doi
stringlengths 28
28
| title
stringlengths 19
311
| abstract
stringlengths 217
5.08k
| plain language summary
stringlengths 115
4.83k
| article
stringlengths 3.87k
161k
|
|---|---|---|---|---|
10.1371/journal.pntd.0007074 | Comparison of Mycobacterium ulcerans (Buruli ulcer) and Leptospira sp. (Leptospirosis) dynamics in urban and rural settings | Zoonotic pathogens respond to changes in host range and/or pathogen, vector and host ecology. Environmental changes (biodiversity, habitat changes, variability in climate), even at a local level, lead to variability in environmental pathogen dynamics and can facilitate their transmission from natural reservoirs to new susceptible hosts. Whilst the environmental dynamics of aquatic bacteria are directly linked to seasonal changes of their habitat they also rely on the ecological processes underpining their transmission. However data allowing the comparison of these ecological processes are lacking. Here we compared the environmental dynamics of generalist and vector-borne aquatic bacterial pathogens in the same unit of time and space, and across rural and urban habitats in French Guiana (South America).
Using Leptospira sp. and Mycobacterium ulcerans we performed an environmental survey that allowed the detection of both pathogens in urban vs. rural areas, and during rainy vs. dry weather conditions. All samples were subjected to qPCR amplifications of LipL32 (Leptospira sp.) and IS2404 and KR (M. ulcerans) genetic markers. We found (i) a greater presence of M. ulcerans in rural areas compared with Leptospira sp., (ii) that modified urban environments were more favourable to the establishment of both pathogens, (iii) that Leptospira sp. presence was enhanced during the rainy season and M. ulcerans during the dry period, and (iv) differences in the spatial distribution of both bacteria across urban sites, probably due to the mode of dissemination of each pathogen in the environment.
We propose that in French Guiana simplified and modified urban ecosystems might favour leptospirosis and Buruli ulcer emergence and transmission. Moreover, disease risk was also constrained by seasonality. We suggest that the prevention of aquatic bacterial disease emergence in impoverished urban areas of developing countries would benefit from seasonal diseases targeted surveys, which would maximise limited budgets from cash-strapped health agencies.
| Many emerging pathogens are zoonotic and transmit from their abiotic reservoir to wild animals, domesticated animals and humans. It is now well known that environmental changes lead to variability in their dynamics in the environment and contribute to changes in the infectious risk. Many aquatic bacteria are responsable for major public health concerns, and more importantly in developing countries where access to drinking-water and sanitation is often limited. Whilst their environmental dynamics are directly linked to seasonal changes of their habitat, they also rely on the ecological processes underpining their transmission, i.e. directly transmitted vs. vector-borne. However, few studies have compared such environmental dynamics despite the fact that it would help to better characterise the infectious risk in the environment, as well as to better monitor the emergence of infectious diseases. Our aim was to provide data on the prevalence of generalist vs. vector-borne aquatic bacterial pathogens in the environment that would further allow the comparison of their environmental dynamics in the same unit of time and space, and across rural and urban habitats. We showed that urbanization and seasonality are two important factors underlying Buruli ulcer and leptospirosis disease emergence in French Guiana (South America), and propose that the mode of transmission of such environmental pathogens might have a detrimental role in disseminating the infectious agent in the environment.
| During the last decades infectious diseases have considerably increased in incidence and new pathogens have emerged and/or re-emerged [1,2]. The majority of known pathogenic species are represented by human pathogens (61%), and most of these are zoonotic [3]. Zoonotic pathogens are widespread in the environment and often transmit from their abiotic reservoir to wild animals (biotic reservoir) but also to domesticated animals and humans (susceptible hosts). Moreover, emerging/re-emerging pathogens are opportunists and respond to changes in host range and/or pathogen, vector and host ecology [3,4]. Thus environmental changes, even at a local level, leads to variability in pathogen dynamics in the environment and contributes to changes in the infectious risk [5].
Biodiversity changes through fragmentation and degradation of natural habitats, and particularly in tropical areas, increase contacts between wildlife, domestic animals and humans, facilitating the transmission of environmental pathogens from natural reservoirs to new susceptible hosts [2]. Biodiversity loss is now well recognized to be associated with the increase in emergence of infectious diseases [2,6,7]. Urbanization and agricultural intensification change land-use, population size and population density, but also impact the interactions between pathogens-vectors-hosts and thus may affect the spread of environmental pathogens [7,8]. Also, whilst the exact transmission routes of many tropical diseases remain unclear, variability in climate, even at a local scale, has been reported to affect the prevalence of infectious pathogens in the environment, as well as their transmission dynamics [9,10].
Many aquatic bacteria are responsable for major public health concerns, and more importantly in developing countries where access to drinking-water and sanitation is often limited (for example Vibrio cholerae, Salmonella enterica, Shigella sp., Leptospira sp., Mycobacterium sp., etc.) [11]. Whilst the environmental dynamics of such pathogens are directly linked to their habitat seasonal changes (i.e. water temperature, pH, oxygen level, salinity, sedimentation/turbidity, presence of biofilm, rainfall patterns, etc.), they also rely on the ecological processes underpining their transmission. For instance, a bacterium directly transmitted from the environment might be more constrained by the local habitat parameters when compared to a bacterium disseminated through a vector, thus depending on the availability, abundance and ecology of that particular vector. However, few studies have described such environmental dynamics in a singular unit of time and space, despite the fact that such description would help to better characterize the infectious risk in the environment (e.g. urban vs. contryside), as well as to better monitor the emergence of infectious diseases.
Mycobacterium ulcerans and Leptospira sp. are two pathogenic bacteria found in tropical areas that are accidentally transmitted to humans from the aquatic environment [5,8,12]. These pathogens are responsible for Buruli ulcer and leptospirosis, respectively, that account for significant morbidity and mortalities among impoverished urban settlements [5,8,12,13]. Whilst M. ulcerans is considered as a generalist pathogen (i.e. associated with different taxa of the aquatic trophic network) with no clear transmission routes to humans [8], Leptospira sp. are transmitted to humans through contact of skin lesions or mucous membranes with contaminated surface water or soil [12], but is mainly disseminated in the environment via urinary secretions of rodent populations which act as a major reservoir for pathogenic leptospires [5]. Land-use changes (i.e. deforestation) in tropical areas were correlated with increased prevalences of M. ulcerans in the environment [14]. Urbanization, associated with increased population density and inadequate sanitation (precarious sewer systems and trash accumulation), favours rodent populations expansion and thus increases leptospirosis risk [5]. Also, local weather patterns are important drivers for both diseases transmission since Buruli ulcer cases are associated to rainfall patterns, with cases occurring during the dry season following a flooding event [9], and leptospirosis outbreaks frequently occur during periods of seasonal rainfall and flooding [15,16,17]. Therefore, Amazonian environmental conditions are highly favorable for the persistance of both bacteria in aquatic systems [18,19] and disease cases are notably reported in French Guiana (South America) [13,20]. Focusing on these two aquatic pathogens as model systems we performed an environmental survey along the French Guiana coastline with the objective to test the following hypothesis:
Finally, we discussed about the likely impact of the transmission mode, e.g. vector-borne versus generalist pathogen, on the spatio-temporal dynamics of both bacteria in the environment.
Between november 2015 and march 2017 a total of 18 rural aquatic sites were sampled monthly for water and sediments. Rural sites were located along the French Guiana coastline and also along the Sinnamary river. These sites were selected based on previous sampling campaigns in French Guiana [19] and represented ponds and oxbows characterized by low water level and stagnant water, either shaded or sunny, composed of a community of aquatic taxa and surrounded either by vegetation or a dense tropical rainforest (Fig 1, Table 1).
In parallel, 51 urban water bodies were also sampled for water and/or sediments from september 2016 to october 2017 among three urban centers: Cayenne (2441.27 inhabitants/km2), Rémire-Montjoly (519.97 inhabitants/km2) and Matoury (236.37 inhabitants/km2) (Fig 1, Table 1). Urban sites were selected based on the location of leptospirosis and Buruli ulcer cases and corresponded to small ditches. Disease cases were mapped and we selected water bodies that fitted the following conditions: (i) being close to Buruli ulcer and/or leptospirosis cases and (ii) showing the ecological conditions prone to sustain the bacteria in the environment (i.e. small water body with low water level and biofilm development for M. ulcerans, and prone to harbour rodents for Leptospira sp.). Water was collected in the middle of the water body, from the water column between 0–1 m below the surface and kept in 1.5 L plastic bottles stored on ice and transported to the laboratory. Also the first layer of sediments (0–1 cm depth) was collected in 30 mL tubes stored on ice and transported to the laboratory. Samples were kept at 4°C until DNA extractions (performed within 24 h for water and 48 h for sediments). Moreover all sites were surveyed during both the dry (september 2016/october 2016/july 2017/october 2017) and the rainy (february and may 2017) seasons in order to compare the distributions and prevalences of both environmental pathogens in space and time (Table 2).
Disease cases were reported at the Cayenne Hospital and were provided by Dr. Loïc Epelboin (Infectious Disease Unit) and Prof. Pierre Couppié (Dermatology Unit). Leptospirosis database provided by Dr. Loïc Epelboin was based on the diagnosis for each patient that has been reported at the Cayenne Hospital and the Centre National de Référence de la Leptospirose at the Pasteur Institute in Paris. Leptospirosis cases reported in French Guiana were analyzed and made available for the period 2014–2017 by the Cayenne Hospital, the Pasteur Institute in Cayenne and the Biomnis laboratory. Buruli ulcer database was built by Prof. Pierre Couppié and colleagues and cases were available from 1969 to 2017. However to be consistent in our comparison of disease cases and positive environmental sites we mapped only cases diagnosed between 2015–2017, occurring thus over the same timescale.
Water samples (1.5 L) were first filtered onto 1.6 μm GF/C glass microfiber filters (Whatman) and then through 0.45 μm cellulose nitrate membrane filters (Merck Millipore). These later filters were air dried and kept at -20°C until further analysis. Total DNA was extracted from filtered water using the DNeasy PowerWater extraction kit (Qiagen) following the manufacturer’s recommendations. For sediment samples, 250 mg of sediments were used to extract DNA using the DNeasy PowerSoil extraction kit (Qiagen). Extracted DNA was kept at -20°C.
To detect and quantify M. ulcerans DNA in environmental samples, we performed two TaqMan qPCR runs; one targetting the insertion sequence IS2404 and one targetting the ketoreductase B (KR) domain of the mycolactone polyketide synthase gene that is specifically found in the virulence plasmid of M. ulcerans strains. To amplify IS2404 genetic marker, we used the following primer and probes: IS2404 forward primer 5’-ATTGGTGCCGATCGAGTTG-3’, IS2404 reverse primer 5’-TCGCTTTGGCGCGTAAA-3’ and IS2404 probe FAM-CACCACGCAGCATTCTTGCCGT-BHQ1 [21]. For KR amplification we used KR forward primer 5’-TCACGGCCTGCGATATCA-3’, KR reverse primer 5’-TTGTGTGGGCACTGAATTGAC-3’, and KR probe FAM-ACCCCGAAGCACTG-MGBNFQ [19]. The qPCR reaction consisted of 1X TaqMan Gene Expression Master Mix (LifeTechnologies), 0.3 μM (final concentration) of each primer, 0.1 μM (final concentration) of the probe, 5 μl of DNA and water adjusted to a final volume per reaction of 25 μl. For KR we followed the same protocol except that we used the probe at a final concentration of 0.25 μM. An internal positive control (IPC) was added in each IS2404 reaction in order to test for the presence of PCR inhibitors in the environmental samples. In each qPCR plate, a positive (M. ulcerans DNA at a concentration of 105 bacteria/mL) and negative (DNA replaced by water) controls were included. The positive control for M. ulcerans consisted of genomic DNA purified from a cultured strain from French Guiana (strain 1G897) and provided by Laurent Marsollier (ATOMYCA, Université d’Angers). This positive control was also used in our study to run standard curves based on serial dilutions of purified DNA from 105 to 100 bacteria/mL (in triplicates). Standard curves allowed us to determine a threshold value above which we considered our samples as negative (CT-values > 38). The assays were run in duplicates on an Applied Biosystems 7300 Real Time PCR system, with the following program: one cycle at 50°C for 2 min, one cycle at 95°C for 10 min, followed by 45 cycles at 95°C for 15 sec and at 60°C for 1 min. Only samples with cycle threshold values < 38 for both IS2404 and KR markers in 1 out of 2 replicates were considered as positives. In all assays the negative controls remained negative.
To date, 22 species of Leptospira have been described and arranged into 3 groups based on their pathogenicity; pathogenic species (L. interrogans, L. kirschneri, L. borgpetersenii, L. mayottensis, L. santarosai, L. noguchii, L. weilii, L. alexanderi, L. kmetyi, L. alstonii), intermediate species of unclear or low pathogenicity (L. broomii, L. fainei, L. inadai, L. licerasiae, L. wolffii), and saprophytic species which are free-living cells in water and soil and are not infectious (L. biflexa, L. idonii, L. meyeri, L. terpstrae, L. vanthielli, L. wolbachii, L. yanagawae) [22]. Whilst pathogenic and intermediate Leptospira species are infectious for humans or animals [22], most diagnotic PCR tools only detect Leptospira from the pathogenic cluster and fail to detect intermediate species [23]. Among these tools the TaqMan qPCR assay targetting the lipL32 gene is commonly used to detect pathogenic Leptospira [5,23,24] since it encodes outer membrane proteins and virulence factors found in pathogenic species [22]. This qPCR assay has been optimized for both sensitivity and specificity, allowing thus to detect and characterize Leptospira sp. in low number or in samples that contain high concentrations of non-Leptospira DNA [22,23]. However since this target gene is highly conserved among Leptospira species it does not allow the discrimination between species. Therefore, to detect the presence of Leptospira sp. DNA we performed a qPCR targetting the lipL32 gene. To do so, we used forward primer LipL32-45F 5’-AAGCATTACCGCTTGTGGTG-3’, reverse primer LipL32-Rb 5’-GAACTCCCATTTCAGCGAT-3’ and the probe LipL32-189P FAM-AAAGCCAGGACAAGCGCCG-BHQ1 [23]. The qPCR reaction consisted of 1X TaqMan Gene Expression Master Mix (LifeTechnologies), 0.7 μM (final concentration) of each primer, 0.15 μM (final concentration) of the probe, 5 μl of DNA and water adjusted to a final volume per reaction of 25 μl. In each qPCR plate, a positive (L. santarosai DNA at a concentration of 102 bacteria/mL) and negative (DNA replaced by water) controls were included. The positive control for Leptospira sp. consisted of genomic DNA of the strain L. santarosai that was provided by the Pasteur Institute in Paris (Pascale Bourhy, Centre National de Référence de la Leptospirose). Standard curves were run with serial dilutions of genomic DNA from L. santarosai from 105 to 100 bacteria/mL (in triplicates). The assays were run on an Applied Biosystems 7300 Real Time PCR system, with the following program: one cycle at 50°C for 2 min, one cycle at 95°C for 10 min, followed by 45 cycles at 95°C for 15 sec and at 60°C for 1 min. Only samples with cycle threshold values < 40 were considered as positives. In all assays the negative controls remained negative.
The prevalences of Leptospira sp. and M. ulcerans in the environment were calculated for each sampling period based on the ratio between the number of sites found positive and the total number of sites tested (%). Whilst we measured the quantity of DNA rather than the quantity of live bacteria in the environment [25], the variation in DNA concentration between sites and between each sampling periods was used as a proxy of bacterial abundances in the environment.
Statistics were performed with R version 3.5.1 (R Development Core Team). We used raw data to perform a logistic binomial regression (package stats, function glm) in order to test for the effect of seasonality (dry and rainy seasons) on the pathogen’s presence in the environment (significance threshold: p-value < 0.05). Maps were created with QGIS (Las Palmas, version 2.18.20).
Among the 18 rural sites tested for the presence of Leptospira sp. DNA, only one site (R9) was found positive for this pathogen (Fig 2A, Table 1). Moreover, this site was found positive at only one sampling period, in february 2017, leading to a total of 1/201 (0.5%) sample recorded positive for Leptospira sp. DNA in rural areas in French Guiana. These 18 rural sites were also tested for the presence of M. ulcerans DNA and we found 4/18 sites (R5, R9, R13, R17) that harboured DNA of this mycobacteria, leading to a total of 12/201 (6%) samples that were positive for M. ulcerans DNA (Fig 2A, Table 1); site R5 was positive in november 2015 only, site R9 was positive in january, february, april, july, october and november 2016, site R13 was positive in december 2015 only, and site R17 in august, october and november 2016 (Supporting Information S1 Table). Therefore these results show a greater number of sites found positive for M. ulcerans in natural aquatic systems compared with Leptospira sp.. Also, M. ulcerans DNA was found in remote prestine sites with no human contact on the upper part of the Sinnamary River (R5), and one site (R9) was suitable for both pathogens.
In parallel, 51 urban water bodies were also sampled for water and sediments from september 2016 to october 2017 around Cayenne, Rémire-Montjoly and Matoury (Table 1). We found a total of 34/169 (20%) samples positive for Leptospira sp. DNA and 27/169 (16%) samples positive for M. ulcerans DNA (Fig 2B, Table 2). These results suggest that urban habitats are more favourable to the establishment of Leptospira sp. and M. ulcerans, which exhibited higher number of positive sites through time when compared with rural environments (20% vs. 0.5% and 16% vs. 6%, respectively).
The only rural site found positive for Leptospira sp. DNA corresponds to the rainy season (average rainfall of 270.4 mm, Table 2). In contrast, the majority of rural sites found positive for M. ulcerans DNA were positive during the dry periods (see Table 2). A total of 7/11 rural sites (64%) were found positive for M. ulcerans DNA during the dry season.
Among the urban sites, the prevalence of Leptospira sp. DNA increased during the rainy season to 47.2% and 22.2% in February and May respectively, and reached its minimum during the dry period (Supporting Information S1 Map, Table 2). Although M. ulcerans DNA prevalence was more stable in the environment across seasons, most positive sites were observed during the dry period from 11.8% to 66.7% depending on the months (Supporting Information S2 Map, Table 2). Our results showed a correlation between the prevalences of each pathogen in the environment and seasonality, such that the prevalence of pathogenic leptospires was enhanced by rainfall while M. ulcerans’s prevalence in aquatic sites was more related to drought (Fig 3). These observations were confirmed by the binomial regression models that showed that the environmental dynamics of Leptospira sp. and M. ulcerans were significantly different between the dry and the rainy seasons. Indeed, environmental sites had 2.86 times (95% CI 1.14–8.19; p-value = 0.0339) higher odds of M. ulcerans positivity during the dry season, while Leptospira sp. had 5.20 times (95% CI 2.32–12.63; p-value = 0.00012) higher odds of being detected in the rainy season (Fig 4).
Here our aim was to follow and describe the environmental dynamics of two aquatic bacteria, potentially pathogenic to animals and/or humans, across the French Guiana territory including the Amazon tropical rainforest. Our environmental survey describe the presence of both pathogens in rural and urban sites, with M. ulcerans being more often encountered in natural (undisturbed) rural habitats compared with Leptospira sp.. As proposed before, our results suggest the ubiquitous nature of the mycobacterium M. ulcerans in the environment [19]. According to Combe et al. [8], this pathogen is likely to be widely distributed in suitable natural aquatic systems and, under specific environmental conditions could become more abundant in the system, resulting an increased risk of Buruli ulcer emergence. Whilst M. ulcerans DNA was more often found in rural aquatic sites than Leptospira sp., we observed that one site (R9) was suitable for both pathogens, which was the only rural site located close to poor human settlements and frequently visited by livestock, such as cows and pigs, and domestic animals such as dogs, cats and poultry. This suggest that the presence of Leptospira sp. in rural environments would need the presence and activity of humans, livestock and/or domestic animals, that would represent sustainable host populations for this pathogen. The presence of Leptospira sp. in peri-domestic water samples from rural households has already been reported in southern Chile, with the presence of dogs and a high density of rodent populations being associated with positive puddles in the lower income households [26]. Pathogenic leptospires could be permanently present in the aquatic environment, and more specifically in watered soils as recently showed in New Caledonia [12], but only detected by qPCR when their abundance is increased by the proximity with human settlements and the availability of animal hosts that could also represent more attractive areas for rodent populations harbouring and sharing the bacteria in surrounding aquatic sites [5].
Also, the results suggest that modified urban environments are more favourable to the establishment of Leptospira sp. and M. ulcerans, with higher positivity for both pathogens when compared with rural environments (20% vs. 0.5% and 16% vs. 6%, respectively). Here the difference in the number of sampling sites and the different sampling scale in rural (18 water bodies, 17 months, 1 samples, n = 306) vs urban areas (51 water bodies, 6 months, 1 sample, n = 306), could constrain our interpretation when comparing the presence of both pathogens in these environments. However, in French Guiana most of the population live along the coastline and only 5 centers can be considered as urban (based on infrastructure development, population density, etc.): Cayenne (2441,27 inhabitants/km2), Rémire-Montjoly (519,97 inhabitants/km2), Matoury (236,37 inhabitants/km2), Kourou (12,14 inhabitants/km2) and Saint-Laurent du Maroni (9,03 inhabitants/km2) [27]. Our sampling effort covered 3 of these urban centers (Cayenne, Rémire-Montjoly and Matoury), and each show higher number of positive sites for Leptospira sp. and M. ulcerans when compared to rural sites. Moreover our sampling stategy was designed to reflect the differences in number of human cases in urban vs rural settings, with 99,3% and 8% of human cases for leptospirosis and Buruli ulcer occuring in urban populations versus 0,73% and 27,3% of cases occuring in rural ones, respectively. As the number of human cases was extremely low in rural settings it was important to have more regular sampling and over a wider geographical area in order to not miss any environmental signal. In urban settings this was not an issue and as such we adopted seasonal based sampling over the whole urban habitat. Therefore our results are consistent with the distribution of human cases across French Guiana, with much less cases in rural than in urban areas [20]. Therefore, we do not observe in villages located in the countryside clusters of human cases as observed in urban environments. In addition, a lack of replicates (1 sample/site) could potentially be a limit as sometimes many samples at a site can result in a single positive sample, or even none (from our field experience). However, our results show that even with such sampling effort (1 sample/site), we had no difficulties in detecting the presence of both pathogens in the environment, and confirmed their presence across seasons similar to what was previously reported on multiple samples [6,9,14]. Therefore, the sampling effort did not overestimated the presence of these pathogens.
In urban environments these pathogens seems to establish, colonize and share similar ecological niches (e.g. benthic algae and watered soils from 1–5 cm depth for pathogenic leptospire [12]; algae biofilm and watered soils from 1–10 cm depth for M. ulcerans, personnal data), habitats that also exist in rural settings but which are for some biotic reasons less favourable to their development. Casanovas-Massana and collaborators (2018) found that both sewage water and standing water were reservoirs for pathogenic Leptospira sp. in a urban area in Salvador, Brazil [5]. It shows that simplified urban ecosystems with less predators to rodents, low level trophic networks due to pollution and increased contact with humans would favour leptospirosis and Buruli ulcer emergence and transmission.
Other diseases are also known to be more prevalent in urban areas, such as dengue fever due to the availability of suitable human-created micro-environments for Aedes sp. mosquitoes breeding and eggs laying [28], or even for water-borne and enteric diseases with a oral-fecal transmission in areas with poor sanitation infrastructure [7]. Since most human emerging and/or re-emerging infectious diseases are zoonotic, increased urbanization in developing tropical countries would tend to increase the frequency of contact between wildlife and humans, representing an increased risk of disease emergence [7]. Indeed, drivers such as land-use changes (i.e. from natural toward deforested areas, agriculture intensification, road building) modify the ecology of the pathogen-vector-host, as well as increase contact between pathogen-vector and humans (i.e. increase human population densities, pollution, unsanitary conditions). The prevention of aquatic bacterial diseases emergence and transmission in tropical areas, where millions of people are currently living and where half of the world’s population will live by 2050 [7], would necessarily result from improved infrastructure and sanitation in impoverished urban areas of developing countries.
Here we showed a synchronisation between the presence of each pathogen in the environment and seasonality, with higher number of positive sites for pathogenic leptospires during the rainy season while M. ulcerans’s presence in aquatic sites was more related to drought (Fig 3, Fig 4). Previous findings indicated that leptospirosis outbreaks frequently occurred during periods of seasonal rainfall and flooding events in endemic areas [5,15,16,17]. Moreover, seasonal (i.e. rain vs drought) conditions leading to increased human exposure to contaminated water are known to be important drivers for leptospirosis transmission, and the proximity of households to open drainage systems and direct contact with sewage, flooding water and runoff were associated with increased risk of infection [29–33]. Similarly, a recent study conducted in Brazil found higher bacterial concentrations in urban environmental sites (sewage and standing water) during the rainy season when compared with the dry period, indicating thus a seasonal effect [5]. Also, the link between Buruli ulcer cases and seasonal patterns has been identified in several studies, showing that Buruli cases occurred during the dry season that followed rainfall events [reviewed in reference 8]. These observations were confirmed by long-term time series of Buruli ulcer cases [9] and climatic models [34,35] that revealed robust correlations between disease incidence and seasonality, with the disease being reported in French Guiana after dry periods following periods of heavy rainfall. Moreover in French Guiana Morris et al. (2014) have linked Buruli ulcer cases with extreme weather events such as La Niña, that are responsible to cause short dry periods during the rainy season [9].
Here the most interesting findings rely on the comparison of the environmental dynamics of the two aquatic bacterial pathogens in the same unit of time and space across both rural and urban environments. Indeed, we found that both pathogens are ubiquitously distributed in aquatic sites (persistent with low burden) although this seems more obvious for M. ulcerans in rural settings, and are both able to survive under similar ecological conditions. However, our seasonal survey clearly showed that the presence of each pathogen in the environment were heterogeneous and depended on different climatic patterns; whilst the presence of pathogenic leptospires in the environment was enhanced by rainfall, M. ulcerans emergence was boosted by drought that followed rainfall and flooding events. These results suggested that in French Guiana the infectious risk for each disease does not occur at the same period of the year, and is constrained by seasonality. Such local prevalence variability due to seasonal patterns might result from the pathogen’s life cycle or the dynamics of reservoir and/or host populations.
Looking at the spatial distribution of Leptospira sp. and M. ulcerans in the urban environment show that positive sites for Leptospira sp. are much more widely distributed when compared with M. ulcerans positive sites that are locally constrained within small neighborhoods (Fig 2B). We propose that such difference in the spatial distribution of both bacteria across urban sites could also be explained by the mode of dissemination of each pathogen in the environment (i.e. environmental dissemination of M. ulcerans vs. vector-borne/animal-borne for Leptospira sp.). During rainfall with increased oxygen levels and alkaline pH (up to pH 8.0), low salt concentrations, and/or the dilution of sewage toxic compounds, Leptospira sp. flourish [5,12,36] and are ingested by rodents and other carrier mammals. Flooded urban habitats, favour the re-distribution of rodents across cities, thus dispersing leptospires from one site to another mainly via urinary excretions. It does result in an increase of cases during the rainy season over a wide urban range. Such patterns have been also observed in other settings around the world where leptospirosis epidemics occurred in the rainy season that followed heavy rainfall [37,38,39]. Alternatively, Ferreira de Albuquerque et al. (2017) reported that capybaras (Hydrochoerus hydrochaeris) were massively infected by leptospires in the western Amazon region [40]. These small mammals are present in urban areas in French Guiana and could thus further play a role in the environmental dissemination of pathogenic leptospires. Unfortunately studies on rodent infections with Leptospira sp. are very scarce and old in French Guiana [41]. In contrast, the ecological conditions favouring M. ulcerans emergence in the environment are known to rely on higher water temperature, low pH, low oxygen levels and the presence of algal biofilm, conditions typically encountered during the dry period in the tropics [reviewed in reference 8]. In addition, M. ulcerans is not transmitted by a specific vector, but rather was found to be associated with a large range of aquatic invertebrates. Several studies showed that aquatic organisms of low/mid trophic level usually exhibit greater bacterial loads compared with organisms of a higher trophic level [reviewed in reference 8]. For instance M. ulcerans seems to have a specific association with gathering collectors and filter feeders. After anthropogenic (i.e. deforestation) or natural (i.e. changes in weather, flooding) changes, stagnant water bodies are prone to rapid local abiotic changes (temperature, pH, oxygen, etc.) associated with a rapid turnover of the biotic community (i.e. changes in functional diversity) and leading to an increase in favourable hosts harbouring M. ulcerans [8]. Based on the current knowledge on M. ulcerans ecology it appears clearly that its distribution is locally constrained by habitat and aquatic hosts communities highlighting the relative clustering of human cases within urban units in French Guiana.
Whilst the wide presence of M. ulcerans along French Guiana’s coast was already known, this is the first survey that screened for the presence of pathogenic leptospires across the territory (including the Amazon tropical rainforest). Until recently it was assumed that there were few leptospirosis cases in French Guiana, compared to West Indies for instance, probably because of the acidity of the soil across the Guiana shield preventing bacterial development. However our results clearly showed that (i) both pathogens are present in the environment in French Guiana, and (ii) urbanization and seasonality are two important factors underlying Buruli ulcer and leptospirosis emergence. Also we propose that the mode of transmission (i.e. generalist vs. vector-borne) of environmental pathogens might have a detrimental role in disseminating the infectious agent in the environment. To better monitor diseases emergence, we suggest that future studies should focus on determining which specific socio-economic and environmental factors are underlying the spatio-temporal distribution of emerging infectious pathogens.
|
10.1371/journal.pntd.0006274 | Study of diagnostic accuracy of Helmintex, Kato-Katz, and POC-CCA methods for diagnosing intestinal schistosomiasis in Candeal, a low intensity transmission area in northeastern Brazil | Control initiatives have successfully reduced the prevalence and intensity of schistosomiasis transmission in several localities around the world. However, individuals that release low numbers of eggs in their feces may not be detected by classical methods that are limited by low sensitivity. Given that accurate estimates of prevalence are key to implementing planning control actions for the elimination of schistosomiasis, new diagnostic tools are needed to effectively monitor infections and confirm transmission interruption. The World Health Organization recommends the Kato-Katz (KK) thick smear as a parasitological test for epidemiological surveys, even though this method has been demonstrated to underestimate prevalence when egg burdens are low. The point-of-care immunodiagnostic for detecting schistosome cathodic circulating antigen (POC-CCA) method has been proposed as a more sensitive substitute for KK in prevalence estimations. An alternative diagnostic, the Helmintex (HTX) method, isolates eggs from fecal samples with the use of paramagnetic particles in a magnetic field. Here, a population-based study involving 461 individuals from Candeal, Sergipe State, Brazil, was conducted to evaluate these three methods comparatively by latent class analysis (LCA). The prevalence of schistosomiasis mansoni was determined to be 71% with POC-CCA, 40.% with HTX and 11% with KK. Most of the egg burdens of the individuals tested (70%) were < 1 epg, thereby revealing a dissociation between prevalence and intensity in this locality. Therefore, the present results confirm that the HTX method is a highly sensitive egg detection procedure and support its use as a reference method for diagnosing intestinal schistosomiasis and for comparative evaluation of other tests.
| Schistosomiasis is a parasitic infection that is caused by flatworms that live inside venous vessels. The parasite eggs are eliminated with human feces. They hatch in the water and the free swimming larvae infect snails. The larvae develop in the snail tissues and, on reaching an infective stage, are released into the water and can penetrate human skin to restart the cycle. Millions of people on several continents have schistosomiasis, although sanitary improvements, treatment, and environmental control have reduced the intensity of transmission in various localities around the world. In order to eliminate schistosomiasis, identification of infected individuals is critical. However, in areas where transmission has declined, the methods used to detect eggs in human feces are not sufficiently sensitive. Therefore, the aim of the present study was to evaluate new and more sensitive methods for obtaining a diagnosis of schistosomiasis. This study compared the relative performance of the POC-CCA method, which detects parasite molecules released in urine and the Helmintex (HTX) method, which isolates eggs from large volumes of feces in a magnetic particle-based assay, together with the Kato-Katz method. In comparison with the egg detecting methods (Kato-Katz [KK] and HTX), the POC-CCA method exhibited limitations in detecting low intensity infections, while the HTX method was confirmed to be a highly sensitive diagnostic method for schistosomiasis. These results provide insights into the deployment of diagnostic tools for efforts to eliminate schistosomiasis in low endemic regions.
| Schistosomiasis is a common infection that affects over 290 million individuals, especially in Sub-Saharan Africa, Asia, and South America [1]. In Brazil, the sole agent of schistosomiasis is Schistosoma mansoni, responsible for intestinal schistosomiasis. This species is endemic to northeastern and southeastern regions in Brazil, although focal transmission sites have been reported in other regions [2].
It is challenging to diagnose schistosomiasis in areas of low endemicity where prevalence and worm burden have decreased [3]. Classical diagnostic methods lack sensitivity in populations where effective control measures have reduced transmission or in areas where the parasite has recently been introduced [4,5]. Detection methods that employ antibodies [6], antigens [7], or DNA [8] have exhibited high sensitivity but reduced specificity compared to microscopy-based assays [9] and they are semi-quantitative [10]. The Kato-Katz (KK) fecal smear method [11] is recommended by the World Health Organization for routine use in epidemiological surveys as part of control measures in endemic areas [12]. The KK method has exhibited good performance in high endemic areas and is still applied in diagnostic surveys due to its ease of application and specificity. However, KK is not an accurate diagnostic in many situations, for example in situations where part of a population has been previously treated and low egg burden is present in stool [13]. In addition, because the volume of stool needed for the KK method is very small (< 50 mg), and eggs may be unevenly distributed in feces [14,15], a large fraction of true positives may be missed with the KK method [13].
To address these limitations, the Helmintex (HTX) method was developed [16] to specifically detect light infections. The HTX method is based on interactions between S. mansoni eggs and superparamagnetic particles in a magnetic field. Seeding experiments have demonstrated 100% sensitivity with this method for egg burdens higher than 1.3 epg [16]. Biophysical properties of the egg surface that may contribute to the performance of the HTX method have also been extensively studied [17,18]. Furthermore, Favero et al. [19] recently proposed an optimization of the HTX method that makes it less time consuming and more efficient for field surveys.
A point-of-care immunodiagnostic for detecting schistosome cathodic circulating antigen in urine (POC-CCA method) has been proposed as a substitute for the KK method based on its estimated higher sensitivity and operational advantages, especially in highly endemic areas. However, even with the advantages of the POC-CCA method, there is still a need for a highly sensitive and direct method for detecting eggs that can serve as a reference in performance evaluations. Other direct diagnostic methods, like biopsy, are not feasible for populational-based studies. Antigen detection methods, like POC-CCA, may indicate the presence of worms that are not excreting eggs at that time, but further studies are required for extensive evaluation of its specificity. Thus, the aim of the present study was to compare by latent class analysis the performances of the HTX, KK, and POC-CCA methods in an endemic area for schistosomiasis in northeastern Brazil and to evaluate the following hypotheses: i) highly sensitive methods can be evaluated in medium-high endemic areas rather than in low endemic areas if they include large numbers of low intensity infections and ii) the HTX method has the capacity to serve as a reference egg detection method due to its high sensitivity.
Between October and November 2015, a prospective community- and geographically-based study was conducted in the locality of Candeal, Municipality of Estancia, State of Sergipe, Northeastern Brazil (11° 16' 04" S 37° 26' 16" W). Approximately 700 people live in Candeal, a restricted area with houses in close proximity that is well delineated by a federal highway (BR-101) on its west side, a stream to the south, and farms on its northern and eastern borders. Schistosomiasis morbidity was not addressed. Routine yearly fecal convenience sampling and examination by KK (but with poor coverage) is followed by treatment provided by local health service.
A total of 580 individuals living in Candeal provided written informed consent to participate in this study. For the children and teenagers included in this study (aged 1–17 years), consent was provided by their parents or legal guardian. The protocol for this study was approved by the PUCRS Ethics Committee (register 48809715.1.0000.5336).
Study design and reporting follow the Standards for the Reporting of Diagnostic accuracy (STARD-2015: http://www.stard-statement.org/) (S1 Checklist and S1 Flow Diagram).
Each individual received a large container (1 L) and was directed to collect an entire evacuation. Feces were processed immediately upon their arrival in the laboratory.
A commercial KK kit (HelmTest, Biomanguinhos, Brazil) was employed for the preparation of slides with fecal smears, according to the manufacturer's instructions. Briefly, each sample was placed on a paper, then a metal mesh was pressed over it. The sieved feces were then scraped into a plastic circular template mounted on a glass slide so that the template was filled. After removing the template, cellophane coverslips presoaked in a glycerin-malachite green solution were placed over the sieved feces. Two slides of each sample were prepared, labeled, and vertically stored in plastic boxes to ensure that no contact occurred between the slides. The boxes were kept in a refrigerator until they were examined by light microscopy. Each slide was completely screened by optical microscopy at a final magnification of 100× for the identification and quantitation of S. mansoni eggs. Egg per gram (epg-KK) values were calculated based on the average number of eggs counted on two slides, and multiplied by 24.
The HTX method was performed as previously described by Favero et al. [19] (Fig 1). Briefly, 30 g of feces was dissolved and fixed in a 10% Tween-20/70% ethanol solution (v/v). After 30 min, each suspension was passed through a 500 μm metal mesh, transferred to a conical flask, and washed until a clear supernatant was obtained. The suspension was then successively sieved through metal meshes with openings of 150 μm and 45 μm, respectively. The fraction retained by the last sieve was suspended in a 30% (v/v) ethyl acetate aqueous solution, homogenized and centrifuged for 10 min. at 200 xg. The pellet was collected after discarding the debris ring at the top of the aqueous phase (a modified Ritchie method) [20]. Each pellet was transferred to a microtube containing 19 μL of iron oxide paramagnetic particles (Bangs Labs, USA). After the pellets and particles were allowed to homogenize for 30 min with orbital rotation, the microtubes were placed in a magnetic rack (Bangs Labs, USA) for 3 min. Unbound material was discarded before each tube was removed from the rack. The magnetic-responsive pellets were then resuspended in 100 μL of 0.9% aqueous NaCl solution (w/v) and stored at -4°C until analyzed. To prepare the samples for microscopy analysis, each sediment was suspended and stained in 3% ninhydrin (Sigma-Aldrich, USA) in 70% ethanol (v/v) and homogenized by pipetting. Each suspension was evenly spread over 5 cm × 2.5 cm filter papers (24-μm pore) (UNIFIL, Brazil), identified, and kept for examination by optical microscopy (magnification, 100×). At the time of microscopy, the filters were moistened with drops of 70% ethanol (v/v) before the total number of eggs present were counted. The filters were stored separately in paper envelopes to avoid cross-contamination between samples. The sum of the eggs detected on all of the filter papers was divided by 30 to express the number of epg of feces. Throughout the text “epg” is meant to be the egg burden estimated by HTX.
Each participant received a 200 mL container for urine collection. From each sample, 4 mL of urine was aliquoted and stored at -20°C for subsequent analyses.
POC-CCA tests were performed according to the manufacturer's instructions (Rapid Medical Diagnosis, Pretoria, South Africa). Briefly, a drop of urine was placed in a cassette and then a drop of developing reagent was added. Each cassette was kept at room temperature and the presence of a control line and a test line was checked exactly 20 min after the application of each sample. The results were recorded and independently verified by three trained observers. These results were also recorded by a digital camera under identical exposure settings. These images were reviewed by the same three observers for classification of intensity of test bands according to criteria (weak, medium, strong) proposed by Silveira et al. [21]. For most samples, the three sets of observations were in agreement. When different scores were recorded for a sample, the predicted infection status of the samples was reviewed by all three observers. If this review could not resolve the status, the final result was determined based on the observations of two of the three observers. “Trace” was considered a faint line with at least part of its limits not defined or absent. The instructions of the manufacturer states that “positivity” is any color that develops at the expected test site, but we register the result “trace” as defined above to allow a detailed comparative evaluation of the diagnostic methods, as previously reported by other authors [21, 22, 23].
Venous blood was collected. Serum was stored at -20°C for transportation and then was stored at -80°C at the laboratory for future serological studies. No adverse effects were reported after collection of any biological samples.
The rationale for KK and HTX test positivity is that identification of an egg is pathognomonic for infection. Eggs of S.mansoni were identified using criteria as described by Favero et al. [19]. Performers and readers were blinded to other tests results or any clinical/epidemiological information.
Assuming no diagnostic test is a “gold-standard”, relative diagnostic performance was accessed by latent class analysis (LCA) for each of the tests conducted based on sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of the tests. Latent class models estimate prevalence and diagnostic accuracy based on the observed data from different tests. It is assumed that 2 latent classes correspond to groups of truly infected and non-infected individuals [24]. The 461 qualitative results from KK, POC-CCA and HTX methods were starting data points for LCA available in R software, Package e1071 (https://cran.r-project.org, assessed on November 4, 2017).
Classification as “false-positive” or “false-negative” was based mainly on the estimates from LCA but it should not be taken as definitive since in the present study neither irregular daily egg elimination in feces (false-negative results from egg-detecting tests, KK and HTX) nor laboratory cross-contamination (HTX false-positive results) could be ruled out.
The Fisher exact test was used to compare the proportions of positive results obtained from KK, HTX and POC-CCA. The Cohen’s Kappa coefficient was used to evaluate the agreement between the methods. The Student’s t-test, ANOVA and Tukey’s test were used to compare the means of epg in each category of POC-CCA band intensity.
A total of 681 individuals from Candeal, Brazil agreed to submit at least one type of biological sample for analysis in this study. A total of 461 (68%) participants donated feces, blood, and urine samples. Age groups comprised of 49 (10.7%) individuals less than 7 years-old, 92 (20%) school-aged-children (7 to 14 years-old) and 319 (69.3%) teenagers and adults. In order to compare all three methods, only results from those participants who provided all three specimens were examined (Tables 1–5 and S1 Table).
The results obtained from analyzing the fecal samples with the HTX and KK methods, and analyzing the urine samples with the POC-CCA method, are compared in Tables 1–5. The prevalence estimates that were obtained varied according to the method used. For example, a total of 187 (40.6%) and 55 (11.9%) samples were positive for S. mansoni eggs according to the HTX and KK methods, respectively (S2 Table). When the samples were analyzed with the POC-CCA method, 330 (71.6%) and 187 (40.6%) samples were positive for schistosomiasis when trace results were considered positive rather than negative, respectively (Tables 2–5).
The relative diagnostic performance of the tests was estimated by LCA and the resulting values are presented in Table 6. The KK and HTX methods were in agreement regarding 54 (11.7%) positive samples and 273 (59.2%) negative samples. However, in 133 (28.9%) samples, schistosomiasis was only diagnosed by the HTX method (kappa coefficient = 0.329).
The urine samples were analyzed with a POC-CCA kit and the results were recorded in two different ways (Tables 2–5). If “trace” results were treated as positive, as recommended by the POC-CCA kit manufacturer, 34 (7.4%) and 8 (1.7%) individuals that were diagnosed as positive for S. mansoni infection by the HTX and KK methods, respectively, would be incorrectly classified as negative by the POC-CCA method. In contrast, when a comparison was made of the POC-CCA data with the examination of 187 samples with egg detected by HTX, 177 (38.4%) and 283 (61.4%) individuals would have false-positive results, respectively. In the subsets of samples containing < 1 epg or ≥ 1 epg, the POC-CCA method produced the lowest proportion of true-positive results (44% vs. 88%, respectively) and the highest proportion of false-negative results (24% vs. 3%, respectively) (Table 7)
The estimates of schistosomiasis prevalence varied from 11.9% (KK) to 71.6% (POC-CCA, with “trace” considered positive). The prevalences estimated from examining KK-slide1 and KK-slide2 were identical (8.7%), yet were lower than the prevalence estimate that was calculated when the results from slides 1 and 2 were combined (11.9%) (S3 Table). When prevalence estimates were analyzed by the HTX and POC-CCA methods (with the “trace” considered negative), the value was identical (40.6%), but Kappa coefficient was low (0.156) indicating poor agreement between these methods (Table 8).
The majority of the infected individuals examined presented low egg burdens. For example, among the 187 positive cases diagnosed by the HTX method, 131 (70%) cases involved < 1 epg (Fig 2). In contrast, 56 epg was the highest result obtained with the HTX method. When the epg numbers obtained for the samples with the HTX and KK methods were compared, 46 and 14 samples, respectively, had between 1 and 12 epg, while 23 samples had epg values >50.
Egg burdens estimated with the KK method were 2.1 to 720 times higher than those estimated with the HTX method in 96% of the samples, resulting in a correlation coefficient of 0.5615 (Fig 3). Meanwhile, for only two samples the HTX method estimated 1.3- and 1.5-times higher epg values than KK (S1 Table).
The average egg burden for POC-CCA band intensity categories were similar: 0.30 (strong), 0.33 (medium), and 0.26 (weak), in the epg <1 samples subset. Within the epg ≥1 group, there was an increase in the epg average from 3.37 (weak) to 11.61 (medium) and 14.70 (strong). The proportions of samples with strong reactivity have increased from 3.5% (epg<1) to 23.5% (epg≥1), while those with weak reactivity decreased from 71.9% (epg<1) to 39.2% (epg≥1) (Table 9).
Among the 461 fecal samples that were analyzed by the HTX method, only 7 samples had volumes less than the standard 30 g typically analyzed (25 g, 22 g, 20 g, 20 g, 12 g, 12 g, and 12 g). The number of eggs detected in these samples were 5, 4, 90, 3, 10, 24, and 1, respectively; which corresponds to epg estimates of 0.1, 0.13, 3, 0.1, 0.33, 0.8, and 0.03, respectively. In this group of samples, eggs were only detected by the KK method in one sample (the 12 epg sample). These results indicate that the HTX method performs well with fecal masses less than 30 g.
Andrews [25] previously proposed that the sensitivity of egg-detecting methods increases with the volume of biological material that is examined. The HTX method is applied to 30 g of feces and was developed after observing that S. mansoni eggs could be isolated from feces based on their interactions with paramagnetic particles in a magnetic field [16]. Thus, rather than screening filtered fecal samples, as occurs with the KK method, the eggs that are present in a larger volume of feces can be concentrated into a smaller volume with the HTX method in order to be more easily screened by microscopy. In seeding experiments, HTX method was 100% sensitive with egg burdens higher than 1.3 epg [16]. HTX processing takes approximately 3h and its current estimated cost is US$ 3 per sample (a single KK slide preparation costs US$ 0.2 and POC-CCA costs US$ 1). More recently, the HTX method has been improved by introducing a detergent (Tween 20) at the concentration step, and then staining the final sediment with ninhydrin prior to microscopic evaluation [19]. As a result, significantly less time is spent screening sediment samples, reducing the overall cost of the HTX method. However, it is also recognized that even with recent optimizations of the HTX procedure [19], the HTX method remains labor intensive and not applicable as a routine field diagnostic. Therefore, it is proposed that the HTX method should serve as a reference method for evaluating other methods.
The KK method is operationally simple and inexpensive, and is the diagnostic method recommended by the WHO for epidemiological studies [12]. However, this method lacks sensitivity when fewer eggs are present in a sample [13]. In a number of observational studies where “infected” and “non-infected” individuals were evaluated based on use of the KK method, false negatives probably occurred preventing a correct interpretation of the data [26]. In the present study, the HTX method exhibited higher sensitivity than the KK method. If this is confirmed in future studies, then the HTX method would represent the best method for obtaining a precise determination of infection status by egg detection. This determination is particularly critical for vaccine efficacy evaluations, individual clinical diagnoses, and control of cure efforts, especially in non-endemic countries [26,27,28].
Superior sensitivity of the HTX method compared with KK was previously demonstrated in field-based studies that were conducted in low endemic areas in Brazil [29,30]. In the present survey that was conducted in Candeal, Brazil, the HTX method detected eggs in 29% of the samples that were negative according to the KK method. In addition, the prevalence estimated by the HTX method was 3 times higher than the KK method (40% vs. 11%, respectively) (Table 1). The assessment of relative diagnostic performance by latent class analysis also clearly indicated that the HTX method provided high sensitivity and displayed an overall better performance (Table 6).
Egg burdens are predominantly low among infected individuals in the Candeal community, with 70% of them harboring less than 1 epg. It is noteworthy that low infection intensity is also associated with morbidity and should be targeted in late stages of schistosomiasis elimination [31]. This locality has been under surveillance and regular treatment for many years by the local Ministry of Health authorities. As a result, a high-prevalence, yet low infection-intensity profile has developed in the community. This is in contrast with the more typical coupling of high prevalence and intensity of infections. The potential for prevalence and intensity of infections to be dissociated should be considered in future epidemiological studies and should be used to adjust control measures appropriately. In addition, classification of endemicity needs to account for both prevalence and intensity [3].
With the exception of two samples, egg burdens were found to be higher with the KK method than with the HTX method (Fig 3). While both methods include concentration steps (sieving–KK and HTX; isolation with magnetic particles–HTX; see Fig 1), estimation of epg by the HTX method derives from absolute counting of eggs in 30 g of sample and the KK method estimates epg based on an extrapolation of egg counting in 42 mg of sample. Consequently, the latter potentially contributes to overestimated epg values. This interesting aspect is consistent with discussions in the field regarding the randomness of S. mansoni egg distribution in feces [14]. With the KK method only examining 42 mg of fecal samples, the possibility that eggs are unevenly distributed would become more evident when lower numbers of eggs are present. This was observed in the present study. It is also possible that HTX underestimates epg because of its estimated egg recovery of aproximately 27% in seeding experiments [19].
POC-CCA is a rapid antigen-based detection test that is applied to urine samples. It has received increasing attention as a promising point-of-care field diagnostic tool, especially based on its use in high endemic areas (e.g., areas with high prevalence and intensity of infections). However, evaluations of this rapid test in low endemicity (specifically low intensity) areas is urgently needed [32,33,34]. The set of samples evaluated in the present study provided an opportunity to directly evaluate the performance of the POC-CCA method with predominantly low intensity infections in comparison with a very sensitive egg detection method (HTX). After LCA analysis, a higher probability of false-positive results is indicated by low positive predictive values (PPV = 46.7% when “trace” is considered positive) (Table 6). Variability in daily egg excretion may explain POC-CCA positive and egg-negative detection and this issue should be addressed in future studies together with appropriate protocol adjustments to minimize cross-contamination in order to avoid false-positive egg detection. Furthermore, performance of the POC-CCA method was worse in the subset of samples that contained less than 1 epg, with a lower detection of “true-positives” and a higher number of “false-positive” results observed (Table 7). Correlation of band intensity and egg burden is also poor, especially with samples containing less than 1 epg (Table 9). Thus, the limitations of the POC-CCA assay for diagnosis of schistosomiasis in individuals that eliminate low numbers of eggs in stool were demonstrated.
In conclusion, the results of the present study support the two initial hypotheses. First, medium-highly endemic areas (defined by prevalence) are suitable for evaluating the diagnostics performance of egg detection methods if a large number of low intensity infections are present, as is the case in Candeal. Thus, “low endemicity areas” with low numbers of positive samples should be avoided when evaluating detection methods. Second, the HTX method is very sensitive and should be used as a reference method for diagnosing intestinal schistosomiasis and for comparative evaluation of other tests. The HTX method should also be considered for use in the monitoring and certification of transmission interruption.
|
10.1371/journal.ppat.1000328 | Identification of Rhoptry Trafficking Determinants and Evidence for a Novel Sorting Mechanism in the Malaria Parasite Plasmodium falciparum | The rhoptry of the malaria parasite Plasmodium falciparum is an unusual secretory organelle that is thought to be related to secretory lysosomes in higher eukaryotes. Rhoptries contain an extensive collection of proteins that participate in host cell invasion and in the formation of the parasitophorous vacuole, but little is known about sorting signals required for rhoptry protein targeting. Using green fluorescent protein chimeras and in vitro pull-down assays, we performed an analysis of the signals required for trafficking of the rhoptry protein RAP1. We provide evidence that RAP1 is escorted to the rhoptry via an interaction with the glycosylphosphatidyl inositol-anchored rhoptry protein RAMA. Once within the rhoptry, RAP1 contains distinct signals for localisation within a sub-compartment of the organelle and subsequent transfer to the parasitophorous vacuole after invasion. This is the first detailed description of rhoptry trafficking signals in Plasmodium.
| The malaria parasite Plasmodium falciparum is a eukaryotic organism with multiple membrane bound organelles with discrete functions. The rhoptry is an unusual secretory organelle that participates in host cell invasion and the formation of a specialised vacuole that the parasite occupies during the intracellular part of its lifecycle. Rhoptries contain an extensive collection of proteins, but little is known about how these proteins are trafficked to their destination. Understanding determinants of rhoptry protein trafficking will help us to identify novel rhoptry proteins, and may provide targets for therapeutic intervention. In the current study, we focussed on the trafficking of the rhoptry protein RAP1. By making parasites that express regions of RAP1 fused to Green Fluorescent Protein (GFP), we were able to map in detail the domains of RAP1 that are necessary for correct trafficking. We also provide evidence that RAP1 is targeted to rhoptries via its interaction with another rhoptry protein, RAMA. This is the first detailed description of rhoptry trafficking signals in Plasmodium.
| Malaria is a disease that causes severe morbidity, mortality and socio-economic hardship in tropical and sub-tropical areas of Africa, South America and Asia. Plasmodium falciparum causes the most serious form of the disease and is responsible for more than 2 million deaths annually [1]–[3]. The development and implementation of novel intervention strategies in the form of drugs, vector control measures and an effective vaccine remains an urgent global health priority [4].
Plasmodium spp. belong to the phylum Apicomplexa – protozoan parasites characterised by a complex lifecycle consisting of invasion followed by rounds of intracellular replication. The invasion is mediated by a set of molecules distributed on the parasite surface and within specialised apical secretory organelles. Regulated secretion from these organelles allows the parasite to adhere to an appropriate target cell, invade and induce the formation of a specialised parasitophorous vacuole (PV) in which it subsequently resides (reviewed in [5]).
Rhoptries are the largest of the Plasmodium secretory organelles and contain more than 20 proteins, many of which are unusual and have no recognisable orthologues, even in the closely related apicomplexan parasite Toxoplasma gondii (reviewed in [6]). Rhoptries are pear-shaped and membrane bound, and in transmission electron micrographs the bulb and neck appear to form distinct sub-compartments. The neck is electron-lucent while the bulb is electron-dense and contains internal membranes reminiscent of multivesicular endosomes in higher eukaryotes [7]–[9]. Individual proteins are not distributed throughout the rhoptry but are sub-compartmentalised within either the bulb or the neck [10]–[12].
Rhoptry biogenesis occurs by sequential fusion of Golgi-derived vesicles which deliver protein cargo into the rhoptry lumen [9],[13]. Rhoptry proteins pass through the endoplasmic reticulum (ER) and the Golgi [14],[15], but specific targeting signals which direct protein sorting into rhoptry destined vesicles remain poorly understood. In mammalian cells, sorting of transmembrane proteins is mediated by cytoplasmic adaptor complexes (APs) which recognise specific motifs (e.g. the YXXΦ motif, where Φ is a hydrophobic amino acid) within their cytoplasmic tails. APs select cargo for inclusion into a transport vesicle and recruit coat components (e.g. clathrin) necessary for vesicular budding and transport [16],[17]. This mechanism has been shown to operate in Toxoplasma, and may also be conserved in Plasmodium [18],[19]. However, most Plasmodium rhoptry proteins described to date do not possess a transmembrane region and cytoplasmic tail, implying the existence of additional sorting pathways [6].
One possibility is that sorting within the Golgi occurs via a clustering mechanism whereby proteins en route to a particular destination aggregate into distinct sub-domains [20]. The rhoptry associated membrane antigen (RAMA) is a glycosylphosphatidyl inositol (GPI)-anchored protein that is expressed early in the asexual red blood cell (RBC) cycle. Most rhoptry proteins are expressed at the late trophozoite stage but RAMA is first synthesised during the late ring stage, before the appearance of recognizable rhoptries, and appears to temporarily accumulate within compartments of the secretory pathway [15]. This unusual expression pattern suggests that RAMA may be involved in rhoptry biogenesis and protein targeting. Fluorescence Resonance Energy Transfer (FRET) experiments indicate that RAMA interacts with the low molecular weight (LMW) rhoptry complex [15]. The LMW complex is a heterodimer composed of rhoptry associated protein 1 (RAP1), and RAP2 or RAP3 [21]. Rhoptry targeting of the LMW complex occurs via the N-terminus of RAP1, although the mechanism is not understood [22]. We hypothesised that RAMA acts as an escorter for RAP1 to recruit RAP1, −2 and −3 into a rhoptry-destined protein complex.
Here we have used expression of heterologous reporter constructs and pull-down assays to map the RAP1 targeting signals and define the RAMA-RAP1 interaction. Our results provide evidence of a novel mechanism for trafficking of proteins to this unusual secretory organelle.
In P. falciparum schizont stage parasites, RAP1 is localised in the rhoptry bulb [21],[23]. Previously, it has been shown that the first 344 amino acids of RAP1 are sufficient for rhoptry targeting [22]. To more precisely define these targeting signals, we used constructs consisting of regions of RAP1 fused to green fluorescent protein (GFP). GFP was chosen as a reporter because it has previously been used in a variety of studies in Plasmodium and does not possess any endogenous targeting signals. When expressed on its own, GFP localises to the parasite cytoplasm. However, addition of sorting signals can result in trafficking of GFP to compartments of the secretory system [24],[25]. The expression of RAP1-GFP chimeras was driven by an inducible promoter with a pattern of expression similar to merozoite surface protein 2 (MSP2) [26]. A late stage promoter was selected to avoid aberrant targeting as a result of incorrect timing of expression [27],[28].
To verify that GFP could be trafficked to rhoptries, we initially generated two constructs – GFP fused to amino acids 1-344 of RAP1 (RAP1-344) and GFP fused to the entire RAP1 sequence (RAP1-FL). The constructs were introduced into P. falciparum 3D7 parasites and the trafficking of GFP was followed using fluorescence microscopy (Figure 1A). As expected, both constructs produced a punctate pattern of staining in schizonts, characteristic of localisation within the apical secretory organelles. Surprisingly however, the two constructs demonstrated subtly different localisation patterns. For RAP1-FL, GFP fluorescence co-localised with the rhoptry bulb marker RAMA [15], and mimicked the localisation of native RAP1 in wild-type parasites (Figure 1Aii and 1Aiii). In contrast, for RAP1-344, GFP staining only partially overlapped and was anterior to a larger RAMA-positive structure (Figure 1Ai). To ascertain the precise localisation of the RAP1-344GFP chimera, we performed double labelling experiments with PfRON4 (a rhoptry neck marker) [29] and apical membrane antigen 1 (AMA1, a microneme marker) [30] using specific antibodies (Figure 1B). Our results strongly suggest that for RAP1-344, GFP is not localised in micronemes (Figure 1Bi) but is localised in the rhoptry neck (Figure 1Bii).
To explore whether rhoptry neck localisation of truncated RAP1 was an artefact of our heterologous expression system, we reanalysed the original rap1 truncation mutant (D10ΔRAP1)[22]. This mutant, generated by single cross-over homologous recombination in the parasite line D10, has a truncated rap1 gene expressing amino acids 1-344 of RAP1 under the control of its native promoter. Interestingly, the same pattern was observed for D10ΔRAP1 as for RAP1-344, with GFP co-localising with PfRON4 (Figure 1Biii). To further confirm this finding, we localised RAP1 in D10ΔRAP1 and its parent line using immunoelectron microscopy (Figure 1C). In D10, native RAP1 is localised in the rhoptry bulb, whereas in D10ΔRAP1 the truncated RAP1 protein is localised in the rhoptry neck, adjacent to PfRON4. Taken together, this data strongly suggests that RAP1 contains a bi-partite rhoptry signal: amino acids 1-344 are sufficient for targeting RAP1 to the rhoptry and amino acids 344-782 are necessary to avoid re-localisation of the protein from the bulb of the rhoptries to the neck.
Having confirmed the ability of RAP1-344 to target GFP to the rhoptries, we set out to define the minimal region sufficient for rhoptry targeting. To this end, we generated a series of N-terminal RAP1 truncation-GFP fusions (Figure 2A and S1). RAP1-244, RAP1-144, RAP1-65 and RAP1-55 were all able to direct trafficking of GFP to the rhoptries. In contrast, for the RAP1-35 construct, GFP fluorescence produced a ‘cluster of grapes’ pattern. Co-localisation with serine repeat antigen 5 (SERA5) (Figure 2Aiii), confirmed that RAP1-35-GFP was targeted to the parasitophorous vacuole (PV), the default destination for the secretory pathway [24],[25]. RAP1 possesses a typical N-terminal signal sequence that is presumably cleaved upon entry into the ER [31]. SignalP analysis of the RAP1 sequence predicts that this cleavage occurs between amino acids 21 and 22. Replacement of the RAP1 signal sequence with a signal sequence from the acyl carrier protein (ACP – normally targeted to the apicoplast) [24] had no effect on rhoptry localisation (Figure 2B). Our data strongly suggests that the signal sequence of RAP1 directs the protein into the secretory pathway. Information contained in amino acids 22-55 (hereafter referred to as the RAP1 rhoptry signal) is then sufficient to divert the protein to the rhoptries.
In T. gondii, proteins that are targeted to the rhoptries can contain multiple signals that are independently sufficient but not necessary for correct localisation [32],[33]. To determine whether this is the case for RAP1, we generated a construct that contains the ACP signal peptide fused to amino acids 56-782 of RAP1 (i.e. the entire RAP1 sequence lacking the signal peptide and the rhoptry signal) fused to GFP (Figure 2C). Although this construct was partially targeted to discrete foci that co-localised with RAMA, the bulk of the fluorescence was distributed in the PV. This data suggests that amino acids 22-55 of RAP1 are necessary for optimal targeting to the rhoptries.
Having defined the RAP1 rhoptry signal, we were interested in the mechanism by which this region mediates targeting. Since RAMA is refractory to genetic deletion [34], we were unable to study the trafficking of RAP1 in RAMA deletion mutants. Furthermore, repeated attempts to overexpress full length RAMA, or RAMA lacking various domains (e.g. R1, R2 or R3 repeats) failed (results not shown), presumably due to toxic effects of overexpression of this protein. Instead, we decided to map the RAMA-RAP1 interaction in vitro. We reasoned that if RAMA acts as an escorter for the LMW complex, it should interact with the RAP1 rhoptry signal which is responsible for correct targeting of the complex. To test this hypothesis, we initially made a recombinant His6-tagged RAP1 protein representing amino acids 22-152 (RAP1(22-152)), which contains within it the RAP1 rhoptry signal, and used it in a pull-down assay (Figure 3). Our results indicate that RAP1(22-152) but not MSP4 (an irrelevant His6-tagged protein) bound RAMA in a schizont stage parasite extract (Figure 3A). To confirm these findings and more precisely map the RAP1 binding site within RAMA, we made RAMA-GST fusion proteins representing amino acids 482-758 (RAMAD), 759-840 (RAMAE), 759-798 (RAMAE1) and 799-840 (RAMAE2). We used these proteins together with RAP1(22-152) in pull-down assays (Figure 3B). RAMAE and RAMAE1 both bound to RAP1(22-152), whilst GST alone did not bind. Truncation of the C-terminus of RAP1(22-152) did not affect RAMAE binding, whereas deletion of the RAP1 rhoptry signal from RAP1(22-152) (construct RAP1(57-152)) abolished RAMAE binding (Figure 3C). Taken together these results demonstrate that the RAP1 rhoptry signal, involved in the targeting of the LMW complex to the rhoptries, acts as the binding site for the C-terminus of RAMA.
We had mapped the RAP1 rhoptry signal and the RAMA binding site to the N-terminus of RAP1. Due to low expression levels of RAP1-GFP chimeras we could not directly confirm RAMA binding by immunoprecipitation. Instead, we made a series of RAP1(22-152) mutant proteins and tested them in pull-down assays against RAMAE (Figure 4A). The same amino acids were also mutated in the RAP1-55 targeting construct so that their affect on RAP1 targeting in vivo could be examined (Figure 4B and S2) We focussed on residues 30–55 as these contain at least part of the information required for correct trafficking of RAP1. Amino acid alignment of RAP1 orthologues from different Plasmodium spp. failed to identify any potential conserved motifs within the RAP1 rhoptry signal (data not shown). Mutation of negatively charged residues (aspartate 39, 43 and 44) to either non-polar (alanine) or positively charged (arginine) residues failed to disrupt either rhoptry targeting or RAMA binding (Figure 4ii and 4iii). By contrast, mutation of aromatic residues (at positions 40, 42, 45, 47 and 48) to glycine abolished the RAMA-RAP1 interaction and resulted in mistargeting of GFP to the PV (Figure 4iv).
To analyse the individual importance of each of the aromatic residues we made mutants where only some of the aromatic residues were changed to glycines. Mutation of residues 40, 42, and 45 was insufficient to alter either RAMA binding or in vivo targeting (Figure 4Av and 4Bv). Simultaneous mutation of residues 40, 42, 45 and 47 or 47 and 48 abolished RAMA binding (Figure 4Avi and 4Avii). The same mutations in the RAP1-55 targeting constructs resulted in significant mistargeting of GFP to the PV, although some chimeric GFP could be observed in rhoptries (Figure 4Bvi and 4Bvii). This is likely a reflection of the sensitivity of the in vitro assay. In vivo, the reduced affinity of the interaction results in partial mistargeting, whereas in vitro the interaction falls below detectable levels. These results indicate that although residues 47 (tyrosine) and 48 (tryptophan) play a significant role in RAP1 targeting, it is the overall nature of the RAP1 rhoptry signal that is important.
The interaction between RAMA and RAP1 in vivo was initially demonstrated by FRET, a technique that measures photon transfer between two fluorophores that are in close proximity [15]. In our attempts to affinity purify the RAMA-RAP complex from schizont stage parasites, we found that only a small amount of RAMA co-precipitated with RAP1, and vice versa (results not shown). This data is consistent with previous studies [21], [35]–[37], and suggests that the RAMA-RAP interaction is transient. Both RAMA and RAP1 are synthesised as pre-proteins that are proteolytically processed within nascent rhoptries, presumably by a rhoptry-resident protease [15],[38]. We hypothesised that this processing may serve to dissociate the transient RAMA-RAP complex. The N-terminal pro-peptide of RAMA is unusually large and comprises more than 50% of the entire protein [15]. The N-terminus of the mature RAMA protein (RAMA p60) has recently been mapped using N-terminal sequencing (cleavage occurs between residues 477L and 478Q). Analysis of RAMA orthologues from different Plasmodium spp. indicates that the protease responsible for this cleavage recognises the sequence (D/E)SFL(Q/E) [39]. We examined the primary structure of RAMA and found that this sequence and/or closely related sequences are repeated eight times within the pro-peptide region but are not present within RAMA p60 (Figure 5). A putative cleavage site was also identified at amino acids 67-71 (ESFLE) of RAP1. Cleavage of the RAP1 pro-peptide has been mapped upstream of A124 and involves the removal of approximately 40 amino acids (in addition to the signal peptide) [38]. We attempted N-terminal sequencing of immunoaffinity-purified RAP1, but did not obtain any data presumably due to N-terminal blockage of the protein. We also performed a trypsin digestion and liquid chromatography-mass spectrometry (LC-MS) analysis. In two independent analyses, we obtained >60% coverage of RAP1 downstream of the putative cleavage site, but did not detect any peptides upstream of the cleavage site. The most N-terminal peptide detected corresponded to amino acids 74-91 of RAP1 (results not shown). The peptide corresponding to amino acids 71-73 (which would be present if cleavage occurs between 70L and 71E) is too small to be detected. This data, in combination with previously published data, strongly suggests that both RAMA and RAP1 are processed by the same rhoptry-resident protease.
Many, though not all, rhoptry proteins are secreted during merozoite invasion and are transferred to the PV of nascent ring stage parasites where they presumably play a role in the establishment of the PV membrane [40]. Earlier studies using D10ΔRAP1, have demonstrated that full length RAP1 is transferred to the PV during invasion, whereas truncated RAP1 is not [22]. Given our finding that the C-terminus of RAP1 contains a rhoptry bulb retention motif (see above), it is possible that RAP1 secretion is dependent on correct sub-organellar localisation. To more precisely map the signals within RAP1 required for rhoptry bulb retention and PV transfer, we generated a further series of RAP1 truncation-GFP fusions that included regions of the C-terminus of the protein. These parasites were examined by fluorescence microscopy both at schizont stage (to establish rhoptry bulb or rhoptry neck localisation) and at ring stage (to ascertain transfer to the PV). As expected, RAP1-344GFP, which is localised in the rhoptry neck (Figure 1Bii), was not transferred to the PV during invasion (Figure 6i). In contrast, for the full-length RAP1, RAP1-644 and RAP1-544 constructs, chimeric GFP was localised in the rhoptry bulb at schizont stage (Figure 1Aii and S3), and could be observed as a rim of fluorescence around newly formed ring-stage parasites indicating transfer to the PV (Figure 6iii and S3). The RAP1-444GFP chimera appeared to be only partially localised in the rhoptry bulb (Figure S3), but was nonetheless transferred to the PV during invasion (Figure 6ii). These results indicate that amino acids 344-444 of RAP1 are required for transfer of the protein to the PV. Our attempts to confirm sub-organellar localisation of the RAP1-GFP chimeras using immunoelectron microscopy were unsuccessful due to the relatively low level of expression of episomal constructs. However, our confocal microscopy results provide preliminary evidence that amino acids 344-544 of RAP1 are required for correct sub-organellar localisation of the protein within the rhoptry.
Apical organelles of apicomplexan parasites play a key role in invasion of target cells and the subversion of host cell function. Rhoptries of P. falciparum merozoites contain a complex proteome including components that have been identified as potential vaccine candidates (reviewed in [6]). However, little is known about mechanisms of rhoptry biogenesis and discharge.
In the present study, we examined the trafficking of the rhoptry protein RAP1. RAP1, together with RAP2 or RAP3, form the heterodimeric LMW complex which is localised in the rhoptry bulb of schizonts [21],[23]. During invasion, the LMW complex is secreted from the rhoptries and transferred to the PV of the nascent ring-stage parasite [35]. Truncation of the C-terminus of RAP1 results in disruption of its interaction with RAP2/RAP3 and causes RAP2 (and probably RAP3) to be retained in the ER [22]. In contrast, truncated RAP1 is still targeted to rhoptries, but is not transferred to the PV during invasion [22].
Our results confirm and expand on these earlier observations. Using expression of GFP chimeras we were able to show that information present between amino acids 23 and 55 of RAP1 is necessary and sufficient for optimal targeting to the rhoptries. We compared the RAP1 rhoptry signal to protein regions that have been implicated in rhoptry targeting in Toxoplasma, as well as other Plasmodium rhoptry proteins but were unable to identify a conserved motif. This suggests that the RAP1 rhoptry signal is specific for the LMW complex and it may be that unlike proteins targeted to the apicoplast [41] or exported into the host RBC [42],[43], many proteins destined for the rhoptries do not possess a common targeting signal. We have provided evidence that RAMA, a protein synthesised in the late ring stage and GPI-anchored in the Golgi lumen, acts as an escorter for the LMW complex via a direct association with the N-terminus of RAP1. Bulky aromatic amino acid clusters are known to be important for protein-protein interactions. In the case of the RAMA-RAP1 interaction, it appears that the overall organisation of aromatic residues within the RAP1 rhoptry signal is important for correct binding. However, in the absence of structural information, we cannot determine whether any or all of these residues directly contact RAMA, or whether disruption of RAMA binding and mistargeting in our mutants occurs as a result of conformational perturbation caused by glycine substitution.
RAP1 appears to possess a distinct signal for localisation within the rhoptry bulb and subsequent transfer to the PV during invasion. These findings are consistent with an earlier study in Toxoplasma which demonstrated that the pro-domain of the rhoptry protein ROP1 directs trafficking of a reporter to the rhoptry neck, whereas full-length ROP1 is preferentially enriched in the bulb [44]. The mechanism by which proteins can be partitioned within a single membrane bound organelle is not understood. Our data argues for the presence of a bulb-retention motif within the C-terminus of RAP1 which may allow interaction with other rhoptry bulb proteins (e.g. RAP2 and −3). It is worth noting that RAP1 is a major constituent of detergent-resistant microdomains (DRMs) in schizont stage parasites [37]. RAP1 has no obvious lipid anchor and is likely recruited into DRMs via association with some other protein.
Whether or not localisation of RAP1 in the rhoptry bulb is necessary for transfer of the protein to the PV per se, is not clear. One possibility is that rhoptry neck proteins are secreted before rhoptry bulb proteins and are deposited onto the surface of the target RBC. In contrast, rhoptry bulb proteins are trapped within the PV because their secretion occurs after the formation of the tight junction between the parasite membrane and the RBC membrane. The alternative explanation is that amino acids 344-444 of RAP1 contain a specific protein-protein interaction motif (e.g. necessary for interaction with RAP2 and −3) which is required for transfer to the PV. Detailed mapping of other sub-organellar localisation signals and PV transfer signals will help to differentiate between these two alternatives.
Based on the data presented above, we propose a model whereby RAMA binds RAP1 in the Golgi lumen and recruits RAP1, −2 and −3 into a complex (Figure 7). GPI-anchored proteins have a tendency to cluster in lipid rafts, and thus the complex is presumably anchored within a lipid raft at the Golgi exit face [45],[46]. Other proteins (e.g. the RhopH proteins which also interact with RAMA) may be recruited into the raft as well, thus generating rhoptry destined aggregates [15].
DRM clustering associated with protein oligomerisation has been shown to be essential for polarised trafficking of GPI-anchored proteins to the apical membrane in epithelial cells (reviewed in [47],[48]). Several of the known P. falciparum rhoptry proteins, including RAMA and RAP1, are associated with DRMs and it is tempting to speculate that this mechanism is involved in differential sorting of proteins within the Golgi. Interestingly, none of the known micronemal proteins have been found associated with DRMs, whereas several merozoite surface proteins do associate with DRMs [45]. This suggests the presence of distinct regions of membrane at the Golgi exit face which are defined by their protein and/or lipid composition that bud off as individual vesicles. Each vesicle then presumably interacts with specific components of the cellular trafficking machinery, possibly via a transmembrane escorter. In Toxoplasma, the cytoplasmic adaptor complex AP-1 has been implicated in rhoptry protein trafficking. A study by Hoppe and colleagues demonstrated that AP-1 binds in vitro to a region of the Toxoplasma rhoptry protein ROP2 that is sufficient to mediate rhoptry targeting in vivo [18]. The biological relevance of this finding has recently come into question as ROP2 appears to lack a transmembrane domain that is necessary in order for the ROP2 targeting region to be exposed at the Golgi exit face and available for binding to AP-1 [49]. Nonetheless, components of vesicular trafficking machinery, including AP-1, have been identified in the P. falciparum genome but their precise roles remain to be determined (reviewed in [40]).
Upon arrival at the rhoptry, the RAMA-LMW complex is dissociated by proteolytic cleavage [15],[38]. The presence of putative cleavage sites in the N-terminus of RAP1 and RAMA suggests that a single rhoptry-resident protease is responsible for their processing [39]. Cleavage of the N-terminus of RAP1 releases the LMW complex from RAMA. This may allow the LMW complex to interact with other proteins in the rhoptry bulb, potentially via the bulb-retention domain of RAP1 identified in this study [37]. In turn, degradation of the N-terminus of RAMA may release it from the hypothetical transmembrane escorter.
Proteins destined for the apicoplast or mitochondrion each possess an appropriate signal that allows their post-translational translocation into a pre-formed organelle [41], [50]–[53]. In contrast, many proteins destined for the apical secretory organelles appear to be targeted by a clustering mechanism. In Toxoplasma, the soluble microneme proteins MIC1, MIC3 and MIC4 are targeted via an interaction with transmembrane escorter proteins [54],[55]. In Plasmodium, microneme proteins of the EBL family are targeted courtesy of a conserved luminal domain presumably via interaction with a transmembrane escorter [56],[57]. In the current study, we present evidence that proteins can be similarly targeted to rhoptries via the formation of transient complexes that are packaged into transport vesicles and dissociated by proteolytic processing upon arrival at their destination. Given that most Plasmodium rhoptry proteins are not type 1 membrane proteins and therefore lack a cytoplasmic tail, it is likely that targeting to rhoptries via this mechanism is the rule rather than the exception.
P. falciparum asexual stage parasites were maintained in human erythrocytes (blood group O+) at a hematocrit of 4% with 10% Albumax (Invitrogen) [58]. P. falciparum 3D7 parasites were originally obtained from David Walliker at Edinburgh University. Cultures were synchronised as previously described [59].
All oligonucleotide primers used in this study are listed in Table S1. GFP fusion proteins for localization studies were encoded in transfection constructs under the regulation of the tetracycline-inducible expression system [26]. Regions of RAP1 were PCR amplified from P. falciparum 3D7 genomic DNA. For mutagenesis experiments, mutations were introduced into primers during synthesis. PCR products were digested with PstI and MluI and cloned in frame upstream of GFP. RAMAE1 and RAMAE2 recombinant fragments were PCR amplified from P. falciparum cDNA and cloned as previously described [15] into the GST-fusion vector pGEX-4T-1 (GE Healthcare). RAP1 recombinant proteins were PCR amplified from P. falciparum 3D7 genomic DNA. PCR products were digested with NcoI and XhoI and cloned into the His6-fusion vector pET28b in frame upstream of the His6 tag. Constructs were sequenced and confirmed to be free of unintended mutations.
His6-tagged RAP1 recombinant proteins were expressed in E. coli BL21 (DE3) (Novagen) and purified using TALON Metal Affinity Resin (Clontech) in accordance with manufacturer's instructions. RAMA-GST fusion proteins were expressed in E. coli BL21 (DE3) and purified using glutathione resin (Sigma) as previously described [15]. Protein expression was analysed using SDS-PAGE and immunoblotting with anti-His6 or anti-GST antibodies. Protein concentration was determined using the Bradford Assay (Bio-Rad).
Purified recombinant proteins were buffer exchanged into pull-down buffer (50 mM Na2HPO4, 75 mM NaCl, 0.1% TrionX-100, 5 mM imidazole, pH 7.4). P. falciparum 3D7 parasites were extracted from parasitised RBCs by lysis with 0.15% (w/v) saponin in phosphate buffered saline and solubilised in RIPA buffer (50 mM TrisCl (pH 8.8), 150 mM NaCl, 1% NP40, 0.5% sodium deoxycholate, 0.1% SDS) containing Complete Mini protease inhibitor cocktail (Roche). After lysis on ice for 5 min the insoluble material was spun down and the supernatant collected. The supernatant was diluted 1 part in 10 in pull down buffer prior to use. For the pull-down assay, 100 µg of the various His6-tagged RAP1 recombinant proteins were used as bait. These proteins were immobilised on TALON Metal Affinity Resin (Clontech) and incubated with either GST-fusion proteins or parasite lysate O/N at 4°C. The resin was washed with pull-down buffer and specifically bound proteins were eluted using imidazole (20 mM Na2HPO4, 0.5 M NaCl, 400 mM imidazole, pH 7.4). Eluted proteins were analysed on Coomassie stained SDS-PAGE gels and by immunoblotting with anti-RAMA [15] or anti-GST antibodies.
P. falciparum 3D7 parasites were transfected as described previously [52] with 100 µg of purified plasmid DNA (Qiagen). Positive selection for transfectants was achieved using 10 nM WR99210 and 0.5 µg/ml Anhydrotetracycline to prevent transgene expression.
Anhydrotetracycline was removed from parasite cultures 72 h prior to live imaging (in the presence of 10 nM WR99210) to allow expression of the GFP fusion. Prior to microscopy, parasites were incubated in culture medium containing 100 ng/ml 4′,6-diamidino-2-phenylindole (DAPI; Roche Molecular Biochemicals). Fluorescence images of schizont stage parasites were captured using a Carl Zeiss Axioskop microscope with a PCO Sensicam and Axiovision 2 software. For immunofluorescence assays, schizont stage parasites were fixed using 4% paraformaldehyde (ProSciTech) and 0.0075% glutaraldehyde (ProSciTech) as previously described [41]. After blocking in 3% bovine serum albumin (Sigma) the cells were incubated for 1 hour with rabbit anti-RAMA [15], mouse anti-AMA1 [60], mouse anti-RAP1 or rabbit anti-PfRON4 (Richard and Cowman, manuscript in preparation) antibodies. Bound antibodies were then visualised with Alexa Fluor-594 anti-rabbit IgG or anti-mouse IgG (Molecular Probes) diluted 1∶1000. Parasites were mounted in Vectashield (Vecta Laboratories) containing DAPI.
Parasites for electron microscopy immunolabeling were fixed and prepared as described previously (Healer et al., 2002). The primary antibodies used were mouse monoclonal anti-PfRAP-1 (1/500), rabbit anti-PfRON-4 (1/100). Samples were washed, then incubated with secondary antibodies conjugated to either 10 nm or 15 nm colloidal gold (BB International). Samples were then post-stained with 2% aqueous uranyl-acetate then 5% triple lead and observed at 120 kV on a Philips CM120 BioTWIN Transmission Electron Microscope.
|
10.1371/journal.pcbi.1007081 | ACAP1 assembles into an unusual protein lattice for membrane deformation through multiple stages | Studies on the Bin-Amphiphysin-Rvs (BAR) domain have advanced a fundamental understanding of how proteins deform membrane. We previously showed that a BAR domain in tandem with a Pleckstrin Homology (PH domain) underlies the assembly of ACAP1 (Arfgap with Coil-coil, Ankryin repeat, and PH domain I) into an unusual lattice structure that also uncovers a new paradigm for how a BAR protein deforms membrane. Here, we initially pursued computation-based refinement of the ACAP1 lattice to identify its critical protein contacts. Simulation studies then revealed how ACAP1, which dimerizes into a symmetrical structure in solution, is recruited asymmetrically to the membrane through dynamic behavior. We also pursued electron microscopy (EM)-based structural studies, which shed further insight into the dynamic nature of the ACAP1 lattice assembly. As ACAP1 is an unconventional BAR protein, our findings broaden the understanding of the mechanistic spectrum by which proteins assemble into higher-ordered structures to achieve membrane deformation.
| Membrane remodeling is needed for a wide range of cellular events. Our current understanding of how proteins bend membrane to achieve such remodeling has come, in large part, from studies on proteins that contain the BAR domain. These studies have elucidated that the BAR domain dimerize to form a crescent-shaped structure. This structure induces membrane deformation through scaffolding and/or insertion of an amphipathic loop into the membrane. Some BAR-containing proteins also contain a PH domain in tandem with their BAR domain. We have found previously that one such protein, known as ACAP1, relies primarily on the PH domain, rather than the BAR domain, for membrane deformation. To explain this phenomenon, we have utilized, in the current study, molecular dynamics simulations, Monte Carlo calculations, and together with Cryo-electron sub-tomography experiments, to gain insights into how the ACAP1BAR-PH protein assembles into an unusual protein lattice through multiple dynamics stages. The results are further verified by functional experiments.
| Membrane deformation is needed for a wide range of cellular processes, including intracellular transport, organelle biogenesis, cell division, and cell motility [1,2]. Some of the best characterized proteins that deform membrane possess a Bin-Amphiphysin-Rvs (BAR) domain [3–8]. Studies on how this domain structure induces membrane curvature have suggested two mechanisms. One way involves scaffolding [5,9,10]. The dimerization of the BAR domain produces a curved, banana-like structure, which can then impose curvature onto the underlying membrane through electrostatic interactions. A second way involves protein insertion into the membrane [4,11,12]. Some BAR domains possess an amphipathic helix, which can insert into one leaflet of the membrane to create asymmetry between the bilayers, resulting in curvature induction. In recent years, a more detailed understanding of how BAR proteins induce membrane curvature has come from high-resolution electron microscopy (EM) studies, which couples cryo-EM with protein crystallography, resulting in an atomic-level view of how BAR proteins are organized into higher-ordered structures on membrane for curvature induction [5,8,11,13,14]. Emerging from these studies has been a general paradigm for how BAR proteins deform membrane. Briefly, the dimeric BAR structure acts as the basic repeating unit, which propagates along its length through tip-to-tip interactions, and also laterally through side-to-side interactions, to assemble into lattice structures that appear as “criss-crossing” strands on membrane for curvature induction [15,16]. A number of recent work has revealed how BAR domains [17–19] or banana-shaped rods [20] cluster to form scaffolds on membranes. While the lattice assembly was shown to be dynamical in nature, the intermediary stages of the assembly process have been less clear.
We have recently uncovered a different paradigm for how a BAR protein deforms membrane. A subset of BAR proteins possesses the BAR domain in tandem with a Pleckstrin Homology (PH) domain. Early studies found that the PH domain in these proteins is critical for membrane deformation, but the explanation had remained elusive [21–24]. We recently addressed this puzzle in studying a GTPase-activating protein (GAP) for ADP-Ribosylation Factor 6 (ARF6), known as ACAP1 (Arfgap with Coil-coil, Ankryin repeat, PH domain 1). Besides its traditional role as a regulator of the ARF6, ACAP1 also acts as an ARF6 effector [25]. This involves ACAP1 functioning as a coat protein, which deforms endosomal membrane to generate transport carriers for recycling to the plasma membrane [26–28]. To achieve a better understanding of how ACAP1 acts in this capacity, we recently pursued structural studies on the BAR-PH tandem of ACAP1 (ACAP1BAR-PH), which is the minimal portion of ACAP1 sufficient for membrane deformation. The solved structure suggested that, rather than the BAR domain engaging the membrane, the PH domain contacts the membrane. We then pursued functional studies to show that a loop in the PH domain likely inserts into the membrane to impart curvature. Thus, rather than its BAR domain, the main driver of membrane deformation for ACAP1 is its PH domain [8].
We also pursued high-resolution EM-based studies to gain insight into how ACAP1BAR-PH organizes into a higher-order lattice structure to achieve membrane deformation. The result revealed another key difference between how ACAP1 versus how a conventional BAR protein deforms membrane. Whereas conventional BAR domains contact the membrane along the entire length of their curved structure, through the concave side [29,30], only one end of the ACAP1BAR-PH dimer contacts the membrane. Consequently, whereas the basic repeating unit of protein lattices formed by conventional BAR proteins is the dimer, the tetramer constitutes the basic repeating unit in the ACAP1 lattice, which is achieved by the end of an ACAP1BAR-PH dimer that does not contact the membrane interacting instead with the mid-portion (arch) of another dimer, resulting in an “end-to-arch” interaction between two dimers in forming a tetramer [8]. Here, we have pursued multiple complementary approaches to gain insight into how ACAP1 assembles into this unusual lattice structure for membrane deformation.
We had previously reconstructed an ACAP1BAR-PH protein lattice on tubulated portions of liposomes, which predicts the structural organization needed for membrane deformation [8]. Overall, the ACAP1BAR-PH dimer is predicted to oligomerize, both longitudinally and laterally, in forming helical strands that wrap around a tubular membrane with regular periodicity, resulting in an “criss-crossing” appearance by which ACAP1 coats the membrane (Fig 1a). Initially, performing closer inspection of this arrangement, we could appreciate three major regions of protein contacts (highlighted by green, blue, and magenta boxes in Fig 1a). The relative positions of these three interfaces can be further defined when considering a grouping of six contiguous ACAP1BAR-PH dimers within the lattice structure, which consist of two dimers in the upper helical strand (defined as positions N-2 and N-1), two dimers in the middle strand (defined as positions N and N+1), and two dimers in the lower strand (defined as positions N+2 and N+3) (Fig 1a).
Interface I (green box in Fig 1a) is an inter-strand interaction, and involves the two dimers of the upper strand (positions N-2 and N-1) contacting the two dimers of the middle strand (positions N and N+1). This interface is further highlighted in Fig 1b, in which the relative position of one dimer in the upper strand (N-1, colored orange) is shown in relation to one dimer in the middle strand (N, colored cyan). Interface II (blue box in Fig 1a) is an intra-strand interaction, and involves two adjacent dimers in the middle strand interacting with each other (positions N and N+1). This interaction is further highlighted in Fig 1c, with one dimer (position N, colored in cyan) interacting with the other dimer (position N+1, also colored in cyan). We had previously noted the unusual nature of this contact, as it involves an “end-to-arch” interaction between the PH domain (at the end) of one dimer with the BAR domain (at the mid-section) of the other dimer in generating a tetramer, which is also the basic repeating unit of the lattice structure [8]. Interface III (magenta box in Fig 1a) is another inter-strand interaction, and involves two dimers in the middle strand (positions N and N+1) interacting with two dimers in the lower strand (positions N+2 and N+3). This interaction is further highlighted in Fig 1d, in which the relative position of a dimer in the middle strand (position N, colored cyan) to that of two dimers in the lower strand (positions N+2 and N+3, colored in orange) is shown.
Our previous reconstruction of the ACAP1BAR-PH lattice did not achieve sufficient resolution to identify specific residues that mediate these three major interfaces of contacts. Thus, we initially sought to refine the resolution by pursuing a type of molecular dynamics (MD) simulation, known as MD flexible fitting (MDFF). This is a computational approach that employs MD simulations to fit atomic structures into cryo-EM density maps [31] and has been successfully applied to improving the structural details of multiple macromolecular assemblies [32–35]. A grouping of 12 contiguous ACAP1BAR-PH protein dimers were embedded onto the cryo-EM density map (summarized in S1 Fig; map resolution 14 Å). This resulted in ~140,000 protein atoms being analyzed, or ~2,340,000 total atoms when the solvent and ions were also included. The crystal structure of ACAP1BAR-PH protein (PDB: 4NSW) was used for the fitting.
Protein configurations before (colored in green) and after (colored in magenta) simulations are shown in Fig 1e, with further details of typical refined regions shown in Fig 1f and 1g. The Fourier Shell Correlation (FSC) profile between the map and the model validated the improvement of the structure after simulation (Fig 1h). The result also revealed that a number of interacting residues would be missed by the rigid-body docking method (S2 Fig), and many of these residues are predicted to reside in the three major interfaces (S3 Fig and S1 Table).
In the more refined structure, interface I is predicted to be composed of two contacting regions (Fig 2a), with the upper of these two contacts highlighted in Fig 2b, and the lower contact highlighted in Fig 2c, 2d, and 2e. For the upper contact, multiple residues (R236, E239, Q240, Q247 and K248) in the α4 helix of the BAR domain in the dimer N are predicted to interact with the same group of residues in the dimer N-1. This interaction occurs symmetrically and in an anti-parallel fashion, which would be analogous to two persons shaking their right hands. The simulation results further predicted that residue Q240 forms H-bonds with Q247 and K248 in this contact. For the lower contact, simulation results predicted that multiple residues (D99, H103, Q107, R118, D122, R125, D126, R129, Q150 and E154) in the α2 helix of the BAR domain in the dimer N would interact symmetrically and in an anti-parallel fashion to the same group of residues in the dimer N-1. This contact could be further sub-divided into three regions, denoted as left (Fig 2c), central (Fig 2d) and right (Fig 2e). The central region has multiple charged residues (R118, E122, R125, D126, R129), with the dimer N interacting symmetrically and anti-parallel with the dimer N-1 (Fig 2c), while the left (Fig 2d) and right (Fig 2e) regions have several polar residues. Overall, Interface I is created by four salt-bridges, residue pairs E122-R125, and E122-R129, twice in each dimer pair, and six H-bonds, residues Q107-E154, E122-R125, E122-R129, twice in each dimer pair due to anti-parallel symmetry (summarized in Table 1).
Interface II is predicted to be created by a portion of the BAR domain in the dimer N+1 forming a binding pocket, and a portion of the PH domain in the dimer N forming a loop that inserts into the binding pocket (Fig 2f). Specifically, a portion of the α0 helix (residues 1 to 20) of the BAR domain in the dimer N+1 is predicted to form a binding pocket, while residues 276 to 282 of the PH domain in the dimer N is predicted to form the insertion loop (designated as Loop1 and located between the β1 and β2 sheets of the PH domain). The refined model also suggested some specific interactions. These include salt bridges and H-bonds formed by residue D6 of the BAR domain in the dimer N+1 interacting with residue K281 of the PH domain in the dimer N. H-bonds are also formed between residues D6 of the BAR domain (in the dimer N+1) and S277 of the PH domain (in the dimer N), and between residues E9 of the BAR domain and N278 of the PH domain. In addition, the insertion loop (Loop1 in the PH domain of the dimer N) is found to interact with part of the α4 helix (residues 234 to 245) of the BAR domain in the dimer N+1. This interaction should further stabilize the main contact described above, created by the insertion of Loop1 in the dimer N into the binding pocket formed by the α0 helix in the dimer N+1 (Fig 2f). An H-bond was also observed between R241 of the BAR domain in the dimer N and S277 of the PH domain in the dimer N+1 (Fig 2g and Table 1).
Interface III is predicted to be generated by two α helices in the BAR domain of the dimer N interacting with the BAR domain in the dimer N+2 and the PH domain in the dimer N+3 (Fig 2h). Specifically, D92 of α2 helix in the BAR domain of the dimer N forms a salt bridge and H-bond with R236 in the α4 helix of the BAR domain in the dimer N+2 (Fig 2i and S1 Table). Another part of the α2 helix (charged residue K82) in the dimer N contacts with the β4/β5 loop of the PH domain (charged residues D310 and D311) in the dimer N+3 (Fig 2i).
Overall, as the electrostatic interactions are about one order of magnitude higher than the van der Waals interactions, additional analysis suggested that charged residues at the three interfaces should provide the main driving force for the protein contacts (S1 Table).
We next sought to confirm the above predictions through functional studies. For interface I, we generated three sets of mutations. One set targeted a cluster of clustered charged residues, R236, E239 and K248, which were predicted to participate in the upper contact in Interface I (see Fig 2b). Mutation of these residues to alanines (R236A/E239A/K248A) resulted in mutant 1 (Mut1) (Fig 3a). A second set of mutations targeted another set of charged residues R118, E122, R125 and D126, which were predicted to participate in the lower contact in Interface I (see Fig 2d). Mutation of these residues to alanines (R118A/E122A/R125A/D126A) resulted in mutant 2 (Mut2) (Fig 3a). We also generated mutant 3 (mut3), which combined the mutations in Mut1 and Mut2 (R118A/E122A/R125A/D126A/R236A/E239A/K248A) (Fig 3a). For Interface III, we targeted a predicted critical interaction between the D92 residue in the BAR domain of one dimer and the R236 residue in the BAR domain of another dimer (see Fig 2i). Mutation of these residues to alanines (D92A/R236A) generated mutant 4 (Mut4) (Fig 3a).
Interface II was more challenging to disrupt. This contact involves two ACAP1BAR-PH dimers interacting through an “end-to-arch” interaction in forming a tetramer (see Fig 1c). Thus, disruption of this interaction required that we only target one of the two PH domains in the ACAP1BAR-PH dimer. We had previously overcome this hurdle by noting that ACAP1BAR-PH dimerizes in a symmetrical and anti-parallel fashion, and this orientation could be preserved by covalently linking two ACAP1BAR-PH monomers in a “head-to-tail” fashion [8]. Importantly, the resulting fusion protein (referred as BARPH-BARPH) was shown to be functional, retaining the ability to tubulate liposomes similar to that seen for wild-type ACAP1BAR-PH (which dimerizes through non-covalent interaction) [8]. Thus, to target Interface II, we generated a dimer fusion protein and then mutated residues S277, N278, F280, and K281 in only one of the two PH domains in this fusion protein, resulting in mutant 5 (Mut5) (Fig 3a).
We then pursued functional studies. Initially, we examined membrane binding by ACAP1, and found that all five mutations impaired the recruitment of ACAP1 to membrane to some extent (Fig 3b). Subsequently, we pursued additional approaches to assess membrane deformation by ACAP1. First, we examined the ability of ACAP1 to induce liposome tubulation, as done previously [8], and found that all mutations affected this ability of ACAP1 (Fig 3c). We also pursued a second assay. ACAP1 acts as a coat protein in generating transport carriers for endocytic recycling, and we had previously established the reconstitution of ACAP1-dependent carrier formation from endosomal membrane [8]. Performing this reconstitution, we confirmed that all five mutants also reduced the ability of ACAP1 to support vesicle formation from endosomal membrane (Fig 3e). We also performed MD simulations of Mut3, Mut4 and Mut5, which revealed disassociation of dimers from tetramer state, leading to disruption to the protein lattice (S4 Fig). Thus, these results confirmed in complementary ways that the MDFF simulations had identified key protein-protein contact sites that enable dimeric ACAP1 to assemble into a higher-ordered lattice structure for membrane deformation.
We then considered a potential clue. Whereas the mutations had modest effects on the membrane-binding assay, they exhibited more severe effects in the two assays of membrane deformation, i) liposome tubulation and ii) carrier formation from endosomal membrane. This disparity suggested that the initial stage of lattice assembly, which involves the recruitment of ACAP1 to membrane, may be dynamic, and thus could not be captured completely by a simple membrane-binding assay. Thus, to explore this possibility, we next embarked on simulation studies, which are better suited in capturing dynamic situations.
We initially pursued an algorithm based on parallel tempering Monte Carlo (PTMC) simulation [36], which investigates the orientation by which the ACAP1BAR-PH dimer is adsorbed onto a negatively charged membrane surface under different surface charge densities (SCD) and ionic strengths (IS). Simulation parameters (SCD and IS) are listed in S2 Table. The parameters for modelling the membrane surface have been described previously [37]. Moreover, a coarse-grained united-residue model was employed, i.e., each amino acid of the protein was reduced to an interaction site centered at the α-carbon of the residue, with parameters as previously described [37]. Since the inter-molecular interactions between the charged surface and the protein are important, and the intra-molecular interactions of the protein itself are less important, the protein structure was kept rigid. This modelling approach has been successfully applied previously to elucidate the adsorption orientation of lysozyme [36] and antibodies [37] on charged surfaces.
For the ACAP1BAR-PH dimer (PDB ID: 5H3D), which was predicted to be neutrally charged, there was no significant electrostatic repulsion with the negatively charge surface. When IS and SCD were set as 0.18 M and -0.127 C·m-2, respectively, “one-end-on” orientation (shown in Fig 4a) became favored, with the potential energy (-303.5 kJ/mol), which is lower than that of symmetrically binding (-274.4kJ/mol). We also manually constructed a symmetric dimer structure, and similarly the asymmetric binding model is more favorable. These findings predicted that the ACAP1BAR-PH dimer would be approaching the membrane asymmetrically, and thereby explaining why its final configuration in the lattice structure shows only one of the two ends in the dimer contacting the membrane.
We sought to confirm this prediction in two ways. First, we sought validation that PTMC simulation would accurately predict the recruitment behavior of BAR proteins that have been well characterized. For an F-BAR protein (PDB ID: 2EFK [38]) (Fig 4b), which has -6e net charges, the electrostatic repulsion still existed, due to the relatively uniform distribution of charges on protein surface and a smaller dipole moment. In this case, adsorption strength was relatively weak. At a low surface charge density, electrostatic interactions were similar to van der Waals interactions. At a high surface charge density, electrostatic interactions became dominant, and this was caused by the shielding effect of solution ionic strength on the surface charge. We obtained almost the same optimal orientation with SCD of 0.007 and 0.127, which was a “lying-on-side” orientation that involves the entire length of the protein interacting with the model surface. Notably, this mode of membrane interaction has been predicted previously as an intermediate stage of lattice assembly for this BAR protein [5]. We further noted that the key residues predicted to mediate membrane binding include K56, E92, K104, Q107, K114, K122, R125, Q160, A167, Q170, and K174 (S3 Table), and a number of these residues have already been confirmed by previous functional studies [5,10,38].
For the N-BAR protein (PDB ID: 1X03 [12]) (Fig 4c), which has -20e net charge, there was significant electrostatic repulsion with negatively charge surface, and consequently adsorption strength was significantly reduced. However, due to the uneven distribution of charged residues on the protein surface and the shielding effect of the strong solution ionic strength on the electrostatic repulsion, the negatively charged protein still adsorbed onto the negatively charge surface. With increasing surface charge density, adsorption became stronger. This resulted in almost the same optimal orientation with SCD = 0.007 and SCD = -0.127. In these cases, the “two-end-on” orientation was the optimal orientation. Moreover, as shown in S3 Table, the key adsorption residues were predicted as R174, Q175, G176, K177, I178, and E182, with the positively charged R174 and K177 interacting electrostatically with the negatively charged surface being predicted to be the main driving force. Notably, these predictions have also been confirmed previously by functional studies [4,12].
We next pursued a second way of validating the results of the PTMC simulations on ACAP1. Besides predicting an asymmetric approach to the membrane by ACAP1, PTMC simulations also predicted specific residues that are critical for this behaviour. These residues could be sub-divided into two regions of ACAP1, with one cluster (F280, K281, D322, and E325) located in the PH domain, and another cluster (R147, R148, A149, Q150, and Q151) located in the BAR domain. A critical role for the clustered residues in the PH domain was expected, as our previous structural elucidation of the ACAP1 lattice on membrane revealed that this region provides the sole means by which the lattice contacts the underlying membrane [8]. We had also performed functional studies that confirmed this situation [8]. In contrast, a role for the clustered residues in the BAR domain was unexpected, as this region was not observed to contact the membrane in the previously elucidated structure of the ACAP1 lattice on membrane [8]. Thus, we next pursued functional studies to confirm this unexpected finding.
We generated two types of mutations. When the R147 and R148 residues were mutated to glutamates (R147E, and R148E; Mut 6; Fig 4d), we found that membrane binding of the ACAP1BAR-PH protein was reduced to some extent (Fig 4e). When the entire cluster was converted to alanines (R147A, R148A, A149, Q150A, and Q151A; Mut 7; Fig 4d), membrane binding was affected similarly (Fig 4e). In the liposome tubulation assay, we found that the mutations reduced membrane deformation by ACAP1 more dramatically (Fig 4f). Similarly, we found that that mutations had a more dramatic effect in reducing the reconstitution of ACAP1-dependent endocytic recycling carrier formation (Fig 4g).
We further noted that these results on mutants 6 and 7 paralleled those seen above for the effects of mutants 1–5. That is, the membrane-binding assay was only modestly affected by the mutations, while the assays of membrane deformation, liposome tubulation and carrier formation from endosomal membrane, were affected more drastically. Moreover, as the residues in the BAR domain were not seen to contact the membrane in the ACAP1 lattice structure that we had previously elucidated [8], the collective considerations suggested that the BAR domain residues likely participated in ACAP1 contacting the membrane in a dynamic manner.
The position of the residues in the BAR domain also suggested how this dynamic recruitment could occur. In the solved structure of the ACAP1 dimer, these residues are located in close proximity to the residues in the PH domain, which we had previously documented to participate in membrane contact [8]. Moreover, when considering that the residues in the BAR domain are positioned more laterally than these residues in the PH domain, we concluded that the participation of the BAR domain residues would result in the ACAP1 dimer initially contacting the membrane in a more “tilted” manner than that would have been predicted if only the residues in the PH domain were involved (S4 Fig).
Unlike other BAR proteins, such as PICK1 [39], which form tetramer or octamer in the solution, ACAP1 exists as a dimer at the concentration from 10 to 50 μM [8]. We next addressed a fundamental question arising from the prediction that the ACAP1 dimer would initially contact the membrane through only one of its two ends. As this dimer is structurally symmetrical, how can it behave asymmetrically for membrane contact? To gain further insight, we next pursued additional simulation studies.
Initially, we performed multiple independent MD simulations of a single ACAP1BAR-PH dimer in solution (Fig 5a). By examining the fluctuation of Cα atoms in the protein backbone, we uncovered that the ACAP1BAR-PH dimer was intrinsically asymmetric in its dynamics. B-factors computed from atomic displacement of the residues indicated the relatively active regions on the molecular surface of the PH domain (Fig 5b). Remarkably, residue loops in the PH domain, which facilitated the interaction between the ACAP1BAR-PH dimer and the membrane surface were also found to be dynamic in simulations, even when no membrane was present. The two PH domains of the protein dimer showed different but consistent root-mean-square-fluctuation (RMSF) profiles in all three independent simulations (Fig 5c). One of the PH domains (PH1) always had relatively larger fluctuations in Loop 1 (residue 276–282) (>2.5 Å) than the other PH domain (PH2). Also, the RMSF of Loop 4 (residue 322–235) in one PH domain, serving as the linking residues between Loop 2 and Loop 3, was higher than that in the other PH domain. These fluctuations were predicted to disrupt membrane binding by the PH domain on one end of the ACAP1BAR-PH dimer greater than that of the PH domain in the other end. Note that the initial protein structure of MD simulations is the crystal structure 4NSW, which consists of two chemically identical chains. In our MD simulations, either PH domain may possess a higher RMSD. This phenomenon may come from an allosteric effect [40]. The asymmetry of the RMSF are consistent with the “one-end-on” configuration, i.e., it would be much easier for PH2 to bind to the membrane than PH1.
Principle component analysis (PCA) of the MD trajectories further supported the above conclusion. The two monomers within the ACAP1BAR-PH dimer were found to exert different but correlated dynamics even without the presence of the membrane (Fig 5d). In the first few low-frequency modes (sorted by the associated eigenvalues in descending order), introduction of a model membrane surface altered the residue fluctuation correlation matrix for both monomers. Generally, the two monomers had a stronger dynamic correlation without the lipid molecules. Integrating more normal modes ceased the difference between proteins, regardless of the presence of a model membrane. Projection of the trajectories onto the first two essential dynamics of the protein also revealed two distinct dynamical states (S7 Fig). Further analysis employing the time-lagged independent component analysis (TICA) [41,42], which finds a subspace with maximized autocorrelation, also show that the two dynamical states are distinguishable and independent of minor revision to molecular force-field (S8 Fig). Thus, these findings suggested that the distal regions of the ACAP1 dimer exert unique internal dynamics even in solution, and this behavior diminishes after it comes toward the membrane.
We then pursued another line of investigation that further reinforced the dynamic nature by which ACAP1 assembles into its protein lattice structure. Previous studies on the assembly of BAR proteins into lattice structures on membrane have sought insight into intermediary stages by examining the coating organization of these proteins on non-tubulated portion of liposomes, which is predicted to reveal a stage of lattice assembly toward its final functional form [5]. Taking a similar strategy, we initially also observed ACAP1BAR-PH to coat liposomes on both non-tubulated and tubulated regions (Fig 6a). Thus, to gain insight into an intermediary stage of ACAP1 lattice assembly, we next sought to deduce how it is organized on the non-tubulated portions of liposomes.
Sub-tomogram averaging suggested that ACAP1 on these liposomes formed curved densities, which are organized into particle units that are ~25 nm long and ~8 nm wide. These particles were flanked by adjacent similarly shaped particles that run roughly parallel (Fig 6b). We fitted a structural model of ACAP1BAR-PH dimer (PDB: 4CKG) onto the curved density map and found that the coated layer is longer than the length (~16 nm) of the ACAP1BAR-PH dimer. Thus, each particle unit was predicted to have more than one dimer. Further analysis revealed that the EM map could match closely with two ACAP1BAR-PH dimer molecules packed with each other via the interactions between the PH domain and BAR domain of the adjacent protein dimer in the same row (Fig 6c). Notably, these tetramers exhibit a more elongated shape as compared to tetramers that we had previously observed on tubulated liposomes.
Collectively, the above findings suggested that the ACAP1BAR-PH dimer packs onto planar membrane (non-tubulated portions of liposomes) with a different arrangement than that previously seen on curved membrane (tubulated portions of liposomes). Thus, we concluded that we have detected an intermediary stage of ACAP1 lattice assembly on membrane. In this case, it involves tetramers of ACAP1 having already been formed on membrane, but not having reached its final configuration seen on tubulated portions of liposomes. Importantly, such a result further reinforced the dynamic nature of the ACAP1 lattice assembly.
We have pursued multiple, and complementary, approaches to gain insights into how ACAP1 assembles into an unusual lattice structure for membrane deformation. The unusual organization was originally revealed by pursuing high-resolution, structural, and EM-based studies [8]. However, due to the limited resolution previously achieved, we were unable to address a key question. What are the specific protein-protein contacts that mediate this unusual lattice organization?
In the current study, we initially pursued MDFF simulations to refine the model, which predicted specific residues to participate in forming the key contact points. The nature of the residues involved suggested that electrostatic interactions would be the main driver in assembly of the ACAP1BAR-PH lattice structure. Further notable was that a number of the interacting residues predicted by MDFF simulations would have been missed by the rigid-body docking method, and importantly many of these residues are predicted to participate in the major contact points within the lattice.
We then pursued functional studies to confirm the predicted key contact points. One assay examined ACAP1 binding to membrane, while two other assays assessed membrane deformation by ACAP1, these being liposome tubulation and carrier formation from endosomal membrane. Although all three assays were affected by mutations of the predicted residues, a notable difference was that the mutations affected the ability of ACAP1 to deform membrane more severely than its ability to bind membrane. Thus, when also considering that the two assays of membrane deformation are predicted to interrogate a more complex situation than the simpler membrane-binding assay, we concluded that ACAP1 is likely recruited to the membrane more dynamically than that could be detected by the membrane-binding assay, which monitored a more static situation.
We then pursued simulation studies, as they are better suited for dynamic situations. ACAP1 exists in solution as a dimer that forms a symmetrical curved structure. However, unlike the conventional BAR proteins, which have been predicted to be recruited to the membrane symmetrically, having both ends of their dimeric curved structure contacting the membrane, PTMC simulations predicted that the ACAP1 dimeric structure would be recruited to the membrane asymmetrically, having only one of the two ends of the curved structure contacting the membrane. This prediction also suggested a key puzzle to address. How can a symmetrical structure bind to the membrane in an asymmetric fashion? Pursuing further simulation studies, our results predicted that the PH domain in the ACAP1 dimer exhibits dynamic fluctuations in solution, which would explain why the ACAP1 dimer initially contacts the membrane asymmetrically.
We had previously pursued a simpler simulation approach, which allowed us to model only how the concave side of the curved ACAP1 dimer approaches the membrane in a perpendicularly fashion. In the current study, pursuing a more comprehensive analysis, which queries all angles by which ACAP1 approaches the membrane, we have achieved a remarkable insight. The simpler simulation that we had previously pursued identified key residues in the PH domain to be critical for membrane binding. This was expected, as the previous structural elucidation of the ACAP1 lattice also shows these residues to be involved in membrane binding[8]. In contrast, the more comprehensive simulation analyses that we have pursued in the current study predict that a region in the BAR domain would also participate in ACAP1 contacting the membrane to initiate lattice assembly. This was unexpected, as this region of ACAP1 had not been observed to contact the membrane in our previous structural elucidation of the ACAP1 lattice.
Importantly, the location of these BAR domain residues also suggests how they could be participating in membrane contact in a dynamic manner. Relative to the PH domain that contacts the membrane, the predicted residues in the BAR domain is located more proximal and lateral. Thus, the participation of these residues in membrane binding predicts that the curved dimeric ACAP1 structure would contact the membrane in a more “tilted” fashion. Notably, such a scenario is reminiscent of how a conventional BAR protein has been predicted to undergo assembly to form a functional protein lattice on membrane, as its curved dimer has also been proposed to contact the membrane initially through its side rather than perpendicularly through its concave surface. Thus, despite exhibiting notable differences in their final lattice organization on membrane, with the dimeric ACAP1 exhibiting asymmetric contact with the membrane and the conventional BAR domain exhibiting symmetric contact with the membrane, both types of lattices are predicted to transit through an intermediary stage of assembly that involve the more lateral surface of their curved dimeric structure coming into contact with the membrane.
We also pursued another line of investigation that further reinforces the dynamic nature by which ACAP1 assembles into a lattice structure for membrane deformation. We had previously pursued structural and EM-based studies to elucidate a lattice structure formed by ACAP1 on tubulated liposomes, which is predicted to be the protein organization that deforms membrane. In the current study, we have further combined structural and EM-based studies with computational approaches to uncover how ACAP1 is organized on the non-tubulated portion of liposomes, which is predicted to gain insight into an intermediary stage of lattice assembly. As cryo-electron tomography and sub-tomogram averaging suggested a stage of lattice assembly in which tetramers of ACAP1 have been formed but have not reached their final configuration seen on tubulated liposomes, we conclude that the dynamic assembly of the ACAP1 lattice extends even to the point when tetramers have been formed. Thus, ACAP1 exhibits dynamic behavior not only in its initial stage of recruitment to membrane, but also subsequently during its assembly on membrane into a higher-ordered structure for membrane deformation.
ACAP1BAR-PH (a.a. 1–377) and different mutants were cloned into the plasmid pGEX-6P-1 (GE Healthcare), expressed as GST-tag fusion proteins in Escherichia coli BL21 (DE3) cells. Cells were grown in Terrific Broth medium at 37°C until the OD at 600 nm reached 1.2~1.5, and then induced at 16°C for 16 ~18 hours with 0.2 mM IPTG. Cells were harvested and re-suspended in PBS buffer (140 mM NaCl, 2.7 mM KCl, 10 mM Na2HPO4, 1.8 mM KH2PO4, pH 7.4) and lysed by sonication. After centrifugation for 30 minutes at 15,000 rpm, the supernatant was collected and incubated with glutathione-Sepharose 4B at 4°C, and then was washed by PBS buffer. After cleavage using Precision Protease (GE Healthcare) to remove the GST tag, the eluted target proteins were changed to buffer A (50 mM Hepes, pH 7.4, 50 mM NaCl) and stored at -80°C. Site-directed mutations of select residues were performed by overlap PCR. To introduce mutations to only one subunit of dimeric BAR-PH, one copy of BAR-PH was inserted into plasmid pGEX-6P-1 with nonstop, and then another copy with Deletion (S277-N278-F280-K281) was cloned into pGEX-6P-1-BAR-PH, to generate a tandem fusion version Mut5 (BAR-PH-Deletion), with a sequence (GGGSGGRLGSSNSG) as a linker between BAR-PH and deletion.
All lipids were purchased from Avanti Polar lipids. Lipid mixtures were similar to that previously described [8], containing 40% phosphatidylcholine (DOPC), 30% phosphatidylethanolamine (DOPE), 20% phosphatidylserine and 10% of L-α-phosphatidylinositol-4,5-bisphosphate (PI(4,5)P2). They were dried under gas nitrogen and then kept under vacuum for at least three hours. Dry lipid mixtures were suspended in 50 mM HEPES, pH 7.4, 50 mM NaCl for 30 minutes at 37 °C, frozen in liquid nitrogen, and thawed at 37 °C for 5 cycles, and extruded through membrane filters of 0.2 μm for the production of 200 nm liposomes. For the sedimentation assay, the 200 nm liposomes (1 mg/ml) and ACAP1BAR-PH proteins (0.2 mg/ml) were incubated for 60 minutes at room temperature before ultra-centrifugation at 250,000 g for 15 minutes. The supernatants and pellets were then subjected to SDS-PAGE analysis.
The reconstitution system was performed essentially as previously described [8]. Briefly, to collect total membranes and cytosol, HeLa cells were incubated with biotin-conjugated transferrin (Tf) at 4°C for 1 hour and then at 37°C for 15 minutes, which allows a pool of Tf receptor (TfR) at the cell surface to accumulate at the early endosome in tracking endosomal membranes. Cells were then resuspended in buffer (20mM HEPES, pH 7.4, 150 mM NaCl), followed by homogenization by passing through a 23-gauge needle 16 times on ice. The homogenate was then subjected to centrifugation to obtain total membranes (with early endosomes labeled by biotin-conjugated Tf) and cytosol.
To reconstitute recycling vesicles from endosomal membrane, total membranes and cytosol, collected as described above, were incubated with 1mM GTP at room temperature for 30 minutes. To detect the level of recycling vesicles formed after this incubation, the sample was subjected to centrifugation at 13,000 x g for 20 minutes at 4°C to derive pellet fraction (P), which contains organellar membranes, and supernatant fraction, which containing vesicles and cytosol (S). Recycling vesicles were detected in the supernatant fraction by blotting for biotin-conjugated Tf using a horseradish peroxidase-conjugated streptavidin.
To assess the effect of different mutations of ACAP1 on the formation of recycling vesicles, HeLa cells were treated with siRNA against ACAP1, followed by the collection of total membranes and cytosol, as described above. Cytosol was also collected from HeLa cells that overexpressed different mutant forms of myc-tagged ACAP1. Cytosol from the two sources of cells (treated with siRNA against ACAP1 or overexpressing different mutant ACAP1) were mixed at 9:1 ratio, respectively, to obtain physiologic level of different mutant ACAP1 expressed in the cytosol. This resulting cytosol was then incubated with total membrane derived from HeLa cells treated with siRNA against ACAP1. The level of recycling vesicles generated after this incubation was then assessed through the tracking of biotin-conjugated Tf, as described above.
Simulations employed cryo-EM density maps from one of two classes of protein lattices associated with tubular membranes of various radius. The system contained ~140,000 protein atoms and ~2,340,000 total atoms, including solvent. Six protein tetramers served as the basic configuration unit for MDFF simulations. Initial structures of the protein were based on the crystal structure of ACAP1BAR-PH protein (PDB: 4NSW). Proteins were solvated in a box of TIP3P water molecules [43] with 180 mM KCl using VMD [44]. Periodic boundary conditions were introduced to the system.
The MDFF simulations were performed using NAMD 2.11 [45] with CHARMM36 force field [46]. A timestep of 1 fs has been used. Cut-off distance was 10 Å for the non-bonded interactions. Temperature was maintained at 310 K using a Langevin thermostat coupled to all heavy atoms with a damping coefficient of 5 ps-1. Restraints for secondary structures were introduced to the system. Symmetrical restraints [47] were also introduced for simulating part of a helical structure.
A total of 27 ns of trajectory was generated. Only data after reaching equilibrium were taken for further analysis. As a result, the final 15 ns of trajectories were used for analysis unless otherwise specified. All quantities shown are the averaged values over the simulation windows.
A salt bridge was defined as a pair of acidic oxygen and basic nitrogen atoms separated by less than 4 Å of distance. A hydrogen bond (H-bond) is defined as a pair of polarized oxygen, nitrogen or sulphur atoms separated by less than 3.5 Å of distance and forming an angle less than 30° with the adjacent hydrogen atom. The occupancy of a salt bridge/H-bond is defined as the percentage of time that the salt bridge/H-bond existed throughout the simulations.
We have also constructed molecular systems, in which a single ACAP1BAR-PH dimer was placed in solvent with ions. Initial structures of the protein were based on the crystal structure of ACAP1BAR-PH protein (PDB: 4NSW). The systems were solvated and ionized with TIP3P water molecules [43] and 180 mM KCl ions using VMD [44]. Periodic boundary conditions were introduced. Three independent simulations have been performed.
The MD simulations were performed using NAMD 2.11 package [45]. The molecular force-fields used were either CHARMM36 [46] or CHARMM36m [48]. A timestep of 2 fs has been used. Electrostatic interactions were calculated using the particle mesh Ewald sum method [49] with a cutoff of 12 Å. Before production runs, the system was minimized in energy, heated to 310 K, and pre-equilibrated by step-wisely releasing harmonically restrained protein backbone and water oxygen atoms. Simulations were then continued in the constant NPT ensemble with 310 K and 1 atm. Langevin thermostats with a damping coefficient of 0.5 ps-1, and Langevin-piston barostats [50] with a piston period of 2 ps and a damping time of 2 ps were used.
In total five independent 500 ns of trajectories were generated for an ACAP1 dimer in ionic solvent. Only data after reaching equilibrium were taken for further analysis. Unless otherwise specified, the last 200 ns of data were used for analysis. All quantities presented in this article are averaged values over the chosen windows. The RMSF profile of two PH domains were calculated from three of the trajectories (using CHARMM36 force-fields). Convergence of the RMSF profiles have been confirmed by using a moving 50 ns window starting at time = 100, to 500 ns and lack of large deviations along the principle components of motions (S6 Fig).
A coarse-grained united-residue model [37] was employed to explore the preferred orientation of BAR domain proteins on negatively charged surfaces under different SCD and IS. In the model, each amino acid was reduced to an interaction site centred at the α-carbon of the residue.
As the objective of PTMC simulations is to obtain the favourable orientation of BAR proteins on model charged surfaces, and when also considering that the inter-molecular interactions between protein and surface are important, while the intra-molecular interactions within the protein itself are less important, the protein structures were kept rigid. The charged surfaces had both van der Waals’ (VDW) and electrostatic interactions with the protein. To mimic the membrane bilayer, surfaces were assigned net negative charges and the SCD were calculated according to membranes adopted in experiments. The simplified flat model considered the most essential two factors, surface charge density and ionic strength, which could make the flat model provide a reasonable prediction of protein orientation on membrane surfaces.
The parameters for model surfaces were taken from our previous works [37]. Five replicas, each in the canonical ensemble, were simulated in parallel at different temperatures of 310 K, 500 K, 800 K, 1500 K and 2500 K, which ensured sufficient energy overlap between neighbouring replicas to allow for the acceptance of configuration swaps. The swaps were performed every 500 cycles.
The adsorption and preferred orientation of five BAR domain proteins were studied at different IS and SCD. Orientation angle (θ) is used to quantitatively characterize the orientation of adsorbed proteins on surfaces, which is defined as the angle between the unit normal vector to the surface and the unit vector along the dipole of a protein. The cosine value of this angle (cosθ) was calculated for each possible orientation. The orientation and corresponding preferred configuration of each protein on different charged surfaces at different ionic strengths were then derived. The direction of dipole was also calculated. The total potential energy (Utot), VDW potential energy (UVDW), electrostatic potential energy (Uele) and the cosθ of each protein adsorbed on surfaces at different SCD and IS were also calculated.
The Monte Carlo (MC) simulation in each replica was carried out in a box of 30 nm × 30 nm × 30 nm. Initially, the protein was put 10 nm above the surface with a random orientation. During simulations, the protein was translated and rotated around its centre of mass. The displacement of each move was adjusted to ensure an acceptance ratio of 0.5. A total of 15×106 MC cycles were carried out, in which the first 5×106 cycles for equilibrium and another 10×106 cycles for production. Besides, 310K is the target temperature.
The ACAP1BAR-PH and mutant proteins (1 mg/ml) were incubated with liposomes (0.5 mg/ml) for 60 minutes and then the mixture was applied onto a glow-discharged carbon-coated EM grid and stained with uranyl acetate. The EM grids were examined with a transmission electron microscope (FEI Tecnai20 or Talos) and the micrographs were recorded with a Gatan UltraScan1000 CCD camera under the nominate magnification of 9,600X or with a FEI Ceta camera under the nominate magnification of 13,500X.
The sample preparation for Cryo-electron tomography was the same as previously described[8]. And tomographic data collection was also the same as described before[8]. Tilt images were aligned using Markerauto[51]. Tomograms were reconstructed using weighted back projection (WBP)37.To obtain average results, 976 individual particles were manually picked and cut out using IMOD36, To avoid any kind of over-fitting, the initial model for sub-volume alignment was generated by averaging all particles in random orientations[52] and refined using RELION 1.4[53]. Then the model was low pass filtered to 60 Å before subjecting into PEET37 for further refinement. The final STA procedure was carried out using PEET, following an iterative angular refinement step. To be noted that the parameter flgWedgeWeight in PEET was set to 0 to include the information in the missing wedge. The final searching step of Euler angle is 1°. Cryo-EM maps were displayed, and fitted with atomic models using UCSF Chimera[54].
|
10.1371/journal.ppat.1003200 | Glucocortiocoid Treatment of MCMV Infected Newborn Mice Attenuates CNS Inflammation and Limits Deficits in Cerebellar Development | Infection of the developing fetus with human cytomegalovirus (HCMV) is a major cause of central nervous system disease in infants and children; however, mechanism(s) of disease associated with this intrauterine infection remain poorly understood. Utilizing a mouse model of HCMV infection of the developing CNS, we have shown that peripheral inoculation of newborn mice with murine CMV (MCMV) results in CNS infection and developmental abnormalities that recapitulate key features of the human infection. In this model, animals exhibit decreased granule neuron precursor cell (GNPC) proliferation and altered morphogenesis of the cerebellar cortex. Deficits in cerebellar cortical development are symmetric and global even though infection of the CNS results in a non-necrotizing encephalitis characterized by widely scattered foci of virus-infected cells with mononuclear cell infiltrates. These findings suggested that inflammation induced by MCMV infection could underlie deficits in CNS development. We investigated the contribution of host inflammatory responses to abnormal cerebellar development by modulating inflammatory responses in infected mice with glucocorticoids. Treatment of infected animals with glucocorticoids decreased activation of CNS mononuclear cells and expression of inflammatory cytokines (TNF-α, IFN-β and IFNγ) in the CNS while minimally impacting CNS virus replication. Glucocorticoid treatment also limited morphogenic abnormalities and normalized the expression of developmentally regulated genes within the cerebellum. Importantly, GNPC proliferation deficits were normalized in MCMV infected mice following glucocorticoid treatment. Our findings argue that host inflammatory responses to MCMV infection contribute to deficits in CNS development in MCMV infected mice and suggest that similar mechanisms of disease could be responsible for the abnormal CNS development in human infants infected in-utero with HCMV.
| Intrauterine infection with human cytomegalovirus (HCMV) is a leading cause of developmental brain damage. In the U.S., an estimated 2,000 infants a year develop brain damage as a result of intrauterine infection with HCMV. In this study, we examined the contribution of host immune responses induced by CMV infection to abnormal development of the CNS by treating neonatal mice infected with MCMV with glucocorticoids. We found that glucocorticoid treatment of infected mice decreased the inflammatory response within the CNS without altering the level of virus replication. In addition, abnormalities in the structure of the cerebellum, as well as abnormalities in granule neuron precursor cell proliferation were normalized in MCMV infected mice following glucocorticoid treatment. These studies suggest that the host immune response to CMV infection is damaging to the developing CNS and that it may be possible to limit CNS disease by modulating inflammation. Moreover, understanding how inflammation and the immune response may alter the developmental program within the CNS could offer important insight into the mechanisms of disease leading to abnormal brain development following intrauterine infection.
| Viral infections in the fetus and young infant are well described causes of abnormal brain development that often result in permanent neurological sequelae, including disorders of motor and cognitive functions. Altered CNS development and neurologic disease have been documented in the developing fetus and young infant following infection with a number of viruses, such as herpes simplex virus (HSV), rubella, lymphocytic choriomeningitis (LCMV) and human cytomegalovirus (HCMV) [1]–[7]. A variety of mechanisms can lead to interruption of the developmental program of the CNS including: damage to the brain parenchyma secondary to apoptotic or necrotic loss of resident cells within the CNS, damage to the supporting vasculature and microvascular supply of the CNS resulting in decreased blood flow and/or damage to the blood brain barrier, altered cellular positioning and disruption of synapse formation leading to a failure in neuronal connectivity and circuitry formation [8], [9]. In the case of infection with viruses that exhibit specific cellular tropism, the loss or dysfunction of specific populations of resident cells within the CNS often underlies disease. In other cases, cellular tropism is broad and disease is thought to result from direct viral damage to supporting structures, such as the vasculature or the glial architecture. Additionally, indirect mechanisms of disease following CNS infection include viral induced host inflammatory responses [10], [11]. Host responses following virus infections often lead to more global CNS damage secondary to the production of soluble effector molecules that can amplify proinflammatory responses of resident cells as well as promote cytotoxic activity by effector cells of the adaptive immune system [12]–[23]. Although these mechanisms of disease, as well as other proposed mechanisms, are consistent with clinical findings in patients with viral encephalitis, a precise description of the pathogenesis of CNS disease in virus infected human fetuses and infants is often limited by the lack of informative tissue specimens.
Because of limitations inherent in studies of the human CNS, small animal models have been developed to elucidate mechanisms of disease associated with viral infections of the developing CNS. These models have utilized a number of different viruses including HSV, murine cytomegalovirus (MCMV), LCMV, alphaviruses and more recently West Nile Virus (WNV) [4], [24]–[30]. Studies of CNS disease following both peripheral and intracerebral HSV inoculation have described a necrotizing encephalitis, which is more severe in animals with deficits in innate and adaptive immunity [31]–[33]. However, more recent studies have argued that in addition to the direct cytopathic effects associated with HSV replication, host derived innate immune responses contribute to CNS damage in infected mice [34], [35]. Similarly, experimental models employing LCMV infection have provided direct evidence that host-derived inflammation is a major component of CNS disease [4], [36]. In these models, limiting CD8+ virus specific T lymphocyte responses, or more global immunosuppression, dramatically reduced the severity of CNS disease [4], [37]. The contribution of immunopathological responses are particularly relevant to disease in young animals because expression of inflammatory genes during the dynamic developmental program of the CNS appears to result in a disease phenotype that differs from that seen in adult animals. Thus, substantial CNS damage in young infants could result from infection with viruses that are infrequently pathogenic in adults. In contrast, an effective immune response does appear to be necessary to limit the severity of CNS infection with alphaviruses and WNV [24]–[26], [38]–[42]. Responses derived from the adaptive immune system, in particular the production of antiviral antibodies, determine the susceptibility of newborn animals to alphavirus infection of the CNS [25], [38], [43], [44]. Thus disease outcome in young animals with viral infections of the CNS reflects a balance between unregulated inflammation and the control of virus replication [18], [32], [45]–[51].
Intrauterine infection with HCMV is the most common cause of congenital (present at birth) infection in humans and occurs in approximately 1/200 live births in the United States [52]. A small but significant number of newborn infants infected in-utero exhibit a variety of neurodevelopmental abnormalities secondary to HCMV infection of the CNS [5], [6]. Because little is known about the mechanisms of disease associated with this intrauterine infection, we developed a murine model of CNS infection that utilizes peripheral inoculation of newborn animals with limiting amounts of MCMV. In contrast to other murine models that have utilized intracranial inoculations of MCMV almost exclusively, the model we have developed uses intraperitoneal inoculation of limiting amounts of MCMV and requires virus replication in the periphery, viremia and neuroinvasion. These latter features of this murine model, particularly the hematogenous spread to the CNS, appear to more closely recapitulate the presumed pathogenesis of fetal CNS infection with human cytomegalovirus. MCMV infection of the brain in these newborn mice results in a focal, non-necrotizing encephalitis with little evidence of specific cellular tropism but with global and symmetric deficits in brain development [53]. Altered development occurred in areas of the brain that exhibited no evidence of viral proteins or nucleic acids, suggesting that inflammatory responses to infection, and not direct effects of virus infection, were responsible for the altered development in the brain of neonatal animals [53]. To determine the potential role of host derived inflammation as a mechanism of disease in this model, we first needed to separate the linkage between virus replication and host inflammatory responses. This was accomplished by treating young animals with corticosteroids to limit host responses, and therefore inflammation, during virus infection. Although inflammation in MCMV infected animals was reduced at several levels, viral replication was unaffected. More importantly, the anti-inflammatory activity of corticosteroids attenuated the previously described developmental abnormalities in the cerebella of infected animals. This finding strongly argued that virus replication was not a direct cause of the developmental abnormalities within the CNS following MCMV infection and suggested that inflammatory responses played a major role in the disease phenotype [53].
In an earlier report we described altered cerebellar development, including delayed cortical lamination, associated with MCMV infection of the CNS in newborn mice [53]. Disruption of lamination within the cerebellar cortex was frequently observed; however, altered lamination in areas immediately adjacent to virus infected cells was atypical in an overwhelming number of examined sections. Thus, histologic evidence of direct virus cytopathology as a cause of abnormal lamination of the cerebellar cortex was rare (Figure 1A). The predominant histopathologic findings of this CNS infection were widely distributed foci of virus infected cells and surrounding mononuclear cells throughout the cerebrum and cerebellum [53]. In contrast to the focal nature of virus infection and mononuclear cell infiltration, defects in cerebellar morphogenesis were global and, most importantly, symmetric as illustrated by the delayed foliation and reduced cerebellar area in virus infected animals (MCMV) compared to uninfected (control) animals at post-natal day (PND) 8 (Figure 1B). Notably, studies of infants infected in-utero by HCMV have also described global and symmetric deficits in brain morphogenesis without a significant component of focal or asymmetric loss of brain parenchyma, in the majority of documented cases [6], [54]–[61]. From these findings, we have proposed that global alterations in cerebellar development are likely associated with soluble factors produced by the host inflammatory response and not related to direct effects of viral cytopathology.
To characterize the nature of the inflammatory response in the cerebellum of infected animals, we analyzed several immunologic parameters in the brains of control and infected animals at PND8. This time point was selected because virus replication in the CNS was established and deficits in cerebellar development were clearly observable [53]. Initially, we assayed the phenotype of CNS mononuclear cells in control and virus infected animals. Although CD8+ and CD4+ T-lymphocyte infiltrates, peripheral blood macrophages and activated microglia could be readily detected in the cerebellar parenchyma at PND14, mononuclear cells were present in the CNS of MCMV infected mice by PND8, prior to the detection of infiltrating T-lymphocytes [62]. Mononuclear cells isolated from control and infected brains were stained with two markers for tissue macrophages, F4/80, a marker for cells of myeloid lineage and CD45, a pan-leukocyte marker. The differential expression of CD45 by F4/80+ cells has been employed to distinguish between quiescent microglia (low), activated microglia (intermediate) and infiltrating macrophages (hi) [63]. In control animals, F4/80+ cells expressing CD45hi/int were present in low abundance (3.0%) (Figure 2A). We observed an increase in the proportion of CD45hi/intF4/80+ cells in the CNS of infected mice (9%) (Figure 2A) [62]. Furthermore, MHC class II expression was increased in this population of cells in MCMV infected mice, a finding consistent with the activation of these cells following infection (Figure 2B). These results demonstrated an increase in the inflammatory response within the CNS, including increased activation of resident macrophages and recruitment of peripheral blood macrophages early in infection, prior to the appearance of virus specific CD8+ T-lymphocytes. To further define the activation state of brain macrophages in the CNS of MCMV infected mice, cerebellar sections from PND8 control and infected animals were stained with anti-Iba-1, a marker for activated microglia/macrophages [64], [65]. In sections from the cerebella of control mice, few Iba-1+ cells were observed (Figure 2C). However, the number of Iba-1+ cells in the cerebellum was significantly increased following infection with MCMV (Figure 2C). In addition, Iba-1 staining was observed in the meningial layer within the cerebellum of MCMV infected animals, suggesting an infiltration of cells from the periphery (Figure 2C). Importantly, cellular infiltrates and activated mononuclear cells in the cerebellum were readily detected in the parenchyma of the cerebellum and not limited to foci of virus infected cells (data not shown), suggesting that the generalized inflammation observed in the brains of MCMV infected mice was induced by soluble mediators produced in response to virus infection. Finally, we attempted to determine the frequency of Iba-1+ cells with an ameboid morphology suggestive of activated microglia and/or macrophages as compared to Iba-1+ cells with a ramified morphology consistent with quiescent or resting microglia/macrophages. We found cells consistent with both morphologies in infected and control animals but were unable to definitively assign differences in populations between the two experimental groups (data not shown).
Given the increase in the number of Iba-1+ cells and the increased activation of CD45hi/intF4/80+ cells, we next quantified the expression of inflammatory cytokines in virus-infected cerebella by quantitative real time PCR. We selected several proinflammatory cytokines, as well components of interferon induced responses (IFIT1 and STAT1), as markers for inflammation in the cerebella of infected animals. The expression of TNFα (10-fold), IFNβ (7-fold), STAT1 (10-fold) and IFIT1 (175-fold) were significantly increased in infected animals as compared to control animals (Figure 2D). Together, these results demonstrated that by PND8 activated cells of the innate immune response and proinflammatory cytokines were present in the developing cerebellum of mice infected with MCMV as newborns.
Thus far our findings suggested that soluble factors produced by the inflammatory response to virus infection in the CNS were responsible for the global alterations in cerebellar development. Endogenous glucocorticoids have been demonstrated to protect against immune-mediated pathology in MCMV infected adult mice, suggesting that treatment with glucocorticoids could alter the pathological changes in the CNS of MCMV infected newborn mice [66], [67]. To examine the effects of glucocorticoid treatment on postnatal cerebellar development, control and MCMV infected mice were treated with dexamethasone (dexa), a glucocorticoid with potent anti-inflammatory activity, which has been routinely used in the treatment of CNS inflammation in both clinical medicine and experimental animal models of human disease [17], [68]–[72]. Control and MCMV infected newborn mice were treated daily with dexa or vehicle on PND4-6 and liver, spleen, brain and cerebellum were isolated from all groups on PND8. There was no significant difference in the number of plaque forming units (PFU) of virus in the spleen, liver or brain of dexa treated/infected animals when compared to vehicle treated/infected animals, signifying that treatment with dexa had minimal effects on viral replication (Figure 3A). We next assessed whether dexa treatment exhibited an anti-inflammatory effect following MCMV infection. Dexamethasone significantly reduced the frequency of CD45hi/intF4/80+ macrophages in the brains of infected mice compared to vehicle treated/infected mice (Figure 3B). Interestingly, the frequency of CD45lo F4/80+ cells was reduced in the brains of MCMV infected mice as compared to control and dexa treated mice suggesting that the number of quiescent, or resting, microglia was decreased in infected animals, perhaps secondary to an increase in activated microglia in this experimental group ( Figure 3B). A reduction of MHC class II expression in this population was also observed in dexa treated/infected mice (data not shown). Similarly, the number of Iba-1+ cells was significantly decreased in the cerebellum of dexa treated/infected mice compared to vehicle treated/infected mice (Figure 3C). Consistent with the findings described above, the expression of IFIT1 was significantly decreased in the cerebellum of infected animals following treatment with dexa (Figure 3D). We also determined that dexa treatment normalized the expression of IFIT2 and STAT1 in the cerebellum of MCMV infected mice (Figure 3D). Together, these results demonstrated that dexa treatment decreased inflammation in the CNS of MCMV infected animals without significantly altering levels of virus replication.
The finding that dexa treatment of MCMV infected mice significantly reduced the inflammatory response in the CNS raised the possibility that dexa treatment could also prevent the aberrant cerebellar development observed following infection. Dexamethasone treatment of infected mice normalized the expression of the developmentally regulated genes gli1 and N-myc (both effectors of the sonic hedgehog (SHH) pathway responsible for granule neuron proliferation), as well as GABRA6 (a marker for granule neuron differentiation) and CDK5 (primarily expressed in differentiated neurons) (Figure 4A) [73]–[75]. Notably, some of these genes have previously been shown to be altered following MCMV infection [53]. However, dexa treatment of control animals also resulted in a significant reduction in the expression of both GABRA6 and CDK5 in the cerebellum when compared to control animals receiving only vehicle (Figure 4A). These differences in expression were not due to an effect of dexa on transcription because the expression of Zic2, a transcription factor expressed predominantly in granule neuron progenitors, was unaltered following treatment (Figure 4A) [76]. Morphometric measurements from the cerebella of infected mice demonstrated that the increased thickness of the EGL, previously associated with delayed migration of granule neuron progenitors, appeared to have been normalized following treatment with dexa (data not shown). However, the EGL in dexa treated/control animals was decreased in thickness compared to vehicle treated/control animals (data not shown). Dexamethasone treatment of control mice also resulted in a significant decrease in cerebellar area when compared to vehicle treated/control mice (Figure 4B). In addition, the cerebellar area of dexa treated/infected animals was further decreased compared to vehicle treated/infected animals (Figure 4B). Importantly, we did not observe a significant increase in activated caspase 3 staining in sections from these mice, indicating that increased apoptosis of granule neuron progenitor cells (GNPCs) did not contribute to the reduced size of the cerebellum in dexa treated/infected animals (data not shown). These findings suggested that dexa treatment of MCMV infected mice resulted in significant off-target effects in cerebellar development, a result that would limit the interpretation of findings from our studies of cerebellar development in dexa treated animals. Similar off-targets effects of dexa on cerebellar development have been previously described and thought to be secondary to the anti-proliferative effects of this specific glucocorticoid on GNPCs [77], [78]. Finally, our findings raised the possibility of an additive effect of dexa and MCMV infection on cerebellar development.
The off-target effects of dexa on cerebellar development have been attributed to the resistance of this glucocorticoid to inactivation by 11β-hydroxysteroid dehydrogenase type 2 (11β-HSD2), an enzyme which is highly expressed in the postnatal cerebellum in rodents as well as humans [78]–[80]. This enzyme is induced by SHH during development of GNPCs in the cerebellar cortex and appears to be protective in terms of limiting both the apoptotic and anti-proliferative effects of corticosteroids [78], [79], [81], [82]. In contrast to dexa, other glucocorticoids such as hydrocortisone and prednisolone can be inactivated by 11β-HSD2 and have not been associated with the level of off-target effects observed following treatment with dexa [78]. Thus, we repeated the previous experiments using prednisolone (pred), a corticosteroid with predominant glucocorticoid activity, which has also been used to attenuate inflammation associated with infections of the CNS, both in animal models and clinical medicine [69], [83]–[86]. Control and MCMV infected newborn mice were treated once a day on PND4-7 with vehicle or pred. This time course of treatment was necessary secondary to the shorter in-vivo half-life of pred compared to dexa (Figure 5A) [87], [88]. Initially, we determined the effects of pred treatment on virus replication in MCMV infected mice. We found no significant difference between the level of virus replication in the liver or brain of pred treated animals compared to vehicle treated/infected animals. However, minimal increases in viral genome copy number were observed in both the spleen and cerebellum of pred treated/infected animals (Figure 5B).
We next determined the effect of pred treatment on the frequency of Iba-1+ cells in the cerebellum of both uninfected and MCMV infected mice. As described previously, the number of Iba-1+ cells was increased in the cerebellum of MCMV infected mice compared to control mice (Figure 5C). Following pred treatment, the frequency of Iba-1+ cells was reduced in MCMV infected animals (59% reduction) compared to vehicle treated/infected animals (Figure 5C). The number of Iba-1+ cells in pred treated/control animals was not significantly different from the number of positive cells in the cerebellum of vehicle treated/control animals (Figure 5C). The observed reduction of Iba-1+ cells in the cerebellum of pred treated/infected mice indicated that pred decreased macrophage/microglia activation in the CNS of newborn mice following MCMV infection. In agreement with our previous findings, treatment of infected mice with pred also decreased the frequency of CD45hi/intF4/80+ macrophages in the CNS of infected mice (Figure 5D). Treatment of control animals with pred had no significant effect on either the CD45hi/intF4/80+ macrophage population or the CD45lo F4/80+ resting microglial population (Figure 5D). The observed reduction of Iba-1+ cells in the cerebellum and the decreased percentage of CD45hi/intF4/80+ macrophages in the CNS of pred treated/infected mice indicated that pred decreased the number of activated macrophage/microglia in the CNS of newborn mice following MCMV infection.
We next determined the effects of pred treatment on the expression of proinflammatory cytokines previously shown to be elevated in the cerebellum following MCMV infection (Figure 2D). Consistent with the findings described above, we observed a reduction in the transcription of TNFα (25%), IFNβ (70%) and IFIT1 (65%) within the cerebellum of MCMV infected mice treated with pred (Figure 6A). Pred treatment also decreased cytokine levels of IFNβ (25%) and IFNγ (43%) within the cerebellum (Figure 6B). Interestingly, cytokine levels of TNFα were not affected following pred treatment. These results illustrated that treatment with pred could attenuate MCMV induced inflammation in the CNS independent of changes in virus replication, thereby uncoupling the level of virus replication and the host inflammatory response within the cerebellum.
Since treatment with pred significantly reduced the inflammatory response in the CNS and has been reported to lack the off-target effects observed with dexa, we next determined if pred treatment could also limit the abnormal development of the cerebellum that was observed in MCMV infected animals. Because of the large number of mice used in these experiments, the variation in animal size and the size dependent variation in brain area, we normalized measurements of cerebellar area between experimental groups by expressing cerebellar area as a percentage of brain area. The ratio of cerebellar area/brain area was found to be similar for pred treated/control and pred treated/infected animals when compared to vehicle treated/control animals; however, vehicle treated MCMV infected mice showed a significant reduction in this ratio (Figure 7A). These results confirmed the decrease in cerebellar area previously observed following infection with MCMV and, more importantly, demonstrated normalization of altered cerebellar size in infected mice by treatment with pred. These findings were consistent with our hypothesis that inflammatory mediators, released in response to MCMV infection, were a primary cause of altered cerebellar development.
In addition to the decrease in cerebellar area, we have previously documented an increase in the thickness of the EGL in MCMV infected animals [53]. Since treatment of infected mice with pred lead to normalization of cerebellar area, we next determined whether this treatment would also normalize the increased thickness of the EGL following infection. As expected, the EGL was thicker in MCMV infected mice compared to control mice. This abnormality in cerebellar development was corrected in infected mice following treatment with pred (Figure 7B, D). There was no measureable difference in the thickness of the EGL in control animals treated with pred compared to vehicle treated/control animals (Figure 7B, D). To determine if the increase in the thickness of the EGL following infection was secondary to an increase in cellularity, the number of GNPCs in the EGL was quantified. Consistent with an increase in thickness, we found an increase in the number of GNPCs within the EGL following infection (Figure 7C). Concomitant with normalizing the increased thickness of the EGL, treatment of infected mice with pred also normalized the number of GNPCs within the EGL. We did not find any significant difference in the number of GNPCs in the EGL between vehicle treated/control animals or pred treated/control animals (Figure 7C). The normalization of MCMV induced abnormalities in the morphogenesis of the cerebellar cortex following treatment with pred demonstrated that we could limit morphogenic abnormalities within the cerebellum of infected mice by modulating inflammatory responses.
Previously, we documented that following infection, morphological deficits within the cerebellum coincided with a significant reduction in the transcription of developmentally regulated genes expressed within GNPCs [53]. Since pred treatment reduced inflammation and corrected morphological deficits within the cerebellum of infected mice, we hypothesized that pred treatment could also correct abnormalities in the transcription of these genes. Similar to our studies using dexa, we assayed gli1, N-myc, GABRA6 and CDK5 expression in the cerebella of uninfected and MCMV infected mice treated with vehicle or pred. Consistent with our previous findings, expression of both GABRA6 and CDK5 was decreased following infection with MCMV when compared to control mice (Figure 7E). Following treatment with pred the expression of both genes was normalized within the cerebella of MCMV infected mice. Similarly, the transcription of gli1 and N-myc was elevated in the cerebellum following infection and treatment of infected mice with pred decreased the expression of both genes (Figure 7E). Importantly, pred treatment had no effect on the transcription of gli1, N-myc, GABRA6 or CDK5 in control animals (Figure 7E). As a control, the expression of Zic2 was analyzed and was found to be similar in the cerebella of all groups (Figure 7E) [76]. These results indicated that decreasing inflammation in MCMV infected animals by treatment with pred normalized the expression of developmentally regulated genes in the absence of measurable off-target effects.
In MCMV infected mice, the upregulation of gli1 and N-myc was inconsistent with the deficit in GNPC proliferation observed in our previous studies [53]. This suggests that an alternative mechanism could be responsible for the deficit in GNPC proliferation within the cerebellum of infected mice [53]. Given our previous findings (increased thickness of the EGL, decreased GNPC differentiation, decreased GNPC migration to the IGL and decreased thickness of the IGL), we postulated that a block or delay within the GNPC cell cycle, downstream from the actions of gli1 and N-myc, would be most consistent with our observations. The failure of GNPCs to complete a program of proliferation in the EGL would prevent their differentiation and subsequent migration into the IGL. This mechanism would also account for the increased cellularity of the EGL and the decreased cellularity of the IGL in infected animals [53], [89]–[91]. To investigate this possibility, PND8 animals were injected with BrdU, a marker of cells in S phase. Serial sections from the cerebellum were stained with antibodies reactive with BrdU and Ki67, a marker of cycling cells, and the number of positive cells was quantified for each marker (Figure 8A). No difference was observed in the percent of total GNPCs that were positive for Ki67 in the EGL of MCMV infected animals compared to control animals (Figure 8B). However, a decrease in the percent of cycling cells (Ki67+) positive for BrdU was detected in infected animals when compared to control animals (Figure 8C). The decrease in BrdU reactivity within GNPCs of infected mice was therefore not secondary to a decrease in the overall number of cells in the cell cycle. Moreover, the previously described minimal level of apoptosis of GNPCs in either group of animals indicated that there is likely a block or delay in the cell cycle of GNPCs following infection [53].
If the inflammatory response in the CNS of infected mice contributed to the block/delay in the proliferation of GNPCs, our results described above would argue that the anti-inflammatory effects of pred could alleviate this block and restore the proliferative capacity of GNPCs in the EGL. Analysis of Ki67 expression in pred treated groups revealed that the percent of GNPCs in the cell cycle was similar to that of infected or control animals that were treated with vehicle (Figure 8B). When compared to vehicle treated/control animals there was no significant difference in the percent of BrdU+ cells in EGL of pred treated/infected animals indicating that pred treatment of infected animals normalized the deficit in GNPC proliferation associated with MCMV infection (Figure 8C). Importantly, the percent of BrdU+ GNPCs in the EGL of pred treated/control animals was not significantly different from vehicle treated/control animals. Together, these findings argue that pred treatment alleviated alterations in the cell cycle of GNPCs that were associated with MCMV infection. Furthermore, these results support our hypothesis that modulating the inflammatory response following MCMV infection could limit deficits in cerebellar morphogenesis, likely through reversing the delay in GNPC proliferation.
To further define the disruption in the cell-cycle of GNPCs following infection we assayed the levels of two cyclins, cyclin D1 and cyclin B1, in control and MCMV infected mice. Levels of cyclin D1 were not significantly different between control or infected animals, suggesting that infection with MCMV did not alter the signals associated with entry of GNPCs into G1 (data not shown) [92]. Similarly, pred treatment did not alter cyclin D1 levels in infected or control animals (data not shown). Although there was no observable difference in the levels of total cyclin B1 expression between infected and control mice (Figure 8D), the level of phosphorylated-cyclin (p-cyclin) B1, a marker for G2/M, was decreased within the cerebella of infected animals compared to control animals (Figure 8D) [93], [94]. Together with the decreased number of BrdU+ GNPCs, this data further argued for a block/delay in the cell cycle following infection. Cerebella from both control and MCMV infected mice treated with pred displayed levels of p-cyclin B1 that were similar to vehicle treated/control mice (Figure 8D). Although this data did not reveal the precise point where cell cycle progression was delayed, it further confirmed that altered development of the cerebellum in infected animals was associated with delayed proliferation of GNPCs within the EGL. Treatment with pred corrected this deficit and normalized the morphological abnormalities within the cerebellum following infection. These results were consistent with a mechanism in which the developmental abnormalities associated with focal encephalitis in MCMV infected newborn mice resulted from the host inflammatory response as opposed to a direct virus-mediated mechanism.
Previously we have shown that intraperitoneal inoculation of newborn mice with MCMV resulted in a focal CNS infection that involved all regions of the brain that but did not exhibit specific cellular tropism [53]. Histologically, the foci consisted of a small number of virus-infected cells, mononuclear cells and reactive astroglial cells [53]. Although there was no observable difference in the size of the cerebrum between infected and uninfected animals, cerebellar hypoplasia was readily apparent in infected animals and was associated with delayed foliation and decreased area of the cerebellar cortex, findings attributable to the decreased proliferation of GNPCs within the EGL [53]. Morphogenic abnormalities of the cerebellar cortex included increased thickness of the EGL, decreased thickness of the IGL, abnormal arborization of Purkinje neuron dendrites and thinning of the molecular layer [53]. Interestingly, the altered morphogenesis of the cerebellum was symmetric even though foci containing virus infected resident cells and infiltrating mononuclear cells were scattered widely throughout the parenchyma of the cerebellum. These later findings strongly argued that the developmental abnormalities were secondary to a soluble mediator generated during virus-induced inflammatory responses in the CNS and not from direct cytopathic effects of virus infection. In this report, we have described findings consistent with this mechanism; specifically, evidence that attenuation of inflammatory responses in infected mice, by treatment with anti-inflammatory glucocorticoids, normalized developmental abnormalities in the cerebellum without affecting the level of virus replication.
Our results demonstrated that several measures of GNPC proliferation were altered in MCMV infected mice, including a decrease in the frequency of cells in S phase and a decrease in the levels of phospho-cyclin B1 within the EGL of MCMV infected mice. Several explanations could account for these findings, including a decrease in the number of GNPCs entering the cell cycle, premature exit of GNPCs from the cell cycle and a block or delay in the cell cycle of GNPCs following infection. Premature exit of GNPCs from the cell cycle represented an obvious explanation for the decreased cerebellar size but other measures of GNPC proliferation were inconsistent with this explanation. The increased cellularity of the EGL following MCMV infection and the similar percentages of Ki67+ GNPCs in infected and control mice argued that there was no difference in the number of GNPCs entering the cell cycle nor was there an increased number of GNPCs exiting the cell cycle. Because we found a decrease in certain markers of proliferation but no change in the number of cycling GNPCs following MCMV infection in this study as well as in a previous study, a more consistent interpretation of our data is that the cell cycle of GNPCs in the EGL is prolonged in MCMV infected animals [53]. Prolongation of the cell cycle could delay the completion of the programmed proliferation and subsequent differentiation of GNPCs that is required for normal morphogenesis of the cerebellar cortex. Variation in the rate of cell division of GNPCs in the EGL has been described, suggesting that the duration of the cell cycle in these cells is not autonomous and can be influenced by extracellular cues [90], [95], [96]. Though we have not fully characterized the nature of this alteration in the cell cycle of GNPCs, it was reversible, in that the delay was corrected when MCMV infected animals were treated with glucocorticoids.
Although a unifying mechanism for the normalization of cerebellar development in pred treated MCMV infected mice remains incompletely described, our results were most consistent with a decrease in the inflammatory response in the CNS leading to normalization of the proliferative capacity of GNPCs in the cerebellar cortex. This mechanism is based on previous studies that have demonstrated that GNPCs undergo what is thought to be a programmed number of cell divisions prior to exiting the cell cycle, entering a differentiation program and then migrating from the EGL into deeper layers of the cerebellar cortex [90], [95], [96]. This well choreographed developmental pathway has been extensively studied and many of the molecular signals associated with this pathway have been described [74], [91], [95]–[100]. We are proposing that if the cell cycle of GNPCs is prolonged, subsequent to inflammation in the cerebellum, then normal morphogenesis of the cerebellar cortex fails to take place and the expression of developmentally regulated genes that depend on differentiation and correct cellular positioning will be delayed. Findings from this study are consistent with a reversible, generalized slowing of the GNPC cell cycle in infected mice. Reversal of this slowing could be expected to result in a rebound in GNPC proliferation, permitting the completion of the developmentally programmed cell divisions, differentiation into migrating granule neurons, migration into the IGL and expression of the associated differentiation genes. The reversibility of this mechanism is consistent with the partial resolution of defects in cerebellar development observed in vehicle treated MCMV infected mice following virus clearance and regulation of the inflammatory response later in infection [53]. Additional support for the reversibility of a slowing of the cell cycle has been reported in a study of 11β-HSD2 −/− transgenic mice treated with corticosterone [79]. Findings from this study demonstrated a rebound in the cerebellar area and the size of the IGL in these transgenic mice following withdrawal of steroid treatment [79]. Even though the effector molecules and pathways that lead to altered proliferation of GNPCs and cerebellar development in this model of a human CNS infection remain undefined, such a mechanism could argue for a common pathway leading to the developmental abnormalities associated with inflammation following infection of the developing brain of the fetus and newborn infant by a number of microbial agents. Alteration in the rate of proliferation of progenitor cells in the developing CNS could lead to deficits in developmental, stage dependent cell positioning and potentially result in a number of long term neurological abnormalities.
A recent study that carefully detailed the effects of glucocorticoids on the developing cerebellum described several phenotypes following treatment with different glucocorticoids [78]. These investigators demonstrated that the phenotypic response of GNPCs to glucocorticoids was dependent on the presence of 11β-HSD2, an enzyme that is expressed at higher levels in the cerebella of both newborn rodents and humans as compared to other regions of the CNS [78]–[80], [101]. Previous studies have indicated that the inactivation of glucocorticoids by 11β-HSD2 limits the anti-proliferative and apoptotic inducing activities of endogenous and exogenous glucocorticoids [78], [79]. Because dexamethasone (dexa) is not efficiently inactivated by 11β-HSD2, treatment of neonatal mice with dexa resulted in increased GNPC apoptosis (short term treatment) or decreased GNPC proliferation (chronic treatment), secondary to exit from the cell cycle presumably from accelerated GNPC differentiation [77], [78]. Interestingly, in this study chronic prednisolone (pred) treatment resulted in an intermediate phenotype due to the inactivation of this specific glucocorticoid by 11β-HSD2 [78]. Our findings were consistent with the results presented in this report in that treatment with dexa, but not pred, resulted in a significant decrease in the size of the cerebellar cortex in both uninfected and infected mice. We also noted that in two independent experiments the cerebellar area in dexa treated/infected mice was smaller than that of both dexa treated/control mice or vehicle treated MCMV infected mice. These findings suggested that the effects of dexa and MCMV infection were additive and raised the possibility that the effect of dexa on GNPC proliferation in this setting differed from those that followed MCMV infection. Interestingly, dexa treatment did result in normalization of the expression of genes associated with GNPC differentiation (GABRA6 and CDK5) in the absence of normalization of GNPC proliferation, a finding consistent with accelerated GNPC differentiation in animals following treatment with dexa [77], [78]. The premature exit of GNPCs from the cell cycle likely accounted for the cerebellar hypoplasia and decreased cerebellar area that was observed in dexa treated animals. In contrast, when infected mice were treated chronically with pred, we observed a correction of the abnormal cell cycle of GNPCs that was also associated with normalization of the morphogenic abnormalities in the cerebellar cortex. Following normalization of the cell cycle in pred treated animals, GNPCs completed their programmed proliferation in the EGL, migrated into the deeper layers of the cerebellum and expressed development specific genes. We have not identified a specific mechanism(s) to explain the correction of proliferation deficit(s) in GNPCs following pred treatment, but it is unlikely that in pred treated mice, GNPCs exited the cell cycle and differentiated as was observed in dexa treated mice. This argument is based on three findings; (i) a similar frequency of GNPCs were cycling in both pred treated and vehicle treated mice, (ii) the frequency of BrdU+ GNPCs in the EGL was increased following pred treatment and (iii) measures of cerebellar morphogenesis (EGL thickness, cerebella area and EGL cellularity) were normalized in infected mice following treatment with pred. Several experimental models of CNS infection in newborn animals have also noted beneficial outcomes following treatment with anti-inflammatory agents, but in some cases and in contrast to our findings, increased disease severity secondary to increased replication of the microorganism was also observed [17], [67], [68]. Experimental rodent models of herpes simplex encephalitis have demonstrated a beneficial effect of steroid treatment when combined with an antiviral agent suggesting that host-derived inflammation contributes to the outcome of CNS infection with this virus [102], [103]. In findings that paralleled our results, treatment of Borna disease virus (BDV) infected adult rats with dexa limited inflammation and also appeared to improve neurologic function in infected animals [17]. In clinical medicine, the use of glucocorticoids to limit CNS inflammation in patients with mycobacterial infections of the brain is well established [69], [70]. These agents have also been utilized to limit neurological sequelae that follow bacterial meninigitis associated with pyogenic bacteria [71]. Several studies have demonstrated that glucocorticoids efficiently limit the innate immune response to microorganisms in the CNS, including the expression of proinflammatory cytokines, chemokines and interferon stimulated genes [17], [104]. However, the use of glucocorticoids, particularly dexa, in young infants remains controversial because of the well documented adverse effects this agent has on brain development [105], [106].
The importance of SHH in the proliferation of GNPCs in the cerebellar cortex has been studied extensively [107]–[113]. The proliferation of these neuron progenitors in response to SHH has been reported to involve the transcription factors gli1 and N-myc [109], [114]–[117]. It was therefore somewhat unexpected to find that expression of both gli1 and N-myc was increased in the cerebella of MCMV infected mice as compared to control mice. Interestingly, we noted that transcription of patched (Ptch) was also increased in the cerebella of MCMV infected mice, a finding that paralleled the increased expression of gli1 and could represent a regulatory response to SHH induced responses [118], [119]. We do not have a definitive explanation for the increase in gli1 and N-myc expression but noted that when MCMV infected mice were treated with glucocorticoids the expression of these SHH effectors was normalized. Consistent with our observations, previous reports have suggested that proinflammatory cytokines can modulate the SHH pathway [120], [121]. As an example, increases in GNPC proliferation have been documented in transgenic mice with constitutive IFNγ expression in the CNS [122]. In these engineered mice, SHH and gli1 expression was induced by IFNγ via a STAT1 dependent pathway. More recent studies have reported that IFNγ treatment of cultured granule neurons leads to increased proliferation and that STAT1 binds directly to the SHH promoter [123], [124]. Interestingly, both IFNγ and STAT1 were upregulated in the cerebella of MCMV infected mice coincident with an increase in the expression of N-myc and gli1 (Figure 3D; Figure 6B). Moreover, treatment with pred reduced the cytokine levels of IFNγ and normalized the expression of both N-myc and gli1 following MCMV infection. Studies of cytokines during CNS development have detailed both neuroprotective and deleterious roles, suggesting a delicate balance between the homeostatic and immune functions of cytokines in the developing CNS [125]–[128]. Our findings suggest that cytokines released following neonatal infection with MCMV could have deleterious effects on developing GNPCs within the cerebellum and that modulating the inflammatory response associated with this infection could limit damage to the developing CNS.
An important aspect of this study is that the pathological and histopathological findings in this murine model appear very similar to those reported in human infants with congenital CMV infection. The focal encephalitis, characteristic of MCMV infection in mice, has also been noted in autopsy findings from infants with congenital HCMV infections. Furthermore, in this model histopathological findings of mononuclear cell infiltrates and reactive gliosis, termed micronodular gliosis, are remarkably similar to those found in infected human infants [55], [57], [60], [129], [130]. Cerebellar hypoplasia is an invariant finding in this murine model and also frequently reported in infants with congenital HCMV infections that have been studied by imaging or, in a smaller number, following autopsy [57], [131], [132]. Reports describing MRI findings in infants with congenital HCMV infection have suggested that cerebellar hypoplasia is characteristic of this intrauterine infection. However, it should also be noted that the murine model we have developed has a significant limitation, dictated by the route of virus inoculation and the age of the developing brain at the time of infection. CNS development in newborn mice is believed to be at a stage similar to that of a mid to late 2nd trimester human fetus. Thus, in the murine model we have developed, cortical damage associated with an earlier gestational age of fetal infection will not be adequately modeled. Yet it is also important to note that the vast majority of infants with congenital HCMV infections also do not exhibit structural damage to the cerebral cortex, raising the possibility that only a minority of infants are infected early in gestation. In agreement with this possibility, recent studies have provided evidence suggesting that transmission of virus to the developing fetus occurs more frequently in the later stages of pregnancy [133]. Thus, with the awareness of limitations inherent in studies carried out in rodents, we would argue that the findings we have generated from our studies suggest that inflammation in the developing brain should be considered a potential contributor to at least some of the developmental abnormalities that have been associated with intrauterine HCMV infections. Furthermore, if inflammation and the soluble mediators present in the CNS account for the altered proliferative capacity of neural progenitor cells, our results could be extrapolated as a potential explanation for maldevelopment of the brain associated with other intrauterine infections resulting in CNS inflammation.
Even though our findings in this murine model of congenital CMV infection have demonstrated a beneficial effect of glucocorticoid therapy in maintaining the developmental program during MCMV infection, we cannot directly extrapolate our findings in this model system to human disease or other infections of the CNS. However, the potential intersections between neurodevelopmental pathways and those that contribute to CNS inflammation in neonatal animals would suggest that more selective approaches to limiting CNS inflammation could open new therapeutic avenues and lead to improved outcomes. These approaches combined with antiviral therapy, to limit virus replication until host responses can efficiently clear virus from the CNS, could offer a more optimal approach for management of this important perinatal infection. Further exploitation of this model could provide insight into the feasibility of such an approach and perhaps aide in defining markers of CNS inflammation, allowing for a more selective introduction of anti-inflammatory therapy.
All animal breeding and experiments were performed in accordance to the guidelines of the University of Alabama – Birmingham Institutional Animal Care and Use Committee (IACUC) in strict compliance with guidelines set forth by the NIH (OLAW Assurance Number - A3255-01). Research was conducted under a protocol approved by IACUC. All experiments done at the University of Rijeka were in accordance with the University of Rijeka – Croatia animal use and care policies in accordance to the guidelines of the animal experimentation law (SR 455.163; TVV) of the Swiss Federal Government.
Infection of mouse pups was performed as previously described [53]. Briefly, newborn Balb/c mice (6–18 hrs post-partum) were infected with 500 PFU of MCMV-Smith (ATCC VR-1399) by i.p. (intraperitoneal) inoculation. Control and MCMV infected pups were treated on PND4-6 by i.p. injection with dexamethasone sodium phosphate (dexa; APP Pharmaceuticals); 1 mg/kg in 50 µl of sterile PBS. Dexa was administered once a day and mice were sacrificed on PND8 between 36 and 42 hours after the last treatment was administered. For Prednisolone experiments, animals were treated with prednisolone sodium phosphate (pred; commercial pharmacy); 7 mg/kg (equivalent to 1 mg/kg dexa) in 50 µl of sterile PBS on PND4-7. Treatments were administered once a day and mice were sacrificed on PND8 between 16 and 18 hrs post injection. As a control, uninfected and MCMV infected animals were given i.p. injections with 50 µl sterile PBS alone (vehicle). Animals were sacrificed on PND8, perfused with ice cold PBS and organs were harvested and processed for the appropriate downstream application. All mice were purchased from The Jackson Laboratory (Bar Harbor, ME).
Stocks of MCMV-Smith strain were propagated by infection of mouse embryonic fibroblasts (MEFs). Infected media was harvested at 5–7 days post-infection and frozen at −80°C. For dexa experiments, organs were collected, weighed and homogenized. A 10% homogenate in media was utilized for standard plaque assays [134]. For pred experiments, organs were collected and DNA was isolated using Trizol according to the manufacturer's instructions (Roche Applied Science). 1 µg of DNA was then used for quantitative real-time PCR with the following primers for MCMV IE-1 Exon 4: Forward: 5′-GGC TTC ATG ATC CAC CCT GTT A – 3′; Reverse: 5′-GCC TTC ATC TGC TGC CAT ACT – 3′. Primers were used at a concentration of 250 nM/reaction. The following FAM-TAMRA (BHQ-2) probe was used at a concentration of 300 nM/reaction for real-time detection: 5′-/56-FAM/AGC CTT TCC TGG ATG CCA GGT CTC A – 3′. Real time PCR was performed by Taqman based assay using the StepOne Plus system from Applied Biosystems (Carlsbad, CA).
For immunofluorescence studies, mice were injected on PND8 with 50 µg/g of BrdU (Sigma Aldrich) in 1× PBS, 6 hrs. prior to harvest. Mice were then perfused with PBS and brains were fixed in 4% paraformaldehyde (PFA) overnight, cryoprotected in 30% sucrose-PBS and embedded in Tissue Tek O.C.T. compound (Andwin Scientific). 8-µm sagittal sections were cut using a Leica cryostat. Cut sections were dried for 4 hours at room temperature (RT), rehydrated in 1× PBS then used for immunofluorescence assays. For Iba-1 staining, sections were blocked in 1× PBS, .05% Triton X-100, 20% normal goat serum, 5% BSA for 2 hr. at RT. Sections were then stained with anti-Iba-1 overnight at 4°C. Subsequently, sections were washed with PBS, .05% Triton X-100 and then incubated for 2 hrs. at RT in the dark with secondary antibody, followed by a 15 min. incubation with TOPRO-3 iodide (1∶1000, Molecular Probes) at RT. Following staining for Iba-1, sections were post-fixed with 2% PFA for 20 min. at RT. Sections were washed and mounted using Vectashield Fluorescent mounting medium (Vector Laboratories). For BrdU/Ki67, sections were blocked in 1× PBS, 1% Triton X-100, 20% normal goat serum, 1 M glycine, 5% BSA for 1 hr. at RT. Blocking was followed by a 2 N HCL acid wash for 10 min. on ice, 10 min. at RT and 20 min at 37°C. Sections were then buffered in .1 M Borate buffer for 12 min. at RT, washed in PBS, 1% Triton X-100 and labeled as previously described. Primary antibodies utilized in this study were anti-Iba-1 (1∶200, Wako, Japan), anti-Ki67 (1∶200, ab66155; Abcam), anti-IE1 (Chroma101 [53]) and anti-BrdU (1∶50, ab6326; Abcam). Secondary antibodies used were: Alexa Fluor 594 - conjugated anti-Rabbit; Alexa Fluor 488 - conjugated anti-mouse (Molecular Probes) and Goat anti-Rat – FITC (Southern Biotech), respectively. Images of stained sections were collected by using an Olympus Fluoview confocal microscope (20× objective for Iba-1 and 60× objective for BrdU/Ki67). For cell counts, images were saved as TIFF files and opened in Image J [135]. An area box was created and the number of cells in the EGL within this box was counted for each section.
Frozen sections were used for all morphometric measurements. EGL measurements were done on serial sections using Image J software. Measurements were obtained from sections stained with BrdU, Ki67 and TOPRO3. Images were collected with a confocal microscope. 4 measurements were taken from the primary fissure of the EGL in each section and 8 serial sections were measured per animal. For area measurements, the first 5 sections in each series were stained with 1% cresyl violet in ethanol for 10 min. followed by washing with 1× PBS until dye no longer ran off. Sections were mounted with 50% glycerol, 50% PBS and pictures were taken using an Olympus BX41 microscope with a 2× objective. Representative sections showing a close up of the cerebellum used in the paper were obtained with a 4× objective. Cerebellar area and brain area was measured using Image J software [135].
CNS mononuclear cells were isolated by using a percoll density gradient protocol [62]. Isolated cell populations were stained in FACS buffer (2% BSA and 0.2% sodium azide) for 30 min at 4°C in the dark and fixed in 2% PFA. All samples were stained with CD45-FITC and F4/80-APC (eBioscience) and MHCII-IA/IE (Biolegend). Samples were acquired using a FACSCalibur (BD Biosciences) flow cytometer and analyzed using FlowJo7.6.1.
Due to low cell number and poor cell viability, mononuclear cell isolations from neonatal brain was performed as follows for prednisolone treated groups. Brains were homogenized using a GentleMACs tissue homogenizer (Milteniy Biotech). Homogenates were strained through a 40 µm nylon strainer, followed by centrifugation at 400×g for 4 min at 4°C. Homogenates were washed once with 1×PBS (without Ca++/Mg++) and centrifuged again at 400×g, 4 min at 4°C. Mononuclear cells were isolated by resuspending the pellet in a 37% continuous Percoll gradient followed by centrifugation at 690×g for 20 min, 4°C with gentle braking. Pellets were washed once with FACS buffer (1×PBS, 2% BSA, .2% Sodium Azide), then lysed for 5 min with 1 ml RBC lysis buffer (Sigma Aldrich). Lysis was inhibited by adding 10 mls FACS buffer and the pellet was collected by centrifugation (400×g, 4 min at 4°C). Pellets were again washed with FACS buffer, followed by resuspension in FACS buffer with FC block (1∶100, eBioscience). Mononuclear cells were blocked for 30 min on ice, counted using a TC20 cell counter (Bio-Rad) and 100 µl of cell suspension was transferred to individual wells of a round bottom, polystyrene 96 well plate. 100 µl of FACS buffer was added to each well and the plate was centrifuged (400×g, 4 min at 4°C) to pellet the cells. Mononuclear pellets were washed 2× with FACS buffer, followed by staining with CD45 – PerCP (1∶300), Cd11b – PE (1∶200) and F480 – FITC (1∶300) (eBioscience) for 1 hr at 4°C in the dark. Following staining, 150 µl of FACS buffer was added to each well and cells were pelleted by centrifugation. Cells were again washed 2× with FACS buffer followed by fixation with 4% PFA for 20 min at 4°C in the dark. Following fixation, cells were washed 2× with FACS buffer, resuspended in 200 µl FACS buffer and transferred to 5 ml polystyrene FACS tubes (BD Falcon). Samples were acquired using a FACSCalibur (BD Biosciences) flow cytometer and analyzed using FlowJo7.6.1. Dexamethasone experiments were repeated using this protocol and data were compared to the previous protocol. No differences were observed in the frequency of CD45lo or CD45hi/int/F480+ mononuclear cell populations in any group when compared to our previous findings; however, mononuclear cell numbers were greatly improved.
Total cerebellar RNA from control and experimental mice was isolated using Trizol reagent (Roche Applied Science); 500 µl Trizol/cerebellum according to manufacturer's protocol. cDNA from each sample was synthesized using the Superscript III First Strand synthesis kit (Invitrogen). Taqman based real time PCR was employed for determining the mRNA expression of genes of interest in experimental animals relative to uninfected controls. Taqman assay mixes for TNF-α (Mm99999068), IFN-β (Mm00439552), STAT1 (Mm00439518), IFN-γ (Mm99999071), gli1 (Mm00494645), N-myc (Mm00476449), Zic2 (Mm01226725), CDK5 (Mm00432437) and GABRA6 (Mm01227754) were obtained from Applied Biosystems. Real time PCR was performed using the StepOne Plus system from Applied Biosystems. The housekeeping gene 18S was used as a control for all experiments. The fold change (target gene expression relative to 18S) for control animals was set to a value of 1 +/− SEM and the relative fold change for each experimental group was determined by normalizing to control animals.
Cerebella were harvest from PND8 animals. Samples were pooled (3 cerebella/sample) and homogenized in ELISA buffer (1×PBS, .25% Triton X-100) containing protease/phosphatase inhibitors (Thermo Scientific). Lysates were rotated for 20 min at 4°C then sonicated 3× for 5 sec, followed by centrifugation at 12K× g for 10 min at 4°C. Aliquots were made and stored at −80°C until use. ELISAs were performed according to the manufacturer's instructions: TNFα (eBioscience), high sensitivity IFNγ (ebioscience, San Diego, CA) and IFNβ (PBL Interferon Source). Cytokine concentrations (pg/ml) were normalized for amount of tissue used (mg).
Cerebella harvested from control and experimental groups at PND8 were homogenized in RIPA buffer (50 mM Tris-HCl, NaCl 150 mM, 1% NP-40, 0.25% Na-Deoxycholate, 1 mM EDTA) containing protease/phosphatase inhibitors (Thermo Scientific) and cleared of insoluble material by centrifugation at 12K× g. 50 µg of protein solubilized in sample buffer (5% SDS,2% 2-mercaptoethanol, Tris pH 8) and separated by SDS-PAGE electrophoresis using a 10% acrylamide gel. Electrophoretically separated proteins were immobilized on nitrocellulose membranes and used for Western blot analysis. Membranes were probed overnight at 4°C for actin (1∶1000, MAB1501; Millipore), cyclin D1, cyclin B1 and phospho-Cyclin B1 (Ser 147) (1∶500, 2978, 4138 and 4131 respectively; Cell Signaling Technology). Immunoblots were incubated for 1 hr with HRP-conjugated anti-mouse or anti-rabbit secondary antibodies (Southern Biotech) then developed with ECL reagent (Perkin Elmer). Densitometry was performed using Quantity One software (Bio-Rad) and levels of protein were normalized to actin for each lane.
Statistical significance of comparisons of mean values was assessed by a two-tailed Student's t test, one-way analysis of variance (ANOVA) followed by Bonfferronni's multiple comparison test, two-way ANOVA followed by Bonfferronni's posttest, or a Mann-Whitney test using Prism 4 software (GraphPad).
|
10.1371/journal.pbio.1000396 | Drosophila microRNAs 263a/b Confer Robustness during Development by Protecting Nascent Sense Organs from Apoptosis | miR-263a/b are members of a conserved family of microRNAs that are expressed in peripheral sense organs across the animal kingdom. Here we present evidence that miR-263a and miR-263b play a role in protecting Drosophila mechanosensory bristles from apoptosis by down-regulating the pro-apoptotic gene head involution defective. Both microRNAs are expressed in the bristle progenitors, and despite a difference in their seed sequence, they share this key common target. In miR-263a and miR-263b deletion mutants, loss of bristles appears to be sporadic, suggesting that the role of the microRNAs may be to ensure robustness of the patterning process by promoting survival of these functionally specified cells. In the context of the retina, this mechanism ensures that the interommatidial bristles are protected during the developmentally programmed wave of cell death that prunes excess cells in order to refine the pattern of the pupal retina.
| In spite of continuous challenges from the ever-changing environment, biological systems exhibit incredible stability in their developmental and physiological processes. In addition to extrinsic variability caused by environmental fluctuations, cells face intrinsic variability arising from the inherent noise of gene expression and of other molecular processes. microRNAs, which act as post-transcriptional regulators of gene expression, are beginning to be recognized for their ability to confer robustness to biological systems by buffering the effects of noisy gene expression. Although noise often is viewed as destabilizing, some biological processes make use of noise in order to make stochastic decisions. In this paper, we describe a role for microRNAs in preventing the stochastic elimination of excess cells in the developing fly retina. After the sense organs that make up the eye have been specified, pruning of excess cells occurs through the action of the gene hid, the expression of which triggers cell death. Specific mechanisms are needed to protect specialized cells which need to be maintained to ensure that only excess cells are eliminated. We report that a pair of related microRNAs, miR-263a/b, protect sense organs during this pruning process by directly acting upon and limiting the expression of the proapoptotic gene hid. This example, illustrates a novel function for miRNAs in ensuring developmental robustness during apoptotic tissue pruning.
| Organogenesis requires the organization of different cell types into precise spatial patterns. The Drosophila compound eye has proven to be a useful model system in which to investigate how such ordered patterns are established and maintained. The mature retina consists of ∼750 regular units, called ommatidia. Each ommatidium consists of eight photoreceptors, four cone cells, and two primary pigment cells. Individual ommatidia are separated by a layer of secondary and tertiary pigment cells. The “interommatidial” lattice also includes sense organs called interommatidial bristles (IOB). The IOB are mechanosensory hair cells, which may help the fly to avoid damage to the eye surface. IOB develop from a distinct set of sensory organ precursors (SOP), specified at discrete positions among the array of interommatidial cells [1].
In the developing eye imaginal disc, a field of naïve cells is produced by proliferation and the requisite number of ommatidial precursors is selected in a process of spatially patterned cell-type specification [2]. Short-range signaling by the initially specified R8 photoreceptor cell determines the fate of surrounding cells to make the full complement of neuronal cells needed for the ommatidium. Accessory cells, such as pigment cells, are then selected from the surrounding field of interommatidial cells. As in most developing neuronal systems, progenitor cells are over-produced, and excess cells eliminated by apoptosis after the correct pattern has been generated. In the eye imaginal disc, excess interommatidial cells are removed by two waves of programmed cell death during early pupal stages to produce the near-perfect array of ommatidia found in the adult eye [3],[4]. A patterning process based on “pruning” of excess cells requires a mechanism to protect important functionally specified cells.
Mechanisms to ensure robustness are an important feature of developmental systems that can be subject to perturbation. MicroRNAs (miRNAs) have been proposed to play a role in conferring robustness during development [5],[6]. This is exemplified by miR-7, which has been shown to contribute to the robustness of regulatory networks that ensure correct sense organ specification in Drosophila [7]. Although miR-7 is not required under normal conditions, SOP patterning was compromised when miR-7 mutant flies were subjected to environmentally challenging conditions. miRNAs act as post-transcriptional regulators that limit levels of target gene expression. This property makes them well suited to buffer fluctuating levels of gene activity. It may also make them well suited to serve a protective function during patterned tissue pruning.
In this report we present evidence that the miR-263a/b family of miRNAs contributes to the robustness of sense organ development. During apoptotic tissue pruning, functionally specified cells such as photoreceptors and mechanosensory organs are protected, while excess cells are eliminated. Mechanisms to ensure survival of specific cells are needed. Tissue pruning in the developing retina depends on activity of the pro-apoptotic gene hid [8], however the mechanisms that govern the decision as to which cells are lost are not fully understood. In the absence of miR-263a/b sensory bristles are lost, like other cells, in a stochastic manner. Through a process of experimental validation we identify hid, among over 50 candidates examined in vivo, as a biologically important target of miR-263a/b in this context. While hid and other proapoptotic genes are targeted by other miRNAs, including bantam and the miR-2 family [5],[9],[10], none of these interactions has been shown to affect apoptotic pruning. Thus miR-263a/b may have a dedicated antiapoptotic role to ensure the robustness of sense organ development in a fluctuating developmental landscape.
miR-263a is located near the bereft locus on chromosome 2L (Figure 1A). cDNA evidence has indicated that bereft encodes a spliced transcript, however, one without an obvious protein-coding region [11]. Expression of this cDNA in transgenic flies did not rescue the defects that were attributed to bereft mutants [11]. In this light, we asked if miR-263a might be the functional product of the bereft locus. To address this, ends-out homologous recombination was used to generate a small deletion removing miR-263a. Three hundred and fifty nucleotides including the miRNA hairpin were replaced with a mini-white gene cassette (Figure 1A) [12]. The absence of the mature miR-263a miRNA was confirmed by Northern blot using total RNA isolated from adult flies homozygous mutant for the targeted allele (Δ263a, Figure 1B). Mature miR-263a was also missing in flies carrying the bereft24 allele in trans to the Δ263a deletion allele (Δ263a/bft, Figure 1B), as well as in other bereft mutants (Figure S1). The bereft24 allele is a 2.8 kb deletion that removes the first exon of the bereft transcript (Figure 1A). The absence of mature miR-263a in these flies suggests that miR-263a is the functional product of the bereft locus.
bereft mutants show defects in the formation of a variety of external sense organs, including loss of the IOB of the eye [11]. In miR-263a homozygous mutants and in Δ263a/bereft24 flies ∼80% of IOB were missing (Figure 1C, 1D). The Drosophila genome encodes a second miRNA closely related in sequence to miR-263a (Figure S2A). We generated a miR-263b deletion allele (Δ263b) by homologous recombination and confirmed that mature miR-263b was absent in the mutant (Figure S2B). IOB numbers were only modestly reduced in flies lacking miR-263b alone (Δ263b/Df(3L)X-21.2; Figures S2C, 1D). However, we observed a significant increase in the loss of IOB in flies lacking both miR-263a and miR-263b compared to miR-263a alone (Figures S2C, 1D). These observations suggest that both the miR-263a and miR-263b miRNAs contribute to IOB formation, with miR-263a playing the major role.
In addition to the IOB phenotype, the miR-263a and miR-263b mutants exhibit other milder defects. The number of large mechanosensory bristles (macrochaetae) on the head and thorax was reduced in miR-263a mutant flies compared to controls (Figure S3). Although the magnitude of the reduction in bristle number was small, the difference was statistically significant. There was no significant enhancement of this phenotype in the miR-263a miR-263b double mutant (Figure S3). In addition, the miR-263a mutant showed reduced viability compared to control flies. Although miR-263b showed little effect alone, the miR-263a miR-263b double mutant showed a stronger viability phenotype (Figure S4). Our further analysis focused on the bristle phenotypes.
In order to verify that the bristle phenotypes are due to loss of the miRNAs, we performed genetic rescue experiments. To this end, we produced Gal4 “knock-in” alleles of miR-263a and miR-263b, in which the miRNA hairpin sequences were replaced by Gal4 and mini-white (using a modified targeting vector; [13]). Flies carrying the miR-263a-Gal4 allele in trans to bereft24 or Δ263a displayed IOB loss (Figure 2A, 2B; unpublished data). Restoring miR-263a expression under the control of miR-263a-Gal4 using a UAS-miR-263a transgene fully suppressed the loss of IOB in miR-263a mutant flies (Figure 2A, 2B). Measurement of mature miR-263a by quantitative PCR showed that less than 20% of the normal expression level was sufficient to achieve a full rescue (Figure 2B, green bars). A lower level of Gal4-independent expression that results from leakiness of the UAS-miR-263a transgene also conferred partial rescue of IOB loss in the Δ263a/bft background (Figure 2A, 2B). The bristle loss on head and thorax observed in miR-263a mutants was also rescued by Gal4-dependent expression of UAS-miR-263a (Figure S3). These data confirm that absence of miR-263a is responsible for the loss of mechanosensory bristles observed in bereft mutants.
Residues 2–8 at the 5′ end of a miRNA, known as the seed region, are thought to play an important role in miRNA target recognition [10],[14]. miR-263a and miR-263b differ in sequence, with the seed region being shifted by one residue (Figure S2A). Thus it might be expected that they would have different target spectra. Nonetheless, expression of UAS-miR-263b under miR-263b-Gal4 control was able to rescue the miR-263a mutant phenotype (Figure 2C, 2D). Rescue occurred at levels of miR-263b several-fold above normal (Figure 2D, green bars), suggesting that miR-263b can replace miR-263a when over-expressed. In light of the observation that loss of miR-263b has a milder impact than loss of miR-263a, these results imply that the two miRNAs have targets in common in their role during IOB development.
Because of the greater dependence of IOB development on miR-263a, we focused on the miR-263a mutant for more in-depth analysis. Mechanosensory organs are composed of four cells produced by two rounds of asymmetric division of a SOP: the sensory bristle (called the shaft cell), its socket cell, a neuron, and its sheath cell [15]. In bereft mutants, all four of these cells are present and properly specified upon completion of IOB cell fate determination [11]. Therefore, miR-263a must act at a later stage, after the asymmetric division of the SOP.
To determine when bristle development fails, we examined pupal retinas using an antibody to the cell junction protein DE-cadherin [16]. At 24 h after puparium formation (APF), the hexagonal array of ommatidia is clearly defined; bristle progenitor cells are visible at alternate corners in the hexagonal array of ommatidia (arrows, Figure 3A). At this stage, miR-263a mutant retinas were indistinguishable from the controls and bristle shaft progenitor cells were present in normal numbers (Figure 3B).
Approximately one third of the interommatidial cells present at 24 h APF undergo apoptosis during the following 12 h [1],[3]. By 40 h APF, a single row of interommatidial cells surrounds each ommatidium. Bristle shaft progenitor cells appear as brightly labeled cells at three of the six corners (arrows, Figure 3C, 3D). In miR-263a mutant retinas, the majority of these cells were missing at 40 h APF (arrowheads, Figure 3E, 3F). Pax2 protein expression marks the nuclei of bristle shaft and sheath cells of external sensory organs (Figure 3G; [17],[18]). The bristle shaft cell grows by an unusual type of cell cycle called endoreplication, in which DNA replication takes place without cell division [19],[20]. These cells have increased ploidy and therefore larger nuclei (arrows, Figure 3G) than the sheath cells (arrowheads). In miR-263a mutant retinas many of the larger Pax2 positive nuclei were missing, consistent with bristle shaft cell loss (Figure 3H). We made use of the Gal4 knock-in alleles to direct UAS-GFP reporter expression in the endogenous miR-263a and miR-263b expression domains. Triple labeling to visualize GFP and Pax2, together with DE-cadherin (Figure 3I, 3J), showed that both miRNAs are expressed in the bristle shaft cells during the developmental window in which bristles are lost in the mutants.
The loss of bristle shaft cells by 40 h APF raised the possibility that they might be eliminated during the normal wave of apoptotic pruning of interommatidial cells. To test this possibility, we made use of miR-263a-Gal4 to express the anti-apoptotic protein p35 in miR-263a expressing cells. p35 has been shown to be effective as an inhibitor of apoptosis in Drosophila [21],[22]. Expression of UAS-p35 using miR-263a-Gal4 suppressed IOB loss in miR-263a mutant flies (Figure 4A, 4B). Similarly, over-expression of the anti-apoptotic protein DIAP1, a direct target of the proapoptotic protein Hid [23], was able to prevent IOB loss in miR-263a flies (Figure 4A, 4B). We also monitored programmed cell death in the pupal retina at 35 h APF by visualizing double strand DNA breaks caused by apoptotic endonucleases. In control retinas, we did not observe apoptosis of the Pax2-expressing bristle shaft or sheath cells (Figure 4C). However, apoptotic Pax2-expressing nuclei corresponding to bristle shaft cells were seen in miR-263a mutants (arrows, Figure 4D). The total number of apoptotic cells/ommatidium was not significantly elevated in the mutant (Figure 4E), but there was a statistically significant increase in the number of apoptotic cells that were Pax2-expressing bristle shaft cells (p<0.001). These findings indicate that miR-263a acts to protect these sense organs from the wave of programmed cell death that sweeps over the retina during early pupal development.
miR-263a has several hundred computationally predicted targets [24],[25]. Among these are genes involved in cell proliferation and cell death and a broad range of other biological processes. Endogenous targets are often upregulated in miRNA mutants. Therefore over-expression of a biologically important target in miR-263a expressing cells would be expected to result in IOB loss, phenocopying the miR-263a mutant phenotype. miR-263b-Gal4 was used to drive over-expression of target genes in this series of experiments because it has higher Gal4 activity than miR-263a-Gal4. We selected 56 predicted targets for analysis (Table S1). Only two of the candidates caused bristle loss when expressed under control of miR-263b-Gal4: Cyclin E and head involution defective (hid).
Cyclin E is an essential cell cycle regulator, required for normal cell proliferation and for endoreplication [26]. Endoreplication plays an important role in the growth of bristle shaft and socket cells [19]. Over-expression of Cyclin E has been shown to interfere with endoreplication [27],[28] and can suppress bristle shaft cell growth [29]. If Cyclin E over-expression is the cause of the bristle loss in miR-263a mutants, limiting their capacity to express Cyclin E should suppress this phenotype. Bristle loss occurs between 24 and 40 h APF in the mutants. RNA was prepared from pupae at 30 h APF, because the majority of the bristles are not yet lost at this stage. Cyclin E mRNA levels were elevated by ∼2.5-fold in RNA samples from miR-263a mutants (Figure S5). Removing one copy of the Cyclin E gene restored the mRNA to near normal levels but did not rescue the IOB loss phenotype (Figure S5). Although Cyclin E is upregulated in the miR-263a mutant, this does not appear to contribute to the bristle loss phenotype.
The other candidate, hid, encodes an inducer of cell death in Drosophila. hid has been shown to play a role in the late stage cell death pathway in the retina [22]. hid expression under miR-263b-Gal4 control caused loss of IOB (Figure 5A). To determine whether hid might be a biologically relevant target of miR-263a in vivo, we compared hid mRNA levels in RNA samples from mutant and control pupal eye discs. hid mRNA was 1.6-fold higher in the mutants (Figure 5B). This difference was abolished in miR-263a mutant flies rescued by expression of UAS-miR-263a under miR-263a-Gal4 control.
To test whether hid over-expression is the cause of bristle loss, we reduced hid activity in the miR-263a mutant background by introducing the hid05014 loss of function allele [22]. This genetic combination restored IOB numbers to ∼60% of normal levels (Figure 5C, 5D). To further reduce hid activity we made use of the W1 allele, which expresses an antimorphic form of hid [30], and found a further restoration of IOB number (Figure 5C, 5D). Similarly, reducing hid levels by expression of a UAS-hid-RNAi transgene under the control of miR-263b-Gal4 produced a strong suppression of the miR-263a mutant phenotype (Figure 5C, 5D). Taken together, these data suggest that miR-263a serves to prevent apoptosis in the IOB precursors by limiting hid expression during the wave of interommatidial cell pruning.
The hid 3′UTR contains four potential miR-263a binding sites (Figure 6A). To address whether hid is a direct target of miR-263a, we generated luciferase reporter constructs carrying the full length endogenous hid 3′UTR or mutant versions in which two nucleotides of each predicted miR-263a site were mutated to compromise pairing to the miRNA seed region (Figure 6A, in red). In S2 cells, co-expression of the luciferase reporter carrying the intact sites with miR-263a significantly reduced luciferase activity (Figure 6B, p<0.001). This was attributable to reduced luciferase mRNA levels (Figure 6C). These effects were not observed in cells expressing the mutant form of the hid reporter (Figure 6B, 6C). We also analyzed the effect of miR-263b on the 3′UTR of hid. Although miR-263b differs from miR-263a by three residues, including position 1 of the seed region, hid is also a predicted target of miR-263b (Figure S6; [24],[25]). Coexpression of miR-263b also significantly reduced luciferase activity from the reporter carrying the intact hid 3′UTR but not from the reporter in which the miRNA sites were mutated (Figure 6B). Therefore, miR-263b and miR-263a can each act directly via these sites to regulate hid mRNA levels. Differences in the quality of the sites for the two miRNAs may contribute to the apparent difference in their relative potency observed in vivo.
To further assess the functionality of these sites in vivo, we generated transgenic flies expressing the two hid 3′UTR luciferase reporters. Luciferase activity levels were compared in pupal retinas dissected from control animals and miR-263a mutants. There was no difference in luciferase activity for the transgene carrying the mutant form of the hid reporter, but the reporter with the intact sites clearly showed increased luciferase activity in the miR-263a mutant (Figure 6D). A similar increase was observed in the level of luciferase mRNA from the hid reporter with the intact sites, but not from the reporter with the mutated sites (Figure 6E). Comparable results were obtained comparing GFP reporter transgenes with intact and mutated sites in control and miR-263a mutants (Figure S7). We also observed an increase in the level of the endogenous mature hid mRNA in the mutant, but not in the level of the hid primary transcript, measured by qRT-PCR using intron-specific primers (Figure 6F). Taken together these experiments provide evidence that miR-263a acts directly via the sites identified in the 3′UTR to regulate hid mRNA levels in vivo. These effects are posttranscriptional, most likely reflecting destabilization of hid transcripts.
It has been reported previously that excess hid activity eliminates photoreceptors and pigment cells, effectively ablating the eye, while sparing the IOB [22],[31]. The result was a tuft of IOB and undifferentiated cuticle in place of the eye. Further increase of hid activity led to loss of these cells as well. Based on these observations, it was proposed that IOB might contain high levels of a negative regulator of cell death. To ask whether miR-263a/b might be responsible for this activity, we compared the effects of reducing miR-263a/b activity in animals expressing the constitutively active form of Hid, hid(Ala5), in the eye. Reducing miR-263a levels by removing one copy of the miR-263a gene led to fewer IOB, producing a more sparse appearance in the tuft of bristles (Figure 7A, 7B). Because the morphology of the hid(Ala5) eyes is strongly perturbed, the effect of reducing the miRNA can be quantified most reliably by counting the number of empty sockets. Every socket should have an IOB hair cell, so the empty socket indicates a specified sense organ lacking the bristle shaft cell. Although the variance of this measure is high, there was a statistically significant increase of ∼2-fold in the proportion of empty sockets (p<0.005), indicating loss of IOB due to reduced miR-263a activity. Thus reduced activity of the miRNA enhanced the GMR:hid(Ala5) phenotype, suggesting that the miRNA has protective activity even in these extreme conditions of hid over-expression.
Finally, we asked whether the loss of the other mechanosensory bristles in miR-263a mutant flies was also a consequence of apoptosis due to elevated Hid activity. To address this we introduced the antimorphic allele of hid, W1, into miR-263a miRNA mutant flies and found that mechanosensory bristle loss on head and thorax was also significantly suppressed (Figure S3; p<0.001). These observations suggest that miR-263a/b play a protective role, preventing the loss of mechanosensory cells due to hid-induced apoptosis.
Robustness of a biological system can be thought of in terms of the mechanisms that ensure stability. In developmental terms, perturbation can come in the form of fluctuating levels of intercellular signaling and/or gene expression and can be intrinsic or of environmental origin. Gene regulatory networks have properties that can help to confer stability by buffering the effects of fluctuations in gene expression (reviewed in [32]). Computational analysis has suggested that miRNAs are over-represented in gene regulatory networks in animals, suggesting that they confer useful regulatory possibilities [33],[34]. However to date few examples have been investigated experimentally in terms of biological processes that confer robustness during development of multicellular organisms. A recent study has provided compelling evidence for a miRNA acting to confer robustness to sensory organ specification in Drosophila [7]. In this report we examine the role of miR-263a/b in conferring robustness to sensory organ survival during a developmental pruning process.
miR-7 was shown to act in two molecularly distinct feed-forward loops required for sense organ specification [7]. One pair of feedforward motifs involve the transcription factors Yan and Pointed, which mediate Notch and EGF signaling to control R8 photoreceptor specification. The second involves the transcription factors E(spl) and Atonal to control SOP specification. In both examples miR-7 is induced by one of the transcription factors to confer repression on the other. The requirement for miR-7 activity in these patterning processes is not evident under normal, environmentally stable, conditions. However, it can be revealed under destabilizing conditions, including severe environmental fluctuation, or in sensitized genetic backgrounds. Thus miR-7 acts to provide stability to these molecular networks.
We have explored the possibility that miR-263a/b might function in a regulatory feed-forward network to control hid both directly and indirectly. The RAS/MAPK pathway has been reported to regulate hid activity at two levels. Hid activity is controlled by direct phosphorylation mediated by MAPK [31]. Signalling through the EGF/RAS/MAPK pathway protects cells of the developing eye from apoptosis, through MAPK mediated regulation of Hid activity. In addition MAPK signalling represses hid transcription [8]. Intriguingly, an upstream element of the MAPK pathway, Ras85D, is a predicted target of miR-263a. In this scenario, miR-263a would act directly to repress hid and indirectly via the RAS/MAPK pathway (illustrated in Figure S8). Negative regulation of Ras85D by miR-263a would repress MAPK activity and alleviate repression of hid transcription and of Hid protein activity. The result is a so-called “incoherent” motif [32], in which the two branches have opposing effects on their shared target. A prediction of this model is that hid transcription should decrease in the miR-263a mutant due to elevation of MAPK activity. However, as shown in Figure 6F, hid primary transcript levels were not significantly affected in the miR-263a mutant, although mature hid mRNA levels increased due to loss of miRNA direct mediated repression. As a further test, Ras85D mRNA levels were measured in pupal eye discs dissected from control animals and miR-263a mutants. There was no significant difference (Figure S8). Although the topology of this predicted network suggested a potential role in control of hid activity, we do not find sufficient evidence to support the biological relevance of this network in vivo. This example highlights the importance of experimental validation in vivo in assessing such predictions.
Our findings suggest a role for the miR-263a/b miRNAs in conferring robustness of a different sort, ensuring the survival of sense organ cells, after they have been specified by the developmental patterning process. In this scenario the fluctuating cellular landscape derives from triggering cell death through randomly variable activity of the pro-apoptotic gene hid. Under normal conditions, this rarely, if ever, causes bristle loss. However in miR-263a/b mutants sporadic bristle loss was seen and was attributable to elevated hid activity. As in the case of the macrochaetae, loss of individual interommatidial cells is a stochastic process. In each of the single mutants we observed a variable loss of IOB, suggesting that the chance of any given nascent IOB cell succumbing to apoptosis has increased in the absence of the protective effect of miR-263a/b. The overall robustness of the pruning process is impaired.
In the miR-7 case, the mutants do not show any defect under normal conditions, but the limits to the robustness of the system can be revealed by environmental perturbation. This is consistent with a scenario in which robustness derives from a gene regulatory network designed to buffer molecular perturbation. Based on the observations of Li et al. [7], we examined whether the severity of the miR-263a/b mutant phenotype would be affected by environmental fluctuation to increase noise but found no effect (unpublished data). We also did not find evidence that miR-263a/b act in the context of a gene regulatory network. Instead, miR-263a/b appears to function in a different context, acting as a buffer in a biological process that is inherently stochastic. In this way miRNA activity is used to ensure that apoptotic cell death is not allowed to compromise specific cells. It is noteworthy that as little as 20% of normal miR-263a levels are sufficient to support IOB development. This implies a considerable buffering capacity to ensure that the process of IOB formation is robust in a fluctuating developmental landscape.
Several other microRNAs have been implicated in the control of apoptosis in Drosophila. bantam miRNA functions during tissue growth and regulates hid, to prevent proliferation induced apoptosis [9]. However, under normal conditions bantam regulation of hid does not appear to impact upon apoptotic pruning or on survival of sense organs (our unpublished observation). miR-14 has also been reported to be anti-apoptotic [35]. miR-14 mutants do not impact on IOB apoptosis ([36] and our unpublished observations). Similarly, miR-8 mutants show apoptosis in the CNS [37] but do not have an IOB phenoytpe. Finally, members of the miR-2 family of miRNAs have been shown to regulate the propaptotic genes, reaper, grim, and sickle in S2 cell over-expression assays or in reporter transgene assays in vivo [10],[38]. Injection of antisense oligonucleotides to deplete members of this family have been reported to cause apoptosis in the embryo [39], but none of the mutants that affect the members of this family have yet shown any role in apoptotic pruning ([40] and our unpublished observations). We do not exclude the possibility that potential phenotypes might be masked by functional redundancy among family members. The available evidence suggests that miR-263a/b may have a dedicated role in controlling hid-induced apoptosis during developmental pruning of interommatidial cells.
Most miRNAs are predicted computationally to have many possible targets. Yet our survey of over 50 candidates yielded only one target, hid, for which we have functional evidence in vivo. It may be of interest in this context to consider data from analysis of Drosophila miRNA target predictions. A high proportion of predicted targets are regulated in cell-based miRNA over-expression assays (sample refs: [5],[41]–[44]). This provides evidence that the miRNA, when present abundantly, can regulate the predicted target site. There are fewer examples in which over-expression of a predicted target can be shown to contribute directly to causing a specific miRNA mutant phenotype in vivo. In most such cases only one or a few targets have been implicated (reviewed in [45],[46]; see also [47]–[49]). For miRNA mutants that have been studied in detail, evidence has begun to emerge that different aspects of the mutant phenotype may result from misregulation of different targets in different tissues. miR-8 is a good example, with well characterized phenotypes linked to different targets in three different tissues: Atrophin for neurodegeneration in the CNS [37], Enabled for neuromuscular junction development [48], and U-shaped in the adipose tissue to control tissue growth [49]. Finding a few different key targets in different tissues may prove to be a common theme.
The genetic evidence presented here identifies hid as a key target of the miR-263 family in supporting bristle development. A priori we cannot exclude that there might be other important targets. But we note that only hid, of the 56 candidates tested, fulfilled two essential criteria: (1) being able to mimic the miRNA mutant phenotype when over-expressed in the miRNA expressing cells and (2) being able to suppress the miRNA mutant phenotype when its level was reduced in the miRNA expressing cells. Further investigation of the miR-263 family miRNA mutants may lead to identification of targets important for other aspects of the miRNA function, such as the reduced viability observed in the double mutants.
Drosophila miR-263a and miR-263b are members of a conserved family of miRNAs, including mammalian miR-183, miR-96 and miR-182, and miR-228 in C. elegans. Interestingly, members of this family display conservation of expression in ciliated sensory organs in vertebrate and invertebrate organisms [50]. miR-183, miR-96, and miR-182 are expressed in sensory hair cells in mammalian auditory and vestibular organs, as well as in sensory cells of the eye and ear in zebrafish and chicken [51]–[55]. C. elegans miR-228 is expressed in chemosensory and mechanosensory sensilla [50]. Drosophila miR-263a and miR-263b are expressed in sense organ precursors in embryos [11],[56] and in mechanosensory organs of the eye, antenna, and haltere ([11],[50], this report). The high degree of sequence conservation and expression in sensory organs across phyla raises the possibility that a common ancestor of these miRNAs was associated with sensory cell development and function [50].
Further support for the idea of conservation of function comes from the observation that mutations affecting miR-96 have been identified as the cause of auditory hair cell degeneration and non-syndromic progressive hearing loss in mice and humans [57],[58]. Depletion of all miRNAs using conditional dicer mutants in the mouse also leads to defects in inner ear hair cell development [59],[60]. Whether there is more than a coincidental similarity to the role of miR-263a/b in support of sensory hair development in Drosophila remains to be determined. Superficially the way in which these sense organs are lost appears to differ. In the fly, the mechanosensory cells are lost due to apoptosis in the miR-263a/b mutants. In the mammalian systems, mature differentiated sensory cells appear to be lost through degeneration. In the case of the miR-96 mutant this could be due to inappropriate regulation of genes that are not normally miR-96 targets due to the change in sequence of the mutant miRNA seed, but in the case of dicer conditional mutants it is more likely due to loss of normal miRNA mediated target regulation. Whether this involves apoptosis is not known. Intriguingly, there is evidence suggesting an anti-apoptotic role for miR-182 and related family members in human cancers [61],[62]. So the possibility of an underlying conservation of mechanism exists.
Canton-S flies were used as the wild-type control. EP lines were obtained from the Bloomington, Szeged, and Exelixis stock centres. The hid UAS-RNAi strain was obtained from the VDRC. bft24, bft225, and bft6B were provided by Rolf Bodmer. hid05014 and GMR:hid(Ala5) were provided by Hermann Steller. W1, Df(3L)X-21.2, UAS-p35, and UAS-Diap1 were obtained from the Bloomington stock centre. UAS-miR-263a and UAS-miR-263b lines were made by cloning a 300 base pair genomic fragment containing the miRNA hairpin into the 3′UTR of dsRed in pUAST, as described in [10]. The GFP and luciferase hid 3′UTR reporters were made by cloning the 2.2 kb hid 3′UTR after GFP or luciferase, under control of the tubulin promoter [5],[9]. hid UTR reporters with mutated miR-263a/b sites were generated by PCR using primers designed to change the seed region from TGCCA into TCCGA. PCR products were sequence verified.
Ends-out homologous recombination was performed as described [12]. As miR-263b is located in an intron of CG32150, we removed the intron-containing mini-white gene cassette and confirmed that splicing of CG32150 mRNA was not affected in the ΔmiR-263b mutants by comparing the level of spliced mRNA using qRT-PCR. miR-263a and miR-263b Gal4 knock-in alleles were made using a modified targeting vector [13].
Luciferase reporters and miRNA expression plasmids were expressed under the control of the tubulin promoter. S2 cells were transfected in 6-well plates with 1,000 ng of miRNA expression plasmid or empty vector, 500 ng of firefly luciferase reporter plasmid, and 500 ng of Renilla luciferase DNA as a transfection control. Transfections were performed with triplicate technical replicates in at least three independent experiments. At 60 h post-transfection, dual luciferase assays (Promega) were performed on a portion of the transfected cells. The other portion was pelleted and dissolved in Trizol reagent (Invitrogen) for total RNA extraction. For luciferase assays on pupal retinas, retinal tissue was dissected and immediately lysed in passive lysis buffer (Promega). Luciferase activity was normalized to total protein content, measured on the same sample using the Bradford method (Bio-Rad).
Northern blots on small RNA were carried out according to [63]. 5 µg of total RNA extracted from adult flies were loaded per lane. The blot was probed with an oligonucleotide complementary to miR-263a, 5′end-labeled with [32]-P. For miRNA qRT-PCR, primer sets designed to amplify mature miR-263a and miR-263b were obtained from Applied Biosystems. Reverse transcription was done on 100 ng of total RNA. miRNA levels were calculated relative to miR-8, after having confirmed that miR-8 levels remain constant in the relevant fly strains. For mRNA qRT-PCR, total RNA was treated with RNase-free DNase (Promega) to eliminate DNA contamination. First strand synthesis used random hexamer primers and SuperScript RT-III (Invitrogen). Samples were RNaseH-treated after the RT reaction and used for qRT-PCR. Measurements were normalized to mitochondrial large ribosomal RNA (mtlrRNA1, AAAAAGATTGCGACCTCGAT and AAACCAACCTGGCTTACACC) or to the transfection control Renilla luciferase mRNA (CGGACCCAGGATTCTTTT and TTGCGAAAAATGAAGACCT). Primers for hid pre-mRNA: TGAAGGTGTTCTCCGATTCC and ATCTCACCCAGCGCTCTTTA. Primers for mature hid mRNA: GAGAACGACAAAAGGCGAAG and CAAAACGAAAACGGTCACAA. Firefly luciferase primers: CCGCCGTTGTTGTTTTG and CTCCGCGCAACTTTTTC. GFP primers: GCAGTGCTTCAGCCGCTA and AGCCTTCGGGCATGGC.
Adult flies were fixed overnight in 2.5% glutaraldehyde at 4°C, washed 3×15 min with PBS, dehydrated in a series of ascending ethanol concentrations, critical point dried, mounted on stubs, and coated with gold. Eyes were imaged with a JSM-6360LV scanning microscope (JEOL, Tokyo, Japan). The orientation, contrast, and brightness of the images were adjusted using ImageJ. For the quantification of bristle numbers, a high-resolution image of a whole eye was printed and the maximal visible surface delimited, usually 300–500 ommatidia. The number of visible IOBs was counted and divided by the total number of corners where IOBs would be expected or, for Figure 7, by the total number of bristle sockets. 10–30 eyes were analyzed for each genotype.
To stage pupae, white pre-pupae were collected and aged at 25°C until dissection. Pupal eye imaginal discs were dissected and fixed in 4% formaldehyde for 20 min on ice. The following primary antibodies were used: rat anti-Dcad2 1∶40 (Developmental Studies Hybridoma Bank), rabbit anti-DPax2 1∶50 (a gift from Erich Frei and Markus Noll), and chicken anti-GFP 1∶1000 (Abcam). Fluorescent secondary antibodies were from Jackson Laboratories. Samples were mounted in Vectashield (Vector Laboratories). Detection of apoptotic cells in pupal eye discs was done using the Apoptag ISOL dual fluorescence kit (Chemicon). Immunofluorescence images were collected using a Leica SPE confocal microscope and processed using ImageJ. Quantification of apoptotic nuclei was done using z-projections of confocal sections. Total apoptotic nuclei as well as apoptotic shaft cell nuclei were counted and normalized to the total number of ommatidia analyzed.
|
10.1371/journal.pgen.1006371 | A Large Genome-Wide Association Study of Age-Related Hearing Impairment Using Electronic Health Records | Age-related hearing impairment (ARHI), one of the most common sensory disorders, can be mitigated, but not cured or eliminated. To identify genetic influences underlying ARHI, we conducted a genome-wide association study of ARHI in 6,527 cases and 45,882 controls among the non-Hispanic whites from the Genetic Epidemiology Research on Adult Health and Aging (GERA) cohort. We identified two novel genome-wide significant SNPs: rs4932196 (odds ratio = 1.185, p = 4.0x10-11), 52Kb 3’ of ISG20, which replicated in a meta-analysis of the other GERA race/ethnicity groups (1,025 cases, 12,388 controls, p = 0.00094) and in a UK Biobank case-control analysis (30,802 self-reported cases, 78,586 controls, p = 0.015); and rs58389158 (odds ratio = 1.132, p = 1.8x10-9), which replicated in the UK Biobank (p = 0.00021). The latter SNP lies just outside exon 8 and is highly correlated (r2 = 0.96) with the missense SNP rs5756795 in exon 7 of TRIOBP, a gene previously associated with prelingual nonsyndromic hearing loss. We further tested these SNPs in phenotypes from audiologist notes available on a subset of GERA (4,903 individuals), stratified by case/control status, to construct an independent replication test, and found a significant effect of rs58389158 on speech reception threshold (SRT; overall GERA meta-analysis p = 1.9x10-6). We also tested variants within exons of 132 other previously-identified hearing loss genes, and identified two common additional significant SNPs: rs2877561 (synonymous change in ILDR1, p = 6.2x10-5), which replicated in the UK Biobank (p = 0.00057), and had a significant GERA SRT (p = 0.00019) and speech discrimination score (SDS; p = 0.0019); and rs9493627 (missense change in EYA4, p = 0.00011) which replicated in the UK Biobank (p = 0.0095), other GERA groups (p = 0.0080), and had a consistent significant result for SRT (p = 0.041) and suggestive result for SDS (p = 0.081). Large cohorts with GWAS data and electronic health records may be a useful method to characterize the genetic architecture of ARHI.
| Age-related hearing impairment (ARHI) is one of the most common sensory disorders. While ARHI effects can be mitigated with current technologies, it cannot be cured or eliminated. It is thus hoped that identification of genetic influences on ARHI may one day lead to curative therapies. Towards this goal, the current study utilized electronic health record data from non-Hispanic whites in the Genetic Epidemiology Research on Adult Health and Aging (GERA) cohort to conduct a genome-wide association study of ARHI, and tested the significant variants for replication in other GERA race/ethnicity groups, independent GERA phenotypes, and self-reported ARHI from the UK Biobank. We discovered two genome-wide significant SNPs. The first was novel and near ISG20. The second was in TRIOBP, a gene previously associated with prelingual nonsyndromic hearing loss. Motivated by our TRIOBP results, we also looked at exons in known hearing loss genes, and identified two additional SNPs, rs2877561 in ILDR1 and rs9493672 in EYA4 (at a significance threshold adjusted for number of SNPs in those regions). These results suggest that large cohorts with GWAS data and electronic health records may be a useful method to characterize the genetic architecture of ARHI.
| Age-related hearing impairment (ARHI), or presbycusis, is one of the most common sensory impairments [1,2], affecting 25% of individuals over age 65 and 50% of individuals over age 80 [3]. ARHI impacts speech understanding, makes it much more difficult to communicate, and leads to an overall lower quality of life [4]. The effects of ARHI can be mitigated by amplification devices and assistive listening devices, but it is not curable and the effects cannot be completely eliminated. The best hope for cure lies in identifying all the physiologic and environmental factors contributing to ARHI and developing interventions that address these risks. There are a number of contributing factors to ARHI, including early noise exposure, medication history, and genetics [1,5–11]. It is hoped that by identifying the genetic basis of ARHI, targeted therapies can be developed to mitigate this risk.
ARHI has a clear genetic contribution; in a study of twins, hearing loss after age 64 was shown to have a heritability of 47% [12]; another study based on self-reported hearing loss in twins over age 75 reported a heritability of 40% [13]. During the past decade, genome-wide association studies (GWAS) have tried to uncover risk variants for ARHI, but no genome-wide significant or suggestive loci have been found that have been successfully replicated [3,14–16]. One study was based on 846 ARHI cases and 846 controls [3], while three other studies used principal components (PCs) of hearing impairment thresholds at several frequencies in 3,417 European individuals [15], 2,161 Belgian individuals [14], and 352 Finnish Saami individuals [16]. Fransen et al. attempted to replicate suggestive SNPs from previous papers, but failed to do so [14]. Of note, the sample sizes in these studies are smaller than those used to study other complex traits [17].
It is clear from these prior studies that much larger sample sizes are needed to better delineate the genetic factors associated with ARHI. Towards this goal, we utilized 6,527 non-Hispanic white ARHI cases and 45,882 controls identified from electronic health records (EHR) of members of the Kaiser Permanente Northern California (KPNC) health care system who participated in the Kaiser Permanente Research Program on Genes, Environment, and Health (RPGEH) Genetic Epidemiology Research on Adult Health and Aging (GERA) cohort for discovery. Loci with genome-wide significance identified in the GERA non-Hispanic whites were then tested for independent replication in 1,025 Latino, East Asian, and African American GERA cases and 12,388 controls, in addition to 30,802 UK Biobank self-reported cases and 78,586 controls. We also tested for replication in two related measured quantitative traits—speech discrimination score (SDS) and speech recognition threshold (SRT), stratified by case/control status, to construct independent tests. These measures were only available on a subset of the GERA individuals. Additionally, we assessed replication of previously-described sub-genome-wide significant loci in the GERA cohort. Motivated by our GWAS results, we also examined the GWAS results at known hearing loss genes at reduced significance thresholds adjusted for the proportion of the genome being tested to account for lack of power in these regions. Finally, we looked at array-based heritability in the GERA non-Hispanic whites.
The multi-ethnic GERA cohort participants in this study were part of the Kaiser Permanente Research Program on Genes, Environment, and Health (RPGEH), which has been described in detail [18,19]. Briefly, participants were an average of 62.9 years old (SD = 13.8) at sample collection, with an average length of membership of 23.5 years in Kaiser Permanente Northern California, comprehensive EHR, survey (five pages including information on demographic factors, behaviors, and self-reported health, but no questions on hearing), and genome-wide genotyping data.
We used as our discovery cohort the largest GERA subgroup of non-Hispanic whites (6,527 cases, 45,882 controls), with our replication cohort in GERA consisting of Latinos/East Asians/African Americans with 481/398/146 cases and 5,215/5,040/2,133 controls, respectively (workflow of GERA phenotyping in Fig 1A). Compared to the controls, the cases were more often male and older (Table 1). There were 4,903 total individuals (4,231 non-Hispanic white, 298 Latino, 249 East Asian, and 125 African American) for whom audiologist notes were available (the majority in cases), and could be used to derive quantitative measures of speech recognition threshold (SRT) and speech discrimination score (SDS), as well as history of noise exposure (Table 1).
Within each race/ethnicity group, we also tested ancestry principal components (PCs) [18] for association with ARHI, adjusting for the potential confounders of diabetes, hypertension, and osteoporosis. In non-Hispanic whites, we found a significant increase in ARHI in those with northwestern vs. southeastern European ancestry (p = 9.2×10−10), but it explained very little of the variance (0.12%). No significant associations were found in Latinos, East Asians, or African Americans, but we had less statistical power in these groups.
In the discovery stage of the GWAS, we analyzed the GERA non-Hispanic whites (workflow of genotyping of GERA and replication cohorts in Fig 1B and 1C and of analysis in Fig 1D, Manhattan plot in Fig 2, Q-Q plot in S1 Fig). The genomic inflation factor was reasonable for the sample size being tested (genotyped λ = 1.037, imputed λ = 1.053) [20]. We detected two novel genome-wide significant (p<5x10-8) loci associated with ARHI.
The first SNP, rs4932196, was at 15q26, b37 position 89,253,268 (GERA non-Hispanic white odds ratio (OR) = 1.185, p = 4.0x10-11, frequency of the risk allele = 0.810, zoomed in plot of locus in Fig 3A), 52kb 3’ of ISG20 and 638kb 5’ of ACAN. There were several genome-wide significant and suggestive typed SNPs around the top (imputed) SNP–rs6496519, p = 1.3x10-10, call rate (CR) = 99.9%, r2 = 0.953; rs35701059, p = 7.8x10-9, CR = 99.9%, r2 = 0.858; rs11073807, p = 1.5x10-7, CR = 99.8%, r2 = 0.389 –giving strong evidence that the signal was not driven by genotyping error. Further, the same top SNP was replicated in the meta-analysis of the GERA Latinos, East Asians, and African Americans (OR = 1.192, p = 0.00094, one-sided for all replication tests since the hypothesis is that the effect is in the same direction), and was in the same direction in each individual group (Table 2). The same SNP was also replicated in UK Biobank individuals (OR = 1.028, p = 0.015) although that analysis was based on a self-reported cross-sectional phenotype that was different from the GERA individuals (see Methods). Meta-analysis of all four GERA race/ethnicity groups together gave an OR = 1.186, p = 2.8x10-13, with no evidence of heterogeneity (I2 = 0, p = 0.82); we did not include the UK Biobank in the meta-analysis due to differences in the phenotype. On the subset of the cohort that had audiologist notes, the SNP association with overall transformed SRT was suggestive (β = 0.043, higher values indicate worse hearing, p = 0.039, stratified by case/control status for an independent test, see Methods), and the association with overall SDS less so (β = 0.023, higher values indicate worse hearing, p = 0.13), although both were in the same direction as the ARHI phenotype (Table 2).
The second SNP, rs58389158, is an imputed indel at 22q13.1, b37 position 38,128,283 (GERA non-Hispanic white OR = 1.132, p = 1.8x10-9, frequency of the risk allele = 0.421, zoomed in plot of locus in Fig 3B), in an intron of the long form of TRIOBP (Table 2). The signal initially appeared to be driven by one genotyped SNP, rs5750477 (p = 1.8x10-8), with a lower CR = 90.8%, and r2 = 0.77 with rs58389158. Although the CR was low, genotype cluster plots of the SNP showed well separated clusters with only modest overlap. However, to ensure the association was not driven by genotyping artifacts, we removed rs5750477 from the analysis and re-imputed rs58389158. The SNP did not impute quite as well as previously (r2 = 0.75 as opposed to r2 = 0.90 with the SNP), and had a slightly less significant p-value (p = 1.0x10-7), which likely reflects reduced power due to poorer imputation accuracy. Although the SNP did not show evidence of association in the replication meta-analysis of the GERA Latinos, East Asians, and African Americans (OR = 1.041, p = 0.22), the effect was in the same direction. In addition, the SNP association was replicated in the UK Biobank data (OR = 1.036, p = 0.00021), and was strongly associated with transformed SRT (β = 0.090, p = 1.9x10-6). For SDS, the SNP effect was in the same direction but not statistically significant (β = 0.009, p = 0.30).
We also looked for a sex difference in the OR of ARHI with the top SNPs in the GERA non-Hispanic whites. There was no significant evidence of heterogeneity (rs4932196 I2 = 55.4, p = 0.13; rs58389158 I2 = 66.9, p = 0.082) between males and females (rs4932196 ORmale = 1.143, ORfemale = 1.234; rs58389159 ORmale = 1.166, ORfemale = 1.085). Also, no additional loci were identified in sex-specific GWAS analyses. Finally, no additional SNPs at the two novel loci were discovered in a conditional analysis including the top two SNPs.
We tested 58 previously reported sub-genome-wide significant SNPs [3,14–16] for replication in GERA non-Hispanic whites. No SNP reached a Bonferroni corrected threshold of 0.00086, with only three SNPs being of marginal significance 0.01<p<0.05 (S1 Table).
Motivated by our TRIOBP results, we also examined SNPs from our GWAS results in 132 known Mendelian hearing loss genes [21] in GERA non-Hispanic whites at a reduced significance threshold adjusted for the proportion of the genome being tested to account for potential lack of power in these regions. For each of the genes, we looked separately at exonic SNPs, specifically non-synonymous and synonymous coding changes, and then SNPs that were eQTLs for that gene in any GTeX tissue (no human auditory tissues available; each highlighted in S2 Fig) [22]. We identified two significant SNPs: rs2877561 in ILDR1 (p = 6.2x10-5, Table 3, Fig 3C; Bonferroni α level for synonymous changes 9.3x10-5) and rs9493627 in EYA4 (p = 0.00011, Table 3, Fig 3D; Bonferroni α level for nonsynonymous changes 0.00025). The first SNP, rs2877561, replicated in the UK Biobank (p = 0.00057), had a non-significant but consistent effect in the other GERA groups (p = 0.42), and was significant for both GERA SRT (p = 0.00019) and SDS (p = 0.0019). The second SNP, rs9493627, replicated in the UK Biobank (p = 0.0095), the other GERA race/ethnicity groups (p = 0.0080), and was marginally significant and in the correct direction for SRT (p = 0.041) and suggestively so for SDS (p = 0.081).
We also examined the age of onset distributions for the four identified SNPs (empirical cumulative distribution functions, S3 Fig). For three of the SNPs, there was no pattern towards earlier onset for the predisposing allele (rs4932196 p = 0.24, rs58389158 p = 0.75, rs2877561 p = 0.97); however, for SNP rs9493627 in EYA4, there was a suggestive trend (p = 0.044) towards earlier onset with the number of risk alleles.
The heritability explained by all of the genome-wide SNPs was estimated to be a somewhat modest 8.7% (95% CI = 2.9%-14.4%). This estimate depends on the assumed prevalence of ARHI in the cohort; we assessed the sensitivity to this assumption, finding that the heritability estimate was 10.7% (95% CI = 3.6%-17.8%) when using twice the prevalence. We note that this is below previous estimates from twin studies [12,13], though we discuss later why this may be an underestimate (see Discussion). In addition, the amount of variance explained by the four SNPs found here was very small at a combined 0.43% (rs49321 0.18%, rs58389185 0.12%, rs2877561 0.064%, rs9493627 0.056%) suggesting there are still many more loci to be found.
In this large cohort comprised of members of KPNC, we identified two novel SNP associations in the GERA non-Hispanic whites that showed replication in at least two of three subsequent analyses: meta-analysis of GERA Latinos, East Asians, and African Americans; independent related quantitative traits in a subset of GERA; and in the UK Biobank self-report data. Of note, we were unable to replicate any of the previously-described suggestive GWAS loci. However, we additionally found two SNPs associated with ARHI when specifically looking at variants in exons in previously-identified Mendelian hearing loss genes. Finally, we estimated only modest genome-wide heritability.
The indel rs58389158 on 22q13.1 that we identified to be associated with ARHI has a potential functional mechanism through the gene TRIOBP/RP1-37E16.12, a filamentous actin (F-actin) binding protein that is associated with the TRIO guanine nucleotide exchange factor and regulates actin cytoskeleton organization. Nonsense, missense, and frameshift mutations in this gene have been previously associated with recessive prelingual nonsyndromic hearing loss [23–27], and it has been shown to be expressed in 11 human tissues in the short isoform and 3 in the long isoform, including expression in the cochlea [26], where its potential role in hearing loss has been described in detail. The SNP is 5773bp 3’ of exon 7 in the long NM_001039141 isoform (24 exons, exon 7 the largest at 3319bp) and 1021bp 5’ of exon 8 (which is 115bp) of TRIOBP (and upstream of the other shorter two isoforms NM_007032, 14 exons, and NM_13862, 8 exons). It is highly correlated with rs5756795 (r2 = 0.96/0.97/0.97/0.94 in 1000 Genomes European/Admixed American/East Asian/African populations, respectively; b37 position = 38,122,122, 6,091bp from rs58389158), a missense variant in exon 7 (TTC to CTC, F [Phe] to L [Leu]) that was also genome-wide significant in GERA non-Hispanic whites (p = 2.8x10-9). According to SeattleSeq Annotation v138 [28], the rs5756795 variant is predicted to be not likely deleterious (PolyPhen2 probability of being damaging 0.006, predicted benign [29]; grantham score = 22, range from 5–215, higher more deleterious [30]), while the Combined Annotation Dependent Depletion (CADD) score [31] was borderline (13.5, scores greater than 10 indicate that the variant is predicted to be among the 10% most deleterious substitutions in the human genome); the variant also showed high conservation scores (PhastCons = 0.996, range from 0–1, with higher being more conserved [32]; Genomic Evolutionary Rate Profiling (GERP) score 2.89, ranges from -12.3 to 6.17, with 6.17 being most conserved [33]). There is potential that the missense variant itself may be affecting the protein produced, or the regulatory region around it may be affecting the transcript produced. While numerous mutations in TRIOBP have been associated with hearing loss, neither of these variants has been previously reported to be associated with hearing loss.
This GWAS also identified rs4932196 on chromosome 15. The mechanism through which this SNP leads to hearing loss is unclear, but 2 genes in the vicinity of rs4932196 and/or correlated with it (r2>0.80) could be relevant. The SNP is 52kb 3’ of gene ISG20, and 685kb 5’ of ACAN, which codes for Aggrecan. Aggrecan is a major component of the extracellular matrix of cartilaginous tissues while the ISG20 protein is involved in pathways such as interferon signaling.
To identify potential mechanisms in humans of rs4932196, we assessed human tissue expression results from the ENCODE project through Haploreg v4.1 [34], which includes many different human tissue types, but no auditory tissues. The database showed that the SNP (or those highly correlated with it) lies in a region that is transcriptionally active; it is within DNAse hypersensitivity sites that are transcriptionally active in 8 tissues, and it affects the binding motifs for 38 transcription factors. The database also suggested that the SNP may be in a regulatory region–it is within enhancer histone marks for 16 different tissues. Lastly, the database showed that the SNP likely affects ISG20 or ACAN expression. The expression Quantitative Trait Locus (eQTL) analysis, which tests for an association of a SNP with the expression of genes, showed that rs4932196 and the SNPs highly correlated around it are associated with expression of both ISG20 (p ranges from 0.0013 to 6.7x10-8 from whole blood) and ACAN (p ranges from 2.9x10-5 to 9.2x10-6 in GTex 2015 cells transformed from fibroblasts, a cell type that synthesizes the extracellular matrix and collagen); Haploreg includes eQTL results from GTex, GUEVADIS, and 10 other studies. These results do not indicate whether ISG20 or ACAN is the more likely mechanistic explanation for the association with ARHI.
Since human auditory tissue data were unavailable, we looked at the expression of the two genes in mouse auditory tissue using the Shared Harvard Inner Ear Database (SHIELD) [35]. Data from mice show that ACAN is expressed in mouse auditory tissue. Scheffer et al. looked at the expression of the genes in the cochlea and utricle at several mouse developmental stages [36]. The highest estimated expression was in the P7 developmental stage in the cochlear non-hair cells. The overall expression in the cochlear hair cells was significantly different from that in the non-hair cells (FDR = 0.00012, fold change = 0.03, S4 Fig). ACAN expression was higher in the cochlea than in the utricle, but the difference was not significant (FDR = 0.37, fold change = 6.2). Also, postnatal expression was higher (but not significantly) than embryonic expression (FDR = 0.31, fold change = 6.7). Liu et al. tested for gene expression differences between cochlear outer and inner hair cells in P25 to P30 mice and did not find a significant difference for ACAN (FDR = 0.948, fold change = 0.99) [37]. Lastly, Shin et al. tested for differential expression between the spiral and vestibular ganglia at several developmental times [38] and found no evidence for any expression differences (S4 Fig).
Mouse studies also support a role for ISG20 in mouse auditory tissue. Expression results for the ISG20 gene were similar to those for ACAN, except that the strongest expression was estimated to be in the utricle (S4 Fig). There were no significant expression differences between hair cells and non-hair cells (FDR = 0.15, fold change = 0.17), cochlea and utricle (FDR = 0.35, fold change = 0.21, utricle higher than cochlea), postnatal and embryonic (FDR = 0.51, fold change = 4.63), or cochlear outer and inner hair cells (FDR = 0.56, fold change 1.2). Finally, spiral and vestibular ganglia showed no significant differential expression of ISG20. The mouse expression results provide perhaps slightly more support for ACAN than ISG20 as the gene near rs4932196 that is associated with hearing loss.
Because of our TRIOBP results, we also looked at known Mendelian hearing loss genes, particularly the exons, and discovered two SNPs, which also had evidence of replication. The first SNP, rs2877561 is a synonymous mutation in exon 2 in ILDR1, a gene encoding an immunoglobulin-like domain containing protein. Previous work has found 10 different homozygous mutations in the gene that cause autosomal recessive prelingual nonsyndromic moderate to profound hearing loss, that was more pronounced at higher frequencies; one of these includes a 35bp deletion at the exon 2 splice acceptor site [39,40]. The ILDR1 gene was also shown to be expressed in the cochlea in mice, at lower to intermediate levels in hair cells, and higher levels in some supporting cells [39]. The SNP rs2877561 had high conservation scores for a nucleotide change (PhastCons 0.996, GREP 3.650, see Discussion of these scores above), but a very low CADD score (0.383, see Discussion above). The second SNP, rs9493627, is a missense mutation (GGC to AGC, G [Gly] to S [Ser]) at position 25 in exon 11 of EYA4, a gene that encodes a transcriptional activator, interacting with other protein families to regulate early development. Previous mutations in the gene have been shown to cause postlingual progressive autosomal dominant hearing loss, resulting in stable flat sensorineural deafness without the influence of presbycusis [41]. Two mutations causing premature stop codons were found in exon 12 with a single individual having a mutation in exon 11 at position 1270, very close to rs9493627 at position 1287. The variant rs9493627 itself had no Polyphen evidence of being damaging (0.969), had high conservation scores (PhastCons 1.000 and GERP score 5.490), a Grantham score of 56, and a CADD score of 35.0 (see Discussion of these scores above).
Our discovery of these two additional SNPs in exons of known hearing loss genes suggests potential regions of the genome to focus on for finding additional SNPs associated with ARHI. It also provides more evidence suggesting a potentially similar etiology for ARHI as hearing loss.
The use of a large cohort with comprehensive, longitudinal EHRs provided a much larger sample size than has been previously analyzed for ARHI, albeit with a slightly different phenotype than those based on hearing frequency thresholds. The advantage of such an approach is the large numbers of individuals that can be analyzed, while the disadvantage is that the cohort is not specifically characterized for hearing loss, but has to be inferred indirectly. Because of late and insidious onset of age-related hearing loss, and the fact that our primary analysis was based on ICD9 diagnosis, we expect that the ARHI effect size estimates may be biased downward and the power to detect novel loci diminished due to misclassification of individuals who did not seek treatment. It likely also dampened our genome-wide heritability estimate. However, to support the validity of our phenotype, we did see large differences in the quantitative measures SRT and SDS between cases and controls. Unfortunately we did not have data on noise exposure except for what was available in the audiologist notes in the reduced cohort–again, another limitation of a dataset not specifically designed to capture data regarding hearing loss.
The degree to which the two genome-wide significant SNPs were replicated in the other GERA race/ethnicity groups, the other GERA quantitative phenotypes, and the UK Biobank data, varied. Reasons for weaker associations in the other GERA race/ethnicity groups include both different LD structures as well as a much smaller sample size. The latter is also a reason for reduced power of analysis of the related quantitative GERA phenotypes. Finally, the UK Biobank was based on a self-reported phenotype largely at one cross-sectional survey, as opposed to our diagnosis that required having two ICD-9 diagnostic codes and an age at diagnosis. In addition, the UK Biobank individuals were at least a decade younger, on average, yet, surprisingly, had a higher proportion of cases, also indicating some inconsistency between the UK Biobank and KP phenotypes.
Our study did not replicate any previously reported sub-genome-wide significant findings seen in prior GWAS studies; this could be due to power or slightly different phenotypes, or potentially because these previously reported analyses were based on sub-genome-wide-significant loci rather than genome-wide significant loci. Although the Glutamate Receptor Metabotropic 7 (GRM7) locus has shown some replication [3], it has failed to do so in more recent results [14]. Our ARHI phenotype was also slightly different from the quantitative phenotypes used in most of these studies, which were calculated from principal components of several hearing frequency thresholds (i.e., the minimum loudness at which a hearing frequency can be heard, repeated for several frequencies, which constitute the points shown on an audiogram).
In summary, we have utilized a general cohort with a large number of middle-aged and older individuals and clinical data from EHRs to discover four loci associated with ARHI. One of these SNPs may be a missense variant in a known hearing loss gene. The second is close to two potential candidate genes through which it may exert its effect. The other two SNPs were exon variants discovered in known hearing loss genes. Studies are ongoing on these SNPS to determine how they interact with their respective surrounding candidate genes. Studies of other large genotyped cohorts with searchable EHR records, as well as the use of more precise phenotypes (e.g., scanned audiograms on a subset of the cohort) will continue to improve our understanding of the genetics underlying ARHI.
Our primary analysis used non-Hispanic white individuals from the RPGEH GERA cohort, which has been previously described [18,19]. Hearing tests are part of routine care in the Kaiser Permanente health care system, and the majority of audiologist record notes are stored in the EHR, although some scan in handwritten notes. The audiograms themselves are not coded directly into the EHR numerically, and instead may be scanned images. For the current analysis, our primary phenotype was constructed by querying the EHR for ARHI related phenotypes from 01/1996-12/2014. A total of 16,123 unique individuals had at least 1 diagnosis of the ICD-9 codes 388.01 (presbycusis, 137 recorded diagnoses on 107 unique individuals), 389.12 (bilateral neural hearing loss, 135 recorded diagnoses on 63 unique individuals), and 389.19 (bilateral sensorineural hearing loss, 52,711 recorded diagnoses on 16,045 unique individuals). To help ensure the validity of cases and eliminate any potential errors in the EHR, we required ARHI cases to have at least 2 ICD-9 entries of any of these three codes, resulting in 8,285 individuals. We also ran our GWAS with a phenotype definition of at least one diagnosis; this slightly dampened our results. After excluding a small number of individuals with any single ICD-9 code for ear damage of transient ischemic deafness (388.02), noise effects on inner ear unspecified (388.10), acoustic trauma (explosive) to ear (388.11), noise-induced hearing loss (388.12), sudden hearing loss unspecified (388.2), and abnormal auditory perception unspecified (388.40), 8,111 individuals remained. For the controls, we began with the 86,790 individuals who were free of any of the ICD-9 codes used to identify cases. We further excluded individuals with any single ICD-9 368.XX (other disorders of the ear) or other 388.XX (hearing loss) code, resulting in 61,811 individuals. After excluding those who had a Current Procedural Terminology (CPT) code for hearing aids, 61,689 individuals remained. Finally, after excluding at random genetic first-degree relatives identified using King robust [42], we were left with a total of 7,569/58,652 cases/controls in GERA. We used the 6,527/45,882 GERA non-Hispanic white cases/controls in our discovery cohort. Note that we also required 2 or more ICD-9 codes for diagnosis of the phenotypes we used as covariates–hypertension 401.XX and 997.91, osteoporosis 733.XX, and diabetes 250.XX, traits which have been shown to be potentially associated with ARHI.
We also examined a secondary set of hearing related phenotypes on a much smaller subset of the cohort that had audiologist notes in the EHR data (the majority from cases). Speech Discrimination Scores (SDS), or the percentage of words that can be recognized when speech is loud enough to be heard comfortably, and Speech Recognition Threshold (SRT) measurements, or the decibel level at which an individual can understand 50% of spoken words tested, were available on 4,903 unrelated GERA individuals (9,454 total measurements, as some individuals had multiple independent measurements at different times) whose EHR included notes taken by their audiologist. Most electronic notes followed a specific template, making extracting certain phenotype information easier. For our outcome, we extracted both SDS and SRT scores, averaging the left and right ear scores. We also attempted to quantify a crude noise exposure history indicator variable based on recorded text from audiologist notes regarding potential noise exposure occupations and activities from these notes (only available on the same subset, so only SDS and SRT were adjusted for noise). To identify potential noise exposure words that had been recorded in each individual’s notes, we began by constructing an exhaustive list of all possible words that were in these notes. We identified a total of 31,825 unique words from the combined set of all individuals’ notes. This was a short enough list that we could simply manually inspect each of these unique words to determine all noise exposure variables given by patients. Once all possible noise exposure variables were identified, we searched for either presence of the noise variable vs. absence of each of the noise exposure variables in the text (e.g., patient denies vs. patient reports). If a noise variable was identified, then that individual was assumed to have a history of noise exposure. If noise variables were absent in the audiologist notes, we assumed the individual had no history of noise exposure.
The Kaiser Permanente Northern California Institutional Review Board (study #CN-13-1643-H) and the University of California San Francisco Human Research Protection Program Committee on Human Research (study #13–12476) approved this research project. All participants provided written informed consent.
A total of 110,266 GERA cohort individuals were genotyped on one of four race/ethnicity-specific Affymetrix Axiom arrays optimized for individuals of European (EUR), Latino (LAT), East Asian (EAS), and African American (AFR) race/ethnicity [43,44]. Quality control was performed on an array-wise basis, which has been described [19]. Briefly, 102,998 individuals passed QC in the first pass genotyping round with individual DishQC (DQC)>82% and individual CR>97%. A total of 85 individuals failed X chromosome heterozygosity tests (male≤20%, female≥80%), leaving 102,913 individuals (step added not in [19]). Genotypes were filtered by package (plates of 96 samples were grouped by similar assay conditions into 58 packages), retaining SNPs with CR≥90%, variance ratio≥31, and male/female frequency differences≤15% [19]. SNPs with poor duplicate concordance were removed, and those with overall call rate<60% were removed, as described in [19]. Here, we additionally required a stricter array per-SNP call-rate of 90%, resulting in 665,046 (EUR), 775,597 (LAT), 702,405 (EAS), and 863,961 (AFR) SNPs, respectively. Finally, to avoid SNPs with low minor allele counts within each race/ethnicity group, we excluded SNPs with a minor allele frequency (MAF) less than 0.0015 (EUR), 0.02 (LAT), 0.025 (EAS), and 0.065 (AFR), leaving a total number of SNPS of 659,803 (EUR), 648,979 (LAT), 588,493 (EAS), 543,158 (AFR), respectively, and a total of 1,188,134 unique genotyped SNPs available for analysis across all race/ethnicity groups.
Imputation was also performed on an array-wise basis, and before the MAF cutoff described above was applied. Genotypes were first pre-phased with Shape-it v2.r72719 [45]. We then imputed 31,085,734 variants from the 1000 Genomes Project (phase I integrated release, March 2012, with Aug 2012 chromosome X update, with singletons removed) as a cosmopolitan reference panel with Impute2 v2.30 [46–48]. The estimated quality control metric used in this study, rinfo2, is the info metric from Impute2, which gives an estimate of the correlation of the imputed genotype to the true genotype [49]. SNPs that did not impute with high quality (rinfo2<0.8) were removed. The rinfo2 and MAF exclusion criteria for the imputed SNPs resulted in a final number of SNPs of 9,469,183 (EUR), 8,090,486 (LAT), 6,517,021 (EAS), and 7,829,026 (AFR), and a total of 11,910,003 unique imputed SNPs available for analysis across all race/ethnicity groups.
For our discovery cohort primary analysis, we used the GERA non-Hispanic whites, employing the conventional genome-wide significance level of 5x10-8. Data from each SNP was modeled using additive dosages, which account for the uncertainty in imputation [50]. We ran a logistic regression on ARHI diagnosis, including covariates for age (at diagnosis for cases, and at last follow-up for controls), diabetes, hypertension, and osteoporosis, and also included sex and the first ten ancestry PCs for non-Hispanic whites to adjust for genetic ancestry/population stratification; the PC analysis has been previously described [18]. For computational efficiency we first ran a logistic model including all covariates except the SNP, and collapsed them into a single covariate for each individual as the sum of the coefficients times each of the covariates for each individual, and then ran a logistic model including only the SNP and the collapsed covariate. Including a single covariate with the SNP genotype reduced the computing time, without loss of accuracy because the SNP effects are quite modest and have virtually no influence on the other covariates. We further confirmed that our top hits showed no appreciable difference fit this way versus the full model (the odds ratio estimates were identical).
For the secondary phenotypes available on a subset of the cohort, we transformed the quantitative phenotypes by taking the square root of SRT, and log(100—SDS + 1) to make them more normally distributed. We ran a mixed model regression analysis on each of the transformed SDS and transformed SRT values (to account for repeated measures), including the same covariates as described above in the analysis of ARHI, as well as noise exposure. We analyzed the transformed SDS and SRT phenotypes separately in ARHI cases and controls, and then meta-analyzed them, to construct a test that was independent of the ARHI test.
To determine whether the novel SNPs identified in GERA non-Hispanic whites constituted replicable findings, we evaluated the SNPs in 481/5,215 GERA case/control Latinos, 398/5,040 GERA East Asians, and 146/2,133 GERA African Americans (there were an additional 17 South Asian cases, too few to analyze), in addition to 30,802/78,586 self-reported case/control individuals from the initial preliminary release of the UK Biobank; complete details of this cohort are available at www.biobank.ac.uk, and the cohort has been previously described [51]. For the SRT and SDS phenotypes, there were 298 Latinos, 249 East Asians, and 125 African Americans available for analysis. For the three GERA minority groups, we used the analysis methods described above, but adjusting for the first six genetic ancestry PCs in each of the three minority groups. A strict Bonferroni α-level for two tests is 0.05/2 = 0.025. In addition, all replication tests are based on a single direction alternative hypothesis (i.e., the effect size in the same direction as in the discovery), therefore we report one-sided P-values.
Cases for the UK Biobank were identified by a self-reported yes to the question “Do you have any difficulty with your hearing?” (variable 2247.0/1/2.0) and a yes to the question “Do you find it difficult to follow a conversation if there is background noise (such as a TV, radio, children playing)” (2257.0–2.0). Individuals who reported being completely deaf were excluded. Controls were identified as those answering no to both of the previous hearing questions, as well as not reporting having a hearing aid (3393.0–2.0). Age was reported as the age at the time of survey (as opposed to age of diagnosis, which was unavailable; cases average age = 58.8, sd = 7.4; control average age = 55.6, sd = 8.1). Analysis was based on a logistic regression model including the SNP as the dependent variable and adjusting for sex (22001.0.0), hypertension (defined as average of SBP≥140 or DBP≥90; variables 93.0–2.0–1, 94.0–2.0–1, 4079.0–2.0/1, 4080.0–2.0–1), self-reported diabetes (2443.0–2.0), self-reported occupational/loud music noise exposure (4825.0–2.0, 4836.0–2.0), and 10 ancestry PCs. First-degree relatives identified by King robust [42] were randomly removed. Individuals were further restricted to those whose self-reported ancestry (21000.0–2.0) was from any white group and whose global genetic ancestry PC1<50 and PC2<50, where these PCs were calculated from the entire cohort (22009.0.1–2). This resulted in a total of 30,802 cases and 78,586 controls. We then re-calculated ancestry PCs in this group using a set of 371,038 very high quality SNPs (call rate>99%, MAF>1%, and LD filtered so no two SNPs had r2>0.5) and using 50,000 random individuals with the remainder projected, as described previously [18].
We also tested 58 previously described sub-threshold suggestive SNPs for replication in this study. The phenotypes of three studies have been based on PCs of several hearing frequency thresholds (3,417 European ancestry individuals in [15], 2,161 Belgian individuals [14], and 352 Finnish Saami individuals [16]). We included in this analysis any SNPs reported in their supplemental material with P<1x10-5. We also included the GRM7 SNPs from the study of 846 cases and 846 controls [3]. For the replication p-value threshold we used 0.05 divided by the number of SNPs compared (α = 0.00086), and we also report any nominal associations (p<0.05).
We examined genetic variation at 132 known hearing loss genes for association with ARHI [21] using our GWAS results at reduced significance thresholds to reflect the proportion of the genome being tested to account for potential lack of power in these regions. For each of the genes, we looked separately at exonic SNPs, specifically non-synonymous and synonymous coding changes, and then SNPs that were eQTLs for that gene in any GTeX tissue (no human auditory tissues available) [22]. We looked ±50Kb of each gene, or up to any eQTL. This led to 414,466 imputed SNPs, or 4.7% of the genome (the usual genome-wide correction is for 1 million tests, despite more SNPs, to correct for correlation, so a Bonferroni correction for percentage of the genome would be 0.05/47000 = 1.1x10-5, which no SNPs met). We further looked into the 253 nonsynonymous exon changes; to determine the correction factor for multiple testing, we decomposed the correlation matrix of these SNPs (based on pairwise LD r-square values) into the M eigenvalues λ1, …, λM, and solved for M* such that (∑m = 1,…,M* λm)/(∑m = 1,…,M λm ≥ c, for c = 0.95 [52], which has additionally shown to perform well [53]. For the nonsynonymous changes we found that M* = 202, for a Bonferroni correction of α = 0.00025. For the 909 synonymous changes, we found that M* = 536, for a Bonferroni correction of α = 9.3x10-5.
We estimated the additive array heritability of ARHI using Genome-wide Complex Trait Analysis (GCTA) v1.24 [54]. Since array heritability estimates may potentially be more sensitive to artifacts than GWAS results [55], we restricted our analysis to the largest group of individuals, non-Hispanic whites who had been run on the same reagent kit and type of microarray [19], and imposed the following extra filters on the autosomal SNPs: Hardy-Weinberg equilibrium test p<0.05 (in controls), significant differences in case-control missing p<0.05, and absolute MAF differences >0.15 when compared with the 1000 Genomes Project. Finally, we also employed an LD filter so that no two neighboring pairwise SNPs had r2>0.8, leaving 427,157 SNPs. In all analyses we further removed individuals outside 5 standard deviations of the first two genetic ancestry PCs, and also removed individuals so that no two remaining individuals had an estimated kinship>0.025 in a manner that maximized the remaining sample size, using PLINK v1.9 [56]. The remaining sample had a total of 4,603 cases and 34,136 controls. The heritability estimate depends on the prevalence of the disease through a liability threshold model; we used the prevalence of ARHI in the non-Hispanic whites in our cohort of 12.5%, and explored several other estimates (we note this is an age-averaged threshold). In addition, we estimated the variance explained by the ARHI associated SNPs [57].
|
10.1371/journal.ppat.1002737 | Anopheles Imd Pathway Factors and Effectors in Infection Intensity-Dependent Anti-Plasmodium Action | The Anopheles gambiae immune response against Plasmodium falciparum, an etiological agent of human malaria, has been identified as a source of potential anti-Plasmodium genes and mechanisms to be exploited in efforts to control the malaria transmission cycle. One such mechanism is the Imd pathway, a conserved immune signaling pathway that has potent anti-P. falciparum activity. Silencing the expression of caspar, a negative regulator of the Imd pathway, or over-expressing rel2, an Imd pathway-controlled NFkappaB transcription factor, confers a resistant phenotype on A. gambiae mosquitoes that involves an array of immune effector genes. However, unexplored features of this powerful mechanism that may be essential for the implementation of a malaria control strategy still remain. Using RNA interference to singly or dually silence caspar and other components of the Imd pathway, we have identified genes participating in the anti-Plasmodium signaling module regulated by Caspar, each of which represents a potential target to achieve over-activation of the pathway. We also determined that the Imd pathway is most potent against the parasite's ookinete stage, yet also has reasonable activity against early oocysts and lesser activity against late oocysts. We further demonstrated that caspar silencing alone is sufficient to induce a robust anti-P. falciparum response even in the relative absence of resident gut microbiota. Finally, we established the relevance of the Imd pathway components and regulated effectors TEP1, APL1, and LRIM1 in parasite infection intensity-dependent defense, thereby shedding light on the relevance of laboratory versus natural infection intensity models. Our results highlight the physiological considerations that are integral to a thoughtful implementation of Imd pathway manipulation in A. gambiae as part of an effort to limit the malaria transmission cycle, and they reveal a variety of previously unrecognized nuances in the Imd-directed immune response against P. falciparum.
| The immune response of the mosquito vector of Plasmodium has proven to possess powerful anti-Plasmodium defense capabilities. As the major regulators of these immune responses, signaling pathways, particularly the Imd pathway which seems especially capable of eliminating malaria parasites, have become attractive candidates targets for malaria-control interventions. Although the general anti-parasitic activity of the Imd pathway has been established, the particular components of the pathway involved and the physiological conditions under which the pathway is capable of limiting infection are mostly unknown. Awareness of these major players and conditions is crucial for adapting the Imd pathway into an intervention strategy. We report that while several members of the Imd pathway are critical for such a response, others are dispensable. We also show that timing of the response with regard to infection and intensity of infection exposure both influence the effectiveness of an Imd-derived anti-Plasmodium response while the status of the gut flora does not. Taken together, this data lays the essential groundwork for effective intervention based on manipulation of this pathway that can severely limit mosquito infection with human malaria parasites.
| Malaria remains one of the world's most devastating infectious diseases, and its successful control will require a multifaceted approach involving a combination of multiple strategies [1], [2]. A multitude of potential methods to prevent mosquito-to-human transmission exist, including several based on manipulating the mosquito's immune response. Despite the lack of an adaptive immune system, mosquitoes are able to quickly and efficiently mount an innate immune response against bacteria, viruses, fungi, and parasites, including Plasmodium. In fact, such an immune response is responsible for one of the most dramatic reductions in the parasite population during the Plasmodium life-cycle: a log-fold loss in parasite numbers as ookinetes cross the mosquito's midgut cells and develop into oocysts. This drop in parasite number is due, at least in part, to an effective cellular and systemic anti-Plasmodium immune assault. Boosting the mosquito's immune response to a level that would fully eliminate this already vulnerable parasite stage could effectively terminate the malaria transmission cycle.
Immune signaling pathways are key regulators of insect immune defenses and are therefore attractive candidates for genetic modification to create a mosquito with an immune response that overwhelms the parasite. Although the Toll and Jak-Stat pathways control immune attacks that limit the development of P. berghei, P. falciparum, and/or P.vivax [3]–[6], the immune deficiency (Imd) signaling pathway has emerged as the most effective pathway in terms of activity against the human malaria parasite. We have previously shown that over-activating the Imd pathway by silencing the gene encoding its negative regulator, Caspar, or over-expressing the gene encoding the REL2 transcription factor confers almost complete protection from cultured P. falciparum in laboratory reared A. gambiae, A. stephensi, and A. albimanus; others have observed a requirement for PGRP-LC, one of the receptors activating the Imd pathway, in the response of wild-caught A. gambiae to field isolates of P. falciparum, and still others have reported a role for REL2, in the response of the recently colonized Ngousso A. gambiae strain to NF54 P. falciparum [4], [7]–[9]. Thus, the Imd pathway has emerged as a critical factor in the ability of malaria-transmitting mosquitoes to kill P. falciparum, and this influence is translatable from laboratory to field, most likely across multiple Anopheles species and Plasmodium strains.
We have shown that an early activation of the REL2 transcription factor in the midgut tissue, at 6–14 hours after ingestion of an infected blood meal is affective at targeting P. falciparum [10]. A snapshot of the mosquito's global gene regulation, potentiated by over-activation of the Imd pathway (via caspar silencing), indicated that thioester-containing protein 1 (TEP1), fibrinogen immunolectin 9 (FBN9), and a leucine-rich repeat family member (LRRD7/APL2) are three of the major players in the Plasmodium killing that occurs in caspar-silenced mosquitoes of the A. gambiae Keele strain [4], [10]. In addition, another leucine-rich repeat family member, APL1A, was identified as a REL2-controlled antiplasmodial effector in the Ngousso strain [8]. These genomic and functional studies of downstream effectors mediating the Imd-dependent infection phenotypes have provided important mechanistic insights and have encouraged the hypothesis that vector control methods targeting the Imd pathway are inherently multifaceted at the effector level. However, the high degree of biological complexity that characterizes the vector-parasite interaction requires a deeper understanding of Imd pathway-directed killing of Plasmodium at the physiological level.
In this study, we addressed some of the remaining key questions regarding the impact of the Imd pathway on P. falciparum infection in the A. gambiae Keele strain, with the goal of facilitating the identification of optimal strategies for Imd pathway manipulation.
We first addressed the contribution of specific Imd pathway components in the anti-Plasmodium defense. Existing data have implicated the negative regulator Caspar, the transcription factor REL2, and the pattern recognition receptor PGRP-LC in the defense against malaria so we concentrated our efforts on the Imd pathway components known in Drosophila to interact with or between these components and that have a clear 1-to-1 ortholog in Anopheles (Figure 1), though Anopheles immune signaling pathways do not necessarily mimic those in Drosophila. As examples, mosquitoes do not have an ortholog of Dif, one of the transcription factors downstream of the Toll pathway in Drosophila [11], Anopheles possesses two functional isoforms of REL2, while flies have only one [12] and TAB2 does not have a reliable ortholog in A. gambiae. We specifically sought to identify the range of Imd pathway genes that have an impact on P. falciparum infection, so we assessed the contribution of each potential pathway member to both the natural (infection only) and the artificially enhanced (infection plus caspar silencing) immune response against P. falciparum.
We then determined which parasite life-cycle stages are vulnerable to an Imd pathway-derived attack. After it enters the midgut lumen, Plasmodium completes its fertilization to form a zygote, transitions to the motile ookinete form, penetrates the midgut epithelium, and transforms into the oocyst stage. Since most assessments of immunity enumerate parasites at the oocyst stage, it is often unknown which stage(s) are targeted by anti-Plasmodium mechanisms, yet this information could drastically affect how an anti-Plasmodium strategy is designed.
It has long been known that mosquitoes' endogenous bacteria can affect Plasmodium development [13], [14], but a recent cohort of studies exploring the tripartite interactions between vector, parasite, and the vector's intestinal microflora at the molecular level have revealed complexities that can drastically affect immune responses and Plasmodium densities in mosquitoes [7], [9], [15], [16]. An important consideration for any effector mechanism that is acting against both parasites and bacteria (such as the Imd pathway) is that its activity may be elicited either directly by the parasite or indirectly by the proliferation of gut bacteria that occurs concurrently with blood-feeding, and how that elicitation occurs is likely to be crucial for intervention planning. This concern is particularly important for the Imd-driven response to P. falciparum because gut-specific over-expression of REL2 in A. stephensi has a significant effect on P. falciparum, highlighting the immune contribution of tissues harboring bacteria [10]. Therefore, in the present study we also asked whether bacteria are required for the effectiveness of caspar silencing in limiting P. falciparum.
To date, all analyses of the effect of the Imd pathway and its downstream components on P. falciparum infection have utilized an exceptionally virulent strain of P. falciparum that is capable of producing high intensities of infection. Recent work from Mendes et al. [17] has revealed differential gene expression profiles in mosquitoes that were given higher or lower parasite exposures, suggesting that the anti-Plasmodium response can vary with the infection intensity. To assess the potential role the Imd pathway in this differential response, we also assessed the outcome of infection following three different levels of parasite exposure in mosquitoes in which several Imd pathway members and downstream effectors had been silenced.
To assess the contributions of Imd pathway components to the anti-Plasmodium defense, we used an RNA interference approach to silence individual genes in order to determine whether each was necessary for P. falciparum infection. A double-silencing assay was also performed to determine whether those components with an effect on P. falciparum could reverse the decreased infection that was observed when caspar was silenced, thereby identifying any factors that are essential for the caspar silencing-mediated immune defense in addition to the routine defense against P. falciparum.
Silencing of the Imd pathway factors imd, fadd, caspL1 (dredd), and rel2 resulted in median P. falciparum oocyst infection intensities that were at least two-fold greater than those of the control group treated with dsRNA against GFP (Figure 2A–2D, Table S1). Of these genes, only silencing of rel2 had a significant effect on infection prevalence (Figure 2H and Table S1). Co-silencing of the four pathway members (imd, fadd, caspL1, and rel2) with caspar completely reversed the typical resistance to infection observed when only caspar was silenced; i.e., median infection intensities were not significantly different from those of the single-silenced groups and did not exhibit the absence of infection typically observed following caspar silencing (Figure 3) and [4].
Single-silencing of ikk-gamma increased the median number of oocysts per midgut by 2.3-fold over that of the GFP dsRNA-treated control mosquitoes, yet co-silencing ikk-gamma with caspar only partially reversed the caspar-silencing resistance; silencing of ikk-gamma in conjunction with caspar returned the median infection intensities to control levels (54% of the single-silencing levels) (Figure 2E). Similarly, silencing rel2-L increased the median oocyst load by 2.47-fold and significantly increased prevalence, yet co-silencing did not reverse the effect of caspar silencing; median intensities in the rel2-L/caspar-silenced mosquitoes were only 70% of the control levels (28% of the single-silencing levels) (Figure 2F and 2H).
Depletion of the TAK1 protein had no significant effect on P. falciparum infection; median oocyst levels in silenced mosquitoes reached 22 oocysts per mosquito, nearly identical to the median of 22.5 oocysts per mosquito in GFP dsRNA-treated controls (Figure 2G). Accordingly, silencing caspar still resulted in significant repression of P. falciparum intensity and prevalence, even when tak1 was silenced (Figure 2G and 2H). TAK1 is a branch point bridging the Imd and JNK pathways; therefore, to ensure that caspar is controlling only the Imd pathway, we assessed how the depletion of jnk would affect P. falciparum and found no effect (data not shown).
Although Plasmodium parasites are susceptible to mosquito immune responses at multiple stages of their development, previous analyses of caspar silencing have measured only the changes in oocyst numbers, an approach that cannot distinguish between activity against a specific stage and that against multiple stages. To discover which stage(s) of development is (are) halted by caspar silencing, we developed an assay in which the time of dsRNA injection was varied. In this assay, peak silencing occurred only after most parasites had matured to a specific stage. Time course analysis of caspar silencing revealed that efficient depletion of caspar mRNA throughout the mosquito body could be achieved within a day of injection, and the effect would persist for at least 6 days (Figure 3A). Based on this kinetic profile, we administered dsRNA against caspar or GFP at 3 and 6 days post-infection to specifically target early and late oocysts, respectively. Previous analyses of Caspar have utilized silencing at 3 to 4 days pre-infection, a time at which one can also assess the effect on ookinetes (Figure 2) [4]. We have also previously shown that over-expression of REL2 in the midgut tissue following a blood meal results in the inhibition of pre-ookinete stages and/or killing of ookinetes in the lumen prior to invasion [10]. We cannot, however, exclude the possibility that caspar, which is upstream of REL2, also controls a REL2-independent branch of the Imd pathway.
Administered 3 days post-infection, caspar silencing significantly reduced the number of developed oocysts when compared to GFP dsRNA-treated controls. Although the mosquitoes were not rendered resistant to infection, prevalence of infection was reduced by 12.3% and the intensity of infection was still severely reduced: GFP-dsRNA treated mosquitoes harbored a median 45.5 oocysts per gut, whereas caspar-silenced mosquitoes harbored only 10.5, a decrease of 77.3% (Figure 3B, Table S2A). For comparison, silencing genes encoding negative regulators of the Toll (Cactus) and Stat (Pias) pathways, two pathways involved in the killing of parasites [4], [5], also had an effect on early oocyst development; caspar silencing exhibited the strongest degree of infection inhibition, and cactus silencing the weakest (data not shown).
In contrast to treatments affecting the pre- and early oocyst stages, silencing treatments designed to target the late-oocyst stages (silencing at 6 days post-infection) were far less effective in limiting the number of parasites per midgut in caspar-silenced and control mosquitoes, although, surprisingly, an appreciable degree of infection inhibition was still observed. In these experiments, the median number of surviving oocysts in the caspar-silenced mosquitoes was 36, as compared to 56 in the GFP dsRNA-treated mosquitoes, representing a decrease of 45.7% while prevalence was not impacted (Figure 3C, Table S2B).
Several studies have illuminated the importance of tripartite interactions between the mosquito's immune system, the parasite, and the mosquito's intestinal flora during Plasmodium infection [7], [9], [15], [16]. For some anti-parasite gene regulation and killing mechanisms, the presence and appropriate composition of the bacterial populations in the gut are required [9], [15], [16]. In Drosophila, the Imd pathway is a primary regulator of the response against intestinal bacteria [18]–[20], and we have observed that REL2 over-expression exclusively in the mosquito midgut offers resistance to parasites [10], leading us to question whether bacteria are necessary for the activation of the Imd pathway during caspar silencing, or whether the Imd pathway activation provided by the bacteria under normal conditions is simply supplanted by the artificial Imd pathway activation mediated by caspar silencing. To answer this question, we administered an antibiotic cocktail to mosquitoes before treating them with dsRNA against caspar or GFP and subjecting them to P. falciparum infection. We have previously shown that antibiotics can be used to eliminate the majority of the bacterial population from the mosquito midgut [9], [21]. As had previously been shown, we found that GFP dsRNA-treated mosquitoes were more susceptible to Plasmodium infection if they had been pre-treated with antibiotics; aseptic mosquitoes harbored almost twice as many oocysts as their GFP dsRNA-treated septic counterparts (Figure 4A, Table S3). However, the lack of bacteria in antibiotic-treated mosquitoes had no effect on the ability of caspar silencing to severely limit parasite development; both septic and aseptic mosquitoes treated with dsRNA against caspar displayed median oocyst levels of 0 and had much lower prevalence of infection than did either GFP dsRNA-treated group, though not significantly different from one another (Figure 4B, Table S3).
Earlier studies have featured a model system composed of a highly virulent parasite strain and highly susceptible mosquito strain. While we have previously shown that the Imd pathway is effective against P. falciparum in other Anopheles strains [4], recent data have revealed the importance of infection intensity in the generation of immune responses directed against Plasmodium [17]. To determine whether Imd-derived responses are more or less effective against different levels of infection, we subjected mosquitoes to a standard administration of dsRNA against various Imd pathway members and downstream effectors; then, at 3 to 4 days after the dsRNA injection, we fed them on P. falciparum-infected blood with a more dilute or more concentrated gametocyte culture than that used in the standard protocol. Oocyst counts indicated that, indeed, low-, medium-, and high-intensity infections had been achieved, with roughly log-fold differences (median = 1, 7, and 86, respectively) from the levels in GFP dsRNA- treated control mosquitoes (Figure 5, Tables S4A–C) and that the infection phenotypes observed following silencing were not consistent for the three levels of infection intensity (Figure 5). For example, silencing all forms of apl1 significantly increased the infection levels (when compared to the GFP dsRNA-treated group) only at low (a 2-fold increase) and medium (a 2.6 -fold increase) infection intensities. Specific paralog silencing suggested that APL1C was most effective in influencing low-level infections (a 2-fold increase), while APL1B was the most influential paralog at medium infection intensities (a 2.3-fold increase). In our experiments, APL1A depletion had no effect on P. falciparum infection under any condition.
As was seen when we silenced all forms of apl1, knockdown of another member of the leucine-rich repeat-containing family, lrrd7, was effective in low- and medium-, but not high-intensity infections. Importantly, we noted that both the prevalence and intensity of infection were significantly increased by silencing lrrd7 prior to low levels of exposure to P. falciparum, but only infection intensity was significantly altered by lrrd7 silencing prior to medium-level exposure. Thus, the contribution of a gene to the anti-Plasmodium response is not only dependent on the level of exposure but also on the method of data collection used (Figure 5 and Tables S4A–C).
As other studies have shown, we found no discernible effect of a third LRR-containing effector, LRIM1, during P. falciparum infection at either low or high intensity. However, we surprisingly observed that in the context of a medium-level infection, silencing of LRIM1 did substantially enhance infection (a 3.1-fold increase) (Figure 5).
Interestingly, silencing the major pathway component, Imd, was only effective at increasing infection intensities at high infection levels (a 3.2-fold increase), the level closest to that at which pathway analysis is typically performed (Figure 5). Nevertheless, caspar silencing was effective in limiting infection, regardless of the exposure dose; in essence, caspar silencing was effective in limiting low-, medium- and high-intensity P. falciparum infections. The only other tested effector to display this consistent effect was TEP1 (Figure 5).
As a control, we checked the efficiency of the gene silencing of the Imd pathway components and downstream effectors genes by measuring the expression of their gene-specific RNAs and observed a reduction of 48–92% in their regular expression patterns (Table S5).
Despite the fact that the negative regulator of the Imd pathway, Caspar, controls a branch of the mosquito immune system that determines the mosquito's resistance to human malaria parasites, until now little has been known about the genes/proteins controlled by this regulator, when they exert their effects on the parasite, and whether this activity is dependent on a tripartite relationship with endogenous bacteria or is influenced by infection intensity. The answers that we have obtained to these essential questions about the biology of Caspar should facilitate the use of the Imd pathway for the development of malaria control strategies.
In the present study, we first addressed the contribution of selected components of the Imd pathway to the normal mosquito response against P. falciparum (single-silenced and infected) and the Caspar-controlled response against P. falciparum in relation to other Imd pathway factors (double-silenced and infected). These experiments revealed three different phenotypes for Imd pathway members: 1) full participation in both anti-P. falciparum responses and caspar-mediated resistance; 2) weaker participation in anti-P. falciparum responses, with unclear contribution to the response directed by caspar; and 3) no participation in anti-P. falciparum immunity.
For the first group of components, silencing genes encoding four of the known components of the Imd pathway (Imd, FADD, CaspL1 and REL2) resulted in a significant increase in the intensity of the midgut infection when compared to GFP dsRNA-treated controls and an ability to reverse the resistance caused by caspar silencing. Importantly, co-silencing Imd, FADD, CaspL1, and REL2 with caspar gave median infection intensities that were almost identical to those of the single-silenced groups. This result suggests a complete dependence of the caspar-silenced infection phenotype on these pathway members. This finding is not surprising, since Drosophila Caspar has been suggested to regulate the Dredd-dependent cleavage of REL2, and Imd and FADD are co-activators of Dredd [22]. Thus, the four components that produced both the strongest single-silencing phenotype and the most complete reversion in double-silencing experiments were also the components that have been hypothesized to interact most directly with Caspar.
In contrast, the second group of components produced reasonable increases in infection level in response to single silencing, yet only a partial reversion of the caspar phenotype. Reduced levels of IKK-gamma led to an appreciable increase in oocyst load when compared to control silencing treatments; the co-silencing of caspar and IKK-gamma showed a mild reversion of the caspar phenotype, but to levels that were not significantly elevated over those for the GFP dsRNA-treated control (as was consistently observed for the four genes in the first group). This result could suggest either a lesser involvement in caspar-dependent immune responses than for the other components, a dual role for IKK-gamma in caspar-dependent and caspar-independent responses (the independent form interfering with a complete reversion of the dependent), or a caspar-independent role in antagonizing P. falciparum response that overwhelms the agonistic output of Caspar.
REL2-L is also a member of the second group. Experiments in the Drosophila model system have strongly suggested that Imd pathway activation results in the cleavage of the ankyrin repeat region of the C-terminus of Relish, likely by Dredd, freeing the active form of Relish for translocation to the nucleus [23], [24]. In Anopheles, both a full form of the Relish ortholog (REL2-L) and a short form lacking ankyrin repeats (REL2-S) are independently transcribed [12]. Because there are no unique sequences for targeting only REL2-S with dsRNA, the distinct roles of the two forms are unclear. However, because the long form can be targeted independently of the short form (by targeting dsRNA to the region encoding the ankyrin repeats) the contributions of REL2-L to a phenotype can be quantified independently of the contributions of all REL2 forms. Mitri and colleagues [8] have reported that silencing only rel2-L has no effect on the prevalence of P. falciparum infection, although infection intensities were not reported in their study. We observed in the present study that silencing rel2-L had a noticeable effect on oocyst burden in single-silencing assays but did not result in the same potent immune response against P. falciparum as did silencing all forms of rel2. In addition, while co-silencing of caspar and rel2-L did not result in complete refractoriness, double-silencing was unable to reverse the phenotype, even to the level of infection intensity observed for the GFP dsRNA-treated control group, much less to the single-silencing levels observed when all forms of rel2 were targeted (Figure 1D). The inability to target REL2-S leaves the identification of distinct roles for the short and long forms still unclear. We can therefore only conclude that both forms of REL2 are able to participate in the normal response of A. gambiae to P. falciparum, with the short form being a major constituent of caspar-mediated resistance to that parasite and the long form a more minor component.
The third group of components showed no effect on infection when either single-silenced or double-silenced and had no effect on the outcome of caspar silencing. In Drosophila, TAK1 is a kinase that is able to activate both JNK and IKKg [25], [26]. Our data showed no observable role for TAK1 in the defense against P. falciparum and no requirement for TAK1 in the caspar-silencing infection phenotype. Caspar is purported to control the cleavage of REL2 by CASPL1/Dredd [22]; TAK1's role is more likely to be that of an activator/mediator of the branch point between the Imd and JNK pathways [27]–[29]. Therefore, TAK1 may be dispensable during the activation of REL2 that occurs in caspar-silenced mosquitoes and, if so, it should not be considered a major target in future applications. Consistent with this conclusion that TAK1 is not a major player in anti-P. falciparum responses, our further investigation into the JNK pathway side of TAK1's responsibilities revealed that P. falciparum is unaffected by JNK silencing. Since this screen is directed toward anti-Plasmodium responses, we cannot remark on the contributions of TAK1 (or other Imd pathway genes) during other infections in Anopheles.
Taken together, these data suggest that the proteins hypothesized to act most intimately with Caspar (Imd, FADD, CASPL1, and REL2) are the most effective players in the mosquito defense against P. falciparum and that the Imd pathway is uniquely potent against this parasite. Based on these data, we propose a model of the A. gambiae Imd pathway (illustrated in Figure 1) in the context of P. falciparum infection. Since this model was generated on the basis of data from RNAi-based gene silencing, researchers must bear in mind the caveats associated with this methodology: i.e., the extent of the silencing is dependent on the silencing efficiency, mRNA turnover, tissue specificity of expression, and the ability of the dsRNAs to reach the appropriate tissues. It is also possible that additional components not included in this targeted screen are part of the anti-Plasmodium Imd signaling module; including known Imd pathway genes not assessed in this screen, unknown genes serving as novel members or unknown genes serving as a functional equivalents of known genes (such as TAK1) in mosquitoes. Nevertheless, the strong infection-intensity phenotypes obtained by silencing multiple Imd pathway members suggest that this module within the pathway is a major player in the anti-P. falciparum defense employed by A. gambiae.
Previous analyses of Caspar and Cactus have utilized a standard silencing protocol developed to assess immune responses directed against the ookinete stage of Plasmodium [3], [4]. This protocol is widely used because the ookinete stage represents a bottleneck in terms of parasite population that is thought to be caused at least in part by the mosquito's immune responses. However, depending on the silencing kinetics of a particular gene, this approach either ignores immune molecules that are effective at later stages, when transcript levels of the target gene have recovered, or is unable to distinguish effects at earlier or later stages because silencing occurs throughout. These complications were made apparent by a study that found a role for the Stat pathway against early oocysts but not ookinetes [5]. Because the silencing of caspar occurs quickly, but mRNA levels recover within 5 to 6 days (Figure 3A), identifying the parasite stages affected by the Imd pathway can be achieved by varying the times at which dsRNA is injected. This method has revealed that ookinetes are most affected by Imd pathway activation, resulting in mosquito resistance to the parasites, but there is also a significant contribution of the pathway to limiting early oocysts (Figure 3B) [4]. In addition, caspar silencing is weakly, but significantly, effective in limiting the development of late oocysts, in agreement with our previous finding that REL2 activation in the midgut tissue also results in the inhibition of late oocysts, and possibly sporozoite stages, of P. falciparum [10].
Together, these studies indicate that there is an extended window of opportunity in the parasite lifecycle for the mosquito to respond to and combat P. falciparum by means of the Imd pathway, but there is an optimal time of response within that window. We hypothesize that this timing is the reason that pre-arming the mosquito with downstream effectors by artificially activating the Imd pathway through caspar silencing allows the mosquito to mount a rapid, strong immune response in the midgut epithelium and hemolymph that is effective in killing the most susceptible stages of Plasmodium. Under normal conditions, the mosquito must first sense infection then activate the Imd pathway, at which point the effectors can respond; the efficiency of killing is reduced as the time required to complete these steps increases. By silencing caspar, we can circumvent the detection and activation steps, so the effector mechanisms are quickly established and at the ready for early killing, at the most susceptible stage of the parasite. This hypothesis has been corroborated by our previous study in which we found that an earlier-than-normal enrichment of the REL2 protein through transgenic over-expression resulted in a profound decrease in P. falciparum infection [10].
Understanding the importance of timing is clearly crucial for manipulating this biology in future studies addressing the Imd pathway as part of an integrated malaria control strategy. The message from these experiments is that early Imd pathway activity is preferable, and the pre-ookinete stages are the most vulnerable to an attack in the midgut tissue [10]. However, such a response can still be antagonistic to the parasite at later time points in the lifecycle.
Several studies have reported a significant contribution of the endogenous flora to the generation of a mosquito's anti-parasitic responses. In addition to the direct interaction between bacteria and parasites, the exponential increase in midgut bacterial loads during blood-feeding elicits immune gene expression and activates immune mechanisms at the same time that Plasmodium parasites are present in the lumen and invading the midgut epithelial cells [21]. These immune mechanisms attack or limit Plasmodium indirectly but adequately [9], [10]. If the resistance to P. falciparum is not solely due to immune activation operating via manipulation of Imd pathway expression but is instead dependent on normal activation of the Imd pathway by bacterial PAMPs (that is merely augmented by caspar silencing), any control strategy based on the Imd pathway will be affected by the mosquito's individual flora. Detectable infection levels in mosquitoes lacking endogenous bacteria suggest that the lack of negative regulation in the Imd pathway is sufficient to confer resistance to P. falciparum. Standard antibiotic treatments eliminate 99% of the endogenous bacteria [8], [9], but it is possible that PAMPs from dead bacteria or shed peptidoglycan still exist in antibiotic-treated mosquitoes. Thus, it is possible that a basal level of bacteria or bacterial components is required to begin Imd pathway activation, and the lack of negative regulation perpetuates the up-regulation of anti-microbial effectors that kill Plasmodium. However, the observation that the required basal level of elicitation can occur in mosquitoes with <1% of the normal flora suggests that the differences in bacterial loads or flora composition would have only minimal effect on P. falciparum resistance directed by the Imd pathway.
By manipulating the dosage of P. falciparum gametocytes available during blood feeding, we have been able to control the average infection intensity in a given group of mosquitoes. These experiments showed that the requirement of a given pathway component or effector, assessed by RNAi, is often dependent on that average infection intensity (but not always, as evidenced by Caspar and Tep1) (Figure 5). Effectors that are known to be required at higher Plasmodium doses yet seem ineffective at low doses may not be up-regulated or otherwise activated in control mosquitoes at lower doses. This hypothesis is supported by recent transcriptomic data suggesting that, for some immune genes, expression is dependent on the infection intensity [17]. Alternatively, it is possible that RNAi-based assays are not sufficiently capable of depleting specific transcripts to have an impact on a low-intensity infection; this situation would be particularly true for genes exhibiting a rapid rate of transcription and/or encoding proteins that have potent anti-Plasmodium effects at low concentrations.
Effectors known to be required at lower but not higher doses could be influenced by a saturation effect, in which Imd pathway effectors are being produced in quantities that are sufficient to kill lower numbers of parasites but insufficient to deal with infections above a certain level. This would be the case if only a small, finite number of anti-Plasmodium effectors are produced and therefore available for combating parasites. This type of effector would not have an effect on infections at high intensities in control mosquitoes, and therefore silencing the gene(s) that encode them would show no effect. Inefficiency of some effectors at higher Plasmodium doses may also be governed by infection-intensity gene regulation if high levels of infection cause those genes to be down-regulated, as in, for example, a feedback loop. Such genes would theoretically be produced at sufficient levels in control mosquitoes during low- or medium-level infections but would be down-regulated, and therefore less effective, at higher doses.
Our data suggest that the medium- intensity infection levels are optimal for studying anti-Plasmodium defenses under laboratory conditions, since measurable effects can be seen for most genes, and statistical analyses can be made (in contrast to very low-infection assays). However, our data also indicate that looking only at medium-level intensities can lead to unfounded assumptions about the physiological relevance of the examined genes during infection under natural field conditions, and complexities of pathway regulation can be masked.
Our study also offers a number of interesting and novel insights into other aspects of anti-Plasmodium defenses that have not been addressed in previous studies using only standard infection intensities:
First, in our assessment of the requirement for LRIM1, LRRD7, and all three paralogs of APL1, we found that some components were necessary for low-, medium- or high-level infection exposures, but none were required at all levels of exposure, perhaps reflecting redundancy or shared roles among this group (Figure 5). How those roles are assigned or regulated with respect to the number of successful ookinete invasion events has yet to be determined. Our analysis of the LRR-containing proteins revealed that the APL1 paralogous genes behaved differently in our experiments than in those reported by Mitri et al. [8]. While we confirmed their report of a role for APL1 genes in limiting P. falciparum, our data are not in agreement with regard to the specific paralogs involved. Most strikingly, we did not find a significant role for the APL1A paralog in the anti-P. falciparum immune response (Figure 5), but we did for APL1B or APL1C, depending on the intensity of infection. The APL1 gene family has exhibited a complex sequence evolution, including an exceptionally high degree of polymorphism in some strains, with a recent selective sweep occurring in others [30]. Therefore, although our study confirms a role for APL1 gene family members during P. falciparum infection, the differences we saw in regard to which family members are playing the effector role may be explained by the possibility that we are assaying mosquitoes with different versions of APL1 sequences, or the fact that all our infection assays used a significantly higher infection intensity than did those of Mitri et al.
Second, the results of infection exposure have revealed a novel role for LRIM1 during P. falciparum infection. LRIM1 was originally identified as a powerful mediator of parasite killing in a rodent malaria model but later shown to have little observable effect on P. falciparum [31], [32]. Our data indicate that LRIM1 does contribute to the anti-P. falciparum response, but only at medium levels of infection intensity (with a trending but non-significant effect at low intensity) (Figure 5). We believe that the discrepancy between our studies can be explained by the fact that the study showing a lack of contribution of LRIM1 [32] was performed with a different mosquito species and a field P.falciparum infection model that generated a low infection intensity (indeed, in our experiments, LRIM1 did not display anti-Plasmodium activity at low infection intensity). The fact that LRIM1 has been linked to the TEP1/APL1 anti-Plasmodium mechanism [33], [34] suggests that it is very likely to play a role in anti-Plasmodium activity, but it is possible that it may not be required (or is redundant) at very low infection intensities, or that RNAi-mediated depletion is not be sufficient to reduce it to a level that would have any impact on a low-intensity infection.
Third, the fact that caspar silencing had an effect at very high infection intensity whereas Imd silencing did not may suggest that other branches of the Imd pathway act upstream of Caspar and in parallel with the Imd receptor. The existence of multiple downstream signal circuits would also explain the robustness of the caspar silencing phenotype and the variety of downstream effectors it regulates. However, the identities of such circuits are unknown.
Fourth, we found that no matter how concentrated or dilute a P. falciparum-laden blood meal, silencing caspar still greatly reduced the resulting oocyst infection and, conversely, silencing tep1 significantly increased the infection. These results suggest that these two components are essential factors in the anti-Plasmodium defense for which no substitutes are allowed, whereas the other factors may be to some degree redundant.
Taken together, our finding suggest that members of the Imd pathway are reasonable first targets for vector-based malaria control interventions, and analyses of such interventions should necessarily include facets of mosquito-parasite infection biology such as the level of the infection and the parasite stage(s) affected. Clearly, some pathway members would be more effective than others: Specifically, Imd, CaspL1, or REL2 would be good positive regulators to manipulate, along with Caspar as the negative regulator, and we have confirmed this conclusion for REL2 using transgenic methodology [10]. The target should be affected (a positive regulator enhanced or negative regulator repressed) within the first 24 after feeding to have the greatest effect on the ookinete stage, and the treatment could be more effective if it were designed to prolong the enhancement/repression through to the pre-oocyst stage. The fact that the mature oocyst stage was marginally affected by the Imd pathway means that an enhancement/repression lasting longer than 3 to 4 days post-feeding may be unnecessary but beneficial; this timing would be amenable to adjustment in order to compensate for a low impact on mosquito fitness. In a laboratory setting, transient activation of the Imd pathway (via RNAi or caspar and transgenic over-expression of REL2) has no observable impact on mosquito fitness, yet is potently anti-parasitic [4], [10]. A similar observation has previously been made in the fruitfly Drosophila [35]. Moreover, an Imd pathway-based intervention could also be successful, regardless of the status of the endogenous bacteria; such an approach would avoid potential species- or environment-specific pitfalls, and yet the target of the intervention would be able to effectively kill the parasite even at low levels of infection.
Components of the immune response of the mosquito vector of Plasmodium have emerged as favorable candidates for targeted malaria-control interventions. However, the immense technical effort required to build such interventions is not trivial, and therefore the preliminary refinement of candidate selection must be thorough. Here we present data that not only identify the biological mediators of the Imd pathway-driven immune response that can render mosquitoes almost completely refractory to P. falciparum but also answer major questions about when and how that response is generated. We show that while several members of the Imd pathway are critical for such a response, others are indispensable, and the anti-Plasmodium response decreases in potency as the parasite matures, with the pre-oocyst stages being most vulnerable and the mature oocyst stage the least. We also show that caspar-mediated refractoriness can occur without regard to microbial flora or the intensity of the Plasmodium exposure, revealing the delicate yet robust control this negative regulator exerts over the Imd pathway and indicating the pathway's potential for broad applicability.
This study was carried out in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health. The protocol was approved by the Animal Care and Use Committee of the Johns Hopkins University (Permit Number: M006H300). Commercial anonymous human blood was obtained from Interstate Bloodbank and used for parasite cultures and mosquito feeding and informed consent was therefore not applicable. The Johns Hopkins School of Public Health Ethics Committee has approved this protocol.
Keele strain A. gambiae mosquito larvae were raised in 30×34 cm trays (∼250 larvae per tray) with cat food pellets added daily and ground fish food supplemented upon water change. Adults were reared in a 20×20×20 cm3 cage and sustained using a 10% sugar solution at 27°C and 80% humidity with a 12-h light/dark cycle according to standard procedures [36].
Assays were performed according to a standard protocol [37]. Genes encoding previously identified genes of the Anopheles Imd pathway response and genes encoding 1-to-1 orthologs of Imd components known in Drosphila were targeted by synthesizing sense and antisense RNAs from ∼300- to 600-bp PCR-amplified gene fragments using the T7 Megascript kit (Ambion) and primers indicated in Table S5. About 69 nl dsRNA (2–3 µg/µl) in water was introduced into the thorax of cold-anesthetized 2- to 4-day-old female mosquitoes using a nano-injector (Nanoject, Drummond) with glass capillary needles according to Blandin et al. [38]. Double-silencing experiments were performed in the same manner, except that dsRNAs targeting each gene were mixed and concentrated so that each dsRNA was present at 2–3 µg/µl. Depletion of dsRNA targets after silencing was quantified; silencing efficiencies are given in Table S5.
Mosquitoes were fed on NF54 gametocytes in human blood through a membrane feeder at 37°C at various time points, depending on the stage being examined: 3–4 days after dsRNA injection for assessment of ookinetes (Figures 2, 4, and 5), 2 days prior to dsRNA injection for early oocysts (Figure 3B), and 6 days prior to dsRNA injection for late oocysts (Figure 3C), and at various dilutions for high-, medium- and low-infection exposures. All mosquitoes were subsequently maintained at 24°C until 7 to 8 days post-feeding, when midguts were dissected and stained with 0.1% mercurochrome in PBS, and oocyst numbers were recorded using light microscopy (Olympus). Each assay was done with at least 25 mosquitoes, and data represent the results of at least three independent assays. P-values were determined using Mann-Whitney tests; further statistical analysis was performed using Fisher's Exact test and Kruskal-Wallis tests with Dunn's Multiple Comparison Summary (Tables S1, S2, S3, S4).
Mosquitoes were silenced for caspar or GFP (as a control) 2 days after emergence and incubated under normal conditions. Each day at the same time, ∼10 mosquitoes from each group were collected and homogenized for total RNA extraction. Assays were then performed according to a standard protocol [37]. Total RNA from adult females was extracted using the RNeasy kit (QIAGEN), quantified using a Beckman DU640 spectrophotometer, and subjected to reverse transcription using Superscript III (Invitrogen) with random hexamers. Real-time quantification was performed using the QuantiTect SYBR Green PCR Kit (Qiagen) and ABI Detection System ABI Prism 7000. Primer sequences are given in Table S5. All qPCR reactions were performed in triplicate; to verify the specificity of the PCR reactions, melting curves were obtained for each data point. The levels of expression in gene-silenced samples were determined by normalizing the cDNA levels using the ribosomal protein S7 gene and compared to controls treated with dsRNA against GFP.
Groups of 80–100 recently emerged (>1-day-old) female A. gambiae mosquitoes were sequestered in pint-capacity paper cups and maintained on either 10% sucrose or 10% sucrose supplemented with 10 µg/mL penicillin-streptomycin and 15 µg/mL gentamicin [9]. For each replicate, 5–10 midguts from each group were dissected and homogenized in PBS, and serial dilutions were plated on LB agar to confirm a 1,000-fold reduction in bacterial loads when antibiotics were administered in the sucrose solution. Comparisons of antibiotic-treated and untreated mosquitoes utilized the Plasmodium infection, RNA extraction, PCR amplification, and other methods described above. Primer sequences are given in Table S5.
|
10.1371/journal.ppat.1003259 | Dendritic Cell-induced Activation of Latent HIV-1 Provirus in Actively Proliferating Primary T Lymphocytes | HIV-1 latency remains a formidable barrier towards virus eradication as therapeutic attempts to purge these reservoirs are so far unsuccessful. The pool of transcriptionally silent proviruses is established early in infection and persists for a lifetime, even when viral loads are suppressed below detection levels using anti-retroviral therapy. Upon therapy interruption the reservoir can re-establish systemic infection. Different cellular reservoirs that harbor latent provirus have been described. In this study we demonstrate that HIV-1 can also establish a silent integration in actively proliferating primary T lymphocytes. Co-culturing of these proliferating T lymphocytes with dendritic cells (DCs) activated the provirus from latency. Activation did not involve DC-mediated C-type lectin DC-SIGN signaling or TCR-stimulation but was mediated by DC-secreted component(s) and cell-cell interaction between DC and T lymphocyte that could be inhibited by blocking ICAM-1 dependent adhesion. These results imply that circulating DCs could purge HIV-1 from latency and re-initiate virus replication. Moreover, our data show that viral latency can be established early after infection and supports the idea that actively proliferating T lymphocytes with an effector phenotype contribute to the latent viral reservoir. Unraveling this physiologically relevant purging mechanism could provide useful information for the development of new therapeutic strategies that aim at the eradication of HIV-1 reservoirs.
| Combination therapy can suppress the viral load in HIV-1 infected individuals to undetectable levels, but does not lead to complete virus eradication. Even after many years of successful therapy the virus is still present in long-lived cells as a latently integrated provirus. HIV-1 can re-establish systemic infection from this reservoir when therapy stops. Purging attempts in patients have been unsuccessful and HIV-1 latency remains a formidable barrier to virus eradication. Different cellular reservoirs that harbor latent HIV-1 proviruses have been described to consist mainly of resting memory T lymphocytes. Yet how this reservoir in memory T lymphocytes is established is still unclear as infection of these cells is very inefficient. In this paper we demonstrate that HIV-1 can establish a latent provirus in activated effector T lymphocytes. We observed that for every virus producing cell there is at least one other cell harboring a latent provirus, illustrating that latent infections occur frequently. Proliferating T lymphocytes are generally short-lived and their contribution to the total cellular reservoir thus seems limited. However, these activated T lymphocytes can revert into resting memory T lymphocytes and become part of the long-lived viral reservoir.
| Combined antiretroviral therapy (cART) is able to suppress the HIV-1 plasma RNA load in patients to undetectable levels. The treatment, however, does not lead to complete virus eradication. Even after many years of successful cART the virus can rebound from latently infected cellular reservoirs and re-establish systemic infection upon therapy interruption [1]–[5]. Proviral latency is an effective strategy to sustain long-term infection by evading the immune system as long as viral antigens are not expressed and presented. Cells latently infected with HIV-1 remain a formidable barrier towards virus eradication and therapeutic attempts to purge these reservoirs have thus far been unsuccessful [6]–[8].
The pool of latent proviruses is established early during infection and forms a steady source of integrated proviral DNA lasting a lifetime for infected individuals [9], [10]. Early onset of cART reduces the size of the viral reservoir but does not prevent its formation [11]. HIV-1 establishes latent proviral integration mainly in T lymphocytes, but viral reservoirs in monocytes and dendritic cells have also been described [12]–[16].
How the reservoir in memory T lymphocytes is established remains unclear. Infection of quiescent memory T lymphocytes is inefficient due to incomplete reverse transcription and integration [17], [18]. Linear non-integrated cDNA is rapidly degraded with a half life of approximately 1 day, suggesting that de novo infection of memory T lymphocytes is unlikely to play a major role in formation of this long-lived viral reservoir [18]. However, it has been shown that cytokine stimulation of quiescent T lymphocytes can increase the HIV-1 infection efficiency by boosting reverse transcription and integration processes without inducing cell proliferation or up-regulation of cellular activation markers [19]–[24]. These integrated HIV-1 proviruses are transcriptionally insufficiently active to support the production of new viral particles and the resting T lymphocyte may thus become part of the long-lived latent reservoir. An alternative hypothesis for the formation of the latent reservoir is that actively proliferating T lymphocytes become infected with a transcriptionally silent provirus [25]–[27]. This latently infected proliferating T lymphocyte will not be recognized by the immune system and the proliferating cell can revert to a memory T lymphocyte, thus contributing to the long-lived viral reservoir.
We and others previously demonstrated that silent HIV-1 proviral integrations occur in T cell lines [28]–[35]. In this study, the presence of latent proviruses in primary proliferating T lymphocytes was studied. To show that silent integration does not equal a defective provirus, one should demonstrate that the provirus can be purged out of latency. Conventional anti-latency treatments, such as TNFα that is effective in T cell lines, had no effect on the latent provirus in actively proliferating primary T lymphocytes, in agreement with the results of other groups [36], [37]. Therefore, an alternative anti-latency treatment was explored. Co-culturing of the actively proliferating T lymphocytes with dendritic cells (DCs) was found to trigger a robust activation of the latent provirus.
Our results demonstrate that a natural mechanism based on cell-cell contact can purge HIV-1 from latency and support the idea that actively proliferating T lymphocytes contribute to the latent viral reservoir. Understanding the natural mechanisms that activate latent HIV-1 provirus may lead to novel intervention therapies to overcome latency.
To study HIV-1 proviral latency, the transcriptionally silent provirus must be distinguished from a defective provirus. This can be achieved by purging the silent provirus out of latency and measuring production of viral proteins such as the major structural protein Gag or its CA-p24 domain. We previously developed a latency assay and demonstrated that TNFα, which is a strong activator of the transcription factor NF-κB, could purge HIV-1 out of latency in the SupT1 T cell line [28], [35]. We reported that HIV-1 frequently establishes latent infection in these actively dividing T cells. Here we used this assay to test several known anti-latency drugs on primary PHA-activated T lymphocytes. As expected, treatment of HIV-1 infected SupT1 cells with TNFα yielded a 3-fold increase in the percentage of CA-p24 positive cells, but no such effect was observed in primary T lymphocytes (Fig. 1A). The phorbol esters prostratin and PMA can indirectly increase HIV-1 transcription via activation of the protein kinase C (PKC) signaling route [38], [39]. Treatment of infected SupT1 cells with prostratin increased the percentage of CA-p24 positive cells 1.5-fold, whereas no or even a small negative effect was observed for primary T lymphocytes (Fig. 1B). PMA treatment reduced the percentage of CA-p24 positive primary T lymphocytes while it did not change the percentage of CA-p24 positive SupT1 cells (Fig. 1C). Activation of the PKC route by stimulating the T cell receptor (TCR) with the cross-linker phytoheamagglutinin (PHA) increased the number of CA-p24 positive SupT1 cells 1.7-fold, but like PMA reduced the percentage of CA-p24 positive primary T lymphocytes (Fig. 1D).
Other activators of latent HIV-1 provirus in T cell lines include histone deacetylase (HDAC) inhibitors, such as sodium butyrate (NaBut) and trichostatin A (TSA) [40], [41]. These compounds prevent deacetylation of histone tails thereby creating a more open DNA chromatin conformation and this improves the accessibility of the HIV-1 promoter in the long terminal repeat (LTR) for transcription factors. In SupT1 cells, both NaBut and TSA activated latent provirus 2-fold when used at the highest concentration, but the compounds had no effect on the percentage of CA-p24 positive primary T lymphocytes (Fig. 1D and E). DMSO and ethanol, used to dissolve NaBut and TSA, did not affect transcriptional activity of latent provirus (Fig. 1F). These results indicate that many of the known anti-latency drugs can indeed purge HIV-1 out of latency in the SupT1 T cell line but not in primary proliferating T lymphocytes. Thus, either PHA-activated primary T lymphocytes do not harbor latent HIV-1 infections, or the anti-latency drugs used at the indicated concentrations are not sufficient to activate HIV-1 provirus from latency in these primary cells.
Dendritic cells (DCs) regulate T and B cell responses via cell-cell contact in combination with secretion of specific cytokines and chemokines [42], [43]. To investigate if a more physiological cell-based stimulus could activate HIV-1 from latency in primary T lymphocytes, the cells were co-cultured with immature monocyte-derived dendritic (DCs). The HIV-1 infected T lymphocyte culture was split 24 h after infection into a mock treated culture and a co-culture with DC (Fig. 2A). In the latency assay new rounds of virus replication and virus transmission from DC to T lymphocyte are prevented by the fusion inhibitor T1249. The cells were harvested after 24 hours, stained for intracellular CA-p24 and analyzed by flow cytometry. The percentage of CA-p24 positive T lymphocytes increased significantly from 2.2% in the control culture to 5.2% upon co-culture with DCs (Fig. 2B). This 2.4-fold activation shows that HIV-1 can frequently establish a latent provirus early after infection of PHA-activated T lymphocytes and DCs can induce proviral gene expression from the silent provirus to re-initiate virus production (Fig. 2C). Similar results were obtained with CD3/CD28-activated T lymphocytes instead of PHA-activated T cells (Fig. S1).
To confirm that the PHA-activated T lymphocytes have an effector phenotype they were stained with different antibodies to detect immune phenotype markers by flow cytometry. The PHA-activated T lymphocytes expressed low levels of CD69, CD127 and CCR7 and high levels of CD25 and CD45RO, as expected of effector T lymphocytes (Fig. S2A). For most markers the expression level, measured with the mean fluorescent intensity (MFI), did not change as a result of virus production, except for a significant increase in CD25 and CD45RO expression in the CA-p24 positive T lymphocytes compared to CA-p24 negative cells (Fig. S2B). The increase in CD25 expression was even more pronounced in the CA-p24 positive T lymphocytes that were co-cultured with DCs (Fig. S2C and S2D).
To investigate whether DCs induce apoptosis of the HIV-1 infected T lymphocytes, the cells were analyzed for the presence of the phospholipid phosphatidylserine (PS), an early apoptosis marker. Co-culturing of T lymphocytes with DCs slightly increased the percentage of PS positive cells but this was observed for the complete T lymphocyte population and not specifically for the CA-p24 positive cells, demonstrating that increased CA-p24 expression is not caused by the onset of apoptosis (Fig. S3).
To investigate whether contact with DCs also enhances the virus production per cell, we inspected the MFI of CA-p24 positive T lymphocytes. The MFI of DC versus mock treated cells was compared and the ratio of the two values was calculated (Fig. 2D). There was no significant difference, indicating that virus production per individual T lymphocyte does not increase. To investigate the total virus production in the cell culture, secreted CA-p24 was measured in the culture supernatant with ELISA. Co-culturing of the T lymphocytes with DCs increased the extracellular CA-p24 production in the culture supernatant 2-fold, but this was not significantly different from the extracellular CA-p24 measured in the mock treated culture (Fig. 2E). When the extracellular CA-p24 production was corrected for the increased number of intracellular CA-p24 positive cells, no nett difference was observed (data not shown). These results demonstrate that DCs induce more T lymphocytes to produce HIV-1 but that virus production per cell does not change.
The conventional anti-latency drugs that can activate provirus from latency in SupT1 cells are insufficient to activate latent provirus in primary T lymphocytes. However, latent provirus in T lymphocytes can be activated upon co-culture with DCs. To investigate whether DCs can also activate HIV-1 from latency in the SupT1 T cell line, HIV-1 infected SupT1 cells were mock treated or co-cultured with DCs for 24 hours. The percentage of CA-p24 positive SupT1 cells increased from an average of 2.7% in the mock treated culture to 4.1% in the co-culture with DCs. This 1.5-fold increase is not significantly different, as underscored by the comparison to the 4.7-fold activation upon TNFα treatment (Fig. 3). These combined results indicate that distinct pathways seem to trigger activation of latent provirus in SupT1 T cells versus primary T lymphocytes.
In the latency assay DCs are added to T lymphocytes 24 hours after a single round HIV-1 infection to allow for completion of the reverse transcription and integration processes. Several control experiments were performed to investigate if DCs influence delayed reverse transcription or integration processes rather than transcriptional activation of latent HIV-1 provirus. First, the integrated DNA copy numbers were analyzed. Infected T lymphocytes were co-cultured with DCs or mock treated and an aliquot of the cultures was analyzed for CA-p24 expression by flow cytometry, which showed the expected 3-fold induction as the percentage of CA-p24 positive cells increased from 2.8% in the mock treated culture to 8.6% in the DC co-culture (Fig. 4A). The remaining cells were pelleted, subjected to proteinase K treatment, and the HIV-1 DNA copy number was analyzed with a TaqMan assay that detects the number of integrated HIV-1 copies with primers binding to repetitive chromosomal Alu segments in combination with primers specific for HIV-1 DNA. The T lymphocytes co-cultured with DCs appeared to have higher integrated viral copy numbers compared to the mock treated culture, but this trend was not statistically significant (Fig. 4B). To further monitor the effects on integration, the latency assay was done in the presence or absence of the integrase inhibitor Raltegravir, which was added at the start of the DC co-culture. Raltegravir caused a small but significantly reduction of the DC-mediated provirus activation. Nevertheless, the 2-fold induction of latent provirus remained, showing that DCs can influence the HIV-1 integration process but also activate latent provirus (Fig. 4C). To eliminate the effect on the early events of the HIV-1 replication cycle, the latency assay was repeated 9 days after infection. At this time all reverse transcription and integration processes should be completed. To allow for prolonged culturing, the T lymphocytes were activated with beads coated with CD3 and CD28 antibodies instead of PHA and infected according to the standard latency assay, except that the culture was split into 3 cultures on day 2 post infection. The first culture was mock treated, the second co-cultured with DC's and the third was maintained for 1 week. The mock treated and DC co-cultured T lymphocytes were harvested after 24 hours and analyzed by flow cytometry. On day 9 post infection the third culture was split into two cultures; one mock treated and one co-cultured with DC's. Both were harvested after 24 hours and analyzed by flow cytometry. The percentage CA-p24 positive T lymphocytes increased 2-fold by DC co-culture executed on either day 2 or day 9 post infection (Fig. 4D). These results show that PHA-activated T lymphocytes still harbor latent provirus at one week after infection and that this provirus can become transcriptionally active upon T lymphocyte contact with DCs.
To unequivocally show that PHA-activated T lymphocytes harbor latent provirus the latency assay was performed with HIV-1 infected CD4 expressing T lymphocytes. Productively infected T lymphocytes down-regulate CD4 expression at the cell surface via the viral Nef protein, one of the early HIV-1 proteins, whilst latently infected T lymphocytes retain normal CD4 expression levels [44]. To study if DCs induce gene expression of truly latent provirus, PHA-activated T lymphocytes (mixed population) were infected with HIV-1 according to the latency assay. A single day after infection half of the T lymphocyte culture was used to select CD4 expressing cells with magnetic beads (CD4 selected, Fig. 5A). To determine selection efficiency, the percentage of CD4 expressing cells was compared by flow cytometry analysis. In a representative experiment starting with a cell population of which 50% expressed CD4, we could enrich up to 94% (Fig. 5B). As expected, the CD4 selected cells exhibited decreased CA-p24 positivity (Fig. 5C). Both mixed and CD4 selected T lymphocytes were co-cultured with DCs or mock treated for 24 hours, and the CD3 positive T lymphocyte population was analyzed for CD4 and CA-p24 expression by flow cytometry (Fig. 5D). Co-culturing the infected CD4 selected T lymphocytes with DCs induced the percentage of CA-p24 positivity to increase while the cells that became CA-p24 positive lost CD4 expression (Fig. 5E and data not shown). The average 2.6-fold DC-mediated activation of latent provirus in the CD4 selected T lymphocytes was similar to the 2.5-fold activation in the whole population T lymphocyte population (Fig. 5F). Thus, the latent HIV-1 provirus in PHA-activated T lymphocytes – assayed by unaffected CD4 expression – is sensitive to transcriptional activation upon T lymphocyte DC contact.
To investigate possible differences in establishment and activation of latent proviruses between individuals, DCs and T lymphocytes were isolated from six healthy blood donors and tested in the latency assay. The percentage of mock treated CA-p24 positive T lymphocytes ranged from 1.7% for donor B to 5.6% for donor D (Fig. 6A). Despite such differences in infection rate, latent provirus activation by stimulation with autologous DCs was apparent for all donors. The DC-mediated fold activation from latency ranged from 2-fold for donor A to 3.5-fold for donor D (Fig. 6B). Next, we investigated whether latent HIV-1 proviruses in T lymphocytes could be activated by co-culturing with allogenic DCs. Infected T lymphocytes from donor D and E were mock treated or co-cultured with DCs from donor D or E. Both autologous and allogenic DCs similarly increased the percentage of CA-p24 positive T lymphocytes by 3-fold (Fig. 6C).
It has previously been shown that activation of latent HIV-1 in quiescent memory T lymphocytes can be achieved by activating the cells via TCR stimulation with CD3/CD28 specific antibodies or using IL-2 or ionomycin [45]–[47]. Since the T lymphocytes in our latency assay are activated via TCR stimulation with PHA prior to HIV-1 infection, we tested whether further TCR stimulation could increase the number of CA-p24 positive cells. Reactivation with CD3/CD28 antibodies did not increase the percentage of CA-p24 positive cells (Fig. 7). Treating the T lymphocytes with IL-2 or ionomycin, which activates the NFAT transcription factor, also had no significant effect on the percentage of CA-p24 producing cells. The T lymphocytes used in this study are all TCR stimulated and still harbor latent provirus that can be activated by DCs. This demonstrates that TCR stimulation is not sufficient to activate all latent provirus in proliferating T lymphocytes.
The C-type lectin DC-SIGN is a cell surface molecule expressed on DCs that facilitates HIV-1 infection of T lymphocytes in cis or in trans [48], [49]. Furthermore, DC-SIGN can induce HIV-1 transcription in DCs themselves [50]. To investigate whether DC-SIGN is also involved in the activation of latent HIV-1 provirus in primary T cells, the HIV-1 infected T lymphocytes were mock treated or co-cultured with DCs in the presence or absence of the C-type lectin competitor mannan (Fig. 8A). Addition of mannan did not influence DC-mediated provirus activation. To further study a possible role for DC-SIGN, T lymphocytes were co-cultured with either Raji or modified Raji cells expressing DC-SIGN (Raji-DC-SIGN). DCs triggered a 3-fold activation, whereas both the Raji and Raji-DC-SIGN cells could not purge provirus out of latency (Fig. 8B). The combined results indicate that DC-SIGN is most likely not involved in DC-mediated HIV-1 activation from proviral latency.
To investigate the requirements for DC-mediated activation of latent provirus, we first studied the effect of co-culturing the T lymphocytes with increasing numbers of DCs (ratio DC∶T; 1∶150–1∶1,5). The CA-p24 positive T lymphocytes increased from an average of 1.4% in the mock treated culture to an average of 2.5% in the DC co-culture at a ratio of 1∶150, yielding a 1.8-fold activation of latent provirus. Increasing DC numbers enhanced proviral activation to 4.8% CA-p24 positive T lymphocytes, representing 3.5–fold activation with 1 DC per 1.5 T lymphocytes (Fig. 9A). To investigate the role of DC-secreted components, cell-free DC culture supernatant was added to infected T lymphocytes. As a control, cell-free culture supernatant of HEK 293T cells was used. The DC-supernatant induced a 2.2-fold activation whereas HEK 293T supernatant showed no activation (Fig. 9B). This result demonstrates that the DC culture medium contains (a) DC-secreted factor(s) activating latent HIV-1 provirus. This is not mediated by IL-4 or GM-CSF that are used to differentiate the monocytes into DCs as both cytokines did not induce activation of the latent provirus (Fig. S4). Co-culturing of infected T lymphocytes with freshly washed DCs, thus removing soluble factors, induced a 2.5-fold increase in percentage of CA-p24 positive cells, while washed DCs combined with DC supernatant gave the strongest (3.7-fold) activation (Fig. 9C). This demonstrates that a combination of DCs and their secreted factors induce the strongest activation. Next, we investigated if the activation of latent provirus can be inhibited by blocking the DC-T lymphocyte cell-cell interaction. Since TCR-stimulation does not have an effect on the latent provirus in the PHA-activated T lymphocytes (see Fig. 1) and activation of latent provirus is induced by both autologous and allogenic DCs (see Fig. 6), we chose to use antibodies that specifically target the general adhesion molecules ICAM-1, ICAM-2 or ICAM-3 (Fig. 9D). The presence of ICAM antibodies did not have an effect on the percentage of CA-p24 positive T lymphocytes in the absence of DCs (left part of the panel). The co-culture with DCs increased the percentage of CA-p24 positive T lymphocytes 5-fold. Blocking ICAM-1 interactions between the DC and T lymphocyte significantly reduced the activation by 75% to 2-fold. Addition of ICAM-2 or ICAM-3 antibodies, on the contrary, did not inhibit the DC-mediated increase in CA-p24 positive T lymphocytes. Together, these results demonstrate that both cell-cell contact between DC and T lymphocyte and (a) secreted DC factor(s) induce activation of latent HIV-1 provirus.
We previously developed a fast and relatively simple method to study HIV-1 latency in T cell lines and showed that the virus can be purged out of latency with TNFα, genistein and 5-Aza [28], [35]. In this study, our latency assay was used to demonstrate that HIV-1 can establish a latent proviral integration in primary PHA-activated T lymphocytes. Conventional anti-latency drugs (TNFα, NaBut, Prostratin, TSA, PMA or PHA), were not sufficient to activate the latent provirus but the virus was purged out of latency by co-culturing the T lymphocytes with dendritic cells (DCs). Co-culturing with DCs, activated gene expression from the latent HIV-1 provirus in the T lymphocytes by 2- to 5-fold. This may seem modest in comparison to other latency models [20], [29], [45], [47], [51], [52], but we note that in other studies the latently infected cells are usually selected, either by clonal selection or by specific outgrowth. In contrast, our latency assay is performed on the bulk culture without any form of selection. The obtained 2- to 5-fold activation means that for every virus-producing lymphocyte there are 1 to 4 lymphocytes that harbor a silent provirus that can be activated by DCs. In fact, since we cannot rule out that some latent proviruses are unresponsive to DC-activation, the actual latent reservoir may be underestimated.
In resting memory T lymphocytes, activation of the latent provirus is predominantly mediated via cellular activation with αCD3/CD28 (TCR-stimulation), IL-2 or via activation of the NFAT transcription factor with ionomycin [36], [45]–[47]. These stimuli did not have an effect on the silent provirus in activated T lymphocytes, most likely because the transcription factors involved are already active. This observation illustrates that the establishment of a silent provirus is not necessarily due to the absence of certain transcription factors, which was proposed to be the major cause for the establishment of latently infected quiescent memory lymphocytes [36]. Importantly, this also shows that the molecular mechanism leading to the activation of latent HIV-1 can differ between different T lymphocyte subsets, depending on their activation status.
A remaining question is whether HIV-1 latency is caused by silencing of a transcriptionally active provirus or the result of silent proviral integration. If silent integration occurs, the infected proliferating T lymphocyte will not be recognized by the immune system, favoring the transition to a latently infected memory cell. Our results showing that DCs can activate latent HIV-1 already 2 days after the initial infection support previous observations obtained in T cell lines that the latent phenotype is established immediately after provirus integration and is not due to down-regulation of an initially transcriptional active provirus [28], [29], [34].
We have not yet fully characterized the molecular interactions required for the DC-mediated HIV-1 activation from latency. However, our results indicate that both (a) secreted DC-component(s) and the cell-cell interaction between DC and T lymphocyte trigger activation of latent provirus. HIV-1 activation did not require autologous T lymphocytes and DCs, suggesting that the cell-cell interaction is not self-restricted. Blocking the DC-T cell interaction with anti-ICAM-1 antibodies strongly inhibited the activation of latent provirus. ICAM-1 is expressed on the DC and binds to leukocyte function-adhesion molecule-1 (LFA-1) on the T lymphocyte, an interaction that is important for the process of antigen presentation [53]–[55]. We and others previously showed that the interaction of LFA-1 and ICAM-1, but not ICAM-2 and ICAM-3, is crucial for efficient HIV-1 transmission and that DC subsets that express higher levels of ICAM-1 transmit HIV-1 more efficiently [56]–[58]. It would be interesting to investigate if DC subsets with higher ICAM-1 levels are also better activators of latent provirus. Further characterization of the cellular contact molecules that can purge HIV-1 from latency and testing their efficiency on latently infected memory T lymphocytes is of great interest, as our results strongly suggest that cellular activation via TCR-stimulation is not sufficient to activate all latent proviruses.
Purging the provirus from latency via physiologically relevant cell-cell interactions supports the idea that the viral reservoir is dynamic. In this study we show that immature monocyte-derived DCs can activate latent HIV-1 in activated T lymphocytes. Marini et al. demonstrated that mature monocyte-derived DCs can activate latent provirus in resting memory T lymphocytes [47]. If these cell-cell contacts activate latent virus in vivo, local outbursts of virus production can occur despite suppressive therapy. However, the situation in vivo is more complex as mature myeloid DCs have been reported to inhibit productive infection by inducing latency [59]. This indicates that different DC subsets may have opposing effects on viral latency. Thus, the cellular HIV-1 reservoir could be activated or silenced depending on the type of DC that is encountered. We are currently investigating the influence of different DC subsets (myeloid and plasmacytoid) on latent HIV-1 provirus in proliferating T lymphocytes.
In this study we demonstrate that HIV-1 can establish a silent integration in actively proliferating T lymphocytes. The latently infected T lymphocytes will escape immune-surveillance as long as no viral peptides are expressed and presented on the cell surface. Although proliferating T lymphocytes are generally short-lived such that their contribution to the total HIV-1 reservoir will be limited, these cells can return to the resting state of memory T lymphocyte and thereby contribute to the long-lived viral reservoir. How important this interaction is in vivo and whether it can be used in ‘shock and kill’ approaches [60], [61] for the complete eradication of the HIV-1 reservoir in patients remains to be studied.
HEK 293T cells were grown as a monolayer in Dulbecco's minimal essential medium (Gibco, BRL, Gaithersburg, MD) supplemented with 10% (v/v) fetal calf serum (FCS), 40 U/ml penicillin, 40 µg/ml streptomycin and nonessential amino acids (Gibco, BRL, Gaithersburg, MD) at 37°C and 5% CO2. The human T lymphocytic cell line SupT1 (ATCC CRL-1942) was cultured in advanced RPMI 1640 medium (Gibco BRL, Gaithersburg, MD) supplemented with 1% (v/v) FCS, 20 mM glucose, 40 U/ml penicillin, and 40 µg/ml streptomycin. The Raji cell line and Raji-DC-SIGN cells were cultured in RPMI 1640 medium (Gibco, BRL, Gaithersburg, MD) containing 10% FCS. The immature monocyte-derived dendritic cells (DCs) were prepared as previously described [62]. In short, human peripheral blood mononuclear cells (PBMCs) were isolated from buffy coats (Central Laboratory Blood Bank, Amsterdam, The Netherlands) by use of a Ficoll gradient. Monocytes were subsequently isolated with a CD14 selection step using a magnetic bead cell sorting system (Miltenyi Biotec GmbH, Bergisch Gladbach, Germany). Purified monocytes were cultured in RPMI 1640 medium containing 10% FCS and differentiated into DCs by stimulation with 45 ng/ml interleukin-4 (rIL-4; Biosource, Nivelles, Belgium) and 500 U/ml granulocyte macrophage colony-stimulating factor (GM-CSF; Schering-Plough, Brussels, Belgium) on day 0 and 2, and used on day 6. The remaining PBMCs were controlled by PCR for the absence of the CCR5 Δ32 allele and frozen in multiple vials. When required, the PBMCs were thawed, activated with phytohemagglutinin (PHA, Remel, 5 µg/ml for 2 days, 2 µg/ml for 3 days activation) or CD3/CD28 immunomagnetic beads (ratio cell∶beads 1∶1 for 3 days, Dynal, Invitrogen) and cultured in RPMI medium supplemented with 10% FCS and recombinant IL-2 (rIL-2, Novartis) at 100 U/ml. On day 2 of culture, CD4+ T lymphocytes were enriched by depleting CD8+ T lymphocytes using CD8 immunomagnetic beads (Dynal, Invitrogen). The CD4+ T lymphocytes were cultured for 3 days in RPMI medium with rIL-2 and 10% FCS.
Plasmid DNA encoding the CXCR4-using HIV-1 LAI primary isolate [63] was transiently transfected in HEK 293 T cells with the calcium phosphate method as described previously [64]. Virus supernatant was harvested 2 days after transfection, sterilized by passage through a 0.2 µm filter and stored in aliquots at −80°C. The concentration of the virus stocks was determined by CA-p24 ELISA.
Culture supernatant was heat inactivated at 56°C for 30 min in the presence of 0.05% Empigen-BB (Calbiochem, La Jolla, USA). The CA-p24 concentration was determined by twin-site ELISA with D7320 (Biochrom, Berlin, Germany) as capture antibody and alkaline phosphatase-conjugated anti-CA-p24 monoclonal antibody (EH12-AP) as detection antibody. Quantification was performed with the lumiphos plus system luminescence reader (Lumigen, Michigan, USA) in a LUMIstar Galaxy (BMG labtechnologies, Offenburg, Germany). Recombinant CA-p24 produced in a baculovirus system was used as standard.
Mannan (Sigma) was used at a final concentration of 40 µg/ml. Raltegravir (ISENSTRESS/MK-0518) was obtained through the AIDS Research and Reference Reagent Program and used at the final concentration of 50 µg/ml. The fusion inhibitor T1249 (WQEWEQKITALLEQAQIQQEKNEYELQKLDKWASLWEWF) was obtained from Pepscan (Therapeutics BV, Lelystad, The Netherlands) and used at a final concentration of 0.1 µg/ml.
PHA (Remel), PMA (Sigma), Prostratin (Sigma-Aldrich), TNFα (Invitrogen), Trichostatin A (TSA, Sigma) and Sodium butyrate (NaBut, Aldrich) were used at the indicated concentrations. Recombinant IL-2 (Novartis) was used at a final concentration of 100 U/ml and ionomycin (Sigma-Aldrich) at 100 µg/ml. αCD3/CD28 beads (Dynal, Invitrogen) were used at a ratio of 1 bead per cell.
For intracellular CA-p24 measurement we used the RD1- or FITC-conjugated mouse monoclonal α-CA-p24 (clone KC57, Coulter). For CD3 staining the APC- or FITC-conjugated α-CD3 (BD Bioscience) was used. Annexin V-APC (BD Pharmingen) was used to stain for the early apoptosis marker phosphatidyldserine. To characterize the T lymphocyte immunophenotye, α-CD69-PE (BD Bioscience), α-CD45-RA-PE (Pharmingen), α-CD25-FITC (BD), α-CD45-RO-FITC (DAKO), α-CD127-PE (BD Pharmingen), and α-CCR7-PE (BD Pharmingen) were used. For DC staining purified α-CD83-APC (BD Bioscience), α-CD86-PE (BD Pharmingen), α-HLA-DR-PerCPCy5 (BD Bioscience), α-CD14-FITC (BD Bioscience) and α-DC-SIGN-PE (R&D Systems) antibodies were used. To block DC-T lymphocyte cell-cell (ICAM) interactions in the co-culture, α-CD54 (Peli Cluster; the Netherlands), α-CD102 (RD Systems) or α-CD50 (Immunotech) antibodies were used.
HIV-1 infected cells were used in the latency assay as described previously [35]. In short, PHA- or CD3/CD28-activated CD4+ T lymphocytes (1.0 or 2.0×106 cells) were infected with HIV-1 (20 ng CA-p24). Free virus was washed away after 4 hours and the cells were cultured with the fusion inhibitor T1249 to prevent new infections. At 24 hr after infection the CD4+ T lymphocytes (1.5×105/well) were either mock treated, treated with anti-latency drugs or co-cultured with DCs, Raji or Raji-DC-SIGN cells (0.5×105/well). After another 24 hr, the cells were harvested and intracellular CA-p24 was detected by FACS flow cytometry. Virus production was also determined by measuring extracellular CA-p24 in the culture supernatant by ELISA. The percentage of CA-p24 positive cells in the treated culture was divided by the percentage of CA-p24 cells in the mock treated culture and used as a measure for proviral latency (fold activation). The CD4+ T lymphocytes were co-cultured with autologous DCs unless when co-cultured with allogenic DC's (as indicated in the figure legend). To block cell-cell interactions between T lymphocyte and DC antibodies specific for human ICAM-1 (CD54), ICAM-2 (CD102) or ICAM-3 (CD50) were added to the co-culture at the final concentration of 10 µg/ml. To select CD4 expressing cells in the HIV-1 infected culture a magnetic bead cell sorting system was used according to the manufactures instructions (Miltenyi Biotec GmbH, Bergisch Gladbach, Germany). One Way ANOVA and student T test (2-tailed) were used to evaluate if observed differences between groups are significant (Graphpad Prism, version 5). P values * = p<0.05, ** = p<0.01, *** = p<0.001.
Cells were fixed in 4% formaldehyde for 10 minutes at room temperature and subsequently washed with FACS buffer (PBS supplemented with 1% FCS). The cells were permeabilized with BD Perm/Wash buffer (BD Pharmingen) and antibody staining was performed in BD Perm/Wash or FACS buffer for 1 hr at 4°C. Excess of unbound antibody was removed and the cells were analyzed on a BD FACSCanto II flow cytometer with BD FACSDiva Software v6.1.2 (BD biosciences, San Jose, CA) in FACS buffer. The live population was defined based on forward/sideward scatter and analyzed for CD3 and intracellular CA-p24 positivity. Gate settings were fixed between samples for each experiment. To measure the membrane phospholipid phosphatidylserine as a marker for early apoptosis, the cells were stained with Annexin V prior to cell fixation.
The DC phenotype (negative for CD14, low levels of MHC class II (HLA-DR), CD83 and CD86 and high levels of DC-SIGN) was confirmed by FACS flow cytometry [57].
TaqMan assay was used to quantify the number of HIV-1 DNA copies in infected cultures. In summary, cells were resuspended in Tris-EDTA (10 mM pH 8.3) containing 0.5 units/µl proteinase K (Roche Applied Science), incubated for 1 hr at 56°C and 10 min at 95°C and directly used for quantitative PCR amplification. The number of input cells was determined using TaqMan reagents for quantification of β-actin DNA (AB, Applied Biosystems) according to the manufacturer's instruction. To quantitate integrated proviral DNA copy numbers a pre-amplification was done with primers detecting the repetitive Alu sequence [65] in combination with primers specific for the HIV-1 LTR. The pre-amplified DNA was subsequently quantified by real-time PCR as previously described [66].
|
10.1371/journal.ppat.1007950 | Equine arteritis virus long-term persistence is orchestrated by CD8+ T lymphocyte transcription factors, inhibitory receptors, and the CXCL16/CXCR6 axis | Equine arteritis virus (EAV) has the unique ability to establish long-term persistent infection in the reproductive tract of stallions and be sexually transmitted. Previous studies showed that long-term persistent infection is associated with a specific allele of the CXCL16 gene (CXCL16S) and that persistence is maintained despite the presence of local inflammatory and humoral and mucosal antibody responses. Here, we performed transcriptomic analysis of the ampullae, the primary site of EAV persistence in long-term EAV carrier stallions, to understand the molecular signatures of viral persistence. We demonstrated that the local CD8+ T lymphocyte response is predominantly orchestrated by the transcription factors eomesodermin (EOMES) and nuclear factor of activated T-cells cytoplasmic 2 (NFATC2), which is likely modulated by the upregulation of inhibitory receptors. Most importantly, EAV persistence is associated with an enhanced expression of CXCL16 and CXCR6 by infiltrating lymphocytes, providing evidence of the implication of this chemokine axis in the pathogenesis of persistent EAV infection in the stallion reproductive tract. Furthermore, we have established a link between the CXCL16 genotype and the gene expression profile in the ampullae of the stallion reproductive tract. Specifically, CXCL16 acts as a “hub” gene likely driving a specific transcriptional network. The findings herein are novel and strongly suggest that RNA viruses such as EAV could exploit the CXCL16/CXCR6 axis in order to modulate local inflammatory and immune responses in the male reproductive tract by inducing a dysfunctional CD8+ T lymphocyte response and unique lymphocyte homing in the reproductive tract.
| A distinctive feature of equine arteritis virus (EAV) is its ability to establish long-term persistent infection in the stallion reproductive tract in the presence of strong immune and inflammatory responses. The data presented herein provides insight into the molecular signature of the inflammatory response during persistent infection in the male reproductive tract, and shows that long-term persistence is associated with the predominance of specific CD8+ T lymphocyte transcription factors that drive the inflammatory process in the reproductive tract, along with the upregulation of inhibitory receptors and CXCL16/CXCR6, a chemokine axis strongly implicated in EAV persistence. Furthermore, the host’s CXCL16 genotype drives the changes in transcriptional factors that favors EAV persistent infection. These findings have a broad translational importance in the immunopathogenesis of EAV and other persistent viral infections in the male reproductive tract of animals and humans, as well as in the prevention and treatment of such infections.
| Equine arteritis virus (EAV) is a positive-sense, single-stranded RNA virus that belongs to the family Arteriviridae, order Nidovirales [1]. EAV is the causative agent of equine viral arteritis (EVA), an economically important systemic, reproductive and respiratory disease of equids [2–8]. Transmission of EAV can occur through the respiratory or venereal routes by acutely infected horses or solely through the venereal route by persistently infected stallions [4, 8–10]. EAV infection in horses can be either asymptomatic or associated with a wide range of clinical signs, including dependent edema, conjunctivitis, periorbital or supraorbital edema, respiratory distress, urticaria and leukopenia [2–4, 8, 11–18]. Infection of pregnant mares can result in abortion or birth of congenitally infected foals that frequently develop a fatal bronchointerstitial pneumonia or pneumoenteric syndrome [19]. Most importantly, EAV can establish long-term persistent infection in the reproductive tract of stallions (carrier state) resulting in continuous shedding of infectious virus in their semen [2–4], which guarantees the perpetuation of the virus in equine populations [2–4, 7–11, 20, 21]. EAV persistent infection is testosterone-dependent [22] and can last from several weeks or months (i.e., virus shedding in semen ≤ 1 year following infection [short-term carrier]) to years or even life-long (i.e., virus shedding in semen >1 year following infection [long-term carrier]). Furthermore, persistently infected stallions do not exhibit clinical signs of disease or impairment of fertility [4, 8–10, 18, 20, 21, 23–25]. To date, the immunopathogenesis of persistent EAV infection in the reproductive tract of the stallion is not fully elucidated and is currently under investigation in our laboratory.
Recently, it has been shown that the outcome of EAV infection in the stallion is dependent on host genetic factors, clearly associated with a specific allele of the CXCL16 gene (CXCL16S) that encodes for the C-X-C motif chemokine ligand 16 (CXCL16). Importantly, it has been demonstrated that CXCL16S acts as a cellular receptor for EAV while CXCL16R does not [26]. Furthermore, it has also been shown that EAV has a specific tropism for a subset of CD8+ T and CD21+ B lymphocytes and stromal cells primarily in the ampullae and to a lesser extent in the other accessory sex glands (vesicular, prostate and bulbourethral glands) of persistently infected stallions [23, 25, 27–29]. Moreover, EAV persists in the male genital tract despite the presence of strong inflammatory (mediated mainly by CD8+ T lymphocytes) and EAV-specific humoral and mucosal antibody responses [23, 24]. Also, it has been recently demonstrated that EAV long-term persistent infection is associated with the specific downregulation of microRNA (miRNA) eca-mir-128 in seminal exosomes along with an enhanced expression of CXCL16 in the ampullae, a putative target of eca-mir-128, at the site of persistent infection [30]. Understanding the mechanisms of EAV persistence in the stallion reproductive tract is critical to the success of efforts to develop novel therapeutics for elimination of the carrier state. Thus, the long-term goal of our studies is to specifically identify the mechanism(s) of EAV persistent infection in the stallion reproductive tract. In this study, we hypothesized that persistent EAV infection induces a specific immunological milieu in the stallion reproductive tract that favors viral immune evasion by modulating the host’s local immune and inflammatory responses at the site of viral persistence, a process driven by specific transcription factors and the CXCL16/CXCR6 chemokine axis.
The clinical outcome and establishment of EAV persistent infection after intranasal challenge with EAV KY84 strain in this group of stallions (n = 8) has been previously described [18, 23, 24]. Of the 8 infected stallions, six stopped shedding in <1 year post-infection and were classified as short-term carrier stallions (Table 1). Conversely, two of the 8 stallions continued to shed EAV in their semen for >1 year post-infection and were classified as long-term carrier stallions (Table 1). A naturally infected, long-term carrier stallion (stallion E) was also included in the study as previously described [23]. All long-term carrier stallions carried the CXCL16S allele (Table 1). All experimentally infected stallions were humanely euthanized at 726 days post-infection (dpi) and tissues collected for analysis.
In order to obtain a comprehensive understanding of the molecular basis of the host response mechanisms to EAV at the site of persistence, we performed comparative whole transcriptome analysis of the ampullae from naïve (n = 3), long-term (n = 3) and short-term (n = 6) carrier stallions collected at the end of the study (726 dpi). The analysis workflow for this study can be depicted in Fig 1. A total of 1,056 and 748 differentially expressed genes (DEGs, false discovery rate [FDR] < 0.1 and log2 fold-change > 1 and < -1) were identified in long-term and short-term carrier stallions compared to the naïve group (n = 3), respectively. Among the DEGs observed in long-term carrier stallions, a total of 896 genes were found to be upregulated (log2 fold-change > 1 over the naïve group, 84.8%) while 160 genes were downregulated (log2 fold-change < -1 over the naïve group, 15.2%). Similarly, a clear majority of the DEGs in short-term carrier stallions were upregulated (647 genes, 86.5%) while a total of 101 genes were downregulated (13.5%). Furthermore, 459 common DEGs were identified when comparing both long-term and short-term carrier stallions to the naïve group. Among these, 386 genes (84%) were upregulated while 73 genes (16%) were downregulated (Fig 2A). Functional annotation analysis of commonly upregulated DEGs was performed using DAVID and PANTHER bioinformatics tools [32, 33], and demonstrated that these were mainly involved in biological processes associated with adhesion (cell adhesion, extracellular matrix organization, integrin-mediated signaling, leukocyte migration and cell-matrix adhesion; Fig 2B). Regarding their molecular function, commonly upregulated genes were significantly associated with protein binding (heparin, integrin, collagen, extracellular matrix and actin binding; Fig 2C). Relevant biological pathways involving commonly upregulated genes are depicted in Fig 2D. Pathway analysis using Ingenuity Pathway Analysis (IPA, Qiagen, Valencia, CA) identified similar pathways involved including granulocyte/agranulocyte adhesion and diapedesis, integrin signaling, protein kinase signaling and epithelial adherens junction signaling, among others (S1 Table). No statistically significant gene ontology (GO) terms for biological processes or molecular functions were obtained for common downregulated genes.
For the identification of specific molecular signatures in the inflammatory response during long-term EAV persistence in the ampullae, we additionally performed differential gene expression analysis between long-term and short-term carrier stallions. Comparative whole transcriptome analysis demonstrated that 390 genes were differentially expressed between these two groups, with a high proportion of genes being upregulated in long-term compared to short-term carrier stallions (284 genes [72.8%]; Fig 3A). DEGs were categorized based on selected GO terms (biological process; S1 Fig) and their expression patterns in long-term, short-term carrier and naïve stallions are depicted in Fig 3B. There was a clear upregulation of genes associated with all these biological processes in long-term carrier stallions (Fig 3B). Additional upregulated genes associated with other immune-related biological processes (e.g. effector functions, antigen processing and presentation, sensing/signaling/transcriptional regulation, among others) are shown in Table 2, some of which presented a strong upregulation (fold change > 2). In the case of downregulated genes, muscle contraction, structural constituent of muscle and actin binding were identified as the only significant biological process and molecular functions, respectively.
Pathway analysis using IPA identified that the DEGs observed between long-term and short-term carrier stallions were primarily involved in several T-lymphocyte associated canonical pathways (Fig 3C). Among the top 25 canonical pathways identified, nine were predictively activated (z score ≥ 2) in long-term carrier stallions and, interestingly, these included the type 1 T helper lymphocyte (Th1) pathway, interferon signaling, regulation of the immune response by nuclear factor of activated T-cells (NFAT), and cytotoxic T lymphocyte-associated protein 4 (CTLA-4) signaling in cytotoxic T lymphocytes. Other significant canonical pathways involved in T lymphocyte and natural killer cell-mediated responses were also observed, although activation predictions could not be determined (Fig 3C). In summary, a clear majority of the DEGs in long-term carrier stallions demonstrated to be upregulated, with specific involvement in adaptive (specifically T lymphocyte-associated) and innate immune responses, including pathways related to the regulation of the immune response.
The inflammatory response to EAV during long-term persistent infection was characterized both histologically and immunohistochemically (IHC). Histopathological examination of the ampullae from long-term carrier stallions showed a moderate to severe, multifocal lymphoplasmacytic ampullitis (S2 Fig). Interestingly, the inflammatory infiltrates were characterized by extensive numbers of CD8+ T lymphocytes, particularly in the lamina propria of the luminal villi along with their intra- and sub-epithelial localization (S2 Fig). The inflammatory response was also characterized by lower numbers of CD4+ T lymphocytes [23] and a significantly higher number of mononuclear cells expressing granzyme B in long-term compared to short-term carrier stallions (p-value = 0.0141, Fig 4A–4C). Since maintenance of EAV long-term persistent infection is testosterone-dependent, immunohistochemical (IHC) evaluation of the androgen receptor (AR) was undertaken to assess the cellular expression within the ampulla. IHC analysis identified its widespread nuclear expression in glandular epithelia and stromal cells in all stallions, and inflammatory (mononuclear) cell infiltrates in long-term and short-term carrier stallions (Fig 4D–4F). Interestingly, a significantly higher number of AR+ cells were found within inflammatory infiltrates of long-term compared to short-term carrier stallions (p-value = 0.0237). In addition, lymphocyte and epithelial cell proliferation were evaluated by Ki-67 immunostaining [34], which demonstrated that neither of these cell types was actively proliferating in any of the experimental groups.
In terms of the chemokine and cytokine profile associated with the persistent inflammatory response, RNAseq analysis identified the upregulation of the T lymphocyte-associated C-C motif chemokine ligand 2 and 5 (CCL2 and CCL5), as well as a subset of related C-X-C motif chemokine ligands including CXCL9, CXCL10 and CXCL11. Relative gene expression analysis by RT-qPCR demonstrated that CCL5, CXCL9 and CXCL10 were significantly upregulated in long-term compared to short-term carrier and naïve stallions (p-values < 0.0001) as well as in short-term carrier compared to the naïve group (p-value = 0.0253). CXCL11 was uniquely upregulated in long-term carrier (p-values < 0.0001) and not statistically different between short-term carrier and naïve stallions (p-value > 0.05, Fig 5A). Similarly, the expression of CXCR3, the common chemokine receptor for CXCL9, CXCL10 and CXCL11, was significantly higher in long-term carrier stallions (p-values < 0.0001, Fig 5A). These findings suggest a specific role of C-X-C motif chemokines in the homing of lymphocytes into the reproductive tract during long-term persistence. Furthermore, RT-qPCR analysis of interferon gamma (IFNG), tumor necrosis factor alpha (TNFA) and interleukin 2 (IL2) demonstrated that both IFNG and TNFA were strongly upregulated in long-term carrier stallions (p-values < 0.0001, Fig 5A), which could be associated with activation of the Th1 pathway (see below). However, no statistically significant differences in the relative expression of IL2 were observed between long-term and short-term carrier stallions (p-value > 0.05). Taken together, EAV persistent infection is mainly associated with the infiltration of CD8+ T lymphocytes and granzyme B+ cells with lower numbers of CD4+ T lymphocytes. In addition, EAV persistence is associated with the upregulation of C-X-C homing chemokines (C-X-C motif chemokine ligands) and their specific receptors (C-X-C motif chemokine receptors), and limited lymphocyte proliferation at the site of persistence.
In order to further understand the molecular elements that drive the inflammatory response during EAV long-term persistence, we analyzed the expression of transcription factors (TFs) combined with transcription factor binding, upstream regulator and molecular network analysis. Differential gene expression analysis identified a subset of TFs that were differentially upregulated in long-term compared to short-term carrier and naïve stallions (FDR < 0.1 and log2 fold-change over naïve group >1, Table 2, Fig 3C). In order to understand the biological significance of these TFs in the regulation of the DEGs identified between long-term and short-term carrier stallions (390 genes), examination of TF usage was undertaken. For this purpose, we performed upstream regulator analysis on IPA (filtering solely for nucleic acid-binding molecules/TFs) and identified a subset of significant TFs (n = 16) acting as upstream regulators for the DEG dataset (S2 Table). Noteworthy, these included interferon regulatory factors (IRF1, IRF4, IRF9), signal transducer and activator of transcription 1 and 4 (STAT1 and STAT4), nuclear factor of activated T-cells cytoplasmic 2 (NFATC2), T-box transcription factor TBX21 (TBX21 or T-bet), PR domain zinc finger protein 1 (PRDM1, also known as transcriptional repressor B lymphocyte-induced maturation protein-1/BLIMP-1), eomesodermin (EOMES) and IKAROS family zinc finger 1 (IKZF1), among others. These results were complimented by TF binding site analysis using CiiiDER analysis tool (Centre for Innate Immunity and Infectious Diseases, Hudson Institute of Medical Research, Victoria, Australia) [35]. Transcription factor enrichment analysis demonstrated that several TFs were significantly over-represented in long-term carrier stallions including IRF1, IRF2, STAT1-2, PRDM1, EOMES, NFATC1, NFATC2, NFATC3, basic leucine zipper ATF-like transcription factor (BATF), proto-oncogene c-Rel (REL) and nuclear factor NF-kappa-B p65 subunit (RELA) (Fig 5B). The TFs identified and differentially expressed in long-term carrier stallions were selected for further analysis. These included IRF1, IRF4, STAT1, STAT2, TBX21, BATF, PRDM1, EOMES and NFATC2, and were involved in four immune-related molecular networks from the top 10 molecular networks identified by IPA (cell-mediated immune response, antimicrobial/inflammatory response, humoral immune response and immunological disease, Fig 5C). RT-qPCR analysis of the selected TFs was performed to confirm their relative expression (Fig 5A). While both long-term and short-term carrier stallions presented higher levels of expression of BATF, EOMES, IRF4 and TBX21 compared to the naïve group (p-values ≤ 0.0004 and p-values ≤ 0.0398, respectively), long-term carrier stallions showed a significantly higher upregulation of these transcripts compared to the short-term carrier group (p-values ≤ 0.0004). Interestingly, IRF1, NFATC2, PRDM1, STAT1 and STAT2 demonstrated to be solely upregulated in the long-term carrier group (p-values ≤ 0.0045), and no significant differences were observed in their relative expression between short-term carrier and naïve stallions (p-values > 0.1636; Fig 5A). Thus, this suggests that these TFs could act as the specific drivers of the local inflammatory/immune response during persistent infection.
Interestingly, transcriptomic analysis via RNAseq and subsequent RT-qPCR confirmation also demonstrated the upregulation of a subset of genes associated with the Th1 differentiation process during long-term persistence. These specifically included Th1-specific TFs STAT1, STAT4, and TBX21 (master Th1 transcription factor). In contrast, no relative expression differences in Th2-specific TFs GATA binding protein 3 (GATA3; master Th2 transcription factor) and STAT6 were observed (Table 2 and Fig 6). Overall, the combined use of differential gene expression, TF binding, upstream regulator and molecular network analyses identified a group of TFs strongly involved in the regulation of the gene expression profile at the site of EAV persistent infection, including cytotoxic and Th1-mediated responses.
To confirm our previous observation, we performed immunohistochemical staining for EOMES, TBX21, PRDM1 and NFATC2 in the ampullae of long-term, short-term carrier and naïve stallions. In agreement with our transcriptomic and RT-qPCR data, immunohistochemical (IHC) analysis demonstrated a significantly higher number of EOMES+, TBX21 (T-bet)+, PRDM1 (BLIMP-1)+ and NFATC2+ infiltrating lymphocytes in the ampullae of long-term carrier when compared to short-term carrier stallions (p-values < 0.05; Fig 7A–7L). Overall, the number of T lymphocytes expressing EOMES, TBX21 (T-bet) and NFATC2 was significantly higher (median score of 4 [>200 positive cells/five 40X magnification fields]), while those expressing PRDM1 (BLIMP-1) was moderate (median score of 3 [100–200 positive cells/five 40X magnification fields]). Subsequently, we compared the expression of EOMES, TBX21 (T-bet), NFATC2 and PRDM1 (BLIMP-1) in a total of five inflammatory infiltrates across long-term carrier stallions (n = 3) in order to determine their cellular predominance. This analysis revealed that a significantly higher number of T lymphocytes expressed EOMES and NFATC2, a moderate number expressed TBX21 (T-bet) and a lower proportion expressed PRDM1 (BLIMP-1; Fig 7A–7L and Fig 8), suggesting that EOMES and NFATC2 constitute the main drivers of the inflammatory response in long-term carrier stallions. Unfortunately, no cross-reactive antibodies against equine IRF1, IRF4, STAT1 and STAT2 could be identified and, therefore, we could not demonstrate the cellular expression of these TFs in inflammatory infiltrates.
To further assess the relationship of this subset of TFs with the DEGs observed in the ampullae of long-term carrier stallions, we identified specific targets and positively correlated genes under their transcriptional regulation based on a database search using the Ingenuity Knowledgebase (IPA) and Immuno-Navigator database [36]. Among their target/correlated genes, several of these were identified in the DEG dataset derived from the ampullae of long-term carrier stallions (Fig 9 and S3 Table). Taken together, we determined that EOMES and NFATC2 constitute the most predominant TFs in inflammatory infiltrates among those analyzed, with several DEGs under their transcriptional regulation at the site of persistence.
Since chronic, persistent viral infections have often been associated with the development of T-cell exhaustion and we identified a set of TFs that could also be associated with this process (namely EOMES, NFATC2, PRDM1 [BLIMP-1], IRF4 and BATF), we hypothesized that a similar immune process is likely to be involved in the reproductive tract during EAV long-term persistence. Therefore, we evaluated the gene expression profile of several inhibitory receptors previously identified to be associated with this T-cell exhaustion [37–39]. While whole transcriptome analysis identified an upregulation of CD160 and CD274 (programmed cell death 1 ligand 1 [PD1-L1]) along with the aforementioned TFs in long-term carrier stallions (Table 2), analysis of other receptors of interest (i.e. programmed cell death 1 [PDCD1 or PD-1], CTLA-4, CD244, lymphocyte-activation gene 3 [LAG3], T-cell immunoglobulin and mucin-domain containing-3 [HAVCR2 or TIM-3] and semaphorin 7A [SEMA7A]) was hampered by low coverage of these genes in the short-term carrier stallions. Therefore, we used RT-qPCR to detect the expression of these genes in the inflammatory infiltrates of long-term and short-term persistently infected stallions. Interestingly, a significant expression of CD160, CD244, CD274, CTLA-4, HAVCR2, PDCD1 and SEMA7A mRNA was evident in both long-term (high expression, p-values ≤ 0.0045) and short-term carrier (intermediate expression, p-values ≤ 0.0334) compared to naïve stallions (Fig 5A). LAG3 was uniquely upregulated in the ampullae of long-term carrier stallions (p-values < 0.0001), with no statistically significant differences between short-term carrier and naïve stallions (p-value = 0.2729). IHC analysis demonstrated a significantly higher number of CTLA-4+ T lymphocytes in inflammatory infiltrates of long-term carrier stallions, while these were very low in tissues derived from short-term carrier or naïve stallions (p-values < 0.05, Fig 4G–4I).
Finally, we analyzed the expression of two TFs (activator protein 1 [AP-1 or JUNB] and Fos proto-oncogene [FOS or AP-1 transcription factor subunit]), previously shown to be downregulated during T-cell exhaustion [40]. No significant differences were identified in the relative expression of JUNB between groups (p-values > 0.05) and, although expression of FOS was significantly higher in long-term (p-value = 0.0162) and short-term carrier (p-value = 0.0253) compared to naïve stallions, no differences were observed between the former two groups (p-value = 0.5895, Fig 5A). In summary, we have identified a strong upregulation of inhibitory receptor transcripts and a higher number of CTLA-4+ T lymphocytes in inflammatory infiltrates of long-term carrier stallions, while no alterations in the expression of AP-1 related TFs were observed.
Previously, we have demonstrated that the expression of CXCL16 is enhanced in the reproductive tract of long-term persistently infected stallions [30]. In agreement with this observation, whole transcriptome analysis of the ampullae identified the differential upregulation of CXCL16 in this group compared to short-term carrier and naïve stallions (FDR ≤ 0.00005, Table 2). RT-qPCR and RNAscope in situ hybridization (ISH) were used to quantify and localize the expression of CXCL16 in the ampullae. As previously reported, CXCL16 expression was abundant in the glandular epithelium and inflammatory infiltrates of long-term carrier compared to short-term carrier stallions (median ISH score = 4, p-value = 0.0256, Fig 10A–10C and Fig 10G). Interestingly, the highest expression was observed in the luminal epithelium and inflammatory infiltrates adjacent to the lumen (Fig 10C). Similarly, we evaluated the expression of CXCR6 at the site of persistence. Even though the expression of CXCR6 mRNA was comparatively lower to that of CXCL16 mRNA (Fig 10D–10G), RT-qPCR and RNAscope ISH analysis demonstrated an evident upregulation of CXCR6 in long-term carrier stallions compared to the other groups (p-values ≤ 0.0093 and p-values < 0.05, respectively [Fig 10G]). While CXCR6 expression in short-term carrier stallions was higher than in naïve stallions as determined by RT-qPCR (p-value = 0.0086), no significant differences were identified by RNAscope ISH (p-value > 0.05; Fig 10G). In contrast to CXCL16, CXCR6 was solely expressed by lymphocytes present in inflammatory infiltrates and co-localized with CXCL16 mRNA signal (Fig 10C and Fig 10F). Since CXCL16/CXCR6 axis is associated with the activation of the mammalian target of rapamycin/RAC-alpha serine/threonine protein kinase (mTOR/Akt) signaling pathway [41], we evaluated the expression of phosphorylated Akt (pAkt) in the ampullae of long-term carrier stallions. While its expression was high and variable (ranging from scattered to widespread) in glandular epithelial cells from both long-term and short-term carrier stallions, a significantly higher number of pAkt+ inflammatory cells were observed in the former group (p-value = 0.0094, Fig 7M–7O).
To determine the relationship of CXCL16 and CXCR6 in the context of the DEGs identified in long-term persistently infected stallions, we retrieved a list of genes positively correlated with both CXCL16 and CXCR6 (r ≥ 0.5) from the Immuno-Navigator database [36] and intersected it to the DEGs. Among 1,309 genes positively correlated with CXCL16 (r ≥ 0.5) that were retrieved from the database, 59 were differentially expressed in long-term persistently infected stallions (Table 3). Toll-like receptor 8 (TLR8), integrin subunit alpha X (ITGAX, also known as CD11c), TYRO protein tyrosine kinase binding protein (TYROBP) and NOD-like receptor family CARD domain containing 4 protein (NLRC4) were among the strongly correlated genes (r ≥ 0.8; Table 3 and S4 Table). Similarly, among 192 genes positively correlated with CXCR6 (r ≥ 0.5) that were retrieved from the database, 45 were differentially expressed in this group (Table 3 and S4 Table). Even though the correlation to CXCR6 was moderate (r ≤ 0.75), several granzymes and effector molecules, TFs and T cell immune synapse components were identified. Interestingly, the expression of the chemokine receptor CXCR3 was positively correlated to that of CXCR6. In summary, we demonstrated the upregulation of CXCL16 and CXCR6 at the site of persistence in long-term EAV carrier stallions, with expression of CXCL16 in glandular epithelial cells and lymphocytes, whereas the expression of CXCR6 was restricted to lymphocytic infiltrates. Intersection of genes positively correlated to CXCL16/CXCR6 axis obtained from public databases with our DEG dataset demonstrated that several of these genes were upregulated in the ampullae during long-term viral persistence and, therefore, directly or indirectly associated with the CXCL16/CXCR6 axis.
Recently, it has been determined that long-term persistent infection is associated with the presence of the dominant CXCL16S allele (CXCL16S/CXCL16S or CXCL16S/CXCL16R). Among the EAV-infected stallions used in this study (n = 9), all long-term carrier stallions (n = 3) were either homozygous or heterozygous for the CXCL16S allele (Table 1). While the majority of the short-term carrier stallions were homozygous for the CXCL16R allele (4/6, Table 1), 2/6 were heterozygous for the CXCL16S allele. Even though the genotype-trait association is strongly correlated, some stallions carrying the CXCL16S allele can stop viral shedding earlier in the course of infection for reasons that remain to be determined. When performing differential gene expression analysis based on whole transcriptome analysis from the ampullae following EAV infection, we observed that differences in the gene expression pattern were likely linked to the genetic background of the animal (i.e., CXCL16 genotype, Fig 3A and Fig 11A). Therefore, we performed differential gene expression analysis between stallions that were homozygous or heterozygous for the CXCL16S allele (n = 5) and those homozygous for the CXCL16R allele (n = 4). A total of 542 DEGs were identified between genotype groups (FDR < 0.1), among which 188 genes (34.7%) were upregulated (log2 fold-change > 1 over CXCL16R/CXCL16R group) and 272 genes (50.2%) were downregulated (log2 fold-change < -1 over CXCL16R/CXCL16R group) in the ampullae of stallions carrying the CXCL16S allele (Fig 11A). The remaining DEGs (n = 82) presented a fold-change between 1 and -1. Functional annotation analysis of upregulated genes in the CXCL16S group demonstrated their involvement in biological processes associated with the immune response, with molecular functions mostly related to binding, catalytic, receptor and chemokine activity (S3 Fig). As observed for long-term carrier stallions, the upregulated genes were mostly involved in immune-related pathways, specifically related to T lymphocyte and NK cell responses. Conversely, downregulated genes in the CXCL16S group were associated with cell adhesion and extracellular matrix organization with relevant pathways related to integrin signaling, focal adhesion/cadherin signaling, extracellular matrix-receptor interaction and cytoskeletal regulation. Taken together, DGE analysis between CXCL16S and CXCL16R stallions demonstrated that the gene expression profile in the ampullae is driven by the host genotype, with a clear majority of upregulated genes associated with immune response functions.
Co-expression network analysis allows the identification of groups of coordinately expressed genes (modules), which may represent specific transcriptional networks. Network analysis is therefore based on correlation analysis, whereby highly correlated genes often share biological features and these gene modules can be considered as “gene circuits” responsible for specific cellular functions [42–44]. Furthermore, network analysis allows the identification of highly connected “hub” genes within modules that likely represent control points. Since differential gene expression analysis may not explain all transcriptional interactions, we performed WGCNA in order to identify gene co-expression patterns associated with the CXCL16 genotype and the CD3+ T lymphocyte susceptibility phenotype. A set of 12,303 genes were analyzed using WGCNA package in R (Department of Human Genetics and Department of Bioinformatics, University of California Los Angeles, Los Angeles, CA) [45, 46] and the percentage of susceptible CD3+ T lymphocytes to in vitro EAV infection was used as the quantitative trait (Table 1) [28, 31]. Network construction and module detection elicited a total of 24 modules (color-coded, Fig 11B–11D) with a range of 35 to 1,535 genes. Module eigengenes (ME, defined as the first principal component of a given module) were computed for each module and considered as a representative of the gene expression profiles in a specific module. Based on their module eigengenes, several modules showed a positive correlation with other modules in the network and clustered together in the eigengene dendrogram (S4 Fig).
To identify gene modules correlated with the CD3+ T lymphocyte susceptibility phenotype and CXCL16 genotype, we tested the correlation between the ME and the trait (presence of CD3+ T cell susceptibility to EAV infection as demonstrated by flow cytometric analysis) [28, 31]. Two gene modules (namely blue [n = 1,198 genes] and lightyellow [n = 130 genes]) were positively correlated to the trait (r > 0.5, p-value < 0.05; Fig 11E) and clustered together in the eigengene dendrogram (S4 Fig). GO analysis (biological process) for the gene list derived from the blue module (S5 Table) demonstrated that these genes were involved in biological processes associated with the immune response. Interestingly, this module gathered several of the TFs shown to be associated with long-term EAV persistence including IRF1, IRF4, BATF, EOMES, PRDM1, STAT1 and STAT2, among others. While no statistically significant GO terms were retrieved for the genes in the lightyellow module, it also contained immune-related genes among which TBX21 and NFATC2 were observed (S5 Table).
In order to identify the gene members that likely drive module expression, we selected highly connected “hub” genes within these modules. For this purpose, we set cut-off values for module membership values (MM ≥ 0.90), significance (p-value < 0.05) and gene significance (GS ≥ 0.5) (S6 Table) [47, 48]. MM values are computed for each gene as the correlation of its expression profile with the module eigengene of a given module and are highly related to the intramodular connectivity [45]. Since highly connected intramodular “hub” genes tend to have a high MM within their module, this measure can be used to identify “hub” genes [45]. Gene significance is the correlation of each gene’s expression profile to the trait and, thus, can be used as a measure of the biological relevance of a gene with respect to the trait of interest. Interestingly, among the top 20 “hub” genes (MM ≥ 0.95) in the blue module, we identified CXCL16 and several other immune-related genes (Table 4 and S6 Table), which were also among CXCL16 first neighbors (Fig 12A and 12B). The lightyellow module contained other important immune “hub” genes including AT-hook transcription factor (AKNA), Runt-related transcription factor 3 (RUNX3), TBX21, NLRC3 and NLRC5, among others (Table 4, Fig 12A and Fig 12C and S6 Table).
Finally, since TFs are intricately associated with the control of gene expression networks, we performed a weighted-gene co-expression analysis specifically on 494 TFs derived from CXCL16S stallions. Network construction and module detection elicited a total of five modules with a range of 51 to 188 genes. From the module eigengene dendrogram, we clearly identified a module (green, 51 genes [S7 Table and S4 Fig]) with a different co-expression pattern and which contained several TFs of interest involved in the T lymphocyte-mediated response including EOMES, IRF1, IRF4, TBX21, PRDM1 and NFATC2 (S7 Table). “Hub” TFs in the green module (MM ≥ 0.90 and p-value < 0.05,) can be depicted in Fig 12D. Interestingly, 5/12 TFs were differentially expressed between CXCL16S and CXCL16R (homozygous) stallions and presented a high degree of connectivity (Fig 12D). In summary, we identified two specific gene modules positively correlated with the CD3+ T lymphocyte susceptibility phenotype and the CXCL16 genotype. We determined that CXCL16 is clearly a “hub” gene and highly interconnected within a transcriptional module encompassing diverse immune-related genes including TFs associated with EAV persistence.
The mechanisms by which EAV persists in the male reproductive tract are not fully understood and have been recently the subject of extensive investigation in our laboratory [23–25, 28–30]. It has been demonstrated that long-term persistence is associated with the presence of a specific allele encoding the chemokine CXCL16 (namely, CXCL16S), with a dominant pattern of inheritance and with EAV receptor activity [26, 28, 29]. Additionally, EAV has the ability to persist despite the induction of a strong systemic immune response and local inflammatory and mucosal antibody responses at the site of persistence [23, 24]. Recent studies showed that long-term persistence is associated with an upregulation of CXCL16 at the site of persistence along with a downregulation of the microRNA eca-mir-128 in seminal exosomes, a putative modulator of CXCL16 [30]. These provide a strong premise that EAV employs a complex strategy to evade host immunity and that host factors play a critical role in long-term viral persistence. However, the immunopathogenesis of viral persistence in the reproductive tract remains to be elucidated (Fig 13). We hypothesized that long-term EAV persistence induces a specific immunological microenvironment in the stallion reproductive tract that facilitates evasion of host immunity. The study presented herein is the first one undertaken to evaluate transcriptional changes following EAV infection, explicitly providing insight into the molecular elements driving the local host response during long-term viral persistence in the stallion reproductive tract.
Global transcriptome analysis of the ampullae derived from long-term carrier stallions along with inflammatory cell immunophenotyping revealed a T lymphocyte-mediated response predominantly driven by a CD8+ T lymphocyte infiltration and functionally evidenced by the upregulation of CD8+ T lymphocyte-specific transcripts including effector molecules (e.g. granzymes and Fas ligand), chemokines/cytokines (e.g. CCL2, CCL5, IFNG, TNFA) and TFs (e.g. BATF, EOMES, NFATC2, TBX21, IRF1, IRF4, STATs). Additionally, there is transcriptional evidence that support the involvement of infiltrating CD4+ T lymphocytes in the Th1 pathway. While a higher number of DEGs were observed between long-term carrier and naïve stallions, there was an overlap of common DEGs in long-term and short-term carrier stallions. These overlapping (upregulated) genes were mostly associated with adhesion properties and leukocyte migration and, therefore, likely involved in both the active inflammatory response and in its resolution in the long-term carrier and those stallions that cleared EAV infection, respectively. Both experimentally and naturally infected long-term carrier stallions used in this study were infected with the same strain of EAV (EAV KY84) and no differences were observed in regard to neutralizing antibody levels in serum and semen, viral output in semen, number of EAV-infected cells and viral tropism within the reproductive tract, inflammatory response, expression levels of seminal exosome-associated miRNA eca-mir-128, and the gene expression profile in the ampullae among these stallions [23, 30]. A recent study on EAV intra-host evolution also demonstrated that EAV KY84 evolutionary rate and genomic sites under selective pressure in the reproductive tract are similar between these experimentally and naturally infected stallions [49].
Interestingly, immunophenotypic characterization of the inflammatory infiltrates in long-term carrier stallions confirmed the scattered inflammatory cells expressing AR. EAV persistence is testosterone-dependent [8, 22] and the immunomodulatory effects of androgens in virus-specific adaptive immune responses have been extensively studied with other viruses [50–52]. The expression of AR in the inflammatory infiltrates clearly indicates that androgens can have a direct impact in the modulation of local effector functions and, thus, in the maintenance of persistently infected cells. However, further studies are required to elucidate the role of AR-responsive elements and testosterone during persistent infection.
Chronic viral infections can progressively trigger CD8+ T lymphocyte hyporesponsiveness (T-cell exhaustion, T-lymphocyte dysfunction or anergy) [38, 39, 53]. This is related to the hierarchical loss of CD8+ T lymphocyte functional properties including proliferation, cytokine production (e.g., IFN-γ and IL-2) and cytolytic responses [38, 39]. Although the molecular signatures of T-cell exhaustion are not fully comprehended [38–40, 42, 53–55], the overexpression of cell surface inhibitory receptors (e.g., PD-1, CTLA-4 and others) mainly mediates CD8+ T lymphocyte dysfunction [38, 39, 56]. Here, we demonstrated the upregulation of inhibitory receptor transcripts including PDCD1 and its ligand, PD-L1 (CD274) and TFs (EOMES, NFATC2 and PRDM1) that may be involved in the inability of local CD8+ T lymphocytes to clear EAV infection. The similar expression levels of IL2 between long-term and short-term carrier stallions and lack of Ki67 expression are indicative of the limited proliferative capacity of local CD8+ T lymphocytes. Surprisingly, we observed an upregulation of IFNG and TNFA transcripts and granzymes during long-term persistence. Previous studies have reported a similar trend despite the poor ex vivo cytotoxic activity of CD8+ T lymphocytes and suggested that alterations in vesicle trafficking and/or cytoskeletal reorganization could be responsible for the poor cytotoxic activity [40]. Even though GO enrichment analysis on downregulated genes from long-term carrier stallions did not retrieve statistically significant biological processes, these genes are likely involved in mesenchyme migration, cell-substrate junction assembly and actin filament organization. Therefore, additional studies to understand the relationship between vesicle transport/cytoskeletal reorganization and CD8+ T lymphocyte functionality during EAV persistence are required. In order to fully comprehend the functional status of local CD8+ T lymphocytes during persistence, future studies will be focused on immunophenotyping purified CD8+ T lymphocytes derived from the ampullae via multicolor flow cytometry and single-cell RNAseq combined with analysis of epigenetic modifications and functional assays (i.e. cytotoxicity and cytokine production) to assess local CD8+ T lymphocyte functionality ex vivo.
In this study, we identified the predominance of the CD8+ T lymphocyte-associated TFs EOMES and NFATC2 during long-term persistence. EOMES and TBX21 (T-bet), two T-box transcription factors, are key drivers of CD8+ T lymphocyte differentiation and their balance is essential for functional differentiation [57]. Interestingly, it has been demonstrated that CD8+ T-betdim EOMEShigh T lymphocytes express high levels of surface inhibitory receptors with poor effector functions during chronic human immunodeficiency virus infection [58]. Along with other members of the NFAT family, NFATC2 plays a versatile role during both T cell activation and T cell hyporesponsiveness in its monomeric/homodimeric form as well as by formation of NFAT::AP-1 and NFAT::FOXP3 TF complexes [54]. While the cooperation of NFAT/AP-1 leads to a functional immune response, its imbalance can induce anergy or exhaustion and the lack of cooperation with AP-1 strongly drives expression of exhaustion-related inhibitory receptor genes [54, 59]. Also, NFATC2 cooperates with IRF4 at key genomic loci that control the transcriptional signature of exhausted T cells [55]. Consequently, it is reasonable to hypothesize that these two overabundant TFs may govern CD8+ T lymphocyte hyporesponsiveness during long-term EAV persistence in the reproductive tract. Further studies to identify their direct targets in the reproductive tract via chromatin immunoprecipitation sequencing (ChIP-seq) and elucidate their functional relationship using siRNA-mediated gene silencing on purified CD8+ T lymphocytes derived from the ampulla of long-term persistently infected stallions are warranted.
It has been previously suggested that EAV-infected lymphocytes present a restricted homing pattern allowing them to persist within the male reproductive tract [23]. Here, we identified a group of C-X-C motif chemokines and receptors associated with T lymphocyte homing. Specifically, CXCL9, CXCL10 and CXCL11, and their receptor, CXCR3, may likely be associated with specific lymphocyte homing into reproductive tract tissues as observed following Chlamydia trachomatis infection [60]. However, the predominance of CXCL16 and its receptor, CXCR6, is a distinctive feature of long-term EAV persistence and likely involved in recruitment of CD8+ T lymphocytes and other inflammatory cells into the site of persistence [61–64]. The expression of CXCL16 in the glandular epithelium and lymphocytic infiltrates along with its co-localization with CXCR6+ lymphocytes closely associated with the glandular epithelium emphasize their likely functional relationship. Given the presence of intra- and sub-epithelial lymphocytes and the close association of infected lymphocytes with the glandular epithelium [23], it is likely that the CXCL16/CXCR6 chemokine axis drives the chemotaxis of infected lymphocytes through the epithelial lining and contributes to the process of viral shedding in semen. Additional in vitro studies evaluating the role of the CXCL16/CXCR6 chemokine axis in chemotaxis of purified CD8+ T lymphocytes derived from the ampulla of persistently infected stallions will involve the use of recombinant CXCL16S and CXCL16R isoforms as well as anti-CXCR6 and pertussis toxin as a Gi-protein coupled receptor inhibitor for CXCR6. Even though the mucosal epithelium expresses high levels of CXCL16, recent studies demonstrated that virus is not harbored in the epithelial cells of the ampullae or other accessory sex glands [23]. This observation is supportive of the fact that ongoing studies in our laboratory suggest that EAV requires additional cellular entry factors, among which vimentin seems to play an important role. Thus, the lack of vimentin expression in the mucosal epithelium may be determinant for the lack of susceptibility of CXCL16+ epithelial cells in the ampullae to EAV infection [23]. While initial experiments involved vimentin knock-out and overexpression of CXCL16 in HEK293 cells, additional in vitro experiments using equine-derived cell lines (equine endothelial cells and ampulla-derived fibroblasts) are in progress.
Recent studies demonstrated the strong association between the CXCL16 genotype and the establishment of long-term EAV persistence [28, 29]. Herein, we showed that the host’s genotype also drives the gene expression profile in the ampulla following EAV infection. While EAV did not establish long-term persistence in 2/5 CXCL16S stallions following experimental infection, their gene expression profile had a similar trend to that observed in long-term persistently infected, CXCL16S stallions. Since the correlation is not complete [29], other factors such as breed, differences in immune responses, epigenetic modifications, among others may be responsible for clearance of EAV infection and are under investigation. Among these, we have previously demonstrated that the CXCL16S variant of CXCL16 and the CD3+ T lymphocyte susceptibility phenotype associated with this allelic variant occur across horse breeds, although differences in penetrance between different breeds were observed [27, 29]. These differences also concur with the varying seroprevalence of EAV between breeds and imply that this trait has most likely appeared in a common ancestor to current horse breeds [27]. This suggests that mutations in CXCL16 (CXCL16S) have permitted EAV adaptation to the horse population and, despite differences in penetrance, it has promoted the maintenance of EAV across horse breeds worldwide. Here, we demonstrated that the CXCL16 genotype shapes the transcriptome profile in the ampullae; however, the use of mixed breed stallions precludes analysis of breed-specific differences. Further studies comparing the gene expression profiles between groups of EAV-infected stallions belonging to breeds with high and low allelic frequencies for CXCL16S are required to understand breed influence in the transcriptome.
The identification of genes correlated with the CXCL16/CXCR6 axis in our transcriptome dataset and abundance of CXCL16+ and CXCR6+ cells determined that this axis is significantly involved in shaping the local inflammatory response. Network analysis detected two important modules of co-expressed genes significantly correlated with the CD3+ T lymphocyte susceptibility trait and, thus, with the CXCL16 genotype. This analysis identified that CXCL16 is a “hub” gene, providing strong evidence that this chemokine plays a critical role in the local CD8+ T lymphocyte response along with other co-expressed gene neighbors that are involved in this specific gene module. This module included several immune-related genes associated with long-term EAV persistence, suggesting its significant implication in the regulation of the local immune response. Further studies are required to fully comprehend the gene interactions within this specific module and the functional role of CXCL16. Similarly, we defined a TF network in CXCL16S stallions and identified that most of the TFs identified by TF site enrichment and quantitative IHC analyses were related members of a single TF module. Taken together, these findings indicate that there are specific transcriptional modules strongly associated with CXCL16 and a network of closely related TFs that cooperatively drive the gene expression profile during EAV persistence. However, additional studies to understand their functional relationship as well as their role in the modulation of the CXCL16/CXCR6 axis in the reproductive tract are warranted.
Whether EAV persistence is the result of the specific immunological environment in the reproductive tract (i.e., ampullae) or EAV infection leads to the conditioning of a specific immune milieu in the reproductive tract of persistently infected stallions that favors its long-term maintenance is still to be determined. However, this as well as previous studies from our laboratory strongly support the latter, suggesting that persistent EAV infection shapes multiple aspects of the immunological microenvironment within the reproductive tract (i.e. the CD8+ T lymphocyte response and the chemokine/chemokine receptor profile) that favors the maintenance of long-term persistence in stallions with CXCL16S genotype. Further sequential (time-course) in vivo studies evaluating the process of establishment of EAV persistence are warranted in order to completely understand the immunopathogenesis of persistent infection in the stallion reproductive tract and the role of testosterone during this process.
In conclusion, the study presented herein identified that the local CD8+ T lymphocyte response during EAV persistent infection is orchestrated by specific TFs (mainly EOMES and NFATC2) and likely modulated by the upregulation of inhibitory receptors. Most importantly, this study provides further evidence of the implication of CXCL16 and its receptor, CXCR6, in the pathogenesis of persistent EAV infection and a linkage between the CXCL16 genotype and the gene expression profile driven at the site of persistence (Fig 13). These findings strongly suggest that EAV exploits the CXCL16/CXCR6 chemokine axis in order to modulate local immune responses at the site of persistent infection. However, this response is complex and likely involves not only this chemokine axis but also CD8+ T lymphocyte hyporesponsiveness associated with either anergy or T-cell exhaustion. It is yet to uncover how these complex host factors, including the recently identified seminal exosome eca-mir-128, are functionally involved in the modulation of viral persistence in the stallion reproductive tract.
This study was performed in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health. The Institutional Animal Care and Use Committee (IACUC) at the University of Kentucky, Lexington, KY approved this protocol (number 2011–0888). Stallions were humanely euthanized by pentobarbital overdose following the American Veterinary Medical Association (AVMA) guidelines for the euthanasia of animals, and all efforts were made to minimize suffering.
Peripheral blood mononuclear cells (PBMCs; University of Kentucky, Lexington, KY) were cultured in complete RPMI medium as previously described [31]. The virulent Bucyrus strain (VBS) of EAV [65] was used for in vitro infection of PBMCs for CD3+ T lymphocyte phenotyping while the KY84 strain of EAV (passage 1 in equine endothelial cells [EECs], University of Kentucky, Lexington, KY) [16] was used for experimental infection of stallions as previously described [23, 31].
A total of 12 adult (age ranging between 4–20 years old), sexually mature stallions were obtained from an outside vendor and used in the study, including naïve (n = 3), EAV experimentally (n = 8) and naturally (n = 1) infected (Table 1) [23, 24]. These were maintained at the UK Maine Chance Farm, University of Kentucky, Lexington, KY and confirmed seronegative (titer <1:4) for antibodies to EAV before initiation of the study according to the World Organisation for Animal Health (OIE) standardized virus neutralization test as previously described [18, 23]. Following experimental infection and monitoring for 726 days postinfection (see below), 2/8 stallions were classified as long-term persistently infected (long-term carriers, duration of viral shedding in semen >1 year) while 6/8 stallions were classified as short-term carriers (duration of viral shedding in semen approximately ≤1 year). In addition, a long-term, naturally infected carrier stallion was included in the study. A control group of naïve stallions (n = 3) remained unexposed and unvaccinated against EAV.
Eight stallions (L136 –L143) were intranasally inoculated in October 2011 with 3.75 x 105 plaque-forming units per ml [PFU/ml] of tissue culture fluid containing the KY84 strain of EAV (passage 1 in EECs, University of Kentucky, Lexington, KY) [16] and delivered in 5 ml EMEM using a fenestrated catheter and monitored as previously described [12, 18, 23]. Clinical sample collection including semen samples to monitor viral persistence was performed as previously described [13, 18, 23, 24].
Stallions were humanely euthanized by pentobarbital overdose following the American Veterinary Medical Association (AVMA) guidelines for the euthanasia of animals. Necropsy examination and tissue collection was performed two years post-infection (726 dpi). For this study, the ampullae was collected and used for downstream experiments. In order to collect comparable sections across individual stallions, the length of the ampulla was determined and 1 cm section at its midpoint was bilaterally and aseptically collected from each stallion during postmortem examination and archived at -80°C, snap-frozen in O.C.T. compound (Tissue-Tek, Sakura Finetek U.S.A., Torrance, CA) and stored at -80°C, or formalin-fixed and paraffin-embedded (FFPE) as previously described [23]. These tissues were subsequently used for all experimental procedures.
Sections of formalin-fixed paraffin-embedded (FFPE) tissues (5 μm) were stained with hematoxylin and eosin (H&E) following a standard laboratory procedure prior to histological evaluation. Tissue sections were scrutinized by an experienced veterinary pathologist who was blinded as to the carrier status of the stallions, and a morphological diagnosis was provided as previously described [23].
A panel of monoclonal and polyclonal antibodies against several cellular markers and transcription factors were utilized for immunohistochemical (IHC) staining (Table 5). IHC staining was performed using the Bond Polymer Refine Detection kit or the Bond Polymer Refine Red Detection kit (Leica Biosystems, Buffalo Grove, IL) as described below. A goat anti-mouse IgG conjugated with Alexa Fluor 488 (Life Technologies, Grand Island, NY) was used for immunofluorescence. Dual immunofluorescence staining for flow cytometric analysis was performed with mouse monoclonal anti-equine CD3 (clone UC F6G, University of California Davis, Davis, CA) and anti-EAV non-structural protein 1 (nsp1, clone 12A4) directly conjugated with Alexa Fluor 488. A goat anti-mouse IgG1 conjugated to R-phycoerythrin (R-PE) was used as a secondary antibody for the anti-equine CD3 (Southern Biotech, Birmingham, AL).
Stallions were genotyped based on the presence of 4 single nucleotide polymorphisms (SNPs) located in the CXCL16 gene (CXCL16S and CXCL16R) by Sanger sequencing as previously described [29] and by the use of an allelic discrimination TaqMan real-time PCR (Table 1).
Peripheral blood mononuclear cells (PBMCs) were obtained by gradient centrifugation using Ficoll-Paque PLUS (GE Healthcare, Little Chalfont, UK) as previously described [31]. A total of 1x107 PBMCs were infected with the VBS strain of EAV at a multiplicity of infection (MOI) of 2 for 36 h as described and subsequently dual stained for flow cytometric analysis [31]. Mock-infected PBMCs were used as negative controls and cultured under identical conditions. A total of 1x106 cells were stained with anti-equine CD3 for 30 min followed by an R-PE conjugated goat anti-mouse IgG1 and subjected to intracellular anti-EAV staining using an anti-nsp1 antibody conjugated to Alexa Fluor 488 as previously described [31]. Acquisition was performed on a FACScalibur (BD, San Jose, CA). Lymphocytes were gated based on forward and side scatter parameters, and analyzed by two-color plots of anti-EAV nsp1 (FL-1) versus CD3 surface antigen (FL-2). The percentage of dual-positive (CD3+nsp1+) cells was determined by quadrant statistics using BD CellQuest (Table 1).
Total RNA was isolated from cryosections of ampullae (naïve stallions, n = 3; short-term carrier, n = 6 and long-term carrier stallions, n = 3). Briefly, ten μm frozen sections (5 to 6) were collected into 1.5 ml tubes using a cryostat (Leica Biosystems) and immediately placed on dry ice. Subsequently, total RNA was isolated using an RNeasy Micro kit (Qiagen) following the manufacturer’s recommendations, and included an on-column DNase treatment. RNA yield was determined by fluorometry (Qubit RNA HS Assay kit, ThermoFisher Scientific) and quality was assessed using an Agilent RNA 6000 Pico kit (Agilent Technologies, Inc.) according to the manufacturer’s instructions.
Isolated RNA was submitted to Exiqon Services (Vedbæk, Denmark) and Qiagen Genomic Services (Hilden, Germany) for library preparation and sequencing, respectively. Library preparation was performed using the TruSeq stranded total RNA sample preparation kit with rRNA depletion (Illumina Inc., San Diego, CA). Libraries for each batch were pooled in equimolar ratios and sequenced on two High Output NextSeq500 runs with 2x50bp paired-end read length (2x30 million reads/sample) plus 2x8bp for demultiplexing. After sequencing, raw data were demultiplexed using the bcl2fastq v. 2.17 software (Illumina Inc.). FastQ for each of the two were combined and QC was performed using the FastQC software package v. 0.10.1 (Babraham Bioinformatics, Babraham Institute, Cambridge, UK).
Adaptor trimming and quality control were performed using TrimGalore Version 0.4.4 (Babraham Bioinformatics) and reads were subsequently aligned to the Equus caballus reference genome (EquCab2.0) [66] using Burrows-Wheeler Aligner (bwa; Version 0.7.12) [67]. Reads were annotated to the equine reference transcriptome available in the Ensembl database (EquCab2.88; www.ensembl.org) using Cufflinks (Release 2.2.1) [68]. Read counts were normalized as fragments per kilobase of exon per million mapped reads (FPKM) and differential gene expression analysis was performed on normalized read counts. Cuffdiff was used to perform differential gene expression analysis between experimental groups (pairwise comparisons): naïve vs long-term carrier stallions, naïve vs short-term carrier stallions, long-term carrier stallions vs short-term carrier stallions and CXCL16S (homozygous and heterozygous) stallions vs CXCL16R (homozygous) stallions [68]. Significance was established if the false discovery rate (FDR) was <0.1. DEGs were defined by FDR < 0.1 and log2 fold-change > 1 (upregulated) or < 1 (downregulated). Mapping statistics are shown in S8 Table.
To investigate the molecular and biological functions of differentially expressed genes and those derived from modular analysis, DAVID Bioinformatics Resources (version 6.8 [https://david.ncifcrf.gov/], National Institute of Allergy and Infectious Disease [NIAID], National Institutes of Health [NIH]) [33, 69] along with the PANTHER classification system (www.pantherdb.org) [32] were used to functionally annotate genes based on gene ontology (biological process, molecular function and protein class). Ingenuity Pathway Analysis (IPA, Qiagen) was used to perform canonical pathway analysis, upstream regulator analysis with a special emphasis on transcription factors and identification of the most significant molecular networks.
Differentially expressed genes between long-term and short-term carrier stallions were analyzed using a recently developed transcription factor analysis software (CiiiDER, Centre for Innate Immunity and Infectious Diseases, Hudson Institute of Medical Research, Victoria, Australia) [35]. CiiiDER can identify over-represented transcription factors that may be potentially playing an important role in regulating the genes of interest by comparing the predicted binding sites present in a list of co-regulated genes to those found in an appropriate background list of genes. A background gene list was generated by selecting genes that were not differentially expressed (FDR > 0.1) and had a very low fold-change (log2 fold-change between -1 and 1) as recommended. Enrichment analysis was performed as suggested and a Fisher’s exact test built-in the algorithm was used to identify significantly over-represented (enriched) transcription factors [35].
Additionally, total RNA was isolated from ampullae for RT-qPCR analysis using the RNeasy Mini kit (Qiagen) according to the manufacturer’s recommendations as previously described [30], including on-column DNase treatment.
Expression levels of selected genes were determined by RT-qPCR (S9 Table). Total RNA was reverse transcribed using the High Capacity cDNA Reverse Transcription kit (ThermoFisher Scientific) following the manufacturer’s recommendations. The reaction (20 μl) included 10 μl of total RNA (2 μg), 2 μl of 10X RT Buffer, 0.8 μl 25X dNTP Mix (100 mM), 2 μl of 10X RT Random Primers, 1 μl of MultiScribe Reverse Transcriptase, 1 μl of RNase inhibitor and 3.2 μl of nuclease-free water. The reverse transcription reaction was incubated for 10 min at 25°C, followed by 120 min incubation at 37°C and a final step at 85°C for 5 min. cDNA was diluted 1:5 in nuclease-free water and stored at -20°C until used. For qPCR, the PowerUp SYBR Green Master Mix (ThermoFisher Scientific) was used. Briefly, 1 μl of cDNA was added to a reaction volume (9 μl) containing 2X PowerUp SYBR Green Master Mix (5 μl), assay-specific forward and reverse primers (0.25 μl of a 20 μM stock, final concentration 0.5 μM for each primer) and RNase-free water (3.5 μl). The cycling conditions included an initial UDG (50°C for 2 min) and PCR activation steps (95°C for 2 min) followed by 40 cycles of denaturation at 95°C for 15 s and annealing/extension at 60°C for 1 min. Melt curve analysis was performed to check for non-specific amplifications along with the inclusion of non-template controls. Specific forward and reverse primers were designed using the Primer-BLAST tool (NCBI, NIH) and primer sequences and gene accession numbers are shown in S9 Table. qPCR efficiencies and Ct values were determined using LinRegPCR v2017.0 [70], with efficiencies in the range of 90.2–100.2%. qPCR reactions were performed in duplicate and Ct values >37 were not used for analysis. Gene expression data were normalized to three housekeeping genes (GAPDH, ACTB, and GUSB) as previously described [30].
CXCL16 and CXCR6 expression was evaluated by TaqMan quantitative real-time PCR. Reverse transcription was performed as indicated above. Complementary DNA was diluted 1:1 in nuclease-free water and stored at -20°C until used. A custom TaqMan Gene Expression assay for equine CXCL16 was used as previously described [30]. In addition, a custom TaqMan Gene Expression assay for equine CXCR6 was developed by a commercial company (ThermoFisher Scientific) using the mRNA sequences derived from GenBank accession number XM_005600758.3. The qPCR reaction and cycling conditions were performed as previously described [30]. RT-qPCR efficiencies, normalization and analysis were performed as indicated above.
A CXCL16 mRNA-specific probe based on the GenBank accession number XM_001504756.5 was developed by a commercial company (Advanced Cell Diagnostics [ACD], Newark, CA) as previously described [30]. A CXCR6 mRNA-specific probe based on the GenBank accession number XM_005600758.3 was developed, containing a total of 20 double Z branched DNA probe pairs spanning a target region of 874 bp of the mRNA sequence (nt position 154–1028). The probes were supplied in a ready-to-use format, and their specificity was evaluated using lymphoid tissues (palatine tonsils; University of Kentucky, Lexington, KY), an equine endothelial cell line known to abundantly express CXCL16 as previously described [30] and a stable cell line expressing equine CXCR6 (HEK-CXCR6; University of Kentucky, Lexington, KY) [29]. Probes specific to dihydrodipicolinate reductase B mRNA of Bacillus subtilis and peptidylprolyl isomerase B (PPIB) were used as negative and positive controls to assess assay specificity and RNA integrity in FFPE tissues, respectively. The RNAscope ISH assay was performed using the RNAscope 2.0 HD Red Detection Kit (ACD, Hayward, CA) as previously described [30]. For CXCL16 mRNA-specific ISH, signal in the region of interest was quantified as previously indicated [30]: (0), no staining or <1 dot every 10 cells at 40X magnification; (1), 1–3 dots every 10 cells at 40X magnification; (2), 4–10 dots/cell (visible at 20-40X magnification); (3), >10 dots/cell with scattered cells presenting dot clusters (visible at 20X magnification); (4), >10 dots/cell with frequent cells presenting dot clusters (visible at 20X magnification). For CXCR6 mRNA-specific ISH, signal was quantified as follows: (0), no staining or rare positive (1 dot) cells at 40X magnification; (1), scattered positive cells (1–3 dots) at 40X magnification; (2), 1–3 dots/cell (visible at 20-40X magnification); (3), >3 dots/cell with scattered cells presenting dot clusters (visible at 20X magnification); (4), >3 dots/cell with frequent cells presenting dot clusters (visible at 20X magnification).
For IHC, cryosections (10 μm, only for CD8, CD4 and CTLA-4 antigens) or sections of FFPE tissues (5 μm) were mounted on positively charged Superfrost Plus slides (Fisher Scientific, Pittsburgh, PA) and processed as previously described [23]. Specific retrieval conditions for each antigen are shown in Table 5. For Ki-67, phosphorylated Akt, NFATC2, EOMES, BLIMP-1, T-bet, CD8, CD4, AR and granzyme B immunostaining was performed using the Bond Polymer Refine Detection kit (Leica Biosystems, Buffalo Grove, IL). The slides were incubated with 3% hydrogen peroxide (5 min), followed by incubation with the primary antibody diluted in ISH/IHC Super Blocking (Leica Biosystems) for 1 h at room temperature. This was followed by incubation with a rabbit anti-mouse IgG (8 min) followed by a polymer-labeled goat anti-rabbit IgG conjugated to HRP (8 min) in the case of mouse primary antibodies. In the case of primary antibodies of rabbit origin, tissue sections were directly incubated with the polymer-labeled goat anti-rabbit IgG conjugated to HRP after primary antibody incubation. 3,3'-diaminobenzidine tetrahydrochloride (DAB) was used as the substrate and the slides were incubated for 10 min. For NFATC2 and granzyme B immunostaining, sections were incubated with a ready-to-use copper sulfate solution (DAB Enhancer, Leica Biosystems) to enhance the signal. Finally, sections were counterstained with hematoxylin and mounted as previously described [23, 24, 71]. For CTLA-4, the Bond Polymer Refine Red Detection kit (Leica Biosystems) was used. After incubation with the primary antibody (1h at room temperature), tissue sections were incubated with a rabbit anti-mouse IgG (20 min) followed by a polymer-labeled goat anti-rabbit IgG coupled with alkaline phosphatase (AP, 30 min). Fast Red was used as the chromogen (15 min), and counterstaining and mounting was performed as indicated above. Palatine tonsil sections were used as both positive and negative (irrelevant primary antibody) controls (S5 Fig). Immunostaining was semi-quantitatively scored based on the cumulative number of positive cells in five high magnification (40X) microscopic fields (Table 6). To quantify and compare the predominance of specific transcription factors in inflammatory infiltrates present in long-term carrier stallions, the number of positive cells in five specific inflammatory infiltrates were counted at 100X magnification. Proliferation was assessed specifically on epithelial cells and infiltrating lymphocytes by means of Ki-67 immunostaining [34].
Snap-frozen tissue sections (10 μm) were obtained and stained for CD8 as previously described [23]. Following fixation, permeabilization and blocking, sections were incubated with mouse anti-equine CD8 for 1 h at room temperature in a humidity tray (Table 5), followed by a F(ab')2 fragment of goat anti-mouse IgG conjugated with Alexa Fluor 488 (Life Technologies) diluted 1:200 in 5% normal goat serum (Jackson ImmunoResearch, West Grove, PA) for 1 h at room temperature in a humidity tray. Nuclear counterstaining was performed with a mounting medium containing 4',6-diamidino-2-phenylindole (DAPI; Vector Laboratories, Burlingame, CA) and observed under a Nikon Ti fluorescent microscope (Nikon Corporate, Tokyo, Japan).
Weighted gene co-expression network analysis was used to construct co-expression networks using the R package “WGCNA” (Department of Human Genetics and Department of Bioinformatics, University of California Los Angeles, Los Angeles, CA) as previously described and following the steps shown under WGCNA tutorials [45]. Gene co-expression networks were generated for the whole transcriptome of CXCL16S and CXCL16R stallions (n = 6 per group) and TFs in CXCL16S stallions based on their gene expression profiles. A total of 12,303 genes were selected based on expression in at least 50% of the samples. Following construction of a matrix of pairwise correlations between all pairs of genes across the samples, a weighted adjacency matrix was generated by raising co-expression similarity to a power β = 16 as determined for this sample set. Subsequently, a topological overlap matrix (TOM) was constructed and used as input for hierarchical clustering analysis. Gene modules (i.e. genes with high topological overlap) were identified using a dynamic tree cutting algorithm (deepSplit = 2, cutHeight = 0.25) and visualized by heatmap plot of the topological overlap of the gene network. Module relationships were summarized by a hierarchical clustering dendrogram and heatmap plot of module eigengenes. These steps were performed using the automatic network construction and module detection functions with default parameters except maxBlockSize = 12,500 and minModuleSize = 30. The associations between the gene modules and the trait of interest (percentage of CD3+ T lymphocytes susceptible to in vitro EAV infection/CXCL16 genotype) were tested by correlating MEs to trait measurements. Module-trait associations were visualized using a heatmap plot. Gene ontology analysis (biological process) was performed on gene lists derived from selected modules (r > 0.5 and p-value < 0.05) using DAVID as indicated above. Module memberships (MM, i.e. the correlation of each gene’s expression value with the module eigengene of a given module as a measure of the intramodular connectivity) and gene significance (GS, i.e. the correlation between the gene expression profile and the trait as a measure of biological relevance) were calculated [45, 47]. Modules correlated with the trait were analyzed using the NetworkAnalysis tool in Cytoscape (version 3.1.0) [72]. The genes (network nodes) having MM ≥ 0.90, p-value < 0.05, and GS ≥ 0.5 were identified as intramodular hub genes [73]. The gene co-expression networks for selected modules of genes were visualized using Cytoscape.
Data distribution, descriptive statistics, plots, and statistical tests were generated using JMP13 Pro statistical analysis software (Cary, NC). Heatmaps were built using Package 'd3heatmap' in R. Whole transcriptome data was analyzed as indicated above (see Whole transcriptome sequencing data analysis). RT-qPCR data (-ΔCt values) were analyzed using a one-way analysis of variance (ANOVA; JMP13 Pro) and post-hoc comparisons were performed using the Student’s t-test [74]. Statistical significance was set at a p-value < 0.05. Finally, RNAscope ISH and IHC data were subjected to a non-parametric test (Kruskal-Wallis) using JMP13 Pro and statistical significance was set at a p-value <0.05. To evaluate the predominance of specific TFs in inflammatory infiltrates during long-term persistence, the average number of positive cells among the long-term persistently infected stallions (n = 3) was calculated and analyzed using a one-way ANOVA and post-hoc comparisons were performed using the Student’s t-test. Statistical significance was set at a p-value < 0.05.
The RNA sequencing data from this study were deposited in the Gene Expression Omnibus (GEO, NCBI, NIH) database under study GSE114982 (accession numbers GSM3161940- GSM3161951).
|
10.1371/journal.pbio.1000215 | Cytoplasmic Relaxation of Active Eph Controls Ephrin Shedding by ADAM10 | Release of cell surface-bound ligands by A-Disintegrin-And-Metalloprotease (ADAM) transmembrane metalloproteases is essential for signalling by cytokine, cell adhesion, and tyrosine kinase receptors. For Eph receptor ligands, it provides the switch between cell-cell adhesion and repulsion. Ligand shedding is tightly controlled by intrinsic tyrosine kinase activity, which for Eph receptors relies on the release of an inhibitory interaction of the cytoplasmic juxtamembrane segment with the kinase domain. However, a mechanism linking kinase and sheddase activities had remained elusive. We demonstrate that it is a membrane-proximal localisation of the latent kinase domain that prevents ephrin ligand shedding in trans. Fluorescence lifetime imaging microscopy and electron tomography reveal that activation extends the Eph receptor tyrosine kinase intracellular domain away from the cell membrane into a conformation that facilitates productive association with ADAM10. Accordingly, EphA3 mutants with constitutively-released kinase domains efficiently support shedding, even when their kinase is disabled. Our data suggest that this phosphorylation-activated conformational switch of EphA3 directly controls ADAM-mediated shedding.
| The Eph transmembrane receptors are part of the receptor tyrosine kinase family and play important roles in communication between neighbouring cells. An Eph receptor binds to its ligand, membrane-tethered ephrin, on a neighbouring cell so as to form a stable complex and activate downstream signalling events. One such event is regulation of ADAM10, a transmembrane protease of the ADAM metalloprotease family, which provides a feedback mechanism to Eph signalling. ADAM10 is located on Eph-expressing cells and cleaves ephrin from its membrane tether on the opposite cell (through its so-called sheddase activity), thereby separating the cell-cell connection and allowing the signalling complex to internalise. In other biological contexts, activity of the ADAM metalloprotease family underlies signalling mechanisms such as oncogenic EGF-receptor transactivation, adhesion molecule shedding and cytokine/chemokine release. In general, ADAM function is enhanced when receptor tyrosine signalling is active and repressed when tyrosine kinase signalling is inhibited. However, the mechanism through which receptor tyrosine kinase signalling regulates ADAM10, have remained elusive. By combining fluorescence lifetime imaging microscopy (FLIM) and electron microscopic tomography of EphA3, we have demonstrated in live cells at molecular resolution that tyrosine phosphorylation of activated EphA3 triggers a measurable movement of the kinase domain away from the plasma membrane. Only this conformation of the EphA3 kinase domain away from the plasma membrane permits ADAM10 to come close enough to EphA3 so that it can reach its tightly EphA3-bound substrate, ephrin-A5. Our findings delineate a new regulatory concept in cell-cell communication, whereby control over proteolytic sheddase activity is provided by an activation-induced switch in the conformation of the cytoplasmic domain of a receptor tyrosine kinase, rather than by a cytosolic signalling pathway.
| The ADAM (A-Disintegrin-And-Metalloprotease) transmembrane proteases fulfil essential functions during normal and pathological tissue- and organ-development [1]. ADAM10 and 17 in particular are widely expressed and knock-out mice lacking expression of either gene show severe, lethal defects in early development, in the formation of somites and the central nervous system (ADAM10) and the cardiovascular system (ADAM10/17). They have important roles in receptor tyrosine kinase (RTK) and Notch signalling, highlighted by phenotypic resemblance of ADAM 10/17 knockouts with those of Notch, the epidermal growth factor receptor (EGFR) and EGFR ligands [2],[3],[4],[5]. ADAM10 and 17 both target a range of EGFR ligands with distinct specificities [6], while ADAM10 cleaves both Notch and its ligand delta, as well as other targets with prominent roles in disease including amyloid precursor protein, interleukin 6 receptor [5], cadherins [7], and ligands for Eph RTKs (Ephs) [8],[9].
Ephs and their membrane bound ligands (ephrins) control cell positioning during normal and oncogenic development by modulating cell-cell adhesion and cell-cell segregation or repulsion [10]. Similar to ADAMs, they function in developmental processes including somite formation, neural and cardiovascular development [11],[12], which, together with their common expression patterns, supports functional evidence for the critical role of ADAM10 in Eph biology [8],[9]. Eph function relies on the direct contact between Eph- and ephrin-expressing cells, which triggers the assembly of signalling clusters at the cell-cell interface [13] and initiates Eph “forward” and ephrin “reversed” signals into the respective cells [10]. For repulsion to proceed it is essential that the multivalent [14],[15] signalling complexes that tether Eph- and ephrin-expressing cells are disrupted, allowing the cells to retract via ensuing actin cytoskeletal rearrangement [16]. In the case of EphA/ephrin-A signalling clusters it was demonstrated that ephrin-shedding by ADAM10, constitutively associated with Ephs on the opposing cell [9], is required for repulsion to occur [8],[9].
ADAM proteins are produced as inactive precursors that become catalytically active upon removal of the prodomain during secretion. Interestingly, there is little evidence for a substrate cleavage sequence motif, and the regulation of proteolytic specificity is achieved by interaction of substrates with the non-catalytic disintegrin and cysteine-rich extracellular ADAM domains. For ephrin cleavage, a substrate recognition module within the cysteine-rich domain of ADAM10 specifically binds only the high-affinity ligand-receptor complex to ensure that only Eph receptor-bound ephrins are cleaved [9].
n addition to this direct control of ADAM10-facilitated shedding, substantial evidence documents intracellular regulation of ADAM proteases [4],[5]. ADAM activity is enhanced upon activation of (receptor) tyrosine kinase signalling by growth factors, phorbol esters, or phosphatase inhibitors, while tyrosine kinase inhibitors or dominant-negative RTK mutants attenuate ADAM activity [4]. This protein kinase-controlled ADAM activity is an essential component of the autocrine, mitogenic signalling that is triggered by G-protein coupled [17],[18] or stress-induced EGF receptor transactivation [17],[19]. Conversely, ADAM shedding of L-selectin during leukocyte trafficking [20] is blocked by Calmodulin (CaM), via its binding to the 17-residue L-selectin intracellular domain (ICD), while CaM inhibitors trigger shedding of the L-selectin ectodomain [21]. Surprisingly, while ADAM family members harbour potential protein docking motifs [4], cytoplasmic-truncated ADAM17 is fully functional [22], and signalling mechanisms regulating ADAM activity have remained elusive.
To elaborate the intracellular regulation of ADAMs by RTK signalling we investigated ADAM10 catalysed ephrin-A5 cleavage that is mediated by EphA3-expressing cells, as previous studies demonstrated that this sheddase activity depends on ephrins binding and activating the EphA3 RTK [8],[9]. Our results suggest that a conformational change in the EphA3 ICD, which upon activation moves the kinase domain away from the plasma membrane, relieves a steric hindrance preventing productive association with ADAM10. We demonstrate that this loss of steric hindrance, resulting from extension of the active Eph ICD, rather than classical signalling via intermediate proteins, regulates the sheddase activity of ADAM10.
Eph activation, phosphorylation, and signalling relies on the assembly of multimeric Eph/ephrin complexes [11],[12]. For in vitro experiments, recombinant proteins comprising two ephrin extracellular domains fused onto the Fc portion of human IgG (ephrin-Fc) can be clustered with anti-Fc antibodies to elicit Eph activation [23],[24],[25]. We tested the cleavage of clustered or non-clustered ephrin-A5-Fc by cell surface ADAM10 in cultures of HEK293T (transformed human embryonic kidney cells) cells expressing either wild type (Wt) EphA3 or mutant, kinase-inactive EphA3[K653M]. Immunoblot analysis confirmed that the release of ephrin-A5 from the Fc complexes was dependent on pre-clustering and indeed was greatly reduced in cultures of cells expressing the kinase-inactive EphA3 mutant (Figure 1A, Figure S1A).
Surprisingly, however, co-immunoprecipitation analysis revealed robust binding of ADAM10 to an EphA3 mutant lacking the whole ICD (EphA3[ΔICD], Figure S1B). This implies that the ADAM10/EphA3 association, which is necessary for ephrin cleavage and involves constitutive as well as ephrin-augmented interactions of their extracellular domains [8],[9], does not require the contribution of the EphA3 ICD. We therefore assessed if cytoplasmic-truncated ADAM10[ΔICD] or EphA3[ΔICD] could catalyse cleavage of Alexa-labelled ephrin-A5-Fc that had been conjugated to Protein-A-coated Dynabeads, an experimental approach previously used to characterise ephrin shedding by ADAM10 [9]. In agreement with earlier studies [8], over-expression of non-functional ADAM10 lacking the MP domain (ADAM10ΔMP) acts as a dominant negative protein to effectively prevent ephrin shedding (Figure 1B). By contrast, over-expression of cytoplasmic-truncated ADAM10[ΔICD] did not notably affect ephrin-A5 cleavage and internalisation (Figure 1B), indicating that the ADAM ICD may not be required for its sheddase activity [22].
Likewise, cells over-expressing EphA3[ΔICD] efficiently supported ephrin-A5 shedding from Dynabeads, as evident from the marked cell surface labelling with fluorescent ephrin that had been released from the beads (Figure 1C). The lack of efficient internalisation of the cleaved ephrin-A5 into cells in this case suggested that the EphA3 ICD is required for internalisation of the ligand/receptor complex, but is not essential for ligand cleavage. Cell surface labelling was efficiently blocked by ADAM metalloprotease inhibitors (Figure S2), consistent with ADAM-dependent shedding of ephrin-A5 [9].
To reconcile the observations that ephrin cleavage requires Eph kinase activity but still occurs in the absence of the entire ICD, we considered recent studies demonstrating an activation- and phosphorylation-dependent release of the Eph juxtamembrane (JM) segment from an inhibitory interaction with the kinase domain [26]. The existence of this structural switch, which converts a static/constrained conformation of the JM domain into a dynamic/relaxed one [26],[27], was recently confirmed also for EphA3 [28].
We hypothesized that the inactive/constrained JM segment positions the kinase domain close to the membrane, a configuration that imparts a steric obstruction to the productive ADAM10/EphA3 interaction and thereby controls ephrin shedding. To test this hypothesis we examined whether forced approximation of the EphA3 kinase domain to the plasma membrane affects ADAM10 association and function. For these experiments we designed a series of EphA3 mutants (Figure 2A) including: i) “EphA3[ΔJX]” short (“S”), lacking JM residues 591–614 and replicating the previously-reported EphB2[Δ599–621] [26]; ii) “EphA3[ΔJX]” long (“L”) lacking all JM residues 567–614; iii) “EphA3[2YE]” where Y→E substitutions of JM tyrosines generate an unfolded JM domain [26]; and iv) EphA3[2YE-KM], a kinase-inactive form of EphA3[2YE]. All mutants were expressed at the cell surface and functional in ephrin-A5 binding (Figure S3). Loss of the JM tyrosines Y596 and Y602 in the truncated (EphA3[ΔJXS]) or Y→E substituted (EphA3[2YE]) JM domain reduced ephrin-A5 induced phosphorylation (Figure 2B) without loss of EphA3 kinase activity (Figure S4A). By contrast, only marginal phosphorylation of EphA3[ΔJXL] (Figure 2B) suggests that the very close proximity of the kinase domain to the inner membrane leaflet impedes the substrate interaction for this mutant.
Importantly, all EphA3 mutants retained the capacity to associate and co-immunoprecipitate with ADAM10 (Figure 2C, Figure S4B), in agreement with our previous finding that ADAM interacts with EphA3 via specific regions in their extracellular protein domains [9]. However, compared to Wt EphA3 or EphA3-[2YE], binding of the [ΔJXS] and [ΔJXL] mutants to ADAM10 was notably reduced, supporting our hypothesis that approximation of the EphA3 kinase domain to the plasma membrane imparts steric obstruction to ADAM10 binding that is relieved during JM domain unfolding. Also, binding to ADAM10 of “kinase-dead” [2YE-KM] was reduced compared to EphA3-[2YE] (Figure 2C), suggesting either that Eph kinase activity may play a role in facilitating the ADAM10 interaction or that JM domain unfolding of the [2YE-KM] mutant is incomplete, as implied from the crystal structure [27].
To test our hypothesis of a conformational switch in the EphA3 JM domain that controls ADAM10 access and ephrin shedding, we compared the ability of Wt and mutant EphA3 receptors to support ADAM-catalysed shedding from ephrin-A5-coated beads and internalisation into EphA3-expressing cells. Confocal microscopy confirmed that indeed both EphA3 JM-truncations significantly affected the capacity to promote ephrin-A5 shedding, whereas the [2YE] mutant behaved comparable to Wt EphA3 (Figure 3A, 3B). Interestingly, kinase-compromised EphA3[2YE-KM] with an unfolded JM domain behaved similar to cytoplasmic-truncated EphA3[ΔICD] (Figure 1C): cell surface ephrin-staining away from the beads revealed ability of this mutant to support ephrin shedding but failure to internalise the shed ligand (Figures 3A, 3B and S5A), also evident when soluble, pre-clustered Alexa594ephrin-A5-Fc was used as substrate (Figure S5B). Since EphA3[2YE] bearing an intact kinase but lacking JM tyrosines is internalised normally, this argues for EphA3 endocytosis requiring tyrosine kinase activity and/or phosphorylation, likely of the remaining critical phosphorylation site within the Eph kinase activation-loop [13]. We confirmed the ability to support ephrin-shedding using immunoprecipitation analysis, revealing similar levels of cleaved ephrin-A5 in cultures of EphA3[2YE] and EphA3[2YE-KM] cells (Figure 3C).
These experiments indicate that it is the proximity of the EphA3 kinase domain to the plasma membrane rather than its kinase activity per se that determines if shedding by ADAM10 is inhibited or promoted. For further evidence, and to clarify if there is any contribution from signalling intermediates that may communicate between the Eph and ADAM cytoplasmic domains, as implied from previous studies [4],[5], we compared ephrin-A5 shedding by cells co-expressing EphA3 mutants together with either Wt ADAM10 or with cytoplasmic-truncated ADAM10[ΔICD] (Figures 3D and S6B). ADAM10[ΔICD] would be expected to overcome the steric hindrance exerted by EphA3 JM mutants, compared to full length ADAM10. To avoid the potential ambiguity caused by the presence of endogenous, Wt ADAM10/EphA3 complexes, we performed these experiments in mouse embryonic fibroblasts (MEFs) from ADAM10 KO mice [2] lacking any detectable ADAM10 expression (Figure S6A). Similar to our findings in HEK293T cells, ephrin-A5 shedding in these MEFs was apparent upon co-expression of either Wt or [ΔICD] ADAM10 together with Wt EphA3, and was greatly reduced when either of the EphA3 JM mutants EphA3[ΔJXL] or EphA3[ΔJXS] were expressed with Wt ADAM10. Importantly, co-expression of cytoplasmic-truncated ADAM10[ΔICD] together with EphA3[ΔJXL] or EphA3[ΔJXS] “rescued” the inhibitory effect of the JM positioning of the Eph kinase domain and resulted in shedding comparable to that seen with the Wt ADAM and Eph proteins (Figures 3D and S6B).
Together, these experiments demonstrate that ephrin-A5 shedding by ADAM10 is controlled by steric hindrance exerted by the membrane-proximal EphA3 kinase domain, which prevents the functional interaction with ADAM10 that is needed for efficient substrate (ephrin) cleavage to occur.
In addition to controlling RTK function, ADAMs are key modulators of cell–matrix interactions [29], and ADAM17-catalysed exodomain shedding regulates the function of the leukocyte adhesion protein L-selectin [20]. Of note, L-selectin shedding is blocked by CaM binding to the L-selectin cytoplasmic domain and is promoted by CaM inhibitors [21], indicating a similar regulation by steric hindrance. Intriguingly, these inhibitors also trigger metalloprotease-dependent EGFR signalling [30], further suggesting that steric hindrance, in this case imparted by CaM binding within the EGFR JM region [31], may promote ADAM-catalysed ligand release.
To test the hypothesis that a bulky protein domain at the JM position would impair a productive ADAM/EphA3 alignment, the EphA3 cytoplasmic domain was replaced with that of L-selectin. We surmised that CaM-loaded EphA3/L-selectin could not effectively promote ephrin cleavage, while conversely inhibition of CaM binding to this chimeric receptor using CaM inhibitors should favour ADAM10 association and ephrin-A5 cleavage. Control experiments confirmed inhibition of CaM binding (Figure S7A) and increased ADAM10 association (Figure S7B). Indeed, shedding from ephrin-A5-Fc coated beads was markedly higher in inhibitor-treated than in untreated cells expressing EphA3/L-selectin (Figure 4A, 4B). Furthermore, immunoblotting of cleaved ephrinA5-Fc from cultures of EphA3/L-selectin cells (Figure 4C) confirmed shedding in CaM-inhibitor-treated cells but not in control cells, at levels that are comparable to those observed in EphA3[2YE-KM] cell cultures (Figure 4C). Likewise, engineering of inactivating mutations into the CaM-binding domain [21] (EphLsel EE) notably increased the capacity of these cells to support ephrin shedding as compared to Wt EphA3-L-selectin cells (Figure S7C). Of note, in these experiments ephrin-A5 labelled the cell membrane but was not internalised into cells with EphA3/L-selectin, confirming the need for the intact EphA3 cytoplasmic domain for endocytosis. We confirmed CaM inhibitor-induced shedding also of cell bound ephrin-A5, using co-cultures of green-fluorescent protein (GFP)-ephrin-A5-expressing and EphA3/L-selectin-expressing cells (Figure S8A). In agreement with these imaging experiments, immunoblotting of cleaved GFP-ephrin-A5 recovered from GFP-ephrin-A5-expressing cells that had been co-cultured with EphA3/L-selectin-cells revealed CaM inhibitor-induced shedding, which is absent in inhibitor-treated control cells (Figure S8B). Thus, CaM binding to the EphA3/L-selectin protein effectively regulates shedding of ephrin-A5, further demonstrating that ADAM10 activity is controlled by steric constraints in the JM region of a (chimeric) EphA3 mutant that is devoid of tyrosine kinase– and kinase-dependent signalling activity.
To this point our analysis strongly argues for the notion that the “relaxed” and “constrained” conformations of active and inactive EphA3, respectively, would direct functional or dysfunctional alignment of ADAM10 with EphA3. To examine in intact cells, if indeed activation uncoils the EphA3-kinase domain away from the plasma membrane, we developed a Förster resonance energy transfer (FRET) imaging approach [32] that is sensitive to the distance between the EphA3-COOH (C)-terminus and the plasma membrane. Here, we used fluorescence lifetime imaging microscopy (FLIM) to monitor FRET between EphA3-GFP and the inner membrane leaflet of Cos7 cells labelled with membrane-targeted tkRasRFP (red-fluorescent protein) (Figure 5A) [33].
Confocal FLIM analysis of live cells activated with clustered ephrin-A5-Fc revealed that fluorescence lifetimes (τ) of cell-surface EphA3-GFP increased (showing reduced FRET) 10–20 min after stimulation (Figure 5B, 5C and Figure S9A, S9B), indicating a drop of the cytoplasmic domain from a membrane-proximal to a membrane-distal conformation. The receptor population with increased fluorescence lifetimes (activated EphA3) returned to pre-stimulation levels at 40 min, likely reflecting de-phosphorylation of activated EphA3. However, energy transfer from GFP (donor) to tkRasRFP (acceptor) will not only depend on their distance but also on the local acceptor density that may vary as a function of time and space in a FLIM time-lapse series. To account for this, the FRET rate (kT) per acceptor density (kT/acceptor) that does not depend on the concentration of acceptors in the plasma membrane [34] needs to be determined. We estimated this parameter from the slopes of a linear fit to the fluorescence rates (τ−1)–acceptor intensity (Iaccep) 2D-histograms of the confocal images at selected time points (Figure 5C). Ephrin-A5 stimulation of EphA3 resulted in a significant decrease in kT/acceptor values (Figure 5C), indicating an increased GFP-RFP distance. The maximal decrease in the kT/acceptor ratio was observed after 20 min stimulation and was followed by partial recovery of the FRET efficiency to that seen with inactive EphA3. By comparison, a confocal FLIM time series of ephrin-A5 stimulated cells expressing constitutively active EphA3[2YE]-GFP or inactive EphA3[3YF]-GFP, containing Phe-replacements of all critical tyrosine residues (Figure 5D, 5E) [16],[27] yielded no significant change in the kT/acceptor slopes, indicating that in this case stimulation does not notably change the distance between GFP and RFP (Figure 5D, 5E). The lower kT/acceptor value for EphA3[2YE]-GFP and the higher value for EphA3[3YF]-GFP as compared to non-stimulated EphA3[wt]-GFP are consistent with constitutively extended and constitutively constrained conformations, respectively, of these receptor mutants.
We note that single cell FLIM analysis of the Wt EphA3 conformation during stimulation exhibits considerable variance, reflecting different mixtures of active and inactive receptor populations at each spatially resolvable volume element in the image. We therefore compared the constitutive conformations of kinase active EphA3[2YE]-GFP with that of inactive EphA3[3YF]-GFP. We computed cumulative 2D-histograms of fluorescence rates versus acceptor intensities for the [2YE]-, [3YF]-, and [2YE-KM]-mutants using fluorescence lifetime/acceptor intensity images of cells for each of the mutant receptors (Figure 5F) obtained with wide-field frequency-domain FLIM (Figure S9C [35]). The energy transfer rate, kT/acceptor, was calculated from the slope of a linear fit to the fluorescence rate—acceptor intensity 2D-histograms in which the intercept was set to the measured fluorescence rate of the donor (GFP) in the absence of acceptor. The EphA3[2YE]-GFP mutant exhibited significantly (p = 7×10−14) lower FRET efficiencies (as apparent from kT/acceptor, right panel, Figure 5F) than the inactive EphA3[3YF]-GFP mutant, indicating its extension from the plasma membrane and confirming the conformational change of the Wt EphA3 ICD observed upon stimulation of live cells. The FRET efficiency of EphA3[2YE-KM]-GFP was in between these extremes, suggesting that kinase-dead Eph with a flexible JM domain adopts an intermediate position between a fully extended and constrained cytoplasmic domain, as previously suggested [27]. The FRET rate (kT) is proportional to the fourth power of the distance between the donor chromophore and acceptor plane [34]. From the fourth power root of the ratio of kT/acceptor of EphA3[3YF]-GFP and EphA3[2YE]-GFP (Figure 5F), we can thus estimate that the C-terminus of relaxed ([2YE]) EphA3-GFP is 1.36±0.06 times further away from the plasma membrane than that of constrained ([3YF]) EphA3-GFP.
To examine at increased resolution the change in the span between the plasma membrane and the carboxy-terminus of the latent and activated receptor, we used electron microscopy (EM) to image the EphA3 C-terminus that had been labelled with streptavidin-conjugated Qdots (SA-Qdots [36]). We achieved the site-specific targeting with SA-Qdots by engineering onto the EphA3 C-terminus a biotin acceptor peptide (AP), which can be specifically biotinylated using the E. coli biotin ligase (BirA) [37].
We validated the feasibility of the approach by binding SA-Qdots to an NH2-terminal AP-tagged and biotinylated EphA3 (APN-EphA3). Confocal microscopy revealed that SA-Qdot staining of APN-EphA3 cells correlated with anti-EphA3 staining and shifted to a cytoplasmic compartment upon ephrin-stimulation (Figure 6A), suggesting intact endocytosis of Eph-signaling clusters [13]. EM of these cells revealed Qdots at the outer cell surface at discernible distances from the plasma membrane (Figure 6B). Considering that a reasonably broad range of estimates (17–29 nm) likely reflects flexibility of the AP-tag linker region, this provides an apparent distance between Qdots and plasma membrane of ∼24 nm (Figure 6C).
In order to image the span of the EphA3 cytoplasmic domain we analysed cells co-expressing a cytoplasmic form of BirA together with EphA3, AP-tagged at the C-terminus (APC-EphA3). Microinjection of SA-Qdots into APC-EphA3-expressing cells and sectioning of non-permeabilised cells allowed EM of EphA3-bound Qdots at the intact plasma membrane (Figure 6D). We used electron tomography with alignment, reconstruction, and segmentation of 3D images (Figure 6E) to estimate Qdot/membrane distances. For APC-EphA3[3YF] cells (Figure 6D, right panel), these ranged from 8–18 nm, with an average span from the membrane of approximately 12 nm (12.32+/−3.1 nm, Figure 6F). Expression of Wt EphA3 or of EphA3[2YE] rapidly leads to perturbation of the plasma membrane due to EphA3 activation and endocytosis (Figure S10) and prevented assignment of Qdot/membrane distances with and without ephrin stimulation. To allow comparison of data from EM tomography with our FRET analysis, we therefore analysed APC-EphA3[2YE], representing EphA3 with relaxed JM regions, while co-expressing the clathrin-assembly protein AP180 to block endocytosis [38] of activated receptors in these cells (Figure 6D, left panel). The distribution of Qdot positions in these cells was clearly different to APC-EphA3[3YF] cells, suggesting for activated EphA3[2YE] an average span of approximately 19 nm (19.37+/−3.8 nm, Figure 6F), approximately 1.6±0.5 times wider than that of inactive [3YF]EphA3. This relative distance increase is consistent with that determined by FLIM (above) and confirms a notable extension of the activated receptor away from the plasma membrane.
Intracellular regulation of ADAM sheddases is known to control the release of transmembrane growth factor precursors and the activation of corresponding growth factor receptors. It was identified as a cause for EGF receptor transactivation almost a decade ago [17],[19]. However, the mechanism linking kinase and sheddase activities has remained elusive since its inception. We have now elucidated a previously unrecognized conformational switch that is embedded in the cytoplasmic domains of Eph receptors and ADAMs and controls ADAM-function and Eph signalling.
Furthermore, marking the EphA3 C-terminus with fluorescent (GFP) and electron-dense (Qdots) tags allowed for the first time to demonstrate in live cells at molecular resolution that receptor activation and tyrosine phosphorylation triggers a measurable shift of the kinase domain away from the plasma membrane. The increased span of activated EphA3 that we estimated by FLIM and EM tomography is consistent with partial extension of the 64-residue (G569 – N633) receptor JM domain with a maximal theoretical (β-sheet) span of ∼20 nm. While it would seem formally possible that extension of the activated EphA3 ICD reflects an unfolding of the linker connecting the kinase with the C-terminal SAM domain, recently elucidated crystal structures of the EphA3 ICD argue against this possibility [28]: in the structures of the active and of the inactive form, this C-terminal linker is tethered to the base of the kinase, indicating that the C-terminal part of the EphA3 cytoplasmic domain maintains a rigid, kinase-associated configuration.
Together, our data suggest a model (Figure 7) where the inactive, membrane-proximal receptor kinase domain obstructs the productive alignment with ADAM10 that is necessary for effective ephrin cleavage. This alignment of the ADAM and Eph extracellular domains relies on a shift of the activated kinase domain away from the membrane and on docking of the ADAM10 substrate-recognition site to the high-affinity Eph/ephrin complex [9]. Such a conformational switch provides rapid and precise control of ADAM10-sheddase activity and challenges the relevance of signalling intermediates that are thought to be involved in the control of ADAM10 sheddase activity [4],[5]:
These findings have considerable implications for the understanding of Eph signalling: Currently it is established that contacts between Eph and ephrin-expressing cells that fail to activate robust Eph phosphorylation will lead to cell spreading and cell-cell adhesion while cell-contact induced Eph activation and phosphotyrosine signalling result in cell rounding and cell segregation [11],[12],[39]. Studies demonstrating the critical role of ADAM10-catalysed ephrin shedding for cell repulsion revealed that interaction with a cleavage-resistant ephrin mutant leads to persisting Eph/ephrin contacts but does not prevent cell rounding (axon collapse) [8], suggesting independent—but tightly synchronised—processes during cell repulsion. They further imply that the cell-biological consequence of an Eph/ephrin contact is determined only during assembly of the Eph/ephrin complex: Thus, synchronisation of cell rounding and segregation requires a molecular switch that rapidly relays the signalling competence of the Eph kinase to the protease controlling ephrin shedding.
We now have elaborated the molecular mechanics of this relay in which kinase-active and kinase-inactive Eph receptors adopt distinct protein configurations that allow productive and unproductive association with ADAM10, respectively. Previously, we demonstrated that processed, catalytically active ADAM10 lacking the inhibitory pro-domain [5] is associated with EphA3 also in the absence of ephrin-A5 contact [9]. In the current context this implies that ADAM10 is “on standby” to release Eph-bound ephrin from interacting cells in the moment EphA3 becomes tyrosine-phosphorylated and adopts a conformation that allows ADAM10 alignment for optimal substrate access. Such a mechanism provides for the synchronised Eph-triggered cell rounding and segregation that is observed during cell-cell repulsion.
A host of stimuli that promote ADAM-mediated shedding, including most prominently phorbol esters and calcium ionophores, have been described [5]. However, while interaction with signalling proteins, in particular via SH3-binding motifs, have been postulated, it has remained unclear if and how the different agents could modulate ADAM activity [4]. The concept of regulation via cytosolic intermediates has been challenged in particular by the finding that PMA (phorbol-12-myristate-13-acetate) efficiently activates an ADAM17 mutant lacking the ICD [22]. Furthermore, CaM inhibitor and calcium-ionophore-induced shedding of EGFR-ligands by ADAM10 is retained partially by a mutant lacking the ICD [40]. Our findings now reveal an entirely novel concept of RTK/ADAM regulation whereby the conformation of the cytoplasmic domain directly regulates ADAM activity rather than involving intermediate signalling proteins and kinase activity per se.
It is tempting to speculate that steric hindrance represents a conserved mechanism for receptor-regulated ADAM activity. Regulated L-selectin shedding provides an important example of a very different receptor system that may be controlled by a mechanism, where CaM binding to the L-selectin ICD [21] and its ensuing conformational change seem to hinder the ability of ADAM17 to shed L-selectin. Notably, upon protein binding to its target site, CaM changes from an elongated to a globular structure [41] with very similar dimensions (4–5 nm diameter) to the RTK kinase domain. Our observation that CaM-binding to an EphA3/L-selectin chimeric receptor can also regulate ADAM10 cleavage of ephrins would seem to confirm steric hindrance as a mechanism regulating Eph-associated ADAM activity and to suggest this as a more widely conserved concept of ADAM regulation. For example, steric hindrance could also explain the regulation of ADAM activity by other RTKs such as the EGFR, which binds CaM within the JM region [31] and controls ADAM-facilitated ligand shedding in an activation dependent manner [5]. Indeed, EGFR signalling can be triggered in a metalloprotease-dependent manner by CaM inhibitors [30], preventing CaM access to a binding site within the EGFR JM region [31].
Interestingly, our data suggest that ephrin cleavage and its receptor-mediated internalisation may be controlled separately, since EphA3 with “relaxed” JM but inactive kinase domain effectively supports ephrin cleavage but is not internalised. While details of Eph endocytosis mechanisms remain to be elaborated, this finding is consistent with signalling-dependent RTK endocytosis potentially involving the ubiquitin ligase Cbl [42]. Importantly, effective internalisation of the EphA3 2YE mutant suggests that the JM tyrosines may not be essential for this endocytic signalling mechanism.
Lastly, we have developed a novel imaging strategy, which bridges the gap between structural and cell-biological imaging, to provide functional information for individual proteins in whole cells at molecular resolution, and which could have applications in a wide range of cell-biological systems. Our integrated FLIM- and EM-based analyses reveal that Eph RTK activation triggers, in addition to tyrosine phosphorylation, a measurable extension of the ICD towards the cytosol. The previously unforeseen functional consequences of this conformational change for downstream Eph signalling and the regulation of ADAM10 activity are likely to have important implications for the understanding of ADAM-regulated biological processes in development and disease.
Inactive EphA3 was made by substitution of residue K653 to M in EphA3-GFP [13]. Insertion into bovine ADAM10-HA [9] of a KpnI restriction site at C698 and removal of the ICD (retaining the C-terminal HA tag) yielded ADAMΔcyto. For EphA3-L-selectin, a NheI restriction site at EphA3 G565 together with annealed L-selectin ICD oligonucleotides (forward: 5′CTAGGAGATTAAAAAAAGGCAAGAAATCCAAGAGAAGTATGAATGACCC-ATATTAA; reverse: 5′CTAGTTAATATGGGTCATTCATACTTCTCTTGGATTTCTT-GCCTTTTTTTAATCTC) were inserted, with a terminal stop codon. For EphA3-APN the AP-tag [37] was inserted after the EphA3 signal sequence (after Gly20), and for EphA3-APC the AP-tag was inserted into a XmaI site engineered into the EphA3 C-terminus (Val983).
Cleaved ephrin-A5 was extracted from pooled Protein-A Sepharose-pre-cleared lysates and culture supernatants of cells that had been treated with pre-clustered or non-clustered ephrin-A5-Fc by using EphA3-Fc coupled to Protein-A-Sepharose. Pull-downs were analysed by anti-ephrin-A5 immunoblot. Cleavage of cell-surface ephrin-A5 was assayed in 1-h co-cultures of ephrin-A5-expressing HEK293T cells and EphA3/L-selectin transfected cells by extracting ephrin-A5 from cell lysates with EphA3-Fc coated Protein-A Sepharose. Where indicated, cells were treated prior to ephrin-A5-stimulation with CaM inhibitors trifluoperazine, calmidazolium (Calm), or W7, or metalloprotase inhibitors TAPI1 or GM6001 (Calbiochem). For CaM-co-precipitation EphA3/L-selectin and Wt EphA3 tagged with a biotin AP were biotinylated with BirA [37] and recovered on SA dynabeads. Other co-immunoprecipitation experiments were performed as indicated with anti-ADAM10 mAb (R&D Systems), anti-ADAM10 polyclonal Ab39177 (Abcam), with anti-EphA3 mAb IIIA4 [9] pre-coupled to mini-leak™ agarose (Kem-En-Tec, Copenhagen), and with anti-phosphotyrosine Sepharose (4G10, Upstate Biotechnology). Transient expression of all EphA3 constructs was optimised by transfecting each at four cDNA concentrations and selecting samples with similar expression levels by Western blotting total lysates. Western blotting was performed with antibodies against ephrin-A5 (R&D systems), EphA3 [43], HA (3F10, Roche), ADAM10 (Biogenesis and Abcam Ab39177), phosphotyrosine (4G10, Upstate Biotechnology), and CaM (Upstate Biotechnology).
3-channel confocal microscopy was performed by sequential scanning on Olympus FV1000 or Leica SP5 confocal microscopes. Quantitation of internalised ephrin-A5-associated fluorescence was achieved using ImageJ or Metamorph image analysis software by selecting regions of cells to exclude bead-associated fluorescence. Microscopic evaluation of ephrin cleavage by EphA3/L-selectin expressing cells, where ephrin-A5 was not internalised but remained complexed at the plasma membrane, was done by estimating the level of Alexa488-ephrin labelling relative to the expression level of the Alexa647IIIA4 anti-EphA3 antibody-stained receptor [15] on cell membranes (Figure 4B). Interactions between cell-surface ephrin-A5 and EphA3/L-selectin were analysed using ephrin-A5-GFP transfected cells [9].
Time-domain confocal FLIM was performed in transiently transfected Cos7 cells grown on coverslips or glass bottom dishes (MatTek Corp.). FLIM images were obtained using an Olympus Fluoview 1000 microscope, equipped with a Picoharp 300 photon counting setup (Picoquant, Germany). GFP was excited with a 470 nm diode (Sepia II, Picoquant, Germany). Images of 512×512 pixels were acquired detecting approximately 108 photons. Images of the donor fluorescence decays were processed using the SymPhoTime software package (v4.2, Picoquant) and the calculated average fluorescence lifetime (τ) images are presented in pseudo-colour. The average fluorescence lifetime τ(xy) images were calculated from the parameters (a1,a2,τ1,τ2) of a double exponential fit of the fluorescence decay curves [F(x,y,t)] in each pixel:(1.1)At pixel x, y the average fluorescence lifetime is:(1.2)τ−1-acceptor intensity (Ia) 2D-histograms were computed from the confocal FLIM images as described below for wide-field frequency-domain FLIM except that a bin size of 100 counts was used for the acceptor intensity.
For wide-field frequency-domain FLIM (Figure S9) we used an IX70 inverted microscope (Olympus, Japan) equipped with a 100/1.4 NA oil immersion lens, a 476 nm argon laser and narrow-band emission filter (HQ510/20; Chroma) for GFP, a 100-W mercury arc lamp with high Q Cy3 filter set (excitation filter, HQ545/30; dichroic, Q580LP; emission filter, HQ610/75) for RFP, and a dichroic beamsplitter (Q495 LP; Chroma Technology, Brattleboro, VT) and narrow-band emission filter.
Raw FLIM data were processed in IPLab (Scanalytics, Fairfax, VA, USA) to generate a binary mask for the intensity threshold operation data and a mask for the ROI used in background correction of the raw data. Using the raw FLIM data and mask, phase- and modulation lifetime images were generated using scripts written in Python programming language (http://www.python.org) with the Numarray extension for numerical computing (http://www.stsci.edu/resources/software_hardware/numarray) further augmented with low-level routines written in C [44],[45]. A cumulative 2D-histogram of fluorescence lifetime (τ) versus acceptor intensity (Ia) was generated from the multiple fluorescence phase-lifetime images (≥16 images) and corresponding acceptor intensity images using a bin size of 320 intensity units (arbitrary units). The standard error in the fluorescence lifetimes for each bin was calculated from the averages of all the images. The donor to acceptor energy transfer rate kT normalized to acceptor density (kT/acceptor) was obtained from the slope of a linear fit to the τ−1−Ia (acceptor intensity) 2D-histograms. The τ−1−Ia 2D-histograms were fitted to a linear equation:(1.3)in which prior knowledge of the fluorescence lifetime in the absence of acceptor (measured, τd = 1.96 ns) was used to constrain the intercept, kd, to 0.51. The slope kT/acceptor is proportional to the energy transfer rate per acceptor yielding 1.9+/−0.16 for EphA3[3YF]-GFP, 0.56+/−0.08 for EphA3[2YE]-GFP and 1.27+/−0.16 for EphA3[2YE-KM]-GFP. The relative distance increase from GFP to the plasma membrane, comparing both conformations, (R2YE/R3YF) can be calculated from:(1.4)yielding a distance increase of GFP to the plasma membrane of 1.36+/−0.06 for EphA3[2YE]-GFP relative to EphA3[3YF]-GFP.
For EM we biotinylated AP-tagged EphA3 receptors in intact cells using either exogenous or co-transfected biotin ligase (BirA) [37], as indicated, before labelling with SA-Qdots605 (Invitrogen). Labelled cells were washed in PBS, fixed in 2.5% Glutaraldehyde, 2% sucrose for 40 min, and prepared on ice for EM by “epon” embedding: cells were rinsed in CaCo buffer (30 min), post-fixed in 2% osmium tetroxide (40 min), washed (water), and stained with 0.5% uranyl acetate (30 min). Fixed, washed cells were dehydrated in graded ethanol solutions, embedded in epon 812 (Serva), and hardened (48 h) at 60°C. Epoxy-embedded blocks were cut into 50 or 250 nm sections (Leica Ultracut S microtome) and mounted on Formvar coated grids. Grids were post-stained with led-citrate (1 min) at room temperature, rinsed with water, and air dried. APC-EphA3 expressing cells, stably co-expressing APc-EphA3 together with a cytoplasmic form of BirA for efficient biotinylation of the AP-tagged EphA3 C-terminus, were either microinjected with Qdots prior to fixation (where indicated) or were fixed in 4% PFA, 0.5% Glutaraldehyde, 2% sucrose (30 min), permeabilised with 0.1% Triton ×100, and incubated with SA-Qdots605 for 1 h. Washed samples were then fixed in 2.5% Glutaraldehyde, 2% sucrose, and prepared for EM as described above. This approach partially solubilises the plasma membrane and required computer-assisted assignment of the exact plasma membrane/cytoplasm boundaries.
We collected at room temperature single-axis tilt series of chemically fixed cells at 1–2° angular increment between −67° to +67° using CM200 and Tecnai 30 microscopes (FEI, Eindhoven, The Netherlands) and the Tietz tomography interface (Tietz, Gauting, Germany) for data acquisition. Serial EM images were recorded on 2 k×2 k and 4 k×4 k pixel CCD cameras at a defocus level of −2 µm, with a pixel size at the specimen level of 0.7 nm. We aligned the projection images of the samples using cross-correlation techniques. The merit figure of the aligned tilt-series had a value of approximately 1 nm, indicating no significant shrinkage of the sample. Reconstructions were performed [46] using weighted back-projection algorithms and visualized with isosurface and volume-rendering techniques in the Amira software package (Mercury Computer Systems, San Diego, CA, USA, www.amiravis.com). We de-noised three-dimensional images with nonlinear anisotropic diffusion and semi-automatically segmented those using erosion and dilation operations after roughly segmenting regions of the reconstructions manually. Plasma membranes localisation in the electronic images was semi-automated, with their boundaries determined using dilation and erosion operations. Qdot detection was fully automated according to their size and contrast using thresholding techniques [46].
|
10.1371/journal.pgen.1005120 | Systems Biology of Tissue-Specific Response to Anaplasma phagocytophilum Reveals Differentiated Apoptosis in the Tick Vector Ixodes scapularis | Anaplasma phagocytophilum is an emerging pathogen that causes human granulocytic anaplasmosis. Infection with this zoonotic pathogen affects cell function in both vertebrate host and the tick vector, Ixodes scapularis. Global tissue-specific response and apoptosis signaling pathways were characterized in I. scapularis nymphs and adult female midguts and salivary glands infected with A. phagocytophilum using a systems biology approach combining transcriptomics and proteomics. Apoptosis was selected for pathway-focused analysis due to its role in bacterial infection of tick cells. The results showed tissue-specific differences in tick response to infection and revealed differentiated regulation of apoptosis pathways. The impact of bacterial infection was more pronounced in tick nymphs and midguts than in salivary glands, probably reflecting bacterial developmental cycle. All apoptosis pathways described in other organisms were identified in I. scapularis, except for the absence of the Perforin ortholog. Functional characterization using RNA interference showed that Porin knockdown significantly increases tick colonization by A. phagocytophilum. Infection with A. phagocytophilum produced complex tissue-specific alterations in transcript and protein levels. In tick nymphs, the results suggested a possible effect of bacterial infection on the inhibition of tick immune response. In tick midguts, the results suggested that A. phagocytophilum infection inhibited cell apoptosis to facilitate and establish infection through up-regulation of the JAK/STAT pathway. Bacterial infection inhibited the intrinsic apoptosis pathway in tick salivary glands by down-regulating Porin expression that resulted in the inhibition of Cytochrome c release as the anti-apoptotic mechanism to facilitate bacterial infection. However, tick salivary glands may promote apoptosis to limit bacterial infection through induction of the extrinsic apoptosis pathway. These dynamic changes in response to A. phagocytophilum in I. scapularis tissue-specific transcriptome and proteome demonstrated the complexity of the tick response to infection and will contribute to characterize gene regulation in ticks.
| The continuous human exploitation of environmental resources and the increase in human outdoor activities, which have allowed for the contact with arthropod vectors normally present in the field, has promoted the emergence and resurgence of vector-borne pathogens. Among these, Anaplasma phagocytophilum is an emerging bacterial pathogen transmitted to humans and other vertebrate hosts by ticks as they take a blood meal that causes human granulocytic anaplasmosis in the United States, Europe and Asia, with increasing numbers of affected people every year. Tick response to pathogen infection has been only partially characterized. In this study, global tissue-specific response and apoptosis signaling pathways were characterized in tick nymphs and adult female midguts and salivary glands infected with A. phagocytophilum using a systems biology approach combining transcriptomics and proteomics. The results demonstrated dramatic and complex tissue-specific response to A. phagocytophilum in the tick vector Ixodes scapularis, which reflected pathogen developmental cycle and the impact on tick apoptosis pathways. These dynamic changes in response to A. phagocytophilum in I. scapularis tissue-specific transcriptome and proteome demonstrated the complexity of the tick response to infection and contributes information on tick-pathogen interactions and for development of novel control strategies for pathogen infection and transmission.
| Anaplasma phagocytophilum (Rickettsiales: Anaplasmataceae) is an emerging zoonotic pathogen transmitted by Ixodes ticks of which the major vector species are I. scapularis in the US and I. ricinus in Europe [1]. This intracellular bacterium infects tick midguts [2] and salivary glands [3] and vertebrate host granulocytes causing human, canine and equine granulocytic anaplasmosis and tick-borne fever of ruminants [4–8]. Human granulocytic anaplasmosis is the second most common tick-borne disease in the United States and tick-borne fever is an established and economically important disease of sheep in Europe [8, 9].
The molecular mechanisms used by A. phagocytophilum to infect and multiply within vertebrate hosts including the inhibition of neutrophil apoptosis have been well characterized [5, 10–14]. Anaplasma infection in the tick vector has been shown to modulate gene expression and tick proteins have been identified that interfere with bacterial acquisition and/or transmission [15]. However, little information is available on the impact of pathogen infection at both transcriptome and proteome levels and the molecular pathways affected by A. phagocytophilum to establish infection in ticks. Recently, Ayllón et al. [16] demonstrated that A. phagocytophilum infection inhibits tick intrinsic apoptosis pathway resulting in increased infection and Severo et al. [6] defined a role for ubiquitination during bacterial colonization of tick cells. However, as shown for other tick-pathogen models [17], information is not available on the tick tissue-specific responses to A. phagocytophilum infection. These facts underline the importance of defining strategies by which these bacteria establish infection in the tick vector.
As recently shown for Drosophila melanogaster, arthropod transcriptomes and proteomes are dynamic, with each developmental stage and organ presenting an ensemble of transcripts and proteins that give rise to substantial diversity in their profile [18]. Characterization of tissue-specific responses and cellular pathways in ticks in response to infection with A. phagocytophilum by use of high-throughput omics technologies is essential for understanding tick-pathogen interactions and to provide targets for development of novel control strategies for both vector infestations and pathogen infection/transmission [15, 19, 20]. However, the application of a systems biology approach to the study of non-model organisms such as tick-pathogen interactions poses challenges including the analysis of large datasets in order to extract biologically relevant information and interpret changes in gene expression in relation to simultaneous changes in the proteome [21–23]. The I. scapularis genome is the only tick genome sequenced (GenBank accession ABJB010000000) but limitations in genome assembly and annotation add additional complexity to the characterization of the molecular events at the tick-pathogen interface [23–25]. Thus, the design of experiments combining tick transcriptomics and proteomics require the integration of these different datasets to identify relevant biological processes and molecules. This challenge can be addressed by assessing global transcriptome and proteome changes and studying specific pathways such as immune response and apoptosis that are important for pathogen infection and transmission by ticks.
In this study, we characterized global tissue-specific response and apoptosis signaling pathways in I. scapularis infected with A. phagocytophilum. Apoptosis was selected for pathway-focused analysis due to its role in A. phagocytophilum infection of tick cells [16]. Nymphs and female midguts and salivary glands were selected for this analysis using a systems biology approach combining transcriptomics and proteomics data. These tick developmental stages and tissues were selected for this study because nymphs are the main vectors for pathogen transmission to humans and animals while midguts and salivary glands play a major role during pathogen acquisition, multiplication and transmission [15, 26]. The hypotheses addressed in this study included that tick tissue-specific response to infection reflects pathogen developmental cycle and A. phagocytophilum infection impacts on tick apoptosis pathways in a tissue-specific manner. The results showed that A. phagocytophilum infection results in complex and dramatic tissue-specific changes of the tick transcriptome and proteome and further extended our understanding of the role of selected biological pathways during bacterial infection and multiplication in the tick vector.
A. phagocytophilum, as other obligate intracellular bacteria, evolved to manipulate host cells to establish infection [27]. Pathogen survival requires the alteration of cell native functions to allow infection, multiplication and transmission. The impact of pathogen infection on host cell function is reflected by changes in the transcriptome and proteome, something that was characterized here at tissue-specific level in ticks infected with A. phagocytophilum. Two independent samples were collected and processed for each tick developmental stage and tissue. After RNAseq, 2.1–4.1 Gbp (Ave±SD; 2.8±0.6) high quality reads were obtained for nymph, adult female midgut and salivary gland samples in infected and uninfected ticks with 101±2 bp average read length and less than 10% (0–8%) variation between replicates (S1 Table). These reads were aligned to the I. scapularis reference genome using TopHat and resulted in 16083, 12651 and 11105 gene transcripts in nymph, midgut and salivary gland samples, respectively with 16293 (99–231014) bp average length and 48±6%GC content (S2 Table). The number of unique gene transcripts mapped among all samples (17503) represented 85% of the predicted 20486 protein-coding genes in the I. scapularis genome [28]. Of the mapped transcripts, 8516 (53%), 5394 (43%) and 2487 (22%) were differentially expressed in response to A. phagocytophilum infection in nymph, midgut and salivary gland samples, respectively (P<0.05; Fig 1A and S2 Table). Probably due to the fact that whole internal organs were analyzed in nymphs, the number of differentially expressed genes in the nymphs was similar to the total number of differentially expressed genes in adult female midguts and salivary glands together (Fig 1A), suggesting that other tissues did not contribute much to the transcriptome in nymphs. However, differences were observed in the number of up- and down-regulated genes in different samples with a higher number of down-regulated genes in nymphs and midguts while in salivary glands the number of up- and down-regulated genes was similar (Fig 1A). We used P<0.05 for differential gene expression analysis, but a high proportion of the differentially expressed genes were significantly different between infected and uninfected samples at P<0.001 (Fig 1A), providing additional support for the transcriptomics data.
Proteomics analysis resulted in the identification of 7418 unique proteins, representing 36% of the predicted proteins encoded by the I. scapularis genome [28]. The number of proteins identified in nymphs (738) was lower than the number of proteins identified in midguts (4195) and salivary glands (6324), but the fraction of proteins matching I. scapularis identifications was similar between samples (53–66%), supporting that sampling did not affect protein assignations. Of the identified proteins, 67, 330 and 533 were differentially represented in response to A. phagocytophilum infection in nymphs, adult female midguts and salivary glands, respectively (Fig 1B and S3 Table).
Despite the difference between the number of mapped transcripts and proteins due to the lower resolution of protein identification when compared to transcriptomics [29], the coverage of the tick proteome reported here was high for ticks [23]. Similar to the transcriptomics analysis, differences were observed in the number of over- and under-represented proteins in different samples with a similar number of over-represented proteins in midguts and salivary glands but 2.7-fold more under-represented proteins in the salivary glands than in the midguts (Fig 1B).
At the individual mRNA and protein levels, a moderate (R2 = 0.4) correlation was obtained for the entire dataset but for genes and proteins highly up- and down-regulated/represented correlation did not exist (S1 Fig). The discrepancy between mRNA and protein levels could be explained by delay between mRNA and protein accumulation which requires sampling at different time points and/or the role for post-transcriptional and post-translational modifications in tick tissue-specific response to A. phagocytophilum infection. For example, apoptosis is often regulated at the post-transcriptional level [30].
The analysis of the total number of differentially expressed genes and represented proteins identified in tick samples highlighted dramatic tissue-specific differences in tick response to A. phagocytophilum infection. To characterize the complexity of the effect of pathogen infection on tick tissues, gene and protein ontology (GO) analyses were conducted to allow for a better characterization of tissue-specific differences in response to infection.
The GO analysis revealed that as expected for the incomplete annotation of the I. scapularis genome, many of the genes and proteins were assigned to unknown (“Others”) biological process (BP) or molecular function (MF) (S2 and S3 Figs). Nevertheless, cellular process, metabolic process and regulation were the most represented BP while catalytic activity and binding were the most represented MF in all tissues for both transcripts and proteins (S2 and S3 Figs). However, tissue-specific differences were also found that were more evident at the mRNA than at the protein level (S2 and S3 Figs), thus illustrating the complexity of the tick tissue-specific response to A. phagocytophilum infection.
The GO analysis is redundant because the same gene/protein may participate in more than one BP or MF, a problem that can be overcome in part by considering as one category in the analysis highly expressed/represented genes/proteins to reduce the number of entries per category. The analysis of tick genes/proteins whose expression/representation varied in more than 50-fold/5-fold further illustrated the complexity of tissue-specific differences in response to infection (S4 and S5 Figs). The total number of highly differentially expressed/represented genes/proteins suggested that the impact of bacterial infection on tick gene expression was more pronounced in nymphs and adult female midguts than in adult female salivary glands (Fig 2A). The hypothesis is that tick tissue-specific response to infection reflects pathogen developmental cycle. In adult female midguts, bacterial infection had the highest impact on tick gene/protein expression/representation through down-regulation of immune system and/or cellular process and up-regulation of metabolic process, while in salivary glands the bacteria had a lower impact on cellular processes because it does not need to replicate and are ready for transmission to vertebrate hosts by feeding ticks (Fig 2B). However, as a dynamic process, bacterial replication at earlier developmental stages may also affect cellular processes in salivary glands. These results reflected A. phagocytophilum developmental cycle in adult female tick tissues in which the intracellular reticulated, replicative form more abundant in midgut cells converts to the non-dividing infectious dense-core form more abundant in the salivary glands where bacterial transcription and translation are more active than replication [26].
Apoptosis is one of the pathways affected by intracellular bacteria such as A. phagocytophilum to establish infection in vertebrate host cells [31] and preliminary results suggested a role for apoptosis during infection of tick cells [16]. Our hypothesis is that A. phagocytophilum infection impacts on tick apoptosis pathways in a tissue-specific manner. To test this hypothesis, putative apoptosis pathway genes were annotated and then used to characterize tissue-specific differential gene/protein expression/representation in response to bacterial infection in combination with functional analyses.
The annotation of the putative apoptosis pathway genes in I. scapularis was based on sequence identity to genes reported in other organisms and used to characterize the tissue-specific response to A. phagocytophilum infection (Figs 3A-3C and 4A and 4B; S4 Table). All apoptosis pathways described in other organisms were identified in I. scapularis (Fig 4C). Each pathway requires specific triggering signals to begin an energy-dependent cascade of molecular events that activate the Caspase-dependent apoptosis execution pathway [32]. At least in humans, the Perforin/Granzyme pathway can only work in a Caspase-independent fashion through Granzyme A (Fig 4C) [32]. However, the ortholog for the Perforin gene was not identified in I. scapularis in these studies (Fig 4A and 4C).
Apoptosis pathway genes were differentially expressed in I. scapularis nymphs and adult female midguts and salivary glands with little overlapping between the different samples, thus providing additional evidences for the complexity of tissue-specific response to bacterial infection (Figs. 3A-3C, 4A, 4B and 5A; S4 Table). Four, 18 and 22 apoptosis pathway components were identified in both transcriptome and proteome in nymphs, adult female midguts and salivary glands, respectively (Fig 5B), and some of these molecules also showed differences between infected and uninfected samples at the protein level (S4 Table). These results suggested a role for apoptosis pathways during A. phagocytophilum infection of I. scapularis.
Some of the intrinsic apoptosis pathway components demonstrated a clear pattern of gene/protein differential expression/representation among the various samples (Fig 5C). In nymphs and adult female midguts, a tendency was observed towards gene up-regulation without an effect on protein representation in response to infection. However, in adult female salivary glands genes were down-regulated in response to infection with Caspase protein under-represented in infected ticks.
One of the problems associated with gene/protein annotations based on sequence identity is that function may not be necessarily identical between organisms. Therefore, functional characterization is ultimately needed to support gene/protein annotation and predicted function. In ticks, RNA interference (RNAi) is the most widely used technique for functional analysis [33]. The intrinsic apoptosis pathway has been implicated in A. phagocytophilum infection of tick cells [16] and was therefore selected for functional analysis using RNAi (Fig 5D).
The results revealed significant gene knockdown after dsRNA-mediated RNAi (Table 1). Gene knockdown for all selected intrinsic apoptosis pathway genes except Porin resulted in reduced tick weights (Fig 6A). These results differed from previous experiments in which Porin knockdown did result in reduced tick weigh [16]. One likely explanation for this discrepancy is the difference in the percent of gene expression silencing obtained in midguts, the most important organ in tick feeding, between both experiments (93% in [16] vs. 73% here (Table 1)), thus reinforcing that the role of Porin in tick feeding is marginal. The number of ticks that completed feeding was reduced in ticks injected with Bcl-2 and IAP dsRNAs (Fig 6B) and suggested a role for these genes during tick feeding. However, although a tendency was observed towards higher A. phagocytophilum DNA levels in ticks after RNAi for most of the selected intrinsic apoptosis pathway genes (Fig 6C), this effect was statistically significant for Porin only when compared to control dsRNA-injected ticks (Fig 6D and 6E). The results suggested that these genes do not have the same function reported in other organisms or the possible role of these genes on pathogen infection was not as relevant as that of Porin. Ayllón et al. [16] reported that A. phagocytophilum infection of tick cells results in down-regulation of mitochondrial Porin, thus providing a mechanism for subversion of host cell defenses to increase infection. This result was corroborated in these studies in which higher A. phagocytophilum DNA levels after Porin gene knockdown was found in both midguts and salivary glands (Fig 6D and 6E). Interestingly, among selected intrinsic apoptosis pathway genes, Porin was the only one consistently showing higher mRNA levels in unfed than in fed tick developmental stages and tissues (Fig 7A), suggesting an effect of tick feeding on Porin expression that may also contributed to Porin down-regulation in infected adult female salivary glands.
Tick feeding and infection with A. phagocytophilum may also affect Cytochrome c expression as part of the effect on the intrinsic apoptosis pathway (Fig 5C). Tick feeding resulted in variable Cytochrome c mRNA levels in different tissues and developmental stages with lower levels in fed larvae, nymphs and adult male midguts but not in female ticks and adult male salivary glands when compared to unfed ticks (Fig 7B). Infection with A. phagocytophilum resulted in up-regulation of Cytochrome c in adult female midguts but down-regulation in the salivary glands (Fig 7C), in agreement with Porin expression in response to infection (Fig 5C). The knockdown of intrinsic apoptosis pathway genes did not affect Cytochrome c mRNA levels in adult female tick midguts, but the effect in salivary glands suggested a complex mechanism by which tick cells respond to changes in the expression of these genes (Fig 7D). Taken together, these results showed a complex effect of tick feeding and A. phagocytophilum infection on Cytochrome c mRNA levels.
Although Porin and Cytochrome c expression was down-regulated in infected tick salivary glands, differences in protein levels between uninfected and A. phagocytophilum-infected tick salivary glands were not found (Fig 5C). These results were corroborated by immunocytochemistry (Fig 8A), demonstrating that differences between infected and uninfected tick salivary glands were not at the protein level but in the localization of Cytochrome c (Fig 8B). While Cytochrome c was distributed in the cell cytoplasm of uninfected tick salivary glands, in A. phagocytophilum-infected tick salivary glands Cytochrome c was mainly localized within organelles that probably correspond to mitochondria (Fig 8C). Although the mechanism(s) regulating mitochondrial permeability and the release of Cytochrome c during apoptosis are not fully understood, Bcl-2 may acts through the voltage-dependent anion channel or Porin, which in turn may play a role in regulating Cytochrome c release [34]. Taken together, these results demonstrated that A. phagocytophilum infection results in down-regulation of Porin expression in tick salivary gland but not midgut cells, which did not translate in different protein levels but resulted in the inhibition of Cytochrome c release as the anti-apoptotic mechanism to facilitate bacterial infection (Fig 8C).
The extrinsic apoptosis pathway is composed of Death ligand/receptor interactions such as Fatty acid synthase (FAS) ligand (FasL)/receptor that activate apoptosis (Fig 4C). A putative FasL-coding gene was not identified in the I. scapularis genome sequence, but the identification of the Fas apoptotic inhibitory molecule and Death receptors suggested that still uncharacterized FasL-like ligands may be present in ticks. Two genes were annotated as coding for Death receptors but except for down-regulation in nymphs for one of them, their expression did not change in response to pathogen infection and were not identified in the tick proteome (S4 Table). FAS is a central enzyme in de novo lipogenesis [35] but the inhibition of FAS causes apoptosis [36–39]. Interestingly, 24 genes were annotated as FAS-coding genes (Fig 3B). In general, most of the putative FAS proteins were not identified in the tick proteome, suggesting low protein levels or problems with the annotation of these genes (Fig 9A). Nevertheless, 6 of the putative FAS genes were corroborated at the protein level (Fig 9A). The analysis was then focused on the changes in FAS mRNA and protein levels in response to A. phagocytophilum infection that revealed different patterns in tick nymphs and adult female midguts and salivary glands (Fig 9A). Thirteen FAS genes were down-regulated in tick nymphs while two FAS genes were up-regulated in adult female midguts in response to infection. In adult female salivary glands, FAS gene expression could not be assessed but A. phagocytophilum infection resulted in 4 under-represented FAS proteins (Fig 9A).
Two different mechanisms mediated by the extrinsic [36, 37] and intrinsic [38, 39] pathways have been proposed to explain the apoptosis induced by the inhibition of FAS. However, the activation of the intrinsic apoptosis pathway is associated with mitochondrial oxidative stress and respiratory chain impairment, independent of FAS inhibition [39], thus suggesting that tick salivary glands may be responding to A. phagocytophilum infection by promoting apoptosis to limit bacterial infection through induction of the extrinsic apoptosis pathway. In this way, activation of the extrinsic apoptosis pathway in infected salivary glands may serve to counteract, at least in part, bacterial inhibition of the intrinsic apoptosis pathway. The activation of the extrinsic apoptosis pathway after FAS inhibition may be mediated by different mechanisms including possible interactions between FAS and FasL [36, 37, 40].
Phylogenetic analysis of putative I. scapularis FAS proteins suggested functional redundancy (Fig 9B), thus encouraging the use of FAS inhibitors and not RNAi for the functional characterization of these molecules during A. phagocytophilum infection of tick cells. Despite the increase in the number of apoptotic uninfected tick cells in culture, a 2 to 3 fold increase in the percent of apoptotic cells after 48 h of treatment with 5, 10 or 20 μg/ml of the FAS inhibitor Cerulenin was observed (Fig 9C). These results suggested that, as in other organisms [41], Cerulenin had an effect on cultured tick cells by promoting apoptosis through FAS inhibition. After 48 h of A. phagocytophilum infection of tick cells, the percent of apoptotic cells decreased in the presence of 0, 10 and 20 μg/ml of Cerulenin (Fig 9D), probably reflecting the effect of bacterial infection on the inhibition of the intrinsic apoptosis pathway. However, as expected for the Cerulenin induction of the extrinsic apoptosis pathway, A. phagocytophilum DNA levels decreased after 48 h of treatment as compared with infection levels in the absence of Cerulenin (Fig 9E). These results corroborated the effect of FAS inhibition on reducing A. phagocytophilum infection of tick salivary glands by activating the extrinsic apoptosis pathway in response to bacterial infection.
The Janus kinase/signal transducers and activators of transcription (JAK/STAT) pathway has been implicated in apoptosis signaling in vertebrate hosts infected with A. phagocytophilum [13], but has not been previously characterized in infected ticks. The JAK/STAT pathway genes were down-regulated in nymphs, up-regulated in adult female midguts and not affected by bacterial infection in adult female salivary glands (Fig 10A). In vertebrate hosts, A. phagocytophilum infection activates the JAK/STAT pathway to inhibit neutrophil apoptosis while mycobacteria and Brucellae inhibit this pathway to overcome host adaptive immunity [13]. The results in ticks suggested that similar mechanisms might occur during A. phagocytophilum infection by decreasing immunity in nymphs while inhibiting cell apoptosis in midgut cells to facilitate and establish infection. However, none of the JAK/STAT pathway components were identified in the tick proteome (S4 Table), thus precluding from comparing mRNA and protein levels in infected tick samples.
To verify the possible role of the tick JAK/STAT pathway during A. phagocytophilum infection, a preliminary experiment was conducted treating infected tick cells with JAK and/or STAT inhibitors (Fig 10B). The results showed that while the STAT inhibitor did not affect bacterial infection, treatment with the JAK inhibitor and the combination of STAT and JAK inhibitors did result in the reduction of A. phagocytophilum DNA levels when compared to control cells incubated with growth medium alone. These results supported a role for the tick JAK/STAT pathway during A. phagocytophilum infection.
The validation of RNAseq and proteomics data is important in order to provide additional support for the results obtained in these studies. However, although real-time RT-PCR is easy to perform to validate RNAseq data, few antibodies are available against tick proteins for validation of proteomics data. Herein, 16 genes were selected to validate RNAseq results by real-time RT-PCR (S6A Fig). Analysis of mRNA levels by real-time RT-PCR in individual samples from infected and uninfected tick nymphs, adult female midguts and salivary glands corroborated RNAseq results by demonstrating that gene up- or down-regulation was similar between RNAseq and RT-PCR analyses for most samples (S6B Fig). The minor differences observed between the results of both analyses could be attributed to intrinsic variation in gene expression and the fact that approximately 85% of the ticks used for RNAseq were infected [42] while for RT-PCR all ticks were confirmed uninfected or infected with A. phagocytophilum before analysis. Nevertheless, a positive correlation was obtained for absolute differential expression values between RNAseq and RT-PCR (S6C Fig). For the validation of proteomics data only nymph proteins were available after completion of the studies and two antibodies against intrinsic apoptosis pathway proteins, Porin and Cytochrome c, were used for Western blot analysis (S6D Fig) and immunofluorescence (Fig 8A). The results corroborated proteomics results in adult female tick salivary glands and nymphs and showed that although Cytochrome c was not identified by proteomics in nymphs (Fig 5C), Western blot results did not show any difference between infected and uninfected ticks (S6D Fig).
The experimental approach used in this study using systems biology showed a dramatic and complex tissue-specific response to A. phagocytophilum in the tick vector, I. scapularis. The results demonstrated that tick tissue-specific response to infection reflected pathogen developmental cycle and the impact of A. phagocytophilum infection on tick apoptosis pathways in a tissue-specific manner.
All apoptosis pathways described in other organisms were identified in I. scapularis, except for the absence of the Perforin ortholog in the Perforin/Granzyme pathway, and tissue-specific differences were found in the response to A. phagocytophilum infection. Although an ortholog for FasL was not identified in I. scapularis, other Death ligand/receptor interactions may activate the extrinsic apoptosis pathway. Functional characterization using RNAi demonstrated that Porin silencing significantly increased tick colonization by A. phagocytophilum but did not affect tick feeding, thus illustrating how bacterial inhibition of Porin expression increases tick vector capacity for this pathogen. In tick nymphs, the results suggested a possible effect of bacterial infection on the inhibition of tick immune response but further experiments are required to address this hypothesis. In tick midgut cells, the results suggested that A. phagocytophilum infection inhibited cell apoptosis to facilitate and establish infection through up-regulation of the JAK/STAT pathway genes. Bacterial infection inhibited the intrinsic apoptosis pathway in tick salivary glands but not in midguts by down-regulating Porin expression that resulted in the inhibition of Cytochrome c release as the anti-apoptotic mechanism to facilitate bacterial infection. However, tick salivary glands may be responding to A. phagocytophilum by promoting apoptosis to limit bacterial infection through induction of the extrinsic apoptosis pathway. In summary, the results suggested that A. phagocytophilum uses different mechanisms to establish infection in I. scapularis nymphs and adult female midguts and salivary glands (Fig 11), supporting the observation that the pathogen uses similar strategies to establish infection in both vertebrate and invertebrate hosts [16]. A. phagocytophilum has a type IV secretion system that translocates effector molecules to host cells to exert their activity on transcription and apoptosis and favor bacterial infection [27, 31]. These effectors have not been fully characterized but may be responsible for some of the changes shown here to occur in tick transcriptome and proteome in response to bacterial infection. These dynamic changes in response to A. phagocytophilum in I. scapularis tissue-specific transcriptome and proteome demonstrated the complexity of the tick response to infection and will contribute to characterize gene regulation in ticks.
Animals were housed and experiments conducted with the approval and supervision of the OSU Institutional Animal Care and Use Committee (Animal Care and Use Protocol, ACUP No. VM1026).
I. scapularis ticks were obtained from the laboratory colony maintained at the Oklahoma State University Tick Rearing Facility. Larvae and nymphs were fed on rabbits and adults were fed on sheep. Off-host ticks were maintained in a 12 hr light: 12 hr dark photoperiod at 22–25°C and 95% relative humidity. Nymphs and adult female I. scapularis were infected with A. phagocytophilum by feeding on a sheep inoculated intravenously with approximately 1x107 A. phagocytophilum (NY18 isolate)-infected HL-60 cells (90–100% infected cells) [42]. In this model, over 85% of ticks become infected with A. phagocytophilum in nymphs, midguts and salivary glands [42]. Ticks (200 nymphs and 100 female adults) were removed from the sheep 7 days after infestation, held in the humidity chamber for 4 days and dissected for DNA, RNA and protein extraction from whole internal tissues (nymphs) or midguts and salivary glands (adult females). Adult midguts and salivary glands were washed in PBS after collection to remove hemolymphs-related cells. Uninfected ticks were prepared in a similar way but feeding on an uninfected sheep. Two independent samples were collected and processed for each tick developmental stage and tissue. Total RNA, DNA and proteins were extracted from uninfected and A. phagocytophilum-infected nymph, midgut and salivary gland samples using the AllPrep DNA/RNA/Protein Mini Kit (Qiagen, Valencia, CA, USA). Ten individual nymphs and female ticks were dissected and samples collected to characterize A. phagocytophilum infection and the mRNA levels of genes selected after RNA sequencing (RNAseq).
Total RNA quality was evaluated using the Agilent 2100 Bioanalyzer RNA Nano Chip (Agilent Technologies, Santa Clara, CA, USA). For RNAseq sample preparation, the TruSeq Stranded mRNA Sample Prep Kit (Illumina, San Diego, CA, USA) was used according to the manufacturer's protocol. Briefly, 10 μg of each total RNA sample was used for polyA mRNA selection using streptavidin-coated magnetic beads, followed by thermal mRNA fragmentation. The fragmented mRNA was subjected to cDNA synthesis using the SuperScript II reverse transcriptase (Life Technologies, Grand Island, NY, USA) and random primers. The cDNA was further converted into double stranded cDNA and, after an end repair process, was finally ligated to Illumina paired end (PE) adaptors. Size selection was performed using a 2% agarose gel, generating cDNA libraries ranging in size from 200–500 bp. Finally, the libraries were enriched using 15 cycles of PCR and purified by the QIAquick PCR purification kit (Qiagen, Valencia, CA, USA). The enriched libraries were diluted with elution buffer (Qiagen) to a final concentration of 10 nM. Each library was run at a concentration of 7 pM on one Illumina Hiseq 2000 lane using 100 bp sequencing (CD BioSciences, Shirley, NY, USA). In the case of paired-end reads, distinct adaptors from Illumina were ligated to each end with PCR primers that allowed reading of each end as separate runs. The sequencing reaction was run for 100 cycles (tagging, imaging, and cleavage of one terminal base at a time), and four images of each tile on the chip were taken in different wavelengths for exciting each base-specific fluorophore. For paired-end reads, data were collected as two sets of matched 100-bp reads. Reads for each of the indexed samples were then separated using a custom Perl script. Image analysis and base calling were done using the Illumina GA Pipeline software.
TopHat [43] was used to align the reads to the I. scapularis (assembly JCVI_ISG_i3_1.0; http://www.ncbi.nlm.nih.gov/nuccore/NZ_ABJB000000000) reference genome. TopHat incorporates the Bowtie algorithm to perform the alignment [44]. TopHat initially removes a portion of reads based on quality information accompanying each read, then maps reads to the reference genome. TopHat allows multiple alignments per read (up to 40 by default) and a maximum of 2 mismatches when mapping reads to the reference genome. The mapping results were then used to identify “islands” of expression, which can be interpreted as potential exons. TopHat builds a database of potential splice junctions and confirms these by comparing the previously unmapped reads against the database of putative junctions. Default parameters for TopHat were used. Raw counts per gene were estimated by the Python script HTSeq count [http://www-huber.embl.de/users/anders/HTSeq/] using the reference genome. The raw counts per gene were used by DEGseq [45] to estimate differential expression at P<0.05.
Proteins were digested using the filter aided sample preparation (FASP) protocol [46]. The FASP method allows processing total SDS lysates of essentially any class of protein from biological samples of any origin, thus solving the long-standing problem of efficient and unbiased solubilization of all cellular proteins irrespective of their subcellular localization and molecular weight. Briefly, samples were dissolved in 50 mM Tris-HCl pH8.5, 4% SDS and 50 mM DTT, boiled for 10 min and centrifuged. Protein concentration in the supernatant was measured by the Direct Detect system (Millipore, Billerica, MA, USA). About 150 μg of protein were diluted in 8 M urea in 0.1 M Tris-HCl (pH 8.5) (UA), and loaded onto 30 kDa centrifugal filter devices (FASP Protein Digestion Kit, Expedeon, TN, USA). With this method, the sample is solubilized in 4% SDS, then retained and concentrated into microliter volumes in an ultrafiltration device. The filter unit then acts as a ‘proteomic reactor’ for detergent removal, buffer exchange, chemical modification and protein digestion. Notably, during peptide elution, the filter retains high-molecular-weight substances that would otherwise interfere with subsequent peptide separation [46]. The denaturation buffer was replaced by washing three times with UA. Proteins were later alkylated using 50 mM iodoacetamide in UA for 20 min in the dark, and the excess of alkylation reagents were eliminated by washing three times with UA and three additional times with 50 mM ammonium bicarbonate. Proteins were digested overnight at 37°C with modified trypsin (Promega, Madison, WI, USA) in 50 mM ammonium bicarbonate at 40:1 protein:trypsin (w/w) ratio. The resulting peptides were eluted by centrifugation with 50 mM ammonium bicarbonate (twice) and 0.5 M sodium chloride. Trifluoroacetic acid (TFA) was added to a final concentration of 1% and the peptides were finally desalted onto C18 Oasis-HLB cartridges and dried-down for further analysis. For stable isobaric labeling, the resulting tryptic peptides were dissolved in Triethylammonium bicarbonate (TEAB) buffer and labeled using the 4-plex iTRAQ Reagents Multiplex Kit (Applied Biosystems, Foster City, CA, USA) according to manufacturer's protocol. Briefly, each peptide solution was independently labeled at room temperature for 1 h with one iTRAQ reagent vial (mass tag 114, 115, 116 or 117) previously reconstituted with 70 μl of ethanol. Reaction was stopped after incubation at room temperature for 1 h with diluted TFA, and peptides were combined. Samples were evaporated in a Speed Vac, desalted onto C18 Oasis-HLB cartridges and dried-down for further analysis as previously described. Labeled peptides were loaded into the LC-MS/MS system for on-line desalting onto C18 cartridges and analyzing by LC-MS/MS using a C-18 reversed phase nano-column (75 μm I.D. x 50 cm, 3 μm particle size, Acclaim PepMap 100 C18; Thermo Fisher Scientific, Waltham, MA, USA) in a continuous acetonitrile gradient consisting of 0–30% B in 145 min, 30–43% A in 5 min and 43–90% B in 1 min (A = 0.5% formic acid; B = 90% acetonitrile, 0.5% formic acid). A flow rate of ca. 300 nl/min was used to elute peptides from the reverse phase nano-column to an emitter nanospray needle for real time ionization and peptide fragmentation on orbital ion trap mass spectrometers (both Orbitrap Elite and QExactive mass spectrometers, Thermo Fisher Scientific). For increasing proteome coverage, iTRAQ-labeled samples were also fractionated by cation exchange chromatography (Oasis HLB-MCX columns) into six fractions, which were desalted and analyzed by using the same system and conditions described before. For peptide identification, all spectra were analyzed with Proteome Discoverer (version 1.4.0.29, Thermo Fisher Scientific) using a Uniprot database containing all sequences from Ixodida (May 17, 2013). For database searching, parameters were selected as follows: trypsin digestion with 2 maximum missed cleavage sites, precursor and fragment mass tolerances for the Elite of 600 ppm and 1200 mmu, respectively, or 2 Da and 0.02 Da, respectively for the QExactive, carbamidomethyl cysteine as fixed modification and methionine oxidation as dynamic modifications. For iTRAQ labeled peptides, N-terminal and Lys iTRAQ modification was added as a fixed modification. Peptide identification was validated using the probability ratio method [47] and false discovery rate (FDR) was calculated using inverted databases and the refined method [48] with an additional filtering for precursor mass tolerance of 12 ppm. Only peptides with a confidence of at least 95% were used to quantify the relative abundance of each peptide determined as described previously [49].
Protein quantification from reporter ion intensities and statistical analysis of quantitative data were performed as described previously using QuiXoT [50, 51]. For iTRAQ data, only the intensity of the reporter ions within 0.4 Da windows around the theoretical values was considered for quantification. Reporter intensities were corrected for isotopic contaminants by taking into consideration the information provided by the manufacturer. The intensity of the reporter peaks was used to calculate the fitting weight of each spectrum in the statistical model as described previously [51]. Outliers at the scan and peptide levels and significant protein-abundance changes were detected from the z values (the standardized variable used by the model that expresses the quantitative values in units of standard deviation) by using a false discovery rate (FDR) threshold of 5% as described previously [51]. Results are the mean of two replicates.
The gene and proteins ontology (GO) analysis for Biological Process (BP) and Molecular Function (MF) was done using the STRAP software (Software for Researching Annotations of Proteins; [http://www.bumc.bu.edu/cardiovascularproteomics/cpctools/strap/] developed at the Cardiovascular Proteomics Center of Boston University School of Medicine (Boston, MA, USA) [52]. For annotation of selected pathways, gene identifiers were obtained from VectorBase (www.vectorbase.org) and compared to the corresponding pathways in D. melanogaster, Anopheles gambiae, Aedes aegypti and Homo sapiens. Regression analysis of biological processes in infected tick nymphs, adult female midguts and salivary glands was conducted using Excel normalizing against the total number of differentially expressed genes and represented proteins and excluding transcripts and proteins without known assignations.
For RNAi, oligonucleotide primers containing T7 promoters (S5 Table) were used for in vitro transcription and synthesis of dsRNA as described previously [16], using the Access RT-PCR system (Promega) and the Megascript RNAi kit (Ambion, Austin, TX, USA). The unrelated gene Rs86 dsRNA was synthesized using the same methods described previously and used as negative control [16]. The dsRNA was purified and quantified by spectrophotometry. Unfed adult ticks (N = 20 females per group) were injected with approximately 0.5 μl dsRNA (5x1010-5x1011 molecules/μl) in the lower right quadrant of the ventral surface of the exoskeleton of ticks [53]. The injections were done using a 10-μl syringe with a 1-inch, 33 gauge needle (Hamilton, Bonaduz, Switzerland). Control ticks were injected with the unrelated Rs86 dsRNA or were left uninjected. After dsRNA injection, female ticks were held in a humidity chamber for 1 day after which they were allowed to feed on sheep inoculated intravenously with A. phagocytophilum (NY18 isolate) as described before with 20 male ticks per tick feeding cell [42]. Two sheep, Sheep 11 and Sheep 15, were used with 11 cells each to feed ticks injected with gene-specific dsRNAs and the Rs86 dsRNA and uninjected controls. Ten female ticks per group were collected after 7 days of feeding and midguts and salivary glands dissected for DNA and RNA extraction using Tri Reagent (Sigma-Aldrich, St. Louis, MO, USA) following manufacturer instructions. RNA was used to characterize gene knockdown by real-time RT-PCR with respect to Rs86 control and DNA was used to characterize A. phagocytophilum infection by PCR [16]. Remaining ticks were allowed to feed until full engorgement and tick mortality and weight were determined in individual female ticks collected after feeding. Tick weight was compared between ticks injected with test genes dsRNA and Rs86 control dsRNA by Student's t-test with unequal variance (P = 0.05). The number of ticks completing feeding was compared between ticks injected with test genes dsRNA and Rs86 control dsRNA by one-tailed Fisher's exact test (P = 0.05).
A. phagocytophilum DNA levels were characterized by msp4 real-time PCR normalizing against tick 16S rDNA as described previously [16]. Normalized Ct values were compared between ticks injected with test genes dsRNA and Rs86 control dsRNA by Student's t-test with unequal variance (P = 0.05).
The expression of selected genes was characterized using total RNA extracted from individual nymphs and/or female midguts and salivary glands. All ticks were confirmed as infected or uninfected by real-time PCR analysis of A. phagocytophilum msp4 DNA in midguts and salivary glands. Real-time RT-PCR was performed on RNA samples using gene-specific oligonucleotide primers (S5 Table) and the iScript One-Step RT-PCR Kit with SYBR Green and the CFX96 Touch Real-Time PCR Detection System (Bio-Rad, Hercules, CA, USA). A dissociation curve was run at the end of the reaction to ensure that only one amplicon was formed and that the amplicons denatured consistently in the same temperature range for every sample. The mRNA levels were normalized against tick 16S rRNA and cyclophilin as described previously using the genNorm method (ddCT method as implemented by Bio-Rad iQ5 Standard Edition, Version 2.0) [16]. Normalized Ct values were compared between test dsRNA-treated ticks and controls treated with Rs86 dsRNA or between infected and uninfected ticks by Student's t-test with unequal variance (P = 0.05).
For analysis of mRNA levels in different tick developmental stages, total RNA was extracted from eggs (three batches of approximately 500 eggs each), fed and unfed larvae (three pools of 50 larvae each), fed and unfed nymphs (three pools of 15 nymphs each), and fed and unfed males and females adults tick tissues (4 ticks each) were used for real-time RT-PCR as described before but normalizing against tick cyclophilin and ribosomal protein S4 [GenBank: DQ066214] using oligonucleotide primers rsp4-F: 5’-GGTGAAGAAGATTGTCAAGCAGAG-3’ and rsp4-R: 5‘-TGAAGCCAGCAGGGTAGTTTG-3’.
Antibodies against Porin [16] and Cytochrome c (H-104: sc-7159; Santa Cruz Biotechnology, Inc. Dallas, TX, USA) were used for Western blot and immunofluorescence studies. Total proteins used for proteomics from infected and uninfected nymphs (2 μg from each sample) were methanol/chloroform precipitated, resuspended in Laemmli sample buffer and separated on a 15% SDS-PAGE gel under reducing conditions. After electrophoresis, proteins were transferred to nitrocellulose membranes (Bio-Rad, Hercules, CA, USA), blocked with SuperBlock blocking buffer in TBS (Thermo Scientific) and incubated overnight at 4°C with rabbit polyclonal anti-Porin (dilution 1:1000) or anti-Cytochrome c (dilution 1:200) antibodies. To detect the antigen-bound antibody, membranes were incubated with goat anti-rabbit IgG conjugated with horseradish peroxidase (dilution 1:10,000; Sigma-Aldrich). Immunoreactive proteins were detected by chemoluminescence using the SuperSignal West Pico chemoluminescent substrate (Thermo Scientific), visualized with an ImageQuant 350 Digital Imaging System (GE Healthcare, Pittsburgh, PA, USA), quantified using the ImageQuant TL 7.0 software (GE Healthcare) and normalized against total proteins. Normalized protein levels (N = 2) were compared between samples by χ2 test (p = 0.05). Positive controls (C+) corresponded to recombinant I. scapularis Porin expressed in Escherichia coli (5 μg) and human HL60 cells for Porin and Cytochrome c Western blots, respectively.
For immunofluorescence, adult ticks were infected with A. phagocytophilum as described before. Female ticks were removed from the sheep 7 days after infestation, held in the humidity chamber for 4 days and fixed with 4% paraformaldehyde in 0.2M sodium cacodylate buffer, dehydrated in a graded series of ethanol and embedded in paraffin. Sections (4 μm) were prepared and mounted on glass slides. The paraffin was removed from the sections with xylene and the sections were hydrated by successive 2 min washes with a graded series of 100, 95, 80, 75 and 50% ethanol. The slides were treated with Proteinase K (Dako, Barcelona, Spain) for 7 min, washed with PBS and incubated with 3% bovine serum albumin (BSA; Sigma-Aldrich) in PBS for 1 h at room temperature. The slides were then incubated for 14 h at 4°C with primary antibodies diluted 1:100 to 1:300 in 3% BSA/PBS and after 3 washes in PBS developed for 1 h with goat-anti-rabbit IgG conjugated with FITC (Sigma-Aldrich) (diluted 1:160 in 3% BSA/PBS). The slides were washed twice with TBS and mounted in ProLong Antifade reagent (Molecular Probes, Eugene, OR, USA) or in mounting medium containing DAPI (Vector Laboratories, Peterborough, UK). The sections were examined using a Leica SP2 laser scanning confocal microscope (Leica, Wetzlar, Germany). Sections of uninfected ticks and IgGs from preimmune serum were used as controls.
The I. scapularis ISE6 tick cell line (provided by U.G. Munderloh, University of Minnesota, USA) was cultured in L15B300 medium and inoculated with the human NY18 isolate of A. phagocytophilum propagated in HL-60 cells as described previously [16]. Uninfected cells were cultured in the same way, except with the addition of 1 ml of culture medium instead of infected cells. Uninfected and infected cultures (three independent cultures with approximately 5x105 cells each) were seeded in 24 well plates and treated with FAS inhibitor Cerulenin (Santa Cruz Biotechnology, Heidelberg, Germany) at 0, 5, 10 and 20 μg/ml and sampled at 0 h and 48 h after treatment. Apoptosis was measured by flow cytometry using the Annexin V-fluorescein isothiocyanate (FITC) apoptosis detection kit (Immunostep, Salamanca, Spain) following manufacturers protocol. It detects changes in phospholipid symmetry analyzed by measuring Annexin V (labelled with FITC) binding to phosphatidylserine, which is exposed in the external surface of the cell membrane in apoptotic cells. Cells were stained simultaneously with the non-vital dye propidium iodide (PI) allowing the discrimination of intact cells (Annexin V-FITC negative, PI negative), early apoptotic cells (Annexin V-FITC positive, PI negative), late apoptotic/necrotic cells (Annexin V-FITC positive, PI positive) and dead cells (Annexin V-FITC negative, PI positive). All samples were analyzed on a FAC-Scalibur flow cytometer equipped with CellQuest Pro software (BD Biosciences, Madrid, Spain). The viable cell population was gated according to forward-scatter and side-scatter parameters. The percentage of apoptotic cells (including early apoptotic, late apoptotic/necrotic and dead cells) was determined by FACS after Annexin V-FITC and PI labeling. Total DNA was extracted from 200 μl of a tick cell suspension using the RealPure Spin Kit (Durviz, Valencia, Spain) following the manufacturer's recommendations. A. phagocytophilum DNA levels were characterized by msp4 real-time PCR normalizing against tick 16S rDNA as described before [16]. The percent of apoptotic cells and normalized A. phagocytophilum DNA levels were compared between cells analyzed at 0 and 48 h of Cerulenin treatment and/or bacterial infection by Student's t-test with unequal variance (P = 0.05; N = 3).
The I. scapularis ISE6 tick cells were cultured and infected with the human NY18 isolate of A. phagocytophilum as described above. Infected cells were treated with 400 nM of the pan JAK inhibitor (GLPG0634; MedChem Express, New Jersey, USA), 9.2 μM of the STAT3 inhibitor (Cryptotanshinone; MedChem Express, New Jersey, USA) or a combination of both at the same concentration. Control cells were incubated culture medium alone. The inhibitors were added at the same time as the bacteria and then sampled at 48 h to extract total DNA to determine A. phagocytophilum DNA levels as described above. A. phagocytophilum DNA levels were compared between treated and control cells by Student's t-test with unequal variance (P = 0.05; N = 4).
FAS amino acid sequences were aligned with MUSCLE (v3.7) configured for high precision [54] and the ambiguous regions were removed with Gblocks (v0.91b) [55]. The phylogenetic tree was reconstructed using the maximum likelihood method implemented in PhyML (v3.0 aLRT) [56, 57]. Internal branch confidence was assessed by the bootstrapping method (1000 bootstrap replicates). Graphical representation and editing of the phylogenetic tree were performed with TreeDyn (v 198.3) [58].
|
10.1371/journal.ppat.1003107 | Bacterial Colonization of Host Cells in the Absence of Cholesterol | Reports implicating important roles for cholesterol and cholesterol-rich lipid rafts in host-pathogen interactions have largely employed sterol sequestering agents and biosynthesis inhibitors. Because the pleiotropic effects of these compounds can complicate experimental interpretation, we developed a new model system to investigate cholesterol requirements in pathogen infection utilizing DHCR24−/− mouse embryonic fibroblasts (MEFs). DHCR24−/− MEFs lack the Δ24 sterol reductase required for the final enzymatic step in cholesterol biosynthesis, and consequently accumulate desmosterol into cellular membranes. Defective lipid raft function by DHCR24−/− MEFs adapted to growth in cholesterol-free medium was confirmed by showing deficient uptake of cholera-toxin B and impaired signaling by epidermal growth factor. Infection in the absence of cholesterol was then investigated for three intracellular bacterial pathogens: Coxiella burnetii, Salmonella enterica serovar Typhimurium, and Chlamydia trachomatis. Invasion by S. Typhimurium and C. trachomatis was unaltered in DHCR24−/− MEFs. In contrast, C. burnetii entry was significantly decreased in −cholesterol MEFs, and also in +cholesterol MEFs when lipid raft-associated αVβ3 integrin was blocked, suggesting a role for lipid rafts in C. burnetii uptake. Once internalized, all three pathogens established their respective vacuolar niches and replicated normally. However, the C. burnetii-occupied vacuole within DHCR24−/− MEFs lacked the CD63-postive material and multilamellar membranes typical of vacuoles formed in wild type cells, indicating cholesterol functions in trafficking of multivesicular bodies to the pathogen vacuole. These data demonstrate that cholesterol is not essential for invasion and intracellular replication by S. Typhimurium and C. trachomatis, but plays a role in C. burnetii-host cell interactions.
| Clustered receptors associated with cholesterol-rich microdomains, termed lipid rafts, are thought to provide plasma membrane signaling platforms that bacterial pathogens can subvert to gain entry into host cells. Moreover, cholesterol has been implicated as a critical structural lipid of several pathogen-occupied vacuoles. Cumulative data supporting these models have principally been derived using inhibitors of cholesterol metabolism and various sterol sequestering compounds, agents that can lack specificity and cause unwanted cellular affects. Here, we employed a new system to investigate pathogen reliance on cholesterol for host cell colonization that utilizes mouse embryonic fibroblasts that can synthesize precursor sterols, but not cholesterol. Cells lacking cholesterol displayed strong defects in lipid raft-based signaling. However, no defects were observed in entry, vacuole development, and growth of Salmonella enterica and Chlamydia trachomatis, bacterial pathogens previously shown to rely on cholesterol for optimal host cell parasitism. Entry by Coxiella burnetii, the bacterial cause of human Q fever, was significantly decreased in cholesterol-negative cells as was trafficking of membranous material to the pathogen vacuole. However, subsequent bacterial replication was unaltered. Our results should prompt a reevaluation of the overall importance of cholesterol in bacterial pathogenesis with the described experimental system providing an alternative approach for such studies.
| Cholesterol is essential for proper membrane structure and function in eukaryotic cells. In the plasma membrane, the high cholesterol content (20–25% of total plasma membrane lipid) significantly influences membrane fluidity and permeability [1], [2]. In addition, cholesterol-rich membrane microdomains, or “lipid rafts”, sequester proteins involved in signal transduction, membrane fusion, and phagocytosis [3]. Cholesterol also regulates endosomal trafficking [4]–[6] and serves as a precursor for steroids and vitamins [7]. Defects in cholesterol biosynthesis and/or trafficking can have severe consequences, including increased susceptibility to oxidative stress and apoptosis [8]–[10] as well as impaired lipid raft signaling and altered caveolae structure [11]–[13]. At the organismal level, defects in cholesterol trafficking and storage can lead to neurodegeneration and splenomegaly, while a complete lack of cholesterol during fetal development is fatal [14].
Mammalian cells have two primary ways of obtaining cholesterol: uptake of exogenous cholesterol through low density lipoprotein (LDL) and biosynthesis of endogenous cholesterol. De novo cholesterol synthesis occurs in the endoplasmic reticulum where the first sterol intermediate, lanosterol, is further modified by 19 enzymatic reactions of demethylation, hydroxylation, and double bond reduction to generate the final sterol product, cholesterol. At the terminal step, the carbon 24 double bond of desmosterol is reduced by a Δ24 sterol reductase. In the absence of this enzyme, membrane cholesterol is replaced by its precursor, desmosterol. The mammalian Δ24 sterol reductase, DHCR24/Seladin, is a bifunctional protein with an enzymatic role in cholesterol biosynthesis and a non-enzymatic role in conferring resistance to oxidative stress [10], [15], [16].
Cholesterol is considered a critical factor in host cell colonization by several bacterial pathogens. To gain entry into host cells, many bacteria target proteins enriched in plasma membrane lipids rafts including αVβ3 integrin [17], E-cadherin [18], and ganglioside GM1 [19]. Furthermore, depletion of plasma membrane cholesterol with methyl-ß-cyclodextrin limits secretion of type III effector proteins by Salmonella enterica serovar Typhimurium and Shigella flexneri, resulting in decreased host cell invasion [20]. Filipin labeling shows high sterol levels in the membrane of intracellular compartments harboring pathogens such as Coxiella burnetii [21], Chlamydia trachomatis [22], and S. Typhimurium [23], leading to the hypothesis that cholesterol is critical for biogenesis of the pathogen-occupied vacuole. Another intracellular bacterium, Anaplasma phagocytophilum, recruits host cell cholesterol as a cell envelope constituent [24]. Cholesterol potentiates A. phagocytophilum infection of HL-60 cells [25] with trafficking of the sterol to the pathogen-occupied vacuole involving both LDL uptake and Niemann-Pick Type C pathways [25], [26]. In vivo, cholesterol promotes A. phagocytophilum infection of apolipoprotein E-deficient mice [27]. Pharmacological reagents that block LDL uptake dramatically inhibit A. phagocytophilum vacuole development and replication [25], while similar events are observed with C. burnetii and C. trachomatis infection when either cholesterol uptake or biosynthesis pathways are blocked [21], [22].
Commonly used cholesterol biosynthesis inhibitors and sequestering agents have pleotropic effects that can obscure the exact roles of cholesterol in host-pathogen interactions. For example, U18666A inhibits both trafficking of LDL [28], [29] and de novo cholesterol synthesis [30]. In addition, de novo synthesis inhibitors typically target cholesterol synthesis immediately upstream or downstream of lanosterol, therefore blocking synthesis of both intermediate sterols and cholesterol. Cholesterol-depleting compounds, such as methyl-ß-cyclodextrin, are toxic and significantly alter membrane properties such as protein diffusion and fluidity [31], [32]. Cells treated with methyl-ß-cyclodextrin also quickly replenish cholesterol-depleted membranes, thereby limiting experimental design. Collectively, these effects make defining a precise role for cholesterol in host-pathogen interactions challenging.
To circumvent the off-target effects of cholesterol biosynthesis inhibitors and sequestering agents, we established cholesterol-free cells using DHCR24−/− mouse embryonic fibroblasts (MEFs) [10]. Using this system, we examined the ability of the bacterial pathogens C. burnetii, S. Typhimurium, and C. trachomatis to colonize cells in the absence of cholesterol. Surprisingly, and in contrast to previous reports, we found that cholesterol was not required for efficient invasion and growth of C. trachomatis and S. Typhimurium. However, our experiments revealed a role for cholesterol in C. burnetii host cell entry as well as trafficking to the pathogen vacuole.
The mammalian enzyme DHCR24 catalyzes the final step in cholesterol biosynthesis by reducing a double bond at carbon 24 [33] (Figure. 1A). In the absence of this enzyme, desmosterol, the immediate precursor of cholesterol, becomes the dominant sterol in cellular membranes. We hypothesized that DHCR24−/− cells would provide a stable, cholesterol-free tissue culture system to study host-pathogen interactions. MEFs were isolated from a mating of heterozygote DHCR24+/− mice and identified as DHCR24−/− MEF lines by polymerase chain reaction (PCR) genotyping (Figure 1B). The absence of DHCR24 protein was confirmed by immunoblotting (Figure 1C). Although DHCR24−/− MEFs cannot synthesize cholesterol, cultivation of cells in standard culture media with serum provides a rich source of exogenous cholesterol. To obtain cholesterol-free cells with no source of endogenous or exogenous cholesterol, DHCR24−/− MEFs were adapted to medium lacking serum but containing the necessary primary fibroblast growth factors. Sterol analysis by high pressure liquid chromatography (HPLC) confirmed the absence of cholesterol in DHCR24−/− MEFs adapted to serum-free media (referred to as −cholesterol MEFs) (Figure 1D, top panel), with desmosterol now present as the primary sterol. When DHCR24−/− MEFs were grown in media supplemented with cholesterol (+cholesterol MEFs), cholesterol was preferentially incorporated into cellular membranes (Figure 1D, middle panel). As expected, cholesterol was the dominant sterol in wild type DHCR24+/+ MEFs even after adaptation to serum-free media (Figure 1D, bottom panel).
Desmosterol can replace cholesterol in tissue culture cells without major effects on growth and morphology [11], [34], [35]. However, in the absence of cholesterol, cells have impaired lipid raft function [10], [12]. To examine lipid raft function in −cholesterol MEFs, we first examined uptake of substrates through different endocytic processes. The fluid-phase marker dextran is internalized by cells through pinocytosis, a non-receptor mediated form of endocytosis. In contrast, receptor-mediated endocytosis can be either lipid raft-dependent (e.g., cholera-toxin B) or -independent (e.g., transferrin). Internalization of dextran and transferrin, molecules that are both internalized independent of lipid rafts, was identical between −cholesterol and +cholesterol MEFs (Figure 2A). However, uptake of cholera toxin-B (CT-B), a process that relies on toxin binding of ganglioside GM1 in lipid rafts [36], was dramatically impaired in −cholesterol MEFs. By electron microscopy, only 9% of −cholesterol MEFs contained 10 or more CT-B-positive endosomes, compared to 80% of +cholesterol MEFs (Figure 2B and Figure S1). These results suggested that lipid raft-mediated uptake is defective in −cholesterol MEFs while other endocytic pathways function normally.
We next determined if lipid raft-mediated signaling was impaired in −cholesterol MEFs by investigating epidermal growth factor (EGF) receptor-mediated signal transduction. Binding of EGF by EGF receptor in lipid rafts triggers receptor dimerization and autophosphorylation, which leads to activation of multiple signaling proteins, including Akt [37]–[39]. Immunoblotting and densitometry was used to measure phosphorylated Akt after EGF stimulation. There was a 50% reduction (p = 0.0373) in phosphorylated (activated) Akt in −cholesterol MEFs when compared to +cholesterol MEFs (Figure 2C). Collectively, our data indicate that lipid raft function is significantly impaired in −cholesterol MEFs.
Studies using methyl-ß-cyclodextrin to deplete host cells of cholesterol suggest that cholesterol and/or cholesterol-rich lipid rafts facilitate entry of the intracellular bacteria S. Typhimurium and Chlamydia trachomatis, organisms that utilize a type III secretion system (T3SS) to actively induce their uptake [40]. The role of cholesterol in C. burnetii entry has not been explored; however, the pathogen enters passively through endocytic pathways [41], [42]. Recognizing the caveats of using cholesterol sequestering agents for pathogen uptake experiments, we conducted bacterial invasion assays using −cholesterol MEFs. We hypothesized that if lipid rafts are utilized by these pathogens to invade host cells, entry should be significantly decreased in −cholesterol MEFs. For C. trachomatis and C. burnetii, immunofluorescence staining was used to enumerate the average number of intracellular bacteria per cell, while a gentamicin protection assay was employed to measure the percentage of internalized S. Typhimurium. Interestingly, the infection efficiency for both S. Typhimurium and C. trachomatis was nearly identical between −cholesterol and +cholesterol MEFs (Figures 3A and 3B), indicating cholesterol is not required for host cell entry by these pathogens. The Salmonella pathogenicity island 1 (SPI1)-encoded T3SS was required for efficient bacterial internalization by both −cholesterol and +cholesterol MEFs, with a 50-fold decrease in invasion of a SPI mutant compared to wild type bacteria (Figure 3B). In contrast, uptake of C. burnetii by −cholesterol MEFs was decreased by 87% (p = 0.0009) when compared to +cholesterol MEFs (Figure 3A). This effect was not due to decreased C. burnetii adherence as pathogen attachment to -cholesterol or +cholesterol MEFs was similar (Figure S2).
A study by Hayward et al. [20] demonstrated that depletion of cholesterol from NIH3T3 fibroblasts by treatment with methyl-ß-cyclodextrin inhibits translocation of the S. Typhimurium SPI1 effector proteins SopB and SopE. Furthermore, the type III secretion system 1 (T3SS1) translocon protein, SipB, was shown to bind cholesterol with high affinity using in vitro binding assays [20]. To further investigate the requirement of cholesterol in type III effector translocation, we used an adenylate cyclase (CyaA) assay to quantify translocation of two SPI1-associated effectors, SopB and SlrP, during S. Typhimurium infection of −cholesterol or +cholesterol MEFs. MEFs were infected with wild type S. Typhimurium expressing fusions of SopB or SlrP to Bordetella pertussis CyaA. At 1 hour post infection (hpi), cytosolic cAMP levels were measured by enzyme immunoassay and normalized to the number of bacterial colony forming units (CFU). As expected, −cholesterol and +cholesterol MEFs infected with wild type bacteria not expressing CyaA fusions showed normal levels of cAMP (Figure 3C; 0.046 and 0.033 fmol cAMP/CFU, respectively). In contrast, −cholesterol and +cholesterol MEFs infected with S. Typhimurium expressing either SopB-CyaA or SlrP-CyaA showed similar increases in levels of cAMP (Figure 3C; approximately 4 fmol cAMP/CFU), indicating translocation of effector fusions into the host cell cytoplasm. Thus, translocation of at least two S. Typhimurium T3SS1 effectors is independent of membrane cholesterol.
To determine whether internalized C. burnetii, S. Typhimurium, and C. trachomatis can productively infect cells in the absence of cholesterol, we examined pathogen vacuole formation and replication during infection of −cholesterol and +cholesterol MEFs. MEFs were fixed and labeled by immunofluorescence at times post-infection when mature pathogen vacuoles are present. For all three bacteria, no notable difference in vacuole size or morphology was observed between −cholesterol and +cholesterol MEFs (Figure 5A). Furthermore, growth assays revealed no significant differences in overall bacterial replication (Figure 5B). However, development of infectious C. trachomatis elementary bodies (EB) from non-infectious reticulate bodies (RB) appeared delayed in −cholesterol MEFs, with fewer infectious C. trachomatis present at 24 hpi (p = 0.02).
Given the defective uptake of C. burnetii by −cholesterol MEFs (Figure 3A), we further characterized the C. burnetii-containing vacuole, which normally has characteristics of a large phagolysosome [53]. Vacuoles were immunostained for the endolysosomal markers CD63, Rab7, flotillin-2, syntaxin 7, syntaxin 8, or vamp7, and assayed for the presence of active cathepsin. With the exception of CD63, endolysosomal markers were associated with C. burnetii vacuoles regardless of the presence or absence of host cell cholesterol (data not shown). CD63 is a late endosome/multivesicular body marker [54] and is typically associated with both the C. burnetii vacuolar membrane and lumen [55]. However, CD63 was absent from the vacuole lumen in −cholesterol MEFs (Figure 6A). This observation correlated with ultrastructural differences in the contents of the C. burnetii vacuole (Figure 6B). In DHCR24+/+ wild type MEFs and DHCR24−/− mutant MEFs with cholesterol added back, and as noted in other cell lines [56], a large amount of non-bacterial material was observed in the C. burnetii vacuole lumen, including multi-lamellar membranes and vesicles (Figure 6B). However, in DHCR24−/− MEFs without cholesterol, there was a striking absence of this material (Figure 6B). Although the source and function of these membranous structures are unknown, we can speculate that at least some of the material is derived from multivesicular bodies (MVBs), based on the CD63 labeling of C. burnetii vacuole lumen. Together, these data suggest that trafficking of late endosomes/MVBs to the C. burnetii vacuole requires cholesterol.
C. burnetii is unique among bacteria in encoding two eukaryote-like sterol reductase homologs [57], [58]. One of these, CBU1206, is a functional Δ24 sterol reductase, as indicated by yeast complementation experiments [59]. The presence of this enzyme in C. burnetii suggested that the bacterium might synthesize cholesterol from mammalian sterol precursors, such as desmosterol. To test this hypothesis, we analyzed sterols from uninfected and infected −cholesterol MEFs by HPLC to determine if infected cells contained cholesterol. We found no evidence for cholesterol biosynthesis in C. burnetii-infected MEFs (Figure 7), which had a sterol profile virtually identical to uninfected cells. To confirm that cholesterol was not being made at very low levels, we labeled infected cells with 14C-acetate and analyzed the sterol profile by HPLC with an in-line scintillation detector. Even with this more sensitive detection method, there was no detectable cholesterol in C. burnetii-infected MEFs (data not shown).
Elucidating the role of cholesterol and lipid rafts in host-pathogen interactions has been challenging. The majority of studies have relied on cholesterol sequestering agents and biosynthesis inhibitors to remove or deplete host cell cholesterol. Cholesterol sequestering agents such as methyl-ß-cyclodextrin physically remove cholesterol from membranes, in particular the plasma membrane. While this method efficiently depletes cholesterol, several concerns must be recognized. First, standard treatment of cells with methyl-ß-cyclodextrin (5–10 mM) removes up to 90% of the total cellular cholesterol, resulting in dramatic effects on cellular morphology and viability [31]. Second, cells tightly regulate total cholesterol levels as well as concentrations across organelles. Removal of plasma membrane cholesterol can therefore alter cholesterol distribution throughout the cell and result in increased cholesterol synthesis and trafficking [60]. Finally, cyclodextrins remove cholesterol from both lipid raft and non-lipid raft domains, and there is some evidence that non-cholesterol membrane components such as phospholipids are also extracted [31]. As a result, membrane properties such as fluidity and protein distribution are drastically altered. Other agents used to interfere with cholesterol functions, such as filipin and nystatin, have similar negative effects on the host cell [61]. Another approach to cholesterol depletion utilizes biosynthesis inhibitors, such as statins and U18668. While inhibitors can efficiently lower cholesterol levels, the approach suffers from two major problems. First, the majority of available inhibitors target enzymes early in the pathway. Consequently, the resulting decrease in all sterols makes it difficult to directly associate results with cholesterol depletion. Second, the majority of the inhibitors have pleotropic and/or off-target effects. For example, U18666A inhibits trafficking of LDL-derived cholesterol [28], [29], de novo cholesterol synthesis [30], and lipid organization of membranes by directly binding to membranes [62].
When adapted to serum-free media, DHCR24−/− MEFs lack cholesterol but contain all of the upstream sterols. In the place of cholesterol these cells accumulate desmosterol, a sterol that differs from cholesterol by a single double bond at the carbon 24 position. Previous work in J774 murine macrophages, which also lack the DHCR24 enzyme, demonstrates that desmosterol can replace cholesterol in the regulation of cellular sterol homeostasis and proliferation [35]. However, desmosterol does not functionally replace cholesterol in lipid rafts [10]–[12]. Indeed, we found lipid raft-mediated uptake and signaling to be dysfunctional in −cholesterol MEFs.
Many studies have examined the role of cholesterol and lipid rafts in pathogen-host cell interactions. However, to our knowledge, these studies have all utilized methyl-ß-cyclodextrin and/or non-specific inhibitors. Our cholesterol-free cell system allowed examination of the importance of cholesterol in host cell colonization of three intracellular bacterial pathogens (C. burnetii, S. Typhimurium, and C. trachomatis) without the pleiotropic effects of these pharmaceutical agents.
Invasion of host cells by the obligate intracellular bacterium C. trachomatis is promoted by cytosolic translocation of effector proteins via a T3SS [63]. However, conflicting reports exist on the role of cholesterol in the invasion process. Jutras and colleagues [64] found that C. trachomatis associates with detergent-resistant plasma membranes, and that bacterial uptake is decreased by 80% in cells treated with methyl-ß-cyclodextrin. In contrast, Gabel et al. [65] saw no effect on entry when cells were treated with methyl-ß-cyclodextrin, filipin, or nystatin. Our results agree with the latter report, as we saw no difference in C. trachomatis invasion of MEFs with or without cholesterol.
Once internalized, C. trachomatis resides in a vacuole (or inclusion) that disconnects from the endocytic pathway and acquires characteristics of a Golgi-derived vesicle [66]. Here, infectious EBs differentiate into metabolically active RBs. Several lines of evidence suggest a role for cholesterol in C. trachomatis inclusion development and replication. Filipin labeling indicates that both the inclusion membrane and bacteria contain sterols [22], [67], while HPLC analysis demonstrates the presence of cholesterol in the bacteria [22]. Furthermore, sterol delivery appears to be Golgi-dependent [22]. We found that cholesterol was not essential for productive infection by C. trachomatis, as the number of infectious units at 48 hpi was identical between MEFs with or without cholesterol. However, we did observe a delay in the onset of the logarithmic phase of the organism's growth cycle, suggesting cholesterol is involved in RB to EB transition. This may reflect a defect in trafficking to the inclusion, perhaps resulting in a decrease in nutrients important for C. trachomatis development.
Our data also demonstrate that cholesterol is not essential for type III secretion and productive infection by S. Typhimurium, a finding that directly contradicts previous reports [20], [68]. These disparate results may reflect different experimental approaches, with previous studies using methyl-ß-cyclodextrin for cholesterol depletion. As discussed earlier, this treatment alters the membrane properties of cells. Indeed, Garner et al. [68] observed that methyl-ß-cyclodextrin treated cells “exhibited a rounder morphology,” although they did not find a difference in cell viability. Hayward et al. [20] utilized in vitro binding assays to demonstrate binding of the S. Typhimurium T3SS protein SipB to cholesterol complexed with methyl-ß-cyclodextrin. However, other sterols, such as desmosterol, were not tested, nor was the ability of SipB to bind model membranes, a closer cellular mimic. By immunofluorescence, Hayward et al. [20] also showed defective secretion of the T3SS effector SopB in methyl-ß-cyclodextrin-treated cells. Using a more sensitive CyaA assay, we demonstrate here the cholesterol-independent translocation of the effectors SopB and SlrP.
Based on filipin staining, the S. Typhimurium vacuole accumulates cholesterol during host cell infection [23]. However, inhibitor studies suggest that non-sterol precursors of the cholesterol biosynthetic pathway are required for S. Typhimurium intracellular growth, and that cholesterol itself is not essential [69]. While these studies were not done in a truly cholesterol-free system (i.e., normal serum conditions were used), our data support the conclusion that cholesterol is not essential for S. Typhimurium growth in host cells.
Unlike C. trachomatis and S. Typhimurium, C. burnetii passively enters host cells through receptor-ligand interactions, triggering classical actin-dependent phagocytosis [41], [42], [70]. C. burnetii entry into cholesterol-free cells is significantly decreased, suggesting uptake occurs through cholesterol or lipid raft-mediated pathway. Our data using blocking antibodies and vitronectin demonstrate that αVβ3 integrin is involved in entry into MEFs in a cholesterol-dependent manner. Furthermore, FAK, a key component of integrin signaling, is required for efficient C. burnetii entry. Together, these data suggest that C. burnetii utilizes lipid raft-mediated αVβ3 integrin signaling to gain entry into host cells. Although significantly decreased, C. burnetii entry into −cholesterol MEFs still occurs, suggesting the pathogen can also enter by non-lipid raft-associated receptors that function normally and/or by lipid raft-associated receptors that function inefficiently due to raft disruption.
Based on intense staining by the sterol-binding fluorophor filipin, we previously showed that the membrane of the mature C. burnetii vacuole is sterol-rich [21]. In the same study, inhibitors of host cell sterol biosynthesis and uptake inhibited C. burnetii vacuole formation and growth [21]. Here, we demonstrate that C. burnetii vacuole formation and replication in −cholesterol MEFs is similar to +cholesterol MEFs. We conclude from these data that precursors of cholesterol, but not cholesterol per se, are required for optimal infection by C. burnetii. The C. burnetii-occupied vacuole of −cholesterol MEFs does show a striking absence of CD63-positive membranous material that we speculate represents MVBs. Thus, trafficking to and fusion with the C. burnetii vacuole of some vesicular compartments appears to depend on cholesterol, although these events are clearly not required for pathogen replication.
The C. burnetii human DHCR24 homolog CBU1206 has sterol reductase activity when ectopically expressed in yeast [59]. Thus, we postulated that CBU1206 activity during C. burnetii infection of DHCR24−/− MEFs might rescue the cholesterol-negative phenotype of these cells to result in enhanced pathogen growth. However, synthesis of cholesterol was not detected in infected cells; thus, the precise role of CBU1206 in C. burnetii colonization of mammalian cells remains unresolved.
To our knowledge, this is the first study to address the role of cholesterol in host-pathogen interactions without the use of pleiotropic inhibitors or compounds that dramatically change membrane dynamics. While our results argue that cholesterol is not absolutely required for in vitro host cell colonization by three different intracellular pathogens, it does not eliminate the possibility that these pathogens target cholesterol and lipid rafts during in vivo infection, or that cholesterol is important under specific cellular conditions.
This study was carried out in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health. The animal protocol used in this study was approved by the Rocky Mountain Laboratories Institutional Animal Care and Use Committee (Protocol Number: RML 2008-08).
C. burnetii Nine Mile RSA439 (phase II, clone 4) was propagated in African green monkey kidney (Vero) cells (CCL-81; ATCC, Manassas, VA) grown in RPMI with 10% fetal bovine serum (FBS) (Invitrogen, Carlsbad, CA). Bacteria were purified from host cells at 28 days post infection (dpi) as previously described [71], and stored at −80°C. Chlamydia trachomatis LGV-434, serotype L2, was propagated in HeLa 229 cells (CCL-2; ATCC) in RPMI +10% FBS. Bacteria were purified by Renografin density gradient centrifugation [72], and stored at −80°C. Wild type Salmonella enterica serovar Typhimurium (S. Typhimurium) SL1344 [73] and the isogenic ΔSPI1::kan mutant [74] have been previously described. S. Typhimurium bacteria were grown in 2 ml LB-Miller broth for 16 to 18 h at 37°C with aeration (225 rpm), then subcultured in 10 ml LB-Miller broth (1∶33 dilution) for 3.5 h (225 rpm). Prior to infection, bacteria were centrifuged at 8,000xg for 2 min and the bacterial pellet resuspended in an equal volume of Hank's Balanced Salt Solution (HBSS; Mediatech, Manassas, VA).
Heterozygote DHCR24+/− mice (C57BL/6 genetic background) were generously provided by Quark Biotech, Inc. (Ness Ziona, Israel). Individual 15 to 17 day embryos were harvested and digested with trypsin, and fibroblasts cultured in DMEM supplemented with 10% FBS and 100 U ml−1 penicillin/streptomycin (Invitrogen). DNA was isolated from both individual embryos and fibroblasts using a DNeasy Blood and Tissue DNA isolation kit (Qiagen, Valencia, CA). PCR genotyping was conducted as previously described [75].
To generate stable lines, MEFs were passaged every three days at 3.8×105 cells per 25 cm2 flask in DMEM containing 10% FBS. Once stable lines were obtained (approximately passage 20), they were adapted to serum-free media using Fibroblast Basal Medium supplemented with compounds contained in a fibroblast serum-free growth kit (ATCC). MEFs were then continuously cultured in serum-free fibroblast media with or without SyntheChol, a water-soluble cholesterol media supplement (Sigma-Aldrich, St. Louis, MO) [76].
MEFs negative for focal adhesion kinase (Du3 cells; FAK−/−) and wild type cells (Du17 cells; FAK+/+) [51], [52] were grown in DMEM supplemented with 10% FBS.
To determine DHCR24 protein levels, confluent MEF monolayers from a 25 cm2 flask were washed twice with phosphate-buffered saline (PBS; 1 mM KH2PO4, 155 mM NaCl, 3 mM Na2HPO4, pH 7.2), trypsinized, and pelleted at 1500xg for 5 min. Cell pellets were resuspended in sodium dodecyl sulfate (SDS) sample buffer (0.5% SDS, 50 mM Tris, 150 mM NaCl) and the protein concentration determined using a DC protein assay kit (Bio-Rad, Hercules, CA). Ten micrograms of protein per lane were separated by 10% SDS-polyacrylamide gel electrophoresis (SDS-PAGE) and transferred to nitrocellulose. The membrane was probed with a mouse polyclonal antibody directed against human DHCR24 (Abnova, Walnut, CA) for 1 h, followed by incubation with an anti-mouse immunoglobulin G secondary antibody conjugated to horseradish peroxidase (HRP) (ThermoScientific, Rockford, IL). Following development using chemiluminesence (ThermoScientific), the membrane was treated for 1 h with 0.1% azide in PBS to destroy the chemiluminescent signal, then reprobed for glyceraldehyde-3-phosphate dehydrogenase (GAPDH) as a loading control using rabbit anti-GAPDH (Cell Signaling, Danvers, MA) and anti-rabbit-HRP (ThermoScientific).
To analyze Akt phosphorylation, confluent MEF monolayers were starved in serum-free media without growth supplements for 3 h. Cells were stimulated with 50 ng ml−1 recombinant mouse epidermal growth factor (EGF; ATCC) for 2 min prior to lysis in SDS sample buffer as indicated. Proteins were separated on 4–20% SDS-PAGE gradient gels (Bio-Rad, Hercules, CA), then transferred to nitrocellulose. The membrane was probed with rabbit anti-phospho Ser473 Akt (Cell Signaling), total Akt (Cell Signaling), and mouse anti-ß actin (Abcam, Cambridge, MA). Densitometry was conducted using a Kodak Image Station 4000 MM (Eastman Kodak, Rochester NY), and phospho Akt levels normalized to actin. Phospho Akt signals were compared between EGF-stimulated MEFs and mock stimulated MEFs.
MEFs were scraped into fresh media, counted, and the cell density adjusted to 1×105 cells ml−1. Resuspended cells were plated onto an ibidi-treated channel μ-slide VI0.4 (Ibidi, Verona, WI) and allowed to adhere. Prior to labeling, the ibidi dish was chilled on ice for 5 to 10 min. Transferrin Alexa Fluor 546 (50 µg ml−1, Invitrogen) or dextran Alexa Fluor 647 (1 mg ml−1, Invitrogen) was added in serum-free media, then the culture dish was incubated on ice for 15 min followed by incubation at 37°C for 15 min. The cells were washed twice with cold media, then treated with basal media (pH 3.5) to remove extracellular label. Cells were fixed for 15 min on ice with 2.5% paraformaldehyde (PFA) in PBS. Identical capture settings were used to obtain 0.4 µm slices with a modified Perkin-Elmer UltraView spinning-disk confocal system connected to a Nikon Eclipse Ti-E inverted microscope. ImageJ (written by W.S. Rasband, National Institutes of Health, Bethesda, MD) was used to quantitate intensity per cell area, with identical threshold settings. At least 20 cells were measured per condition for each experiment (n = 2).
Lipids were extracted using a modified Bligh-Dyer protocol [77]. Confluent monolayers from a 25 cm2 flask were washed twice with PBS, scraped into 10 ml PBS, and centrifuged for 5 min at 1500xg. The cell pellet was resuspended in 1.6 ml PBS and transferred to a glass tube. Chloroform:methanol 1∶2 (6 ml) was added and the solution vortexed. Following the addition of 2 ml chloroform and 2 ml PBS, the aqueous and organic phases were separated by centrifugation at 1500xg for 5 min. The lower organic phase was transferred to a new glass vial, dried under nitrogen, and resuspended in a small volume of acetonitrile:methanol (50∶50). Sterols were analyzed by HPLC using a C18 reverse phase column (Alltima HP C18 HL 5 µm, 250×4.6 mm, Grace Davison Discovery Sciences, Deerfield, IL) at 51°C, with a mobile phase of methanol:acetonitrile 50∶50 at 1 ml min−1. Sterol fractions were detected with an evaporative light scattering detector set at 60°C. The retention times of desmosterol and cholesterol were determined using commercially available standards (Avanti Polar Lipids, Alabaster, Alabama).
Fluorescence-based in versus out assays were employed for C. burnetii and C. trachomatis. MEFs were scraped into fresh media, counted, and the concentration adjusted to 1.5×105 cells ml−1. Resuspended cells were plated into individual channels of an ibidi-treated channel μ-slide VI0.4 (Ibidi, Verona, WI). C. burnetii and C. trachomatis were added to MEFs at a multiplicity of infection (MOI) of 200 (based on genome equivalents) and 10 (based on inclusion-forming units), respectively, and incubated at 37°C in 5% CO2 for 2 h (C. burnetii) or 1 h (C. trachomatis). Cells were then fixed with 2.5% PFA for 15 min on ice, followed by three washes with PBS. All subsequent steps were done at room temperature. Cells were blocked for 15 min in 1% bovine serum albumin (BSA) in PBS, then incubated for 15 min with rabbit polyclonal antibody directed against C. burnetii or a mouse monoclonal antibody (L21-45) directed against C. trachomatis serovar L2. After six washes with PBS to remove any residual antibody, cells were permeabilized for 15 min with 0.1% Triton X-100 in 1% BSA-PBS, followed by incubation for 15 min with guinea pig polyclonal antibody directed against C. burnetii or rabbit polyclonal antibody directed against C. trachomatis. After five washes with PBS, cells were incubated with secondary antibodies for 15 min (for C. burnetii, Alexa Fluor 546 anti-rabbit and Alexa Fluor 488 anti-guinea pig; for C. trachomatis, Alexa Fluor 546 anti-mouse and Alexa Fluor 488 anti-rabbit). After washing five times with PBS, samples were mounted in ProLong Gold containing DAPI (4′,6-diamidino-2-phenylindole; Invitrogen). The number of intracellular (green only) bacteria per cell were counted. Experiments were conducted three times in triplicate, with 100 cells counted per replicate. The results are expressed as the total number of intracellular bacteria divided by the total number of host cells.
For blocking assays, cells were plated on ibidi-treated channel slides as described above. Prior to infection, cells were incubated with 120 µl of blocking antibody or protein at 10 µg ml−1 for one h. Mouse anti-αV integrin (monoclonal 272-17E6), mouse anti- αVβ3 integrin (monoclonal 27.1), mouse vitronectin and mouse fibronectin were obtained from Abcam (Cambridge, MA). Mouse IgG1 isotype control antibody was obtained from BD Biosciences (San Jose, CA). C. burnetii was added to MEFs at an MOI of 200 in media containing corresponding antibody or protein, and after a 2 h incubation, the slides were fixed and processed for in/out staining as described above. Experiments were conducted three times in duplicate.
For S. Typhimurium CFU-based invasion assays, MEFs were seeded at ∼50% confluency in 24-well tissue culture treated plates approximately 24 h prior to infection. Bacteria were added to cells at an MOI of ∼100 and incubated for 10 min at 37°C. Extracellular bacteria were removed by aspiration, monolayers washed three times with HBSS, then fresh growth media was added to cell cultures. Following a 20 min incubation, cells were treated with growth media containing 50 µg ml−1 gentamicin for 1 h to kill extracellular bacteria. For enumeration of intracellular bacteria, monolayers were lysed in 1 ml 0.2% (w/v) deoxycholate in PBS and serial dilutions were plated onto LB agar plates to determine CFU. Experiments were done three times in duplicate, and internalized bacteria expressed as a percentage of the inoculum.
MEFs were plated on ibidi-treated channel μ-slide VI0.4 as described above. C. burnetii was added to cells at an MOI of 200 and incubated on ice for 30 min. Cells were then fixed with 4% PFA for 15 min on ice, followed by three PBS washes. All subsequent steps were done at room temperature. After blocking in 1% BSA-PBS for 15 min, bacteria were labeled for 15 min with guinea pig anti-C. burnetii. After 3 washes with PBS, cells were incubated for 15 min with Alexa Fluor 546 anti-guinea pig and Alexa Fluor 488 wheat germ agglutinin to label adherent bacteria and the host cell plasma membrane, respectively. After three washes with PBS, cells were mounted with ProLong Gold with DAPI, and the number of attached bacteria counted. Experiments were done twice in triplicate, and results are expressed as the total number of attached bacteria divided by the total number of cells.
For measurement of type III effector translocation, wild type S. Typhimurium bacteria were electroporated with low-copy number plasmids expressing SopB-CyaA-SigE [78] or SlrP-CyaA under the control of their native promoters. Overlap extension PCR was used to create a plasmid encoding the N-terminal 207 amino acids of SlrP fused to the catalytic domain of CyaA and under the control of the slrP promoter. slrP and approximately 470 bp of upstream region were amplified from S. Typhimurium SL1344 genomic DNA with the oligonucleotide pairs slrP-Xba-Fw (5′ tgc tct aga gcg agt cat cgt tac cat ggc tcg 3′) and slrP-cya-Rv (5′ acc agc ctg atg cga ttg ctg atc gag tat cag agt agt tat ctg ctc 3′). The catalytic domain of Bordetella pertussis CyaA was amplified from pPipB(1-210)-CyaA [79] with the oligonucleotide pairs cya-SlrP-OE-Fw (5′ cag ata act act ctg ata ctc gat cag caa tcg cat cag gct ggt tac 3′) and cyaA-Eco (5′ ccc gga tcc gat atc ttc atc gat aac tgt cat agc cgg 3′). The resulting amplicons were purified and mixed for a second round of PCR with slrP-Xba-Fw and cyaA-Eco. This amplicon was then cloned into pCR2.1TOPO (Invitrogen), released by EcoRI digestion and ligated into EcoRI-digested pMPMA3ΔPlac [80] to create SlrP-CyaA.
MEFs were infected as described above with wild type S. Typhimurium expressing or not expressing the SopB-CyaA-SigE or SlrP-CyaA fusion proteins. At 1 hpi, MEFs were washed twice with HBSS, then lysed in 1 ml 0.2% DOC for CFU enumeration, or 300 µl lysis buffer 1B (supplied with the cAMP immunoassay kit, GE Life Sciences, Piscataway, NJ) supplemented with 0.1 M HCl for CyaA assays. CyaA samples were rocked at room temperature for 10 min, neutralized with 1 M NaOH, then stored at −20°C. Samples were clarified by centrifugation at 14,000xg for 10 min at 4°C, then CyaA assays conducted using the non-acetylation enzymatic immunoassay procedure as described by the manufacturer (GE Life Sciences). The concentration of cAMP was normalized to CFUs collected at 1 hpi.
MEFs were plated at 5×104 cells per well in a 24-well tissue culture plate and allowed to grow to confluency. MEFs were infected with C. burnetii at an MOI of 25 for 2 h in 250 µl serum-free media. MEFs were washed twice with PBS, then incubated for the indicated times in serum-free media with or without SyntheChol. At each timepoint, the media was transferred to a 2 ml microfuge tube, the cells trypsinized and combined with the media. Following centrifugation at 20,000xg for 10 min, the pellet was resuspended in 200 µl water and added to a half volume of 0.1 mm zirconia/silica beads (BioSpec Products, Bartlesville, OK). Cells were lysed by bead beating in a FastPrep FP120 (Thermo Scientific) at setting #5 for 40 sec, the lysate spun briefly, then the sample heated at 100°C for 10 min. Beads and cell debris were pelleted at 20,000xg for 2 min, then 100 µl of the supernatant transferred to a new tube. Genome equivalents using a dotA probe were determined by quantitative PCR as previously described [56]. Growth assays were done three times in duplicate.
An inclusion-forming unit assay was used to quantify C. trachomatis growth [81]. Bacteria were added to MEF cultures at an MOI of 10 in 250 µl serum-free media, then cell cultures incubated on ice for 15 min followed by a 30 min incubation at 37°C. MEFs were washed twice with PBS, then fresh serum-free media with or without SyntheChol was added to the wells. At the indicated time points, the media was removed and the cells lysed by treatment with 500 µl of water for 5 min. After vigorous mixing, 10-fold serial dilutions were made in RPMI with 10% FBS. Bacterial suspensions were added to confluent HeLa cell monolayers that were then incubated at 37°C in 5% CO2 for 24 to 36 h. Infected HeLa cells were fixed with methanol, then inclusions stained with rabbit anti-C. trachomatis L2 antibody and anti-rabbit Alexa Fluor-488 secondary antibody. The number of inclusions in 15 fields at 20× were counted, and the experiment was done three times in duplicate.
To quantify S. Typhimurium intracellular replication, MEFs were infected as described for entry assays. After the initial 50 µg ml−1 gentamicin treatment, the gentamicin concentration was reduced to 10 µg ml−1 and replenished at 12 hpi when needed. CFU enumeration of intracellular bacteria was conducted as described above.
To assay pathogen vacuole development, infected MEFs in ibidi-treated channel μ-slide VI0.4 were fixed with 2.5% PFA, washed three times with PBS, then blocked/permeabilized for 15 min at room temperature with 0.1% saponin in 1% BSA-PBS. Primary antibodies used were rat anti-LAMP1 (BD Biosciences, San Jose, CA), guinea pig anti-C. burnetii, rat anti-CD63 (R&D Systems, Minneapolis, MN), and rabbit anti-C. trachomatis. Wild type S. Typhimurium constitutively expressing mCherry [82] was used for infections. Cells were mounted in ProLong Gold with DAPI. Widefield microscopy was done on Nikon Eclipse TE2000 epifluorescence microscope. Image analyses were done with ImageJ.
MEFs were scraped into fresh media, spun at 500xg for 5 min, then washed once in cold 2% BSA-PBS. All subsequent manipulations were done on ice. Following a 10 min incubation in 2% BSA-PBS, mouse anti-αVβ3 integrin (monoclonal 27.1) or mouse IgG1 isotype was added at a dilution of 1∶250. The cells were incubated for one h, washed twice with BSA-PBS, then incubated for 30 min with anti-mouse Alexa Fluor 647. Following four washes, the cells were analyzed by flow cytometry. Data were obtained on a LSR II flow cytometer (BD Biosciences) and analyzed using FlowJo version 8.3.3 (Tree Star, Inc., Ashland, OR).
Cells in 6-well plates were infected with C. burnetii for 4 days, washed twice with PBS, then fixed overnight in 2.5% glutaraldehyde/0.5 M sucrose in 0.1 M sodium cacodylate pH 6.8. Samples were processed and visualized with a model H7500 electron microscope (Hitachi High-Technologies USA, Pleasanton, CA) at 80 kV as previously described [83]. To visualize cholera toxin-B (CT-B) uptake, cells in 6-well plates were incubated with 1 ug ml−1 CT-B conjugated to HRP for 20 min on ice. After 3 washes with cold PBS, warm growth media was added and the cells incubated for 15 min at 37°C. Cells were washed an additional three times with cold PBS, then fixed with 2.5% glutaraldehyde/0.5 M sucrose in 0.1 M sodium cacodylate pH 6.8 for 1 h at room temperature before washing with 50 mM Tris-HCL (pH 7.4) plus 7.5% sucrose. HRP activity was detected by development with metal enhanced 3,3′-diaminobenzidine reagent (Thermo Scientific) for 1 h at room temperature prior to fixation and processing as described above. The number of HRP-positive endosomes in −cholesterol or +cholesterol MEFs were counted (n>50 cells), and cells scored as having more or fewer than 10 positive endosomes.
Unless otherwise noted, results are presented as the mean of 3 independent experiments +/− S.D, and statistical significance determined by two-tailed unpaired student t test (Prism, GraphPad Software Inc, La Jolla, CA).
The sequence for CBU1206 is available in the NCBI database under accession number NC_002971.3.
|
10.1371/journal.pgen.1006190 | A MutSβ-Dependent Contribution of MutSα to Repeat Expansions in Fragile X Premutation Mice? | The fragile X-related disorders result from expansion of a CGG/CCG microsatellite in the 5’ UTR of the FMR1 gene. We have previously demonstrated that the MSH2/MSH3 complex, MutSβ, that is important for mismatch repair, is essential for almost all expansions in a mouse model of these disorders. Here we show that the MSH2/MSH6 complex, MutSα also contributes to the production of both germ line and somatic expansions as evidenced by the reduction in the number of expansions observed in Msh6-/- mice. This effect is not mediated via an indirect effect of the loss of MSH6 on the level of MSH3. However, since MutSβ is required for 98% of germ line expansions and almost all somatic ones, MutSα is apparently not able to efficiently substitute for MutSβ in the expansion process. Using purified human proteins we demonstrate that MutSα, like MutSβ, binds to substrates with loop-outs of the repeats and increases the thermal stability of the structures that they form. We also show that MutSα facilitates binding of MutSβ to these loop-outs. These data suggest possible models for the contribution of MutSα to repeat expansion. In addition, we show that unlike MutSβ, MutSα may also act to protect against repeat contractions in the Fmr1 gene.
| The repeat expansion diseases are a group of human genetic disorders that are caused by expansion of a specific microsatellite in a single affected gene. How this expansion occurs is unknown, but previous work in various models for different diseases in the group, including the fragile X-related disorders (FXDs), has implicated the mismatch repair complex MutSβ in the process. With the exception of somatic expansion in Friedreich ataxia, MutSα has not been reported to contribute to generation of expansions in other disease models. Here we show that MutSα does in fact play a role in both germ line and somatic expansions in a mouse model of the FXDs since the expansion frequency is significantly reduced in Msh6-/- mice. However, since we have previously shown that loss of MutSβ eliminates almost all expansions, MutSα is apparently not able to fully substitute for MutSβ in the expansion process. We also show here that MutSα increases the stability of the structures formed by the fragile X repeats that are thought to be the substrates for expansion and promotes binding of MutSβ to the repeats. This, together with our genetic data, suggests possible models for how MutSα and MutSβ, could co-operate to generate repeat expansions in the FXDs.
| The fragile X (FX)-related disorders (FXDs) are repeat expansion diseases that result from an increase in the length of a CGG/CCG-repeat tract in the 5’ UTR of the FMR1 gene (reviewed in [1]). This expansion occurs from an unstable premutation (PM) allele that contains 55–200 repeats. The repeat is prone to expansion in germ line and somatic cells in humans and in a FXD mouse model with a targeted insertion of ~130 FX-repeats [2–4]. The molecular basis of this instability is not known. Individual strands of the FX repeat form hairpins and other atypical structures some of which may be folded and include mismatches [5–12] and current thinking is that these structures are the substrates for the expansion pathway [13].
We have previously shown that a number of different pathways that affect repeat instability are active in a mouse model of the FXDs, one that gives rise to expansions, one that results in the error-free repair of the expansion substrate and perhaps two different contraction pathways [14, 15]. We have also shown that the mismatch repair (MMR) complex MutSβ, a heterodimer of MSH2 and MSH3, is required for 98% of germ line and all somatic expansions in the FXD mouse [14]. This is consistent with what has been seen in some, but not all mouse models of other repeat expansion diseases [16–18]. MutSα, the other MSH2-containing complex present in mammals, has been shown to either have no effect or to protect against repeat instability in various mouse models [16, 17, 19]. For example, in a mouse model for myotonic dystrophy type 1 (DM1), MutSα protects against somatic expansions [19] and in a mouse model for Friedreich ataxia (FRDA) MutSα protects against both germ line expansions and contractions [17]. However, FRDA is also unique amongst the repeat expansion diseases studied thus far in that MutSα has also been shown to be involved in generating somatic expansions in the mouse model and in patient-derived induced pluripotent stem cells [20]. Whether or not this involvement of MutSα reflects some unique property of the GAA/TTC-repeats is not known. Furthermore, why MutSα protects against, rather than promotes, germ line expansions is also an open question [17].
As part of an effort to better understand the mechanisms of repeat instability in the FXD mouse model, we examined the somatic and intergenerational instability of the FX repeat in mice lacking MSH6, the MSH2-binding protein in the MutSα heterodimer. These data, together with our biochemical studies on the binding of these complexes to CGG- and CCG-repeats have interesting implications for the mechanism of repeat instability in the FXDs.
Our previous work indicates that in the FXD mouse model expansion, contraction and error-free pathways co-exist in germ line cells [4]. This can complicate the interpretation of intergenerational transmission data since a decrease in expansions can result either because the expansion pathway has become less efficient or because a pathway that protects against contractions has been impaired, or some combination of both. However, the interpretation of somatic instability data is simpler since all evidence to date suggests that the contraction pathway is not active in adult somatic cells in this mouse model [3, 4, 15].
Since somatic expansion is much more extensive in males than in females in the FXD mouse model [21], we examined the effect of the loss of MSH6 on expansion in different organs of male mice that were 6 months old. This time point was chosen since Msh6-/- male mice rarely survive beyond this age. However, since somatic expansion is clearly discernable in Msh6+/+ males at this age, any effect of the loss of MSH6 can be readily detected. To examine the effect of the loss of MSH6 on somatic instability in male mice we carried out PCR across the repeat and then resolved the PCR products by high-resolution capillary electrophoresis. Analysis of the PCR products produced from somatic tissue of Msh6+/+ animals at weaning at 3 weeks of age (tail 1) or in organs that do not show somatic expansion, like heart, typically reveal a Gaussian distribution of PCR products with relatively little deviation of these products from the mean (Fig 1A).
These PCR profiles are indistinguishable from those obtained from samples taken at birth [3]. Some of these products represent strand-slippage products that are generated when amplifying through long repeat tracts. In particular, the PCR products smaller than the major allele that do not change with genotype, age or tissue, fall into this category. We then used these PCR profiles to determine the somatic instability index (SII), a quantitative measure of the extent of repeat expansion [22]. In MSH6+/+ males the SII for heart was -0.1 (Fig 1B). This negative number is not evidence of contractions since the SII in heart does not change with age and the PCR profile seen in old animals corresponds to the original allele size determined at birth ([3] and Fig 1A). The negative value likely reflects contribution of the products of strand-slippage to the SII. In organs other than the heart, the SII was positive with the lowest SII being seen in kidney and the highest in liver and testes as we had previously observed [3].
The loss of MSH6 was associated with a significant reduction in the SII in many organs of male mice (Fig 1B). The organs most affected are those with the highest level of expansion in Msh6+/+ animals, namely, the tail, brain, liver and testis. However, the distribution of products smaller than the major allele are similar in all tissues including the tail sample taken at weaning and the heart, an organ that shows little, if any instability (Fig 1A). We also did not see evidence of somatic contractions in Msh2-/- mice that lack both MutSα and MutSβ [23]. Thus the failure to see evidence of contractions in Msh6-/- mice is not the result of an offsetting effect of MutSβ-mediated expansions. We can therefore conclude that the reduced SII in Msh6-/- mice is not the result of contractions that have now become apparent as a result of the loss of MSH6. Rather the reduction must reflect either a direct or indirect role of MSH6, and thus MutSα, in generating somatic expansions.
Female Msh6+/+ mice show less somatic expansion than males [21]. This makes it difficult to see significant effects of the loss of MSH6 in young animals. We thus confined our examination of somatic instability in Msh6-/- females to the few that survived to 12 months of age. Note that despite the females being twice as old as the males, the SII in Msh6+/+ females was still lower than it was in most of the corresponding organs of 6 month old males, consistent with reduced somatic instability in females (Fig 1B). Nonetheless, a role for MSH6 in generating somatic expansions was apparent in Msh6-/- females albeit only in tail and ovary (Fig 1B). Thus, the loss of MSH6, like the loss of MSH3, reduces the extent of somatic expansion. However, while somatic expansions are completely eliminated in Msh3-/- males and females on a similar genetic background [14], some expansion is still evident in Msh6-/- mice of both sexes.
We hypothesized that the loss of MSH6 would also affect germ line expansions with loss of two copies of the gene having a larger effect than the loss of one copy. We thus examined the transmission of the PM allele on intergenerational transfer from Msh6+/+, Msh6+/- and Msh6-/- parents. The Jonckheere-Terpstra test for ordered alternatives showed that there was a statistically significant trend towards fewer expansions with decreasing Msh6 gene dosage (p<0.001 for both paternal and maternal transmission). Pairwise comparisons demonstrated that while the expansion frequencies in the offspring of Msh6+/- parents was not significantly different from the expansion frequency in the offspring of Msh6+/+ parents, the progeny of Msh6-/- males and females had significantly fewer expansions than the progeny of either the Msh6+/+ (Fisher’s exact test; p = 0.0003 for paternal and p<0.0001 for maternal transmission respectively; Fig 2) or the Msh6+/- parents (Fisher’s exact test; p = 0.008 paternal and p<0.0001 for maternal transmission respectively; Fig 2).
There was also a significant difference in the distribution of the transmitted alleles for both maternal and paternal transmissions (Mann-Whitney U test; p<0.0001 for both males and females). There is no evidence to date to suggest that somatic and germ line expansions occur by different mechanisms in the FXD mouse. Thus the simplest interpretation of our data is that the decline in germ line expansions seen in Msh6-/- animals reflects a contribution of MutSα to the germ line expansion process. This would be above and beyond the 2% of expansions that are MSH2-dependent, but MSH3-independent that we previously attributed to MutSα [14]. However, in contrast to the 80:20 ratio of unchanged to contracted alleles seen in Msh3-/- males [14], in Msh6-/- males the ratio was 50:50 (27% vs 29% of the total alleles). The ratio of unchanged to contracted alleles in Msh2-/- mice is intermediate between the two (60:40) consistent with the combined contribution of MutSβ and MutSα complexes to the overall distribution of residual alleles [23]. The decline in the proportion of unchanged alleles in Msh2-/- and Msh6-/- animals relative to Msh3-/- mice may reflect an additional role for MutSα in protecting against contractions which occur in the germ line, but not somatic cells. Thus the decline in expansions seen on intergenerational transmission in Msh6-/- mice may represent some combination of the reduced efficacy of the expansion pathway together with the reduced efficacy of the pathway that protects against contractions.
We have previously shown that loss of MSH3 results in a change in the distribution of contraction sizes that are seen on intergenerational transmission [14]. Specifically while animals wildtype with respect to mismatch repair show a bimodal distribution of repeat sizes with the first modal class having lost 1–2 repeats and a second modal class having lost >7 repeats, Msh3-/- mice show a significant loss of alleles falling into the second modal class. Most notably in Msh3-/- males all contractions involved the loss of just a single repeat. This would be consistent with a role for MutSβ in generating larger contractions.
To assess the contribution of MutSα to contractions we examined the distribution of contracted alleles in Msh6-/- animals. Msh6-/- males die young making it difficult to collect enough data on the contraction sizes of paternally transmitted alleles. Therefore we analyzed the effect of the loss of MSH6 on the distribution of contraction sizes by doing small pool PCR on sperm DNA isolated from 2 month old Msh6-/- males (Fig 3A). The expansion frequency in Msh6+/+ sperm was generally lower than that observed in the live born progeny of Msh6+/+ animals. This could reflect the difference in the ages of the sperm donors (2 months) versus the fathers (2–6 months), since there is a progressive increase in the proportion of expanded alleles with age [4]. A contribution of low level of contamination of the sperm sample with less expansion-prone somatic cells also cannot be completely excluded. However, the expansion frequency was also lower in the sperm of Msh6-/- mice than in the progeny of Msh6-/- males. Thus, as expected, there were fewer expansions and more contractions than in the sperm of Msh6+/+ males of the same age (Fig 3B, Fisher’s exact test; p<0.0001). In addition, the distribution of alleles in Msh6-/- and Msh6+/+ gametes was significantly different by the Mann-Whitney U test (p<0.0001). This is generally consistent with the data derived from analysis of the progeny of Msh6-/- males (Fig 2).
In any event, in contrast to what is seen in Msh3-/- animals, the distribution of contractions in Msh6-/- sperm was similar to that seen in Msh6+/+ sperm (Mann-Whitney U test; p = 0.14). Our data thus suggest that MSH6, and therefore MutSα does not severely impact the distribution of contraction sizes as does MutSβ [14].
It has been suggested that MSH2 partitions between available pools of MSH3 and MSH6 and thus that the loss of MSH6 should thus not lead to a decrease in MutSβ [24–26]. To verify this we compared the levels of each of the three proteins in various organs of Msh2-/-, Msh3-/-, Msh6-/- mice and mice WT for all three proteins. As can be seen in Fig 4, the absence of MSH2 led to a complete loss of bands with the predicted mobility of MSH3 and MSH6, consistent with previous data demonstrating that the formation of MutSα and MutSβ complexes protects their subunits from degradation [24, 26, 27]. The loss of MSH6 resulted in a much larger decrease in the levels of MSH2 than did the loss of MSH3 in all organs tested. However, as can be seen in Fig 4, the levels of MSH3 are comparable in the brain and testes of Msh6+/+ and Msh6-/- mice and after normalizing to β-actin, no significant difference in the levels of MSH3 were detected. Two MSH3 bands were seen the liver and ovary of Msh6+/+ and Msh6-/- animals that were absent in extracts from Msh3-/- animals. These bands are thus likely to be MSH3-related. A similar pair of bands was seen in mouse spleen extracts using a MSH3 antibody that was directed to a similar region of the protein as the antibody we used [28]. However, in that report, only one band was detected with an antibody that recognizes a very N-terminal epitope. The N-terminal end of MSH3 known to be prone to degradation [29, 30] and it is possible that the smaller of the two bands represents a proteolytic degradation product of MSH3 in which the N-terminus had been lost. Thus, while the original levels of MSH3 in these organs are difficult to determine unequivocally, the data from brain and testes suggests that the effect of the loss of MSH6 on somatic and germ line expansion is not due to an indirect effect on the levels of MSH3, at least in some of the most expansion-prone organs in these animals.
We have previously shown that MutSβ binds to loop-outs formed by CCG- and CGG-repeats [14]. To see whether the same was true of MutSα, we examined the binding of this protein to substrates containing a loop-out of (CCG)13 or (CGG)13. These substrates were modeled on those used previously to examine MutSβ binding to CAG-repeats [31]. We also included MutSβ with a deletion of the unstructured N-terminal region of MSH3 [30]. This region of MSH3 is not involved in DNA or nucleotide binding [32] and this MutSβ complex has the same binding affinities for homoduplexes, tailed substrates and insertion/deletion loops (IDLs) as complexes containing the full length MSH3 protein, as well as the same rate constants and ATPase activities on these substrates [30]. Use of this MSH3 variant has the advantage of producing a DNA:protein complex with a mobility that is distinctly different from that of the DNA:MutSα complex. Both MutS complexes were of equivalent concentration as evidenced by the fact that equivalent amounts of protein contained equivalent amounts of MSH2 (S2 Fig, panel A). There was also no evidence of any degradation of the subunits as evidenced by the single products detectable on western blotting with antibodies to MSH2, MSH3 and MSH6 (S2 Fig, panel A).
As expected MutSα does not bind well to either homoduplex DNA or a loop-out of (CA)3, a good substrate for MutSβ-mediated but not MutSα-mediated repair (S2 Fig, panel B). In contrast, MutSβ binds effectively to the (CA)3 loop-out with even low concentrations of protein being able to shift almost all of the substrate. Limited binding of MutSβ to the homoduplex was also seen (S2 Fig, panel B). This binding is much less extensive than the binding of MutSβ to the (CA)3 loop-out as evidence by the fact that no unbound (CA)3 substrate was seen at a protein concentration of 0.8 nM, while most of the homoduplex remained unbound even at the highest protein concentration tested (20 nM). Binding of MutSβ to homoduplexes has been previously reported where it has been attributed to end binding [33–35].
MutSα binds to a substrate containing a G•T mismatch (S2 Fig) and to (CCG)13 and (CGG)13 loop-outs (Fig 5). It also binds to (CAG)13 and (CTG)13 loop-outs (S2 Fig, panel B). However, MutSα binds less well to the repeat substrates than to the G•T mismatch since binding of MutSα to G•T mismatch depletes all of the free probe at the highest protein concentration, while some free probe remains with all the repeat substrates. MutSα binding to the repeat substrates is also less extensive than the binding of MutSβ (compare lanes 2 and 3 and 12 and 13 of Fig 5).
MutSα stimulates MutSβ binding to a canonical MMR substrate containing a 2 nucleotide insertion/deletion when present at a high MutSα:MutSβ ratio [36]. To test whether this was also true for FX repeat-containing substrates we compared the binding of MutSβ in the presence and absence of an excess of MutSα. MutSβ binding to both canonical and repeat containing substrates produced multiple DNA:protein complexes as can be seen in Fig 5 and S2 Fig. Multiple DNA:MutSβ complexes have been previously reported for both yeast and human proteins binding to canonical MutSβ substrates [34, 35, 37] as well as (CAG)13 loop-outs [31]. The different MutSβ containing products could reflect either multiple MutSβ molecules binding to a single DNA molecule or to alternative binding modes. MutSβ binding to (CCG)13 and (CGG)13 substrates was increased in the presence of MutSα. This was evidenced most clearly as an increase in the amount of the DNA:MutSβ complex with the second highest mobility (DNA:MutSβ2; compare lanes 3 and 4 and 13 and 14 of Fig 5). This is not likely to be a non-specific effect since the addition of much higher concentrations of BSA do not have the same effect (S2 Fig, panel D). The increase in MutSβ binding was associated with a decrease in the amount of the MutSα-shifted band. Since the substrate has not been depleted, this decrease is unlikely to reflect competition between MutSα and MutSβ for binding to the substrate. Rather, it may reflect the incorporation of MutSα into one or more higher molecular weight species. Indeed at higher MutSα concentrations, a new shifted product, indicated by the open arrowheads in Fig 5, is apparent. This product is associated with a decline in the levels of the 2 most rapidly migrating DNA:MutSβ complexes. It is not seen in the absence of MutSβ even when very high concentrations of MutSα are used (lanes 8 and 9 and 18 and 19 of Fig 5). It thus likely represents complexes containing both MutSα and MutSβ. In reactions containing both MutSα and MutSβ a small amount of a second novel band is also seen with the CCG-substrate (indicated by the grey arrowhead in Fig 5, lane 17). This band may represent the result of binding of multiple MutSβ and MutSα complexes to the CCG substrate or complexes in which the binding modes differ from the complex with the faster mobility.
We have previously shown that MutSβ is able to increase the stability of the CCG-loop-out at physiological temperatures. To assess whether MutSα binding had the same effect, we monitored the thermal denaturation of the oligonucleotide in the presence of BSA or MutSα as previously described [14]. Since the 5’ end of the oligonucleotide was labeled with 5-carboxy-X-rhodamine (ROX), a fluorescence donor and the 3’ end was labeled with IOWA Black RQ, a fluorescence acceptor, the stability of the hairpins could be assessed in the presence of protein by monitoring the effect of increasing temperature on the fluorescence at 608 nm, the ROX emission wavelength. The oligonucleotide was denatured and cooled under conditions in which the repeats are known to form hairpins [7, 10, 38–42]. The oligonucleotide was then mixed with either BSA or MutSα and the thermal denaturation of the oligonucleotides monitored as previously described [14]. The melting curves obtained for both protein-CCG-repeat mixtures fit a two-state model (S3 Fig). While the best-fit for CCG-repeat melting in the presence of MutSα was within acceptable limits, the data suggest that the process by which the oligonucleotide melts in the presence of MutSα may be more complex than it is either in the presence of BSA or MutSβ [14]. The thermodynamic parameters derived from analysis of the melting curves are shown in Table 1. As for MutSβ, the presence of MutSα resulted in higher apparent ∆Gs at 37˚C than is seen in the presence of BSA. This suggests that MutSα, like MutSβ, increases the stability of the CCG-repeat structure at physiological temperature. However, the significant differences in the enthalpy of melting (∆Hm) of the oligonucleotide in the presence of MutSβ (52.4 ± 4.1 kcal/mol; [14]) and MutSα (81.3 ± 3.0 kcal/mol) suggest that the consequence of binding of these two complexes differs. This may reflect the very different modes of binding of these complexes to their substrates [30, 34].
We have previously shown that the loss of MSH2 eliminates all germ line and somatic expansions in the FXD mouse and that most of these expansions are MutSβ-dependent since the loss of MSH3 eliminates almost 98% of germ line expansions and all somatic ones [3, 14]. The remaining 2% of MSH2-dependent germ line expansions are presumably the result of MutSα-dependent events. However, we show here that loss of MSH6, and thus MutSα, reduces both the germ line and the somatic expansion frequency by much more than 2% (Figs 1–3).
A comparison of the relative levels of MSH2 in Msh3-/- and Msh6-/- mice showed that the loss of MSH6 resulted in a greater decrease of MSH2 than the loss of MSH3 (Fig 4). The fact that no MSH3 or MSH6 is seen in Msh2-/- mice (Fig 4) is consistent with previous reports suggesting that the levels of MSH2, MSH3 and MSH6 are interdependent and that there is very little free MSH3 or MSH6 in cells [24, 25]. Thus our data would be consistent with the interpretation that more MSH2 is in a heterodimer with MSH6 than is in a heterodimer with MSH3, i.e., that MutSα is more abundant than MutSβ in these animals. This finding is consistent with what has been reported for human cells [43, 44] and mice with a mixed C57BL6/129/OLA/FVB background [16], but not with what is seen in FVB mice [28]. While we did not assess the absolute amount of MSH3, a comparison of the relative levels of MSH3 in Msh6+/+ and Msh6-/- mice showed that the loss of MSH6 did not result in a detectable decrease in MSH3 levels in expansion-prone organs like brain and testes (Fig 4). Since MSH2 levels were reduced in Msh6-/- mice it is possible that MSH6 is acting indirectly via decreasing the amount of MSH2 available to form the MutSβ complex. However, since MSH3 levels are not significantly lower in Msh6-/- mice and MSH3 is thought to be stable only when in the MutSβ complex [24, 25], MSH6 may well be playing a different role in the expansion process.
No comparable decrease in expansions is seen in Msh6-/- mice in models of other repeat expansion diseases where MSH3 has been implicated in the expansion process and where a similar excess of MutSα was seen [16]. In addition, the loss of MSH6 in the same mouse strain or in human cells does not result in reduced repair of typical MutSβ substrates [45–49]. Thus significant redeployment of MutSβ to other sites in the genome to compensate for the loss of MutSα is also unlikely to account for the decrease in germ line and somatic expansions seen in Msh6-/- mice. However, since very few germ line expansions and no somatic ones are detected in mice that lack MutSβ [14], MutSα is not able to efficiently substitute for MutSβ in the expansion process despite the relative abundance of MutSα in these animals (Fig 4). A contribution by both MutSα and MutSβ to repeat expansion is consistent with our previous observations that while MutSβ levels alone do not correlate well with the levels of somatic instability across 5 different organs, a better correlation is seen when the levels of both MutSβ and MutSα are considered [3].
Thus, our data suggest that the involvement of MutSα in repeat expansion is not unique to somatic expansion of GAA/TTC-repeats in FRDA, contributing to both germ line and somatic expansion in the FXD mouse. Whether MutSα acts independently of MutSβ in FRDA unknown. The nature of MutSα’s role in the expansion process in the FXD mouse is also unclear. MutSα may be acting to facilitate MutSβ-dependent expansion by increasing the stability of the expansion substrates as it does in vitro (Table 1), or by protecting the substrates from repair by another mechanism, thus allowing more time for the hairpins or other atypical structures to be processed by MutSβ to generate expansions. MutSα also promotes binding of MutSβ to the repeat substrates (Fig 5) in a manner reminiscent of MutSα’s effect on MutSβ binding to a canonical MutSβ substrate [36]. This property may reflect yet another way that MutSα could facilitate MutSβ-mediated repeat expansions.
A role for MutSα in repeat expansion has not been observed in mouse models of CTG/CAG-repeat expansion diseases despite the fact we have shown that MutSα binds to those repeats in vitro (S2 Fig). It may be that the effect of the loss of MutSα is only apparent under certain circumstances. For example, it may be that in the FXD mouse model where the expansion frequency is high, the amount of the expansion substrate formed in the germ line exceeds the processing capacity of MutSβ acting alone. Under these conditions the effect of MutSα would become apparent. When only moderate levels of the expansion substrate are produced, an effect of the loss of MutSα might only be seen in mice with reduced MutSβ levels since the available MutSβ in Msh3+/+ animals may be sufficient to process all the expansion substrates without the assistance of MutSα. At the other end of the spectrum when the expansion substrates are present only at very low levels, either one of the MutS complexes may be sufficient to process them. This may explain the perplexing observation that in a mouse model of Huntington disease, loss of MSH2 eliminates germ line expansions but neither the loss of MSH3 nor the loss of MSH6 had any effect on the expansion frequency [18]. This idea would also be consistent with our observation that in the FXD mouse, the loss of MSH6 had more of an effect on somatic expansion in males than in females (Fig 1), since the expansion process is less extensive in females [21] and thus the requirement for MutS proteins may be lower. Thus, the different effects of MutSα and MutSβ on expansion in different models, in germ line versus somatic cells, or in males and females, may not necessarily reflect differences in the mechanisms of expansion, but rather differences in the levels of the MutS complexes relative to the substrates that potentially could be processed to generate expansions.
In addition to contributing to the generation of expansions, our data suggest that MutSα may also act to protect against germ line contractions as evidenced by the reduction in the proportion of unchanged alleles in Msh2-/- [23] and Msh6-/- animals (Fig 2) relative to Msh3-/- mice [14]. Protection against germ line contractions by MutSα would be consistent with what has been reported for both the GAA/TTC-mouse model [17] and a CAG/CTG-mouse model [18]. Protection against contractions by MutSα also would be consistent with a typical MMR process albeit one that is triggered by an atypical repair substrate. The nature of the FX hairpins with the high frequency of single mismatches may account for the ability of MutSα to bind to and coordinate their repair. The ability of MutSα to contribute both to error-free repair and to expansions may reflect MutSα’s ability to participate in more than one DNA repair pathway [50, 51].
We have recently demonstrated that a hypomorphic mutation in Polβ, a key DNA polymerase involved in base excision repair (BER), reduces expansion in the FXD mouse [52]. How MutSα and MutSβ interface with the BER pathway to generate expansions in these models remains an open question. One possibility is that MutSβ and MutSα act downstream of DNA damage excision to stabilize loop-outs formed during strand-slippage and strand-displacement synthesis that is mediated at least in part by Polβ. We speculate that normal signaling by MutSα results in MMR of these loop-outs resulting in error-free repair, while MutSβ, alone or together with MutSα, can channel them into a different repair pathway that results in expansions.
This work was carried according to ARAC guidelines and procedures as outlined in the Guide for the Care and Use of Laboratory Animals, U.S. Government Principles for the Utilization and Care of Vertebrate Animals Used in Testing, Research, and Training and Public Health Service Policy on Humane Care and Use of Laboratory Animals. This work was approved by the NIDDK Animal Care and Use Committee (ASP: K021-LMCB-12 and K021-LMCB-15).
Oligonucleotides were obtained from Integrated DNA technologies (IDT, Coralville IA) and are listed in Table 2. Purified human MutSα was a kind gift of Drs Hsieh and Geng (NIDDK, NIH). Purified human MutSβ was a kind gift of Drs Yang and Li (NIDDK, NIH). This MutSβ complex contained a “trimmed” version of MSH3 containing amino acids 211–1125. This MutSβ complex has the same binding affinities for homoduplexes, tailed substrates and IDLs as complexes containing the full length MSH3 protein, as well as the same rate constants and ATPase activities [30].
The generation of the FXD mice was described previously [2]. These mice are on a C57BL/6 background. The Msh6+/- mice were generated previously [49, 53] and cryopreserved embryos were obtained from the NCI Mouse Repository (Frederick, MD). These mice are also on a predominantly C57BL/6 background. Live born pups were generated from these embryos by implantation into the oviduct of pseudopregnant recipients using standard procedures. F2 Msh6+/- parents were bred to generate Msh6+/+, Msh6+/- and Msh6-/- littermates. Multiple breeding pairs from the same parents were set up for each genotype. The litters for each genotype considered for this analysis had a similar parental age distribution. This was the same genetic background and breeding strategy that we had used previously to examine the effect of the loss of MSH3 on the expansion frequency [14]. Mice were maintained in accordance with the guidelines of the NIDDK Animal Care and Use Committee and with the Guide for the Care and Use of Laboratory Animals (NIH publication no. 85–23, revised 1996).
Sperm was isolated from the cauda epididymis as previously described [54], pelleted twice by centrifugation at 500 g for 5 min and the pellet resuspended first in PBS and then in 100 μl of a solution containing a 90:10 mixture of ATL lysis buffer (Qiagen, Valencia, CA) and a 20 mg/ml proteinase K solution (Invitrogen, Carlsbad, CA). The samples were then incubated at 55°C overnight before the addition of 30 μl of 5 M NaCl. The resultant precipitate was pelleted by centrifugation and the supernatant transferred to a new tube and mixed with 130 μl of ethanol. The DNA was then pelleted by centrifugation and dissolved in TE by incubation overnight at 55°C. This protocol results in little, if any, contamination with somatic DNA [54]. Genomic DNA from mouse tails was extracted using KAPA Mouse Genotyping Kit (KAPA Biosystems, Wilmington, MA). Genomic DNA from other tissues was extracted using a Maxwell16 Mouse tail DNA purification kit (Promega, Madison, WI) according to the manufacturer’s instructions. Msh6 genotyping was carried out with Taq DNA polymerase in standard buffer with either the M010/M011 primer pair to detect the WT allele and M012/M013 to detect the mutant allele. The PCR parameters were 1x 94°C for 1 min., 35x (94°C for 1 min., 60°C for 2 min. and 72°C for 1 min), followed by 1x 72°C for 3 min. The presence of the PM allele and its repeat number was determined using a fluorescent PCR assay and FraxM4 and FraxM5 primer pair as described previously [3]. The somatic instability index (SII) was calculated from the GeneMapper profiles of DNA from different organs as previously described [3, 22] and used to evaluate the extent of somatic expansion in adult mice. For small pool PCR analysis from sperm, the DNA was diluted to 3 pg/μl (roughly 1 haploid genome equivalent/μl). The diluted DNA was then subjected to nested PCR. The first round of PCR was carried out using the primers FraxC and FraxF in a 25 μl PCR mix as described previously [55]. One microliter of this PCR mix was used in second round of PCR with the FraxM4 and FraxM5 primers. Roughly 50% of the reactions contained a PCR product, consistent with the idea that each positive PCR likely represents the products of amplification of DNA from a single sperm cell. An exact Jonckheere-Terpstra test of trend in ordered counts was carried out using StatXact software (version 8; Cambridge, Massachusetts). Fisher’s exact test was carried out using the GraphPad QuickCalcs website (http://www.graphpad.com/quickcalcs). The Mann-Whitney U test was carried out using VassarStats (http://vassarstats.net/). We set the significance level (α) at 0.050 for the pairwise comparisons. For the comparisons of WT, heterozygous and homozygous null animals this corresponds to p = 0.015 after adjusting for multiple testing using the (relatively conservative) Bonferroni correction. Hartigans’ dip test was calculated using the dip.test command in the R diptest library.
Total protein extracts were prepared from flash frozen brain, liver, testes and ovary of 6-month old mice. Tissues were homogenized using a tissue homogenizer (Precellys 24, Bertin Technologies, Berlin, Germany) with T-PER protein extraction reagent (Pierce Biotechnology, Inc, Rockford, IL) supplemented with complete, Mini, EDTA-free protease inhibitor cocktail (Roche Applied Science, Indianapolis, IN). Nuclear extracts of liver proteins were prepared using the NE-PER Nuclear and cytoplasmic extraction reagents (Pierce Biotechnology, Inc, Rockford, IL) according to the manufacturer’s instructions. The protein concentrations were determined using a Bio-Rad protein assay kit (Bio-Rad, Hercules, CA). Proteins were heated for 10 minutes at 70°C in LDS-Sample Buffer (Life Technologies, Grand Island, NY), resolved by electrophoresis on either 3–8% NuPAGE Novex Tris-Acetate gels (Life Technologies) or 4–12% NuPAGE Novex Tris-Bis gels (Life Technologies) and transferred to nitrocellulose membranes using the iBlot transfer apparatus (Life Technologies) according to the manufacturer’s instructions. Membranes were blocked for one hour at room temperature in 5% ECL Prime blocking agent (GE Healthcare Bio-Sciences) in TBST, then incubated overnight at 4°C with antibodies to MSH2 (ab70270, Abcam, Cambridge, MA) at a concentration of (1:10000), MSH3 (sc-271079, Santa Cruz, Dallas, TX) at a concentration of (1:1000) and MSH6 (BD 610918, BD Biosciences, Franklin Lakes, NJ) at a concentration of (1:1000). The secondary antibodies (anti-mouse IgG, NA931V and anti-rabbit IgG, NA934V, GE Healthcare Bio-Sciences) were both used at a dilution of 1:5000. After addition of the ECL Prime detection reagent (GE Healthcare Bio-Sciences), the blot was imaged using a Fluorchem M imaging system (Proteinsimple, Santa Clara, CA). Beta-actin (anti-mouse ab8227, Abcam, Cambridge, MA) was used as a loading control for total cell extracts and lamin B (ab16048, Abcam, Cambridge, MA) for nuclear extracts. A representative example of a full blot of testes protein extracts showing binding to MSH2, MSH3, MSH6 and the loading control β-actin is shown in S1 Fig. Western blots were repeated several times and always included molecular weight markers and extracts from the appropriate null mice as negative controls. To evaluate whether the loss of MSH6 affected the levels of MSH3 in Msh6-/- mice, knowledge of the absolute levels of each protein is not necessary. Since the levels of MSH3 in each group of animals was tested with equivalent amounts of protein using the same antibody on the same gel, the avidity of the MSH3 antibody relative to the avidity of the MSH6 or MSH2 antibodies is not an issue. We thus were able to directly compare the levels of MSH3 in WT, Msh3-/- and Msh6-/- animals by determining the amount of each protein relative to β-actin (total protein extracts) or lamin B1 (nuclear extracts) using the AlphaView software for FluorChem Systems (Proteinsimple, Santa Clara, CA). The levels of MSH2 and MSH6 in these animals were determined in the same way.
The oligonucleotides used in EMSA were prepared as described previously [14]. The binding reactions were carried out using the Gelshift chemiluminescent EMSA kit (Active Motif, Carlsbad, CA) according to the manufacturer’s instructions using the indicated amounts of purified human MutSα and human MutSβ and 2 fmoles of the duplexed oligonucleotides as described previously [14].
The oligonucleotide used for thermal analysis consisted of a single strand of DNA comprised of 10 copies of CCG with the 5’ end labeled with 5-carboxy-X-rhodamine (ROX) and the 3’ end with IOWA Black RQ. The oligonucleotide was prepared as described previously [14] and MutSα or BSA was added to 360 nM as indicated. Thermal denaturation was monitored as described previously [14]. The melting curve was consistent with a two-state model (S3 Fig) and the thermodynamic parameters were thus derived from the melting curve using a two-state model (closed and open states).
|
10.1371/journal.pntd.0003863 | Characterization of the Burkholderia mallei tonB Mutant and Its Potential as a Backbone Strain for Vaccine Development | In this study, a Burkholderia mallei tonB mutant (TMM001) deficient in iron acquisition was constructed, characterized, and evaluated for its protective properties in acute inhalational infection models of murine glanders and melioidosis.
Compared to the wild-type, TMM001 exhibits slower growth kinetics, siderophore hyper-secretion and the inability to utilize heme-containing proteins as iron sources. A series of animal challenge studies showed an inverse correlation between the percentage of survival in BALB/c mice and iron-dependent TMM001 growth. Upon evaluation of TMM001 as a potential protective strain against infection, we found 100% survival following B. mallei CSM001 challenge of mice previously receiving 1.5 x 104 CFU of TMM001. At 21 days post-immunization, TMM001-treated animals showed significantly higher levels of B. mallei-specific IgG1, IgG2a and IgM when compared to PBS-treated controls. At 48 h post-challenge, PBS-treated controls exhibited higher levels of serum inflammatory cytokines and more severe pathological damage to target organs compared to animals receiving TMM001. In a cross-protection study of acute inhalational melioidosis with B. pseudomallei, TMM001-treated mice were significantly protected. While wild type was cleared in all B. mallei challenge studies, mice failed to clear TMM001.
Although further work is needed to prevent chronic infection by TMM001 while maintaining immunogenicity, our attenuated strain demonstrates great potential as a backbone strain for future vaccine development against both glanders and melioidosis.
| Burkholderia mallei and B. pseudomallei are the causative agents of glanders and melioidosis, respectively. In addition to the recent rise in cases of glanders and the endemicity of melioidosis worldwide, these pathogens have gained attention as potential bioweapons. Further, these pathogens have huge potential for aerosol delivery and often produce fatal infection amongst untreated individuals. Both pathogens are difficult to treat, and even with antibiotic intervention, patients relapse or get re-infected. A big challenge for vaccine development against these pathogens includes identification of broadly protective antigens and a better understanding of the correlates of protection from both acute and chronic infections. Our study is the first to demonstrate significant protection against a lethal challenge with both Burkholderia species. Because TMM001 persists in immunized mice, we propose that this attenuated organism is a promising backbone-based strain from which a legitimate vaccine candidate can be generated.
| Melioidosis and glanders are severe zoonotic diseases caused by two closely related Gram-negative pathogens known as Burkholderia pseudomallei and B. mallei, respectively [1,2]. The genomic relatedness between these two pathogens suggests that B. mallei is a host-adapted clone of B. pseudomallei, which evolved from a process of reductive evolution. Genes retained by B. mallei share 99% sequence identity with their B. pseudomallei orthologs and of those, 650 genes have been identified as putative virulence determinants via in silico genomic subtraction from non-pathogenic Burkholderia species [3]. In addition, the presence of very few B. mallei specific genes suggest it’s possible to generate a live attenuated vaccine with a B. mallei backbone that can cross-protect against both melioidosis and glanders [4].
Where B. pseudomallei is an environmental saprophytic pathogen ubiquitous in soil and fresh water surfaces, B. mallei is an obligate mammalian pathogen that typically infects solipeds (horses, donkeys, etc) [1,5]. Despite epidemiological differences, the clinical and pathological manifestations of B. pseudomallei or B. mallei infections bear striking resemblance. Both pathogens can be contracted via the cutaneous, oral and/or inhalational routes. Depending on the dose and route of transmission, B. pseudomallei or B. mallei infection may result in an acute or chronic disease. Clinical manifestations of acute infection from either disease, which include fever, malaise, abscess formation, pneumonia and sepsis, are non-specific. The lack of pathognomonic symptoms, in addition to their ability to cause silent infection, makes rapid and accurate diagnosis problematic for these Burkholderia infections. Since mortality rates among severe infections are high, and there are no reliable antibiotic therapy or licensed pre- and post-exposure vaccines, both pathogens remain top candidates for bioterrorist use and thus have been classified as category B, tier 1, biothreat agents [1]. The destructive potential of B. pseudomallei and B. mallei has heightened concerns among public health officials due to the increased potential of opportunistic infection among growing populations of diabetic and immunocompromised people [2]. For military personnel and susceptible individuals, the availability of a vaccine would be the most efficacious and cost-effective way to protect from disease.
Progress in vaccine development shows formulations consisting of subunits or live-attenuated strains are the most effective in conferring protection against both pathogens. Subunit vaccines consisting of purified protein [6]; recombinant Hcp proteins [7]; lipopolysaccharide (LPS) [8,9]; truncated recombinant proteins LolC and PotF [10]; and outer membrane vesicles (OMV) [11] have achieved the greatest protection to date. While encouraging, subunit vaccines provided only partial protection, which is attributed to their inability to generate broad protective immunity, specifically cell mediated immunity [12].
Live attenuated vaccines are recognized for their ability to elicit strong broad immune responses that provide long-lasting protection [12]. Thus far, attenuated mutants lacking a functional purN, purM, aroB, ilvl, or bipD genes in B. pseudomallei, and ilvl or DD3008 (capsule) genes in B. mallei have been evaluated for their protective potential [12]. Although these candidates have proven capable of providing significant protection during the acute stage of infection, none have yet to afford full protection during the chronic stage of infection.
To create a live attenuated B. mallei mutant that will generate a protective immune response against chronic infection, we focused on iron transport systems as a target of mutagenesis. For a majority of bacterial pathogens, the acquisition of iron and iron complexes has long been recognized as major pathogenic determinant and thus also represent a promising target for vaccine development. In the host environment, free iron is too scarce and iron complexes are too large to diffuse effectively through porin channels. To survive in these growth-limiting conditions, bacteria utilize siderophores and/or high-affinity outer-membrane receptors to uptake iron and iron complexes [13]. In the case of B. pseudomallei and B. mallei, very little information exists concerning iron uptake mechanisms in the host and their roles in virulence. In one study, Kvitko el at., generated single, double and quadruple B. pseudomallei mutants defective in siderophores and/or hemoglobin utilization [14]. While mutants defective in these systems are often attenuated, the B. pseudomallei mutants remained fully virulent in a murine model of acute melioidosis [14]. Failure to eliminate virulence was attributed to redundancy in the iron transport system, citing a reliance on alternative iron sources and acquisition mechanisms.
To negate this redundancy, we targeted the inner membrane energy transfer protein TonB, an essential component that interacts with all outer membrane receptor proteins that carry out high-affinity binding and energy dependent iron uptake [15,16,17]. When assessed in multiple models of infection, tonB mutants displayed severe attenuation compared to their wild-type homologs [18,19,20,21]. In the case of K. pneumoniae, Hsieh et al. showed 100% protection in challenge mice previously vaccinated with the tonB mutant homolog [20]. Thus, Burkholderia TonB-dependent iron-transport systems, specifically their contribution to survival, persistence and potential as targets for attenuation, should be investigated further. In this communication, we describe the construction and characterization of a B. mallei tonB mutant as a backbone strain for subsequent vaccine development against acute inhalational murine glanders and melioidosis.
All manipulations of B. mallei were conducted in CDC/USDA-approved and registered biosafety level 3 (BSL3) facilities at the University of Texas Medical Branch (UTMB), and experiments with select agents were performed in accordance with BSL3 standard operating practices. The animal studies were carried out in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health. The protocol (IACUC #0503014B) was approved by the Animal Care and Use Committee of the UTMB.
The bacterial strains and plasmids used in this study are listed in Table 1. All E. coli strains were grown in Luria-Bertani (LB) media at 37°C or 30°C, as required. For all the experiments, all B. mallei strains were taken from freezer stocks, plated on LB agar containing 4% glucose (LBG), and incubated at 37°C for 3 days. For liquid cultures, a few colonies (2–3) were inoculated into 20 mL of LBG broth and incubated overnight with agitation at 37°C. When employing antibiotic selection, we used kanamycin and polymyxin B at concentrations of 50 μg/mL and 30 μg/mL, respectively. For counter-selection, co-integrates were grown in YT broth (10 g of tryptone and 10 g of yeast extract) and then plated on sucrose agar (YT agar supplemented with 5% sucrose), as described by Hamad et al. [22]. When appropriate, LBG broth and agar were supplemented with FeSO4 at a concentration of 200 μM. Unless otherwise stated, wild type B. mallei ATCC 23344 or CSM001 (B. mallei Lux), B. mallei TMM001 (tonB mutant), and TMM002 (pTonB-comp) were used in all experiments.
Cloning methods were performed as previously described [22]. Chromosomal and plasmid DNA were isolated by using the DNeasy Qiagen Blood and Tissue kit, and the QIAGEN Plasmid Mini Kit, respectively (Qiagen, Inc., Valencia, CA). Polymerase chain reaction (PCR) products were purified with either the QIAquick PCR purification kit or QIAquick gel extraction kit (Qiagen). Restriction enzymes and T4 DNA ligase were purchased from NEB and used in accordance with the manufacturers’ instructions (New England Biolabs Inc., Ipswich, MA). Primers used in this study were purchased from Sigma-Aldrich Co (St. Louis, MO). DNA fragments obtained for cloning were amplified with Phusion High-Fidelity DNA polymerase (New England Biolabs) by using the following touchdown PCR protocol: 1 cycle of 95°C for 5 min, 29 cycles of 95°C for 30 sec, 70°C to 55°C (-5°C/cycle) for 30 sec, 72°C for 2 min, 29 cycles of 95°C for 30 sec, 55°C for 30 sec, 72°C for 30 sec, and 1 cycle of 72°C for 7 min.
Matched adaptamers containing 3’ enzyme restriction sites and 5’ complementary sequences were amplified via touchdown PCR. The sequences of the PCR primers were as follows: ΔtonB US forward primer (AAG CTA GCC CTC GGC GCG GCG ATC CGC GAC GT) (underlined sequence indicates NheI site); ΔtonB US reverse primer (CGG TAT TGC CGA GAT TAA CGG TGC GGC ACG TCG T); ΔtonB DS forward primer (ACG ACG TGC CGC ACC GTT AAT CTC GGC AAT ACC G); and ΔtonB DS reverse primer (CCA AGC TTT ACG AGC ATG ACG TCG ACG AGC GGC GTC ATG TTG) (underlined sequence indicates HindIII site). The adaptamers were fused together via splicing by overlap extension (SOE) PCR to create a 1794-bp chimeric fragment containing sequences flanking the tonB gene plus its first 33 codons. The chimeric fragment was digested with NheI and HindIII and ligated into the pMo130 vector to create the allelic exchange plasmid pTonB-allex. The pTonB-allex plasmid was then transformed into E. coli S17-1 and introduced into B. mallei via conjugal transfer. Merodiploids were selected based on their growth on kanamycin (Km) and polymyxin B (Pbx) agar plates and ability to turn yellow after exposure to pyrocathecol. Single deletion mutants were counter selected on YT agar supplemented with 5% sucrose and 200 μM FeSO4.
After the tonB mutant was screened for Pxb resistance and Km sensitivity, deletion was confirmed via PCR amplification, followed by sequencing of the tonB gene and flanking DNA regions by using the following primers: confirmation forward primer (5’ GCG CCA CGC GGC CGA TTG CCG CTT TCT) and confirmation reverse primer (ACA GAA CCG TGC CGT CGC TTT). To restore the tonB mutant (renamed TMM001) to wild-type function, pMo168 carrying a functional tonB gene plus its native promoter was used for complementation. Briefly, a fragment containing the wild-type tonB gene plus approximately 120 bp of its upstream sequence flanked by enzyme restriction sites was amplified by using the following PCR primers: complementation forward primer (CCG CTA GCC TGA TTT TCC GCA AGT GAT GCA GCA CT) (underlined sequence indicates NheI site) and complementation reverse primer (CCA AGC TTT TAA TCG GTC AGA GTG AAG TCA TAA GGC) (underlined sequence indicates HindIII site). The fragment was then digested with NheI and HindIII and ligated into the pMo168 plasmid to create pTonB-comp. After transformation into E. coli S17-1, pTonB-comp was introduced into B. mallei via conjugal transfer. TMM001 containing pTonB-comp was isolated via selection on LBG + Km agar plates and confirmed by PCR amplification, followed by sequencing of the region flanking the tonB gene by the same primers used to confirm the tonB mutation.
Overnight cultures were used to inoculate 50 mL of LBG with 6 x 106 CFU of each strain. Inoculated cultures were then incubated with agitation at 37°C. At the indicated time points, 1 mL aliquots from each culture were taken to measure optical density at 600 nm. Individual data points represent the OD600 mean ± standard deviation (SD) of three independent experiments. A significant difference due to treatment over time was ascertained via two-way ANOVA. Significant differences (p ≤ 0.05) of each OD600 reading was determined at every time point compared to wild type using one-way ANOVA followed by Dunnett’s multiple comparison test.
Overnight cultures were diluted to 1 x 105 CFU/ml in LBG + 200 μM of 2,2’-dipyridyl and poured onto plates, as previously described [23]. Disks containing iron sources were placed on the surface of the LBG plates, which were incubated at 37°C for 48 h. Disks contained 10 μL of the following compounds at the specified concentrations: hemin, 8.0 μM; hemoglobin, 4.5 μM; myoglobin, 4.5 μM; transferrin, and lactoferrin, both at 30 μM or FeSO4, 10 mM [23]. Iron utilization was quantified by measuring the diameter of growth around the disk.
Ten μl samples of overnight cultures, grown in LBG or LBG + 200 μM FeSO4 or 200 μM 2,2’-dipyridyl, were spotted onto CAS agar plates and incubated at 37°C. Halos were then monitored and the diameter of color change was measured over the course of the next 4 days. For the CAS agar, solutions were prepared as previously described [24]. An unpaired t test with equal standard deviation was performed on halo measurements to ascertain a significant difference (p ≤ 0.05) between the strain-specific halos produced.
Female, 6- to 8-week-old BALB/c mice obtained from Harlan Laboratories (Indianapolis, IN, USA) were housed in microisolator cages under pathogen-free conditions. Animals were provided with rodent feed and water ad libitum and maintained on a 12 h light cycle. Before experiments, mice were afforded an adaption period of at least 1 week. Humane endpoints were strictly observed and time of death was recorded upon death of the animal or at the study’s end. Animals were observed closely throughout the study for clinical symptoms (immobility, dyspnea, paralysis) and moribund animals were anesthetized and then euthanized via cervical dislocation.
Anesthetized BALB/c mice (n = 8 per treatment) were inoculated i.n. with the indicated CFU of TMM001, grown in LBG ± 200 μM FeSO4 and diluted in phosphate-buffered saline (PBS) in a total volume of 50 μL (25 μL/ naris). Mice were monitored and deaths recorded over a period of 14 days. Survival curves were generated and analyzed by using the Kaplan-Meier method. A significant difference (p ≤ 0.05) in survival curves was ascertained via a log-rank test.
Anesthetized mice (n = 8 per treatment) were challenged i.n. with 1.5 x 104 CFU/50 μL of the B. mallei bioluminescent reporter strain CSM001, and TMM001 in LBG ± 200 μM FeSO4. At 24, 48 and 72 h post challenge, BALB/c mice were euthanized and necropsied for organ collection. The lungs, liver and spleen were homogenized in 1 mL of PBS by using a tissue grinder (Covidien, Mansfield, MA), and then the bacteria were enumerated by standard plate counts on LBG + 200 μM FeSO4. Significant differences (p ≤ 0.05) in colonization at 24 and 48 h were individually determined via one-way ANOVA followed by Tukey’s multiple comparisons test. Significant difference (p ≤ 0.05) in colonization at 72 h was extrapolated by using an unpaired t test with equal standard deviation.
Anesthetized mice (n = 8 per treatment) were immunized i.n. with PBS or the indicated CFU of TMM001 diluted in PBS in a total volume of 50 μL (25 μL/ naris). Mice were challenged 21 days post immunization with 1.5 x 105 CFU (~220 LD50) of CSM001 (LD50 of 6.81x102 CFU) or 9 x 102 CFU (~3 LD50) of wild-type B. pseudomallei K96243 (LD50 of 3.12x102 CFU) [25,26], diluted in PBS in 50 μL (25 μL/ naris). Mice were monitored, and deaths were recorded until the end of the study. Survival curves were generated and analyzed by the Kaplan-Meier method. A significant difference (p ≤ 0.05) in survival curves was ascertained via log-rank test. To find significant differences in individual treatment, when compared to the PBS-treatment control, an additional log rank test was employed in which an adjusted definition of significance (p ≤ 0.05/ the number of pair wise comparisons) was used.
Bioluminescent images were acquired on an IVIS Spectrum (Caliper Corp., Alameda, CA, USA), as previously described [25]. Briefly, anesthetized BALB/c mice placed in the isolation chamber were transferred to the imaging chamber, which was then connected to an internal anesthesia delivery system that maintained 1–2% isoflurane. Bioluminescence signaling was measured after three minutes’ exposure with no excitation (filters blocked) and an open emission filter to capture all luminescent signals from labeled bacteria. To depict the differences in intensity of the signal, bioluminescence was represented in the images with a pseudo-color scale ranging from red (most intense) to violet (least intense). Scales were manually set to the same values for every comparable image to normalize the intensity of the bioluminescence across time points.
Serum extracted from PBS or TMM001-vaccinated BALB/c mice at 21 days post treatment, was evaluated for B. mallei-specific IgG1, IgG2a and IgM using the Ready-Set-Go! ELISA Kit (Affymetrix eBioscience, San Diego, CA) as instructed by the manufacturer. Briefly, microplates (Costar, Cambridge, MA) were coated with 10 μg/ml of heat inactivated B. mallei and incubated overnight at 4°C. Wells were then washed twice with PBS, 0.05% Tween-20, and then blocked over night with the Assay Buffer provided in the kit. After the wells were washed, a 1:10,000-fold dilution of sera samples was added to the appropriate wells, followed by the detection antibody provided by the kit. After 3 h incubation, the wells were washed four times before 100 μL of the substrate solution was added. After 15 min incubation, 100 μL of stop solution consisting of 2 N H2SO4 was added, and absorbance was measured at 450 nm with the Epoch microplate spectrophotometer (Winooski, VT). An unpaired Student’s t test was performed to ascertain a significant difference (p ≤ 0.05) in B. mallei-specific Ig levels between the PBS and TMM001-treated mice.
At the indicated time points, necropsies were performed to collect the lungs, liver and spleen. Organs were instilled with 10% formalin, paraffin-embedded, and processed for histopathology. Hematoxylin and eosin-stained slides were examined and blindly scored by a pathologist for the follow observations: perivascular and peribronchial inflammatory infiltrates, necrosis and microabscesses in the lung; granulomas, necrosis and histocytosis in the spleen; and inflammation and necrosis in the liver. Severity of pathology was scored using the following combined scale: 0 (unremarkable), 1 (minimal), 2 (mild), 3 (moderate) and 4 (severe). Pathology scores were combined with a percent factor associated with the extent of the damage (0–25%, 25–50%, 50–75%, 75–100%) and added together to give the total score for each organ. Each image is representative of three replicates per treatment. A two-way ANOVA was performed on each organ individually to assess a significant difference in treatment over time. Student’s t test was performed to ascertain a significant difference (p ≤ 0.05) between the treatments of each organ, individually, at 0 and 48 h.
At the indicated time points following challenge, whole blood was collected by cardiac puncture. The blood was stored in microvette tubes without anti-coagulant and incubated at room temperature for 20 min to permit clotting. Serum was collected after centrifugation of the tubes and stored at -80°C. Samples were inactivated as previously described [27] and verified for sterility. Serum chemokine/cytokine levels were measured by using the murine bioplex ELISA kit (BioRad Bio-Plex Pro Mouse Cytokine 23-plex Assay) according to the manufacturer’s specification. Serum sample were diluted 1:4 in PBS and expression of the following molecules was determined: interleukin (IL)-1α, IL-1β, IL-2, IL-3, IL-4, IL-5, IL-6, IL-9, IL-10, IL-12 (p40), IL-12 (p70), IL-13, IL-17A, eotaxin, granulocyte–colony-stimulating factor (G-CSF), granulocyte–macrophage colony-stimulating factor (GM-CSF), gamma interferon (IFN-γ), keratinocyte-derived chemokine (KC), monocyte-chemotactic protein (MCP-1), macrophage inflammatory protein (MIP)-1α, MIP-1β, RANTES, and (tumor necrosis factor) TNF-α. Data values represent the mean ± the SEM of three animals per treatment and were ascertained as previously described [27]. Out of range values above the asymptote of equation (> OOR) we set to the highest extrapolated value to provide a conservative estimate that allowed statistical analysis. A significant difference (p ≤ 0.05) in individual serum cytokine levels in PBS vs. TMM001-treated mice was determined by using the Mann-Whitney test.
A previously described method for genetic manipulation via allelic exchange was used to create an unmarked tonB mutant in the B. mallei strain ATCC 23344 [22]. To ensure the mutant phenotype was not due to polar effects incurred during mutagenesis, the TMM001 was transformed with the plasmid pTonB-comp, which carries the intact tonB gene plus its native promoter (Table 1). Unlike the wild type, TMM001 appears as bright yellow colonies that discolor the surrounding media when grown in Luria Bertani + 4% glycerol (LBG) plates (S1 Fig). This phenotype in iron transport mutants has been attributed to the unregulated production and accumulation of iron-bound siderophores, which are yellow-to-brown in color, in contrast to uncolored iron-free siderophores [28,29,30]. The wild-type phenotype was restored when TMM001 was complemented, which grew as muted yellow-beige colonies with no media discoloration.
To determine the effect of the tonB deletion on growth rate and iron requirement, growth curves were performed with the following strains and broth conditions: wild type in LBG, TMM001 in LBG ± 200 μM FeSO4 (Fig 1). When grown in LBG, TMM001 exhibited a reduced growth rate, displaying a longer lag phase, compared to that of the wild type. When grown in LBG + 200 μM FeSO4, the growth rate of the TMM001 increased substantially approaching that of the wild type. Notably, TMM001 grown in iron-supplemented media maintained wild-type growth rates showing statistically significant differences only after 25h of growth.
To determine if the deletion of tonB in B. mallei resulted in differential siderophore production, both the wild-type and TMM001 were seeded on CAS agar. The CAS media was used because when strong iron chelators, such as siderophores, are secreted, they are able to strip the dye complex of iron, which results in the formation from blue to orange/yellow zones (S2 Fig). Siderophore secretion zones were measured after 96 h and calculated as the diameter of the halo minus the diameter of bacterial colony on the filter disk. TMM001 produced significantly larger halos (33.3 ± 0.5 mm) compared to those of the wild type (12.3 ± 0.6 mm). These results are in line with previously studies that show iron transport mutants hypersecrete siderophores in a futile attempt to acquire iron [28,30,31,32,33,34,35].
A disk diffusion assay was performed to examine the ability of the TMM001 to utilize the following sources of iron: FeSO4, hemoglobin, hemin, lactoferrin, and transferrin. Iron assimilation was determined by measuring the diameter (mm) of bacterial growth around the disk containing specific iron sources placed on iron-depleted media (S1 Table). The wild-type strain was able to grow by utilizing all iron sources, while TMM001 was only capable of utilizing FeSO4, the only iron source acquired by a TonB-independent process.
In previous characterization studies of our acute respiratory murine inhalational glanders model, we observed that the 50% lethal dose using B. mallei strain ATCC 23344 was 7.4 x 104 CFU/50 μL (Torres lab experimental data). To establish the role of tonB in B. mallei virulence, we challenged BALB/c mice intranasally (i.n.) with 1.5 x 105 CFU, 1.5 x 106 CFU and 1.5 x 107 CFU of TMM001 grown in LBG ± 200 μM FeSO4 and monitored them for survival up to day 14. The Kaplan-Meier curve shows an inverse correlation between the dose and/or iron concentration and the mouse survival rate (Fig 2). Despite growth conditions, all BALB/c mice challenged with 1.5 x 107 CFU of TMM001 succumbed to infection by 4 days post challenge. At lower doses, the effect of supplementing TMM001 with 200 μM FeSO4 on survival was still apparent. At day 14, survival increased from 62.5% to 100% and 0% to 12.5% when BALB/c mice received a challenge dose of 1.5 x 105 CFU and 1.5 x 106 CFU of the TMM001, respectively, which was grown in LBG alone.
We next enumerated bacterial counts in infected organs to determine the role of TonB in B. mallei’s ability to disseminate and colonize target tissues. BALB/c mice challenged i.n. with 1.5 x 104 CFU of the wild-type CSM001 or TMM011 grown in LBG ± 200 μM FeSO4 were euthanized at 24, 48 and 72 h post challenge. At each time point, the lungs and spleen were processed and plated for CFU quantification. Compared to CSM001, the numbers of TMM001 recovered from the lungs were significantly reduced at 24 h (★ p ≤ .05) and 48 h (★★★★ p ≤ .0001), independent of growth conditions (Fig 3A). A similar trend was observed in the spleen with significantly reduced numbers of TMM001 compared to the CSM001 at 24 h (★ p ≤ .05) and 48 h (★★★ p ≤ .001) (Fig 3B). When grown in LBG + 200μM FeSO4 prior to challenge, TMM001 resembled CSM001, showing no statistical difference in the number of bacteria recovered from the lungs. However, a statistical difference was seen in the recovery of TMM001 grown in FeSO4 in the spleen at 72 h (★ p ≤ .05) (Fig 3A and 3B). BALB/c mice challenged with the CSM001 expired before the 72 h time point and data are not presented.
To evaluate the protective efficacy of TMM001 against CSM001 challenge, BALB/c mice received PBS, 1.5 x 104 CFU or 1.5 x 105 CFU of TMM001 (grown in LBG only), via the i.n. route. At 21 days post-immunization, vaccinated mice were challenged i.n. with 1.5 x 104 CFU of CSM001. The wild-type homolog CSM001, containing a luminescent reporter, was used to assess the protective potential of TMM001 via real-time in vivo monitoring. All infected PBS-treated BALB/c mice died by day 4, presenting with a calculated median survival of 3 days post challenge (S3 Fig). In contrast, infected mice immunized with TMM001 at a dose of 1.5 x 105 CFU or 1.5 x 104 CFU showed 100% (★★★ p = 0.0003) and 87.5% (★★★ p = 0.0003) survival, respectively.
Dissemination and colonization of CSM001 was monitored in TMM001-treated and naïve BALB/c mice using IVIS at 72 h post challenge and every 7 days thereafter until the experiment ended. At 72 h post challenge, PBS-treated BALB/c mice exhibited a luminescent signal associated with anatomical locations corresponding to the lungs, liver, spleen and brain. However, this signal was not detected at similar locations in BALB/c mice immunized with TMM001 (S4 Fig). To evaluate whether TMM001 immunization resulted in the production of sterile immunity, BALB/c mice surviving the experimental challenge were euthanized and organs harvested to be analyzed for gross pathology and bacterial persistence. Although the lungs and livers showed no signs of evident pathology, BALB/c mice presented with splenomegaly accompanied by multiple splenic abscesses (S5 Fig, panels D-F), which mirrors spleens at stage 3 of murine melioidosis infection, as we previously described [27]. Bacterial counts were only recovered from the spleens of mice immunized with a dose 1.5 x 105 (334,666 ± 70,465 CFU per spleen) and 1.5 x 104 (61,917 ± 18,217 CFU per spleen) CFU of TMM001. Based on the phenotypic yellow pigment of the colonies, polymyxin B resistance and kanamycin sensitivity, we were able to conclude that all bacteria recovered were TMM001 and not CSM001.
In an attempt to eliminate persistence of the attenuated TMM001 strain, as well as to reduce organ pathology, an attenuated strain titration study was initiated to identify the lowest immunization dose that still provided 100% protection. The TMM001 titration study used the following CFUs for immunization: 1.5 x 104, 1.5 x 103 and 1.5 x 102. Twenty-one days post-immunization, three mice from each immunization group were euthanized, and organs and serum were harvested for histopathological and cytokine analysis. Forty-eight hours after CSM001 (1.5 x 104 CFU) challenge, an additional 3 mice from each treatment were euthanized, and organs and serum were harvested for histopathological and cytokine analysis. As previously observed, all PBS-treated mice challenged with B. mallei CSM001 died by day 4, with a median survival of 3 days (Fig 4). The titration curve exhibits a significant dose-dependent increase in survival in TMM001-treated mice challenged with CSM001. All mice immunized with 1.5 x 102 CFU expired by day 15, with an increased mean survival of 9 days (★★ p = 0.0016). Mice immunized with 1.5 x 103 CFU or 1.5 x 104 CFU, survival up to 28 days increased to 62.5% (★★ p = 0.0016) and 100% (★★★ p = 0.00016), respectively. Assessment of bacterial burden in surviving animals showed the spleen and, to a lower extent, the liver chronically infected in the TMM001-treated but not the CSM001 strain.
The generation of murine humoral immune responses to B. mallei following treatment with mock (PBS) or TMM001 was determined by analysis of sera using ELISA. Compared to mock-vaccinated mice, sera from TMM001-treated mice had significantly higher titers of B. mallei-specific IgM and IgG antibodies (Fig 5). Mean differences in absorbance for IgG1, IgG2a, and IgM were 5.4-fold (p = 0.0009), 4.8-fold (p = 0.0106), and 10.9-fold (p = 0.0028) higher, respectively, in TMM001-vaccinated mice.
The mouse tissues (lungs, liver and spleen) from the TMM001 titration study (n = 3 per treatment) at 0 h and 48 h post-challenge were processed for histology. Representative images of the lungs, liver and spleen from PBS- and TMM001 (1.5 x 104 CFU)-immunized mice are presented in S6 Fig. At 0 h, the lungs, livers and spleens of PBS-treated mice were unremarkable, presenting as normal healthy tissue with normal architecture (S6 Fig, panels A-C). BALB/c mice immunized with TMM001 presented with mild-to-moderate changes in pathology: perivascular and peribronchial inflammatory infiltrates in the lung sections (S6 Fig, panel D), hepatitis with multifocal necrosis and scattered abscesses in the liver sections (S6 Fig, panel E), and necrosis of follicles and accumulation of neutrophils in spleen sections (S6 Fig, panel F). At 48 h post challenge with CSM001 (1.4 x 104 CFU), PBS-treated mice showed moderate-to-severe pathological changes, such as abscesses and multifocal inflammatory infiltrates in the lungs (S6 Fig, panel G and Fig 6A), areas of hepatocellular necrosis, occasional abscesses with necrotic cores and areas of focal necrosis in the liver (S6 Fig, panel H), and congestion of the red pulp, proliferation of large foamy macrophages (inset of Fig 6, panel C) and necrosis affecting the mantle zone (S6 Fig, panel I and Fig 6, panel C). Similarly, TMM001-immunized mice showed moderate-to-severe changes in pathology, but with a few differences. In the lungs, large, multifocal inflammatory infiltrates, as well as abscesses, were present with focal consolidation observed as well (S6 Fig, panel J and Fig 6, panel B). The liver presented with hepatitis and multiple foci of hepatocellular necrosis (S6 Fig, panel K), and large granulomas were formed in the spleen (S6 Fig, panel L and Fig 6, panel D). Histopathology scores showed significant differences due to treatment over time in the lungs (★★★★ p ≤ 0.0001), liver (★★★★ p ≤ 0.0001) and spleen (★★ p ≤ 0.001). When comparing the differences in treatment at 0 h and 48 h, the lungs (★ p = 0.05), liver (★ p = 0.05) and spleen (★ p = 0.05) showed a robust trend toward significance (Fig 6, panels E-G). Overall, TMM001-immunization alone does cause some histopathology as evident by the histopathology at 0 h. That being said, PBS-immunized animals exuded more extensive pathology 48 h after CSM001 challenge compared to TMM001-immunized animals.
Sera that was collected at 48 h post challenge from PBS- and TMM001-treated mice was used to identify pro-inflammatory cytokine and chemokine responses that correspond with disease outcome. Prior to challenge, a similar baseline expression of cytokines and chemokines was detectable in serum of representative animals from both the PBS- and TMM001-immunized animals (Fig 7A). Following CSM001 challenge, the overall cytokine/chemokine expression increased markedly in both PBS- and TMM001-immunized animals (Fig 7B) compared to baseline, consistent with our previous observations of innate immune responses to Burkholderia species [27]. An attenuation of the pro-inflammatory serum cytokine/chemokine response to challenge was observed in the TMM001-treated compared to PBS control. The reduction of several pro-inflammatory mediators due to TMM001-treatment was significant, including IL-6 (p = 0.049), GM-CSF (p = 0.037), MCP-1 (p = 0.022), and RANTES (p = 0.032) (Fig 7). A trend for reduction of several other pro-inflammatory IL-1β (p = 0.097), G-CSF, (p = 0.067) and KC (p = 0.05) due to TMM001-treatment was also observed (Fig 7).
We next tested TMM001 for its protective potential against B. pseudomallei in an acute inhalational model of murine melioidosis. Mice received 1.5 x 104 CFU of TMM001 and at 21 days post-immunization, they were challenged with 9.0 x 102 CFU (3 LD50) of B. pseudomallei strain K96243 [26]. All PBS-treated BALB/c mice died by day 5 post-challenge and displayed a median survival of 5 days (Fig 8). In mice immunized with TMM001, survival was increased to 75% (★★★, p ≤ 0.001) at the end point of 36 days. As with the previous B. mallei study described above, the TMM001 strain, but not the wild-type B. pseudomallei, were recovered from immunized mice who presented with splenomegaly accompanied by abscesses.
To date, immune correlates of protection for B. mallei and B. pseudomallei are not clearly defined. Due to their intracellular lifestyle, these pathogens use an array of virulence factors to invade, replicate, and cause pathogenesis from within host cells, which can impede immune detection and, in some cases, protection. An extensive review of the literature suggested to us that an ideal vaccine for both pathogens would induce robust humoral and cell-mediated responses [1,36]. Thus, we decided to examine live attenuated vaccines, as these are often cited as the most efficacious approach to vaccine development against intracellular pathogens because they mimic natural infection, inducing both humoral and cell-mediated immunity, without causing disease. Moreover, exposure to the live attenuated strain allows the immune system to customize a protective response, in addition to generating an immune memory for lifelong protection against infection.
In growth curve experiments, it was found that TMM001 was unable to maintain wild-type growth kinetics (Fig 1). Upon supplementing the culture with free iron, TMM001 exhibited increased growth rates more reminiscent of the wild-type, which is illustrated by a shorter lag phase and prolonged maintenance of wild-type growth kinetics. In a separate growth curve study, full rescue of the wild type phenotype in TMM011 was achieved after both the starter and sub-culture were supplemented with free iron. The correlation between free iron concentration and the growth rate of TMM001 illustrates the importance of TonB as a facilitator of iron transport, which has a direct impact on bacterial fitness.
The results of our survival study show an inverse correlation not only between TMM001 dose and survival but also between concentration of free iron and survival (Fig 2). Compared to the wild-type B. mallei strain (LD50 of 7.5 x 104 CFU), TMM001 is approximately 3-fold more attenuated when grown with FeSO4 (LD50 of 2.38 x 105 CFU); and when grown in LBG alone, the tonB mutant is attenuated by approximately 7-fold (5.59 x 105 CFU). Differences in virulence are consistent with the data of the dissemination study which showed the lowest burden in animals infected with TMM001 and higher bacterial burdens in animals infected with TMM001 grown with FeSO4 (Fig 3). In both experiments, FeSO4 supplementation failed to fully reverse attenuation of TMM001 in vivo. This outcome was not unexpected as the concentration of free iron in the host (10-24M [37]) is well below that is needed to sustain bacterial replication. While FeSO4 supplementation would prolong its survival and therefore increase its virulence, TMM001 would be unable to sustain the wild-type phenotype once its internal stores of iron are exhausted. Lack of full complementation by FeSO4 supplementation can also be attributed to the role of TonB in the import of other substrates. While the majority of TonB-dependent transport systems function to uptake iron, vitamin B12, nickel chelates, and carbohydrates can also be transported by this mechanism. Overall, decreased mortality observed in animals challenged with the TMM001 grown in LBG alone illustrates the importance of iron and its TonB-mediated acquisition to virulence.
In a series of TMM001 titration studies, it was empirically determined that a dose of 1.5 x 104 CFU of TMM001 resulted in 100% protection (Fig 4) and CSM001 clearance following challenge. Protected animals developed strong B. mallei-specific IgG1, IgG2a, and IgM responses (Fig 5), which we attribute to TMM001-mediated protection. The observation and correlation of strong IgG and IgM elicitation and protection are cited often in Burkholderia vaccine studies [38,39,40,41]. In human cases, it was found that patients with less severe, localized infection produced detectable Burkholderia-specific IgM antibody titers, whereas none were detected in patients suffering from acute disseminated infection [39]. Thus, it is plausible to suggest TMM001 treatment protects against lethal infection by neutralizing bacteria and/or preventing their dissemination to target organs via antibody-mediated mechanisms.
TMM001 immunization resulted in pathological differences that may explain increased survival and protection. In general, histopathological scoring shows a robust trend toward significant differences in the pathology seen in the lungs, liver and spleen of PBS- vs. TMM001-immunized animals (Fig 6, panels E-G). Further analysis of these tissues revealed two discriminatory elements of pathologic damage between vaccine treatments. First, despite the finding that the lungs and livers from both PBS- and TMM001-immunized animals displayed some degree of tissue damage, the pathological changes in TMM001-immunized mice were much less severe (S6 Fig, panel A). Second, the differential alteration in spleen architecture implied that the PBS- and TMM001-immunized animals responded differently to infection. For example, splenic tissues from PBS-treated mice show a diffuse response to injury (i.e. diffuse severe histiocytosis), while splenic tissue from TMM001-immunized mice showed a focal response to injury (i.e. granuloma formation) (S6 Fig, panels C-D). These histological observations suggested to us that immunization with TMM001 may result in the induction of an immune response that produces a different type of tissue damage, in addition to confining infection to prevent disseminated disease, an important cause of morbidity and mortality in many diseases [39,42,43,44].
The histopathological differences between PBS- and TMM001-vaccinated mice suggest that TMM001 reduces disease by attenuating immune-mediate pathology at sites of bacterial proliferation. Our observation that pro-inflammatory cytokine/chemokine responses are attenuated following TMM001 treatment further supports this conclusion. In models of murine melioidosis, it has been established that increased expression of IL-1β and IL-6 follow B. pseudomallei dissemination and coincide with acute sepsis and mortality [45,46]. Clinical evidence further suggests a correlation between elevated serum levels of IL-1β and IL-6 and poor prognosis in patients with septic melioidosis [47,48,49]. Our previous studies demonstrated that pre-treatment with CpG oligonucleotides protected mice from B. pseudomallei exposure and reduced pro-inflammatory cytokine/chemokine (e.g. IL-1β, IL-6, G-CSF, KC, MCP-1) expression in the lung [35]. In the study by Judy, et al., a moderate pro-inflammatory response was associated with protection while excessive inflammation caused pulmonary pathology [26]. Further, the protective effects of CpG treatment to reduce lung pathology were attributed to a reduction in neutrophil and inflammatory monocyte recruitment [26,50]. Similarly, we have previously shown that the virulence of B. pseudomallei strains in direct comparisons corresponds with excessive production of pro-inflammatory cytokines and chemokines that recruit neutrophils and monocytes [27]. These observations in clinical and animal model studies support a role for exacerbated pro-inflammatory responses to mediate lung pathology in disease due to Burkholderia species. Thus, treatment with TMM001 activates a moderate pro-inflammatory cytokine/chemokine response associated with protective immune responses and attenuates the exacerbation of this response that is associated with neutrophil infiltration and immune-mediated tissue damage.
Lastly, since B. mallei and B. pseudomallei are genetically closely related, TMM001 was further tested for its potential to provide protection in an acute inhalational model of murine melioidosis. The significant cross protection seen in TMM001-treated mice provides an optimistic outlook for the development for a single vaccine for both pathogens. Immunization with TMM001 resulted in full protection and clearance of CSM001 when tested in an acute respiratory model of murine glanders. This live attenuated strain is unique not only because it provided full protection against both acute and chronic stages of infection but also because it imparted significant cross protection against B. pseudomallei infection.
It is hypothesized that the persistence of viable bacteria is key for protective potential. In previous vaccination studies, the failure to provide successful long-term protection has often be attributed to the quick removal of live attenuated strains from the host [51,52]. Thus, we believe the resulting long-term protection is link to the ability of TMM001 to evade rapid clearance. This notion is supported by vaccination studies which reported long-term survivors to be generally colonized at the end of the study [51,52,53]. It is plausible to propose that this persistence increases the accessibility of the immune system to protective antigens or it might contribute to the development of an environment adverse to wild-type colonization via chronic elicitation of the immune response.
Although its persistence is important for its protective potential, TMM001 is able to colonize and maintain in the host. Before securing approval from the division of select agents and toxin (DSAT) for removal of this strain from the Health and Human Services select agent list and becoming a legitimate vaccine candidate, the ability of TMM001 to cause chronic infection needs to be addressed. Studies are now focused on using TMM001 as a backbone to generate a further attenuated strain with the introduction of additional mutations. As TMM001 is only 7-fold less virulent than the wild-type B. mallei strain, we believe this to be the best strategy for optimization and do not anticipate problems with over-attenuation. Currently we are targeting genes that are contributing to persistence with the intention of developing a more attenuated strain that can persist long enough to elicit a protective immune response without establishing chronic infection. For example, we are focused on genes involved in TonB-independent mechanisms of iron assimilation. Bacterial transport systems that are shown to transport iron in this manner include the following: FbpABC transport system of Neisseria gonorroeae¸ SfuABC transport system of Serratia marcescens, VctPDGC ABC cytoplasmic membrane transport system of Vibrio cholera, etc [Ref]. Looking for homologs of these systems in B. mallei could provide optimal targets for further attenuation. Overall, we believe the present study represents a significant advancement in the battle against pathogenic Burkholderia infections, in which TMM001 could be further optimized to become an effective vaccine against glanders, melioidosis, or other Burkholderia infections.
|
10.1371/journal.pmed.1002396 | Gabapentin, opioids, and the risk of opioid-related death: A population-based nested case–control study | Prescription opioid use is highly associated with risk of opioid-related death, with 1 of every 550 chronic opioid users dying within approximately 2.5 years of their first opioid prescription. Although gabapentin is widely perceived as safe, drug-induced respiratory depression has been described when gabapentin is used alone or in combination with other medications. Because gabapentin and opioids are both commonly prescribed for pain, the likelihood of co-prescription is high. However, no published studies have examined whether concomitant gabapentin therapy is associated with an increased risk of accidental opioid-related death in patients receiving opioids. The objective of this study was to investigate whether co-prescription of opioids and gabapentin is associated with an increased risk of accidental opioid-related mortality.
We conducted a population-based nested case–control study among opioid users who were residents of Ontario, Canada, between August 1, 1997, and December 31, 2013, using administrative databases. Cases, defined as opioid users who died of an opioid-related cause, were matched with up to 4 controls who also used opioids on age, sex, year of index date, history of chronic kidney disease, and a disease risk index. After matching, we included 1,256 cases and 4,619 controls. The primary exposure was concomitant gabapentin use in the 120 days preceding the index date. A secondary analysis characterized gabapentin dose as low (<900 mg daily), moderate (900 to 1,799 mg daily), or high (≥1,800 mg daily). A sensitivity analysis examined the effect of concomitant nonsteroidal anti-inflammatory drug (NSAID) use in the preceding 120 days. Overall, 12.3% of cases (155 of 1,256) and 6.8% of controls (313 of 4,619) were prescribed gabapentin in the prior 120 days. After multivariable adjustment, co-prescription of opioids and gabapentin was associated with a significantly increased odds of opioid-related death (odds ratio [OR] 1.99, 95% CI 1.61 to 2.47, p < 0.001; adjusted OR [aOR] 1.49, 95% CI 1.18 to 1.88, p < 0.001) compared to opioid prescription alone. In the dose–response analysis, moderate-dose (OR 2.05, 95% CI 1.46 to 2.87, p < 0.001; aOR 1.56, 95% CI 1.06 to 2.28, p = 0.024) and high-dose (OR 2.20, 95% CI 1.58 to 3.08, p < 0.001; aOR 1.58, 95% CI 1.09 to 2.27, p = 0.015) gabapentin use was associated with a nearly 60% increase in the odds of opioid-related death relative to no concomitant gabapentin use. As expected, we found no significant association between co-prescription of opioids and NSAIDs and opioid-related death (OR 1.11, 95% CI 0.98 to 1.27, p = 0.113; aOR 1.14, 95% CI 0.98 to 1.32, p = 0.083). In an exploratory analysis of patients at risk of combined opioid and gabapentin use, we found that 46.0% (45,173 of 98,288) of gabapentin users in calendar year 2013 received at least 1 concomitant prescription for an opioid. This study was limited to individuals eligible for public drug coverage in Ontario, we were only able to identify prescriptions reimbursed by the government and dispensed from retail pharmacies, and information on indication for gabapentin use was not available. Furthermore, as with all observational studies, confounding due to unmeasured variables is a potential source of bias.
In this study we found that among patients receiving prescription opioids, concomitant treatment with gabapentin was associated with a substantial increase in the risk of opioid-related death. Clinicians should consider carefully whether to continue prescribing this combination of products and, when the combination is deemed necessary, should closely monitor their patients and adjust opioid dose accordingly. Future research should investigate whether a similar interaction exists between pregabalin and opioids.
| Gabapentin is a drug often used together with opioids to treat chronic pain, and both of these drugs have been shown to suppress breathing, which can be fatal.
Concomitant opioid use can also increase the amount of gabapentin absorbed by the body, potentially leading to higher risks when these drugs are used together.
Despite these potential risks, so far no studies have examined whether the concomitant use of gabapentin and opioids really increases the likelihood of accidental opioid-related death and whether this risk is dependent on gabapentin dose.
If there is an important interaction between these drugs, this would be of high clinical importance given the common combined use of these drugs.
We conducted a matched case–control study among people treated with opioid painkillers in Ontario, the most populous province of Canada.
We determined concomitant gabapentin exposure among 1,256 individuals (cases) who died of an opioid-related cause and 4,619 matched controls.
We found that concomitant gabapentin and opioid exposure was associated with a 49% higher risk of dying from an opioid overdose.
Clinicians should take great caution when combining gabapentin and opioids.
Patients treated with opioids and gabapentin should be closely monitored, and may need to have their doses adjusted to avoid potential drug overdose.
| Prescription opioid overdoses are an ongoing public health concern across North America, contributing to more than 15,000 deaths in the United States in 2015 [1]. Most opioid-related deaths result from respiratory depression, and co-administration of central nervous system (CNS) depressants is an important and avoidable risk factor for death. Recent evidence showed that co-prescription of benzodiazepines with opioids increases the risk of overdose death nearly 4-fold [2], leading to the introduction of black box warnings on the packaging for both products in August 2016 [3]. Furthermore, prescription opioid use has been shown to be highly associated with future risk of opioid-related death, with 1 of every 550 chronic opioid users dying within approximately 2.5 years of their first opioid prescription [4].
Gabapentin is an anticonvulsant commonly used as an adjunct for the treatment of chronic pain [5]. Although gabapentin is widely perceived as safe [5,6], drug-induced respiratory depression has been described when gabapentin is used alone or in combination with other medications [7–10]. Indeed, the product monograph was amended in 2014 to warn about possible respiratory depression when combined with opioids [11]. Potential risk factors for gabapentin-related respiratory depression include advancing age, renal insufficiency, chronic lung disease, and dose. The role of dose is particularly important in light of data indicating a 44% increase in systemic gabapentin exposure following its administration with morphine [8–10,12,13], likely reflecting increased drug absorption from lowered intestinal motility [12].
Because gabapentin and opioids are both commonly prescribed for pain, the likelihood of co-prescription is high [14–17]. However, to our knowledge, no published studies have examined whether concomitant gabapentin therapy is associated with an increased risk of accidental opioid-related death in patients receiving opioids. We investigated this question in a cohort of adults receiving prescription opioids in Ontario, Canada. We hypothesized that individuals co-prescribed opioids and gabapentin would have a higher risk of opioid-related death, and that there would be a dose–response gradient with increasing gabapentin dose.
We conducted a population-based nested case–control study of adults dispensed opioid analgesics under the Ontario Public Drug Programs between August 1, 1997, and December 31, 2013. All residents of Ontario receive publicly funded physician and hospital care. Public drug coverage for prescription medications is provided to individuals aged 65 years and older, as well as those who are unemployed, are receiving disability benefits, have high drug costs relative to their net household income, are receiving home care services, or reside in a long-term care home. The study protocol (S1 Text) was approved by the research ethics board of Sunnybrook Health Sciences Centre, Toronto, Ontario.
We used the Ontario Drug Benefit (ODB) database to identify prescription medications dispensed to eligible residents of Ontario over the study period, and the Ontario Health Insurance Plan database to identify services rendered by physicians. We used the Canadian Institute for Health Information’s Discharge Abstract Database and National Ambulatory Care Reporting System to identify diagnoses and procedures provided during hospital admissions and emergency department visits, respectively. We used the Registered Persons Database, which contains information on every Ontarian ever issued a health card, to identify demographic information. We used the Ontario Cancer Registry and the Ontario Diabetes Database (ODD) to capture diagnoses of cancer and diabetes, respectively. The ODD is a database constructed using administrative claims data that was shown to have a sensitivity of 86%, specificity of 97%, and positive predictive value of 80% against primary care physician diagnoses as the external criterion in a validation study [18]. Similarly, we used an algorithm in our hospitalization and physician claims data to define chronic kidney disease that has been shown in a validation study to have a specificity of 97% against laboratory measures of serum creatinine as the external criterion [19]. Finally, we abstracted detailed information on confirmed opioid-related deaths from the Office of the Chief Coroner of Ontario using methods described previously [20]. In Ontario, all sudden or unexpected deaths are investigated by a medical coroner to determine cause and manner of death. This rigorous investigation incorporates interviews as well as autopsy findings and postmortem toxicology to confirm cause of death. Our data capture all cases in which the findings of this investigation confirmed that opioids contributed to death, either alone or in combination with another drug or alcohol. Therefore, overdose deaths due to opioids alone or due to the combined sedating effects of opioids and gabapentin would be identified in this database. These data are used regularly to study opioid overdose deaths in Ontario [4,21–23]. All datasets were linked using unique, encoded identifiers, and were analyzed at the Institute for Clinical Evaluative Sciences (ICES; https://www.ices.on.ca).
We identified a cohort of ODB-eligible individuals aged 15 to 105 years who were treated with at least 1 opioid prescription over the study period, including oral formulations of morphine, codeine, oxycodone, meperidine, and hydromorphone, as well as transdermal fentanyl patches. We excluded prescriptions for rarely used opioids (such as pentazocine or anileridine), parenteral or intranasal opioid formulations, and methadone, which in Ontario is principally used to treat opioid use disorders.
We defined cases as individuals from within this cohort who died from an opioid-related cause over the study period, and excluded opioid overdoses deemed to be suicides or homicides by the investigating coroner. We defined the index date as the date of death. The index date for potential controls was randomly assigned according to the distribution of index dates for included cases. We excluded individuals with invalid identifiers and those with a prior diagnosis of cancer or evidence of palliative care in the 6 months preceding the index date. Consequently, our analyses were limited to patients receiving opioids for non-cancer pain. We required that all study patients have at least 1 opioid prescription overlapping with their index date, and at least 6 months of continuous eligibility for public drug benefits prior to their index date.
To increase the comparability of cases and controls, we utilized a disease risk index to generate predicted probabilities of dying of an opioid-related death, using multiple demographic characteristics, medical problems, and psychiatric disorders, as described previously [22,24]. Details of the components of this risk index can be found in S1 Table. We used incidence density sampling to match each case with up to 4 controls on their disease risk index (within 0.2 standard deviations), age (within 5 years), sex, year of index date (within 1 year), and history of chronic kidney disease (prior 5 years). When a full number of matches could not be found, we matched as many controls as possible to each case, and any available controls were analyzed.
We defined recent concomitant gabapentin exposure on the basis of at least 1 prescription in the 120 days preceding each individual’s index date. In a secondary dose–response analysis, we stratified the gabapentin dose into low dose (<900 mg daily), moderate dose (900 to 1,799 mg daily), and high dose (≥1,800 mg daily) to align with dose ranges suggested in the product monograph (effective dose range 900 to 1,800 mg/day). Two post hoc sensitivity analyses were conducted. In the first, we further stratified high-dose gabapentin use into high dose (1,800 to 2,499 mg daily) and very high dose (≥2,500 mg daily) to investigate the association between opioid-related death and higher gabapentin doses. In the second post hoc sensitivity analysis, we defined gabapentin exposure on the basis of a gabapentin prescription where the days’ supply overlapped the index date. To test the specificity of our findings, we conducted a prespecified sensitivity analysis in which we examined recent concomitant exposure to nonsteroidal anti-inflammatory drugs (NSAIDs) in the prior 120 days because NSAIDs are not expected to independently increase the risk of opioid-related death among patients receiving opioids.
We ascertained the average daily dose of opioids dispensed to each individual in the cohort by identifying all opioid prescriptions overlapping their index date. The daily dose of each prescription was calculated and converted into approximate milligrams of morphine or equivalent (morphine milligram equivalent [MME]) using morphine equivalence ratios defined by the Canadian National Opioid Use Guideline Group [25]. Opioid dose was grouped into categories as follows: <20 MME, 20 to 49 MME, 50 to 99 MME, 100 to 199 MME, and ≥200 MME.
We also identified a number of other patient characteristics, including sociodemographic characteristics (age, sex, urban/rural location of residence, income quintile), past medication use, Charlson Comorbidity Index (0, 1, 2 or more), history of alcohol use disorder, and health services utilization (S1 Table).
We summarized patient characteristics using descriptive statistics, and used standardized differences to compare cases and controls. Standardized differences are often used instead of p-values when studying large cohorts, and values > 0.1 are generally considered to represent a meaningful difference [26]. Missing data for any covariates in the analysis were reported as separate categories. In accordance with ICES privacy policies, whenever a cell count was ≤ 5, the cell count was censored to avoid potential re-identification of personal health information.
We used conditional logistic regression to compare the odds of dying of opioid-related causes among opioid recipients co-prescribed gabapentin with the odds among those prescribed opioids alone. We estimated odds ratios (ORs) and 95% CIs for all comparisons, and adjusted all models for a number of important covariates, including opioid dose, age, medication use in the prior 120 days (pregabalin, selective serotonin reuptake inhibitor [SSRI] antidepressants, other antidepressants, benzodiazepines, other psychotropic drugs/CNS depressants, and methadone/buprenorphine, each considered separately), the number of drugs dispensed in the past 6 months, receipt of a long-acting opioid formulation during the exposure window, diagnosis of alcohol use disorder in the prior 3 years, Charlson Comorbidity Index, chronic lung disease, diabetes, number of opioid prescribers in the past 6 months, and number of pharmacies from which each person was dispensed opioids in the past 6 months. In our sensitivity analysis of concomitant NSAID use, we also adjusted the model for gabapentin use in the past 120 days.
To estimate the potential number of people at risk of combined opioid and gabapentin use, we created a cohort of gabapentin users in the last year of our study (calendar year 2013) and defined a period of ongoing gabapentin use on the basis of a prescription refill within 150% of the days’ supply of the prior prescription. We estimated the prevalence of co-prescription of gabapentin and opioids by identifying all individuals in this cohort who received at least 1 prescription for an opioid during the period of ongoing gabapentin use.
All analyses used a type I error rate of 0.05 as the threshold for statistical significance and were performed using SAS statistical software (version 9.4; SAS Institute, Cary, North Carolina).
The reporting of this study is in accordance with the REporting of studies Conducted using Observational Routinely collected health Data (RECORD) statement (S1 RECORD Checklist) [27].
We identified 2,914,971 ODB-eligible individuals who received a prescription opioid over the study period; of these, 1,391 potential cases met our inclusion criteria (Fig 1), and 1,256 (90.3%) of these were matched to at least 1 control, leading to a total of 4,619 controls included in our study. All cases had prescription opioids found on postmortem toxicology, and 24 (1.9%) also had heroin present in their system at the time of death. Of those with heroin involved in their death, fewer than 6 had been exposed to a prescription for gabapentin in the prior 120 days.
Baseline characteristics of cases and controls are presented in Table 1. The majority of cases (94.5%) and controls (94.2%) were aged less than 65 years, and over 40% (42.1% of cases and 43.8% of controls) were in the lowest income quintile. As expected, cases tended to receive higher opioid doses, were more likely to have received a long-acting opioid during the exposure window, and were more likely to have recent exposure to antidepressants, benzodiazepines, and other CNS depressants. Cases had more visits to physicians and a greater number of prescriptions in the past 6 months.
Overall, 12.3% of cases (155 of 1,256) and 6.8% of controls (313 of 4,619) were prescribed gabapentin in the prior 120 days. In the primary analysis, we found that the odds of an opioid-related death was 49% higher among individuals recently exposed to gabapentin and opioids (adjusted OR [aOR] 1.49, 95% CI 1.18 to 1.88, p < 0.001) compared to those exposed to opioids alone, even after extensive adjustment for potential confounders, including opioid dose (Fig 2). In a sensitivity analysis considering only gabapentin prescriptions where the days’ supply overlapped the index date, the results were very similar (aOR 1.46, 95% CI 1.12 to 1.89, p = 0.005).
In our secondary analysis exploring the effect of gabapentin dose, we found that exposure to a moderate (900 to 1,799 mg daily) or high dose (1,800 mg daily or more) of gabapentin was associated with a nearly 60% increased odds of opioid-related death compared to exposure to opioids alone (aOR 1.56, 95% CI 1.06 to 2.28, p = 0.024, for moderate doses; aOR 1.58, 95% CI 1.09 to 2.27, p = 0.015, for high doses), whereas exposure to a low gabapentin dose was not significantly associated with an increased odds of opioid-related death (aOR 1.32, 95% CI 0.89 to 1.96, p = 0.174; Fig 2). In a post hoc sensitivity analysis, very high dose gabapentin use (2,500 mg daily or more) was associated with a nearly 2-fold increased odds of opioid-related death (aOR 1.83, 95% CI 1.04 to 3.22, p = 0.036).
Finally, in our sensitivity analysis to test the specificity of our findings, as expected, we found no significant association between recent exposure to concomitant NSAIDs and opioid-related death (aOR 1.14, 95% CI 0.98 to 1.32, p = 0.083).
In our exploratory analysis of patients at risk of combined opioid and gabapentin use, we found a total of 98,288 gabapentin users in calendar year 2013, of whom 45,173 (46.0%) received at least 1 concomitant prescription for an opioid. Furthermore, among our matched cases and controls, approximately 8% of opioid users had also recently received a prescription for gabapentin.
In this large study spanning more than 16 years, we found that approximately 8% of patients receiving opioids were co-prescribed gabapentin and that co-prescription was associated with a 50% increase in the risk of dying of opioid-related causes. A very high dose of co-prescribed gabapentin was associated with a near doubling of this risk. In contrast, and as expected, no such risk was observed among opioid recipients concomitantly prescribed NSAIDs. Our findings thus support the existence of a life-threatening drug–drug interaction between gabapentin and opioids in routine clinical practice.
The mechanism by which gabapentin may increase the risk of death in opioid users likely reflects both a pharmacodynamic and pharmacokinetic interaction [5]. More specifically, it likely reflects additive respiratory depression as well as increased gabapentin concentrations with concomitant opioid use [12]. A pharmacokinetic interaction most likely reflects increased gabapentin absorption, which occurs primarily in the upper small intestine [5]. Thus, opioid-induced slowing of gastrointestinal transit could prolong the time spent within this narrow absorption window and increase gabapentin bioavailability [5].
Our study has important implications for public health, particularly given the high degree of co-prescription. Almost 10% of patients treated with an opioid in our study also used gabapentin, while nearly half of patients treated with gabapentin were co-prescribed opioids. Similarly, studies from the United States and United Kingdom have estimated that between 15% and 22% of people with opioid use disorder are also misusing gabapentin [17]. Gabapentin is frequently used as an adjunct to opioids for neuropathic pain syndromes, but physicians may not be aware of the potential for respiratory depression with this drug; thus, increased awareness among patients and clinicians about the potential for a life-threatening interaction between these drugs is essential. When co-prescription is necessary, strategies for minimizing the sequelae of this interaction should be considered, including cautious dose titration, dose adjustment in the setting of co-morbid lung and kidney disease, and avoidance of other CNS depressants. In addition, patients treated with this combination should be instructed to seek medical attention immediately if symptoms of opioid overdose occur. Finally, because of pregabalin’s pharmacologic similarities to gabapentin, it is possible that pregabalin imparts a similar risk of overdose and death among opioid users, a hypothesis supported in part by cases of respiratory depression associated with this drug [28]. We were unable to test this hypothesis in our study due to the low prevalence of pregabalin prescribing over our study period (pregabalin only became available on the public drug program in 2013), but suggest that this is an important area of future research.
This study has several strengths, including its large size, the use of population-based coroner’s records to ascertain opioid-related deaths, and the specificity of the association of gabapentin co-prescription and opioid-related death (as evidenced by there being no such association between co-prescription of NSAIDs and opioid-related death). However, some limitations merit discussion. First, our study was limited to a population of individuals eligible for public drug coverage in Ontario, which includes the elderly (aged 65 and older) and younger individuals receiving social assistance. Approximately 95% of the cases and matched controls were younger than 65 years, which led the overall cohort to be generally of lower socioeconomic status (over 40% were in the lowest income quintile). Therefore, these findings may not be generalizable to the broader population of opioid users. Second, we were only able to identify prescriptions reimbursed by the government and dispensed from retail pharmacies. We could not determine drug adherence, whether individuals obtained additional drugs through cash payments or illicit purchases, or whether they were using their prescription opioids as prescribed. While it is possible that this could bias our analysis towards a significant finding if people concomitantly taking gabapentin and opioids were more likely to source opioids illicitly, our finding that heroin involvement in opioid-related deaths was highly concentrated among people not exposed to gabapentin suggests that this had no major influence on our findings. Third, the CIs in our dose–response analysis showed considerable overlap, and therefore the suggestion of a dose–response gradient should be interpreted with caution. Fourth, we were unable to determine the indication for gabapentin use, and so could not confirm whether these medications were being used to treat pain or for indications such as anxiety or depression. However, it is likely that the majority of gabapentin prescribing in this study was for a pain indication due to its concurrent use with opioids. Finally, despite matching on several demographic and clinical variables (including a disease risk index), our cases and controls differed on a number of measured covariates. Confounding by indication is also possible, particularly among patients receiving the highest doses of gabapentin or opioids, for whom severe pain could be a manifestation of serious underlying illness. However, we believe that confounding by indication did not have considerable influence on our findings for several reasons. First, we used a disease risk index to match cases to controls on a number of factors, including diagnoses commonly associated with pain. Second, because we adjusted for opioid dose in all of our analyses, our findings for gabapentin exposure are representative of the additional risk of opioid-related death beyond that which is explained by escalating opioid doses. Third, we adjusted our analyses for markers of severity of underlying disease, including comorbidity, medications, and health service use, and excluded individuals receiving palliative care and those with a prior cancer diagnosis. Finally, our tracer analysis found no association of NSAID co-prescription with opioid-related death, suggesting that underlying pain is not obfuscating our findings, and further mitigating concerns about confounding by indication.
In this study we found that, among patients prescribed opioids, co-prescription of gabapentin was associated with a considerable increase in the risk of opioid-related death, particularly at higher doses. The clinical consequences of a potential drug–drug interaction are clear given the large number of people at risk of this fatal outcome. Clinicians should consider carefully whether to continue prescribing this combination of products and, when co-prescription is deemed necessary, should closely monitor their patients and adjust opioid dose accordingly. Future research should investigate whether a similar interaction exists between pregabalin and opioids.
|
10.1371/journal.pgen.1004981 | Eye Selector Logic for a Coordinated Cell Cycle Exit | Organ-selector transcription factors control simultaneously cell differentiation and proliferation, ensuring the development of functional organs and their homeostasis. How this is achieved at the molecular level is still unclear. Here we have investigated how the transcriptional pulse of string/cdc25 (stg), the universal mitotic trigger, is regulated during Drosophila retina development as an example of coordinated deployment of differentiation and proliferation programs. We identify the eye specific stg enhancer, stg-FMW, and show that Pax6 selector genes, in cooperation with Eya and So, two members of the retinal determination network, activate stg-FMW, establishing a positive feed-forward loop. This loop is negatively modulated by the Meis1 protein, Hth. This regulatory logic is reminiscent of that controlling the expression of differentiation transcription factors. Our work shows that subjecting transcription factors and key cell cycle regulators to the same regulatory logic ensures the coupling between differentiation and proliferation programs during organ development.
| Organs develop from groups of undifferentiated cells that proliferate and differentiate into specific cell types. During development, the coupling between proliferation and differentiation programs ensures that enough cells of the different cell types are generated. This is critical for proper organ formation and function. Here, we use the developing Drosophila eye to examine how the coupling between these two programs is achieved. During eye development, progenitors are amplified before they exit the cell cycle and enter the differentiation program. This amplification step depends on an expression burst of the mitotic trigger string/cdc25, which, by forcing cells into mitosis, synchronizes cells in G1 just before differentiation onset. Thus string regulation acts as a hub where differentiation and proliferation programs are integrated. We identify a DNA element that controls the burst of string expression prior to differentiation, and show that it is regulated by the same gene network that triggers eye development. The transcription factor Pax6/Eyeless is a key regulator in this network. Eyeless acts cooperatively with Sine oculis and Eyes absent to regulate string, through a positive feed-forward loop. This loop is negatively modulated by the progenitor-specific transcription factor Homothorax/Meis1. This work shows that transcription factors that instruct cells to acquire an eye fate also control their proliferation regime, thus guaranteeing the coupling between proliferation and differentiation.
| Selector genes are transcription factors that instruct the development of organs. The processes under the control of selector genes include the assignation of cell fates and their organ-specific responses to extracellular signals [1]. But organ development also requires the faithful execution of proliferation programs to ensure the expansion of progenitor cells and their coordinated exit from the cell cycle prior to the onset of differentiation. This coordinated cell cycle exit is critical to regulate organ size during development and to ensure tissue homeostasis during adult life. The power of selector genes to control the differentiation state of cells and their proliferation regimes explains why abnormal expression of these transcription factors is often associated to cancer [reviewed in 2,3]. However, how selector genes carry out the coordination between proliferation and differentiation programs is still unclear.
Structures of the nervous system, such as the retina, in which complex arrays of different cell types need to be assembled from multipotent proliferative progenitors, are especially sensitive to impairments of proliferation control mechanisms [reviewed in 4,5]. It is therefore likely that selector genes coordinate cell cycle exit with the processes of differentiation and patterning by co-regulating the transcription of cell cycle and patterning genes. However, this control may be direct or mediated by intermediate transcription factors.
The eye selector function is exerted by a network of transcription factors and signaling pathways, with many of the network genes shared by invertebrates and vertebrates. The Pax6 selector genes are on top of the retinal determination (RD) gene network in both animal groups [6]. Pax6 mutations are responsible for aniridia [7,8], while Pax6 overexpression is associated with retinoblastoma cancer progression through promotion of proliferation and cell survival [9–11].
In Drosophila, the RD gene network comprises a number of transcription factors and nuclear proteins, that includes members of conserved gene families: The Pax6 paralogues eyeless (ey) and twin of eyeless (toy); the Six family genes Optix (Six3) and sine-oculis (so; Six1,2); So’s partner, eyes absent (eya); dachshund (dac); and the Meis1 homologue homothorax (hth). These genes are not only connected through transcriptional cross-regulation, but also have been found to engage in protein complexes [reviewed in 12,13].
Research during the past years is yielding an increasingly clearer picture of how the process of eye specification and retinal patterning in Drosophila is controlled [reviewed in 13,14]. The eye primordium (also called “eye disc”) derives from the So-expressing embryonic cephalic neuroectoderm [15]. Within this domain, toy activates ey expression during late embryogenesis, which results in the specification of the eye-progenitor cells [16]. During larval life, ey-expressing progenitors are maintained proliferative and multipotent as long as they express hth [17–19]. Repression of hth starts during the third and last larval stage (L3), mediated by Decapentaplegic (Dpp a BMP2/4-like molecule) and Hedgehog (Hh) signals produced at a moving signaling center, called “morphogenetic furrow” (MF). hth repression is key, as it allows the upregulation of so, eya and dac [17,19]. Coinciding with hth repression, the expression of string (stg)-the Drosophila cdc25 phosphatase homologue [20–22]- is upregulated and this drives cells through a few consecutive mitotic rounds (the first mitotic wave, FMW), resulting in G1-synchronized ey-so-eya-dac-expressing cells (retinal precursors) [17,19,20]. The expression of ey, so and eya turns on the expression of the bHLH gene atonal (ato), the fly homologue of ath5/atoh7, which is necessary for the differentiation of precursor cells into photoreceptors, lens and pigment cells of the retina [23–26].
The information processing devices in networks such as the RD gene network are cis-regulatory elements (CREs), DNA sequences that allow binding of specific combinations of transcription factors, which in turn regulate transcription of the CRE target genes [27]. Therefore, CREs are key to understand the logic that drives the developmental processes directed by a gene network. In the Drosophila RD gene network, CREs from ey [28,29], so [30,31], eya [32], dac [33]; optix [34] and ato [24–26,35] have been isolated and studied in molecular detail. Not surprisingly, all rely on direct Pax6 input and at least so, dac and ato CREs also integrate direct regulation by the So:Eya complex [24–26,31,33]. But all of these genes are transcription factors, not effector genes. Is the logic acting upon transcription factors the same as that controlling specific outputs of the network’s function—such as cell cycle control?
In this paper we have addressed this issue by investigating the direct regulatory logic acting upon the eye-specific stg CRE. During Drosophila retina development, a transcriptional burst of stg is associated to the transition from proliferative progenitors to cell cycle quiescent precursors [19,20,22]. This peak of stg drives progenitors, which are mostly in the G2 phase of their cell cycle, through the FMW, leading to their G1 synchronization [19,36]. This synchronicity is essential: In stghwy mutants, which lack specifically this peak of stg expression, precursors are specified but do not become G1-synchronized. As a result, the patterning of the retina is aberrant [22]. Therefore, the study of stg transcriptional regulation in the eye offers an ideal model to understand how organ specific cell cycle and patterning programs are coupled during development.
We identified a distal 5′stg CRE, which we named stg-FMW (First Mitotic Wave) enhancer. When stg expression was driven by stg-FMW enhancer, it rescued the eye defects of stghwy mutants, indicating that stg-FMW contains most, if not all the regulatory information required for the accurate spatial-temporal expression of stg at the progenitor-precursor transition. Within this element, we characterized two positive inputs: one from both Pax6 proteins, Ey and Toy, and one from So:Eya. Interaction with these transcription factors occurs through two binding sites. We also identified one negative input: Hth. In agreement, assays in vivo suggested that Hth hampers Ey activation of stg-FMW. This fact could explain mechanistically the negative action of Hth on stg transcription. The picture that emerges is of a coherent feed-forward loop in which Ey and Toy play partially redundant activating roles, together with So:Eya, on stg transcription. Moreover, this activation is modulated by the negative input of the meis1 gene, hth.
As an entry point into the molecular mechanisms by which selector transcription factors activate organ-specific programs of cell division, we searched for an eye specific regulatory element of stg. Lehman and co-workers had scanned 38 Kb of the stg locus (from-35 to +3 relative to the transcription start site) and uncovered several CREs [21 and Fig. 1A]. These included CREs active in embryos and imaginal discs, but none of the fragments studied recapitulated the strong stripe of stg expression anterior to the MF (Fig. 1B). We re-analyzed a similar interval of 38.7 Kb (from the stg transcription start site [Chr3R: 25.081.410] to CG14506 [Chr3R: 25.120.100], the gene located immediately upstream of stg) by generating a new set of tiled reporter transgenes with an average fragments length of 5 Kb. Contiguous fragments overlap each other an average length of 1,5 Kb (Fig. 1A). This approach was selected to avoid splitting blocks of conserved sequence, as sequence conservation is often a landmark of CREs [37]. Again, none of the fragments from this interval revealed an expression pattern reminiscent of stg in the eye disc.
Together with the Lehman study, our results suggested that the eye-specific CREs should be located further upstream [21]. To try to define the expected limit of the stg regulatory landscape we used several landmarks. First, the analysis of an extended genomic region revealed the existence of two class I insulator binding sites [38,39], one immediately downstream of the stg transcript (Chr3R: 25077239) and another downstream of Cnx99A (Chr3R: 25138877), delimiting a region of 61,6 Kb (Fig. 1A). Binding of class I insulators helps to establish chromatin boundaries between genes [39,40]. Therefore, we considered that this region might comprise the stg regulatory landscape and should include unidentified stg CREs. This interval includes CG14506 as well. However, this transcript is not conserved in all Drosophila species sequenced, although the adjacent sequences are highly conserved, suggesting that CG14506 is a bystander gene within the stg locus. Second, a regulatory mutation in the stg gene, highway (stghwy), had been shown to be associated to an insertion of an uncharacterized DNA sequence at around 30 Kb upstream of the stg transcription start site. The stghwy is a viable allele that results in slightly reduced, roughened eyes [22]. In stghwy mutant eye discs the peak of stg expression at the progenitor-precursor transition is lost (Fig. 1B, C). As a consequence, cells fail to undergo G1 arrest, and accumulate in G2, with high levels of mitotic cyclins, such as cyclin B [Fig. 1G, H and 22]. Since stghwy is an eye-specific regulatory allele of stg, we reasoned that the stghwy insertion might be affecting the CRE we were looking after, perhaps having landed in its vicinity.
A primer walking strategy was next employed to identify the nature of the DNA element and the exact insertion point in stghwy. Molecularly, we defined the mutation associated with stghwy as an insertion of a gypsy transposable element between positions Chr3R: 25115094 and Chr3R: 25115097 (Fig. 1A and S1A Fig.). Gypsy transposable elements are known to block enhancer-promoter interactions when located in between them [reviewed in 41]. This finding suggested that the insertion in stghwy was likely impairing the contacts between the eye-specific CRE and the stg promoter. Further, it predicted that the eye CREs should lie between the genes CG14506 and Cnx99A. When we extended our reporter transgene study to this region, we identified a fragment of 4.8 Kb, located distal to CG14506 and 52 Kb away from the stg promoter. This fragment was sufficient to drive expression of the reporter gene (destabilized Green Fluorescent Protein, (dGFP)) in eye discs, both in a stripe anterior to the MF as well as in cells posterior to it (Fig. 1A and S1B Fig.). In addition, this fragment showed enhancer activity in the dorsal anterior region of the eye disc, where the prospective ocellar region resides, and in the lamina region of the optic lobes. We named it stg-VisualSystem (stg-VS).
We next subdivided stg-VS into smaller overlapping fragments. This allowed the identification of two enhancer elements, of 539bp and 690bp respectively, that drive expression in different cell populations of the eye disc (Fig. 1A, D, E). The remaining sub-fragments of stg-VS failed to drive expression in the visual system or elsewhere. The 539bp enhancer drives strong dGFP expression in the FMW domain and precursor cells and recapitulates stg expression in the eye field after differentiation onset (S2 Fig.). Accordingly, the 539bp enhancer was called stg-First Mitotic Wave (stg-FMW) (Fig. 1D). The 690bp element drives expression in the ocellar domain, and in a subset of cells posterior to the MF. This fragment was named stg-EyeOcelli (stg-EO) (Fig. 1E). stg-FMW and stg-EO are expressed in adjacent, non-overlapping domains (Fig. 1F, F’) and together reconstitute the eye disc-specific pattern of stg transcription. Anteriorly, expression driven by stg-FMW abuts the Hth expression domain (Fig. 1F, F’), as was previously shown for stg mRNA [19]. Expression of stg-EO in the eye field overlaps the so-called second mitotic wave [SMW, 42]. To test that stg-FMW is a functional stg enhancer, we attempted to rescue the stghwy phenotype, by driving stg expression using a stg-FMW-GAL4 driver in stghwy homozygous individuals. stg-FMW-GAL4>UAS-stg rescued the adult eye phenotype and the pattern of cyclin B accumulation in L3 eye discs of stghwy mutants (Fig. 1G-I). This result supports the idea that stg-FMW is a functional, eye-specific stg CRE and, together with the data on enhancer activity throughout the stg locus, suggests that it may be the sole CRE responsible for stg expression at the FMW.
The stg-FMW sequence shows a high degree of conservation (Fig. 2A). Using JASPAR and TRANSFAC models [43,44] we predicted the existence of putative transcription factor binding sites for components of the RD network. For the identification of Ey binding sites we generated our own position weight matrix from a set of published binding sites [24,30,34,45] (Fig. 2B and S3 Fig.). Evolutionarily conserved Ey binding sites in the genome of 12 Drosophila species were filtered using the CBS platform [46]. Two highly conserved regions were identified, which we refer to as Binding Site 1 (BS1) and BS2 (Fig. 2A, C). BS1 contains partially overlapping putative binding sites for Ey/Pax6 and So (Fig. 2C). BS2 contains one Ey/Pax6 conserved binding site, and a highly conserved consensus site for Hth lies adjacent to it (Fig. 2A, C).
To test the in vivo relevance of BS1 and BS2, we mutated the bases fitting the Ey/Pax6 consensus at each site. Transgenic lines carrying mutant versions of the stg-FMW enhancer harboring mutations in BS1 (stg*BS1), in BS2 (stg*BS2) or in both sites (stg *BS1+*BS2) were analyzed (Fig. 2D-G). Neither the stg*BS1 nor the stg*BS2 single mutants showed altered temporal or spatial expression (Fig. 2E, F), suggesting that the remaining site suffices for enhancer activity during eye development. However, when the two sites were simultaneously mutated (stg*BS1+*BS2), enhancer activity was lost (Fig. 2G). This shows that both sites are redundant for enhancer activity in vivo.
Since mutation of both Ey/Pax6 consensus-binding sites abolished enhancer activity, we next assayed whether Ey was required for stg-FMW activity. The expression of stg-FMW remains unaffected in clones where ey expression has been knocked-down using RNAi (Fig. 2H). As toy, a second Pax6 gene, is expressed coextensively with ey in the eye primordium [16], we tested if Toy was required for regulation of stg-FMW. As observed with ey, toy downregulation through RNAi did not affect enhancer activity (Fig. 2I).
Since Ey and Toy have similar expression patterns and binding site preferences [16,31,47], we next tested a potential redundant function of Ey and Toy in stg-FMW regulation. For this, we generated clones of cells in which both genes were simultaneously knocked-down by co-expression of ey-RNAi and toy-RNAi. In these cells the activity of stg-FMW was abolished in a cell-autonomous way (Fig. 2J). This result shows that both Ey and Toy redundantly activate the stg-FMW enhancer. A redundant function between Ey and Toy was further supported by the finding that in ey2 homozygous imaginal discs, where toy expression is maintained [16], the pattern and levels of expression of stg mRNA or stg-FMW were not affected (S4A-G Fig.).
To explore a potential “division of labor” between the two sites, with each of them specializing in only one of the two Pax6, we generated clones of ey-RNAi and toy-RNAi in the presence of stg-FMW mutated versions (stg*BS1 and stg*BS2) (S4H-K Fig.) Upon mutation of BS1 or BS2, downregulation of Ey did not abolish enhancer activity (S4F,G Fig.). Downregulation of Toy did not impact on the activity of the single-mutant versions of stg-FMW either (S4J, K Fig.). These results show that one Ey/Pax6 binding site suffices for enhancer activity, and that Ey and Toy do not have preferential binding in vivo.
Region BS1 also contains a putative binding site for So (Fig. 2C). So is known to physically interact with the transcriptional co-activator Eya to regulate downstream genes [48]. We next tested the role of So and its transcriptional co-activator Eya in the regulation of stg-FMW. Both genes lay downstream of Ey in the RD gene network [reviewed in 14,49] and stg has been previously identified as a transcriptional target of the Eya:So complex [50]. Loss of function of Eya (S5A Fig.) or its downregulation by means of RNAi (Fig. 3A) in cell clones resulted in a cell-autonomous loss of enhancer activity in the precursor domain. A similar result was obtained when So expression was knocked down using RNAi (S5B Fig.). This loss of enhancer activity coincided with the maintenance of high levels of Hth expression (Fig. 3A” and S5B Fig.).
We had previously shown that Hth could act as a repressor of stg transcription [19]. Additionally, Eya:So are negative regulators of Hth expression during eye development [17]. Therefore, the observed loss of stg-FMW enhancer activity could result from either the loss of Hth repression, or alternatively reflect a positive requirement of Eya:So for stg-FMW activation. To discriminate between these two hypotheses, we first checked whether ectopic expression of Hth could repress stg-FMW. In Hth-expressing clones stg-FMW activation was delayed, but not repressed (Fig. 4A). These findings were qualitatively different from the ones obtained upon RNAi-mediated eya knock-down, where loss of enhancer activity was always observed, irrespective of the position of the clones within the precursor cell domain. This suggested that indeed Eya:So acted as stg-FMW activators. To test this issue avoiding any interference by Hth, we generated clones of cells simultaneously mutant for eya (eya null) and hth (hth RNAi), using the MARCM system (Fig. 3B) [51]. In eya- hth- cells, stg-FMW activity was always lost in a cell-autonomous manner. However, in these eya- hth- double mutant cells expression of Ey was maintained (S5C Fig.). Therefore, these results show that Eya:So are required as stg-FMW activators independently of their role as hth transcriptional repressors.
Since the RD nuclear protein Dac has also been found as part of the Eya/So complex [52] we tested if Dac also played a role in stg transcriptional regulation. In clones of a dac-null allele (dac3) the expression of stg-FMW remained unaltered (S5D Fig.), indicating that Dac is not a partner of Eya:So in the regulation of stg-FMW enhancer. This finding further indicates that different Eya:So targets may rely on the formation of different protein complexes.
Previous results suggested that Hth was a transcriptional repressor of stg [19]. However, as we described before, ectopic expression of Hth delayed the onset of stg-FMW activation, but did not block it (Fig. 4A), suggesting that hth could be involved in the precise timing of stg-FMW expression rather than in repressing it. To test this idea, we generated hth-mutant clones of a strong allele (hthP2) [53]. Since hth-clones grow poorly [18,19,54], we gave them a growth advantage by using the Minute technique [55]. In hth- M+ clones the anterior border of stg-FMW expression was shifted anteriorly (Fig. 4B). Therefore hth is required for the precise spatio-temporal activation of stg-FMW, delaying its initiation. Hth is a transcription factor and its action could be mediated through direct interaction with the stg-FMW enhancer. In fact, we identified a potential Hth BS in the stg-FMW sequence (Fig. 2C). However, mutation of this site (stg*hth) did not result in changes in stg-FMW expression (Fig. 4C and S6C Fig.). Although this result does not rule out a direct Hth-DNA interaction through a non-canonical site on the stg-FMW enhancer, it points to an indirect effect. In fact, it has been previously shown that Hth and Ey can form a protein complex in vivo [17]. The possibility that Hth affects stg-FMW through Ey is explored below.
Our results show that during eye development Ey/ Toy and So plus Eya are all necessary to activate stg-FMW, although in the eye neither the Pax6 genes Ey/Toy or Eya/So are sufficient to do so. Molecularly, mutational analysis of the Pax6 binding sites suggested that Ey and Toy could exert their function through direct binding to BS1 and BS2. To test this hypothesis directly and grasp the molecular interactions underlying stg-FMW activity, we performed chromatin immunoprecipitation followed by quantitative real-time PCR (ChIP-qPCR) experiments. We used ectopic gene expression in wing discs as they can be used as a “blank slate” where to assess the functional consequences of expressing RD genes, including Ey. In addition, since in the wing disc Hth expression is restricted to the hinge, we bypass the potential repressor effect of Hth on Ey activity in most of the disc. To drive gene expression we used the dpp-GAL4 line, which is expressed in a stripe that bisects the wing disc along its anterior-posterior (A/P) axis (Fig. 5A). dpp-GAL4-driven Ey expression (dpp>ey) was sufficient to promote activation of the enhancer throughout the Dpp expression domain (Fig. 5B and S7 Fig.), in agreement with the potent eye-inducing ability of Ey [56,57]. In contrast, Toy was only able to induce expression from stg-FMW in a small subset of cells in the ventral hinge region (Fig. 5C). This suggests that although in the eye imaginal disc both Ey and Toy have the ability to promote enhancer activity, Ey is a stronger regulator of stg-FMW than Toy. We next analyzed the in vivo binding of Ey to stg-FMW by ChIP-qPCR in dpp>ey wing discs. We designed primers so that we could detect binding to region 1 (stg-BS1) or region 2 (stg-BS2). As positive control we used a region in the ato-3′ enhancer known to be bound by Ey [24] (Fig. 5I). As expected, we detected a high enrichment of Ey at ato-3’ relative to our negative control (Fig. 5I). ChIP-qPCR analysis showed that Ey binds to both BS1 and BS2, reinforcing the results described above showing that both sites are used in vivo. We consistently recovered higher amounts of chromatin from BS2 than from BS1, suggesting that Ey’s binding affinity towards BS2 region is higher (Fig. 5I), and that this site might be preferentially used by Ey in vivo.
Our previous experiments showed a requirement for the Eya:So complex in stg-FMW activation, and identified a putative So binding site on region BS1. Ectopic assays in the wing showed that co-expression of Eya and So (dpp>Eya,So) was able to activate the enhancer in a subset of hinge cells located along the A/P boundary (Fig. 5D and S7 Fig.). However, ectopic expression of So, alone or together with Dac, was not sufficient to activate stg-FMW. This observation supports the existence of an Eya:So complex within the precursor domain, whose targets are distinct and independent of the Dac:Eya:So complex. Eya and So can act as transcriptional regulators of Ey [29,49] and it could be argued that the observed stg-FMW activity might be indirect and due to Ey up-regulation. To test this point, we checked if ectopic expression of Eya:So in the Dpp domain induced Ey expression. Although ectopic Ey expression was easily detected in the antennal imaginal disc, we systematically failed to detect Ey expression in the wing or leg imaginal discs of dpp>Eya,So larvae (S7D,G Fig.). These results are in agreement with previous observations [16,48,58]. Nevertheless, and to rule out the possibility that undetectable levels of Ey might contribute to the activation of stg-FMW upon ectopic Eya:So expression (Fig. 5D), we used an RNAi to knock ey expression down when co-expressing Eya and So (dpp>eyRNAi,Eya,So; Fig. 5E). In these conditions, ectopic stg-FMW was induced in the same subset of cells as when induced by Eya and So alone (Fig. 5D, E). This shows that, in ectopic assays, the Eya:So complex has the capacity to promote transcription from the enhancer independently of Ey. This finding allowed us to test if BS1, which contains a putative So binding site, was indeed required for the activation of stg-FMW by Eya+So. In case this hypothesis were true, mutation of BS1 should preclude stg-FMW activation. To test this point, we checked Eya:So’s ability to activate the enhancer upon mutation of BS1 or BS2, when Ey expression was simultaneously attenuated (dpp>eyRNAi,Eya,So). In this background, mutation of BS1 (stg*BS1) prevented Eya+So from activating the enhancer (Fig. 5F). In contrast, when stg*BS2 was used, the pattern and expression levels of the reporter gene upon Eya+So expression were similar to those of wild-type stg-FMW (Fig. 5G). These results suggest that Eya:So complex most likely regulates stg-FMW activity through binding to stg-BS1. To test this hypothesis we performed ChIP-qPCR experiments using an HA-tagged Eya protein (Eya:HA). As Eya lacks a DNA binding domain, its association with DNA would only occur if forming a complex with its DNA-binding partner So [59]. Thus, Eya ChIP can be used as a read-out of Eya:So target DNA binding. In dpp>Eya:HA wing discs, anti-HA ChIP-qPCR showed enrichment of stg-BS1 and stg-BS2, although only that for BS2 was statistically significant. ato-3’, which was again used as positive control, showed also a significant enrichment, as so did the banA enhancer (also included as control; see below), although to a lower extent (S7H Fig.). Taken together, our results show that the Eya:So complex is able to bind BS2 (and likely also BS1). However, Eya:So regulation of stg-FMW relies mostly on BS1.
In addition to the positive regulators Toy, Ey, Eya and So, our experiments indicate that Hth contributes to the precision of the onset of stg-FMW expression, delaying its activation. The fact that a mutation that eliminates the single canonical Hth binding site does not affect the enhancer’s expression suggested that Hth’s action could be indirect, perhaps mediated through its known interaction with Ey [17]. To test this point, we evaluated the ability of Ey to activate stg-FMW in the presence of ectopic Hth (Fig. 5H). In the wing imaginal disc, ectopic expression of Hth strongly reduces the ability of Ey to activate transcription of the reporter gene, which can only be detected in spots in hinge cells (compare Fig. 5H with 5B). To address if Hth counteracts Ey positive role on stg transcription by preventing Ey’s binding to chromatin, we performed ChIP-qPCR assays in wing discs upon simultaneous expression of an HA-tagged Ey plus Hth (dpp>HA:Ey,Hth) (Fig. 5I, red bars). The amount of Ey bound to chromatin regions stg-BS1 and stg-BS2 in dpp>HA:Ey,Hth was slightly reduced compared to dpp>HA:Ey (Fig. 5I, blue bars). A stronger reduction was observed for ato-3′, the activity of which is known to be directly regulated by Ey binding [24,25]. These results show that Hth only moderately hampers Ey binding to its target DNA sites, something that could be happening through a direct Hth:Ey interaction. Additionally, we noted that banA, a CRE from the bantam gene, a known direct Hth target in the eye is also bound by Ey (Fig. 5I) [18]. In contrast to stg and ato sequences, we observe a 2-fold enrichment of the banA sequence upon ectopic co-expression of Hth and Ey. This is in agreement with the known binding of Hth to banA and likely reflects the previously described ability of Hth to interact with Ey [17,18].
Selector genes lie atop organ-specific gene regulatory networks (GRNs), but it is still unclear what is the depth of their connectivity—i.e. whether selectors regulate a first layer of transcription factors that then relay their information, through consecutive layers down onto specific effector genes (those that determine the actual properties of the cells), or if they regulate the expression at all levels of those GRNs, connecting both to transcription factors and effector genes. This control, in any case, is established by their binding to specific CREs and still, in most organogenetic processes, our knowledge of the molecular logic used by selector genes in GRNs to control gene expression is fragmentary.
The Drosophila RD gene network is a good example of this. Despite the vast knowledge of its main components and their contribution to eye development, there is not much evidence about the molecular mechanisms that underlie their function. In particular, how interactions among the different RD gene network components take place and contribute to retina development, by acting not only on other components of the network (all transcription factors or nuclear co-factors) but also on effector genes. In this study we have addressed this question by investigating the mechanisms that regulate stg transcription in the developing eye.
stg codes for the universal phosphatase that triggers the G2-M transition [60]. Upregulation of stg expression during L3 is essential for the synchronous exit from mitosis of retinal progenitors, while simultaneously ensures their amplification at the FMW in order to produce a sufficient number of retinal precursors. It therefore works as an effector gene during the progenitor-precursor cell state transition.
We identified the eye-specific stg CRE and showed that it contains two conserved Pax6 binding sites. Drosophila has two Pax6 paralogues, toy and ey [16]. The expression of toy starts during early embryogenesis and is required for the activation of ey transcription in the eye primordium during late embryogenesis. During larval development, both toy and ey are coexpressed in the undifferentiated cells of the developing eye primordium [16]. However, while loss of ey function during larval stages results in smaller or absent eyes [61,62], no function in the eye had been attributed to the larval expression of toy. Here we show that both ey and toy act as positive regulators of the stg-FMW CRE, and that in the absence of ey, toy suffices to maintain stg-FMW CRE activity. However, ectopic experiments in the wing show that their activating capacity differs, with Ey proving to be a more efficient activator of stg CRE than Toy. This is consistent with a less powerful eye-inducing ability of Toy compared to Ey [16,56,57]. The discrepancy between the functional equivalence of Ey and Toy in the eye and their different eye-inducing ability in ectopic assays could be explained if Ey expression could facilitate the accessibility of Toy to (at least some) Ey targets. This would happen in the eye (where toy activates ey very early in the development of the eye primordium) but not in the wing, where none of the two Pax6 genes are normally expressed.
Our work shows that Ey is able to bind both BS1 and BS2, but shows higher affinity towards BS2 in vivo (Fig. 5I), suggesting that this site might play a key role in the enhancer activity. In agreement, we found that mutation of BS2, although not affecting stg-FMW pattern, causes a significant reduction in its expression levels (S6 Fig.). On the other hand, the transcriptional complex Eya:So is able to bind to BS1 and BS2 with similar affinities, but genetic analysis suggests that interaction with BS1 is critical for stg-FMW activity (Fig. 5E-G). However, while this analysis derives from wing disc assays, the mechanism of action in the eye disc might be more complex. While in the wing disc Ey shows a superior stg-FMW induction, in the eye removing Eya:So results in loss of enhancer’s activity, despite the fact that Ey and Toy expression remain. This suggests a model in which Toy/Ey and Eya:So cooperate to activate stg-FMW enhancer. A similar cooperation between Ey and So has been recently described for the activation of ato CREs [24,25], which are also active anterior to the MF with a pattern similar to that of stg-FMW [24–26].
The picture that emerges is that of a feed-forward loop, in which Pax6 genes activate Eya and So expression and then Pax6 and Eya:So control stg-FMW through direct binding. The engagement of Ey/Pax6 and Eya:So in a positive feed-forward loop has also been reported for the activation of dac, even though in this case two separate enhancer elements are involved [33]. Therefore, a similar gene regulatory motif involving Ey and Eya:So operates to control the expression of transcription factors and stg, this latter an effector gene. This may be a general feature of the gene networks where Pax6 proteins participate. For example, during the development of the vertebrate eye lens, Pax6 and c-Maf are similarly engaged in a positive feed-forward loop to activate the expression of crystallin genes [63]. This suggests that synergistic interactions among transcription factors within the same GRN determine the specificity of their recruitment to cell type-specific CREs.
A key step towards the activation of stg-FMW is the repression of Hth, which is mediated by Dpp and Hh [19]. Hth interferes with the coherent feed-forward loop formed by Ey and Eya:So (Fig. 6) at two points: First, Hth moderately hampers Ey binding to stg-CRE, something that could contribute to the temporal shifts that this enhancer suffers upon manipulating hth function (this work). This could happen through a direct Hth-Ey physical interaction [17]. And second, Hth also acts as a transcriptional repressor of Eya [17]. The resulting GRN allows integration of extracellular signals with tissue specification resulting in a short pulse of stg transcription as soon as MF-produced Dpp represses Hth. This pulse is thus made coincidental with the transition from progenitors into precursors (Fig. 6). The need of both Ey and Eya/So inputs for the enhancer’s activation acts as a molecular coincidence detector that ensures that the enhancer will only be active when the regulatory state of the cell is “correct”, avoiding spurious stg activation.
Our results also point to a role for hth regulation as a precision mechanism, acting to guarantee a sudden, rather than gradual, activation of stg. It is through hth regulation that the system integrates the extracellular cues with the activity of the selector genes. This mechanism ensures coupling of growth with tissue specification. That hth and its vertebrate homologues may play a similar role in Ey/Pax6-regulated processes than the one we have described for stg CRE is a tantalizing hypothesis that needs to be investigated. Interestingly, loss of function of Hth does not suffice for enhancer activation in all cells of the anterior domain (Fig. 4). This seems to indicate that additional factors or signaling inputs contribute to stg-FMW activation. Dpp and Hh signaling are the obvious candidates. However, ectopic activation of either pathway does not change stg-FMW activity in progenitor cells (S8 Fig.) Altogether our data suggests the existence of still unknown anterior factors/signaling inputs that contribute to the regulation of stg-FMW expression onset.
The role of Pax6 genes in cancer development appears to be linked with their function during organ development. They act as oncogenes in organs where their expression correlates with the maintenance of the progenitor state, as is the case of the retina and pancreas [reviewed in 2]. In both organs, the maintenance of Pax6 expression during adult stages associates with a failure to undergo differentiation and to tumor development. In contrast, cdc25 is commonly up-regulated in tumors, as expected from a mitotic gene, but this up-regulation is not tumor type-specific [60]. Our results raise the possibility that Pax6 genes may regulate cell cycle genes in collaboration with Eya/Six proteins also during vertebrate organogenesis, something that might be linked with their oncogenic potential.
The following fly stocks were used: w1118, stghwy [22], w; FRT82BhthP2/TM6B [64], w; dac3 FRT40A/ CyO [65], w; eyaE8 FRT40 [66,67], UAS-Toy [16], UAS-eya, UAS-so [24], UAS-so, UAS-dac (kindly provided by F. Pignoni), UAS-soRNAi (VDRC 8950); UAS-eyaRNAi (VDRC 43911); UAS-hthRNAi (VDRC 12764); UAS-eyRNAi (VDRC 42845) and UAS-toyRNAi (VDRC 15919), ey2 (Bloomington Stock Center) [61]. Standard genetic techniques were used to introduce stg-FMW reporters in the different genetic backgrounds. All crosses were kept at 25°C unless otherwise stated. Cells mutant for hthP2 were recovered using the Minute technique [55]. The fly strain yw,hsFLP; FRT82BhsCD2, y+M/TM2 was used. Mutant tissue was identified by the absence of CD2 staining. Clones were induced between 24 and 48 h or 48 and 72 h after egg laying (AEL) by a 45′ heat-shock (hs) at 37°C. The Flip-Out method [68] was used to induce gain of function clones. The line yw,hsFLP, act>hsCD2>Gal4 was used. Clones were generated at 36–60 h AEL, by a 20′ hs at 35.5°C. Flies were kept at 25°C, except when UAS-RNAi lines were used, in which case they were transferred to 29°C after hs. Dpp-Gal4/TM6B (FBti0002123) and Dpp-Gal4,UAS-GFP/ MKRS (kindly provided by M. Dominguez) were used to ectopically express RD genes in the wing imaginal disc.
The FlyC31 system was used to generate all transgenic lines used in this study. Transgenes were inserted in either 2L (22A) or 3R(86FA) attP sites [69]. Insertions on either landing site yielded similar results. Two reporter vectors were used for assaying enhancer activity: pRVV54 that uses nuclear lacZ as reporter [70], and pBPUwdGFP which uses destabilized GFP as reporter [71]. stg-FMW was cloned into pBPGUw [72] vector to generate stgFMW-Gal4 line. Overlapping DNA fragments, covering >60 Kb of stg locus were amplified by PCR and introduced into either pBPUwdGFP or pRVV54 using the Gateway System. Mutant versions of stg-FMW were cloned into pBPUwdGFP and inserted in 2L (22A) and 3R(86A) sites, as wild-type versions of the enhancer. The megaprimer method was used to generate mutations on putative Ey and Hth binding sites [73]. Primers delimiting stg-FMW enhancer sequence and used for enhancer mutagenesis are listed in S1 Table.
Imaginal discs were dissected and fixed according to standard protocols. Primary antibodies used were guinea-pig anti-Hth [74], rabbit anti-PH3 (Sigma), rabbit anti β-galactosidase (Cappel), mouse anti β-galactosidase (Sigma), mouse anti-CD2 (Serotec), rabbit anti-GFP (Molecular Probes), mouse anti-Ey (Clements et al., 2008) and rabbit anti-cyclin B [75]. Mouse anti-Eya, rat anti-ELAV (7E8A10), and mouse anti-cyclin B were from Developmental Studies Hybridoma Bank (Iowa University). Fluorescently labeled secondary antibodies were from Molecular Probes. Anti-mouse-HRP (Sigma) was used for immunoperoxidase staining. Digoxigenin labelled stg RNA probe was produced from cDNA clone LD47579 (BDGP). ImageJ was used to quantify pixel intensities (http://imagej.nih.gov/ij/).
A PCR based approach was used to map and characterize, at the molecular level, the nature of stghwy allele. Several primer combinations spanning the genomic region Chr3R: 25.081.410 to Chr3R: 25.141.369 (Drosophila Genome Release 5) were used to amplify fragments of approximately 5 Kb of DNA from control (w1118) and stghwy flies. An insertion was detected between genomic coordinates Chr3R: 25114731 and ChR3R: 25115810. Primers flanking and within this region were employed to amplify and sequence stghwy DNA.
Wing imaginal discs from wandering L3 larvae of the following genotypes, dpp>Ey-HA, GFP; dpp> Ey-HA, Hth-GFP and dpp> Eya:HA, GFP were used for Chip-qPCR analysis. Chromatin was prepared essentially as described in Estella et al. [76]. 30 μg of soluble chromatin, with a size average of 200 bp, was incubated with 3 μg of rabbit anti-HA antibody (AbCam). Ey:HA and Eya:HA bound chromatin complexes were pulled down with protein G magnetic beads (Invitrogen) according to Sandmann et al. [77]. Chromatin was eluted with 100 mM NaHCO3. To reverse crosslinking, samples were incubated overnight at 65°C after the addition of 160 mM NaCl. DNA was purified via phenol-chloroform extraction and ethanol precipitation. The PCRs were performed on 1:50 dilutions of the ChIP and input samples. Primers were designed to specifically amplify regions BS1 and BS2, which are 100 bps apart. Primers on ato-3’ enhancer were used as positive control [24]. Primers used are described in S1 Table.
|
10.1371/journal.pcbi.1004462 | The Impact of Different Sources of Fluctuations on Mutual Information in Biochemical Networks | Stochastic fluctuations in signaling and gene expression limit the ability of cells to sense the state of their environment, transfer this information along cellular pathways, and respond to it with high precision. Mutual information is now often used to quantify the fidelity with which information is transmitted along a cellular pathway. Mutual information calculations from experimental data have mostly generated low values, suggesting that cells might have relatively low signal transmission fidelity. In this work, we demonstrate that mutual information calculations might be artificially lowered by cell-to-cell variability in both initial conditions and slowly fluctuating global factors across the population. We carry out our analysis computationally using a simple signaling pathway and demonstrate that in the presence of slow global fluctuations, every cell might have its own high information transmission capacity but that population averaging underestimates this value. We also construct a simple synthetic transcriptional network and demonstrate using experimental measurements coupled to computational modeling that its operation is dominated by slow global variability, and hence that its mutual information is underestimated by a population averaged calculation.
| This work demonstrates how different sources of variability within biochemical networks impact the interpretation of information transmission. These sources are the intrinsic noise generated within the pathway of a single cell, variability due to initial conditions and/or global parameters across the population. A theoretical analysis of a simple signaling pathway and experimental exploration of a synthetic circuit are used to discuss the contributions of these sources of variability to information transmission using mutual information as a metric.
| To survive in challenging conditions, cells need to detect, transduce, and process signals from their environment. A cell’s ability to precisely process environmental signals is limited by intrinsic fluctuations and variability of its cellular processes. This variability takes root in the stochastic nature of biochemical reactions. For a given pathway, this includes the stochastic steps involved in transcription and translation [1–4] as well as diffusion-reactions, dissociations, allosteric changes, and degradation of biological molecules. A signal propagates across cellular networks through molecules undergoing these various reactions, and gets distorted and altered by their probabilistic nature. Therefore, metrics for quantifying the limits of faithful information propagation (signaling fidelity) in biological pathways are crucial for understanding their information processing and transduction capabilities.
Mutual information [5] (MI) is a natural metric for characterizing information transmission between the inputs of a stochastic network and its nodes. MI quantifies the level of precision with which a given node(s) in a network estimates and responds to an input(s) by accounting for both the mean and variability in the response. Recent studies have used MI to characterize information transmission between environmental inputs and transcription factors in a number of genetic circuits [6–10]. In these studies, steady-state MI was computed for a variety of in silico networks to assess their stationary response as a function of input dose. More recently, these ideas were extended to optimize time-dependent MI in delay circuits with binary inputs, and MI was used to discuss maximally informative network topologies in these contexts [11]. In addition, time dependent MI calculations were used to obtain fundamental limits on the suppression of molecular fluctuations for different network topologies [12].
Several experimental studies have also used MI to assess signaling fidelity. MI was used as a metric to argue that negative feedback enables dose-response alignment and enhances information transmission in the pheromone pathway in yeast [13]. Similarly, MI was used to estimate time-dependent information transfer in tumor necrosis factor (TNF) signaling, and to assess transmission bottlenecks in this system [14]. Recently, robustness and compensation of information transmission in different pathways and pharmacological perturbations were attempted in PC12 cells using similar measurements [15]. These experimental studies relied on driving isogenic cell populations with various inputs, and then calculating the mutual information based on the overall variability in the population response. Such calculations mostly found low MI values, suggesting that cellular pathways might have on average low information transmission capacity. In this work, we argue that these calculations often under-estimate MI of a pathway in a single cell, since they do not account for 1) variability in initial conditions and 2) variability that is extrinsic to the pathway. The overall effect of these two sources of variability is that any single cell has a quantitatively distinct input-output relationship [3, 4, 16] and that calculations that take this into account are needed for more accurate estimation of MI from experimental data. By assuming that extrinsic variability manifests as cell-to-cell differences in a global parameter, such as translation capacity, we demonstrate in a simple in silico circuit that mixing cells with different parameters sets (and/or different initial conditions) reduces the value of the computed MI. We also argue this point experimentally by building a simple synthetic circuit that exhibits strong extrinsic variability, and then demonstrating with the help of computational modeling that single cells within the population have a larger mutual information than that exhibited by the averaged population. These results indicate that cells might possess higher capacity for information transmission than previously appreciated.
To compute mutual information in a given biological network, we apply simple step functions [14] of the appropriate environmental input to N populations of the same isogenic cells. The step function is mathematically defined as x(t) = 0 for t < 0 and x(t) = X+ for t ≥ 0, where X+ is a constant within a given population. For each of the N populations, X+ is sampled from a discrete uniform distribution, pu(x+), over the range of interest (0 to Xmax). The uniform distribution represents an unbiased distribution (other than the choice of Xmax) that has been routinely applied to steady-state mutual information calculations [6]. Experimentally, one can implement this scheme by growing replicas of the same culture in an N-well plate and stimulating each well with a different step function as defined above (Fig 1a). For a given population n and sampled input amplitude X+(n), the stochastic time-dependent response of measurable proteins y = [y1 … ym] of the population at t will be p(y, t∣X+(n)). For a general x+ between 0 and Xmax, we interpolate the central moments of adjacent sampled distributions to construct p(y, t∣x+). The time-dependent mutual information is then given by
I ( x ( t ) ; y , t ) = ∑ x ( t ) ∑ y p ( y , t | x ( t ) ) p ( x ( t ) ) log 2 p ( y , t | x ( t ) ) p ( y , t ) = ∑ x + ∑ y p ( y , t | x + ) p u ( x + ) log 2 p ( y , t | x + ) p ( y , t ) = I ( x + ; y , t ) (1)
The value of N (that is the number of experiments) can be chosen based on well established methods [14] to approximate the MI in Eq (1) (see Materials and Methods for further details).
We first illustrate MI calculations using an in silico model of a simple signaling cascade (Fig 1b). Here the input Xn(t) causes the transformation of the inactive molecule Y 1 * to its active form Y1. Y1 could be a receptor or transcription factor responsive to the given input. Y1 in turn activates transcription of Y2, whose mRNA is denoted as My2 (chemical equations are detailed in Materials and Methods with the parameter values listed in Table 1). The uniform input distribution is between 0 nM and Xmax = 250 nM. We found that for this system and its corresponding parameters, N ≥ 20 was a conservatively large number of experiments to approximate MI.
We first assumed that this circuit is isolated from the rest of the cell, and that any stochasticity it exhibits is only the result of its chemical reactions (intrinsic variability). When this system is unstimulated (t ≤ 0), its molecular species assume a joint steady-state distribution, p ( y 1 * , y 1 , M y 2 , y 2 , t ≤ 0 ). As an example, we show the marginal distribution of Y 1 * in Fig 1c. This distribution represents the range of initial conditions in Y 1 * that a population containing this network would exhibit before any input is applied.
We will first compute the MI of the network while ignoring this initial distribution of states, assuming that all cells in the population start from the same initial condition (for state S1, this is the mean of the initial joint distribution, see Fig 1c) which we refer to as a homogeneous initial condition. This could be thought about as the mutual information of one cell in that population. We plot the time-dependent mutual information between the input and the different species of the circuit: I(x+; y1, t∣S1), I(x+; My2, t∣S1), I(x+; y2, t∣S1) (Fig 1d). The MI from the input to y1, I(x+; y1, t∣S1), has rapid dynamics, peaking initially and decaying with time to a steady-state. The initial peak in this MI is due solely to the activation and inactivation of y1, while the subsequent decrease to steady-state is due to the fluctuations in the synthesis and degradation of y1. By contrast, the MI from the input to My2 (I(x+; My2, t∣S1)) is slower and on the order of tens of minutes, while that of the protein y2 (I(x+; y2, t∣S1)) is on the order of hours. This is not unexpected, as the mutual information signals for each species follow the causality of the circuit where y2 shows the largest delay.
The increase of I(x+; y2, t∣S1) as a function of time has an intuitive explanation in terms of y2 dynamics. To visualize this, we plot y ‾ 2 ( n , t ), the mean of y2 as well as y ‾ 2 ( n , t ) ± σ y 2 ( n , t ) versus X+(n) for t = 0, 75, and 750 minutes (Fig 1e, red lines), where σy2(n, t) is the standard deviation in y2. We will refer to these plots as the time-dependent dose response relationships. For t = 750, more values of x+ are resolvable from measurement of y2 than at time t = 75. For example, for x+ > 150, steps in the dose response curves constrained between the standard deviations (Fig 1e, black lines for t = 75 and 750 minutes) approximate how well a measurement in y2 can infer the value of x+. At time 750, about 2 steps are resolvable allowing for two distinct ranges of x+ to be inferred. While for t = 75 minutes, only one distinct range of x+ is inferrable. The larger the number of resolved states, the higher the value of the mutual information.
When mutual information is calculated between the input and a given node over the entire time duration of the signals, the mutual information between the input and each successive node has an upper bound equal to that of the prior node. This is known as the data processing inequality [5]. However, since we are evaluating the time-dependent MI at a given time t, the instantaneous value y2 can have more information about the input than y1. Indeed, at t approximately greater than 150 minutes, we find that the MI I(x+; y2, t∣S1) is greater than I(x+; y1, t∣S1) or I(x+; My2, t∣S1) (Fig 1d).This is because for the particular parameter set used in this example, the noise propagated from y1 onto y2 is averaged out, and the only variability in y2 stems from its own production and degradation. As a result, I(x+; y2, t∣S1) can be modulated to be higher or lower than I(x+; y1, t∣S1) by changing the rates of y2 production and degradation [17]. On the other hand, increasing the number of y1 molecules would increase its mutual information as this would reduce the noise in the y1 signal. Therefore, the mutual information at each node of this pathway can be modulated through choice of kinetic parameters. Similar observations that filtering can improve time-dependent MI between success nodes have been discussed in the context of other types of pathways [18].
Next, we examined mutual information while accounting for the fact that cells assume a distribution of initial states across the population upon receiving the input stimulus. We do so by incorporating the pre-stimulus steady state initial joint distribution into the MI calculations. This variability in initial conditions transiently reduces the MI (Fig 1f). At steady-state, the mutual information curves computed for a single or a distribution of initial states eventually converge onto each other at approximately t = 2750 minutes (Fig 1f, inset). This convergence at longer times occurs because a population in which every cell assumes the same exact initial conditions will eventually produce a heterogeneous distribution of states due to the intrinsic stochasticity of the biochemical reactions. For the values of parameters used in this example, the convergence of the two MI curves proceeds very slowly. Therefore, even when only intrinsic fluctuations are present, with no extrinsic contributions to variability, and for a given distribution of initial conditions, a single cell still transiently assumes, on average, a higher time-dependent mutual information than the whole population. In our case, this difference is very modest.
Thus far, in our MI calculations, we have only accounted for variability in initial conditions given a single parameter set for the pathway. More realistically, any given pathway in a cell is subjected to variability through coupling to other cellular activities. This is known as extrinsic noise to distinguish it from intrinsic noise generated by the pathway itself. There are many extrinsic sources of variability that cellular pathways experience. For example, different cells may contain different numbers of polymerases or ribosomes, and hence have different capacities for transcription and translation [3]. This extrinsic variability can be accounted for in many ways, the simplest is to assume that the transcription or translation rate constants themselves can assume different values in different cells across the population.
To demonstrate the contribution of extrinsic variability to MI calculations, we consider a simple case where cells in the population have different translation rates. To do so, we add a stochastic global variable, G, which affects the protein creation rates such that β ^ y 1 * = β y 1 * G / G ‾ and β ^ y 2 = β y 2 G / G ‾, where β y 1 * and βy2 are the nominal values for the parameters used above. In this way, the protein creation rates keep their mean value, but fluctuate because of their coupling to G. For this example, G follows a memoryless birth/death process such that the mean of G is G ‾ = β g / γ g (βg, γg are the birth and death rates). It follows that G has a coefficient of variation given by η g = 1 / G ‾.
First, setting G ‾ = 50, we chose βg = 1.5 × 10−6 mol-s−1 and γg = 3 × 10−8 s−1. These values establish a stationary distribution of states, which we use as an initial distribution for the MI calculations. Fluctuations in the translation rate induce extra variability in the pathway components (compare the initial distributions of Y 1 * in Fig 2a to Fig 1c). As a result, mutual information calculations with this added extrinsic variability (and using the population distribution of initial conditions) show that I(x+; y2, t) is now drastically reduced compared to the case when a single parameter set is used to represent the lack of global variability (compare black line in Fig 2b with value in Fig 1d). Here also, as expected, MI calculations from a single initial state corresponding to parameters G = G ‾ (state S1), G = G ‾ − G ‾ (state S2) and G = G ‾ + G ‾ (state S3), generate high transient values (red (S1), blue (S2) and green (S3) curves in Fig 2b). This discrepancy between single cell and population MI is further highlighted by examining the time-dependent dose response relationship between y2 and x+ at t = 750 (Fig 2d (full population) and Fig 2c (S1, S2, and S3). Again, the sub-populations generated from S1, S2, S3 each have little variability (high mutual information) relative to the full population.
While constraining G ‾ = 50, we investigated the time-dependent MI for different values of βg and γg. Our original choice of γg = 3 × 10−8 forces G, and hence the translation rates β ^ y 1 * and β ^ y 2, to fluctuate very slowly. Therefore, the convergence of the MI values computed from a single initial condition versus the full distribution also proceeds slowly. As γg increases, this convergence proceeds faster (Fig 2e). Therefore, these results indicate that the mutual information of a pathway can be severely underestimated by population-based measurements if the pathway is subjected to global fluctuations that proceed on a slower timescale than the pathway itself.
Next, we sought to probe the major determinants of mutual information for a simple synthetic transcriptional circuit (Fig 3a). In this circuit, a constitutively expressed transcription factor Y 1 * interacts with a small molecule X+, leading to the activation of the transcription factor. The active transcription factor Y1 translocates into the nucleus and activates expression of a gene Y2.
In our implementation, Y 1 * is an estradiol (input X+) responsive chimeric transcription regulator (TR) consisting of three fused elements: an activation domain (from MSN2), a lipid-binding domain (from the human estradiol receptor, hER-LBD), and a DNA binding domain (from GAL4). When estradiol binds to the LBD, the activated TR Y1 translocates to the nucleus and controls the expression of promoters containing Gal4-binding-sites. Therefore, the protein Y2 (in this case a fluorescent protein) is produced from a Gal4-responsive GAL10 promoter (See Materials and Methods for more details). At the same time, Y 1 * is produced from an altered version of the promoter of the alcohol dehydrogenase 1 gene (ADH1). We constructed two strains for measurement purposes. Strain 1 contains the circuit in addition to two copies of the GAL10 promoter, one driving YFP and the other driving mCherry. Strain 2 contains the circuit, but this time with two copies of the ADH1 promoter, one driving the production of Y 1 * and the other driving the production of YFP (which we will refer to as Y1r). The same strain also contains a GAL10 promoter driving the production of the mCherry (Y2) protein. These strains were useful for two reasons. First, we wanted to establish how mutual information computations depend on the ability to simultaneously measure different quantities in a circuit (e.g. Y1 and Y2 versus Y2 alone). Given that this necessitates the use of two fluorescent proteins, in this case YFP and mCherry, we wanted to ascertain that the results we obtain are qualitatively independent of the choice of fluorophores, given that mCherry has lower dynamic range than YFP with higher background fluorescence and hence increased noise at low concentrations.
For each strain, we subjected 12 exponentially growing populations (wells) of cells cultured in non-repressive media to input concentrations of estradiol (x+) log-sampled between 0 and 100 nM. The 12 measurement points sufficiently sampled the dose response relationships. The number of cells measured from each well was greater than 3000, ensuring good statistics for approximating the MI [14]. All cultures were started from zero estradiol concentrations. Samples were taken at t = 0, 65, 165, 330 and 580 minutes. Fig 3b shows the time-dependent dose-response relationships of estradiol versus y2 (in this case YFP, strain 1) for these timepoints, where fluorescence values were normalized with respect to side scatter in order to minimize the effects of cellular volume and shape dependent differences.
The dose-response relationships of y2 normalized by their respective maximum mean values (Fig 3d) exhibit an interesting trend: for the last 3 time points, the traces for the mean and variability are very similar to each other. The only outlier to this trend is the time point at t = 65 minutes after stimulation (Fig 3d). For this timepoint, fluorescence is weak and strongly overlaps with autofluorescence and folding delays, and therefore the true signal cannot be accurately estimated. Autofluorescence and folding delay also contributes, albeit less dramatically, to the measurement at the t = 165 minutes timepoint (Fig 3d). The mCherry measurements (strain 1 or strain 2) generated the same trend (S1a and S1b Fig) albeit with a noisier outcome than YFP due to the limited dynamic range of mCherry. As a consequence, the y2 (YFP) and y1r (YFP) experimental measurements from the two strains can be used in combination for comparison of modeling with data. The fact that variability in the y2 data irrespective of the fluorescent protein does not decrease with increasing mean values suggests that dominant fluctuations are unlikely to be intrinsic to the pathway.
Fig 3c plots measurements of y1r (YFP, strain 2). Unexpectedly, despite the common assumption that the ADH1 promoter has constitutive and constant expression, we found that it exhibits a modest dependence on estradiol. We do not know the root of this dependence, but it is likely to reflect the influence of the circuit itself on the metabolic state of the cell, hence affecting ADH1 promoter activity. Overall, the growth rate of these strains is independent of estradiol for concentrations under 100 nM over the duration of the experiment (S1c and S1d Fig), and therefore this effect can be compensated for in the mutual information calculations. It is worth noting here that we are making the assumption that despite the fact that YFP (Y1r) and Y 1 * are different proteins sharing only the same transcription rate (since both are driven by the ADH1 promoter), they share the same dominant noise characteristics. This would be the case if their intrinsic noise, which can be different, is insignificant compared to a dominant source of extrinsic noise affecting both. Next, we present data and modeling demonstrating that, indeed, noise in both Y1 and Y2 is most likely dominated by the same extrinsic global component.
Since the measured distributions are approximately gaussian for the majority of estradiol concentrations (S2 Fig) and the synthetic circuit (Fig 3a) follows the same basic chemical equations as the simple pathway we have studied in Fig 1b, we used this already established model to computationally explore different noise scenarios (see Materials and Methods for a more technical justification of the model). Specifically, we simulated the model (parameter values listed in Table 2) with both intrinsic variability and added global extrinsic parameter variability as sources of stochasticity. The data we collected are in fluorescent units, therefore we set our model to arbitrarily yield maximum y2 protein expression levels of about 2500 molecules, likely an underestimation of the actual system. However, this choice constitutes a scaling factor and does not affect any of our results. We also accounted for the estradiol dependence of ADH1 (Fig 3c) by adding to the model a term depicting the modest estradiol dependent repression of this promoter.
For global parameter variability, we again chose to focus on the parameters affecting protein expression. We potentially could model the global parameter variability with cell-to-cell heterogeneity in the protein degradation rates. However, given that our experimental data does not measure the expression of genes involved in either of these processes, i.e. no way to experimentally distinguish the source(s) of global parameter variability, we chose to model global variability in the protein creation rates. Following the same procedure as in the previous section, we added a stochastic global parameter, G, which affects the protein creation rates for Y1* and Y2 such that β ^ y 1 * = β y 1 * G / G ‾ and β ^ y 2 = β y 2 G / G ‾. The noise in the experimental Y1r data is approximately .155, therefore modeling Y1 and Y1r using Poissonian statistics sets the mean of the global noise variable to G ‾ ≈ 42. We first assumed that global parameter fluctuations are slow relative to the circuit timescales (γg = 3 × 10−6). Simulating the model with this slow global source of fluctuations (SGF model, (Fig 3e–3g)) generated profiles for normalized y2 (Fig 3g) that recapitulated the highly similar variance envelopes of the experimental time-dependent dose responses (Fig 3d). This behavior was a characteristic feature of the model for any γg < = 3 × 10−6. By contrast, as the global fluctuating variable assumes a faster timescale (γg > 3 × 10−6), the variability envelopes in the normalized time-dependent dose response of y2 started to diverge from each other (S3a and S3b Fig, γg = 3 × 10−4). As expected, the system modeled with intrinsic variability only (G ‾ = ∞, parameter values listed in Table 3) shows a normalized time-dependent dose response in which variability decreases as a function of time as the protein levels increase (S3c and S3d Fig). Given the data in Fig 3b, if the fluctuations were purely intrinsic, the ratio of the standard deviation to the mean between times 165 and 580 minutes would decrease by a factor of approximately 1.7. This is a change we should be able to detect in our data since for the number of cells sampled, the error in estimating the means and standard deviations in the dose response relationships are .5 percent and 2 percent, respectively. However, as previously discussed, the experimental data shows that this ratio is relatively invariant for the last 3 time points (Fig 3d, inset) while increasing for the both the fast global fluctuations model (S3b Fig, inset) and the intrinsic variability model (S3d Fig, inset). Our argument is further strengthened by the fact that in order to capture the noise observed in y1r with the intrinsic variability model for the first timepoint, we had to set the y1r mean copy number in the model to an unrealistically low value for a strong promoter such as ADH1 (approximately 40 proteins), further indicating that variability is unlikely to be intrinsic. The results for the SGF model for γg < = 3 × 10−6 are not an artifact of the estradiol dependence of ADH1 since an SGF model without this effect yields indistinguishable results (S3e and S3f Fig). We therefore conclude that the dominant source of variability in this synthetic circuit is likely to be due to a globally slow fluctuating variable. This is consistent with previous results, which also indicated that global parameters play a dominant role in cell to cell variability and that these parameters exhibit fluctuations at a slower timescale than fluctuations of processes involved in gene expression [4].
In terms of mutual information, the fact that the normalized time-dependent dose responses coincide in terms of their variability (Fig 3d) implies that the experimentally computed mutual information I(x+, y2, t) at t = 165, 330 and 580 minutes should be similar. This is indeed the case (Fig 4a, solid black). Importantly, I(x+, y2, t) peaks and reaches a plateau at approximately 1 bit, at an earlier time than when y2 reaches its steady-sate. This further lends credence to the idea that the variability in the population is dominated by global parameter variability. Gratifyingly, the model with ‘slow’ global parameter fluctuations (with γg = 3 × 10−6) also captures the time-dependent mutual information seen in the data without any further parameter tuning (Fig 4a, solid blue).
Since slow global fluctuations seem to dominate in this circuit, our analysis above indicates that the population mutual information might be under-estimating the fidelity of a single cell. To illustrate this point, we used the model to computationally isolate and compute the mutual information I(x+; y2, t∣Si) for single cells S1, S2 and S3 as defined in the computational example above. These calculations yield a substantially higher MI value than the population MI for the time span simulated (Fig 4a). Evidently, and as explained above, the MI for S1, S2, or S3 will eventually converge back to the whole population MI, but here it will do so on a much slower timescale than that of the system. For example, the dose response and distribution of y2 at time 580 minutes when the system is started from S1, S2 and S3 (Fig 4b) still shows tighter variability than that of the full population. Allowing for intrinsic variability in the initial conditions, i.e. starting with cells with G = G ‾ at time zero (state Sg) yields a similar MI value to that of I(x+; y2, t∣S1) for γg = 3 × 10−6 (Fig 4a).
Finally, we explored how simultaneous measurement of y1r and y2 affects mutual information calculations. Calculations using the model indicate that as expected, knowledge of y1r improves the estimate of mutual information. For a slow globally fluctuating variable (γg = 3 × 10−6), the joint mutual information I(x+; [y1r y2], t) is larger than I(x+; y2, t). It can be shown that I(x+; [y1r y2], t) = I(x+; y1r, t) + E[I(x+; y2, t∣y1r)] where E[I(x+; y2, t∣y1r)] is the expected value of I(x+; y2, t∣y1r). Since the influence of estradiol on y1r adds (albeit very slightly (Fig 3c (data) and Fig 3f (model)) to the mutual information, i.e. I(x+; y1r, t) > 0, we normalized for this effect. To do so, at a given time, we set the y1r mean at each estradiol value to the value of the y1r mean at zero estradiol while adjusting the variance to preserve the noise in y1r at each estradiol value. Importantly, this operation does not affect correlation between y2 and y1r at each estradiol value, but enforces I(x+; y1r, t) = 0. We confirm that this does not change our conclusions that knowledge of y1r improves the estimate of mutual information (compare Fig 4c blue, dashed black and dashed magenta).
For comparison, we can carry out mutual information from the data obtained using Strain 2 in which both y1r and y2 are measured. In this strain, y2 is the fluorescent protein mCherry which has a limited dynamic range. Importantly, at the highest estradiol values and peak mCherry signal (time 580 minutes), the measured correlation between y1r and y2 (greater than .79) is less than ten percent below the model predictions. Even at these signal levels the noise in the mCherry signal still deteriorates the correlation. For decreasing values of estradiol the correlations become increasingly inaccurate. Therefore, the values of the MI cannot be quantitatively compared to the model which was fitted to YFP data. However, the qualitative trend of increased MI due to measurement of y1r relative to computing the MI with no knowledge of y1r should also hold. This is indeed seen to be the case (Fig 4d). This insight is in agreement with recent work [19] that studied mutual information in the RAS/ERK pathway. Nuclear ERK (erknuc) was used as a readout of pathway information transmission. The MI at time t between this readout and the input x was conditioned for single cell ERK levels, using measurement of total ERK (erktot). It was also shown that I(x; [erktot erknuc], t) is greater than I(x; erknuc, t). Therefore, simultaneous measurements of different cellular variables improve estimates of mutual information capabilities of single cells.
In this work, we illustrated how variability in initial conditions across a population, as well as slow-fluctuating extrinsic (global) variables can generate low values for the population mutual information in response to an input. We also demonstrated that when subpopulations of cells that have similar parameters or initial states are isolated, their mutual information values are transiently much higher than those of the whole population. These findings are important in light of the fact that many previous studies have found that extrinsic variability is a substantial contributor to pathway fluctuations. Indeed, our own experimental data using a synthetic circuit also implicated extrinsic fluctuations as a major source of variability. As a result, cells in a population cannot be considered to be the same noisy channel for mutual information calculations. Rather, each cell is a different noisy channel possessing its own parameters. Recent work [20] using light-inducible input signals [21, 22] to a mammalian RAS/MAPK pathway observed that different isogenic single cells have quantitatively different dose-response relationships. Interestingly, for the RAS/MAPK mammalian system, the dose-repsonse relationships were repeatable for hours within a given single cell [20], suggesting slow global parameters that affect that pathway for that duration.
A direct assessment of mutual information requires repeated time-resolved measurements in single cells. Another strategy to better approximate mutual information is to simultaneously measure a large number of interconnected variables, including global states. This might be increasingly feasible with breakthrough technologies such as mass-cytometry (a.k.a. CyTOF) [23] as well as improvements in fluorescent reporter technologies. In the mean time, however, we have demonstrated that computational modeling, especially with respect to the patterns of time-dependent variability, can generate valuable insights into whether intrinsic or extrinsic fluctuations dominate variability in a circuit. These results produce a more accurate quantification of mutual information, and therefore promise to generate a more realistic assessment of signaling fidelity in cellular circuits.
Our results and those from [20] support a view in which individual cells have distinct transfer functions over relevant signaling timescales, and have superior signaling fidelity (> 1.5 bits) than estimated from pooled measurements of a population. From this perspective, it could be the case that a diversity of high fidelity but different single cell signaling transfer functions across the population is a beneficial trait. However, some situations might arise where variability in population signal transmission capacity is not desirable. In this case, cells might use strategies such as negative feedback to constrain this variability. In either case, cells might also capitalize on the integration of signals from many pathways that respond to a given input(s) in order to generate a desired population response. In this view, each such pathway will add to the mutual information of the desired cellular output (e.g. level or activity of a transcription factor), allowing the population to further circumvent in this way any information fidelity bottleneck. Researchers of the subject are likely to encounter both situations, and perhaps a revised form of population mutual information might be needed to quantify these effects, along with the formulation of new information theoretic metrics. As an example, for any given input x(t), the mutual information I ( y 2 ; y 1 r * , t ∣ x ( t ) ) gives us a sense of the diversity (or spread) in responses in y2 given the cell-to-cell variability encoded in y 1 *. S4 Fig shows the results of this metric applied to the simple signaling cascade (Fig 1b) for different input step function amplitudes x+ and for different times. We envision these kind of metrics to reflect the different subpopulations with similar parameters within a given population and to serve as a potential tool to quantify how cell-to-cell variability across a population might change in structure due to various time-dependent inputs.
Finally, most studies to date have focused on variability in populations of non-communicating cells. Information fidelity in cells that communicate, for example through quorum sensing for bacterial communities or cell-to-cell mechanical coupling for tissues, is still largely unstudied. How cell-to-cell communication modulates global variability and variability in initial conditions across a population, and hence mutual information of cellular pathways, is a topic that should be explored in order to determine whether and when multicellularity offers a beneficial strategy in terms of signaling fidelity.
Because we are using a finite number of experiments, the input distribution pu(x+) is sampled with N discrete points. In practice, these points are spaced to accurately sample the input-output transfer function p(y, t∣x+) for x+ ranging from 0 to Xmax. The time-dependent mutual information is then calculated with this data. For values of x+ between the sampled values, p(y, t∣x+) is approximated by linearly interpolating the moments of the adjacent sampled distributions. Since the distributions generated by systems in this paper are approximately gaussian (and approximately negative binomial at very low x+ for the synthetic circuit data), only the means and covariances are required. A larger number of experiments (N) generates a more accurate approximation of mutual information. However, we observed that convergence to accurate MI values does not increase monotonically with N for the logarithmic sampling of the doses response that we have adopted. Rather, convergence proceeds exponentially, followed by marginal gains in accuracy as N increases. Therefore, for every N, we examine the last three sample number values, N, N−1 and N−2. Given their measured convergence rates, we can extrapolate an upper bound on the MI at an infinite number samples. We choose N whose calculated MI at N is within 1 percent of the extrapolated upper bound.
The chemical equations for the circuit in Fig 1b are
∅ ⟶βy1* Y1* ⟶γy1*Y1* ∅ (2a)
X + Y 1 * ⟶ θ x X Y 1 * Y 1 ⟶ θ y 1 Y 1 Y 1 * (2b) Y 1 ⟶ γ y 1 Y 1 ∅ (2c) ∅ ⟶ f 1 ( y 1 ) M y 2 ⟶ γ m 2 M y 2 ∅ (2d) ∅ ⟶ β y 2 M y 2 Y 2 ⟶ γ y 2 Y 2 ∅ (2e)
where f 1 ( y 1 ) = β m 2 * + β m 2 y 1 n 1 y 1 n 1 + y 1 0 n 1. The propensities of the reactions appear above the reaction arrows. The system is a simple cascade of reactions where the input X activates Y1, and subsequently the Y1-dependent transcription of Y2. The parameter values are tabulated in Table 1.
Here the mean total number of Y1 molecules, active and inactive, is β y 1 * / γ y 1 * = 1500. The max mean numbers for Y2 mRNA and Y2 protein are βm2/γm2 = 200 and β m 2 β y 2 γ m 2 β y 2 = 2000, respectively. This system has only a single stationary solution. This allows us to approximate and efficiently calculate the master equation with a local affine assumption using the first two moments Eqs (6) and (7) taken from [24].
The formulation that we assume in our model and data consists of a system of well-stirred chemical reactions with N molecular species. For some environmental input X(t), we define the pathway state Y(t) to denote the vector whose integer elements Yi(t) are the number of molecules of the ith species at time t. If there are M elementary chemical reactions that can occur among these N species, then we associate with each reaction rj (j = 1, …, M) a non-negative propensity function defined such that aj(Y(t)) τ+o(τ) is the probability that reaction rj will happen in the next small time interval [t, t+τ], as τ → 0. The polynomial form of the propensities aj(y) may be derived from fundamental principles under certain assumptions [25]. The occurrence of a reaction rj leads to a change of νj ∈ ZN (the set of nonnegative integers) for the state Y. νj is therefore a stoichiometric vector that reflects the integer change in reactant species due to a reaction rj.
This set of well-stirred chemical reactions can be represented by the joint probability density function P(y, t∣ X(t)) which describes the probability of the system being in state y at time t, given the environmental signal X(t). The evolution of P(y, t∣X(t)) is given by
∂ P ( y , t | X ( t ) ) ∂ t = ∑ j = 1 M [ a j ( y - ν j ) P ( y - ν j , t | X ( t ) ) - a j ( y ) P ( y , t | X ( t ) ) ] (4) Eq (4) is the so-called chemical master equation (CME)[26, 27].
To approximate the CME with moment equations, we approximate the propensity function aj(y) with a locally affine Taylor series expansion [24] about the mean of the distribution, z(t), to get
a j ( y ) ≈ a j ( z ( t ) ) + ∑ i = 1 N ∂ a j ( y ) ∂ y i | y = z ( t ) [ y i - z i ( t ) ] (5)
From the time dependent mean equation for the kth species is
∂ z k ( t ) ∂ t = ∑ j = 1 K ν j k a j ( z ( t ) ) (6)
and the time dependent covariance equation for the kth and k′th species is
∂ C k k ′ ( t ) ∂ t = ∑ j = 1 K ( ν j k ∑ i ∂ a j ( y ) ∂ y i | y = z ( t ) C i k ′ + ν j k ′ ∑ i ′ ∂ a j ( y ) ∂ y i ′ | y = z ( t ) C k i ′ + ν j k ν j k ′ a j ( z ( t ) ) ) (7)
The calculation of the mutual information requires probability distributions. Given that we solve the first two moments, we constrain our distributions to be either a negative binomial distribution or a normal distribution. For cases when μ k < 3 ( C k k ), we apply the negative binomial distribution since it only requires the first two moments and is non-negative. The negative binomial is very close to a normal distribution for μ k > 3 * ( C k k ) and we therefore apply the normal distribution in these regions. The value of 3 used is heuristic, but the tail of the normal distribution at negative values is negligible at this point. For linear transcriptional systems, the negative binomial is a natural steady state solution [28] which was our motivation for applying it. Importantly, our data never violated any constraints required by the negative binomial distribution, for example, μk ≤ Ckk. Note that the negative binomial distribution is only required for our modeling of the synthetic circuit data. Our theoretical example in the first half of the paper has large enough basal levels at zero input which always keeps it in the normal distribution regime. As a demonstration of the validity of the moment approach, S5 Fig shows very good agreement in the distributions derived from stochastic simulations (SSA) and the moment equations for the synthetic circuit model (γg = 3 × 10−6, initial condition S1).
Here we discuss how multi-variate MI measurements relates to MI measurements from particular initial conditions: We start with the distribution p(ym, ys, t∣x(t)) where ym are the dynamic cellular pathway/network signals, ys are the slowly fluctuating pathway component quantities relative to the timescale of a given experiment, and x(t) is the input signal(s). The time dependent mutual information is
I ( x ( t ) ; [ y m y s ] , t ) = ∑ x ( t ) ∑ y s ∑ y m p ( y m , y s , t | x ( t ) ) p ( x ( t ) ) log 2 p ( y m , y s , t | x ( t ) ) p ( y m , y s , t ) = ∑ x ( t ) ∑ y s ∑ y m p ( y m , t | y s , x ( t ) ) p ( y s , t | x ( t ) ) p ( x ( t ) ) log 2 p ( y m , t | y s , x ( t ) ) p ( y s , t | x ( t ) ) p ( y m , t | y s ) p ( y s , t ) (8)
where the second line is simply a chain-rule representation. In addition to the assumption that the quantities of ys are fluctuating extremely slowly, we will also impose that the quantities in ys are independent of x(t). This results in p(ys, t∣x(t)) = p(ys, t) ≈ p(ys). The time dependent MI is approximated as
I ( x ( t ) ; [ y m y s ] , t ) ≈ ∑ x ( t ) ∑ y s ∑ y m p ( y m , t | y s , x ( t ) ) p ( y s ) p ( x ( t ) ) log 2 p ( y m , t | y s , x ( t ) ) p ( y s ) p ( y m , t | y s ) p ( y s ) = ∑ y s p ( y s ) ∑ x ( t ) ∑ y m p ( y m , t | y s , x ( t ) ) p ( y s ) p ( x ( t ) ) log 2 p ( y m , t | y s , x ( t ) ) p ( y m , t | y s ) = ∑ y s p ( y s ) I ( x ( t ) ; y m , t | y s ) = E [ I ( x ( t ) ; y m , t | y s ) ] (9)
Finally, we can examine the mutual information between ys and ym for a given input signal(s) x(t) using the formula
I ( y m ; y s , t | x ( t ) ) = ∑ y s ∑ y m p ( y m , t | y s , x ( t ) ) p ( y s , t | x ( t ) ) log 2 p ( y m , t | y s , x ( t ) ) p ( y m , t | x ( t ) ) (10)
|
10.1371/journal.pcbi.1002659 | Evolutionary Dynamics on Protein Bi-stability Landscapes can Potentially Resolve Adaptive Conflicts | Experimental studies have shown that some proteins exist in two alternative native-state conformations. It has been proposed that such bi-stable proteins can potentially function as evolutionary bridges at the interface between two neutral networks of protein sequences that fold uniquely into the two different native conformations. Under adaptive conflict scenarios, bi-stable proteins may be of particular advantage if they simultaneously provide two beneficial biological functions. However, computational models that simulate protein structure evolution do not yet recognize the importance of bi-stability. Here we use a biophysical model to analyze sequence space to identify bi-stable or multi-stable proteins with two or more equally stable native-state structures. The inclusion of such proteins enhances phenotype connectivity between neutral networks in sequence space. Consideration of the sequence space neighborhood of bridge proteins revealed that bi-stability decreases gradually with each mutation that takes the sequence further away from an exactly bi-stable protein. With relaxed selection pressures, we found that bi-stable proteins in our model are highly successful under simulated adaptive conflict. Inspired by these model predictions, we developed a method to identify real proteins in the PDB with bridge-like properties, and have verified a clear bi-stability gradient for a series of mutants studied by Alexander et al. (Proc Nat Acad Sci USA 2009, 106:21149–21154) that connect two sequences that fold uniquely into two different native structures via a bridge-like intermediate mutant sequence. Based on these findings, new testable predictions for future studies on protein bi-stability and evolution are discussed.
| Proteins are essential molecules for performing a majority of functions in all biological systems. These functions often depend on the three-dimensional structures of proteins. Here, we investigate a fundamental question in molecular evolution: how can proteins acquire new advantageous structures via mutations while not sacrificing their existing structures that are still needed? Some authors have suggested that the same protein may adopt two or more alternative structures, switch between them and thus perform different functions with each of the alternative structures. Intuitively, such a protein could provide an evolutionary compromise between conflicting demands for existing and new protein structures. Yet no theoretical study has systematically tackled the biophysical basis of such compromises during evolutionary processes. Here we devise a model of evolution that specifically recognizes protein molecules that can exist in several different stable structures. Our model demonstrates that proteins can indeed utilize multiple structures to satisfy conflicting evolutionary requirements. In light of these results, we identify data from known protein structures that are consistent with our predictions and suggest novel directions for future investigation.
| New functional proteins are likely to evolve from existing proteins. Most existing proteins, however, are under selection to conserve their existing native structure in order to maintain functionality (and also to avoid aggregation and proteolysis). Without such selective constraints, the accumulation of random mutations would soon render a protein nonfunctional. When the same gene (protein) is under two selection pressures, i.e. to evolve a new functional structure while conserving its existing structure, an adaptive conflict arises. This adaptive conflict scenario is at the heart of most contemporary theories of molecular evolution, such as the popular Neofunctionalization and Subfunctionalization models (as reviewed in [1], [2]). However, these models generally require gene duplications to take place before adaptive conflicts can be resolved. This implies that such models can only explain long-term processes that involve many unlikely events, such as the occurrence of a suitable gene duplication event, followed by retention, fixation in the population, and additional beneficial or neutral point mutations in one or both gene copies. Only then would an adaptive advantage become possible. Because of these potential drawbacks, a more recent model (Escape from Adaptive Conflict, EAC) emphasizes the sufficiency of single-gene, multi-functional proteins during short term adaptive conflicts [3]. Similar ideas have been proposed earlier in terms of the concept of “gene sharing” [4], [5]. In fact, a gene duplication of a multi-functional protein is more likely to be successful than duplicating a protein with only a single function: first, because a new function is already present – thus it does not have to first evolve the new function in a rare mutant carrying a gene duplication; second, functional divergence can be faster because the multiple functions have already been responding to conflicting selection pressures; and, finally, retention and fixation of the duplication is more likely because the second copy can immediately provide higher activity levels through higher protein concentrations for the multiple protein functions, none of which would likely have been fully optimized in a single-gene, multi-functional protein.
Indeed, there is increasing evidence that proteins have a significant capacity for multi-functionality. Not only are many enzymes known to exhibit promiscuity for nonnative reactions and substrates [6]–[8], multi-functionality has also been linked to proteins with two or more stable conformations [9]–[12]. These proteins can be called bi- or multi-stable. A few naturally occurring cases of such proteins are known, such as the prion protein that can assume different structures. One of these structures can aggregate to cause neurodegenerative pathologies such as mad-cow and Creutzfeldt-Jakob diseases [13], [14]. Protein bi-stability was also found in the cysteine-rich domain proteins (minicollagen) that form the walls of Cnidarian organelles called nematocysts [15]. Different conformers of these protein domains exhibit distinct patterns of disulfide bridges and perform different functions. Another example is the antiviral protein RhTC, which was found to target different HIV viruses by allowing a dynamic active site to assume very different conformations [16]. More generally, emerging evidence is lending support to the view that functional promiscuity in enzymes may frequently be based on thermodynamic fluctuations of conformational sub-states [17]. However, this may not always be the case, for example, if the functional promiscuity is mediated by changes of catalytic residues that do not cause conformational changes. An evolutionary theory of structure-based multi-functionality requires detailed knowledge of the sequence-structure relationship in proteins, as emphasized by the theory of neutral networks [18]–[23]. A neutral network consists of a connected set of sequences that fold into the same native (maximally stable) structure, and a pair of sequences in the network is connected if and only if they differ by one point mutation. Proteins can tolerate a number of mutations (mostly of surface amino acids [24], [25]) without losing their native structure. It has been shown experimentally that the neutral networks of two protein structures can be directly connected, such that one or two mutations can cause a switch from one native structure to the other [9], [26]–[28].
Because actual protein sequence space is too vast for computational — let alone experimental — exploration using current resources, we rely on a well-established explicit-chain biophysical model with exhaustive sequence-to-structure mapping [22], [23], [29]–[33] to provide a model of protein sequence space consisting of sequences with up to six-fold degenerate native state (i.e. proteins with up to six native structures). This model, termed the “hydrophobic-polar” (HP) model, is based on the central role of hydrophobic interactions in protein structures [29]. Earlier studies using the HP model but with non-degenerate native states have revealed that sequence space consists of distinct islands of neutral networks corresponding to unique native structures, which can be bridged either by single-site mutations (substitutions) [22], [32] or recombinational jumps [30]. A key feature of neutral networks arising from the HP model and similar simple exact models is a funnel-like distribution of free energy values around a most stable, and mutationally robust, prototype sequence [23], [34]–[36], or consensus sequence [37]. These funnels can act as attractors on evolving proteins outside the neutral network by allowing for selection of excited (non-native) conformational states, the stabilities of which increase with every incremental step toward the prototype sequence of that excited state [32]. More recently, the model was used to show an association between evolvability and phenotypic variation [33]. Some sequences in HP and HP-like models have been shown to have multiple native structure [23], [29] and even exhibit prion-like behaviors [38], [39]. However, an extensive account of sequence spaces with degenerate native structures is lacking and most theoretical studies of protein neutral networks to date have not considered the implications of multiple native structures [40]–[47].
In this context, our main aims here are to investigate: (i) where do bridge proteins preferably locate in sequence space, (ii) the manner in which bi-stability is distributed in the sequence-space neighborhood around bridge proteins, and (iii) the role of opposing selection pressures in the evolutionary dynamics that may take advantage of bi-stability. Toward these goals, we will first describe below the characteristics of the sequence space in our simple biophysical protein chain model. We will show that bridge proteins, and bi-stable proteins in general, have a high potential for facilitating evolution under adaptive conflicts. We will further demonstrate that this potential originates from a nonrandom distribution of bi-stability in sequence space. Subsequently, we will apply the concepts and insights gained from our simple model to real protein structures. In particular, we will describe bi-stability in a well-documented experimental case and also in a larger set of putative bi-stable proteins in the Protein Data Bank (PDB).
Our analysis of an entire model protein sequence space demonstrates that viability of proteins with degenerate native states can confer an advantage under adaptive conflicts. In such situations, extensive overlaps exist between stability funnels of neutral networks, with bi-stable bridge proteins situated at the interface between networks. Although detailed characteristics of real protein sequence space remain to be elucidated, based on our model results we have little doubt that the investigation of bi-stability and evolvability is a promising area of future research. Bi-stability, however, cannot be the only evolutionary response to adaptive conflicts, because the two alternative conformations are mutually exclusive and thus the function of the protein can never be fully optimized. In this regard, the role of gene duplications would also be crucial. We leave this topic for another study [81].
Here we have employed a simple biophysical protein chain model to infer general properties of bi-stable proteins and their distribution in sequence space. The model used here is a simple exact model with an explicit representation of the protein conformations on a two-dimensional lattice. Despite their simplicity, such models capture essential features of the sequence-to-structure mapping of real proteins (see discussion in Results), and have provided significant insights into protein folding and evolution (reviewed in Refs. [31], [35]). The simple exact modeling approach allows a complete description of a system, but clearly such models are only a caricature of reality. In particular, only a limited variation of stability and bi-stability is allowed in our simple model, resulting in an appreciable percentage of sequences adopting two native structures with identical stability. Real proteins, in contrast, are unlikely to have exactly equal native stability in bridge proteins. Nonetheless, inasmuch as the goal of theoretical/conceptual models is to make predictions that can be tested experimentally, the main testable prediction of this work is that bi-stability can be increased or decreased by mutations leading either towards or away from bridge proteins, which are sequences that enjoy maximum bi-stability.
While the fraction of actual bridge proteins is unknown, one may speculate how the HP model relates to real proteins. For example, consider the following argument: Our simple model only allows for 10 different energy states ( HH contacts). If the conformational ensemble of a real protein was mapped onto 10 equally sized bins of energy, the lowest-free energy bin (highest stability) could contain two structures with similar yet non-identical stabilities such that the protein may function as a bi-stable bridge (e.g. see Table 2). This relaxed definition of a bridge could entail that one structure would still be much more stable than the other (as in the case of in Alexander et al. [27]). As a consequence, perhaps many such bridge proteins do not have easily measurable bi-stability because one structure remains dominant over the other. Nevertheless, the known examples of functional promiscuity suggest that even such unequal bi-stability may be of biological relevance. However, it is important to note that bi-stability can only occur if the two alternative native (or near-native) states are both thermodynamically accessible on time scales that are relevant for molecular functions.
The consequence of bi-stability landscapes (Figure 2) for evolution is that proteins evolving under adaptive conflict for two alternative structures (whose extended neutral networks are connected in sequence space) are automatically directed towards bi-stable states, and that the dynamics of this process do not have to rely entirely on random genetic drift. Bridge proteins may thus be created in the laboratory by providing appropriate combinations of selection pressures, or known bridge proteins can be stabilized towards one of their structural sub-states. So far, this gradual shift in bi-stability was studied in terms of structural phenotypes; but the same concept should also apply to other definitions of phenotypes that depend upon structural stability.
The simple fitness function in the present study rewards increased protein stability. This fitness function has provided significant insights; but it does not fully capture the subtle relationship between conformational stability and biological function in real proteins [82]. Too much stability can be detrimental for protein function, for example. More sophisticated biophysical models will need to be developed to incorporate such effects.
Future work should also improve the computational methods for determining bi-stability changes of in-silico mutated PDB structures. In this regard, the discrepancy between FoldX and Rosetta predictions in Figure 3a is noteworthy. Using these algorithms, only local structural optimization around PDB structures for and was performed in the present study. We made no attempt in global structural optimization, which amounts to using an amino acid sequence as the only input to determine its native structure, i.e., solving the protein folding problem for the given sequence. For this much more challenging task, scoring functions such as Rosetta that rely on comparative modeling have difficulties when presented with sequences that have a high degree of identity but fold to different structures nonetheless. The ability of Rosetta to arrive at the correct structure can be greatly enhanced by considering not only the amino acid sequence but also including experimental NMR chemical shift data as input [83], as has been demonstrated for the system [84]. This finding underlines that the scoring function alone is insufficient for this system. As emphasized recently by van Gunsteren and coworkers, the energetics that govern the structural transition between and is highly delicate and cannot yet be accounted for atomistically using current force fields [85]. The quest for an accurate energy function for protein folding will likely remain a great challenge for years to come. In this light, the Rosetta criterion we adopted to obtain the present protein conformational diversity dataset (Dataset S1) is, inevitably, tentative. Nevertheless, based on the theoretical framework we developed and the general trend observed here, this dataset should serve as an impetus and provide useful candidates to be evaluated by future experimental investigations.
Our evolutionary simulations (Figures 4, S3, and S4) are idealized scenarios that do not realistically capture evolution in natural populations, where usually only a small portion of sequence space would be explored by individuals within a population that are related to each other by common ancestry. Our master equation approach and the calculated steady state therefore only give a general evolutionary trend: given enough time and mutations, a population will acquire the most bi-stable proteins. Nevertheless, we have shown that the nature of bi-stability landscapes (Figure 2) – where incremental shifts of excited state stability can lead towards increased bi-stability – have the potential to speed up adaptation under adaptive conflict, whenever such stability shifts are advantageous. Evolutionary experiments will be needed to test these predictions under natural conditions.
The increasing knowledge of promiscuous enzymes and the high evolvability of new enzyme functions [86] suggests that enzymes are in general mutationally robust for their native functions, while at the same time accepting mutations that enhance promiscuous functions. An apparently neutral mutation may therefore actually be adaptive. Even an apparently detrimental (destabilizing) mutation might promote a promiscuous function that is only beneficial under certain environmental conditions that the experimenter may not be aware of.
The theory of neutral networks is impacted by the inclusion of degenerate native-state structures in that the notion of “neutrality” is moderated. While the strictest definition of neutrality (no change in protein activity/stability whatsoever) is usually not realistically applicable, a weaker definition (neutral, if the overall native structure is conserved, but a small loss of stability is tolerated) can be reconciled with experimental data. One can also go one step further and define neutral networks as fuzzy sets, where set membership is a continuous (not a binary) function over the interval [87]. Degenerate native states could be easily incorporated into such a definition. Our biophysical model shows that, at least in theory, excited state conformations may contribute to promiscuous functions, and could therefore be included into the “fuzzy” neutral set of all sequences that have some non-zero probability of forming that conformation. The membership to a fuzzy sequence set could be provided by the fractional population of the conformation (Equation 1 in Methods). Neutrality depends on the strength of the selection pressures involved, so that membership to a fuzzy neutral set as defined above requires a certain threshold of minimum stability. In the same manner as a falling sea level will expose more habitable land mass, a reduced selection pressure will allow for a larger number of viable protein variants.
The intrinsic mutational robustness of neutral networks has been proposed to promote evolvability, i.e. the capacity to evolve towards new phenotypes [57], [88]. High robustness allows a population to accumulate many neutral variants within a neutral network. Some of these variants may be mutationally close to other phenotypes. We have shown that the inclusion of proteins with degenerate native states into neutral networks also enhances evolvability by providing more viable sequences between neutral networks. Compared to only proteins with non-degenerate native states, these additional sequences can access a substantial number of additional phenotypes. However, strong selection pressures would generally prevent evolution from utilizing degenerate native states, especially if only one of the native states is beneficial. The higher the native-state degeneracy, the lower the stability of a particular structure (see Figure 1b), and the lower the selection pressure would have to be for viability. If more than one native-state structure is beneficial, and if fitness effects are additive, a low stability may be compensated by providing multiple beneficial structures. Therefore, evolvability requires weak selection pressures in our model.
Draghi et al. [88] have found an analytical solution to the general problem of how robustness and evolvability are related. Their results are general enough to be applied to any system (biological or non-biological) that exhibits robustness. In particular, they have provided a biological example of RNA phenotypes. However, their study does not provide any information specific to proteins, because the necessary parameters cannot be measured easily. Proteins are fundamentally different from RNA: structure formation in proteins is largely determined by hydrophobic-polar interactions, which are largely absent in RNA. Consequently, proteins and RNA do not share similar genotype-phenotype relationships [89]. The results from our simple protein model are consistent with the general predictions by Draghi et al. that evolvability increases with robustness, given two conditions: first, robustness is relatively low (only of mutations in sequences belonging to the same neutral network are neutral in our model; detailed data not shown); and second, only a small fraction of phenotype space can be accessed from each point in genotype space (true for our model, since the number of mutations per sequence is limited to 18, while phenotype space consists of 1475 stable structures [22]). One of the measures of evolvability that they use, and that we also have used here, is the number of mutationally accessible new phenotypes per genotype. An alternative measure is the time (e.g. number of generations) a population takes to adapt to a new beneficial phenotype. These two measures, however, capture different aspects of evolvability: one is the potential to quickly access many different phenotypes if the need arises (a concept followed by some experimentalists working on promiscuous enzymes [90]), while the other is adaptation to one specific phenotype that is under selection (a scenario we have investigated previously [32]). Here, we have followed the first approach of measuring evolvability, because we also impose the important additional requirement of conservation of the existing phenotype. With this restraint, the new beneficial phenotype is never fully reached by adaptation, especially since we refrain from a binary definition of neutral network membership (see previous section). Dual phenotypes (as exhibited by bi-stable proteins) have not been considered by Draghi et al. or any other theoretical study on neutral networks. By allowing dual phenotypes, which evidently also exist in nature, we allow an evolutionary compromise, whereas a binary definition of neutral networks completely prohibits adaptation as long as the need for conservation exists. In addition, our results also have consequences for the case of “unopposed” adaptation (without conservation), at least as far as modeling efforts are concerned: the true connectivity (evolvability) between neutral networks could be significantly underestimated, if proteins with degenerate native states are not considered. Both scenarios — a complete shift of selection pressures from one phenotype to another [32], [88] and adaptive conflict (present study) — are important fields of investigation since both are likely to exist in nature.
The true robustness and evolvability parameters of proteins remain largely unknown. It appears plausible, however, that proteins may have become the dominant type of biopolymer (as opposed to RNA, or other unknown biopolymers that might have existed during early stages of evolution), in part because they produce the right balance between robustness and evolvability that allows for fast adaptation.
Bi-stability as a factor for protein evolution (as opposed to conformational changes that are part of the same protein function) is currently based on a few mostly artificial example cases, but has not been widely observed in natural settings. This may be caused, in part, by experimental limitations in protein structure determination, and possibly also by a lack of research focus. Conformational diversity, as a more general case of bi-stability, has only recently gained broader attention [11], [59], [91], but much of its potential for evolution remains unexplored. We propose that bi-stability is particularly beneficial in complex and quickly changing environments that are likely to create adaptive conflicts. One important example could be the evolutionary arms-race between hosts and parasites. Bacteria and viruses have limited genetic material for adaptation to act upon, therefore these organisms might benefit from bi-stable and thus bi-functional proteins. Further studies in this direction will be instructive.
Our model folds polymers of length that are configured on a two-dimensional square lattice. The model sequences have a binary residue alphabet (H for hydrophobic, P for polar). This simplicity makes it possible to enumerate all possible structures, or conformations (self-avoiding walks on the lattice) for all HP sequences. The energy function only includes one type of favorable energy, which is assigned for each hydrophobic intra-chain contact in any of the structures. Despite the simplicity in its construction, short-chain two-dimensional HP models have been shown to capture the essential physics of the sequence to native structure mapping of real proteins [29], [52]. The simplicity of the HP model allows for exact computation of the partition function — which takes full account of the energies of all structures, and thus permits an exact determination of the fractional population of each structure, which we use here as a stability measure. Specifically, gives the probability of a protein with sequence to fold into (adopt) structure :(1)where is the energy per hydrophobic-hydrophobic (HH) contact, is the number of such contacts in a conformation (thus total energy ), is the Boltzmann constant and is absolute temperature. Conformation has HH contacts. The summation in Equation 1 is over all possible values in the entire conformational space , and denotes the density of states of sequence [23]. For any given HP sequence , the native-state degeneracy is the number of structures with the highest number of HH contacts, . In the present study, and were chosen to provide conditions generally favorable to the folding of sequences. As in some of our earlier studies [23], we have used throughout the present work.
If the number of HH contacts in and are denoted by and , respectively, the difference for a given sequence is a measure of stability difference for that sequence (as used in Figure 2) because is directly related to the fractional populations and , viz., it follows from Eq. 1 that(2)The system of two adjacent neutral networks that we showed in Figures 2 and 4 as examples comprises one core neutral network (A; blue) with 48 sequences or the corresponding extended neutral network that includes an additional 84 sequences with , as well as another core network (B; red) with 20 sequences or the corresponding extended neutral network that includes an additional 40 sequences with . The Hamming distance between the two prototype sequences is 2, and the intra-chain contact difference between the native-state structures and is also 2 (Figure S2).
In our model, the fitness of an HP sequence evolving under selection for two beneficial structures and is given by(3)where(4)and is an upper bound for the contribution of stability to fitness [81]. In all the computational results presented in this paper except those in Figure 5, the same was assumed for and for simplicity, whereas two different upper bounds and were used to gain a broader perspective in Figure 5. The upper bounds serve as a selection pressure because a low allows for destabilization of the protein, without fitness costs, whereas a high does not tolerate destabilization.
Let be the set that contains all sequences in the extended neutral networks of two structures and . In our master-equation formulation of population dynamics [30], [81], the population of sequence at time is a function of sequence populations at time :(5)where and are, respectively, the mutation rate and sequence length chosen for the present study. is the population of at time , and is the population of the adjacent sequences of , denoted here by , in the sequence network , where two sequences are adjacent if and only if they can be converted to each other by a single mutation. The factor is introduced to keep the total population normalized to 1 to facilitate comparisons of distributions at different time steps. The factor is a reproduction term that is determined by the relative fitness of sequence , being the average fitness (weighted by population) of all at time .
Population dynamics were calculated from an initial state () in which only the prototype sequence of one network (A) was populated (Figure S4). The steady state was reached by iterating Eq. 5 until the values remained essentially unchanged over many generations. For a given network topology, the steady state is independent of the initial state. In this regard, it should be noted that for some of the control calculations in Figure S3 only the initially populated network were populated at steady state because in those cases the two networks were disconnected by the artificial removal of bridge sequences in the control simulations.
The above master-equation approach presupposes an effectively infinite population. To assess the effect of finite population on steady-state distributions, we have also conducted Monte Carlo simulations under the same general conditions with respect to selection pressure and mutation rate (Figure S4) [81]. Similar to the initial conditions in the master-equation formulation, every Monte Carlo simulation was initialized with a population consisting of identical individuals each carrying the prototype sequence . At each subsequent time step, a random number between 0 and 1 was drawn for each of the 18 monomers in each of the 1000 sequences. If the random number was less than , the monomer was mutated ( or , depending on whether the initial monomer was H or P), and fitness was then recalculated in accordance with Equation 3. Multiple mutations in one sequence can occur in one time step; but these events were very rare under the chosen value for . Evolution thus proceeded essentially in discrete steps of single point mutations. After all mutations were performed for a given time step, a new population was selected for the next time step by the following consideration: As in the master-equation formulation, the relative fitness of individual in the population with fitness is , where . Let and for (thus ). The 's resulting from this construction are the boundaries of discrete bins in with widths equal to the values. Now, to select an individual, a random number was drawn and individual was selected if . This procedure picks an individual by letting the random number fall into one of the bins. By repeating this procedure times, a new population of 1,000 individuals was selected. Because the same individual could be picked multiple times and some individuals might not be picked at all, fitter individuals would be statistically over-represented in the next generation, as they should.
For illustrative purposes, the sequences belonging to the two adjacent neutral networks in Figure 2a were depicted as nodes placed by the Fruchterman-Reingold algorithm [92] that simulates physical spring forces between connected nodes. This algorithm serves to keep edge lengths as equal as possible, resulting in a network layout that roughly reflects the sequence connectivity relationships, i.e. sequences differing by many mutations are also farther apart in the two-dimensional node layout. Stability difference (see above) was then added as a third axis for the drawing in Figure 2a.
The NMR model 1 of (PDB code 2FS1) and the X-ray structure of (PDB code 1PGA) were used as the wildtype structures in our analysis. The two wildtype sequences have a sequence identity of around . In addition to the wildtype pair, we considered also the sequence pairs in Refs. [27], [69] that are intermediate mutants between the two wildtypes and have pairwise sequence identity of , , , , , and . For any one of these sequences, only one — but not both — of the and structures was experimentally inferred to be native [27], the other was a hypothetical excited-state structure. To estimate the stability difference between excited- and native-state structures, we modeled the free energy of every sequence in both the and structures by “threading” each mutant sequence into a modified and a modified structure that were locally optimized for the given sequence. Two different methods, namely Rosetta and FoldX, were employed for this computation.
In the Rosetta approach (PyRosetta v2.0 implementation [93]), mutations were introduced using the “PackRotamersMover” routine to produce the sequence variants, then each of the two wildtype PDB structures embodying the mutant sequence were optimized using the FastRelax method, which is currently the best-performing free energy minimization method of Rosetta [71]. FastRelax was applied three times in a row to each wildtype PDB structure to ensure that the resulting structures were as optimized as possible and had comparable free energy scores. The same FastRelax procedure was also applied to the two wildtype sequences. Free energy scores were computed by the standard energy function of Rosetta with undamped Lennard-Jones repulsions (“hard rep”) [72].
In the FoldX approach, the mutagenesis engine (“BuildModel”) and the standard energy function of FoldX were used to generate and evaluate the sequence variants. For each sequence, the “Repair” function of FoldX was used to optimize the side-chain orientations. In contrast to the Rosetta approach that allows for movement of all atoms to achieve local optimization of the structure, FoldX (version 3.0) [70] only optimizes side-chain orientations but leaves the backbone unchanged, resulting in less structural optimization (from the PDB wildtype) for any given sequence. A comparison of the performance of Rosetta and FoldX in our analysis of the system is provided in Figure 3c.
To determine the hydrophobic contact density for a given all-atom protein structure (Figure 3d), the number of C-atom pairs from different amino acid residues and the total number of inter-residue atomic contacts were counted. An atomic contact is defined by an inter-atomic distance of less than . Computation of contacts was performed using the P3D Python module [94]. Among several possible choices of threshold separation, we found that a threshold separation of in the definition of produced the best illustration of the native-structure switch between and (Figure 3d). As in the lattice HP protein model [73], only contacts between residue pairs that are at least 3 positions apart along the chain sequence were counted in the measure. Conceptually, the difference in hydrophobic contact density plotted in Figure 3d for the all-atom protein structures corresponds roughly to , where and are, respectively, the total number of contacts of structures and in our biophysical protein chain model. We note that is a simple measure of hydrophobic contact density that does not rely on a hydrophobicity scale (e.g., that of Kyte-Doolittle [95]). It takes contributions from the carbon atoms in hydrophobic as well as non-hydrophobic residues. For instance, in the present application to the system, contains contribution from the C-atoms in the polar residue lysine in the core of both and [27].
All 7989 redundant protein structure clusters were obtained from the protein conformational database PCDB (version 2, August 2011) [59]. Each entry in PCDB contains a cluster of CATH [64] domain structures that correspond to the same sequence. The largest conformational difference (max PCD) between two structures of the same cluster was determined, using the RMSD values (in ) that were already included in PCDB (obtained using MAMMOTH [59], [96]). Stability value of each structure in a pair with max PCD was calculated with Rosetta [93] by the standard energy function (see above). If a structure had unfavorable energy (), the FastRelax method (see above) was applied until a favorable energy () was reached. Potential bridge proteins were identified by the criteria described above in Results. The set of proteins we thus obtained is listed in Dataset S1 with the cause(s) of conformational diversity provided by PCDB.
|
10.1371/journal.pgen.1005239 | Rescue of DNA-PK Signaling and T-Cell Differentiation by Targeted Genome Editing in a prkdc Deficient iPSC Disease Model | In vitro disease modeling based on induced pluripotent stem cells (iPSCs) provides a powerful system to study cellular pathophysiology, especially in combination with targeted genome editing and protocols to differentiate iPSCs into affected cell types. In this study, we established zinc-finger nuclease-mediated genome editing in primary fibroblasts and iPSCs generated from a mouse model for radiosensitive severe combined immunodeficiency (RS-SCID), a rare disorder characterized by cellular sensitivity to radiation and the absence of lymphocytes due to impaired DNA-dependent protein kinase (DNA-PK) activity. Our results demonstrate that gene editing in RS-SCID fibroblasts rescued DNA-PK dependent signaling to overcome radiosensitivity. Furthermore, in vitro T-cell differentiation from iPSCs was employed to model the stage-specific T-cell maturation block induced by the disease causing mutation. Genetic correction of the RS-SCID iPSCs restored T-lymphocyte maturation, polyclonal V(D)J recombination of the T-cell receptor followed by successful beta-selection. In conclusion, we provide proof that iPSC-based in vitro T-cell differentiation is a valuable paradigm for SCID disease modeling, which can be utilized to investigate disorders of T-cell development and to validate gene therapy strategies for T-cell deficiencies. Moreover, this study emphasizes the significance of designer nucleases as a tool for generating isogenic disease models and their future role in producing autologous, genetically corrected transplants for various clinical applications.
| Due to the limited availability and lifespan of some primary cells, in vitro disease modeling with induced pluripotent stem cells (iPSCs) offers a valuable complementation to in vivo studies. The goal of our study was to establish an in vitro disease model for severe combined immunodeficiency (SCID), a group of inherited disorders of the immune system characterized by the lack of T-lymphocytes. To this end, we generated iPSCs from fibroblasts of a radiosensitive SCID (RS-SCID) mouse model and established a protocol to recapitulate T-lymphopoiesis from iPSCs in vitro. We used designer nucleases to edit the underlying mutation in prkdc, the gene encoding DNA-PKcs, and demonstrated that genetic correction of the disease locus rescued DNA-PK dependent signaling, restored normal radiosensitivity, and enabled T-cell maturation and polyclonal T-cell receptor recombination. We hence provide proof that the combination of two promising technology platforms, iPSCs and designer nucleases, with a protocol to generate T-cells in vitro, represents a powerful paradigm for SCID disease modeling and the evaluation of therapeutic gene editing strategies. Furthermore, our system provides a basis for further development of iPSC-derived cell products with the potential for various clinical applications, including infusions of in vitro derived autologous T-cells to stabilize patients after hematopoietic stem cell transplantation.
| Studying the molecular pathology of human disease is often difficult due to the limited availability of particular primary cells, their limited lifespan, or because complex developmental differentiation procedures cannot be easily followed in vivo. In vitro disease modeling with induced pluripotent stem cells (iPSCs) provides a practical alternative, and the study of several disorders has benefitted enormously from the convergence of three key technologies: modern genomics that links genetic variants to disease phenotypes, the ability to generate patient-specific iPSCs that can be differentiated into cell types affected by disease, and powerful tools for editing complex genomes [1,2].
T lymphocytes play an important role in adaptive immunity against invading pathogens or in fighting tumor cells. A natural microenvironment for T-cell lymphopoiesis is provided by the thymus. Inherited defects in T-cell function or in T-cell development can lead to severe combined immunodeficiency (SCID), a group of life threatening disorders of the immune system [3]. Radiosensitive SCID (RS-SCID; OMIM #602450) is characterized on the molecular level by dysfunctional non-homologous end-joining (NHEJ), the most important pathway to repair DNA double strand breaks (DSBs). In human patients, defective DNA repair can lead to a cellular hypersensitivity to ionizing radiation. Moreover NHEJ is essential for physiological B- and T-lymphocyte development as it plays an important role in the B-cell receptor (BCR) and T-cell receptor (TCR) recombination process [4]. The diversity of BCRs and TCRs results from the multitude of variable (V), divers (D) and joining (J) gene segments that are almost randomly reassembled in a process called V(D)J recombination. During V(D)J recombination, specific enzymes cleave at specific recombination signal sequences flanking these gene segments and NHEJ factors play a crucial role in reassembly and final ligation of these gene segments [5,6]. The NHEJ process involves a number of different enzymes, including DNA-dependent protein kinase (DNA-PK). DNA-PK is a polyprotein complex, formed by the Ku70/Ku80 heterodimer and the DNA-PK catalytic subunit (DNA-PKcs) [7], that binds to DNA end structures and serves as a docking site for additional NHEJ factors that mediate DNA repair [8]. Hypomorphic mutations in PRKDC, the locus encoding DNA-PKcs, have recently been described for radiosensitive T and B deficient SCID patients [9]. Hence, DNA-PK dependent signaling is a paradigmatic example of how a single molecule can be simultaneously involved in both, DNA repair and T- and B-cell development, and of how such a process can be disturbed by a single point mutation. These particularities make PRKDC an optimal target for novel site-specific gene therapy approaches, such as designer nuclease mediated genome editing.
For disease modeling, iPSCs can be generated from affected somatic cells by expression of four transcription factors Oct4, Sox2, Klf4 and c-Myc [10,11]. Similar to pluripotent embryonic stem cells, iPSCs have the capacity for unlimited self-renewal, are permissive for transfection with foreign DNA, and importantly, can be expanded in a clonal fashion for characterization. Thus far, iPSCs have been derived from several patients suffering from different hematopoietic and immunological disorders and have been used for disease modeling and gene targeting approaches [12]. Several protocols for in vitro [13–21] and in vivo [22,23] differentiation of iPSCs to hematopoietic cells have been reported. The availability of Notch ligand based cell culture systems, such as the murine stromal cell line OP9-DL1, allows for further differentiation of hematopoietic stem cells into T-cells in vitro [24,25]
Targeted genome modification in iPSCs is an essential tool in disease modeling [12], and gene editing with designer nucleases has developed into a powerful instrument, which has been successfully applied to generate various genetically modified model organisms or human cells to study gene function or the pathophysiology of disease causing mutations. Designer nucleases, like meganucleases [26], zinc-finger nucleases (ZFNs) [27], transcription activator-like effector nucleases (TALEN) [28], or the clustered regularly interspaced short palindromic repeats (CRISPR)/Cas9 system [29], induce site-specific DNA double strand breaks (DSBs) at chosen sites. These DSBs activate one of two major DNA repair mechanisms, NHEJ or homology directed repair (HDR), which can be employed to disrupt genes or to target the integration of exogenous donor DNA sequences to a specific site in the genome, respectively [30].
The goal of this study was to establish an in vitro disease model for T-cell deficiencies and to employ this model to evaluate a designer nuclease-based genome editing strategy. To this end, we generated iPSCs from adult ear fibroblasts of NOD.SCID mice, a model for RS-SCID [31], and established a protocol to recapitulate T-lymphopoiesis from iPSCs in vitro. We used ZFNs to edit DNA-PK deficient fibroblasts and iPSCs and demonstrated that designer nuclease mediated gene correction led to rescue of DNA-PK dependent signaling, normal radiosensitivity, restoration of T-cell maturation, and polyclonal TCR recombination. We hence provide proof that the combination of two promising technology platforms, iPSCs and designer nucleases, with a protocol to generate T-cells in vitro represents a powerful paradigm for SCID disease modeling and the evaluation of therapeutic gene editing strategies.
In the murine disease model RS-SCID is caused by a T-to-A transversion mutation in exon 85 of the prkdc locus. The introduced premature stop codon (Y4046*) leads to an 83 aa long C-terminal truncation of the encoded DNA-PKcs protein, leading to decreased protein stability and low kinase activity [31]. ZFNs targeted to intron 84 of prkdc were generated using the OPEN platform [32] and their activity verified by in vitro cleavage assays and plasmid-based recombination assays (S1 Fig). To restore function of DNA-PK, we generated a donor DNA encompassing the wild-type cDNA sequence of prkdc exons 85 and 86, preceded by a splice acceptor site and followed by a poly(A) signal (Fig 1A). Targeting an intron allowed us to co-introduce a neomycin selection marker cassette to enrich for cells that underwent correct gene targeting.
To validate our targeting strategy, fibroblasts from a 12-week old male NOD.SCID mouse, in which the SCID mutation in prkdc was confirmed by sequencing, were isolated. Upon culturing in vitro these cells transformed spontaneously, probably due to their intrinsic DNA repair deficiency. The fibroblasts were transfected with various ratios of donor DNA to ZFN expression plasmids before G418 selection was applied. An inside-out PCR strategy was used to verify correct gene targeting in polyclonal cell populations (Fig 1B). All samples transfected with ZFN expression plasmids and donor revealed successful gene targeting. Splicing of exon 84 to the integrated cDNA was verified by inside-out reverse transcription (RT)-PCR (Fig 1C). To determine the efficiency of the gene targeting approach, cell clones were generated by single cell dilution. Six out of 20 analyzed clones showed correct targeting.
To confirm that re-routed splicing of exon 84 to the artificial exon 85/86 restored DNA-PK activity, cells were treated with camptothecin (CPT), a compound known to induce DSBs during DNA replication by blocking topoisomerase I. Under these experimental conditions, RPA2 is exclusively phosphorylated by DNA-PK at the stalled replication forks [33]. Upon CPT treatment of SCID fibroblasts (Fib.S), a gene edited fibroblast clone (Fib.T) and a donor-containing clone (Fib.D), phosphorylation of RPA2 was detected in Fib.T cells, but not in Fib.S and Fib.D cells (Fig 2A). The fibroblast cell line NIH-3T3 served as a positive control.
Fibroblasts of RS-SCID mice are sensitive to gamma-irradiation or the radiomimetic drug bleomycin [34]. To verify that successful gene targeting could abrogate radiosensitivity, colony survival assays with bleomycin were conducted. We found that the corrected cell line Fib.T displayed similar resistance to the drug as NIH-3T3 cells, while both Fib.D and Fib.S cells were highly sensitive to bleomycin (Fig 2B). In conclusion, successful ZFN-mediated genome editing restored activity of DNA-PK, which was able to phosphorylate downstream target proteins and to rescue the radiosensitive phenotype of RS-SCID cells.
While fibroblasts served as an important model to evaluate DNA-PK dependent signaling, the full therapeutic potential of genome editing at the prkdc locus can only be assessed in lymphoid cells. iPSCs have the capacity for unlimited self-renewal, allowing long-term in vitro culture and generation of single-cell derived subclones. As iPSCs can be differentiated into hematopoietic cells, including T lymphocytes, they are an ideal platform for disease modeling and the evaluation of gene therapeutic approaches. We generated iPSCs from fibroblasts isolated from a 6-week-old NOD.SCID mouse by transduction with a polycistronic lentiviral vector expressing the reprogramming factors Oct4, Klf4, Sox2 and c-Myc [35]. Since the DNA repair-deficient phenotype interferes with efficient reprogramming [36], we conducted the experiment under hypoxic conditions and added ascorbic acid to reduce damage by reactive oxygen species (ROS) [37]. In addition, small molecule inhibitors for MAP kinase (MEK), glycogen synthase kinase 3 (GSK3) and TGF-beta were used, which have been reported to permit derivation of iPSCs of NOD-derived mouse strains and enhance the reprogramming progress [38,39]. All analyzed iPSC clones expressed pluripotent stem cell markers (S2 Fig), and RT-PCR demonstrated expression of the embryonic stem cell-specific genes in a NOD.SCID iPSC clone (iPS.S6; Fig 3A). In addition, cells from ectodermal (neural rosette-like structures), endodermal (gut-like structures) and mesodermal (smooth muscle patches) origin were detected in teratoma derived from clone iPS.S6 (Fig 3B). Genome integrity was assessed before and after ZFN mediated genome engineering by spectral karyotyping (Figs 3C and S2). The NOD.SCID iPSC clone iPS.S6 displayed no gross genetic abnormalities and was used for subsequent gene targeting experiments. In summary, we showed that DNA-repair deficient NOD.SCID fibroblasts could be reprogrammed into iPSCs that display pluripotent behavior and characteristics similar to murine embryonic stem cells.
For targeted genome editing, cells of iPSC clone iPS.S6 were nucleofected with donor and ZFN expression plasmids. Following selection and clonal expansion, inside-out PCR amplification was applied on genomic DNA to detect correct targeting. Of note, 41 out of 46 analyzed clones (89%) showed correct integration of the artificial exon 85/86. Extended PCR analysis of five targeted iPSC clones verified correct 5´- and 3´-junctions between genomic and donor DNA, respectively. An allelic discrimination PCR confirmed mono-allelic targeting in all cases (Fig 4A). Furthermore, expression of the DNA-PKcs encoding mRNA and re-routed splicing to artificial exon 85/86 was validated by inside-out RT-PCR (Fig 4B). All targeted iPSC clones were positive for expression of pluripotency markers (Fig 3A), formed all three germ layers in teratoma assays (Figs 3B and S2), had an intact karyotype (Figs 3C and S2), and did not show any signs of NHEJ-mediated mutagenesis at the top 15 predicted off-target sites in the mouse genome (S1 Table, S1 Text).
The polycistronic lentiviral vector used for generation of iPSCs contained Flp recognition target (FRT) sites in the U3 region of the long-terminal repeats, which allowed us to excise the reprogramming cassette using retroviral-mediated transfer of Flp recombinase [40]. Southern blot analysis confirmed successful removal of the lentiviral vector genome (S3 Fig) and targeted integration of the artificial exon 85/86 into intron 84 of the prkdc locus (S3 Fig).
Since DNA-PK is essential for V(D)J recombination, the RS-SCID immunophenotype is characterized by a lack of T and B-lymphocytes [41]. The stromal cell line OP9-DL1 leads to activation of the DL1-mediated Notch signaling in co-cultured cells, which in turn is a prerequisite to induce the T-lymphoid program in multipotent hematopoietic progenitors [24]. Initial experiments showed that OP9-DL1 co-cultivation of C57BL/6-derived lineage negative bone marrow cells enabled the differentiation of these multipotent stem cells through all (CD4-/CD8-) double-negative (DN) thymocyte stages, as determined by CD25 and CD44 surface expression. Further culturing of these T-cell precursors on OP9-DL1 led to the generation of CD4+/CD8+ double-positive (DP) T lymphocytes, which expressed the beta chain of the T-cell receptor (TCRß), indicating that these cells have successfully undergone V(D)J recombination and beta-selection in vitro (Fig 5).
Since V(D)J recombination is initiated at the DN2 (CD44+/CD25+) stage and beta-selection occurs at the DN3 (CD44-/CD25+) stage, we hypothesized that corrected RS-SCID iPSC-derived hematopoietic progenitor cells (HPCs) should be able to differentiate to CD4+/CD8+ double-positive T lymphocytes, while T-cells derived from uncorrected SCID-derived iPSCs would stop at the DN2 thymocyte stage due to their defect in V(D)J recombination (Fig 5A). To this end, we established an embryoid body (EB)-based differentiation protocol for the generation of HPCs from iPSCs. Differentiated and dissociated EBs from all iPSC clones contained cells carrying the early hematopoietic surface markers CD41 and cKit (S4 Fig). Co-cultivation of these cells on OP9-DL1 stroma cells induced differentiation towards T-lymphocytes. After two weeks, thymocyte maturation of iPSC-derived HPCs was measured by flow cytometry, revealing the presence of CD44+/CD25- (DN1), CD44+/CD25+ (DN2), CD44-/CD25+ (DN3), CD44-/CD25- (DN4), and CD4+/CD8+ (DP) cells from wild-type iPSCs (iPS.WTX; Fig 5). As hypothesized, T-cell differentiation of NOD.SCID iPSCs (iPS.S6X) was blocked in early DN1 and DN2 thymocyte stages and these T-cell precursors showed neither expression of CD4/CD8 nor TCRß. In contrast, differentiation from genetically corrected iPSC clones (iPS.T25X) reached DN3 and DN4 stages as well as the CD4+/CD8+ DP T-cell stage, with a fraction of cells expressing TCRß (Fig 5B). Although the same experimental conditions were applied, the absolute numbers of generated T-cells varied in between different experiments.
To confirm T-cell receptor recombination on the genome level, V(D)J recombination was verified by spectratyping. Control T-cells isolated from the thymus and in vitro generated T-cells from bone marrow lineage negative cells showed a polyclonal T-cell repertoire at Vß chains 1, 6, 8.1, 8.3, 10, 12, 14 and 20 (Figs 6 and S5). While V(D)J recombination was undetectable in T-cell precursors derived from SCID iPSCs, T lymphocytes derived from WT or gene targeted iPSCs underwent V(D)J recombination and revealed a polyclonal T- cell repertoire.
In summary, we developed a protocol, which allowed us to model T-cell differentiation in vitro. We showed that iPSCs can be differentiated into hematopoietic progenitors and further to various stages of thymocyte development. While wild-type and corrected NOD.SCID iPSCs could be maturated into CD45+ CD4+/CD8+ DP T-cells that express TCRß, differentiation of DNA-PK-deficient cells stopped at the DN2 thymocyte stage. These results provide a proof of concept that iPSC-based in vitro disease modeling is able to reflect in vivo thymocyte maturation and that such modeling can be used for both to investigate T-cell maturation defects and to validate gene therapy strategies.
SCID is a group of monogenetic disorders of the immune system characterized by the absence of T-cells, sometimes in combination with a lack of functional B-lymphocytes and/or natural killer cells. RS-SCID is a special form of SCID disorders and serves as a paradigm for radiosensitivity and immunodeficiency. On top of the absence of T- and B-lymphocytes, the pathophysiology of RS-SCID is characterized by a strong sensitivity of all somatic cells to radiation and DNA damaging agents due to a defective DNA repair pathway. The underlying mutations are found in genes coding for NHEJ factors, including LIG4 [42], Artemis [43], XLF [44] and DNA-PKcs [9].
Disease modeling based on patient-derived iPSCs is particularly valuable when studying rare disorders, like RS-SCID, for which patient cells are not easily accessible, have a limited lifespan, or do not develop due to a differentiation block. Designer nuclease-based gene editing in iPSCs makes this instrument even more attractive because it enables scientists to correlate genotype to phenotype in an isogenic background, either by creating disease models through the insertion of disease specific mutations in normal cells [45] or by correcting the underlying genetic mutation back to wild-type in patient-derived iPSCs [16,46]. Particularly in combination with genetic engineering, iPSCs are preferred over fibroblasts because of their unlimited proliferative potential and their ability of clonal expansion.
Hematopoietic differentiation protocols offer the possibility to investigate maturation of various blood lineages in vitro, e. g. to study the impact of genomic mutations on protein function in mature blood cells or where specific mutations lead to a block in lymphopoiesis, myelopoiesis, or erythropoiesis [17,18,46]. While designer nuclease-based gene editing in iPSCs has been established in several labs, differentiation of genetically modified iPSCs to mature immune cells has remained challenging. Differentiation of iPSCs derived from a patient suffering from X-linked chronic granulomatous disease (X-CGD) to granulocytes was the first example to show functional correction of a genetic defect by targeted integration of a gp91phox expression cassette into the putative safe harbor site AAVS1 [15]. Myeloid differentiation from patient-derived iPSCs for disease modeling and/or drug development has also been established e.g. for severe congenital neutropenia [47] and pulmonary alveolar proteinosis [20].
Differentiation of iPSCs to lymphocytes, on the other hand, has been reported only from a few labs [18,19]. In the present study, we describe an improved in vitro differentiation procedure for iPSCs to T-cells that is based on previously published protocols [18,48,49], and, to our knowledge, use this protocol for the first time to model the functional defects of an immunodeficiency in vitro and to investigate the effect of genetic engineering of disease iPSCs on T-cell maturation. Because the generated hematopoietic progenitor cells supported the maturation through all early stages of thymocyte differentiation, including V(D)J recombination and beta-selection, we were able to reproduce the stage-specific block induced by the point mutation in the prkdc locus in vitro. This setup can also be used to screen for genotype-phenotype correlations or to characterize the consequence of newly identified genetic mutations on T-lymphopoiesis and/or T-lymphocyte function in more detail. For instance, as compared to in vivo models, individual effects of the microenvironment, cytokines and/or small molecules affecting T-cell maturation and expansion, like IL-7 or IL-2, can be analyzed by simple addition to the culture medium. Moreover, existing stroma-free models can be further developed [50] to identify factors downstream of Delta-like Notch ligands that promote T-cell development. Finally, the efficiency of T-cell related gene therapy approaches can be assessed in vitro, without the need of hematopoietic stem cells of the patients.
In our study we applied ZFNs for genetic modification of RS-SCID iPSCs. The generation of highly specific ZFNs can be rather challenging and several studies have described off-target cleavage activity of ZFNs [51,52]. While the specificity of ZFNs can be improved, e.g. by optimizing the DNA binding properties of the zinc-finger arrays [32], selecting appropriate linker domains [53,54] and employing obligate heterodimeric FokI nuclease domains [55,56], alternative designer nucleases, such as TALENs [28] and CRISPR/Cas9 based nucleases [29], are easier to engineer.
Our system provides a basis for further development of iPSC-derived cell products with the potential for various clinical applications. However, although we have tried to transplant iPSC-derived hematopoietic stem/precursor cells into NOD.SCID mice, we did not observe any engraftment of these cells. This is in line with published data showing that transplantation worked only with iPSC-derived hematopoietic stem/precursor cells that were produced in vivo [22,23]. Further studies will be needed to establish optimal culture conditions to generate transplantable stem cells in vitro. Hence, combining in vitro protocols with physiologic in vivo differentiation seems more promising. For example, transplantation of iPSC-derived early thymocyte progenitor populations could allow for thymic reconstitution and maturation to create polyclonal T-cell effector populations [50]. Infusions of in vitro derived autologous T-cells could be used to stabilize patients suffering from primary immunodeficiencies, like SCID or hemophagocytic lymphohistiocytosis, or after conventional hematopoietic stem cell transplantation to close the gap until graft-derived lymphocytes arise. Moreover, given the clinical success of autologous T-cells expressing tumor specific chimeric antigen receptors (CARs) [57], iPSC-derived autologous CAR-T-cells represent an interesting alternative to current protocols, as recently shown [19]. Finally, autologous, CCR5 knockout iPSC lines could present a source to provide HIV patients with HIV-resistant T-cells to reconstitute the adaptive immune system [58]. However, before iPSC-based cell therapies can enter clinical practice, safety concerns, especially with regard to the generation of iPSC-derived teratoma, have to be addressed and full functionality of iPSC-derived cells proven.
In conclusion, our study describes an iPSC-based disease model for RS-SCID. Our in vitro protocol allowed us to differentiate iPSCs to T-cells and to analyze the influence of NHEJ deficiency on V(D)J recombination. Moreover, it emphasizes the significance of designer nucleases as a tool in generating isogenic disease models and their future role in producing iPSC-based, patient-specific, genetically corrected autologous transplants for various applications in the clinic.
NIH.3T3 and HEK293T cells were cultured in DMEM (Biochrom) supplemented with 10% FCS (PAA), penicillin/streptomycin (P/S; PAA), L-glutamine (Biochrom) and sodium pyruvate (PAA). OP9 and OP9-DL1 cells (obtained from Juan Carlos Zúñiga-Pflücker) were expanded in OP9 medium [alpha-MEM (Gibco), 20% OP9-tested FCS (PAA), P/S and L-glutamine]. Primary mouse ear fibroblasts were cultured in MEF medium [DMEM low glucose (PAA) with 15% FCS, L-glutamine, nonessential amino acids (NEAA; Gibco), P/S, 100 μM of ß-mercaptoethanol (Sigma-Aldrich), sodium pyruvate and 50 μg/μl phospho-ascorbic acid (P-VitC, Sigma-Aldrich)]. ES.CCE cells were cultivated in ES medium [Knockout-DMEM (Gibco) with 15% ES-tested FCS (PAA), P/S, L-glutamine, NEAA, 150 mM monothioglycerol (MTG, Sigma-Aldrich) and ESGRO mouse LiF (Millipore)]. iPSCs were cultivated in iPS medium [Knockout-DMEM supplemented with 15% ES-tested FCS, NEAA, P/S, L-Glutamine, 100 μM of ß-mercaptoethanol and ESGRO mouse LiF, 50 μg/μl of P-VitC, 4 μM of SB431542, 1 μM of PD0325901 and 3 μM of CHIR99021 (all Axon Medchem, together termed 3i) and passaged with Accutase (Gibco). ES.CCE cells and iPSCs were cultivated either on irradiated C3H or CF-1 MEF feeders on gelatin-coated plates or feeder-free in vented flasks (Sarstedt). Lineage negative cells (HSC) were isolated by flushing the tibiae and femurs of C57BL/6N mice (Charles River) and purified by magnetic cell sorting (MACS) with the Lineage Cell Depletion Kit (MACS Miltenyi) according to the manufacturer’s protocol. Cells were stained with Trypan Blue (Sigma-Aldrich) and counted at 100x microscope magnification prior to in vitro T-cell differentiation. Cell clones were generated either by limiting dilution (fibroblasts) or colony picking (iPSCs). All but HEK293T cells were cultivated under hypoxic conditions (7% CO2 / 5% O2).
Prkdc-specific zinc-finger arrays (S1 Fig) were generated with the OPEN protocol [32]. To generate ZFNs, the zinc-finger arrays were codon-optimized (GeneArt) and cloned into pRK5 vectors, with and without NLS [59], containing the cleavage domains of wild-type FokI or the obligate heterodimeric FokI variant KV/EA [55] and the LRGS linker [54]. The target plasmid pCMV.LacZsPK∂GFP was generated by replacing the “31” target site of pCMV.LacZs31∂GFP [59] by the ZFN target site aGTTTGCGCCtaactGAAGGTGACa (capital letters indicate target site for ZFN). The donor plasmid pJet.SAE8586Neo (Fig 1A) consists of (i) a splice acceptor (SA) [60]; (ii) a cDNA consisting of prkdc exons 85 and 86, which was PCR amplified from pMEPK7 (kindly provided by Masumi Abe) with primers PRK-F/PRK-R (S2 Table); (iii) an SV40 polyadenylation signal (pA); (iv) a NeoR cassette comprise the aminoglycoside phosphotransferase coding sequence flanked by the HSV thymidine kinase promoter and an SV40 pA (kindly provided by Stefan Weger); (v) left and right homology arms, which were PCR amplified from Fib.S gDNA.
For expression analysis, ZFNs were expressed in HEK293T for immunoblotting as previously described [59]. The in vitro cleavage assay was basically performed as defined before [61]. Briefly, a target DNA was amplified by PCR from Fib.S gDNA using primers IV-F/IV-R (S2 Table). ZFNs were in vitro transcribed/translated with the TNT SP6 Coupled Reticulocytes Lysate System (Promega), 150 ng of target DNA was mixed with the reticulocyte lysates, incubated for 1.5 h at 37°C, and analyzed on a 1.5% agarose gel. The plasmid-based gene targeting assay was conducted as described before [59]. Flow cytometry to determine the percentage of EGFP and REX positive cells was performed on FACSCalibur with CellQuestPro software (BD Biosciences).
For targeted integration into Fib.S fibroblasts, 1x105 cells were transfected 24 h after seeding with Lipofectamine 2000 (Life Technologies). 1.6 μg of endotoxin-free DNA was mixed with 4.8 μl of transfection reagent in 200 μl OptiMEM (Gibco). The ZFN expression plasmids were co-transfected with the donor pJet.SAE8586Neo at different ratios and filled up with pUC118 to 1.6 μg. Selection with 500 μg/ml of G418 (Sigma-Aldrich) was applied 5 days after transfection for 7 days. iPSCs were grown feeder-free before and after transfection. 3x106 cells were nucleofected with 10 μg of pJET.SAE8586Neo and 5 μg of each ZFN expression plasmid using the Mouse ES Cell Nucleofector Kit (LONZA) and Nucleofector II with program A-030. After 5 days of recovery, G418 selection was applied for 7 days at a concentration of 400 μg/ml. After 1 week, iPSC clones were isolated and cultivated on feeders.
Genomic DNA was extracted with the QIAamp DNA Blood Mini Kit (QIAGEN). G418 selected fibroblast and iPSC clones were analyzed for legitimate targeted integration by inside-out PCR using Phire Hot Start II DNA polymerase kit (Thermo Scientific). RNA was isolated with TRIzol (Life Technologies), and all RT-PCR reactions performed with the QuantiTect Reverse Transcription Kit (QIAGEN). All used primers are listed in S2 Table. For Southern blot analysis [62], genomic DNA was digested with EcoRV or BamHI, separated on a 0.8% agarose gel and transferred to Biodyne B nylon membrane (PALL Life Sciences). DNA was hybridized with a 32P-labeled fragment of PRE (for detection of the reprogramming vector) or NeoR (for detection of donor copies) using the DecaLabel DNA Labeling Kit (Fermentas). Labeled HindIII digested Lambda DNA was used as a marker.
To measure DNA-PK dependent RPA2 phosphorylation, 8x105 fibroblasts were treated with 1 μM of camptothecin (Sigma-Aldrich) for 1 h. Cells were harvested in RIPA buffer supplemented with Complete Protease Inhibitor and PhosSTOP phosphatase inhibitor cocktails (both Roche). Western blot was basically performed as described before [63,64]. RPA2 and ß-actin were detected with rat anti-RPA32 (1:1000, 4E4, Cell Signaling) and rabbit anti-ß-actin (1:1000, Cell Signaling), respectively, and visualized with HRP-conjugated anti-rat and anti-rabbit antibodies (1:20,000, Dianova) and West Pico Chemiluminescence substrate (Thermo Scientific). For the colony survival assay, 1x105 fibroblasts were treated 1 day after seeding with the indicated amounts of bleomycin (Sigma-Aldrich) for 2 h. Cells were washed with PBS, trypsinized and 5,000 cells seeded into a 10-cm plate (N = 3). After 4 days the plates were stained with 0.5% (w/MeOH) crystal violet (Sigma-Aldrich) and colonies counted.
Murine adult fibroblasts were extracted from ears of 6-week old NOD/ShiLtJ and NOD.CB17-Prkdc scid/J male mice as described before [62]. Fibroblast from 12-week old NOD.CB17-Prkdc scid/J mouse gave rise to spontaneously transformed Fib.S. The “4-in-1” reprogramming vector pRRL.PPT.SF.mOKSMco.idTom.PRE, co-expressing the transcription factors Oct4, Klf4, Sox2 and c-Myc with the fluorescent marker tdTomato, has been previously described [35]. To generate versions that allow for Flp recombinas-mediated excision (pRRL.PPT.SF.mOKSMco.idTom.PRE.FRT), FRT sites were introduced into the promoter-deprived U3 region. Virus production has been described elsewhere [65]. The reprogramming was conducted as described before [35]. Briefly, NOD.CB17-Prkdc scid/J or NOD/ShiLtJ-derived fibroblasts were seeded in MEF medium on gelatin-coated 6-well-plates at 8x104/well for transduction. After 2 days, cells were transduced with an MOI of 5 and incubated for 8 h, following 2 times washing with PBS. MEF medium with 2 mM VPA (Sigma Aldrich) was added. After 4 days medium was changed to iPS medium with VPA, and after 7 days 3i was added. After 14 days, emerging iPSC colonies were isolated and expanded for characterization. A total of 12 iPSC clones derived from NOD.CB17-Prkdc scid/J (iPS.S) were initially characterized by assessing expression of SSEA-1 by flow cytometry and staining of alkaline phosphatase (Millipore) followed by documentation with the Olympus IX71 system. Determination of the vector copy number (VCN), teratoma formation, Flp recombinase-mediated excision, fluorescence in situ hybridization (FISH) and pluripotency factors RT-PCR analysis have been described previously [35,62,66]. Clone iPS.S6 was used for gene targeting and three out of 41 corrected clones (iPS.T8, iPS.T25, iPS.T44) were characterized in detail. The parental uncorrected clone iPS.S6 was included as a negative control, a wild-type NOD/ShiLtJ derived clone (iPS.WT) as a positive control.
The protocol was adapted from previously published work [48,49]. For embryoid body (EB) formation, iPSCs were split with Collagenase IV (Gibco) and 5x104 cells were cultured in suspension plates in 2 ml of EB medium [IMDM (Biochrom AG) with 15% ES cult FCS (Stem Cell Technologies), 5% PFHM II (Gibco), P/S, L-Glutamine, 50 μg/ml P-VitC, 150 mM MTG, 200 μg/ml human transferrin (Sigma-Aldrich)] in a normoxic incubator on a shaker at 60 rpm. At day 2.5, 0.5 ml of EB medium plus cytokines rhBMP-4, activinA, rhVEGF165 and rhFGF-2 at 5 ng/ml final concentration each (all R&D Systems) was added. At day 8, EBs were harvested, washed with PBS and collected in Trypsin-EDTA, diluted 1:15 in Collagenase IV. After 30 min, 2.5 ml of cell dissociation buffer (Gibco) was added and cells transferred through a 70-μm mesh. Hematopoietic progenitor cells (HPCs) were washed with PBS and analyzed for CD41/cKit expression by flow cytometry prior to hematopoietic expansion. To this end, 106 EB-derived HPCs were cultivated for 3 days under hypoxic conditions in STFV medium [IMDM, 10% OP9-tested FCS, P/S, L-glutamine, 10 ng/ml mSCF, 20 ng/ml mTPO, 100 ng/ml rhFlt3-L (all Peprotech), and 40 ng/ml rhVEGF165 (R&D Systems) (final concentration each)]. At day 3, cells were harvested through a 100-μm mesh and washed with PBS prior to in vitro T-cell differentiation. To this end, up to 3x105 expanded HPCs or 0.5-1x105 HSCs were added in T-cell differentiation medium [OP9 medium, supplemented with 1 ng/ml mIL-7 (Peprotech) and 5 ng/ml rhFlt3-L]. After 3 days, 2 ml medium was added and cultivation continued for up to 4 weeks. Every 7 days cells were harvested through a 100-μm mesh, washed with PBS, transferred to a new OP9-DL1 cell layer, and analyzed for T-cell differentiation by flow cytometry.
For flow cytometric analysis, cells were resuspended in FACS buffer [PBS supplemented with 2% FCS, 1 mM EDTA and 0.1% sodium azide (both Sigma-Aldrich)]. To stain for pluripotency marker SSEA-1, iPSCs were rinsed with PBS and stained with biotinylated anti-SSEA-1 antibody (eBioscience) diluted in FACS buffer for 20 min at 4°C. After rinsing the secondary staining was performed with a streptavidin-APC antibody (eBioscience). Hematopoietic cells were pretreated with Mouse BD Fc block (BD Biosciences) before antibody staining. Antibody staining was performed for 20 min at 4°C. EB-derived HPCs were stained with CD41-PE, cKit-APC, or respective isotype controls (all eBioscience). iPSC-derived T-cells were stained with CD44-PE and CD25-APC, or CD4-PE and CD8-APC. Viability staining with 7-AAD was performed for 2 min during the last rinsing, before samples were measured on a FACSCalibur. Alternatively, iPSC-derived T-cells were stained with CD45-APC-Cy7, CD4-PerCPR-Cy5.5, CD8-PE-Cy7 (all BD Biosciences), CD44-PE, CD25-APC, TCRß-FITC (eBioscience) and DAPI, before analysis on a FACSCanto II with FACSDiva (BD Biosciences). All samples were analyzed with FlowJo software (Tree Star). T-cell receptor diversity was analyzed by CDR3 spectratyping as previously described [67].
All experiments were performed at least three times. Error bars represent standard deviation (SD). Statistical significance was determined with a two-sided Student's t-test with unequal variance.
The National Center for Biotechnology Information (NCBI) Nucleotide database (http://www.ncbi.nlm.nih.gov/nuccore) accession number for the ZFN target site in intron 84 of the prkdc gene on mouse chromosome 16 is AB030754: 189732.
|
10.1371/journal.pgen.1005299 | MreB-Dependent Inhibition of Cell Elongation during the Escape from Competence in Bacillus subtilis | During bacterial exponential growth, the morphogenetic actin-like MreB proteins form membrane-associated assemblies that move processively following trajectories perpendicular to the long axis of the cell. Such MreB structures are thought to scaffold and restrict the movement of peptidoglycan synthesizing machineries, thereby coordinating sidewall elongation. In Bacillus subtilis, this function is performed by the redundant action of three MreB isoforms, namely MreB, Mbl and MreBH. mreB and mbl are highly transcribed from vegetative promoters. We have found that their expression is maximal at the end of exponential phase, and rapidly decreases to a low basal level upon entering stationary phase. However, in cells developing genetic competence, a stationary phase physiological adaptation, expression of mreB was specifically reactivated by the central competence regulator ComK. In competent cells, MreB was found in complex with several competence proteins by in vitro pull-down assays. In addition, it co-localized with the polar clusters formed by the late competence peripheral protein ComGA, in a ComGA-dependent manner. ComGA has been shown to be essential for the inhibition of cell elongation characteristic of cells escaping the competence state. We show here that the pathway controlling this elongation inhibition also involves MreB. Our findings suggest that ComGA sequesters MreB to prevent cell elongation and therefore the escape from competence.
| In bacterial cells, like in their eukaryotic counterparts, precise spatiotemporal localization of proteins is critical for their cellular function. This study shows that the expression and the localization of the bacterial actin-like MreB protein are growth phase-dependent. During exponential growth, we previously showed that MreB, together with other morphogenetic factors, forms discrete assemblies that move in a directed manner along peripheral tracks. Here, we demonstrate that in cells that develop genetic competence during stationary phase, transcription of mreB is specifically activated and MreB relocalizes to the cell poles. Our findings suggest a model in which MreB sequestration by the late competence protein ComGA prevents cell elongation during the escape from competence.
| In response to nutritional deprivation and high population density, the rod-shaped model Gram-positive bacterium Bacillus subtilis enters stationary phase and develops diverse environmental adaptations, namely competence for genetic transformation, sporulation, cannibalism or biofilm formation [1]. These developmental programs are exquisitely regulated in order to anticipate starvation and optimize the survival of at least a fraction of the population. During the development of these adaptations, cells initiate a large reorganization of gene expression [2,3], protein localization [4,5] and cell shape [5].
In the case of genetic competence, the central regulator ComK activates the expression of more than a hundred genes [2,6,7]. Competence development in B. subtilis is a well-known bistable system [1]. Only a small fraction of a population (2 to 10%) expresses the ComK-dependent genes, and thus the large majority of the population remains in the non-competent state [8,9]. Within the ComK regulon, twenty-eight genes are essential for genetic transformation [10], a process defined as the genetic alteration of a competent cell by incorporation of foreign DNA in its genome. The remaining genes upregulated in the presence of ComK may be involved in functions other than transformation. Accordingly, it was proposed to rename the ComK-determined physiological state the K-state, a more neutral term than genetic competence [2]. For instance, it has been shown that growth is inhibited during the escape from competence. When the environmental conditions improve (e.g. upon dilution into fresh medium), non-competent cells rapidly resume growth whereas competent cells remain in a growth-limited state during which both cell elongation and cell division remain inhibited for more than 90 minutes before they start to grow again [11,12]. This delay relative to non-competent cells is thought to constitute a tightly regulated checkpoint to allow the repair of the chromosome following homologous recombination of the transforming DNA, before replication initiation [11,12]. Growth inhibition during the escape from competence is controlled at two levels: cell elongation is inhibited through the late competence peripheral protein ComGA [11] and cell division is inhibited by ComGA and the highly conserved protein Maf [11,12]. The ComGA-mediated mechanism that inhibits cell elongation during outgrowth remains unknown. After exhibiting a diffuse localization in the cytoplasm, ComGA accumulates preferentially at polar clusters where it co-localizes with other competence proteins to form the transformation machinery [4,13]. Upon dilution into fresh medium, ComGA stays at the poles for 120 minutes before delocalizing, presumably through degradation or inactivation, ultimately reversing elongation inhibition [12].
Among the different classes of proteins regulating bacterial cell elongation, the bacterial actin-like MreB proteins have been the most studied over the past fifteen years. MreB proteins (Mre, for Murein cluster e) are essential for cell morphogenesis in most non-spherical bacteria [14,15]. In exponentially growing rod-shaped cells, MreB proteins localize in membrane-associated assemblies that rotate perpendicularly to the long axis of the cell [16–21]. These MreB structures are thought to control cell elongation by directing the assembly and movement of macromolecular complexes that effect synthesis of the sidewalls (cell cylinder) during growth [14,16,17].
In B. subtilis, sidewall elongation during vegetative growth is controlled by the redundant action of three MreB isoforms: MreB, Mbl and MreBH [22]. mreB and mbl are essential under normal growth conditions [23,24], while mreBH is essential only under certain adverse conditions [22,25]. The mreB gene is found in the third position of an operon composed of seven genes; immediately upstream the mreCD morphogenes and the minCD division-related genes, and downstream maf, involved in division inhibition during competence [12], and radC, of yet unknown function. It has been shown that several promoters are located within or upstream the mreB operon [12,26,27]. mbl is found immediately downstream spoIIID, a gene encoding a sporulation-specific transcriptional regulator [28] and usd, a gene located upstream spoIIID and necessary for its translation [29]. A sigma-E dependent promoter, activating the mbl expression during sporulation, is located upstream usd and spoIIID [26,30]. However, it has been shown that expression of mbl during vegetative growth is ensured by a sigma-A dependent promoter located between spoIIID and mbl [26]. Finally, mreBH forms an operon with a small gene of unknown function, ykpC [26]. Transcription of the mreBH operon is driven by the alternative sigma factor sigma-I, which is induced during heat shock [31]. The specific expression of the three mreB isoforms, from different promoters depending on different sigma factors, is in agreement with their partial functional redundancy upon various stress conditions [22].
Interestingly, mreB and mbl were identified as competence-induced genes in a transcriptomic study [2]. However, a detailed profile of expression of these two genes throughout growth and stationary phase remained to be characterized, and a possible role of MreB-like proteins in stationary phase adaptations was not investigated so far. Here, we report a new role associated to MreB during genetic competence in B. subtilis. We show that mreB (but not mbl) belongs to the ComK regulon, and that in competent cells MreB forms a complex with several competence proteins. Additionally, MreB co-localizes with ComGA in polar clusters. We finally show that ComGA-dependent growth inhibition displayed by cells escaping the K-state also involves MreB. We propose a model in which ComGA sequesters MreB in order to prevent cell elongation during outgrowth and therefore the escape from competence.
In previous transcriptional profiling studies of B. subtilis grown to competence, all the genes of the mreB operon were found to be down-regulated in comK mutant cells relative to wild-type cells [2]. mbl was also down-regulated but only when mecA, which codes for the adaptor protein that targets ComK for proteolysis [32], was knocked-out to increase the percentage of competent cells [33]. It was proposed that expression of both mreB and mbl was ComK-dependent and thus induced during competence, although it could not be excluded that mbl expression was affected by ComK only in the pleiotropic mecA background [2].
We examined whether transcription of mreB, mbl and/or mreBH was specifically induced during competence. Fragments of different sizes (500 to 2300 bp) upstream the open-reading frames of mreB (Fig 1A), mbl (S2A Fig) and mreBH (S2B Fig) containing several promoters were fused to the firefly luciferase (luc) coding sequence. In the case of mreB, three promoters were previously identified: P1, upstream the maf-radC-mreBCD-minCD operon [12,26]; P2, inside maf [27] and P3, between radC and mreB [26,27] (Fig 1A). P1 and P3 are dependent on the major housekeeping sigma factor sigma-A, while P2 is dependent on extracytoplasmic sigma factors [26,27,34]. P1 also contains ComK binding boxes (Fig 1A) and was shown to drive expression of maf during competence [12]. We measured the transcription rate from three fragments upstream mreB: PmreB123, containing the three promoters; PmreB23, containing promoters P2 and P3, and PmreB3, containing P3 (Fig 1A) during growth (measured by OD600, S1 Fig) in competence medium (CM). During exponential growth, expression of luc fused to PmreB123, PmreB23 and PmreB3 was virtually identical (Fig 1B). The transcription rate progressively increased to reach a maximum during the transition from exponential growth to stationary phase, which marks the beginning of competence (T0). This indicated that in exponentially growing cells expression of mreB comes from P3. No transcript generated from P1 and P2 could be detected, even if P1 has been shown to drive the low, basal expression of maf during exponential growth [12]. Upon entering stationary phase (after T0), the transcription rate from all three fragments rapidly decreased (Fig 1B). Transcription from PmreB23 (P2 + P3) and PmreB3 (P3 alone) exhibited a relatively sharp and progressive decrease, reaching a low basal level approximately 3 h after T0. However, expression from PmreB123 (P1 + P2 + P3) was significantly higher and exhibited a prominent burst about 2 hours after T0 (T2, which corresponds to the time of maximal competence [35]). Thus, in stationary phase there was a substantial (4–6 fold, see inset in Fig 1B) increase in mreB transcription that came from P1, the promoter in front of the operon. This was consistent with the recent finding that maf is expressed during competence from P1, regulated by the master regulator ComK [12]. As expected, when we monitored mreB transcription in a comK mutant, the transcriptional burst observed at T2 was abolished and the transcription rate from PmreB123 was comparable to that from the two shorter fragments PmreB23 and PmreB3 (Fig 1C).
Two promoters were previously identified for mbl: P1, a sigma-E-dependent promoter located upstream the usd gene and P2, a sigma-A-dependent promoter right upstream mbl (S2A Fig) [26,30,36]. Like mreB, mbl was transcribed predominantly during exponential growth, and maximum of expression was reached right before T0 (S2C Fig). Expression of mbl in exponentially growing cells came exclusively from P2. In contrast to mreB, however, expression of mbl was not reactivated in stationary phase and was not affected by ComK (S2C Fig), even though a small peak can be observed around T2. mbl was previously reported to be over-expressed in comK mutant cells only when mecA was also knocked-out [2]. Since mecA mutants are very pleiotropic [37,38], our results indicate that activation of mbl transcription in the comK- mecA- background was indirect, resulting from secondary effects of the absence of mecA. For mreBH, only a sigma-I-dependent promoter, induced during heat shock, has been identified [31]. Consistently, no transcription of mreBH was detected during growth in competence medium (S2D Fig).
Taken together, our findings indicate that mreB, but not mbl and mreBH, is a competence-induced gene, regulated by ComK.
To provide insight into a possible role of MreB in competent cells, we sought to identify MreB binding partners during competence. To this end, MreB was fused to the sequential peptide affinity (SPA) tag [39]. Unlike cells lacking MreB, cells containing spa-mreB as only copy of mreB in their genome displayed normal morphology in both exponential and stationary phase (S3 Fig) indicating that the SPA-MreB fusion was functional. The strain expressing the SPA-MreB fusion was grown to T2 in CM at 37°C, and MreB-associated proteins were purified and identified by mass spectrometry. Strains expressing no SPA-tagged protein and a SPA fusion to PerR, a non-related protein of B. subtilis, were used as negative controls. Interestingly, several competence proteins (ComGA, Maf, ComEB, ComC and ComFA) were specifically and reproductively detected in the MreB pull-down complexes (Table 1). Among these proteins, ComGA was the most abundant in the complex based on the Protein Abundance Index (PAI, established according to [40]). ComGA was co-purified with SPA-MreB well above the contaminant value found in the control strains (Table 1), indicating that their co-purification was specific. ComEB, ComC and ComFA were specifically co-purified, and Maf was greatly enriched in the SPA-MreB eluate relative to the control strains (Table 1). Taken together, these results indicated that MreB is associated with several competence proteins in B. subtilis.
Next, we determined whether MreB displays a specific localization in the subpopulation of competent cells. We have shown that expression of mreB is complex, driven from three different promoters (Fig 1). To avoid a possible artifact of overexpression and/or misregulation, we replaced mreB by gfp-mreB at the native locus expressed under control of the native mreB regulatory sequences (Pnativegfp-mreB), without leaving any scar or resistance cassette in the vicinity (see Methods for details, S4A Fig and S1 Movie). We then analyzed nativeGFP-MreB localization in cells that natively expressed a functional ComGA-RFP fusion (PnativecomGA-rfp) as a marker for competence. Strikingly, in stationary phase cells (T2), most nativeGFP-MreB signal disappeared from the membrane and became diffuse in the cytoplasm (‘b’ cells in Figs 2A and S4C). In competence-expressing cells at T2, in addition of exhibiting a diffuse signal, nativeGFP-MreB formed clusters at one or both cell poles (‘a’ cells in Figs 2A and S4C). All polar MreB clusters (n > 200) were found to co-localize with ComGA polar clusters, while no nativeGFP-MreB signal was found in 15% (n > 200) of ComGA polar assemblies (Figs 2A and S4C). In the absence of comGA, MreB polar clusters were never observed and nativeGFP-MreB fluorescence signal was diffuse in all cells (n > 200) (Fig 2B). Control experiments showed that this co-localization was not due to bleed-through of the bright ComGA-RFP signal into the GFP channel (S4E Fig). In a given field of view, the integrated fluorescence signal of nativeGFP-MreB per cell was more than 3 times higher in competent (AU = 67.2 ±17.6, n = 102) than in non-competent (AU = 20.8 ±11.6, n = 103) cells, indicating that ComK-dependent expression of mreB (Fig 1) leads to increased levels of MreB protein in competent cells.
In exponential growth, a functional nativeMbl-GFP fusion displayed the characteristic ‘motile patches’ localization (S4B Fig and S2 Movie). However, in contrast to nativeGFP-MreB, at T2 a nativeMbl-GFP fusion was still localized in membrane-associated patches (albeit no longer motile, S4 Movie) along the sidewalls, which did not co-localize with ComGA polar clusters (S4D Fig). Thus, in competent cells MreB, but not Mbl, relocalizes into polar clusters that colocalize with, and are dependent on, the multi-functional competence protein ComGA.
ComGA was first described for its essential role in natural genetic transformation [2]. We then tested if MreB could play a role during this process. Strikingly, transformation efficiency of in-frame mreB null mutants was increased about a hundred fold relative to the wild-type strain, while mbl and mreBH mutants had transformation efficiencies comparable to that of the wild-type (Table 2). However, in cells lacking mreB, both the percentage of competent cells and the timing of competence development were not affected (S5A and S6A Figs respectively).
High concentrations of magnesium (Mg2+) rescue the viability and shape defects of mreBs and other mutants involved in different aspects of cell wall synthesis by a yet unknown mechanism [14]. It has been proposed that Mg2+ may stiffen the cell wall, compensating for structural defects associated to the absence of mreB [41]. CM is traditionally supplemented with 5 mM Mg2+ [35]. Remarkably, increasing Mg2+ concentrations in CM progressively rescued the mreB transformation phenotype (Table 2). At 25 mM Mg2+, the transformation efficiency of mreB mutant cells was down to wild-type levels (Table 2). Taken together, these results suggested that the effect of MreB in transformation is indirect. They also raised the interesting possibility that specific cell wall defects could promote transformation in B. subtilis. One hypothesis was that the assembly or localization of the transformation apparatus across the cell wall was affected in the absence of mreB. To investigate this, we compared the localization of the transformation machinery in the wild-type and mreB mutant backgrounds using our nativeComGA-RFP fusion. The dynamic localization of ComGA during competence has been extensively described [4]. In wild-type cells developing competence ComGA first appears diffuse in the cytoplasm (S7B Fig). Then, ComGA forms clusters associated to the inner face of the membrane with an important bias for the regions near the poles (S7C and S7D Figs), where it co-localizes with other main competence proteins to form the transformation machinery [4]. The number of ComGA focus per wild-type competent cell varies from one to nine, but the large majority of wild-type competent cells (41%, n>1500) display a single ComGA polar cluster (S5C and S5D Figs). The percentage of competent cells that displayed nativeComGA-RFP clusters at T2 (S5B Fig) and among these the number of ComGA clusters (S5C Fig) were significantly higher in cells lacking mreB relative to wild-type cells. More specifically, the majority of mreB mutant competent cells (37%, n>1500) displayed three foci (S5C and S5E Figs). Expression of the comGA gene (S6B and S6D Figs) and ComGA protein levels (S6E Fig) were nevertheless unaffected in the mreB mutant. At high Mg2+ concentrations, mirroring the recovery of wild-type transformation efficiency of the mreB mutant, the distribution of the number of ComGA clusters per competent mreB mutant cell shifted back to wild-type levels (S5B Fig). We concluded that MreB is not directly required for natural transformation and that ComGA localization might be impacted by the cell wall integrity.
It was shown that ComGA is also required for inhibition of cell elongation in cells exiting competence [11], while MreB directs cell elongation in exponentially growing cells [14,16,17]. We hypothesized that MreB could be involved in inhibition of cell elongation during competence escape through its association with ComGA. To test this, we performed outgrowth experiments using a ComK-GFP construct to distinguish competent from non-competent cells, as previously described [11]. Wild-type, ΔcomGA, ΔmreB and ΔmreB ΔcomGA mutant strains were grown to T2, when maximal competence is achieved, and diluted 20-fold into fresh medium. Samples were taken prior to dilution (T2) and 90 minutes after dilution (T2+90) for size and morphology characterization. At T2 competent and non-competent cells were indistinguishable in length for all strains (S3 Table) [12]. At T2+90, non-competent cells had resumed growth and division [11,12]. Competent cells of the wild-type strain (Fig 3A) were only slightly longer than at T2 (S3 Table), confirming the previously reported growth limitation imposed during the escape from competence [11]. ΔmreB competent cells also remained in a growth-limited state after 90 minutes of outgrowth and were significantly shorter than wild-type competent cells (Fig 3C and 3F and S3 Table) as previously reported for exponentially growing mreB mutant cells [41]. In contrast, ΔcomGA competent cells were filamentous and often bent (Fig 3B and 3F and S3 Table), indicating that ComGA directly or indirectly inhibits cell elongation during the escape of competence [11]. However, when mreB was knocked out in the ΔcomGA mutant strain, the filamentous phenotype of ΔcomGA competent cells was rescued, and the average cell length of the ΔmreB ΔcomGA mutant was similar to that of wild-type competent cells at T2+90 (Fig 3D and 3F and S3 Table).
Wild-type rod shape is restored in mreB and mbl null mutants by addition of 25 mM Mg2+ to the growth medium, while addition of 2.5 mM Mg2+ is sufficient to restore wild-type growth rate of mreB mutants [41,42]. Consistently, ΔmreB mutant cells were viable and displayed wild-type growth and moderate cell shape defects in classic CM (i.e. 5mM Mg2+) (S8 Fig and S5 Movie). To exclude an indirect effect due to the inability of mreB-like mutants to elongate properly at low Mg2+ concentrations, we repeated the outgrowth experiments in CM containing 25 mM Mg2+. At T2+90, the length of ΔcomGA ΔmreB competent cells was similar in conventional CM (5 mM Mg2+) and in CM with 25 mM Mg2+ (Fig 3F and S3 Table). In contrast, deletion of mbl ameliorated but did not rescue the ΔcomGA filamentous phenotype, and in the presence of 25 mM Mg2+ ΔcomGA Δmbl competent cells filamented like ΔcomGA competent cells (Fig 3F). Taken together, these results indicated that MreB plays a direct role in the growth limitation imposed during the escape from competence. However, it was still plausible that in the absence of MreB, competent ΔcomGA cells did filament but started dividing during the 90 minutes of outgrowth, as previously shown for Δmaf ΔcomGA mutant cells [12]. If this was true, the average length of ΔmreB ΔcomGA double mutant cells would first increase (during filamentation) and then decrease (upon initiation of cell division) between T2 and T2+90. Measurement of the length of competent cells at different times during the outgrowth experiment showed that ΔmreB ΔcomGA cells length slightly but progressively increased their average length from T2 to T2+90 (Fig 3G), excluding that they had filamented and then divided.
These findings suggested that ComGA-mediated inhibition of cell elongation during the escape from competence also involves MreB. One possibility is that ComGA directly or indirectly sequesters MreB in competent cells to delay the initiation of cell elongation upon outgrowth. According to this prediction, over-production of MreB could totally or partially bypass the ComGA checkpoint and thus promote elongation of competent cells during outgrowth. Consistently, when native levels of MreB were increased by expressing the functional spa-mreB fusion (S3 Fig) in the presence of the endogenous copy of mreB, competent cells filamented in a manner similar to ΔcomGA cells after 90 minutes of outgrowth. The mean length of cells overproducing SPA-MreB was almost twice the mean length of wild-type cells and cell length distribution was much broader, with cells exceeding 20 µm in length (Fig 3F).
We found that at the time of maximum competence (T2) MreB forms polar clusters that co-localize with ComGA polar clusters and are dependent on the presence of ComGA (Fig 2). In addition, our findings suggest that MreB is involved, alongside ComGA, in the inhibition of cell elongation during outgrowth. We then verified if the localization of MreB and ComGA was still correlated during the escape from competence. After 90 minutes of outgrowth, MreB polar clusters were still present and co-localized with ComGA clusters in wild-type competent cells (Fig 3H). However, in the filamentous ΔcomGA competent cells, MreB had already re-localized into motile patches along the sidewalls (Fig 3I). These results suggested a direct correlation between ComGA-dependent polar localization of MreB and the absence of elongation during the escape from competence.
Our findings above suggest a model in which ComGA would directly or indirectly sequester MreB in competent cells. Unfortunately, difficulties to purify active recombinant MreB proteins currently unable biochemical work with MreB proteins of B. subtilis [14] and thus the direct interaction between MreB and ComGA could not be tested in vitro. No direct protein-protein interaction between MreB and ComGA was detected in pairwise yeast two-hybrid assays using full-length proteins (S9 Fig). False negatives are nevertheless frequent in two-hybrid assays [43], and thus the absence of interaction in yeast did not exclude a true protein interaction. Alternatively, we analyzed the effect of expression of comGA during exponential growth. In wild-type cells, comGA is exclusively expressed during competence [12]. In the same background, unnatural expression of comGA during exponential phase from an inducible promoter, was reported to have no effect on growth [11]. However, we reasoned that defects due to the sequestration of MreB by ComGA could be masked by the partial functional overlap between the three MreB isoforms [22]. Thus, we analyzed the effect of over-expression of comGA from the very strong (although poorly repressed) hyperspank promoter (Phs) in both wild-type and mbl mutant cells growing in rich (LB) medium. The mbl mutant strain grew almost like the wild-type strain in LB (Fig 4A), and a low percentage of cells (11%, n = 400 at OD = 0.15) displayed mild morphological defects (arrows in Fig 4D). Expression of comGA in the wild-type background had virtually no effect on growth (Fig 4A) [11] and morphology (Fig 4B and 4C). However, growth of the mbl mutant carrying the Phs-comGA-rfp construct, was significantly affected both in the absence and (to a bigger extent) in the presence of inducer (Fig 4A). This result clearly indicated that over-expression of comGA is toxic in the absence of Mbl. Furthermore, the majority (65%, n = 400 at OD = 0.15) of comGA-overexpressing mbl mutant cells showed progressive bulging and aberrant morphologies including Y-shaped cells and polar bulges characteristic of mreB (but not mbl) mutant cells [22,44] (Fig 4E), indicating impairment of cell morphogenesis and explaining the lethal effects on growth. We concluded that when comGA is expressed in exponentially growing cells, MreB cannot fully compensate for the absence of Mbl. These findings were consistent with the hypothesis that ComGA sequesters MreB to prevent cell elongation and limit growth. We could not test the effect of expression of comGA on the localization of MreB in the mbl mutant background because GFP fusions to MreB do not support growth in a Δmbl ΔmreB background.
In bacterial cells, like in their eukaryotic counterparts, proteins localize to specific locations, often in a dynamic manner, during growth. Spatiotemporal localization of proteins is critical for their function and orchestrates cellular processes. In exponentially growing B. subtilis cells, the mreB gene is highly expressed and MreB assembles into membrane-associated patches that move processively around the cell to control sidewall elongation [16–20]. Here, we show that when B. subtilis cells enter stationary phase in competence medium, expression of mreB drastically decreases and MreB delocalizes from the membrane exhibiting a largely diffuse localization in the cytoplasm. Such transcriptional regulation of mreB and the disassembly of MreB patches from the membrane may inhibit deposition of peptidoglycan along the sidewalls during stationary phase. Additionally, we show that expression of mreB is reactivated in cells that develop competence. In competent cells, MreB relocalizes in polar clusters together with the late competence protein ComGA. Co-localization of MreB and ComGA at the cell poles persists for at least 90 minutes of outgrowth into fresh media. MreB subsequently relocalizes as motile patches along the sidewalls to reinitiate elongation. Altogether, these findings underline the importance of dynamic regulation of gene expression and protein localization for bacteria to adapt to changing environmental conditions.
In cells lacking mreB, transformation efficiency was increased a hundredfold and the number of membrane-associated ComGA clusters was significantly higher than in wild-type cells. Both phenotypes were however rescued by high Mg2+ concentrations, suggesting that (i) MreB is not directly required for natural transformation in B. subtilis, and (ii) assembly of the transformation apparatus might be affected by structural features of the cell wall, as Mg2+ has been proposed to rigidify weakened cell-walls [41]. The transformation apparatus, which includes a type IV pilus-like structure that traverses the thick cell wall and is required for binding and importing the transforming DNA [45], preferentially localizes near the poles at the junction between the cylinder and the polar caps [4]. This region represents the interface between the sidewalls, which are intensively reshaped during growth, and the almost inert cell wall at the poles. Interestingly, this region is also chosen by phage SPP1 to bind and inject its DNA into the cytoplasm of B. subtilis [46]. During infection, SPP1 has to irreversibly bind to its receptor, YueB, encoded by a putative type VII secretion system gene cluster in B. subtilis [47,48]. YueB extends across the cell wall and also localizes at the junction between the cylinder and the polar caps [46]. Thus, this structurally differentiated region of the cell wall may contain positional information for the assembly of structures that need to cross the cell envelope. Initial assembly of the transformation apparatus pilus-like structure at these sites could then direct the localization of cytoplasmic competence-induced proteins such as ComGA at the inner leaflet of the cytoplasmic membrane. Specific defects in the structure or the organization of the cell wall of mreB mutant cells may favor the assembly of additional transformation apparatus at ectopic sites. Consistently, it has been shown that the absence of mreB induced the apparition of multiple sites containing polar material in E. coli cells [49,50]. Furthermore, inactivation of MreB in Pseudomonas aeruginosa led to the mislocalization of a normally polar type IV pilus [51].
We show here that in competent cells mreB is specifically transcribed from the same promoter than maf and that MreB protein levels are increased relative to non-competent cells. Competent comGA mutant cells filament upon dilution into fresh medium [11]. These long comGA mutant cells are unable to divide because Maf is still present and inhibits cell division [12]. When mreB was deleted in a ΔcomGA background, competent cells did not filament during the early stages of competence escape. When mbl was deleted, the average length of ΔcomGA cells exiting competence was also slightly reduced. High Mg2+ concentrations fully rescued ΔcomGA cells elongation in the absence of mbl but not in the absence of mreB. Taken together, these findings indicate that elongation of cells escaping competence primarily depends on MreB and cannot be rescued by the redundant action of Mbl and/or MreBH. Mbl could nevertheless play a mild secondary role in this process. Consistently, a low level of transcription of mbl was detected during stationary phase at T2, while expression of mreBH was completely switched off. When mreB was overexpressed in a wild-type background, cells escaping competence exhibited a filamentous phenotype, like ΔcomGA cells. We hypothesize that in this condition excess of MreB can bypass the ComGA-mediated inhibition of elongation and activate cell wall synthesis. Finally, confirming the implication of the two proteins in order to limit cell elongation, MreB was found (i) in the same complex than several competence proteins and (ii) co-localizing with ComGA polar clusters at T2 and throughout the 90 minutes following dilution into fresh medium.
In the light of our results, we propose the model presented in Fig 5, in which ComGA inhibits cell elongation during the escape from competence by sequestering MreB, either directly or indirectly. No direct interaction between MreB and ComGA was detected in yeast two-hybrid assays and such interaction cannot be tested in vitro because active recombinant MreB of B. subitlis is currently not available for biochemical work [14]. However, we show here that expression of comGA in exponentially growing mbl mutant cells induces growth and morphological defects similar to those of mreB mutants. This result suggests that ComGA may be able to sequester MreB during exponential phase too. Therefore, if ComGA and MreB do not interact directly, then the potential protein(s) mediating their interaction during competence is (are) also expressed during vegetative growth. However, to date, all proteins found to co-localize with ComGA at the poles of competent cells are specifically over-produced during competence [4,52]. How the ComGA-MreB interaction is mediated remains an important question for future work.
General principles governing protein localization include capture by a cellular factor (e.g. interacting protein, DNA binding site, membrane domain or substrate) and self-assembly, where polymerization/depolymerization dictate the location of a protein at a given time [53]. Polymerization may also be regulated by binding proteins, like in the case of eukaryotic actin, where a myriad of actin-binding proteins (ABPs, [54,55]) regulate actin activity and dynamics. ABPs can nucleate, cross-link, bundle, anchor and regulate the state of polymerization of polymeric, filamentous actin (F-actin), and they can cap and stabilize the monomeric, globular actin (G-actin) pool in the cytoplasm.
Here, we show that in cells entering stationary phase, MreB dissociates from the sidewalls and becomes diffuse in the cytoplasm. Interestingly, it has been recently shown that the concentration of lipid-linked peptidoglycan precursors regulates the association of MreB to the membrane [56]. When precursors are depleted, MreB filaments disassemble into the cytoplasm. During the entry into stationary phase peptidoglycan precursor depletion probably occurs [57], as the metabolism slows down and the need of cell wall synthesis decreases, potentially explaining MreB relocalization. However, the details of the mechanism regulating the dynamic localization of MreB remain unknown. Numerous studies have identified a number of proteins that modulate FtsZ ring formation in B. subtilis [58–62] while the first ABP-like protein regulating MreB has yet to been found. It is plausible that one or several ABP-like protein(s), sensing the peptidoglycan precursor’s availability, promote MreB depolymerisation and/or stabilize the monomeric form of MreB in the cytoplasm.
In addition, we propose a model in which ComGA would sequester MreB in competent cells to prevent its localization to the sidewalls and therefore cell elongation. ComGA could then be considered as a new cellular regulator of the actin-like protein MreB. Only one protein that spatially regulates the MreB proteins has been reported in bacteria [63]. Indeed, the progressive depletion of RodZ leads to the misassembly of MreB into non-spiral structures before inducing a total loss of shape in Escherichia coli [63]. While RodZ can be considered as a positive regulator favoring the assembly of MreB at the right sites, ComGA could be classified as a negative regulator preventing the canonical MreB localization along cylindrical sidewalls.
We suggest that sequestration by ComGA spatially regulates MreB during competence in B. subtilis. When ComGA is eventually degraded or inactivated allowing competent cells to resume growth [11], excess of MreB relative to non-competent cells would be free to rapidly form membrane-associated patches and initiate fast elongation. ComK-dependent induction of mreB expression during competence would therefore compensate for the timing disadvantage imposed by genetic transformation. The two levels of regulation (i.e. gene expression and protein localization) might generate and orchestrate the pathway controlling simultaneously a delay in growth and a way to compensate for it.
It has been shown that MreB, Mbl and MreBH display partial functional redundancy in B. subtilis [22]. Overexpression of any one of the isoforms is sufficient to sustain lateral peptidoglycan synthesis and maintain cell shape in normal growth conditions. However, no single MreB isoform could support growth in various stress conditions, suggesting that multiplicity of MreB isoforms may become essential in specific environmental conditions [22]. Here we show that unlike mreB, mbl and mreBH are not specifically expressed during genetic competence. Consistently mbl and mreBH mutants displayed no competence-associated phenotypes. This specialization of MreB in competence further suggests that each isoform could be essential for specific environmental adaptations. A sigma-E sporulation specific promoter has been detected upstream mbl [26,30,36], while mreBH is part of the SigI regulon induced during heat stress [31,64]. Similarly to MreB in the context of competence, the localization and/or activity of Mbl and MreBH could be modulated by a regulator specifically produced during their respective adaptation. Future studies will reveal whether Mbl plays a role in sporulation and MreBH in stress response.
Bacillus subtilis strains were constructed by natural genetic transformation with selection for the appropriate antibiotic resistance marker. For transformation, competent cultures were prepared and incubated in competence medium (CM) with transforming DNA (~1 µg/ml) for 30 minutes at 37°C [35]. When needed, B. subtilis chromosomal DNA was prepared as detailed in [65]. Transformants were selected using 100 µg/ml spectinomycin, 10 µg/ml kanamycin, 5 µg/ml chloramphenicol, 16 µg/ml phleomycin and 1 µg/ml erythromycin. All the plates used to select transformants contained 25 mM of Mg2+. The details of all the new constructs in this publication are presented below. All new constructs were sequenced after introduction in the B. subtilis chromosome. B. Subtilis strains were grown in CM or LB media. When needed, the CM Mg2+ final concentration was increased to 25 mM. Strains are listed in S1 Table.
Because some of our genes of interest are in the middle of operons, we decided to clone our constructs (promoter + RBS + luciferase) at the ectopic amyE locus.
Fragments of different lengths upstream the genes of interest (mreB, mbl and mreBH) and ending right before the genes RBS were amplified by PCR from the B. subtilis chromosome. To amplify the fragments PmreB123, PmreB23, PmreB3 (Fig 1A), Pmbl12, Pmbl2 (S2A Fig) and PmreBH1 (S2B Fig) we used the primers MCS-PmreB1-F and RBS-PmreB-R, MCS-PmreB2-F and RBS-PmreB-R, MCS-PmreB3-F and RBS-PmreB-R, MCS-Pmbl1-F and RBS-Pmbl-R, MCS-Pmbl2-F and RBS-Pmbl-R and MCS-PmreBH1-F and RBS-PmreBH-R respectively. In parallel, we amplified by PCR the upstream (amy-Front and choramphenicol cassette) and downstream (amy-Back and luciferase gene) amyE fragments from the plasmid pUC18cm-luc [66] using primers amyF-F and MCS-R and primers amyR-R and MCS-F respectively. Finally, using the Gibson method based on isothermal assembly [67], we joined the three fragments to obtain the PCR product “amy-F–Cm–Promoter–RBS–Luc- amy-R”. The final PCR product was used to transform strain NC57 by selection for cloramphenicol resistance.
Luciferase experiments were performed as we previously described in [68].
All primers are listed in S2 Table.
A method developed to construct scar-less and marker-less deletions in the genome of B. subtilis [69], was adapted to insert the gfp directly upstream mreB, at the native locus.
The first step was to delete, in the recipient strain (NC101, NeoR), the radC gene which is positioned right before mreB, by inserting a deletion cassette (PhleoR). The cassette was first amplified by PCR from plasmid pUC19-K7-010 [69] using the primers K7PH-F and K7PH-R. Then, the regions upstream (radC front) and downstream (radC back) the radC gene were amplified using the primers HindIII-Pmaf-F and Phleo-radC-R or Phleo-radC-F and HindIII-mreB-R, respectively. Finally, the three fragments were joined using the Gibson method [67] to obtain the following PCR product n°1:“radC front–Phleo cassette–radC back”. Transformation of the recipient strain (168 Δupp) with this PCR product generated strain NC102 which is NeoS and PhleoR.
Then, the deletion cassette was replaced by a fragment that re-introduced the radC gene and inserted gfp in front of mreB. This fragment was constituted by two blocks, namely Pmaf-maf-radC (block 1) and gfp-mreB (block 2). These blocks were amplified by PCR using the following primers: Pmaf-F and GFP-radC-R (for block 1) and RBS-mreB-GFP-F and mreB-R (for block 2). The gfp-mreB block was amplified from chromosomal DNA of strain 3723 [41]. The two blocks were then joined using the Gibson method [67] to generate the PCR product n°2: Pmaf-maf-radC-gfp-mreB. This final PCR product was used to transform the strain NC102 to obtain strain NC103 (NeoR and PhleoS), which now contains gfp right in front of mreB inside its own operon. Cells of this strain (NC103) and its derivatives, in which Pnative-gfp-mreB is expressed as the only copy of mreB in the genome, were viable and displayed almost wild-type growth and morphology, indicating that the fusion is virtually functional (S4A Fig and S1 Movie).
All primers used are listed in S2 Table.
We decided to express the comGA-rfp fusion under the control of the native comGA promoter (PcomGA) from the thrC locus. The Gibson method [67] was used to join four PCR fragments corresponding to the upstream (thrC front) and downstream (thrC back) regions of the thrC gene, the comGA promoter and orf, and the mrfpruby gene. These fragments were amplified using the primers hom-F and pDG1664-MCS-R (thrC front), pDG1664-MCS-R and thrB-R (thrC back), pDG1664-MCS-PcomGA-F and RFP-comGA-R (PcomGA-comGA) and comGA-RFP-F and pDG1664-MCS-RFP-R (mrfpruby). The four fragments were joined to produce the final PCR product “thrC front–PcomGA−comGA–mrfpruby- thrC back”. The thrC front and thrC back (which also contains an erythromycin resistance cassette) fragments were amplified from plasmid pDG1664 [70]. The mrfpruby gene was amplified from chromosomal DNA of strain RWSB5 [16]. The final PCR product was used to transform the NC57strain to generate strain NC118. In this strain and its derivatives, ComGA-mRFPruby displays the expected dynamic of localization during competence [4], indicating that the fusion is virtually functional (S5A–S5D Fig).
All primers are listed in S2 Table.
The method was comparable to the construction of the natively expressed comGA-rfp fusion described above. The Gibson method [67] was used to join four PCR fragments corresponding to the upstream (thrC front) and downstream (thrC back) regions of the thrC gene, the comGA gene and the mrfpruby gene. The Phyperspank promoter was introduced through the thrC front fragment. The four fragments were amplified using the primers hom-F and pDG1664-MCS-R (thrC front), pDG1664-MCS-R and thrB-R (thrC back), pDG1664-MCS-comGA-F and RFP-comGA-R (comGA) and comGA-RFP-F and pDG1664-MCS-RFP-R (mrfpruby). The four fragments were joined to produce the final PCR product “thrC front–Phyperspank−comGA–mrfpruby- thrC back”. The thrC front (that contains the Phyperspank promoter) and thrC back (that also contains an erythromycin resistance cassette) fragments were amplified from the pDP150 plasmid [71]. The mrfpruby gene was amplified from chromosomal DNA of strain RWSB5 [16]. The final PCR product was used to transform the wild type strain (168) to generating strain NC208.
All primers are listed in S2 Table.
We decided to clone the rfp gene under the control of the comK promoter at the amyE locus. The Gibson method [67] was used to join four fragments corresponding to the upstream (amy front) and downstream (amy back) regions of the amyE gene, the comK promoter and the mrfpruby gene. These fragments were amplified using the primers amy-F and PcomK-amy-R (amy front), RFP-amyR-F and amyR-R (amy back), amyF-PcomK-F and RFP-PcomK-R (PcomK) and PcomK-RFP-F and amyR-RFP-R (mrfpruby) respectively. The amy front and back (which also contains a spectinomycin resistance cassette) fragments were amplified from plasmid pDG1730 [70]. The mrfpruby gene was amplified from chromosomal DNA of strain RWSB5 [16]. The four fragments were joined to produce the PCR product “amy front–PcomK−mrfpruby- amy back”. The final PCR product was used to transform the wild type strain (168), inserting the PcomK- mrfpruby construct, at the amyE locus and selecting for chloramphenicol resistance.
All primers are listed in S2 Table.
Translational fusion between the SPA-encoding (Sequential Peptide Affinity) and mreB open reading frame was cloned at the ectopic amyE locus under control of the xylose-inducible promoter Pxyl (pSG-SPA-Nter). pSG-Spa-Nter was generated by replacing the GFP contained in pSG1729 [72] by affinity purification tags (Sequential Peptide Affinity, or SPA) [39], right downstream from the Pxyl promoter. However to generate a N-ter fusion, the tags were inverted in comparison to the original SPA construct (i.e. Flag-TEV site-CBD). The inverted SPA tag was synthesized by Genscript. Then, the mreB open reading frame was PCR-amplified using primers ac-983/ac984, and cloned into the pSG-Spa-Nter vector, using the XhoI and EcoRI restriction sites. The resulting pAC637 plasmid (pSG-Pxyl-spa-mreB) was transformed into B. subtilis strain 4281 (ΔmreB::cm) and selected for resistance to spectinomycin, to obtain strain ABS1370. Finally, we used chromosomal DNA of strain ABS1370 to transfer by natural transformation the amyE::Pxyl-spa-mreB (Spc) construct in strain NC60 to obtain strain NC66.
Chromosomal DNA from strain Bas013 [73] was used to transform the wild-type strain (168) and transfer the Pxyl-perR-spa fusion. Chromosomal DNA of strain NC60 was then used to sequentially incorporate by natural transformation the mcComS and ComK-GFP constructs to generate the final strain NC135.
Experiments were carried out as previously described [66,68]. In brief, the high instability of the luciferase, used as transcriptional reporter in B. subtilis, allows us to approach the measurement of a rate of expression [66], with a relatively small contribution from the cumulative effect of transcription. This particular characteristic of luciferase is in stark contrast with the behavior of other reporters, e.g. β-galactosidase. All the strains used in the luciferase experiments carried a multi-copy plasmid, mcComS [74], in order to increase the percentage of competent cells (from 2% to 35% in the wild-type background in the conditions used here, see Fig 4A).
For detection of luciferase activity, strains were first grown in LB medium to an optical density at 600 nm (OD600nm) of 2. Cells were then pelleted and resuspended in fresh competence medium, adjusting all the cultures to an OD600nm of 2. These pre-cultures were then diluted 20 fold in fresh competence medium and 200 µl was distributed in each of two wells in a 96-well black plate (PerkinElmer). 10 µl of luciferin (PerkinElmer) was added to each well to reach a final concentration of 1.5 mg/ml (4.7 mM). The cultures were incubated at 37°C with agitation in a PerkinElmer Envision 2104 Multilabel Reader equipped with an enhanced sensitivity photomultiplier for luminometry. The temperature of the clear plastic lid was maintained at 38°C to avoid condensation. Relative Luminescence Units (RLU) and OD600nm were measured at 2 minutes intervals. The data were plotted as RLU/OD (luminescence readings corrected for the OD) versus time from inoculation.
B. subtilis strains were transformed using chromosomal DNA of strain BD4893 carrying a spectinomycin marker [35]. The number of transformants was evaluated by plating the transformed cultures on LB agar plates containing spectinomycin. Each transformation culture was also plated on non-selective LB agar in dilution series to establish the viable cell count. Transformation efficiency was calculated by dividing the number of transformants by the viable count of each strain.
The strains containing the SPA fusions were grown to T2 in competence medium supplemented with 0.4% xylose (to induce the SPA fusions). The cultures were then centrifuged and promptly frozen in liquid nitrogen. The xylose concentration used was chosen in order to optimize the SPA fusions production and minimize the shape and growth phenotypes associated to the over-expression of MreB. The frozen cells pellets were then disrupted by cryogenic grinding (4 cycles of 2 minutes, always maintaining the cupules and the pellets in liquid nitrogen). The powder recovered from the grinding was resuspended in buffer A (Tris-HCl pH7,5 10 mM, NaCl 150 mM, EDTA 0,2 mM, Triton 0,1 mM and proteases inhibitors) and centrifuged to eliminate cell debris. SPA-MreB, PerR-SPA and No-SPA containing protein complexes were then isolated and analyzed as described in [75].
Cultures were grown in competence medium at 37°C from single freshly isolated colonies on plates containing the appropriate antibiotic selection. Samples for microscopic observation were taken at T2 (2 hours after the beginning of competence development) and T2+90 (90 minutes after dilution of a T2 culture in fresh competence medium) and immobilized on 1% agarose-coated microscope slides.
Bacteria were imaged with an inverted microscope (Nikon Ti-E) equipped with a 100× oil immersion objective and an environmental chamber maintained at 37°C. Conventional epifluorescence Images were recorded on phase-contrast and fluorescence channels (472/30-nm excitation filter and 520/35-nm emission filter for GFP, 562/40-nm excitation filter and 641/75-nm emission filter for RFP) with an ORCA-R2 camera (Hamamatsu). Images were processed with NIS-Elements (Nikon) software. Exposure time was set up to 200 ms for nativeGFP-MreB and 500 ms for nativeComGA-RFP.
All TIRFM images were acquired on the same inverted microscope with a diode-pumped solid-state laser (Cobolt Calypso, 50mW, 491nm) and an Apo TIRF 100x oil objective (Nikon, NA 1.49). All images were collected with an electron-multiplying charge-coupled device (EMCCD) camera (iXON3 DU-897, Andor) with a gain of 300. Incidence angles and z-position were adjusted individually for all channels to obtain comparable evanescent wave penetration depth and focus position.
In order to follow B. subtilis growth over time, we used a microfluidic flow chamber technique (CellAsic part of EMD Millipore). The technology is divided in two parts: a perfusion control system and a microfluidic plate (specific for bacteria, B04A) that keeps cells in a single focal plan and allow us to induce and follow events during many generations.
The day before the experiment, strains were grown on selective plates. The next day, cells were resuspended in competence medium to OD = 1. 1µl of this resuspension was used to inoculate 1mL of fresh competence medium. Once the cultures reached early exponential phase, cells were injected in the chamber and incubated under a continuous flow (5µl/hour) of medium at 37°C.
In order to characterize ComGA-RFP foci at the single cell level, phase contrast and fluorescence images were taken simultaneously for cells grown to stationary phase (T2) in competence media. Fields of view of both images were used to generate sub-images displaying individual cells by applying a two-step algorithm. First, each single cell was detected by applying segmentation to phase-contrast images, resulting sub-images of individual cells with cell contours. Next, diffraction-limited comGA foci in each cell were identified in fluorescence images. Examples of individual cells are presented in S5 Fig Custom image processing codes (S10 Fig) were implemented in Matlab (Mathworks).
Kymograph analysis was applied to obtain the rotation speed of MreB patches as we previously described [16]. In brief, a series of parallel lines were created from one cell pole to the other (every other pixel), all perpendicular to the cell midline. Next, kymographs were generated, corresponding to movement of MreB patches at all positions along the cell longer axis. Finally, angles of the clear MreB traces on the kymographs were used to calculate the rotation speed.
Length of competent cells was measured using the Metamorph software (Molecular Devices). Phase contrast images were used and the distance from one pole to the other was evaluated. Length of competent cells during the outgrowth experiment is shown as boxplots (refers to Fig 3F). The blue box edges indicate the first and third quartile while the red line indicates the median of the data set. In addition, the whiskers indicate the 5th and 95th percentiles and individual red points indicate outliers. All values with means, standard deviations (SD) and sample sizes are listed in S3 Table. Boxplots were plotted using Matlab 2013. The statistical significance of the differences observed is presented in S3 Table.
Whole cell extracts were fractionated by SDS-PAGE and transferred to a polyvinylidene difluoride membrane using a transfer apparatus according to the manufacturer’s protocol (Bio-Rad). After incubation with 5% nonfat milk in TBST (10 mM Tris, pH 8.0, 150 mM NaCl, 0.05% Tween 20) for 60 minutes, membranes were incubated with antibodies against GFP (1:10000) overnight at room temperature. Membranes were washed 3 times for 10 minutes with TBST and incubated with a 1:10000 dilution of anti-rabbit antibodies for 2h. Blots were washed with TBST three times and developed with the “ECL Prime” kit (Amersham) according to the manufacturer’s protocols. The Chemidoc system (Bio-Rad) was used to reveal the membrane and the Image Lab™ software (Bio-Rad) to analyze the intensity of the bands.
Saccharomyces cerevisiae cells expressing B. subtilis selected proteins as GAL4 BD fusions were mated with cells expressing either the same or another protein as GAL4 AD fusions as presented in [76]. For each fusion, two independent yeast clones were used. Binary interactions were revealed by growth of diploid cells after 5 days at 30°C on synthetic complete medium lacking leucine, uracil and histidine (to select for expression of the HIS3 interaction reporter, annotated-H). Specific interactions were reproduced independently at least three times.
|
10.1371/journal.pmed.1002604 | Carbon trading, co-pollutants, and environmental equity: Evidence from California’s cap-and-trade program (2011–2015) | Policies to mitigate climate change by reducing greenhouse gas (GHG) emissions can yield public health benefits by also reducing emissions of hazardous co-pollutants, such as air toxics and particulate matter. Socioeconomically disadvantaged communities are typically disproportionately exposed to air pollutants, and therefore climate policy could also potentially reduce these environmental inequities. We sought to explore potential social disparities in GHG and co-pollutant emissions under an existing carbon trading program—the dominant approach to GHG regulation in the US and globally.
We examined the relationship between multiple measures of neighborhood disadvantage and the location of GHG and co-pollutant emissions from facilities regulated under California’s cap-and-trade program—the world’s fourth largest operational carbon trading program. We examined temporal patterns in annual average emissions of GHGs, particulate matter (PM2.5), nitrogen oxides, sulfur oxides, volatile organic compounds, and air toxics before (January 1, 2011–December 31, 2012) and after (January 1, 2013–December 31, 2015) the initiation of carbon trading. We found that facilities regulated under California’s cap-and-trade program are disproportionately located in economically disadvantaged neighborhoods with higher proportions of residents of color, and that the quantities of co-pollutant emissions from these facilities were correlated with GHG emissions through time. Moreover, the majority (52%) of regulated facilities reported higher annual average local (in-state) GHG emissions since the initiation of trading. Neighborhoods that experienced increases in annual average GHG and co-pollutant emissions from regulated facilities nearby after trading began had higher proportions of people of color and poor, less educated, and linguistically isolated residents, compared to neighborhoods that experienced decreases in GHGs. These study results reflect preliminary emissions and social equity patterns of the first 3 years of California’s cap-and-trade program for which data are available. Due to data limitations, this analysis did not assess the emissions and equity implications of GHG reductions from transportation-related emission sources. Future emission patterns may shift, due to changes in industrial production decisions and policy initiatives that further incentivize local GHG and co-pollutant reductions in disadvantaged communities.
To our knowledge, this is the first study to examine social disparities in GHG and co-pollutant emissions under an existing carbon trading program. Our results indicate that, thus far, California’s cap-and-trade program has not yielded improvements in environmental equity with respect to health-damaging co-pollutant emissions. This could change, however, as the cap on GHG emissions is gradually lowered in the future. The incorporation of additional policy and regulatory elements that incentivize more local emission reductions in disadvantaged communities could enhance the local air quality and environmental equity benefits of California’s climate change mitigation efforts.
| Climate change policies to reduce greenhouse gas (GHG) emissions can also reduce emissions of hazardous co-pollutants, such as air toxics and particulate matter.
Decreases in GHG emissions are therefore also likely to provide health benefits by improving local air quality to communities near regulated facilities.
Globally, socioeconomically disadvantaged communities are often disproportionately exposed to hazardous air pollutants due to emissions from facilities nearby.
We examined temporal patterns in GHG and co-pollutant emissions with respect to neighborhood demographics under California’s cap-and-trade program—the world’s fourth largest carbon trading market.
We assessed GHG and co-pollutant (particulate matter, nitrogen oxides, sulfur oxides, volatile organic compounds, and air toxics) emission patterns and the social equity implications of California’s cap-and-trade program before (2011–2012) and after (2013–2015) the initiation of carbon trading.
Facilities regulated under California’s cap-and-trade program are disproportionately located in disadvantaged neighborhoods.
Statistical analysis found that co-pollutant emissions from regulated facilities were temporally correlated with GHG emissions, and most regulated facilities (52%) reported higher annual average local (in-state) GHG emissions after the initiation of trading, even though total emissions remained well under the cap established by the program.
Since California’s cap-and-trade program began, neighborhoods that experienced increases in annual average GHG and co-pollutant emissions from regulated facilities nearby had higher proportions of people of color and poor, less educated, and linguistically isolated residents, compared to neighborhoods that experienced decreases in GHGs.
To our knowledge, this is the first study to assess social disparities in GHG and co-pollutant emissions under an existing carbon trading program.
Although GHG emission reductions could bring about significant air quality and health benefits for California’s disadvantaged residents, thus far the state’s cap-and-trade program has yet to yield such localized improvements in environmental equity.
Policy and regulatory incentives to enhance local GHG emission reductions in disadvantaged communities could yield greater local air quality and environmental equity benefits from California’s climate change mitigation strategies.
Future regulatory efforts should systematically track trends in hazardous co-pollutant emissions associated with GHG emissions from stationary and transportation-related sources and assess how they impact socioeconomically disadvantaged populations.
| GHGs, including carbon dioxide (CO2), indirectly impact health by causing climate change but are not directly harmful at the concentrations typically found in outdoor air. However, GHG emissions from the combustion of fossil fuels are accompanied by other hazardous co-pollutants such as particulate matter (PM), ozone-forming nitrogen oxides (NOx), and volatile organic compounds (VOCs) that cause respiratory and cardiovascular disease and increases in mortality [1]. Decreases in GHG emissions from combustion are thus likely to provide short- and long-term health benefits by improving local air quality [2]. Several studies estimate that the economic cost savings of reduced air-pollution-related illness and death often outweigh the costs of GHG mitigation [3–5].
Globally, socioeconomically disadvantaged communities are often disproportionately exposed to hazardous air pollutants [6,7]. In the US, regulation under the Clean Air Act has led to significant improvements in ambient air quality even while the economy and population have grown [8]. However, many air toxics remain unregulated, and some industrial facilities are exempt from regulation due to their vintage, size, or location. Moreover, many US cities are out of compliance with ambient air quality standards, and stark racial, ethnic, and class-based inequalities in exposure to air pollutants remain [9]. For example, in the US, Asian American, African American, and Hispanic individuals (herein referred to as “people of color”) have higher estimated lifetime cancer risks from exposure to hazardous ambient air pollutants compared to white individuals [10]. Similarly, based on location of residence, average outdoor nitrogen dioxide levels are 38% higher for people of color than for non-Hispanic white individuals, and reducing ambient concentrations to the level experienced by white individuals would reduce ischemic heart disease mortality by an estimated 7,000 deaths per year [11].
Strategies to reduce GHG emissions could be structured to also maximize the ancillary health benefits of reducing these social inequalities in exposure to air pollutants that have persisted despite decades of regulation. Indeed, emerging evidence suggests that designing air quality regulations to improve conditions for those who are most negatively impacted can also efficiently improve overall outcomes at the population level. For example, Levy et al. examined the equity and efficiency benefits of a suite of hypothetical rollouts of emission-control technology at US power plants, by simulating scenarios by which reductions of sulfur dioxide (SO2), NOx, and fine PM (PM2.5) could be distributed to achieve national emission caps. The authors applied a source–receptor matrix to determine pollutant concentration changes associated with various control scenarios and mortality reductions, and estimated changes in the spatial inequality of health risk, applying the Atkinson index for health risk inequality. Study results found that reductions in spatial inequality in mortality associated with SO2 and PM2.5 emissions were correlated with higher total mortality reductions [12]. A later study looking at controls on tail-pipe emissions on public buses in Boston, using a similar source–receptor matrix method and inequality metric, found similar results [13].
Cap-and-trade has emerged as the dominant regulatory mechanism for pricing carbon and reducing GHG emissions from large stationary sources around the world. Under a cap-and-trade system, regulated companies must surrender tradable emission permits, or “allowances,” equal to the amount of GHGs they emit (typically, 1 allowance equals 1,000 kg [1 metric ton (t)] CO2 equivalent [CO2e]). The cap on emissions is set by the total allowances issued, which is designed to decrease over time to secure aggregate gains. As the cap is lowered, regulated companies can reduce their GHG emissions (e.g., through energy efficiency measures, new technologies, or switching to less GHG-intensive fuels) or purchase excess allowances from other regulated entities that are able to reduce their emissions more cheaply. Most cap-and-trade programs also allow industries to purchase carbon offset credits generated from projects in sectors outside of the cap and often outside of the legal jurisdiction of the program—such as forestry or agriculture projects in other states or countries—that can be used in place of allowances. The market-based approach of cap-and-trade ostensibly lowers emission reduction costs and enhances industry support for climate change mitigation policies [14].
Some economists and environmental justice advocates argue that efficient climate regulation requires deeper GHG reductions in locations where the health benefits of co-pollutant reductions are likely to be greatest, and that this objective cannot be accomplished with the geographically unrestricted trading characteristic of cap-and-trade in which all GHG reductions are treated equally regardless of where they occur [15]. Offsets may further undermine improvements to local air pollution by undercutting financial incentives for industries to reduce emissions on site. Unless the location and co-pollutant intensity of GHG emissions are incorporated into the design of a cap-and-trade system, carbon trading could also potentially widen social inequities in exposure to localized hazardous co-pollutants because GHG-emitting facilities, which are disproportionately located in disadvantaged communities, are able to purchase allowances or offsets rather than reduce their emissions [15–17]. However, to our knowledge, no studies have examined trends in co-pollutant emissions or social disparities in emission reductions under an existing carbon trading program in order to inform climate policy design.
Using data from January 1, 2011–December 31, 2015, which includes the first 3 years of California’s cap-and-trade program, we evaluated temporal and sector-specific trends in emissions of GHGs and hazardous co-pollutants overall and with respect to multiple measures of neighborhood demographics and disadvantage. Specifically, our analysis sought to examine the following questions: (1) What are the demographic characteristics of neighborhoods (census block groups) surrounding facilities that are currently regulated under California’s cap-and-trade program? (2) Since the program’s implementation, what patterns are evident in terms of the relationship between local (in-state) GHG and co-pollutant emissions across and between industry sectors? (3) What is the relationship between neighborhood demographics and temporal patterns in local GHG and co-pollutant emissions? (4) What trends do we observe in terms of companies that utilize offsets as part of their regulatory compliance obligations and their local emissions of GHGs and co-pollutants?
California’s cap-and-trade program regulates carbon dioxide, methane, nitrous oxide, and fluorinated GHGs from power plants, refineries, industrial facilities, fuel suppliers, and other entities that emit over 25,000 t CO2e of GHGs per year, with biogenic CO2 being exempt. The program covers 3 types of GHG emissions: (1) direct emissions within the state (“local” emissions); (2) indirect emissions from electricity imported from outside state boundaries; and (3) starting in 2015, geographically distributed emissions from fuels such as gasoline and natural gas. Beginning in 2013, industries were required to hold allowances equal to their GHG emissions (1 allowance = 1 t CO2e). Over 90% of allowances were freely allocated during the first compliance period of 2013–2014, with the balance auctioned or reserved for price containment. The total number of allowances in circulation, or “cap,” decreases by 3%–3.5% annually between 2015 and 2020 in order to meet a cumulative GHG reduction target of 15% from 2015 to 2020. In addition, companies can meet 8% of their compliance obligation by purchasing GHG emission reduction credits generated by offset projects located in the US (1 offset = 1 t CO2e). Thus, by design, the 3%–3.5% annual reduction in GHG emissions set by the decreasing cap can be achieved entirely via offset projects. Cutbacks in the use of more carbon intensive energy sources imported from outside the state (such as electricity generated from coal-fired rather than natural gas power plants) can also be used by regulated entities to meet emission reduction goals in lieu of in-state reductions.
Recognizing the social and environmental equity concerns related to cap-and-trade, California passed legislation requiring that 25% of the revenue generated by the program be invested in climate mitigation measures located in or benefitting disadvantaged communities [18]. These communities are defined geographically based on CalEnviroScreen, a spatial mapping tool that combines 21 indicators of environmental quality and population vulnerability to identify communities most burdened by multiple sources of pollution and that may be especially vulnerable to their effects [19]. CalEnviroScreen incorporates measures of ambient pollution and proximity to pollution sources, most of which are not regulated under cap-and-trade; these measures include hazardous waste sites, polluted water bodies, traffic density, pesticide usage, drinking water quality, and ambient air quality measures for ozone and PM2.5. CalEnviroScreen also includes indicators of population vulnerability including low educational attainment, poverty, linguistic isolation, and unemployment, and measures of health status, because of the evidence that social stressors and underlying chronic health conditions may exacerbate the adverse effects of pollution exposures [20]. A recent systematic review of relevant human and animal studies using the Navigation Guide protocol [21] assessed the combined impact and interaction of prenatal exposure to stressors and chemicals, including air pollution, on developmental outcomes. For the most common outcome (fetal growth), the authors evaluated risk of bias, calculated effect sizes for main effects of individual and combined exposures, and found that, in human studies, effect estimates for pollutants were stronger for groups exposed to higher levels of social stressors [22].
We utilize neighborhood demographic measures from the US Census and the CalEnviroScreen designation of “disadvantaged communities”—which are the 25% of California census tracts that score the worst on measures of environmental quality and population vulnerability—to analyze the distribution of GHG and co-pollutant emissions from facilities regulated under California’s cap-and-trade program.
GHG and co-pollutant emissions from facilities regulated under California’s cap-and-trade program were downloaded from the Pollution Mapping Tool (formerly known as the Integrated Emissions Visualization Tool) of the California Air Resources Board (CARB) for the calendar years 2011–2015 (https://www.arb.ca.gov/ei/tools/pollution_map/). The locations (latitude and longitude) of facilities were obtained separately from CARB and were based on geo-coding of facility-reported addresses. The locational information was manually cleaned using satellite imagery from Google Earth to verify the location of GHG facilities. Facility-level GHG emissions are self-reported to the State of California under the Regulation for the Mandatory Reporting of Greenhouse Gas Emissions (mandatory reporting regulation [MRR]) program [23] and include self-reported estimates of annual carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and fluorinated GHG emissions that have been verified by an independent third party. Our analysis focused on “emitter covered” emissions (local emissions), which correspond to localized, in-state emissions resulting from “the combustion of fossil fuels, chemical and physical processes, vented emissions,” and “emissions from suppliers of carbon dioxide” as well as emissions of GHGs other than CO2 from biogenic fuel combustion. Emissions are given in units of CO2e based on the 100-year global warming potential factors given in Title 40, Part 98, of the Code of Federal Regulations, Subpart A, Table A-1, as published in the Federal Register on October 30, 2009 (http://www.arb.ca.gov/cc/reporting/ghg-rep/regulation/subpart_a_rule_part98.pdf).
Facility-level emissions of PM, NOx, sulfur oxides (SOx), and VOCs are self-reported by regulated facilities under the California Emission Inventory Development and Reporting System (CEIDARS) program. Reporting under CEIDARS is required every 4 years but may be more frequent depending on the administrative air basin and facility. We made several adjustments to harmonize the MRR and CEIDARS datasets. First, the GHG emissions for 3 hydrogen plants were allocated to nearby refineries because they primarily produce hydrogen for those refineries and because the facilities appear to report jointly to CEIDARS. Second, we summed co-pollutant emissions from oil and gas facilities based on a cross-walk file provided by CARB in order to harmonize the data with oil and gas GHG emissions that are reported on a more aggregated basis to the MRR. Finally, 4 facilities merged into or were acquired by 2 other facilities during the study period. Emissions prior to the merger for these facilities were combined for consistency in temporal reporting.
Data on the annual stack emissions of air toxics were downloaded from the US Environmental Protection Agency’s Risk-Screening Environmental Indicators (RSEI) model using the EasyRSEI application and matched to regulated California GHG facilities based on their name and spatial proximity (https://www.epa.gov/rsei). In a few cases, if the facilities in the RSEI and MRR databases were near each other but the names did not match, an internet search was used to confirm that one company was a subsidiary of the other. RSEI emission estimates come from data that are self-reported to the Toxics Release Inventory (TRI) as required by Section 313 of the Emergency Planning and Community Right-to-Know-Act of 1986 [24]. Facilities must report to the TRI if they are in a specific industry sector (such as mining, utilities, manufacturing, and hazardous waste facilities), employ 10 or more full-time employees, and manufacture, process, or handle a TRI-listed chemical in sufficient quantities (https://www.epa.gov/toxics-release-inventory-tri-program/basics-tri-reporting). Only a fraction of facilities regulated under California’s cap-and-trade program are required to report to TRI.
Facilities were initially categorized according to the first 2 digits of the North American Industry Classification System (NAICS) codes given in the MRR. In order to facilitate the analysis, we then grouped several categories together to achieve a greater number of facilities in each category as follows: educational, healthcare and social assistance, professional, scientific, technical, public administration, and other services were categorized as “public services”; mining, quarrying, and oil and gas extraction were categorized as “oil and gas production/supplier”; facilities in the “utilities” category with a NAICS description of “steam and air-conditioning supply” were grouped as “co-generation”; support activities for transportation, agriculture, forestry, fishing and hunting, utilities, arts, entertainment, recreation, information, wholesale trade, administrative and support, and waste management and remediation services, and missing values were recoded as “other.” The category of paper, chemical, mineral, and petroleum manufacturing was renamed “other manufacturing.” All other categories are as coded by NAICS.
Finally, we conducted several additional data cleaning steps. We concluded from a visual inspection of the 43 facilities that reported 0 emitter covered (i.e., local) GHG emissions during 1 or more years that many of these 0 values were likely not true 0s, but artifacts of the accounting rules that govern which emissions are covered under the program. Most facilities reporting 0 emitter covered GHG emissions reported total GHG emissions during the same year, and had reported emitter covered GHG emissions proportional to their total GHG emissions in all other years. Therefore, we replaced 0 emitter covered GHG values with the value of total GHGs reported by that facility during the same year, multiplied by the ratio of emitter covered to total GHGs reported the prior year, or the subsequent year if the prior year was not available. Finally, 5 GHG facilities reported 0 GHG emissions and then stopped reporting in subsequent years. We assumed these facilities ceased operations during the study period and assumed 0 GHG emissions for all years after reporting ceased.
We defined neighborhoods on the basis of 2010 vintage census block group boundaries provided by the US Census Bureau (https://www.census.gov/geo/maps-data/data/cbf/cbf_blkgrp.html). Block groups are generally contiguous geographic areas that contain between 600 and 3,000 people and can vary in size depending on population density. Geographic block group centroids and the distance between block group centroids and GHG facility locations were calculated using ArcGIS (ESRI, Redlands, CA). We considered 2 buffer distances when assigning block groups to nearby GHG facilities based on their geographic centroid: 1 mile (1.6 km) and 2.5 miles (4.0 km).
Demographic information for each block group was obtained from the American Community Survey 2011–2015 5-year estimates (https://www.census.gov/acs/www/). White individuals were defined as those who self-identified as white race but not Hispanic ethnicity. People of color were defined as all other individuals, including those who identified as multiracial or of Hispanic ethnicity. Poverty was defined as twice the federal poverty level to reflect increases in the cost of living and California’s high cost of living relative to the rest of the country [25]. CalEnviroScreen 3.0 scores for all census tracts were obtained from the California Office of Environmental Health Hazard Assessment (https://oehha.ca.gov/calenviroscreen/report/calenviroscreen-30). Block groups are nested within census tracts, larger geographic units that contain between 1,200 and 8,000 people. We assigned block groups the CalEnviroScreen score of their census tract in order to compare CalEnviroScreen rankings near GHG facilities to those of neighborhoods in the rest of the state.
Some oil and gas facilities report GHG emissions on an aggregate basis. In order to more accurately characterize neighborhood demographics near the sites of the pollutant emissions, we obtained information on the geographic location of drilling sites for 19 oil and gas facilities from CARB. These drilling site locations were included when ascertaining whether a block group was near a regulated facility. If a block group contained or was near several drilling sites belonging to 1 facility, we considered it to be near 1 GHG facility rather than multiple.
Information on the allocation of allowances was compiled from the California Code of Regulations (17 CA ADC § 95841 and 17 CCR § 95870) and CARB publications on the public allocation of allowances and estimates of state-owned allowances [26–28]. We obtained the number of allowances and offsets surrendered by each company at the completion of the first compliance period from CARB’s 2013–2014 compliance report [29]. Information on individual offset projects was compiled from CARB documents on offsets issued as of August 10, 2016 [30], and individual project descriptions provided in the American Carbon Registry and Climate Action Reserve carbon offset registries (http://americancarbonregistry.org; http://www.climateactionreserve.org).
The construction of the final dataset for analysis is shown in S1 Fig. All statistical analyses were conducted in R (R Foundation; https://www.r-project.org). Mann–Whitney–Wilcoxon tests were used to test for differences in neighborhood demographics near facilities because demographic variables were not normally distributed. Emissions data were also highly skewed, so we used log values in our analysis in order to reduce the non-normality of model residuals. For the cross-sectional analysis, a series of simple linear regressions was used to examine the correlation between GHG and co-pollutant emissions cross-sectionally for each industry category, with log(t co-pollutant) as the outcome and log(t GHG) as the predictor variable using the most recent year with both values available and greater than 0 for each facility.
For the longitudinal analysis, a series of mixed effects regression models was used to estimate the correlation between GHG and co-pollutant emissions over time, with log(t co-pollutant) as the outcome and log(t GHG) as the predictor variable. We included a random slope and a random intercept for facility. This approach allowed us to incorporate missing values during years when facilities did not report; missing values were assumed to be missing at random. In order to be able to incorporate 0 values, a small constant equivalent to the reporting threshold divided by the square root of 2 was substituted for 0 values following convention for left-censored, skewed environmental data. The reporting threshold was considered to be 0.05 in the original units of each data source (short tons for CEIDARs, metric tons for GHGs, and pounds for air toxics) since the lowest reported values were 1, and we assumed values below 0.5 would have been rounded down to 0. We conducted a sensitivity analysis to examine the effect of the imputed value for 0s on our results. Coefficients from the regression models can be interpreted as estimates of the percent change in co-emissions associated with a 1% change in emitter covered GHG emissions, either comparing across facilities in the case of the cross-sectional analysis or over time in the case of the longitudinal analysis.
Finally, we also applied a multivariable logistic modeling strategy to assess the independent effect of multiple block group demographic variables on the odds of an increase in annual average GHGs and co-pollutant emissions from nearby facilities before (January 1, 2011–December 31, 2012) versus after (January 1, 2013–December 31, 2015) implementation of the cap-and-trade program.
GHG-emitting facilities regulated under California’s cap-and-trade program are disproportionately located in disadvantaged communities (Fig 1). The relative differences between neighborhoods within 2.5 miles (4.0 km) (based on their geographic census block group centroids) of a regulated facility as compared to neighborhoods located beyond 2.5 miles were, on average, 59% higher in population density, 34% higher in the proportion of residents of color, 23% higher in the proportion of poor residents, 64% higher in the proportion of residents with low educational attainment, and 80% higher in the proportion of linguistically isolated households in which no one age 14 years or older speaks English very well (Table 1). A higher proportion of neighborhoods near facilities were designated by CalEnviroScreen as disadvantaged (38% versus 19% of neighborhoods not near facilities) (Table 1). Similar but generally smaller relative differences also existed at the smaller, 1-mile (1.6 km), buffer distance; one exception was a larger relative difference in the proportion of poor residents within 1 mile (54%) (see S1 Table).
The majority of facilities (52%) had higher annual average local GHG emissions (a change in mean aggregate emissions of 6,773,670 t) after implementation (2013–2015) of the cap-and-trade program as compared to the 2 years prior to implementation (2011–2012) (S4 Table). A majority of facilities also increased their annual average PM2.5, VOC, and air toxics emissions during this time period (51%, 57%, and 52%, respectively), while a minority increased their annual average NOx and SOx emissions (46% and 44%, respectively). While the program has claimed an overall reduction in total annual average GHG emissions during this period, this decrease was primarily achieved through indirect reductions associated with cutbacks in purchases of more carbon intensive electricity (such as electricity generated from coal-fired rather than natural gas power plants) imported from outside the state, rather than reductions in local emissions within California (see S2 Fig). Changes in GHG emissions within California during this period varied by industry sector. For example, while 70% of co-generation facilities decreased annual average emissions in 2013–2015 relative to 2011–2012, 75% of cement plants increased emissions. Cement plants had the highest median increase in local GHG emissions, followed by electricity generators, oil and gas producers, food and beverage manufacturing, and refineries (Fig 2).
GHGs and hazardous co-pollutants emitted by facilities regulated under California’s cap-and-trade program were positively correlated when comparing across facilities. The strength of the correlation between GHG and co-pollutant emissions across facilities varied by co-pollutant and industry sector. Co-pollutant emissions tended to rise most steeply with GHG emissions among public service facilities (for PM2.5), metal and machinery manufacturing facilities (for NOx), refineries (for SOx), co-generation facilities (for VOCs), and other manufacturing facilities (for air toxics), while co-pollutant emissions overall tended to be the most tightly correlated with GHG emissions (based on the model R2) among cement plants and refineries (Table 2).
GHG and co-pollutant emissions were also correlated within facilities over time. On average, a 1% change in annual GHG emissions at the facility level was accompanied by a 0.91%, 0.66%, 0.66%, 0.63%, and 0.48% change in air toxics, NOx, PM2.5, SOx, and VOCs, respectively (all p < 0.001) (see Fig 3). This association indicates that reductions in GHG emissions can be expected to result in reductions in co-pollutant emissions (and vice versa). Imputing slightly higher values for reported emissions of 0 in the analysis did not affect these effect estimates by more than 3%. We did not observe a one-to-one relationship between changes in GHG and co-pollutant emissions, which may be a result of the fact that emission reductions can be achieved using diverse strategies. For example, a facility may use scrubbers to reduce PM emissions, but potentially increase GHG emissions in the process because this pollution reduction strategy is more energy intensive. Alternatively, a facility could undertake energy conservation efforts that reduce energy use and thereby GHG emissions, but have little impact on air toxics or other co-pollutant emissions that result from processes unrelated to energy production.
Given the spatially clustered nature of facilities regulated under California’s cap-and-trade program, we examined the relationship between neighborhood demographics and changes in emissions from all facilities located nearby (≤2.5 miles from the geographic block group centroid). We found that compared to neighborhoods that experienced decreases in aggregate annual average GHG emissions after versus before implementation of carbon trading, neighborhoods that experienced increases of both annual average GHGs and annual average co-pollutants were more likely to be designated by CalEnviroScreen as disadvantaged and had higher proportions of residents of color, higher rates of poverty, higher rates of low educational attainment, and higher proportions of linguistically isolated households (Table 3). Population density was generally lower in the neighborhoods that experienced emission increases, except for those where both GHGs and air toxics from nearby facilities increased. Similar differences were also noted using a 1-mile buffer distance (see S2 Table). Logistic modeling results assessing the odds of block groups experiencing increases of GHGs and each co-pollutant compared to block groups that experienced decreases in either indicate that less densely populated block groups and those with higher proportions of residents of color and residents with low levels of educational attainment had an increased odds of experiencing an increase in GHGs and co-pollutants independent of the proportion of poor or linguistically isolated residents (Table 4). Results for other demographic variables such as the proportion of residents living in poverty and the proportion of linguistically isolated households were less consistent across models for each of the co-pollutants. Overall, among those block groups that experienced increases in GHG and co-pollutant emissions, the median and range of emission changes in metric tons was as follows: GHGs, 14,320 (1.7–1,137,000); PM2.5, 2.78 (0.03–113.6); NOx, 5.35 (0.03–223.2); SOx, 0.51 (<0.001–132.7); VOCs, 2.96 (0.015–296); and air toxics, 0.37 (0.001–95.6).
During the first compliance period (2013–2014), offset credits represented more than 4.4% of the total compliance obligation (credits and offsets surrendered for each metric ton of GHGs emitted), or over 4 times the targeted reduction as established by the cap in GHG emissions from 2013 to 2014. The majority of the offset credits (75.6%) were generated by out-of-state projects. Overall, most offset projects were in forestry (46.3%) and destruction of ozone-depleting substances (45.6%). Facilities owned by companies that used offsets emitted significantly higher levels of GHGs than those owned by companies that did not use offsets (see S3 Table). For example, the 10 companies using the most offset credits during the first compliance period were responsible for 82% of offsets surrendered and 43% of total covered GHG emissions. Facilities owned by companies that used offset credits also emitted more PM2.5, NOx, SOx, and air toxics over the same time period, although these differences were not statistically significant. Conversely, average VOC emissions were lower among companies that used offsets (p = 0.001). While companies using offsets tended to be larger emitters overall, their annual average changes in GHGs and co-pollutant emissions after (2013–2015) versus before (2011–2012) the implementation of carbon trading were statistically indistinguishable from those of companies not using offsets (data not shown).
California’s efforts to slow climate change by reducing GHG emissions have the potential to bring about significant air quality and health benefits to the state’s less advantaged residents. GHG-emitting facilities tend to be located in neighborhoods with higher proportions of residents living in poverty and people of color, and the temporal correlation between GHG and co-pollutant emissions indicates that incentivizing deeper reductions in local GHG emissions could bolster the environmental equity goals articulated in California’s climate change laws. Our results, however, indicate that, thus far, the cap-and-trade program has not yielded this set of localized improvements in environmental equity.
Prior analyses of emission trading programs found little evidence that they produced socially inequitable outcomes. For example, studies of the US Acid Rain Program to reduce sulfur dioxide emissions from coal-fired power plants and of Southern California’s Regional Clean Air Incentives Market (RECLAIM) program to reduce NOx and SOx emissions from large facilities such as power plants, refineries, and manufacturing facilities found no evidence that the locations of emissions or purchases of allowances were disparate with respect to the racial/ethnic makeup or income of surrounding neighborhoods [34–36]. One exception is an analysis that incorporated dispersion modelling of emissions and found that high-income neighborhoods benefitted more from RECLAIM than did low-income neighborhoods and that, conditional on income, African American individuals benefitted more, and Hispanic individuals benefitted less, than white individuals [37]. Our analytical approach differs from that taken in most prior studies because we use a neighborhood—rather than facility-level—perspective to evaluate changes in aggregate emissions. Such an approach is warranted in our context because polluting facilities are clustered in space and many Californians live in close proximity to multiple facilities, as shown in Fig 1.
Our results also suggest that although California’s total GHG emissions are below the cap set by the cap-and-trade program, results have been underwhelming with respect to local (in-state) GHG emissions, which increased on average for regulated facilities in several industry sectors (with a net increase of mean local GHG emissions of 6.7 million t CO2e from 2011–2012 to 2013–2015, and a median facility-level increase of 600 t across all facilities that we analyzed). The lack of deeper reductions in local emissions may be due to an initial overallocation of allowances that resulted in an oversupply of cheap allowances on the market. Emissions at the initiation of the carbon trading program were lower than expected due to the economic downturn related to the Great Recession of 2008, and the initial allocation of allowances was thus far greater than the metric tons of regulated emissions. There was a larger aggregate decrease in local GHG emissions in 2015 compared to prior years (see S2 Fig), suggesting that greater reductions may be achieved going forward as the cap is lowered further. However, banking of excess allowances from early years of the program [38] and the substantial use of offset credits suggest that there may continue to be little reduction in in-state emissions. The quantity of offsets allowed thus far under the program is worrisome because the validity of GHG emission reductions claimed under offset projects is controversial given the challenge of verifying if they are truly additional and would not have occurred in the absence of the cap-and-trade program [39–42]. Offset credits included in our analysis were primarily generated from forestry projects outside the state that do not offer the same benefits as localized co-pollutant emission reductions. Recent California legislation (AB 398) seeks to address this issue by reducing the use of offset credits generally, while also increasing the proportion of allowable offsets that are generated from in-state projects [43]. Under the current cap-and-trade program, offset credits can make up as much as 8% of the total amount of allowances used for compliance by a regulated company. However, AB 398 will reduce this amount. From 2021 to 2025, up to 4% of a covered company’s compliance obligation can be met by offsets, and half of these must be in state or “provide direct environmental benefits” to California. From 2026 to 2030, up to 6% of a covered company’s compliance obligations can be met by offsets, with at least half generated from in-state projects.
In summary, our study results reflect preliminary local GHG and co-pollutant emissions and social equity patterns of the first 3 years of California’s cap-and-trade program for which data are currently available. One limitation of our analysis is that it was restricted to regulated industries and was not able to include an assessment of the emission patterns and equity implications of GHG reductions from transportation-related sources. In addition, ongoing investments of a significant portion of California’s cap-and-trade revenue in disadvantaged communities as mandated by law [18] to mitigate climate change could also potentially incentivize deeper local GHG and co-pollutant reductions in the future. As data to examine these issues become available, future research can more holistically assess the extent to which GHG and co-pollutant emission patterns from both industrial and transportation sources may be shifting due to changes in industrial production decisions, cap-and-trade revenue investments, and policy initiatives that encourage deeper in-state emission reductions, particularly in disadvantaged communities.
Some analysts have cautioned against integrating air quality into climate policy, and argue that co-pollutants are best regulated under existing laws such as the US Clean Air Act [44]. However, others note that the most cost-effective climate regulation would achieve GHG reductions in locations where the health benefits are greatest [15]. Our analysis suggests that California’s climate policy could better harmonize efforts to reduce GHGs with improvements to local air quality, and that market-based strategies in general could provide greater overall benefits by incentivizing localized GHG reductions in disadvantaged and highly polluted neighborhoods. For example, other emission trading programs have restricted trading and raised the price of allowances within high-pollution areas in order to promote deeper reductions in disproportionately impacted neighborhoods [45]. In addition, regulated firms could be required or incentivized to purchase offsets that are linked to local projects that reduce GHG emissions and also improve air quality in the regions where their facilities are located; such local offset projects could include electrification of railyards and ports, cleaning up truck fleets, or financing retrofits to reduce GHGs and co-pollutant emissions from other local emission sources. Such local offset projects could enhance government oversight and promote community partnerships in project monitoring and emission verification.
The administrative costs of such an integrated strategy are likely to be modest, particularly since a small number of industry sectors and facilities present the greatest opportunities to achieve air quality co-benefits [15]. It would require more systematic temporal and spatial tracking of the air quality and environmental equity impacts of cap-and-trade through annual and verifiable GHG and co-pollutant emission reporting by each regulated facility, combined with facility- and company-specific allowance allocations and trading information, including the use of offsets. These data are beginning to be made publicly available, which will enable more effective and timely regulatory oversight of emission temporal patterns and future research on the health and environmental equity impacts of cap-and-trade. Ultimately, applying regulatory and analytical tools that address the contributions of GHG emission sources to local cumulative air pollution burdens could support better integration of the sustainability and environmental equity goals of California’s climate laws and inform carbon pricing efforts elsewhere.
|
10.1371/journal.ppat.1000059 | Lentiviral Vpx Accessory Factor Targets VprBP/DCAF1 Substrate Adaptor for Cullin 4 E3 Ubiquitin Ligase to Enable Macrophage Infection | Vpx is a small virion-associated adaptor protein encoded by viruses of the HIV-2/SIVsm lineage of primate lentiviruses that enables these viruses to transduce monocyte-derived cells. This probably reflects the ability of Vpx to overcome an as yet uncharacterized block to an early event in the virus life cycle in these cells, but the underlying mechanism has remained elusive. Using biochemical and proteomic approaches, we have found that Vpx protein of the pathogenic SIVmac 239 strain associates with a ternary protein complex comprising DDB1 and VprBP subunits of Cullin 4–based E3 ubiquitin ligase, and DDA1, which has been implicated in the regulation of E3 catalytic activity, and that Vpx participates in the Cullin 4 E3 complex comprising VprBP. We further demonstrate that the ability of SIVmac as well as HIV-2 Vpx to interact with VprBP and its associated Cullin 4 complex is required for efficient reverse transcription of SIVmac RNA genome in primary macrophages. Strikingly, macrophages in which VprBP levels are depleted by RNA interference resist SIVmac infection. Thus, our observations reveal that Vpx interacts with both catalytic and regulatory components of the ubiquitin proteasome system and demonstrate that these interactions are critical for Vpx ability to enable efficient SIVmac replication in primary macrophages. Furthermore, they identify VprBP/DCAF1 substrate receptor for Cullin 4 E3 ubiquitin ligase and its associated protein complex as immediate downstream effector of Vpx for this function. Together, our findings suggest a model in which Vpx usurps VprBP-associated Cullin 4 ubiquitin ligase to enable efficient reverse transcription and thereby overcome a block to lentivirus replication in monocyte-derived cells, and thus provide novel insights into the underlying molecular mechanism.
| Monocyte-derived tissue macrophages play crucial roles in infection by primate lentiviruses. Human and simian lentiviruses of the HIV-2 and SIVsm/mac lineages encode a virion-bound virulence factor termed Vpx. Vpx is required to establish infection specifically of monocyte-derived cells, but the underlying molecular mechanism is unclear. In this study we characterize how the replication of SIVmac is blocked in the absence of Vpx and how Vpx overcomes this block. We find that Vpx is required for efficient reverse transcription of the incoming RNA genome, suggesting that Vpx acts early following virion entry into the macrophage, probably on events linked to virion uncoating and/or reverse transcription. We also identified a Vpx-associated ternary protein complex that is the key mediator of Vpx function specifically in macrophages. This complex links Vpx to the cellular machinery that mediates protein ubiquitination and degradation. Together, we describe the immediate downstream effector, the molecular machinery and a tentative mechanism that lentiviral Vpx uses to enable reverse transcription in macrophages. Our findings should lead to the conception of new strategies to control macrophage infection by human and simian lentiviruses.
| Vpx accessory proteins are virulence factors encoded by viruses of the HIV-2/SIVsm/SIVmac lineage of primate lentiviruses. vpx gene disruption results in greatly reduced rates of virus replication in monocyte-derived cells, such as differentiated macrophages, but has no overt effect in primary T lymphocytes, as well as T and monocytic cell lines [1],[2],[3]. Intact vpx gene is required for optimal replication of these viruses in the infected host [4],[5]. Thus, it is thought that the role of Vpx in natural infection is to enable the establishment of virus reservoirs in macrophages.
Vpx is recruited into viral particles through the interaction with the p6 component of Gag [6],[7], and thus is available to facilitate an early event in the virus life cycle upon virion entry into the target cell. Indeed, an early study revealed that Vpx is required for efficient transport of preintegration complexes to the nuclei of infected macrophages [3]. In more recent studies HIV-2 and SIVsm Vpx proteins were found to promote accumulation of reverse transcribed viral genomes upon infection of dendritic cells (DCs) and this effect may reflect the ability of Vpx to overcome a proteasome dependent mechanism that inhibits an as of yet unidentified early event in the viral replication cycle [8]. How Vpx intersects this ubiquitin-dependent proteasomal protein degradation mechanism is unclear.
Vpx is a paralogue of Vpr accessory factor encoded by all known lineages of primate lentiviruses [9]. Although their amino acid sequences are closely related, the two proteins have different roles along the viral life cycle. For example, Vpr has the ability to activate DNA damage checkpoint and thereby arrest cells in the G2 phase of the cell cycle, while Vpx does not possess this function (reviewed in [10]). Results from recent proteomic studies revealed that lentiviral Vpr proteins associate with components of the ubiquitin proteasome system (UPS), such as Vpr Binding Protein (VprBP, GenBank NM014703) termed also DDB1 and CUL4-associated factor 1 (DCAF1), damaged DNA-binding protein 1 (DDB1, GenBank U18299), DET1 and DDB1 associated 1 (DDA1, GenBank DQ090952) and Cullin 4 (GenBank NM001008895, NM003588) ([11],[12],[13] reviewed in [14]). Cullin 4 is a scaffold protein that assembles a family of E3 ubiquitin ligase complexes. DDB1 is an obligatory subunit of all Cullin 4 E3's that bridges the catalytic cores organized on the Cullin 4 scaffold to a substrate-recruiting subunit, and VprBP/DCAF1 is a putative substrate adaptor for Cullin 4-based E3 ubiquitin ligases ([15] reviewed in [16],[17]). Evidence has been obtained showing that these interactions provide Vpr with the ability to modulate specifically the intrinsic catalytic activity of the Cullin 4 E3 containing VprBP and with a potential to influence the recruitment of substrate proteins for ubiquitination by Cullin 4, which in turn leads to the activation of DNA damage checkpoint [13],[18].
Since Vpx, similarly to Vpr, probably functions as an adaptor protein, we have used a combination of biochemical and proteomic methods to identify downstream effectors of Vpx encoded by the pathogenic SIVmac 239 strain. Here we show that SIVmac Vpx also binds DDA1-DDB1-VprBP complex, which links Vpx to Cullin 4, thus extending the previous observation that another SIV Vpx variant can bind VprBP [11]. Importantly, we demonstrate that VprBP, and its interaction with Vpx, are required for efficient macrophage transduction by SIVmac. Surprisingly, in the absence of Vpx, the incoming RNA genome is reverse transcribed very inefficiently. These findings indicate that Vpx facilitates macrophage infection by acting prior to and/or during reverse transcription, rather than by facilitating nuclear transport of the fully reverse transcribed preintegration complex, as has been thought previously ([3], reviewed in [10]). Together, our findings identify the UPS system and the VprBP associated protein complex as cellular machinery and immediate downstream effector that Vpx uses to promote replication of cognate primate lentiviruses in cells of monocyte/macrophage lineage, and provide novel insights into the underlying mechanisms.
Two complementary strategies were used to identify cellular proteins that are bound by SIVmac 239 Vpx. As one approach, U937 monocytic cell populations were transduced with BABE-puro retroviral vectors stably expressing Vpx tagged at its N-terminus with a triple HA-FLAG-AU1 epitope tag (hfa-Vpx). The population of positively transduced cells was then selected with puromycin and expanded in spinner cultures for biochemical experiments. Surprisingly, we observed that U937 cells that stably expressed Vpx grew more slowly than the control U937 population transduced with an empty BABE-puro vector (data not shown), suggesting that Vpx is toxic and/or cytostatic to these cells. This in turn raised the possibility that chronic Vpx expression could lead to selection of escape variants where Vpx interaction with cellular proteins is not faithfully reproduced. Therefore, as an additional approach hfa-Vpx was expressed transiently in human embryonic kidney 293T (HEK 293T) cells by calcium phosphate co-precipitation. Next, Vpx and its associated proteins were purified from U937 and HEK 293T detergent extracts by sequential immunoprecipitations with anti-HA- and anti-FLAG- epitope antibodies, each followed by elution with the respective peptide epitope.
The immunoprecipitates were proteolyzed without prior separation of protein bands by SDS-PAGE and peptide mixtures analyzed by multidimensional protein identification technology (MudPIT, [19]). Interestingly, the most abundant cellular polypeptides we found associated with Vpx both in U937 and HEK 293T cells, but were absent from control purifications from cells that did not express Vpx were DDA1, DDB1 and VprBP/DCAF1 (see Table 1). Significantly, DDB1, an obligatory subunit of all known Cullin 4 based E3 ubiquitin ligases [16], VprBP, a known Vpr-binding cellular protein that has been recently shown to bind DDB1 and postulated to function as a substrate receptor for Cullin 4 E3 ubiquitin ligase [15],[20] and DDA1, a DDB1-binding protein that links to a negative regulator of Cullin4 E3 ubiquitin ligases [21], were thus identified as relatively abundant Vpx-associated proteins. Notably, DDB1, VprBP and DDA1 were recently shown to assemble a ternary complex that associates with Vpr proteins of HIV-1 and SIVmac and mediates activation of DNA damage checkpoint by these accessory factors [13]. Thus, our observations suggested that Vpx and its Vpr paralog both act through the DDA1-DDB1-VprBP complex, even though the two proteins execute distinct functions.
To verify the data from MudPIT analyses, experiments were performed to confirm that Vpx associates specifically with the endogenous DDB1-VprBP-DDA1 complex. hfa-Vpx was transiently expressed in HEK 293T cells. Then, hfa-Vpx and its associated proteins were immunoprecipitated from detergent extracts prepared from the transfected cells with anti-FLAG-affinity resin, separated by SDS-PAGE and analyzed by western blotting with antibodies specific for VprBP, DDB1 and DDA1. As shown in Figure 1A, VprBP, DDB1 and DDA1 were readily detected in immune complexes isolated from hfa-Vpx expressing, but not from control, HEK 293T cells. Thus these data confirm that Vpx associates with DDB1, VprBP and DDA1 (compare lane 2 with 1).
The finding that Vpx associates with DDA1, VprBP and DDB1 was not entirely surprising because SIVmac Vpx amino acid sequence is approximately 25% and 50% identical to those of HIV-1 and SIVmac Vpr proteins, respectively, and because some of the previously tested SIVmac/HIV-2 Vpx variants were reported to bind VprBP [11],[22]. Previous studies have demonstrated that Vpr binds DDA1-DDB1-VprBP complex via its C-terminal α-helical region [11],[13]. Given the high degree of sequence identity between Vpx and Vpr proteins, this raised the possibility that Vpx binds the above complex in a manner similar to that seen with Vpr [11]. To test this and to develop mutant Vpx proteins defective for the interaction with DDA1-DDB1-VprBP, we substituted amino acid residues located in the C-terminal α-helical region of Vpx that are conserved in Vpr proteins (see Figure 1B). Mutant Vpx proteins were then transiently expressed in HEK 293T cells, immunoprecipitated via their FLAG tags, and immune complexes analyzed by Western blotting. As shown in Figure 1A, alanine substitution for the conserved glutamine Q76 (Q76A) disrupted Vpx ability to associate with DDA1, VprBP and DDB1. Also, alanine substitution for the conserved phenylalanine F80 (F80A) and arginine substitution for histidine 82 (H82R) had similar effects. Of note, the corresponding mutations in HIV-1 Vpr were previously shown to disrupt the binding to the VprBP-associated protein complex ([11], data not shown). Finally, mutating the conserved glycine G86 and cysteine C87 residues (GC86NG) did not have a detectable effect. We conclude that Vpx binds the DDA1-DDB1-VprBP complex via its C-terminal domain, probably using an interaction surface that is also conserved in the Vpr protein.
The VprBP-DDB1 module was found to bind Cullin 4 and to participate in a functional Cul4-DDB1[VprBP] E3 ubiquitin ligase complex [13]. Therefore, we tested whether Vpx can associate with Cullin 4 and, if so, whether VprBP mediates this association. hfa-Vpx was transiently expressed together with myc-tagged Cullin 4A isoform (m-Cul4) and/or myc-tagged VprBP (m-VprBP) in HEK 293T cells, and anti-FLAG immunoprecipitates were analyzed for Cullin 4 by immunoblotting. As shown in Figure 2, VprBP co-expression dramatically elevated the levels of Cullin 4 associated with wild type Vpx, but not with the Vpx(Q76A) variant that is unable to interact with VprBP and DDB1 subunits of the E3 complex (compare lane 4 with 2 and 5). We conclude that VprBP links Vpx to the Cullin 4-based E3 complex.
Notably, we observed that the Vpx-associated Cullin 4 migrated as a doublet (see lane 4). The slower migrating form of Cullin 4 was much less abundant in immune complexes assembled with VprBP in the absence of Vpx (lane 9), and co-migrated with the neddylated form of Cullin 4 shown previously to be induced by HIV-1 Vpr (see Figure S1, and ref. [13]). As expected, the upshifted Cullin 4 isoform was not detected in the E3 complex containing the DDB2 substrate receptor, which is catalytically repressed in the absence of damaged DNA (lane 7, see ref. [23]). Notably, in contrast to Vpr, the Vpx-induced modification was much less pronounced, and did not lead to a robust increase in catalytic activity of the associated E3 (Figure S1). We conclude that SIVmac Vpx is a much less potent inducer of Cullin 4 neddylation and E3 catalytic activity, than HIV-1 NL43 Vpr.
The ability of Vpx to enable infection of primary macrophages is well documented, yet the immediate downstream mediator(s) of Vpx remains unknown [1],[2],[3]. Therefore, experiments were performed to assess whether the interaction with VprBP and its associated E3 complex is important for Vpx's ability to facilitate macrophage transduction by SIVmac 239. Since this function is probably mediated by the virion-bound Vpx molecules, our initial experiments assessed the ability of the mutant Vpx proteins to be incorporated in SIVmac 239 virions.
VSV-G pseudotyped single cycle SIVmac 239(GFP) viruses encoding wild type or mutated Vpx variants that do not bind VprBP, or possessing an inactive vpx coding sequence due to termination codon substitutions for methione codons M1 and M62 were produced from HEK 293T cells. All viruses contained a frameshift mutation in the env gene which prevented expression of a functional Env glycoprotein, and expressed GFP marker protein from an IRES element positioned immediately downstream of the nef gene (SIVmac 239(GFP)). A reference panel of virions containing decreasing amounts of wild type Vpx were also produced from HEK 293T cells transiently co-expressing SIVmac 239(GFP) proviral construct possessing wild type vpx gene mixed with an isogenic construct containing the M1- and M62- mutated vpx, at 1∶3, 1∶7, or 1∶15 ratio. Virions were partially purified and concentrated by pelleting through 20% sucrose cushion and then analyzed by immunobloting for p27 Capsid and for Vpx. As shown in Figure 3A, the Q76A and F80A substitutions had only minor effects on the abilities of the mutant proteins to be incorporated into the virions (compare lanes 5 and 6 with 1–4). The H82R substituted Vpx was incorporated into viral particles very poorly (data not shown), and therefore was not studied further.
Next we measured the abilities of the VSV-G pseudotyped single cycle virions to transduce human monocyte derived adherent macrophages. Monocytes obtained from human peripheral blood mononuclear cells (PBMC) by negative selection for CD3, CD7, CD16, CD19, CD56, CD123 and Glycophorin were differentiated into macrophages in the presence M-CSF. Macrophage cultures were then infected with normalized virion preparations and transduction efficiencies of the wild type and mutant viruses were quantified by flow cytometric analysis of GFP expression in the infected cell populations. As controls, CD4+ T lymphocytes purified from PBMC by positive selection for CD4 and activated by phytohemagglutinin in the presence of IL-2, and Jurkat T cells, were also infected and analyzed in parallel. As shown in Figure 3B, panels 2–6, wild type Vpx stimulated macrophage transduction by up to 100-fold, in a dose-dependent manner (5.1% vs 0.06% GFP-positive cells). Significantly, Vpx(Q76A), or Vpx(F80A), substituted Vpx failed to support macrophage infection (panels 7 and 8), even though the mutant Vpx molecules were efficiently incorporated into the virions. In contrast, all viruses displayed similar infectivities to primary CD4+ T lymphocytes and Jurkat T cells (panels 9–16 and 17–24), indicating that Vpx is not required for transduction of primary T cells and established T cell lines, consistent with previous observations [2]. Thus, the Q76A and F80A changes link SIVmac Vpx ability to enhance macrophage transduction to its interaction with VprBP and its associated E3 ubiquitin ligase complex.
Vpx proteins encoded by HIV-2 viruses also enhance transduction of monocyte-derived cells, but a previous report suggested that they may be unable to bind VprBP [22],[24]. This in turn raised a question whether HIV-2 Vpx uses a VprBP-independent mechanism to enable macrophage infection. To address this issue we asked whether Vpx variant encoded by HIV-2 Rod proviral clone binds VprBP. We chose this particular Vpx variant because it is required for the ability of HIV-2 Rod to transduce primary macrophages and, therefore, is functional [24]. Of note, it is evident from phylogenetic analyses that both the Rod Vpx and SIVmac 239 Vpx are representative of two major groups of HIV-2 Vpx variants (see Figure S2). As shown in Figure 4A, wild type, but not Q76A-substituted, Rod Vpx protein readily bound VprBP in a transient expression assay in HEK 293T cells (compare lanes 1 and 2). Next we assessed the abilities of both proteins to enhance macrophage transduction by a single cycle SIVmac 239(GFP) reporter virus. We found that only wild type Rod Vpx rescued the infectivity of single cycle SIVmac 293(GFP) reporter virions that were devoid of SIVmac Vpx, even though both the wild type and Q76A substituted Rod Vpx variants were incorporated into the virions to similar extents (Figure 4B and 4C). We conclude that the interaction with VprBP is a conserved function of SIVmac and HIV-2 Vpx proteins, and that both use VprBP to enable macrophage infection.
Vpx was reported to be essential for efficient reverse transcription and/or nuclear import of lentiviral genomes in monocyte-derived cells [3],[8]. Hence we examined the effect of Q76A and F80A substitutions in Vpx on reverse transcription (RT) of the incoming SIVmac genomes by real-time quantitative fluorescent PCR. Macrophages were transduced with a reference panel of VSV-G pseudotyped single cycle SIVmac 239(GFP) virions containing decreasing amounts of wild type Vpx, or Vpx(Q76A) and Vpx(F80A) variants, characterized in Figure 3A. DNA was isolated from the transduced cells 18 hours and 72 hours later and RT intermediates were quantified by real time PCR with four sets of primers shown in Figure 5A. The primers were designed to amplify strong-stop DNA (early), RT products synthesized immediately following minus strand transfer (U3), or a region of the gag gene located approximately 8000 nucleotides distal from U3 (gag), as well as late RT products synthesized following successful plus strand transfer (late). As shown in Figure 5B, these analyses revealed that reverse transcription was defective following infection with virions lacking, or containing suboptimal amounts of Vpx. First, the steady state levels of the early strong-stop RTs were approximately 10-fold lower in the absence of Vpx and the magnitude of the decrease was inversely correlated with the Vpx virion content. Second, the levels of U3, gag and late RTs were progressively lower upon infection with Vpx-deficient virions (approx 100-fold, 300-fold and 1000-fold, respectively) at the 18 hour time point. These differences were less pronounced at the 72 hour time point. Importantly, Vpx was not required for efficient reverse transcription following infection of Jurkat T cells. Together, these observations indicate that Vpx is required for events that lead to an efficient initiation and progression of reverse transcription of SIVmac genome in macrophages.
Next we tested the effects of Q76A and F80A substitutions in SIVmac Vpx for its ability to enable efficient reverse transcription of the SIVmac genome in macrophages. As shown in Figure 5C, both Vpx variants conferred a Vpx-deficient virion phenotype. Since Q76A and F80A each disrupts Vpx binding to VprBP, these findings link the interaction with VprBP to Vpx ability to facilitate reverse transcription of lentiviral genome in macrophages.
To obtain further insight into the role of VprBP, we knocked down its expression in macrophages by RNA interference (RNAi, [25]). As illustrated in Figure 6A, a pool of small interfering RNAs (siRNA) targeting VprBP, but not the control non-targeting siRNAs, severely diminished VprBP expression (compare lane 3 with 1 and 2). Two days following initiation of RNAi macrophages were infected with VSV-G pseudotyped single cycle SIVmac 239(GFP) reporter virus and the transduction efficiency was assessed 3 days later. Flow cytometry analysis of GFP expression revealed that nontargeting siRNA decreased transduction efficiencies by only approximately 30% and the magnitude of this effect was constant over a wide range of siRNA concentrations (Figure 6B, compare panels 4 and 6 with 2). A similar result was observed with another non-targeting siRNA pool (data not shown). These observations indicate that non-specific engagement of RNAi machinery had only a minor negative effect on macrophage transduction by SIVmac 239. In contrast, RNAi to VprBP decreased transduction efficiency by approximately 10-fold at a lower dose, and 30-fold at a higher dose of the targeting siRNA (compare panel 3 with 4 and 5 with 6). These experiments were repeated 4 times and we consistently observed a decrease in transduction efficiency following RNAi to VprBP, ranging between 6-fold and >100-fold. To further exclude the possibility that the observed resistance of VprBP-depleted macrophages to SIVmac infection is caused by the off target effects of the siRNA pool targeting VprBP, additional experiments were performed using individual VprBP-specific siRNAs (Figure S3). We observed good correlation between the abilities of the four siRNAs to knock down VprBP expression and to disrupt macrophage transduction by SIVmac 239(GFP) reporter virus. Of note, VprBP-depletion in U2OS cells did not compromise transduction of these cells by SIVmac 239(GFP) reporter virus regardless of the presence or absence of Vpx (Figure S4). Together these data indicate that VprBP is required for efficient macrophage transduction by SIVmac.
If VprBP indeed facilitates macrophage transduction through the interaction with Vpx, we expected the arrest of SIVmac replication in the absence of VprBP and that in the absence of Vpx to be similar in nature. To test this prediction we examined the steady state levels of SIVmac 239(GFP) late reverse transcription products 72 hours post infection of VprBP-depleted and control macrophage populations, by real time PCR. As expected, the levels of late reverse transcripts were approximately 100-fold lower in VprBP-depleted versus non-targeting siRNA treated, or untreated macrophages (Figure 6C, compare VprBP to scr, or none). Together our data indicate that VprBP has an important role in macrophage transduction by SIVmac and that this function requires Vpx.
Vpx enables efficient transduction of monocyte-derived cells, such as macrophages and DC's by SIVsm/mac and HIV-2 viruses; however the mechanism that mediates this effect has not been identified. Our findings link this Vpx function to its ability to interact with components of the ubiquitin proteasome system and identify a ternary protein complex - comprising DDA1, DDB1 and VprBP/DCAF1, a putative substrate receptor for Cullin 4-based E3 ubiquitin ligase - as the immediate downstream effector that Vpx uses to promote macrophage transduction. Importantly, the DDB1-VprBP/DCAF1 module was previously shown to participate in a functional Cullin 4 E3 ubiquitin ligase complex [13]. Together, these findings support a model in which Vpx usurps the Cullin 4 E3 ubiquitin ligase utilizing the VprBP/DCAF1 to overcome a block to lentivirus replication upon its entry into monocyte-derived cells.
Our data indicate that Vpx acts early following virion entry into macrophages to allow efficient initiation as well as completion of reverse transcription of the incoming SIVmac RNA genomes. This can be clearly seen from a >10-fold decrease in steady state levels of early reverse transcription products and 103-fold decrease in late reverse transcripts upon challenge with Vpx-deficient virions. These phenotypes could result from defects in virion uncoating and/or in its transit into a permissive cytoplasmic compartment.
A similar, albeit less dramatic, loss of vpx function phenotypes were previously reported for other SIVsm/mac viral isolates and/or vpx alleles upon infection of monocyte-derived DCs [8]. The finding that Vpx is required for efficient reverse transcription in macrophages was somewhat surprising, because it has been thought that this factor acts at a later stage in the replication cycle by enabling the import of the fully reverse transcribed preintegration complex into the nucleus [3]. Our findings, taken together with these previous observations, indicate that SIVmac replication is restricted by the same mechanism in DCs and in macrophages. Thus, it is important to refocus future studies towards post entry events that precede reverse transcription in these monocyte derived cells.
The phenotype of Vpx-deficient virions is reminiscent of that resulting from a block to retrovirus replication imposed by tripartite motif protein 5α (TRIM5α) restriction factors. TRIM5α is a E3 ubiquitin ligase that inactivates the incoming virions, probably by deregulating their uncoating so rapidly that the late reverse transcripts fail to accumulate [26],[27],[28]. Also, the observation that proteasome inhibitors partially rescue reverse transcription of Vpx-deficient viruses in DCs is consistent with the idea that SIVmac virions may be targeted by a TRIM5α-like restriction, or by another E3 ubiquitin ligase in monocyte-derived cells [8]. Whereas these observations raise the possibility that Vpx could act by counteracting TRIM5α, we note that this is not likely, because TRIM5α is expressed in Jurkat T cells [29], which we found not to restrict Vpx-deficient SIVmac virions.
How does Vpx facilitate reverse transcription in macrophages via its interaction with VprBP? As mentioned above, a recent study suggested that the replication of SIVmac cells could be restricted, at least in part, by an as yet unidentified E3 ubiquitin ligase [8]. We initially considered that VprBP-linked Cullin 4 E3 complex could be that enzyme and that Vpx counters the restriction by inhibiting its activity. However, our data from RNAi experiments revealed that VprBP is not required for the restriction to occur and, therefore, do not support this possibility. Furthermore, the incoming virions probably contain at most only several hundred Vpx molecules, similar to Vpr, which also is virion recruited through its interaction with Gag p6 [6],[30],[31]. Therefore, it is difficult to envision that the limited amounts of virion-bound Vpx would be able to saturate and inhibit the cellular pool of VprBP-associated Cullin 4 E3 complexes, even by a noncompetitive mechanism.
Instead of blocking SIVmac replication, our evidence indicates that VprBP is required for Vpx to overcome the block, implying that Vpx uses VprBP-associated E3 to enable reverse transcription in macrophages. Notably, the same VprBP-associated ubiquitin ligase was shown previously to be targeted by a Vpx paralogue, Vpr, which stimulates the intrinsic catalytic activity ofthis E3 [13]. The findings that both Vpx and Vpr interact with VprBP in a similar manner via their C-terminal regions, and that both interactions lead to post-translational modification of their associated Cullin 4 subunits suggest that Vpx also usurps the VprBP-associated E3, probably to inactivate a cellular factor that inhibits lentivirus replication in macrophages and DC's. Indeed, viral accessory proteins are known to utilize E3 ubiquitin ligases to direct ubiquitination and proteasomal degradation of cellular proteins that mediate innate immunity to viral infection [32].
Both Vpx and Vpr bind VprBP through similar molecular interactions, yet the functional outcomes are different. Vpr uses VprBP-associated E3 to activate DNA damage checkpoint controlled by the Ataxia-telangiectasia and Rad3-related (ATR) kinase, while Vpx does not have this function and, instead, enables efficient reverse transcription of SIVmac genome in monocyte-derived cells [33]. These different outcomes likely reflect that Vpr and Vpx recruit different sets of substrates for ubiquitination by the same E3 enzyme [34],[35], and that they affect differently the activities of their associated Cullin 4 E3s (see Figure S1). It will be important in the future to identify cellular proteins whose ubiquitination is altered by Vpx and Vpr in order to advance the understanding of these important virulence factors.
In summary, our findings provide novel insights into the mechanism by which Vpx enables macrophage infection, as they link this function to Vpx interaction with VprBP and its associated Cullin 4 E3 ubiquitin ligase complex. Further studies of how Vpx manipulates protein ubiquitination through its interaction with VprBP should lead to detailed understanding of the biochemical mechanism that limit replication of primate lentiviruses in monocyte-derived cells, and how it is countered by viruses of the HIV-2/SIVmac/sm lineages. This knowledge in turn will likely lead to the conception of new strategies aimed to prevent the virus from establishing reservoirs in these cells.
pCG expression vectors expressing epitope tagged VprBP/DCAF1, DDB1, DDA1, Cullin 4A, and Vpr proteins of HIV-1 NL43 and SIVmac 239 viruses were described previously [13]. SIVmac 239 Vpx was tagged with hfa- triple epitope tag and subcloned into BABE(puro) and pCG vectors [13]. HIV-2 Rod vpx gene was amplified by PCR from Rod proviral clone [24] kindly provided by Michael Emerman (Fred Hutchinson Cancer Research Center, Seatte). Mutations were introduced using QuikChange XL II kit (Stratagene, La Jolla, CA, United States) and confirmed by DNA sequencing.
HEK 293T cells were transfected by calcium phosphate co-precipitation method. Detergent extracts and anti-FLAG immune complexes were analyzed by immunobltting as described previously [36]. FLAG-, HA- and myc- epitope tagged proteins were detected with anti-FLAG M2 (Sigma-Aldrich, St. Louis, MO, United States), 12CA5, and 9E10 monoclonal antibodies (mAb), respectively. The following antibodies were also used: anti-DDB1 (37-6200) from Zymed (Invitrogen, Carlsbad, CA, United States), anti-α-adaptin (AC1-M11) from Alexis Corp (San Diego, CA, United States), anti-Gag SIVmac 251 (13-112-100) from Advanced Biotechnologies Inc. (Columbia, MD, United States) and anti-Vpx 6D2.6 Vpx hybridoma supernatant. DDA1 and VprBP were detected with rabbit sera raised to recombinant proteins [13].
SIVmac 239 Vpx and its associated proteins were purified from U937 cells stably expressing hfa-tagged Vpx, or HEK 293T cells transiently expressing hfa-Vpx by two sequential immunoprecipitations via FLAG and HA epitope tags, each followed by competitive elution with the appropriate peptide epitope. MudPIT analysis was performed as described previously [13]. Tandem mass (MS/MS) spectra were interpreted using SEQUEST [37] against a database of 82242 sequences, consisting of hfa-tagged SIVmac Vpx, usual contaminants, and 40873 human proteins, as well as, to estimate false discovery rates, randomized amino acid sequences derived from each non-redundant protein entry. Peptide hits from multiple runs were compared using CONTRAST [38].
Vpx and Vpr mutations were introduced into a single cycle SIVmac 239(GFP) reporter proviral clone containing a frameshift mutation in the env gene constructed by filling in a unique ClaI site [39]. A proviral clone deficient for vpx was constructed by substituting methionine and serine codons at positions 1 and 2 in vpx with threonine and termination codons, respectively, such as not to alter the overlapping vif gene. The second consecutive methionine codon (M62) in vpx was also changed to a termination codon to prevent the possibility that a truncated C-terminal fragment of Vpx protein will be expressed. Mutagenesis was performed with QuikChange XLII kit (Stratagene) using 1.3 kb PacI-SphI fragment of SIVmac 239(GFP) provirus [39] that comprises vif and vpx open reading frames, subcloned into pCR 2.1 vector, as a template. All mutations were confirmed by DNA sequencing and reintroduced into SIVmac 239(GFP) proviral clones containing a frameshift mutation in env, by exchanging the 1.3 kb PacI-Sph1 restriction fragment. VSV-G pseudotyped single cycle viruses were produced from HEK 293T cells transiently transfected with proviral clones and a VSV-G expression plasmid. In some experiments vpx-defective SIV were complemented in trans with wild-type or mutant Vpx proteins expressed from cotransfected pCG vectors. Culture medium was harvested 24 hours after transfection, cell debris removed by centrifugation at 7,000 rpm for 10 minutes and virus containing supernatants were then treated with DNAse I (Roche) for 60 minutes at 30°C. Viral particles were partially purified and concentrated by pelleting through 20% sucrose in 10 mM Tris-HCl [pH 7.4], 100 mM NaCl, 1 mM EDTA cushion at 27,000 rpm for 3 hours. Virion preparations were normalized based on reverse transcriptase assays and/or infectivity to Jurkat T cells, and stored at −70°C.
Monocytes obtained from human PBMCs by negative selection for CD3, CD7, CD16, CD19, CD56, CD123 and Glycophorin using Monocyte Isolation Kit II (Miltenyi Biotec Inc., Auburn, CA, United States), were plated in 24 well plates at 4–7×105 cells/well and differentiated into macrophages by culturing in DMEM supplemented with 10% fetal bovine serum (FBS), Macrophage-Colony Stimulating Factor (M-CSF, 50 ng/ml, R&D Systems, Minneapolis, MN, United States) for 6 days. Cells were fed every alternate day by replacing one half of the cell culture medium with fresh medium. Purity of CD14+ cells obtained by negative selection with the Monocyte Isolation Kit II from Miltenyi usually ranged between 95% and 99% while the final purity of the adherent macrophage population was typically greater than 99.9%. RNA interference was initiated at day 6 and followed by infections with SIVmac on day 8. Cells were harvested for QPCR analysis of reverse transcription products 18 hours to 72 hours post infection. Flow cytometry analysis of GFP expression was performed 4 days post infection. Macrophages were detached from wells by trypsin treatment, resuspended in 1% paraformaldehyde and GFP expression analyzed by flow cytometry. CD4+ T cells were purified from PBMC using CD4+ T cell isolation kit (Miltenyi Biotec) and stocks were frozen in 107 cell aliquots. Stocks were plated in 5 ml of RPMI 1640 supplemented with 10% FBS, 2 mM glutamine, 10 mM HEPES, pH = 7.4, 50 µM β-mercaptoethanol, and containing phytohemagglutinin (PHA, 10 µg/ml) and recombinant human IL-2 (10 u/ml, Roche) in single wells of a 6 well plate. After 48 hours cultures were diluted into the same medium but without PHA and 5×105 cell aliquots were infected in the total volume of 2 ml in wells of a 24 well plate. Expression of GFP marker protein was quantified 48 hours post infection by flow cytometry.
SIV reverse transcription products were quantified by real time PCR on ABI PRISM 7700 SDS. A typical reaction contained 50 ng of DNA isolated with DNAeasy Kit (Qiagen, Valencia, CA, United States) from infected or control cells and SYBR Green PCR master mix in a total volume of 25 µl (Applied Biosystems, Foster City, CA, United States). Early reverse transcription products were amplified with ERT.2.s (5′-CTTGCTTGCTTAAAGCCCTCTT-3′) and S.ERT.as (5′-CAGGGTCTTCTTATTATTGAGTACC-3′) primers, U3 with U3.SIV.s (5′-ATCATACCAGATTGGCAGGATT-3′) and U3.SIV.as (5′-GAAGTTTGAGCTGGATGCATTA-3′), gag with SIV.GAG.s (5′-ATTAGTGCCAACAGGCTCAGA-3′) and SIV.GAG.as (5′-GCATAGTTTCTGTTGTTCCTGTTT-3′), late with SIV.1 (5′-AGCTAGTGTGTGTTCCCATCTC-3′) and SIV.3 (5′-TACTCAGGAGTCTCTCACTCTCCT-3′).
Serial dilutions of known amounts of a plasmid containing SIVmac293 provirus, served as a copy number standard to generate standard curves.
Macrophages cultured in 24 well plates (Becton & Dickinson, San Jose, CA, United States) were fed with antibiotic free 10% serum containing DMEM 24 hrs before transfections. Cells were transfected with 1–200 pmol aliquots of a control nontargeting pool of siRNA (D-001206-14-05, Dharmacon, Lafayette, CO, United States) or ON-TARGET plus SMARTpool siRNA targeting human VprBP (L-021119-01), or individual VprBP-specific siRNAs (VprBP1 sense: GAUGGCGGAUGCUUUGAUAUU, antisense: UAUCAAAGCAUCCGCCAUCUU; VprBP2 sense: GGAGGGAAUUGUCGAGAAUUU, antisense: AUUCUCGACAAUUCCCUCCUU; VprBP3 sense: ACACAGAGUAUCUUAGAGAUU, antisense: UCUCUAAGAUACUCUGUGAUU; VprBP10 sense: CCACAGAAUUUGUUGCGCAUU, antisense: UGCGCAACAAAUUCUGUGGUU) using Lipofectamine 2000 (Invitrogen) according to manufacturer's instructions. Briefly, siRNA stocks were prepared in phosphate buffered saline (PBS). Liposomes were formed using 4 µl of Lipofectamine 2000 per well, as recommended by manufacturer (Invitrogen). 4–6 hours post transfection culture medium was replaced with fresh antibiotic-free DMEM supplemented with 10% FBS. U2OS cells were plated at 5×104/well of 12 well plate in 1 ml of DMEM supplemented with 10% FBS and 2 mM glutamine in the absence of antibiotics 24 hours before initiation of RNAi. Cells were transfected with lipofectamine 2000 (4 µl/well) containing indicated amounts of siRNA duplexes in 0.5 ml of medium, and the medium was replaced with 1 ml of fresh medium 5 hours later. 48 hours following initiation of RNAi cells were harvested for immunoblot analysis of VprBP expression, or infected with SIVmac or HIV-1 derived vectors. Transduction efficiencies were quantified by flow cytometric analysis of GFP expresssion. |
10.1371/journal.pntd.0000988 | A Secure Semi-Field System for the Study of Aedes aegypti | New contained semi-field cages are being developed and used to test novel vector control strategies of dengue and malaria vectors. We herein describe a new Quarantine Insectary Level-2 (QIC-2) laboratory and field cages (James Cook University Mosquito Research Facility Semi-Field System; MRF SFS) that are being used to measure the impact of the endosymbiont Wolbachia pipientis on populations of Aedes aegypti in Cairns Australia.
The MRF consists of a single QIC-2 laboratory/insectary that connects through a central corridor to two identical QIC-2 semi-field cages. The semi-field cages are constructed of two layers of 0.25 mm stainless steel wire mesh to prevent escape of mosquitoes and ingress of other insects. The cages are covered by an aluminum security mesh to prevent penetration of the cages by branches and other missiles in the advent of a tropical cyclone. Parts of the cage are protected from UV light and rainfall by 90% shade cloth and a vinyl cover. A wooden structure simulating the understory of a Queenslander-style house is also situated at one end of each cage. The remainder of the internal aspect of the cage is covered with mulch and potted plants to emulate a typical yard. An air conditioning system comprised of two external ACs that feed cooled, moistened air into the cage units. The air is released from the central ceiling beam from a long cloth tube that disperses the airflow and also prevents mosquitoes from escaping the cage via the AC system. Sensors located inside and outside the cage monitor ambient temperature and relative humidity, with AC controlled to match ambient conditions. Data loggers set in the cages and outside found a <2°C temperature difference. Additional security features include air curtains over exit doors, sticky traps to monitor for escaping mosquitoes between layers of the mesh, a lockable vestibule leading from the connecting corridor to the cage and from inside to outside of the insectary, and screened (0.25 mm mesh) drains within the insectary and the cage. A set of standard operating procedures (SOP) has been developed to ensure that security is maintained and for enhanced surveillance for escaping mosquitoes on the JCU campus where the MRF is located. A cohort of male and female Aedes aegypti mosquitoes were released in the cage and sampled every 3–4 days to determine daily survival within the cage; log linear regression from BG-sentinel trapping collections produced an estimated daily survival of 0.93 and 0.78 for females and males, respectively.
The MRF SFS allows us to test novel control strategies within a secure, contained environment. The air-conditioning system maintains conditions within the MRF cages comparable to outside ambient conditions. This cage provides a realistic transitional platform between the laboratory and the field in which to test novel control measures on quarantine level insects.
| Novel vector control strategies require validation in the field before they can be widely accepted. Semi-field system (SFS) containment facilities are an intermediate step between laboratory and field trials that offer a safe, controlled environment that replicates field conditions. We developed a SFS laboratory and cage complex that simulates an urban house and yard, which is the primary habitat for Aedes aegypti, the mosquito vector of dengue in Cairns Australia. The SFS consists of a Quarantine Insectary Level-2 (QIC-2) laboratory, containing 3 constant temperature rooms, that is connected to two QIS-2 cages for housing released mosquitoes. Each cage contains the understory of a “Queenslander” timber house and associated yard. An automated air conditioning system keeps temperature and humidity to within 1°C and 5% RH of ambient conditions, respectively. Survival of released A. aegypti was high, especially for females. We are currently using the SFS to investigate the invasion of strains of Wolbachia within populations of A. aegypti.
| Dengue is the most abundant arboviral infection in the tropics, with 50–100 million cases and 5 million people at risk annually [1]. Currently, there is no available human vaccine, thus dengue prevention is limited to control strategies attacking the mosquito vectors Aedes aegypti and Aedes albopictus. Outside of community education programs and source reduction campaigns that seek to remove artificial containers that produce the vectors, most government programs rely upon insecticides to reduce vector populations. These methods are often inefficient, costly and ineffective. Furthermore, many populations of A. aegypti have developed physiological resistance to many pesticides, rendering them ineffective [2].
Thus, novel population control strategies are being developed to control vectors of dengue and other mosquito-borne diseases. Releases of genetically modified (GM) A. aegypti that are refractory to dengue infection and transmission, and the inundative release of sterile males are currently being developed to reduce populations of A. aegypti [3], [4]. Our research group is investigating the use of strains of the endosymbiotic bacteria Wolbachia pipientis to induce life-shortening and dengue virus interference in populations of A. aegypti. The Wolbachia infection is driven to fixation in populations of A. aegypti via a cytoplasmic incompatibility mechanism. The wMelPop strain is known to shorten the life span of A. aegypti reducing the number of individuals that survive long enough to transmit dengue [5], and can also interfere with the replication and transmission of several viruses in A. aegypti, including DENV-2 and chikungunya virus [6]. Entomopathogenic fungi are also being studied for their life-shortening impact on A. aegypti [7].
Novel control strategies require confirmation under field conditions before they can be deployed operationally. Furthermore, experiments involving population replacement methods involving GM and novel agents such as Wolbachia and fungi that are conducted out of the laboratory must be under tight containment to avoid accidental release. A cross-disciplinary scientific working group developed guidelines for testing of gene drive systems within secure flight cages [8]. These facilities, termed “semi-field system” (SFS) [9], typically consist of secure biocontainment laboratory for insect rearing, secure field cage for experimental release, and associated security features such as fencing, moats and pass-coded gates. Within the cages, experimental houses or huts simulating domestic premises to be tested are featured. This is especially important for A. aegypti, a mosquito that typically feeds on humans and harbours within houses and other human premises. Natural substrates of soil, grass and native vegetation are included. Natural larval habitat such as puddles for Anopheles malaria vectors and artificial containers for Aedes are included. Biocontainment structures typically include double-door atriums, air curtains, mosquito surveillance traps, double layers of insect-proof screening, screened water drains, etc. In the tropics, facilities must often be built to withstand heavy rain and strong winds, often to tropical storm, cyclone or hurricane strength.
We describe a SFS that features a biocontainment level 2 laboratory/insectary that connects directly to 2 identical Quarantine Insectary Containment level 2 (QIC-2) semi-field cages. This new facility, the James Cook University Mosquito Research Facility (MRF), is currently being used to investigate the impact of wMelPop and wMel strains of Wolbachia infection on survival of A. aegypti, and the dynamics of its spread within a population of wild type A. aegypti. Each of the SFS cages contains the ground floor of a simulated Queenslander house and associated yard. Queenslander houses are typically timber houses set on concrete or wooden pillars, and are common throughout much of Queensland, Australia (http://en.wikipedia.org/wiki/Queenslander_%28architecture%29). They are generally unscreened to maximise ventilation, and the ground floor is often not fully enclosed, allowing free access to mosquitoes. Dengue transmission is often most intense in suburbs dominated by these older types of housing [10]. We describe the security and containment features of the MRF, measure the environmental conditions inside and outside the cage and the impact of its climate control system, and also examine the survival of wild type A. aegypti within the cage. Finally, we provide standard operating procedures (SOP; File S1) designed to prevent escape of released mosquitoes.
Human ethics approval for use of human volunteers to blood feed colony (dengue free) A. aegypti was obtained (JCU Human Ethics H2250). Volunteers were examined for fever before each blood feeding, excluded if feverish, and could withdraw at anytime. Written consent was obtained from all staff involved in blood feeding.
The MRF was constructed to provide a simulated Cairns urban environment, under QIC-2 containment levels (http://www.daff.gov.au/aqis/import/general-info/qap/class7/quarantine_approved_criteria_qap_class_7.2_quarantine_insectary_containment_level_2_qic2_facilities), for testing novel control strategies on A. aegypti. The MRF is built on 133 m2 of land on the Smithfield campus of James Cook University (16°48′58”S, 145°41′15”E) located ca. 15 km northwest of the city of Cairns, Queensland Australia. Cairns is located in the wet tropics of northern Queensland, and has a pronounced wet and dry monsoonal climate; the mean daily temperature ranges from 21°C in winter to 27°C in summer, and an average of 1992 mm of rain falls annually (Australian Bureau of Meteorology; http://www.bom.gov.au/index.shtml). Cairns has a history of dengue outbreaks [10], [11], and A. aegypti are present in most urban areas. The campus building site was chosen as it is practical for researchers but, more importantly, it is situated within tropical rainforest and is isolated from urban areas of Cairns where A. aegypti is common. Thus, we think that any escaping A. aegypti are highly unlikely to breed with existing populations in the Cairns region. Construction on the MRF began in March 2008 and finished in January 2009. The cost of the facility in $AUS was $469,000 for the cage, $888,000 for the laboratory and $364,000 for the air conditioning system including controller. Total cost was $1,721,000; with Goods and Services Tax (10%) this was $1,893,000. Of this total, 55% was material costs, and 45% labor. The MRF design (Figure 1) allowed us to provide direct and secure access between the rearing laboratory and the SFS cages. Two cages were built so that treatment and control experiments could be conducted simultaneously. A service road connects to a loading bay located near the entry to the MRF laboratory.
The screening of the cages reduced incoming light and thus potential solar gain. We measured light passing through the cage layers into the SFS cages using a Extech EasyView EA30 light meter (Extech Instruments Corporation, Waltham, MA 02451 U.S.A.) during mid day on clear conditions. We measured temperature and RH inside and outside each cage to test the ability of the shade cloth awning and AC system to maintain ambient conditions. Data loggers (Esis Hygrocon DS1923, Esis Pty Ltd, PO Box 450, Pennant Hills NSW 1715 AUSTRALIA) were set 24 cm above ground on a 8 L plastic bucket located within the Queenslander house and in the yard in the center of each cage, and run while the AC was on and off to investigate the impact of solar gain and the AC unit on conditions within the cages. Outside, 2 data loggers were set, one under a shaded tree ca. 1.5 m off the ground (equivalent to 1.5 m Stevenson screen height used by Bureau of Meteorology) and the other set on a upturned bucket in a shaded area adjacent to cage A. None of the data loggers were exposed to direct sunlight that could heat the unit and provide inaccurate temperature readings.
Several systems are deployed at the MRF SFS to provide security against vandalism and to minimise the accidental release of insects. The cages are surrounded by 2 m high fencing topped with barbed wire to prevent access by animals and humans. Each cage has an auto-locking door that could only be opened once the entry door into the vestibule was closed. Before being opened, the entry door activated an air curtain above the cage side door that blew air downward over the entryway. The interior also had overlapping screens composed of fine polyester Tentex 72007 cloth (located on the vestibule side) that had a metal chain weight sewn into the bottom to ensure the screens securely overlapped. All doors entering the laboratory are auto-locking, and keys are only available to JCU staff working on the project. The doors have all been fitted with rubber seals. In total, there are 6 doors (3 from cage to insectary, and 3 from insectary to external) between each cage and the external exit of the MRF SFS. Within each vestibule entry into the cage, a BG-Sentinel trap (BGS, Biogents GmbH, Regensburg, Germany) [12] runs continuously and a sweepnet is provided for staff to capture any escaped mosquitoes. All drains within the cages have stainless steel basket screens (0.25 ml) covered with fine mesh socks that are regularly inspected and cleaned. The external and internal walls of the cage are inspected for damage weekly. All supply air and return air grilles are fitted with 0.25 mm stainless steel mesh within the MRF facility. Fire extinguishers are located within the cages and laboratory, and fire detectors are located in the laboratory, air-conditioning system and plant rooms. The building is fitted with a “Notifier” system that automatically dials out to the fire brigade and campus security personnel in the event of a fire alarm. A set of SOPs (File S1) are used to maintain surveillance and security within the MRF SFS.
Extensive monitoring is conducted on the JCU campus to detect mosquitoes that may have escaped the SFS. Sticky ovitraps [13] and 4 BGS traps are also situated in buildings near the MRF, and are serviced weekly. Sticky traps consisting of 700 BGS ml red plastic cups containing a sticky panel insert are placed within the containment space between the mesh layers of each portal frame (Figure S1). If required, breaches of the cage sections can be rectified by replacement of the independently fitted double layers of 0.25 BGS mm stainless steel mesh. Any mosquitoes collected are identified in the laboratory, and A. aegypti are sent to University of Queensland for identification of Wolbachia infection. The presence of Wolbachia was detected by polymerase chain reaction analysis using primers specific to the wMelPop IS5 insertion sequence as described in [14]. Several times a year container surveys are conducted on the JCU campus, and potential A. aegypti larval habitat is removed or treated with S-methoprene pellets.
We sampled cohorts of male and female A. aegypti released within the MRF cage to determine their preferred resting sites. Three cohorts of 120 female and 60 male pupae were allowed to emerge in the cages at two day intervals. Mosquitoes were provided with daily human blood meals and access to oviposition sites as per regular experiment procedures. Separate areas in the cages were surveyed with a Prokopack aspirator [15] 3–7 days post-emergence. The cages were divided into five sections; (facing into the cage) left garden, inside Queenslander, right garden, behind Queenslander and front entrance of cage, and were surveyed in that order. Three surveys were performed at around dusk, when mosquitoes were less active, and three surveys were performed in mid-morning prior to blood-feeding. The dusk collections were performed 7, 8, 9 days after the first release of pupae and the day collections were performed 9, 10 and 11 days after the first release of pupae. One person (PHJ) performed all aspirator surveys and followed a specified route around objects (eg, plants, light fittings, furniture, sweaty towels) in each section. Mosquitoes were released back into the cages at the end of each survey. Data for all survey times were combined. For each cage, Fisher’s Exact Test was used to compare the total number of female and male mosquitoes captured within the Queenslander structure compared with those captured elsewhere in the cage.
A cohort of known numbers of equally aged male and female Ae. aegypti were allowed to synchronously emerge in each cage to estimate daily survival within each MRF cage. Mosquitoes (F1 obtained from populations collected from over 280 ovitraps set in suburbs across Cairns) were reared in the MRF insectary as a single large cohort. Larvae were reared in 3.4 L white buckets with approximately 2 L of water (ca. 100–150/bucket) and fed a diet of fish food (Tetramin). Temperature was maintained at 26°C with a 12∶12 photoperiod. Pupae were sexed using size as an indicator and 2500 female and 2500 male 0–24 hr old pupae were placed in buckets and allowed to emerge in each cage (total 5000 mosquitoes per cage).
Mosquitoes within the SFS cages were blood fed on 1–2 human volunteers for 10 min. at around 10 AM each day. Two BGS traps were set in the Queenslanders in each cage and run for 30 min. and the mean number of male and female captured Ae aegypti calculated. Samples were not returned to the cages. After 22 days, all remaining mosquitoes were captured using BGS traps and human-bait sweepnet collections.
Mosquito oviposition took place in 8 ovibuckets placed in the yard area of each cage. The ovibucket consisted of a 4 litre plastic bucket filled with 2 BGS L of a 20% hay infusion; a 10×15 cm red flannel cloth strip was attached inside the bucket as an oviposition substrate. Half of the ovibuckets in each cage were changed every three days so each ovibucket was in the cage for 6 days.
Daily survival rates (DSR) were estimated using BGS trap sample and final trap-out data. Both methods of analysis assume that mortality is independent of age and are potentially biased as BGS trap samples were not returned to the cages [16], [17]. However, linear analyses were used for both estimates as the recapture rate was low (overall 10–12% of the total initial population), survival was high and data from the first collection period was very low.
Mean BGS trap collections (+1) for females and males in each cage were loge transformed and fitted by linear regression against time (day of sample day 0 to day 15 for females and day 11 for males). The DSR were calculated from the resulting slopes [18]. For the DSR estimate for females using log-linear regression, the first sample point on day 3 was excluded from the regression as fewer mosquitoes were collected in the BGS-trap on day 3 than on the next sample day, day 7 (Figure 4A), likely due to a poor collection of teneral adult females by the BGS trap [19] on day 3.
Male DSR were estimated based on samples from day 3 to day 11.
The DSR based on the remaining number of female mosquitoes collected in each cage on day 22 was estimated by solving for p in the exponential decay equation where n is days and y is the number of mosquitoes on that day.
As no males were collected in the final trap-out or in the BGS traps after day 7 (Figure 4B), day 11 and 0 males were used to estimate DSR.
Ambient light entering the cage was reduced by 98–99% (Table 1), and was reduced by well over 99% within the Queenslander. Temperature and relative humidity within the cages accurately tracked ambient conditions outside the cage during the Sept 2009 period (Figure 5, Figure S3). Indeed, the AC system appeared to reduce daily peak temperatures by about 2–3°C, suggesting that the shade cloth awning above the cage helped prevent significant solar gain within each cage. Temperature and RH were comparable between the two cages. The mean absolute difference in hourly temperature inside and outside the cage was 0.92 and 1.02°C, respectively, for cage A and B with the AC turned off; and 0.71 and 0.99°C, respectively, with the AC turned on. For RH, the mean absolute difference was 5.6% and 5.5%, respectively, for cage A and B with the AC turned off; and 2.9% and 4.8%, respectively, with the AC turned on. Temperature and RH within the Queenslander were comparable to both outside ambient and yard conditions within each SFS cage (Table 2). The level of solar gain was not high, and reflects the 99% reduction in light entering the cage. Thus, temperature did not become extreme when the AC system was off, although the AC did appear to reduce highest temperatures in the afternoon. Aberrant RH peaks within both cages during the day was caused by water from the automated sprinkler system. Long term temperatures in both cages remained comparable (Figure S3), with cage A ca. 0.5°C warmer than cage B. Especially hot afternoon temperatures in early February 2010 exceeded 35°C, but were nearly identical inside and outside both cages.
The exterior drainage system prevented overrunning and flooding within the MRF cage due to heavy tropical rains. Indeed, no evidence of flooding within the MRF cage has been observed despite extreme rain events in excess of 300 ml within 24 hr. Overflow of interior drains from rain penetrating the cage screens has also not been observed. The soil base of the cage allows much of the storm water to percolate out of the cage rather than being flushed through the floor drains.
There is no evidence of A. aegypti escaping from the MRF-SFS. Aedes aegypti were occasionally captured in the BGS traps and sticky ovitraps located on the JCU campus. From February to June, 2009 a total of 47 (30 female and 17 male) A. aegypti were collected in 4 BGS traps from February to May 2009. During this time 14,800 non-infected and 48,000 Wolbachia-infected A. aegypti had been released in the cages. But none of the A. aegypti collected from the external traps was positive for Wolbachia by PCR assay. Whilst the absence of Wolbachia does not preclude the possibility that these mosquitoes escaped from the SFS cages, an alternative source of the mosquitoes was usually located. For example, the majority (25/47) of the A. aegypti were captured in one fortnight in a BGS trap located near an A. aegypti field bioassay from which adult mosquitoes had inadvertently escaped. Also, A. aegypti had been detected on campus before the cages were operational. Mosquito trapping and inspections detected larvae in potted plant bases, drain sumps and tyres. Although these sites were treated, breeding may have persisted.
Unwanted arthropods, such as millipedes, phorid flies, ants and some spiders, were observed in the SFS cages. These were probably introduced before screening of the cages was completed, and may also have been entered the cages from contaminated mulch or ornamental plants. Many remained in the cages despite the steam-cleaning of the mulch. Most arthropod populations were self-limiting while spiders and their webs were removed by hand. Ants may been present in the site soil or tunnelled beneath fencing and ratwalls into the cages. These were subsequently controlled by placing ant baits containing AmdroTM (0.73% hydramethylnon) within protective plastic petri dishes inside and outside each cage. A few geckoes (the exotic Hemidactylus frenatus (Dumeril and Bibron)) that probably invaded the Queenslander before the cages were screened were also found in each cage. These were removed by hand or by spraying with DettolTM (active ingredient Chloroxylenol (4-chloro-3,5-dimethylphenol)). Whilst the use of Dettol is not an approved method for removing geckos, it was the only effective one available. Spraying Dettol at the gecko would cause it to jump off the wall onto the floor rather than running to a crevice in the Queenslander wall or ceiling. Once on the floor the gecko could be quickly caught and killed by freezing.
For cage A, significantly more A. aegypti females and males were collected inside the Queenslander structure compared with all the other areas of the cage (Fisher’s exact test, p = 0.02). This was less apparent in Cage B where similar number of females were captured in the Queenslander, (Fisher’s exact test, p = 0.32), but fewer mosquitoes were collected overall (Figure 6).
The mean numbers of female and male A. aegypti collected in the BGS traps was consistent between the two cages (Figure 4; Figure S2). The day 22 trap-out collected 1,073 and 880 females from Cage A and Cage B, respectively; no males were collected from either cage. Estimated daily survival rate of females was similar for both cages across both estimation methods, ranging from 0.92–0.96 (Table 3). The DSRs for males were much lower, but there was nearly a 30% difference between estimates from the two methods, perhaps owing to different termination days.
The MRF provides a secure insectary for the production of mosquitoes and replicate quarantine level 2 SFS cages for conducting of experimental releases. The temperature and relative humidity within both SFS cages closely tracks ambient conditions outside the cages. We had fears that solar gain within the cages would result in high daytime temperatures that could be lethal to mosquitoes. Temperatures over 50°C were reported within the SFS in Tanzania, Africa [9]. However, the Tanzanian SFS had no AC system, and no protective awning to reduce solar gain. Our AC system was able to help maintain mean daily maximum temperatures within 1–2°C of external ambient (Figure 5, Figure S3). The multiple layers of shade cloth and screening reduced incident light within the cage by 98–99% (Table 1). This, coupled with ventilation facilitated by the void of 20–30 cm void beneath the shade cloth, ensured that the cage did not heat up appreciably when the AC was turned off (Figure 5). Temperature and relative humidity within the SFS Queenslander were similar to those recorded in the SFS yard, but light incidence was considerably reduced. Temperature and RH with in the SFS Queenslander are comparable to those occurring within a typical well-ventilated Queenslander house. Data loggers set from 1–8 Dec. 2007 in three rooms within a Queenslander house in Cairns demonstrated that average temperature was within 1°C of external Stevenson screen height temperature (S. Ritchie, unpublished data). However, cooler, high-humidity microclimates did exist in sheltered, moist areas such bathroom and laundry. The moist towels placed in the Queenslander within our SFS would have also provided a cooler, high humidity microclimate.
A simple awning system also minimised solar gain and excessive temperature within in two smaller cages (7 m×6 m×4 m high) near the MRF-SFS. These cages were built of 0. 25 ml Tentex polyester covered with a 0.2 m elevated 90% shade cloth awning. The mean maximum daily temperature (from 10 Feb. – 1 Mar 2010 using data loggers set 0.24 above ground) in these cages was only 0.44°C and 0.17°C higher than ambient (J. Darbro, unpublished data). Thus, data from both the MRF-SFS and the adjacent small cages indicate that a simple elevated awning of shade cloth will provide shade and ventilation, preventing high solar gain and extreme temperatures within the cage. This would be a cheaper alternative to air conditioning units.
Aspirator collections within the SFS cages indicated that most mosquitoes harboured within the Queenslander structure. Furthermore, we do not observe large numbers of mosquitoes resting on the cage walls. These observations indicate that the MRF SFS simulates a typical north Queensland urban environment for A. aegypti. Daily survival rate of female A. aegypti was quite high within the MRF SFS. Estimated daily survival rate of 0.92–0.96 was obtained from a released cohort of females A aegypti. Male DSR was much lower, (ca. 0.5–0.78), suggested that they died from starvation due to a lack of food or feeding. Either the flowering plants available in the cage were not suitable, or the males spent less time feeding compared with other behaviours such as mating. However, the high DSR estimates for females may be unrealistic high. Certainly mortality from predation, insecticide exposure and desiccation during prolonged flights are minimised within the cage. Female mosquitoes also had ready and easy access to a blood source (volunteer blood feeders were available every day) and oviposition sites, and thus were likely to expend less energy in searching for hosts or oviposition sites than wild mosquitoes.
We acknowledge that the MRF SFS has limitations. Due to the high construction costs, we were limited to only two SFS cages. Thus, experimental replication will be minimal, requiring multiple sequential experiments in some instances. These experiments could be further complicated by seasonal differences between sequential runs. Environmental conditions within the SFS cage are also different from the natural urban environment. While temperature and RH were comparable to external ambient conditions (Table 2, Figure 5), the screening and shade cloth greatly reduced light and wind within the cage, and the limited space within the cage would may have greatly restricted flight activity. These could impact mosquito survival and the potential infection by agents such as Wolbachia. Thus, results from SFS experiments must be interpreted with caution, especially regarding extrapolation to field conditions.
The MRF SFS is a highly secure environment. No Wolbachia-infected A. aegypti have been detected outside the SFS. Adult mosquitoes would have to escape through a double layer of 0.25 mm stainless steel, limiting these events to a breech of the containment by damage to the structure by flying tree branches, sabotage or vehicular collision. Barring a breech of the cage screening, a mosquito would have to fly through 6 secure locked doors to escape. Both are highly unlikely events. Adult mosquitoes could oviposit in free water or even mulch within the cage. However, all drains have secure 0.25 mm mesh baskets that would contain larvae. The oviposition buckets are the only source of free-standing water in the SFS cage. Thus, larvae hatched from eggs laid on mulch and other wet areas would not develop into adults. Nonetheless, care must be taken to eliminate free standing water in areas like plant axils and drains. In some instances regulatory bodies may require that genetic material not leave the SFS. Water from drainage and direct contact with the soil could allow for transfer of genetic material in our cage without the escape of living mosquito eggs, larvae or adults. A sealed concrete foundation, together with collection of waste water, would have to be used to prevent this. Finally, insects and other animals entered the cage in some instances. Most invaded the cage before it was screened, entered in mulch and plants brought into the cage or may have burrowed from the soil. Care must be taken to ensure contamination is minimal, and harmful mosquito predators, such as ants and geckoes, are eliminated.
Nonetheless, contained SFS cages offer excellent opportunity to conduct research on insects. The secure environment prevents release of quarantine insects; to date, no Wolbachia-infected A. aegypti have been detected in surveillance traps on the JCU campus. The cage allows for the release of cohorts of known numbers. Thus, the direct impact of a control measure can be estimated by comparing changes in population between control and treatment cages. This approach as been used to study the impact of pesticides, repellents and parasite-vector interactions (for a review see Ferguson et al. [9]). Cohort cage studies can also be used to study the behaviour of mosquitoes [9], and to estimate the relative efficacy of traps [20]. The MRF SFS could also be used to conduct insecticide and repellent evaluations under controlled semi-field conditions without the ethical dilemma of disease risk. We hope to investigate the impact of competing oviposition containers on efficacy of ovitraps such as sticky ovitraps and lethal ovitraps. Furthermore, detailed studies on A. aegypti behaviour, such as the microclimate of preferred harbourage sites, can be conducted on released cohorts within the Queenslander structure. While we have not established populations within the cage, we believe it would be relatively easy to do so as has been done with Anopheles [9].
For our studies with Wolbachia, we will be able to observe the rate of Wolbachia invasion within a population of wild A. aegypti. These studies will measure the penetration of Wolbachia within wild A. aegypti after simultaneous release of known ratios of Wolbachia-infected and uninfected A. aegypti. This will occur over several generations and be used to estimate the time to fixation We are currently conducting invasion experiments using the wMelPop and wMel strains.
|
10.1371/journal.pgen.1003090 | Genome-Wide Fine-Scale Recombination Rate Variation in Drosophila melanogaster | Estimating fine-scale recombination maps of Drosophila from population genomic data is a challenging problem, in particular because of the high background recombination rate. In this paper, a new computational method is developed to address this challenge. Through an extensive simulation study, it is demonstrated that the method allows more accurate inference, and exhibits greater robustness to the effects of natural selection and noise, compared to a well-used previous method developed for studying fine-scale recombination rate variation in the human genome. As an application, a genome-wide analysis of genetic variation data is performed for two Drosophila melanogaster populations, one from North America (Raleigh, USA) and the other from Africa (Gikongoro, Rwanda). It is shown that fine-scale recombination rate variation is widespread throughout the D. melanogaster genome, across all chromosomes and in both populations. At the fine-scale, a conservative, systematic search for evidence of recombination hotspots suggests the existence of a handful of putative hotspots each with at least a tenfold increase in intensity over the background rate. A wavelet analysis is carried out to compare the estimated recombination maps in the two populations and to quantify the extent to which recombination rates are conserved. In general, similarity is observed at very broad scales, but substantial differences are seen at fine scales. The average recombination rate of the X chromosome appears to be higher than that of the autosomes in both populations, and this pattern is much more pronounced in the African population than the North American population. The correlation between various genomic features—including recombination rates, diversity, divergence, GC content, gene content, and sequence quality—is examined using the wavelet analysis, and it is shown that the most notable difference between D. melanogaster and humans is in the correlation between recombination and diversity.
| Recombination is a process by which chromosomes exchange genetic material during meiosis. It is important in evolution because it provides offspring with new combinations of genes, and so estimating the rate of recombination is of fundamental importance in various population genomic inference problems. In this paper, we develop a new statistical method to enable robust estimation of fine-scale recombination maps of Drosophila, a genus of common fruit flies, in which the background recombination rate is high and natural selection has been prevalent. We apply our method to produce fine-scale recombination maps for a North American population and an African population of D. melanogaster. For both populations, we find extensive fine-scale variation in recombination rate throughout the genome. We provide a quantitative characterization of the similarities and differences between the recombination maps of the two populations; our study reveals high correlation at broad scales and low correlation at fine scales, as has been documented among human populations. We also examine the correlation between various genomic features. Furthermore, using a conservative approach, we find a handful of putative recombination “hotspot” regions with solid statistical support for a local elevation of at least 10 times the background recombination rate.
| Recombination is a biological process of fundamental importance in population genetic inference. The crossing-over of homologous chromosomes during meiosis results in the exchange of genetic material and the formation of new haplotypes. Accurate estimates of the recombination rate in different regions of the genome help us to understand the molecular and evolutionary mechanisms of recombination, as well as a host of other important phenomena. For example, recombination rate estimates are needed in assessing the impacts of natural selection [1], [2], admixture [3], and disease associations [4].
Recombination rates have been observed to exhibit a number of interesting heterogeneities: they are known to vary in magnitude and distribution between species (e.g., [5]–[7]), between populations within species [8],[9], and between individuals within populations [9]–[12]. There is also substantial variation in different regions of the genome at different scales. At the broad-scale, for example, recombination rates in humans are known to be correlated negatively with the distance from telomeres [13], while at the fine-scale, recombination events cluster in narrow hotspots of 2 kb width [4], [13], [14]. In humans, hotspots are typically defined as those with statistical support in favor of at least a five-fold increase of the recombination rate [13] over the background or surrounding region, and many hotspots suggest a ten- or even hundred-fold increase. Such hotspots exhibit a powerful influence on the recombination landscape; 70–80% of recombination events in humans occur in 10% of the total sequence [8]. Extensive fine-scale variation and recombination hotspots have also been found in other species, including chimpanzees [7], Arabidopsis thaliana [15] and yeast [16].
The picture in Drosophila is however less clear. Broad-scale maps of recombination have been constructed for D. melanogaster by fitting a third-order polynomial to each chromosome arm [17], [18]. These give an overview of the distribution of recombination along each arm, quantifying for example earlier observations of declining recombination rates with proximity to the telomeres and centromeres. Variation on finer scales has been inferred by studies of linkage disequilibrium (LD) and by breeding experiments. Rapid and consistent decay in LD [19] leads to an absence of long haplotype blocks. There is scant evidence for hotspots either at the intensity or prevalence of those found in humans. Experimental studies of variation have produced local, fine-scale maps in D. melanogaster [20], D. persimilis [21], and D. pseudoobscura [22], [23], providing a resolution typically on the order of 100 kb in the regions analyzed. These experimental results suggest that regions of fine-scale variation—including some mild “hotspots” [22]—do exist in several Drosophila species. For example, Singh et al. [20] study a 1.2 Mb region of the X chromosome in D. melanogaster, and find 3.5-fold variation in this region, though no hotspots by the criterion mentioned above. These experimental approaches are cumbersome to recapitulate, however.
A number of crucial questions concerning Drosophila therefore remain unanswered. It is not known to what extent this variation is further localized to finer scales, or how common such variation is across the genome. Further, intra-specific differences in recombination rate have not been characterized. However, the advent of ambitious projects (e.g., see the Drosophila Genetic Reference Panel [18] and the Drosophila Population Genomics Project [24]) sequencing tens of D. melanogaster genomes each from different global populations raises the exciting prospect of addressing these and other questions. The patterns of LD in a random sample of contemporary genome sequences taken from a population contain a great deal of information regarding historical recombination events, and from these we can infer recombination rates across the genome. A number of sophisticated and computationally-intensive statistical approaches have been developed for inferring recombination rates from such data [14], [25]–[27] and for testing for the presence of recombination hotspots [28], [29], and are ostensibly suitable for this task. In particular, LDhat [14], [25], [30] is a useful software package which scales well to large datasets, and it has therefore been applied to estimating recombination rates in humans [4], [8], [13], [14], chimpanzees [7], dogs [31], yeast [16], and microbes [32], among others.
Estimating fine-scale recombination rates from recently published D. melanogaster genomes is, however, challenging for several reasons: First, these data exhibit a much higher density of single nucleotide polymorphisms (SNPs) than those of other species and of earlier technologies. For example, the African data considered in this paper exhibits a mean SNP rate of about 1 SNP per 38 bp for a sample of size 22, far higher than those of other recent sequencing projects (e.g., [8]). This promises an unprecedented opportunity to localize recombination rate variation to very fine scales, but making full use of these data raises further challenges in computational and statistical efficiency. Second, data generated from short-read sequencing technologies give rise to numerous missing alleles. It would be highly advantageous to be able to make use of sites in which some alleles are missing without the exponential increase in LDhat's running time that this entails. Third, the background recombination parameter in D. melanogaster is known to be an order of magnitude higher than in humans (the species for which LDhat's prior distributions and parameters are typically calibrated) and it is not clear how this will affect the accuracy of subsequent rate estimates. Fourth, there is a growing consensus that a considerable fraction of the genome of some Drosophila species is influenced by adaptive substitutions [2], [33]. Recurrent selective sweeps combined with genetic hitchhiking affect patterns of variation across many kilobases of sequence and have the potential to invalidate inferences of recombination, even leading to the possibility of spurious signals of recombination hotspots [34], [35]. By contrast, the footprints of positive selection in recent human evolution are less widespread [1]. The model underlying LDhat assumes a neutrally evolving population of constant size. While LDhat is known to be robust to mis-specification of the demographic model [14], its susceptibility to the effects of selection is less clear cut.
In this paper, we develop a new method, called LDhelmet, which addresses the above critical issues. While it employs a reversible-jump Markov Chain Monte Carlo (rjMCMC) mechanism similar to that of LDhat, our method has a number of modifications that render key advantages. Briefly, by utilizing recent theoretical advances in asymptotic sampling distributions [36]–[41], we introduce several analytic improvements to the computation of likelihoods in the underlying population genetic model, which reduce Monte Carlo errors and simultaneously provide likelihoods for all relevant samples with an arbitrary number of missing alleles. Our refinements further improve accuracy by allowing us to make full use of a quadra-allelic mutation model in which realistic mutation patterns between the four nucleotides A, C, G, T can be taken into account. Additionally, we utilize information from the available genomes of outgroup species by using them to infer a distribution on the ancestral allele at each polymorphic site in D. melanogaster. Taken together, our method enables us to compute fine-scale, genome-wide recombination rates with considerably improved accuracy and efficiency. LDhelmet generally produces recombination maps that are less noisy than that of LDhat's. In particular, while LDhat can infer spurious hotspots under certain types of selection, we demonstrate that our approach is much more robust.
We apply our method to data taken from two D. melanogaster populations, one from North America and the other from Africa, and estimate fine-scale recombination maps for each population. Then, through a wavelet analysis, we capture levels of variability and correlation of the two recombination maps, and provide a quantitative view of genome-wide inter-population comparison of recombination rates in D. melanogaster. We also employ the wavelet analysis to examine the correlation between various genomic features, including recombination rates, diversity, divergence, GC content, gene content, and sequence quality. At the fine-scale, we perform a conservative, systematic search for evidence of the existence of recombination hotspots and find a handful of putative hotspots each with at least a tenfold increase in intensity over the background rate. Also, we compare our recombination rate estimates with existing experimental genetic maps.
Our software LDhelmet and the fine-scale recombination maps described in this paper are publicly available at http://sourceforge.net/projects/ldhelmet/.
We applied our method to samples from two populations of D. melanogaster: Raleigh, USA (RAL) and Gikongoro, Rwanda (RG). The RAL dataset consisted of the genomes (Release 1.0) of inbred lines sequenced at a coverage of by the Drosophila Population Genomics Project [24] (DPGP, http://www.dpgp.org/). The RG dataset comprised genomes (Release 2.0) from haploid embryos sequenced at a coverage of by the Drosophila Population Genomics Project 2 (DPGP2, http://www.dpgp.org/dpgp2/DPGP2.html).
Using the procedure described in Materials and Methods, we were able to designate the ancestral allele in 1,755,040 of 2,475,674 high quality (quality score ) SNPs in the RAL sample (70.9%), and 2,213,312 out of 3,134,295 high quality SNPs in the RG sample (70.6%). These collections of polarized SNPs yielded the following estimates for the mutation transition matrix , with rows and columns ordered as A, C, G, T:These results imply that simple diallelic models are inadequate for the Drosophila populations. As expected, we see a transition:transversion bias. We also observe a higher overall mutation rate away from C and G nucleotides—this pattern persists even after excluding CpG sites from our analysis (not shown). Indeed, each of the four nucleotides exhibits its own characteristic mutation distribution. There appears to be no significant difference between the transition matrices for the two populations. This is partly explained by the shared history of the two populations: There were 2,990,025 sites for which: (i) data were available in both populations, (ii) two alleles were observed in the combined sample, and (iii) one of the two alleles was assignable as ancestral. Of these, 925,569 (31.0%) were polymorphic in both populations, 800,118 (26.8%) were private to RAL, 1,262,109 (42.2%) were private to RG, and 2,229 (0.1%) were fixed differences.
For simplicity, in the analysis described in this paper, we used the same mutation transition matrix for all sites in the genome. However, we note that our method can easily handle site-specific mutation transition matrices at no extra computational cost; see Materials and Methods: for details.
To assess the accuracy of estimated recombination maps, we carried out an extensive simulation study with various simple recombination patterns, first assuming selective neutrality (the case with natural selection is discussed in the subsequent section).
The simulations assumed a finite-sites, quadra-allelic mutation model, with the mutation transition matrix shown above and the population-scaled mutation rate per bp. We used these transition matrix and mutation rate in LDhelmet's inference. For LDhat, we used the corresponding effective mutation rate per bp (see Estimation of mutation transition matrices). Incidentally, we note that per bp is the estimated effective mutation rate for the autosomes of RAL lines [24].
Figure 1 shows representative examples of LDhelmet's and LDhat's results. As the figure illustrates, our method LDhelmet generally produces recombination maps that are less noisy than that of LDhat's; in particular, LDhelmet produces spurious “spikes” less frequently than does LDhat. To illustrate the impact of the spikes on the total genetic distance, the corresponding cumulative recombination maps comparing LDhelmet and LDhat are shown in Figure S1. Additional comparisons between LDhelmet and LDhat can be found in Table S1, and SNP statistics of the datasets are listed in Table S2.
In general, we observed that LDhelmet is able to identify the location of hotspots reliably. Furthermore, in the scenario considered in the second row of Figure 1, the width and height of the hotspot could be estimated very accurately; on average the total rate in the hotspot region could be estimated within 2.5% of the true value.
To test the performance of LDhelmet in a more realistic scenario, we simulated 1 Mb regions each with a substantial amount variation in recombination rate and with a high average rate representative of the interior of the D. melanogaster X chromosome. To specify realistic levels of recombination rate variability in these regions, we took as the true recombination map a 1 Mb excerpt from our estimated map for the RAL sample. To specify realistic absolute levels of recombination, we rescaled this map to match the mean (per megabase) recombination rates inferred for the X chromosomes of RAL and of RG. In Figure 2, LDhelmet's estimated recombination maps for these two scenarios are illustrated in blue, while the true maps are shown in red. These results demonstrate that, even when the average recombination rate is high, LDhelmet with our chosen block penalty in the rjMCMC is able to capture the pattern of fine-scale variation rather well. However, we note that in the top plot of Figure 2, in which case the true average rate is per kb, the estimated map tends to be slightly more variable than the true map. In contrast, if the true average recombination rate is substantially higher, as in the bottom plot of Figure 2 with the true average rate of per kb but otherwise the same pattern of variation, the estimated map tends to be somewhat smoother than the true map. Clearly, there is no single block penalty value that is universally optimal in all cases, but the value we have chosen seems to yield reasonable results for D. melanogaster (see Materials and Methods for further details on the choice of block penalty).
It has been previously shown [34], [35], [42] that hitchhiking can induce seemingly similar patterns of linkage disequilibrium as that created by recombination hotspots, while McVean [43] has argued that the precise signatures of selective sweeps and hotspots actually differ. To test the robustness of our method to natural selection, we simulated data under various scenarios with positive selection and recombination rate variation, and assessed the impact on our estimates of recombination rates. We generated data using a range of values for the selection strength and fixation time. See Simulation study on the impact of natural selection for details of the simulation setup.
The results of LDhelmet and LDhat for a few cases are shown in Figure 3; each plot shows the results for 25 simulated datasets illustrated in 25 different colors. The corresponding cumulative recombination maps are shown in Figure S2. For both methods, the estimated recombination maps are in general noisier than that for the neutral case (c.f., Figure 1), though LDhelmet is still more robust than LDhat. As can be seen in Figure 3, LDhat tends to produce false inference of elevated recombination rates near the selected site more frequently than does LDhelmet. A more detailed comparison is provided in Table S3 and SNP statistics of the datasets are listed in Table S2. Overall, although strong positive selection causes more noise and fluctuations in our estimates, it does not seem to produce a strong bias to the extent that would consistently lead to false inference of recombination hotspots.
The noise in our estimates of the recombination rate in the presence of selection depends on several factors. Specifically, we observed that the accuracy of our estimates decreases as the selection strength increases, whereas the accuracy improves as the distance between the selected site and the region of estimation increases. Furthermore, the more recent the time of fixation, the noisier are the estimates.
In addition to the case of a single, recent selective sweep, we also assessed the impact of recurrent selective sweeps [44], [45] on the estimation of recombination rates. Assuming that beneficial mutations fixate randomly at a given rate, we simulated three different sets of datasets with a background recombination rate of per kb, as detailed in Simulation study on the impact of natural selection. The degree to which recurrent sweeps reduce diversity in each model is summarized in Table S4. In model RS3, which has infrequent but strong sweeps, the mean number of SNPs reduces by more than a factor of relative to the neutral model. Such a drastic drop in diversity significantly reduces the ability to perform accurate statistical inference of recombination. To infer the location of a recombination hotspot, for example, at least a few SNPs must be present in the hotspot and near its edges.
The results of recombination rate estimation under recurrent sweep models are summarized in Table 1 and Table 2. Compared to a single sweep, recurrent selective sweeps tend to decrease the accuracy of recombination rate estimates more noticeably. Furthermore, infrequent but strong selective sweeps (model RS3) have more severe impact on the accuracy than do frequent but weaker selective sweeps (model RS1). As discussed above and can be seen in Table 2, detecting recombination hotspots in model RS3 would pose a great challenge. Overall, LDhelmet generally underestimates the recombination rate in the presence of selection, suggesting that it is unlikely to produce spurious hotspots because of selection.
We also tested our method on datasets simulated under a variety of demographic scenarios. Specifically, the demographic models we considered are those proposed by Haddrill et al. [46], and by Thornton & Andolfatto [47], comprising two exponential growth models and two bottleneck models. As in the neutral simulations, we assumed a finite-sites, quadra-allelic mutation model, with the mutation transition matrix and the mutation rate per bp. See Simulation study on the impact of demographic history for details on the other parameters used in the simulations.
Table 3 and Table 4 show the results of recombination rate estimation in this simulation study. Although the estimates are clearly less accurate compared to the case with constant population size, they are reasonably accurate in most cases. Note that the overall trend is to underestimate the true rates, in some cases only slightly.
As in the case of recurrent selective sweeps, demography may decrease diversity, thus hindering statistical inference of recombination. Table S4 includes the SNP statistics for the demography models we considered. In model B2, which involves a very recent bottleneck, a reduction in diversity by about a factor of was observed, partly explaining the particularly poor estimates of the recombination rate. Table S5 shows that the average SNP density of the D. melanogaster data considered in this paper; note that the average SNP density of each chromosome is substantially greater than the SNP density observed in simulation model B2.
The population-specific average recombination rate for each major chromosome arm is summarized in Table 5, which shows that the average rate for the African (RG) population is higher than that for the North American (RAL) population. This difference could be explained partially, but not entirely, by a difference in population size. Note that the average recombination rate in the X chromosome appears to be higher than that in the autosomes, much more so in RG than in RAL. Table 5 shows the ratio of the average recombination rate of RG to that of RAL for each chromosome arm. Although the ratio is more or less consistent for the autosomes, the ratio for the X chromosome is significantly higher. Hence, a difference in population size could explain the higher recombination rate estimates in RG for the autosomes, but it does not explain the significant increase in the recombination rate for the X chromosome of RG over RAL. Furthermore, for RAL, that the observed average recombination rate of the X chromosome is higher than that of autosomes is unexpected given that an excess of LD is observed on the X chromosome of this population [18], [24].
In both populations, arm 3R has a notably reduced recombination rate compared to the other arms. This reduction is more pronounced in RG than in RAL, which could be partly explained by the fact that, in African populations, arm 3R has the largest number of common inversions [48].
To study the effect of sample size on the estimation of recombination rates, we subsampled a 2 Mb excerpt of chromosome arm 2L from each population over several repeated trials. We performed the subsampling on an excerpt rather than the entire genome for computational reasons. The averages of the estimates are shown in Table S6. Despite a slight increase in the estimate as sample size increases, the effect is small and appears to diminish with increasing sample size. We also analyzed the whole-genome RAL dataset down-sampled to match the sample size (i.e., 22) of RG. As Table 5 shows, the genome-wide average estimates produced using 22 genomes of RAL were only slightly lower than those produced using all 37 genomes. Encouragingly, our estimate ( per kb) of the recombination rate for the X chromosome of RG is similar to the previous estimates for other African populations obtained using a different method: Haddrill et al. [46] estimated , and per kb for the X recombination rate in three African populations.
To assess the effect of SNP density, we thinned the SNPs on chromosome arm 2L and chromosome X of the RG dataset to the corresponding SNP densities of RAL, and performed inference on the resulting data. The results summarized in Table S7 show that although SNP density indeed influences the ability to estimate recombination rates, the impact is not nearly large enough to account for the difference between the observed recombination rates of RAL and RG on the X chromosome.
Finally, as there exist several inversions in D. melanogaster, we analyzed regions of inversion excluding individuals known to carry the inversion [49]. The comparison of excluding individuals with inversions and the original analysis is shown in Table S8. Note that for each inversion, only a small number of individuals carry it. We found that excluding the individuals with inversions did not significantly affect the recombination rate estimates.
LDhelmet's fine-scale recombination maps for RAL and RG are illustrated in Figure 4; files containing the corresponding numerical values are publicly available. To assess the accuracy of our recombination estimates obtained via statistical analysis of population genetic variation data, we compared them to genetic maps obtained experimentally.
Singh et al. [20] examined the fine-scale recombination rate variation over a 1.2 Mb region of the D. melanogaster X chromosome using a genetic mapping approach, by crossing an African line with a line presumably of North American origin (a cross between two lines from Bloomington Drosophila Stock Center). For their experiment, Singh et al. genotyped SNPs and identified two flanking genes, white and echinus, with visible phenotypes. They found statistically significant heterogeneity in this region, with differences in rate up to -fold. In Figure 5, estimates from LDhelmet for both the RAL and RG samples are shown, along with the genetic map from [20]. Both estimates from LDhelmet mostly fall within the confidence intervals of the empirical estimate, with the exception of the outermost intervals. The three maps share the same overall shape, including the location of the highest peak. We find -fold variation in the RG estimate, which is comparable to the -fold variation obtained by Singh et al. The high correlation among the three maps give us confidence that our estimates from the statistical analysis of population genetic data accurately represent the true underlying recombination map.
In a second study, we compared our chromosome-wide recombination estimates with a consensus genetic map for each chromosome arm based on data hosted at the FlyBase website (http://www.flybase.org [50]). To facilitate a comparison with this map, resolution of which is roughly 200 kb, we binned our data into the same cytogenetic subdivisions [24] and LOESS-smoothed the results, with a span of 15%; a correspondingly LOESS-smoothed version of the FlyBase data was kindly provided to us by C.H. Langley. A comparison of the maps is shown in Figure 6; evidently, the three estimates show broad agreement, each capturing key features such as the spike in recombination near position 10 Mb on arm 2L, as well as a series of dramatic changes in recombination rate across chromosome X. When the recombination map for RAL is regressed on the FlyBase maps, the coefficient of determination, or proportion of variability explained by the simple linear regression model, is and for chromosome arms 2L, 2R, 3L, 3R, and X, respectively; the corresponding values for RG are , and . These correlations are lower than those seen in a comparison of statistically- versus experimentally-derived maps in humans (e.g. [13]), though in that case the experimental data from pedigrees were of higher quality. As noted by Langley et al. [24], data on which the FlyBase map is based is highly edited and based on heterogeneous experimental conditions with sometimes conflicting results.
As discussed in the sec:introduction, it is well known that in humans and many other eukaryotes recombination tends to cluster in recombination hotspots, regions of approximately 2 kb wide in which the rate of recombination may be one or more orders of magnitude higher than the background rate [4], [12]–[14]. However, it is an open question whether hotspots exist in the D. melanogaster genome, or to what extent recombination rates vary on a fine scale.
We first searched for the most extreme forms of recombination rate variation—namely, recombination hotspots. Using a highly conservative approach described in Materials and Methods, we initially identified nineteen and five putative autosomal recombination hotspots from the RAL and RG data, respectively. Of these, respectively six and four were also detected by the hotspot detection software sequenceLDhot [29]. These ten hotspots, the width of which ranges between 0.5 kb and 6.8 kb, are listed in Table 6. All were found in genic regions, with all except one overlapping exons and one contained within an intron. An example of a RAL hotspot is shown in Figure 7, where we also show the RG recombination map. The fine-scale recombination maps in this region for the two populations are clearly highly correlated, with both RAL and RG exhibiting a tenfold increase in recombination rate within almost identical 4 kb intervals, though only the hotspot of RAL was also found by sequenceLDhot. We note that the power of sequenceLDhot may be further reduced by the apparent preference (not shown) for putative hotspots to reside in regions in which the “local” background rate is higher than that of the chromosome arm as a whole. In light of these factors, it is likely that several more hotspots would have been found in one or both populations under a more relaxed definition, though it is clear that they are far scarcer, and less hot, than in humans.
It is apparent from both RAL and RG maps shown in Figure 4 that recombination rates vary on multiple scales, from the very fine to the very broad, as has been observed in a number of other species [7], [13]–[16]. It is clear, for example, that recombination rates tail off towards each end of the arm, with the reduction towards the telomere much more precipitous than the pericentromeric reduction. The latter reduction is evident from as far as the start of heterochromatic sequence a few megabases from the centromere, in agreement with other broad-scale estimates of recombination [17], [18], although we do not find a complete absence of recombination here.
Figure 8 shows that the recombination rate in the X chromosome between positions 10 kb and 20 kb is noticeably higher than the rate in the subtelomeric region to the right. This trend is much more pronounced in the North American X than in the African X, consistent with a previous study by Anderson et al. [51]. The telomere-associated sequence (TAS), located to the left of position kb, was not available in our data, but Anderson et al. provided evidence that the TAS region in the North American X exhibits even higher recombination rate than that in the subtelomeric region between positions 10 kb and 20 kb.
As shown in Figure 4, the largest difference between the estimated recombination maps of the two populations is observed in the X chromosome. First, the recombination map in the African X is generally much higher than that in the North American X. Second, there is noticeably less variation in the estimated African X recombination map. As mentioned earlier in the discussion of our simulation study, when the average recombination rate is as high as that of the African X, the amount of variation in our estimated map tends to be somewhat lower than the true variation. Hence, the observed reduction in variation could be partially attributed to our method being not sensitive enough in that range of very high rates. More generally, it is also true that Fisher's information for data on sequence variation is lower in regions of high recombination (), which could create an inherent limitation in our ability to infer recombination rate changes here.
The use of wavelets enables us to compare how changes in the rate of recombination along the genome correlate with other genomic features. For each population we computed pairwise correlations between the detail coefficients of the following features: diversity (mean fraction of pairwise differences between each individual in the population, within sequenced nucleotides), divergence (fraction of differences between the reference sequences of D. melanogaster and D. simulans), GC content, gene content (fraction of sites annotated as exonic), and sequence quality (Phred score), as well as the recombination rate, with each feature measured in 250 bp windows (see Materials and Methods). Results are shown in Figure 12 and Figure S12, and follow a similar analysis performed by Spencer et al. [53] on human data. From these results we can make a number of observations detailed below.
We have developed a new method, LDhelmet, which is able to provide accurate estimates of recombination rates using genomic data from D. melanogaster. Although our focus has been on this species, the features of our method should offer improvements in the estimation of recombination in other species too. For example, the desire to efficiently incorporate sites in which some alleles are missing is a common issue when data are generated by next-generation sequencing technologies. We believe that our method will find many further applications in other datasets.
Using our method, we have performed a genome-wide comparison of fine-scale recombination rates between two populations of D. melanogaster, one from Raleigh, USA (labeled RAL) and the other from Gikongoro, Rwanda (labeled RG). While earlier studies have largely been confined to regions of moderate resolution, we find extensive fine-scale variation across all chromosomes and in both populations. A notable difference between the two recombination maps is the higher overall recombination rate in RG than in RAL. Our method estimates the composite parameter , where is the effective population size and is the (female) rate of recombination per generation, so this difference is partly explained by a difference in effective population size. However, further differences between chromosomes—namely, the inflated recombination rates in the X chromosome relative to autosomes—lead us to invoke biological differences too, particularly the role of polymorphic inversions. There may also be other, unappreciated, biological factors causing an increase in on the X chromosome.
In addition to the higher absolute rate of recombination in RG, a further difference between the populations merits discussion: the relative increase in recombination on the X chromosome compared to the autosomes is much more pronounced in RG than in RAL. In the African population, estimates of the ratio lie in the range , whereas in the North American population they lie in the range (Table 5). There are several possible explanations for the difference between the two populations. First, RAL may have experienced a historical population bottleneck. The effect of a population bottleneck on LD is stronger on the X chromosome than on the autosomes [59] (a similar effect on diversity is also seen [60]). Thus, a population bottleneck leads to an increase in LD on the X chromosome over and above the increase on the autosomes. A bottleneck in the non-African population is a sensible proposition since D. melanogaster is a human commensal of African origin which has colonized North America more recently. Bottlenecks in non-African populations of D. melanogaster have been inferred from genetic data by others [46], [47]. Furthermore, as shown in our simulation study, bottlenecks tend to cause our method to underestimate the true recombination rate, so the bottleneck explanation would be consistent with the fact that our recombination rate estimates for RAL are lower than that for RG. Second, the impact of polymorphic inversions may be greater in RG, since the African population has a high frequency of polymorphic inversions in the autosomes and in the centromere-proximal X. The observed increase in the recombination rate in the African X could be partially attributed to interchromosomal effect [61], [62]. A third possible explanation is the more efficient role of selection on the X chromosome when nonneutral mutations are recessive: such mutations can more easily be exposed to the action of selection in their hemizygous state in males. This effect will be more pronounced in RAL if it has undergone greater selective pressures, as seems likely in its adaptation to a new environment. Unraveling the relative importance of these possible explanations merits further investigation.
At fine-scales, we also find extensive differences between the recombination maps of the two populations, for which a simple difference in effective population size is not a sufficient explanation. Wavelet coherence analysis reveals high correlation at broad scales but regions of low correlation at fine scales, as has been documented among human populations, and in comparison between humans and chimpanzees [8], [9]. The advantage of a wavelet coherence approach is that it further identifies the locations of similarities and differences. However, the causes of these differences remain to be understood. One noteworthy result of our analysis is that changes in diversity are a strong positive predictor of changes in recombination in one population, even when the recombination map of the other population is included as a covariate. A possible explanation for this observation is that the two populations have undergone separate selective sweeps, with sufficient impact on the genome that the correlation between recombination and diversity can still be detected even when the recombination map of the other population is used as a covariate. We note that a partial overlap in the signature of selective sweeps was also found by Langley et al. [24]. Using a metric based on valleys of diversity, they found that 44% of diversity valleys in RAL overlapped with those found in an African sample. There are of course other possible explanations for the observed correlations between diversity and recombination; it is known that background selection—the loss of neutral diversity due to linked deleterious mutations—can also induce such a correlation (see Charlesworth [63], [64] and references therein). The relative importance of these types of selection in distinguishing the two populations is obviously deserving of further study.
Access to a fine-scale map lets us address a crucial question of the distribution of recombination in Drosophila: whether they localize into recombination hotspots. Using a conservative approach, we found a few regions with solid statistical support for a local elevation of at least 10 times the background recombination rate (Table 6). With the caveat that we used a high block penalty in the rjMCMC and employed a stringent hotspot detection strategy, overall our findings support the belief that extreme localization of recombination into hotspots is not prevalent in D. melanogaster; in humans, on the other hand, the list of well-supported hotspots exceeds 30,000 [4], many of which exhibit much more than a tenfold increase and have a common mechanism for recruiting the recombination machinery [6], [10], [11]. Singh et al. [20] therefore reserve the term “recombination peaks” for the milder variability they find, and it could be the case that what we have found in this paper are the most extreme examples of these peaks. Having said that, we also note that, as discussed earlier in our simulation study, the ability to perform accurate statistical inference of recombination (in particular, detecting hotspots) gets significantly reduced when recurrent strong selective sweeps are in play. It is hence possible that there are actually more hotspots in the D. melanogaster genome than our study could find.
We have focused on estimating and characterizing the recombination map itself and on its correlation with a set of important genomic annotations, but given such a map one can tackle many further problems. The question of primary sequence influences of recombination localization can now be addressed with much greater power. In humans, the 13 bp motif CCNCCNTNNCCNC has been found to be over-represented in hotspots, consistent with its recruitment of the protein PRDM9 which has been implicated in the hotspot usage [6], [11]. Searches for motifs in Drosophila that correlate with fine-scale recombination rate have been undertaken in D. pseudoobscura [22], [23], D. persimilis [21], and D. melanogaster [58]. Motifs that correlate with fine-scale recombination in humans are also significant in some of these species [21], [23], which is unexpected given the rapid turnover of motif usage in humans and chimpanzees [6]. In a recent pedigree study, Miller et al. [58] were able to localize with high precision fifteen crossover events on the X chromosome of D. melanogaster. From these they identified the 7 bp motif GTGGAAA as significantly enriched in the vicinity of these crossovers. Further study is required to validate this motif and to search for others, and our maps should prove useful in this regard.
Finally, our work should be of interest since a fine-scale recombination map is a prerequisite of studies seeking to estimate the influence of natural selection on the genome [1]; those lacking such a map retain this caveat [2]. Although these inferences of recombination and selection rely on the same data and have the potential to distort each other, it is reassuring that our method is robust to the influence of positive selection, and that it shows good agreement with existing experimental estimates of recombination. In our simulation studies we focused on the effects of hard sweeps, since they are thought to be an important mode of adaptation in Drosophila [2], [33], [45] and are expected to have the strongest effect on patterns of variation. Aside from additional noise resulting from a reduction in diversity, there is little bias introduced by failing to include selection in the assumed model, at least under the parameters we considered. This is consistent with the observation that a recurrent sweep model does not have a striking effect on LD beyond that predicted by the reduction in diversity [59]. Nonetheless, further investigation is warranted on the effects of other types of selection, and on the development of methods that can account for recombination and selection jointly.
The mean coverage of the RAL data was . Regions of residual heterozygosity and regions of identity-by-descent between genomes were masked in the RAL data, in addition to a quality filter of Q30 applied to both populations. Preliminary analysis by the DPGP2 group found evidence of admixture among of the RG lines we considered, in addition to evidence for minor levels of identity-by-descent between genomes. To maintain a reasonable sample size, these regions were not masked in the results presented in this paper. We did repeat several of our analyses with these regions excluded and generally found little difference. Despite the extensive filtering, which increases the amount of missing data, the runtime complexity of our method does not increase from a lack of data, as it does for LDhat.
The data were divided into overlapping blocks of 4,400 SNPs each, with 200 SNPs of overlap on either end of a block. For every block, LDhelmet was run for 3,000,000 iterations after 300,000 iterations of burn-in. The map for each chromosome or chromosome arm was constructed by removing 200 SNPs from the ends of the blocks and concatenating the blocks together.
The aim of our method is to infer the fine-scale map of the population-scaled recombination rate in D. melanogaster, in which recombination occurs only in females. The population-scaled recombination rate between a pair of sites in the chromosome is defined as , where is the effective population size for X and is the probability of recombination between the sites per generation per X chromosome in females. The population-scaled recombination rate between a pair of sites in an autosome is defined as , where is the effective population size for the autosome and is the recombination rate between the sites per generation per autosome in females. Furthermore, and are defined as and , where and denote the effective number of female and male individuals in the population. If we assume , we obtain and .
In contrast to recombination, mutation occurs in both males and females. We denote the X chromosome mutation rates in females and males as and , respectively, and the autosomal mutation rates in females and males as and, respectively. Then, the population-scaled mutation rates for X and the autosomes are given by and , respectively. Further, if , then the expressions simplify to and .
In our statistical model, we allow the recombination rate to vary across the genome. We use to denote generically the population-scaled recombination map, which is a function of genomic position. For ease of notation, we do not add a subscript to to distinguish between X and autosome; it should be clear from the context which is intended. Similarly, we use to denote generically the population-scaled mutation rate.
Our objective is to estimate the recombination map from population genomic DNA sequence data. Our approach introduces several key improvements to the method LDhat [14], [30] (v2.1 used throughout), which was first developed for estimating fine-scale recombination maps in humans. Below is a brief description of LDhat, followed by the details of our improved method LDhelmet.
Given a sample of chromosomes from a population, LDhat estimates the recombination map within a Bayesian setting, placing a prior on the map. To avoid overfitting, is assumed to be a step function (i.e., a piecewise constant function). The prior is a distribution on the number of times changes value, the locations of such changes, and the value of each piecewise constant segment. LDhat employs reversible-jump MCMC (rjMCMC) [65] to sample from a posterior distribution over a sample space of step functions where different parts of the space have different numbers of parameters.
Denote the likelihood of and by , where represents a set of phased haplotypes. Rather than compute the full likelihood, which is in general intractable except for a very small sample, LDhat computes an approximation known as the pairwise composite likelihood [30], [66]. For every pair of SNPs in a short region, the pairwise likelihood is computed under the coalescent with recombination, and the product over all such pairwise likelihoods serves as an approximation to the full likelihood. This approach scales well to large datasets, and has been demonstrated through simulation studies to provide a reasonable approximation to the full likelihood [30].
The two-locus likelihoods are precomputed and stored in a lookup table for computational efficiency. In LDhat, the two-locus likelihoods for sample configurations with no missing data are precomputed by importance sampling [67]. Then, the likelihoods for sample configurations with missing data are computed (and stored) as they are encountered during data analysis, by marginalizing over appropriate configurations with no missing data; the running time of this procedure is exponential in the number of missing entries in the configuration.
There is one likelihood table for every choice of mutation parameter , and likelihoods are precomputed over a grid of the recombination parameter . In LDhat, the default is over and a two-allele model is assumed, with mutation transition matrix P at each site. That is, when a mutation event occurs, the allele changes to the other type with probability .
To accommodate the higher recombination rate observed in D. melanogaster, we introduce several key modifications to LDhat to improve the accuracy and robustness of recombination map estimation. These modifications are summarized as follows:
The computational tractability of the first two modifications listed above is dependent on the following approximation we employ: We assume that each site underwent at most one mutation in the entire genealogical history of the sample. This assumption is reasonable for small values of , as is the case for D. melanogaster, and it provides several computational advantages, described in the following sections.
For generating two-locus likelihood lookup tables, we replace importance sampling with solving recursion relations [68] (see also [36]–[39], [69]). These recursion relations necessitate the solution of large systems of equations in the possible observed sample configurations. However, the one-mutation-per-site assumption leads to gains in efficiency that make such systems soluble. To illustrate, consider first a random sample drawn from a single locus. We use the notation to denote the probability that a sample of alleles taken at random from the population in some fixed order leads to the one-locus configuration m, where is the number of samples with allele ; if we are modeling, say, the evolution of DNA nucleotides, then and . (It is implicit that this probability is also a function of the mutation transition matrix at this locus.) It is well known (e.g., [70]) that satisfies(1)for which a closed-form solution is not known in general. Here, denotes a unit vector with th entry 1 and the rest zero. In a later section, we describe a method for using outgroup data to infer which of the alleles in our samples is ancestral. When the identity of the ancestral allele (i.e., the allele of the most recent common ancestor of the sample) is presumed known, say type a, the appropriate boundary condition for use with (1) isAs an alternative to working with (1), we can seek a solution for the joint probability of obtaining the configuration with the event that it arose as the result of precisely mutation events in the history of the sample, a probability we denote by . Then we have [70]:(2)withThe advantage of the one-mutation-per-site assumption is then apparent: is known in closed-form [40], [41]:(3)where the only nonzero entries of are and , corresponding to a sample comprising copies of the ancestral allele type and copies of a derived allele type . Hence, in this case we entirely circumvent the need for a numerical solution to a large system of linear equations. Provided the mutation rate per site is sufficiently small, the error should be negligible.
We can make similar gains in a two-locus model by reducing a large system of equations to a much smaller system, albeit one that still requires a numerical solution. The idea is similar to that described above, though notation is more complicated: the precise form of the system is provided in Text S1. In the present paper, the largest sample size we work with is . This leads to a very large system of equations that must be solved: Accounting for symmetries, the total number of complete configurations of size is approximately 1,300. When we count all configurations encountered in the RAL data—including those with missing alleles—this number rises to . In the two locus case, the quantity of interest is , the probability of obtaining the two-locus configuration together with the events that there was precisely one mutation event at each of the two loci. Here, denotes the mutation rate and denotes the recombination rate between the two loci. Provided we work with the reduced system of equations for as outlined above, it becomes feasible to solve the system for every sample of size , and thus to generate exactly solved lookup tables for later use. Table S9 shows the running time of this recursion-based likelihood computation as a function of sample size .
Because the two-locus recursion relation is solved jointly for every configuration, this also gives us exact solutions for every subconfiguration at no extra computational cost. In particular, we emphasize that we also obtain likelihoods for all relevant configurations with any missing data, at no extra computational cost. By contrast, when LDhat encounters a configuration in which some alleles are missing, its approach is to marginalize over missing alleles by summing over the relevant entries in its lookup table for fully-specified haplotypes, but the time required for this computation is exponential in the number of missing alleles. The extent of missing data in the D. melanogaster genomes is such that this approach is impracticable. On the data we analyzed, we masked all alleles with a quality less than . For the RAL lines, about of the data was missing, and for the RG lines, about of the data was missing. The more missing data there is, the more expensive marginalization becomes, and the greater the number of distinct configurations present in the data.
One key advantage of our approach is that it can make use of all four alleles (A, C, G, T) in sequence data, together with the ancestral alleles inferred from outgroup sequences. This is achieved with modifications to the boundary conditions of the appropriate two-locus recursion described above. In combination with the one-mutation-per-site assumption, this allows us to use a full transition matrix to model realistic mutation patterns between nucleotides, with no significant amount of extra computation: Suppose the ancestral allele at each of a given pair of segregating sites is known to be and , respectively. At the first site some chromosomes exhibit a derived allele, and at the second site some chromosomes exhibit a derived allele. Because of the one-mutation-per-site assumption and the decoupling of the genealogical and mutational processes under neutrality, it is easy to see that the likelihood of this two-locus configuration has a dependence on only through a single multiplicative factor . Hence, this expression can be factorized completely out of the two-locus likelihoods and hence from our lookup tables. The remaining quantity, which represents the probability of observing a particular configuration up to the identities of the alleles involved, can be multiplied by the relevant pair of entries in for any observed combination of nucleotides. To be precise, if denotes our solution to the system of equations described above, this argument shows we can write(4)for some function independent of . [The single-locus analogue of this result is evident in equation eq:one-locus-solution.] We then need to store only . If later we see the same combination of haplotype counts but for a different combination of nucleotides, we can reuse this quantity and multiply it by different relevant entries in . For simplicity, in our analysis we used the same for each site in the genome, but note that, because of the factorization in (4), it is possible to use different mutation transition matrices for different sites at no extra computational cost.
This approach easily generalizes to the case where the ancestral allele is not known or where we only have a distribution on the ancestral allele at each site. We can simply take the weighted average over each of the four possible combinations of ancestral alleles, weighted with respect to their distributions. In the case where no information is known about the ancestral alleles, this reduces to using the stationary distribution of as the distribution over ancestral alleles at each site.
Because we are now able to make full use of a quadra-allelic mutation model, we developed a method to estimate the mutation transition matrix from empirical data, for subsequent use in our recombination rate inference. We use the following parsimony-based method to estimate by inferring the ancestral allele at each site in D. melanogaster by comparison with aligned outgroup reference genomes of D. simulans, D. erecta, and D. yakuba. We designate the ancestral allele at each dimorphic site in D. melanogaster using the following rule. If the alleles of the three outgroups are not all missing at this site and together exhibit precisely one of the four possible nucleotides, and if this allele agrees with one of the two observed in D. melanogaster, then this is designated as the ancestral allele. Otherwise, it is considered unknown and discarded from the analysis. (We also discarded triallelic and quadra-allelic sites.) A related approach is used in the Drosophila Population Genomics Project in the estimation of divergence. We tried both more and less restrictive parsimony rules, as well as excluding CpG sites from our analysis; neither variation substantially altered our results.
Given a large collection of SNPs in our dataset for which the ancestral allele is known, we can infer the identities of the alleles involved in the mutation event at each polymorphic site. For example, an A/G polymorphism with A ancestral implies a historical AG transition. The relative frequencies of each type of event, normalized to account for varying genomic content of the four nucleotides, determines our empirical estimate of . To be precise, let denote the total number of A nucleotides in the D. melanogaster genome, of which , , and have been inferred to be AC, AG, and AT polymorphisms, respectively. (For consistency we restrict all these definitions only to those monomorphic or dimorphic sites for which sufficient, consistent outgroup information is also available, as required above.) We make analogous definitions for and , for each . Finally, let , the largest empirical frequency of mutation away from any particular nucleotide. The appropriate choice for is given byDivision by ensures that, without loss of generality, one entry in the diagonal of is zero. By allowing the diagonal entries of to be nonzero, different nucleotides can have different overall mutation rates. The total “effective” mutation rate—that is, mutations not involving the diagonal entries of —is calibrated against classical infinite-sites-based estimators: for RAL this is per bp (autosomes) and per bp (X chromosome). For RG we used per bp for all chromosomes. Since we are to use a general quadra-allelic model in which both effective and ineffective mutations are permitted to occur, the appropriate choice of for use with is such that it exhibits the same overall rate of effective mutations:
When it is not known which of the two alleles at a polymorphic site is ancestral, one can use the stationary distribution of as a prior distribution over the ancestral allele. However, when additional information is available, such as sequence data from an outgroup, we can use the information to update our prior beliefs about the identity of the ancestral allele, thus allowing a more accurate estimate of the recombination map. In our application, we used the D. simulans outgroup information to update our prior distributions on the ancestral alleles of the D. melanogaster samples. Specifically, for each D. melanogaster genome, we used the software psmc [71] to estimate, at each site, a distribution on the time to the most recent ancestor (TMRCA) of the D. melanogaster and D. simulans genomes. Given the TMRCA, we integrate over possible mutations occurring according to along the two branches, to obtain a distribution on the ancestral allele. Finally, for each site, we aggregate each of these pairwise distributions into a single distribution on the ancestral allele, and use this distribution in the computation of our likelihoods. Further details are provided in Text S1.
Recall that LDhat's lookup tables are precomputed over a grid: . For a pair of sites with a recombination rate greater than 100, the likelihood at is used as an approximation. This can create systematic errors in the likelihood [36]. Instead, for we compute accurate approximations to the two-locus likelihood using the method of Padé summation described in Jenkins & Song [38]. Briefly, one Taylor expands about and uses the method of Jenkins & Song to compute the first few terms in the expansion. In practice this Taylor series rapidly diverges for values of of interest, but it can be made into an accurate, convergent approximation of by replacing this truncated series with a rational function approximation whose own Taylor series agrees as far as possible, a technique known as Padé summation. We modified the analysis of Jenkins & Song to account for our new system of equations described above (see Text S1). We precompute 11 Padé coefficients (up to in the Taylor series expansion of the likelihood about ) for every sample configuration of size , which gives an extremely accurate approximation for every (not just integral values). Usually, the “join” between the Padé approximant for and the true likelihood for is indistinguishable. We also employ a “defect heuristic” [38] with threshold parameter to correct for potential effects from singularities in the Padé approximants. As in the direct computation of the likelihoods from the system of equations, obtaining the Padé coefficients for a given configuration also yields the coefficients for all its subconfigurations. This approach is therefore well-suited to data with a large proportion of missing data. Table S9 shows the running time for the Padé coefficient computation as a function of sample size .
One can imagine that it would be useful to have a more refined lookup table in regions of higher curvature of the likelihood. In such regions simply using integral values of might be too coarse. Since the lookup tables will be used for every conceivable pairwise dataset, we should be interested in the expected curvature of the likelihood curve at , across datasets drawn under a model with the same . (That is, the curvature at some is most important for datasets that we are likely to see when the recombination rate really is .) This is reflected by Fisher's information:which can be estimated from an existing lookup table using the second-order central difference operator. As is evident from Figure S3, curvatures are generally higher in the range , and so we changed the increment between values in the lookup table from 1 to 0.1 in this range.
LDhelmet places a prior distribution on the number of change points, the positions of the change points, and the heights of the change points in the recombination map. The prior on the number of change points is, as in LDhat, a Poisson distribution with mean equal to , where is the number of SNPs in the data and is a user-defined parameter called the block penalty. The positions of the change points are distributed uniformly, and the distribution on the heights of the change points is user-settable as exponential, gamma or log-normal.
One should be mindful that LDhat was designed for background recombination rates an order of magnitude less than that used in the simulations. In particular, LDhat implements the exponential prior but the mean is hard-coded for human data. Adjusting the mean of the prior according to the expected background recombination rate is necessary to obtain meaningful results. For example, using a prior suitable for humans on Drosophila-type data produces poor estimates with little to none of the true variation in the underlying recombination map (simulations not shown). To facilitate a comparison, we modified the source code of LDhat such that its prior was similar to the one used by LDhelmet. Without such modifications, the estimates from LDhat were not comparable to LDhelmet's estimates. In the simulations and analysis, we used an exponential prior with the mean adjusted for the expected background rate of D. melanogaster.
The block penalty controls the extent of variation in the estimated recombination map. In general, the higher the block penalty, the smoother the estimated map. We carried out a simulation study to choose a conservative penalty value to reduce false positive inference of hotspots, at the expense of tolerating more false negatives.
In this simulation study, we considered the following three scenarios: no recombination variation (constant rate), moderate variation (with a hotspot of width kb and intensity the background rate), and high variation (with a hotspot of width kb and intensity of the background rate, such as that seen in humans). We simulated datasets of each kind, with a fixed background rate of in all cases.
After considering a variety of evaluation metrics for measuring the accuracy of an estimated map, we found the -distance between the true map and the estimated map to be the simplest to interpret and assess, where the -distance is the sum of the point-wise differences between the true and estimated maps. For the three scenarios described above, Figure S17 shows the average -distances between the true recombination maps and the estimated maps for various block penalty values and recombination landscapes. For each dataset, we ran LDhelmet for 250,000 iterations after a 50,000 iteration burn-in. We observed that noise from overfitting is reduced for higher block penalties. Based on our simulation study, we chose a conservative block penalty of in our analysis of the real data.
In our simulation study for evaluating the choice of block penalty on realistic data (Figure 2), we used the program MaCS [72] to simulate a 1 Mb region with a highly variable recombination map. (We used and ; output was postprocessed to incorporate an empirical quadra-allelic mutation model.) The map's variability was taken from a 1 Mb excerpt of the estimated recombination map of the X chromosome for the RAL sample. The total recombination rate for the region was then rescaled to match the mean (per Mb) rate of the RAL X chromosome (to create a “RAL-like” map) or the RG X chromosome (to create a “RG-like” map; see Figure 2).
In order to simulate datasets that had been affected by natural selection, we focused on modeling the effects of sites experiencing positive, genic selection, i.e. selective sweeps. We investigated two modeling scenarios: First, the effect of a single, strong sweep with its strength, fixation time, and location treated as fixed parameters. Under some parameter combinations, we expect such sweeps to substantially reduce observed polymorphism levels. Second, we considered data generated under the influence of a recurrent sweep model, in which the ages and genomic locations of sweeps occur randomly. In this scenario, we chose the parameters of the model (selection coefficient and rate of fixation of beneficial mutations) such that expected polymorphism levels were concordant with observations in D. melanogaster. While the second scenario is likely to be a more realistic model for the forces affecting variation in D. melanogaster genomes, its inherent randomness introduces additional noise. The first scenario allows us to study the effects of a sweep with particular characteristics under a controlled environment.
Under both scenarios, we again simulated data under three possible recombination landscapes: a flat recombination rate of per kb except for a 2 kb-wide hotspot at the center of the sequence, of relative strength 1 (no hotspot), 10, or 50; we also post-processed all outputs to allow for a full quadra-allelic mutation model, using the mutation transition matrix . To reconstruct the recombination maps of simulated data, we used the following parameters for LDhelmet and LDhat: 250,000 iterations after 50,000 iterations of burn-in for LDhelmet, and 1,000,000 iterations after 100,000 iterations of burn-in for LDhat. We chose the number of iterations such that the two methods would require about the same computational time.
In order to simulate datasets that had been affected by a nonstandard demographic history, we used the software msHOT [74]. We investigated four realistic demographic histories:
(G1) Exponential growth at rate 100 initiated generations ago (a tenfold increase by the present time),
(G2) Exponential growth at rate 10 initiated generations ago (a fivefold increase by the present time),
(B1) A bottleneck initiated generations ago, with a transient reduction to size lasting generations,
(B2) A bottleneck initiated generations ago, with a transient reduction to size lasting generations.
The first three models were proposed by Haddrill et al. [46] as reasonable fits to their (African) data, while the fourth is taken from [47] for a European population. We note that the precise demographic history of D. melanogaster populations remains poorly understood, and that these models simply serve as reasonable examples for investigating the robustness of our method. It is probable that there exist better fitting demographic models; indeed, Haddrill et al. ultimately favor their bottleneck model over any growth model.
We simulated 100 datasets under each model and under each of three recombination landscapes: a flat recombination rate of per kb except for a 2 kb-wide hotspot at the center of the sequence, of relative strength 1 (no hotspot), 10, or 50. This provided independent datasets in total. We also post-processed all outputs from the infinite-sites-based software to allow for a full quadra-allelic mutation model, using the mutation transition matrix and the mutation rate per bp. We ran LDhelmet for 250,000 iterations after 50,000 iterations of burn-in.
We used a conservative approach to identify candidate recombination hotspots. From the recombination maps for RAL and RG we first identified putative hotspots—regions in which the recombination rate exceeded ten times the mean for that chromosome arm, and which were greater than 500 bp in length. We discarded regions of length less than 500 bp on the grounds that such narrow peaks can be produced occasionally as spurious artifacts of the rjMCMC procedure.
To further filter the remaining candidate hotspots, we applied an independent method, sequenceLDhot [29], to the same data, in order to test for the presence of hotspots in these regions. The software uses a computationally-intensive importance sampling framework to construct likelihood ratios in sliding windows to evaluate the evidence for the presence of a hotspot in that window. To reduce computation time we focused on 50 kb regions centered on the autosomal putative hotspots. We modified sequenceLDhot's default parameters, which are tuned for interrogating human data, as follows. We used per site, and for the background recombination rate we used the estimated mean across the local 50 kb containing the hotspot of interest. We specified the software's grid for hotspot likelihoods to be in the range 10–100 times the background rate, and tested windows of 500 bp sliding in steps of 250 bp, using a composite likelihood comprising ten SNPs. Other parameters were unchanged. We reduced SNP density to be comparable to the data on which the software had been calibrated [29], by discarding sites with any missing alleles and singleton SNPs, though we obtained similar results without such a reduction (not shown). In constructing our final list of candidate hotspots, we retained only those which overlapped one of sequenceLDhot's ‘extended hotspot regions’, constructed conservatively from windows with a likelihood ratio greater than 10. To improve power in the search for hotspots, we included five additional lower coverage RG genomes in this analysis.
To put the recombination maps into a suitable time-series format, we used the (log-transformed) cumulative recombination rate across each bp window. We found that this provided good resolution at high frequencies, with little further improvement using smaller bins. To facilitate a comparison between RAL and RG, we used the maps estimated from sample size in both populations.
|
10.1371/journal.pntd.0006205 | Environmental enteric dysfunction pathways and child stunting: A systematic review | Environmental enteric dysfunction (EED) is commonly defined as an acquired subclinical disorder of the small intestine, characterized by villous atrophy and crypt hyperplasia. EED has been proposed to underlie stunted growth among children in developing countries. A collection of biomarkers, organized into distinct domains, has been used to measure different aspects of EED. Here, we examine whether these hypothesized relationships, among EED domains and between each domain and stunting, are supported by data from recent studies.
A systematic literature search was conducted using PubMed, MEDLINE, EMBASE, Web of Science, and CINAHL between January 1, 2010 and April 20, 2017. Information on study objective, design, population, location, biomarkers, and results were recorded, as well as qualitative and quantitative definitions of EED. Biomarkers were organized into five EED domains, and the number of studies that support or do not support relationships among domains and between each domain with stunting were summarized.
There was little evidence to support the pathway from intestinal permeability to microbial translocation and from microbial translocation to stunting, but stronger support existed for the link between intestinal inflammation and systemic inflammation and for intestinal inflammation and stunting. There was conflicting evidence for the pathways from intestinal damage to intestinal permeability and intestinal damage to stunting.
These results suggest that certain EED biomarkers may require reconsideration, particularly those most difficult to measure, such as microbial translocation and intestinal permeability. We discuss several issues with currently used biomarkers and recommend further analysis of pathogen-induced changes to the intestinal microbiota as a pathway leading to stunting.
| Globally, one-quarter of children under the age of five are affected by poor linear growth, known as stunting. Interventions, including giving children supplemental foods or improving hygiene to prevent diarrhea, have only been partially successful at restoring normal growth. Environmental enteric dysfunction (EED) is a disease characterized by damage to the lining of the small intestine and is thought to contribute to stunting, though the exact mechanism is still unclear. EED was first diagnosed by removing samples of the intestinal lining and analyzing them under a microscope; however, these procedures are costly and invasive, and recent research has focused on discovering easier ways to identify EED. These tests focus on the many interconnected aspects of EED, including damage and function of the intestinal wall, inflammation, and presence of pathogenic bacteria outside the gut. We conducted a systematic review to evaluate the evidence of relationships between each aspect of EED and stunting. We found the most evidence for the relationship between inflammation and stunting, but less evidence for the relationship between stunting and the presence of pathogenic bacteria outside the gut. Our results suggest that EED may be more complex than previously conceived and that some frequently used EED tests may need reconsideration.
| One-quarter of children under the age of 5 years are stunted, defined as a height-for-age > 2 standard deviations below the median as defined by the World Health Organization growth standards. Children whose linear growth is impaired during the first 1000 days after conception have an increased risk of poor cognitive development and educational performance, lost productivity and lower adult earnings, chronic diseases, and mortality over their lifetime [1–3].
There is a well-recognized network of interacting determinants that underlie stunting [4–13]. For many years, studies have focused predominantly on nutrition-specific interventions for stunting; however, previous systematic reviews highlight that neither food quantity nor quality fully explains impaired linear growth in children [14,15]. Diarrhea has been proposed as a major contributor to growth failure in young children, though results are inconsistent [16–19]. While diarrheal episodes in the first few months after birth lead to increased prevalence of stunting at 24 months [20], catch-up growth between diarrheal episodes can be sufficient for linear growth recovery in some children [21].
Environmental enteric dysfunction (EED) is commonly defined as an acquired subclinical disorder of the small intestine, characterized by villous atrophy and crypt hyperplasia. Previous reviews have described the history and epidemiology of environmental enteric dysfunction in detail [1,22–26] and have refocused attention on EED as a potential cause of stunting in developing countries. Exposure to bacteria through fecal contamination is postulated to induce morphological changes, leading to intestinal epithelial damage, increased permeability, and microbial translocation into the lamina propria. This invasion prompts an influx of inflammatory cells to the intestine and leads to local and systematic inflammation, resulting in the reallocation of resources normally directed toward child growth and development, and disruption of hormonal pathways that regulate growth plate activity in long bones. Chronic inflammation and reduced intestinal nutrient absorption are also hypothesized to affect brain development, inducing lasting negative effects on cognition, educational achievement, and linear growth [27].
There are currently no clear diagnostic criteria for EED, which presents a major problem in investigating the role of EED in stunting, and in evaluating treatment and prevention strategies. Intestinal biopsy is used to diagnose diseases with similar pathological changes, such as celiac disease [28]; however, collection of small bowel biopsy samples is technically and ethically infeasible in young children. Over recent years, studies have evaluated a range of potential biomarkers of EED, with a general agreement that these should be organized into distinct domains to measure different aspects of the pathogenic pathway that characterizes EED. Studies have included noninvasive biomarkers of intestinal damage and repair, epithelial permeability and absorption, digestion, epithelial morphology, intestinal inflammation, microbial drivers, systemic immune activation, and non-small intestine organ function.
Multiple research groups have used this domain-based approach, focusing on longstanding physiologic relationships to study the complex mechanisms that may underlie EED. Here, we examine whether these relationships are supported by recent and rich new data from studies conducted between 2010–2017, building on the review conducted by Denno et al for the time period 2000–2010 [29]. We define five contributing domains of EED to provide supporting evidence for our two aims: (i) to evaluate the relationships between individual EED domains; and (ii) to evaluate the relationships between each EED domain and stunting. We focus on stunting as the primary outcome in this review, as it is the most common outcome of the included studies and is objectively measured. Although our ultimate interest is in cognition and child development, these have not been as commonly measured, and the mechanistic pathways between stunting and neurodevelopment remain unclear [30].
Our search strategy followed PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines for the reporting of systematic reviews [31]. A search for articles in any language between January 1, 2010, and April 20, 2017, was conducted using PubMed, MEDLINE, EMBASE, Web of Science, and CINAHL. Abstracts were independently screened by two reviewers (K.H. and M.M.) and full-text articles that were related in any way to “environmental enteric dysfunction”, “environmental enteropathy”, or “tropical enteropathy” were selected for review. The reference lists of all review articles and original publications were also screened for any relevant studies. Published study abstracts were also included in this review. Disagreements regarding study inclusion were resolved by consensus.
Our population of interest included individuals of all ages for whom two or more EED domains, or at least one domain and stunting, were measured. Only human studies were included. While most studies focused on children under 5 years, no age restriction was imposed in the search criteria as some adult studies provide valuable histopathological data that is less frequently collected in young children. Studies in both developed and developing countries were included in the search, though only one study in the final selection included individuals from a developed country [32].
Studies selected for inclusion can be categorized into three groups: (i) observational studies in which EED was defined as either an exposure or as an outcome; (ii) studies investigating potential EED biomarkers and/or identification/diagnosis of EED; and (iii) intervention studies designed to treat or prevent EED. A standardized data abstraction form was used to extract information from each study. Abstracted data included: study objective, study design, location, subject eligibility and description of study population, inclusion or exclusion of subjects with diarrhea or human immunodeficiency virus (HIV), results and final conclusions. Biomarkers and diagnostic tests were recorded, as well as any qualitative and quantitative definitions of EED provided by the authors (S1 Table).
To review the evidence supporting the complex mechanisms that may contribute to child stunting we have elected to organize results according to these five EED domains: (1) intestinal damage and repair, (2) permeability and absorption, (3) microbial translocation, (4) intestinal inflammation, and (5) systemic inflammation. These domains were determined by consensus from reviewing previous conceptual frameworks and descriptions [2,3,23,24,29,33–41]. Each domain and its respective non-invasive biomarkers are described below.
Studies were included if they reported results for two or more EED domains, or at least one domain and stunting. In the situation where multiple studies included the same EED measurements on the same study subjects, only the most complete report was included in the review. For each of the 5 EED domains, we first present the number of studies that support (or not) the relationships of each domain to stunting. Second, we present the number of studies that report data in support of the relationship between each EED domain and the other domains, where data are available. These associations are summarized in Tables 1 and 2.
The electronic search identified 598 potentially relevant abstracts and articles. Two additional records were identified from checking reference lists. A total of 190 records remained after duplicates were removed. 126 articles were excluded based on a review of the title and abstract (Fig 1). Of the 64 reports selected for full-text review, 24 were excluded: 7 studies reported results for one domain but did not include measurements of linear growth [71–77], 6 studies reported measurements of linear growth but did not report domain measurements [78–83], and 1 study abstract did not report results for domains or stunting [84]. Five abstracts [85–89] and five full-text articles [90–94] included overlapping EED measurements on the same study subjects and were excluded from our review. The final review therefore includes 40 reports (5 conference abstracts, 35 full articles).
Of the 40 reports included in this analysis, 10 (25%) of the studies were conducted in Central or South America, 16 (40%) were conducted in Asia, and 29 (73%) were conducted in Africa. Half of the studies were conducted in rural locations, and 1 study was conducted in both urban and rural populations, while 9 (23%) studies did not specify the setting. Thirty-six (90%) reports included participants under the age of 5 years, and 19 (48%) studies were restricted to children less than 2 years. Three studies were conducted exclusively in adults. Diarrhea was used as an exclusion criterion in 16 (40%) studies; 16 (40%) studies included participants regardless of diarrheal status and 8 (20%) studies did not mention diarrhea in their analyses. Twenty-nine (73%) reports did not specify the HIV status of participants. Seven (17%) studies used HIV as an exclusion criterion and 4 (10%) studies included individuals regardless of HIV status.
EED has traditionally been defined as a primary gut disorder that initiates a chain of events from intestinal permeability and microbial translocation, via their impact on immune activation, to stunting. In this review, we found that there was fairly little evidence to support the pathway from intestinal permeability to microbial translocation and from microbial translocation to stunting. There was stronger support for the link between intestinal inflammation and systemic inflammation, and between intestinal inflammation and stunting. Other relationships presented conflicting pictures. For example, evidence for or against the role of intestinal damage in intestinal permeability and intestinal permeability/absorption in stunting was evenly split across studies.
These results suggest (i) that the relationships between EED domains are inconsistent; (ii) that the associations between EED domains and stunting are variable, with the best evidence for intestinal inflammation and stunting; (iii) that some domains are harder to measure than others, which may have led to a bias in study findings; (iv) there remains no consistent definition of EED; and (v) the number of studies comparing histopathological analysis of small bowel biopsies to non-invasive biomarkers is lacking. As EED is characterized by small bowel morphological changes, more studies are needed to investigate associations between biopsy samples and a range of non-invasive biomarkers from each domain.
The L:M test has been measured in numerous populations, but procedural details (fasting prior to ingestion, sugar dosage, time of urine collection, assay method) vary, which hinders comparisons between studies; moreover, reference cut-points for the diagnosis of children with EED using the sugar absorption test have not been established [50]. Twelve studies used L:M ratio cut-points of 0.06–0.07 [33,63,101,115,117,125] or 0.10–0.15 [55,108,111,112,103,126] to define EED. Three studies categorized EED as moderate or severe, where L:M values of 0.28–0.45 were used for severe cases [101,104,112]. Seven studies did not report normal L:M cut-point values [32,70,76,96,98,107,114]. The assignment of normal values is challenging as other non-EED related factors can influence the movement of sugars across the epithelium, including age, gastrointestinal motility, variations in gastric emptying, recent diarrhea, mucosal blood flow, and renal clearance [127], resulting in individual and population variability in L:M values. One large multi-site study by Kosek et al compared L:M values to L:M z-values, standardized by age and sex [113]. While L:M values were significantly associated with HAZ up to 15 months, the L:M z-scores were not associated with HAZ at any time points. This study emphasizes the importance of addressing age-specific norms in dual-sugar clearance and illustrates that the assignment of L:M cut-point values may lead to greater risk of EED misclassification in some populations.
Some performance measures, such as the L:M test, may be under- or overestimated in a single population based on its overall characteristics (e.g. higher incidence of diarrhea, prevalence of stunting). For example, two studies of children in rural Malawi found no association between stunting and L:M values [108,111], but each cohort low high mean HAZ values (-2.4 ± 1.3 and -2.8 ± 1.1, respectively) and used high L:M cut-points to classify EED (0.15 and 0.10, respectively). Thus, the values of L:M were likely elevated even in those not classified with EED. Conversely, a third study in Burkina Faso found no association between L:M and stunting, but the cohort was less stunted (mean HAZ = -1.5 ± 1.1) and had a low mean L:M value (0.042; 95% confidence interval: 0.030, 0.072) [101]. The proportionally greater permeability in all subjects with higher L:M values, and the proportionally lower stunting in subjects with lower L:M values, provide evidence in favor of assessing permeability, particularly in cohorts with high variability in the underlying prevalence of stunting.” Moreover, EED may not be a binary (present/absent) condition but a population shift in intestinal structure, wherein some individuals with EED-like characteristics (increased permeability, malabsorption) may not exhibit gut morphological changes. The variability in underlying characteristics presents challenges in interpreting results from heterogeneous study populations.
Finally, lactulose permeability may not accurately reflect the ability of microbes, toxins or larger molecules to pass through the epithelium. The molecular weight of lactulose (342 Da) is significantly smaller than that of antigenic molecules such as LPS (10–20 kDa) [127]. Holes in the tight junction complex that allow paracellular lactulose permeation may not be large enough to allow the movement of microbes or their byproducts across the epithelium. These factors may partly explain why permeability and absorption were not consistently associated with other EED domains, especially inflammation, or stunting.
The association between microbial translocation and stunting was observed in a study of infants in The Gambia [128] over a decade ago, but few studies included in this review support this relationship. Assuming that stunting is a primary outcome of EED, we propose that current tests for microbial translocation may not be appropriate for EED identification. However, strong evidence supports the relationship between intestinal inflammation and stunting, and it is also possible that factors other than microbial translocation may be responsible for the intestinal inflammation observed in individuals with EED. Several investigators [3,113,129] have suggested that pathogen colonization may promote chronic inflammation and induce changes to intestinal microbiota, contributing to EED and stunting, though the exact mechanism is unknown. The intestinal microbiota contains over 500 different species of bacteria, as well as assorted viruses, archaea, fungi and yeasts; the composition and function of the microbiota is largely determined by a combination of dietary, host genetic, inflammatory, clinical (e.g., antimicrobial exposure), and other environmental factors [130]. To our knowledge, only one human study has investigated microbiota dysbiosis in individuals with EED, which was categorised based on L:M ratios [112]. Three of the 6 differentially abundant bacterial genera—Megasphaera, Mitsuokella, and Sutterella—were enriched in children with EED compared to children without EED; the other 3—Succinivibrio, Klebsiella, and Clostridium_XI—were depleted.
Small intestinal bacterial overgrowth (SIBO) may also contribute to intestinal inflammation [131] and EED. A study of urban Bengali children investigated the relationship between the glucose hydrogen breath test, an indicator of SIBO, with EED domains and outcomes [132]. SIBO was not associated with the L:M ratio nor systemic inflammatory markers. However, children with SIBO had significantly worse linear growth and higher concentrations of calprotectin, an intestinal inflammatory marker, compared to those without SIBO. A study of Burmese children observed a similar association between SIBO and linear growth faltering [133] but this association was not present in slum-dwelling children in Brazil [134]. Additional evidence for the relationship between stunting and SIBO is needed.
The inconsistencies highlighted in this review indicate that EED may be more complex than previously conceived. It is possible that EED is not a single entity, but instead a set of phenotypes dependent on unique environmental exposures that vary geographically. The small intestine has a limited repertoire of responses to insult, and enteropathy resulting from multiple potential exposures may have a similar appearance. Some elements of EED may even be adaptive, rather than pathologic, for children living in conditions of poor water, sanitation and hygiene [50]. It is certainly not firmly established that EED is always consequential to linear growth, or indeed that it is definitively associated with stunting. Study subject selection (e.g., including subjects with diarrhea, HIV, or severe acute malnutrition) may influence EED biomarker results. Other potential confounders, including malnutrition, diarrhea, food insecurity, the intestinal microbiota/microbial factors, concomitant treatments (e.g., antimicrobial therapy) that were measured in some studies, but not all, may influence individual study results and our conclusions.
Recent human intervention studies suggest that micronutrient supplementation is not sufficient for EED recovery [115,120,135]; only one study of Zambian adults observed significant improvement in intestinal morphology after micronutrient supplementation [77]. Morphological studies are more feasible in animals than humans and have the potential to reveal key associations that are otherwise difficult to assess in human studies. An experimental study to evaluate the effects of malnutrition and exposure to Bacteroidales and E. coli in mice observed that malnourished mice with bacterial exposure experienced increased permeability and tail length (a surrogate for length of mouse), as well as blunted intestinal villi characteristic of EED [136]. Mice with a malnourished diet—regardless of bacterial exposure—also had greater microbiota dysbiosis and intestinal permeability, but without blunted intestinal villi. Mice with bacterial exposure but normal diets experienced normal tail growth and no change in permeability. These results suggest an interaction may exist between malnutrition and the presence of specific bacterial species, where EED features are present only in individuals with both. It should be noted that this study used mouse tail length as a surrogate for mouse length; how these findings relates to human linear growth remains unclear.
This review summarizes evidence for each pathway in a consensus framework model of EED (and its hypothesized component/contributing domains) and stunting. Information from a wide range of published articles representing very different biomarker measurement methods, heterogeneous study populations, and different analysis and reporting methods, was reviewed. We did not assess study quality in this review given the heterogeneity, nor did this review include studies of EED in vitro and in vivo models. Many studies did not report associations between domains; these missing or non-reported data may have influenced our conclusions. This is a consistent feature of EED-related studies, as already highlighted by Denno et al in their review of biomarkers from 2000–2010 [29,50]. The studies included in that review, which categorized biomarkers into eight EED domains, provided conflicting evidence for the links between permeability and linear growth [119,137–142], permeability and intestinal inflammation [137,143], as well as systemic inflammation and linear growth [138,140]. Microbial translocation was not included as a domain in their review, but one study reported a positive association between IgG endotoxin-core antibody and permeability [137]. No conclusive set of biomarkers for EED diagnosis were identified in either the 2000–2010 or in our review of the literature from 2010–2017.
The domains and specific pathways defined and investigated in our review are open to debate; however, we focused on pathogenic processes that have been long been hypothesized to be part of EED. Other potential domains (e.g. digestion, microbial drivers) have been identified within EED pathways [3,35–37], but these domains were minimally reported or missing from selected studies and were not included in our results.
In this review, we evaluated individual pathways between domains within EED and between each domain and stunting using studies published between 2010–2017. We found evidence to support the link between intestinal and systemic inflammation and stunting, but little support for the link between microbial translocation and stunting within the limits of current tests. There was conflicting evidence for the associations between intestinal damage and intestinal permeability, as well as intestinal damage and stunting. These results suggest that current biomarkers and proposed mechanisms of EED pathogenesis may need reconsideration, and future studies of pathogen-induced changes to the intestinal microbiota should investigate alternative pathways of the effect of intestinal and systemic inflammation on growth in children.
|
10.1371/journal.ppat.1003828 | Nodular Inflammatory Foci Are Sites of T Cell Priming and Control of Murine Cytomegalovirus Infection in the Neonatal Lung | Neonates, including mice and humans, are highly susceptible to cytomegalovirus (CMV) infection. However, many aspects of neonatal CMV infections such as viral cell tropism, spatio-temporal distribution of the pathogen as well as genesis of antiviral immunity are unknown. With the use of reporter mutants of the murine cytomegalovirus (MCMV) we identified the lung as a primary target of mucosal infection in neonatal mice. Comparative analysis of neonatal and adult mice revealed a delayed control of virus replication in the neonatal lung mucosa explaining the pronounced systemic infection and disease in neonates. This phenomenon was supplemented by a delayed expansion of CD8+ T cell clones recognizing the viral protein M45 in neonates. We detected viral infection at the single-cell level and observed myeloid cells forming “nodular inflammatory foci” (NIF) in the neonatal lung. Co-localization of infected cells within NIFs was associated with their disruption and clearance of the infection. By 2-photon microscopy, we characterized how neonatal antigen-presenting cells (APC) interacted with T cells and induced mature adaptive immune responses within such NIFs. We thus define NIFs of the neonatal lung as niches for prolonged MCMV replication and T cell priming but also as sites of infection control.
| Neonates are highly susceptible to a number of infections that usually cause disease only in immunocompromised individuals, most likely because of their incompletely developed immune system. Although this phenomenon has been frequently observed, immune responses of neonates remain largely undefined upon infections with viruses. There is lack of knowledge about the spatio-temporal dynamics of host-virus interaction, especially in comparative infection models of neonates and adults. In this study, with the use of virus reporter mutants, we provide elaborate insight into these aspects in the mouse model of CMV infection. We define hallmarks of virus tropism, early cellular immune responses and general infection dynamics, findings that are fundamental to understand neonatal antiviral immunity. Furthermore, we found that neonatal APCs induce T cell responses in nodular inflammatory foci of the lung, a process which was supposed to be restricted to lymphoid organs. However, the MCMV-specific T cell response was qualitatively different in neonates from that in adults, possibly explaining - in part - the higher susceptibility of newborns. These observations expand our understanding of where adaptive immunity can be initiated, highlights the importance of early local cellular immune responses and sheds more light on neonatal antiviral immunity.
| CMV infection shows an extraordinary high prevalence worldwide which increases with age [1], [2], but the majority of infected humans stays asymptomatic. Clinical symptoms dominate in neonates who suffered from congenital infection, postnatal infection of preterm low birth-weight infants, or in immuno-compromised adults [1], [3], [4], [5], [6]. The prevalence of CMV infection is already high in the very young who seem to be carriers of high viral loads and participate in the shedding of virus [7]. These observations imply that CMV infection is not sufficiently controlled by the immune system at the very early life. Accordingly, neonatal mice are more susceptible to infections with MCMV than adult mice [8], [9], [10], [11], [12], [13], [14]. Similar findings have been reported for other pathogens including Respiratory Syncitial Virus, Listeria monocytogenes, Herpes Simplex Virus type 1, Influenza Virus, and Pneumocystis [15], [16], [17], [18] suggesting that neonatal mice in general are more vulnerable to infections. The mechanisms behind this phenomenon as well as the differences in antiviral immunity between the very young and adults remain largely undefined [19], [20].
To understand and predict the outcome of a virus infection, it is of great importance to know where the infection is localized and what types of antiviral immune responses are initiated locally. Human cytomegalovirus (HCMV) DNA has been detected in several body fluids like blood, breast milk, saliva, urine, and bronchoalveolar fluid [3], [5], [7]. Thus, mucosal surfaces are most likely a primary target of postnatal CMV infection and indeed several routes of virus transmission have been suggested in neonates and children. Oral infection by contaminated breast milk and droplet infection of the lung by infectious saliva have been proposed in several studies [21], [22], [23], [24], [25]. MCMV has been widely used to investigate CMV infection in vivo in the mouse model [24], [26]. Infections have been extensively studied in adult mice after systemic administration of the pathogen while the natural infection routes of MCMV, including transmission to newborns, remains a matter of debate [27]. Therefore, it is still unknown which mucosal tissues are targets for viral entry and which cell types become infected to such a challenge.
Studying MCMV infection in adult mice has unmasked many aspects of the complex interplay between this pathogen and the immune system. Whereas CD8+ T cells are supposed to be major effectors of the host to control of MCMV infection there is also strong evidence that NK cells as well as CD4+ T cells contribute to keep the virus from undisturbed replication [24], [28]. However, the composition of the neonatal immune system seems to differ in many ways if compared to that of adults [29], [30]. This phenomenon is supplemented by the fact that there are enormous changes of immunity during the very first steps of life, especially in mucosal tissue [31]. However, the features of antiviral immunity in neonates infected with MCMV have not been investigated in detail. Accordingly, it is currently not known how the neonatal immune system responses to MCMV infection and why neonates subsequently suffer from increased morbidity and mortality.
Here, we investigated the primary virus tropism in mucosal surfaces of neonatal mice with the use of recombinant viruses expressing suitable reporter proteins [32]. Comparative analysis of lung infection in neonatal and adult mice reflected characteristics of HCMV infection in terms of virus tropism and histopathology. Accordingly, primary mucosal MCMV infection in neonatal mice led to a pronounced systemic viral spread and simultaneously caused disease, whereas adults rapidly coped with the infection. The clonal expansion of MCMV-specific CD8+ T cells in both adults and neonates was paralleled by virus control although the time course differed between the two groups. Interestingly, MCMV infection attracted myeloid cells to form morphological unique nodular inflammatory foci (NIF) in the neonatal lung. Within these structures MCMV-infected cells were destroyed and subsequently engulfed by local APCs. Notably, using in situ 2-photon microscopy, we visualized priming of naïve CD8+ T cells in NIFs of the neonatal lung suggesting that the neonatal organism allows the local differentiation of myeloid cells into APCs that directly cross-present antigen within NIFs. Thus, this study provides fundamental new insights in early antiviral immune responses during mucosal infections of neonatal mice.
To investigate which mucosal surfaces of neonatal mice can be infected we applied MCMV via different routes. Since virus transmission by contaminated breast milk has been reported in humans [22], [25], we firstly analyzed whether the mucosa of the gastrointestinal tract is susceptible to infection. Following oral application, fluorescent latex microspheres (0.5 µm in diameter, applied in PBS) were detected in the distal colon within 24 h, confirming sufficient ingestion of the inoculum (Figure 1A). When we fed neonates with high doses (106 PFU) of the recombinant MCMV-3D that encodes the fluorescent protein mCherry as well as Gaussia luciferase [32] we could neither detect mCherry+ infected cells in the oral cavity (Figure 1B), esophagus, stomach, small or large bowel (Figure 1C+D) nor luciferase activity in organ homogenates of the gastrointestinal tract (Figure 1E). We therefore concluded that carrier-free MCMV does not infect the neonatal intestine via the oral route.
Pneumonitis is a frequently observed manifestation of HCMV infection [5], [33], [34]. It has been previously reported that MCMV does infect the lung of adult mice after intranasal and subcutaneous application as well as the adult and neonatal lung after intraperitoneal (i.p.) injection [21], [35], [36], [37]. To investigate whether the lung mucosa could be a direct target for virus infection in neonatal mice we established a procedure to infect the lung by inoculation of virus into the laryngopharynx (designated as “laryngopharyngeal (l.p.) infection” throughout the article). High numbers of mCherry+ infected cells and dose-dependent expression of luciferase in organ homogenates were detected in the lung within 1 day after virus application (Figure 1F–H). Histological analysis showed solitary infected cells mainly in the epithelium of distal respiratory ducts and terminal bronchioles but not in the epithelium of the trachea (Figure 1I). Approximately 50% of the infected cells were pro-surfactant protein C-expressing type 2 alveolar epithelial cells [38] (Figure 1J, Figure S1A). No CD45+ cells of hematopoietic origin were found to be infected (Figure 1K, Figure S1B). Together, the data illustrate that the neonatal lung epithelium is highly susceptible to MCMV infection.
Human CMV infection has not only been described in the lung but also in numerous organs and tissues such as the liver, brain, spleen, vascular endothelium and the kidney [3], [5], [39]. Likewise, in models for systemic MCMV infection multiple organs have been reported to become infected [10], [14], [35], [40], [41], [42]. We analyzed various organs after lung infection and found that by day 8 post infection (p.i.) mCherry+ cells could be detected in all investigated neonatal organs (Figure 2A). As described above for the lung, most infected cells were CD45− demonstrating their non-hematopoietic origin. Various parenchymal cells were found to be infected and the spatial proximity to CD31+ vascular endothelial cells suggested hematogenous virus dissemination (Figure 2A). Surprisingly, we also found infected cells next to CD31+ vascular endothelial cells in the intestine (Figure S2A) indicating that hematogenous viral spread can lead to infection of the intestine, while oral application of the virus failed to infect the gut (see Figure 1). To gain insight into the dynamics of viral dissemination we determined luciferase activity to screen for virus spread to various organs following primary infection of the lung. All screened organs of neonatal mice including the salivary glands, liver, brain, spleen, and kidney possessed luciferase activity after primary lung infection (Figure 2B). However, whereas infection of the lung was already detectable one day p.i. (Figure 1H) viral activity in these organs was delayed by approximately six days (Figure 2B), an observation that also supports the idea of a barrier function of the lung preventing massive viral systemic exposure [43]. A comparative analysis with weight-adapted virus doses revealed that in adult mice luciferase was detected only in organ homogenates of the salivary glands and the spleen after intranasal infection with MCMV-3D (Figure 2C). Apart from the salivary glands, which have been described to be a place of ongoing virus persistence [44], [45], none of the tissues examined, neither neonatal nor adult, showed luciferase activity three weeks after infection (Figure 2B+C). Therefore, neonates as well as adults are able to cope with respiratory cytomegalovirus infection. However, when monitoring the body weight as a parameter of the health status, differences between adult and neonatal mice could be observed. While in the latter both a low dose (5×104 PFU) as well as a high dose (106 PFU) MCMV-3D infection temporally coincided with the diminished increase in body weight when compared to mock infected animals (Figure 2D), there was no effect in this respect in MCMV-3D-infected (106 PFU) adult mice (Figure 2E). The pronounced vulnerability of neonatal mice to MCMV infection can be explained by productive virus infection in all organs that is accompanied by cachexia. We continued with low dose (5×104 PFU) lung infections in further experiments in neonates to avoid excessive virus exposure to the neonatal organism.
We speculated that an impaired antiviral immune response in the neonatal lung might be the cause of the massive systemic viral spread and disease. Therefore, we investigated early cellular immune responses in neonates via histological analysis of the lung 5 days after low dose infection (MCMV-3D; 5×104 PFU), prior to the onset of viral dissemination. At this time-point numerous areas containing multiple infected cells with dense infiltration of CD45+ cells could be detected (Figure 3A, framed areas). These infiltrates were exclusively found around foci of infected cells, apparently sheathing them and causing a nodular appearance of the lung. Similar histopathology has been described in pulmonary HCMV-infection of immuno-compromised adults and termed “nodular inflammatory foci” [46]. Therefore, we equally termed these areas of the neonatal lung “nodular inflammatory foci” (NIF), defined as multiple juxtapositioned MCMV-infected cells and associated immune cell infiltrate.
Besides the presence of NIFs we found no evidence for further profound pathological alterations of the lung (Figure 3B). However, we frequently observed some solitary infected cells within the lung that were not contacted by CD45+ cells suggesting that they may escape immune surveillance (Figure 3A, arrow heads). Alternatively, these cells could also reflect earliest stages of NIF development. Most of the infected cells within the NIFs were neither pro-surfactant protein C-expressing type 2 alveolar epithelial cells nor CD45+ hematopoietic cells. Instead, cell morphology and position matched that of stromal cells such as fibroblasts (Figure 3C). Hence, in addition to type 2 alveolar epithelial cells also other cell types become infected during the course of infection.
Further analysis of the infiltrated cells identified them as a variety of CD11b+, CD11c+ and/or F4/80+ myeloid cells and the abundance of these cells account for the “nodular” morphology of the inflammatory foci (Figure 3D+E). In contrast, only few T and B cells were present (Figure 3E, Figure S3A–C). Natural killer cells were also present as illustrated by analysis of Ncr1gfp/+ transgenic mice (Figure 3F) and NK1.1 cell surface expression (Figure S3D). Further high resolution analysis revealed heterogeneous patterns of mCherry signals within the NIFs. The mCherry fluorescence intensity varied between infected cells suggesting that lung parenchymal cells were either differently permissive to infection or had been infected at different time-points and therefore were in different phases of viral replication (Figure 3G; arrows). In addition to multiple infected cells we found some cellular mCherry+ remnants that most likely were derived from infected apoptotic cells as described previously for MCMV-infected cells in the salivary glands [47] (Figure 3G; arrow heads). Interestingly, we found some of these remnants to be situated within CD45+ cells suggesting engulfment of mCherry-containing cell debris by myeloid cells (Figure 3G). We found remnants in CD11c+ cells and F4/80+ macrophages (Figure 3H+I). CD169+ macrophages were also present in NIFs at high frequencies and similarly contained mCherry+ remnants (Figure 3J). In contrast, few CD103+CD11c+ DCs were found to be present in NIFs but occasionally formed close contacts with infected cells (Figure S3E). Thus, NIFs are clearly different from bronchus-associated lymphoid tissue (BALT), tertiary lymphoid structures of the lung that are localized next to bronchi and characterized by the presence of B cell follicles with separated T cell areas [48], [49]. Instead, NIFs appear to be areas of viral replication although myeloid cells present in NIFs can engulf remnants of infected cells and probably function as APCs.
To gain comprehensive insight into the genesis of the NIFs and their role in viral clearance, we performed a comparative analysis of neonatal and adult lung sections at different time-points after infection with MCMV-3D. Over an observation period of three weeks we constantly found solitary infected cells in the neonatal lung that were not in contact with any hematopoietic cell (Figure 4A+B). However, multiple infected cells could be detected side by side 3 days p.i. suggesting cell-to-cell spread of MCMV in vivo (Figure 4A+B). NIFs could be detected in the neonatal lung from day 3 until at least day 8 p.i. but disappeared, to a large extent, by day 12 p.i. (Figure 4A+B). Accordingly, luciferase activity as well as the number of infected cells per lung slice did not decrease within the first 8 days p.i. (Figure 4C+D). Instead, NIFs appeared to be niches of ongoing virus replication possibly by recruiting susceptible fibroblasts [50]. The disappearance of the NIFs was associated with declining numbers of infected cells (Figure 4A,B+D). Interestingly, solitary infected cells that were not targeted by immune cell infiltrates were still present three weeks after infection (Figure 4A+B). In addition, appearance of NIFs coincided with the presence of mCherry+ cell remnants indicating immune cell-mediated destruction of infected cells within these structures (Figure 4B+E).
To compare these findings to those in the adult lung, we intranasally infected 6–8 week old mice with a weight-adapted dose of MCMV-3D. As observed for neonates, the virus also infected type 2 alveolar epithelial cells (unpublished data) and a comparable infection pattern to that observed in neonates was evident in adult mice within the first 3 days p.i. (Figure 4A). Likewise, a localized inflammation with immune cell infiltration was found around foci of infected cells. Although the morphology, composition and localization of these infiltrates may differ from the ones found in neonates we also named these structures “NIFs” of the adult lung. However, in contrast to the situation in neonates, the number of remnants of infected cells peaked already at day 5 p.i. in adult NIFs and only few morphologically intact mCherry+ infected cells could still be identified (Figure 4A, D–F). By day 8 p.i. luciferase activity had decreased from a peak at day 3 p.i. (Figure 4C). Accordingly, only residuals of cell infiltrates remained but few solitary infected cells were still detectable (Figure 4A+D). Luciferase activity as well as a comparative quantification of infected cells and mCherry+ remnants per lung slice showed a clear delay of virus control in the neonatal lung as compared to adults (Figure 4 C–F). These data demonstrate that neonatal mice can cope with most of the infected cells in the lung. However, although neonates induce a cellular immune response and form NIFs, they suffer from a persistent lung infection for up to three weeks. During the first 8 days infiltrating immune cells in the neonatal lung tolerate ongoing infection and fail to prevent spread of the infection to neighboring cells whereas adult mice start to contain viral replication in the lung already within the first 4 days.
CD8+ T cells have been implicated as major contributors to MCMV infection control in adult mice [28]. To test the hypothesis that the ongoing MCMV infection in the lung of neonates from day 1 until day 8 p.i. could be due to a limitation in the CD8+ T cell response we analyzed the presence of CD8+ T-cells which recognize the immunodominant MCMV epitope M45 [51]. M45-specific CD8+ T cells showed a massive expansion at day 8 p.i. in adult mice and already turned to the contraction phase at day 12 p.i. (Figure 5A–C). In contrast, M45-specific CD8+ T cells in neonates were hardly detectable before day 12 p.i. in lung or lung draining lymph nodes (Figure 5A–C). As the expansion of M45-specific CD8+ T cells showed a temporal coincidence with the initiation of virus control in neonates at ∼day 12 p.i. (Figure 4A–E) we depleted CD8+ T cells to investigate their importance in the clearance of infection (Figure 4D and Figure S4A). After depletion, we found higher luciferase activity in the lung, liver and all other organs analyzed (Figure 5E, Figure S4B). In addition, unlike the control group, CD8+ T cell-depleted neonatal mice showed NIFs which contained increased numbers of viable infected cells (Figure 5F) confirming the contribution of CD8+ T cells to controlling MCMV infection in neonatal mice.
Next, we performed adoptive transfers of CD8+ T cells into MCMV-infected neonatal mice to determine whether this treatment could abrogate MCMV infection. For this, we took advantage of lymphocytes from transgenic mice (OTI), which express a recombinant, Kb-restricted T cell receptor that exhibits high affinity to MHC class I bound SIINFEKL peptide [52]. To that end, we infected neonatal mice either with MCMV-3D or MCMV-3DΔvRAP virus mutants [32]. Both viruses encode the SIINFEKL peptide but MCMV-3DΔvRAP lacks the “viral regulator of antigen presentation” genes encoding for the MHC class I evasion proteins gp48/m06 and gp40/m152 [53], [54]. In contrast to MCMV-3D, this mutant is therefore expected to lack the ability to interfere with MHC class I peptide surface expression as was recently shown for a related virus mutant [55]. We speculated that MHC class I bound SIINFEKL peptide presentation on MCMV-3DΔvRAP-infected cells would allow direct recognition of infected cells by OTI T cells and decreased luciferase activity in organs of these animals would be an in vivo indicator for the activity of cytotoxic T lymphocytes (CTL). Two days p.i. and at the day of infection we adoptively i.p. transferred various numbers of naïve CD8+ T cells from OTIxGFP mice and analyzed the animals six and ten days after transfer, respectively (Figure 5G and Figure S5A). In animals infected with MCMV-3DΔvRAP the reduction in luciferase activity in the lung and liver depended on the number of OTI T cells transferred (Figure 5H). Moreover, MCMV-3DΔvRAP-infected animals which received high numbers of OTI T cells showed no NIFs and only very few solitary infected cells could be found in the neonatal lung (Figure 5I and Figure S6C). The antiviral effect was also observed in the spleen, kidney, and brain of these mice (Figure S6B). In contrast, we did not observe a robust impact on luciferase activity within the neonatal lung and most organs tested 6 or 10 days after adoptively transferring OTI T cells into MCMV-3D-infected mice (Figure 5H, Figure S5A+B). Even in the presence of adoptively transferred OTI T cells these mice still possessed NIFs that harbored multiple infected cells (Figure 5I and Figure S6C). However, in MCMV-3D-infected neonatal animals, the liver significantly benefited from the transferred cytotoxic T lymphocytes (Figure 5H and Figure S5B). Previous reports have shown that already the deletion of one gene (m152) for the MHCI immune evasion leads to virus attenuation even in BALB/c neonatal mice [56]. In line with this report, in our model with infection of C57BL/6 neonatal mice we saw a trend to lower luciferase activity in the lungs of MCMV-3DΔvRAP-infected mice if compared to MCMV-3D-infected neonates, but the difference was not significant (Figure 5H, “MCMV-3D no OTI” vs. “MCMV-3DΔvRAP no OTI”, p = 0.0675, unpaired t-test). In summary, the CD8+ T cell response to MCMV infection in neonates is strikingly different from the response in adults and likely contributes to delayed virus control in neonates.
Priming of naïve CD8+ T cells is supposed to take place in secondary lymphoid tissue [57], and we have recently shown that T cells can also be primed in tertiary lymphoid tissues such as BALT [49]. Since we found many APCs in the NIFs of the neonatal lung, we wondered whether they could assist in priming naïve T cells already at the site of infection. To test this hypothesis, we adoptively i.p. transferred purified naïve CD8+ T cells from OTIxGFP mice into neonates infected with the reporter viruses MCMV-3D or MCMV-2D (Figure 6A). The latter lacks the sequence encoding the SIINFEKL peptide [32]. Within one day of transfer we found in lung draining lymph nodes of MCMV-3D, but not MCMV-2D-infected neonates, a considerable proportion of OTI T cells to express CD69, indicating T cell activation and arguing that neonatal lymph nodes are able to prime CD8+ T-cells (Figure S7A+B). To further investigate the priming capability of cells in the NIFs we performed in situ 2-photon microscopy of lung explants from MCMV-infected neonatal mice. Surprisingly, naïve OTI T cells accumulated already within 1 day after i.p. transfer in NIFs of MCMV-3D, but not of MCMV-2D-infected mice (Figure 6B+C, Movie S1). These T cells in MCMV-3D-infected neonates showed a slowed migration behavior in NIFs, similar to that of naïve T cells that are primed in the lymph node as reported earlier [58]. In contrast, peribronchial T cells which were not next to infected cells were not confined 1 day after transfer (Movie S2 - Scene 1). Within 2 days of transfer, a high proportion of OTI T cells in NIFs showed a lymphoblastic appearance and enlarged nuclei in MCMV-3D but not MCMV-2D-infected mice (Figure 6B–D, Movie S3). Additionally, after we subcutaneously treated MCMV-3D-infected neonates with a pulse of the nucleoside analog 5-ethynyl-2′-deoxyuridine (EdU) 2 days after T cell transfer and sacrificed the animals within 4 hours, immunohistology revealed a high frequency of proliferating EdU+ OTI T cells (Figure 6E). Furthermore, these T cells within NIFs became highly motile within 4 days of transfer (Movie S2 - Scene 2). Together, these data indicate that neonatal APCs in NIFs can induce OTI T cells to pass the classical priming program directly at the site of infection, including confined migration behavior after antigen-recognition, subsequent lymphoblastic appearance, cell proliferation and increased cell migration after the differentiation into CTLs.
Activated OTI T cells were detected as early as 48 hours after transfer within NIFs indicating that T cells were also primed in these structures. To formally exclude the possibility that activated T cells present in NIFs were initially primed in the lung-draining lymph node we blocked the egress of T cells from lymph nodes by treating neonates from the time of adoptive transfer of OTI T cells with the functional sphingosine 1-phosphate receptor antagonist FTY720 (Figure 7A) [59]. Four days after cell transfer, the frequency of OTI T cells in MCMV-2D-infected mice was extremely low in all compartments analyzed (Figure 7B). These cells did not proliferate and did not express the effector/memory marker CD44 (Figure S8A). In contrast, most of OTI T cells in lung draining lymph nodes of MCMV-3D-infected neonates had started to proliferate and expressed CD44, indicating that they experienced antigen (Figure 7C). Furthermore, we found significantly more OTI T cells in lung-draining lymph nodes of FTY720-treated MCMV-3D-infected neonates than in control animals and hardly detected OTI T cells in the blood of neonates which received FTY720, confirming the blockade of T cell egress from lymph nodes by this drug (Figure 7B). Despite the inhibition of T cell egress from lymph nodes, the frequency of OTI T cells in the lungs of FTY720-treated neonates was comparable to that in the control group (Figure 7B). Furthermore, OTI T cells in the lung of FTY720-treated animals showed proliferation and CD44 expression that were similar to those in the control group (Figure 7C). Conclusively, these data confirm the hypothesis that activated T cells present in NIFs have also been activated in these structures.
As these data suggested that neonatal APCs can potently prime T cells in lymph nodes and in NIFs we wondered if a small T cell receptor repertoire in neonates and therefore a low precursor frequency for MCMV-specific T cells accounted for the delay in clonal expansion of M45-specific CD8+ T cells (Figure 5A). Consequently, we adoptively transferred 107 polyclonal CD45.1+CD8+ T cells from adults into neonates at the time of MCMV-3D infection and treated these animals with FTY720 to prevent egress of lymph node-primed T cells (Figure 7D). We speculated that the adult T cell repertoire contains T cells with M45-reactive TCRs and that this adoptive transfer would substitute for the missing MCMV-reactive CD8+ T cells in neonates. As clonal expansion of M45-specific T cells peaked at ∼8 days p.i. in adults (Figure 5A–C) we also analyzed neonates at day 8 p.i. (Figure 7D). Of interest, we found a considerable frequency of M45-specific T cells in the neonatal lung within the CD45.1+CD8+ T cell fraction (Figure 7E). In addition, the transferred CD45.1+ cells (with ∼90% of CD45.1+ cells being CD8+ T cells; unpublished data) were situated within NIFs suggesting the accumulation of MCMV-specific CTLs in NIFs (Figure 7F). In summary, these data support the hypothesis that APCs in neonatal NIFs are capable of presenting MCMV peptides (including M45) to naïve CD8+ T cells to directly prime these cells at the site of infection. Furthermore, as the adoptive transfer of polyclonal adult CD8+ T cells led to expansion of M45-specific clones, it is likely that the low precursor frequency of MCMV peptide-specific CD8+ T cells accounts for the delayed clonal expansion of M45-specific CTLs in neonates.
Finally, we aimed to further characterize T cell priming in non-lymphoid tissue by 2-photon microscopy of NIFs in the neonatal lung. In particular, we wondered if we could observe interactions between APCs and CD8+ T cells. Since CD11c is mainly expressed by dendritic cells and alveolar macrophages [60], we infected neonatal CD11c-YFP transgenic mice with MCMV-3D. Four days later we adoptively transferred purified naïve CD8+ T cells from OTIxCFP mice (Figure 8A). Numerous OTI T cells could be found in a dense network of CD11c+ APCs in the NIFs within 1 day of T cell transfer (Figure 8B, Movie S4). Interestingly, OTI T cells were in direct contact with APCs, but only occasionally with cells infected with the reporter virus MCMV-3D that carries the MHC class I immune evasion genes and is therefore expected to interfere with MHC class I peptide presentation (Figure 8B+C). Most of the contacts observed between OTI T cells and APCs were stable and some lasted for more than 30 minutes (Figure 8D). Of interest, APCs formed cell protrusions which connected OTI T cells with infected cells (Figure 8B, Movie S4). Most of the contacts between APCs and OTI T cells occurred when the APC itself was in contact with an infected cell (Figure 8E). Histological analysis of NIFs revealed intensive synapse formation of OTI T cells with CD169+ macrophages which contained remnants of infected cells (Figure 8F). These data support the idea that MCMV-specific cytotoxic CD8+ T cells can be primed by myeloid cells, potentially by CD169+ macrophages, of virus-induced NIFs in the lung and that these myeloid cells contribute to the local antiviral immune response.
In the present study we established an animal model for mucosal MCMV infection in neonatal and adult mice and describe the spatio-temporal distribution of virus infection at the single-cell level. Compared to classical MCMV infection models using systemic application (i.p. or i.v.) of the virus that leads to primary infection of multiple organs, the approach used in this study allows investigation of immune responses at mucosal surfaces. Our data suggest that infection of the respiratory tract serves as a previously underestimated entry organ for CMV in neonates and that other organs become infected after primary virus replication and hematogenous spread. This virus is known to persist in salivary glands and infectious virus can be found in saliva of infected humans. Thus, CMV may be transmitted via virus-containing saliva to the respiratory tract. This transmission route may be of clinical importance especially for postnatal infection of highly susceptible preterm low birth-weight infants. In neonatal and adult lungs type 2 alveolar epithelial cells were frequently found to be infected with MCMV, a cell type that also has been suggested as a target for HCMV [61], [62]. Our observations are also in accordance with the model of cell-to-cell spread of infectious virions in the lung since we could visualize that different neighboring cell types became infected at later time-points after primary infection. The proximity of alveolar epithelial cells, fibroblasts and vascular endothelial cells within the lung [63] suggests the sequential infection of these cells as an imaginable route for virus particles to enter the blood stream from the pulmonary alveoli and spread systemically within the host. Indeed, infection of the gastrointestinal tract was evident in neonatal mice after hematogenous virus spread but not after virus administration via the oral route. After oral application, low pH in the stomach and bile in the duodenum supposedly prevent infection of the small and large intestine with the enveloped MCMV. Though, the neonatal stomach is not very acidic in the first days of life and therefore may allow the virus to enter the duodenum as an infectious particle. However, it was unexpected that neither the oral cavity nor the esophagus seem to be mucosal entry sites for MCMV. A study by Wu and colleagues suggested that neonatal mice can be infected after oral application of carrier-free MCMV as well as virus-containing milk cells [64]. However, these authors did not analyze infection of the gastrointestinal tract itself but instead found viral transcripts in the lung and other organs 4 days after oral delivery of virus. Therefore, it is currently not known which cells are first targeted by MCMV after oral application. Nevertheless, the data presented in our study suggest that after systemic spread from a primary site of infection such as the respiratory tract, MCMV can disseminate to any vascularized corner of the body and virus infection of the colon may actually occur from the “blood-side” and hike through the epithelium rather than start at the apical side of the epithelium.
To prevent, or at least reduce, systemic dissemination of pathogens efficient antiviral defense mechanisms has to be induced very early after infection of mucosal tissues. Following MCMV lung infection neonatal mice failed to prevent the systemic spread of virus originating from infected cells of the lung. An impaired first line antiviral defense is most likely the cause for the high susceptibility of neonates to virus infection and explains prolonged viral replication in the lung and pronounced virus dissemination with subsequent infection of various organs. Still, virus spread to other organs was usually not observed during the first 5 days of infection in neonates and in adults, and the majority of infected cells was cleared after 3 weeks suggesting that local immune responses of the neonatal lung to some degree help to reduce systemic spread of the virus.
Several of our observations support the hypothesis that the formation of NIFs contribute to control MCMV infection of the neonatal lung: i) infected cells of the lung were efficiently removed at locations where NIFs were induced, ii) remnants of infected cells were always found to be associated with NIFs, iii) macrophages within the NIFs contained fragments of lysed, virus-infected cells indicating that NIF macrophages locally remove infectious virions, and iv) NIFs provided an environment that allowed priming of antigen-specific cytotoxic T lymphocytes.
Immunohistology identified NIFs to primarily consist of MCMV-infected cells and myeloid cells including macrophages (F4/80+; CD169+) as well as DCs (CD11b+CD11c+; CD103+) while only few lymphocytes and NK cells were present. These features clearly distinguish NIFs from induced BALT that develops after the clearance of infections and is characterized by large and separated T and B cell zones and that has been shown to act as a general priming site for T cells [49]. Although T cells are sparse, our data indicate that APCs can efficiently prime naïve CD8+ T cells directly in NIFs. T cell priming is a multistep process which has been extensively characterized in secondary lymphoid organs such as lymph nodes. There, following recognition of antigen presented by APCs T cells undergo an extensive proliferation and differentiation program that lasts for at least three days. During this period all lymphocytes are trapped within these organs by a process known as lymph node shut down, which also prevents the release of activated T cells. Therefore, it is unlikely that those OTI T cells that were observed to rapidly proliferate in NIFs 2 days after their adoptive transfer were initially primed in lung-draining lymph nodes. The idea that T cells are directly primed in NIFs is further supported by the finding that proliferating T cells were also present in NIFs of FTY720-treated mice, where T cell egress from lymph nodes is blocked. Furthermore, 2-photon microscopy studies revealed intensive interaction and synapse formation of APCs and OTI T cells within NIFs. In addition, approximately 75% of the APCs that contacted OTI T cells simultaneously interacted with infected cells indicating that APCs, which are not actively infected, actually cross-present viral antigens to naïve T cells that differentiate to mature CTLs.
It is unknown which entry portal is used by naïve T cells to enter NIFs and what signaling molecules are involved. As data on essential molecules for homing of lymphocytes into the lung is sparse this needs to be addressed in future studies. Furthermore, it is currently unclear which subset of the CD11c+ APCs observed actually cross-presents antigen in the NIFs. We recently identified lung-derived CD103+ cells to cross-present antigen to CD8+ T cells in lung-draining lymph nodes [65]. Thus it seems possible that CD103+ DCs also cross present antigen directly in NIFs and indeed immunohistology identified few CD11c+ CD103+ DCs to be in direct contact with infected cells. Alternatively, some of the newly recruited monocytes and/or DC progenitors undergo a differentiation program within NIFs that allows the local generation of cross-presenting DCs. Interestingly, CD169+ macrophages were present at high frequencies within NIFs. CD169+ lymph node macrophages have recently gained considerable attention since they were identified to play important roles in controlling spread of lymph-derived virus, in presenting lymph-derived antigen to B cells and to cross-present lymph-derived apoptotic tumor cells to induce cytotoxic T cell responses [66], [67], [68]. In NIFs, CD169+ macrophages not only contained remnants but also contacted infected cells and simultaneously formed synapses with OTI T cells suggesting that these cells actually cross-present antigen and therefore contribute to the control of MCMV infection in the neonatal lung.
Antibody depletion of CD8+ T cells clearly promoted virus replication in MCMV-3D-infected neonates emphasizing an important role of cytotoxic T cells in MCMV control. As we observed activated endogenous CD8+ T cells in MCMV-infected neonates at day 8 (Figure 5A, CD44 expression and unpublished data) it is likely that MCMV epitopes were recognized by CD8+ T cells at that time. Additionally, the cytotoxic CD8+ T cells activated in NIFs are seemingly fully functional since naïve OTI T cells adoptively transferred into neonates differentiated to cytotoxic effector T cells and efficiently reduced the viral load in all organs analyzed of mice infected with MCMV-3DΔvRAP. Activated OTI T cells had only a limited effect in mice infected with the MCMV-3D variant. These data indicate that the MCMV-encoded vRAP proteins, m06 and m152, efficiently prevent killing of MCMV-infected cells by CD8+ T cells as shown by others before [56], [69], [70], [71], [72]. These observations suggest that, in principle, neonatal mice can prime CD8+ T cells and induce CTL-mediated antiviral immunity. Interestingly, the generation of CTLs in the present animal model is in line with a previous report showing expansion of CMV-specific CD8+ T cells in newborns upon HCMV infection [73]. This raises the question why particularly the CTL response should be responsible for the higher susceptibility of neonates to MCMV infection. First, the frequencies of lymphocytes are in general lower in neonatal than in adult mice [30]. Furthermore, our observation that M45-specific CD8+ T cells efficiently expand in neonates once adoptively transferred from adult donors suggest that a low precursor frequency - rather than a general defect in T cell priming in neonates - contributes to the late expansion of M45-specific CD8+ T cells. These findings are in line with reports showing that the neonatal TCR repertoire and hierarchy differ from that of adults [74], [75]. Therefore, low numbers of MCMV-specific CTLs in combination with a reduced clonal repertoire and diminished variety of recognized viral proteins may account for the vulnerability of these young organisms.
We also observed NK cells and CD4+ T cells in NIFs but it is currently unclear to what degree these cells contribute to the anti-MCMV response in these structures. NK cells have been proposed to lack multiple activating receptors during the very first days of life [76]. Additionally, neonatal myeloid cells have been reported to produce only low levels of IL-12 [77] and subsequent low IFN-γ responses by T cells and NK cells may diminish antiviral immunity in neonates. As CD4+ T cells also contribute to control of MCMV infection it is likely that low precursor frequencies of both, MCMV-specific CD4+ and CD8+ T cells, account for the vulnerability of neonates to MCMV infection. In summary, the high susceptibility of neonatal mice to viral infection may be the result of an impaired innate and a delayed adaptive antiviral immune response that allows prolonged local virus replication and extreme systemic viral spread with multi-organ disease and cachexia.
In both, the adult and neonatal lung solitary infected cells were still present when all the infected cells had been removed from the NIFs. These findings suggest the existence of micro-anatomical niches which allow immune evasion of infected cells. Likely, innate immune responses are needed to allow migration of immune cells to places of viral infection which then leads to removal of MCMV-infected cells or inhibition of viral replication. Possibly, in some cells MCMV infection does not trigger these early responses and therefore the first steps of inflammation are not initiated. Alternatively, solitary cells might result from secondary infections with viruses released from other organs such as the salivary glands. The fate of these infected cells, apparently ignored by the immune system, clearly deserves further attention since it cannot be excluded that latent infection is finally established in such cells.
The present study is to our knowledge the first to describe and profoundly characterize NIFs as well as solitary infected cells in the neonatal lung upon MCMV infection. Interestingly, CMV-associated interstitial pneumonia with formation of nodules is one among various reported lung manifestations of CMV infection in immuno-compromised adults [33], [46]. As it is currently unclear what factors determine the type of lung manifestations in human CMV patients, the mouse model presented in this study might help to shed light on the pathogenesis of CMV lung disease as well as the definition of crucial antiviral immune responses to control CMV infection in the lung.
In summary, this study provides profound insight into host-pathogen interaction upon viral challenge of the lung of neonatal mice. The localized accumulation of primarily myeloid immune cells at the site of infection represents an essential feature for the formation of NIFs in the neonatal lung. These structures allow the local induction of adaptive immune responses and moreover represent the anatomical correlate where the control of MCMV infection takes place.
Mice were all on a C57BL/6 background, bred at the central animal facility of Hannover Medical School under specific pathogen free conditions and/or purchased from Charles River Laboratories. ß-actin-eGFP mice [78] and ß-actin-eCFP [79] mice were crossed to ovalbumin-transgenic TCR (OTI) mice [52] and the F1 cross was labeled as OTIxGFP and OTIxCFP, respectively; CD11c-YFP [60]; Ncr1+/gfp [80].
All animal experiments were performed according to the recommendations and guidelines of the Federation of European Laboratory Animal Science Associations (FELASA) and Society of Laboratory Animals (GV-SOLAS) and approved by the institutional review board and the Niedersächsische Landesamt für Verbraucherschutz und Lebensmittelsicherheit (AZ33.9-42502-04-10/0225 and AZ33.12-42502-04-12/0921).
MCMV mutants have been described previously [32] and were produced and titrated on mouse embryonic fibroblasts. MCMV-2D encodes Gaussia luciferase and mCherry, MCMV-3D carries additionally a sequence within the m164 ORF encoding the SIINFEKL peptide. The MCMV-3DΔvRAP mutant is identical to MCMV-3D except that it lacks the m06 and m152 ORFs. All reporter viruses lack the m157 ORF that encodes a ligand for the activating receptor Ly49H present on NK cells in C57BL/6 mice [81]. C57BL/6 wildtype or CD11c-YFP mice were mated and dams were kept with their litter. Neonatal mice were infected on their first day of life (<24 h old); “oral” inoculations were performed by repeated moistening of the mouth with fluid up to a volume of 10 µl (for control applications we used 3×109 Fluoresbrite YG Microspheres, Polysciences Europe GmbH), for l.p. inoculations a volume of 10 µl was administered by probing of the laryngopharynx with a pipette and extension of the neck. Adult C57BL/6 wildtype mice (6–8 weeks old) were anesthesized (100 mg/kg BW ketamine and 5 mg/kg BW xylazine) and 20 µl of virus solution was applied to each nostril for “intranasal” infection.
CD8+ T cells were isolated with MACS CD8+ T cell isolation kit (Miltenyi Biotec) from lymph nodes and spleen of OTIxGFP or OTIxCFP mice and had a purity of 85–95%. MCMV-2D and MCMV-3D-infected neonatal mice received equal numbers (5×106 cells) of naïve CD8+ T cells via i.p. application (Figure 6). CD8 T cells were depleted by intraperitoneal application of RmCD8.2 mAb (25 µg/g body weight; Figure S4A). FTY720 was given subcutaneously (5 µg/g body weight) on a daily basis. The first administration was given at the time of cell transfer (Figure 7).
Right heart ventricle was perfused with PBS until blood cells were removed from the lung. Fragmented tissue was digested with Collagenase D (Roche, 0.5 mg/ml) and DNAse I (Roche, 0.025 mg/ml) for 45 min at 37°C, meshed through 40 µm Falcon® Cell Strainer and leukocytes isolated with Lympholyte®-M.
Leica MZ16 epifluorescence microscope was used for whole organ images. For histology organs were fixed in 2% PFA and 30% sucrose for 30 min and embedded in OCT compound (Tissue-Tek, Sakura). 7 µm-thick organ slices were stained after appropriate blocking with depicted antibodies. Images were taken with an AxioCam MRm camera (Carl Zeiss) attached to Axiovert 200M fluorescence microscope (Carl Zeiss) with PlanApochromat objectives 10×/0,45, 20×/0,75 and 40×/0,95 (magnification/numerical aperture) and processed with AxioVision 4.8 software. Images of HE stained sections were taken with Olympus BX61 microscope and ColorView IIIu camera with UPlanSApo objectives (4×/0,16 and 40×/0,90) and processed with cell∧P 5.0 (Olympus Europe). All images were processed with Microsoft Office Picture Manager. Cell strainers (BD Falcon) were used to prepare suspensions for FACS analysis or cell purification from lymph node or spleen cells. Cells were processed with LSRII Cytometer and data was analyzed with BD FACSDiva Software (6.1.3) or WinList 6.0 software. The following antibodies (clones) were used after adequate blocking of Fc receptors: B220-Cy5 (RA3-3A1), CD103-PE (M290), CD169-AlexaFluor647 (MOMA-1), CD11b-AlexaFluor488 (M1/70), CD11c-APC (N418), CD3-AlexaFluor488 (17A2), CD3-PE (17A2), CD31-biotinylated (MEC13), CD4-biotinylated (GK1.5), CD4-PerCP (RM4-5), CD44-eFluor450 (IM7), CD45-APC (30-F11), CD69-PerCP/Cy5.5 (H1.2F3), CD8a-APC/Cy7 (53-6.7), CD8b-Cy5 (Rm CD8-2), CD8b-AlexaFluor488 (Rm-CD8-2), F4/80-APC (BM8), NK1.1-PE (PK136), pro surfactant protein C (AB3786) combined with anti-rabbit-Cy5 (Jackson ImmunoResearch), TCR-Vα2-PE (B20.1). Streptavidin-Cy5 (eBioscience), Streptavidin-APC/Cy7 (BD-Pharmingen), Cell Proliferation Dye eFluor® 670 (eBioscience), M45-tetramer-PE provided by Ramon Arens.
Single organ preparations were performed after perfusion of supplying blood vessels with PBS. Organs were kept in PBS, homogenized with TissueLyser II (Qiagen) and supernatants were measured for luciferase expression after addition of “native Coelenterazine” (Synchem) with Lumat LB 9507 (Berthold Technologies). For lung, salivary glands, gut and liver 1∶10 dilutions were performed for measurements. The following organs were analyzed: lung (Figure 1 complete lung, Figure 5 lobes of right lung including trachea), gut (from proximal esophagus to distal colon), salivary glands (all sublingual and submaxillary), brain (down to the bulb), spleen, liver (complete liver of neonates, only left lobe from adults), kidney (right only). Luciferase measurements of organs from non-infected animals were used as controls and data was normalized to means of control measurements to determine the detection limits.
Neonatal lungs were explanted and 400 µm-thick lung slices were prepared with use of a Tissue Chopper (McIllwain). Lung slices were fixed on a imaging chamber using tissue adhesive (Surgibond) and kept in oxygenated (95% O2/5% CO2) RPMI 37°C medium (Invitrogen) containing 5 g/L glucose. Imaging was performed with Olympus BX51 upright microscope equipped with a 20×/0.95 water immersion objective. A MaiTai Ti∶Sa pulsed IR laser (Spectra-Physics) was set to 920 nm for excitation of eGFP (as well as Ncr1gfp/+ and DAPI for Figure 3 F) or 860 nm for excitation of eCFP and YFP. A second laser excited mCherry with 1100 nm generated from an optical parametric oscillator (OPO; APE, Berlin). Z-stacks of up to 30 images from 300×300×60–160 µm (Movie S1, S2, S3) or 150×150×60–160 µm (Movie S4) viewfields were acquired every 20–30 seconds to generate time-lapse series. Data was analyzed with Imaris 7.x (Bitplane Scientific Software) and processed with MAGIX Video deluxe 2013.
7 µm-thick lung sections of 2–4 animals per time-point and group were performed at comparable anatomical positions (central lung, slices including right and left lobes and main bronchi). “Viable” infected cells were distinguished from cell “remnants” by the following criteria: morphology (smooth edge, round shaped with or without elongations), nucleus (clear non-fragmented DAPI signal present) and cell size (larger than 5 µm) (Figure 3F). Mean of two counted slices per animal was calculated for Figure 4D and E. Area of inflammation was determined by manual measurement of CD45+ stain signal using AxioVision 4.8 software (Figure 4F).
Neonatal mice were subcutaneously injected with 125 µM EdU and sacrificed within 4 h after injection. Histological staining was performed with Click-iT EdU Imaging Kit (Invitrogen).
Statistical analysis was performed with Prism 4 (Graph-Pad Software, Inc.). Unpaired t-test for comparison of 2 groups or ANOVA one-way analysis for >2 groups. Statistical significance was depicted as follows: *, p<0.05; **, p<0.01; and ***, p<0.001.
|
10.1371/journal.pntd.0000900 | The Epidemiology and Clinical Spectrum of Melioidosis: 540 Cases from the 20 Year Darwin Prospective Study | Over 20 years, from October 1989, the Darwin prospective melioidosis study has documented 540 cases from tropical Australia, providing new insights into epidemiology and the clinical spectrum.
The principal presentation was pneumonia in 278 (51%), genitourinary infection in 76 (14%), skin infection in 68 (13%), bacteremia without evident focus in 59 (11%), septic arthritis/osteomyelitis in 20 (4%) and neurological melioidosis in 14 (3%). 298 (55%) were bacteremic and 116 (21%) developed septic shock (58 fatal). Internal organ abscesses and secondary foci in lungs and/or joints were common. Prostatic abscesses occurred in 76 (20% of 372 males). 96 (18%) had occupational exposure to Burkholderia pseudomallei. 118 (22%) had a specific recreational or occupational incident considered the likely infecting event. 436 (81%) presented during the monsoonal wet season. The higher proportion with pneumonia in December to February supports the hypothesis of infection by inhalation during severe weather events. Recurrent melioidosis occurred in 29, mostly attributed to poor adherence to therapy. Mortality decreased from 30% in the first 5 years to 9% in the last five years (p<0.001). Risk factors for melioidosis included diabetes (39%), hazardous alcohol use (39%), chronic lung disease (26%) and chronic renal disease (12%). There was no identifiable risk factor in 20%. Of the 77 fatal cases (14%), 75 had at least one risk factor; the other 2 were elderly. On multivariate analysis of risk factors, age, location and season, the only independent predictors of mortality were the presence of at least one risk factor (OR 9.4; 95% CI 2.3–39) and age ≥50 years (OR 2.0; 95% CI 1.2–2.3).
Melioidosis should be seen as an opportunistic infection that is unlikely to kill a healthy person, provided infection is diagnosed early and resources are available to provide appropriate antibiotics and critical care.
| Melioidosis is an occupationally and recreationally acquired infection important in Southeast Asia and northern Australia. Recently cases have been reported from more diverse locations globally. The responsible bacterium, Burkholderia pseudomallei, is considered a potential biothreat agent. Risk factors predisposing to melioidosis are well recognised, most notably diabetes. The Darwin prospective melioidosis study has identified 540 cases of melioidosis over 20 years and analysis of the epidemiology and clinical findings provides important new insights into this disease. Risk factors identified in addition to diabetes, hazardous alcohol use and chronic renal disease include chronic lung disease, malignancies, rheumatic heart disease, cardiac failure and age ≥50 years. Half of patients presented with pneumonia and septic shock was common (21%). The decrease in mortality from 30% in the first 5 years of the study to 9% in the last five years is attributed to earlier diagnosis and improvements in intensive care management. Of the 77 fatal cases (14%), all had known risk factors for melioidosis. This supports the most important conclusion of the study, which is that melioidosis is very unlikely to kill a healthy person, provided the infection is diagnosed early and resources are available to provide appropriate antibiotics and critical care where required.
| Melioidosis is the clinical disease following infection with the soil and water bacterium Burkholderia pseudomallei [1], [2]. It occurs in humans and a wide variety of animals and is thought to usually follow percutaneous inoculation. In addition, inhalation of aerosolized bacteria probably occurs during severe weather events such as tropical storms, aspiration is documented with near drowning and ingestion can occur, especially in grazing animals but also from mastitis-associated infected breast milk [3], [4]. Zoonotic transmission is described but is exceedingly uncommon, as are person-to-person transmission, nosocomial transmission and laboratory-acquired infection.
While melioidosis can present as a rapidly fatal septicemic illness and B. pseudomallei is now considered a potential biothreat agent, there remain major gaps in understanding the global distribution, epidemiology and pathogenesis of this infection. The known endemic distribution of B. pseudomallei is expanding well beyond the traditional melioidosis-endemic regions of Southeast Asia and northern Australia, with recent case reports of melioidosis from the Americas, Madagascar, Mauritius, India and elsewhere in south Asia, China and Taiwan [5], [6]. It remains unclear to what extent this reflects true expansion of endemicity rather than unmasking of the long-standing environmental presence of the bacterium.
Since October 1989 we have prospectively documented all cases of melioidosis in the tropical “Top End” of the Northern Territory of Australia. We described the presentations of the first 252 cases after 10 years of the Darwin prospective melioidosis study [7] and we now present the findings from 540 cases over 20 years.
This study was approved by the Human Research Ethics Committee of the Northern Territory Department of Health and Families and the Menzies School of Health Research (HREC 02/38) and data were analysed anonymously. The Top End has a population of around 150,000 in an area of 516,945 km2, with almost 125,000 living in the Northern Territory capital city of Darwin (12°S). All patients with culture-confirmed melioidosis in the Top End from October 1st 1989 until September 30th 2009 were included. Investigation, treatment and follow-up were supervised in all cases in consultation with the Infectious Disease Department at Royal Darwin Hospital, the 350 bed referral hospital for the Top End. We followed all patients until death or after completion of therapy. Hazardous alcohol use was defined as greater than an average daily consumption of six standard drinks (60 g alcohol total) for males and four (40g alcohol total) for females. Chronic lung disease was defined as a documented diagnosis of chronic obstructive airways disease. Chronic renal disease was defined as a creatinine of >150 umol/L (N. R.<90 umol/L) before the melioidosis illness or after completion of therapy if not previously documented. Septic shock was defined as the presence of hypotension not responsive to fluid replacement together with hypoperfusion abnormalities manifest as end organ dysfunction [8].
Patient details were stored in a database and analysed using Stata version 10 (Stata Corporation, Texas). Chi-squared or Fisher exact tests were used to assess categorical variables; p<0.05 was considered significant and risk ratios and 95% confidence intervals were then calculated. To identify associations with a fatal outcome and with presentation with pneumonia and with bacteremia we conducted multivariable logistic regression analyses with stepwise backwards elimination of patient demographic and risk factor variables, with odds ratios and 95% confidence intervals calculated.
There were 540 cases and 77 deaths (14%) attributable to melioidosis over the 20 years. Ages ranged from 8 months to 91 years (median 49 years). There were 26 children ≤15 years old (5%) and two of these died, one with congenital heart disease and one with severe rheumatic heart disease. 372 patients (69%) were male and 281 Indigenous Australians (52%). 262 patients (49%) lived in the suburbs of Darwin, 65 (12%) on rural properties (“blocks”) outside Darwin city, 37 (7%) in the regional towns of Katherine and Nhulunbuy and 169 (31%) in remote Indigenous communities. Four infections were considered acquired in sub-tropical central Australia and three were acquired elsewhere in tropical northern Australia outside the Northern Territory.
Table 1 shows patient risk factors and outcomes by risk factor. There were only 2 patients with confirmed HIV infection, although a small number were not tested and 1 patient was seropositive for HTLV-I. Mortality was significantly higher in those with chronic respiratory disease (19% vs 13%; risk ratio = 1.5 (95% CI 1.1–2.4); p = 0.048). Although no other individual risk factor, including diabetes, was predictive of mortality, the absence of any risk factors was strongly predictive of survival; of the 106 (20%) with no identified risk factor for melioidosis, only two died (2%); both elderly, aged 75 and 82 years, respectively.
407 patients (75%) were considered to have exposure to environmental B. pseudomallei through their recreational activities and 96 (18%) had direct exposure through occupational activities including gardening and outdoor maintenance, plumbing, building construction, plant machine operation and military exercises. Only 103 (19%) had no evident environmental or recreational exposure. In 118 (22%) cases there was a specific exposure scenario that was considered the likely infecting event. These included skin wounds sustained whilst working outdoors or gardening, or while playing sports such as soccer and rugby on muddy playing fields, or while fishing in fresh water rivers, hunting such as chasing feral pigs through tropical savannah swamps and motor vehicle accidents involving wet soil exposure. Several cases were in disabled people who rarely ventured outside their accommodation but were potentially exposed to aerosolised bacteria during storms. Regional clusters of cases occurred following severe weather events such as the Katherine river flood in January 1998, and Category 5 tropical cyclone Thelma, which hit the Tiwi Islands in December 1998 [9]. One cluster with nine cases of melioidosis and four deaths was attributed to confirmed B. pseudomallei contamination of the un-chlorinated water supply in a remote Aboriginal community [10].
Overall 436 (81%; 95%CI 77%–84%; p<0.005) presented during the wet season (November 1st–April 30th) and mortality was higher in cases presenting in January (23/102 (23%) died; p = 0.007) than in other months. Pneumonia was a significantly more common presentation in the peak monsoonal months of December to February (172/280; 61%) than in the other 9 months (106/260; 41%; p<0.001).
Of all presentations, 461 (85%) were considered acute (defined as symptoms present for less than 2 months) and from recent infection. 60 (11%) were chronic in nature (defined as symptoms present for over 2 months; 25 pneumonia, 23 skin ulcer(s), 12 others). These chronic infections were considered to be mostly acquired during the current or preceding wet season, with the delay until presentation explaining some of the cases diagnosed during the dry season. 17 of the 53 (32%) cases who presented during the mid dry season months of June 1st to September 30th fulfilling the definition for chronic melioidosis. Patients with chronic melioidosis were less likely to be diabetic than those with acute melioidosis (20% vs 42%; p<0.001), with 42% having no identified risk factor in comparison to 17% of those with acute disease (p<0.001). Only 1 of 60 patients (2%) with chronic melioidosis died (p<0.001).
The remaining 19 (4%) patients were thought to have reactivation of disease from a latent focus of B. pseudomallei infection, based on long-standing prior radiological abnormalities and/or known long-standing positive melioidosis serology (13 pneumonia, 2 bacteremia no focus, 2 genitourinary infection, 1 each soft tissue infection and skin abscess). Those with presumptive reactivated melioidosis were more likely to have underlying chronic lung disease (47% compared with 25% for all others; p = 0.03) and rheumatic heart disease and/or congestive cardiac failure (32% compared with 6% for all others; p<0.001) and 5/19 of these died (26% compared with 14% for all others; p = 0.13).
The clinical presentations and outcomes are shown in Table 2. Overall 298 (55%) patients were bacteremic. Pneumonia was the commonest principal clinical presentation on admission (278 cases; 51%), followed by genitourinary infection (76 cases; 14%) and skin infection (68 cases; 13%). There were 20 (4%) patients presenting with septic arthritis and/or osteomyelitis and 14 (3%) with neurological melioidosis, of whom 10 presented with meningo-encephalitis, 2 with myelitis and 2 with cerebral abscesses. Bacteremia without an evident clinical focus was also a common presentation (59 cases; 11%), with severity of illness ranging from rapidly fatal septic shock to a clinically very mild febrile illness. When septic shock occurred it was usually present on or within 24 hours of admission. Of the 116 patients (21%) with septic shock, 58 (50%) died from acute fulminant melioidosis. In contrast, for those without septic shock on presentation, mortality was 4% overall (19/424); even in the 195 of those without septic shock who were bacteremic, only 13 (7%) died. Of the 106 patients with no identified risk factor, 23 (22%) were bacteremic and 6 (6%) had septic shock and the only deaths in this group were the 2 elderly patients as already noted.
Table 3 shows significant risk factors in the 278 melioidosis patients with a primary presentation of pneumonia. On univariate analysis age ≥50 years, diabetes, excessive alcohol consumption and rheumatic heart disease and/or congestive cardiac failure were each associated with a propensity for presentation with pneumonia in comparison to other presentations. However on multivariable analysis diabetes and age were not independent predictors of a presentation with pneumonia, while chronic lung disease, excessive alcohol consumption and rheumatic heart disease and/or congestive cardiac failure were.
Table 4 lists internal organ abscesses and other foci of infection. Following findings early in the study of frequent internal collections, CT scanning of abdomen and pelvis has been routinely performed on all patients with melioidosis since around 1995. Prostatic abscesses were present in 76 males (20%), the majority of which required drainage [11]. In comparison to case series from Thailand, hepatic abscesses were uncommon and as with splenic and renal abscesses rarely required drainage. Three women had mastitis and three men had epididymo-orchitis. Lymphadenitis (sometimes suppurating), muscle abscesses, diffuse myositis and cellulitis were all seen but were uncommon. Four patients had para-intestinal masses which were considered possible primary infection following ingestion of B. pseudomallei, as was a presentation with a ruptured large gastric ulcer with subphrenic abscess and suppurative peritonitis. Mediastinal widening on chest X-ray and CT scan was seen, sometimes with clearly enlarged mediastinal lymph nodes and usually in association with pneumonia (12/17 cases). Four patients had suppurative pericarditis, three with contiguous pulmonary infection and one without evident pulmonary infection who developed acute pericardial tamponade requiring emergency thoracotomy and a pericardial window. Two had mycotic pseudo-aneurysms and one woman presented with a ruptured uterus from a massive uterine wall abscess.
In addition to the initial principal clinical presentation, subsequent clinically-evident secondary foci were not uncommon and examples are shown in Table 5. Secondary pneumonia was especially common in those presenting with genitourinary infection, septic arthritis/osteomyelitis and bacteremia without an apparent clinical focus, but was unusual in those presenting with skin infection. Secondary foci were also less common in those presenting with pneumonia, although brain abscesses and septic arthritis requiring surgery occurred in this group. Of note, the pattern of secondary neurological melioidosis was different from the encephalomyelitis seen as a primary presentation. Of the eight patients with secondary neurological disease, all were blood culture positive (in comparison to 3/14 of those with primary neurological melioidosis; p = 0.001) and 5 had abscesses (4 intracranial, 1 spinal cord).
121 patients (22%) were admitted to the Royal Darwin Hospital Intensive Care Unit (ICU) and of these 40 (33%) died. In the ICU 97 were ventilated (41 died; 42%) (Table 6) and 60 received granulocyte colony-stimulating factor (G-CSF) (15 died; 25%). Three patients also received activated protein C therapy (1 died). Of the 77 deaths overall, 75 were during the initial hospital admission, with the time from admission to death in these ranging from 0 to 111 days (median 3 days). Two patients were dead on arrival at hospital, 8 died on the day of admission, and 7 died the day after admission.
Of the 465 patients surviving the initial infection, 30 (6%) re-presented with culture-confirmed recurrent melioidosis subsequent to completion of antibiotic therapy, with 2 deaths in this group. Of these 30, 25 were considered to have relapse of an unsuccessfully eradicated infection, usually resulting from poor adherence to antimicrobial therapy. In these cases, the time from initial admission to first relapse was 3.6–28 months (median 8 months), with one of these patients dying. Two of these patients had a second relapses (25 and 27 months after their first relapse); one of these patients died during the second relapse and the other patient had a third relapse 5 years after the second relapse. For those surviving the initial admission, diabetes was more common in those who relapsed (16/25 (64%) vs 166/440 (38%); p = 0.016), as was bacteremia on admission (18/25 (72%) vs 221/440 (50%); p = 0.034).
There were 5 patients with recurrent melioidosis where the B. pseudomallei isolates were different from the original isolate by pulsed-field gel electrophoresis or multilocus sequence typing (MLST). One patient with cystic fibrosis had three separate presentations with melioidosis at ages 10, 14 and 18 years. There was a good response to therapy each episode, with each B. pseudomallei isolate being a different sequence type (ST) and frequent sputum cultures between episodes being consistently culture negative for B. pseudomallei. He was considered to have been re-infected on three separate occasions. Three other patients with recurrent melioidosis but disparate isolates on typing were also considered likely to have new infections, occurring 14, 58 and 72 months after the initial infection, respectively. One further patient had disparate isolates on MLST from presentations 8 months apart and was thought to have relapse of a probable initial infection with multiple B. pseudomallei strains, with the osteomyelitis of the second presentation being evident clinically during the first presentation. Clinically apparent re-infection with B. pseudomallei is therefore thought to have occurred in only 4/465 (1%) patients surviving the initial admission, despite most survivors remaining in the melioidosis-endemic location, with many having persisting risk factors and continuing environmental exposure to B. pseudomallei.
Of the 463 patients who did not die from melioidosis, all except one have eventually cleared their infection with antibiotic therapy. This is a patient with moderately severe bronchiectasis who presented with a productive cough at age 61 years, with B. pseudomallei cultured from sputum. Her sputum has remained consistently B. pseudomallei culture positive for 8 years, despite multiple courses of intravenous and prolonged oral antibiotics and also a lobectomy of the most severely bronchiectatic lung lobe. She nevertheless remains generally well.
Table 6 shows decreasing mortality over the 20 years of the study that was not explained by either increasing recruitment of less sick patients or fewer risk factors in patients. To further assess associations with mortality we included the following categorical variables in the initial logistic regression model; age (≥50 years), indigenous ethnicity, each of the risk factors from Table 1, location, and presentation in December, January or February. No individual risk factor was a significant independent predictor of mortality (data not shown), although in a similar logistic regression model with bacteremia as the outcome, independent predictors of bacteremia were indigenous ethnicity, age ≥50 years, diabetes, hazardous alcohol use, chronic renal disease, malignancy and immunosuppression (Table 7). Therefore our final model for mortality from melioidosis incorporated presence or absence of any of the defined risk factors as a dichotomous variable. In this model the independent factors associated with mortality were age ≥50 years (OR 2.0; 95% CI 1.2–3.3) and presence of any risk factor (OR 9.4; 95% CI 2.3–39), but not indigenous ethnicity, geographical location or season (Table 8).
Serological surveys suggest that most infections with B. pseudomallei are asymptomatic, with over half of teenagers seropositive in the highly endemic region of northeast Thailand [12]. It was estimated that for children in northeast Thailand approximately 1 in 4600 antibody-producing exposures results in clinical infection [13]. The Darwin prospective melioidosis study provides strong support for the vast majority of melioidosis cases being from recent infection, with 81% of cases presenting during the monsoonal wet season, similar to a figure of 75% in Thailand [14]. Nevertheless latency of B. pseudomallei with subsequent reactivation is well recognised, being described as long as 62 years after infection in a returned World War II prisoner of war infected in southeast Asia [15]. It was estimated from serology studies that following the Vietnam War around 225,000 US service personnel may have been infected with B. pseudomallei [16]. This was called the “Vietnamese time bomb”, but the subsequent number of melioidosis cases following return to the USA has been comparatively small. Reactivation from a latent focus was considered to have occurred in only 19/540 (4%) cases in the Darwin study.
We previously estimated the average annual incidence rate of melioidosis in the Top End of the Northern Territory to be 19.6 cases per 100,000 population, with an estimated rate in diabetics of 260 cases per 100,000/year [17]. Yearly rates between 1990 and 2002 ranged from a low of 5.4/100,000 in 1993 to a high of 41.7/100,000 in 1998, a year with two severe tropical cyclones with intense rainfall and winds. This compares to northeast Thailand, with an average annual melioidosis incidence rate between 1997 and 2006 of 12.7/100,000 and with a highest rate of 21.3/100,000 in 2006 [18].
In the Darwin study 75% of melioidosis cases reported recreational activities that would result in exposure to environmental B. pseudomallei and 18% had clear occupational exposure. Both males (69%) and indigenous Australians (52%) were over-represented, most likely reflecting increased environmental exposure. There were 118 cases (22%) where history revealed a likely specific infecting event. B. pseudomallei is common in the urban environment of Darwin and most of the 49% of patients in the study who lived in the city of Darwin were infected in the city environs, including domestic gardens and yards. Mortality in the Darwin study was not linked to geographical location, being actually higher (although not statistically significantly so) in the urban population than in the rural and remote population (Table 8). In contrast, in northeast Thailand 81% of cases of melioidosis were in rural rice farmers and their children [14]. In Singapore melioidosis has occurred in construction workers, gardeners and military personnel, but in that tropical island city state, where over 80% of people live in high-rise apartments, the reasons for infection often remain unclear [19].
Earlier in the Darwin prospective melioidosis study we established that the incubation period for acute melioidosis following specific infecting events was 1–21 days (mean, 9 days) [20]. The incubation period, clinical presentations of melioidosis and outcomes are thought to be determined by a combination of bacterial load infecting the individual, putative B. pseudomallei strain differences in virulence, mode of infection and, most importantly host risk factors for disease [21]. For instance, less severe disease with symptoms present for over 2 months before presentation (chronic melioidosis) was significantly less common in diabetics and was more commonly seen in those without underlying risk factors.
The association between inhalation as a route of acquisition and increased severity of disease with higher mortality than percutaneous exposure is well recognised for anthrax, plague and tularaemia, but appears to have been under-appreciated in melioidosis. While the association between melioidosis and rainfall is well established [14] and there is epidemiological support for inhalation of aerosolised B. pseudomallei during severe weather events resulting in a pneumonic presentation with higher mortality [9], [19], [22], the overall contribution of inhalation of B. pseudomallei in comparison to percutaneous inoculation remains entirely unclear. Support for inhalation of B. pseudomallei from this study includes that 61% of admissions during the peak monsoonal months of December to February were with pneumonia, in comparison to only 41% in the other 9 months, plus the recognition that mediastinal lymphadenopathy is not uncommon.
Diabetes is the most important risk factor for melioidosis, followed by hazardous alcohol use, chronic lung disease and chronic renal disease [7], [14], [17], [19], [23], [24], [25]. Malignancy, immunosuppression and thalassemia are also recognised risk factors [24]. In the Darwin study 39% of patients were diabetic, with nearly all having adult onset type 2 diabetes. Rates of diabetes from other endemic locations were 57% in the largest series from Thailand [24], 48% in Singapore [19], 60% in Taiwan [26], 38% in bacteremic melioidosis patients in Malaysia [27] and 42% in north Queensland, Australia [25]. When considering the estimated prevalence of diabetes in the whole population, we previously calculated the risk of melioidosis in diabetics in the tropical Top End of the Northern Territory to be 21.2 (95% CI 17.1–26.3) times the risk in non-diabetics [17], which is similar to data from Thailand [14].
A lack of association of melioidosis with HIV infection [28], [29] supports a limited role for adaptive immunity in protection against acquisition of and mortality from melioidosis, despite evidence for a cell-mediated immune response to B. pseudomallei [30], [31]. We proposed that a unifying hypothesis for the predominance of diabetes, excessive alcohol consumption and chronic renal disease in melioidosis patients was the critical role of innate immunity and especially robust neutrophil function in controlling infection with B. pseudomallei [7]. The specific defects in neutrophil function in diabetes, alcohol excess and renal disease have been well described and were the basis for trialling therapy with granulocyte-colony stimulating factor (G-CSF) in melioidosis [32], [33]. The dysfunctional neutrophil hypothesis is supported by a study in a mouse model showing a critical role for neutrophils in resistance to melioidosis [34] and a recent study from Thailand showing that, in comparison to non-diabetics, otherwise healthy diabetics had neutrophils displaying impaired phagocytosis of B. pseudomallei, reduced migration in response to interleukin-8 and an inability to delay apoptosis [35]. The occurrence of melioidosis in chronic granulomatous disease also supports a key role for neutrophils [36].
Our clinical impression is that the risk for melioidosis in those with hazardous alcohol use may often be directly related to binge drinking rather than chronic liver disease, with high blood alcohol levels at the time of exposure to B. pseudomallei inhibiting protection against bacterial propagation and dissemination. This is consistent with earlier studies on neutrophil function in alcohol intoxication [37], [38]. An additional potential pathogenetic mechanism for more severe disease in those with hazardous alcohol intake is the induction by alcohol of bacterial genes encoding various potential virulence mechanisms, as recently shown in transcriptional profiling studies of Acinetobacter baumannii grown in the presence of alcohol [39]. In the Darwin study hazardous alcohol use but not diabetes was an independent predictor of presentation with melioidosis pneumonia (Table 3). In addition to neutrophil dysfunction, alcohol excess also adversely affects many other components of innate pulmonary host defences, from decreased ciliary beat frequency to impaired alveolar macrophage phagocytosis and inhibited cytokine responses [40]. Various aspects of adaptive pulmonary immunity are also affected by alcohol, involving both cellular and humoral responses.
We have noted two comorbidities previously unrecognized as potential risk factors for melioidosis. Chronic lung disease was an independent predictor of pneumonic melioidosis, which may reflect defective innate immunity such as impaired alveolar macrophage function [41]. Rheumatic heart disease and cardiac failure may predispose to melioidosis by similar mechanisms [42].
It is being increasingly recognised that patients with cystic fibrosis are at substantial risk of infection with B. pseudomallei if they live in or travel to endemic regions [43]. Chronic infection can occur, with acute flares of pneumonia and progressive deterioration of lung function, as also seen with B. cepacia infection in cystic fibrosis [44], [45], [46]. Patients with cystic fibrosis should consider avoiding travel to locations where melioidosis is common. One Darwin patient with cystic fibrosis has had three separate infections with different genotypes of B. pseudomallei. In addition, there is only 1/463 survivors in the Darwin study in whom clearance of B. pseudomallei has not been possible. This patient has severe bronchiectasis and has had persisting pulmonary infection for 8 years; such inability to eradicate B. pseudomallei from sputum has only been previously documented in cystic fibrosis [43].
Around half of melioidosis cases present with pneumonia, which can be part of a fatal septicaemia, a less severe unilateral infection indistinguishable from other community-acquired pneumonias or a chronic illness mimicking tuberculosis [2], [47]. In the Darwin study mortality was 49% in those with pneumonia who also had septic shock, in comparison to 6% in those with pneumonia without septic shock and 4% in those with chronic pneumonia. Early clinical descriptions and animal studies showed that melioidosis pneumonia can follow percutaneous infection [48], but the proportions of our pneumonia cases which were from percutaneous exposure, inhalation or aspiration are unknown. Nevertheless the finding of mediastinal widening on chest X-ray and CT scan in some melioidosis patients is analogous to inhalational anthrax.
Other presentations in the Darwin study range from skin lesions without systemic illness [49], to overwhelming sepsis with abscesses disseminated in multiple internal organs. Genitourinary [11], bone, joint and neurological infections [50], [51], [52] are all well recognised. One manifestation of melioidosis commonly seen in Thailand [53], but not seen over the 20 years of the Darwin study is children presenting with parotid abscesses. The reasons for this difference remain unclear.
The dramatic presentation of melioidosis brainstem encephalitis or myelitis has been noted to be more commonly seen in Australia than in Thailand [1], [3]. Recent mouse studies have suggested that such neurological presentations may result from direct entry of B. pseudomallei to the brain from the nasal mucosa via the olfactory nerve or similar pathways [54]. Genetic differences between B. pseudomallei strains may account for regional clinical variations. It was recently demonstrated that the global B. pseudomallei population probably evolved from an ancestral Australian population which subsequently spread to Southeast Asia [55]. One possible explanation for the neurological disease being more common in Australia is differences in propensity between B. pseudomallei populations for actin-based motility of bacteria along nerve pathways, conferred by variants of the BimA gene which have been found to be geographically restricted [56]. The concept of direct brain invasion by B. pseudomallei in the Darwin cases of primary melioidosis meningo-encephalomyelitis is supported by the low bacteremia rate in comparison to all 8 of those with secondary brain infections being bacteremic.
Most of the less common presentations of melioidosis seen in the Darwin study have also been described from other locations. These include mycotic aneurysms [57], epididymo-orchitis [58], pericarditis [59] and mastitis with maternal to child transmission of melioidosis [4]. The common presence of diverse internal organ abscesses necessitating routine imaging is also well recognised [1], [60], [61]. In contrast to Thai studies, where spleen and liver abscesses predominate [60], prostate abscesses were extremely common in the Darwin series, being present in 76/372 (20%) males. While liver, spleen and renal abscesses responded to prolonged antibiotic therapy, prostatic abscesses usually required drainage, whether primary or secondary [11]. Although unusual, the presence of para-intestinal masses supports that ingestion can occasionally be the primary route of infection in humans, as is more commonly seen in grazing animals [62].
Whatever the initial clinical presentation, secondary foci of infection are common in melioidosis (Table 5), presumably from bacteremic spread and reflecting the high rate of bacteremia overall (55%). Of those 59 patients presenting with bacteremia without an apparent focus, 25 (42%) subsequently developed an evident secondary focus of infection. Secondary pneumonia and septic arthritis were especially common.
Therapy of melioidosis requires prolonged antibiotics to cure infection and prevent relapse [63]. In the Darwin study 25/465 (5.4%) patients who survived the initial infection relapsed after treatment, with a median time to relapse of 8 months from initial admission, in comparison to 86/889 (9.7%) and 6 months from commencement of oral therapy in Thailand [24]. Choice and duration of and compliance with antibiotic therapy were the strongest indicators of risk for relapse in both locations. Diabetes was significantly associated with risk for relapse in the Darwin series but not in Thailand, while in both locations bacteremia on initial admission was associated with relapse, although only significantly so in Thailand.
Genotyping of B. pseudomallei from recurrent melioidosis has shown that reinfection can also occur but is less common than relapse [24], [64]. In northeast Thailand reinfection occurred in 30/899 (3.4%) patients, making the incidence of melioidosis reinfection substantially higher than that of primary infection [24]. This is in contrast to the Darwin patients, where reinfection was documented in only 4/465 (1%), despite most having persisting risk factors and continuing exposure and even raising the possibility of some acquired immunity to reinfection following melioidosis. Simultaneous infection with more than one strain of B. pseudomallei has been shown to very uncommon (2/133 cases in Thailand) [65], but was thought likely in one of the patients in this study.
Mortality from melioidosis in the Darwin study was 14%, with 75 of the 77 deaths occurring during the initial hospital admission and only 2 deaths from relapsed melioidosis. Mortality during the first 5 years of the study (from October 1989) was 30% and during the last 5 years (until October 2009) was 9% (p<0.001). These rates compare with 49% mortality in the large Thai study from 1986–2004 [24], with mortality now decreasing in that region [18], 65% in bacteremic patients in Malaysia during 1976–1991 [27], 16% in Singapore between 1998–2007 [19], 22% in Taiwan between 2000 and 2005 [26] and 25% in north Queensland between 1996 and 2004 [25].
The decreasing mortality over the 20 years of the Darwin study cannot be attributed to ascertainment bias from improved diagnosis of less severe cases. Indeed, the bacteremia rate was higher in the last 5 years (66%), possibly reflecting both more frequent repeat culturing in suspected cases and improved laboratory detection of low level bacteremia. The median age, rates of septic shock, percentages with various risk factors and the proportion with no risk factors did not change significantly over the 20 years (Table 6). The overall bacteremia rate of 55% compares with up to 65% in Thailand [24][66], 50% in Singapore [19] and 60% in north Queensland [25].
We attribute the improved survival over time to a combination of earlier diagnosis of melioidosis through increased community and health staff awareness of the possibility, earlier treatment with ceftazidime or meropenem [67] and probably most importantly, access to and improvements in intensive care management of the septic patient. In many melioidosis-endemic regions renal replacement therapy and other resources for managing the metabolic abnormalities and organ dysfunction seen in severe sepsis are limited and without these mortality in septicemic melioidosis will remain high [2], [24], [68]. A greater proportion of patients were ventilated in the second half of the Darwin study and mortality in patients with septic shock was 100% in the first five years and decreased to 27% in the last 5 years (Table 6). Our initial optimism of potential benefit from G-CSF therapy in septicemic melioidosis [32] has been tempered by a randomized controlled trial in Thailand which showed that G-CSF conferred no mortality benefit in severe melioidosis in that setting [33]. Nevertheless those treated with G-CSF in that study had a longer duration of survival, suggesting that if state-of-the-art ICU therapy is available G-CSF may be beneficial.
A decrease in mortality in melioidosis similar to that seen in Darwin has also occurred in Singapore, where mortality in 1989–1996 was 40% in comparison to the more recent 16% [19]. It is notable that during the more recent series from Singapore there was a period in March–April 2004 with case numbers, proportion with pneumonia (83%) and mortality (53%) all higher than at other times [19]. This cluster of cases followed heavy rainfall and strong winds. Genotyping showed a diversity of strains, excluding a point source outbreak and the severe disease in this cluster was attributed to a possible shift to inhalation of aerosolized B. pseudomallei [19], [69]. This is analogous to clusters seen in the Darwin study following severe weather events [9].
This study provides strong support for the presence of specific host risk factors being the most important determinant of mortality from melioidosis. Older age is also recognised as a risk factor for melioidosis [14], [17], [19] and in the Darwin study age ≥50 years was an independent predictor of death from melioidosis (Table 8). Of the 77 deaths from melioidosis over the 20 years, 2 were in elderly patients without other evident risk factors and all of the other 75 fatal cases had at least one of the specific recognised risk factors listed in Table 1. That severe disease is very uncommon in melioidosis in patients without risk factors is evident from the much lower rates of bacteremia (22%) and septic shock (6%) in these patients in the Darwin study. The association of diabetes with bacteremia in patients with melioidosis has been noted in Thailand [23]. Nevertheless, while all the listed risk factors including diabetes were independently associated with bacteremia in the Darwin study (Table 7), no individual risk factor apart from age was an independent predictor of mortality. This reflects that 80% of melioidosis cases in the Darwin study had at least one risk factor irrespective of age and it was the presence of any of these risk factors that was highly predictive of mortality (OR 9.4; 95% CI 2.3–39).
Although the higher proportion of presentations with pneumonia during the peak monsoonal months of December to February supports a role for inhalation and although mortality was higher on univariate analysis during these 3 months, multivariate analysis showed that seasonality was not itself a significant independent predictor of mortality (Table 8). Furthermore, while severe melioidosis is associated with an array of pathogen induced immune dysregulation [70], that no death occurred in a patient without risk factors does not support an important role for cytokine-related human genetic polymorphisms in determining outcomes in melioidosis [71]. Therefore, although disease may be more severe following inhalation and/or higher infecting load of B. pseudomallei, the only predictor of mortality from melioidosis is the presence of defined risk factors such as diabetes, hazardous alcohol use, chronic lung or renal disease and older age.
In conclusion, melioidosis should be more seen as an opportunistic pathogen that is very unlikely to kill a healthy person, provided the infection is diagnosed early and resources are available to provide appropriate antibiotics and critical care where required.
|
10.1371/journal.pntd.0001952 | Effectiveness of the Viet Nam Produced, Mouse Brain-Derived, Inactivated Japanese Encephalitis Vaccine in Northern Viet Nam | Japanese encephalitis (JE) is a flaviviral disease of public health concern in many parts of Asia. JE often occurs in large epidemics, has a high case-fatality ratio and, among survivors, frequently causes persistent neurological sequelae and mental disabilities. In 1997, the Vietnamese government initiated immunization campaigns targeting all children aged 1–5 years. Three doses of a locally-produced, mouse brain-derived, inactivated JE vaccine (MBV) were given. This study aims at evaluating the effectiveness of Viet Nam's MBV.
A matched case-control study was conducted in Northern Viet Nam. Cases were identified through an ongoing hospital-based surveillance. Each case was matched to four healthy controls for age, gender, and neighborhood. The vaccination history was ascertained through JE immunization logbooks maintained at local health centers.
Thirty cases and 120 controls were enrolled. The effectiveness of the JE vaccine was 92.9% [95% CI: 66.6–98.5]. Confounding effects of other risk variables were not observed.
Our results strongly suggest that the locally-produced JE-MBV given to 1–5 years old Vietnamese children was efficacious.
| Japanese encephalitis (JE) is a disease caused by a flavivirus transmitted by mosquitoes. Although pigs and wild birds are main reservoirs of the disease, it is occasionally transmitted to humans. The majority of infections in humans are asymptomatic. In persons developing encephalitis, JE has a high case-fatality rate and, among survivors, JE frequently causes persistent neurological sequelae and mental disabilities. Therefore, it is a public health concern in many parts of Asia and many countries vaccinate against JE. Since 1997, children in Vietnam are vaccinated in high risk areas and receive a locally-produced vaccine. This study is aimed at evaluating the effectiveness of the Vietnamese JE vaccine through a case-control study, in which 30 cases and 120 controls were enrolled. The effectiveness of the JE vaccine was 92.9% [95% CI: 66.6–98.5], which suggests that the locally-produced JE vaccine given to 1–5 year old Vietnamese children was efficacious.
| Japanese encephalitis (JE) is a mosquito-borne flaviviral disease endemic in many regions of Asia [1]. Culex tritaeniorhynchus, the principal mosquito vector of the JE virus (JEV), preferentially breeds in rice fields [2], [3]. Swine are potent amplifiers of the virus and exhibit rapidly after virus transmission considerable viral loads. Thus, Culex mosquitoes breeding in rice fields and feeding on swine, are critical ecological factors favoring JE transmission to humans in rural areas. Prior to the availability and introduction of vaccines, JE was a significant cause of mortality in the northern provinces of Viet Nam with an annual incidence of 5–15/100,000 [4].
Most JE infections (96%–99.9%) are asymptomatic or present as a mild disease only with rather non-specific flu-like symptoms. However, among symptomatic patients who exhibit symptoms of encephalitis and/or serious neurologic infection, the case-fatality ratio can be as high as 10%–30% [5], [6]. Among survivors, 30% to 50% of individuals suffer from chronic, severe neuropsychiatric disabilities [5]–[7]. Why only a small proportion of infected individuals experience severe disease is not clear. Reasons may include host genetic factors, but also virulence factors of differing virus strains. Children younger than 15 years are at the highest risk of infection and the incidence peaks at three to ten years of age [7]. JE infections efficiently induce protective immunity [8] and seroprevalence studies indicate almost universal exposure to the infection in endemic areas by adulthood [9].
Specific antiviral treatment for JE is not available [10] and care of patients strongly depends on supportive measures. Vaccination is the primary strategy for prevention of infection [1] and has been shown to dramatically reduce the disease incidence in South Korea [11], [12], Japan [13], China [14], [15], Thailand [15] and Taiwan [16]. In Viet Nam, children receive three pediatric doses (0.5 ml/dose) of a locally-produced, mouse brain-derived, inactivated JE vaccine (MBV; Nakayama strain; VaBiotech, National Institute for Hygiene and Epidemiology (NIHE), Ha Noi, Viet Nam) with the first two doses at one year of age given at an interval of two weeks followed by a third, booster dose one year later. The production of the MBV in Viet Nam was initiated in 1989 and supported by technology transfer from Japan. Bridging studies suggest that the Vietnamese MBV has an immunogenicity similar to that of the Japanese vaccine, reaching nearly 100% immunogenicity in children after the application of two doses [1]. The vaccine was integrated into Viet Nam's national Extended Program of Immunizations (EPI) in selected districts of Ha Tay and Hai Phong provinces in 1997.
Hospital-based surveillance of all patients presenting with an acute encephalitis syndrome (AES) is ongoing in the Ha Tay and Hai Phong Provincial Hospitals in the North of Viet Nam. Two provincial hospitals in the Ha Tay Province as well as the National Pediatric Hospital in Ha Noi are used as referral hospitals for JE surveillance since January 1, 2004. These hospitals jointly account for approximately 95% of all cases of acute encephalitis notified in the Ha Tay Province. In Hai Phong province, cases are identified through the national AES surveillance system. Cerebrospinal fluid (CSF) is collected at admission. The presence of immunglobulin-M (IgM) antibodies to JEV (anti-JEV) in CSF is defined as one of the criteria for a JE diagnosis [17]–[19]. Initial testing of specimens is performed at the National Pediatric Hospital Laboratory and Laboratory of Ha Tay Preventive Medicine Center, and confirmatory testing of all specimens was done by sending coded samples to the Department of Virology, Armed Forces Research Institute of Medical Sciences (AFRIMS), Bangkok, Thailand.
The objective of the present study was to assess in a case-control design the effectiveness of this MBV JE vaccine.
The study was conducted according to ethical principles consistent with the International Guideline for Ethical Reviews of Epidemiologic Studies [20]. The Institutional Review Board of the International Vaccine Institute, Seoul, Republic of Korea, and the local ethical committee of NIHE, Ha Noi, Viet Nam approved the study and granted ethical clearance. Vaccination status' of participants were assessed from vaccination records of the health centers. During visits of the households of children that were aimed at assessing the distances of piggeries and ricefields from the houses, oral informed consent was obtained from parents/guardians of cases and controls and documented in the questionnaire. Oral consent was considered appropriate for the study and approved by both Ethical Review Boards.
For this matched case-control study, patients with a confirmed diagnosis of JE and younger than 15 years of age were identified from the database of the surveillance hospitals from January 2004 to December 2007. Older children were not recruited, as they could not have received JE vaccines through the national immunization program.
Controls were chosen from the birth registry of all births in the health center of the village of the case and matched for gender, age (+/− six months), and proximity of their house.
Only after four controls had been selected from birth registry and agreed upon by the study team, the vaccination record books were opened and the individual vaccination status was assessed. JE vaccination is provided during an annual mass campaign and vaccination records are kept at the health centers.
The houses of cases and controls were visited and as an indicator of potential exposure to infection and the distances to piggeries and rice fields were assessed by the study team after obtaining oral informed consent from the adult/guardian present.
A Student's t-test for unequal distributions was used to compare the age distribution of cases and controls. A chi-square test was employed to compare cases and controls for the proportion of males, proportion living within ≤50 meters of a piggery and percent living ≤30 meters of rice fields. A matched conditional logistic regression model was employed to evaluate the protective effect of immunization. A single dependent (JE disease) and independent variable (immunization status) was entered into the model. Odds ratios (OR), 95% confidence intervals (CI), and p values were calculated from model parameters. The protective effect of immunization was assessed for residents receiving three or more doses relative to two or less doses. Vaccine effectiveness (VE) was calculated as VE = 1−OR×100 [21]. The threshold of statistical significance was p<0.05. All statistical analyses were performed using the Stata v 10.0 software (Stata Corp., Texas, USA).
We identified 30 laboratory-confirmed cases of JE infection and these were matched to 120 controls. After selection of controls was completed, it became evident that two controls were chosen who had no information on their immunization status in the birth registries. These two controls were excluded from the calculation of vaccine effectiveness. As cases and controls were matched for age and gender, no significant differences were observed between cases and controls for these variables. Cases and controls resided equidistantly from rice fields and piggeries (Table 1).
Only individuals who had received the full course (three doses) of JE vaccine were considered as vaccinated; two cases and two controls received one and two cases and six controls two doses of JE vaccine, respectively, and were considered as not vaccinated (Table 2).
Among the 30 laboratory-confirmed cases, the proportion of cases vaccinated was 60.0%, compared to 86.4% among the 118 control individuals. Based on the matched analysis vaccine effectiveness reached 92.9% [95% CI: 66.6–98.5%] (Table 2).
Three previous trials have evaluated the effectiveness of the JE MBV. A randomized double-blinded study conducted in northern Thailand, using JE MBV produced in Thailand, yielded an overall effectiveness of 91% [95% CI: 70.0–97.0] [22]. Another trial in Taiwan evaluated a Taiwanese vaccine and revealed an effectiveness of approximately 85% when two or more doses were administered [23]. A case-control study in Thailand showed an effectiveness of 94.6% in children ≥18 years of age [24]. The present matched case-control study suggests that the MBV produced in Viet Nam yields results that are similar to those of the Thailand and Taiwan studies.
Even though the MBV has an excellent efficacy, its usage was recently restricted after considerable safety concerns were raised. Severe reactions such as hypersensitivity, including generalized urticaria and angioedema, occurred at far higher rates than observed in other routine vaccinations [25], [26], [27], [28]. Consequently, Japan no longer recommends JE MBV vaccination [29], [30] and recently introduced a Vero cell-derived JE vaccine; other countries are currently in the process of phasing out the MBV (e.g., Thailand, Sri Lanka). Nevertheless, MBV safety issues have not led to any restrictions in its use in Viet Nam. However, Viet Nam is currently developing its own Vero cell-derived vaccine, which is foreseen to be available for use in 2014/2015.
The Vietnamese government has announced that it would eliminate clinical JE by 2015. However, in contrast to poliomyelitis virus, for which humans are the only hosts, the JE virus is enzootic and is, therefore, unlikely to be entirely eradicated from the environment. A recent JE surveillance study has shown that, even in the virtual absence of manifest human disease after vaccination, JEV is still widespread among swine [31], showing that active transmission is perpetuated and that protective immunity of humans through persistent vaccination is a key measure to preventing disease in humans [13]. Therefore, controlling clinical JE disease through vaccination would not impact on reducing or eradicating the circulation of the virus within the vectors and animal hosts as JE is not transmitted from person to person and JE vaccination does probably not confer herd immunity [32].
Depending on the potency of enzootic transmission and the age-specific risks of natural human infection, the age for primary vaccination differs between countries. Most countries give one to two booster doses after the initial three-dose regimen [1].
In contrast to Japan and Korea, where nation-wide changes in lifestyle have provided additional contributions to the control of JE [11], [33], rural areas in Viet Nam have so far largely remained agrarian. Rice paddies cover extensive geographic areas, and little changes only have occurred in the natural environment and living conditions. Even though the effectiveness of the vaccination program appears to be high, an annual average incidence of 3.4/100,000 was observed among children less than 10 years of age. The current JE immunization program may be improved by immunizing younger children (6–23 months of age) who are at the highest risk of patent infection, and providing a booster dose at 3–5 years after initial immunization with the three-dose regimen, reducing waning immunity in immunized children [1].
An inherent problem of inactivated vaccines is that due to their low immunogenicity, multiple vaccinations are required in order to induce and maintain sustained levels of protective immunity. Following the initial three doses given at immunization, the effectiveness of the MBV declines over years [34]. Therefore, the introduction of one or more booster doses using the adult formulation to children at school age is recommended to ensure protective immunity against JE using MBV.
|
10.1371/journal.pgen.1002376 | Histone H3K56 Acetylation, CAF1, and Rtt106 Coordinate Nucleosome Assembly and Stability of Advancing Replication Forks | Chromatin assembly mutants accumulate recombinogenic DNA damage and are sensitive to genotoxic agents. Here we have analyzed why impairment of the H3K56 acetylation-dependent CAF1 and Rtt106 chromatin assembly pathways, which have redundant roles in H3/H4 deposition during DNA replication, leads to genetic instability. We show that the absence of H3K56 acetylation or the simultaneous knock out of CAF1 and Rtt106 increases homologous recombination by affecting the integrity of advancing replication forks, while they have a minor effect on stalled replication fork stability in response to the replication inhibitor hydroxyurea. This defect in replication fork integrity is not due to defective checkpoints. In contrast, H3K56 acetylation protects against replicative DNA damaging agents by DNA repair/tolerance mechanisms that do not require CAF1/Rtt106 and are likely subsequent to the process of replication-coupled nucleosome deposition. We propose that the tight connection between DNA synthesis and histone deposition during DNA replication mediated by H3K56ac/CAF1/Rtt106 provides a mechanism for the stabilization of advancing replication forks and the maintenance of genome integrity, while H3K56 acetylation has an additional, CAF1/Rtt106-independent function in the response to replicative DNA damage.
| Loss of replication fork integrity is a primary source of genetic instability. In eukaryotes DNA synthesis is rapidly followed by its assembly into chromatin, and these two processes are tightly connected. Defective chromatin assembly mutants accumulate DNA damage and are sensitive to genotoxic agents, even though the mechanisms responsible for this genetic instability remain unclear because chromatin assembly also plays essential roles in transcription, silencing, DNA repair, and checkpoint signaling. A good example is the acetylation of histone H3 at lysine 56, which promotes histone deposition by the chromatin assembly factors CAF1 and Rtt106. In this case, the absence of this modification also causes a loss of structural and/or coding information at chromatin. Here we show that defective replication-coupled chromatin assembly leads to an accumulation of recombinogenic DNA damage by affecting the integrity of advancing, but not stalled, replication forks. Therefore, we propose that H3K56ac/CAF1/Rtt106-dependent chromatin assembly provides a mechanism for the stabilization of replication forks. Besides, H3K56 acetylation promotes replicative DNA damage repair/tolerance through a function that is independent of CAF1/Rtt106 and likely subsequent to its deposition at chromatin, revealing this modification as a key regulator of genome integrity.
| Problems in DNA replication are a direct cause of genetic instability and are associated with early tumor development [1]. This instability is linked to a high susceptibility of the replication forks to become stalled, damaged or even broken, and for this reason understanding of the scenarios that threaten replication fork integrity is crucial, but also the mechanisms that promote replication fork repair and restart. Cells are endowed with a complex network of checkpoints mechanisms that coordinate DNA damage repair with cell cycle progression [2]. Thus, during S phase, arrested or damaged forks trigger a signal transduction cascade ending up in the phosphorylation of effector kinases (e.g., Rad53 in Saccharomyces cerevisiae) that lead to specific responses such as the maintenance of replication fork stability, inhibition of late replication origins, DNA repair modulation and cell cycle arrest [3]. The presence of a sister chromatid provides a unique opportunity to repair and rescue the forks by homologous recombination (HR), even though the molecular mechanisms by which HR repairs and/or tolerates replicative DNA damage remain unclear [4].
In eukaryotes DNA is packaged into a highly specialized and dynamic nucleoprotein structure called chromatin, which is actually the substrate for cell machineries that deal with DNA. The repetitive unit of chromatin, the nucleosome, is formed by ∼146 base pairs of DNA wrapped 1.65 times around an octamer of histones. Nucleosome assembly of the replicated DNA is conducted by histone chaperones and chromatin assembly factors that first deposit two heterodimers of histones H3 and H4 to form a core (H3/H4)2 tetramer to which an H2A/H2B dimer binds on each side [5]. This provides the substrate for a plethora of ATP-dependent remodeling and histone modifier complexes that will eventually set up the specific chromatin structures required for the regulation of each DNA metabolic process. Replication coupled (RC)-chromatin assembly occurs rapidly after the passage of the replication fork and involves physical interactions between components of the replisome with chromatin assembly and remodeling factors; e.g., the replication processivity factor PCNA interacts with the chromatin assembly factor CAF1 [6], [7], the PCNA loader RFC with the histone chaperone Asf1 [8] and the MCM helicase complex with Asf1 and the chromatin remodeling complex FACT [9]–[11]. These interactions may facilitate nucleosome assembly but also help disrupt chromatin ahead of the fork. Besides, these interactions have been proposed to coordinate the flow of histones ensuring the exact supply at the fork [10], a process that is also regulated at the level of DNA and histone synthesis during the cell cycle [12]–[14].
Newly synthesized histones H3 and H4 are acetylated before being deposited at the fork, and this modification is required for nucleosome assembly [15]–[19]. Histone H4 is acetylated at lysines 5 and 12 by the acetyltransferase Hat1, this acetylation pattern being highly conserved from yeast to humans [15], [20], [21]. Histone H3 is also acetylated at its amino terminal tail, though the pattern is more variable among organisms. In the budding yeast H3 is acetylated at lysines 9 and 27 by the acetyltransferases Rtt109 and Gcn5 [22]. Additionally, histone H3 and H4 are acetylated in their globular domains at positions K56 and K91 by Rtt109 and Hat1, respectively [19], [23]–[26]. A detailed molecular analysis in yeast has recently deciphered part of the mechanisms of H3/H4 deposition during DNA replication. Thus, Asf1 binds to newly synthesized H3/H4 dimers [27] and presents them for acetylation of H3K56 by Rtt109 [23], [24]. This histone modification enhances the binding affinity of H3 to the chromatin assembly factors CAF1 and Rtt106 and of CAF1 to PCNA, thus promoting histone deposition at the proximity of the fork [17]. This process is also facilitated by direct interactions between CAF1 with Asf1 and Rtt106 and Asf1 with Rtt109 [26],[28]–[30]. Similarly, lysine acetylation at the amino terminal tail of H3 by Gcn5 enhances histone binding to CAF1 and Rtt106 and promotes RC chromatin assembly [16], suggesting that lysine acetylation might be a general mechanism to regulate the interaction of histones with chromatin assembly factors. In addition to newly synthesized histones, cells recycle parental histones that result from the disassembly of the chromatin ahead of the replication fork, a process in which Asf1 is also involved [10].
A number of results have clearly shown over the last few years that defective chromatin assembly causes genetic instability. In plants and human cells, the absence of CAF1 causes inhibition of DNA synthesis, accumulation of DNA damage and activation of the S-phase checkpoint [31], [32]. In yeast the disruption of a Gcn5-containing complex causes an accumulation of recombinogenic DNA damage [16], while the absence of H3K56 acetylation in asf1Δ, rtt109Δ and H3K56R mutants increases the frequency of HR and gross chromosomal rearrangements (GCRs) [23], [24], [33], [34]. Similarly, defective chromatin assembly by partial depletion of histones causes replication defects and hyper-recombination [35], [36]. In addition to the accumulation of DNA damage, chromatin assembly mutants are usually sensitive to genotoxic agents that impair DNA replication; thus, acetylation of H3K56 and lysines at the amino terminal tails of H3 and H4 prevent DNA damage sensitivity by non-redundant mechanisms [17], [23]–[25], [27], [37], [38]. Similarly, a mutant lacking Cac1 – the largest subunit of CAF1 – and Rtt106 is defective in RC-chromatin assembly and replicative DNA damage repair/tolerance [17]. However, the mechanisms by which chromatin assembly prevents the accumulation of DNA damage and the sensitivity to replicative DNA damage remain unknown. This is in part due to the fact that many of the players functioning in RC-chromatin assembly do it as well in replication independent chromatin assembly processes like DNA repair and checkpoint recovery; e.g., Asf1 and CAF1 are required for chromatin assembly and checkpoint turning off upon DNA double-strand break (DSB) repair [39]–[41]. In addition, it is difficult to discern whether the role of a histone mark in the DNA damage response (DDR) is prior or subsequent to histone deposition and whether it has a coding or a structural role.
We have recently shown that defective chromatin assembly by partial depletion of H4 is rapidly followed by the collapse of replication forks, which are efficiently rescued via HR, suggesting that correct nucleosome deposition is required for replication fork stability [42]. This approach, however, needs to be validated for specific chromatin assembly mutants. Here we have dissected the H3K56ac-dependent CAF1 and Rtt106 chromatin assembly pathways in terms of HR, checkpoint activation, replication fork stability and response to different genotoxic agents. Our results indicate that defective nucleosome assembly by impairment of H3K56ac-dependent CAF1 and Rtt106 pathways increases HR by affecting the integrity of advancing, but not stalled, replication forks. In contrast, H3K56ac is required after replicative DNA damage for CAF1/Rtt106-independent DNA repair/tolerance mechanisms that are likely to occur after its incorporation into chromatin.
The histone chaperone Asf1 interacts with the histone acetyltransferase Rtt109, and both proteins are required for acetylation at lysine 56 of newly synthesized histone H3 [23], [24], [26], [43]. Consistent with a role for this histone modification in preventing DNA damage accumulation, the absence of H3K56 acetylation in asf1Δ, rtt109Δ and H3K56R mutants increases the frequency of genetic recombination and budded cells with foci of the recombination protein Rad52 fused to the yellow-fluorescence protein (Rad52-YFP) (Figure 1; [23], [24], [34]). As previously shown for rtt109Δ [24], we confirmed that the increase in recombination mediated by asf1Δ was due to its incapability acetylating H3 on lysine 56, as the frequency of genetic recombination and Rad52-YFP foci in asf1Δ H3K56R was as in the single mutants (Figure 1A and 1B).
Histone H3K56 acetylation marks nucleosomes incorporated into chromatin via both RC and replication independent mechanisms [44], [45]. Thus, we first assessed whether the observed increase in recombination was linked to defects in replication-independent chromatin assembly. In this regard, Asf1 interacts with the HIR complex (formed by Hir1, Hir2 and Hir3 in yeast) [46] with which promotes replication-independent chromatin assembly [47]. We analyzed recombination in the absence of Hir1 since this subunit is required for the integrity and histone deposition activity of Asf1/HIR [47]. As shown in Figure 1C and 1D, disruption of the HIR complex in hir1Δ did not affect recombination.
Acetylation of H3K56 is also involved in RC-nucleosome assembly. It promotes both the transfer of H3/H4 to the chromatin assembly factors CAF1 and Rtt106 and the binding of CAF1 to PCNA [17]. Consequently, hyper-recombination in asf1Δ, rtt109Δ and H3K56R could be associated with defective histone deposition but also with a loss of structural and/or coding information because of the absence of H3K56ac at chromatin. To distinguish between these possibilities we analyzed the role of CAF1 and Rtt106 in preventing the accumulation of recombinogenic DNA damage; CAF1 and Rtt106 have redundant chromatin assembly functions as shown by the fact that cac1Δ rtt106Δ, but not cac1Δ and rtt106Δ, is defective in histone deposition [17]. Besides, the levels of H3K56ac are not affected and its deposition at chromatin is delayed but not prevented in cac1Δ rtt106Δ [17]. While the single mutants cac1Δ and rtt106Δ were not affected in HR, the double mutant cac1Δ rtt106Δ increased the frequency both of genetic recombination and budded cells with Rad52-YFP foci as compared to the wild type (Figure 1C and 1D), indicating that CAF1- and Rtt106-dependent chromatin assembly pathways prevent the accumulation of recombinogenic DNA damage. Besides, the triple mutant asf1Δ cac1Δ rtt106Δ displayed the same frequency of genetic recombination as asf1Δ and cac1Δ rtt106Δ, suggesting that H3K56ac avoids hyper-recombination through its function in CAF1/Rtt106-dependent chromatin assembly. Nevertheless, the triple mutants displayed a slight but significantly higher frequency of cells with Rad52 foci than asf1Δ and cac1Δ rtt106Δ, suggesting the existence of additional, non-overlapping functions of H3K56ac and CAF1/Rtt106 in preventing the accumulation of DNA damage.
Another feature of asf1Δ, rtt109Δ and H3K56R is the activation of the DNA damage checkpoint in the absence of DNA damaging agents as determined by partial phosphorylation of Rad53 [23], [48], [49]; as shown in Figure 1E, only the simultaneous absence of CAF1 and Rtt106 led to the activation of Rad53. Therefore, our results indicate that defective RC-nucleosome assembly causes accumulation of recombinogenic DNA damage and checkpoint activation. However, and strikingly, the absence of Rad52 did not increase the amount of phosphorylated Rad53 in asf1Δ as determined by western blot and in situ kinase assays (Figure 1E and 1F), suggesting that accumulation of recombinogenic DNA damage and checkpoint activation are not genetically linked.
Histone deposition and DNA synthesis are tightly connected during DNA replication. We therefore hypothesized that defective nucleosome assembly in asf1Δ, rtt109Δ, H3K56R and cac1Δ rtt106Δ mutants might affect replication fork integrity, which in turn would generate genetic instability. To address this possibility we followed the fate of replication intermediates (RIs) in wild type and mutants by 2D-gel electrophoresis. For this, cells were synchronized in G1 with α-factor and released into S phase, and DNA samples were analyzed at different times to follow the progression of replication forks from the early replication origin ARS305 (Figure 2A). Replication initiation and early elongation can be followed with probe Or by the formation of a bubble arc that reverts to a single Y-arc of large Y-shaped molecules when forks cross the nearest restriction site (Figure 2B, left panel), while replication fork progression along adjacent restriction fragments can be followed with specific probes by the accumulation of a complete arc of single Y-shaped molecules (Figure 2B, central panel). Finally, converging forks and Holliday junction (HJ)-like structures can be detected by the accumulation of double Y- and X-shaped molecules, respectively (Figure 2B, right panel).
The amount of RIs at the origin during the kinetics (i.e., the sum of bubbles, Ys and Xs at region Or of all time points combined), taking the total amount of wild-type RIs as 100, was reduced to ∼50% in asf1Δ and rtt109Δ (Figure 2C). In agreement with this defect being mediated by the lack of acetylation at H3K56 in asf1Δ and rtt109Δ, the total amount of RIs in a H3K56R mutant was 33% (Figure 2D). An increased drop in RIs was noticed in H3K56R as compared to asf1Δ and rtt109Δ (Figure 2C and 2D), which might be due to either an additional effect by reduced levels of histones – strains in Figure 2D have one instead of two H3/H4 genes – or the specific change to arginine. Therefore, the absence of H3K56 acetylation causes a loss of RIs. It should be noted that this reduction was also observed at adjacent DNA fragments, even though the effect became less evident at fragment B because of the loss of synchrony in the peak of RIs as the forks move away from the origin.
Next, we decided to address whether the loss of RIs in mutants defective in H3K56 acetylation was due to defective chromatin assembly as previously shown for recombination and checkpoint activation. For this, the amount of replication forks from cac1Δ, rtt106Δ and cac1Δ rtt106Δ mutants synchronized in G1 and released into S phase was analyzed. As shown in Figure 3A, whereas the single mutants cac1Δ and rtt106Δ accumulated wild-type levels of RIs, the double mutant cac1Δ rtt106Δ displayed a ∼50% reduction in the amount of RIs at the origin, indicating that CAF1- and Rtt106-mediated chromatin assembly pathways have redundant roles in preventing the loss of replication forks. Besides, the levels of RIs in cac1Δ rtt106Δ were the same as in asf1Δ and rtt109Δ (∼50%), suggesting that the major role of H3K56 acetylation in replication fork stability is through its function in chromatin assembly. Consistently, the reduction in RIs in the triple mutant asf1Δ cac1Δ rtt106Δ was neither synergistic nor additive as compared to asf1Δ (69±3%; Figure 3B), though this drop opens the possibility that H3K56ac and CAF1/Rtt106 have also additional, non-overlapping functions in preventing the loss of RIs. Finally, we observed that the total amount of RIs at the replication origin ARS315 was also significantly reduced in asf1Δ and cac1Δ rtt106Δ as compared to wild type (∼64 and ∼44%; Figure S1), indicating that the loss of RIs was not restricted to ARS305.
In order to determine why defective chromatin assembly causes a loss of RIs, we first assessed the possibility that forks break during DNA extraction. Contrary to this, the loss of RIs in asf1Δ determined by collecting and digesting the DNA in agarose plugs to preserve its integrity was similar to that obtained with standard DNA extraction protocols (Figure S2).
Alternatively, this loss of RIs might be due to differences in replication initiation, either in the efficiency or in the synchrony of the firing. As a first approach to assess this possibility we analyzed cell cycle progression in chromatin assembly mutants. FACS and budding analyses showed that most G1 cells reached G2/M in all mutants (Figure 4A, 4B and 4C). Besides, neither asf1Δ nor rtt109Δ displayed a significant delay in completing S phase compared to the wild type (Figure 4A and 4C), suggesting that the loss of RIs in these mutants is not due to defects in replication initiation; in contrast, H3K56R was clearly retarded as compared with its wild type. Also, while cac1Δ and rtt106Δ were not affected, cac1Δ rtt106Δ mutants displayed a slight but significant delay (Figure 4A and 4C) that might influence the amount of RIs. However, the reduction in RIs in the triple mutant asf1Δ cac1Δ rtt106Δ was neither synergistic nor additive as compared to asf1Δ (Figure 3B), which is not affected in cell cycle progression. Therefore, the delay in the progression through S phase seems not to be the main cause for the loss of RIs in cac1Δ rtt106Δ, even though the 30% drop in the triple asf1Δ cac1Δ rtt106Δ versus the single asf1Δ mutant leaves open the possibility that a fraction of the drop in RIs reflects some defects in replication initiation.
Since FACS and budding analyses estimate whole genome duplication, we cannot rule out the possibility that cells progress normally through S phase but having problems in the firing of some specific origins that could be compensated with altered programs of initiation and/or elongation. Likewise, a slow advance through S phase does not necessarily reflect a defect at a specific replication origin. Therefore, we first asked whether the loss of RIs was a consequence of inefficient ARS305 firing. In this regard, a defect in replication initiation would lead to a complete single Y-arc indicative of passive replication of the ARS305 fragment by forks coming from a neighbor origin. Even though the shape of the single Y-arc in the mutants was as in the wild type (Figure 2 and Figure 3), we cannot discard that the region were replicated later either from ARS305 or from a fork originated elsewhere. Therefore, we decided to determine the efficiency of replication initiation of the origin ARS305. Previous works have shown that asf1Δ, rtt109Δ and H3K56R are proficient in the activation of this origin [8], [50]. We studied replication initiation in our strains with a similar approach [42]; cells arrested in G1 with α-factor were released into S phase in the presence of hydroxyurea (HU) for 50 minutes, which causes the stalling of the forks in the proximity of the origin by depletion of available dNTPs. RT-PCR quantification of the total amount of DNA at the origin relative to an unreplicated fragment both in G1 and HU-arrested cells showed no significant defects in the firing of ARS305 in any of the mutants tested (Figure 4D).
Next, we asked whether the loss of RIs was due to differences in the synchrony of the firing of replication from ARS305. Contrary to this possibility, chromatin assembly mutants displayed the same kinetics of RI accumulation as the wild type, with a peak for the ARS305 region at 20–30 minutes upon G1 release (Figure 2 and Figure 3). This was not the case for H3K56R, in which the slow accumulation of RIs might explain its difference with asf1Δ and rtt109Δ (Figure 2D). Importantly, chromatin assembly mutants displayed a similar drop in RIs when released into S phase for 1 and 2 hours – what ensures that most cells have fired ARS305 (Figure 4D) – in the presence of HU (see below), which stalls forks close to the origin and thereby minimizes putative differences in synchrony. Consequently, the loss of RIs in chromatin assembly mutants is not associated with defective replication initiation and therefore may reflect a loss of integrity of the replication forks as they move away from the origin.
We have shown that chromatin assembly mutants display both a loss of RIs and an increase in recombination. Indeed, the stronger is the loss of RIs the higher is the percentage of cells with recombination foci. This correlation led us to hypothesize that the increase in recombination might result from the repair of collapsed replication forks. To address this possibility, we analyzed the role of Rad52, essential for DNA repair by HR [51], in the replication of cells lacking Asf1. As shown in Figure 5A, the amount of RIs dropped from about 54% in asf1Δ and rad52Δ to 14% in asf1Δ rad52Δ, being this drop not associated with defects in the kinetics of RI accumulation or in the firing of ARS305 (Figure 4D). This synergistic reduction of RIs in asf1Δ rad52Δ suggests that HR participates in the rescue of collapsed forks from ARS305 in asf1Δ. Consistently, asf1Δ rad52Δ cells displayed a delay in completing S phase (Figure 4A and 4C). These results provide an explanation for the accumulation of recombinogenic DNA damage in chromatin assembly mutants and the slow growth of asf1Δ rad52Δ cells (Figure 5B; [49]).
Defective H3K56 acetylation in asf1Δ, rtt109Δ and H3K56R causes a reduction in the amount of ChIP-detected replisome components in the presence of HU that has been thought to be responsible for their high sensitivity to drugs that stall replication forks [8], . Those experiments, however, do not provide information about the integrity of DNA at the fork and cause of the collapse, which could be a defect in chromatin assembly but also the absence of H3K56 acetylation at chromatin. Besides, our previous results suggest a role for this modification in keeping the stability of unperturbed replication forks, leaving its role unresolved on stalled replication forks. Therefore, we followed the fate of RIs in cells synchronized in G1 and released into the S phase in the presence of HU, which leads to the stalling of the wild-type forks at the proximity of the origin with a peak of RIs at 60 minutes upon α-factor release (Figure 6A; [42], [52]). A similar kinetics of replication fork stalling was observed in asf1Δ (Figure 6A), indicating that synchrony was not affected; however, and consistent with previous ChIP analysis [8], [26], [50], the total amount of stalled RIs over the whole region (i.e., the sum of bubbles, Ys and Xs of all fragments, either of all time points combined (Figure 6A) or at 1 hour (Figure 6B)), taking the total amount of wild-type RIs as 100, dropped to ∼30% in asf1Δ and rtt109Δ (Figure 6A and 6B) and this reduction was not due to a distinctive distribution of the stalled forks along the DNA (Figure S3). Also, a similar drop in RIs was observed in cac1Δ rtt106Δ (Figure 6B), indicating that proper chromatin assembly is required to prevent the loss of RIs in the presence of HU. Therefore, HU further decreases the amount of RIs in chromatin assembly mutants from approximately 50 to 30%.
In principle, this enhanced loss of RIs in the presence of HU might be linked to a role for chromatin assembly in keeping the stability of both advancing and stalled replication forks, but also to a defect in resuming DNA replication upon HR-dependent fork rescue as a consequence of the HU-induced depletion of available dNTPs. In this case, however, the HU would not have any additional effect on replication fork stability in the absence of Rad52. As previously shown [42], the amount of RIs in rad52Δ was not affected by HU (∼50%; Figure 5A and Figure 6B), indicating that Rad52 is not required for the stability of stalled replication forks but likely for the rescue of damaged replication forks. Importantly, the amount of RIs in asf1Δ rad52Δ was not affected by the presence of HU (∼15%; Figure 5A and Figure 6B), suggesting that Asf1, and by extension H3K56 acetylation, has a minor role in the stability of stalled replication forks. In addition, and consistent with the idea that HU partially prevents the restart of replication forks, asf1Δ cells released into S phase in the presence of HU displayed a 2-fold increase in X-shaped molecules (Figure 6C). Unfortunately, the slight accumulation of X-shaped molecules in rad52Δ leaves an insufficient margin to determine the Rad52 dependency of the X-shaped molecules accumulated in asf1Δ.
These results argue against a defect in the stability of stalled replication forks as a causative factor of the high sensitivity of asf1Δ, rtt109Δ and H3K56R to HU. Accordingly, the double mutant cac1Δ rtt106Δ was not sensitive to HU (Figure 7A), despite this strain displaying a similar loss of RIs as asf1Δ and rtt109Δ. In agreement with the growth assay, cac1Δ rtt106Δ was not required for stalled forks restart as determined by treating G1 released cells with 200 mM HU for 1 hour and checking their ability to resume DNA replication by FACS analysis (Figure 7B) (note that cac1Δ rtt106Δ displayed a similar delay during the S phase in the absence of HU (Figure 4A)). Strikingly, asf1Δ cells also resumed DNA replication after 1 hour in 200 mM HU and progressed to the following cell cycle without previous arrest (Figure 7B); consistently, asf1Δ cells did not display defects in checkpoint recovery and were viable (data not shown; [53], [54]). In summary, H3K56ac/CAF1/Rtt106-mediated chromatin assembly has no role in the stability and restart of forks stalled by HU, and therefore the loss of RIs observed in HU has to be of advancing replication forks.
Our previous results indicate that the role of H3K56ac in preventing sensitivity to chronic treatment with HU is independent of CAF1/Rtt106, suggesting that is a function separate from chromatin assembly and likely subsequent to its deposition at chromatin. A global epistatic analysis of pairs of gene deletions revealed a connection between Asf1 and Rtt109 with the Rtt101 ubiquitin ligase complex [53], which appear to promote fork progression through damaged DNA by HR [55]–[57]. However, as previously shown and in contrast to asf1Δ and rtt109Δ, rtt101Δ was not sensitive to HU (Figure 7A; [56], [57]).
H3K56ac, and by extension Asf1 and Rtt109, are also required for growth in the presence of drugs that impair the advance of the replication forks by DNA damage, such as the topoisomerase I inhibitor camptothecin (CPT) or the DNA alkylating agent methyl methane sulfonate (MMS) (Figure 7A; [23]–[25], [27], [39]). Again, these sensitivities could be associated with the role of H3K56ac in chromatin assembly. A comparative analysis showed that although the double mutant cac1Δ rtt106Δ was sensitive to both drugs, in particular to high concentrations, this sensitivity was much milder than that displayed by asf1Δ and rtt109Δ (Figure 7A, see CPT at 7.5 µg/ml and MMS at 0.005%), suggesting that the main role of H3K56ac in response to CPT and MMS is also independent of CAF1/Rtt106 and subsequent to its deposition into chromatin. The ubiquitin ligase complex Rtt101 has been shown to be required for MMS- and CPT-induced HR [55] and for checkpoint recovery (Figure 7C; [53], [55], [57]). Our comparative analysis showed that rtt101Δ was not as sensitive to MMS and CPT as asf1Δ and rtt109Δ (Figure 7A); thus, these results suggest that H3K56ac promotes fork progression through damaged DNA via Rtt101-mediated HR and, to a lesser extent, CAF1/Rtt106-mediated chromatin assembly.
To further understand the role of the CAF1/Rtt106 chromatin assembly pathway on MMS and CPT resistance, we analyzed the ability of cac1Δ rtt106Δ to resume DNA replication upon the treatment of G1 released cells with a high concentration (0.033%) of MMS. cac1Δ rtt106Δ cells resumed and completed DNA replication but remained partially arrested in mitosis (Figure 7B) as a consequence of a delay in checkpoint deactivation (Figure 7C), being these phenotypes much stronger in asf1Δ and rtt101Δ in agreement with the sensitivity assay.
H3K56 acetylation is a histone modification required for chromatin assembly. Notably, mutants defective in H3K56 acetylation (asf1Δ, rtt109Δ and H3K56R) accumulate recombinogenic DNA damage as determined by genetic recombination, cells with Rad52 foci and molecular analysis of sister-chromatid exchange [23], [24], [34]. How H3K56 acetylation prevents DNA damage accumulation is not predictable, however, because its role in chromatin assembly is associated not only with replication but also with other processes that influence HR, such as transcription, silencing, DSB repair or DNA damage tolerance [58]. We first ruled out a role for replication-independent chromatin assembly as a disruption of the HIR/Asf1 complex in hir1Δ exhibited wild-type levels of recombination. Alternatively, and in agreement with a model in which spontaneous genetic instability stems from defective DNA damage repair/tolerance, hyper-recombination might result from defective repair/tolerance and channelling to HR of spontaneous DNA lesions. In this case, DNA damage induction with genotoxic agents to which these mutants are sensitive should further increase their levels of recombination. In contrast, Asf1, Rtt109 and the Rtt101 complex are required for HR induced by MMS and CPT [55]. Given that Asf1 and Rtt109 are not required for DSB-induced HR, both ectopic and sister-chromatid recombination [34], [49], [55], hyper-recombination in cells defective in H3K56 acetylation may be associated with the generation of DSBs. Accordingly, GCRs are mediated by the DSB-repair pathway of non-homologous end-joining and are prevented by HR in asf1Δ [33].
H3K56 acetylation enhances the binding affinity of H3 to CAF1 and Rtt106, two factors with redundant histone deposition functions during replication [17]. We show that only the RC-chromatin assembly defective cac1Δ rtt106Δ, but not the RC-chromatin assembly proficient cac1Δ and rtt106Δ, leads to recombinogenic DNA damage and checkpoint activation, and that the main role of H3K56ac in preventing hyper-recombination is mediated by CAF1 and Rtt106. Therefore, RC-chromatin assembly prevents the accumulation of recombinogenic DNA damage.
We show that chromatin assembly mutants display a loss of RIs that is not due to defects in replication initiation, and that there is a correlation between the loss of RIs and the increase in HR. Besides, the absence of Rad52, essential for HR, further increases the loss of RIs in asf1Δ. These results, together with the reported loss of replisome integrity in H3K56 acetylation mutants in the presence of HU [8], [26], [50] despite the fact that they are not affected in the stability and rescue of stalled replication forks (Figure 6 and Figure 7), strongly suggest that defective RC-chromatin assembly causes a loss of integrity of the advancing replication forks, and that HR participates in the rescue of these forks using the sister chromatid. Consistent with this, asf1Δ accumulates spontaneously sister-chromatid exchange products [34].
This loss of integrity may end up in the collapse of some of the forks, which can render unprotected DNA ends susceptible of being processed by HR [59]–[62] but that are difficult to be detected by 2D-gel analysis unless a homogeneous and stable population of intermediates accumulates. In particular, the detection of broken intermediates is not easy because the breakage of single Ys leads to linear molecules, while the breakage of bubbles leads to a mixture of asymmetric Ys that do not run at a defined arc. Additionally, defective chromatin assembly might generate DNA structures that are lost due to the running conditions required for the visualization of the RIs by 2D-gel analysis. Similarly, the reduction in the total amount of detectable RIs in chromatin assembly mutants in spite of the fact that they complete replication opens the possibility that the rescue of the collapsed forks and subsequent completion of DNA replication are not associated with the formation of a canonical replication fork [63] or reflects an asynchronous fork rescue along the DNA region. Finally, we cannot rule out that a fraction of the drop in the amount of RIs to be a consequence of problems in the initiation of replication of a subpopulation of cells as suggested by the analysis of cell cycle progression in cac1Δ rtt106Δ mutants.
Strikingly, defective chromatin assembly hardly affected (asf1Δ, rtt109Δ) or delayed just 10–20 minutes (H3K56R, cac1Δ rtt106, asf1Δ cac1Δ rtt106Δ) the time required for DNA duplication despite the loss of RIs. Replication fork rescue by HR cannot account for completion of DNA replication because asf1Δ rad52Δ cells are also capable of completing DNA duplication (Figure 4). Additional mechanisms may operate in the rescue of the collapsed replication forks; in this regard, it has recently been shown that asf1Δ accumulates ribosomal DNA repeats by a novel mechanism that is independent of HR but needs replication processivity functions known to be required for break-induced replication [64]. This work is consistent with our proposal that chromatin assembly mutants accumulate broken forks and that there may be mechanisms other than HR involved in the repair of these breaks. We have observed that the loss of RIs is not specific of forks coming from ARS305 (Figure S1); however, we cannot rule out the possibility that not all chromatin regions display the same replication defects, that a proportion of the forks are functional but are lost during the 2D-gel analysis, and that chromatin assembly mutants counteract the instability of the replication forks by altering the program of replication initiation and/or increasing the rates of replication elongation. In this frame, it is possible that an “open” chromatin structure in these mutants favors alternative outputs of collapsed fork rescue and DNA replication as suggested above. Genome-wide analyses have to be conducted to address these possibilities.
Why are replication forks unstable under conditions of defective RC-chromatin assembly? These mutants are proficient in checkpoint activation (Figure 1 and Figure 7; [23], [34], [48], [49], [53], [65]), ruling out a defect in this mechanism of replication fork stability as responsible for the loss of RIs. In fact, the absence of checkpoint proteins in asf1Δ affects cell progression during the S phase [54], suggesting that chromatin assembly and replication checkpoints have non-redundant functions in replication fork stability. In principle, the loss of RIs and the increase in HR could be associated with defects in chromatin structure as a consequence of the lack of H3K56 acetylation at chromatin. This modification breaks a water-mediated histone-DNA interaction at the point of entry and exit of the nucleosomal DNA that modulates chromatin compaction [25], [66]–[68]. Also, this modification might recruit chromatin factors required for fork stability. We do not favor these possibilities in cac1Δ rtt106Δ because this mutant expresses acetylable H3K56, although its deposition at chromatin appears to be delayed and might generate regions behind the fork with reduced H3K56ac [17].
Alternatively, replication fork instability might result from defective chromatin disassembly and/or transfer of parental histones ahead of the fork. In this regard, Asf1, which is also a nucleosome disassembly factor [69], interacts with MCM to coordinate fork progression and parental histone supply ahead of the fork [10]. However, asf1Δ and H3K56R mutants share similar defects in replication fork stability and HR and the effect of asf1Δ is due to defective H3K56 acetylation as determined by epistatic analysis. Since this modification marks preferentially newly synthesized histones [25], our results point to defects in the pathway of newly synthesized histone deposition as the main cause of fork collapse and subsequent repair by HR.
DNA synthesis and histone deposition are physically and genetically connected to ensure the exact supply of histones at the fork [6]–[11]. Histone excess is toxic and cells are endowed with different mechanisms to get rid of non-incorporated histones [12]. The opposite situation, a reduction in the pool of available histones, is also deleterious and phenocopies the defects in fork stability and HR reported here with RC-chromatin assembly mutants [42]. The current study provides additional support to the idea that, under conditions of defective H3/H4 deposition during replication, DNA synthesis and nucleosome assembly could become uncoupled exposing DNA fragments behind the fork. This uncoupling might favor the formation of unstable secondary DNA structures, as it has been proposed to explain the high levels of DNA breakage and contractions at CAG/CTG tracts displayed by asf1Δ and rtt109Δ but not rtt101Δ [70]. Although these structures could be targeted by nucleases, we failed to find single nuclease mutants that alter the frequency of RI loss in asf1Δ (data not shown), a result that is not unexpected because of the redundancy of DNA nucleases in DNA damage repair [71], [72]. Finally, the loss of RIs and the increase in HR could be due to defective stability of stalled forks, as suggested by the observation that the replisome is unstable in the presence of HU in H3K56 acetylation mutants [8], [26], [50]. Here, we present some evidence indicating that only advancing, but not stalled forks, are affected in RC-chromatin assembly mutants. First, the total amount of RIs in chromatin assembly mutants defective in fork rescue by HR (asf1Δ rad52Δ) is not affected by the presence of HU. Second, RC-chromatin assembly mutants (asf1Δ, rtt109Δ and cac1Δ rtt106Δ) are proficient in stalled fork stability and restart upon an acute treatment with HU as determined by FACS analysis, checkpoint recovery and cell viability. Therefore, our results point to defects in the stability of advancing forks as the cause of the genetic instability in RC-nucleosome assembly mutants, further supporting the idea that defective histone deposition uncouples DNA synthesis and nucleosome assembly. Notably, asf1Δ cells treated with HU also exhibited an accumulation of Polα at the fork and an uncoupling of the MCM helicase [8]. We speculate that these alterations in the replisome structure might also occur in the absence of HU. Indeed, Asf1 interacts with MCM [10] and with RFC – which loads PCNA and in this way replaces Polα with Polε and Polδ – [8], and H3K56 acetylation regulates the function of the RFC [73]; it is thereby possible that the absence of Asf1 and/or H3K56ac could specifically alter the distribution of the polymerases and the MCM helicase at the fork.
H3K56 acetylation – and by extent Asf1 and Rtt109 – is required for promoting resistance to replicative DNA damage [17], [23]–[25], [27]. Indeed, there is a correlation between the levels of H3K56 acetylation and the degree of DNA damage sensitivity to genotoxic agents [43]; consistently, H3K56Q, which mimics constitutive acetylation, suppresses asf1Δ sensitivity to HU and CPT [39], [43]. In contrast to H3K56 acetylation mutants, cac1Δ rtt106Δ is only sensitive to high concentrations of MMS and CPT and is not sensitive to chronic treatment with HU, suggesting that the function of H3K56ac in the replicative DNA damage response can be separated from its role in CAF1/Rtt106-mediated chromatin assembly. This points to a role subsequent to its deposition into chromatin. In agreement with this idea, it has recently been shown that a change of lysine 56 to glutamic acid in H3 generates a histone proficient in binding to CAF1 and Rtt106 but sensitive to replicative DNA damage [74]. An epistatic analysis has included Asf1, Rtt109 and the Rtt101 ubiquitin ligase complex into a functional group involved in DNA repair [53]. Rtt101 is recruited to chromatin in response to DNA damage in a process that requires Rtt109 [75], and Asf1, Rtt109 and Rtt101 promotes the repair of replicative DNA damage – but not DSBs – by SCE [34], [49], [55], suggesting that H3K56 acetylation might facilitate the repair of fork-associated DNA lesions other than DSBs by recruiting Rtt101, which in turn would promote HR. This model, however, would not be valid for HU sensitivity, which is Rtt101 independent, and may be related with sustained replication under conditions of low levels of dNTPs.
Besides, our comparative analysis shows that H3K56 acetylation mutants are slightly more sensitive to DNA damage than rtt101Δ, suggesting an additional function for this histone modification in response to replicative DNA damage. This role could be to open the chromatin and facilitate the access of repair proteins to DNA. Other possibility is that H3K56 acetylation promotes checkpoint deactivation via CAF1/Rtt106-chromatin assembly upon the repair of the replicative DNA damage, as previously demonstrated for DSB repair [39], [40]. This is supported by the fact that cac1Δ rtt106Δ becomes temporally arrested at mitosis by sustained phosphorylation of Rad53 upon DNA damage release, even though this defect might also be a consequence of an incomplete accumulation of H3K56ac behind the fork of the double mutant.
Our results in yeast anticipate a similar role for chromatin assembly in the stability of advancing replication forks through the more demanding chromatin structure of mammalian genomes. It will thereby be well worth the effort to address replication fork integrity in human cells defective in RC-chromatin assembly, which are known to arrest in the S phase and accumulate DNA damage [13], [32], [36]. Finally, the results presented here reveal the process of RC-chromatin assembly as a potential target against cell proliferation in cancer therapy, as also suggested by a recent observation showing that human Asf1b is overexpressed in breast tumours [76].
Yeast strains used in this study are listed in Table 1. They all are isogenic to BY4741, except for H3K56R mutants that are isogenic to MSY421. pRS316-SU [77] and pWJ1344 (kindly provided by R. Rothstein, Columbia University) are centromeric plasmids containing the SU inverted-repeat recombination system and RAD52-YFP, respectively. Yeast cells were grown in supplemented minimal medium (SMM), except for nocodazole (NCD) synchronization that were grown in YPD medium [78]. For G1 synchronization, cells were grown to mid-log-phase and α factor was added twice at 1.5 hours intervals at either 0.5 µg/ml (asf1Δ rad52Δ, cac1Δ rtt106Δ and asf1Δ cac1Δ rtt106Δ) or 0.25 µg/ml (rest of strains). Then, cells were washed three times and released into the S phase at different times in fresh medium with or without 0.2 M HU and 50 µg/ml pronase. Cell cycle progression was followed by DNA content analysis (data not shown). To prevent cells from re-entering a new cell cycle in Figure 4B and 4C (bottom), G1-synchronized cells were shifted to YPD with α factor for 1 hour and released into the S phase in fresh YPD medium with 50 µg/ml pronase and 15 µg/ml NCD.
The frequency of Leu+ recombinants generated by recombination between inverted repeat sequences was determined in cells transformed with plasmid pRS316-SU by fluctuation tests as the median value of six independent colonies [77]. DNA damage sensitivity was determined by plating ten-fold serial dilutions from the same number of mid-log phase cells onto medium without or with genotoxic agents at the indicated concentrations.
The proportion of budded cells with Rad52-YFP foci was performed as described previously [34]. Mid-log-phase cells transformed with pWJ1344 were visualized with Leica CTR6000 fluorescence microscope.
DNA content analysis was performed by fluorescence-activated cell sorting (FACS) as reported previously [35]. The percentage of budded cells was determined by counting 200 cells at each time point.
Each replication kinetic was conducted in parallel with the mutants and the wild type. Cell cultures were arrested with sodium azide (0.1% final concentration) and cooled down in ice. Total DNA was isolated either in agarose plugs or with the G2/CTAB protocol as previously reported [42], digested with restriction enzymes, resolved by neutral/neutral two-dimensional-gel electrophoresis as described [79], blotted to nylon membranes and analysed by sequential hybridization of the same membrane with different 32P-labelled probes (for probes along the ARS305 region see [42]; probe for ARS315 was PCR amplified with oligos AACAGCTTCTCTTGCCGTAG and TGTACTGAACCTACCGCTCC). All signals were quantified using a Fuji FLA5100 and ImageGauge as analysis program. Quantification of the RIs was normalized to the total amount of DNA, including linear monomers (n), to the size of the restriction fragment, and to the percentage of cells synchronized in G1; thus, the total amount of RIs at each specific region and time point was calculated as [ΣRIs/Σ(RIs+n×g)]×f, where f is the ratio between the size of the DNA fragment containing the origin and the size of the specific DNA fragment, and g is the proportion of cells in G1 after α-factor synchronization.
Total DNA from mid-log phase cells synchronized in G1 and released into S phase in the presence of 0.2 M HU for 50 minutes was extracted and the amount of DNA at the origin ARS305 and a non-replicated control region (located at ∼7 kb from the late replicating origin ARS609) determined by qPCR (ARS305: oligos CGCCCGACGCCGTAA and GAGCGGCCTGAAATACTGTCA; control region: oligos TACACCAGCCCGGATTTAAG and GACCAGTGGCTGAGTCACAA). The efficiency of replication initiation was calculated as the ratio between the amount of DNA in HU-arrested cells and the amount of DNA in G1-arrested cells at the origin normalized to the same ratio at the control DNA region.
Yeast protein extracts were prepared from mid-log-phase cultures using the TCA protocol as described [35] and run on a 8% and 10% sodium dodecyl sulfate-polyacrilamyde gel for western blot and in situ kinase assay, respectively. Rad53 was detected either with rabbit polyclonal antibody JDI47 [80] (Figure 1E) or with goat polyclonal antibody (yC19) (Santa Cruz Biotechnology, INC) (Figure 7C). The autophosphorylation reaction was performed as described [81].
|
10.1371/journal.pgen.1004018 | Clustering of Tissue-Specific Sub-TADs Accompanies the Regulation of HoxA Genes in Developing Limbs | HoxA genes exhibit central roles during development and causal mutations have been found in several human syndromes including limb malformation. Despite their importance, information on how these genes are regulated is lacking. Here, we report on the first identification of bona fide transcriptional enhancers controlling HoxA genes in developing limbs and show that these enhancers are grouped into distinct topological domains at the sub-megabase scale (sub-TADs). We provide evidence that target genes and regulatory elements physically interact with each other through contacts between sub-TADs rather than by the formation of discreet “DNA loops”. Interestingly, there is no obvious relationship between the functional domains of the enhancers within the limb and how they are partitioned among the topological domains, suggesting that sub-TAD formation does not rely on enhancer activity. Moreover, we show that suppressing the transcriptional activity of enhancers does not abrogate their contacts with HoxA genes. Based on these data, we propose a model whereby chromatin architecture defines the functional landscapes of enhancers. From an evolutionary standpoint, our data points to the convergent evolution of HoxA and HoxD regulation in the fin-to-limb transition, one of the major morphological innovations in vertebrates.
| Hox genes encode transcription factors with crucial roles during development. These genes are grouped in four different clusters names HoxA, B, C, and D. Mutations in genes of the HoxA and D clusters have been found in several human syndromes, affecting in some cases limb development. Despite their essential role and contrary to the genes of the HoxD cluster, little is known about how the HoxA genes are regulated. Here, we identified a large set of regulatory elements controlling HoxA genes during limb development. By studying spatial chromatin organization at the HoxA region, we found that the regulatory elements are spatially clustered regardless of their activity. Clustering of enhancers define tissue-specific chromatin domains that interact specifically with each other and with active genes in the limb. Our findings give support to the emerging concept that chromatin architecture defines the functional properties of genomes. Additionally, our study suggests a common constraint of the chromatin topology in the evolution of HoxA and HoxD regulation in the emergence of the hand/foot, which is one of the major morphological innovations in vertebrates.
| The Hox gene family encodes transcription factors with central roles in patterning of the body plan and organogenesis. Hox genes are grouped into clusters in most animal species, and mammals possess 39 genes divided into four clusters named HoxA to HoxD. In mice, deletion of the HoxA cluster is embryonic lethal [1]–[2] whereas mutants lacking HoxB, HoxC, or HoxD are viable at least until birth [3]–[5]. Inactivation of individual Hox genes identified Hoxa13 as a gene required for proper placenta function and thus embryonic survival [2], [6]–[7]. Mutations in HoxA genes have been found in various human syndromes (e.g. HFGS-OMIM140000, Guttmacher syndrome-OMIM176305, MRKH-OMIM277000) including limb malformations. Studies of gene inactivation in mice demonstrated that genes located at the 5′ end of the HoxA cluster (Hoxa9–13) are required for proper patterning of the three limb segments: the upper arm (humerus; Hoxa9, 10), lower arm (radius and ulna; Hoxa10, 11), and the hand/foot (autopod; Hoxa13) [6], [8]–[11].
Despite their pivotal roles during embryogenesis, little is known about the regulation of HoxA genes. This is in contrast to HoxD, which transcriptional control has been more thoroughly studied, especially in the limb where the HoxD genes play partially overlapping functions with HoxA [12]. Expression at the HoxA and D clusters follows similar dynamics during limb development, and occurs in two phases [12]. In the first phase, which starts at embryonic day 9.5 of development (E9.5), expression at both clusters is comparable suggesting that the control mechanisms are likely similar. During this phase, gene expression generally follows the collinear strategy observed in the trunk, characterized by sequential gene activation from one end of the cluster (Hox1) to the other (Hox13), with early activated genes expressed throughout the limb bud and those activated later (Hox10-13) gradually restricted to posterior cells [13]. In contrast, the expression domains of HoxA and HoxD genes partly differ in the second phase (from E11.5 onwards), suggesting some differences in the regulatory mechanisms controlling the clusters in this later phase.
Previous studies show that transcription at the HoxD cluster is regulated long-distance by enhancers in several tissues (reviewed in [14]). Notably, expression of Hoxd10 to Hoxd13 in the distal part of the limb bud (presumptive hand/foot) is controlled by several remote cis-regulatory sequences located in the gene desert upstream of the cluster [15]. Hands/feet, in particular digits, are evolutionary novel structures and the hallmark of Vertebrate adaptation to terrestrial habitats. The fact that Hoxa10 and Hoxa13 are also expressed in the presumptive hand/foot domain therefore raised the possibility that specific recruitment of HoxA and HoxD gene functions in developing digits stem from the implementation of similar cis-regulatory elements during the fin-to-limb transition. Whereas sequence conservation analysis of the region upstream of these clusters did not identify cognate cis-regulatory elements driving HoxA expression in limbs [16], BAC transgenesis revealed the existence of a “digit” enhancer activity located between 250 and 500 kb upstream of the Hoxa13 gene, in the neighborhood of the 3-hydroxyisobutyrate dehydrogenase (Hibadh) gene [17]. As Hibadh is also expressed in distal limb buds [16], this study could not resolve whether the “digit” enhancer activity detected within that region controls Hibadh, Hoxa10/13, or both. Thus, the enhancer sequence(s) and whether HoxA expression in limbs is regulated by long-range control mechanisms has remained unknown.
It was previously shown that control DNA elements could regulate the expression of remote genes by physically interacting with them [18]. Physical contacts between chromatin segments can be measured using the chromosome conformation capture (3C) methods, a series of assays that use proximity-based ligation to infer the three-dimensional organization of genomes [19]. 3C assays were used to show that regulation of HoxD genes in the presumptive digit domain is mediated by physical contacts with remote enhancers, and led to a model whereby expression of Hoxd10 to d13 associates with the formation of DNA loops between the genes and regulatory islands [15]. This was further supported by Fluorescence In Situ Hybridization data showing the co-localization of HoxD genes and one of its regulatory islands, specifically in digit progenitor cells [20]. Whether the proximity between target genes and regulatory DNA elements requires transcription appears to be loci-dependent and it remains unknown whether a given mechanism prevails over others. Indeed, while such “loops” were sometimes reported in absence of transcription [21]–[22], transcription factors requirement for DNA looping was uncovered at the β-globin locus [23]–[24] and Igh gene [25].
Here, we show that during limb development, expression of HoxA genes is controlled by multiple remote enhancers located upstream of the cluster. In limb cells, these enhancers are grouped into distinct sub-megabase topological domains (sub-TADs) that contact each other and the sub-TADs containing target genes. In head tissues, the topology is drastically different, modifying both gene-enhancer and enhancer-enhancer interactions. Interestingly, enhancers located in the same sub-TAD are active in distinct subset of limb cells suggesting that spatial clustering of enhancers does not simply reflect enhancer co-activity. We also present evidence that enhancer-HoxA contacts are maintained even when enhancer activity is suppressed, suggesting that the HoxA regulatory region acquires a permissive conformation prior to gene activation. We suggest a model whereby sub-TAD formation and/or contacts between sub-TADs define the cis-regulatory network controlling gene expression. From an evolutionary perspective, this first extensive characterization of HoxA regulation in developing limbs provides new insights into the evolution of Hox regulation in the emergence of hand/foot. Our study suggests that while the DNA sequences of the distal limb enhancers for HoxA and HoxD genes are different and have likely emerged independently, chromosome partitioning into topological domains has similarly constrained the evolution of HoxA and HoxD cis-regulatory landscapes underlying the emergence of digits, one of the major morphological innovations in Vertebrates.
To identify enhancer sequences regulating HoxA expression during limb development, we used a combination of genetic and genomics approaches that probe enhancer features in mouse embryos. We first tested whether HoxA transcription in developing limbs involves cis-regulatory sequences outside of the gene cluster itself. To this end, we used two mutant lines with targeted genomic rearrangements at the HoxA cluster [1]–[2] to monitor activation of reporter transgenes by surrounding enhancer activity (Figure 1A,B). Whole mount in situ hybridization shows that a neomycin reporter transgene located downstream of Hoxa1 is not expressed in limbs at E11.5 (Figure 1A, left). In contrast, we found that a hygromycin transgene inserted at the opposite end of the cluster, 3.5 kb upstream of Hoxa13 is robustly transcribed in distal limbs at this stage (Figure 1A, right). These expression patterns correlate well with the expression profile of the endogenous HoxA genes adjacent to the reporter transgenes. Upon deletion of the entire HoxA cluster, the neomycin transgene becomes activated in distal limbs suggesting that sequences upstream of the cluster are sufficient to trigger distal expression (Figure 1B). These results support the previously proposed hypothesis that a 250 kb region in the neighborhood of Hibadh contains an enhancer activity controlling HoxA expression in developing limbs [17].
Given the results described above, we focused our analysis on the genomic region upstream of the cluster. To identify active enhancers in distal limbs, we used dissected distal forelimbs (Figure 1C), which are composed of cells expressing mainly Hoxa10 and a13, but also a small amount of Hoxa9 and a11 from the presumptive wrist domain (mesopod). Active enhancers are characterized by the binding of several proteins including RNA polymerase II (RNAP2), and subunits of Mediator like Med12 [26]. We therefore mapped candidate enhancer sequences by identifying genomic sites enriched in these proteins using chromatin immunoprecipitation combined with deep sequencing (ChIP-seq) in cells isolated from E12.5 distal limb buds (Figure 1D). This data was considered together with previously published datasets derived from whole limb buds for the transcriptional co-activator p300 [27] and acetylated histone H3 lysine 27 (H3K27Ac), which also mark active enhancers [28]. Sequences distinct from proximal promoters (RefSeq) that were bound by RNAP2 and at least one other mark, or by both p300 and H3K27Ac were retained as candidate enhancers. Using these criteria, 19 putative enhancers were identified within 850 kb upstream of Hoxa13 (Figure 1D, top).
The number of candidate sequences identified upstream of HoxA was rather large, particularly compared to HoxD for which seven enhancers have been identified [15]. Also, in contrast to the gene desert surrounding HoxD, the region upstream of HoxA encompasses several genes (Figure 1D). Candidate HoxA enhancers therefore reside amidst other genes including Hibadh, Tax1bp1, and Jazf1, for which expression has been reported in the limb [16]. As ChIP-seq datasets cannot resolve the targets of enhancers, we used a structural approach to assess the potential interactions of the candidate enhancers with HoxA genes. We profiled the interaction pattern of the HoxA cluster with the upstream 850 Kb region in distal limb buds and head tissues using 5C technology combined with deep sequencing [29]–[30], which provides insights into chromatin architecture at high resolution (down to 4–6 kbs on average). We found that the 5′ part of HoxA, containing Hoxa9 to Hoxa13, frequently interacts with several regions upstream of the cluster (Figure 2, top, Figure S1), and that most of these regions contain the candidate limb enhancers (Figure 2, bottom). In contrast, none of the enhancers interact with the 3′ part of the cluster containing Hoxa1 to Hoxa7 (Figure 2), which have no detectable expression in limb buds at this developmental stage. This result is reminiscent of the distal enhancers controlling the 5′ HoxD genes, which are also located upstream of the cluster and specifically interact with genes located in the 5′ part [15]. Interestingly, previous studies based on Hi-C analysis revealed that the HoxA and HoxD clusters each span a junction between two so-called “topologically associated domains” (TADs), with 3′ genes residing into one TAD and the 5′ part extending into the other [31]. TADs are thought to represent a basic unit of chromatin organization at the megabase-scale that is largely conserved between cell types [32]. Our data therefore points to a common relationship between chromatin topology and the limb regulatory landscapes of the HoxA and HoxD clusters.
Interestingly, sequences with the highest interaction frequencies with 5′HoxA genes (e10, 13, 14 and e15, 16, 18) locate farther from the cluster, within the Jazf1 gene, and correspond to those loci most enriched in marks typical of active enhancers (Figure 1D). High interaction frequencies being associated either with stronger, more abundant and/or stable spatial contacts, these data likely reflect a prevalent activity of these enhancers in distal limbs. In contrast to the other enhancers, e1 and e3 do not show enriched interactions with the 5′ part of the HoxA cluster in distal limbs compared to head tissue (Figure 2, bottom). e1 is located close to Evx1, within a region of high interaction frequencies with HoxA both in limb and head tissues. This is not the case for e3 so we further tested interaction frequencies between Hoxa13 and e2 to e5 using 3C (Figure S2). This analysis shows higher frequency of interactions between these enhancers and Hoxa13 specifically in the limb. Yet, based on our 5C data, these interactions are modest compared to those observed for the other enhancers (Figure 2). Interestingly, contacts such as those with e5, e13 and e15 were also observed in the head at low frequencies (Figure 2). As there is no evidence of HoxA expression in the head at the stage analyzed, the contacts observed might be evidence of default chromatin architecture in this tissue. Alternatively, these enhancers may drive HoxA expression in head tissues at levels below detection by whole-mount in situ hybridization. Finally, our 5C data also reveals high interaction frequencies with at least two loci that have no apparent characteristics of transcriptional enhancers (Figure 2 bottom, blue stars). These may reflect additional structural anchors that stabilize the chromatin architecture, such as those mediated by CTCF and Cohesin [33]–[34]. Interestingly, loci bound either by CTCF or cohesin in limb buds have been recently identified [35] and comparison with our data shows that almost all loci interacting with 5′ HoxA genes overlap with either CTCF or cohesin binding (Figure 2, bottom).
Having confirmed the spatial proximity between 5′ HoxA genes and most of our candidate enhancers, we proceeded to test their in vivo activity in the mouse by transgenesis. Putative enhancer sequences were subcloned into vectors carrying the β-globin minimal promoter and lacZ reporter. Except for e1 and e2, X-Gal staining in transgenic embryos shows that all candidate enhancers tested activates transcription in developing limbs (Figure 3, Table S1). Interestingly, the confirmed enhancers exhibit distinct but overlapping activity domains in limb buds, and all trigger expression in the presumptive hand/foot (Figure 3). While some are active mostly in the distal part of the limb (e3, 4, 5, 10, 12, 13), others are functional also in the proximal domain (e5, 16, 18). The only candidate enhancers that fail to trigger reporter expression in our transgenic assays are e1 and e2 (Table S1). The absence of activity for these two candidates indicates either that these sequences are not limb enhancers or that the transgenes did not include all the necessary sequences to reflect their transcriptional activities. For e1, our 5C data (Figure 2) neither supports nor disagrees with it being a HoxA enhancer since it lies within a large region of high interaction frequency. Interestingly, e1 is located within a 50 kb DNA fragment that was previously shown to trigger gene expression in distal limbs [2], suggesting that it is possibly a bona fide limb enhancer but that some sequences required for its activity are absent from the 2.9 kb fragment tested in our transgenic assay. Similarly, absence of X-Gal staining in e2 transgenic embryos does not prove that e2 is not an enhancer. Yet, the fact that it does not strongly interact with 5′ HoxA genes in our 3C and 5C assays suggest that e2 may not be tightly linked to the regulation of HoxA genes. Nonetheless, analysis of the other identified enhancers shows that multiple enhancers with overlapping domain-specific activities regulate transcription at the HoxA cluster in the limb.
While “DNA looping” is associated with long-range transcriptional control, the extent to which spatial structure exists prior to or as a consequence of enhancer activation remains elusive. This issue partly originates from the fact that most studies have compared the spatial distance of enhancers and target gene(s) in tissues expressing the genes with others where they are never expressed. To gain insight into the causative relationship between spatial proximity and long-distance enhancer regulation, we examined the outcome of enhancer silencing on long-range enhancer-gene interactions in developing limbs. During limb development, the transcriptional repressor Gli3R negatively regulates the expression of HoxA genes [36]–[37]. While Gli3 is expressed almost throughout the limb in wild type (wt) embryos, the Gli3R domain is restricted anteriorly as a consequence of posterior Sonic hedgehog (Shh) signaling emanating from the Zone of Polarizing Activity (ZPA), which blocks processing of the full length Gli3 protein into its truncated repressor form [38]. In Shh−/− limbs, the Gli3R functional domain extends posteriorly leading to the down-regulation of HoxA as well as HoxD genes [36]–[37].
Amongst the HoxA-associated limb enhancers identified, we found several that overlap with loci bound by Gli3R in the limb (e3, e5, e9 and e16; [39]). The activity of these enhancers should thus be suppressed in Shh−/− mutant. Of these, e5 is of particular interest because it triggers robust gene expression (Figure 3), and there is no other limb enhancer in its genomic neighborhood allowing us to assess its interaction frequency with HoxA without potential interference from surrounding enhancers. We first verified the activity of e5 in Shh−/− by generating mutant embryos homozygous for Shh inactivation and carrying the e5 transgene. X-Gal staining shows that e5 activity is suppressed in limbs upon inactivation of Shh (Figure 4Aa–d, compare a to b) while still functional in the developing genitalia (Figure 4A, panel d). In contrast, a transgene containing the e1 enhancer, which does not overlap with a Gli-bound locus, remained expressed in a Shh−/− background (Figure 4Af–h) although in a smaller domain consistent with Shh−/−embryos having reduced limb size ([40]; Figure 4A, compare e to f).
To assess whether HoxA-enhancer proximity requires enhancer activity, we measured contacts between Hoxa13 and e5 in wt and Shh−/− distal limb buds from E11.5 embryos. As e5 activity is suppressed in the absence of Shh, the enhancer should no longer interact with Hoxa13 if enhancer activity is required for the contact. 3C analysis shows that e5 interacts with Hoxa13 even in the absence of Shh (Figure 4B). Although interaction frequencies are lower than in wt limbs, the interaction pattern is similar and contacts are much stronger than in the head, which was used as control. These data show that even though e5 silencing may affect the robustness of the interactions, the spatial proximity between e5 and Hoxa13 does not require the transcriptional activity of the enhancer. As Hoxa13 expression is severely reduced in the absence of Shh [37], [41], we next wondered whether the contact pattern of HoxA genes with the distal limb enhancers was similarly preserved in Shh−/− limbs. To address this question, we compared the interaction profile of the HoxA cluster with its upstream regulatory region in wt and Shh−/− limbs, and in the head. For these 5C experiments, we used a modified 3C library protocol optimized for the production of libraries from a small number of cells (see Materials and Methods). This protocol largely recapitulated the contact pattern detected in wt limbs and the head with our standard approach (compare corresponding panels in Figures 2 and 4C). Consistent with our 3C data, this 5C analysis revealed a similar contact pattern between the 5′ HoxA genes and upstream regulatory region in the Shh−/− mutant and wt limbs (Figure 4C, Figure S3). These include contacts with e5 and e16 enhancers, which overlap with Gli3R sites and others like e10 and e13 that are not regulated by Shh. As observed in our preliminary 3C analysis, the contacts were weaker in the Shh−/− suggesting that strengthening a given enhancer-promoter contact upon enhancer activation may impact on the stabilization/strength of other interactions. Together, these data indicate that enhancer activity strengthens, but is not mandatory for spatial proximity between enhancers and their target genes.
The observation that different enhancers drive transcription in the same areas of the limb suggests a possible physical link between some of them. To test this possibility, we extended our 5C analysis to the whole regulatory region. The HoxA cluster was previously found to span the junction between two adjacent TADs in human IMR90 and mouse embryonic stem cells (ES) analyzed with Hi-C at the mega-base scale [31]. We observed a similar megabase scale organization in our samples, where 5′ HoxA genes and distal limb enhancers are located in the same TAD (Figure 5A, B, and Figure S4). We found that this TAD is subdivided into domains of interactions that differ between the limb and head tissues at E12.5 (Figure 5A, B). In addition, contacts between sub-TADs are different in the two tissues. For example, the HoxA sub-TAD containing Hoxa9 to Hoxa13 preferentially forms long-range contacts with the enhancers in the limb (e.g. e10–14, e15–18; Figure 5A, Figure S5), while it interacts strongly with the 3′ HoxA genes in the head (Figure 5C, bottom, Figure S4). Similarly, Evx1, which spatially localizes within its own domain, interacts long-distance with a subset of the identified enhancers in limbs, consistent with its expression pattern being similar to Hoxa13. This is different in the head where Evx1 and HoxA are mostly inactive and the genes form a large interacting domain (Figure 5B,C), which likely reflects chromatin compaction at transcriptionally silent loci. This result raises the possibility that chromatin conformation within TADs could vary in a tissue-specific manner. In support of this, a recent 5C analysis in mouse ES and neural progenitor cells identified tissue-specific topological domains at the sub-megabase scale, termed “sub-TADs” [33]. Our 5C analysis therefore revealed the existence of tissue-specific sub-TAD interactions underlying the regulation of HoxA genes in developing limbs.
The chromatin architecture resulting in the spatial proximity between 5′ HoxA genes, the enhancers, and the promoter of Hibadh suggests that Hibadh itself interacts with HoxA-associated limb enhancers. Indeed, Hibadh shows enriched interaction with e5, e13 and e16 in limb compared to head tissue (Figure S5). Interactions between Evx1 and Hibadh are also enriched in limb compared to head tissue. Our experimental design unfortunately did not retain the promoter region of Tax1bp1 and thus we could not profile its connectivity with the region. As for the promoter of Jazf1, it contacts neither the genes nor the enhancers consistent with the absence of RNAP2 and Med12 at its promoter (Figure 1D), and in agreement with previous work showing that Jazf1 is expressed in distal limbs only at later developmental stages [16]. Together, these results show that a subset of HoxA-associated enhancers likely regulate also Evx1 and Hibadh. Interestingly, there is an extensive connectivity between the enhancers themselves in the limb but not in head tissues (Figure 5A, B). Similarly to the genes, the enhancers partition among different sub-TADs that interact together. This is particularly visible for the most distal ones where e10–14 localized within one sub-TAD, and e15–18 into another (Figure 5A, D). This organization suggests that enhancers are spatially grouped into regulatory modules, which can interact with each other, eventually triggering specific expression patterns. Such interaction between genomic domains is reminiscent of the contacts identified in Drosophila [42]. It is thus likely that long-range gene regulation relies on sub-TAD interactions rather than discrete looping between specific DNA elements. Moreover, interactions between gene and enhancer sub-TADs in the limb strengthened and better defined the position of the corresponding TAD as compared to head tissues (Figure 5, compare A and B). This result suggests that although largely invariant, the partitioning of chromosomes into TADs can be affected by the tissue-specific folding of the chromatin at the sub-megabase level.
In this study, we identified the very first set of bona fide limb enhancers controlling 5′ HoxA gene expression. We show that these enhancers, like the HoxA genes, are grouped into distinct topological domains at the sub-megabase scale, and that long-range contacts between the sub-TADs underlie the expression of 5′ HoxA genes in the developing limb. This result suggests that long-distance regulation of HoxA genes is based on sub-TAD interactions rather than discrete looping between enhancers and target genes. In the head, sub-TAD interactions are barely detectable thus indicating that the chromatin architecture of the region upstream HoxA varies in a tissue-specific manner at the sub-megabase scale. The apparent lack of sub-TAD interactions in the head could also be the consequence of the greater cellular complexity of this tissue, which would equally imply that sub-TAD interactions are cell type/tissue-specific (Figure 5B). A similar conclusion was recently reached based on the comparison between 5C data in mouse ES cells and neural progenitor cells {Nora, 2012 #165} [32]–[33]. The cell-type/tissue specificity of sub-TADs contrasts with the mostly invariant nature of TADs, which partition the genome into topological domains at the megabase scale [31]–[32]. While it was proposed that TADs could represent the structural basis of regulatory landscapes [43], the actual chromatin folding associated with transcriptional activity likely relies mostly on sub-TAD interactions (Figure 6). The diametrically opposed invariant nature of TADs and the tissue-specificity of sub-TADs also imply that distinct structural parameters define them. Accordingly, while arrays of CTCF sites characterize TAD boundaries [31], there is no obvious correlation between CTCF binding and the sub-TAD boundaries observed in limb buds (Figure S5).
Our results also indicate that at least some of the gene-enhancer contacts form independently of enhancer transcriptional activity (Figure 4), and we suggest that this structure largely exists before the gene transcription begins. This view is supported by the existence of interactions with loci for which there is no evidence of transcriptional activity (Blue stars in Figure 2). Moreover, our data shows that enhancers triggering distinct expression patterns in the limb (i.e. active in different cells) actually belong to the same sub-TAD, which further supports the notion that organization of the chromatin into sub-TADs does not simply reflect physical clustering of active enhancers. Chromatin interactions nonetheless appear strengthened by enhancer activity consistent with the recent concept of self-enforcing structure-function feedbacks, considered as a mechanism propagating cell-fate memory [44]. Our data also reveal better-defined boundaries of the 5′ HoxA-containing TAD in limbs, where sub-TADs robustly interact (Figure 5). This result raises the possibility that upon enhancer activation, the robustness of sub-TAD interactions within two adjacent TADs may change and consequently re-define the position of the TAD boundary. This potential TAD/sub-TAD interplay may actually provide a mechanistic explanation for Hoxd9 to Hoxd11 switching from one TAD to the neighboring one in proximal limb compared to distal limb cells [45].
The identification of multiple enhancers controlling 5′HoxA genes in distal limbs raises questions about the potential role and benefits for this apparently complex control mechanism. The evidence that the various enhancers identified have distinct spatial specificities, together with the eventual morphological diversity of the hand/foot, points to the existence of an early molecular heterogeneity among the mesenchymal progenitors of the hand/foot. Accordingly, Shh signaling regulates a subset of enhancers identified here while others are not (Figure 4). Nonetheless, most enhancers also appear to share overlapping functional domains. Interactions between some enhancers may reflect their co-function in some cells, which could correspond to specific cell populations in which a higher HoxA dosage is required. Alternatively, enhancer interactions could be the consequence of a “pre-set” chromatin architecture whereby a series of enhancers is brought in the vicinity of the same target genes, without having necessarily a combined transcriptional input in the same cell. Finally, it should also be mentioned that multiple enhancers with overlapping function can be beneficial, as exemplified with the discovery of shadow enhancers, which compensate for each another in sub-optimal conditions [46]–[47].
The hand/foot (autopod) is one of the major morphological novelties that accompanied Vertebrates adaptation to terrestrial habitats. As autopod development requires the function of HoxA and HoxD genes, the mechanism that led to the emergence of this new Hox function appears as a key molecular event associated with the fin-to-limb transition. By mapping active enhancers in the presumptive hand/foot and testing their interaction with HoxA genes, we provide evidence that HoxA expression in this tissue relies on long-range regulation by multiple enhancers. Previous studies on the regulation of HoxD genes led to the same conclusion [15], [48]–[49] suggesting that HoxA and HoxD genes have been recruited in this evolutionary novel structure through the implementation of a similar regulatory strategy. Yet, sequence comparison between HoxA and HoxD specific enhancers failed to identify obvious conservation thereby favoring a model whereby the recruitment of HoxA and HoxD genes in the presumptive hand/foot was likely achieved through independent implementations of novel cis-regulatory elements. Since these enhancers were identified with a resolution varying between 0.5 and 2 kb, it is possible that they are bound by the same transcription factors but with a distinct layout of their binding sites, as it would be expected from the independent evolution of the HoxA and HoxD regulatory landscapes. It is also possible that some ‘HoxA’ and ‘HoxD’ enhancers are bound by distinct combinations of transcription factors, in agreement with a subset of ‘HoxA’ enhancers having domains of activity within the developing limb distinct from the ‘HoxD’ enhancers (Figure 3; [15]). Notably, the differences in enhancer functional domains are consistent with the specificities of HoxA expression as illustrated in the presumptive digit one domain: while HoxD expression in digit one is restricted to Hoxd13 as a result of the quantitative collinearity [50]–[52], no such phenomenon is observed for HoxA genes, the regulation of which involves a digit one-specific enhancer not identified for HoxD genes [15].
The independent evolution of HoxA and HoxD regulatory landscapes suggested by the absence of obvious sequence conservation of their respective enhancers is further supported by several findings. First, the recent evidence that HoxA and HoxB clusters most likely stem from the duplication of an ancestral HoxA/B cluster [53] implies that putative ancestral regulatory modules common only to HoxA and HoxD should have been lost at HoxB and HoxC. This scenario however appears unlikely to account for the specific HoxA and HoxD regulation associated with hand/feet development as the tandem duplications of the ancestral Hox cluster that led to the four Vertebrate Hox clusters occurred prior to the fin-to-limb transition. Second, there is a major difference in the layout of the HoxA and HoxD regulatory landscapes controlling their expression in the developing hand/feet. While HoxD-associated enhancers are part of a gene desert [15], [48]–[49], a large number of HoxA-associated enhancers are embedded in genes. Notably, HoxA enhancers with the highest enrichment of RNAP2, Med12 and p300, which also show the highest frequencies of interaction with HoxA genes, are located within Hibadh and Jazf1. Moreover, the genomic domain between HoxA and Jazf1, has undergone significant expansion from fish to mice (about 50 kb in fish and 800 kb in mice), indicating that an extensive genomic reshuffling at the HoxA regulatory landscape occurred during the fin-to-limb transition, which further support an independent evolution of the HoxA and HoxD regulatory landscapes. Interestingly, this genome expansion affected both the size of Hibadh, Jazf1 and the intergenic regions. The absence of preferential localization of HoxA-associated enhancers in gene-free regions thus suggests that introns are equally amenable to sequence evolution and emergence of new regulatory elements.
Although enhancers controlling HoxA and HoxD expression in distal limb most likely emerged independently, in both cases the distal limb regulatory landscape is located within the TAD containing the 5′ genes ([45] and this work). As long-range physical contacts between DNA sequences preferentially occur within TADs, it is conceivable that topological constraints have influenced the evolution of HoxA and HoxD regulatory landscapes associated with their distal limb expression. Interestingly, the early/proximal limb regulatory landscape of HoxD was identified on the opposite side of the gene cluster, within a TAD containing the 3′ HoxD genes, and not contacting Hoxd13 [45]. Whether the existence of a TAD boundary within the HoxA and HoxD clusters has favored the differential expression of Hox genes in proximal and distal limb bud or spatially constrained the emergence of proximal and distal limb enhancers remains unclear. Nonetheless, the deleterious modification of proximal limb development upon expression of Hoxa13 or Hoxd13 in early/proximal limb bud [54] raises the possibility that the TAD boundary embedded in both the HoxA and HoxD clusters has influenced the evolution of the tetrapod limb morphology. Although, chromosome partitioning into TADs remains to be characterized in most animal species, the presence of a TAD boundary embedded in each Hox cluster both in mice and humans [31] suggests a possible widespread impact of genome topology on the evolution of Hox regulation, and perhaps more generally on the evolution of regulatory landscapes.
In summary, our study reveals that extensive three-dimensional chromatin interactions control the expression of HoxA genes in developing limbs by forming distinct topological domains containing limb enhancers, which interact with each other and with the topological domains containing their target genes. Although this chromatin architecture is tissue-specific, our data suggests that it forms independently of enhancer activity, and is strengthened upon enhancer activation. Importantly, our data provide evidence that target genes and regulatory elements physically interact with each other through contacts between sub-TADs rather than by the formation of discreet “DNA loops”. From an evolutionary standpoint, the identification of HoxA-associated enhancers in limbs reveals major differences with the HoxD regulatory landscape suggesting that the changes in HoxA and HoxD regulation associated with the emergence of the hand/foot likely occurred through the independent emergence of regulatory sequences but common topological constraints.
The HoxAFlox, HoxADelNeo, IR50, and Shh−/− mice lines were described previously [1]–[2], [40]. Candidate enhancer sequences identified from ChIP-seq data were amplified by PCR using the primer sequences reported in Table S1 and PCR products were verified by sequencing. Enhancer sequences were cloned upstream of the chicken β-globin minimal promoter and the LacZΔCpG NLS reporter. Transgenic embryos were generated by pronuclear injections, and at least three transgenic embryos per construct were analyzed. The stable mouse line for e5 was generated using the same protocol.
X-Gal staining was performed on E12.5 embryos following standard procedures. In situ hybridization was conducted using a standard procedure [55]. Hygromycin and Neomycin probes were generated as described previously [2].
Distal limb and head tissues were dissected at E12.5 for wt and at E11.5 for Shh−/− mice. Tissues were collected in 1×PBS containing 10% FBS (100 µl per embryo), and the samples were incubated 20 min at 37°C with collagenase (0.025% final concentration) to obtain single-cell suspensions. The number of cells in suspension was then counted under the microscope, and each sample was diluted in 9 ml of 1×PBS containing 10% FBS (5 ml for Shh−/− embryos). The cells were then fixed with 1% formaldehyde for 10 min at room temperature. Crosslinking was stopped with glycine (125 mM final concentration), and incubated 5 min at room temperature followed by 15 min on ice. Cells were centrifuged at 400 g for 10 min at 4°C. Supernatants were removed and the cell pellets were flash frozen on dry ice.
Chromatin immunoprecipitation was performed as previously described with some modifications [56]–[57]. Briefly, chromatin from 5 million cells was sonicated using a Branson Sonicator 450D to obtain fragments with average sizes ranging between 100–600 bp. Cell debris was removed by centrifugation at 20,000 g for 15 min at 4°C and aliquots of the supernatant were taken for quantification and to confirm proper sonication. Remaining samples were stored at −80°C until use. Chromatin from 5 million cells was used for each immunoprecipitation. Protein G Dynal Beads (Invitrogen) were incubated 8 hours at 4°C with either 5 or 10 µg of antibodies. The chromatin was then incubated with the beads overnight. Immunoprecipitated complexes were sequentially washed in low salt (150 mM NaCl, 0.1% SDS, 1% Triton X-100, 2 mM EDTA, 20 mM Tris-HCl (pH 8.0)), medium salt (250 mM NaCl, 0.1% SDS, 1% Triton X-100, 2 mM EDTA, 20 mM Tris-HCl (pH 8.0)), LiCl (0.25 M LiCl, 0.5% NP40, 0.5% Na-Deoxycholate, 1 mM EDTA, 10 mM Tris-HCl (pH 8.0)), and 1×TE buffers. The protein/DNA complexes were eluted in an SDS buffer (1% SDS, 50 mM Tris (pH 8.0), 10 mM EDTA) by incubation at 65°C for 15 min on a rotating platform. Crosslinks were reversed by incubating the complexes at 65°C overnight. Samples were treated one hour at 55°C with RNAseA (0.2 µg/ml final concentration) and then with Proteinase K for two hours. Finally, the DNA was purified on QIAquick columns (Qiagen). Specific antibodies for Med12 and RNAP2 were purchased from Bethyl (A300-774A) and Abcam (ab5131), respectively.
The ChIPed material was sequenced on a Hi-Seq 2000 high-throughput DNA sequencer. Sequencing libraries were prepared from 31 ng (RNAP2), 5 ng (Med12), and 345 ng (input) of ChIPed DNA. The libraries and flow cells were prepared by the IRCM Molecular Biology and Functional Genomics platform. The libraries were multiplexed and sequenced on one lane. The sequencing was performed by the McGill University and Génome Québec Innovation Centre following recommendations by the manufacturer (Illumina, San Diego, CA).
For RNAP2, Med12, and the input, we obtained a total of 151,045,509, 110,507,927, and 98,043,425 sequence reads, respectively. The first base of each sequence read was trimmed to ensure high base calling quality. The trimmed reads were mapped to the mouse mm9 genome assembly with Bowtie using the –best parameters [58]. To identify the highly significant RNAP2 and Med12 peaks, we used the MACS 1.4.1 peak finder with the following parameters: --format SAM --wig --bw 250 --mfold 7,30 -pvalue 1e–5 -g mm [59].
Redundant reads were filtered out for peak finding and wiggle file generation. Thus the wiggle files enclose the total number of uniquely mapped and non-redundant reads. After processing the data, the number of sequence reads we obtained was 129,222,085 for RNAP2, 91,816,355 for Med12, and 88,141,136 for the input. The position of RNAP2 and Med12 peaks genome-wide identified in our study is provided in Tables S21 and S22, respectively. We also provide the wig files for the data on chromosome 6 (Dataset S1, S2 and S3).
Limb and head cell pellets were treated as previously described [29], [60]. Briefly, 10 million fixed cells (2.87 million for Shh−/− library used for the 3C experiments) were incubated for 15 min on ice in 200 µl of lysis buffer (10 mM Tris (pH 8.0), 10 mM NaCl, 0.2% NP40, supplemented with fresh protease inhibitor cocktail). Cells were then disrupted on ice with a dounce homogenizer (pestle B; 2×20 strokes). Cell suspensions were transferred to eppendorf tubes and centrifuged 5 min at 2000 g. Supernatants were removed and the cell pellets were washed twice with 100 µl of 1×EcoRI buffer (NEB). After the second wash, the cell pellet was resuspended in 100 µl of 1×EcoRI buffer, and divided into two eppendorf tubes containing 50 µl of cell suspension. 1×EcoRI buffer (337 µl) was added to each tube, and the mixture was incubated 10 min at 65°C with 0.1% SDS final (38 µl). Triton X-100 (44 µl of 10% Triton X-100) was added before overnight digestion with EcoRI (400 Units). The restriction enzyme was then inactivated by adding 86 µl of 10% SDS, and incubating 30 min at 65°C. Samples were then individually diluted into 7.62 ml of ligation mix (750 µl of 10% Triton X-100, 750 µl of 10×ligation buffer, 80 µl of 10 mg/ml of BSA, 80 µl of 100 mM ATP and 3000 Cohesive end Units of T4 DNA ligase). Ligation was carried out at 16°C for 2 hours.
3C libraries were then incubated overnight at 65°C with 50 µl Proteinase K (10 mg/ml), and with an additional 50 µl Proteinase K the following day for 2 hours. The DNA was purified by one phenol and one phenol-chloroform extraction, and precipitated with 0.1 volume of 3M NaOAc pH 5.2 (800 µl) and 2.5 volumes of cold EtOH (20 ml). After at least 1 h at −80°C, the DNA was centrifuged 25 min at 20,000 g at 4°C, and the pellets were washed with cold 70% EtOH. The DNA was resuspended in 400 µl of 1×TE pH 8.0, and transferred to eppendorf tubes for another phenol-chloroform extraction and precipitation with 0.1 volume of 3M NaOAc pH 5.2 (40 µl) and 2.5 volumes of cold EtOH (1.1 ml). DNA was recovered by centrifugation (25 min at maximum speed at 4°C), and washed eight times with cold 70%EtOH. The pellets were then dissolved in 100 µl of 1×TE pH 8.0, and incubated with RNAse A (1 µl at 10 mg/ml) for 15 min at 37°C.
This protocol was used to produce the 5C data for the distal limb, Shh−/− distal limb, and head shown in Figure 4. The protocol is essentially the same as the one described for samples containing 2 to 10 million cells, with some modifications. Briefly, one million fixed cells were incubated for 15 min on ice in 200 µl of lysis buffer (10 mM Tris (pH 8.0), 10 mM NaCl, 0.2% NP40 supplemented with fresh protease inhibitor cocktail). Cells were then disrupted on ice with a dounce homogenizer (pestle B; 2×20 strokes). Cell suspensions were transferred to eppendorf tubes and centrifuged 5 min at 2000 g. Supernatants were removed and the cell pellets were washed twice with 100 µl of 1×EcoRI buffer (NEB).
After the second wash, the cell pellet was resuspended in 50 µl of 1×EcoRI buffer. 1×EcoRI buffer (337 µl) was added to each tube, and the mixture was incubated 10 min at 65°C with 0.1% SDS final (38 µl). Triton X-100 (44 µl of 10% Triton X-100) was added before overnight digestion with EcoRI (400 Units). The restriction enzyme was then inactivated by incubating 30 min at 65°C. Ligation was performed in 600 µl (450 µl of digestion product, 15 µl of 10% Triton-X-100, 60 µl of ligase buffer, 6 µl of 10 mg/ml of BSA, 6 µl of 10 mM ATP, and 300 Cohesive end Units of T4 DNA ligase). Ligation was carried out at 16°C for 4 hours.
3C libraries were then incubated overnight at 65°C with 15 µl Proteinase K (10 mg/ml), and with an additional 15 µl Proteinase K the following day for 2 hours. The DNA was purified by one phenol and two phenol-chloroform extractions, and precipitated with 0.1 volume of 3M NaOAc pH 5.2 (64 µl) and 2.5 volumes of cold EtOH (1740 µl). After at least 1 h at −80°C, the DNA was centrifuged 25 min at maximum speed at 4°C, and the pellets were washed once with cold 70% EtOH. The DNA was resuspended in 50 µl of 1×TE pH 8.0, and incubated with RNAse A (1 µl at 10 mg/ml) for 15 min at 37°C.
As 3C products are quantified by PCR amplification of expected ligation junctions with different primer pairs, differences in PCR primer pair efficiencies must be corrected using control 3C libraries. Control libraries were generated from bacterial artificial chromosomes (BACs) as previously described [29] and contain equimolar ratios of all possible 3C contacts. Briefly, BAC clones covering the HoxA region (mm9, chr6: 51,946,668–52,656,241), and one USP22 control region (mm9, chr11: 60,890,403–61,093,236)) were mixed in equimolar ratio. Mixed BACs were digested with EcoRI and randomly ligated with T4 DNA ligase (5700 Cohesive end Units) overnight at 16°C. BAC libraries were then purified by phenol-chloroform extraction. The libraries were generated with the following BACs: RP23-420L19, RP24-359H1, RP24-242G11, RP-347D14, RP23-305I5 (Invitrogen, CHORI). These libraries were used only to correct primer pair efficiencies during 3C analysis and not in the 5C experiments.
3C primers were designed using the ‘3CPrimer’ program (http://dostielab.biochem.mcgill.ca), and sequences are listed in Table S2. Three reactions using the control BAC library and three reactions using each 3C library were generated for each primer pair. The PCR conditions were described elsewhere [29]. 3C PCR products were resolved on agarose gel containing ethidium bromide and quantified using a ChemiDoc XRS system featuring a 12-bit digital camera and the Quantity One computer software (version 4.6.3; BioRad). Interaction frequencies (IF) were measured by dividing the value of each template PCR reactions by the value of each of the three control PCR reactions. The nine values were then average to determine the normalized interaction frequency. Three biological replicates were averaged after normalization for the wt limb and head. Normalization between different libraries was done using the compaction profiles for the USP22 region and an intergenic region within HoxA region as a reference.
5C primers covering the USP22 region (mm9, chr11: 60,917,307–61,017,307) and the HoxA region (mm9, chr6: 52,099,908–53,050,000) were designed using ‘my5C.primer’ [61] and the following parameters: optimal primer length of 30 nt, optimal TM of 65°C, default primer quality parameters (mer:800, U-blast:3, S-blasr:50). The sequences of these primers are listed in Table S3 and S4. Primers were not designed for large (>20 kb) and small (<100 bp) restriction fragments. Low complexity and repetitive sequences were excluded from our experimental designs such that not all fragments could be probed in our assays. Primers with several genomic targets were also removed.
The universal A-key (CCATCTCATCCCTGCGTGTCTCCGACTCAG-(5C-specific)) and the P1-key tails ((5C-specific)-ATCACCGACTGCCCATAGAGAGG) were added to the Forward and Reverse 5C primers, respectively. Reverse 5C primers were phosphorylated at their 5′ ends. Two experimental designs were used in our study. In the “cluster R” design (anchored 5C scheme, Figure 2, Figure S1, Figure 4C, Figure S3), Reverse 5C primers covered the HoxA cluster while Forward 5C primers tiled the surrounding upstream region. In this design, we used 142 Forward and 39 Reverse 5C primers (133 Forward/30 Reverse for the HoxA region, 9 Forward/9 Reverse USP22 region). In the “FR” design (alternating 5C scheme, Figure 5, Figure S3), alternating Forward and Reverse 5C primers covering the entire HoxA region were used to generate the 5C libraries. This design used 194 primers (86 Forward/90 Reverse for the HoxA region, 9 Forward/9 Reverse USP22 region). Primer sequences are listed in Table S3 (anchored “R” design) and S4 (alternating “FR” design).
5C libraries were prepared and amplified with the A-key and P1-key primers following a procedure described previously [30]. Briefly, 3C libraries were first titrated by PCR for quality control (single band, absence of primer dimers, etc.), and to verify that contacts were amplified at frequencies similar to what is usually obtained from comparable libraries (same DNA amount from the same species and karyotype) [29], [62]–[63]. We also verified the quality of the 3C libraries by generating a compaction profile in the USP22 region. In general, we used approximately 1.5 µg of 3C library per 5C ligation reaction when the libraries were generated from a large number of cells (2×106 to 107 cells). When 3C libraries were generated from a small cell number (106 cells), we used approximately 1 µg of DNA.
Before adding the 3C libraries to the reaction tubes, 5C primer stocks (20 µM) were diluted individually in water on ice, and mixed to a final concentration of 0.002 µM. Mixed diluted primers (1.7 µl) were combined with 1 µl of annealing buffer (10×NEBuffer 4, New England Biolabs Inc.) on ice in reaction tubes. Salmon testis DNA (1.5 µg) was added to each 5C reaction, followed by the 3C libraries and water for a final volume of 10 µl. Samples were denatured at 95°C for 5 min, and annealed at 55°C for 16 hours. Ligation with Taq DNA ligase (10 U) was performed at 55°C for one hour. One tenth (3 µl) of each ligation was then PCR-amplified individually with primers against the A-key and P1-key primer tails. We used 28 cycles based on dilution series showing linear PCR amplification within that cycle range. The products from 2 (for the 3C libraries prepared from a large number of cells) to 8 (for the 3C libraries prepared from 106 cells) PCR reactions were pooled before purifying the DNA on MinElute columns (Qiagen).
5C libraries were quantified on agarose gel and diluted to 0.0534 ng/µl (for Xpress Template Kit v2.0) or 0.0216 ng/µl (for Ion PGM Template OT2 200 kit). One microliter of diluted 5C library was used for sequencing with an Ion PGM Sequencer. Samples were sequenced onto Ion 316 Chips following either the Ion Xpress Template Kit v2.0, and Ion Sequencing Kit v2.0 protocols, or the Ion PGM Template OT2 200 Kit, and Ion PGM Sequencing 200 Kit v2.0 protocols as recommended by the manufacturer's instructions (Life Technologies).
Analysis of the 5C sequencing data was performed as described earlier [30]. The sequencing data was processed through a Torrent 5C data transformation pipeline on Galaxy (https://main.g2.bx.psu.edu/). Briefly, the data was mapped against a customized reference file with TMAP. The reference file contained a list of all possible contacts between Forward and Reverse 5C primers covering our regions. The data was then filtered to remove low-quality reads (MAQ quality score of lower than 30), reads aligning more than two nucleotides away from the reference sequence start site, and reads which do not contain EcoRI restriction sites. This analysis generates an excel sheet containing interaction frequency lists (IFL) as well as a text file, which was used to visualize results using ‘my5C-heatmap’ [61]. Limb-enriched 5C interactions were obtained by subtracting limb and head 5C-seq data. Data was normalized by dividing the number of reads of each 5C contact by the total number of reads from the corresponding sequence run. All scales correspond to this ratio multiplied by 103. The number of total reads and of used reads is provided for each experiment in Table S5. 5C data are provided in Tables S6 to S20 and can be downloaded from our website: http://dostielab.biochem.mcgill.ca/
The limb p300 and H3K27Ac datasets (Acc. No. GSE13845 and GSE30641) are from E11.5 embryos, and were downloaded from the Gene Expression Omnibus (GEO) website http://www.ncbi.nlm.nih.gov/geo/. The my5C-primer and my5C-heatmap bioinformatics tools can be found at http://3dg.umassmed.edu/my5Cheatmap/heatmap.php
|
10.1371/journal.pntd.0000115 | Characterization of the Entamoeba histolytica Ornithine Decarboxylase-Like Enzyme | The polyamines putrescine, spermidine, and spermine are organic cations that are required for cell growth and differentiation. Ornithine decarboxylase (ODC), the first and rate-limiting enzyme in the polyamine biosynthetic pathway, is a highly regulated enzyme.
To use this enzyme as a potential drug target, the gene encoding putative ornithine decarboxylase (ODC)-like sequence was cloned from Entamoeba histolytica, a protozoan parasite causing amoebiasis. DNA sequence analysis revealed an open reading frame (ORF) of ∼1,242 bp encoding a putative protein of 413 amino acids with a calculated molecular mass of 46 kDa and a predicted isoelectric point of 5.61. The E. histolytica putative ODC-like sequence has 33% sequence identity with human ODC and 36% identity with the Datura stramonium ODC. The ORF is a single-copy gene located on a 1.9-Mb chromosome. The recombinant putative ODC protein (48 kDa) from E. histolytica was heterologously expressed in Escherichia coli. Antiserum against recombinant putative ODC protein detected a band of anticipated size ∼46 kDa in E. histolytica whole-cell lysate. Difluoromethylornithine (DFMO), an enzyme-activated irreversible inhibitor of ODC, had no effect on the recombinant putative ODC from E. histolytica. Comparative modeling of the three-dimensional structure of E. histolytica putative ODC shows that the putative binding site for DFMO is disrupted by the substitution of three amino acids—aspartate-332, aspartate-361, and tyrosine-323—by histidine-296, phenylalanine-305, and asparagine-334, through which this inhibitor interacts with the protein. Amino acid changes in the pocket of the E. histolytica enzyme resulted in low substrate specificity for ornithine. It is possible that the enzyme has evolved a novel substrate specificity.
To our knowledge this is the first report on the molecular characterization of putative ODC-like sequence from E. histolytica. Computer modeling revealed that three of the critical residues required for binding of DFMO to the ODC enzyme are substituted in E. histolytica, resulting in the likely loss of interactions between the enzyme and DFMO.
| Entamoeba histolytica is a unicellular protozoan parasite that infects about 50 million people each year and can cause potentially life-threatening diseases such as hemorrhagic colitis and extraintestinal abscesses. The infections are primarily treated by antiamoebic therapy. Drugs of choice for invasive amoebiasis are tissue-active agents, such as metronidazole, tinidazole, and chloroquine. Although drug resistance to E. histolytica does not appear to be a serious problem, there are occasional reports of failure with metronidazole, suggesting that clinical drug resistance may be developing. When identifying a drug target, it is important that the putative target be absent in the host, or, if it is present in the host, that the homologue in the parasite be substantially different from the host homologue so that it can be exploited as a drug target. Such is the case with the enzymes involved in polyamine biosynthesis, a pathway that has been exploited as a target to control disease caused by several parasites. We report, to our knowledge for the first time, molecular cloning, expression, and characterization of the ornithine decarboxylase from E. histolytica, a rate limiting enzyme in the polyamine biosynthesis pathway.
| Entamoeba histolytica is a unicellular protozoan parasite that infects about 50 million people each year and may cause potentially life-threatening diseases such as hemorrhagic colitis and/or extraintestinal abscesses [1]. The infections are primarily treated by antiamoebic therapy. Drugs of choice for invasive amoebiasis are tissue-active agents such as metronidazole, tinidazole, and chloroquine [2]. Although drug resistance to E. histolytica does not appear to be a serious problem, there are occasional reports of failure with metronidazole suggesting the possibility of development of clinical drug resistance [3].
Polyamine biosynthetic pathway is the critical regulator of cell growth, differentiation, and cell death [4]–[6]. Polyamines are involved in nucleic acid packaging, DNA replication, apoptosis, transcription, and translation [7]. The polyamine biosynthetic pathway is a potential target for therapeutic agents against various hyperproliferative disorders, particularly cancer [8]–[10]. Given the importance of the polyamine biosynthetic pathway as a validated therapeutic target in protozoan parasites [11]–[14], we decided to further investigate this pathway in E. histolytica in the hope of extending our attempts at drug discovery to include this medically important parasite.
Ornithine decarboxylase (ODC; EC 4.1.1.17) is the first rate-limiting enzyme in polyamine biosynthesis, catalyzing the decarboxylation of L-ornithine to putrescine. This enzyme is found in a variety of systems ranging from bacteria [15] and protozoa [16] to plants [17] and mammals [18]. The rapid activation of the enzyme by various stimuli such as hormones, growth factors, or stress makes this enzyme a vital mediator in the regulation of polyamine pathway. ODC, like most amino acid decarboxylases, requires pyridoxal-5′-phosphate (PLP) as a cofactor [19]. E. histolytica ODC protein has been biochemically purified from trophozoites of the parasite [20]. Analytical electrophoresis revealed the presence of a major polypeptide of 45 kDa and scarcely noticeable amounts of two other proteins of 70 and 120 kDa. The major polypeptide exhibited amino-terminal sequence homology in the range of 40%–73% with ODCs of other organisms [20].
Biosynthesis of polyamines in parasites has been exploited as a target to control disease caused by several parasites with specific inhibitors of ODC such as α-difluoromethylornithine (DFMO), which is a structural analog of ornithine. DFMO has been found to be an efficient therapeutic agent against ODC up-regulation [13], [14], [21]–[23]. It is a specific and irreversible inhibitor of ODC, and previous studies have shown that DFMO inhibits growth of Giardia lamblia [24], Acanthamoeba castellani [25], Plasmodium falciparum, and some Trypanosoma species [13],[14],[21],[26] but has no inhibitory effect on E. histolytica ODC [20].
In this paper we report, to our knowledge for the first time, molecular cloning, expression, and characterization of a putative ODC-like sequence from E. histolytica, the parasitic protozoan responsible for amoebiasis. ODC is a PLP-dependent enzyme, and in the present work the ability of E. histolytica putative ODC to form complexes with PLP and DFMO was investigated using modeling of the three-dimensional structure.
Restriction enzymes Pfu and Taq DNA polymerases were obtained from MBI Fermantas. All other chemicals were of analytical grade and were available commercially.
All experiments were carried out with E. histolytica strain HM-1:IMSS clone 6, which was obtained from William A. Petri (University of Virginia). The cells were maintained and grown in TYI-33 medium supplemented with 15% adult bovine serum, 2% Diamond's vitamin mix, and antibiotic (0.3 units/ml penicillin and 0.25 mg/ml streptomycin). Cell viability was determined by microscopy using a trypan blue dye exclusion test. Experiments were conducted with cells that showed >90% viability.
A ∼1,242 base pair fragment was amplified from the genomic DNA of E. histolytica using a sense primer with flanking BamH I site (underlined), 5′-CGCGGATCC ATGAAACAAACATCTCTAGAAG-3′, which codes for amino acid sequence MKQTSLE at position 1–21 and one extra base, G, and the antisense primer with a flanking Xho I site (underlined), 5′- CCGCTCGAGAGCATAGTGTGGAATACCAT-3′, which codes for amino acids GIPHYA at position 1,220–1,239 with two extra bases, A and T. Polymerase chain reaction was performed in a 50 µl reaction volume containing 150 ng of genomic DNA, 25 pmol each of gene-specific forward and reverse primers, 200 µM of each dNTPs, 2.5 mM MgCl2, and 2.5 units of Taq DNA Polymerase (MBI Fermentas). PCR cycling conditions were as follows; 94°C for 10 min, followed by 35 cycles of 94°C for 1 min, 47°C for 45 sec, 72°C for 1:30 min. A final extension was carried out for 10 min at 72°C. A single 1,242 bp PCR product was obtained and subcloned into pTZ57R T/A vector (Promega, Madison, USA) and subjected to automated sequencing. Sequence analysis was performed by DNAstar, whereas comparisons with other sequences of the database were performed using the search algorithm BLAST [27]. Multiple alignments of amino acid sequences were performed using CLUSTAL W (http://www.ebi.ac.uk/clustalw/). The phylogenetic tree was constructed using PHYLIP style treefile produced by CLUSTAL W. The ∼1242-bp DNA fragment, amplified by Pfu polymerase (MBI Fermentas), was also cloned into the BamH I-Xho I site of pET 30a vector (Novagen). The recombinant construct was transformed into BL21 (DE3) strain of E. coli.
Expression from the construct pET30a-ODC-like sequence was induced at O.D. of 0.3 with 1 mM IPTG (isopropyl β-D-thiogalactoside) (Sigma) at 37°C for different time periods. Bacteria were then harvested by centrifugation and the cell pellet was resuspended in binding buffer (50 mM sodium phosphate buffer, pH 7.5; 10 mM imidazole, pH 7.0; 300 mM sodium chloride; 2 mM phenylmethylsulphonyl fluoride (PMSF); and 30 µl protease inhibitor cocktail). Lysozyme (100 µg/ml) was added to the cell suspension and kept on a rocking platform for 30 min at 4°C. The resulting suspension was sonicated six times for 20 s with 1 min intervals. The lysate was centrifuged at 20,000g for 30 min at 4°C. The resulting supernatant, which contained protein, was loaded onto a pre-equilibrated Ni-NTA agarose beads (Ni2+-nitrilotriacetate)-agarose beads (Qiagen). The mixture was kept on a rocking platform for 2 h at 4°C. It was centrifuged at 400 g for 30 min at 4°C. The supernatant was discarded and pellet was washed thrice with wash buffer (50 mM sodium phosphate buffer, pH 7.5; 50 mM imidazole, pH 7.0; 300 mM sodium chloride; 2 mM phenylmethylsulphonyl fluoride [PMSF]; and 30 µl protease inhibitor cocktail). The protein was eluted with increasing concentrations of imidazole, pH 7.0. The imidazole was removed by dialysis in 20 mM sodium phosphate buffer, pH 7.5. The purified protein was aliquoted and stored at −80°C.
Genomic DNA was digested with the enzymes XhoI and HindIII and subjected to electrophoresis in 0.8 % agarose gels. The fragments were transferred to nylon membranes (Amersham Pharmacia Biotech) and subjected to Southern blot analysis. For Northern blot analysis, 15 µg of total RNA was fractionated by denaturing agarose gel electrophoresis and transferred onto nylon membrane following standard procedures. Pulsed-field gradient gel electrophoresis (PFGE) was carried out essentially as described earlier [28]. The agarose blocks containing the cells were subjected to PFGE in 1.2% agarose gels using the Gene Navigator system (Pharmacia). The pulse conditions used were 70 s for 15 h, 120 s for 14 h, and 200 s for 7 h at 5.5 V cm−1. Saccharomyces cerevisiae chromosomes were used as size markers. Following the transfer of DNA, RNA, and chromosomes onto nylon membranes, the nucleic acids were UV cross-linked to the membrane in a Stratagene UV cross-linker. Prehybridization was done at 65°C for 4 h in a buffer containing 0.5 M sodium phosphate; 7% SDS; 1mM EDTA, pH 8.0; and 100 µg/ml sheared denatured salmon sperm DNA. The blots were hybridized with denatured α-[PPPP32P]-dCTP-labeled DNA probe (PCR probe described for the E. histolytica putative ODC-coding region) at 10PP6P cpm/ml, which was labeled by random priming (NEB BlotPPKit, New England Biolabs). Membranes were washed, air-dried, and exposed to an imaging plate. The image was developed by PhosphorImager (Fuji film FLA-5000, Japan) using Image Quant software (Amersham Biosciences).
E. histolytica (1×106 cells) were harvested by centrifugation at 16,000 g at 4°C for 10 min, washed with phosphate-buffered saline, pH 7.4. The cell pellet was resuspended in lysis buffer (100 mM Tris-Cl, pH 7.5; 150 mM sodium chloride; 2 mM PMSF; 2 mM iodoacetamide; 2 mM EDTA; 2.5 mM parahydroxymercuricbenzoic acid; 2 mM ethylene glycol-bis (amino ether); and 10 µg/ml proteinase cocktail) and incubated on ice for 10 min. The cells were lysed by freeze-thaw in liquid nitrogen and subjected to sonication for 10 sec with 1 min interval at 4°C, thrice. The lysate was centrifuged at 15,000g for 30 min at 4°C and the supernatant was used for ODC assay, polyamine estimation, and Western blot analysis as mentioned below.
ODC activity was assayed by following the release of 14CO2 from L- [-14C] ornithine [29]. The standard assay mixture containing the supernatant, 200 µM PLP; 12.5 mM DTT; 250 mM Tris, pH 7.5; 2 mM ornithine; and 3 µCi of the radiolabeled ornithine were incubated at 37°C for 1 h. The reaction was terminated by injecting 5 N H2SO4. Activity is expressed in enzyme units in which one unit is nmol of CO2 /mg protein/h. The assay was repeated thrice. Protein concentrations were determined by the method of Bradford [30] using bovine serum albumin as standard.
Quantitative determination of polyamines in crude lysates of E. histolytica was performed by C18 reversed-phase high performance liquid chromatography after precolumn derivatization with dansyl chloride [31]. The results were based on three separate determinations.
The purified recombinant putative ODC-like protein (20 µg) was subcutaneously injected in mice using Freund's complete adjuvant, followed by two booster doses of recombinant putative ODC-like protein (15 µg) with incomplete adjuvant at 2 wk intervals to produce polyclonal antibody against the recombinant putative ODC-like protein. The mice were bled after 2 wk after the second booster, and sera were collected and used for Western blot analysis.
Recombinant putative ODC-like protein and cell lysate (100 µg of protein) from E. histolytica were fractionated by SDS/PAGE blotted on to nitrocellulose membrane using electrophoretic transfer cell (Bio-Rad). Western blot analysis was carried out using the ECL (enhanced chemiluminescence) kit (Amersham Biosciences) according to the manufacturer's protocol. Anti-polyhistidine (mouse IgG2a isotype, Sigma) and polyclonal antibody (1∶500 dilution) against purified recombinant E. histolytica putative ODC generated in mice were used for Western blot analysis.
The structure of the Trypanosoma brucei ODC mutant in complex with DFMO [32] was used as a template to model E. histolytica ODC. In this mutant structure, lysine 69 has been mutated by alanine (K69A). STAMP (structural alignment of multiple proteins) [33], was used for structural alignment of three ODCs from T. brucei (2TOD) (ExPASy [http://expasy.org/] accession number: P07805), H. sapiens (1D7K) (ExPASy accession number: P11926) and M. musculus (7ODC) (ExPASy accession number: P00860). Later, the program JOY (version 5) [34] was used to align and merge three structurally aligned ODCs with the sequence of E. histolytica putative ODC such that properties of both the structure-based alignment for the homologues of known three-dimensional structures and the sequence-based alignment involving E. histolytica putative ODC are reflected in the final alignment used for modeling (Figure 1A). JOY represents structural information and annotates each amino acid residue according to its structural environment. JOY uses local structural features calculated from the atomic coordinates in a PDB file. The three-dimensional model of E. histolytica putative ODC in complex with PLP and DFMO has been built based on the crystal structure of T. brucei ODC by using the program MODELLER [35]. MODELLER generates a three-dimensional structure of a given protein sequence (target) based primarily on its alignment to one or more proteins of known structure (template/templates). The modeling process consists of fold assignment, target-template alignment, structure building, and evaluation. MODELLER implements comparative protein structure modeling by satisfying spatial restraints [36],[37] and performs tasks such as de novo modeling of loops, comparison of protein structures, optimization of various models of protein structures, etc. Interactive graphics like SYBYL (Tripos, St. Louis, Missouri, United States) was used for energy minimization of the modeled structure to relieve the short contacts, if any.
Modeled E. histolytica putative ODC was subjected to energy minimization using the AMBER force field [38] encoded in the SYBYL software. Energy minimization was done in order to rectify all stereochemical inconsistencies and short contacts that may be present in the initial model.
In order to clone the gene encoding putative ODC-like gene, PCR was performed using specific oligonucleotides (as described in the Methods section), whose sequence was based on Genome Sequencing Project of E. histolytica (http://www.tigr.org). Examination of the E. histolytica database predicts a single ODC gene (http://pathema.tigr.org). A single open reading frame consisting of ∼1,242 bp was obtained, cloned, and sequenced. (E. histolytica ODC gene, GenBank [http://www.ncbi.nlm.nih.gov/Genbank/] accession number AY929249).
The open reading frame coded for a putative polypeptide of 413 amino acids, with a predicted molecular mass of ∼46 kDa. The predicted isoelectric point (pI) of E. histolytica putative ODC-like protein (GenBank accession number AAX35675) was determined to be 5.61, comparable to those of proteins from L. donovani (GenBank accession number P27116) (pI 5.29), and T. brucei (GenBank accession number AAA30219) (pI 5.46) (Figure 1). There was only 33% sequence identity with human ODC (GenBank accession number AAA59967), 32% identity with T. brucei (GenBank accession number AAA30219), and 36% identity with Datura stramonium ODC (GenBank accession number CAA61121) sequences (Figure 1B). The sequences of the mammalian ODC has some highly conserved amino acids and regions that are reported to be essential for catalytic activity and dimerization [39]–[41], and these were also found in the putative ODC-like sequence of E. histolytica. The putative ODC-like sequence was 413 amino acids smaller than ODCs from T. brucei, Homo sapiens, and D. stramonium (Figure 1B). The residue that is essential for dimerization of ODC monomers, mediated by glycine-387 in mammals [42], was found to have equivalents in the sequence of the E. histolytica putative ODC-like protein at position glycine-361. The sequence motif PFYAVKCN at position 64–71 of mammalian ODC, which contains the lysine-69 residue to which the cofactor pyridoxal-5′-phosphate binds, is present at position 53–60 of the E. histolytica putative ODC-like protein, although with changes of phenylalanine to cysteine and tyrosine to phenylalanine. The region GPSCNGSD at position 331–338 in the E. histolytica putative ODC-like protein is probably equivalent to consensus sequence GPTCDGLD of the ODC sequences of various eukaryotes. This sequence contains cysteine-360 in mammalian ODC, which is the major binding site of α-difluoromethylornithine (DFMO) [40]. The corresponding cysteine-334 is conserved in E. histolytica putative ODC-like protein. The overall amino acid homology of E. histolytica putative ODC with the mammalian ODC is low, but highly conserved signature motifs responsible for dimerization and catalytic activity were present. Another signature sequence, as predicted by PROSITE, D(I/V)GGGF, is present across varied sequences without exception. Other highly conserved amino acid stretches, i.e., FDCAS, EPGR, FNGF, and GAYT, are also consistently conserved, though the functional significance of these stretches is not known.
A phylogenetic tree was constructed using the E. histolytica putative ODC-like sequence and other representative ODC sequences (Figure 2). The nearest homologue to the amoebic protein, as revealed by the tree, is the plant D. stramonium. The human ODC sequence seems to be farthest from the amoebic one. Among kinetoplastids, L. donovani appears to be the closest homologue, while Trypanosoma not clustering with L. donovani, is quite distantly related.
To determine the E. histolytica putative ODC-like gene copy number, Southern blot studies were performed as described in Materials and Methods, using the 1,242-bp PCR product as a probe (Figure 3). The enzymes used for Southern analysis were Xho I and Hind III, which have no recognition sites in the E. histolytica putative ODC-like gene sequence. A single band was obtained in each case (Figure 3A), demonstrating that it is a single-copy gene. A PFGE blot probed with the 32P labeled 1,242 bp ODC PCR fragment, hybridized to a 1.9 Mb size chromosome. (Figure 3B). Northern blotting of E. histolytica total RNA and PCR-generated ∼1,242-bp gene probe, revealed two transcripts of ∼4.8 and ∼3.5 kb (Figure 3C).
In order to characterize the recombinant protein, the gene sequence encoding the E. histolytica putative ODC-like protein was cloned in-frame in a pET-30a expression vector with its own start ATG codon. The resultant pET-30a E. histolytica putative ODC-like construct was transformed into E. coli, and protein expression was induced as described in Materials and Methods. A protein with molecular weight that matched the estimated ∼48 kDa predicted by the amino acid composition of E. histolytica putative ODC-like protein with His-tag and S-tag present at its N-terminal end was induced (Figure 4). The recombinant protein was purified on a Ni2+-NTA affinity chromatography column (Figure 4A and 4B). To further confirm the size of the protein, a Western blot was done with anti-His antibody that revealed the band of purified product (∼ 48 kDa) (Figure 4C). Recombinant E. histolytica putative ODC-like protein was used to raise polyclonal antibody in BALB/c mice as described in Materials and Methods. The antiserum recognized a ∼48 kDa fusion protein on a Western blot of purified recombinant E. histolytica putative ODC-like fusion protein (Figure 4D). The same antiserum detected a band of anticipated E. histolytica putative ODC at size ∼46 kDa in Western blots of parasite cell extracts, in agreement with the value calculated from the predicted sequence (Figure 4E). Purification of His-tagged E. histolytica putative ODC-like protein by metal affinity chromatography yielded ∼3–4 mg of pure protein from a 1-liter bacterial culture.
ODC activity was measured in the crude E. histolytica lysates and in the recombinant putative ODC-like protein. Detailed study was limited by its remarkable instability. Addition of dithiothreitol (2 mM DTT), a known stabilizer of mammalian ODC [43], to the purified enzyme samples did not improve the stability of the enzyme or its activity. However, we were able to measure the activity by adding 0.002% BRIJ-35 to the reaction mix. The activity obtained in the crude lysate was 4.8±0.8 nmol h−1 mg−1 protein, and the recombinant protein gave an activity of 1,311±7.0 nmol h−1 mg−1 protein (Table 1). Addition of DFMO (10 mM) to the recombinant ODC protein did not have any affect on the ODC activity (1,085±15 nmol h−1 mg−1 protein). The values obtained were not significantly different from that of the control with no DFMO. The Km value for the substrate ornithine was 1.5 mM. The activity obtained here for the recombinant protein was much lower than that reported earlier for the purified protein from E. histolytica [20]. Ammonium sulfate purification of the His-tagged recombinant ODC protein from E. histolytica did not improve the activity of this recombinant protein. Since we were not able to obtain higher Km values using ornithine as the substrate, we checked substrate preference. Decarboxylation of L-arginine and L-lysine was also measured in order to check the substrate preference of the recombinant protein. In the ODC assay we found no activity using arginine and lysine as the substrate.
Analysis of polyamine content of E. histolytica revealed substantial levels of putrescine (137±6.4 nmol/mg protein) compared to spermidine, which is present in very low amounts (6.9±0.2 nmol/mg protein). Spermine was not detected in the lysate (Table 1).
E. histolytica putative ODC-like protein is 413 amino acids long, with 32% sequence identity with the ODCs of T. brucei and H. sapiens. We looked for two important motifs in the sequence of ODC that are essential for binding of DFMO and PLP. The homologues of known three-dimensional structure complexed with DFMO show that the amino acid motif GPSCNGSD corresponds to the binding site for DFMO. DFMO is known to bind to cysteine in this motif. This cysteine is well conserved in the ODC from all three organisms (T. brucei, H. sapiens, and E. histolytica). Despite the presence of the cysteine, E. histolytica putative ODC is not inhibited by DFMO. Concentration of DFMO as high as 10 mM did not inhibit the enzyme activity in vitro. The lysine in the PCFAVKCN motif is an important residue for the interaction of PLP with ODC, which is well conserved in ODC from all three organisms discussed above. Structural analysis of the E. histolytica putative ODC is shown in Figure 5. Known three-dimensional structural analysis suggests that DFMO, which can inhibit T. brucei ODC, makes a hydrogen bond (H-bond) with three residues in the two chains of ODC. The nitrogen (ε) of DFMO forms an H-bond with aspartate-332, and water mediates H-bonds with aspartate-361 and tyrosine-323, both from the same chain (Figure 5A). All the three residues mentioned above with which DFMO is interacting are replaced in E. histolytica putative ODC. Tyrosine-323, aspartate-332, and aspartate-361 of the T. brucei ODC are substituted by histidine-296, phenylalanine-305, and asparagine-334 respectively in the E. histolytica putative ODC. The distance between the nitrogen atom (ε) of DFMO and asparagine-334 is more than 3.4Å in E. histolytica and hence unable to form an H-bond. These three residues have been labeled in the modeled structure (Figure 5B) of E. histolytica.
It should be noted that a deliberately unrealistic model of the E. histolytica putative ODC in complex with DFMO was generated in order to understand why DFMO does not bind to E. histolytica putative ODC. The overall conformation of modeled E. histolytica putative ODC in complex with DFMO and PLP is not very different from that of the T. brucei ODC. The site of PLP binding is fully conserved, thus E. histolytica putative ODC should be able to accommodate it. It is possible that the substitution of important interacting amino acids in E. histolytica putative ODC, makes DFMO unable to bind and hence unable to inhibit the action of E. histolytica putative ODC. However, this mechanism can be experimentally proved only by mutating these residues, namely histidine-296, phenylalanine-305, and ssparagine-334, respectively, and determining if the inhibition is restored.
The polyamines putrescine, spermidine, and spermine are polycationic organic compounds present in all eukaryotic cells, including parasitic protozoans. It was reported earlier that the polyamines are essential for the proliferation of normal cells and for differentiation [6].
Ornithine decarboxylase is the rate-limiting enzyme in the de novo synthesis of polyamines and it catalyses the decarboxylation of ornithine to putrescine and is a highly regulated enzyme. Interest in ODC has arisen mainly from the observation that the development of certain tumors closely correlates with increase in enzyme activity and that specific inhibitors of ODC reduce or stop these malignancies [13]. In addition, because of the critical role of ODC in growth and differentiation of the cells, it has been exploited as a target to control certain parasitic infections with specific inhibitors of ODC such as DFMO, a structural analog of ornithine that has been proved as an efficient therapeutic drug [21].
In this paper, we describe the molecular cloning and characterization of putative ODC-like gene of E. histolytica, a protozoan parasite known for causing amoebiasis. We cloned the putative ODC-like gene of E. histolytica (GenBank accession number AY929249), and there is only 33% identity to H. sapiens ODC. Comparison of the putative ODC-like protein sequence from E. histolytica with other eukaryotic species revealed conserved regions. The sequence PFYAVKCN, which resembles the consensus sequences of PXXAVKC(N), contains the lysine (K) to which the pyridoxal 5′ phosphate cofactor binds. Other highly conserved amino acid stretches, for example, FDCAS, EPGR, and FNGF, are also conserved, although their functional significance of these stretches is not known. Phylogenetic tree analysis showed a close evolutionary relationship of ODC of E. histolytica and the plant D. stramonium. However, comparison of E. histolytica putative ODC-like sequences with L. donovani and H. sapiens showed closer evolutionary relationship with L. donovani.
The recombinant putative ODC from E. histolytica was very unstable. Addition of 2 mM DTT to the enzyme samples did not improve the activity or stability of this enzyme. Earlier reports also show that purified ODC from trophozoites of E. histolytica lost most of the activity after 24 h in unfractionated samples and was reported to be very unstable [20]. In our hands even ammonium sulfate purification of the His-tagged recombinant putative ODC-like protein from E. histolytica did not improve the activity of this recombinant protein. The irreversible inhibitor DFMO (α-difluoromethylornithine) did not inhibit activity of E. histolytica recombinant ODC (data not shown). Similar observations made previously rules out the possibility of its being used as a suitable target for this enzyme [20].
It has been reported that purified preparations of E. histolytica ODC contain a major polypeptide band of 45 kDa and barely detectable amounts of two other proteins of 70 and 120 kDa. Both the 45 and the 70 kDa bands were recognized by a mouse anti-ODC monoclonal antibody [20]. However, in the present study, Western blot analysis of the whole cell lysates of E. histolytica using the polyclonal antibody against E. histolytica putative ODC-like enzyme showed a single band of approximately 46 kDa, and the same antibody recognized the recombinant protein of about 48 kDa, the expected size of the putative ODC-His tag fusion protein.
Analysis of polyamine content of E. histolytica revealed significant levels of putrescine compared to spermidine, which is present in very low amounts. Spermine was not detected in the lysates.
Computer modeling revealed that three of the critical residues required for binding of DFMO to the ODC enzyme are substituted in E. histolytica resulting in the likely loss of interactions between the enzyme and DFMO. These residues correspond to Tyrosine-323, Aspartate-332 and Aspartate-361 in T. brucei ODC homologue and these are substituted by histidine-296, phenylalanine-305, and asparagine-334 respectively in E. histolytica homologue. It is known that Asp-332 and Asp-361 are essential catalytic residues that interact with the substrate [44]. Several members of the ODC family are known to be found in the GenBank database with amino acid substitutions at the position of Asp-332 (D332E) [45]; however, our present study shows that amino acid substitution at Asp-361 (an active site) is unique to E. histolytica putative ODC. Asp-332 is highly conserved in the ODC family and is known to play an important role in substrate binding and catalysis. Shah et al. [45] reported that in Paramecium bursaria chlorella virus-1 ornithine decarboxylase (PBCV-1 DC) the equivalent position is residue 296, which is glutamate; according to the authors this substitution was a key determinant in the change in the substrate specificity from ornithine to arginine. This substitution (D332E) has also been observed in sequences of antizyme inhibitor, which is an inactive ODC homolog that regulates ODC activity (GenBank accession numbers: human, NP_680479; mouse, NP_06125; and rat, NP_072107) [46]. Furthermore, they investigated the impact of the active-site difference at position 332 on substrate specificity and mutated Asp332 (E296D). They reported that this substitution alone was insufficient to produce the observed substrate specificity change in PBCV-1 DC. In the present study we found a unique E. histolytica substitution in the putative ODC-like gene sequence both at Asp-332 and Asp-361; given that the amino acid changes affected the pocket of the E. histolytica enzyme, we wanted to know whether it was likely to cause a significant change in substrate specificity. We checked the activity of the recombinant protein using arginine and lysine as the substrate and found no enzyme activity. Kinetic analysis using ornithine as the substrate showed lower kinetic parameters compared to those reported for well-characterized enzymes from other organisms. It is possible that this enzyme has evolved a novel substrate specificity. In view of this situation we feel that this E. histolytica protein might have other functions, so far unidentified, including a regulatory role.
In conclusion, characterization of the E. histolytica putative ODC-like enzyme and expression of the protein will facilitate studies of structural and functional aspects of the enzyme and could prove to be an important anti-amoebic target. |
10.1371/journal.pmed.1002491 | Long-term trends in mortality and AIDS-defining events after combination ART initiation among children and adolescents with perinatal HIV infection in 17 middle- and high-income countries in Europe and Thailand: A cohort study | Published estimates of mortality and progression to AIDS as children with HIV approach adulthood are limited. We describe rates and risk factors for death and AIDS-defining events in children and adolescents after initiation of combination antiretroviral therapy (cART) in 17 middle- and high-income countries, including some in Western and Central Europe (W&CE), Eastern Europe (Russia and Ukraine), and Thailand.
Children with perinatal HIV aged <18 years initiating cART were followed until their 21st birthday, transfer to adult care, death, loss to follow-up, or last visit up until 31 December 2013. Rates of death and first AIDS-defining events were calculated. Baseline and time-updated risk factors for early/late (≤/>6 months of cART) death and progression to AIDS were assessed. Of 3,526 children included, 32% were from the United Kingdom or Ireland, 30% from elsewhere in W&CE, 18% from Russia or Ukraine, and 20% from Thailand. At cART initiation, median age was 5.2 (IQR 1.4–9.3) years; 35% of children aged <5 years had a CD4 lymphocyte percentage <15% in 1997–2003, which fell to 15% of children in 2011 onwards (p < 0.001). Similarly, 53% and 18% of children ≥5 years had a CD4 count <200 cells/mm3 in 1997–2003 and in 2011 onwards, respectively (p < 0.001). Median follow-up was 5.6 (2.9–8.7) years. Of 94 deaths and 237 first AIDS-defining events, 43 (46%) and 100 (42%) were within 6 months of initiating cART, respectively. Multivariable predictors of early death were: being in the first year of life; residence in Russia, Ukraine, or Thailand; AIDS at cART start; initiating cART on a nonnucleoside reverse transcriptase inhibitor (NNRTI)-based regimen; severe immune suppression; and low BMI-for-age z-score. Current severe immune suppression, low current BMI-for-age z-score, and current viral load >400 c/mL predicted late death. Predictors of early and late progression to AIDS were similar. Study limitations include incomplete recording of US Centers for Disease Control (CDC) disease stage B events and serious adverse events in some countries; events that were distributed over a long time period, and that we lacked power to analyse trends in patterns and causes of death over time.
In our study, 3,526 children and adolescents with perinatal HIV infection initiated antiretroviral therapy (ART) in countries in Europe and Thailand. We observed that over 40% of deaths occurred ≤6 months after cART initiation. Greater early mortality risk in infants, as compared to older children, and in Russia, Ukraine, or Thailand as compared to W&CE, raises concern. Current severe immune suppression, being underweight, and unsuppressed viral load were associated with a higher risk of death at >6 months after initiation of cART.
| Adolescence has been highlighted as an urgent global priority for health by a diverse range of stakeholders.
HIV/AIDS is one of the top 10 leading causes of death in adolescents globally.
Estimates of mortality and progression to AIDS as children approach adulthood are limited.
We investigated long-term risk of death and AIDS-defining events in children and adolescents with perinatal HIV across high- and middle-income country settings in Western and Central Europe, Eastern Europe (Russia, Ukraine), and Thailand.
In our study, in 3,526 patients across 17 countries, median age at cART initiation was 5.2 years, and median duration of follow-up was 5.6 years, with 9,228 person-years of follow-up in those aged ≥10 years and 20,574 person-years overall.
We found that there was a higher risk of mortality in very young children, in Eastern European countries and Thailand, and in patients with current severe immune suppression and low BMI.
We describe outcomes for some of the first generation of children to survive perinatal HIV and reach adolescence.
Our findings, together with other available evidence, suggest an ongoing need for intensive monitoring of very young children with HIV infection, those with severe immunosuppression, and those with a lower BMI than that expected for their age.
| Studies have reported declining mortality since the introduction of combination antiretroviral therapy (cART) about 20 years ago, both in adults and children [1–3]. For example, in the United Kingdom (UK) and Irish nationwide paediatric cohort, among 1,441 children with median age 9 years at last follow-up, mortality rates declined from 8.2 per 100 person-years before 1996 to 0.6 in 2003–2006 [1]. In an Italian multicentre study, survival of children with perinatal HIV significantly improved between 1996 and 1998, following the introduction of cART [4]. Similarly, in a United States cohort of 3,553 children with median age at enrolment of 6 years and median follow-up of 5 years, mortality rates declined from 7.2 to 0.8 per 100 person-years between 1994 and 2000 and remained relatively stable to the end of 2006 [2]. Mortality rates in adults have shown a similar trend, and life expectancy among those with a CD4 count ≥500 cells/mm3 is now thought to be close to that of the general population [5].
However, AIDS is now a top 10 leading cause of death in adolescents globally [6], and evidence suggests worsening outcomes as children with HIV grow up and transition to adult care. European data from individual cohorts suggest that young people with perinatal HIV infection have a higher risk of treatment failure [7–10], care disengagement [11], and death [12] compared to those who acquire HIV in adulthood and children with other routes of transmission. Thus, there is the need for vigilance to ensure that health outcomes in this population who have survived paediatric HIV are maximised as they enter adulthood.
Longitudinal cohort studies are well placed to measure changes in mortality rates over time. However, due to relatively limited numbers of children with perinatal HIV infection in some European countries [13], pooling of data across cohorts is the only practical way of obtaining reliable mortality rates across the European region. The European Pregnancy and Paediatric HIV Cohort Collaboration (EPPICC) is a network of 19 cohorts across 17 countries in Western and Central Europe (W&CE), Eastern Europe (Russia, Ukraine), and Asia (Thailand). In this study, data from these cohorts were used to describe rates and risk factors for mortality and AIDS-defining events in perinatally HIV-infected children and adolescents after initiating cART.
This study was carried out in accordance with the EPPICC Paediatric merger 2014 SOP and the project-specific Concept Sheet (S1 Text). Nineteen cohorts across 17 countries contributed to individual patient data (see S2 Text: “Writing Group members and collaborating cohorts” for list of collaborators). Children were included in this analysis if they had perinatal HIV infection and initiated cART (defined as a ≥3 drug, ≥2 class regimen [excluding unboosted PIs] or ≥3 non-nucleoside reverse transcriptase inhibitor [NNRTI]-only regimen, including abacavir) after 1996 (with no prior antiretroviral therapy [ART] use), aged <18 years. They were at risk from cART initiation until their censor date, defined as the earliest of 21st birthday, last visit in paediatric care, death, or loss to follow-up (as defined by each cohort), with data available until 31 December 2013. Demographic, clinical, laboratory, and treatment-related data from routine clinic visits (typically every 3–6 months) were pooled electronically using a modified HICDEP protocol (www.hicdep.org). Pooled data were subjected to a battery of consistency checks. Data on all children at participating clinics were included; data are pseudo-anonymised, and therefore individual informed consent was not obtained. All cohorts received approval from local and/or national ethical committees. For example, the UK/Ireland CHIPS cohort had approval from the London Central Research Ethics Committee.
Causes of death, along with CD4 and clinical event data, were reviewed by the Project Team and confirmed by the reporting clinician. Cause of death was coded using the International Classification of Diseases version 10 and categorised into 4 groups: HIV-related infectious causes, other HIV-related causes, deaths not directly related to HIV, and those with unknown cause. AIDS events were considered to be reliably reported, as they are an important clinical indicator of disease progression for children in routine care.
Rates of death and first AIDS-defining event were calculated per 100,000 person-years. Rates were summarised overall, within 6 months of ART initiation (‘early’; children were at risk from ART initiation to ART initiation plus 6 months), and after 6 months of ART (‘late’; at risk from ART initiation plus 6 months to their censor date), and rates of first AIDS-defining event were additionally summarised by event. Early deaths and AIDS-defining events were summarised separately, as they were likely to be related to late presentation/initiation of cART. AIDS-defining events were classified according to the US Centers for Disease Control and Prevention 2014 surveillance criteria [14]. Cohorts reported date of AIDS diagnosis as the date of the child’s earliest WHO stage 3/4 or US Centers for Disease Control (CDC) stage C event. Continuous variables were compared using Wilcoxon’s rank-sum test and categorical variables using a chi-squared test.
Risk factors for early death, late death, early first AIDS-defining event, and late first AIDS-defining event were assessed using univariable and multivariable proportional hazard models with inverse-probability-of-censoring weights. The probability of being censored was estimated using logistic regression (including all factors listed below) to account for informative censoring in those lost to follow-up. Weights were calculated as the reciprocal of these estimated probabilities. Association with the following factors at cART initiation were considered: age (continuous), sex, ethnicity (black African, Asian, other, unknown), year of birth (continuous), born abroad (versus in country of cohort), country group (Eastern Europe and Thailand [EE&T] [Russia/Ukraine/Thailand]) versus W&CE (all others), previous AIDS diagnosis (yes, no), year of cART initiation (continuous), initial regimen (NNRTI-based, other), World Health Organization (WHO) severe immune suppression for age (defined as a CD4 lymphocyte percentage [CD4%] <25% for children <1 year of age, <20% for children aged 1–3 years, <15% for children aged 3–5 years, and <200 cells/mm3 or <15% for children aged ≥5 years [15]), viral load (≤100,000 c/mL, >100,000 c/mL), and BMI-for-age z-score (>0, 0–−3, <−3, calculated using WHO child growth standards, 2007).
Time-updated factors considered for late death and first AIDS event were age, severe immune suppression for age, HIV viral load, BMI-for-age z-score, and proportion of time suppressed since cART initiation (≥80% versus <80%, as a proxy for adequate adherence). Values for time-updated factors based on clinical/laboratory measurements were considered to occur on the date recorded and were carried forward for up to 6 months (if no subsequent measurement was recorded), after which they were considered to be unknown. Individuals with missing data for any given factor were classified as missing, with a separate level of the variable (the missing indicator method).
Nonlinear effects of continuous variables were explored using natural cubic splines with 5 knots, and in final models, where appropriate, statistically significant nonlinearity was represented by piecewise linear functions. All characteristics (excluding those that were highly correlated) were included in a multivariable model. Covariate interactions between country group, calendar year of ART initiation, and any other statistically significant (p < 0.05) characteristics were considered and the proportional hazards assumption assessed.
BMI-for-age z-score was chosen rather than weight-for-age z-score, as WHO has normative data across all ages for BMI for age (WHO reference data for weight are only available for children aged <10 years), and to minimise differences due to variation in height for age between ethnic groups in the general population. A sensitivity analysis used weight-for-age z-score based on UK normative data for Western, Central, and Eastern European cohorts and Thai normative data for the Thai cohort. Analyses investigating rates and risk factors for early and late AIDS events separately were not prespecified but conducted following discussion of the results from a combined analysis investigating overall rates of AIDS events. Statistical analyses were performed using Stata version 14.2 (Stata Corporation, College Station, Texas). STROBE recommendations were followed (S1 STROBE Checklist).
Of 3,953 HIV-infected, ART-naïve children in EPPICC at the time of initiating cART, 3,526 (89%) acquired HIV perinatally and were eligible for this analysis; 1,124 (32%) were from the UK or Ireland, 700 (20%) from Thailand, 508 (14%) from Ukraine, and 1,194 (34%) from elsewhere across Europe (Table 1). The median age at cART initiation for the 3,526 participants was 5.2 (IQR 1.4–9.3) years, and the median year of cART initiation was 2006 (2003–2009). Median duration of follow-up was 5.6 (2.9–8.7) years, with 20,574 person-years of follow-up overall (of which 4,531 were of participants aged <5 years, 6,836 aged 5–<10 years, 6,577 aged 10–<15 years, 2,139 aged 15–<18 years, and 512 aged ≥18 years).
At cART initiation, 1,491/2,719 (55%) children overall had severe immune suppression for their age (466/555 [84%] for Thailand) and 663 (19%) had a previous AIDS diagnosis. The proportion of children aged <5 years at ART initiation who had a CD4% less than 15% declined over the study period, from 35% (134/381) in 1997–2003 to 15% (24/157) from 2011 onwards (p < 0.001) (Fig 1), and similarly, the proportion aged ≥5 years with a CD4 count <200 cells/mm3 at cART initiation declined from 53% (194/365) to 18% (36/196) (p < 0.001).
By the end of the study period, 94 (3%) children had died, 453 (13%) had been lost to follow-up, 305 (9%) had transferred to adult care, 314 (9%) had dropped out for other reasons (including having moved to another clinic or withdrawn consent), and 2,322 (66%) were still in follow-up. Among those still in paediatric care, the median age at last visit was 11.6 (7.6–15.0) years.
There were 94 deaths, of which 43 (46%) were within 6 months of cART initiation. Among the early deaths, 37% (16/43) occurred within 1 month and 79% (34/43) within 3 months of cART start. The crude mortality rate was 2,502 (95% CI 1,856–3,374) per 100,000 person-years in the first 6 months of treatment and 270 (206–356) thereafter. Fig 2 shows the decreasing risk of death after the first 6 months of cART, in all calendar periods and country groups. The probability of survival to 5 years after initiating cART was 97.6% (97.0%–98.1%) overall and improved with calendar period from 96.2% (94.8%–97.2%) in 1997–2004 to 98.4% (97.2%–99.1%) for 2008 onwards. Overall, it was 98.7% (98.0%–99.1%) in W&CE countries and 95.8% (94.4%–96.8%) in EE&T countries. The mortality rate was highest in earlier calendar years, peaking at 1,773 (95% CI 1,030–3,054) per 100,000 person-years in 2003 and decreasing to 360 in 2006, after which it was relatively stable at between 122 and 435 cases per 100,000 person-years (Fig 3), with a similar trend for deaths both before and after 6 months of cART (S1 Fig). The peak in mortality rate in 2003 coincided with the introduction of cART in Thailand, and Thailand accounted for over half of the deaths in the periods for both early and late deaths (Table 1).
The median age at death was 9.5 (IQR 3.3–13.9) years; those who died within 6 months of cART initiation were younger than those who died after 6 months (median age 6.5 versus 12.8, p < 0.001). Among those who died after 6 months of cART, the median (IQR) viral load was 9,393 (400–107,654), CD4% was 6% (2%–24%), and CD4 count (among children aged ≥5 years, n = 26) was 68 (9–304) cells/mm3. Twelve (24%) children with late deaths were not taking cART at the time of death, and a further 4 (8%) had a treatment interruption of >30 days in the 6 months prior to death.
Overall, 58 (64%) deaths were due to HIV-related infections, predominantly bacterial infection and/or sepsis (Table 2), and these accounted for 76% (31/41 with cause available) of early deaths and 55% (27/49) of late deaths (p = 0.043). Twenty-four deaths (27%) were from other HIV-related causes (22% versus 31% early versus late, p = 0.355), 8 (9%) were not directly related to HIV (2% versus 14%, p = 0.049), and 4 causes were unknown. Over the calendar periods 2000–2004, 2005–2008, and 2009 onwards, the proportions of deaths caused by HIV-related infections (19/30: 63%, 21/29: 72%, and 18/31: 58%, respectively) and other HIV-related causes (10/30: 33%, 3/29: 10%, and 11/31: 35%, respectively) did not significantly change (p = 0.504 and p = 0.053, respectively). Overall, 45 (50%) deaths were AIDS related (59% of early deaths versus 43% later, p = 0.138), with no discernible trend by calendar year period (8/30: 27%, 15/29: 52%, 11/31: 35%) or country group (EE&T: 30/59, 51%; W&CE: 15/31, 48%; p = 0.824). Twenty-five (27%) children who died did not have an AIDS-defining illness at the time of their death; their most common cause of death was bacterial infection/sepsis.
Of the 51 late deaths, 21 (41%) were in adolescents aged ≥14 years. Median ages at HIV diagnosis and cART initiation of these 21 children were 9.9 (IQR 8.5–11.3) and 10.8 (8.7–11.9) years, respectively, and median duration of cART was 6.2 (4.5–7.6) years. Thirteen (62%) of these deaths were due to an HIV-related infection, 6 to other HIV-related causes, and 2 were unrelated to HIV.
In multivariable analysis, being in the first year of life, EE&T country group, AIDS diagnosis at cART initiation, initiating cART on an NNRTI-based regimen (versus any other), severe immune suppression for age, and low BMI-for-age z-score were associated with increased risk of early death (Table 3). Sensitivity analysis replacing BMI-for-age z-score with weight-for-age z-score resulted in similar estimates (S1 Table).
Rates and risk factors for death after 6 months of cART are shown in Table 4. In multivariable analysis, no baseline characteristics were strongly associated with higher risk of late death. Time-updated characteristics associated with higher risk of death were severe immune suppression for age, current viral load >400 c/mL, and low current BMI-for-age z-score. There was a nonsignificantly elevated risk of late death in EE&T (p = 0.073). After adjustment for other factors, year of cART initiation was no longer associated with early or late death.
In the sensitivity analysis, the model with weight-for-age z-score was broadly similar to the BMI model (S2 Table), but the effect of weight-for-age at ART start was attenuated (>0 adjusted hazard ratio [aHR] 0.57 [0.11–2.88], <−3 aHR 1.07 [0.32–3.63], unknown aHR 1.21 [0.46–3.15] versus −3–0, p = 0.875).
Among those without an AIDS diagnosis by the start of cART (n = 2,863), 278 AIDS-defining events were reported in 237 children: 113 (41%) were from Thailand, 75 (27%) in the UK or Ireland, 48 (17%) in Russia or Ukraine, and 42 (15%) in the rest of W&CE. The overall rate of first AIDS-defining event was 1,549 (95% CI 1,364–1,760) per 100,000 person-years and was higher in the first 6 months of cART (7,305 [6,005–8,887]) compared to later (984 [832–1,163], p < 0.001). The most commonly reported AIDS events were encephalopathy (38 events in 38 children, 14%), tuberculosis (TB)(34 events in 32 children, 12%), and wasting syndrome (31 events in 30 children, 11%) (Table 5).
Among the 663 children with an AIDS diagnosis prior to cART initiation, there were a further 80 AIDS-defining events after initiation of cART in 66 (10%) children. The most common events were encephalopathy (21 events in 21 children, 26%), multiple/recurrent bacterial infections (11 events in 10 children, 14%), and TB (11 events in 8 children, 14%); 61 events had a missing cause.
In multivariable analysis, factors associated with increased risk of the first AIDS-defining event within 6 months of cART initiation were severe immunosuppression and lower BMI-for-age z-score (<−3 versus 0–−3) at cART initiation (S3 Table). Factors associated with increased risk after 6 months of cART were earlier year of cART initiation, initiating cART on an NNRTI-based regimen (versus any other), higher viral load at cART initiation, severe current immune suppression for age for those in EE&T, current viral load >400 c/mL, and lower current BMI-for-age z-score (S4 Table).
Our study included more than 3,500 children and young adults with HIV infection followed across 16 Western, Central, and Eastern European countries and Thailand. Median age at cART initiation was 5 years and median follow-up 5 years, with a quarter of participants being followed for ≥9 years. The median year of birth was 2000, with a quarter being born before 1997; thus, many children were born before the introduction of cART, and outcomes in older children who survived reflect the first cohort of patients with perinatal HIV progressing to adult life [13]. Nearly half of all deaths (46%) and 42% of first AIDS-defining events were within 6 months of cART start in our study. We found that multivariable predictors of early death were very young age, residence in Eastern European countries and Thailand, AIDS diagnosis at cART initiation, initiating cART on an NNRTI-based regimen, severe immune suppression for age, and low BMI-for-age z-score at cART initiation. Time-updated (current) severe immune suppression for age and low BMI-for-age z-score were also associated with late death, as was current viral load >400 c/mL.
Our results confirm that mortality rates in children starting treatment across the European region and Thailand fell markedly since the introduction of cART in 1996 but also suggest that they have remained stable since 2006, at 122 and 435 deaths per 100,000 person-years, respectively. Our 5-year survival probability after initiating cART was 97.6% overall, and was higher in W&CE countries than EE&T. These findings are consistent with results from other studies. For example, a study of HIV-infected youth in the US reported mortality rates of 660 per 100,000 person years (PY) for the period 2008–2014 and a 5-year survival probability of 97.6% [16], and another US study reported survival probabilities of 76% at 6 years for those born in 1991–1996 and exposed to mono or dual therapy, and 91% at 6 years for those born in 1997–2004 and exposed to cART [3].
Interestingly, our results suggest that mortality rates (and also incidence of first AIDS events) in children initiating cART in the European region and Thailand have not declined since 2006. It is encouraging that the proportion of children presenting late decreased over time, but from 2011 onwards, 1 in 7 children aged <5 years and 1 in 5 children aged ≥5 years at cART initiation still presented with CD4% < 15 or CD4 count <200 cells/mm3, despite the CD4 threshold for initiating treatment increasing over time [17]. This highlights the ongoing importance of identifying and testing children most at risk of HIV. Enhanced prophylaxis against infections has been shown to reduce the risk of early death in adults and children with advanced disease in low-income countries [18] and may be relevant in our high-income setting, especially as a third of patients in our study were born abroad, many in sub-Saharan Africa. Further work is needed to ascertain possible contributing factors, such as late HIV diagnosis, suboptimal immune reconstitution despite cART, late access to cART, nonadherence, ART toxicity, and socioeconomic factors. Our results suggest that mortality rates in HIV-infected children initiating cART in the European region are 3–12 times higher than in the general population, as the mortality rate for 0–14-year-olds in 27 countries in the European Union (EU) in 2013 was 35 per 100,000 person-years [19]. Similarly, higher rates of mortality have been found in perinatally HIV-infected older youth compared to the general population in the US [20].
Almost half of all deaths (46%) occurred in the first 6 months of cART, with the majority already having an AIDS diagnosis by the time of cART initiation. Indeed, AIDS may have been an indication for initiating treatment at the time, and with guidelines now recommending universal treatment for HIV [21], it is unlikely that we would observe so many deaths now, with a higher proportion of children being diagnosed earlier (when asymptomatic), through screening. The Therapeutic Research, Education and AIDS Training (TREAT) Asia Pediatric HIV Observational Database (TApHOD) cohort of children in Asia-Pacific found similar results [22], and very young age has been found to predict mortality in other studies [23–26]. In our study, three-quarters of early deaths were due to HIV-related infections, with bacterial/sepsis-related causes being the most common, whilst 22% were due to other HIV-related causes, and only 1 death had a cause not directly related to HIV. Some of these early deaths may have been associated with immune reconstitution inflammatory syndrome [27,28]. The causes of early death contrast with the deaths after 6 months, of which 14% were not directly related to HIV. The trend in causes of death (i.e., HIV-related infections, other HIV-related causes, and not HIV related) did not change over calendar periods, although we had limited power for this. Similarly, we did not detect a decline over the calendar period in the proportion of deaths attributed to AIDS, in contrast to other studies [2,3]. One study of adults with HIV found different trends in the proportion of deaths due to AIDS by region across Europe and Argentina [29], with higher risk of mortality from AIDS-related causes in Eastern Europe and higher non-AIDS mortality in northern Europe.
The strongest predictors of mortality in our analysis were having an AIDS diagnosis at cART initiation, current severe immune suppression for age, and being underweight. These are known risk factors that have been consistently reported in many earlier HIV cohorts [3,20,22]. As expected, children in W&CE countries had a lower risk of death within the first 6 months of cART initiation, perhaps due to better access to medical care, a higher standard of clinical care, higher nutritional status, and better tolerated combinations of cART. One key component of clinical care is vaccination against infectious diseases among children with HIV, and this remains an area in which improvements can be made [30]. Of concern, our findings indicate a raised mortality risk in the first year of life, independent of AIDS diagnosis and immune suppression for age, similar to other study findings [31,32]. This suggests the need for continued vigilance in this group.
Among those who did not have AIDS prior to initiating treatment, 6% of children had a subsequent AIDS-defining event, with the most commonly reported events being encephalopathy, TB, and wasting syndrome. Predictors of first AIDS-defining event were similar to those found for death.
Our study has a number of limitations. We analysed observational data from several cohorts across the European region and Thailand, but events were distributed over a long time period and we lacked statistical power to investigate trends over calendar time of patterns and causes of death in some analyses. Data were not collected in a standardised way across cohorts, but we took time to verify causes of death with reporting clinicians, and each cohort had their own system for data validation and querying. We were unable to analyse data on serious adverse events and CDC disease stage B events, as these were incompletely collected across cohorts. For a small number of children with an AIDS event, information on the type of AIDS event was not available. Around half (52%) of patients came from the UK/Ireland or Thailand, but these countries accounted for 81% of the deaths observed. Conversely, 6 (38%) of the 16 countries that observed no deaths at all (Germany, Greece, the Netherlands, Portugal, Romania, and Sweden) only accounted for 9% of patients.
In conclusion, in our study of more than 3,500 children across the European region and Thailand, mortality rates fell after the introduction of cART in 1997 but have remained stable since 2006, and the prevalence of low CD4 at initiation of cART decreased over the period. Five-year survival probability after initiating cART across the whole period studied was 97.6%. Almost half (46%) of the 94 deaths observed were in the first 6 months of cART, suggesting that close clinical follow-up is recommended over the first 6 months after cART initiation, with enhanced prophylaxis against infections in those presenting with advanced disease. The indication of raised early mortality risk in infants and those in EE&T raises concern and warrants vigilance. It highlights the need to direct additional clinical resources to the care of these groups, as well as further prospective studies evaluating morbidity and mortality in older adolescents.
|
10.1371/journal.pgen.1000216 | A Position Effect on the Heritability of Epigenetic Silencing | In animals and yeast, position effects have been well documented. In animals, the best example of this process is Position Effect Variegation (PEV) in Drosophila melanogaster. In PEV, when genes are moved into close proximity to constitutive heterochromatin, their expression can become unstable, resulting in variegated patches of gene expression. This process is regulated by a variety of proteins implicated in both chromatin remodeling and RNAi-based silencing. A similar phenomenon is observed when transgenes are inserted into heterochromatic regions in fission yeast. In contrast, there are few examples of position effects in plants, and there are no documented examples in either plants or animals for positions that are associated with the reversal of previously established silenced states. MuDR transposons in maize can be heritably silenced by a naturally occurring rearranged version of MuDR. This element, Muk, produces a long hairpin RNA molecule that can trigger DNA methylation and heritable silencing of one or many MuDR elements. In most cases, MuDR elements remain inactive even after Muk segregates away. Thus, Muk-induced silencing involves a directed and heritable change in gene activity in the absence of changes in DNA sequence. Using classical genetic analysis, we have identified an exceptional position at which MuDR element silencing is unstable. Muk effectively silences the MuDR element at this position. However, after Muk is segregated away, element activity is restored. This restoration is accompanied by a reversal of DNA methylation. To our knowledge, this is the first documented example of a position effect that is associated with the reversal of epigenetic silencing. This observation suggests that there are cis-acting sequences that alter the propensity of an epigenetically silenced gene to remain inactive. This raises the interesting possibility that an important feature of local chromatin environments may be the capacity to erase previously established epigenetic marks.
| Epigenetics involves the heritable alteration of gene activity without changes in DNA sequence. Although clearly a repository for heritable information, what makes epigenetic states distinct is that they are far more labile than those associated with DNA sequence. The epigenetic landscape of eukaryotic genomes is far from uniform. Vast stretches of them are effectively epigenetically silenced, while other regions are largely active. The experiments described here suggest that the propensity to maintain heritable epigenetic states can vary depending on position within the genome. Because transposable elements, or transposons, move from place to place within the genome, they make an ideal probe for differences in epigenetic states at various positions. Our model system uses a single transposon, MuDR in maize, and a variant of MuDR, Mu killer (Muk). When MuDR and Muk are combined genetically, MuDR elements become epigenetically silenced, and they generally remain so even after Muk is lost in subsequent generations. However, we have identified a particular position at which the MuDR element reactivates after Muk is lost. These data show that there are some parts of the maize genome that are either competent to erase epigenetic silencing or are incapable of maintaining it. These results suggest that erasure of heritable information may be an important component of epigenetic regulation.
| Whether or not a gene is expressed can depend as much on its location within the genome as its primary DNA sequence. Although proximity to enhancers and suppressors outside the core promoter can affect gene expression, the most dramatic position effects often involve epigenetic silencing of genes placed in proximity to inactive or heterochromatic regions of the genome. In animals, the best example of this process is Position Effect Variegation (PEV) in Drosophila melanogaster [1],[2]. In PEV, when genes are moved into close proximity to constitutive heterochromatin, their activity can become unstable, resulting in variegated patches of gene expression. This process is regulated by a variety of proteins implicated in both chromatin remodeling [3]–[5] and RNAi-based silencing [6]. PEV appears to be the result of the spreading of a compacted chromatin state from heterochromatin to adjacent genes. Given that heterochromatin is largely composed of transposable elements, PEV can be seen as a breakdown in the normal process by which transposable elements and host genes are effectively sequestered from each other. The spread of heterochromatin can be blocked by insulating sites, such as those bound by Suppressor of Hairy-wing [7],[8] and GAGA factor [9],[10]. These proteins are competent to alter the silenced state by actively remodeling chromatin. Interestingly, some of the same proteins, such as GAGA factor, are also involved in the epigenetic regulation of homeobox genes during Drosophila development. These observations suggest that the process by which transposable elements are sequestered from the rest of the genome may have been recruited to regulate host gene expression as well.
Phenomena similar to PEV have also been observed in Schizosaccharomyces pombe. In this case, transgenes integrated into centromeric heterochromatin or silent mating type loci become silenced [1]. Many of the proteins that have been identified that influence this process are conserved among eukaryotes [11], and can affect gene silencing in species as diverse as Arabidopsis thaliana [12] and humans [13]. A number of proteins that influence centromeric silencing in S. pombe have orthologs in Drosophila that regulate PEV [11]. Thus, there are clear and consistent relationships between position effects, chromatin structure and epigenetic silencing.
Although a great deal is known about position effects in Drosophila and fission yeast, very little is known about it in plants. Indeed, there is conflicting evidence as to whether or not they exist at all in plants [14]–[16]. Certainly, transgenes equipped with minimal promoters can respond to local tissue-specific enhancers [17], but position-specific effects on the epigenetic state of genes, such as has been observed in Drosophila and yeast, have not been well documented. In plants, variations in expression of transgenes at various locations have been interpreted as “position effects”. However, the stochastic nature of transgene silencing, variations in copy number and sequence of integrated transgenes and sporadic tissue-culture induced epigenetic variation make interpretation of these experiments difficult.
Ideally, to prove a position effect, the effect should be reversible due to subsequent changes in position. Since transposable elements are mobile, they represent an ideal model for understanding the role of position in gene activity. Among transposable elements, the Mutator (Mu) transposons in maize are particularly useful because they transpose at a high frequency and can be epigenetically silenced in a controlled fashion [18]. Mutator is the most active known plant transposon. In Mu-active lines, Mu elements can duplicate at a 100% frequency; every element makes an average of one duplication every generation [19]. Insertions are into unlinked sites, and the overall mutation frequency in an active line can exceed 50 times that of background [20]. The system is regulated by MuDR elements, which carry two genes: mudrA and mudrB [18]. These genes encode MURA, the putative transposase, and MURB, a helper protein of unknown function. We have derived a minimal version of this transposon system, that includes a single active MuDR element and a single non-autonomous reporter element inserted into a color gene [21]. In the presence of an active MuDR element, the non-autonomous element excises from the color gene during somatic development, resulting in small sectors of revertant tissue. Unlike higher copy number Mu lines, the minimal line does not undergo spontaneous silencing. However, a single derivative of MuDR arose in the minimal line that can heritably silence one or many MuDR elements [22]. This derivative, called Mu killer (Muk), contains a portion of MuDR that has been duplicated and inverted. The Muk transcript forms a perfect 2.4 kb hairpin RNA, which is processed into 26 nt siRNAs [23]. These siRNAs trigger rapid degradation of the mudrA transcript, as well as methylation of the terminal inverted repeats (TIRs) and transcriptional silencing of one or many MuDR elements. After exposure to Muk, MuDR elements generally remain heritably and stably silenced even in the absence of Muk. The availability of the Muk locus has made it possible to target MuDR elements for heritable epigenetic silencing in a controlled and reproducible fashion by making the appropriate genetic crosses.
The minimal Mutator line began with a single active MuDR element that can move from place to place in the genome. It was therefore possible to examine the effects of Muk on duplicate copies of the same MuDR element at various positions. Given that Muk-mediated silencing of MuDR involves trans-acting siRNAs, it seemed likely that, regardless of position, all MuDR elements would be silenced in the presence of Muk. In fact, we have found that silencing is particularly effective when multiple MuDR elements are present (Slotkin and Lisch, unpublished data). However, it was also possible that the degree to which individual elements would remain heritably silenced in the absence of Muk might vary depending on the local context. A screen was developed that made it possible to isolate individual duplications of a single active MuDR element, expose them to Muk, and observe the degree of heritable activity in progeny plants that carried the transposed copies of MuDR but that lacked Muk. This screen lead to the identification of a MuDR element at a specific chromosomal location that failed to maintain a heritable silenced state. We suggest that this phenomenon represents the converse of PEV, in that cis acting sequences in this case are responsible for reversing, as opposed to triggering, epigenetic silencing. The existence of such a locus suggests that an important feature of the epigenome may be the capacity to reverse epigenetic silencing.
All MuDR elements described in this manuscript were derived from a single MuDR element that had been genetically isolated and cloned previously [21]. We have found that there are variations in duplication frequency and somatic activity depending on the position of transposed copies of this element [24]. Therefore, the elements at various positions are given distinct position numbers, indicated by parentheses. Thus, the original element is designated MuDR(p1) and duplicates are given new position numbers as they are characterized.
The derivation of all families described in this manuscript is shown in Figure 1. This diagram follows standard conventions. Females are on the left and males on the right of the “x”. Unlinked loci are separated by a semicolon. MuDR elements at each position (designated “p4” or “p5”) are hemizygous for the insertion. All MuDR elements described here are derived from a single MuDR element originally present in the minimal Mutator line. The derivation of the minimal line, containing a single MuDR element and a single Mu1 element inserted into the a1-mum2 allele of the A1 gene was described in Chomet et al. [21]. In the presence of active MuDR elements, Mu1 excises from the a1-mum2 allele, resulting in characteristically small revertant sectors (spots). These sectors are most readily visualized in the outer layer of the kernel (the aleurone). In the absence of MuDR, the reporter element remains inserted in the A1 gene, and the kernels are uniformly colorless, or pale. The a1-mum2 allele has the additional advantage of being suppressible in the adult tissues (but not in the kernel). In the adult tissues, expression of a functional gene product from a1-mum2 is prevented by the presence of MuDR transposase (MURA), except when Mu1 excises from the allele. This results in characteristically small red (revertant) spots of color on a green (suppressed) background. When the transposase is lost, the adult tissue is uniformly red because the a1-mum2 allele expresses in its absence [21]. This characteristic makes it possible to assay for transposase activity in mature plant tissue. In contrast, the aleurone layer of the kernels, a1-mum2 is not suppressible. Thus, in the absence of the transposase, the kernels are uniformly pale, as can be seen in Figure 2A. All individuals described in this work were homozygous for the a1-mum2 reporter allele. All crosses designated as “test crosses” represent crosses to the a1-mum2 tester, which lacks both functional MuDR elements and Mu killer. The genetic isolation, characterization and cloning of Muk was described in Slotkin et al. [22],[23]. Genetic isolation and characterization of MuDR(p3) was described in Lisch et al. [24]. MuDR(p3) causes a distinctively low frequency of somatic excisions of Mu1 from a1-mum2 in the aleurone of the kernel. When MuDR(p3) transposes to a new position, somatic excision returns to a more typical frequency. Thus, germinally transmitted transpositions of MuDR from position 3 to a more typical position can be detected as heavily spotted kernels in a family segregating for weakly spotted kernels. With respect to the crosses of Muk to plants carrying MuDR(p3), previous work has demonstrated that, when Muk is used as a male parent there is little or no effect on excision of the reporter Mu1 element in the F1 aleurone, but a strong effect on MuDR elements in the F1 embryo and adult plant tissue [22]. Thus, transposed copies of MuDR(p3) can be easily detected as individual kernels with a high frequency of somatic excision of the reporter element in the aleurone, even when exposed to Muk derived from the male parent.
DNA extraction and Southern blotting was as previously described [24]. Briefly, 10 micrograms of DNA was digested with a four-fold excess of restriction enzyme for a minimum of 2 hours, blotted and probed with a series of Mu-specific DNA fragments. Probes: The location of restriction enzyme sites and probes used are illustrated in Figure 3. The probes used to detect MuDR internal sequences (probes A and B) were as described in Slotkin et. al. [22]. The probed used for Mu1 (probe C) was as described in Chomet et al. [21]. The probe for the MuDR TIR was generated by amplifying genomic DNA with primers TIRAF (GAGATAATTGCCATTATAGACGAAG) and TIRAR (AGGAGAGACGGTGACAAGAGGAGTA), which generates a fragment of 219 bp that includes the entire TIR (TIRA) flanking the mudrA gene of MuDR.
Active MuDR elements, regardless of their position would be expected to yield a fragment of 445 bp when digested with HinfI. This size is consistent with a lack of methylation of both the HinfI site within the TIR adjoining mudrA (TIRA) of MuDR elements and of a second site within the first intron of mudrA. Methylation of the TIR HinfI site of TIRA of MuDR elements will yield larger fragments whose size depends on the MuDR insertion sites. Based on the sequence of DNA flanking MuDR(p4) and MuDR(p5), if the TIR HinfI site (but not the internal HinfI site) is methylated the expected fragment sizes are 648 bp and 1003 bp respectively. Similarly, the expected fragment size if the TIR HinfI site is methylated in Mu killer is 500 bp. In each case the expected fragment sizes were observed (Figure 4B).
Hypomethylation of Mu1 HinfI sites has proved to be a highly reliable indicator of MURA activity in our lines; the loss of mudrA transcript is invariably associated with methylation of this site [21],[24]. Methylation of Mu1 elements was examined using HinfI digests probed with an internal fragment of Mu1, as described in Chomet et al. [21]. An unmethylated Mu1 element at a1-mum2 is expected to give a fragment size of 1.3 kb; a methylated Mu1 element at this locus gives a fragment size of 2.1 kb. In all cases, complete digestion of the DNA was confirmed by examination of the ethidium-stained gel.
To determine if full-length MuDR elements were present, SacI was used. MuDR elements have two SacI sites in the terminal inverted repeats (Figure 3). Digestion with this enzyme results in a diagnostic 4.7 kb fragment regardless of chromosomal position. The intensity of this fragment reflects the copy number of the element [24]. To detect transposed copies of MuDR, DNA samples were digested with EcoRI (Figure 2B) or XhoI (Figure 3B). These enzymes cut once within MuDR. Therefore, elements at various positions will give rise to unique fragment sizes.
Cloning of these elements was achieved using inverse PCR. Southern blot analysis had revealed that digestion of samples containing these elements with XhoI yielded MuDR terminal inverted repeat (TIR)-hybridizing fragments of 2.6 and 2.4 kb for MuDR(p4) and MuDR(p5) respectively (Figure 2). 10 micrograms of DNA containing one or the other element was digested with a four-fold excess of XhoI for 4 hours in a total volume of 20 microliters. The reaction was placed at 65 degrees C for 15 minutes, to heat inactivate the restriction enzyme. Two microliters of the reaction was then added to 1 microliter of DNA ligase, two microliters of ligase buffer and 15 microliters of water, and the resulting mixture was incubated for 2 hours at 25 degrees C. The reaction was then heat inactivated for 15 minutes at 65 degrees C. Two microliters of this reaction was then subjected to PCR amplification using primers specific to the MuDR TIR (TIRout: GCTGTCACCTTTCTGTTTTGGCGAT) and a MuDR internal sequence flanking the XhoI site (exon3R: CTAGCTCTTGTTCAGTGACTTCC). These amplifications yielded products of 700 bp and 520 bp for samples containing MuDR(p4) and MuDR(p5) respectively, the expected sizes for these elements based on the XhoI restriction mapping data. Both strands of the PCR products were then sequenced using an ABI sequencer (Applied Biosystems). The sequences of MuDR obtained were identical to known MuDR sequences. The flanking sequences were used to design primers facing inwards towards the MuDR elements. These primers in combination with MuDR TIR primers were used to confirm that we had indeed cloned the elements. Flanking primers were used in combination with TIR-specific primers on DNA samples of plants segregating for each element. For MuDR(p5), primer p5flnkB (CGATTAAGCGCGACGAACACG) was used in combination with RLTIR2 (ATGTCGACCCCTAGAGCA). In a family segregating for MuDR(p5) and MuDR(p4), these primers gave a product of 408 bp in three of three plants carrying only MuDR(p5) and zero of three plants carrying only MuDR(p4). To obtain sequences on the other side of the insertion, the available flanking sequences were used to search DNA databases for maize sequence matches. Perfect matches were used to extend the sequence, which were then used to design primers that would be expected to amplify when used in combination with a MuDR TIR primer. Primer p5flnkA (GGAGCGTGACAGGGGCGGCAGAT) was used with primer TIRAR (AGGAGAGACGGTGACAAGAGGAGTA). The same samples that yielded a product with the p5flnkB/RLTIR2 combination also yielded the expected 405 bp product. When the sequences of the DNA flanking the insertion were compared, they revealed the presence of a 9 bp target site duplication (GGCGTGCGC) diagnostic for Mu insertions. The strategy to confirm the MuDR(p4) was similar. The available sequence was used to design a flanking primer, p4flnkB (CGTGAAAGGTGGAGACTACTGGAA), which was used in combination with the MuDR TIRAR primer. A product of the expected size of 320 bp cosegregated with the presence of MuDR(p4), confirming that we had also cloned sequences flanking MuDR(p4).
In order to screen for new insertions of single MuDR elements, we made use of a MuDR element that exhibits a position effect that results in reduced somatic excision of non-autonomous reporter element from a color gene in the aleurone (Figure 2A)(for alleles and stock construction see Materials and Methods). This effect on somatic excisions of the reporter is fully reversible; when MuDR(p3) transposes to a new position, the high frequency of excision and transposition more typical for MuDR are restored [24]. The advantage of using MuDR(p3) is that, in a family of kernels segregating for this element, new insertions of MuDR(p3) can be readily visualized as individual kernels exhibiting a high frequency of excisions. It should be emphasized that when Muk is introduced through the male lineage, it has no immediate effect on the F1aleurone, but it has a strong effect on the F1 embryo and the resulting plant [22]. Thus, individual kernels that inherit a transposed copy of MuDR(p3) would be expected to exhibit a high frequency of excision, even in the presence of Muk, but plants grown from those kernels would be expected to show reduced or absent MuDR activity.
To perform the screen, one ear of a plant carrying MuDR(p3) was crossed to the a1-mum2 tester (the control cross), and a second ear from the same plant was crossed to a plant that was homozygous for Muk. These and subsequent crosses are portrayed in Figure 1. DNA from plants grown from weakly spotted and pale (non-spotted) sibling kernels derived from the control cross were examined by Southern blot for the presence of MuDR(p3). As expected, all progeny plants grown from weakly spotted kernels carried the diagnostic 6.8 kb MuDR(p3) EcoRI fragment (Figure 2B, lanes 1–4). The other fragments hybridizing to this probe are inactive MuDR homologs (hMuDRs) that do not positively or negatively affect Mu activity in this line [24],[25]. Methylation of Mu1 at a1-mum2 was also assayed because Mu1 methylation has proved to be a highly reliable indicator of MuDR activity. The Mu1 elements in the individuals carrying MuDR(p3) were unmethylated due to the presence of the MuDR(p3)-derived transposase (Figure 2C, lanes 1–4). Sibling plants grown from non-spotted kernels that did not inherit MuDR(p3) (lanes 5–9) carried methylated Mu1 elements, a consequence of the absence of a functional MuDR element.
In the experimental cross (MuDR(p3)/- x Muk/Muk), only plants grown from heavily spotted kernels (which were expected to contain duplicate copies of MuDR(p3)), were examined (Figure 2A, lanes 10–14). In each case, an EcoRI digest revealed that plants grown from these kernels contained at least one new MuDR insertion (red arrows in Figure 2B). Mu elements transpose duplicatively in the germinal lineage [24]. Therefore, the absence of MuDR(p3) in plants grown from some of the heavily spotted kernels was not due to germinally transmitted excisions of MuDR(p3). Mu elements do, however, often transpose just prior to meiosis. Thus some of these plants carried MuDR(p3), while others carried only transposed copies of that element due to independent assortment of the donor and transposed elements. Previous work in our laboratory has demonstrated that although Muk has no effect on MuDR activity in the aleurone if Muk is introduced via the male parent, it has a strong effect on MuDR activity in the F1 embryo and plant [22]. This was observed in the plants grown from the heavily spotted kernels that carried transposed copies of MuDR(p3). Each plant contained Mu1 elements that were methylated, consistent with the loss of transposase in these plants due to the activity of Muk [22] (Figure 2C, lanes 10–14). As described in Materials and Methods, the a1-mum2 allele is suppressible in the adult plant tissue, resulting in red plants in the absence of MuDR activity and green plants with small revertant sectors in its presence [21]. This made it possible to monitor activity by observing plant color. All of the plants carrying Muk in this experiment were red, consistent with the loss of MuDR activity. We conclude from this experiment that each of these plants contained at least one newly transposed copy of MuDR(p3), and that Muk was efficiently silencing all of these elements.
To test for heritability of silencing, each plant carrying a transposed copy of MuDR(p3) was crossed as a female to the a1-mum2 tester. Typically, the ears resulting from a cross of a plant carrying both Muk and one or many MuDR will exhibit a low frequency of spotted progeny kernels, most of which are only weakly spotted [22]. This was true for three of the five individuals examined, and these results are consistent with heritable silencing of transposed MuDR elements in these plants. A fourth plant gave rise to a higher overall percent of spotted progeny (37%), but these kernels were uniformly weakly spotted, and this family was not examined further. In contrast, one plant gave rise to an ear with an unusually high proportion of heavily spotted kernels (Figure 2B, lane 13). Overall, the family derived from the test cross of this plant had 57% (83/147) spotted progeny kernels, roughly half of which (46/83) were more heavily spotted. This plant lacked MuDR(p3) and contained two new MuDR-hybridizing fragments, which we designated MuDR(p4) and MuDR(p5). Progeny kernels were separated into classes based on excision frequency, with the expectation that excision frequency would reflect the degree of heritable activity. The more heavily spotted kernels are designated “heavy” and “medium” in Figure 3. Plants grown from representatives of each excision frequency class were then subjected to Southern blot analysis (Figure 3).
In order to detect the presence of full-length transposed MuDR elements, a SacI digest of DNA from this family probed with a fragment of MuDR was compared to an XhoI digest, also probed with MuDR. SacI cuts in the ends of MuDR and gives rise to a diagnostic 4.7 kb fragment regardless of the element's position; in this genetic background only full-length functional MuDR elements yield a fragment of this size [24]. Because Muk has sequence identity to MuDR in the probe region [23], this derivative of MuDR can also be observed as a 2.5 kb fragment (i.e. lanes 15,16 and 17 in Figure 3A). SacI sites in Muk are subject to partial methylation (Slotkin and Lisch, unpublished data), resulting in the larger, 4.2 kb fragment in plants with Muk as well (red arrow, Figure 3A).
XhoI cuts only once in MuDR. Therefore, elements at various chromosomal positions give rise to unique fragment sizes (Figures 3B and 3D). Our analysis revealed that each of two XhoI segregating fragments contributed to the intensity of the SacI internal fragment. When both XhoI fragments were missing, so was the diagnostic SacI fragment. All spotted kernels gave rise to plants with one or the other XhoI fragment; kernels that lacked both XhoI fragments (Figure 3, lanes 30, 31, 33, 35 and 36) were uniformly pale and did not transmit spotted progeny kernels when plants grown from those kernels were test crossed. We conclude from this analysis that each XhoI fragment represents a full length MuDR element that can condition somatic activity of the reporter. The element that gave the smaller XhoI polymorphism was arbitrarily designated MuDR(p5) and that which gave the larger polymorphism was designated MuDR(p4).
The DNA samples from this family were also digested with HinfI, the methyl-sensitive enzyme that cuts in the ends of the reporter Mu1 element, and probed with Mu1. Strikingly, most (8/10) of the individuals grown from the most heavily spotted kernels contained unmethylated Mu1 elements (Figure 3C). This reversal of Mu element methylation has not been observed before, and suggests that some feature of the MuDR elements in these plants had been altered. All plants that carried unmethylated Mu1 elements carried MuDR(p5) and none of them carried MuDR(p4) by itself. None of these plants carried Muk, but 11/18 (61%) of plants grown from the more weakly spotted kernels did. None of the plants that were grown from weakly spotted kernels had hypomethylated Mu1. Overall, 26/28 (93%) of plants grown from kernels exhibiting any spotting at all carried MuDR(p5). In contrast, only 17/28 (61%) of these plants carried MuDR(p4), as did 3/8 (38%) of the plants grown from pale kernels. These results are consistent with segregation of a single active MuDR element (MuDR(p5)) and a second, much more weakly active element (MuDR(p4)). The presence of Muk in roughly half of plants grown from the weakly spotted kernels demonstrated that this locus had been in the parent and was still competent to silence MuDR elements.
Each plant from the above family was test crossed to determine the heritability of activity. The genetic ratios of spotted to pale kernels in the next generation were used to determine the copy number and degree of heritable activity of MuDR elements in each plant. The resulting families demonstrated an unambiguous relationship between MuDR(p5) and heritable activity as assayed by the number of spotted progeny kernels from these test crosses. Plants carrying only MuDR(p5) and unmethylated Mu1 elements gave rise to an average of 50% spotted kernels, consistent with segregation of a single, fully active MuDR element (Figure 4A and Table 1). Many of the plants examined that lacked Muk and that carried MuDR(p5) carried methylated Mu1 elements in the first generation following the loss of Muk. This suggested that in the leaf tissue of these plants, MuDR remained inactive. However, these plants exhibited a sectored phenotype with respect to expression of the suppressible a1-mum2 allele in the first generation following the loss of Muk (Figure 4D). This phenotype suggests that a reversal of MuDR(p5) was occurring in these plants, but that it was incomplete. Supporting this hypothesis, after a second round of test crossing, these plants gave rise to an average of 49% heavily spotted progeny kernels (Figure 4A and Table 1). Together, these data suggest that MuDR(p5) eventually reactivated in all cases, but in some plants reactivation was delayed. In contrast, plants carrying only MuDR(p4) gave rise to a uniformly low frequency (5%) of very weakly spotted kernels, consistent with a more typically heritable silenced state. Thus, although both MuDR(p4) and MuDR(p5) had been exposed to Muk in a previous generation, MuDR(p4) remained silenced, whereas MuDR(p5) eventually reverted to an active state in all cases once Muk was lost.
Plants carrying both MuDR(p5) and Muk also gave rise to a high frequency (37%) of spotted kernels, an average of 17% of which were heavily spotted (Figure 4A and Table 1). This ratio is consistent with a second generation of escape from Muk, where the progeny of these plants that carried MuDR(p5) but that lacked Muk had restored somatic activity. Thus, even after two successive generations of exposure to Muk, plants carrying MuDR(p5) retained the propensity to reactivate after Muk was lost. In contrast, lineages carrying only MuDR(p4) clearly lacked the propensity to reactivate even after only having been exposed to Muk for a single generation.
We also examined methylation at MuDR TIRs to see if the reversal of Mu1 TIR methylation was associated with a reversal of methylation at MuDR(p5). To do this, DNA that had been assayed for Mu1 methylation (Figure 3C) was again digested with HinfI, blotted and probed with a MuDR TIR fragment (Figure 4B). This analysis revealed that methylation at MuDR(p5) and Mu1 correlated well.
It was important to show that reactivation of MuDR(p5) is a reproducible phenomenon. To do this, a single plant carrying reactivated MuDR(p5) was crossed to the a1-mum2 tester and to a Muk homozygote. Kernels from the resulting families were grown, assayed for Mu element methylation, and test crossed. As expected, all of the plants from the a1-mum2 test cross that inherited MuDR(p5) were unmethylated at Mu1 (Figure 5A) and at MuDR(p5) (Figure 5B). All of these plants gave rise to approximately 50% spotted progeny kernels (Table 2). These data confirmed that MuDR(p5) remained active in a subsequent generation. In the family derived from the cross between the same MuDR(p5)-containing plant and a Muk homozygote, progeny plants contained methylated Mu1 and MuDR(p5) (Figure 5A and B). Nevertheless, when these plants were test crossed, an average of 42% of the progeny kernels were spotted, indicating that MuDR(p5) had again escaped heritable silencing (Table 2). Plants that did not inherit MuDR(p5) did not give rise to any spotted progeny kernels, confirming that activity in this family was specific to MuDR(p5).
After the loss of Muk, MuDR(p4) never reactivated when it was by itself (Table 1). However, MuDR(p4) did become heritably reactivated in the presence of a reactivated MuDR(p5) element, but only when MuDR(p5) was fully active in the generation immediately following the loss of Muk. Plants that carried an active MuDR(p5) element (as judged by hypomethylation of HinfI sites in both MuDR(p5) and Mu1), also carried unmethylated MuDR(p4) elements (Figure 4B, lanes 7 and 8). When these plants were test crossed, they gave rise to an average of 77% spotted progeny, consistent with the independent segregation of two active MuDR elements (Table 1). To test this hypothesis, kernels from one of these families were planted and the resulting plants were subjected to Southern blot analysis (Figure 6A) and were test crossed (Table 3). In this family, both MuDR(p5) and MuDR(p4) cosegregated with Mu activity. All spotted kernels in this family carried either MuDR(p5), MuDR(p4) or both, while none of the pale kernels had either. Plants carrying either MuDR(p5) or MuDR(p4) gave rise to an average of 50% and 48% spotted kernels respectively. Plants that carried both elements gave rise to an average of 78% spotted kernels, consistent with the independent assortment of two unlinked active MuDR elements (Table 3). Those that carried neither element did not give rise to any spotted kernels (data not shown). The elements also showed a positive dosage effect; the most heavily spotted kernels carried both elements (7/7) while the more moderate or weakly spotted kernels (19/19) carried a single MuDR element. These data demonstrate that both elements were active in this family, and that they were the only active elements present. Since MuDR(p4) alone never exhibited reactivation in this family (Table 1) or any other we have examined (see below), we suggest that MuDR(p4) required the presence of active MuDR(p5) to become reactivated.
For comparison, we examined the heritable activity of MuDR(p4) in plants in which there had been a delay in MuDR(p5) reactivation. As described above, these plants carried methylated MuDR and Mu1 TIRs in the generation immediately after the loss of Muk (Figures 3 and 4). However, when these plants were test crossed, they gave rise to an average of 50% heavily spotted progeny kernels (Table 1). One plant and its progeny were examined in detail. In this plant, both MuDR(p5) and MuDR(p4) had remained at least partially inactive in the first generation after the loss of Muk (Figure 4B, lanes 9 and 10, and 4D). Despite having two potentially active elements, this and all similar families segregated only 50% spotted progeny kernels, as if only one of these two MuDR elements had become reactivated in this generation (Table 1). Southern blot analysis of progeny of this plant revealed that MuDR(p5), but not MuDR(p4), co-segregated with activity (Figure 7A). All the plants grown from spotted kernels in this family carried MuDR(p5), but the presence or absence of MuDR(p4) had no effect on activity; three of ten plants grown from spotted kernels carried MuDR(p4), as did seven of nine plants grown from pale kernels. This experiment demonstrated that MuDR(p4) was not active in this family. It also showed that in this generation, an active MuDR(p5) element had no influence on the heritable activity of MuDR(p4). Plants that carried both MuDR(p5) and MuDR(p4), when test crossed, gave rise to only 50% spotted progeny (Table 4 and Figure 7B). Together, these results suggest that MuDR(p4) could be responsive to a reactivated MuDR(p5), but only in the generation immediately following the loss of Muk.
It is unclear as to precisely when MuDR(p5) must be active in order to alter the trajectory of MuDR(p4) silencing. Only those plants that showed hypomethylation at MuDR and Mu1 TIRs that were grown from more heavily spotted kernels gave rise to progeny with active MuDR(p4) elements. This suggests an active MuDR(p5) element was required quite early in development in order to reactivate MuDR(p4). The aleurone and the mature plant are the result of a double fertilization event. One sperm fuses with the egg cell of the female gametophyte to form the embryo. The second fuses to the diploid central cell to give rise to the triploid endosperm. The egg cell and the central cell are derived from a post-meiotic mitotic division in the female gametophyte. With that in mind, it is interesting to note that eight of ten heavily and medium spotted kernels gave rise to plants with hypomethylated Mu elements. In contrast, none of the plants grown from more weakly spotted kernels gave rise to plants with hypomethylated Mu elements. The fact that the methylation status of MuDR(p5) in the mature plant correlated so well with the phenotype of the kernels suggests that MuDR(p5) reactivation that was not delayed most often occurred prior to the post-meiotic mitotic cell division. Together, these data suggest that the window of opportunity for activation of MuDR(p4) by MuDR(p5) may be a very narrow one. Indeed, it may be restricted to the gametophyte, or even meiosis II.
Although somatic excision of a reporter element is a reliable indicator of Mu activity, it only represents one aspect of that activity, which only requires MURA transposase function [26],[27]. Insertional activity, either of the reporter element or of MuDR itself requires both mudrA and mudrB expression. The analysis portrayed in Figure 5 demonstrated that a reactivated MuDR(p5) element could cause new insertions of Mu1. When Mu1 is methylated, the size of the fragments following digestion varies depending on the position of the element. The element at a1-mum2 is 2.1 kb. Other sizes present in single individuals represent independent new insertions of Mu1. The presence of new Mu1 fragments in progeny of plants that carried reactivated MuDR(p5) (Figure 5A, lanes 8 and 13) indicates that this element can cause new insertions of Mu1, consistent with reactivation of both mudrA and mudrB functions.
We also examined the propensity of reactivated MuDR(p5) and MuDR(4) to duplicate themselves by test crossing a series of individuals that carried active versions of either MuDR(p5) or MuDR(p4). In each case, the plants were derived from a family that had segregated genetically for a single active MuDR element. In the absence of new duplications of these MuDR elements, the expectation would be that each resulting family would also segregate 50% spotted progeny kernels. Ratios significantly higher than 50% are the result of MuDR duplication events [21]. The frequency of ears showing ratios of spotted kernels significantly greater than 50% provides an estimate of the duplication frequency, which we have shown can vary from position to position [24]. Of 100 ears derived from plants carrying silenced MuDR(p5) in the presence of Muk, none had ratios significantly greater than 50% spotted kernels (data not shown), indicating that MuDR(p5) does not transpose in the presence of Muk. In contrast, following reactivation we found that both MuDR(p4) and MuDR(p5) were competent to transpose at a frequency of 10% for MuDR(p5) and 18% for MuDR(p4) (data not shown). These data demonstrate that although both somatic and transpositional activity of MuDR(p5) is repressed in the presence of Muk, both manifestations of activity are restored once Muk is lost via genetic segregation.
We also wanted to confirm that the “rescue” of MuDR(p4) in the previous experiment was due to the presence of a reactivated MuDR(p5) element. To test the effects of Muk on MuDR(p4) in the absence of MuDR(p5), plants carrying active MuDR(p4) were crossed to Muk heterozygotes, and the resulting plants were then test crossed (Figure 1). As before, unlike MuDR(p5), which showed clear evidence of reactivation following the loss of Muk, MuDR(p4) remained heritably silenced (Table 5). Thus, MuDR(p4) in the absence of an active MuDR(p5) element showed a typical pattern of heritable silencing after being exposed to Muk.
In order to replicate the “rescue experiment”, a plant carrying active MuDR(p5) and MuDR(p4) elements was crossed to a Muk homozygote. Progeny plants were genotyped and test crossed (Figure 8). Plants that carried only MuDR(p4) and Muk gave an average ratio of spotted kernels of 6%, consistent with our previous result that MuDR(p4) without MuDR(p5) is heritably silenced by Muk. Plants that carried Muk with MuDR(p5) alone or with MuDR(p4) gave an average frequency of spotted progeny of 48%, consistent with reactivation of MuDR(p5) following the loss of Muk. Progeny of this cross that carried both MuDR(p5) and MuDR(p4) but that lacked Muk were test crossed again. One individual gave rise to a ratio of spotted kernels of 68%, consistent with the independent segregation of two active elements. In the next generation, somatic activity segregated with both elements; plants carrying both MuDR(p4) and MuDR(p5) gave rise to a 75% ratio, and those with either MuDR(p4) or MuDR(p5) by itself gave rise to roughly 50% ratios (Table 6). These data strongly support the hypothesis that, although MuDR(p4) is invariably silenced in the absence of MuDR(p5), a reactivated MuDR(p5) element can cause MuDR(p4) to reactivate as well.
If the reactivation effect we observe for MuDR(p5) were a function of position, then we would expect that, if this element transposed to a new position, it would exhibit a more typical heritable response to Muk. To test this hypothesis, plants carrying MuDR(p5), a transposed copy of this element at a second unlinked position and Muk were test crossed (Figure 9). Resulting progeny plants grown from spotted kernels were genotyped for MuDR(p5) and Muk and test crossed a second time (Table 7). Plants carrying MuDR(p5) that lacked Muk gave rise to ears that segregated for one or more active MuDR elements and averaged 55% spotted progeny kernels. In contrast, siblings that inherited only the second MuDR element and not MuDR(p5) gave rise to a much lower frequency of spotted kernels (5%), consistent with the kind of heritable silencing that is typical for MuDR elements after having been exposed to Muk. These results suggest that, while MuDR(p5) reactivates once Muk is segregated away, the duplicate copy of this element remained heritably silenced. These data strongly suggest that the reduction of heritable silencing at MuDR(p5) is a function of chromosomal position and not sequence, since this effect can be reversed following transposition.
In order to determine the local chromosomal environment around MuDR(p5) and MuDR(p4), inverse PCR was used to clone sequences flanking the insertions. DNA from plants carrying either element was digested with XhoI, which gives rise to fragments of 2.6 and 2.4 kb corresponding to MuDR(p4) and MuDR(p5) respectively (Figure 3B). The DNA was then ligated and primers specific to MuDR were used on the circularized fragments to amplify fragments of the expected sizes (see Materials and Methods for details). The products were sequenced on both strands, and the resulting sequences were used to design flanking primers. These primers were then used with MuDR-specific primers on DNA from families segregating for MuDR(p4) or MuDR(p5). In each case, these primer pairs specifically amplified a product only in samples containing the MuDR elements (data not shown). Sequences were extended using publicly available maize genomic sequences, and these sequences were used to design primers matching DNA sequences present to the other side of each element. Nine base pair target site duplications, a characteristic feature of Mu insertions, were identified in each case. Further, XhoI and HinfI sites in the flanking sequences obtained from the public databases also correlated well with data obtained from Southern blot restriction data.
Given its propensity to reactivate, we were particularly interested in sequences flanking MuDR(p5). This element was inserted into the 5′ UTR just 4 base pairs proximal to the start codon of a putative ORF of unknown function (Figure 10), which we designate here Hemera, after the Greek goddess of the day, who was believed to disperse the night's mist each morning. Genes homologous to Hemera can be detected other grasses such as rice and Brachypodium distachyon. This conservation, along with the presence cDNA sequences in the database from several species, including maize, suggests that this gene is functional. The insertion of MuDR(p5) was 69 bp downstream of a 37 bp GA-rich sequence composed largely of GA repeats. Interestingly, although the rice and B. distachyon 5′ UTRs are not homologous to the maize sequence by sequence similarity, each of them has a GA-rich sequence roughly the same distance from the putative start of translation. These data suggest that sequence composition, rather than sequence order, may be conserved at this gene in these three species. Homologues of Hemera are also present in dicots, including papaya, grape, Arabidopsis and poplar. Although some of these sequences carry GA or TC rich regions near the putative start of translation, their positions are not conserved between species (Figure S1).
MuDR(p4) was also inserted into a conserved gene of unknown function (Figure S2). Based on a comparison with cDNAs from several species, it appears that the insertion is into an intron, 401 bp upstream of the putative start of translation. Interestingly, the 5′ portion of this intron contains a region rich in TCs, as does the 5′ portion of the paralogous rice gene, which contains a long GA-rich sequence. Since MuDR(p4) does not reactivate following exposure to Muk, these data suggest GA/TC-rich sequences by themselves are not sufficient to permit reactivation. However, it is possible the presence of these sequences near MuDR(p4) make it particularly responsive to active MuDR(p5). Analysis of additional positions, and combinations of positions will be informative, but an unambiguous demonstration of the propensity for cis-acting sequences will require mutation of those sequences in a transgenic context.
The experiments described here detail a position effect that alters the heritability of the silenced state of a maize transposon. The experiments were possible because of the absence of spontaneous Mu transposon silencing in our lines and the availability of a single silencing locus (Muk) that can reliably and heritably silence MuDR elements. Because heritability is the rule for MuDR silencing by Muk, it was possible to screen for exceptions to this rule in order to uncover variation in the ability of chromosomal positions to maintain silencing over multiple generations. One such exception is MuDR(p5), which fails to maintain silencing. The fact that a transposed copy of MuDR(p5) showed a more typically heritable pattern of Muk-induced silencing demonstrates that MuDR(p5) is exceptional because of its position rather than its sequence.
To our knowledge, this is the only known example of a specific locus competent to reverse epigenetic silencing of flanking sequences. In plants, a related (albeit reversed) phenomenon can be found at the FLC locus in Arabidopsis thaliana. In that case, the FLC gene is apparently competent to alter the activity of neighboring genes via an epigenetically mediated pathway [28]. When a T-DNA encoded resistance gene is integrated near the FLC gene, its expression is down regulated in response to cold temperatures, and this down-regulation is dependent at least in part on components of the small RNA mediated silencing pathway. The difference is that FLC attracts factors that down-regulate gene expression, and Hemera apparently attracts factors that reverse silencing. Nevertheless, in each case it appears that there are cis-acting sequences that can alter the epigenetic state of inserted genes.
Interestingly, the kind of epigenetic resetting we see with MuDR(p5) is typical in animals, although the role of position remains poorly understood. In certain cell types at certain times, massive changes in patterns of histone and DNA methylation are observed. This process, which is thought to be required for the elimination of some epigenetic marks and their replacement with others, is particularly pronounced in the pre-implantation embryo of mammals [29]. The same is true of primordial germ cells, where this process of epigenetic reprogramming is thought to be involved in the restoration of totipotency [30]. In mammals, exceptional instances in which DNA methylation is not lost, are associated with imprinted genes and deeply silenced transposons [31]. In some cases it has been shown that a close association between transposon and host gene can lead to heritable changes in phenotype. For instance, the Agouti viable yellow (A(vy)) locus in mice is under the control of an IAP retrotransposon. Hypomethylation of this element results in expression of the gene and yellow coat color. Epigenetic variants of this allele can be transmitted from generation to generation, and it is hypothesized that the heritable epigenetic effects of on A(vy) are due to a failure to remove epigenetic marks due to the close association of the IAP element with the coding sequence [32].
In Drosophila, changes in the efficiency of epigenetic resetting can have important consequences. A hyperactive version of a JAK kinase, hopTum-1, causes tumor formation. It does so because counteracts heterochromatic gene silencing, which is an important regulatory pathway for tumor suppression [33],[34]. Enhancers of the hopTum-1 allele included several components of the heterochromatin formation pathway, including HP1 and several Suppressors of variegation mutations, which were first identified due to their effects on position effect variegation. Remarkably, not only can hopTum-1 cause tumors in one generation, but it can increase the propensity for the wild-type offspring of mutant flies to have tumors as well [35]. It is hypothesized that the hopTum-1 mutation antagonizes the normal process by which epigenetic states are reset each generation by allowing genes that should be heritably silenced to take on a heritably active state.
Plants are distinct from animals in the sense that they lack a dedicated germ line. Instead, somatic meristem tissue differentiates into germinal cells each generation. A wealth of information suggests that the result of this difference is that epigenetic changes in plants are more readily transmitted from generation to generation [36]. Nevertheless, it is likely that in plants, as in animals, at least a subset of genes in are reset each generation order to ensure that the epigenetic state of each embryo is roughly equivalent. DNA methylation, for instance, increases in the meristem as it ages, and these changes must presumably be reversed each generation [37],[38]. We suggest that Hemera may represent a gene whose epigenetic state must be reset each generation. If Hemera were epigenetically silenced in the floral meristem and upregulated in the embryo, for instance, then perhaps that epigenetic regulation must be relieved during or following meiosis. It will be interesting to see if differences in expression levels of Hemera correlate with changes in chromatin configuration or DNA methylation, and whether or not these changes correlate with changes at MuDR(p5).
It should be emphasized that the variation we observe is not in the propensity to become silenced; MuDR(p5) is effectively silenced by Muk. Given that MuDR(p5) TIR sites are methylated at the HinfI site, it is also unlikely that this element is exclusively inactivated at the post-transcriptional level. Rather, the effect we observed appears to be specifically associated with the efficiency with which transcriptional silencing of this element is heritably propagated in the absence of the trigger. The loss of methylation at MuDR(p5) may not be a passive process; our assay for methylation, a HinfI digest, depends on methylation of a CG site. Since CG methylation can be maintained passively through the activity of maintenance methyl-transferases such as MET1, the loss of methylation at this site may reflect an active de-methylation process. Active demethylation has been observed as a consequence of DNA glycosylase activity in plants, and is often associated with repetitive elements such as transposons [39],[40]. It will be interesting to see whether or not the reversal of methylation we see at MuDR(p5) is due to similar activity in maize. It will be particularly interesting if mutations of maize DNA glycosylase genes affect MuDR(p5) reactivation.
We do not know the cause of the position effect on MuDR(p5). The fact that this element is inserted into an expressed portion of a gene may have been sufficient to reverse silencing, but Mu elements often insert into or near genes and nearly all MuDR elements are silenced when high copy number Mu lines are crossed to Muk [22]. The presence of GA repeats near the insertion is intriguing, as GA repeats have been associated with programmatic changes in chromatin structure and in particular with the active replacement of histones [9]. Although we have not established that this is the case at MuDR(p5), we do note that the rice and B. distachyon homologs of Hemera also have GA-rich sequences just upstream of the start of the ORFs. Although the sequence of the GA-rich regions in the maize, rice and B. distachyon genes are not similar in sequence, they do have similar sequence composition (100%, 89% and 96% GA respectively). These blocks of sequences are roughly the same distance from the first ATG of each gene, 82 bp, 83 bp and 89 bp for maize, rice and B. distachyon respectively. Given the phylogenetic distance between these species (roughly 50 million years [41]), the conserved positioning of these blocks at the same distance from the start of translation in each gene suggests that they may have a conserved function.
In addition to the position effects we observed, our data also suggests that epigenetically determined states of competency can change over time. Specifically, we provided evidence that a silenced MuDR(p4) element could respond to a reactivated MuDR(p5) element, but only for a brief period of time. This was revealed because of variations in the rate at which MuDR(p5) became reactivated. In some cases, it was immediately after the loss of Muk, as evidenced by the high level of somatic activity in the aleurone and the complete loss of methylation in the growing F2 plants (Figures 3 and 4). In these cases, when MuDR(p4) was also present, it too was reactivated. However, in those cases in which MuDR(p5) reactivation was delayed (weakly spotted kernels, variegated a1-mum2 suppression and TIR methylation), MuDR(p4) was not reactivated. In the subsequent generation, even though MuDR(p5) had become fully reactivated, it had no effect on a previously silenced MuDR(p4). We hypothesize that silencing of MuDR elements is a progressive process that involves successively deeper silenced states, from responsive to a second, active element, to refractive to that element. Thus, immediately after Muk was lost due to genetic segregation, MuDR(p4) silencing was not completely established, and so this element was responsive to active MuDR(p5). After a round of meiosis, MuDR(p4) had become fully refractive to MuDR(p5). Perhaps passage through meiosis of a previously silenced transposon acts as a check-point, during which provisionally established silenced states are made more permanent. If our interpretation of the data is correct, then the epigenetic state of MuDR(p4) can change over time, even once the silencing trigger (Muk) has been lost. This is consistent with what we know about silencing mechanisms in plants, in which chromatin remodeling factors, DNA methylation and siRNAs form a self-reinforcing loop [42]. MuDR(p4) silencing may represent an illustration of how this process can deepen a silent state over time, resulting in a shift from competency to respond to a second, active element to a refractive state in the course of a generation. Similarly but conversely, MuDR(p5) may represent a process by which silenced states can be reversed over time through the activity of cis-acting factors. The delay in MuDR(p5) reactivation in many of the plants examined suggests that reactivation, like silencing, can be a progressive process. Our data suggest that even after a trigger is lost, a series of additional and progressive changes can continue to occur. This is perhaps the most fascinating aspect of epigenetic modifications: time matters. Changes triggered in one generation can manifest themselves over multiple subsequent generations.
Historically, an emphasis has been on mechanisms by which epigenetic information is propagated from generation to generation, a classic example being paramutation [43]. Our data suggest that an equally important process may be the erasure of epigenetic modifications that have occurred in plants in the meristem prior to meiosis. The cis-acting factors that appear to be responsible for reversing MuDR(p5) silencing may provide an important clue concerning the mechanism of this erasure.
|
10.1371/journal.pcbi.1002623 | Alternative Protein-Protein Interfaces Are Frequent Exceptions | The intricate molecular details of protein-protein interactions (PPIs) are crucial for function. Therefore, measuring the same interacting protein pair again, we expect the same result. This work measured the similarity in the molecular details of interaction for the same and for homologous protein pairs between different experiments. All scores analyzed suggested that different experiments often find exceptions in the interfaces of similar PPIs: up to 22% of all comparisons revealed some differences even for sequence-identical pairs of proteins. The corresponding number for pairs of close homologs reached 68%. Conversely, the interfaces differed entirely for 12–29% of all comparisons. All these estimates were calculated after redundancy reduction. The magnitude of interface differences ranged from subtle to the extreme, as illustrated by a few examples. An extreme case was a change of the interacting domains between two observations of the same biological interaction. One reason for different interfaces was the number of copies of an interaction in the same complex: the probability of observing alternative binding modes increases with the number of copies. Even after removing the special cases with alternative hetero-interfaces to the same homomer, a substantial variability remained. Our results strongly support the surprising notion that there are many alternative solutions to make the intricate molecular details of PPIs crucial for function.
| The number of known protein-protein interactions (PPIs) grows rapidly, yet their molecular details remain largely unknown. Over the last years, structural biologists have addressed this issue with an increased output of structurally resolved hetero complexes. This wealth now enables statistically significant quantitative statements about interface properties. Here, we addressed the question how interfaces differ when observing the same proteinprotein interaction twice. A new dataset derived from the entire PDB was analyzed employing different definitions for the “same interaction” and a range of interface similarity measures. The hypothesis was that the interface between the same pair of proteins stays the same irrespectively of how often it is measured. Although the results mostly confirm this hypothesis, the surprising finding was how often it was not true: for many comparisons of interfaces, the molecular details of the interaction differed importantly, often without the slightest change of amino acids. In addition, no matter how much “special cases” were sieved out, the essential message remained: interfaces appear immensely plastic. Hand-selected sample structures largely support this view. In general, we complement a series of recent studies focusing either on family-family interactions or exploring other aspects of protein-protein complexes.
| The study of high-resolution three-dimensional (3D) structures of proteins as deposited in the PDB, the Protein Data Bank [1], began with peptides [2], [3] and has increasingly included larger complexes of interacting proteins [4]. These complexes, or PPIs (Protein-Protein Interactions), capture the molecular details of interaction networks. The network view, in turn, has become increasingly important for, e.g., the ranking of genes according to their probability of being causative for a particular disease [5]–[7] as needed for Genome-wide Association Studies (GWAS).
Despite this wealth of high-resolution interaction data, the set of interactions for which the exact molecular mechanisms are known remains immensely incomplete [8] and with it experimental and computational descriptions of binding positions and binding-induced conformational changes [9]–[11]. Nevertheless, studies of available structures have shown that related proteins have similar binding sites [12], that permanent and transient interactions differ so substantially from each other [13] that PPI hotspots can be predicted from sequence [14], [15], and that we can accurately distinguish between specific and unspecific contacts [16]. Many others have addressed related tasks [16]–[23], including even the contribution of water to the binding modes of PPIs [24].
An excellent recent work reviews various types of protein interactions [25]. We want to complement it with a quantitative analysis of the interface variability of external interactions, i.e. interactions between two protein chains coming from different genes. These typically correspond to the edges in a PPI network. The atomic structures of their interfaces often seem to cluster into particular architectures [16], [26] and it has been suggested that they are conserved within and between organisms [26]–[31]. Many authors have also analyzed the molecular details of binding within and between their domain families [32]–[37].
For example, they found that two different SCOP domain families exhibit more than one orientation of binding about 24% of the time [33]. Beside this number, however, only few more details were given about the underlying biological variety and in particular the causes of differential interfaces. The problem we see with this approach is that members of a SCOP family only share similar 3D structures and that the observed variability in binding might simply be explained by sequence variation. In fact, the inference of similarity in structure (homology modeling) is much more accurate than the inference of protein-protein interactions [26]. So far no studies based on significantly sized data sets have addressed the question to which extent the interface between two different proteins is biologically conserved, i.e. excluding diversity due to sequential differences.
Another challenge for the analysis of large-scale data sets have been crystal contacts and the difficulties of automated methods to correct such problems (e.g. the PQS [38] or the PISA [39] service). Authors “addressed” these problems by either entirely excluding different interfaces suspecting that those originated from non-biological contacts, or by leaving it open to which extent their results might have been created by such contacts.
Here, we address both issues. First, we realized that the number and quality of author-assigned biological assemblies in the PDB now suffices to enable a quantitative study like this one. For the large majority of entries, the PDB now provides biologically relevant structures from the crystallographers themselves. Similar to PQS or PISA, they describe a complex as it occurs in the living cell. At the same time, however, they are more accurate and easier to verify than de novo predictions. Therefore, we did not discard any high-resolution complex or interface therein.
Secondly, put most extremely, we ask the question: if X-ray crystallographers measure the same interaction twice, do they get the same result? The main focus is first on the variability of the interaction between identical variants of the same two proteins (SameSeq). In other words, we look at external interactions corresponding to the same pair of protein sequences and estimate how often the interfaces are different (Fig. 1A; Fig. 1B: the red arrow compares two sequence-identical interactions). We then extend our analysis by allowing minor sequence variations in corresponding interactors (e.g. in the form of point mutations; SameProt). However, we still maintain the comparison between essentially the same proteins, because we make sure that a sequence change does not go hand-in-hand with a change of the original protein (Fig. 1B: for the blue interface comparisons, the sequences have changed [S1/S3 vs. S2/S3], but the original proteins [Px/Py] remained the same). Finally, we compared two external interactions corresponding to the same family pair, i.e. “interologs” (Interolog). In a dimer-dimer comparison on this Interolog-level, corresponding interactors still had a similar 3D structure, but their sequences could be very different. (Several authors have been using the term “interolog” [40], [41]; it has the advantage over the term “homolog” that no evolutionary relation is implied in the definition; Fig. 1B, green: interfaces between proteins Px and Py are compared to those between Pz and Py).
Each node in a PPI network typically refers to a UniProt [42] entry. While UniProt stores information about proteins, its first layer of organization is genetic: every entry corresponds to a unique location on a genome. Hence, in order to find reliable structural evidence of PPI network edges, we mined the PDB [43], [44] for interacting proteins which map to different Uniprot/Swiss-Prot [45] identifiers. We extracted such external protein-protein interactions (i.e. interactions originating from two different genes) in the following way: first, we downloaded all author assigned biological assemblies from the PDB. We then retained only X-ray structures that had a resolution <2.5 Å and mapped to at least two different UniProt/Swiss-Prot entries (author assignment available for 99% of all such structures). We primarily used the PDB< = >Swiss-Prot mapping provided by the PDB and only performed the following step if this mapping was not available: we BLASTed [46] the PDB SEQRES sequence (at least 30 residues long) against the Swiss-Prot database, thresholding at E-Values <10-3 and requiring at least 90% of the PDB chain to be aligned. (When we found more than one hit, we took the one with the lowest E-Value; when we had none, we discarded this complex.) Having found those ‘interesting’ complexes, we extracted all interacting pairs of chains pointing to two different Swiss-Prot entries. At this early stage of our procedure, we only required one pair of atoms of the two chains to be closer than 0.6 nm (6 Å) in order to consider them interacting.
Note that in an earlier version of this work, we had exclusively used the PISA service [39] to obtain biologically relevant assemblies. In Section S3.3 in Text S1, we give reasons why we switched to author assigned complexes, an accuracy estimation of PISA in the context of hetero-complexes and other results compiled with the PISA based data set.
Having found all structures of external interactions, we annotated their interfaces. Given a hetero-dimer with chains X and Y (X and Y come from different genes), we considered a pair of residues Rx and Ry as part of the interface if it contained at least one pair of atoms closer than 0.6 nm (6 Å) or if it met all three conditions: (i) both residues changed their accessible surface area upon binding (dASA: replacing the binding partner by water), (ii) Rx had no other interaction partner within 0.6 nm (6 Å), (iii) of all residues in protein chain Y that changed their accessible surface area (ASA), Ry was the closest to Rx. The latter included interactions that fell slightly above the 0.6 nm (6 Å) threshold but should still be considered interacting by their ASA change (we present a brief analysis of the effect of including dASA in the interface annotations in Section S3.1 in Text S1). We annotated each interface residue by two structural descriptors: dASA and d reflecting the distance (in Ångstrøm of the closest binding residue). Having defined all interface residues, we removed each hetero-dimer with fewer than five interacting residues on either chain from our data set. Finally, we assigned each remaining hetero-dimer its “interface copy number”. To this end, we first determined the original complex a hetero-dimer was extracted from. Then we counted how many other hetero-dimers were also extracted from this complex and had exactly the same two SEQRES sequences as the hetero-dimer under consideration. This “interface copy number” was assigned to all these sequence-identical hetero-dimers of the complex (Section S5 in for details).
Overall, we applied nine different interface similarity measures to our data, covering various types of changes. They are defined in detail in Section S2 in Text S1. The variety of these measures guaranteed that we captured as many aspects of “interaction similarity” as possible. We found significant differences between these measures, but with respect to our overall conclusions, we considered it more important to eschew obfuscation than to present all necessary details. Therefore, we used only the two most representative and intuitive measures in the main text, namely the Face Position Similarity and the L_rms. In the following, we refer to “interface” as all the residues that “touch each other” between two interacting proteins (Fig. 1), and as “face” as all the residues on one side of the interface. Also note that we always reduced hetero-dimers to common residues before comparing their interfaces. Please see Section S2 in Text S1 for details of this procedure.
Before we could apply the interface similarity measures to our entire collection of external interactions, we needed to group the structures so that we could differentiate between (and not mix) different types of sequence divergence. This also addressed the redundancy immanent in the PDB in the form of, e.g., overrepresented protein families. We hierarchically clustered the hetero-dimers over three levels, corresponding to increasing levels of sequence divergence (a more technical description of the following procedure can be found in Section S1.1 in Text S1)
First, we assigned two hetero-dimers to the same Level SameSeq group if they corresponded to same pair of SEQRES sequences (Fig. 1B: we add interfaces 1 and 2 to the same Level SameSeq group; other interfaces become single member Level SameSeq groups). Next, we reduced the influence of over-represented proteins. This was achieved by adding Level SameSeq groups to the same Level SameProt group if they corresponded to the same pair of associated Swiss-Prot identifiers (Fig. 1B: Level SameSeq groups S1-S3 and S2-S3 go into one Level SameProt group, S3-S4 to another). Clusters obtained in this way should represent the classical notion of edges and nodes in a PPI network. Our final Level Interolog addressed overrepresented families: we merged Level SameProt groups that pointed to the same pair of Pfam [48] families into one Level Interolog group (Fig. 1B: both Level SameProt groups are merged into one Level Interolog cluster; Fig. S1 in Text S1 for a graphical illustration of the clustering).
Without the grouping above, any distribution of pairwise interface similarities would have been highly dominated by large and well-studied complexes for which many structures are available. Avoiding this bias demanded to group PPIs differently (Levels SameSeq to Interolog) and also to embrace this alternative grouping when trying to infer biologically meaningful similarity distributions. While the following procedure successfully reduced the bias stemming from overrepresented sequences and sequence families, we deliberately left Level SameSeq clusters unchanged in the assumption that all binding modes are biologically meaningful and that eliminating this redundancy would remove more biology than noise (please see Section S1.2 in Text S1 for a more mathematical description of the following procedures).
The same proteins may aggregate to form a homo-oligomer and bind as such a complex to another protein. In this case, the other protein often “sees” different parts of the homomeric chain, resulting in very different external interfaces. For example, a homo-dimer with chains X1 and X2 might bind to another chain Y with two different interfaces (Fig. 2). Hence, we will have two hetero-dimers X1/Y and X2/Y with low interface similarity. As it can be argued whether both of these interfaces should be considered as one big interface or treated separately (Discussion), we analyzed their influence on the distributions D-SameSeq to D-Interolog. To this end, we defined homo-oligomers in two different ways. Firstly, we used the classical notion, namely that all chains of a homomer have the same SEQRES sequence. Secondly, we introduced “structural homomers” as interacting chains from the same family. This corresponded to all complexes that look homo-oligomeric on a structural level (low RMSD; Fig. S4B in Text S1), but can differ in sequence.
Consequently, when comparing two interfaces X/Y and X′/Y′ from two different PDB entries, it was checked whether or not one of the chains X′ and Y′ were part of homo-oligomers (i.e. whether there were homomers X′/X′1/…/X′n or Y′/Y′1/…/Y′m) and whether or not these homo-oligomers had other external interfaces with the same interaction partner as in X′/Y′ (i.e. whether X′ had interfaces with Y′1/…/Y′m or Y′ interfaces with X′1/…/X′n). Having identified the set of all those sequence- or family-identical interfaces (including X′/Y′), they were compared to X/Y. Only if X/Y< = >X′/Y′ was the best match among all alternatives, the corresponding similarity was used. Otherwise, the entire comparison was discarded (Fig. 2.)
Eventually, the roles of X/Y and X′/Y′ are switched, and the procedure is repeated because all interfaces are compared with all others in the distributions D-SameSeq to D-Interolog. In this way asymmetries arising from only considering the oligomeric context of one side of the comparison were filtered out.
We applied “structural homomerization” only in the context of D-Interolog. For the two other distributions, it would have led to comparisons of interfaces between different protein pairs, thereby invalidating the constraints of these distributions. Also note that the above definition only allowed for comparisons of interactions between two different families.
Our complete data set of external protein-protein interactions (PPIs) comprised 37,338 hetero-dimers. We grouped and filtered them on three different levels with decreasing sequence redundancy (Methods). For instance, the first clustering level had 634 groups that contained sequence-identical hetero-dimers from at least two different high-resolution PDB entries. We compiled various statistics on this data set, including the distribution of cluster sizes on each clustering level, of oligomeric states, interface sizes and even of the conservation of protein function (Section S3.2 in Text S1).
In order to capture different facets of interface similarity, we introduced and evaluated nine different similarity measures (Sections S2 and S6 in Text S1). In the following, we focused on the results from two measures (Face Position Similarity and L_rms; Methods), and reported only qualitative findings for the others. The first measure (Face Position Similarity) was most representative for all other seven measures while the second (L_rms) enabled direct comparisons of our results to related work, e.g. to the CAPRI [47] experiments. For each measure, we used our clustering to calculate three different interface similarity distributions, corresponding to increasing levels of sequence divergence between interactions (D-SameSeq to D-Interolog [Methods]). These distributions constitute the main result of this work and are presented in the following. They were calculated such that all proteins and families of our data set contributed equally, regardless of their respective over-representation in the PDB. Finally, we measured how the distributions change when excluding the interface variability introduced by a protein binding differently to the same homomer. We give a short summary of this after the presentation of the unmodified distributions.
When two different experiments measured exactly the same external interaction (distribution D-SameSeq; Methods), usually the interface between the two proteins was identical. Depending on the similarity measure, the number of largely conserved interfaces varied between 60 and 89% (Fig. S3 and Fig. S9 in Text S1). The most representative measure (Face Position Similarity) found the same interface in 75–79% of all cases (Fig. 3A, D-SameSeq, rightmost bar). Conversely, interfaces largely differed between two observations in about 12% (Fig. 3A, Face Position Similarity <0.5).
Other measures introduced in this work were, for instance, very sensitive to side-chain movements (Convex Hull Overlap), or only roughly assessed the conservation of the interface location (Sphere Radius Ratio). Taking into account two similarity measures simultaneously, small differences were observed in as many as 49% of all comparisons (Section S6.3 in Text S1).
In contrast to our measures, the L_rms (used by CAPRI) returned distances in Å for the entire protein rather than for the interface alone. This perspective could capture conformational changes outside the binding regions that would be missed by other measures. L_rms found 64–69% of all “ligands” (per definition the smaller of the two proteins in the interaction) not to exhibit conformational changes and to bind to the larger proteins at the same positions (RMSD <1.0 Å). Conversely, 10–14% of the interfaces differed very substantially between alternative experiments (>9.0 Å). Considering Face Position Similarity and L_rms at the same time suggested that about 1% of all comparisons did not differ by the first but differed substantially (>9 Å) by L_rms (Fig. S11 in Text S1). In other words, at least 1% of all the changes between different experiments can be attributed to conformational changes outside the binding region.
Another CAPRI measure, the I_rms, compared the shapes of the interface regions common to both protein pairs. We found these common regions to be very different in about 4% (e.g. due to a rotation of one of the proteins) and largely conserved in 80% (Fig. S9 in Text S1).
We confirmed the surprising result of interface variability without sequential changes through a variety of additional analyses. The degree to which interfaces were mostly robust (ratio between rightmost and leftmost bars in Fig. 3) was a function of the number of copies of a particular interaction in a complex (i.e., a function of the ‘interface copy number’; Methods; e.g. Fig. 1: S1/S3 observed once in C1): the more copies, the relatively lower the bars on the right and the higher on the left (Fig. S8 in Text S1). But all of this also varied between families and particular complex subgroups (Fig. S7 in Text S1). For instance, MHC (Major Histocompatibility Complex) interactions were much less diverse than others. In fact, they contributed importantly to our overall results, although they constituted only a small fraction of all interactions. Like many before us, we also had to choose key parameters to define an interface (Methods). As previous studies differed in these parameters, we also provided results for several alternative choices (Section S3.1 in Text S1). For instance, we included structures with a resolution >2.5 Å, used 4 Å instead of 6 Å as the minimal distance between two interacting residues or did not consider the change in solvent accessibility upon binding (dASA) when defining interface residues. These additional analyses demonstrated that some of our quantitative results depended crucially on the choice of critical parameters while the qualitative findings did not.
Two hetero-dimers can differ by minor sequence variations and still correspond to the same external interaction. Comparing these slightly different pairs (Fig. 3, D-SameProt) suggested considerably lower interface conservation than for the same pairs (Fig. 3, D-SameSeq): the most conserved bin (0.9–1.0) was reduced by about five percentage points for Face Position Similarity (Fig. 3A black vs. dark gray) and by nine percentage points for the L_rms measure (Fig. 3B black vs. dark gray). These reductions were evenly distributed over the other similarity ranges. This result can be cast into two opposing views. On the one hand, it suggested that a PPI network accurately reflected the interactions because different protein variants only had a small effect on interfaces. On the other hand, there was a significant influence of small sequence changes. For instance, the range of very different interfaces (0.0–0.5) by the Face Position Similarity measure rose from 12% to 17%. In other words, about one interface pair in six differed substantially.
When two experiments measured external interactions that did not correspond to the same protein pair, but to the same family pair (D-Interolog), interface conservation dropped significantly by both measures (Fig. 3, D-Interolog, rightmost bars; Face Position Similarity: 28–36%; L_rms: 7–11%). For Face Position Similarity, these differences largely originated from a shift toward intermediate levels of conservation, suggesting that most changes partially preserved the approximate interface location. The Sphere Radius Ratio supported this interpretation (Fig. S9 in Text S1). Nevertheless, the interfaces with clear dissimilarity also increased from 13% (D-SameSeq) to almost 30% (D-Interolog, Fig. 3, cumulative black to light gray bin with <0.5). This effect was stronger for L_rms: 33–40% of all comparisons were by this measure clearly dissimilar (>9 Å; Fig. 3[B], light gray vs. black). For these strong differences, the effects from conformational changes (Fig. S5 in Text S1) and from local interfaces appeared to act synergistically.
We hypothesized that families of interologs without alternative binding might have similar functions and that the same could be true for families with extreme binding diversity. Unfortunately, only for 18 Level Interolog clusters, interfaces were always maintained (Face Position Similarity >0.9 at 100%), while only 17 others always used very different interfaces (Face Position Similarity <0.5 at 100%). These numbers were too small to permit statistically significant analyses on the functional differences between those interactions. We still provided a list of those cases in Section S8 in Text S1. The two most extreme findings of this analysis were that the Gene Ontology (GO) [49] term “tetrapyrrole binding” appeared over-represented in the interactions that differed, while “cytoskeletal protein binding” appeared over-represented in the interactions that did not change.
With a special filter, we might remove all alternative binding of a protein to the same homomer from D-SameSeq to D-SameFam (Methods). Obviously, filtering out diversity will reduce the signal of diversity observed. Nevertheless, we performed this analysis. As expected, the observed effects dropped significantly (Table 1), most extremely for D-SameSeq, i.e. for the same pairs. The differential behavior between D-SameSeq and D-Interolog might be explained by sequence divergence increasingly leading to very different interfaces for the same protein pair and ultimately to different quaternary states. Despite all the filtering for homomers, varying interfaces remain frequent and still almost one third of the change seen in interologous pairs (D-Interolog) is also observed between the same pairs (D-SameSeq).
Our finding that most interactions form identically when repeating the experiment might not be surprising. The observation that many interactions differed substantially, in contrast, appears much more counter-intuitive. Readers might attribute the difference to some mistake in the way we measure similarity or build our data sets. We addressed these concerns by expanding our analysis in many directions. On top, we analyzed ten case studies in more detail. Three are discussed in detail in the following (Fig. 4), the other seven in Section S7 in Text S1. Our entire collection of interesting protein pairs is available in Dataset S1.
Empirically, we found several reasons for the same two proteins to have different interfaces (D-SameSeq). The simplest was merely technical: some experimental findings may not have been completely correct. We reduced this effect by excluding complexes with resolutions >2.5 Å, but even structures at 1.2 Å can contain errors [56], [57]. Another reason was local flexibility or disorder: many proteins have local regions that are natively unstructured and these often form protein-protein interfaces [58]; such regions are difficult to track experimentally. Often, the N- and C-termini contributed to the observed interface variability. Another reason was environmental differences: despite all efforts, we could not entirely exclude artificial interfaces due to crystal packing. Different pH values could trigger conformational changes, as was the case for small ligands or other interaction partners. The presence of another protein changing the overall structure of a complex played a similar role. In all that, however, we still miss one important aspect: proteins often have evolved to interact in different ways. For such cases of biologically important alternatives, we might interpret the variety observed in a single PDB structure as an example of one protein binding to multiple copies of the same interaction partner.
There were various reasons why variability in binding was higher between sequentially modified proteins than for identical proteins. The modifications that preserved the original protein (D-SameProt) were usually point mutations (i.e. changes of single amino acids, e.g. by site directed mutagenesis or in the form of Nucleotide Polymorphisms [SNPs]). Others included protein tags at the N- or C-terminus (e.g. to facilitate protein purification), post-translational modifications (protein cleavage) and alternative splicing. For interologs (D-Interolog), finally, there was also evolutionary driven sequence divergence. As described before, however, the mere presence of insertions or deletions was not enough for low interface similarity: we reduced structures to common residues before comparing them. Thus, the increase in variability was actually the result of changes in the common parts of two structures.
Using similar measures as we did, other groups [33], [37] have found that many families interact in more than one way. Our analyses support this result. However, they also reveal that the differences in interfaces span the entire spectrum of the distribution, especially for D-Interolog. Only 18 of the 151 pairs of families completely conserved the binding modes. This finding suggests the model of a continuum of binding modes rather than clearly defined groupings, e.g. as obtained by clustering at predefined thresholds. Furthermore, in our results, about one third of the variability observed in a family-family interaction appeared to be protein-intrinsic in the sense that it was also observed between sequence-identical pairs (D-SameSeq), i.e. did not originate from sequence variations (as, e.g. for D-Interolog).
As mentioned before, alternative interfaces might be due to the intrinsic capability of proteins to bind at different positions. This is often encountered among homo-oligomers [59]. In our case, however, it leads to a debatable scenario: a protein A can bind to multiple copies of protein B, all of which alone form a homo-oligomeric complex (Fig. 2). Do we then have to treat the various external interfaces between the same proteins as one interface, or are they indeed individual interfaces that ought to be differentiated? We argue for the second case: first, considering the homomer a requirement for the hetero-interactions implies that by disabling the homomerization (e.g. through site-directed mutagenesis), we also lose the interaction. This is not always the case [60]. Secondly, it is unclear why such a filtering should be limited to homo-complexes. Also the formation of a hetero-multimer could be a requirement for the interaction with another protein. Studying which interactions remain after disabling the potentially highly complex hetero-multimer is much beyond the currently available data. Finally, also the original publication of a complex usually describes different interfaces to the same homomer as separate interfaces.
Our results raise the question whether the molecular details of protein-protein interactions (PPIs) are crucial for function. Protein crystallography captures static views on those molecular details along with some information about the dynamic nature of PPIs. If the details always had to be the same to guarantee function, different experiments would identify the same interfaces. We applied many reasonable ways of measuring interface similarity in order to analyze the consistency of the molecular details of protein-protein interactions between different experiments. For sequence-identical pairs of proteins, i.e. the same biological interaction, most interfaces were almost completely conserved by all measures. However, all measures also revealed an unexpected variety. Depending on how much detail we required to be similar in order to consider two experiments to yield the same results, we found 11–37% of all observations to have significant differences, and up to 10% to be completely different. One important result was that this was a significant fraction of the difference observed between homologous PPIs. Put differently, over a third of the differences in the interactions between pairs of homologous proteins are also observed between identical proteins. These numbers may challenge the notion that the maintenance of the molecular details is crucial for function. At least, our results suggest that there appear to be many alternative solutions to maintain or actually enable the intricate molecular details: change seems an extremely frequent exception for protein-protein interfaces.
|
10.1371/journal.pgen.1005287 | Npvf: Hypothalamic Biomarker of Ambient Temperature Independent of Nutritional Status | The mechanism by which mice, exposed to the cold, mobilize endogenous or exogenous fuel sources for heat production is unknown. To address this issue we carried out experiments using 3 models of obesity in mice: C57BL/6J+/+ (wild-type B6) mice with variable susceptibility to obesity in response to being fed a high-fat diet (HFD), B6. Ucp1-/- mice with variable diet-induced obesity (DIO) and a deficiency in brown fat thermogenesis and B6. Lep-/- with defects in thermogenesis, fat mobilization and hyperphagia. Mice were exposed to the cold and monitored for changes in food intake and body composition to determine their energy balance phenotype. Upon cold exposure wild-type B6 and Ucp1-/- mice with diet-induced obesity burned endogenous fat in direct proportion to their fat reserves and changes in food intake were inversely related to fat mass, whereas leptin-deficient and lean wild-type B6 mice fed a chow diet depended on increased food intake to fuel thermogenesis. Analysis of gene expression in the hypothalamus to uncover a central regulatory mechanism revealed suppression of the Npvf gene in a manner that depends on the reduced ambient temperature and degree of exposure to the cold, but not on adiposity, leptin levels, food intake or functional brown fat.
| Current knowledge does not provide a clear, definite view of central mechanisms controlling energy balance upon cold-activated thermogenesis. Here we show that upon cold exposure lean mice maintain body composition but increase food intake to fuel thermogenesis, whereas cold-exposed mice with DIO utilize endogenous fat stores and then transition to increased food intake as body composition approaches that of the lean controls. Using knockout mice with leptin and Ucp1 gene deficiency our study indicates that the relative energy utilization from food intake and endogenous energy reserves to maintain body temperature during cold exposure is independent of both leptin action and brown fat-linked thermogenesis. Using a combination of genetic and biological approaches, we demonstrate that Npvf gene expression in the hypothalamus is regulated by changes in ambient temperature in a manner independent of the nutritional status of the mouse.
| Reduced ambient temperature will increase thermogenesis and reduce obesity. However its long-term effectiveness as a strategy to reduce obesity has been questioned because of the expectation that increased energy expenditure for the cold environment will increase food intake, thereby neutralizing the weight reducing effects of the cool environment [1], a skepticism also associated with the effectiveness of physical activity as an anti-obesity strategy [2]. This skepticism emerges from the adipostat hypothesis itself, which predicts that reductions in fat mass by cold stimulation will be compensated by increased food intake to maintain its adiposity index [3]. On the other hand, studies on loss of fat mass by increasing thermogenesis with the chemical uncoupler dinitrophenol (DNP) showed that increased food intake does not necessarily occur [4]. Therefore compensation as predicted by the adipostat model may also not occur in association with BAT thermogenesis. Since chemical uncoupling by DNP, or even activation of thermogenesis by adrenergic receptor agonists [5], are unregulated inductions of thermogenesis, compared to normal physiological mechanisms regulating body temperature, the problem of predicting the effectiveness of achieving energy homeostasis from food intake and endogenous energy reserves during cold exposure remains. Specifically, when an individual is exposed to a cold environment how the physiological decision is made to use endogenous energy reserves or to increase food intake and how this decision is influenced by the obese state of the individual is unknown.
Although significant recent research progress has enhanced our understanding of the central control of BAT thermogenesis and energy expenditure in cold-exposed mammals, some areas are yet not well understood. In cold-exposed animals increased thermogenesis is associated with increased feeding, but is not accompanied by a gain of weight [6]. Coordinated increases in thermogenesis and food intake during cold exposure are controlled by signaling events in hypothalamus that are undefined. Within the hypothalamus, only a few genes are known to be differentially regulated in response to reduced ambient temperature [7–12], but one cannot identify a clear pattern of neuropeptide expression characteristic for the hypothalamic response to the cold. The contribution of the selective neuro-hormone systems such as NPY or TRH in the regulation of cold-activated thermogenesis and feeding behavior has been extensively studied using pharmacologic approaches [13,14] or animal knockout models [15–17]. However, neither of these approaches identifies a critical molecule or describes signaling events that account for central mechanisms controlling energy availability and utilization under cold conditions.
In this study, using wild-type B6 and brown fat deficient Ucp1-/- mice with DIO and genetically obese (Lep-/-) mice, we first determined that cold-induced thermogenesis is preferentially fueled by oxidation of fat reserves in individuals with environmental obesity and by food intake in lean individuals. We then analyzed global gene expression in the hypothalamus of cold-exposed mice and found that suppression of Npvf neuropeptide precursor mRNA levels occurred in the three models of obesity. To our knowledge Npvf is the only transcriptional target in hypothalamus known to be selectively regulated by changes in ambient temperature.
A wide range of body weight in genetically identical B6 mice results from their high natural variation in susceptibility to DIO [18]. We utilized this variation together with feeding mice a HFD for different lengths of time to generate mice with a range of adiposity. After 8 weeks or 1 week of feeding a HFD a cohort of mice was produced in which body weight ranged between 32.4 and 43.8g (greater obese mice) and between 24.4 and 32.7g (lesser obese mice) (Fig 1A). Reducing the ambient temperature from 24 to 4°C resulted in an immediate lowering in body weight that was highest on day one and gradually diminished during the succeeding days (Figs 1B and S1A). Although lesser and greater (range of body weight) obese mice showed the same response, the weight loss was larger in the greater obese group than the lesser obese group (Figs 1B, 1C, S1A and S1B). Fat mass was the major endogenous substrate fueling thermogenesis (Figs 1D and S1B). In the greater obese group, after 4 days at 4°C 97.5 kJ of energy came from fat mass and 33.8 kJ from fat free mass. For the lesser obese group, 30 kJ came from fat mass and 22.9 kJ from fat-free mass. Thus, 4 days of cold exposure resulted in total use of endogenous energy that equaled 131.3 kJ for the greater obese mice and only 52.9 kJ for the lesser obese mice (Fig 1D and 1E). After one day at 4°C both groups of mice experienced a slight decline in body temperature (1–2°C), however, by the 2nd day at 4°C all mice were able to thermoregulate and maintain their body temperature at the level at which they started (36 ± 1°C).
If the lesser obese group utilized less of their endogenous energy reserves during cold exposure than the greater obese, then where did the energy for thermogenesis come from? For this we measured food intake. After 16 weeks on the dietary regime at 24°C, as described in the Methods, food intake was 56.5 ± 3.64 kJ/day for the lesser obese and 53.6 ± 2.29 kJ/day for the greater obese (Fig 1F). When mice were transferred to 4°C, food intake immediately increased in the lesser obese mice to 75 kJ/day (35% increase) and to 84 kJ/day (50% increase) after 1 and 4 days, respectively; the increase in food intake was smaller in the greater obese mice going to 55 kJ/day (2% increase) and 67 kJ/day (25% increase), respectively, after 1 and 4 days in the cold. With increasing time at 4°C the difference in food intake between the lesser and greater obese groups was reduced (S1C Fig), consistent with the diminishing difference in fat mass. After the first day of cold exposure the difference in food consumption between mice from 2 cohorts equaled 20.06 ± 4.53 kJ, after 4 days at 4°C it was 14.44 ± 4.23 kJ (Fig 1F) and only 9.53 ± 2.87 kJ after 7 days at 4°C (S1C Fig). Cold-induced thermogenesis is associated with increased consumption of fuel reserves and, as evident in Figs 1F and S1C, mice with lower endogenous fuel reserves compensate by increasing food intake, a process that apparently increases with time as endogenous fuel reserves become depleted. After 4 days at 4°C, regardless of the level of obesity present in the animals before cold exposure, cumulative energy coming from feeding and mobilized endogenous energy stores was comparable in the greater and lesser obese mice (Fig 1G).
For both lesser and greater obese animals linear regression analysis revealed a strong negative correlation between energy reserves (fat and fat free mass) mobilized per day and daily energy consumed during time spent in the cold (R2 = 0.62 for greater obese mice and R2 = 0.75 for lesser obese after 4 days in the cold) (Fig 1H). An equally strong negative relationship was observed when values of adiposity index calculated for each mouse before cold exposure were plotted against daily food intake during 4 days at 4°C (R2 = 0.74 for both greater and lesser obese mice) (S1D Fig).
An important observation is that mice with robust DIO after 8 weeks on a high fat diet at 24°C will concurrently increase food intake and reduce body weight when transferred to an ambient temperature of 6°C (S2A and S2B Fig). They will stabilize both body weight and food intake to a new state of energy balance to maintain body temperature. When they are returned to 24°C food intake returns to the level observed before the cold exposure and they resume the increase in adiposity characteristic of B6 mice.
At 24°C there were no differences in the level of plasma free fatty acids (FFAs) and insulin between the groups (Fig 2A and 2B). After 4 days at 4°C, greater obese mice had significantly elevated levels of circulating FFAs in comparison to lesser obese, consistent with increased fat mobilization in the greater obese animals. Substantial fat mass loss after 7 days of cold exposure resulted in reduced plasma FFAs in both groups of mice. Similarly, after 7 days at 4°C, circulating insulin was decreased in greater and lesser obese mice compared to 24°C (Fig 2B). At 24°C leptin levels were positively correlated with adiposity (Fig 2C). Leptin levels did not drop during the first 4 days at 4°C, only after 7 days in the cold did highly significant reductions in leptin levels occur (Fig 2D).
We evaluated the effects of leptin administration to DIO B6 mice fed HFD or lean B6 mice fed chow diet on the food intake and utilization of endogenous energy substrates before and after cold challenge. Leptin administration at 24°C decreased average daily food intake from 49.12±1.68 to 39.15±1.69 kJ in DIO mice and from 45.40±1.55 to 34.30±1.21 kJ, in chow fed lean mice (Fig 2E). There was no effect of leptin administration on either body weight or body composition of mice at 24°C (Fig 2E). When the ambient temperature was reduced from 24 to 4°C lean mice receiving leptin immediately increased food intake, whereas their body weight and fat mass did not change. On the other hand, cold-exposed and leptin-administered DIO mice immediately utilized endogenous reserves, then as these reserves diminished, they increased food intake (Fig 2E). These results on food intake and fat utilization with leptin administration are not different from the phenotypes in the absence of exogenous leptin (Fig 1B and 1F).
Mice deficient in either leptin or the leptin receptor are cold intolerant when acutely exposed to 4°C; however, they are able to adapt to a lower temperature if the exposure is gradual [19–21], thereby enabling an analysis of energy utilization during a cold challenge. Although there were large differences in body mass and composition between Lep+/? and Lep-/- mice fed a low fat chow diet at 24°C, after 9 days in the cold neither genotype showed significant changes in body weight mass nor composition (Fig 3A–3C).
With no reduction in endogenous energy reserves we looked to an increase in food intake. At 24°C average daily food intake was about 40% higher in leptin-deficient than in the Lep+/? control mice, as previously observed by Coleman [20] (Fig 3D). One would anticipate that this source of energy would be used to fuel thermogenesis, however, reducing the ambient temperature by 3°C per day resulted in an increase in food intake in both control and Lep-/- mice. This food intake curve is displaced upward by an amount corresponding to the difference in food intake between control Lep+/? and Lep-/- mice at 24°C (Fig 3D). Therefore, the rate of increase in food intake per degree Celsius reduction in ambient temperature by the control mice and Lep-/- mice was essentially indistinguishable (Fig 3E). The most striking observation was that Lep-/- mice, already hyperphagic at 24°C, further increased their food intake under a cold challenge. After correcting for the slight changes in body composition that occurred in mice upon cold exposure, the total energy used for cold-induced thermogenesis was equal in leptin-deficient and control mice (Fig 3F). Accordingly, there were no significant correlations either for control lean Lep+/? or for obese Lep-/- mice between the daily increase in food intake and endogenous body fuel reserves mobilized per day in the cold (Fig 3G), in contrast to the significant correlations in DIO mice (Fig 1H).
It is assumed that non-shivering thermogenesis of brown fat is essential for providing the heat to protect the animal from the cold. Indeed Ucp1-/- newborn mice on either the B6 and 129 genetic backgrounds cannot survive the first days of birth in a breeding room maintained at ~23°C and Ucp1-/- adult mice acutely exposed to the cold at 4°C will succumb within 5 hours [22,23]. However, similar to Lep-/- mice, Ucp1-/- mice can adapt to the cold [24]. Ucp1-/- and Ucp1+/? mice were exposed to the cold using the same protocol as that used for Lep-/- mice, except that DIO was first induced at 24°C as with the greater and lesser obese mice (Fig 1A). The level of obesity for the Ucp1+/? resembled that of the greater obese B6.+/+ mice, whereas the Ucp1-/- mice resembled the lesser obese mice (Fig 4A–4C), even though they were fed the HFD for the full 8 weeks. This is expected, since at 24°C Ucp1-/- mice are resistant to DIO [23]. At 24°C food intake was similar for mutant and control mice, whereas the daily energy intake during cold adaptation was higher for Ucp1-/- mice (Fig 4D). Similar to the results of the initial experiment with wild type B6 mice, Ucp1+/? mice which had the greater obese phenotype preferentially lost fat mass during cold adaptation, whereas the Ucp1-/- mice which had the lesser obese phenotype preferentially increased food intake (Fig 4E). Thus, energy balance and substrate utilization in DIO Ucp1-/- mice during cold exposure resembles that of lesser obese wild-type mice.
In summary, UCP1-dependent brown fat thermogenesis is not required to derive the weight reducing benefits of adapting to the cold and there is no mechanism associated with thermogenesis that will increase food intake of the greater obese to preserve the obese state. There is a mechanism, however, to preserve a minimal adiposity index typified by young adult C57BL/6J mice fed a low fat chow diet. Total energy consumption as shown by 6 experimental groups (Figs 1G, 3F and 4E) indicates that energy expenditure during cold exposure is generally similar, except that Ucp1-/- mice are metabolically inefficient and have higher O2 consumption per mouse [25]. The difference among groups describes source of energy for the induction of thermogenesis, endogenous reserves vs food intake, and it is this difference which is the focus of this study.
At 24°C Lep-/- mice are hyperphagic compared to the Lep+/+ or Lep+/- mice (Fig 3D). Reducing the ambient temperature from 24 to 6°C was accompanied by a graded parallel increase in food intake, corresponding to approximately 50 kJ of energy for both control and mutant mice (Fig 3D). Consequently, the same leptin-independent increase in food intake was observed during the transition from 24 to 6°C in both Lep+/+ and Lep-/-. Since the energy content of Lep+/+ and Lep-/- mice was unchanged during cold exposure, thermogenesis is fueled solely by food intake. Accordingly, we predicted that the same changes in gene expression associated with the central regulation of thermogenesis by the hypothalamus must occur in both Lep+/+ and Lep-/- mice during the transition from 24 to 6°C. Microarray analysis of gene expression was performed on hypothalamic tissue dissected from Lep-/- and Lep+/+ mice kept at different temperature conditions, that is, in mice maintained at 24°C (point A, Fig 3D) and in mice in which the ambient temperature had been reduced to 6°C (point B, Fig 3D). We identified a small subset of genes in Lep-/- in common with Lep+/+ mice during the transition from 24 to 6°C (Fig 5A). Among these genes, neuropeptide VF precursor (Npvf), showed a robust down-regulated expression of 4.0 and 3.5 fold in the hypothalamus of cold-exposed Lep-/- and Lep+/+, respectively. A group of genes encoding for G protein-coupled receptors (GPCRs) including the dopamine receptor D1 (Drd1a), adenosine receptor 2A (Adora2a), GABA(A) receptor subunit delta (Gabdr) and Gpr88 as well as some of their downstream targets including cAMP-regulated phosphprotein 21 (Arpp21) and protein phosphatase 1 regulatory subunit 1B (Ppp1r1b) were up-regulated 1.4 to 3 fold in both Lep+/+ and Lep-/- mice following cold exposure. Cold exposure also increased the expression of antidiuretic hormone arginine vasopressin (Avp) gene in both mutant and wild-type animals by 1.8 and 1.4 fold, respectively. Each of the genes expressed in parallel in Lep+/+ and Lep-/- mice were validated by qRT-PCR (Fig 5B).
To further investigate a potential role for Npvf in food intake as a function of cold, we determined its expression in the hypothalamus of mice with different levels of dietary-induced obesity following cold exposure (Figs 1A and S1A). Similar to the experiment with Lep+/+ and Lep-/- mice, Npvf expression was suppressed in both greater and lesser obese mice after the temperature shift from 24 to 4°C, but its expression was not associated with either adiposity or food intake (Fig 6A). Increasing the duration of cold exposure at 4°C from 1 to 7 days gradually amplifies the reduction in Npvf mRNA levels. In an independent experiment DIO mice that were maintained at 4°C for 14 days and then returned to 24°C for 25 days restored their levels of Npvf mRNA to that initially observed at 24°C (Fig 6A). Npvf mRNA expression in hypothalamic tissue showed a positive correlation with ambient temperature. Mice kept for 14 days at thermoneutrality (29°C) had higher expression of Npvf mRNA in hypothalamus than mice maintained at 24°C. Similarly, 2 weeks at 17°C resulted in a reduction of mRNA expression to levels below that observed at 24°C (Fig 6B).
Although modulation of Npvf precursor mRNA occurs during cold-stimulated thermogenesis, an involvement of Npvf in the regulation of non-shivering thermogenesis in brown fat is unlikely. Down-regulation of Npvf mRNA was not influenced by the absence of UCP1 protein (Fig 6C). Acute exposure to the cold requires an immediate response for heat generation and leads to immediate UCP1 production in BAT and WAT. A separate experiment performed to illustrate time-course of changes in the expression of Npvf under low temperature conditions showed that significant suppression in the amount of Npvf mRNA does not occur before 12h at 4°C; a significant decrease in the accumulation of Npvf mRNA in hypothalamus is found after 24h at 4°C compared to 29°C (Fig 6D). Moreover, one week administration of β3-adrenergic agonist CL 316,243 (1mg/kg of body weight) did not result in the suppression of Npvf mRNA in hypothalamus compared to saline-treated control mice (Fig 6E), providing evidence that changes in expression of the Npvf gene are not linked to heat production or brown adipocyte induction in peripheral β3-AR-expressing tissue targets.
Genes associated with food intake in the hypothalamus, thermogenesis in the adipose tissue, and lipid metabolism in the liver and adipose tissues were analyzed by qRT-PCR. No patterns in gene expression could illuminate mechanisms associated with the phenotypes described above (see Supplement; S3A and S3B Fig for thermogenic genes in iBAT and iWAT, S4A and S4B Fig for neuropepetides of feeding behavior, and S5A Fig for genes of fatty acid metabolism in the liver, S5B Fig in iBAT and S5C Fig in iWAT).
We show that the total energy expended by a mouse from food intake and endogenous energy reserves to sustain thermogenesis during cold exposure is independent of the degree of obesity in the animals. This is true in genetically obese Lep-/- mice, chow-fed wild-type mice (Fig 3F) and in wild-type mice and B6.Ucp1-/- with variable levels of DIO (Figs 1G and 4E, respectively). However, in chow-fed mice the energy that is necessary to sustain a thermogenic program to maintain body temperature in the cold comes exclusively from feeding, as observed by others [20,26]; whereas in a wild-type mouse with diet-induced obesity induced by a high-fat diet, the fuel to support thermogenesis is obtained from endogenous energy reserves (mostly fat) and food intake. In DIO mice the source of energy required to maintain body temperature during cold exposure is determined by the degree of obesity. In DIO mice the energy reserves in fat mass are not privileged or restricted as those in a normal wild-type mouse maintained on a low-fat chow diet, rather they are utilized in proportion to their absolute levels. DIO mice with the highest levels of stored fat immediately mobilize fat, subsequently as these reserves become depleted, food intake becomes progressively a larger contributor to the fuel mix. In contrast, those mice that are at the other end of the DIO spectrum, the lesser obese mice, will preferentially increase food intake and use less of their endogenous fuel reserves to support thermogenesis. An important finding is that wild type mice with high levels of adiposity behave in response to cold exposure by the utilization of available energy sources in a manner that is independent of hormonal status, i.e. leptin and insulin. Serum leptin levels measured before cold exposure indicated that leptin resistance should have been higher in the greater obese mice than in the lesser obese, predicting a defense of adipose stores and higher food intake in greater obese mice. However, from the very beginning of cold exposure a defense of the fat status in mice with leptin levels predictive of leptin resistance was not observed. In fact the opposite was observed, with food intake reduced and fat utilization increased in the greater obese mice compared to the lesser obese.
Interestingly, our observations on body composition-dependent differential fuel selection occurring during cold exposure in DIO mice parallels findings in exercising human subjects (32). Moderate to intense physical activity performed regularly and on a long-term basis by lean individuals is compensated for by a corresponding change in food intake while body mass is maintained. On the other hand, obese individuals with excess fat storage do not significantly increase food intake and loss of body fat occurs as a consequence [27]. A return of mice fed a HFD from 6 to 24°C leads to a decrease in food intake and increase in adiposity characteristic of their phenotype on a high fat diet (S2A and S2B Fig). We previously observed the same response of mice fed a high fat diet when energy balance was interrupted with food restriction [18]. Accordingly, mice do not assume increased levels of food intake transiently acquired when they are in the cold, rather food intake is set by the requirements for heat production as originally hypothesized by Brobeck [28]. The long-term defense of body weight in humans and mice has been described and discussed as a consequence of under- and over-feeding [29]; however, mechanisms associated with a negative energy balance resulting from reduced energy intake during dieting may be different from increased energy expenditure in response to cold-induced energy expenditure, since the latter condition is supported by increased food intake and the neutralization of insulin and leptin resistance [2,30].
Wild-type B6 (Lep+/- or Lep+/+) mice fed a low fat chow diet exhibited almost no change in endogenous energy reserves, that is, lean mass or fat mass when the ambient temperature was reduced from 24 to 6°C, but they increased food intake. This observation fits with the thermostatic theory proposed by John Brobeck in the late 1940s, which relates the regulation of body temperature to the control of feeding behavior [28]. Brobeck summed up his theory by saying: “…animals eat to keep warm and stop eating to prevent hyperthermia”. In the present study, the wild-type B6 mouse maintained energy balance and body composition on a normal diet, when exposed to the cold, by increasing calorie intake. Importantly, the Lep-/- mouse behaved in the same manner, it adapted to the cold by increasing food intake in a manner quantitatively indistinguishable from the normal B6 mouse and it preserved its endogenous energy reserves. The β-oxidation of fat stores of Lep-/- mice is not an option for fuel to maintain body temperature [31] and this is a major factor in cold intolerance of leptin-deficient mice during acute exposure [32]. Lep-/- mice sensed that existing fat stores were unavailable and compensated by increasing food intake in a leptin-independent manner. This feeding behavior in the cold underscores the inability of mice with leptin-deficiency to utilize endogenous fat reserves; furthermore it also shows that in the face of a cold challenge fuel for thermogenesis must come from food intake. On the other hand, the wild-type mouse on a low fat chow diet can access its energy reserves in an acute situation, but quickly turns to increased food intake to maintain energy balance. This similarity in the metabolic response to the cold environment between normal and leptin-deficient mouse suggests that leptin is not important for the acute thermogenic phenotype in the Lep-/- mouse, nor for the regulation of food intake during cold exposure by normal wild-type mice fed a chow diet.
Mice with mutations to leptin and the leptin receptor have a thermogenic phenotype in which body temperature drops about 10°C in about 4 hours at an ambient temperature of 4°C [21]; however, as illustrated in Fig 3D they can adapt to the cold when it is gradually reduced. A key feature of cold-induced thermogenesis in normal animals is the increase in food intake that occurs over and above the increase in food intake necessary to support nutrition [33–36]; as exemplified by the remarkable boost in food intake that occurs in lactating females exposed to the cold [26]. This suggests that central mechanisms controlling food intake, as related to nutrition, growth and body composition, may be independent of those associated with cold-induced thermogenesis. A similar idea has been put forth by Speakman and Krol [37], but with a necessary role for leptin in the cold-induced food intake, which we did not see, nor was a role for leptin proposed by Melnyk and Himms-Hagen [6]. We had observed previously, as did Coleman [20], that Lep-/- mice exposed to the cold further increased food intake above that normally occurring in these mice [19]. This preliminary observation has been extended in this study to show that this hyperphagia, which is above that normally occurring in Lep-/- mice fed a chow diet, is very similar in magnitude and kinetics to that occurring in Lep+/+ mice. Accordingly, mechanisms controlling cold-associated food intake in Lep-/- mice are independent of leptin-based regulation of food intake. We tested further the role of leptin in regulating thermogenesis during cold exposure in wild-type DIO mice. Plasma leptin and insulin levels in DIO mice of this study are remarkably similar to mice described in previous studies that were leptin resistant [38]. If the mobilization of fuels for cold-induced thermogenesis in DIO mice is controlled by the leptin resistance at the time of cold exposure, then one would predict that food intake would be high and mobilization of endogenous fat stores would be low. However, within one day of exposure to the cold the opposite phenotype was observed in DIO mice: food intake was low and fat mobilization was high. Even 4 days after cold exposure plasma levels of leptin were not significantly different from those at 24°C; only after 7 days in the cold were the levels of leptin significantly reduced (Fig 2D). Additional leptin administered intraperitoneally to DIO and lean mice did not affect the observed pattern of energy substrate utilization in the cold (Fig 2E). This data additionally suggests that the mechanism controlling food intake during acute cold exposure is independent of leptin signaling. Chronic cold adaptation may involve leptin by another mechanism [19].
The primary motive driving this study was to explore the feasibility of using cold exposure as an anti-obesity strategy. Human studies on brown fat show dramatic inter-individual differences in brown adipocyte content and BAT activity [39,40]. Thus, it is important to assess how the cold-stimulated effect of body weight reduction is influenced when the capacity for thermogenesis in brown fat is variable. The extent to which BAT-mediated adaptive thermogenesis could account for variability in substrate utilization in the reduced ambient temperature is also not known. For these reasons we evaluated the phenotype of DIO Ucp1-/- mice lacking functional brown fat. Ucp1-/- mice are sensitive to the cold; however, they can adapt to the cold if the ambient temperature is gradually reduced [24,41].Therefore, if UCP1 is essential to the thermogenic process, then in its absence the capacity for heat production from brown fat would be severely suppressed and we could expect effects on food intake and or the utilization of endogenous fuels that would differ from the wild-type mouse. As expected, when the ambient temperature was reduced average food intake was higher in the Ucp1-/- mice than in control mice, because these mice are less obese when fed a high-fat diet and they burned less of their endogenous reserves compared to normal Ucp1+/? mice with the greater obese phenotype (Fig 4B and 4D). Thus, there does not seem to be any difference in the pattern of utilization of endogenous food reserves or food intake between UCP1-deficient and wild type mice, provided that these mice have similar adiposity phenotypes as occurs with the lesser and greater obese mice.
QRT-PCR analysis of the expression of several genes in the hypothalamus encoding neuropeptides implicated with food intake did not provide evidence for the involvement of any of the neuropeptides associated with food intake with the possible exception of CART and POMC which have expression reduced by 30% and 50% in Lep+/+ only. However, a microarray analysis of gene expression in Lep-/- and Lep+/+ mice at 24 and 6°C showed that the expression of neuropeptide VF precursor was decreased 4-fold during cold exposure, and a similar level of down-regulation for this gene was observed for all three of the genetic models we have studied. In rodent brain, the sequence of the Npvf precursor gene predicts two–RFamide peptides: RFRP-1 and RFRP-3, also named NPSF and NPVF [42,43]. There is an expanding body of evidence for a role of various–RFamide peptides in the modulation of nociception, hormone secretion, reproduction or blood pressure [44–46]. Finally, although little is known of the functional significance of this particular biological effect, various–RFamide peptides were able to illicit a transient 10–300% induction or suppression of food intake in chicks, rats or mice after i.c.v. injection [44,47–50]. Moreover, food restriction or deprivation, both stimulating hunger and food hoarding, have been shown to be positively correlated with activation of RFRP-3 cells in the DMH of Syrian hamsters [51]. Effects on thermogenesis are unknown. From a functional viewpoint, specific expression of Npvf mRNA in the rodent central nervous system is restricted to a population of neurons localized between dorsomedial hypothalamic (DMH) and ventromedial hypothalamic (VMH) nucleus [42,43,52,53], which is consistent with a putative role in feeding or thermogenic processes [54]. Our observations on lack of association between leptin status and regulation of Npvf mRNA in the cold demonstrate that Npvf system in hypothalamus is unlikely to be leptin responsive, which is in agreement with a recent study, where no evidence for leptin signaling after leptin injection or detection of leptin receptors in RFRP3 expressing neurons was found in mice hypothalamus [55]. Thus, the reduction in Npvf expression at lower temperature when a higher level of energy expenditure (EE) is required suggests that reduction of Npvf releases a brake on EE. Although increased food intake provides the fuel for the increase in EE in lesser obese mice, endogenous fat provides the fuel in greater obese mice. Since Npvf is similarly suppressed in both lesser and greater obese mice, neither endogenous substrate or food intake per se are the signals associated with Npvf expression levels. Lack of association between body energy reserves and hypothalamic Npvf expression was also shown in the recent study in which no significant difference in Npvf mRNA was detected between mice fed high-fat and low-fat diet for 20 weeks [55].
A motive for conducting our experiment was to establish in a mouse model the effects of cold on substrate utilization and long-term effects of cold exposure on food intake after a return to ambient temperature. This study clearly showed that upon cold exposure obese mice fuel their increase in energy expenditure with endogenous fat supplies, whereas lean mice increase food intake. An analysis of three mouse models of obesity suggests that reduced ambient temperature is effective in reducing diet-induced obesity without long-term compensatory increases in food intake. Whether humans will behave in a similar manner needs to be determined. The second part of our study uncovered evidence for a new hypothalamic signaling pathway, involving the Npvf gene, that is regulated in cold-activated thermogenesis. We will work towards determining whether a similar signaling pathway is present in humans.
Breeding pairs of C57BL/6J.+/+, C57BL/6J.Ucp1+/- and C57BL/6J.Lep+/- mice were obtained through the generosity of Dr. Martin Klingenspor of the Technical University of Munich, Germany. All procedures concerned with breeding, housing, maintenance and experimental treatment of the mice were approved by the Local Animal Care and Use Committee for University of Warmia and Mazury, Olsztyn. Guidelines for animal experiments followed EU Directive 2010/63/EU.
The goal of this protocol was to generate a series of mice with a range of adiposities by use of a high-fat diet to determine the effects of cold exposure on changes in food intake and endogenous energy stores. Breeding pairs of C57BL/6J+/+ mice were housed at standard temperature (24±1°C) and maintained in ventilated rooms under a standard-day photoperiod (12:12-h light-dark period, lights on from 0700 to 1900 h) with free access to low-fat diet (PicoLab Rodent Diet 20, LabDiet 5053, 11.9 kcal % fat) and water. At 21 days of age male progeny were weaned and housed in groups of 3–5 in plastic cages with fresh sawdust bedding. Body weight and body composition by NMR (Bruker, BioSpin, Germany) were monitored until mice were 8 weeks of age, at which time mice were individually housed and divided into two nutritional groups matched for similar mean body mass and body fat content to form the lesser and greater obese groups. By 8 weeks of age, when mice were still on a low-fat diet, their body weights ranged from 19.2–25.5g (Fig 1A). Based on the NMR analysis, the distribution in body weights was mainly caused by differences in fat mass, with only a small contribution from fat free mass The greater obese group was fed a high-fat diet (AIN-76A with 33% hydrogenated coconut oil, 58 kcal % fat) from the 8th to 16th week to establish a range of mice with a higher adiposity index. The lesser obese would continue to be fed the low-fat diet (PicoLab Rodent Diet 20, LabDiet 5053, 11.9 kcal % fat) for 7 weeks and then the high fat diet for the 16th week. Mice with body weights and fat mass ranging from 24.4 to 43.8g and 3.7 to 18.8g, respectively, were formed (Fig 1A–1D). The aim of such a dietary intervention was to establish as broad a range in adiposity as possible between two groups of mice and at the same time to induce metabolic adaptations associated with high-fat feeding in the lesser obese mice at the time of cold exposure. In addition to having mice with a variation in adiposity, food intake of the lesser and greater obese mice was measured for the 16th week, just prior to being exposed to the cold. Food intake, initially varied in the lesser obese mice when presented with a highly palatable high-fat diet for the first time; however, on the last day before cold exposure, there was no significant difference in food intake between the lesser and greater obese mice (S1E Fig).
In order to determine the relative contribution of food intake and endogenous energy reserves to fuel cold-induced thermogenesis, individually housed mice in the lesser and greater obese groups of mice were transferred to a cold room at 4°C for either 4 or 7 days. Food intake (high-fat; AIN-76A) and body weight were measured daily and body composition was analyzed by NMR at the end of the cold exposure. To calculate daily energy expenditure in the cold coming from endogenous and exogenous energy sources, fat mass and fat free mass measured after cold exposure were subtracted from fat mass and fat free mass measured before cold exposure and divided by number of days spent in the cold; average daily food intake measured at 24°C was subtracted from average daily food intake measured at 4°C. Energy values in kJ for g of fat mass or fat free mass were calculated as follows: 4.18 kJ/kcal × (9 or 4 kcal/g, respectively). Energy values in kJ for g of low-fat chow diet or high-fat diet were calculated as follows: 4.18 kJ/kcal × (3.07 or 5.44 kcal/g, respectively).
To observe the effects of cold-induced hyperphagia on DIO mice that were returned to an ambient temperature of 24°C, adult C57BL6/J+/+ mice were fed a high-fat diet (AIN-76A) from 8 to 16 weeks of age then transferred to 4°C until food intake stabilized over a course of 16 days. Mice were returned to an ambient temperature of 24°C and the suppression of food intake was monitored for an additional month.
To assess the effects of leptin treatment (1μg/g BW twice a day) on utilization of energy fuel coming from endogenous reserves or food intake, we measured daily changes in food intake, body weight and composition in 8 week-old lean B6 male mice fed chow diet and 16 week-old DIO B6 male mice fed HFD. Leptin was administered for 4 days at 24°C and for an additional 4 days at 4°C.
All mice used in the following studies were adult wild-type C57BL/6J+/+ mice. Mice were placed in individual cages with free access to food (LabDiet 5053) and water. At the end of each experiment mice were sacrificed and hypothalamus was dissected in order to measure the level of Npvf mRNA expression. To establish the influence of different ambient temperature on Npvf expression mice were kept in climate-controlled rodent incubators set to 29 and 17°C for the period of weeks prior to sacrifice. Additional experiment was performed to observe the kinetics of changes in the level of Npvf mRNA in the cold. All mice used in this study were first allowed to acclimate to 29°C for 2 weeks before cold challenge. Temperature of the housing unit was then transitioned from 29 to 6°C and mice were cold-challenged for 6, 12 or 24h. To evaluate the effects of the β3 adrenergic receptor agonist on Ucp1 and Npvf thermoneutrally acclimated mice were injected subcutaneously with 1 mg/kg BW/day CL 316,243 or saline for 7 days.
Mice were anesthetized by the solution of ketamine, xylopan and chlorpromazine (26.6 mg/ml, 1.67 mg/ml and 0.53 mg/ml, respectively, 40μl/10g body weight) and the blood was collected through heart puncture to EDTA coated tubes. After decapitation, interscapular brown adipose tissue depot (iBAT), inguinal white adipose tissue depot (iWAT) and the liver were removed, rapidly frozen in liquid nitrogen and stored at -80°C for subsequent preparation of total RNA. To isolate the whole hypothalamus, the brain was removed and placed on an ice-cooled glass plate with the cortex facing down. The hypothalamus was dissected along the following boundaries: laterally 2 mm either side of the third ventricle, 2 mm dorsally from the base of the brain and rostrocaudally from the optic chiasm to the posterior border of the mammillary bodies. The dissected hypothalami were stored at -80°C until further analysis. The blood was centrifuged for 10 min at 3,000 g, 4°C. Plasma was removed and stored at -80°C until assayed.
Plasma insulin and leptin were measured by enzyme-linked immunosorbent assay with commercial kits (Wide range mouse insulin immunoassay kit, Biorbyt Ltd., Cambridge, UK; Mouse/rat leptin ELISA kit, Phoenix Pharmacuticals, Inc., Burlingame, CA, United States, respectively). Assessment of FFA in plasma was performed with plasma non-esterified free fatty acid detection kit (Zenbio, Inc., Research Triangle Park, NC, United States).
Total RNA was isolated from adipose tissue, liver and hypothalamus using TRI Reagent and BCP phase separation reagent (Molecular Research Center Inc. Cincinnati, OH, United States). RNA was further purified by using the RNAeasy minikit (QIAGEN, Valencia, CA, United States) and stored at -80°C in RNase-free H2O with addition of SUPERase-In (Ambion, Austin, TX, United States) for RNase protection. Quality and quantity of RNA was determined using UV spectrophotometry (Nanodrop) and agarose gel visualization of intact RNA. Quantitative RT-PCR using TaqMan probes and primers (Applied Biosystems, Foster City, CA, United States) was performed with standard curves generated using pooled RNA isolated from corresponding tissues collected from eight 8 week old C57BL/6J.+/+ mice. Probe and primer sequences used to perform the analyses are available upon request. All the gene expression data were normalized to the level of cyclophilin b.
Total RNA was isolated from the hypothalamus of 8 Lep-/- and 8 Lep+/+ mice maintained at 24 and 6°C, as described above. RNA with RNA Integrity number higher than 8.5 (Agilent 2100 Bioanalyzer, Agilent Technologies, Santa Clara, CA) was used for microarray analysis of each individual mouse. RNA was amplified, labeled and hybridized onto chips containing over 56,000 probes of mouse genes (Agilent Single Color SurePrint G3 Mouse GE 8x60K Microarray Kit, G4852A, Agilent Technologies) according to manufacturer’s guidelines. Agilent Feature Extraction software was used for array image analysis. Absolute and comparative analyses were performed using the GeneSpring GX 10 (Agilent Technologies). Quality control filtering after quantile normalization resulted in approximately 33,000 probes. Probes that were not above microarray background signal or whose sequences could not be mapped to Ensembl transcripts were discarded. Fold change of gene expression was calculated based on the normalized signal values. Genes were considered significantly down-regulated or up-regulated if the fold-change was less than -1.4 or greater than 1.4, respectively, and the FDR-corrected P-value was less than 0.05. To validate the reliability of the results obtained from the microarray analysis, we performed qRT-PCR for all genes of interest.
Graphs were created with the GraphPad Prism Software (Version 6.0, GraphPad Software, Inc.; La Jolla, USA). All data sets were analyzed using Student’s test for groups (GraphPad Prism Software). Data are presented as means ± SEM. Differences between the means for all tests were considered statistically significant if P < 0.05.
|
10.1371/journal.ppat.1007870 | Antigenic cartography of immune responses to Plasmodium falciparum erythrocyte membrane protein 1 (PfEMP1) | Naturally acquired clinical immunity to Plasmodium falciparum is partly mediated by antibodies directed at parasite-derived antigens expressed on the surface of red blood cells which mediate disease and are extremely diverse. Unlike children, adults recognize a broad range of variant surface antigens (VSAs) and are protected from severe disease. Though crucial to the design and feasibility of an effective malaria vaccine, it is not yet known whether immunity arises through cumulative exposure to each of many antigenic types, cross-reactivity between antigenic types, or some other mechanism. In this study, we measured plasma antibody responses of 36 children with symptomatic malaria to a diverse panel of 36 recombinant proteins comprising part of the DBLα domain (the ‘DBLα-tag’) of PfEMP1, a major class of VSAs. We found that although plasma antibody responses were highly specific to individual antigens, serological profiles of responses across antigens fell into one of just two distinct types. One type was found almost exclusively in children that succumbed to severe disease (19 out of 20) while the other occurred in all children with mild disease (16 out of 16). Moreover, children with severe malaria had serological profiles that were narrower in antigen specificity and shorter-lived than those in children with mild malaria. Borrowing a novel technique used in influenza–antigenic cartography—we mapped these dichotomous serological profiles to amino acid sequence variation within a small sub-region of the PfEMP1 DBLα domain. By applying our methodology on a larger scale, it should be possible to identify epitopes responsible for eliciting the protective version of serological profiles to PfEMP1 thereby accelerating development of a broadly effective anti-disease malaria vaccine.
| Immunity to human malaria parasites never fully protects against infection, even after a lifetime of exposure. By contrast, protection against severe disease occurs early in life in malaria-endemic areas. Both anti-infection and anti-disease immunity depend on antibody responses to proteins expressed by the parasite on the red blood cell surface which cause pathology. These proteins are extremely diverse thus creating a problem for designing a widely effective vaccine. Despite its importance to vaccine design, however, it is not yet known whether protection against malaria depends on accumulation of exposure to each of the many antigenic types, or whether this is accelerated due to cross-reactivity between antigenic types or some other mechanism. In this study, by applying a novel technique used for describing antigenic diversity in influenza viruses–antigenic cartography—we make the surprising discoveries that children’s serological responses to a panel of diverse surface antigens fall into one of just two qualitatively distinct patterns, and that these almost perfectly predict severity of disease. These alternative serological profiles were found to associate with genetic variants within a small region of the protein. By using the methodology presented here to define the antigenic types and their underlying genetic variants that give rise to the protective version of the serological response, progress towards development of a broadly protective vaccine against severe malaria might be accelerated.
| The surface of red blood cells (RBCs) infected with Plasmodium falciparum contains antigens of parasite origin that are highly immunogenic and genetically very diverse [1]. Diversity in variant surface antigens (VSAs) plays an important role in immune evasion and thus in prolonging infections: this affords parasites more opportunities to transmit to new hosts. Acquisition of antibodies to the most studied family of VSAs–P. falciparum erythrocyte membrane protein 1 (PfEMP1)—is associated with protection against malarial disease [2,3]. Since PfEMP1 also plays a key role in pathology due to its property as an adhesion ligand to host cells [4], this protein family makes an attractive target for vaccine development.
PfEMP1 is encoded by approximately 60 var genes which are genetically diverse within and between parasite genomes, and which recombine, thus potentially presenting a challenge in finding a handful of antigens that could form the basis of a broadly effective vaccine. Despite their genetic diversity, however, var genes structure into distinct groups thus somewhat limiting this pool of variability. These groupings are based on chromosomal position and upstream sequence (Ups) [5,6]; combinations of domains and sub-domains (domain cassettes, DC) [5,7–9]; and homology in short sequence blocks found across the full gene (‘homology blocks’, HB) [7,9] or at positions of limited variability (PoLV) within the DBLα domain [10]. These different classification systems partially overlap [11,12]. Some var genetic groups have been consistently associated with severe disease, denoted here as ‘SM types’, namely, those with Group A-type upstream promoters (UpsA) [13], domain cassettes 8 and 13 [14], the REY motif at the PoLV2 position of the DBLα domain [15], and the presence of two cysteines (cys2) between the PolV3 and PolV4 positions in DBLα [13,16]. PfEMP1 host cell adhesion phenotypes ICAM-1, EPCR and rosetting have been mapped to expression of specific domains or domain cassettes thus providing a clear link between PfEMP1 diversity, adhesion phenotype and disease severity [4].
Antigenic properties of PfEMP1 are, by contrast, poorly understood. It is known that antibodies against var types associated with severe malaria, such as UpsA and DC8, are readily detected in young children living in malaria-endemic regions and develop before antibodies to other types [13,15,17–20]. In addition, VSAs from parasites found in younger children and those with severe disease are more frequently recognized by sera than those from older or more immune children: that is, they are more ‘immunologically common’ [2,13,15,21,22]. However, the antigenic properties that differentiate these types are not known. It has been hypothesised that some var types–those found in younger and sicker children—have immunological properties that differentially affect the quality and efficacy of the PfEMP1 antibody response [13]. Possible reasons for stronger antibody recognition of these types are that they have higher immunogenicity, elevated transcriptional levels, greater protein abundance on the red cell surface, stronger antigenic conservation, wider cross-reactivity or better ability to grow inside the host and thus become immunodominant. Some of these hypotheses are supported by recent studies [13,15,20,23]. Here, we describe the antigenic diversity of PfEMP1 in relation to severe vs. mild malaria in order to understand this further. To do so, we borrow a technique used in the study of immune cross-reactivity of influenza A viruses called ‘antigenic cartography’ [24,25] that is used to annually update flu vaccines in order to cover the ‘antigenic space’ of the circulating pathogen population [26]. Our overall aim is to lay the foundation for inferring the subset of antigens that might form a broadly protective PfEMP1-based vaccine.
We measured sero-reactivity, both IgG and IgM, of anti-sera from 36 children with severe or mild malaria to a panel of recombinant proteins that represented part of the DBLα domain -the DBLα-tag—of dominantly expressed PfEMP1 types found in clinical isolates of 36 children (Fig 1, S1 Fig). 32 of the antigens derived from the 36 children that provided antisera. As expected given the endemicity of malaria in our study site in coastal Kenya, at the time of recruitment, children in our study had substantial prior history of malaria exposure, evident from their generally broad recognition and significantly higher sero-reactivity than unexposed control individuals to recombinant DBLα-tag proteins (Fig 1). Sero-reactivity of IgG with individual DBLα-tag proteins was generally higher than sero-reactivity of IgM at acute disease. This is most likely to be because IgG plasma antibodies reactive with individual DBLα-tags were generated during previous infections while IgM responses present induction of de novo responses from naïve B cells and, to a lesser extent, IgM memory B cells. We used analysis of variance to determine the principal factors affecting sero-reactivity: these revealed that both antisera and DBLα-tag antigens varied substantially in their average reactivity across antigens/sera, respectively explaining 39% and 12% of the total variation for IgG, and 13% and 7% for IgM under analysis Model 1 (Fig 1). These differences were maintained across timepoints (Fig 1, S1, S2 and S3 Figs). In addition, there was strong specificity of reactivity of individual anti-sera to individual DBLα-tag antigens: this explained a further 24% and 15% of the total variation in IgG and IgM reactivity, respectively, after excluding data from homologous (i.e., derived from the same child) DBLα-tag-antiserum pairs. These patterns of sero-reactivity were exploited to produce ‘serological maps’ that reflect shared patterns of reactivity of individual sera with all DBLα-tag antigens, and ‘antigen maps’ that reflect shared patterns of reactivity of individual DBLα-tags with all anti-sera. Distances between points in these maps correspond to differences in reactivity patterns between antigens or between sera, respectively.
Serological maps revealed two distinct clusters which we denote serological Clusters I and II hereon. For IgG, these serological clusters were apparent at the acute stage (Fig 2A) and remained stable throughout the convalescent period (Fig 2B, S1 & S4 Figs). By contrast, for IgM, serological clusters were not evident at the acute stage (Fig 2C) but, as convalescence progressed, despite the overall low sero-reactivity compared to IgG, two clusters emerged which were the same as those for IgG (Fig 2D, S5 Fig). Serological clusters were still evident after pre-adjusting sero-reactivity data for mean antisera reactivity (S4 and S5 Figs vs. Fig 2) and thus were not generated by systematic differences in mean reactivity of sera. Instead, they were driven by the highly specific nature of serum-DBLα-tag reactivities: this was most obvious for serological Cluster II antisera which reacted strongly with DBLα-tag antigens P6433, P6408 and P6430 while serological Cluster I sera did not (Fig 1, S1 Fig).
DBLα-tag antigen maps, by contrast, did not show strong clustering at any timepoint for either IgG or IgM (S6, S7 and S8 Figs). Instead, distances between DBLα-tag antigens were principally a function of differences in a given DBLα-tag antigen’s mean reactivity (S6 Fig). Nonetheless, DBLα-tag antigens grouped into five groups (denoted DBLα-tag antigen Clusters III to VII) using a combination of hierarchical clustering (S7 Fig) and the proportion of antisera strongly recognized (log10 OD > 2) as follows: Antigen Cluster III, ‘high reactivity’ with >75% anti-sera reactive with these DBLα-tags; Antigen Cluster IV, ‘high specificity’ with 40–60% of anti-sera strongly reactive and the remaining anti-sera with low reactivity; Antigen Cluster V, ‘medium specificity and reactivity’ with medium reactivity of anti-sera to a broad set of DBLα-tag antigens (19–64%) that were largely non-overlapping with the highly reactive antigens that defined Cluster IV; Antigen Cluster VI with ‘low reactivity’ with <25% of anti-sera reactive; and Antigen Cluster VII with ‘zero reactivity’ with 0% of anti-sera reactive. Antigen clustering weakened upon pre-adjusting for antigen mean reactivity (S7 and S8 Figs vs. S1 Fig). Antigenic clusters did not significantly associate with serological clusters (S6 Fig, P = 0.20 by chi-squared test of association on 4 d.f.).
Serological clusters almost perfectly aligned to clinical group and thus disease severity: whereas all serological Cluster I sera derived from patients with mild malaria attending the hospital outpatients department (n = 16), all but one (n = 19) of the Cluster II sera derived from patients admitted to the hospital wards (Fig 3A, P < 0.001 by chi-squared test on 1 d.f.). Since host age, parasite density at the time of sampling and average reactivity of the serum did not differ significantly between clinical groups (Table 1, P > 0.05 by t-tests), the association between serological clusters and disease is unlikely to have arisen from different levels of prior exposure in the two clinical groups. There was a significant effect of clinical group on date of sampling because all six plasma samples collected in 2008 were sourced from patients admitted to the hospital (Table 1). However, since the association between serological clusters and disease severity remained highly significant (P < 0.001) after excluding data from the 2008 samples, this confounding was not responsible for the association, as might be the case, for example, if the frequency of severe malaria-causing genotypes in the population changed from year to year.
DBLα-tag antigen clusters did not significantly associate with clinical group (P = 0.36 by chi-squared test on 4 d.f., Fig 3B). However, all three of the DBLα-tag antigens with very high specificity, reacting strongly to approximately half the plasma (antigen Cluster IV), derived from patients with mild disease who had serological Cluster I profiles. DBLα-tag antigen cluster was not significantly associated with other host infection parameters (Table 1).
At the acute stage of infection, IgG sero-reactivity of homologous antigen-antiserum pairs (i.e., both the plasma and parasite isolate from which the dominant DBLα-tag was cloned were sourced from the same child) was significantly lower than for heterologous antigen-antiserum pairs (P < 0.001 under Model 2, Fig 4A vs. 4B and 4C). This effect disappeared in the convalescent phase during which antibodies to homologous DBLα-tags significantly increased (P < 0.001 fitting timepoint as a linear covariate) while those to heterologous DBLα-tags significantly declined (see below). These findings are consistent with those from previous studies showing that children are more likely to become infected with PfEMP1 antigenic types not encountered previously, and that they subsequently mount antibody responses that are primarily specific to the infecting antigenic type [2,27–29].
There were no differences between serological Cluster I and II in the homologous IgG response (P > 0.05 at all timepoints under Model 4, Fig 4A). By contrast, Cluster I and II antisera responded differently to heterologous DBLα-tag antigens. Acute stage plasma belonging to serological Cluster I had lower IgG reactivity to heterologous DBLα-tag antigens from children with the same serological cluster than to antigens from the opposite cluster (Fig 4B vs. 4C, P < 0.01 and P < 0.05 at the A and C2 timepoints under Model 3), similar to the heterology effect for individual antigen-antisera pairs. By contrast, serological Cluster II had similar or higher IgG sero-reactivity to DBLα-tags from children with the same serological cluster than to antigens from the opposite cluster (P = 0.04 for timepoint C3 and P > 0.05 at the A and C2 timepoints under Model 3, Fig 4B vs. 4C). Serological Cluster II profiles further differed from serological Cluster I profiles in that–with the exception of responses to homologous antigens—antisera levels declined significantly during convalescence (Fig 4B and 4C, P < 0.001 for Cluster II vs. P > 0.05 for Cluster I fitting timepoint as a linear covariate in Model 3). Serological cluster and cluster heterology effects for IgM patterns were qualitatively similar, though weaker, to those for IgG (Fig 4D, 4E and 4F).
Combined, these results suggest that children develop one of just two qualitatively distinct serological profiles with respect to the DBLα-tag antigen which align with disease outcome of the infection. Serological Cluster I profiles appear to be broadly reactive, long-lasting and, while less effective in protecting against infection with non-SM than SM types, nonetheless are effective in preventing progression to severe disease. Serological Cluster II profiles, by contrast, while generally stronger at the time of acute infection, appear to be narrowly specific, transient, non-protective against infection with all types—both SM and non-SM—and ineffective in preventing progression to severe disease.
Genetic maps based on the full amino acid sequence of the DBLα-tag region of PfEMP1, like serological maps, revealed two genetic clusters (denoted Genetic Clusters VIII and IX) (Fig 5). These corresponded to the 2-cysteine vs. not-2-cysteine major genetic groupings described previously [10] (Fig 5B, P < 0.001 by chi-squared test). Genetic cluster and PoLV group did not significantly associate with clinical group (Fig 3C, P = 1.00 and P = 0.20, respectively), serological cluster (P = 1.00 and P = 0.13, respectively), or DBLα-tag antigen cluster (P = 0.91 and P = 0.35, respectively) or any of the host and infection variables described above (Table 1).
Sliding window analysis of genetic maps based on 14 amino acid sub-regions in relation to serological, DBLα-tag antigenic and genetic clusters indicated that serological clustering was principally driven by genetic diversity between Segments 2 and 3 at the N-terminal portion of the DBLα-tag ending with PoLV2 (Fig 6A and S9 Fig). This region comprises almost complete S2b and S2c sub-domains of DBLα domains as defined by Rask et al. (2010) [9]. High concordance between serological clustering and genetic diversity in this region was driven by the fact that all but one (P6408) of the nine antigens that contained a REY motif at the PoLV2 position (PoLV Groups 2 and 5) derived from patients with serological Cluster II profiles, while only one REY type was found in patients with serological Cluster I profiles (Fig 7, S9B and S9E Fig). Previous evidence suggests that parasites expressing PoLV/Cys Group 2 [10] and Group 5 [32] are likely to form rosettes, a cytoadhesion phenotype strongly implicated in severe disease [33–35]. This, together with our findings, suggests that epitopes in the S2b sub-domain of DBLα REY sequence type, or elsewhere in the PfEMP1 protein but in strong linkage disequilibrium with the latter, are the target of antibodies that are associated with a milder course of disease.
Genetic clustering was strongly associated with sequence diversity in Segments 4 and 5 at the 3’ end of the S2c sub-domain of DBLα, consistent with location of the cysteines that distinguish two major genetic groups under the PoLV/Cys classification system [10] (Fig 6B).
We explored other genetic aspects of the DBLα domain for their relationship with disease and serological clusters. Recognition scores for homology blocks HB14 and HB64 were significantly elevated in DBLα-tags derived from parasites from patients with mild disease and serological Cluster I profiles, while those of HB54 were elevated in DBLα-tags derived from parasites from patients with severe disease and serological Cluster II profiles (Table 1). These three homology blocks span Segments 1, 2 and 3 of the DBLα domain (Fig 7), thus corroborating the strong association between serological cluster and genetic diversity in the S2b sub-domain described above.
Recognition scores of HB60 and HB36 were significantly positively associated with Genetic Clusters VIII and IX, respectively (Table 1), consistent with their location in Segments 4 and 5 in which variation in the number of cysteines that delineate these clusters occurs (Fig 7).
The analysis above focused on sero-reactivity of plasma to the DBLα-tags of the dominant expressed PfEMP1 in clinical isolates. We next addressed whether underlying genetic characteristics of the full length PfEMP1 protein in the entire parasite population of a given child, such as expression of specific domain cassettes or upstream promoter sequences were associated with serological or antigenic clusters. We found that expression levels of domain cassettes DC8 and DC13 were significantly positively associated with severe disease and serological Cluster II or both (Table 1), consistent with previous studies [14,20]. Expression levels for three out of four other markers of var subtypes associated with severe disease in previous studies were significantly associated with disease in this study (Table 1): two of these markers define DBLα sub-types that belong to the upstream promoter Group A (DBLa2/a1.1/2/4/7 and DBLa1). Surprisingly, a generic group C marker that has been previously associated with mild disease was also elevated in children admitted to hospital: however, expression levels of Group C upstream promoter regions were low in all groups (Table 1).
Global population frequency of the parasite’s var genes, as judged by number of highly similar sequences to the full DBLα–tag in the global var gene sequence database, did not associate with disease severity or clusters (Table 1). Thus, the association between disease severity and antigenic profile was not explained by the rarity of, and hence degree of prior exposure to the var gene products of the infecting isolate.
Overall, our genetic analysis of the parasites in this study confirms the conclusion from previous studies that a subset of genetically defined PfEMP1 types–those with DC8, two cysteines, and Group 2 and Group 5 REY types—are strongly predictive of severe disease. By introducing serology into analysis of this relationship, we have shown that protection against these SM types may depend on serological responses to epitopes in the S2b sub-domain of DBLα, or epitopes elsewhere in the PfEMP1 protein that are in strong linkage disequilibrium with these. By contrast, protection was not associated with putative epitopes in the S2c sub-domain which delineates the two major genetic groups by its number of cysteines, Cys2 and non-Cys2.
This study has revealed that, despite different histories of malaria and diversity of antigenic types, serological responses to PfEMP1 in children fall into one of just two qualitatively different patterns which strongly associate with disease severity. In most children, serological responses to PfEMP1 (here evaluated using recombinant DBLα-tags representing the dominantly expressed PfEMP1) are broadly reactive and long-lasting, but in some children–those that succumb to severe disease—responses are narrower and short-lived. This conclusion, based on the similarity of serological profiles between children to a set of related antigens rather than on the frequency of responses to individual antigens–as in most immuno-epidemiological studies—departs from the traditional view that protection against malarial disease is acquired in a piecemeal fashion through acquisition of specific antibodies to each of PfEMP1’s many antigenic types upon repeated exposure [36,37]. It shifts the focus in understanding severe malaria away from individual antigenic types towards the overall quality of the antibody response.
As in many previous studies [13,15,17–20], we observed that PfEMP1 (here represented by DBLα-tags) from parasites that caused infections which progressed to severe disease were generally better recognized by plasma than those from infections resulting in mild disease (Figs 1 and 4). How does this reconcile with our proposal that patients with severe disease mount serological responses that are less effective and less persistent? Although var genes are very heterogenous in sequence within and between genomes, they share sequence similarities that define domains, homology blocks and binding-sites for endothelial receptors, as also reflected in short shared stretches of sequence similarities within the DBLα-tag region [38]. These sites could be targets of cross-reactive serological responses. Cross-reactive antibody responses can be short-lived when the corresponding antibodies originate from memory B cells that differentiate into short-lived plasma cells, or long-lived when antibodies originate from long-lived plasma cells. Our data suggest that children with mild disease maintain stable antibody responses to heterologous DBLα-tag antigens, with levels generally higher to DBLα-tag antigens derived from parasites of children with severe malaria, while children with severe disease have similar levels of serological responses to heterologous DBLα-tag antigens of all parasite types which drop significantly during convalescence. Given that memory B cells tend to leave germinal centres earlier than long-lived plasma cells during affinity maturation [39], it seems likely that the short-lived serological Cluster II IgG responses originated from short-lived plasma cells that had lower affinity for heterologous DBLα-tags, and that these qualitative differences contributed to the association of serological profiles with disease severity that we observed here.
Remarkably, serological profiles for IgM and IgG were similar at the convalescent stage. IgM memory B cells contribute—and sometimes are the earliest responders [40]–to the rapid boosting of antibody levels during re-infection. However, differentiation of naïve B cells in response to new antigenic variants increases levels of plasma IgM memory B cells too. Although the source of plasma antibodies to the different DBLα-tag antigens has to be further investigated, we thus hypothesise that the different sources and quality of memory B cells may underlie the reason for why children that have developed Cluster I serological profiles in response to previous infections are protected from severe disease, even when they become infected with SM types, while children with Cluster II profiles are not. We further note that the decoupling of the roles of antigen and serological responses in severe disease under this hypothesis contrasts with that for establishment of an infection which clearly depends on highly specific reactivity to individual antigenic types.
Although severe disease is clearly linked with PfEMP1 type, such as expression of DC13, DC8 or group A PfEMP1 associated with rosetting, expression of these SM-PfEMP1 types is not sufficient for the development of severe disease since they are also expressed during first infections, whether or not the child develops severe malaria, and because children readily induce antibodies against SM-PfEMP1 [14]. Our data suggests that this gap is filled by some qualitative feature of the host’s immune response that causes failure to develop an adequate anti-SM-PfEMP1 response. Possible reasons for this include host-derived factors that influence the general quality of the immune response such as haemoglobinopathies which alter display of PfEMP1 on the red cell surface [41]; parasite-induced epigenetic modification of immune cell function [42]; PfEMP1-mediated inflammation resulting in dysregulation of T- and B cell function [43] or antibody-dependent cellular cytotoxic function [44]; and RIFIN-mediated binding to LILRB1 expressed on B cells [45]. Clearly, underlying host genetic factors and cellular events that result in distinct serological profiles between children with mild vs. severe malaria disease require further investigation.
Alternatively, the failure may be directly attributable to the parasite. Theoretical modelling of within-host infection dynamics has led to the hypothesis that in order to produce sequential expression of individual var genes during the course of an infection, the parasite must induce the immune system to produce long-lasting, cross-reactive antibodies as well as transient, highly specific antibodies to the current numerically dominant variant [46]. Empirical studies have shown that, in patients with severe disease, var gene expression is dysregulated, leading to the appearance of multiple var types simultaneously instead of sequentially: this has been linked to down-regulation of parasite histone deacetylase PfSIR2A, an epigenetic silencer of var gene expression [20,47]. The latter two observations, together with our results here, support a model in which hosts mount one of two types of immune response to PfEMP1 –one (Cluster I-like) in which var gene expression is well-regulated, this with assistance from long-lasting antibody seroreactivity, and which culminates in control of severe disease; and the other (Cluster II-like) in which many vars are expressed simultaneously, generating an antibody repertoire that is broader but less effective, presumably as a consequence of generating fewer antibodies to each type and which thus gives the appearance of transience.
Such a model must include an antigen-antiserum specific component, however, since our conclusion that protected children mount broader and longer serological responses than unprotected children does not sufficiently explain the serological reactivity patterns in our data. We found that it was not the overall quantity of plasma antibody, but instead the highly specific nature of the immune profiles which delineated serological Cluster I from Cluster II and mild from severe disease patients. The serological specificity that drove these differences was particularly obvious among a small subset of DBLα-tag antigens (P6433, P6408 and P6430, Fig 1, S7 Fig). Perhaps surprisingly, these ‘indicator’ antigens were only recognized by children with severe disease and all of them derived from children with mild disease (S1 Fig). We therefore interpret strong responses to this subset of antigens in children with severe malaria as an indication of, rather than the cause of, a defective immune response.
A variant-specific basis for the relationship between serological antibody profile and disease outcome is further supported by the results of our analysis relating distinct serological profiles to genetic variation within PfEMP1. We mapped diversity in serological responses to genetic diversity in HB14, a short region of semi-conserved sequence in the S2b sub-domain of PfEMP1’s DBLα domain. HB14 spans a hypervariable loop (Fig 6 & S9 Fig) between two alpha helices that form the core structure of this sub-domain: these are marked by conserved sequences HB3 at the 5’ end and HB5 at the 3’ end. HB5 is believed to be frequently exposed on the surface of PfEMP1 [9]: the adjacent hypervariable region marked by HB14 may also be frequently exposed to the host’s immune system. Just downstream from HB14 and marking the beginning of HB5 is a short motif–REY or RED. REY defines genetic sub-groups (PoLV Groups 2 and 5) which have previously been reported to be associated with severe malaria, particularly rosetting [32]. REY-type HB5 tend to have shorter segments in the HB14 region (Fig 7). Sero-reactivities against REY-containing DBLα-tag sequences are acquired early in life in children living in Papua New Guinea [48]. Taken together, these findings suggest that the length and/or diversity of the hypervariable loop exposed on the surface of PfEMP1, as defined by HB14, might provoke antibody responses that qualitatively differ in their efficacy in controlling progression to severe disease.
Since we did not sequence the full length of the var genes, we could not determine the complete set of genetic variants that best predict serological profile and how these relate to Group A, DC8 and DC13 var types that generate severe malaria. Given the genetic structure of var genes, it is likely that HB14 and HB5 in the DBLα domain are in linkage disequilibrium with cassette types in other domains. A much larger study is required to identify all the potential epitope sites in PfEMP1 that could explain the dichotomous serological profiles that we have revealed here. Such a study would constitute a significant step towards the development of a broadly effective PfEMP1-based malaria vaccine. Unlike vaccines for other highly genetically diverse pathogens such as influenza A, in which antigen components are selected to cover the full antigenic diversity of the currently circulating population, and which require updating for each global hemisphere every year [25,26], the challenge will be to define PfEMP1 types that elicit the long-lasting, protective version of the antibody repertoire, as found in most children in malaria-endemic areas, and which we have begun to define here.
Children were recruited into this study after written informed consent from their parents or guardians. Ethical permission for the study was granted by the National Ethical Review Committees of Kenya (Kenya Medical Research Institute protocol SSC1131) and the Oxford Tropical Research Ethics Committee (OXTREC protocol no. 30–06).
Thirty-six children under the age of 5 years presenting with acute malaria at the Kilifi County Hospital, Kilifi, Kenya each donated a venous blood sample at recruitment (Acute, A), at 4 weeks (Convalescence 1, C1) and at 16 weeks (C2) after the acute malaria episode. Children were either admitted to the hospital ward according to pre-established criteria for severe malaria (Blantyre coma score <5, Hb<5g/dl or respiratory distress [49]) or moderate malaria (admitted to the ward but without any of these severe malaria syndromes): otherwise they were diagnosed as ‘mild malaria’, treated with anti-malarial drugs at the Outpatient Department (OPD) and sent home. Plasma and parasite material were collected upon recruitment and separated and stored using standard procedures [50].
The dominant expressed var gene in each isolate at the time of recruitment was identified based on the DBLα-tag as described previously [50,51]. For 36 of the isolates for which a dominant var transcript was able to be identified, DBLα tags were cloned and expressed in BL21 (DE3) pLysS E.coli cells to give recombinant proteins (‘antigens’) as previously described [50] (S1 Table).
Each antigen was screened for sero-reactivity to each of 36 plasma (‘antisera’) collected at each of the three timepoints (A, C1 and C2). Thirty-two of these plasma samples came from the same children from which the 36 recombinant proteins derived. Sero-reactivity was measured by coating 96-well NuncTM MaxisorbTM immunoplates plates overnight at 4°C with recombinant protein diluted in Tris-buffered saline (TBS) at a concentration of 1μg/ml. Plates were blocked with 3% bovine serum albumin (BSA) in TBS, washed and then incubated in duplicate at a 1:200 dilution of individual sera diluted in 1% non-fat milk powder in TBS. After incubation for 2h at room temperature, plates were washed, then bound antibody was detected with mouse anti-human IgG alkaline phosphatase antibody or AffiniPure donkey anti-human IgM alkaline phosphatase antibody (Jackson ImmunoResearch Laboratories, Inc.) diluted 1:5000 in 1% non-fat milk powder in TBS. The reaction was developed using o-Phenylenediamine dihydrochloride (Sigma-Aldrich) and then stopped with 2M sulphuric acid. Optical density (OD) was read at 450nm in an ELISA microplate reader (BioTek Synergy 4, BioTek Instruments).
Prior to building antigen and serological maps, sero-reactivity levels (log10 OD values) were explored for systematic effects of timepoint, DBLα-tag antigen and antisera by conducting analysis of variance fitting a series of mixed linear models in the lme4 package in R [52]. Model 1 included a fixed effect for timepoint (sometimes fitted as a fixed-level factor and sometimes as a continuous variable) and random effects for antigen and antisera within antigen: the latter term thus tested for interactions between antigens and antisera. Model 2 was as for Model 1 but substituting antisera within antigen with a fixed effect for ‘heterology’, i.e., whether the antigen-antisera pair was from the same patient (homologous) or not (heterologous). Model 3, which was only applied to data from heterologous antigen-antisera pairs, fitted fixed effects for timepoint, serological cluster of the antisera (see below), and serological cluster of the antigen, with all possible interactions between these, and random effects for antigen and antisera. Model 4, which was only applied to data from homologous antigen-antisera pairs, fitted fixed effects for timepoint, serological cluster of the antisera, the interaction between these and a random effect for antisera. Model 5 fitted fixed effects for timepoint and antigen cluster (see below) and random effects for antigen and antisera. Models 6 and 7 were as for Model 5 but fitting genetic cluster (see below) or clinical group instead of antigen cluster. Means for fixed effects after adjusting for other fixed and random effects (least-squares means) were computed using the lsmeans package in R [53]. Pairwise contrasts between fixed effects of interest were formed using the same package with no adjustment for multiple testing. The variation due to antigen, antisera and their interaction was described by the ratio of the corresponding variance estimate (from the random effect) to the total variance remaining after accounting for fixed effects. Significance levels of all fixed and random effects were determined by likelihood ratios from models with and without the term of interest included. Estimates presented are from models in which interaction terms with P > 0.05 were removed.
Associations between categorical variables (disease severity class (‘clinical group’), antigen cluster, serological cluster and genetic cluster, as defined by maps (see below)) and normally distributed host and infection-related variables (parasite density, age, date of collection at the time of recruitment) were tested for significance using F-tests in a fixed effect analysis of variance with the latter as the dependent variables. Associations between categorical variables and non-normally distributed traits (DNA sequence global population frequency, homology block scores, relative gene expression levels for domain cassettes, see section below on genetic characteristics) were tested for significance using a two-sided Wilcoxon rank sum non-parametric test implemented by the ‘wilcox.test’ function in the R stats package [54]. Tests for relationships between clusters and categorical variables were performed using chi-squared tests of association using the ‘chisq.test’ function in the R stats package with Monte Carlo simulation for computation of P-values [54].
Antisera ‘maps’ (a representation of pairwise antisera ‘distances’, with respect to the antigens they ‘read’ antigens) were generated from the 36 x 36 matrix of sero-reactivity data using multidimensional scaling (MDS) implemented by the ‘cmdscale’ function in the stats package of R [54]. The elements of the distance matrix used as input for MDS were computed as (dij/dmax)2 where dij is the Euclidean distance between the vector of sero-reactivities of antisera pairs i and j and dmax is the maximum Euclidean distance for all pairs, calculated using the ‘adjacency’ function in the WGCNA package in R [55]. Maps were fitted in two dimensions and constructed from data with and without adjusting for mean differences in sero-reactivity across antigens of each antisera. Two further aids to visualization of antisera diversity were used. First, heatmaps of the distance matrix were created using the pheatmap package in R [56] and plotted with dendrograms based on hierarchical clustering (‘hclust’ function using the ‘average’ method in the R stats package [54]. Second, networks were constructed from the lowest 20% of distances in the distance matrix using the Davidson-Harel algorithm in the igraph package in R [57]. Antigen maps were similarly constructed but based on distances between pairs of antigens instead of antisera.
Note that the application of ‘antigenic cartography’ here differs from its original use [24,25] in that the plasma were polyclonal rather than monoclonal due to prior history of malaria in study children and the fact that multiple PfEMP1 types are expressed within the lifetime of a single infection. We did not attempt to adjust for previously existing levels of antibodies since our main interest was to relate existing antisera profiles at the time of infection, rather than responses to the current infection, to disease severity.
Genetic maps were constructed by MDS of the distance matrix based on amino acid sequence similarities after alignment using the clustalw algorithm implemented in the Geneious software [58] with gap open and extension penalties of 12 and 3, respectively. The genetic distance matrix was calculated using the ‘seqidentity’ function in the bio3d package in R [59].
Antigenic, serological and genetic clusters were defined by a combination of visual inspection of MDS maps and hierarchical clustering of the distance matrices.
Antigens were categorized based on their amino acid sequence into six ‘PoLV/Cys2’ groups (Groups 1 to 6) based on motifs at four ‘positions of limited variability’ (PoLV) within the DBLα domain and by number of cysteine residues (Cys) - 2 vs. not-2—in the region between the third and fourth PoLV (PolV3 and PolV4) as described previously [10,51]. The two major groups defined by number of cysteines (Groups 1–3 vs. Groups 4–6) further divide into those containing a REY vs. non-REY motif at the second PoLV (PoLV2) (Groups 2 and 5 vs. Groups 1, 3, 4 and 6). REY types are shorter than non-REY types and are further distinct in their sequence between PoLV1 and PoLV2 [10]. Antigens were also classified for the presence of predicted ‘homology blocks’ by analyzing the DBLα-tag sequences on the “varDom” server [9,31]. The presence of specific domain cassettes, particularly (DC) 8 and 13—combinations of protein sequences from the DBLα, DBLα, DBLα and CIDR domains of PfEMP1 that have been previously strongly associated with severe malaria [14,20]–were determined using real-time quantitative PCR for each parasite isolate as described previously [14]. Upstream promoter type (A, B or C) of the antigens was likewise determined by real-time PCR [9,14]. Nucleotide sequences of each DBLα-tag was blasted against the DNA sequence in the global var gene database [30]. The number of var genes in the database which contained DBLα-tag nucleotide stretches with 95% or more identity to each DBLα-tag sequence was counted to estimate the ‘global population frequency’ of the sequence.
To determine whether some sub-regions of the DBLα-tag sequence might better explain antigenic or antisera diversity than others, a sliding window analysis was performed in which a genetic sub-map built from sequence similarity based on windows of 14 amino acids, each offset by one, was assessed for concordance with the consensus antigenic or antisera map. Concordance was statistically evaluated by fitting a linear model to the distance matrix of the genetic sub-map with antigen or serological cluster as a fixed effect using the ‘adonis’ multivariate analysis of variance function in the vegan package in R [60]. 1200 permutations of the data were performed to determine significance levels. Concordance between genetic and antigenic or antisera maps was also assessed by computing the sum of squared differences (SS) between point locations in the two maps after rotating and scale transforming the antigenic or antisera map such that it minimized the SS, as implemented by the ‘procrustes’ function in the vegan package in R [60]. The P-value for observed goodness-of-fit statistic, R2 (= 1-SS), was compared to that of its empirical distribution generated from 1200 random permutations of the antigenic or antisera map using the ‘protest’ function in the vegan package This second statistical test was applied to maps built both across and within antigenic or antisera clusters.
|
10.1371/journal.pntd.0001155 | Comparative Microsatellite Typing of New World Leishmania infantum Reveals Low Heterogeneity among Populations and Its Recent Old World Origin | Leishmania infantum (syn. L. chagasi) is the causative agent of visceral leishmaniasis (VL) in the New World (NW) with endemic regions extending from southern USA to northern Argentina. The two hypotheses about the origin of VL in the NW suggest (1) recent importation of L. infantum from the Old World (OW), or (2) an indigenous origin and a distinct taxonomic rank for the NW parasite. Multilocus microsatellite typing was applied in a survey of 98 L. infantum isolates from different NW foci. The microsatellite profiles obtained were compared to those of 308 L. infantum and 20 L. donovani strains from OW countries previously assigned to well-defined populations. Two main populations were identified for both NW and OW L. infantum. Most of the NW strains belonged to population 1, which corresponded to the OW MON-1 population. However, the NW population was much more homogeneous. A second, more heterogeneous, population comprised most Caribbean strains and corresponded to the OW non-MON-1 population. All Brazilian L. infantum strains belonged to population 1, although they represented 61% of the sample and originated from 9 states. Population analysis including the OW L. infantum populations indicated that the NW strains were more similar to MON-1 and non-MON-1 sub-populations of L. infantum from southwest Europe, than to any other OW sub-population. Moreover, similarity between NW and Southwest European L. infantum was higher than between OW L. infantum from distinct parts of the Mediterranean region, Middle East and Central Asia. No correlation was found between NW L. infantum genotypes and clinical picture or host background. This study represents the first continent-wide analysis of NW L. infantum population structure. It confirmed that the agent of VL in the NW is L. infantum and that the parasite has been recently imported multiple times to the NW from southwest Europe.
| Leishmaniasis is a vector borne disease with a broad spectrum of clinical forms caused by protozoan parasites of the genus Leishmania. Visceral leishmaniasis is the most severe, systemic form of the disease. It is caused by parasites belonging to the Leishmania donovani complex, which includes L. infantum and L. donovani in the Old World (OW) and L. infantum (syn. L. chagasi) in the New World (NW). The identity and origin of the causative agent of VL in the Americas have been the subjects of much debate for decades. Different scientific approaches led to different conclusions, either favouring the hypothesis of indigenous origin of this parasite and its status as distinct species, or a recent importation of L. infantum by European colonists and synonymy of L. infantum and L. chagasi. We performed the first broad population study of these parasites from the NW using highly variable microsatellite markers. The level of heterogeneity and population structure was very low in contrast to the OW. Using a combined data analysis of NW and OW strains we have provided conclusive evidence of recent multiple introductions of L. infantum from Southwest Europe into the New World and for synonymy of L. infantum and L. chagasi.
| In 1937 the causative agent of visceral leishmaniasis (VL) in the New World (also referred to as American visceral leishmaniasis – AVL) was designated as a distinct species, Leishmania (L.) chagasi Cunha & Chagas [1]. Many studies have subsequently concluded that the causative agent is indistinguishable from L. infantum, derived from Europe [2]–[4]. To explore the molecular epidemiology of AVL, we have applied a high resolution population genetic analysis to a vast collection of New World (NW) and Old World (OW) isolates.
Visceral leishmaniasis in the New World extends from the southern parts of the USA [5], [6] and Mexico to the North of Argentina, including countries such as Brazil, Paraguay, Bolivia, Venezuela, Suriname, Guyana, Colombia, Honduras, Panama, Costa Rica, El Salvador, Guadeloupe, Guatemala, and Nicaragua [7]–[11]. Brazil is the country that accounts for the highest number (∼90%) of cases [12], [13]. The principal foci are located in drier, poorly forested areas, although there are several foci in the densely forested Amazon region and the Guianan Ecoregion Complex (GEC), which covers some States of Venezuela and all of Guyana, Suriname and French Guiana and the upper parts of the Brazilian states Amazonas, Roraima, Pará, and Amapá. The main foci here are in Pará, Roraima (Brazil), Bolivar (Venezuela) and parts of Guyana. There are few cases reported from Suriname and no cases from French Guiana except a recently imported canine case [14]–[17].
To a lesser extent, NW L. infantum also causes atypical cutaneous leishmaniasis (atypical CL). This clinical manifestation has been reported since the 1970s mainly from Caribbean countries such as Venezuela, Honduras, Costa Rica, Nicaragua, but sporadically also from Brazil [18]–[23]. Except in Brazil, atypical CL cases are characterised by non-ulcerative skin lesions that are often misidentified as nodular infantile tuberculoid leprosy. Host immuno-genetic factors and/or parasite factors in combination with socio-economical and environmental factors are likely to play a role in determining the varied clinical picture, as in the case of Mediterranean L. infantum infections.
In the NW, domestic dogs are primary reservoirs of infection for humans, but foxes (Cerdocyon thous), native marsupials (Didelphis marsupialis, D. albiventris) and rodents (e.g. domestic rats) have also been found infected not only in urban areas but also in the Amazonian region [11], [14], [15]. The sand fly Lutzomyia longipalpis is the primary vector of L. infantum in the NW [12], [15], however, differences in the sand fly populations [24]–[28] and perhaps also the involvement of other sand fly species (e.g. Lu. evansi) [11],[15],[29],[30] may contribute to the variable clinical manifestations of the disease seen in different geographic regions.
Taxonomically NW L. infantum (syn. L. chagasi) belongs to the L. donovani species complex of the subgenus Leishmania Ross 1903, which in addition includes L. infantum and L. donovani from the OW [7]. There are two different hypotheses on the origin of NW L. infantum, of which the first one is now widely accepted: (1) L. infantum has been imported from Europe during the Spanish and Portuguese colonization carried by dogs or rats, and (2) L. chagasi is indigenous to the Americas [8], [31]–[36]. As summarized by Dantas-Torres [34], [35] these two hypotheses have led to much confusion regarding the nomenclature and at least 6 different nomenclatures are used in the literature. Until now, there have been no extensive studies of the population structure of NW L. infantum with a reasonable number of strains from different regions, environments, hosts and reservoirs and, therefore the taxonomic status of NW L. infantum is still not clear.
To detect structure of Leishmania populations it is essential to use a typing method with a high discriminatory potential. Many previously used methods were not adequate for discriminating at this taxonomic level. One of the most powerful and discriminative DNA-based methods for strain differentiation and population genetics is the analysis of highly variable, co-dominant microsatellite markers. Recently, multilocus microsatellite typing (MLMT) has been used successfully to differentiate L. infantum populations in the Mediterranean region of Europe and North Africa, the Middle East and Uzbekistan [4], [37]–[41] as well as L. donovani populations in the Indian subcontinent and East Africa [42], [43]. This method enabled differentiation even at the intra-zymodeme level, as shown for the predominant MON-1 zymodeme of L. infantum.
In the present study we have applied MLMT for an extensive population survey of NW L. infantum originating mainly from different endemic regions within Brazil, but also from other countries. To our knowledge this is the first comprehensive study of population structure of L. infantum in the NW. We show that NW L. infantum, indeed, was introduced on multiple occasions in recent times from European source populations of L. infantum and is inseparable from them. We also provide substantial new insight into the molecular epidemiology of AVL.
Sources, designation, geographical origins, MLEE identification, if known, and clinical manifestation for the 426 studied strains, including NW L. infantum, OW L. infantum and L. donovani are listed in Table S1. NW L. infantum was represented by 98 strains from Brazil, Paraguay, Colombia, Venezuela, Honduras, Panama, and Costa Rica (Figure 1A, Table 1). Most NW L. infantum came from Brazil and Figure 1B shows the number of strains used from the respective Brazilian endemic regions. Figure 1C depicts the percentages of NW L. infantum strains causing different clinical pictures. The 308 L. infantum from seven European and two North African Mediterranean countries, four countries from the Middle East and Asia, as well as 20 L. donovani strains from East Africa and India were analysed in previous population genetic studies and have been incorporated here to elucidate the phylogenetic position of NW L. infantum in relation to OW L. donovani complex species. Table 1 summarises the number of strains per species according to geographical origin (continent and country), zymodeme and clinical picture. Most of the Brazilian strains were obtained from the Leishmania collection of the Oswaldo Cruz Institute (CLIOC, WDCM731, http://clioc.ioc.fiocruz.br). All strains from CLIOC were typed by MLEE as IOC/Z1 which corresponds to zymodeme MON-1 [44] (unpublished data) (Table S1). Strains from Paraguay were collected in 2000 (Programa Nacional de Leishmaniosis, SENEPA, Ministry of Public Health, Paraguay), and the strains from Venezuela were provided by the Universidad de Carabobo, Centro Nacional de Referencia de Flebotomos de Venezuela (CNRFV-BIOMED-UC). DNA of strains from Panama, Costa Rica and some from Brazil were obtained from the Royal Tropical Institute (KIT), Amsterdam, The Netherlands and from the WHO's Jerusalem Reference Centre for Leishmaniases, Hebrew University – Hadassah Medical School, Jerusalem. Strains from Honduras, which were previously typed by kDNA-RFLP [20] were given by the London School of Hygiene and Tropical Medicine, London, UK.
DNA was isolated using proteinase K- phenol/chloroform extraction [45] or the Wizard™ Genomic DNA Purification System (Promega, Madison, WI, USA) according the manufacturer's protocol, suspended in TE-buffer or distilled water and stored at 4°C until use.
The standard set of 14 primer pairs (Lm2TG, TubCA, Lm4TA, Li41-56, Li46-67, Li22-35, Li23-41, Li45-24, Li71-33, Li71-5/2, Li71-7, CS20, kLIST7031, LIST7039) that we have previously applied for the L. donovani complex was used for amplification of microsatellite containing fragments, as previously described [38], [43]. PCRs were performed with fluorescence-conjugated forward primers. Screening of length variations of the amplified markers was done by automated fragment analysis using capillary sequencers. PCR products from amplified microsatellites were analysed either with the fragment analysis tool of the CEQ 8000 automated genetic analysis system (Beckman Coulter, USA) or the ABI PRISM GeneMapper (Applied Biosystems, Foster City, CA).
Population structure was investigated by the STRUCTURE software [46], which applies a Bayesian model-based clustering approach. This algorithm identifies genetically distinct populations on the basis of allele frequencies. Genetic clusters are constructed from the genotypes identified, estimating for each strain the fraction of its genotype that belongs to each cluster. This clustering method proved superior to distance-based approaches for processing data sets of low variability like those presented by L. infantum. The following parameters were used: “burn-in” period of 20,000 iterations, probability estimates based on 200,000 Markov Chain Monte Carlo iterations. The most appropriate number of populations was determined by calculation of ΔK, which is based on the rate of change in the log probability of data between successive K values [47].
Phylogenetic analysis was based on microsatellite genetic distances, calculated with the program POPULATIONS 1.2.28 (http://bioinformatics.org/~tryphon/populations) for the numbers of repeats within each locus using the Chord-distance [48], which follows the infinite allele model (IAM). Neighbor-joining trees were constructed with the POPULATIONS software and visualized with MEGA [49]. Microsatellite markers as well as populations were analysed with respect to diversity of alleles (A), expected (gene diversity) and observed heterozygosity (He and Ho, respectively), and the inbreeding coefficient FIS applying GDA (http://hydrodictyon.eeb.uconn.edu/people/plewis/software.php).
Genetic differentiation and gene flow was assessed by F-statistics [50] calculating the FST (theta) values (IAM) [51] with the corresponding p-values (confidence test) using the MSA software [52] (for details see Methods S1).
Research in this study was subject to ethical review by the European Commission and approved as part of contract negotiation for Project LeishEpiNetSA (contract 01547): the work conformed to all relevant European regulations. The research was also reviewed and approved by the ethics committee of the London School of Hygiene and Tropical Medicine (approval 5092). The Leishmania strains analysed in this consolidated microsatellite analysis were principally reference strains derived from international cryobanks as CLIOC (registered at the World Data Centre for Microorganisms under the number WDCM731 and recognized as depository authority by the Brazilian Ministry of the Environment, MMA/CGEN Deliberação CGEN 97 de 22/03/2005, Processo 02000.003672/2004-34), the cryobank of the London School of Hygiene and Tropical Medicine (LSHTM), the Centro Nacional de Referencia de Flebotomos de Venezuela (CNRFV-BIOMED-UC), the cryobank of the Royal Tropical Institute (KIT) in Amsterdam, Netherlands and the WHO's Jerusalem Reference Centre for Leishmaniases, Hebrew University, Jerusalem, Israel. They have already been object of many publications. Several other strains were from small prior studies also using other methods, such as the strains from Honduras that were isolated 16–23 years ago and deposited at the LSHTM cryobank [4], [20], [22]. In all cases Leishmania were isolated from patients as part of normal diagnosis and treatment with no unnecessary invasive procedures and with written and/or verbal consent recorded at the time of clinical examination. Data on isolates were coded and anonymised. Isolation of Leishmania during the course of this study and not obtained from historical reference collections, whether from patients or animals, was subject to a local ethical review and approval in Paraguay (human and animal samples) by the Ethical-Scientific Committee at the IICS-UNA, under Code P42/07. All L. infantum and L. donovani strains origin from different central cryobanks (Table S1) and were already the object of many publications [4], [37], [39]–[41], [43].
Ninety-eight strains of L. infantum from seven South and Central American countries with emphasis on Brazil have been studied by microsatellite analysis (Figure 1 and Table 1). Most of these strains were isolated in the drier, poorly forested regions and represented human or canine isolates, but several were isolated from wild animal reservoirs, foxes (Cerdocyon thous) and opossums (Didelphis marsupialis), in the Amazonian forest region (Pará, Colombia). Isolates from different clinical forms (human VL and ACL), as well as three strains from VL/HIV co-infections were included. Table 2 shows the variability measures for the 14 microsatellite loci in NW L. infantum. The number of alleles varied between 2–8, with a mean value of 5.1. The most variable markers were, as in a previous study on OW L. infantum [4], Lm2TG and Li 22–35. The observed heterozygosity was very low (mean Ho = 0.04) and always much lower than the expected (mean He = 0.216). Inbreeding coefficients varied between 0.489 and 1 (mean FIS = 0.818). This disparity between expected and observed heterozygosity and the high FIS values points to a considerable amount of inbreeding and/or population substructuring, reflecting possible Wahlund effect.
STRUCTURE analysis indicated that the sample set of NW L. infantum comprised two main populations as inferred by ΔK calculation (Figure 2). This population structure has been confirmed by distance analysis and the inferred neighbor-joining tree (Figure 3). Population 1 (Pop1-INFNW) which includes 89 of the 98 strains consists of all strains from Brazil, Paraguay and Colombia, and all but one strain from Honduras, regardless of whether they were isolated from cases of VL, VL/HIV+ or CL, or from different animal reservoirs, such as dogs and foxes. It also included the single opossum isolate. Population 2 (Pop2-INFNW) comprises only 9 strains, mostly from the Caribbean region (all strains from Costa Rica, Panama, Venezuela and one strain from Honduras) isolated from VL, CL and canine leishmaniasis (CanL) cases (Table S1). The distribution of the respective populations 1 and 2 among the Central and South American countries is shown in Figure 1A. F-statistics showed significant genetic differentiation between these two populations: FST = 0.761 and p = 0.0001.
A very important observation was that Pop1-INFNW is very homogeneous with only 35 genotypes identified for the 89 strains (proportion of polymorphic strains in pop1 = 39%), whereas in Pop2-INFNW all strains had unique genotypes. Table 3 gives a detailed overview of the number of genotypes found in each country studied and Figure S1 shows a neighbor-joining tree, which is based only on distinct genotypes. Interestingly, 13 out of the 15 Honduran strains in Pop1-INFNW including all CL cases, shared an identical MLMT profile. Another cluster of identical MLMT profiles was found for eight strains of human and canine origin from Mato Grosso do Sul. A third big cluster of identical genotypes comprised 23 strains from Espírito Santo, Rio de Janeiro, Pernambuco, Ceará, and three from Paraguay. Also here an identical MLMT profile was shared by human VL, VL/HIV+ and canine isolates (Figure S1, Figure S2). The difference in the degree of polymorphism between Pop1-INFNW and Pop2-INFNW is also reflected by the mean number of alleles (MNA) of 3.1 for population 1 (N = 89) and of 2.9 for population 2 (N = 9) (Table 4). In both populations the observed heterozygosity was much lower than the expected, leading to high FIS values (Tables 2 and 4).
Both main populations have been tested by STRUCTURE for sub-structures. Two sub-populations were found for each of the main populations (Figure 2). Sub-Pop1B-INFNW (34 strains) comprised 22 of the 24 strains from Mato Grosso do Sul, all fox isolates from Pará, all Colombian strains including the D. marsupialis isolate, and three canine strains from Paraguay. The other 55 strains were members of Sub-Pop1A-INFNW consisting of all strains from Honduras, seven strains from Paraguay and the strains from all other Brazilian foci. Both 1A and 1B sub-populations included strains isolated from all clinical forms of human disease as well as from dogs. Population 2 was divided into Sub-Pop2B-INFNW comprising all Venezuelan strains isolated from human cases of VL and CL and from dogs, and Sub-Pop2A-INFNW consisting of the CL strains from Panama and Costa Rica, and a single VL strain from Honduras. Distance analysis confirmed the inferred subpopulations (Figures S1 and S2). FST analysis however showed that genetic differentiation of the sub-populations was statistically not significant. The assignment of the strains to the respective populations and sub-populations is given in Table S1.
To address questions about the nomenclature and the origin of NW L.infantum we have included in the analysis previously identified MLMT profiles of 308 L. infantum strains from different countries of southern Europe, North Africa, the Middle East and Asia and, as an outgroup, 20 L. donovani strains from East Africa and India [4], [37], [39]–[41], [43] (Table S1, Table 1). Most of the L. infantum strains belong to the zymodeme MON-1, the most ubiquitous in the Old World, however several non-MON-1 strains have been also included. All these strains represented different clinical pictures in humans, namely VL, CL, VL/HIV+, PKDL, as well as canine, fox and sand fly isolates (Table 1).
STRUCTURE analysis of the combined NW and OW L. infantum strains revealed 3 main populations, as deduced from ΔK calculation (Figure 4), which showed significant genetic differentiation (Table 5). The largest population (Pop1-INFNW+OW) was formed by 224 strains from Spain, Portugal, France, Italy (all identified as MON-1) as well as from Brazil, Paraguay, Honduras (all but one strain) and Colombia. The second population (Pop2-INFOW) comprised 121 MON-1 strains from Algeria, Tunisia, Greece, Turkey, Israel, Palestine, Uzbekistan, China, and few from France and Italy. Strains from the New World were not found in this population. The third population (Pop3-INFNW+OW) consisted of 59 strains from southwestern Europe and North Africa (all identified as non-MON-1) as well as from Venezuela, Panama, Costa Rica and a single strain from Honduras. Consequently, population 1 of NW L. infantum (Pop1-INFNW) is part of the southwestern European L. infantum MON-1 population, and population 2 of NW L. infantum (Pop2-INFNW) of the Mediterranean non-MON-1 L. infantum population. The non-MON-1 population was the most variable one, as shown by the highest values for the MNA (9.6), Ho (0.27) and He (0.777), although the sample size was much smaller than that of the other two populations. This is in agreement with previous observations for Mediterranean L. infantum [4]. All three populations showed high FIS values, especially the two MON-1 populations (Table 6).
The two main populations which included the NW L. infantum strains were tested for sub-structures. Population 1 (Pop1-INFNW+OW) was clearly divided into two sub-populations, as inferred by ΔK (Figure 4), with significant genetic differentiation between them (FST = 0.223, p = 0.0001). Sub-Pop1A-INFNW+OW (N = 149 strains) comprised the majority of strains and included strains from the Iberian mainland, Italy, and France as well as the NW strains. Sub-Pop1B-INFOW (N = 75 strains) contained all Balearic strains, strains from Sicily and Campania (Italy) and from the Provence region of France. The sub-structures of the non-MON-1 population 3 (Pop3-INFNW+OW) were not very clear, however a first split led to Sub-Pop3A-INFOW formed by 28 strains from North Africa and Malta. Sub-Pop3B-INFNW+OW comprised 31 strains from Europe and the NW (Figure 4). There is significant genetic differentiation between these sub-populations (FST = 0.25, p = 0.0001). Table 7 presents the variability measures for each of the detected sub-populations. The most homogeneous population was Sub-Pop1A-INFNW+OW, which comprised L. infantum MON-1 (MNA = 3.3), even though its sample size was the largest. High FIS values were detected for each of these sub-populations.
The same clustering and population structure was found by distance analysis of the MS data. Figure 5 shows the neighbor-joining tree inferred from the combined sample set of 426 strains from the Old and New World with L. donovani as outgroup. NW strains of L. infantum are intermingled among the European L. infantum MON-1 and non-MON-1 strains. Identical genotypes were found multiple times for strains of NW and OW L. infantum in population 1 (Pop1-INFNW+OW), e.g. between strains from Mato Grosso do Sul (Brazil) and a strain from Portugal, between strains from eastern Brazil, Paraguay, and from France and Spain. Furthermore, one of the Honduran VL strains was identical to strains from Portugal and France (data not shown). NW strains of L. infantum were concentrated in few major clusters that also contained strains from the Iberian peninsula and France, indicating an expansion of single clones (Figure S3, Figure 5).
Knowledge of the population structure of NW L. infantum was so far very restricted and essentially based on clinical observations and the population structure of the vector. With the development of MLMT an adequate and powerful tool based on highly polymorphic genetic markers that differentiate at intra-species level became available for analysing population structure of the parasite. The present paper shows that there are two main populations of NW L. infantum, that correlate with the separation of L. infantum into MON-1 and non-MON-1 strains [4], [43]. Ninety-one per cent of the 98 NW strains analysed fell into the MON-1 population (Pop1-INFNW), which extends over a huge geographical and ecological range, including strains from all Brazilian, Paraguayan and some Colombian foci, and from Honduras. This situation is very similar to that in the OW, where ∼70% of L. infantum strains isolated in foci in southern Europe, Middle East, Central Asia and North Africa belong to zymodeme MON-1 [53]. The remaining 9% of the NW L. infantum strains belong to the non-MON-1 population 2 (Pop2-INFNW) and, interestingly, all these strains come from the Caribbean region, from Panama, Costa Rica and Venezuela, and a single strain from Honduras. Whether the non-MON-1 strains are generally present exclusively in the Caribbean region needs to be further elucidated.
The focal point of our sampling was Brazil, including most of the known foci. Although Brazil is an ecologically diverse country and different reservoirs and vector populations have been reported, all the 64 Brazilian strains from different VL foci belonged to the MON-1 population 1. The same was true for the neighboring country Paraguay.
The degree of polymorphism in NW L. infantum is much lower than among OW L. infantum. Only 39% of the NW MON-1 strains had individual MLMT profiles compared to about 75% of the OW MON-1 strains analysed in this and in a previous study [4]. The lower variability of NW L.infantum is also reflected by lower MNA and He values for both the MON-1 and non-MON-1 populations (Table 4 and Table S2). Moreover, in contrast to the NW genetically clearly separated geographically determined sub-populations were observed in the OW MON-1 cluster. The lower diversity of NW L. infantum supports the hypothesis of a recent import of selected strains of L. infantum from the Old to the New World [54]. In the case of indigenous parasites we would expect a much higher diversity and more complex population structures.
Despite the low diversity of NW L.infantum we found indications for population sub-structures. The MON-1 sub-populations are mostly geographically determined. Two sub-populations were recognized in Brazil and Paraguay, Sub-Pop1B-INFNW comprised three strains from central Paraguay, all but two strains from the Mato Grosso do Sul focus and those from rural foci in Pará. All other strains were members of the Sub-Pop1A-INFNW. Strains from Colombia and Honduras belonged to the sub-populations 1B and 1A, respectively. There is some hint that the Colombian strains may form a separate population, but this has to be proven with more strains from that country.
Three big clusters were detected that contain genetically identical strains. The first two included strains from the same focus, eight strains from Mato Grosso do Sul and 13 from Honduras, respectively. The third comprised 26 strains from seven states of Brazil, mostly from the eastern part of the country and some from Paraguay. This is evidence for the spread of several single clones in the New World. The homogeneity of strains in Honduras where only three genotypes were identified for 15 strains collected between 1987 and 1994 was surprising and needs further investigation.
The strong heterozygote deficiency and high inbreeding coefficients found for NW and OW L. infantum could result from population subdivision (Wahlund effect), presence of null alleles, natural selection, gene conversion, and inbreeding. Since similar FIS patterns were obtained across all 14 microsatellite loci in this study, null alleles, selection and gene conversion are unlikely to be responsible for the heterozygote deficiency found. Recent studies [55], [56] have demonstrated the existence of Wahlund effect in Leishmania populations which did however account only partially for the high inbreeding and suggested the “existence of population foci at a microgeographic scale, where clonality alternates with sexuality of an endogamic nature”. The strains of NW L. infantum analysed herein were sampled over large areas and further geographic subdivision of the subpopulations identified seems to be quite likely. For clarifying whether the high inbreeding is exclusively due to population subdivision or also to the presence of sexual recombination, mainly among identical or similar organisms, sampling at finer geographical scale would be essential.
There was no strict correlation between clinical pictures and population assignment. Strains isolated from human VL and CL cases, including the atypical CL cases collected exclusively in the Caribbean, were assigned to both the MON-1 and non-MON-1 populations. VL/HIV+ coinfections were found only in the MON-1 population, but due to the small number of these samples so far analysed we cannot draw conclusions. In Pop1-INFNW strains from CL cases from Honduras formed a single cluster of identical genotypes and one of the two VL strains from Honduras was similar to this CL cluster. This confirms previous reports showing by kDNA RFLP and RAPD that the two clinical forms in Honduras and Nicaragua were caused by genetically similar parasites [20], [21]. In addition, we found that at least two different parasite populations circulate in Honduras. Disease susceptibility and clinical manifestation is, however not only affected by parasite factors but also by host conditions, such as malnutrition and the age of the patient (status of the host immune system). In Honduras VL patients were much younger than CL patients. The vector may also play a role, as two species, Lu. longipalpis and Lu. evansi, are present in the Honduran endemic foci.
Obviously, we cannot link the restricted occurrence of CL cases in the Caribbean region with the general population assignment to MON-1 and non-MON-1 strains. From the Old World it is known, that only 20% of the CL cases are due to MON-1 strains and that the majority of CL cases is caused by strains of zymodeme MON-24, besides other dermotropic zymodemes, some of which were represented by several strains in this study. Ninety percent of all VL cases of immunocompetent individuals are caused by MON-1 strains, but there are also several other viscerotropic zymodemes [57], [58]. The tropism of many zymodemes is not clear cut, they are known to cause both VL and CL and the reasons leading to the respective clinical picture seem to be very complex.
The dog is the main reservoir host for NW L. infantum [59] and this was confirmed by the detection of identical MLMT profiles for human and canine strains from different VL foci, such as Mato Grosso do Sul, Ceará, Espírito Santo, Rio de Janeiro (Brazil) and Paraguay. All canine strains were found in the MON-1 population except one from Venezuela which was linked to the non-MON-1 population. This is in agreement with the situation in the OW where the majority of canine leishmaniasis is due to strains of zymodeme MON-1 [58], [60], [61]. Only very few other zymodemes, MON-98, 77, and 108, which are closely related to MON-1, and MON-253 and MON-24 have occasionally been found in dogs.
It has been suggested that natural foci of sylvatic zoonotic transmission may exist beside the main transmission cycle via the domestic dog [62]–[64]. Foxes are considered to be a natural reservoir of VL in different states of Brazil, such as Pará [65], Mato Grosso do Sul [66], Ceará and Piauí [63], Minas Gerais [67], [68], and Amazonas [69]. Didelphis marsupialis has been incriminated as an important reservoir host of NW L. infantum only in Colombia [70]–[72]. Opossums infected by NW L. infantum have, however also been reported from Bahia and Minas Gerais [73], [74]. The role of these animals in natural infection cycles remains however, questionable [73], [75]. MLMT analysis did not reveal separate genotypes for strains from wild animals. The four isolates from foxes and the one from a marsupial were all assigned to the MON-1 Sub-Pop1B-INFNW and interspersed with the human and canine strains (Figure 2, Figure 3 and Figures S1, S2, S33).
The transmission of NW L. infantum, its virulence and clinical picture are likely influenced by coevolutionary interactions between specific parasite and sand fly genotypes, as suggested recently [26], and different sand fly species or subspecies might be involved in the transmission of different L. infantum genotypes in the NW. Lu. longipalpis, the major vector of NW L. infantum, is distributed from Southern Mexico to Northern Argentina and it is considered to be a complex of sibling species [15], [24]–[26], [28]. Arrivillaga et al. (2002) have concluded from mitochondrial sequence (COI) data that Lu. longipalpis in Central and South America consists of at least four clades, which constitute species [27]. These clades may correlate in part with some of the populations or subpopulations of NW L. infantum identified in this study.
The Caribbean non-MON-1 population might possibly be linked to Lu. evansi which was reported as an alternative vector for NW L. infantum in Latin America [15], but was not found in Brazil. Interestingly, most strains from Mato Grosso do Sul and three from Paraguay were found in Sub-Pop1B-INFNW whereas the strains from all other Brazilian and Paraguyan foci were assigned to Sub-Pop1A-INFNW. This could be attributed to the fact that in some foci of Mato Grosso do Sul Lu. cruzi (a species within the Lu. longipalpis complex) has been established as the vector for NW L. infantum and it has been found to be sympatric with Lu. longipalpis in many areas [76]–[82]. It is perhaps also present in bordering areas of Paraguay and Bolivia [77], [79], [80], [83]. Whether different geographically determined L. infantum genotypes correlate with the occurrence of specific sand fly species in those areas should be further investigated.
Our results indicate that L. infantum was introduced from Southwest Europe to the New World several times and at several points along this continent (Figure 6). The genotypes found in specific regions in South and Central America were also found in Europe, especially among the Spanish, Portuguese, French and Italian strains of L. infantum. When analysed together the MON-1 population of NW L. infantum was intermingled with MON-1 strains from the Western European Mediterranean countries and the non-MON-1 population comprised strains from both New and Old Worlds.
There were several waves of immigration of Europeans into the New World especially from Leishmania endemic countries such as Portugal and Spain starting with the arrival of the Conquistadores up to the immigration of settlers during last century. Brought to South America with European immigrants, perhaps many times, the parasites spread rapidly due to migration, urbanization and trade. There is e.g. some indication of expansion of single genotypes (clones) among the Northeast of Brazil and Mato Grosso do Sul, respectively. This introduction of L. infantum is still ongoing as recently shown for an infected dog which was brought from France to French Guiana [17].
This study further confirmed that the agent of VL in the New World is L. infantum and not a separate indigenous species, L. chagasi. This is also supported by i) the identity of the isoenzyme profiles [44], [54], [84]–[88], ii) the identical genotypes obtained by analysing different genomic targets with different molecular techniques [2]–[4], [38], [43], [89]–[92], iii) the fact that L. infantum can be transmitted by several OW sand fly species and infect, develop in and adapt to Lu. longipalpis [32], and (iv) by the fact that New World foxes are phylogenetically closer to Old World wolf-like canids than to Old World foxes and therefore have a high potential to be a reservoir [93]. It still remains unclear, whether neotropical wild animals found to be infected with L. infantum are accidental hosts or real reservoirs. We could show, that parasites isolated from four foxes and one marsupial did not constitute a separate population in the NW and are, thus, not part of separate transmission cycles.
Multiple introductions of the parasite help to explain the immense spread of L. infantum in the NW Furthermore, Lu. longipalpis has been proven to be a permissive vector and fast adaptation is facilitated by modification of the parasite's surface molecules [94], [95]. Thus L. infantum brought to the NW could have easily adapted to the respective local sand fly populations.
Recent, and likely continious migrations to and possibly even from, the NW are further supported by FST values calculated between NW and OW L. infantum, which indicate only little genetic differentiation (data not shown). In contrast, we observed that the populations of L. infantum from southern Europe are more closely related to NW L. infantum than to other populations of OW L. infantum, e.g. from North Africa, Central Asia, the Middle East and even South Eastern Europe. Moreover, parasites indigenous in the NW should be more diverse, but we observed them to be much less diverse than L. infantum or L. donovani from the Old World. This is consistent with a founder effect, i.e. recent introduction of a restricted part of the original L. infantum population, with possible genetic drift or clonal expansion of only some genotypes. As a consequence there is no justification for a taxonomic separation of L. chagasi and L. infantum at species or subspecies level .
The present paper represents to our knowledge the first comprehensive population study of NW L .infantum, in which we have applied a high resolution typing method sensitive enough to detect population structures at intra-species level. We found a very homogenous population structure in Brazil and Paraguay consisting exclusively of MON-1 strains and a mixed population structure including MON-1 and non-MON-1 strains in the Caribbean region. Further studies with refined sampling strategies based on the populations and sub-populations detected in this study will enable more intensive microepidemiological analyses of NW L. infantum genotypes, and their association with reservoirs, vectors, clinical presentation, host immunological status, ecology, geography, and socioeconomic or demographic factors. We have provided conclusive evidence of recent multiple introductions of L. infantum from the Old into the New World including both MON-1 and non-MON-1 genotypes and for the synonymy of L. infantum and L. chagasi.
|
10.1371/journal.ppat.1006138 | Glucocorticoid Insensitivity in Virally Infected Airway Epithelial Cells Is Dependent on Transforming Growth Factor-β Activity | Asthma and chronic obstructive pulmonary disease (COPD) exacerbations are commonly associated with respiratory syncytial virus (RSV), rhinovirus (RV) and influenza A virus (IAV) infection. The ensuing airway inflammation is resistant to the anti-inflammatory actions of glucocorticoids (GCs). Viral infection elicits transforming growth factor-β (TGF-β) activity, a growth factor we have previously shown to impair GC action in human airway epithelial cells through the activation of activin-like kinase 5 (ALK5), the type 1 receptor of TGF-β. In the current study, we examine the contribution of TGF-β activity to the GC-resistance caused by viral infection. We demonstrate that viral infection of human bronchial epithelial cells with RSV, RV or IAV impairs GC anti-inflammatory action. Poly(I:C), a synthetic analog of double-stranded RNA, also impairs GC activity. Both viral infection and poly(I:C) increase TGF-β expression and activity. Importantly, the GC impairment was attenuated by the selective ALK5 (TGFβRI) inhibitor, SB431542 and prevented by the therapeutic agent, tranilast, which reduced TGF-β activity associated with viral infection. This study shows for the first time that viral-induced glucocorticoid-insensitivity is partially mediated by activation of endogenous TGF-β.
| In this study, we investigate how respiratory viral infection interferes with the anti-inflammatory actions of glucocorticoid (GC) drugs, which are a highly effective group of anti-inflammatory agents widely used in the treatment of chronic inflammatory airway diseases, including asthma and chronic obstructive pulmonary disease (COPD). Exacerbations of both asthma (“asthma attacks”) and COPD are often caused by viral infection, which does not respond well to GC therapy. Patients are often hospitalized placing a large burden on healthcare systems around the world, with the young, elderly, and those with a poor immune system particularly at risk. We show that viral infection of airway epithelial cells causes increased expression and activity of transforming growth factor-beta (TGF-β), which interferes with GC drug action. Importantly, we have shown for the first time that inhibiting TGF-β activity in the airways could serve as a new strategy to prevent and/or treat viral exacerbations of chronic airway diseases.
| Exacerbations of asthma and chronic obstructive pulmonary disease (COPD) are commonly associated with airway viral infection, including respiratory syncytial virus (RSV), human rhinovirus (RV) and influenza A virus (IAV) [1, 2]. RSV infection is a major cause of acute respiratory disease (i.e. bronchiolitis), especially in infants and the elderly [3–5]. Most children are infected by RSV at least once by 2 years of age [3]. RSV infection in children does not elicit long-term immunity, and the adaptive immunity following natural infection is poorly protective even in adults. Thus, re-infection occurs throughout life, even with the identical RSV strain [6, 7]. Severe RSV infection in infancy may result in Th2 and Th17-biased responses, that influence allergic airway inflammation[6]. Approximately 50% of the children who had severe RSV bronchiolitis were subsequently diagnosed with asthma [8, 9]. In addition to RSV, RV and IAV are also commonly detected in patients with asthma and COPD exacerbations [2, 10, 11].
During cellular infection, the viral pathogen-associated molecular patterns (PAMPs), such as viral single-stranded (ss) RNA, double-stranded (ds) RNA, dsRNA-like structures (panhandles), the 5’ triphosphate structure of viral RNA or some unidentified RNA structures, are detected by pattern recognition receptors, including toll-like receptors (TLRs), retinoic acid-inducible gene (RIG)-1-like receptors (RLRs), and nucleotide-binding oligomerization domain (NOD)-like receptors (NLRs) [1, 12–14]. Activation of these innate immune receptors induces secretion of primary anti-viral mediators, including Type-I and -III interferons (IFN-α/β and IFN-λ) to combat the viral infection [12]. Simultaneously, respiratory viral infection induces the secretion of an array of other pro-inflammatory cytokines and chemokines to recruit inflammatory cells to the site of infection to facilitate viral clearance. The infiltrating inflammatory cells also release inflammatory mediators that may induce tissue damage and compromise function [12, 15].
There is no effective therapeutic strategy for either RSV or RV infection except for supportive care, including hydration and oxygenation. Development of effective vaccines is challenging due to the immature infant immune system in early-life RSV infection, and to the large number of RV serotypes [5, 16, 17]. The approved antiviral drugs for the treatment of IAV, the M2 ion channel blocker (amantadine and rimantadine) and neuraminidase inhibitors (zanamivir, oseltamivir and peramivir), are associated with adverse effects or have limited efficacy, respectively. The IAV vaccine is updated annually; however, it still gives limited protection [18]. The most commonly used anti-inflammatory drugs for asthma and COPD exacerbations are glucocorticoids (GCs). However, the majority of clinical studies have found that respiratory viral infection responds inadequately to the anti-inflammatory actions of either inhaled or systemic GCs [19–25]. Moreover, the effect of GCs on virus-induced cytokine secretion is controversial. GCs have been shown to inhibit RSV infection-induced interleukin (IL)-8 and macrophage inflammatory protein (MIP-1) secretion from neutrophils [26], and IL-11 production by lung fibroblasts [27]. However, GCs have been shown to have no effect on the RSV-induced release of IL-8 and MIP-1 during infection of either Hep-2 epithelial cells or primary bronchial epithelial cells [28]. The mechanism by which the inflammation associated with respiratory viral infection is unresponsive to GCs treatment remains unclear.
Airway epithelium is a key target for both GC activity and GC resistance [29, 30]. Infection with RSV or RV has been shown to impair GC transactivation in alveolar epithelial A549 cells, bronchial epithelial BEAS-2B cells, and in submerged primary bronchial epithelial cells [31–36]. However, there is limited understanding of the underlying mechanism of viral-induced GC resistance in epithelial cells. Upon viral infection, airway epithelial cells produce an array of pro- and anti-inflammatory cytokines and chemokines including the type-I and -III interferons (IFN-α/β and IFN-λ), TNFα, IL-4, IL-8, IL-13, IL-17, CCL3 and RANTES [37], some of which have been shown to interfere with GC action in epithelial cells: TNFα inhibits GC transactivation in A549 cells and BEAS-2B cells [38]; IL-17 induces GC insensitivity in the human bronchial epithelial cell line, 16HBE14o- [39]; and, IFN-λ-induced JAK/STAT signaling activation is insensitive to GC action in A549 cells and air-liquid interface (ALI) differentiated primary human bronchial epithelial cells (HBECs) [40]. Viral infection of airway epithelial cells with RSV [41], RV [42, 43], or IAV [44, 45] also results in increased expression, secretion, and activity of the pleiotropic growth factor transforming growth factor-β (TGF-β). Moreover, endogenous TGF-β enhances RSV replication by induction of cell cycle arrest in an autocrine manner [41], and increases RV replication by suppression of type I/III IFN expression [42, 43]. Importantly, our group recently found that TGF-β causes a profound impairment of GC activity in A549 cells, BEAS-2B cells and in ALI-HBECs [46, 47].
We therefore hypothesize in the current study that viral-infection induced glucocorticoid insensitivity in epithelial cells is due to activation of endogenous TGF-β. We provide evidence that autocrine activation of TGF-β mediates the GC insensitivity induced by RSV, RV, and IAV infection in epithelial cells. Moreover, we also examined the anti-allergic agent tranilast, which has been widely used in Japan and South Korea [48, 49]. Tranilast has therapeutic effects in many conditions including inflammation, renal fibrosis, autoimmune disorders and cancer. It has been reported that tranilast inhibits the expression and activity of TGF-β in different cell types [50–52]. We show that tranilast inhibits the expression and activity of TGF-β in epithelial cells, and provide the first evidence that TGF-β modulators may be suitable novel therapeutics to restore sensitivity to GC actions during viral infection.
Budesonide (0.01-100nM) induced a concentration-dependent increase in the expression of the selected GC-inducible genes. The expression of most of the genes assessed was markedly impaired by RSV infection at a multiplicity of infection (MOI) of 0.1 virus units/cell for 48 hours (Fig 1). Genes impaired in this manner included those encoding glucocorticoid-inducible leucine zipper (GILZ), which is an anti-inflammatory/anti-proliferative gene; epithelial sodium channel-α subunit (ENaCα), which regulates the airway fluid levels by absorbing Na+ ions; α-1 antichymotrypsin (SERPINA3), which inhibits the activity of proteases; cyclin-dependent kinase inhibitor 1C (CDKN1C), which is a cell cycle negative regulator; pyruvate dehydrogenase kinase isozyme 4 (PDK4), which decreases glycolytic metabolism; and the potassium channel shab-related subfamily B member 1 (KCNB1), which regulates epithelial electrolyte transport. We examined the effect of viral infection on maximum GC response; therefore 100nM budesonide was used for the following gene expression experiments based on the concentration-response curves. Suppression of budesonide-induced glucocorticoid response element (GRE) activity was also observed in BEAS-2B cells infected with MOI 0.1 RSV for 48 hours (S1 Fig). Budesonide at 1nM was used for the GRE activity study, as we previously showed that GRE activity requires lower concentrations of GCs to reach the maximum response compare to the concentrations for GC-inducible gene expression. Although RSV infection did not influence cell viability, it decreased the total cell number (S2(A) Fig). The intracellular expression of RSV A2 strain N gene mRNA, measured as an index of viral load, was unaffected by budesonide (S3 Fig). The expression of GC-inducible proteins ENaCα and the promyelocytic leukemia zinc finger (PLZF, a transcriptional repressor in control of cell proliferation and differentiation) was also examined. We found that RSV infection clearly impaired the expression of budesonide-induced ENaCα and PLZF protein (Fig 2).
As respiratory viral infection is a major trigger of exacerbations of asthma or COPD, we therefore investigated the effect of RSV infection in BEAS-2B cells, which had been pretreated with budesonide for 24 hours or 4 hours, to emulate the sequence of exposure for asthma or COPD patients who are on GC therapy at the time of viral infection. We found that budesonide-induced expression of GILZ, ENaCα and PLZF mRNA was significantly impaired by subsequent RSV infection. Moreover, budesonide-induced expression of PLZF protein was significantly reduced by subsequent RSV infection (Fig 3).
RSV infection significantly increased the expression of TGF-β1 mRNA in BEAS-2B cells (Fig 4). The cells were pre-incubated for 30 min with the TGF-β receptor (activin-like kinase 5 (ALK5/ TGFβR1)) selective inhibitor, SB431542 (1μM using concentration validated in previous studies [46, 47]) to ascertain the activity of the endogenous TGF-β [53]. TGF-β-inducible gene PAI-1 (plasminogen activator inhibitor-1) was used as a consistent marker for TGF-β activity. Inhibition of ALK5 attenuated the RSV-induced mRNA expression of PAI-1 in BEAS-2B cells [54]. Moreover, PAI-1 can also be induced by GC in different cell types [55, 56]. We found, as expected, that budesonide (100 nM) significantly induced the expression of PAI-1 mRNA, and further enhanced the induction by RSV infection (Fig 4).
TGF-β impairs glucocorticoid function in both bronchial epithelial cells (BEAS-2B cell line and primary ALI-HBECs) and pulmonary epithelial cells (A549 cell line) [46, 47]. The impairment of glucocorticoid action in these cell types was found to be dependent on activation of the TGF-β receptor kinase ALK5 [46, 47]. Inhibition of ALK5 using SB431542 (1μM) completely prevented RSV infection—impaired GRE activation in BEAS-2B cells (S1 Fig). Moreover, inhibition of ALK5 using SB431542 completely prevented or significantly attenuated the RSV infection (48 hours)-induced impairment of budesonide-induced expression of GILZ, ENaCα and SERPINA3, with little effect on CDKN1C expression levels (Fig 2). However, SB431542 did not influence the total cell number (S2B Fig), or the intracellular expression of RSV A2 strain N gene (S3A Fig). Similar findings were obtained by pretreating the cells with a structurally distinct ALK5 inhibitor GW788388 (1μM) [53] (S4 Fig). In addition, transfection of ALK5-targeted siRNA resulted in more than 50% knockdown of ALK5 protein expression (S5A Fig). A concomitant impairment of ALK5 activity was confirmed by measurement of TGF-β-induced phosphorylation of Smad2, which is a critical downstream signaling molecule for TGF-β/ALK pathway (S5B Fig). Importantly, transfection of ALK5 siRNA showed similar effects to the ALK5 inhibitors in restoring GC sensitivity.
Infection of BEAS-2B cells with IAV at MOI 0.1 (Fig 5A), RV at MOI 1 (Fig 5B) for 48 hours, or treatment of the cells with Poly(I:C) (10 μg/ml) for 24 hours (Fig 5C), impaired budesonide (100nM) or dexamethasone (30nM)-induced GILZ expression. Inhibition of ALK5 using SB431542 (1μM) prevented IAV, RV or Poly(I:C) impairment of GC-induced GILZ expression. Moreover, inhibition of ALK5 attenuated the IAV, RV or Poly(I:C)-induced PAI-1 mRNA expression Again, budesonide (100 nM) significantly increased PAI-1 expression (Fig 5). However, treatment of BEAS-2B cells with RLR (RIG-1 and MDA-5) ligands Poly(I:C)(HMW)/LyoVec (0.01–1μg/ml) had no effect on dexamethasone (30nM)-induced GILZ and ENaCα expression (S6 Fig).
RSV infection is known to activate a variety of intracellular signaling cascades. A number of the underlying kinases are involved in non-canonical TGF-β signaling pathways, including p38MAPK, ERK1/2, JNK, Akt and NFkB. We found that RSV infection induced the phosphorylation of ERK1/2 kinase in BEAS-2B cells, and pretreatment of the cells with SB431542 showed a trend to decrease the phosphorylation of ERK1/2 (Fig 6A). We therefore further investigated the involvement of the ERK1/2 kinase using the MEK1/2 inhibitor U0126 at 1 μM, at concentration validated in previous studies [46, 47]. Pre-incubation of the cells with U0126 (1μM) reduced the induction of TGF-β and PAI-1 mRNA during RSV infection and attenuated the RSV infection impairment of budesonide-induced expression of GILZ mRNA (Fig 6B).
Stimulation of BEAS-2B cells with the TLR3 ligand Poly(I:C) (10 μg/ml) also induced EKR1/2 phosphorylation (S7 Fig). We next examined whether the viral infection-impaired GC action was mediated by activation of TLR3 using targeted siRNA. Transfection of TLR3-targeted siRNA induced a knockdown of approximately 70%, which was stable throughout the experimental period (72 hours) (Fig 7A). We found that knockdown of TLR3 largely inhibited both RSV and RV-induced TGF-β expression (Fig 7B), and prevented the viral infection impairment of dexamethasone-induced gene expression (Fig 7C and 7D).
TGF-β-induced impairment of glucocorticoid action was partially attributed to attenuated nuclear translocation of GRα in the A549 cell line [47], although this was not observed in the BEAS-2B cell line [46]. We investigated the potential relevance of delayed or reduced GRα translocation, or changes in the level of GRα in RSV-infected BEAS-2B cells. Analysis of BEAS-2B cell cytoplasmic and nuclear extracts indicated that whilst SB431542 did not affect the total GRα protein expression level, 24 hours budesonide treatment (100nM) significantly reduced the expression of GRα protein (Fig 8A). However, neither the expression level nor the GRα subcellular distribution was influenced by RSV infection (Fig 8C). Immunoreactive-GRα was detected in both cytoplasmic and nuclear compartment in vehicle-treated cells. The immunoreactive-GRα level was increased in the nuclear compartment in response to budesonide treatment. However, the localization of GRα in the presence of budesonide was not affected by RSV infection (Fig 8B).
The anti-allergic agent tranilast inhibits the expression and activity of TGF-β in different cell types [50–52]. Importantly, this agent has few and only mild side-effects and is well tolerated [49]. We therefore examined the effects of tranilast at a concentration (100μM) within the range detected in plasma (30–300μM) after oral administration of a therapeutic dose [48], to ascertain its impact on GC impairment by RSV infection in epithelial cells. We found that tranilast inhibited RSV infection-induced mRNA expression of TGF-β1 and PAI-1 (Fig 9A). Pre-incubation of BEAS-2B cells with tranilast prevented/attenuated RSV infection impairment of budesonide-induced mRNA expression of GILZ, ENaCα, PDK4 and CDKN1C (Fig 9B), whilst it did not affect the intracellular expression of RSV A2 strain N gene (S3B Fig).
Expression of the anti-viral cytokines IFN-α, IFN-β, IFN-λ1 (IL29) and IFN-λ2 (IL28A), and the pro-inflammatory cytokines IL-8 and IL-6 were measured. We found that RSV infection markedly induced the mRNA expression of IFN-β, IL29 and IL28A in BEAS-2B cells, whilst modest up-regulation of expression of IFN-α mRNA was observed. None of these expression levels were influenced by budesonide (Fig 10). RSV infection also induced marked expression of pro-inflammatory cytokines, including IL-8 and IL-6 mRNA in BEAS-2B cells. Treatment of the cells with budesonide (100nM) attenuated the RSV infection-induced expression of IL-8 and IL-6 mRNA by more than 80%. Inhibition of ALK5 using SB431542 (1μM) prior to RSV infection did not affect IL-8 expression. However, SB431542 (1μM) reduced the RSV infection-induced IL-6 expression by 30% (S8A Fig). Interestingly, pre-incubation of the cells with tranilast (100μM), a modulator of TGF-β production and activity, reduced RSV infection-mediated expression of both IL-8 and IL-6. Co-treatment with budesonide further inhibited IL-8 expression (S8B Fig)
Primary HBECs were cultured at air-liquid interface (ALI-HBECs) to reach the criteria for ALI differentiation, including TEER values of at least 200 Ω.cm2 to ascertain the formation of functional tight junctions; increased mRNA expression of Tektin-1 (a marker for ciliated cells) and MUC5AC (a marker of goblet cells); and visible cilia and mucus on the differentiated cells [46]. RSV infection increased expression of TGF-β1 and the TGF-β-inducible gene PAI-1 mRNA (Fig 11). The PAI-1 expression was reduced by pre-incubation of the cells with SB431542 (1μM) or tranilast (100μM) for 1 hour prior to RSV infection. RSV infection impaired the dexamethasone-induced mRNA expression of GILZ and ENaCα. Importantly, the impairment of the expression of the genes was prevented by SB431542 or tranilast (Fig 11).
Respiratory viral infection-induced acute bronchiolitis and asthma/COPD exacerbations are worldwide health problems, with a substantial disease burden in the young, the elderly, in adults with chronic lung disease and patients who are immunocompromised [3, 4]. Inhaled or oral glucocorticoids are standard treatments for asthma and COPD, but GCs are generally not effective for treating exacerbations of asthma and COPD, and other inflammatory complications of respiratory virus infection. In this study, we identified that endogenous TGF-β is expressed, induced TGF-β-like activity, increases PAI-1 expression in RSV, RV or IAV-infected bronchial epithelial cells, contributing to the viral infection-induced GC insensitivity. We also showed that treatment of epithelial cells with the anti-allergic agent tranilast reduced the expression and activity of TGF-β, and restored GC sensitivity (Fig 12).
RSV infection impaired GRE activity and the expression of GC targeted genes, including GILZ and ENaCα in the BEAS-2B bronchial epithelial cell line. GILZ is a GC-responsive gene that mediates anti-inflammatory effects of GCs in T cells, macrophages and also epithelial cells [57]. Impairment of GILZ expression by viral infection dampens the anti-inflammatory activity of GCs. ENaC channels in lung epithelial cells regulate the airway surface fluid levels. The attenuation of ENaCα expression by viral infection leads to excess fluid and recurrent infections in the lung [58]. As the primary ALI-HBECs are necessarily cultured in hydrocortisone-containing medium, the response to synthetic GC stimulation is modest. Nevertheless, RSV infection-induced GC activity impairment was also observed in ALI-HBECs. Importantly, we found that RSV infection-impaired expression of GC-responsive gene (ENaCα and PLZF) translates to similar patterns of change in protein levels. This impairment of GC action may explain the lack of clinical effectiveness of GC treatment in RSV-infected patients. Interestingly, we found that RV and IAV also induced TGF-β-like activity, observing increased PAI-1 expression and TGF-β-dependent impairment of GC activity in BEAS-2B cells. Thus, GC resistance is likely a common response to a respiratory viral infection.
The viral pathogen-associated molecular patterns are detected by pattern recognition receptors, including TLRs, RLRs and NLRs, expressed in or on respiratory epithelial cells. We found that GC impairment was also induced by the TLR3 agonist, poly(I:C), a synthetic analog of dsRNA. Poly(I:C) stimulation also activates RLRs (RIG-1 and MDA-5). However, activation of RLRs with Poly(I:C)HMW/LyoVec did not affect the GC actions. RSV and IAV comprise negative-sense ssRNA genome viruses, classified as a paramyxovirus and orthomyxovirus, respectively. RV is classified as a picornavirus and has a positive-sense ssRNA. RSV and RV generate dsRNA intermediates during viral replication cycles, which activate TLR3 [59–61]. IAV does not generate abundant levels of dsRNA in the infected cells. However, TLR3 is thought to recognize as yet unidentified RNA structures during IAV infection [13]. In order to validate that impairment of GC action with each of these viruses is through activation of TLR3 in the infected cells, we chose to knockdown TLR3 using targeted siRNA. Whilst TLR3 expression was reduced approximate 70%, the viral infection-induced TGF-β expression and GC impairment were prevented. These data strongly suggest that viral infection-impaired GC action is at least partially mediated by activation of TLR3 (Fig 12).
Engagement of TLR3 activates multiple transcription factors including NF-κB, mitogen-activated protein kinases (MAPKs), and members of the interferon regulatory factor (IRF) family, which induce the expression of inflammatory cytokines and type I/III IFNs. Since both viral infection and poly(I:C) stimulation induces secretion of various cytokines [62], it is conceivable that the GC impairment was mediated by the release of soluble factors that act in an autocrine manner. Some of these cytokines (such as TNFα, IFNγ, IL4, IL13 and IL17) have been reported to interfere with GC action in epithelial cells [38–40]. Viral infection induces expression and secretion of TGF-β in epithelial cells [41, 42]. Our group has recently shown that TGF-β impairs GRE-dependent transactivation in different epithelial cell types [46, 47]. Moreover, we found that TGF-β was more potent, had a more rapid onset and shows a greater extent of GC impairment than the combination of TNFα, IL-4 and IL-13. We now show that RSV infection induces TGF-β expression and activity in the BEAS-2B cell line and ALI-HBECs. Moreover, we found that RSV infection-induced TGF-β expression was mediated by phosphorylation of ERK1/2, at least partially through activation of TLR3. Inhibition of ERK1/2 activation significantly attenuated the impairment of GC activity by RSV. Inhibition of ALK5 with SB431542 tended to reduce the phosphorylation of ERK1/2. However, we have shown previously that TGF-β-induced GC impairment was not induced by activation of ERK nor other established canonical or non-canonical pathways. A novel TGF-β-inducible mechanism is implicated in the modulation of GC action [46]. Therefore, we believe the contribution of ERK1/2 activation to the viral infection-induced GC action occurs upstream of TGF-β expression, in signaling emerging from activation of TLR3 (Fig 12).
Viral infection induces cytopathic effects reducing cell viability. The cytopathic effect is both time and inoculation dose (MOI) dependent. RSV infection decreased the cell numbers compared to the uninfected cells. However, under the conditions of RSV infection in the present study there were no detectable effects on cell viability, suggesting that viral induced GC impairment is not secondary to reduced cell viability. We found that inhibition of the type I TGF-β receptor ALK5 activity did not impact the viral reduction in cell numbers or viral replication. Interestingly, inhibition of ALK5 activity prevented/attenuated the RSV-impaired GRE activity and the expression of its targeted genes, including GILZ and ENaCα, which suggests that blockade of TGF-β activity increases the GC-mediated anti-inflammatory action and airway fluid regulation. Viral infection induced activation of the TGF-β/ALK5 pathway and subsequent impairment of GC action was further confirmed by knockdown of ALK5 using targeted siRNA. Thus, autocrine TGF-β contributes to the viral infection-induced GC insensitivity in airway epithelial cells, identifying TGF-β signaling as a target for inhibition that can potentially restore GC sensitivity during RSV infection (Fig 12). Moreover, we found that poly(I:C) or viral infection-impaired GC activity was shown after 24–48 hours incubation or infection. A similar latency period has been reported by another group showing that poly(I:C) or RV infection-decreased GC activity only became apparent after a period of hours and reached maximum in 24–48 hours post-treatment [36]. The incubation period fits our conclusion with regard to the time required for dsRNA generation by viral replication, TLR3 activation-induced TGF-β expression and activity. We found that RSV infection also reduced on-going responses to budesonide. This latter experimental design is of relevance to therapeutic patterns in asthma and COPD and offers a potential explanation of the exacerbations upon respiratory viral infection. We suggest that viral infections not only induce inflammatory pathways that are intrinsically insensitive to GC, but also that the asthmatic or COPD inflammation previously controlled by GC is compromised by infection induced TGF-β activity.
RSV infection impaired most of the GC-responsive genes assessed. However, we also found GCs might have beneficial effects in regulation of RSV-induced cytokine expression, as budesonide inhibited RSV-induced mRNA encoding the pro-inflammatory cytokines IL-8 and IL-6, without interfering with the production of IFNs or viral replication. The mechanism of tranilast-inhibition of TGF-β production and activity was unclear. It is likely acting differently from SB431542, but has in common with SB431542 suppression of TGF-β expression and activity. Interestingly, we have shown for the first time that tranilast, but not SB431542, markedly inhibited the RSV-induced expression of IL-8 and IL-6, which suggests the potential for additional beneficial anti-inflammatory activities mediated by tranilast, when used as an anti-allergic agent.
The molecular mechanisms of TGF-β impairment of GC-action in epithelial cells have been extensively studied [46, 47]. The impairment was unrelatedto either the GRα protein level, or to the GRα nuclear translocation in BEAS-2B cells. The GC impairment by TGF-β requires activation of ALK5. However, the signal transduction downstream of ALK5 could not be associated with any known canonical or non-canonical pathways [46, 47]. Similar results have been found with RSV infection-impaired GC action that GRα protein expression or GRα nuclear translocation were not influenced by RSV infection. Moreover, inhibition of ALK5 did not affect the expression of GRα protein level. Current evidence suggests a novel non-canonical signaling pathway being activated. Hypothesis-free approaches, such as proteomics and functional genomics, are being used to further examine the signaling mechanisms subserving GC resistance induced by TGF-β [29].
TGF-β activates a variety of signaling cascades regulating many cellular processes. Global inhibition of TGF-β activity therefore engenders many adverse effects, including excessive inflammation and risk of autoimmunity [56, 63]. Further investigation of the novel signaling mechanism underlying the GC impairment by TGF-β and its more selective targeting may restore GC sensitivity during respiratory viral infection, whilst avoiding the adverse effects that are associated with complete inhibition of TGF-β activity. TGF-β modulators may be an alternative means to restore GC activity without undue adverse effects. The anti-allergic agent tranilast is reported to inhibit the expression and activity of TGF-β in different cell types [50–52]. Importantly, it has few and only mild side-effects and is well tolerated [49]. We found that tranilast inhibits the expression and activity of TGF-β in both BEAS-2B cells and ALI-HBECs. Intriguingly, we show that pre-incubation with tranilast prevented the GC impairment by RSV infection. Further establishing the effectiveness of tranilast in viral infection would support the use of TGF-β modulators for the prevention/treatment of GC insensitivity occurring during RSV infection-induced bronchiolitis or asthma/ COPD exacerbations.
In summary, exacerbations of asthma or COPD associated with respiratory viral infection are resistant to the anti-inflammatory actions of GCs. We identified autocrine TGF-β as a key mediator of the GC impairment. Our studies show for the first time that modulation of TGF-β activity is a potential strategy for restoring the GC sensitivity during viral infection and for prevention of viral exacerbation of chronic airway diseases.
BEAS-2B bronchial epithelial cells (ATCC, Manassas, VA, USA) were cultured as described [47], seeded at 5×104 cells/cm2 in 24 well plates, T-75 flask or chamber slides in Dulbecco’s modified Eagle’s media (DMEM) containing 5% vv-1 heat-inactivated FBS, 15 mM HEPES, 0.2% vv-1 sodium bicarbonate, 2 mM L-glutamine, 1% vv-1 non-essential amino acids, 1% vv-1 sodium pyruvate, 5 IU·mL-1 penicillin and 50 mg·mL-1 streptomycin, and incubated overnight at 37°C in air containing 5% CO2. The cells were then inoculated with RSV at a multiplicity of infection (MOI) of 0.1 TCID50 (50% tissue culture infectious dose) per cell for 1 hour, and incubated for up to 48 hours. The GC transactivation was assessed by incubating the cells with budesonide (Bud, 0.01-100nM) for the last 24 hours to measure the glucocorticoid response element (GRE) activity, or for the final 4 hours, to measure the mRNA expression of the GC-inducible genes. In some experiments, budesonide was added to BEAS-2B cells for 24 hours or 4 hours prior to RSV infection and it was re-added after 1 hour RSV inoculation. The mRNA expression of the GC-inducible genes and also protein were examined after RSV infection for up to 48 hours.
Primary HBECs were purchased from Lonza (Waverley, Australia) and cultured using B-ALI Bulletkit (Lonza) according to the manufacturer’s instructions. The cells were differentiated for more than 21 days at air-liquid interface on fibrillar collagen-coated 24-well Corning Transwell 0.4μm pore polyester membrane cell culture inserts (Sigma-Aldrich, MO,USA) as described [46]. Cell differentiation was confirmed through measurement of trans-epithelial electrical resistance (TEER) and visualization of beating cilia. RSV at a MOI of 0.1 or control culture medium was added onto the apical surface of the cells, which were inoculated for 1 hour, and then incubated for up to 48 hours. Dexamethasone (Dex, 100nM) was applied to the basolateral side of the cells for 5 hours to assess the GC transactivation by measuring the mRNA expression of the GC-inducible genes.
Human RSV, prototype A2 strain (ATCC VR-1540) was cultured in Hep2 cells (also from the ATCC). Viruses were inoculated into Hep2 cells, and incubation continued until a cytopathic effect was observed. Supernatant was removed and the Sucrose-Phosphate-Glutamate-Albumin (SPGA) stabilizer solution was added to the cells. The virus was harvested by scraping the cells and centrifugation the cell suspension at 1,000g for 15 min. clarified supernatants were snap frozen and stored at -80°C RSV was titrated by serially diluting the newly generated stock virus (1/103−1/108) and then inoculating Hep2 cells in flat-bottomed 96-well plates (2.5×104 cells/well). Viral titer was determined by TCID50 assay, defined as the quantity of virus which induces detectable cytopathic effects in 50% of the infected cells after 3–5 days, and was calculated according to Reed and Muench [64].
Human rhinovirus, RV16 strain (ATCC VR-283) was cultured in Ohio-HeLa cells (a kind gift from Dr. Reena Ghildyal). The virus was harvested by scraping the cells without removing the infection media. The cell suspension was centrifuged at 3,000g for 15 min. The viral titer was titrated by using the same methodology as RSV, but in Ohio-Hela cells.
Influenza A virus, HKx31 strain (also known as X-31, a virus strain of H3N2 subtype, a kind gift from Dr. Sarah L. Londrigan) was cultured in the allantoic cavity of 10-day old embryonated chicken eggs (Research Poultry, Research, Victoria, Australia) and titrated on Madin-Darby canine kidney (MDCK) cells (ATCC) by standard procedures and expressed as plaque forming units (PFU)/ml as previously described [65].
BEAS-2B cells for transfection were seeded in 24-well plates overnight. Cells were co-transfected with pGRE-SEAP and pGL3 control plasmids using Lipofectamine 2000 (Invitrogen, Carlsbad, CA), as previously described [46, 47]. Transfected cells were inoculated with RSV at a MOI of 0.1 or control medium for 1 hour, and incubated for 24 hours prior to the addition of Bud (1 nM) or vehicle for further 24 hours. The 24 hour time point for Bud-induced GRE activity was selected based on our previous studies [46, 47]. Supernatants were collected for measurement of secreted SEAP using a chemiluminescence kit (Roche Applied Science, NSW, Australia) as described [46]. Pre-validated siRNA targeting ALK5 and TLR3 (Invitrogen) was transfected using Lipofectamine RNAiMAX (Invitrogen) as described previously [66].
BEAS-2B cells were seeded in 6-well plates overnight. Cells were pre-incubated with SB431542 (1μM) for 30 min prior to RSV infection at MOI of 0.1 or control medium for 3 hours, 24 hours and 48 hours for assessment of intracellular kinase phosphorylation. To assess changes in total GRα, ENaCα and PLZF expression, budesonide (100nM) was added to the cells following 48 hours RSV infection (MOI 0.1) for the last 2 hours or the last 24 hours. In some experiments, PLZF expression was measured after treatment of the cells with budesonide for 4 hours prior to RSV infection for 48 hours. Rabbit polyclonal antibody (pAb) anti-phospho-ERK1/2 (Thr202/Tyr205) and rabbit monoclonal antibody (mAb) anti-Erk1/2 (Cell Signaling) was used to measure the ERK1/2 activation. Rabbit pAb anti-GRα (Santa Cruz Biotechnology) was used to measure GRα expression. Mouse monoclonal antibody anti-PLZF and goat polyclonal antibody anti-αENaC (Santa Cruz Biotechnology) were used to measure the expression of PLZF and ENaCα. The expression level of GAPDH protein (Rabbit pAb; Abcam, Cambridge, UK) was used as a reference control for normalization to account for variation in protein loading. Western blotting was performed as described [66]. Band intensities were quantified by densitometry using the image J program (1.48v, National Institute of Health, USA).
BEAS-2B cells were seeded in a T-75 flask for isolation of cytosolic and nuclear fractions, or in an 8 chamber slide for immunofluorescence staining. Cells were infected with RSV at MOI of 0.1 or control culture medium for 46 hours prior to addition of Bud (100nM) for 2 hours. GRα localization was then determined by subcellular fractionation followed by western blot analysis as described [47]. In separate experiments, immunofluorescence was used to monitor GRα localization with the DAPI-stained nucleus [47].
Cell viability was assessed using the Trypan blue exclusion method, as described [66].
BEAS-2B cells were seeded in 24-well plates overnight. Cells were pre-incubated with SB431542 (1μM) for 30 min prior to RSV infection at MOI of 0.1, RV infection at MOI of 1, IAV infection at MOI of 0.1, or control medium for 44 hours prior to addition of Bud (0.01-100nM) or Dex (30nM) for 4 hours. The 4 hour time point for mRNA expression of the GC-inducible genes was chosen based on our previous studies [46, 47]. In some experiments, tranilast (100μM) or U0126 (1μM) was added 30 min prior to RSV infection. Cells were also treated with TLR3 agonist, polyinosinic-polycytidylic acid (poly(I:C); 10μg/ml); or RLRs ligands, Poly(I:C)(HMW)/LyoVec (0.01–1μg/ml) for 24 hours prior to addition of Dex (30nM) for 4 hours. The mRNA extraction and reverse transcription were performed as previously described [46]. An ABI Prism 7900HT sequence detection system (Applied Biosystems) was used to quantitatively analyze the level of gene expression as previously described [47]. The generation of specific PCR products was confirmed by dissociation curve analysis. 18S ribosomal RNA (18S rRNA) was used as a housekeeping gene. RSV N gene mRNA expression level was determined by the standard curve on the basis of known TCID 50 virus stock. Primer sequences (Table 1) were KiCqStart pre-designed primers from Sigma-Aldrich, or obtained from the literature, or designed using Primer Express software (Applied Biosystems, Mulgrave, Australia) with mRNA sequences from the National Centre for Biotechnology Information (http://www.ncbi.nlm.nih.gov).
Data are expressed as the mean ± SEM. Reported n values represent number of experiments repeated or number of primary cultures used. All data were statistically analyzed using GraphPad Prism 5.0 for Windows (GraphPad Software, San Diego, CA). One-way or two-way analyses of variance (ANOVA) with Bonferroni’s post hoc test were used to analyze the data. A P value less than 0.05 was considered statistically significant.
|
10.1371/journal.pgen.1006433 | The Jujube Genome Provides Insights into Genome Evolution and the Domestication of Sweetness/Acidity Taste in Fruit Trees | Jujube (Ziziphus jujuba Mill.) belongs to the Rhamnaceae family and is a popular fruit tree species with immense economic and nutritional value. Here, we report a draft genome of the dry jujube cultivar ‘Junzao’ and the genome resequencing of 31 geographically diverse accessions of cultivated and wild jujubes (Ziziphus jujuba var. spinosa). Comparative analysis revealed that the genome of ‘Dongzao’, a fresh jujube, was ~86.5 Mb larger than that of the ‘Junzao’, partially due to the recent insertions of transposable elements in the ‘Dongzao’ genome. We constructed eight proto-chromosomes of the common ancestor of Rhamnaceae and Rosaceae, two sister families in the order Rosales, and elucidated the evolutionary processes that have shaped the genome structures of modern jujubes. Population structure analysis revealed the complex genetic background of jujubes resulting from extensive hybridizations between jujube and its wild relatives. Notably, several key genes that control fruit organic acid metabolism and sugar content were identified in the selective sweep regions. We also identified S-locus genes controlling gametophytic self-incompatibility and investigated haplotype patterns of the S locus in the jujube genomes, which would provide a guideline for parent selection for jujube crossbreeding. This study provides valuable genomic resources for jujube improvement, and offers insights into jujube genome evolution and its population structure and domestication.
| A balanced sweetness and acidity taste is among the most important characteristics of fruits. It is generally believed that human selection of sweetness plays a crucial role in the process of domestication from wild to cultivated fruit trees. However, the molecular mechanisms underlying fruit taste domestication still remain unclear. It is also unclear whether taste improvement is mainly determined by positive selection of advantageous traits such as sweetness or negative selection of disadvantageous trait such as acidity. Chinese jujube, domesticated from the wild jujube, is an economically important fruit tree crop in China. In this study, we sequenced and assembled the genome of a dry jujube and analyzed the genetic relationship between cultivated and wild jujubes through genome resequencing. Key genes involved in the acid and sugar metabolism were identified in the selective sweep regions. This finding suggested an important domestication pattern in fruit taste and also provided insights into the fruit molecular breeding and improvement.
| Chinese jujube (Ziziphus jujuba Mill.) (2n = 2x = 24), native to China, is one of the oldest cultivated fruit trees, with more than 7,000 years of domestication history [1]. It belongs to the Rhamnaceae family in the Rosales order. Jujube is valued as a woody crop and traditional herbal medicine, and cultivated on 2 million hectares in China alone, with an annual production of approximately 4.32 million tons [2]. Jujube cultivars have been traditionally classified as fresh or dry, and dry jujubes account for approximately 80% of the total production. Ripe fruits of dry jujube have a coarse texture while those of fresh types have a crisp texture.
Cultivated jujubes were domesticated from their wild ancestors (Z. jujuba Mill. var. spinosa Hu.) through an artificial selection process for important agronomic traits, which resulted in architectural and structural changes in the tree such as a transition from bushes with more thorns to trees with fewer thorns and enlarged fruit sizes [1,3]. As with many agricultural crops, taste attributes of jujube fruits, such as sweetness and sourness, have been the subject of human selection. Fruits of cultivated jujubes have higher levels of sugars (up to 72% of the dry weight), while wild jujube fruits accumulate more soluble organic acids [3,4]. The domestication mechanism of fruit sweetness and acidity taste from their wild relatives is still not well characterized. Therefore, characterization of the sugar and acid metabolism of domesticated and wild jujubes through genome-wide analyses would help elucidate the genomic mechanism underlying fruit sweetness and acidity taste improvement.
The majority of jujube cultivars produce few seeds due to self-incompatibility or cross-incompatibility, which limit the practical artificial breeding of jujube. Gametophytic self-incompatibility (GSI) system is controlled by the S locus and has been found to operate in several Ziziphus species, including Z. jujuba [5–7]. Parents sharing the same S haplotype often result in seedless jujube kernels. Therefore, identification of the self-incompatibility locus (S locus) genes would provide a guideline to facilitate jujube breeding.
Recently, the draft genome of a fresh jujube cultivar ‘Dongzao’ with a high level of heterozygosity was reported, and it provides insights into the ascorbic acid metabolism and the adaptation mechanism to abiotic/biotic stresses [8]. However, little is known about jujube evolution, domestication, and the genetic bases of fruit quality. The genome sequencing of additional diverse jujubes would help us to address these questions, laying the foundation for improved strategies for jujube breeding. Here, we report the genome of a dry jujube cultivar ‘Junzao’ Fig A and Fig B in S1 File,). We also resequenced the genomes of 31 cultivated and wild jujube accessions with a range of geographical distributions. The genome sequences provided insights into the evolution of Rhamnaceae. Integrative transcriptome and resequencing analyses illuminated the genomic mechanisms underlying the domestication events of fruit sweetness and acidity.
Sequencing of the ‘Junzao’ genome resulted in a 351-Mb assembly with contig and scaffold N50 sizes of 34 kb and 754 kb, respectively (Table 1; Table A in S2 File). A k-mer analysis of ‘Junzao’ sequences suggested an estimate genome size of ~350 Mb, consistent with the size estimated from the flow cytometry analysis (Fig C in S1 File; Table B in S2 File). The GC content of the assembled ‘Junzao’ genome was 32.6% (Fig D in S1 File). Approximately 98.3% of the 2,901 expressed sequence tag (EST) sequences and 98.9% of the assembled transcriptome contigs could be mapped to the ‘Junzao’ genome (Table 1; Table C in S2 File). In addition, 99.6% of the core eukaryotic genes were mapped to the ‘Junzao’ genome using CEGMA [9] (Fig E in S1 File) and 93.2% were completely mapped to the assembled ‘Junzao’ genome using BUSCO [10] (Table 1), indicating a high quality of the ‘Junzao’ genome assembly.
Using two high-density genetic linkage maps, we anchored 600 assembled scaffolds to the 12 linkage groups, covering 83.6% (293 Mb) of the assembled ‘Junzao’ genome (Table D in S2 File; Fig F in S1 File). We predicted a total of 27,443 protein-coding genes with an average coding sequence length of 1,136 bp and an average of 4.83 exons (Table E in S2 File), of which 91.2% were mapped to the 12 pseudo-chromosomes. A total of 2.1 million single-nucleotide polymorphisms (SNPs) were detected in the ‘Junzao’ genome, and therefore the heterozygosity level of the genome was calculated as 0.72% (Table F in S2 File). In addition, 2,309 small insertions and deletions (indels) were found to be located in the exonic regions (Table G in S2 File).
The assembled ‘Junzao’ genome was 86.5 Mb smaller than the reported genome of ‘Dongzao’ (437.7 Mb), which was assembled by sequencing the in vitro cultured plantlet [8]. One notable difference between the ‘Junzao’ genome and the reported ‘Dongzao’ genome was the abundance of transposable elements (TEs). A total of 136 Mb of TEs were identified, accounting for 38.8% of the assembled ‘Junzao’ genome, while the reported genome of ‘Dongzao’ contained 204 Mb of TEs (46.8%) (Table 1; Fig G in S1 File; Table H and Table I in S2 File). In addition, a more recent accumulation of TEs was found in ‘Dongzao’ (<1.2 million years ago) (Fig H(a) in S1 File), and a greater proportion of genes were close to the TEs in ‘Dongzao’ than in ‘Junzao’ (Fig H(b) in S1 File). Phylogenetic analysis also indicated a greater expansion of specific LTR retrotransposon clades in the ‘Dongzao’ genome (Fig H(c) in S1 File).
Collinear genome regions between ‘Dongzao’ and ‘Junzao’ were identified (Table J in S2 File). The syntenic blocks in the ‘Dongzao’ genome (326.3 Mb) were 34.1 Mb larger than those in the ‘Junzao’ genome (292.2 Mb). We found that 26.0 Mb (77%) of the 34.1 Mb were repetitive sequences, further supporting that transposons are one of the major factors contributing to the genome size difference between ‘Junzao’ and ‘Dongzao’.
We found that unanchored scaffolds in the reported ‘Dongzao’ genome had many syntenic blocks with the anchored scaffolds, much higher than those in the assembled ‘Junzao’ genome (Fig I in S1 File). Furthermore, read coverage distribution of coding regions in the ‘Dongzao’ genome displayed a heterozygous peak at the half depth of the major homozygous peak, while no heterozygous peak was found in ‘Junzao’ (Fig J. and Fig K in S1 File). These findings suggest that sequences of heterozygous alleles from the same loci (redundant sequences) were sometimes failed to be assembled into consensus sequences in ‘Dongzao’, partially contributing to the larger genome assembly size of ‘Dongzao’ than that of ‘Junzao’. In addition, we identified ~4.9 Mb bacterial sequences in the ‘Dongzao’ genome assembly. Taken together, we suggest that higher levels of repetitive sequences, redundant sequences and bacterial contaminated sequences in the assembled ‘Dongzao’ genome have contributed to the larger genome assembly of ‘Dongzao’ than ‘Junzao’.
A presence-absence variation (PAV) analysis identified 7.8 Mb of ‘Dongzao’-specific sequences containing 354 genes and 14.2 Mb of ‘Junzao’-specific sequences containing 432 genes. Gene Ontology (GO) terms including DNA recombination and DNA integration were found to be significantly enriched in ‘Dongzao’-specific genes (Table K in S2 File). In addition, we identified 131 expanded gene families (930 genes) and 232 contracted families (702 genes) in ‘Junzao’ in comparison with ‘Dongzao’ (Fig L in S1 File).
‘Junzao’ and ‘Dongzao’ are representative cultivars of dry and fresh jujubes, respectively (Fig B in S1 File). Their fruits contain highly different levels of crude fiber, which is derived from the fruit primary cell walls (Table L in S2 File). We found that several families of genes involved in cell wall modification were substantially expanded in the dry jujube ‘Junzao’ compared with ‘Dongzao’, including those encoding glycosyl hydrolases (beta-glucosidases, xyloglucan endotransglucosylase-hydrolases, endoglucanases and polygalacturonases) and those encoding pectin esterases and rhamnogalacturonate lyases (Table M in S2 File).
Eight putative proto-chromosomes of the common ancestor of Rhamnaceae and Rosaceae, two sister families in the order Rosales, were inferred based on the available genome sequences of jujube, peach (Prunus persica) and apple (Malus × domestica) (Fig 1), and they are similar to the nine putative proto-chromosomes of the ancestor of Rosaceae [11,12]
No recent whole-genome duplication (WGD) events were detected in jujube [8] and peach [13] after their divergence, while one such event was identified in apple [14]. Although the numbers of proto-ancestral chromosomes in the Rosaceae increased from eight to nine after its divergence from the Rhamnaceae [11], we were still able to identify a one-to-two relation between the jujube and apple genomes. Considering the intergenomic relations among jujube, peach and apple, we determined the two largest chromosome synteny pairs as follows: 1) jujube chromosome 3, peach chromosome 2 and apple chromosomes 1 and 7, and 2) jujube chromosome 10, peach chromosome 3 and apple chromosomes 9 and 17 (Fig 1), which reflected the recent diploidization of the apple genome [14]. A conserved block was also identified among jujube chromosome 3, peach chromosome 2 and apple chromosome 7, which did not undergo any rearrangements, fissions or fusions and is thus likely derived directly from ancient chromosome III (Fig 1). These results showed that larger syntenic blocks were retained in jujube chromosomes, and illustrated that fewer chromosome fissions, fusions and rearrangements occurred in the jujube genome compared with the peach and apple genomes (Table N in S2 File).
Resequencing the genomes of 31 accessions, including 10 wild jujube individuals (6 typical wild jujubes and 4 semi-wild accessions) and 21 jujube cultivars (Table O in S2 File; Fig M in S1 File), generated a total of 344 Gb of sequences, representing an average depth of 27.8× and an average coverage of 92.5% (Table O in S2 File). After mapping the reads of each accession to the genome of ‘Junzao’, we detected a total of 5,300,355 SNPs. The parameter θπ values [15] indicated that wild jujubes, although represented in our analysis by half the number of accessions (10) as cultivated accessions (21), exhibited greater diversity (θπ = 2.60×10−3) than cultivated jujubes (θπ = 2.19×10−3). The neighbor-joining phylogenetic tree illustrated the domestication process as a transition from wild to cultivated jujubes via certain semi-wild accessions (Fig 2A). In addition, the cultivated jujube group could be further divided into two subgroups that were generally correlated with their geographical distributions in West China and East China (Fig 2A; Fig N in S1 File). A principle component analysis (PCA) generated a similar pattern (Fig 2B) to that of the phylogenetic analysis in that the jujube cultivars formed a tight cluster that was distant from the wild jujube accessions.
Population structure analysis indicated that wild and cultivated jujubes could be divided into two groups when K = 2, although admixed features were observed in 17 accessions covering both cultivated and wild jujubes (Fig 2C). With K = 3, the cultivated populations were further divided into two subgroups corresponding to their geographical distributions (West and East China), whereas the wild population remained relatively uniform. When the K value increased progressively from 3 to 5, new subgroups emerged in the wild jujube group and further differentiation was found within the cultivated jujubes (Fig 2C).
As shown in Fig 3A, jujube fruit had a much higher content of soluble sugars, and lower levels of organic acids than wild jujube fruits (Table P in S2 File), indicating both sweetness and acidity are important traits under human selection. Selective sweep regions covering 1,372 genes were identified in the jujube genome (Fig 3B; Table Q in S2 File). These included four genes, which encode an NADP-dependent malic enzyme (NADP-ME), a pyruvate kinase (PK), an isocitrate dehydrogenase (IDH), and an aconitate hydratase (ACO), all of which play key roles in organic acid metabolism in fruit (Fig 3C; Table R in S2 File). In addition, three vacuolar proton pumps (V-type proton ATPase), transporting H+ into vacuolar, were also in the putative sweep regions. On the other hand, three genes involved in sugar metabolism in fruit, encoding a sucrose synthase (SUSY), a phosphoglucomutase and a 6-phosphofructokinase, and 13 sugar transporters were also identified in the regions of putative selective sweeps.
Expression profiling analysis of sugar- and acid-related metabolism genes showed that a gene encoding a vacuole acid invertase (VAINV), an enzyme that irreversibly catalyzes the hydrolysis of sucrose to glucose and fructose, was expressed at a significantly lower level in the ripe fruits of cultivated jujubes than in those of wild jujubes, possibly contributing to higher sucrose accumulation in the vacuoles of cultivated jujube fruits (Fig 3C). In addition, most genes involved in acid metabolism pathways, including those encoding NADP-ME, PK, phosphoenolpyruvate carboxylase (PEPC), malate dehydrogenase (MDH), shikimate dehydrogenase (SD) and citrate synthesis (CS), were expressed at much higher levels in wild than in cultivated jujube fruits (Table S in S2 File). This trend was also the case for a neutral invertase in the sucrose biosynthesis pathway, which supplies glucose and fructose for organic acid metabolism (Fig 3C). On the contrary, genes involved in decomposing citrate, such as ACO and IDH, were expressed at lower levels in wild than in the cultivated jujubes.
Furthermore, a population differentiation analysis based on a population fixation index (Fst) between dry and fresh jujube groups (Table T in S2 File) uncovered four genes encoding beta-galactosidases and one encoding endo-1,4-beta-xylanase in the highly differentiated regions (Table U in S2 File).
We identified a candidate S-RNase gene (Zj.jz035833030; chromosome 1) and two S-like RNases (Zj.jz026761011 and Zj.jz022467042; chromosomes 7 and 9, respectively) that belong to the T2-RNase family in the ‘Junzao’ genome (Table V in S2 File). We also identified the three T2-RNase genes in the ‘Dongzao’ genome (Table V in S2 File). Phylogenetic analysis further confirmed that Zj.jz035833030 was the S-RNase and that Zj.jz026761011 and Zj.jz022467042 were S-like RNases (Fig 4A). A transcriptome analysis revealed that Zj.jz035833030 (named S1) was specifically expressed in flowers, and the two S-like RNase genes were expressed in all tested tissues (Fig 4B).
We identified four candidate SFB genes near the S1 S-RNase gene on chromosome 1 and inferred that the jujube S locus is likely localized within a narrow region (from 3.8–4.3 Mb) on chromosome 1. However, none of these four SFB genes was specifically expressed in flowers (Fig 4C). A phylogenetic analysis showed that all four SFB genes were clustered together in the same clade with the Prunus SFB (Fig O in S1 File). The structure of the jujube S locus was similar to that of pear [16].
By mapping genome sequencing reads that were generated from the 31 jujube accessions to the putative S1 region, we identified 21 SNPs (9 in the first exon, 11 in the second exon, and one in the intron) and 3 indels (one in the first exon, one in the second exon, and one in the intron). Among the 21 SNPs, 13 were located in the ribonuclease domain of T2-RNase (Fig P in S1 File). According to the SNP pattern in the S-RNase gene, we assigned each accession a corresponding haplotype. We found that S1 was the most common haplotype, and there were 10 accessions with a homozygous S1 locus and 14 with a heterozygous locus (Fig 4D).
We report the 351-Mb genome of the heterozygous dry jujube cultivar ‘Junzao,’ which is 86.5 Mb smaller than the assembled ‘Dongzao’ genome (437.7 Mb) [8]. We performed a series of analyses to characterize the size difference between the two genome assemblies, and revealed that the difference was primarily attributed to different levels of transposable elements, redundant sequences caused by heterozygosity, and bacterial contaminated sequences. In addition, we note that the k-mer and flow cytometry analyses of leaves from a ‘Dongzao’ mature tree indicated a similar genome size (~352 Mb) to that of ‘Junzao’ (Table B and Table W in S2 File). There are several reports of tissue culture-induced genome-level changes in plants, and one of the major underlying factors is the proliferation of transportable elements [17, 18]. Accordingly, the use of in vitro cultured plantlets as the source material for genome sequencing could lead to complex genetic background to some extent.
We resequenced a wide range of jujube cultivars and their wild relatives covering different fruit types (dry or fresh) and geographical distributions. This exploration revealed the various consequences of artificial selection during jujube domestication and elucidated the history of jujube domestication. Genome-wide SNP analysis revealed that naturally grown wild jujubes possessed higher diversity than cultivated jujubes, and the overall genetic diversity of jujube is similar to that of peach [19] but lower than that of several herbaceous species, such as soybean [20], rice [21] and cucumber [22]. A complex genetic background, caused by natural or artificial hybridization, was also revealed, which might explain why some semi-wild jujubes exhibited few traits that could distinguish them from cultivated jujubes, other than a sour taste and smaller fruit and tree sizes. Consistent with the population structure deduced by chloroplast diversity [23], our analyses using various approaches supported the hypothesis of an admixed population structure of wild and cultivated jujubes. We propose that this pattern reflects frequent exchanges of jujube germplasm between human populations from different sites and natural and artificial hybridizations.
The domestication of jujube has involved the selection for the fruit sourness and sweetness profiles, which are determined by acid and sugar metabolism. Analysis of fruit sugar and acid contents in a wide range of apple accessions suggested that fruit acidity rather than sweetness is likely to have undergone selection during apple domestication [24]. In this study, we found that unlike apple, sugar and acid metabolism were both undergone human selection during jujube domestication (Fig 3A). In particular, four genes and several sugar transporters involved in acid and sugar metabolism were determined to be putatively under human selection, of which two genes (NADP-ME and PK) are closely associated with the production of malate, which contributes directly to the acid taste of the fruit [25]. In addition, the expression levels of genes encoding enzymes, such as NADP-ME, PK, PEPC and MDH were substantially lower in cultivated jujube fruits in comparison with wild jujubes. Differential expression of the same gene sets between high-and low-acid apple fruits has also been reported [25, 26].
GSI have been characterized in the Rosaceae, Solanaceae and Plantaginaceae families which involves an S locus [27]. Although GSI is widely present in the Ziziphus species, the molecular mechanism controlling the hybridization behavior in jujubes is unknown. We identified the jujube S locus on the basis of genome sequence information, which was supported by gene expression analysis. Analyses of the S-locus genotypes of the 31 accessions provide guidelines for parent selection for crossbreeding. We have proved this strategy by crossing two random combinations with different S haplotypes, i.e., ‘Dongzao’ × ‘Linyilizao’ [28] and ‘Dongzao’ × ‘Zhongningyuanzao’ [29].
We assembled a draft genome of the dry jujube cultivar ‘Junzao’ covering 351 Mb with contig and scaffold N50 sizes of 34 kb and 754 kb, respectively, which was 86.5 Mb smaller than that of ‘Dongzao’ (437.7 Mb) for which an in vitro culture plantlet was sequenced. Higher levels of repetitive sequences and redundant sequences in the assembled ‘Dongzao’ genome have primarily contributed to the larger genome assembly of ‘Dongzao’ than ‘Junzao’. By comparing the fresh (‘Dongzao’) and dry (‘Junzao’) jujube genomes, we found gene families involved in cell wall modification were largely expanded in ‘Junzao’, which might characterize the difference in fruit quality between dry and fresh cultivars. We reconstructed eight putative proto-chromosomes of the common ancestor of Rhamnaceae and Rosaceae based on the genome sequences of jujube, peach and apple, which elucidated the evolutionary processes that have shaped the genome structures of modern jujubes.
Genome resequencing of 31 geographically diverse accessions of cultivated and wild jujubes illustrated the domestication progress of jujubes and revealed the complex genetic background of jujubes caused by natural or artificial hybridizations. Based on the analysis of selective sweeps, we identified four genes involved in acidity metabolism pathways that encode an NADP-ME, PK, an IDH, and an ACO, all of which play key roles in organic acid metabolism. In addition, three V-type ATPase, enhancing organic acid storage in fruit, were also in the putative sweep regions. Furthermore, SUSY and several sugar transporter genes were determined to be putatively under selection. These findings might elucidate the changes in the sweetness/acidity taste caused by domestication events. We also identified the S-locus genes that controlled gametophytic self-incompatibility, and investigated the haplotype patterns of the S locus in diverse jujube accessions. Our study offers novel insights into the jujube population structure and domestication and provides valuable genomic resources for jujube improvement.
A 9-year-old diploid, highly heterozygous dry jujube cultivar, ‘Junzao’ (voucher number: NWAFU-Junzao001), grown at the Jujube Experimental Station (N 37.13, E 110.09) of Northwest A&F University, Qingjian, Shaanxi Province, China, was used for genome sequencing. Genome sizes of jujubes including ‘Junzao’, ‘Dongzao’ and other 11 accessions were estimated by flow cytometry analyses on the young leaves (Table B in S2 File, S1 File Supplementary notes). The ‘Junzao’ genome was sequenced using a whole-genome shotgun strategy [30]. High-quality genomic DNA was extracted from young leaves using the Qiagen DNeasy Plant Mini Kit (Qiagen, Valencia, CA, USA). A total of 3 μg of DNA was used for each library construction. Short-insert paired-end libraries (180 bp and 500 bp) were generated using the NEB Next Ultra DNA Library Prep Kit for Illumina (NEB, USA) according to the manufacturer’s instructions. Large-insert (2 kb, 5 kb, 10 kb, 15 kb and 20 kb) DNA sequencing libraries were prepared through circularization by Cre-Lox recombination [31]. These libraries were sequenced on the Illumina HiSeq 2000 system. A total of 79 Gb of high-quality cleaned sequences (approximately 227x coverage of the genome) was generated and used for de novo genome assembly (Table X in S2 File). A modified version of SOAPdenovo was developed specifically for the de novo assembly of the highly heterozygous jujube genome (S1 File Supplementary Notes).
Augustus [32], Geneid [33], Genscan [34], GlimmerHMM [35] and SNAP [36] were used for ab initio gene predictions. We also aligned the protein sequences of Arabidopsis thaliana, Capsicum annuum, Citrus clementina, Eucalyptus grandis, Malus × domestica, Oryza sativa, Populus trichocarpa, and Vitis vinifera to the ‘Junzao’ genome using TBLASTN with an E-value cutoff of 1e-5. The homologous genome sequences were then aligned to the matched proteins for accurate spliced alignments using GeneWise [37]. Finally, a total of 36 Gb of high-quality RNA-Seq reads was aligned to the ‘Junzao’ genome using TopHat [38] with default parameters. Based on the RNA-Seq read alignments, Cufflinks [39] was then used for transcriptome-based gene structure predictions. Outputs from ab initio gene predictions, homologous protein alignments and transcript mapping were integrated using EVM [40] to form a comprehensive and non-redundant reference gene set and then filtered by removing the genes with incorrect coding sequences and putative repeat elements (80% coverage).
We combined two genetic maps, described below, to anchor the assembled scaffolds of ‘Junzao.’ First, we used a previously published restriction site-associated DNA (RAD)-based high-density genetic map generated from an inter-specific F1 population to anchor the genome assembly [41]. Second, we constructed a genetic map by using a different F1 population (‘Dongzao’ × ‘Yingshanhong’, 96 progenies), which was also based on the RAD strategy according to Baird et al [42]. High-quality SNP and SSR markers were used to construct a linkage map (Table D in S2 File). The resulting genetic map was used to further anchor the assembled scaffolds of ‘Junzao.’
To better understand the evolutionary processes that shaped the genome structures of jujubes, we reconstructed the putative proto-chromosomes of the common ancestor of Rhamnaceae and Rosaceae, which are sister families in the order Rosales [43]. Protein sequences from 13 plant species (A. thaliana, C. annuum, C. sinensis, M. × domestica, O. sativa, P. trichocarpa, V. vinifera, Cucumis sativus, Pyrus × bretschneideri, Actinidia chinensis, Cypripedium arietinum, Z. jujuba ‘Dongzao’ and Z. jujuba ‘Junzao’) were extracted for building gene families. For alternatively spliced isoforms, only the longest proteins were used in the analysis. An all-to-all BLASTP was used to compare protein sequences with an E-value cutoff of 1e-7, and OrthoMCL [44] was then used to cluster genes from these species into families with the parameter “-inflation 1.5.” MUSCLE [45] was used to generate multiple sequence alignments of proteins in single-copy gene families with default parameters. RAxML [46,47] and a ‘supermatrix’ of protein sequences were used to construct the phylogenetic tree with the maximum likelihood algorithm. A molecular clock model was implemented to estimate the divergence time of these 13 species using McMctree in PAML [48]. To obtain a more accurate result, ‘r8s’ was used to estimate the divergence time based on the constructed tree. Café [49] was used to identify gene families that have undergone significant expansion or contraction in the Z. jujuba ‘Junzao’ genome with a p-value cutoff of 0.05.
The one-to-one collinear regions between ‘Junzao’ (accession number: PRJNA306374) and ‘Dongzao’ [8] were detected using the MUMmer package [50] with the parameters ‘-maxmatch -c 90 -l 40 -d 0.05’. The sequence alignments were performed on the scaffold level between these two jujube genomes. The best reciprocal alignments with length less than 300 bp or an identity less than 90% were discarded, and then the aligned regions within the same scaffold were connected together. Regions were identified as syntenic blocks if there were more than 5 adjacent alignment regions between the two genome sequences.
In addition, we use the “show-snps” program in MUMmer package to detect homozygous SNPs and small indels from the one-to-one alignments. RepeatMasker (http://www.repeatmasker.org/RepeatModeler.html) was used to find repeat elements in sequences that could not be aligned to the ‘Junzao’ genome. Sequences shorter than 100 bp were removed. ‘Dongzao’-specific sequences were obtained after realigning them with the ‘Junzao’ genome and discarding sequences with identities of greater than 95% and gap lengths of less than 100 bp. Potential bacterial sequences, which were identified on the basis of BLAST searches against the GenBank NT database, were excluded. Genes with at least 90% of the CDS regions covered by ‘Dongzao’-specific sequences were defined as ‘Dongzao’-specific genes.
Syntenic blocks shared by the seven species (V. vinifera, P. trichocarpa, Theobroma cacao, A. thaliana, Prunus persica, M. × domestica, and Z. jujuba ‘Junzao’) were identified with MCscanX [51] using the grapevine genome as the reference. Syntenic blocks containing at least 3 gene pairs were retained to reconstruct the genome structure of the seven selected species. Based on the syntenic and overlapping relations of Z. jujuba, P. persica and M. × domestica genomes, we reconstructed the paleo-chromosomes of the common ancestor of Rhamnaceae and Rosaceae using a previously described method [52, 53]. The structures of the V. vinifera, P. trichocarpa, T. cacao and A. thaliana genomes were reconstructed by comparing them with the seven proto-chromosomes of the eudicot ancestor.
Thirty-one jujube accessions were chosen for genome resequencing analysis, including 10 wild jujube individuals (6 typical wild jujubes and 4 semi-wild accessions) and 21 jujube cultivars (Table O in S2 File; Fig M in S1 File). Illumina paired-end genome libraries were constructed for each accession following the manufacturer’s instructions and then sequenced on Illumina HiSeq 2500/4000 platforms, which yielded a total of 363 Gb of raw paired-end sequences. The raw data were processed to remove low-quality bases, adapter sequences, and putative PCR duplicates, resulting in a total of 344 Gb of high-quality paired-end sequences. The cleaned reads were mapped to the ‘Junzao’ genome using BWA [54] with the parameters “mem -t 4 -k -M” [29]. BAM files were processed for SNP calling using the SAMtools mpileup function [55] with the parameters “-m 2 -F 0.002 -d 1000 -u.” High-quality SNPs, which were supported by a coverage depth of 5–1,000, mapping quality >20, distance of adjacent SNPs >10 bp and missing ratio of samples within each group <50%, were retained for subsequent analyses.
Peach was used as the outgroup to construct the phylogenetic tree. The NUCmer program in MUMmer [50] was used to align the peach genome (GenBank accession no. AKXU00000000) with the ‘Junzao’ genome with default settings. SNPs within the best-hit regions were extracted, and then the genotypes of peach were used to provide outgroup information at corresponding positions. The neighbor-joining phylogenetic tree was constructed using Treebest-1.9.2 (http://treesoft.sourceforge.net/treebest.shtml) on the basis of the p-distance. We used the parameter θπ [15] to assess the level of genetic diversity for cultivated and wild jujube populations by scanning whole-genome SNP sites, respectively. PCA was performed using EIGENSOFT [56]. The eigenvectors were obtained from the covariance matrix using the R function ‘eigen.’ The population structure was further inferred using the program FRAPPE [57] with kinship (K) set from 2 to 5 and the maximum iteration of expectation-maximization set to 10,000.
Four methods, i.e., the θπ ratios, pairwise population differentiation (Fst) levels [58], Tajima’s D test [59] and the cross-population composite likelihood ratio test (XP-CLR) [60], were used to identify the selective sweeps associated with jujube domestication events. Briefly, θπ ratios (θπ, wild/θπ, cultivated), Fst and TajimaD were calculated using a sliding window analysis with a window size of 20 kb and a step size of 10 kb. XP-CLR test was performed with the following parameters: sliding window size of 0.6 cM, grid size of 10 kb, maximum number of SNPs within a window of 300, and correlation value for 2 SNPs weighted with a cutoff of 0.95. Genome regions with the top 5% of scores in each of the four methods were identified and those detected by at least two of the methods were identified as selective sweeps. In addition, we used the top 5% highest FST values to characterize the population differentiation between dry and fresh jujubes. Genes within these regions were subjected to GO enrichment analysis using EnrichPipline [61].
We compared the ‘Junzao’ genes to the information in a local database containing known S-RNase gene sequences collected from NCBI with an E-value cutoff of 1e-10 using BLASTN. We then screened for genes belonging to the T2 RNase gene family from the BLAST results because S-RNase genes are members of the T2 RNase family [62, 63]. Candidate S-RNase genes were further screened according to two criteria: the absence of the amino acid pattern 4 ([CG] P [QLRSTIK][DGIKNPSTVY]) [63] and the presence of a maximum of two introns [64]. The S-RNase genes from four families (Rosaceae, Fabaceae, Solanaceae, and Plantaginaceae) and the candidate jujube S-RNases were used to construct the phylogenetic tree using RAxML with a generalized time-reversible (GTR) model of sequence evolution.
Pollen-determinant S-haplotype-specific genes belong to the F-box family. We used a similar BLAST strategy to that described above to search for the F-box genes in the chromosome region in which the candidate S-RNase gene was located. A phylogenetic analysis was performed for those candidate SFB genes together with the known Prunus SFB, Petunia SLFs, Prunus SLF1 and Malus SFBs.
To investigate the expression of the candidate S-RNase and SFB genes, we used RNA-Seq data from leaves, phloem, flowers and fruits of ‘Junzao.’ The SNP calling results derived from the resequencing of 31 accessions were used to reconstruct the two haplotypes of S-RNase gene using HapCUT [65].
Phloem, mature leaves, flowers, and fruits at different stages (expanding fruit, half-red, and full-red) of the ‘Junzao’ cultivar and the wild jujube ‘Qingjiansuanzao’ (8 years old) were collected in 2013 and 2014, respectively. All the samples were immediately frozen in liquid nitrogen. Total RNAs were isolated using a modified CTAB method and then treated with RNase-free DNase I (Promega, USA). First-strand cDNAs were synthesized using a Clontech kit. RNA-Seq libraries were constructed using the NEB Next UltraTM RNA Library Prep Kit (NEB, USA) and sequenced on a HiSeq 2000/2500 system. RNA-Seq reads were mapped to the ‘Junzao’ genome using TopHat [38]. The total numbers of aligned reads (read counts) for each gene were normalized to the reads per kilobase exon model per million mapped reads (RPKM) [66]. DESeq [67] was used to identify differentially expressed genes.
Fruits were collected at three developmental stages: expanding fruit, half-red fruit and full-red fruit. Sugars (fructose, glucose and sucrose) and acids (malic acid, citric acid and succinic acid) were quantified using high-performance liquid chromatography (HPLC, Shimadzu) as described previously [68]. A total of 1 g of the edible part of the dried jujubes was ground and incubated in 50 mL of 80% ethanol in an ultrasonic bath (40 kHz, 45°C, 20 min). The samples were centrifuged at 3,500 ×g for 10 min, and the supernatant was collected in a new tube. The pellet was re-extracted by repeating the above steps. The combined supernatants were evaporated in a rotary evaporator at 45°C and then diluted with deionized water to 10 mL. The diluted extracts were filtered through a 0.45-μm membrane filter prior to HPLC analysis.
Accession codes: Sequence data have been deposited in the GenBank/EMBL/DDBJ nucleotide core database under the accession number LPXJ00000000 (PRJNA306374) and all sequence reads have also been deposited in the online database. The version described in this paper is the first version.
|
10.1371/journal.pcbi.1006944 | Model-based analysis of influenza A virus replication in genetically engineered cell lines elucidates the impact of host cell factors on key kinetic parameters of virus growth | The best measure to limit spread of contagious diseases caused by influenza A viruses (IAVs) is annual vaccination. The growing global demand for low-cost vaccines requires the establishment of high-yield production processes. One possible option to address this challenge is the engineering of novel vaccine producer cell lines by manipulating gene expression of host cell factors relevant for virus replication. To support detailed characterization of engineered cell lines, we fitted an ordinary differential equation (ODE)-based model of intracellular IAV replication previously established by our group to experimental data obtained from infection studies in human A549 cells. Model predictions indicate that steps of viral RNA synthesis, their regulation and particle assembly and virus budding are promising targets for cell line engineering. The importance of these steps was confirmed in four of five single gene overexpression cell lines (SGOs) that showed small, but reproducible changes in early dynamics of RNA synthesis and virus release. Model-based analysis suggests, however, that overexpression of the selected host cell factors negatively influences specific RNA synthesis rates. Still, virus yield was rescued by an increase in the virus release rate. Based on parameter estimations obtained for SGOs, we predicted that there is a potential benefit associated with overexpressing multiple host cell genes in one cell line, which was validated experimentally. Overall, this model-based study on IAV replication in engineered cell lines provides a step forward in the dynamic and quantitative characterization of IAV-host cell interactions. Furthermore, it suggests targets for gene editing and indicates that overexpression of multiple host cell factors may be beneficial for the design of novel producer cell lines.
| Influenza viruses depend on cellular functions at every step of their life cycle and a comprehensive picture of virus-host cell interactions is the key to understand influenza disease and establish antiviral therapies. Over the past decade, this was supported by numerous screening approaches, which identified cellular factors relevant for intracellular virus replication. Ideally, the identification of pro-viral targets should also support the generation of cell lines to optimize influenza virus replication in cell cultures. As a first approach towards this goal, we used a mathematical model to identify mechanisms of viral growth that would be most promising targets for host cell factor manipulation. Based on predictions, we expected a significant increase in virus production if RNA synthesis and virus assembly and virus budding were perturbed, which was partially confirmed by cell lines overexpressing single and multiple selected host cell factors. However, the cell-specific productivity of engineered cell lines was not improved significantly and, according to model-based analysis, this can be explained by adverse changes in kinetic parameters of intracellular replication steps. Finally, results indicate that screening approaches should focus on late time points post infection to identify targets for engineering of cell lines that support high-yield vaccine production processes.
| Influenza A viruses (IAVs) are highly contagious respiratory pathogens that constitute a permanent threat to public health, causing three to five million cases of severe illness and up 650,000 deaths per year [1]. As obligate intracellular parasites, influenza viruses rely on host cellular functions at every step of their life cycle. Thus, to deepen the understanding of virus-host cell interactions is a key step to improve vaccine production and thereby efficiently counteract disease. During the past decade multiple RNAi screens, yeast-two-hybrid approaches and omics studies, allowed for systematic identification of cellular factors that are relevant for the IAV life cycle (recently reviewed by [2]). These factors are commonly grouped into pro- and antiviral factors, which can be used to design new therapeutic and preventive disease measures. So far, the focus of these investigations was mainly on novel antiviral treatment that targets host dependency factors instead of viral factors, which might help to avoid the emergence of viral escape mutants [3–6]. Regarding the design of cell lines for optimized virus production, however, host restriction factors, e.g. factors that belong to cellular antiviral defense mechanisms and which can be downregulated to increase the virus yield in vaccine manufacturing, are of key importance. In the case of poliovirus, for instance, the knockdown of host cell factors that inhibit virus replication in adherent Vero cells was reported to result in a ten-fold increase in virus titers [7]. This promising result, however, could not be reproduced in a recent follow-up study [8]. Another option, pursued in our study, is the overexpression of host dependency factors to facilitate virus replication and increase yields in cell culture-based IAV production. To this end, we chose the lung carcinoma cell line A549 as a model cell line that was previously used in two genome-wide RNAi screens for identification of antiviral targets [9,10] (for further review of relevant RNAi screens the reader is referred to [11]). In these studies, changes in virus replication were measured in cells with temporal modulation of gene expression and evaluated at single time points post infection (p.i.). To complement this approach, we investigate the dynamics of virus replication in cell lines stably overexpressing host cell genes over an extended period.
Since virus-host cell interactions display highly complex dynamics, mathematical modeling approaches are crucial to support the interpretation of time courses of viral components measured in experiments, e.g. intracellular viral RNA copy numbers. In addition, such models help to explain specific steps and outcomes of virus-host cell interaction, to study effects of changes in expression of viral or cellular components, or to make predictions about phenotypic changes after cell line engineering, i.e., inhibition of virus growth or increase in yield. We employed a model of the IAV life cycle that describes virus replication within a single infected adherent MDCK cell [12]. First, we re-calibrated this model to experimental data from infected A549 cells obtained in this study. Second, we predicted which steps of the virus life cycle are most sensitive with respect to cell-specific virus yield and therefore represent promising targets for cell line engineering. To validate model predictions, we integrated various experimental data sets from infection studies performed in A549 cell lines that we modified genetically to overexpress host cell factors previously identified by RNAi screening [9,13–15] and studies performed by other research groups [16–19]. Finally, the resulting parameter sets for IAV replication in single gene overexpression cell lines (referred to as SGOs), were used to predict the outcome of IAV infection in multiple gene overexpression cell lines (referred to as MGOs). While only one of five of the selected SGOs showed a higher virus yield compared to the parental A549 cell line, MGO simulations indicated that there is a potential for a significant increase in virus yield. However, this finding was confirmed only partially in experiments.
Overall, SGOs and MGOs that were established during this study showed an improvement in early release dynamics rather than the expected increase in total virus yield compared to their parental cell line. Using a single cell model of IAV replication, we elucidate this in greater detail and link the overexpression of host cell factors to changes in key parameters of virus growth, which has not been reported before.
The model of IAV replication used in this study is identical to a previously published description of the intracellular life cycle of IAV [12]. In general, we assume that basic mechanisms of IAV replication are similar in different host cell lines, but that values for key parameters of virus growth have to be adapted for each host cell system. While the previous model [12] was calibrated against various experimental data, mostly acquired from infected MDCK cells [20,21], the re-calibration of the model used in this study was based on three sets of in-house experimental data from infected A549 cells (S1 Fig). The available measurements allowed to estimate the kinetic parameters for nuclear import of vRNPs kImp, the synthesis of viral mRNA, cRNA and vRNA (kMSyn, kCSyn, kVSyn) as well as binding of matrix protein 1 (M1) kM1Bind and the release of viral progeny kRel. Statistical testing (Table 1) revealed that kCSyn and kM1Bind were not significantly different in A549 compared to MDCK cells [12]. However, kVSyn was significantly increased and kMSyn significantly reduced in A549 cells, respectively.
Two simplifying assumptions were made to simulate the influence of host cell factors on IAV replication. First, we considered that each step in the virus life cycle was dependent on one host cell factor and secondly, that a change in the expression level of this host cell factor would directly translate into a change of the corresponding kinetic parameter value in our mathematical model for IAV replication. For instance, if a host cell factor responsible for vRNA synthesis is overexpressed, vRNA replication is enhanced, resulting in a higher vRNA synthesis rate. Likewise, the downregulation of the same factor would result in a reduced vRNA synthesis rate.
Based on these assumptions, we performed in silico engineering of A549 cells by perturbing each parameter of our model individually with the objective to maximize virus yield at 24 h p.i. (optimized parameter values are summarized in S1 Table). By comparing the simulated virus release of parental A549 cells to results obtained for in silico optimized cell lines (Fig 1), we observed three possible outcomes upon parameter perturbation: (i) virus release dynamics were not affected significantly, (ii) only onset of virus release was improved, starting at least 1 h earlier compared to the parental A549 cell line and (iii) virus release dynamics were affected significantly leading to an increase in final yield by at least two-fold. The latter was caused by perturbations of parameters that define the most promising targets for cell line engineering, namely steps of viral RNA synthesis, its regulation and virus release (Fig 1, green shaded subfigures). Interestingly, the model predicted that the upregulation of viral mRNA synthesis is beneficial for virus replication whereas synthesis of viral cRNA and vRNA should be downregulated. To investigate this in greater detail we, next, compared the dynamics of the simulated intracellular viral RNAs and protein levels in both upregulation and downregulation scenarios to levels in parental A549 cells (Fig 2). We observed that changes of intracellular replication dynamics were most evident upon manipulation of viral mRNA synthesis (Fig 2, middle panel). Most importantly, the sole increase of the mRNA synthesis rate lead to a higher increase in vRNA levels than the upregulation of the vRNA synthesis rate itself (Fig 2, upper and middle panel second column). This strongly indicates that viral RNA replication in A549 cells is already saturated and only if more viral mRNA, and consequently, more viral proteins were available, more vRNA could be produced and virus release could be enhanced significantly. In addition, the modulation of regulatory steps, which is accounted for in our model by binding of M1 (negative regulator), had only an impact on final RNA and protein levels rather than on the dynamics per se (Fig 2, bottom panel).
To validate our model predictions, we used lentiviral gene transfer to generate A549 cell populations that overexpress specific host cell genes relevant for IAV replication. The host cell factors CEACAM6, FANCG, NXF1, PLD2 and XAB2 were selected from a set of candidate genes determined previously by RNAi screening [9,13–15] and virus-host cell interaction studies [16,17]. An overview of genes and their function in the IAV life cycle is given in S2 Table. The resulting cell populations were subjected to fluorescence activated cell sorting (FACS) to enrich cells that express the transduced gene based on eGFP, which is the co-expressed reporter gene. SGOs that showed stable gene overexpression were infected with A/Puerto Rico/8/34 (A/PR/8/34, H1N1) at a multiplicity of infection (MOI) of 10−4, which is usually applied for vaccine production processes. We compared virus titers of each SGO to that of the parental A549 cell line at selected time points p.i. (Table 2). Assuming that changes in virus release are associated with changes in intracellular mechanisms, we selected SGOs for further characterization of intracellular virus replication based on their HA titer. To facilitate selection, we ranked the HA measurements for each time point and each cell line according to their relative increase compared to the parental A549 cell line. As can be seen by the measurement data and the corresponding ranking values in Table 2, HA titers of all SGOs were increased at early time points p.i., whereas none of the SGOs showed an increase greater than 20% of the final HA titer at the usual time of harvest 72 h p.i. Thus, by modulating the expression level of these host cell factors, it was possible to influence the IAV release dynamics, however, the total virus yield was similar comparing SGOs to their parental cell line.
Next, we performed a detailed characterization of intracellular steps of viral growth in IAV-infected SGOs as well as in the parental A549 cells and an eGFP transduction control (Figs 3–5).
Although only NXF1 SGOs showed a promising increase in virus yield, it seemed that overexpression of host cell factors can influence IAV replication on the intracellular level. Thus, we also explored the possibility whether additive or even synergistic effects on IAV yield could be achieved by overexpressing multiple host cell factors simultaneously. At first, we investigated this option by a computational approach and simulated the virus release of single cells overexpressing different combinations of multiple host cell factors. Since integration of genes into the host chromosome is random, the gene constructs will be inserted at different chromosomal locations with different transcriptional activities and, since transduction follows a Poisson distribution, not every cell will obtain the same number of the gene constructs. Together, these factors influence the strength of overexpression. In addition, the integration process can also have an impact on the gene expression through off-target effects. To account for all these scenarios, which involve some sort of randomness, we used randomized sets of parameters assembled based on the median values of the model parameters kImp, kVSyn, kCSyn, kMSyn, kM1Bind and kRel, previously estimated from experimental data of infected SGOs and the parental A549 cell line. The parameter set of the latter was also included to account for off-target effects. For instance, the parameter set of an MGO may be composed of kImp of XAB2 SGOs, kVSyn of PLD2 SGOs, kCSyn of NXF1 SGOs, kMSyn of FANCG SGOs, kM1Bind of CEACAM6 SGOs, and kRel of the parental A549 cell line. We assume that all transduced genes can be expressed theoretically with the same probability, i.e., that there is an equal chance that kinetic parameters of the SGOs will be selected during randomization. Note, that even if all five candidate genes were transduced, not every MGO single cell will be a phenotypic mixture of all SGOs, but its parameter set could be kImp and kVSyn of the parental A549 cell line, kCSyn and kMSyn of CEACAM6 SGOs and kM1Bind and kRel of the NXF1 SGOs. To generate in silico MGOs, we chose to randomize parameter sets of those SGOs that showed a beneficial change in parameters according to initial model predictions of this study (Fig 1). Thus, we combined parameter sets of the top three candidates with the highest virus release rate kRel (CEACAM6 (C), FANCG (F) and NXF1 (N), CFN in Fig 7), the top three with the lowest cRNA synthesis rate kCSyn (FANCG (F), PLD2 (P) and XAB2 (X), FPX in Fig 7), and the top three with the lowest M1 binding rate kM1Bind (NXF1 (N), PLD2 (P), XAB2 (X), NPX in Fig 7). Finally, we also randomized parameter sets of all SGOs (CFNPX in Fig 7).
In a Monte Carlo approach, we generated multiple randomized parameter sets according to the selected combinations of SGOs and simulated virus infection at MOI 1 for 48 h (S3 Fig). Finally, we evaluated every single cell simulation for the time point at which the first simulated virus particle was released t(VRel≥1) and the fold change in the maximum number of released viral progeny (Fig 7). Interestingly, these model predictions revealed that a single cell overexpressing multiple genes can theoretically yield up to five-fold more virus progeny than its parental cell line if the underlying parameter set was kImp and kMSyn of the parental A549 cell line, kVSyn of XAB2 SGOs, kCSyn of PLD2 SGOs, and kM1Bind and kRel of the NXF1 SGOs. In particular, the earlier virus release started, the higher was the fold increase in the number of viral progeny. While the time point of first virus release followed a normal distribution, the fold change of virus release showed a log-normal distribution with highly productive cells as rare events. Overall, the combinations CFN, NPX and CFNPX showed similar distributions of the simulation read outs, whereas the combination of FPX resulted in a narrower distribution of virus yield with a slightly lower maximum fold increase of four-fold. Finally, this analysis revealed that highly productive cells are rare events in a heterogenous MGO population and their contribution to the population average is negligible, which leads to an increase of less than two-fold in the final virus yield (Fig 7, dashed line in vertical histograms).
The computational analysis of MGOs indicated that overexpressing multiple host cell factors could result in an earlier onset of virus release and, to some extent, also in an improvement of virus yield. To validate these model predictions, we generated populations of A549 cells in which individual cells express random combinations of selected host cell factors at various levels (S4 Table). In particular, we generated three independent cell populations (MGO 1-3) which provide random combinations of all five host cell factors CFNPX, which also covers the phenotypes of combinations CFN and NPX according to simulations. Further, we generated MGO 4 in which the three factors FPX were randomly combined and which should show a slightly different phenotype compared to CFNPX. All MGOs were infected with IAV at MOI 10-4. We chose this MOI according to the SGO screening experiment (Table 2) since under these experimental conditions differences between cell lines were more pronounced than for infections at MOI 1 (Fig 5). Ranking of HA titers revealed that virus release of MGOs was increased at early time points, while final virus yield was not increased significantly in these cell populations compared to the parental A549 cell line (Table 3). Of note, the impact of overexpressing single host cell genes on virus yield could be enhanced by overexpressing multiple of these host cell genes simultaneously, which partially confirms our model predictions on MGOs. In addition, MGO 4 was the only cell line showing less than 40% increase in virus yield at 42 h p.i. compared to the parental A549 cell line. This supports the model prediction that the combination FPX results in a slightly less productive phenotype than other gene combinations.
IAVs depend on host cellular functions to complete their replication cycle. Our aim was to take advantage of this dependency and manipulate the expression of host cell factors that are relevant for IAV replication to improve virus production for vaccine manufacturing. Due to the complexity of virus-host cell interactions mathematical models are required to complement the interpretation of infection experiments. In the present study, we used a re-calibrated model of IAV replication to predict and quantify changes in virus replication in genetically engineered A549 cells.
To account for the influence of host cell factors on steps of the virus life cycle, we made the simplifying assumption that changes in host cell gene expression have a direct impact on kinetic parameters of our model. Although we did not explicitly model physical interactions between host cell factors or cellular pathways with viral components, we were able to identify targets for cell line engineering by evaluating changes in the cell-specific virus release upon parameter perturbations. According to this in silico analysis, both a significant increase in virus yield as well as an earlier onset of virus release could be expected if either viral transcription or translation were significantly enhanced. In contrast, the model predicted that various steps of virus replication need to be downregulated to achieve a higher cell-specific virus yield. For instance, the binding of M1 to nuclear vRNPs, which mediates the nuclear export of vRNPs, should be delayed. The lower the binding rate of M1 kM1Bind, the longer vRNPs serve as template for viral genome replication and transcription inside the nucleus. Accordingly, not only more viral genome copies but also mRNAs will be synthesized and, thus, higher viral protein levels will be achieved (Fig 2, lower panel), which together will benefit virus yield. Furthermore, the model predicts that a decrease in the vRNA synthesis rate, in the cRNA synthesis rate, and a delayed binding of NP to naked viral RNA, needed to form replication-competent vRNPs and cRNPs, will cause an increase in virus yield (Fig 1). These three predictions seem counterintuitive since they cause a slowdown of viral replication. On the other hand, however, this strongly suggests that there is an imbalance between viral RNA replication and viral protein synthesis. While the synthesis of viral genomes is saturated, i.e., the RNA synthesis rates are too high, the supply of viral proteins either needed to form RNPs (NP and polymerases) or needed for virus budding (HA and NA) represents a limiting step in A549 cells. Interestingly, Ueda and colleagues [23] made similar observations when comparing IAV growth in MDCK and A549 cells. While steps of viral replication were similar in both cell lines, A549 cells released fewer virions because both the maturation of glycoproteins and their transport to the plasma membrane were slower compared to MDCK cells. In line with that, parameter perturbation studies with the single cell model for MDCK cells [12] did not point to bottlenecks in viral transcription and translation (S4 Fig). Indeed, the MDCK-based model is more sensitive to a change in the vRNA synthesis rate compared to a change in the protein synthesis rate, while the A549-based model is highly sensitive to changes in the protein synthesis rate (S5 Fig).
We generated cell lines overexpressing host cell genes beneficial for virus replication previously determined by RNAi screening [9,13–15] and studies on virus-host cell interactions performed by other research groups [16–19].
Overall, the maximum virus yield was similar in all A549 cell populations. However, the engineered cell populations released more virus particles at earlier time points compared to the parental cell line during infection studies performed at low MOI. To assure that target genes were stably overexpressed, we confirmed the expression of the functionally linked reporter gene coding for eGFP by flow cytometric measurements during cell culture maintenance (S6 Fig). Furthermore, we determined relative expression levels of the transgenes in SGOs by RT-qPCR (S3 Table). Although the overall number of virus progeny produced by engineered cells was not significantly higher compared to the parental cell line, we could not exclude that intracellular mechanisms of virus replication had changed due to the modulation of host cell gene expression. To elucidate this in greater detail we investigated virus replication dynamics on the intracellular level both experimentally and computationally. With the help of the single cell model, we quantified the changes in key kinetic parameters by fitting to the available experimental data. In contrast to our initial model predictions, both nuclear import rate and viral mRNA synthesis rate were reduced in some SGOs compared to their parental A549 cell line. For instance, the viral mRNA synthesis rate in infected cells overexpressing the nuclear export factor NXF1 was only 60% of the one in parental A549 cells, which alone would lead to a reduction in virus yield by 50%. Still, the NXF1 SGO was the only cell line with a higher cell-specific virus yield when infected at MOI 1 (Fig 5D). The model can only capture these experimental data by an increase in the virus release rate. Hence, the improved virus release rescues virus yields such that despite the adverse changes in viral RNA synthesis, the SGOs release equal or slightly higher amounts compared to the parental A549 cell line. It was reported that inhibition of NXF1 in A549 cells impairs nuclear export of viral mRNAs encoding for NP as well as the surface proteins hemagglutinin (HA) and neuraminidase (NA) [18]. Upon NXF1 overexpression viral mRNA export might be improved, which may lead to an earlier onset of translation, such that viral surface proteins are available earlier compared to the parental A549 cell line, which is less efficient in protein maturation and trafficking [23]. In the single cell model these steps are not explicitly modeled but lumped into a joint release mechanism that depends on the availability of viral proteins and genome copies in the cytoplasm (S1 File, Equation 27). In addition, the importance of the virus release mechanism was also shown by initial model predictions (Fig 1) that identified virus assembly and budding as kinetic bottleneck of virus production.
The overall tendency that an increase in the virus release rate can compensate adverse changes in RNA synthesis steps can also be observed for infected CEACAM6 SGO cells. In contrast to NXF1, CEACAM6 is not directly involved in steps of RNA synthesis but seems to interact with newly synthesized viral NA proteins during infection, which activates the Src/Akt survival pathway in A549 cells as shown by Gaur and colleagues [16]. In the same study, CEACAM6-silenced A549 cells showed reduced levels of viral genome copies and proteins. However, in our study, the overexpression of CEACAM6 was not beneficial for IAV replication. Accordingly, temporal upregulation of CEACAM6 instead of high abundance seems to be crucial for cellular survival signaling during infection. Furthermore, members of the CEACAM family are already upregulated upon infection by different influenza virus strains, as recently also shown for CEACAM1 and CEACAM5 [24]. In particular, CEACAM1 induction triggers the innate antiviral host cell response by suppression of the translational machinery and limits viral spread [25]. Taken together, the ambivalent role of the CEACAM family and, in particular, the functional role of CEACAM6 in cellular survival pathways, may support the finding that the overexpression of CEACAM6 can be disadvantageous for IAV replication. Still, it is remarkable that CEACAM6 SGO cells release equal amounts of progeny virions compared to parental A549 cells, indicating that despite a certain inhibition of replication, the virus maintains a basal level of reproduction.
Except for SGOs NXF1 and CEACAM6, for which the nuclear import rate was slightly reduced (p ≤ 0.1, calculated by one-sided Gauss test), the nuclear import rate of vRNPs was similar in the other SGOs compared to parental A549 cells. For the PLD2 SGO, this was unexpected, since it is known that inhibition of PLD2 results in delayed virus entry and reduced viral titers [19]. Still, overexpressing PLD2 did neither improve virus entry nor virus release in our study. The only change in kinetic parameters, that was in agreement with initial model predictions (Fig 1) and should benefit virus yield, was the reduction of the cRNA synthesis rate to 50% compared to parental A549 cells. However, this alone would result in an increase of virus yield by only about 1.3-fold in simulations, a small improvement that is eliminated by a simultaneous decrease in the mRNA synthesis rate in PLD2 SGOs as determined from the experimental data.
The candidate FANCG interacts with the three viral polymerase subunits (PB2, PB1 and PA) and has a direct influence on polymerase activity according to a minigenome replicon assay using a vRNA-like reporter gene [17]. In this particular assay, it was demonstrated that a FANCG knockdown resulted in a decrease of polymerase activity by 50% while overexpression of FANCG showed a three-fold increase in polymerase activity. According to our initial model predictions, FANCG would have been the most promising candidate to improve virus yield, in particular, if the mRNA synthesis rate was increased (Fig 1). Surprisingly, all viral RNA species showed reduced levels in infected FANCG SGO cells. Although we have only performed two independent experiments to measure intracellular viral RNA levels in infected FANCG SGO cells, RNA copy numbers were lower compared to those in infected A549 cells in the same experiments as well as compared to the averaged RNA levels in A549 cells from all four independent experiments. Taken together, it seems that an overall increase of the viral polymerase activity results in imbalanced virus replication. Therefore, additional simulations were performed to test the effect of increasing all three or different combinations of the RNA synthesis rates simultaneously. However, by only increasing the vRNA synthesis rate, a reduction in virus yield is predicted (S7 Fig), while any other scenario leads to an increase in final yield in simulations (for instance see S8 and S9 Figs). Hence, our experimental observations together with the model-based analysis of this candidate are not in agreement with the study of Tafforeau and colleagues [17]. On the one hand, this may indicate that observations in an (artificial) minigenome replicon assay can only give hints towards changes in mechanisms and that the observation in the context of an infection, i.e., including additional regulatory steps of replication and availability of cellular and viral precursor molecules, can be contradictory. On the other hand, FANCG also has a beneficial function for the host cell, since it is involved in DNA repair mechanisms. We could, therefore, speculate that damage of cellular DNA induced by IAV infection [26] is reduced by overexpressing FANCG. However, we cannot exclude that FANCG plays a pro-viral role by interacting with the viral polymerase.
Similar to FANCG, also XAB2 is involved in DNA repair mechanisms, in particular, in transcription-coupled DNA repair [27]. XAB2 is a host restriction factor for IAV as well as for other viruses, e.g. West Nile virus, Vaccinia virus and HIV-1 [28]. In our study, however, the overexpression of this factor neither improved nor impaired viral reproduction.
In a few infected SGOs the change in various kinetic parameters should be beneficial for virus replication according to model predictions (Fig 1), e.g. a decrease in cRNA synthesis rate upon overexpression of FANCG, PLD2 or XAB2, or an increase in the virus release rate upon overexpression of CEACAM6, FANCG or NXF1. Using a Monte Carlo approach, we analyzed single cell simulations using randomized SGOs parameter sets to predict virus release of MGOs. This analysis revealed that the productivity of single cells follows a log-normal distribution with highly productive cells as rare events. This finding is supported by previous single-cell analyses performed by our group, which investigated the cell-specific productivity of MDCK cells infected by IAV. In particular, they demonstrated that there is a large variability in the productivity of individual cells and that only very few cells are highly productive (with up to 10-fold higher titers compared to the cell population average) [29,30]. Furthermore, the most recent study showed that single cell virus yields are log-normally distributed [30].
While MGO simulations suggest that particular combinations of genes have the potential to yield IAV titers similar to an in silico optimized cell line with an optimal virus release rate or M1 binding rate (open circles, Fig 7), we could not generate MGOs with an elevated overall HA titer. However, it has to be taken into account that all experimental data were acquired from cell populations of genetically modified cells with different combinations and expression levels of host cell genes. Thus, beneficial host cell factor combinations in individual cell clones might be masked. More extensive screening would be required to identify and isolate individual cell clones, which reflect the features predicted in silico.
The present version of the mathematical model of IAV replication is most suited to describe the impact of host cell factors that act directly on individual steps of the virus life cycle, e.g. factors that modulate the activity of the polymerases. The assumption that the influence of such factors also directly impacts kinetic parameters of the model enabled the identification of bottlenecks in virus replication that could be modulated by cell line engineering. Similar model-based approaches were performed previously by others to compare the replicative properties of different influenza virus strains [31,32] and virus replication with and without antiviral treatment [22,33]. While Binder and colleagues [34] compared low and high permissive host cells for hepatitis C virus replication that showed different intracellular basal concentrations of the same host cell factor, we applied the single cell model of IAV replication to quantify changes in key kinetic parameters of virus replication in cell lines overexpressing different host cell factors, which has not been reported before. Still, all these approaches have in common that they are solely computational, focusing on viral dynamics described by a fixed set of equations. As a result, in our study, similar ‘patterns’ of parameter changes were found for cell lines overexpressing host cell factors with very diverse functions, e.g. kImp ↓, kVSyn→, kCSyn→, kMSyn↓, kM1Bind→ and kRel ↑ for both NXF1 and CEACAM6. Therefore, this model-based analysis can only provide indications regarding the general impact of an overexpressed host cell factor. Clearly, further in-depth characterization of the impact of host cell factors on individual steps of virus replication is required on the molecular level to fully comprehend the biological implications of parameter changes determined in the present work. To neglect details of cellular processes and pathways, e.g. cellular transcription and translation or immune response, may limit model predictions. On the contrary, the implementation of proposed functions of candidate host cell factors into the model may lead to biased interpretation of experimental data (self-fulfilling prophecies). More elaborate dynamic models on virus-host cell interactions should not only account for the viral life cycle but also include a mathematical description of the cellular pathways in which the considered host cell factors are involved. Yet, the biological knowledge about how most host cell factors impact the viral life cycle is too sparse and even controversial to be readily implemented into a mathematical framework. To elucidate this in more detail can only be accomplished through experiments which analyze changes in the viral life cycle together with the dynamics of host cell factors and the activity of the corresponding cellular pathways. Regarding the further improvement of quantitative models for intracellular virus replication, this will probably be one of the most challenging tasks to be performed over the next decades. Moreover, we model viral dynamics in an average infected cell and do not account for stochastic effects that play a role at low molecule numbers, i.e., for low MOI infections. We can therefore only estimate parameters from experimental infections performed at high MOI (MOI ≥ 1), which ensures that the majority of cells is infected simultaneously. Thus, the infection propagates synchronously in the cell population and virus release reaches steady state within 24 h. In these high MOI scenarios, replication can also be affected adversely by introducing a high number of non-infectious virions, e.g. defective interfering particles (DIPs). There is already a single cell model available that also describes the impact of DIPs on virus replication [35]. However, since the intracellular mechanisms of DIP interference remain elusive, we think that, the modeling of DIP propagation in engineered cell lines seems unreasonable but should be taken into account in future studies.
Usually, the significance of cellular targets identified from loss of function studies is limited, e.g. due to inefficient knockdown or off-target effects that lead to identification of false positives and false negatives (discussed in [36–38]). In our study, we therefore chose host cell factors relevant for IAV replication that were not only identified in RNAi screens, but have also been described previously in separate studies, except for XAB2. Still, the importance of these factors is mostly inferred from loss of function studies and we simply assume that if the knockdown of a host cell factor results in reduced virus growth, the overexpression of the same factor should improve virus replication. Overall, however, we found that most differences in both intracellular replication and progeny virus release were noticeable, but not statistically significant compared to parental A549 cells. Only when infected at MOI 10−4, engineered cell lines showed higher HA titers at early time points, while the HA titers of all cell lines were similar at time of harvest (72 h p.i.). Hence, we confirmed findings of screens for which changes in virus growth were evaluated at early time points (12–48 h p.i.) after infection at MOIs below one [9,13–15], where a single readout is useful to identify host cell factors that have a strong impact on viral dynamics. Such factors are very interesting in the context of antiviral treatment, for which the interference with virus replication early during infection might promote viral clearance in an in vivo system. Although they are required to complete the replication cycle successfully, such factors might not even limit viral replication at their basal expression level. Hence, their overexpression would not result in any measurable changes of intracellular mechanisms. To improve vaccine production, however, the expression of host cell factors should be increased which improve the maximum cell-specific productivity. For this purpose, screening designs should be re-considered to capture not only dynamics of virus growth but also virus yield at time of harvest. Since large scale high-throughput screens are costly, a first step might be the re-evaluation of already existing screens that considered multiple time points post infection (e.g. [10,39]). Recently, re-evaluation of primary data from various RNAi screens and different virus-host cell interaction studies, i.e., protein-protein interactions, transcriptomic and proteomic data, revealed and validated the impact of host cell factors on virus replication, that were previously unknown [40,41]. This highlights the importance of study design and subsequent bioinformatical analysis, which both strongly contribute to the identification of key host cell factors for intracellular virus replication and release.
Beyond that challenge, we have no indication regarding the optimal level of gene (over)-expression required to achieve a positive impact on virus growth, while avoiding off-target effects. In our study, we used lentiviral transduction without control of the integration site and assumed that cells, for which insertion of the overexpression constructs was beneficial, will propagate well in culture. Indeed, we saw that transduction of different host cell factors resulted in different levels of overexpression (S3 and S4 Tables) and surprisingly, that the cell line with a very low overexpression level of the host cell factor NXF1 was most promising with respect to early virus dynamics. In contrast, a high level of overexpression might stress the biosynthetic capacity of the cell, and result in a competition between expression of candidate genes and viral proteins. It is particularly known that the translation of viral proteins is the energetically most costly step of virus replication [42]. If the synthesis capacity of the cell is exploited by both overexpression of candidate genes and expression of viral proteins, cellular resources needed for virus growth might become limiting. Together, this might explain the observation that SGOs, in particular those showing high expression levels of the candidate gene, produce the same or only slightly higher virus yields compared to the parental A549 cell line. However, experimental proof would be needed to support these speculations. To better control overexpression levels, it might be worthwhile to explore other gene editing methods, e.g. recombinase-mediated cassette exchange [43] or CRISPR/Cas9 [44], for target validation studies. As discussed before, some host cell factors are already enriched upon infection and it might be also interesting to follow their expression levels over time and—based on that—design an inducible overexpression system to control supply of host cell factors in a temporal manner if this is needed for their function [45,46].
Finally, and as shown in a first attempt in this work, mechanistic models of the virus replication cycle are indispensable for evaluation and interpretation of infection data from engineered cell lines. Thus, we envision that screening approaches focusing on virus yield at harvest time points relevant in vaccine production supported by simulation studies using mathematical models for virus replication will enable the design of novel producer cell lines with the final goal to improve cell culture-based vaccine manufacturing. In addition, the combination of both, experimental and computational, approaches using data from well-defined experimental conditions will significantly deepen our understanding of intracellular mechanisms of virus-host cell interactions and support analyses of infectious diseases and virus transmission.
The model used in this study is a detailed mathematical description of intracellular IAV replication as published previously for adherent MDCK cells [12]. It accounts for key steps of the virus life cycle, using a set of ODEs to simulate virus entry, viral RNA and protein synthesis as well as virus assembly and progeny virus release. To predict virus replication and release for A549 cells, we assumed that these do not change mechanistically, but only show differences in their dynamics due to the change in the host cell system. To capture this, we performed a re-parameterization of nuclear vRNP import, viral replication, viral transcription and virus release based on experimental data obtained for infected A549 cells (S1 Fig). As an extension of the original version of this model, we also computed the percentage of nuclear vRNPs fracRnpnuc to fit measurements of nuclear vRNP import obtained by imaging flow cytometry (Fig 3).
The description of the complete mathematical model can be found in S1 File.
Model parameters were estimated in two subsequent steps. First, the nuclear import rate kImp was estimated by fitting the simulated fraction of nuclear vRNPs fracRnpnuc to the mean of the relative fluorescence intensity (FI) of the nucleus fracIntnuc determined by imaging flow cytometry (see Imaging flow cytometry and image analysis). For this, we assumed that the relative increase in FI of the nucleus is correlated directly to the increase in the fraction of nuclear vRNPs caused by nuclear import of the viral genomes which can be stained by a specific antibody (see Imaging flow cytometry and image analysis). In our experiments, we observed an offset for fracIntnuc of approximately 50% at the time point of infection, which is related to the background signal of the nucleus and normally comprises between 40–60% of the cell’s area evaluated during image analysis. To account for this background signal, we applied an offset to the simulation values of fracRnpnuc. Since offset values differed slightly between cell lines and showed occasionally high standard errors (Fig 3), we also estimated this offset value and optimized it with respect to the arithmetic mean and standard error of the first measurement point at zero h p.i. for each cell line. For fitting with parameter set p, we minimized the least-squares prediction error for all available data points at time point t weighted with the maximum measurement value (Eq 4).
After optimization of the nuclear import rate kImp, we fitted our model to intracellular measurements of vRNA, cRNA and mRNA levels obtained from experiments at MOI 50 as well as to progeny particle numbers per cell for experiments at MOI 1. The corresponding set of kinetic parameters p was estimated simultaneously by minimizing the least-squares prediction error based on the decadic logarithm of all state variables n, whereby the error of each variable i was weighted with its maximum measurement value (Eq 5).
To synchronize infection and facilitate parameter inference, we performed infections experiments at high MOI. Thus, due to the high virus concentration at time of infection, RT-qPCR already detected vRNA copies as soon as 1 h p.i. (Fig 4, panel 3). This value cannot be caused by an immediate uptake of all virions but rather stems from vRNAs inside virus particles and/or free vRNAs attached to the cells. Therefore, we applied the intracellular vRNA measurement value at 1 h p.i. as an offset to the simulated amount of vRNAs, as done before similarly in another modeling study of our group [22]. In contrast to this previous study, we did not apply offsets to viral mRNA and cRNA levels, as these RNA species are not part of virus particles and are usually not present in the seed virus supernatant. In particular, cRNA levels at 1 h p.i. were below or close to one copy per cell and have no significant impact on simulation results. Finally, approximately 10 copies of mRNA per cell were detected at 1 h p.i. Since mRNA synthesis starts as early as vRNPs reach the nucleus, these mRNAs are a product of primary transcription and cannot be considered as a plain mRNA offset.
The parameter distributions were determined by parametric bootstrapping performing multiple model fits to 3000 random resamples from the experimental data according to their mean and standard deviation, as detailed elsewhere [47]. We set the medians of the resulting parameter distributions as parameter optima to perform simulations. For the SGO candidate FANCG, only duplicate measurements of the intracellular viral RNA were available. Therefore, we considered a relative standard error of 50%, which was the average relative standard error of all other RNA measurements performed in this study.
The modeling approaches in this work are based on the simplifying assumption that each step of the virus life cycle is directly dependent on the presence of relevant host cell factors and that their influence is changed by manipulating the expression of the corresponding genes. For instance, if a host cell factor crucial for viral RNA synthesis is knocked down, the efficiency of vRNA synthesis is reduced as well, resulting in a lower vRNA synthesis rate. When the same host cell factor is overexpressed, RNA replication is enhanced, which results in a higher vRNA synthesis rate. Using this assumption, we determined the optimal value for individual kinetic parameters of the model by maximizing the number of released progeny virions at 24 h p.i. To predict biologically reasonable values, we constrained the parameter search by a lower bound of factor 0.2 and an upper bound of factor 5 of the original parameter values, respectively.
In this study, lentiviruses were used to modify the expression of host cell factors relevant for IAV replication. Gene editing constructs delivered by lentiviruses are integrated randomly at different chromosomal locations with different transcriptional activity (reviewed in [48]). Therefore, we can anticipate that individual cells within a transduced cell population will show heterogeneity with respect to levels of relative overexpression.
Consequently, the transduction of more than one overexpression construct leads to an even larger heterogeneity in gene expression levels. To simulate IAV production of MGOs, we account for the non-targeted integration of multiple gene constructs by randomly compiling new parametrizations of the single cell model. More precisely, we assume that IAV can propagate in an individual cell of an MGO population with random combinations of kinetic parameters as determined before in detailed characterizations of SGO populations. In addition, to account for the adverse impacts by off-target effects, we also included the parameter set of the unmodified parental A549 cell line for randomization.
To facilitate the interpretation of simulation results for MGOs, we simulated IAV replication with randomly assembled parameter sets for a single cell infection at MOI 1 for 48 h p.i. In a next step, we evaluated each simulation with respect to maximum virus yield and the time point of first virus release, i.e., the time p.i. when the first simulated virus particle was released (VRel≥1).
To assure that a sufficient number of simulations was performed that would allow reasonable conclusions on MGO single cell infections, we repeated simulations with randomized parameter sets n times until the relative deviation between the mean of n-1 and mean of n simulated maximum virus yields reached 1 x 10−8.
Model equations were solved numerically using the CVODE routine from SUNDIALS [49] on a Linux-based system. All model parameter values and initial conditions are given in S5 and S6 Tables. Model files and experimental data were handled within the Systems Biology Toolbox 2 [50] for MATLAB (version 8.0.0.783 R2012b). Parameter values were estimated by the least-squares method as explained before (see Parameter estimation), using the global stochastic optimization algorithm fSSm [51].
To determine the significance level of differences in parameter distributions between parental A549 and engineered cell lines (SGOs) we performed a one-sided Z test (Gauss test) with mean p¯ and variance σ2 taken from the empiric parameter distributions to compute the following test statistic Z:
Z=pA549¯−pSGO¯σA5492n+σSGO2m
(6)
For this, the variance is usually normalized by the sample sizes n and m. However, we set the sample sizes to 1 instead of 3000 for the number of bootstrapped resamples, since the artificially high sample size is otherwise biasing the test result. This was also done previously by others to compare parameters of mutant to wild type viruses [31]. Following their approach, we generally assume that parameters are normally distributed. Only if parameter distributions followed a log-normal form, namely the vRNA synthesis rate kVSyn and the virus release rate kRel, the test statistics were calculated based on the decadic logarithm of these parameters.
To determine statistical significance in differences of measurements from SGOs and the parental A549 cell line, the Kruskal-Wallis test was performed as available in MATLAB (version 8.0.0.783 R2012b).
Human cDNAs encoding CEACAM6, XAB2, FANCG, NXF1 and PLD2 were purchased from the I.M.A.G.E consortium. The cDNA sequences were amplified by PCR and cloned into the bicistronic lentiviral vector pLV-X-GFPneo. This vector was derived from pLVtTRKRAB-Red [52] by integrating the fusion gene of GFP and neomycin phosphotransferase in the second cistron. Lentiviral vectors were produced by transfecting HEK293T cells with the pLV-X-GFPneo and the lentiviral helper plasmids coding for gag-pol, Rev and VSV-G using the calcium phosphate transfection protocol as detailed in [53]. The supernatant was collected two days post transfection, filtered (0.45 μm), titrated and stored at -80°C.
At the day of transduction, the virus supernatant was supplemented with polybrene (8 μg/mL) and added to 1 x 105 A549 cells. After 6 h the virus was removed and cells were cultured in Dulbecco’s Modified Eagle Medium (DMEM, GIBCO) with 10% (v/v) fetal calf serum (FCS, Sigma-Aldrich). On the day after infection, selection with neomycin was started (1 mg/mL G418). G418-resistant cell populations were maintained as transduced populations. FACS was performed to enrich cell populations expressing eGFP.
For generation of MGOs, cells were transduced with two cocktails of two to three different lentivirus stocks each on two consecutive days using MOI 1 per virus.
Parental A549 cells [54,55] and transduced A549 cell lines were maintained in DMEM with non-essential amino acids, 10% (v/v) FCS at 37°C and 5% CO2 atmosphere. Prior to infection, cells were washed twice with phosphate buffered saline (PBS), detached and counted using a Vi-CELL XRTM (Beckman Coulter). Subsequently, 0.4 x 106 cells per well were seeded into multiple 12-well plates and incubated overnight. Infection was performed with an A549-adpated seed virus preparation of influenza virus A/Puerto Rico/8/34 (#3138, Robert Koch Institute Berlin) which had an infectious virus titer of 1.08 x 108 virions per mL as determined by TCID50 (see [56] for detailed description of the TCID50 assay). For infection, cells were washed twice with PBS and virus was added together with serum-free cell culture medium containing trypsin (#T7409, Sigma-Aldrich) at a concentration of 1 x 10-4 units per cell. To support synchronous infection of cells, experiments were carried out at MOI 50 in a reduced volume of 300 μL per well. After 30 min, 700 μL DMEM was added to compensate for liquid losses through evaporation. To investigate the nuclear import of viral genomes, cells were treated with the translation inhibitor CHX (Sigma Aldrich). For this, cells were incubated for 1 h in serum-free culture medium at a CHX concentration of 100 μg per mL. Then, infection was performed by replacing the supernatant with serum-free culture medium containing seed virus, trypsin and CHX.
The amount of total virus particles in the supernatant of infected cells was determined by the hemagglutination assay as described by Kalbfuss and colleagues [57]. The virus titer measured as log10 HA units per test volume (log10 HAU per 100 μL) can be used to estimate the concentration of hemagglutinating particles cvirus with
cvirus=cEry⋅10(log10HAU/100μL),
(7)
assuming that one virus particle per erythrocyte is sufficient to cause agglutination [58,59], where cEry denotes the concentration of chicken erythrocytes added for hemagglutination (2 x 107 cells per mL). The number of virions released per cell was assessed by dividing the virus concentration by the maximum viable cell count obtained in each experiment.
Viral and cellular RNA were purified from cells using the extraction kit ‘NucleoSpin RNA’ (Macherey-Nagel) according to the manufacturer’s instructions. To quantify intracellular viral RNA levels of segment 5 (encoding viral nucleoprotein, NP) polarity- and gene-specific tagged primers (listed in S7 Table) were used for reverse transcription to distinguish between the three different RNA species of the IAV genome (as detailed in [60]). Reference standards were synthesized in vitro using a specific set of primers (listed in S8 Table) and supplemented with 350 ng of RNA from A549 cells to mimic intracellular conditions. In order to determine relative overexpression levels of host cell genes, mRNA of uninfected A549 cells was reverse transcribed using Oligo(dT) primers (listed in S9 Table).
For both, viral and cellular RNA, real time RT-pPCR was performed using the Rotor-Gene SYBR Green PCR Kit and Rotorgene Q (Qiagen) according to the manufacturer’s instructions. The calculation on viral RNA molecule numbers per cell was performed as described in [60]. Relative expression levels of host cell genes in SGOs and MGOs compared to the parental A549 cells were calculated by the 2−ΔΔCT method, using 18S rRNA as a calibrator [61].
For the analysis of nuclear vRNP import, 1 x 106 infected A549 cells were fixated with paraformaldehyde (PFA) at a final concentration of 1% (w/v) for 30 min on ice. Subsequently, samples were transferred to reaction tubes, cells pelleted by centrifugation (8 min, 300 x g, 4°C) and resuspended in 70% ice-cold ethanol before storage at -20°C.
For vRNP and DAPI staining, stored samples were centrifuged (8 min, 300 x g, 4°C) and the cell pellet was resuspended in wash buffer (PBS, 2% (w/v) glycine, 0.1% (w/v) bovine serum albumin (BSA)) and centrifuged as before. Afterwards, the cell pellets were resuspended in 150 μL wash buffer, transferred to 96-well plates and centrifuged once more. Next, cell pellets were resuspended in 25 μL blocking buffer (wash buffer with 1.1% (w/v) BSA) and incubated for 30 min at 37°C. After a final washing step with 200 μL wash buffer, cells were resuspended in 25 μL antibody solution and incubated for 1 h at 37°C. The anti-NP antibody mAb61A5 that preferentially binds oligomerized NP as present in the vRNP complex, was kindly provided by Fumitaka Momose [62]. Upon incubation, cells were washed three times with wash buffer and afterwards 25 μL of Alexa Fluor 647-conjugated polyclonal goat anti-mouse antibody (Life Technologies, #A21235) solution was added to the cells and incubated for 1 h at 37°C. Both the primary and secondary antibody were used at a dilution of 1:500 in wash buffer. Finally, cells were washed three times and the cell pellet was resuspended in 30 μL wash buffer with 2% (v/v) DAPI (Roth, 143 μM stock solution) for nuclear staining. After 5 min of incubation in the dark at room temperature, cells were measured using the ImageStream X Mark II (Amnis, EMD, Millipore) together with the INSPIRE software. For each sample 10,000 single cells were analyzed using the 60x magnification and the 375 nm and 642 nm lasers for excitation of DAPI and vRNP antibody, respectively. Channels 1 (DAPI signal, CH1) and 5 (Alexa Flour 647, CH5) were acquired together with channel 6 (CH6), which records the bright field (BF) image. The laser powers were adjusted according to the value of the ‘raw max pixel’ feature that should be in the range between 200 and 1500 for single-stained positive controls. Furthermore, 1000 single positive cells were measured to adjust the compensation settings.
To evaluate the localization of vRNPs only double positive single cells in focus were selected for analysis. In order to distinguish between nucleus and the whole cell, a nucleus mask and a cell mask were defined according to the DAPI signal on CH1 and the BF image on CH6, respectively (examples are shown in S10 Fig). To determine the relative fluorescence intensity of the vRNP signal (CH5) located in the nucleus, the intensity of the vRNP signal within the nucleus mask was divided by the intensity of the vRNP signal within the whole cell mask.
|
10.1371/journal.ppat.1003487 | Tetherin/BST-2 Antagonism by Nef Depends on a Direct Physical Interaction between Nef and Tetherin, and on Clathrin-mediated Endocytosis | Nef is the viral gene product employed by the majority of primate lentiviruses to overcome restriction by tetherin (BST-2 or CD317), an interferon-inducible transmembrane protein that inhibits the detachment of enveloped viruses from infected cells. Although the mechanisms of tetherin antagonism by HIV-1 Vpu and HIV-2 Env have been investigated in detail, comparatively little is known about tetherin antagonism by SIV Nef. Here we demonstrate a direct physical interaction between SIV Nef and rhesus macaque tetherin, define the residues in Nef required for tetherin antagonism, and show that the anti-tetherin activity of Nef is dependent on clathrin-mediated endocytosis. SIV Nef co-immunoprecipitated with rhesus macaque tetherin and the Nef core domain bound directly to a peptide corresponding to the cytoplasmic domain of rhesus tetherin by surface plasmon resonance. An analysis of alanine-scanning substitutions identified residues throughout the N-terminal, globular core and flexible loop regions of Nef that were required for tetherin antagonism. Although there was significant overlap with sequences required for CD4 downregulation, tetherin antagonism was genetically separable from this activity, as well as from other Nef functions, including MHC class I-downregulation and infectivity enhancement. Consistent with a role for clathrin and dynamin 2 in the endocytosis of tetherin, dominant-negative mutants of AP180 and dynamin 2 impaired the ability of Nef to downmodulate tetherin and to counteract restriction. Taken together, these results reveal that the mechanism of tetherin antagonism by Nef depends on a physical interaction between Nef and tetherin, requires sequences throughout Nef, but is genetically separable from other Nef functions, and leads to the removal of tetherin from sites of virus release at the plasma membrane by clathrin-mediated endocytosis.
| Tetherin (BST-2, CD317 or HM1.24) is an interferon-inducible cellular restriction factor that prevents the release of enveloped viruses from infected cells. Human and simian immunodeficiency viruses have evolved to use different viral proteins to overcome the anti-viral effects of tetherin. Whereas HIV-1 Vpu and HIV-2 Env counteract human tetherin, most SIVs use the accessory protein Nef to counteract tetherin in their non-human primate hosts. Here we show that the mechanism of tetherin antagonism by SIV Nef involves a direct physical interaction between the core domain of Nef and the cytoplasmic domain of tetherin, which results in the removal of tetherin from sites of virus assembly and release on the cell surface by a mechanism that depends on clathrin and dynamin 2. The Nef-mediated internalization of tetherin leads to the accumulation of tetherin within lysosomal compartments, suggesting that, similar to CD4− and MHC I-downregulation, Nef promotes the lysosomal degradation of tetherin.
| Mammalian cells express a number of proteins that inhibit specific steps of virus replication. One such factor, tetherin (BST-2 or CD317), impairs the release of enveloped viruses from infected cells [1], [2], [3], [4], [5]. Tetherin is a type II integral membrane protein with a topology that allows both ends of the molecule to be anchored in lipid membranes [6]. It has an N-terminal cytoplasmic domain followed by a single-pass transmembrane domain, an extracellular coiled-coil domain and a C-terminal glycosyl-phosphatidylinositol (GPI) anchor [6]. Under conditions of interferon-induction, tetherin is upregulated and becomes incorporated into virus particles as they attempt to bud from infected cells [7], [8], [9]. Captured virions are then internalized and routed to lysosomal compartments for degradation by a mechanism that involves interactions between the cytoplasmic domain of tetherin and the endocytosis machinery of the cell [7], [10].
Tetherin has played an important role in shaping the course of lentiviral evolution in primates, having selected for at least three different viral gene products to overcome this restriction factor (reviewed in [11], [12]). Whereas HIV-1 Vpu and HIV-2 Env antagonize human tetherin [4], [5], [13], the majority of SIVs use Nef to counteract the tetherin proteins of their non-human primate hosts [14], [15], [16]. Indeed, HIV-1 Vpu and HIV-2 Env appear to have acquired the ability to antagonize tetherin due to the absence of sequences in the cytoplasmic domain of human tetherin that confer susceptibility to Nef [11], [12].
Alternative models have been proposed for the mechanism of tetherin antagonism by HIV-1 Vpu. Vpu physically associates with tetherin via membrane-spanning domain interactions [17], [18], [19], [20], recruits ßTrCP-2, a component of the Skp1-Cullin1-F-box ubiquitin ligase complex, promoting the ubiquitination of non-lysine residues in the cytoplasmic domain of tetherin [21], [22], and uses the ESCRT-mediated trafficking of tetherin [23] for degradation in lysosomes [17], [19], [24], [25], [26]. There is also evidence that Vpu may antagonize tetherin in the absence of degradation by sequestering the protein in a perinuclear compartment, either by retaining newly synthesized tetherin, or by blocking the recycling of tetherin to the plasma membrane [24], [27], [28], [29]. Vpu-mediated downmodulation of tetherin and enhancement of virus release were also recently shown to be dependent in part on clathrin-mediated endocytosis [30].
The mechanism of tetherin antagonism by HIV-2 Env depends on a physical interaction between Env and tetherin, and a conserved tyrosine-based endocytosis motif in the cytoplasmic tail of gp41 [13], [31], [32]. The sequences required for Env interactions with tetherin are poorly defined, but appear to reside in the extracellular domains of both proteins, as indicated by analyses of recombinant forms of Env and tetherin [13], [33], and the identification of substitutions in the ectodomains of each protein that disrupt tetherin antagonism [32], [33], [34], [35]. HIV-2 Env does not promote the degradation of tetherin, but leads to the internalization and sequestration of tetherin by a clathrin-dependent mechanism, consistent with the trapping of tetherin in recycling endosomes [13], [30], [31].
Comparatively little is known about the mechanism of tetherin antagonism by Nef. The Nef proteins of phylogenetically diverse SIVs, including SIVsmm/mac, SIVagm and SIVcpz, antagonize the tetherin proteins of their non-human primate hosts, but not human tetherin [14], [15], [16]. This specificity maps to a five amino acid sequence that is present in the cytoplasmic tails of non-human primate tetherin proteins (G/D14DIWK18 in rhesus macaques, sooty mangabeys and chimpanzees), but absent from the corresponding region of human tetherin [15], [16]. We previously reported that SIV Nef downregulates rhesus tetherin from the surface of transfected and infected cells [15], [36]. Zhang et al. further demonstrated that this activity is AP-2-dependent [37]. Here we demonstrate a direct physical interaction between SIV Nef and rhesus tetherin, define residues throughout Nef required for tetherin antagonism, and demonstrate that the anti-tetherin activity of Nef is dependent, at least in part, on clathrin-mediated endocytosis.
SIV Nef was tested for a physical interaction with tetherin by co-immunoprecipitation. Tetherin was immunoprecipitated from lysates of 293T cells co-transfected with constructs expressing Nef and either human or rhesus macaque tetherin. Immunoprecipitated proteins were separated by SDS-PAGE, and western blots were probed with monoclonal antibodies to Nef and to tetherin. In accordance with the selective activity of Nef in opposing restriction by tetherin [15], [16], SIV Nef strongly co-immunoprecipitated with rhesus tetherin, but not with human tetherin (Figure 1A).
To determine if this interaction is direct, SIV Nef was tested for binding to peptides corresponding to the N-terminal cytoplasmic domains of rhesus and human tetherin by surface plasmon resonance (SPR). Tetherin peptides were biotinylated at conserved cysteine residues (C25 in rBST-2 and C20 in hBST-2) and coupled to the surface of neutravidin-coated CM5-BIAcore chips to mimic the native orientation of the N-terminus of tetherin on the inner leaflet of the plasma membrane. Recombinant SIVmac239 Nef proteins containing residues 4–263 (Nef4–263) and 96–237 (Nef96–237) were flowed over the immobilized peptides to assess binding. SIV Nef96–237 bound to the N-terminal peptide of rhesus tetherin (Figure 1B), but not to the corresponding peptide of human tetherin (Figure 1C). The dissociation constant and the maximum response for SIV Nef96–237 binding to rhesus tetherin were determined by equilibrium analysis (Kd.app 401+/−114 µM) (Figure 1D). The nearly full-length Nef protein, Nef4–263, also bound to rhesus tetherin (data not shown). However, the Kd of this interaction could not be determined due to artifacts at protein concentrations greater than 300 µM that may reflect Nef dimerization. These results reveal a direct physical interaction between SIV Nef and the cytoplasmic domain of rhesus macaque tetherin.
To identify sequences in SIV Nef that contribute to tetherin antagonism, 103 pair-wise alanine-scanning substitutions were introduced throughout the N-terminal, globular core and the flexible loop regions of SIVmac239 Nef (residues 3–210), and these mutants were tested for their ability to counteract rhesus tetherin in virus release assays (Figure 2). Mutations in the C-terminal domain (residues 211–263) were not tested, since these sequences can be deleted without affecting the anti-tetherin activity of Nef (data not shown). Virus release for 43 of the Nef mutants was reduced to a similar or greater extent than a myristoylation site mutant (G2A), which was previously shown to impair tetherin antagonism [15]. These results were corroborated by western blot analyses comparing p55 Gag expression in cell lysates to the accumulation of p27 capsid (CA) in the cell culture supernatant (Figure S1). This approach identified 9 substitutions in the N-terminal domain (Figure 2B), 27 substitutions in the globular core domain (Figure 2C), and 7 substitutions in the flexible loop region of Nef (Figure 2D) that disrupt tetherin antagonism.
To define residues in SIV Nef that contribute to interactions with tetherin, Nef mutants lacking anti-tetherin activity were tested for binding to rhesus tetherin by co-immunoprecipitation (Figure 3A). The ratios of the band intensities for Nef and tetherin in immunoprecipitates were calculated to quantify differences in binding to tetherin (Table 1). Substitutions at positions 2, 5–6, 66–67, 68–69 and 70–71 in the N-terminal domain, positions 116–117 and 174–175 in the globular core domain, and positions 181–182, 193–194, 195–196 and 199–200 in the flexible loop region diminished the co-immunoprecipitation of Nef with tetherin (Figure 3A, 3B and Table 1). Some of the substitutions in the globular core, particularly at positions 178–179 and 180–181, resulted in reduced levels of Nef protein in cell lysates (Figure 3A and Table 1). Hence, the loss of tetherin binding in these instances may reflect decreased Nef protein stability or expression rather than a disruption of tetherin contact residues. However, many of the core domain mutations that decreased steady-state levels of Nef did not result in a corresponding decrease in binding to tetherin, and a few paradoxically appear to have increased the stability of this interaction (Figure 3A and Table 1). Since many of these mutations did not completely abrogate binding to tetherin, combinations of alanine substitutions were also tested. The co-immunoprecipitation of Nef with tetherin was reduced to nearly undetectable levels by combining substitutions in the N-terminal domain, either alone (residues 66–71), or together with the substitutions in the globular core and flexible loop (x6: residues 66–71, 116–117, 174–175 and 181–182) (Figure 3B).
To determine if residues in the flexible loop of SIV Nef are needed for direct binding to rhesus tetherin, purified Nef proteins with deletions in the flexible loop were tested for binding to the cytoplasmic domain of rhesus tetherin by SPR. Recombinant SIVmac239 Nef96–237 lacking residues 181–200, 181–195, 197–205 and 181–205 were flowed over BIAcore chips coated with a 26 amino acid peptide corresponding to the cytoplasmic domain of rhesus tetherin, as described for Figure 1B. All of the deletion mutants bound to the peptide within a similar range of apparent Kd values (Figure 3C; representative raw SPR data is shown in Figure S2A and S2B). The somewhat lower Kd.app estimates for three of the deletion mutants (Δ181–200, Δ181–195 and Δ181–205) may be due to technical limitations with testing these mutants at high concentrations as a result of protein aggregation, rather than an actual increase in binding affinity. Some of these mutants also showed lower apparent Kd values for binding to a TCRζ chain peptide (Figure S2C–S2E), an interaction that reflects direct binding of the SIV Nef core domain as corroborated by three-dimensional structural data [38]. Hence, these results demonstrate that the flexible loop of Nef is not required for direct binding to rhesus tetherin, implying that surfaces of the core domain are sufficient for the low affinity interaction with the N-terminus of rhesus tetherin observed by SPR.
Cytoplasmic domain variants of tetherin were also tested for binding to Nef by co-immunoprecipitation and SPR. Deletion of the first 10 amino acids of rhesus tetherin (rΔ10) significantly reduced, but did not eliminate, binding to Nef in both assays (Figures 4A, 4B and S3B), indicating that although these residues are not essential for binding to Nef, they contribute to the stability of the interaction. Consistent with previous studies mapping the anti-tetherin activity of Nef to a five amino acid sequence (G/D14DIWK18) that is missing from human tetherin [15], [16], alanine substitutions at positions 14–18 of rhesus tetherin (rA14-A18) diminished Nef binding, whereas the introduction of these residues into human tetherin partially restored binding (Figures 4A, 4B, S3C and S3D). Thus, although the specificity of tetherin antagonism by Nef is dependent on residues 14–18, and these sequences contribute to a physical interaction with Nef, they are not the sole determinant of Nef binding.
These tetherin variants were also tested in virus release assays to determine how Nef binding relates to susceptibility to antagonism. In accordance with partial binding of Nef to rΔ10 and hDDIWK, restriction of virus release by each of these mutants was partially counteracted by Nef (Figure 4C). However, despite a physical interaction between rA14-A18 and Nef that was detectable by co-immunoprecipitation and SPR assays, this mutant was resistant to antagonism and restricted virus release to an extent comparable to human tetherin (Figure 4C). Therefore, although a physical interaction may be necessary for tetherin antagonism by Nef, it is not sufficient. This raises the possibility that the anti-tetherin activity of Nef may require the recruitment of one or more additional cellular factors that participate in interactions with the G/D14DIWK18 sequence.
To determine if substitutions that impair tetherin antagonism also disrupt other activities of Nef, the Nef mutants were tested for CD4-downregulation, MHC I-downregulation and infectivity enhancement; three functional activities of Nef that require distinct protein sequences and cellular pathways [39], [40]. CD4− and MHC class I-downregulation assays were performed by electroporating Jurkat cells with bicistronic constructs that express wild-type Nef, or a mutant Nef protein, together with green fluorescent protein (GFP), and comparing the mean fluorescence intensity (MFI) of CD4 and MHC class I staining on the surface of cells expressing Nef to cells transfected with an empty vector (pCGCG) (Figures S4 and S5). Infectivity enhancement was measured by infecting GHOST X4/R5 cells, which express GFP in response to HIV-1 or SIV infection, with SIVmac239 Δnef trans-complemented with wild-type Nef or each of the Nef mutants, and measuring the percentage of infected GFP+ cells by flow cytometry (Figure S6).
Of the 43 Nef mutants with impaired anti-tetherin activity, only 5 retained the ability to downregulate CD4 within 3 standard deviations of wild-type Nef (Figure 5A, black dotted line). In contrast, 16 of the mutants retained the ability to downregulate MHC I within 3 standard deviations of wild-type Nef (Figure 5B, black dotted line). Whereas substitutions in the N-terminal domain and flexible loop region, with the exception of substitutions at positions 5–6 and 74–75, had little or no effect on MHC I-downregulation, many of the substitutions in the globular core impaired this activity (Figure 4B and S5). In most cases, the loss of MHC I-downregulation corresponded with a partial decrease in Nef protein levels (Table 1), suggesting that the effects of these mutations were not necessarily specific to this function of Nef. Nevertheless, five Nef mutants with impaired anti-tetherin activity, and no significant effects on protein stability, retained the ability to downregulate both CD4 and MHC I molecules. These included Nef mutants with substitutions at positions 106–107, 181–182, 193–194, 199–200 and 209–210 (Table 1). Therefore, tetherin antagonism is separable from CD4− and MHC class I-downregulation.
The infectivity of SIV Δnef trans-complemented with each of the Nef mutants relative to SIV Δnef trans-complemented with wild-type Nef was also determined to assess the effects of the substitutions on Nef-mediated infectivity enhancement. To control for assay-to-assay variation in the susceptibility of the GHOST X4/R5 cells to infection, the percentage of infected cells obtained for each of the Nef mutants was normalized to the percentage of infected cells obtained for wild-type Nef. Nef mutants were considered to retain the ability to enhance virus infectivity if the relative infectivity was at least 5 standard deviations above the infectivity of SIV Δnef trans-complemented with an empty vector (pCGCG) (Figure 5C). This analysis identified 12 Nef mutants that were impaired for infectivity enhancement (Figure S6A–S6C). In accordance with previous observations, the G2A substitution in SIV Nef did not have a significant effect on virus infectivity [41]. Consistent with a study of this function of HIV-1 Nef [40], all of these mutants also lost the ability to bind to dynamin 2 (Dyn2) (Figure S6D). Of these 12 Nef mutants, 10 also exhibited impaired anti-tetherin activity, suggesting that tetherin antagonism and infectivity enhancement may be linked, perhaps by a common dependence on a physical interaction with Dyn2. However, two of the substitutions in the core domain at positions 94–95 and 98–99 that disrupted infectivity enhancement did not significantly affect anti-tetherin activity (Figure 2C and S6B). Moreover, three of the substitutions that disrupted binding to Dyn2 (82–83, 146–147 and 168–169) did not impair binding to rhesus tetherin (Table 2). Thus, Nef appears to use distinct surfaces to bind Dyn2 and tetherin. In addition, since all but 10 of the 43 Nef mutants lacking anti-tetherin activity retained the ability to enhance infectivity, including the 5 mutants that retained both CD4− and MHC class I-downregulation, infectivity enhancement is independent of tetherin antagonism (Figure 5C and Table 1).
AP-2 binds to a pair of conserved motifs in the flexible loop of Nef that are necessary for tetherin antagonism; a di-leucine motif and a di-acidic motif, corresponding to residues E191XXXLM195 and D204D205 of SIVmac239 Nef, respectively (Figure 6A) [37], [42], [43]. Consistent with previous observations [37], substitutions of residues within either of these motifs (positions 193–194, 195–196, 203–204, 205–206) impaired tetherin downregulation by Nef (Figure 6B and 6C). In addition, substitutions at positions 181–182, 199–200 and 209–210, not previously identified as AP-2 binding sites or known to be involved in the anti-tetherin activity of Nef, also impaired tetherin downregulation (Figure 6B and 6D). These results confirm the role of the di-leucine and di-acidic motifs and identify additional sequences in the flexible loop of SIV Nef required for tetherin downmodulation.
Since AP-2 binds to both Nef and tetherin [42], [43], [44], the flexible loop mutants were also tested for their ability to interact with the α-adaptin (α2) and μ2 subunits of AP-2 by co-immunoprecipitation. Endogenous α2 and μ2 were immunoprecipitated in parallel from lysates of parental 293T cells (Figure 6E), or 293T cells that constitutively express HA-tagged rhesus tetherin (Figure 6F), following transfection with Nef expression constructs. Immunoprecipitates were separated by electrophoresis and western blots were probed with antibodies to Nef, α2, μ2 and tetherin. Wild-type Nef co-immunoprecipitated with α2 and μ2, both in the absence and in the presence of tetherin (Figures 6E and 6F). As expected, substitutions in the di-leucine and di-acidic motifs of Nef (193–194, 195–196, 203–204, 205–206) greatly diminished binding to both subunits (Figures 6E and 6F). Substitutions at positions flanking these motifs (181–182 and 209–210) also disrupted binding to α2 and to μ2 (Figures 6E and 6F). Whereas in the absence of tetherin, substitutions at positions 181–182, 203–204, 205–206 and 209–210 eliminated Nef binding to μ2, and substitutions at positions 193–194, 195–196, and 199–200 reduced Nef binding to μ2, (Figure 6E), the binding of these Nef mutants to μ2 was partially restored in the presence of tetherin (Figure 6F). These results suggest that the loss of anti-tetherin activity for each of the flexible loop mutants reflects a deficit in Nef binding to AP-2, and raises the possibility that AP-2 may form a multimeric complex with both Nef and tetherin that stabilizes an otherwise low affinity direct interaction between these two proteins.
Since the activities of HIV-1 Vpu and HIV-2 Env in downregulating tetherin and facilitating virus release were recently shown to be dependent on AP180, a component of the clathrin assembly complex [30], and dynamin 2 (Dyn2), an ubiquitously expressed GTPase required for the scission of vesicular membranes [45], we asked whether tetherin antagonism by Nef also requires AP180 and Dyn2. The effects of dominant-negative mutants of Dyn2 (Dyn2K44A) and AP180 (AP180C) on the surface expression of tetherin, and on virus release, were therefore tested in 293T cells expressing HA-tagged rhesus macaque tetherin. As a control, we also included the dominant-negative mutant of dynamin 1 (Dyn1K44A), which is exclusively expressed in neurons [46]. Changes in tetherin expression on the cell surface were assessed by flow cytometry after co-transfection with constructs expressing Dyn2K44A, Dyn2, Dyn1K44A or AP180C, with or without Nef (Figure 7A). In the absence of Nef, Dyn1K44A, Dyn2K44A and AP180C all slightly increased surface levels of tetherin, whereas wild-type Dyn2 decreased surface levels of tetherin (Figure 7A), which may reflect a role for dynamin and clathrin in the constitutive endocytosis of tetherin [44]. In the presence of Nef, the effects of the dominant-negative mutants were more pronounced. Whereas SIV Nef reduced the surface expression of rhesus tetherin by 2- to 3-fold, as previously reported [15], this effect was almost completely reversed by AP180C and Dyn2K44A (Figure 7A). Although Dyn1K44A also increased the overall levels of tetherin at the cell surface, as shown in transfections with the empty vector, Nef was still able to downregulate tetherin in the presence of Dyn1K44A (Figure 7A). Likewise, in the presence of wild-type Dyn2, Nef further decreased the surface levels of tetherin. These results demonstrate that the downregulation of tetherin by Nef is dependent, at least in part, on clathrin-mediated endocytosis.
To further confirm that Dyn2 and clathrin are required for tetherin antagonism by Nef, virus release for wild-type SIV versus SIV Δnef was compared in the presence and absence of each of the dominant-negative mutants. 293T cells were co-transfected with proviral DNA for SIVmac239 or SIVmac239 Δnef, together with a construct expressing rhesus macaque tetherin and expression constructs for AP180C, Dyn1K44A, Dyn2K44A or Dyn2, and the accumulation of virus particles in the cell culture supernatant was measured by SIV p27 antigen-capture ELISA. Whereas AP180C and Dyn2K44A completely abrogated the resistance of wild-type SIV to rhesus tetherin, as indicated by comparable levels of virus release for SIVmac239 and SIVmac239 Δnef, virus release for wild-type SIV was not significantly affected by Dyn1K44A or Dyn2 (Figure 7B). Western blot analyses of cell lysates confirmed protein expression for tetherin, Nef and the dominant-negative mutants (Figure 7C). None of the dominant-negative mutants inhibited tetherin expression. On the contrary, increased steady-state levels were observed in the presence of AP180C, Dyn1K44A and Dyn2K44A (Figure 7C), consistent with the modest increase in cell surface expression of tetherin in the absence of Nef (Figure 7A).
A role for Dyn2 in the anti-tetherin activity of Nef was further investigated by comparing virus replication of wild-type SIV and nef-deleted SIV with or without Dynasore, a chemical inhibitor of dynamin. A Herpesvirus saimiri-immortalized rhesus macaque CD4+ T cell line [47], was infected with SIVmac239 and SIVmac239 Δnef, treated with IFNα to upregulate tetherin, and maintained in medium with or without Dynasore. While Dynasore had little effect on the replication of SIV Δnef, which was suppressed relative to wild-type SIV by the IFNα-induced upregulation of tetherin, Dynasore significantly reduced wild-type SIV replication (Figure 7D). Indeed, wild-type SIV replication in the presence of Dynasore was comparable to SIV Δnef replication, suggesting that this compound fully negated the resistance provided by Nef to the antiviral effects of tetherin. However, since Dyn2 is also required for infectivity enhancement by Nef [40], these results may reflect an additional effect of Dynasore on viral infectivity.
Changes in the subcellular distribution of tetherin in the presence of Nef were examined in uninfected and SIV-infected cells by confocal microscopy. 293T cells expressing HA-tagged rhesus tetherin were infected with VSV-G-pseudotyped SIVmac239 Δenv and stained for Nef and tetherin. In uninfected cells, tetherin was observed at the plasma membrane and within the trans-Golgi network (Figure 8A and Figure S7), as previously reported [48]. However, in SIV-infected cells, the overwhelming majority of tetherin was observed within intracellular compartments (Figure 8B). To better define the subcellular distribution of tetherin, cells were stained for markers of the trans-Golgi network (TGN46), endosomes (CD63) and lysosomes (LAMP-1). In some cells, tetherin co-localized with TGN46 (Figure 9A), but did not appear to co-localize with CD63 (Figure 9B). These results suggest that Nef may partially retain tetherin within the trans-Golgi network with little or no sequestration in endosomes. However, in the majority of the SIV-infected cells, tetherin was found to co-localize with LAMP-1 (Figure 9C), but not in uninfected cells (Figure S7). These observations were supported by quantifying the extent of tetherin co-localization with TGN46, CD63 and LAMP-1 by calculating the Pearson's correlation coefficients for these markers in twenty SIV-infected cells. Although the distribution of cells exhibiting co-localization of tetherin with TGN46 was heterogeneous (Figure 9D), it was higher than the extent of co-localization with CD63 (P = 0.042). In the case of LAMP-1, the extent of co-localization with tetherin was significantly higher than for either TGN46 (P = 0.0048) or CD63 (P<0.000001). Therefore, similar to the effects of Nef on CD4 and MHC class I trafficking [49], Nef appears to direct tetherin to lysosomes.
The primate lentiviruses have evolved to use at least three different proteins to counteract tetherin; Nef, Vpu and Env [5], [13], [15], [16], [32], [36], [50]. Although a number of studies have addressed the mechanisms of tetherin antagonism by HIV-1 Vpu and HIV-2 Env [4], [5], [13], [18], [23], [27], [29], [30], [31], [51], relatively little is known about the mechanism of tetherin antagonism by Nef–the viral gene product used by most SIVs to counteract the tetherin proteins of their respective hosts. In accordance with the species-dependent activity of Nef in opposing restriction by tetherin [15], [16], we show for the first time that Nef selectively binds to rhesus macaque tetherin, but not to human tetherin. We identify residues in the N-terminus, globular core and flexible loop of Nef that are required for anti-tetherin activity, and demonstrate that, despite substantial overlap with sequences required for CD4 downregulation, tetherin antagonism is genetically separable from this activity, as well as from other Nef functions including MHC class I downregulation and infectivity enhancement. We also show that dominant-negative mutants of AP180 and Dyn2 impair tetherin antagonism by Nef, indicating that this activity is dependent, at least in part, on clathrin-mediated endocytosis.
Co-immunoprecipitation and surface plasmon resonance assays revealed a selective physical interaction between SIV Nef and rhesus tetherin. The specificity of this interaction is determined by binding of the core domain of Nef to the cytoplasmic domain of tetherin, since a truncated form of the SIVmac239 Nef protein, containing the globular core of the protein, was sufficient for binding to a peptide corresponding to the cytoplasmic domain of rhesus tetherin. However, the affinity of this interaction was low (Kd∼400 µM), suggesting that additional Nef sequences, and perhaps one or more cellular co-factors, contribute to the stability of this interaction in virus-infected cells. In support of this, an analysis of alanine-scanning substitutions identified sequences in the N-terminal, globular core and flexible loop domains of Nef that participate in binding to rhesus tetherin. Although the N-terminal domain and flexible loop were dispensable for binding by SPR, these sequences were required to detect an interaction by co-immunoprecipitation. The contribution of the N-terminus of Nef to interactions with tetherin may reflect an indirect effect on membrane association, since the targeting of Nef to cellular membranes is dependent on the myristyolation of a glycine residue at position 2, and structural studies suggest that the N-terminus of Nef is disordered in the absence of phospholipids [52], [53], [54]. Substitutions at positions 116–117, 174–175 in the globular core domain, and positions 181–182, 193–194, 195–196 and 199–200 in the flexible loop region also reduced binding to rhesus tetherin. Since the flexible loop contains a di-leucine and a di-acidic motif (E190xxxLM and D204D) required for binding to the AP-2 subunits (α2-σ2 and μ2, respectively) [42], [55], [56], and substitutions in these sites disrupt tetherin antagonism [37], it is conceivable that AP-2 stabilizes the binding of Nef to tetherin. Indeed, Nef was recently shown to form a trimolecular complex with the μ1 subunit of AP-1 to stabilize an otherwise low affinity bimolecular interaction with the cytoplasmic tail of MHC class I molecules [57], [58]. In support of a possible trimeric complex with AP-2, Nef and rhesus tetherin both co-immunoprecipitated with the μ2 and α2 subunits of AP-2 [24], [44]. Taken together, these results suggest a model in which the specificity of SIV Nef for rhesus tetherin is driven by a direct physical interaction between the core domain of Nef and the N-terminus of tetherin, which is stabilized by residues in the N-terminal domain and flexible loop region, either through direct contacts or indirect effects on membrane association and/or the recruitment of additional cellular co-factor(s).
A systematic analysis of alanine-scanning substitutions throughout the SIVmac239 Nef protein identified a total of 43 mutations that impaired anti-tetherin activity. Substitutions in the C-terminal domain were not tested, since deletion of these sequences did not affect tetherin antagonism. Most of the mutations that disrupted the anti-tetherin activity of Nef also disrupted CD4-downregulation, MHC I-downregulation or infectivity enhancement. In some cases, both CD4− and MHC I-downregulation were lost due to effects on the association of Nef with cellular membranes, such as the G2A mutation and probably also the adjacent substitutions at positions 5–6. In other cases, these activities were lost due to a decrease in Nef expression or stability. While this was most evident for the changes at positions 178–179 and 180–181, some of the substitutions in the globular core domain also had partial effects on steady-state levels of Nef that may account for their reduced activity in CD4 and MHC class I downregulation assays. Although there was substantial overlap with sequences required for CD4-downregulation, five mutations were identified that disrupted the anti-tetherin activity of Nef, while retaining nearly wild-type levels of CD4-downregulation, as well as MHC class I-downregulation and infectivity enhancement. These mutations included alanine substitutions at positions 106–107 in the core domain and at positions 181–182, 193–194, 199–200 and 209–210 in the flexible loop region. Thus, tetherin antagonism by Nef is genetically separable from other functional activities of the protein.
In addition to the sequences identified by Zhang et al. [37], we identified residues in the flexible loop region outside of the known AP-2 binding sites that separate tetherin antagonism from CD4-downregulation. Substitutions at positions 181–182 and 199–200 (residues N181V182 and Q199T200 in SIVmac239 Nef) specifically impaired the anti-tetherin activity of Nef without affecting CD4-downregulation. These residues are well conserved among Nef alleles of SIVsmm/mac and HIV-2 isolates, with identities of 61.1% for N181, 75% for V182, 94.4% for Q199 and 84.7% for T200 (Los Alamos database; http://www.hiv.lanl.gov/content/index). Co-immunoprecipitation assays further demonstrated that these residues contribute to AP-2 binding. Our mutational analysis also identified residues in the N-terminal and globular core domains of Nef that are important for tetherin antagonism. Thus, our results reveal that the anti-tetherin activity of Nef is dependent on complex interactions involving multiple residues in the N-terminus, globular core and the flexible loop regions of the protein.
Nef is a multifunctional accessory protein that interacts with a number of different cellular factors to modulate cellular trafficking [55]. Nef reroutes MHC I molecules from the trans-Golgi network to lysosomes via AP-1 and promotes the internalization and lysosomal degradation of CD4 via AP-2 [55], [59], [60], [61]. Nef also enhances virus infectivity by an undefined mechanism that depends on a physical interaction with Dyn2 [40]. We previously demonstrated that Nef downmodulates tetherin from the surface of SIV-infected and transfected cells [15], [36], and this activity was later shown to occur by an AP-2-dependent pathway [37]. Our experiments with dominant-negative mutants of AP180 and Dyn2 confirm that the internalization of tetherin by Nef, and the capacity of Nef to rescue virus release in the presence of tetherin, depends, at least in part, on clathrin-mediated endocytosis. A role for Dyn2 was further demonstrated by showing that Dynasore, a chemical inhibitor of dynamin, suppressed wild-type SIV replication to an extent comparable to nef-deleted SIV under conditions of interferon-induced upregulation of tetherin. Since Dyn2 is also required for Nef-mediated infectivity enhancement, the inhibition of virus replication by Dynasore may reflect an additional effect of this compound on virus infectivity. Although tetherin antagonism and infectivity enhancement are genetically separable, 10 of the 12 Nef mutants that lost the ability to enhance virus infectivity, and to bind to Dyn2, also lost the ability to counteract tetherin. The concordance of these activities suggests that a physical interaction with Dyn2 may be necessary for both Nef functions. However, two of the mutations in the globular core disrupted infectivity enhancement and binding to Dyn2 without impairing tetherin antagonism. Moreover, three of the mutants with impaired infectivity enhancement and binding to Dyn2 (mutants 82–83, 146–147 and 168–169) did not lose binding to tetherin, suggesting that Nef uses distinct protein surfaces to bind to Dyn2 and to tetherin. Therefore, unlike infectivity enhancement, the anti-tetherin activity of Nef does not depend on a physical interaction with Dyn2.
Consistent with previous studies demonstrating the downmodulation of rhesus tetherin by Nef [15], [36], [37], SIV infection resulted in a striking redistribution of tetherin from the plasma membrane to compartments within the cell. An analysis of the distribution of tetherin in SIV-infected cells revealed co-localization with TGN46 and LAMP-1, but not with CD63, suggesting that in the presence of Nef, tetherin accumulates in the trans-Golgi network and in lysosomes. Localization of tetherin to the trans-Golgi network in uninfected cells has previously been reported [48]. Thus, the contribution of Nef to directing tetherin to that compartment is unclear. The trafficking of tetherin to lysosomes raises the possibility that, similar to the effect of HIV-1 Nef on CD4 and MHC class I molecules [49], SIV Nef may direct rhesus tetherin for lysosomal degradation.
In summary, we show that the mechanism of tetherin antagonism by SIV Nef; (1) involves a direct physical interaction between the core domain of Nef and the cytoplasmic domain of rhesus tetherin, (2) requires sequences throughout the N-terminal, globular core and flexible loop domains, yet is genetically separable from other functional activities of Nef, and (3) depends, at least in part, on clathrin-mediated endocytosis. These results begin to reveal the molecular interactions and cellular pathways by which the majority of the primate lentiviruses counteract the tetherin proteins of their non-human primate hosts.
293T cells were co-transfected with wild-type or nef-deleted SIV proviral DNA (100 ng) and pcDNA3-tetherin or pcDNA3-tetherin mutants (50 ng). Differences in the amount of plasmid DNA in each transfection were offset by the addition of empty pcDNA3 vector (50 ng). Either pCGCG, pCGCG-Nef or pCGCG-Nef mutants (100 ng each) were provided in trans to assess the ability of the Nef mutants to rescue virus release. All transfections were performed in duplicate in 24-well plates seeded the day before at 5×104 cells per well, using GenJet Lipid Transfection Reagents (SignaGen Laboratories, Gaithersburg, MD). Forty-eight hours post-transfection, the amount of virus released into the cell culture supernatant was measured by SIV p27 antigen-capture ELISA (Advanced Bioscience Laboratories, Inc., Kensington, MD), and virus release was expressed as the percentage of maximal particle release in the absence of tetherin, as previously described [15], [36].
Forty-eight hours post-transfection, 293T cell lysates were prepared by harvesting in 2× SDS sample buffer. Virions were recovered from the cell culture supernatant by centrifugation at 13,000 rpm for 2 hours at 4°C, and resuspended in 2× SDS sample buffer. Samples were boiled for 5 minutes, and separated by electrophoresis on 12% SDS-polyacrylamide gels and transferred to polyvinylidine fluoride (PVDF) membranes using a Trans-Blot SD transfer cell (BioRad, Hercules, CA). The membranes were then blocked with 5% non-fat dry-milk in PBS containing 0.05% Tween-20 for 1 hour, and probed overnight at 4°C with one of the following primary antibodies. Tetherin/BST-2 was detected with a mouse polyclonal antibody (abcam cat #ab88523, Cambridge, MA) at a dilution of 1∶500. The SIV Gag proteins p27 and p55 were detected with the mouse monoclonal antibody 183-H12-5C (AIDS Research and Reference Reagent Program, Division of AIDS, NIAID, NIH) at a dilution of 1∶1000. SIV Nef was detected using the mouse monoclonal antibody 17.2 (AIDS Research and Reference Reagent Program, Division of AIDS, NIAID, NIH) at a dilution of 1∶1000. Endogenous β-actin was detected with the monoclonal antibody C4 (Chemicon, Billerica, MA) at a dilution of 1∶1000. HA-tagged Dyn1K44A was detected with the HA-specific mouse monoclonal antibody HA.11 (Covance, Princeton, NJ) at a dilution of 1∶1000. The GFP fusion proteins Dyn2 and Dyn2K44A were detected using an anti-GFP mouse monoclonal antibody (Sigma-Aldrich, St Louis, MO) at a dilution of 1∶1000. The dominant-negative mutant AP180C was detected with a mouse monoclonal FLAG-specific antibody (Sigma-Aldrich, St Louis, MO) at a dilution of 1∶1000. After rinsing the PVDF membranes three times for 10 minutes in PBS 0.05% Tween-20, the blots were probed with an HRP-conjugated goat anti-mouse secondary antibody (Pierce, Rockford, IL) at a dilution of 1∶2000 for 1 hour at room temperature. The blots were then rinsed three more times in PBS 0.05% Tween-20, treated with SuperSignal West Femto Maximum Sensitivity substrate (Pierce, Rockford, IL), and imaged using a Fujifilm Image Reader LAS 3000 (Fujifilm Photo Film Co., Japan).
293T cells (6×105 cells) were co-transfected with constructs expressing wild-type and mutant forms Nef (2 µg) along with rhesus tetherin, human tetherin, tetherin mutants, Dyn2-GFP or empty vector (pCDNA3) (2 µg). Twenty-four hours later, cells were lysed with 400 µl of Lysis buffer (Thermo Scientific, Rockford, IL) and incubated on ice for 30 minutes. Lysates were transferred to a 1.5 ml tube and insoluble cell debris was removed by centrifugation at 3,000 rpm. Cell lysate (200 µl) was set aside to confirm tetherin and Nef expression by western blot analysis, and the rest of the sample (200 µl) was used for immunoprecipitation. Samples for immunoprecipitation were incubated on a rotating platform for 1 hour at 4°C with 1 µg of the anti-tetherin mouse monoclonal antibody 3H4 (Sigma-Aldrich, St Louis, MO). Protein A sepharose beads or Protein A sepharose magnetic beads (50 µl) (GE Healthcare, Piscataway, NJ) were then added, and the incubation was continued overnight at 4°C. The beads were washed ten times in Lysis buffer (500 µl) and boiled in 2× SDS sample buffer. Denatured proteins were separated on 12% SDS-polyacrylamide gels and transferred to PVDF membranes. The blots were probed with the monoclonal antibody 17.2 to detect Nef, a mouse monoclonal to detect GFP (Sigma-Aldrich, St Louis, MO), a monoclonal antibody to detect the α2 subunit of AP-2 (Sigma-Aldrich, St Louis, MO), a rabbit monoclonal to detect the μ2 subunit of AP-2 (abcam, Cambridge, MA) at a dilution of 1∶1000, or a polyclonal antibody against tetherin (abcam, Cambridge, MA) at a dilution of 1∶500. Membranes were next probed with an HRP-conjugated goat anti-mouse antibody (Pierce, Rockford, IL), a goat anti-mouse heavy chain specific antibody (abcam, Cambridge, MA), or goat anti-rabbit secondary antibody (abcam, Cambridge, MA), developed in SuperSignal West Femto Maximum Sensitivity substrate and imaged using a Fujifilm Image Reader LAS 3000 as described above. Quantification of the association between Nef and rhesus tetherin, Nef and AP-2 or Nef and Dyn2 was performed by determining the band density from western blots using ImageJ software (Rasband, W.S., Image, US. NIH, Bethesda, MD, http://rsb.info.nih.gov/ij, 1997–2008).
Synthetic peptides (21st Century Biochemicals) corresponding to the cytoplasmic domain of rhesus macaque (MAPILYDYRKMPMDDIWKEDGDKRCK) and human (MASTSYDYSRVPMEDGDKRCK) tetherin were biotinylated via stable thioester bonds at conserved cysteine residues (underlined in sequences shown above). In human tetherin cysteine at position 9 was replaced by serine (italics) in order to avoid multiple labeling. Additional tetherin peptides (rA14-A18, hDDIWK and rΔ10) were generated to further define the binding interface with Nef. These peptides were also biotinylated at the conserved cysteine residues mentioned above. Biotinylation was carried out for 2 hours at room temperature and pH 6.5 to 7.0 with a 10-fold molar excess of maleimide-PEG2-Biotin (Pierce), followed by reverse-phase HPLC purification using a C18 Vydac 4.6×250 mm analytical column (Vydac, Hesperia, CA) with linear acetonitrile gradient (0–72%) in 0.1% TFA (1 mL/min). Fractions containing the biotinylated peptides were identified by mass-spectrometry, pooled and lyophilized.
A nearly full-length SIVmac239 Nef protein (residues 4–263), the core domain (residues 96–237) and different Nef deletion mutants lacking residues in the flexible loop were expressed in E. coli BL21 (DE3) as 6-His-thioredoxin fusion proteins and purified as described previously by Sigalov et al. [54]. After cell lysis with phosphate/Tris buffered 8 M urea solution (pH 8), the fusion protein was purified by affinity chromatography using NiNTA (Qiagen) under denaturing conditions (8 M urea), and refolded by dialysis against 20 mM Tris pH 8.0, 150 mM NaCl, 0.1 mM DTT. The soluble fusion protein was digested with thrombin (MP Biochmicals) leaving both the full-length protein and the core domain of SIVmac239 with two additional N-terminal residues (GS). Further purification was performed by anion-exchange chromatography (POROS 20 HQ, Applied Biosystems) and size-exclusion chromatography (Superdex 200, GE Healthcare).
Surface plasmon resonance experiments were carried out on a BIAcore 3000 instrument at 25°C. Neutravidin (approximately 30,000 resonance units (RU)) were coupled to a CM5 sensor chip (GE Healthcare) in 10 mM acetate buffer pH 5.0, and 0.005% (v/v) surfactant P20 at 10 µl/min using standard amine coupling protocols. Excess activated dextran carboxylate groups were capped with ethanolamine. Biotinylated peptides of tetherin (rBST-21–26 and hBST-21–21) were captured (3,000 RU) in different neutravidin-coupled experimental flow cells leaving one flow cell unmodified as a neutravidin-only control surface. Nef binding was studied at 25°C in PBS under reducing conditions (5 mM DTT). Purified samples of full-length, core domain or flexible loop deletion mutants of SIVmac239 Nef protein were injected at a flow rate of 5 µl/min over each experimental and control flow cell generating SPR sensorgrams. The sensorgram from the control cell was subtracted from the sensorgram of each experimental flow cell to correct for any nonspecific interaction with the CM5 or neutravidin surface. No regeneration step was required. Experiments were run in triplicate. For equilibrium analysis RU binding levels at equilibrium were extrapolated from each sensorgram (corrected for nonspecific interaction) in the concentration series, and plotted against concentration to derive a binding curve that was fit to a hyperbolic equation y = RUmax*x/(Kd.app+x), where y is the observed RU value, x is the concentration of Nef, and adjustable parameter RUmax and Kd.app are the RU value at saturation and the apparent binding constant (Kd.app), respectively.
Ten million Jurkat cells were electroporated with bicistronic pCGCG constructs (40 µg) that express wild-type Nef, or Nef mutants, and green fluorescent protein (GFP) from a downstream internal ribosomal entry site. Twenty-four hours later, cells were stained with a PerCP-conjugated monoclonal antibody to CD4 (BD Pharmingen, Billerica, MA) and an APC-conjugated monoclonal antibody to MHC-I (HLA-ABC, Dako, Carpintería, CA). After gating on the GFP+ cells, the mean fluorescence intensity (MFI) of CD4 and MHC I expression was determined. Data was collected using a FACSCalibur flow cytometer (Becton Dickenson) and analyzed using FlowJo 8.8.7 software (TreesStar).
293T cells were co-transfected with nef-deleted SIV proviral DNA (100 ng) and either pCGCG, pCGCG-Nef or pCGCG-Nef mutants (100 ng each). All transfections were performed in duplicate in 24-well plates seeded the day before at 5×104 cells per well, using GenJet Lipid Transfection Reagents (SignaGen Laboratories, Gaithersburg, MD). Forty-eight hours post-transfection, the amount of virus released into the cell culture supernatant was measured by SIV p27 antigen-capture ELISA (Advanced Bioscience Laboratories, Inc., Kensington, MD). Next, 50 ng of p27 equivalents for each virus were inoculated overnight onto GHOST X4/R5 cells seeded the day before in 12-well plates at 2.5×104 cells per well. Twenty-four hours later, cells were washed and kept in fresh media. Forty-eight hours post-infection, cells were fixed and analyzed by flow cytometry as described above. The amount of infected cells was determined by calculating the percentage of GFP+ cells, and the infectivity of each Nef mutant relative to wild-type Nef was determined.
293T cells stably expressing HA-tagged rhesus tetherin (5×104 cells) were transfected with 200 ng of pCGCG-Nef (or Nef mutants) or empty vector. In the case of experiments with dominant-negative mutants of endocytic pathways, cells were also transfected with 300 ng of each of the expression vectors coding for the dominant-negative mutants or empty vectors. Twenty-four hours post-transfection, cells were briefly trypsinized and stained for the surface expression levels of tetherin with a primary mouse monoclonal anti-HA antibody (Covance, Princeton, NJ) at a dilution of 1∶4 and a secondary donkey anti-mouse APC-conjugated antibody (BD Pharmingen, Billerica, MA) at a dilution of 1∶40. Cells were gated on the GFP+ population and the levels of tetherin at the cell surface were determined by calculating the MFI. The percentage of tetherin present at the plasma membrane was calculated by dividing the MFI obtained in each transfection by the MFI obtained in transfections with empty vectors. Data was collected using a FACSCalibur flow cytometer (Becton Dickenson) and analyzed using FlowJo 8.8.7 software (TreesStar).
Two million 221 T cells, a Herpesvirus saimiri-immortalized rhesus macaque CD4+ T cell line [47], were infected in duplicate with 20 ng p27 of SIVmac239 and SIVmac239 Δnef. After 3 h of incubation at 37°C, cells were washed three times and resuspended in 5 ml of R20+IL-2 (100 U). One day post-infection cells were treated with 100 U of IFNα, and 8 hours later one of the replicates was treated with 20 µM of Dynasore. Virus replication was monitored at selected time points by p27 antigen-capture ELISA of the culture supernatant.
293T cells stably expressing HA-tagged rhesus macaque tetherin (2×104 cells in a 8-well slide) were infected with VSV-G pseudotyped SIVmac239 Δenv (50 ng p27 eq.). Twenty-four hours later, cells were washed and fixed for 10 minutes in acetone/methanol and blocked for 20–60 minutes with 100 mM glycine diluted in 10% normal goat serum in PBS with 0.2% fish skin gelatin, 0.1% Triton ×100 and 0.02% sodium azide (10% NGS-PBS-FSG-Tx100-NaN3). The cells were then washed three times in 10% NGS-PBS-FSG-Tx100-NaN3, and stained. The mouse monoclonal antibodies 17.2 (IgG1) and 3H4 (IgG2a) were used at a dilution of 1∶250 to stain for Nef and tetherin, respectively. The cells were subsequently stained with Alexa-488- and Alexa-568-conjugated goat anti-mouse secondary antibodies specific for IgG1 and IgG2a, respectively (Invitrogen, Grand Island, NY) (1∶1000), and with TO-PRO3 (Invitrogen) (1∶5000) to visualize cell nuclei. To stain intracellular compartments, rabbit polyclonal antibodies specific for TGN46 (Sigma-Aldrich, St Louis, MO), CD63 (Santa Cruz Biotechnology, Santa Cruz, CA) and LAMP-1 (abcam, Cambridge, MA) were used at a dilution of 1∶50. Next, an Alexa-568 goat anti-rabbit (Invitrogen, Grand Island, NY) was used to detect these cellular markers. In this case, Nef staining was performed by using a secondary Alexa-633-conjugated goat anti-mouse IgG1. After staining, the cells were washed and mounted on slides with antiquenching mounting-medium (Vector Laboratories, Inc). Images were acquired using a Leica TCS SP5 II confocal microscope.
|
10.1371/journal.ppat.1005202 | Dengue Virus Infection of Aedes aegypti Requires a Putative Cysteine Rich Venom Protein | Dengue virus (DENV) is a mosquito-borne flavivirus that causes serious human disease and mortality worldwide. There is no specific antiviral therapy or vaccine for DENV infection. Alterations in gene expression during DENV infection of the mosquito and the impact of these changes on virus infection are important events to investigate in hopes of creating new treatments and vaccines. We previously identified 203 genes that were ≥5-fold differentially upregulated during flavivirus infection of the mosquito. Here, we examined the impact of silencing 100 of the most highly upregulated gene targets on DENV infection in its mosquito vector. We identified 20 genes that reduced DENV infection by at least 60% when silenced. We focused on one gene, a putative cysteine rich venom protein (SeqID AAEL000379; CRVP379), whose silencing significantly reduced DENV infection in Aedes aegypti cells. Here, we examine the requirement for CRVP379 during DENV infection of the mosquito and investigate the mechanisms surrounding this phenomenon. We also show that blocking CRVP379 protein with either RNAi or specific antisera inhibits DENV infection in Aedes aegypti. This work identifies a novel mosquito gene target for controlling DENV infection in mosquitoes that may also be used to develop broad preventative and therapeutic measures for multiple flaviviruses.
| Dengue virus (DENV) is responsible for serious human disease worldwide and the World Health Organization estimates that over 2 billion people are at risk for disease. There are no vaccines or specific antiviral medications currently available for DENV infection. DENV is transmitted to humans by infected mosquitoes during feeding and probing. By examining the effects of virus infection on gene expression, and interactions between virus and vector, we may be able to find new targets for prevention and treatment. Here we look at a mosquito protein, CRVP379, whose gene expression was highly increased during DENV infection in mosquitoes. We show a requirement for CRVP379 during DENV infection in the mosquito and a correlation between the levels of CRVP379 and levels of infection. Our results indicate that the protein may be acting with a putative DENV receptor in the mosquito, prohibitin protein. These data also suggest that blocking CRVP379 function may be used to block DENV infection in the mosquito.
| Dengue virus (DENV) is the most important arbovirus in tropical areas leading to substantial pediatric morbidity and mortality worldwide [1–6]. DENV is transmitted to humans via the bite of an infected mosquito of the Aedes spp. Infection with DENV in humans can result in dengue fever (DF), dengue shock symptom (DSS) and dengue hemorrhagic fever (DHF), the latter two that can lead to severe disease and death. There are no specific antivirals or approved vaccines for use in DENV treatment or prevention [4,7–9]. Current dengue control methods rely mostly on activities to reduce vector population [10,11]. The increase in number of cases despite vector control indicates that these strategies are not as effective as expected, and that new tools need be developed to alleviate disease burden in endemic areas [12–14].
During the last five decades, much effort has been invested in the development of vaccines against DENV [15–23]. One of the obstacles in dengue vaccine development is the potential risk of severe disease mediated by the presence of sub-neutralizing antibodies against virus particles. These antibodies can predispose an individual to severe disease through a phenomenon called antibody-dependent enhancement (ADE), where the virus can infect cells via FcR in mononuclear cells [8,9,24–28]. Traditional vaccine approaches have included live attenuated viruses, recombinant subunits, virus-like particles and plasmid or viral vectors. There are live attenuated and chimeric DENV vaccines that have gone into clinical trials but none have proven to provide complete and lasting protection against all four DENV serotypes [21,29].
An attractive complement to traditional vaccines is to induce an immune response in the vertebrate host (infected or non-infected) that will block virus infection of the mosquito vectors. This would successfully interrupt transmission by inducing antibody responses against non-viral antigens [30]. These type of vaccines are called transmission-blocking vaccines (TBV), since they aim to interfere with pathogen development within the vector, thereby blocking transmission to human hosts [31]. The majority of TBVs designed to inhibit malaria infection are based on the mammalian immune response to pathogen proteins [31]. Another category of TBVs in development are based on arthropod molecules able to reduce pathogen infection in vector tissues [32]. For arboviruses, vector molecules able to interact directly with the pathogen (i.e. ligands/receptors) are highly suitable candidates for blocking transmission [33,34].
The main global transmission vector for DENV is Ae. aegypti. Extensive research has shown that DENV infection of Ae. aegypti induces many varied changes in gene expression [35–44]. Our hypothesis is that genes upregulated during DENV infection are required for virus survival or are related with defense against infection [37]. Consequently, a better understanding of the role of mosquito proteins regulated by DENV infection will reveal important insights into DENV biology and transmission as well as be helpful to the design of an effective TBV against DENV. For example, antibodies directed against mosquito molecules involved in steps of the pathogen life cycle are promising candidates for TBV. In addition, a recent study demonstrated that antibodies against a mosquito C-type lectin, mosGCTL1, effectively interrupts the infection of Ae. aegypti mosquitoes with DENV [34]. Other proteins which genes are unregulated upon infection also show promising capacity of interrupting infection since they are considered important for the microorganism survival. One of these proteins is the tick histamine release factor (tHRF) from Ixodes scapularis upregulated during Borrelia burgdorferi infection. Previous work showed that expression of tHRF is associated with the tick blood feeding and that the silencing its gene by RNA interference or antibodies not only effectively impairs tick feeding but subsecuently decreases B. burgdorferi burden [45].
Using comprehensive microarray analysis to identify key alterations in the Ae. aegypti transcriptome during flavivirus infection, we previously identified 203 mosquito genes that were up- and 202 genes that were down-regulated during infection [35]. Comparative analysis revealed that at least 15 of these genes had differential expression during infection with DENV, Yellow fever (YFV) and West Nile virus (WNV) [35]. One of these conserved, up-regulated genes was a putative cysteine-rich venom protein (AAEL000379), which we named CRVP379. Cysteine-rich venom proteins (CRVPs) are expressed in multiple mosquito tissues including the salivary glands [37,46,47]. Examples of mosquito CRVPs include an An. stephensi peptide annotated as salivary-secreted serine protease inhibitor [48] and a putative cysteine-rich protease inhibitor found in the sialotranscriptome of adult female Culex quinquefasciatus [49]. The specific role of these proteins in mosquitoes remains unknown [46,47].
Here, we describe a requirement for CRVP379 during DENV infection in mosquito cells and live mosquitoes, including a direct correlation between the amount of CRVP379 expressed and the level of DENV infection. We demonstrate the importance of an interaction between CRVP379 and prohibitin, a putative DENV receptor protein in mosquitoes. We also assess the tissue-specific expression of CRVP379 during DENV infection. Finally, we use both RNAi and specific antibody to demonstrate that blocking CRVP379 results in inhibition of DENV infection in Ae. aegypti. These results further our understanding of DENV pathogenesis in the mosquito vector and highlight a potential target protein for the creation of a DENV TBV to break the host-vector transmission cycle.
We previously used microarray analysis to identify a number of Ae. aegypti genes that were significantly up-regulated during infection with DENV and other selected flaviviruses [35]. These genes are likely required for flaviviral infection of Ae. aegypti or are part of the mosquito immune response to viral infection. To elucidate the role of these genes and their corresponding proteins, we reduced gene expression through RNAi knockdown and analyzed the effect on viral infection. We designed siRNA against 100 genes that were significantly up-regulated during DENV infection of Ae. aegypti (S1 Fig). The siRNA was used to silence these genes in an Ae. aegypti cell line, Aag2, and the resulting effects on DENV infection were examined. Cells were infected with DENV 72h after siRNA transfection and analyzed for infection using qRT-PCR 24h post-infection. We found that gene silencing both increased and decreased DENV infection, as expected (S2 Fig). The silencing of 9 genes caused cytotoxicity beyond our ability to accurately measure infection levels. Silencing approximately 55 individual genes decreased DENV infection of the cells to below 60% of control infection (Fig 1A), which is greater than 40% inhibition of infection. A number of these genes encode hypothetical proteins for which the function is not known. Several of our target genes do have putative known functions, including a cytochrome P450 (AAEL009762), histone H3 (AAEL003685) and a cysteine-rich venom protein (AAEL000379).
The cysteine-rich venom protein (CRVP), which we will call CRVP379, was a particularly interesting target. CRVP proteins are known to contain a trypsin inhibitor-like (TIL) domain, which indicates that the CRVP379 protein could be a serine protease inhibitor. Both serine proteases and their inhibitors are known to be involved in DENV infection and pathogenesis in the mosquito vector as well as in mammals [50–55]. The expression levels of 19 mosquito CRVP genes were examined during flavivirus infection with the previous microarray analysis. CRVP379 was the only CRVP significantly up-regulated in Ae. aegypti during infection with any of the 3 prototypic flavivirus infections, including DENV, West Nile virus (WNV) and Yellow Fever virus (YFV), at all timepoints tested (Table 1) [35]. In addition, the 19 CRVP genes that we looked at in our microarray actually have very low sequence identify at the amino acid level (S3 Fig). This indicates that they may not have identical or even similar functions, though they are grouped in a protein family due to the presence of multiple cysteines and a TIL domain. Therefore, we decided to assess the role of CRVP379 in DENV infection of the mosquito vector in greater detail.
Looking at gene expression over time in DENV-infected Aag2 cells, we found that CRVP379 expression increased more than 800-fold when compared to mock-infected cells (Fig 1B, P<0.01). Since the gene was upregulated in mosquito cells during DENV infection, we wanted to examine the phenotype during DENV infection with loss of function experiments. We used RNA interference (RNAi) with siRNA to reduce CRVP379 gene expression. To confirm gene knockdown, levels of CRVP379 were measured after siRNA transfection over time (Fig 1C), and the expression levels remained below 10% at 72h post-transfection. To determine how the reduction of CRVP379 altered DENV infection, we examined infection levels in Aag2 cells at various timepoints from 1 to 24h post-infection during siRNA knockdown. Silencing CRVP379 reduced DENV infection at all timepoints measured, as compared to infection in control cells transfected with siRNA against GFP (Fig 1D). Interestingly, we noticed that levels of CRVP379 were slightly elevated during DENV infection even during siRNA knockdown, when compared to uninfected cells. This can be seen by looking at the fold change in CRVP379 expression in GFP siRNA-transfected cells as compared to CRVP379 siRNA-transfected cells during DENV infection over time (Fig 1E). Together, these results indicated that the silencing effects of RNAi on CRVP379 are slightly overcome by the gene upregulation during DENV infection, but that infection levels still remained quite low when compared to cells with no CRVP379 silencing.
Since we found that the presence of CRVP379 is required for DENV infection, we wanted to test whether increasing CRVP379 levels would enhance infection levels. To do this, we cloned the CRVP379 coding region into the insect expression vector pAc5.1/V5-His (Life Technologies, CA), resulting in pAcCRVP379 vector. This expression vector was transfected into an Ae. aegypti mosquito cell line, Aag2, and the cells were subsequently infected with DENV. A vector expressing GFP was transfected as a control into a separate group of DENV-infected cells. Transfection levels as measured by GFP transfection are over 50%, which will give meaningful results when looking at gene expression and effects on DENV infection (S4 Fig). At 48h post-infection, levels of CRVP379 expression were measured by qRT-PCR. The expression levels of CRVP379 were over 1000 times higher in the cells that were transfected with the CRVP379 plasmid (Fig 2A). We next infected the transfected cells with DENV at 48h post-transfection. At 24 hpi, RNA was isolated and qRT-PCR done to measure DENV infection in the cells. We found that the overexpression of CRVP379 did not increase DENV infection levels in the cells (Fig 2B). This indicated that the endogenous levels of CRVP379 protein are sufficient and that the virus has likely evolved to require only those amounts for optimum infection.
Since we found that CRVP379 was required for DENV infection of mosquito cells, we decided to next look at the requirements for infection in live Ae. aegypti. To do this, we designed dsRNA against the CRVP379 coding region and inoculated mosquitoes via intra-thoracic injection. At days 2, 4 and 8 post-injection, we dissected out midgut tissues and measured levels of CRVP379 expression by qRT-PCR analysis. Although knockdown was not achieved in all tissues tested, a near-complete reduction of CRVP379 expression (over 95%) in midguts was seen 70% of the time by day 8 (Fig 3-all panels). We next examined the effects of silencing CRVP379 on DENV acquisition in the mosquito midgut. Mosquitoes were again injected with dsRNA against CRVP379 or a control dsRNA against GFP protein. At day 4 post-injection, mosquitoes were infected with DENV by blood feeding using a hemotek apparatus. At day 4 post-infection, midgut tissues were dissected out and analyzed for both CRVP379 expression and DENV infection by qRT-PCR. Since not all midgut tissues had reduction in CRVP379 expression, we analyzed each midgut individually in order to examine the effects on DENV infection in midguts that did have reduced CRVP379. The levels of CRVP379 in the selected midguts are shown in Fig 3A. In the midguts that had reduced CRVP379 expression, DENV infection was almost completely inhibited, as compared to infection in control mosquito midguts (Fig 3B, P<0.01). We also analyzed the data after adding back in the midguts that did not have sufficient gene knock down and looked at levels of DENV infection. Fig 3C shows the levels of CRVP379 in these midguts. Interestingly, in midgut tissues where CRVP379 was not knocked down, DENV infection was comparable to levels in the GFP dsRNA-injected mosquitoes (Fig 3D-squares). Plotting the data points as level of DENV versus level of CRVP379, there is a correlation between expression of CRVP379 in the mosquito midgut and level of DENV infection in that same midgut (Fig 3E, r = 0.6442, P<0.0001). This indicates that CRVP379 levels are directly related to levels of DENV infection in the mosquito midgut. Finally, we repeated the RNAi experiment and allowed the DENV infection to disseminate for 7 days. At day 7 post-infection, whole mosquitoes were homogenized and analyzed for both CRVP379 expression and DENV infection by qRT-PCR (Fig 3F). The mosquitoes that received the dsRNA against CRVP379 had a significant reduction in DENV infection levels as compared to the control mosquitoes. This indicates that the reduction of CRVP379 blocks DENV infection in the whole mosquito.
After establishing that DENV requires CRVP379 in both mosquito cells and live Ae. aegypti, we next wanted to investigate the mechanistic role that CRVP379 plays during infection. To do this, we used the tandem affinity purification (TAP) assay to identify putative mosquito proteins that bind CRVP379 during DENV infection. We cloned the coding region of CRVP379 into the NTAP vector (Stratagene, CA), which fuses the gene to purification tags, and transfected this plasmid into Aag2 mosquito cells. Cells were infected with DENV 24h post-transfection and lysed 24h post-infection. The cell lysate was processed and CRVP379 was purified using the expressed tags, along with interacting mosquito proteins. The resulting solution was sent for LC/MS-MS analysis to determine which proteins were pulled out of the mosquito cell lysate by CRVP379 during DENV infection. A separate set of cells was transfected with the NTAP vector expressing GFP as control. Table 2 lists the mosquito proteins that putatively bound CRVP379 and were not identified during control experiments. One of the proteins identified, prohibitin, was previously characterized as binding to DENV in Aedes A7 cells [56] and has also been suggested as a putative DENV receptor in mosquito cells [57]. Since we found that prohibitin binds CRVP379 during DENV infection using the TAP assay, this may indicate that the proteins act together to facilitate DENV entry into mosquito cells.
In establishing prohibitin as a putative receptor in mosquito cells, Kuadkitkan et al used siRNA against the mosquito prohibitin gene to inhibit protein production and saw a significant decrease in DENV infection in mosquito cells [57]. To confirm that prohibitin is required for DENV infection in mosquitoes, we designed dsRNA against the mosquito prohibitin gene and examined the impact of silencing prohibitin on DENV infection. Ae. aegypti were intra-thoracically inoculated with the dsRNA and at 4 days post micro-injection (dpmi), mosquitoes were infected with DENV via blood feeding. At 4 days post-infection (dpi), mosquito midguts (MG) were dissected and analyzed for infection by qRT-PCR analysis. Our results show that DENV infections levels were greatly inhibited by prohibitin silencing as compared to the control mosquitoes (Fig 4A), confirming a requirement for prohibitin in DENV mosquito infection. We next performed co-immunoprecipitation to confirm the protein interaction between CRVP379 and prohibitin. Aag2 cells were transfected with an expression vector coding for His-tagged CRVP379 protein and/or infected with DENV. Cells were then lysed and antibody against the His tag was used to pull down His-CRVP379 from the cell lysate. Western blot analysis identified prohibitin protein in the immunoprecipitate (Fig 4B), demonstrating that His-tagged CRVP379 bound prohibitin during DENV infection in the mosquito cells. We then used immunoflourescent imaging to visualize the putative prohibitin-CRVP379 protein interaction. Aag2 cells were transfected with the His-tagged CRVP379 expression construct and infected with DENV 48 hours post-transfection. At 24 hours post-infection, cells were fixed and stained with antibodies against the His tag and prohibitin protein. Fig 4C shows that the two proteins were highly colocalized during DENV infection. We next wondered whether prohibitin overexpression could rescue the mosquito cells that were resistant to DENV infection due to reduced CRVP379 expression. To investigate this, we transfected CRVP379 siRNA into Aag2 cells and then overexpressed mosquito prohitibin before infecting the cells with DENV. We found that the overexpression of prohibitin did not significantly increase DENV infection in cells with reduced CRVP379, though there was a slight enhancement (S5 Fig). This indicates that, though the proteins may act together to facilitate DENV infection in mosquito cells, prohibitin cannot replace the function of CRVP379 protein. Interestingly, the overexpression of prohibition in control Aag2 cells (with siRNA against GFP) did increase DENV infection (S5 Fig).
Since we found that inhibiting CRVP379 gene expression using RNA interference reduced DENV in Ae. aegypti, we next wanted to try and inhibit protein function with antibody and examine the effects on DENV infection. To do this, recombinant protein consisting of residues 21–128 of CRVP379 was expressed in E. coli along with a GST tag for purification. To generate polyclonal antiserum, rabbits were immunized with the recombinant CRVP379 (rCRVP379). We used the antisera in Western blot analysis to confirm that antibodies would bind the recombinant protein (Fig 5A). We next ensured that the polyclonal antisera contained antibodies that recognized endogenous CRVP379 protein in the mosquito. To test this, we used the antisera to stain Aag2 cells and found that there was a strong reaction between the CRVP379 antisera and protein in the cells (Fig 5B). We then used the antisera to probe mosquito midgut tissue for endogenous protein. Fig 5C demonstrates that the CRVP379 antisera, but not the pre-immune control sera, recognized protein in the dissected mosquito MG tissue. We also used the antisera to probe MG tissue with reduced CRVP379 expression due to RNAi (S6 Fig). To confirm that the antisera did recognize the CRVP379 protein in the mosquito, we ectopically expressed a His-tagged CRVP379 protein in Aag2 cells and used antibody against the His tag along with the CRVP379 antisera. Staining with the CRVP379 antisera colocalized with the anti-His staining, indicating that the antisera recognized the CRVP379 protein (Fig 5D). We then looked at tissue-specific expression of CRVP379 and found that levels are increased in both the salivary glands (SG) and midguts (MG) of DENV-infected mosquitoes, as compared to uninfected mosquito tissues, at all timepoints examined (Fig 5E). We also used ELISA analysis with the CRVP379 antisera using both Aag2 cell lysate, Ae. aegypti salivary gland tissue and Ae. aegypti saliva to confirm that the antisera bound endogenous CRVP379 protein (Fig 5F). Next, we tested the effects of the antisera on DENV infection in Aag2 cells. We used two experimental protocols; in one, the antisera was incubated with the cells for 2h at RT and then infected with DENV (pretreatment group), in the second, antisera and DENV were incubated for 1h at RT and then added to cells (simultaneous group). We used pre-immune sera for a control and also did the same experiment in the Huh-7 human liver cell line as an additional control, as antisera against a mosquito protein should not have an effect on DENV infection in mammalian cells. Infection was analyzed by qRT-PCR analysis at 24 hpi. We found that the antisera against CRVP379 inhibited DENV infection in Aag2 cells at dilutions up to 1/100 (Fig 6A). We also found that incubating the antisera with the cells before DENV infection resulted in a slightly larger reduction in infection levels (Fig 6A). We did not see any reduction in DENV infection in either experimental group using Huh-7 cells (Fig 6B). We then tested the effects of the antisera against CRVP379 on DENV infection in Ae. aegypti. Mosquitoes were fed a mixture of human blood, DENV and either CRVP379 antisera or preimmune sera at 1/10 and 1/100 dilutions. We also used control antisera against two unrelated, GST-tagged mosquito proteins MMP (AAEL003012) and PC (AAEL011045). On 3 dpi, mosquito MG were dissected and qRT-PCR was done to analyze DENV infection. The antisera against CRVP379 significantly reduced the DENV infection in the mosquitoes at both 1/10 and 1/100 dilution as compared to mosquitoes which fed on the preimmune sera (Fig 6C). The antisera against the control GST-tagged proteins did not reduce DENV acquisition in the mosquito MG tested (Fig 6D).
Flaviviruses are known to modify gene expression in their mosquito transmission vectors during infection. Our previous results showed that infection of Ae. aegypti with either DENV, WNV or YFV, modifies expression levels of at least 405 genes [35]. The study of mosquito genes modified during flavivirus infection may lead to the identification of key vector antiviral mechanisms as well as key factors for interruption of the viral life cycle. One of the genes that we identified as being significantly upregulated during DENV infection was the CRVP379 gene. Cysteine-rich venom protein (CRVPs) are members of a large family of cysteine-rich secretory proteins (CRISPs), predominantly found in mammalian males and reptile venom [58]. CRISPs contain characteristic cysteine rich C-terminal domains thought to act as ion channel regulators [58] and are also characterized by their role in proteolytic and defense mechanisms [59]. CRISPs and CRVPs have been described in a broad spectrum of insects and higher vertebrates [59–62].
Recently, Bonizzoni et al found several CRVP genes to have differentially regulated expression during DENV infection of Ae. aegypti, including CRVP379, which was shown to be upregulated on day 1 and day 14 in the mosquito MG during infection [42]. Here, we found that CRVP379 was the only CRVP significantly upregulated in mosquitoes after infection with DENV. Furthermore, knockdown of CRVP379 protein both in vivo and in vitro was able to reduce viral infection, and we found a significant positive association between the level of CRVP and DENV infection. This indicates that CRVP379 is specifically required for DENV infection of Ae. aegypti, at least in our current studies. The genetic variations between both DENV and mosquito strains likely contributes to differences among various studies, and consistent reporting of these discrepancies warrants further research into these variations along with additional transcriptomic analysis of the impact of DENV infection on mosquitoes.
Recent work has shown that another mosquito venom protein, a member of the antigen-5 family (Ag5), is upregulated in the salivary glands of Ae. aegypti during Chikungunya virus infection [63]. This protein is present in the saliva of several insects and is associated with platelet aggregation inhibition in blood sucking arthropods [46,64]. More investigation is needed to determine whether mosquito CRVP proteins and the Ag5 proteins are related or have similar functions during virus infection. Another group of CRVP proteins, the Cysteine-rich secretory proteins, Antigen 5, and Pathogenesis-related 1 proteins (CAP) superfamily, has been descried in the sialotranscriptome of Ae. Aegypti as well as in Culex [49,65,66]. Previous reports indicate that these genes could be preferentially expressed in the salivary glands of female mosquitos, perhaps suggesting an important role during blood feeding. Our study showed that up-regulation of CRVP379 occurs in both midgut and salivary glands of mosquitoes, and expression in salivary glands increased from day 1 to day 7 during DENV infection. This suggests that CRVP379 may be found in the saliva of DENV-infected mosquitoes. We have previously found that Ae. aegypti and certain Anopheles saliva have the ability to induce an antibody response in humans that can be correlated with the level of exposure to mosquito bites and disease status [67,68]. Several insect proteins from the CAP superfamily have also been reported to stimulate mammalian immune responses [69]. Given that CRVP379 has no homologous proteins in humans, we suspect that it will be a potent immunogen if used as a TBV.
Many CRVP proteins contain trypsin inhibitor-like (TIL) domains found in members of the serine protease inhibitor family [70] and functional sequence analysis confirmed that CRVP379 does contain a TIL domain from amino acids 23–79. Serine proteases and their inhibitors are known to have very specific interactions, and they play central roles in many cellular processes [71–73]. In addition, both serine proteases and their inhibitors have been shown to have an impact on DENV infectivity in both mammals and mosquitoes [50,55,74]. As such, we sought to identify the serine protease that CRVP379 potentially bound by using the TAP assay to investigate which mosquito proteins CRVP379 bound during DENV infection. We discovered that CRVP379 interacted with a number of mosquito genes during infection, including histones, ubiquitin and prohibitin.
Previous research has suggested that prohibitin may be a receptor for DENV in mosquitoes, as expression levels of this protein correlate with the susceptibility of DENV infection in both Ae. aegypti and Ae. albopictus cell lines [57]. Prohibitin is a protein pervasive expressed and highly conserved in eukaryotic cells [75] and has been previously described as an inhibitor of cell proliferation [76]. Prohibitin is found in several cellular compartments including nucleus, mitochondria and cytoplasm [57]. Furthermore, a recent report shows that Cry4B, one of the major insecticidal toxins produced by Bacillus thuringiensis israelensis, co-precipitates and co-localizes with prohibitin in Ae. aegypti larva midgut, and this interaction is able to reduce DENV infection under physiological conditions [77]. These findings suggest that the inhibition of proteins that interact with viral receptors may potentially block mosquito infection. Additionally, several proteins have been reported to bind prohibitin conferring resistance to bacteria phagocytosis [78] as well as cell surface expressed binding protein [79]. In DENV infection, it has been suggested that prohibitin interacts directly at the cell surface with the viral envelope protein. Our current work shows that CRVP379 is able to interact with several other mosquito proteins including prohibitin, suggesting that CRVP379 may be involved in virus cell entry along with other putative roles.
In spite of decades of effort, there are currently no approved DENV vaccines available. A recent study with a live-attentuated tetravalent DENV vaccine developed by Sanofi Pasteur has demonstrated partial protection against DENV [22] and shows promising results, though efforts continue to develop vaccines that will confer full protection. A vector-based vaccine would nicely complement these efforts at traditional vaccine development and could contribute as an additional strategy to combat the increasing global spread of DENV. The development of vaccines targeting either a pathogen or vector protein to prevent transmission to human hosts is considered essential to the eradication of many emerging tropical diseases, including malaria. Transmission-blocking vaccines (TBVs) are currently being developed and have been shown to be successful at preventing malaria infection of Anopheles mosquitoes (12–15). One of these, a TBV developed against the Plasmodium protein Pfs25, was able to prevent the transmission of malaria from infected mice to naïve mosquitoes (12,13). Another group found that vaccinating mice with the mosquito protein serpin-2 prevented the transmission of Plasmodium berghei to a naïve group of mosquitoes (16). In addition, an arthropod-specific TBV based on the outer surface protein A (OspA) of Borrelia burgdorferi, the causative agent of Lyme disease, has been shown to protect mice from spirochete infection (17). Proteins of the sand fly have also been used successfully as TBVs to prevent the transmission of Leishmania (18).
Dengue virus is transmitted to humans in saliva during mosquito probing and blood feeding. During this process, mosquitoes take in host factors contained in the blood including host antibodies, complement proteins and immune cells that remain active for several hours post-feeding [80,81]. Previous studies have shown that the presence of antibodies against mosquito proteins are able to disrupt mosquito infection and transmission of pathogens [82,83]. This type of TBV has several advantages over a TBV targeting pathogen antigens, including the ability to target a conserved molecule among vector genera and that the targeted genes may also affect mosquito survival in nature. Recently, Cheng et al demonstrated that antibodies against mosquito C-type lectin proteins were able to block DENV infection in Aedes mosquitoes [34]. Their data strongly suggested that a TBV targeting DENV acquisition in mosquitoes is possible and may be close at hand. Our results inhibiting DENV in Aedes using antisera targeting CRVP379 protein showed a similar reduction in viral infection and suggests that CRVP379 may also be a viable target for the development of a TBV. Importantly, CRVP379 has no homolog in humans and extremely low sequence identity to any protein in the human proteome. This means there should not be any off-target immune reaction targeting self if CRVP379 were to be used to stimulate antibody production in humans.
In the present study, we identified a mosquito protein that was required for DENV infection in mosquito cells, CRVP379. We showed a correlation between this protein and DENV infection levels in vitro and in vivo. The interaction between CRVP379 and prohitibin, a putative viral receptor in mosquitoes, may be the mechanism behind the requirement for infection. In addition, antiserum against CRVP379 protein was able to significantly inhibit DENV acquisition in Ae. aegypti. Given that the CRVP379 protein was also upregulated in the mosquito during infection with WNV and YFV, it stands to reason that a TBV developed against this protein may act to block acquisition and transmission of multiple, globally important flaviviruses. We have also been able to detect an antibody response against CRVP379 in human serum samples, indicating that the protein is immunogenic. We are currently designing studies to correlate levels of these antibodies with putative protection against dengue virus infection and disease severity upon infection.
The Aag2 Ae. aegypti cell line (ATCC, VA) was used for transfection and infection studies. The cells were grown at 30°C and 5% CO2 in DMEM supplemented with 10% heat-inactivated fetal bovine serum (Gemini, CA), 1% penicillin-streptomycin and 1% tryptose phosphate broth (Sigma, MO). Dengue virus stock was grown in C6/36 Ae. albopictus cell line using the same media. The dengue strain used was DENV-2 New Guinea C. Cells were infected at an m.o.i. of 1.0, virus was allowed to propagate for 6–8 days, supernatant was removed, spun down and virus stock was stored at -80°C until use.
S1 Fig provides a complete list of the siRNA molecules used in our in vitro knock-down studies. Dharmafect4 reagent (Dharmacon,) was used to transfect the siRNA into the Aag2 cells according to manufacturer’s instrucitons. For gene knockdown in live mosquitoes, dsRNA was produced from 500bp coding regions of either Ae. aegypti CRVP379, Ae. aegypti prohibitin or GFP. Briefly, PCR was used to produce a DNA template with T7 overhangs that was then used with the Ambion Megascript kit to produce the dsRNA molecules. The dsRNA was transfected into mosquitoes as described.
The Rockefeller strain of Ae. aegypti were infected by blood-feeding, using 400 μL of DENV-infected C6/36 cell supernatant added to 1 mL serum-inactivated human donor blood (The Blood Center, New Orleans, LA). Mosquitoes were fed for 20 minutes at room temperature using a hemotek feeder and maintained in groups of 10 at 30°C, 80% humidity. Mosquitoes were supplied sucrose water as a source of dietary sugar. At the conclusion of experiments, mosquitoes were briefly washed in 70% ethanol and then rinsed in sterile PBS. Organs were dissected in sterile PBS and transferred to Eppendorf tubes separately. Mosquito organs were stored in PBS with protease inhibitors for protein assays and homogenized in RLT buffer (Qiagen, CA) for gene expression assays.
RNA was isolated from infected Ae. aegypti mosquitoes on Days 1, 2, 7 and purified using RNeasy kit (Qiagen, CA) according to manufacturer’s instructions. The quantitative RT-PCR (qRT-PCR) analysis was done using the QuantiFast kit according to manufacturer’s instructions (Qiagen, CA). Oligos for the qRT-PCR reactions were:
DENV envelope: F: 5’-CATTCCAAGTGAGAATCTCTTTGTCA-3’
R: 5’-CAGATCTCTGATGAATAACCAACG-3’; Ae. aegypti Actin: F: 5’-GAACACCCAGTCCTGCTGACA-3’, R: 5’-TGCGTCATCTTCTCACGGTTAG-3’
DNA plasmids were injected according to our published whole-body transfection method [84]. Briefly, Cellfectin II (Invitrogen, CA) was mixed with S2 Schneider’s medium at a 1:1 ratio and then keep at RT for 10 min. Plasmid DNA was combined with this mixture and incubated at RT for 30 minutes before thoracic microinjection into Aedes aegypti. Mosquitoes were injected with 500ng plasmid/300 nL solution. The dsRNA was injected as previously described and as indicated in the figure legends.
Aag2 Ae. aegypti cells were infected with DENV at an MOI of 0.1. At 24 hours post-infection, infected cells and control cells were fixed in 4% paraformaldehyde for 20 min at RT, washed with PBS(-) and then stained for infection using antibodies against CRVP379 (L2 Diagnostics, CT), DENV envelope gene (Millipore, MA) and/or prohibitin (Abcam, MA). The antibodies were diluted in 1% BSA at 1/250 and cells were incubated for 20 minutes at RT. Any secondary antibodies used were standard (anti-mouse or anti-rabbit TRITC and FITC, DAPI and phalloidin), and were diluted according to manufacturer’s instructions. Infection was visualized using fluorescent microscopy, equipment and specifics can be found in figure legends.
All plasmids were prepared using Qiagen miniprep kits (Valencia, CA) after standard transformation into DH5α competent bacterial cells. The tagged virus protein nTAP expression plasmids were made by cloning the coding regions for each viral protein into the N-terminal TAP plasmid (Stratagene, CA).
Solutions were run on a 4–12% SDS-PAGE gel for 1.5 h at 15 milliamps per gel (unless figure legend indicates otherwise). The proteins were then transferred to PVDF membrane. The membrane was blocked with 5% milk in 1% TBST for 1h at RT and then incubated with the appropriate primary antibody overnight at 4°C. The membrane was washed and then incubated with the appropriate horseradish peroxidase secondary antibody for 1h at RT. The protein blots were incubated with ECL substrates (Amersham, NJ) for 5 min at RT and then detected on Kodak film. Antisera production is described below and prohibitin antibody was purchased from Santa Cruz.
The expression plasmids were made from pAc5.1/V5-His A vector (Invitrogen, CA) and cloning was done using PCR along with gene-specific primers as previously described [85]. We used the Qiagen mini-prep kit to isolate DNA from bacterial cultures after transforming DH5-alpha cells. Plasmids were transfected into cells using Effectene (Qiagen, CA) according to manufacturer’s instructions. Briefly, for a 10 cm2 plate, 10 μg of DNA was mixed with 500 μL buffer EC and 32 μL enhancer was added. This was allowed to incubate for 5 min on the benchtop. Then, 30 μL Effectene reagent was added and the solution vortexed briefly. After 10 min incubation, the solution was added to the cells.
The TAP assay was used to identify mosquito cell proteins that interacted with CRVP379 protein. All steps were done at 4°C to maintain the protein interactions. The cell or tissue lysates were applied to streptavidin resin, incubated at 4°C for 2 h, washed, and bound proteins eluted off. A second purification step was done with calmodulin resin and the proteins were boiled off into PBS(-). The eluted proteins were analyzed at the Yale University W.M. Keck Foundation core facility. The eluate was subjected to trypsin digestion followed by LC/MS-MS (liquid chromatography and mass spectometry) for peptide sequencing and identification using the recently completed Aedes aegypti mosquito genome [47]. Putative mosquito proteins were identified via amino acid sequence identity to both known mosquito proteins and their mammalian counterparts using the BLAST software on the NCBI website. Mosquito proteins found to bind the tags alone as well as proteins found to bind tagged green fluorescent protein were eliminated as putative interacting partners.
A recombinant protein consisting of residues 21–128 of CRVP379 was synthetically cloned into the pGEX-6p-1 expression vector (GE Life Sciences) into the BamH1 and Xho1 sites. The recombinant plasmid was transformed into Rosetta DE3 pLys2 E. coli cells. GST-CRVP379 protein was purifed from the bacteria cells as inclusion bodies by passing the E coli cells through a cell disruptor at 20 psi of pressure. Inclusion bodies were used to immunize rabbits to generate polycolonal antisera (CoCalico Biologicals, Reamstown, PA). Prior to immunization, rabbits were bled to obtain pre-immune control sera.
Serum samples were coated onto a 96-well ELISA plate (Thermo Fisher Sci, MA) and incubated overnight at 4°C. The plate was blocked with 1% BSA in PBS(-) and incubated with recombinant CRVP for an hour at RT. The proteins were washed off, antibodies were added for 30 min at RT, washed off and secondary-HRP was added for 30 min at RT, washed off and TMB substrate was added for 20 min at RT. Stop solution was added and the O.D. of the wells was read at 450 nm.
|
10.1371/journal.pntd.0005653 | Tracking the return of Aedes aegypti to Brazil, the major vector of the dengue, chikungunya and Zika viruses | Aedes aegypti, commonly known as “the yellow fever mosquito”, is of great medical concern today primarily as the major vector of dengue, chikungunya and Zika viruses, although yellow fever remains a serious health concern in some regions. The history of Ae. aegypti in Brazil is of particular interest because the country was subjected to a well-documented eradication program during 1940s-1950s. After cessation of the campaign, the mosquito quickly re-established in the early 1970s with several dengue outbreaks reported during the last 30 years. Brazil can be considered the country suffering the most from the yellow fever mosquito, given the high number of dengue, chikungunya and Zika cases reported in the country, after having once been declared “free of Ae. aegypti”.
We used 12 microsatellite markers to infer the genetic structure of Brazilian Ae. aegypti populations, genetic variability, genetic affinities with neighboring geographic areas, and the timing of their arrival and spread. This enabled us to reconstruct their recent history and evaluate whether the reappearance in Brazil was the result of re-invasion from neighboring non-eradicated areas or re-emergence from local refugia surviving the eradication program. Our results indicate a genetic break separating the northern and southern Brazilian Ae. aegypti populations, with further genetic differentiation within each cluster, especially in southern Brazil.
Based on our results, re-invasions from non-eradicated regions are the most likely scenario for the reappearance of Ae. aegypti in Brazil. While populations in the northern cluster are likely to have descended from Venezuela populations as early as the 1970s, southern populations seem to have derived more recently from northern Brazilian areas. Possible entry points are also revealed within both southern and northern clusters that could inform strategies to control and monitor this important arbovirus vector.
| Aedes aegypti (“yellow fever mosquito”) is of great medical concern because it is the primary vector of important viruses causing dengue fever, chikungunya and Zika, as well as increasingly transmitting yellow fever once again. Due to the Zika outbreak, which started in Brazil in 2015 and rapidly spread with million cases reported in Central/South America and the Caribbean, this mosquito has attained special notoriety. Brazil is by far the most affected country not only by Zika, but also by dengue and chikungunya. Interestingly, in 1950s Brazil had been declared “free of Ae. aegypti” after a well-documented eradication program. However, during the last 45 years Ae. aegypti reappeared in Brazil causing several dengue outbreaks, with millions of reported cases. Here, we used genetic data to study the reappearance of Ae. aegypti in Brazil after the putative eradication event. Our results support re-invasion; mosquitoes from the non-eradicated areas in Venezuela invaded North Brazil, later expanding their distribution southwards. We also identify specific locations in Brazil, as possible entry points. This knowledge can inform strategies to control the spread of the vector and of the diseases it transmits.
| Aedes aegypti is one of the most successful worldwide invaders, spreading from its native Africa to most tropical and subtropical regions of the world [1,2] and it is the primary vector of dengue fever, chikungunya and Zika viruses. There is no vaccine for chikungunya and Zika and it is still unclear whether there is an effective vaccine for dengue, so control of Ae. aegypti remains the major target for disease control. The species’ highly anthropophilic behavior, its ability to lay desiccation-resistant eggs, its high passive dispersal, and its tendency to develop pesticide-resistance [3,4], have made the control of this vector extremely difficult. Since Ae. aegypti’s arrival in the Americas, shortly after Europeans in the 16th Century, many dengue outbreaks have been recorded, including several in Brazil and the Caribbean [5]. Brazil together with other South American and Caribbean countries were subjected to an intense eradication program during 1940s-1950s and in the late 1950s [6] were declared Ae. aegypti free. However, after the relaxation of the eradication campaigns, Ae. aegypti reappeared in some of these regions. This could have happened either through re-invasion from non-eradicated regions, such as Venezuela, USA, and some Caribbean islands [6], or recrudescence from relict populations, or a combination of both.
The study of the genetic structure and the identification of likely source populations or isolated Brazilian populations that may be efficiently targeted for control can contribute to develop better strategies to control the spread of this vector and of the diseases that it transmits. It can also provide insights on the roles of environmental and human induced factors that can contribute to vector or disease spread.
A few studies have provided insights on levels of genetic diversity in Brazilian populations. Microsatellites and mitochondrial DNA (mtDNA) revealed moderate [7] to high [8] levels of genetic differentiation between populations, limited gene flow and dispersal capability, especially in urban areas [9]. At the country level, mtDNA [10], insecticide resistant alleles data [11] and microsatellites [7] have shown the existence of two genetically distinct groups in Brazil. According to Monteiro et al. [7] Ae. aegypti from the east, southern and central Brazil are genetically close to populations from the island of Dominica in the Caribbean, whereas the populations from northern Brazil have genetic affinities primarily with northern South American countries (Fig 1). This suggest that the reappearance of Ae. aegypti in Brazil was likely due to two independent re-invasions from non-eradicated neighboring areas, possibly Dominica in the Caribbean and northern South America.
In this study we revisit the analysis of Ae. aegypti genetic diversity in Brazil by screening for genetic diversity at 12 microsatellite loci in 48 Brazilian and non-Brazilian populations, a much denser spatial sampling than in previous studies. We use these data to study the patterns of genetic differentiation among populations of Ae. aegypti in Brazil at a fine geographic scale, to describe their genetic affinities with populations from neighboring regions from both South and North/Central America and the Caribbean, and to reconstruct the history of the re-appearance of Ae. aegypti in Brazil.
Aedes aegypti samples were collected from 46 locations (2 locations were sampled twice, so 48 samples in total) in America and Caribbean (Fig 1A and S1 Table). Samples were either adults from the field or eggs that were collected in multiple ovitraps per location to minimize sampling siblings. Brazilian samples were all collected in ovitraps, following the Brazilian National Network for Monitoring Insecticide Resistance in Ae. aegypti (Morenaa Network) recommended procedures [12]. Samples from generation F0 to F2 (39 out of 48 were F0; S1 Table) were preserved in 70–100% ethanol or dry at -80°C until DNA extraction. Thirty-one samples from Brazil and 17 from outside Brazil (1,875 individual Ae. aegypti mosquitoes in total) were used in the present study (for details see S1 Table). Twelve out of the 31 Brazilian populations and 8 non Brazilian had been studied previously [7] and their data are included in this study (Fig 1).
Total DNA was extracted using the DNeasy Blood and Tissue kit (Qiagen) according to the manufacturer instructions, with an additional RNAse A (Qiagen) step. DNA samples were stored at -20°C until further analysis. Individual mosquitoes were genotyped as described in Brown et al. [13] for twelve previously published [2,13,14] microsatellite loci [A1, B2, B3, A9 (tri-nucleotide repeats), and AC2, CT2, AG2, AC4, AC1, AC5, AG1, and AG4 (di-nucleotide repeats)]. Microsatellite alleles were scored using GENEMAPPER version 4.0 (Applied Biosystems). Raw allele frequencies are available at VectorBase.org, Project ID VBP0000176 and are also available as Supporting Information (S1 Appendix).
All microsatellite loci were analyzed for within-population deviations from Hardy-Weinberg equilibrium (HWE) using the exact HWE test in GENEPOP v.4.5.1 [15,16]. The same software was used for the estimations of the Linkage disequilibrium (LD) among all pairs of loci. Both HWE and LD tests were run with 10,000 dememorizations, 1,000 batches and 10,000 iterations per batch. Bonferroni corrections were applied to the resulting matrices of both HWE and LD. Allele numbers, allelic frequencies, and average observed (Ho) and expected (He) heterozygosities were estimated using GenAlEx [17]. In order to test if the mean observed heterozygosity is significantly lower than mean expected heterozygosity for each one of the studied populations, we performed paired t tests (the homogeneity of variances tested with Barlett's test). GeneAlex was also used for the Analysis of Molecular Variance (AMOVA). Allelic richness (AR) and private allelic richness (Np) were calculated in HPRARE [18,19]. One-way ANOVA was used to test for significant differences on Ho and allelic richness levels between different groups of populations (see results). The probability of null allele occurrence was tested for each population and each locus separately using MICROCHECKER v2.2.3 [20]. The pairwise genetic distances (Fst) and their significance were calculated in Arlequin v3.5.1.2 [21], using 1,000 permutations. To estimate the extent of bias in the Fst estimations because of the presence of null alleles in our dataset, the FreeNA software [22] was used with 1,000 bootstraps replications.
Geographic population structure was evaluated using the Bayesian clustering method implemented in the software STRUCTURE v.2.3 [23], which identifies genetic clusters and assigns individuals to these clusters with no a priori information of sampling location. We conducted different runs using different datasets (see results).
For each dataset the most likely number of clusters (K), was determined by conducting 10 independent runs for each K = 1 to the maximum number of populations included in the analysis. Each run assumed an admixture model and independent allele frequencies (lambda set to one), using a burn-in value of 100,000 iterations followed by 500,000 repetitions. The optimal number of K clusters was determined using the Delta K method of Evanno et al. [24], using the online version of STRUCTURE HARVESTER v.0.6.94 [25]. The program CLUMPP v1.1.2 [26] was used to summarize the results from the 10 independent STRUCTURE runs and provide the Q matrices based on which a population or an individual can be assigned to a specific cluster. The results were plotted using DISTRUCT v.1.1 [27].
To complement the Bayesian analysis, we also performed a Discriminant Analysis of Principal Components (DAPC), using the “find.clusters” option of ADEGENET package [28] in R v.3.1.3 (R Core Team 2015) so in this case, individuals assigned to DAPC-defined clusters.
Mantel tests to assess the significance of correlation between geographic (Euclidean distance as estimated in R based on the localities’ coordinates; S1 Table) and genetic (Fst) distance matrices were performed on different groups, based on the results from the genetic structure analyses (see results). The Mantel tests were conducted using 9,999 permutations and the “ade4” package in R v.3.1.3 (R Core Team 2015). The correlation between geographic and genetic distance was plotted and the correlation coefficient (r) as well as R-squared were estimated using the web version of IBD [29].
To test the degree of assignment of any individual mosquito to a specific population of origin or an inferred structure-cluster (clusters pre-identified based on the Q matrices retained by CLUMPP [26]), we used the program GeneClass2 v2.0 [30]. The self-assignment tests were performed using the original sampling locality or the clusters identified by STRUCTURE (see results). The same software was used in order to identify the first generation migrants between the major geographic regions of our dataset. To distinguish true from statistical migrants (type I error), we selected the Rannala and Mountain criterion [31]. We used the Monte Carlo resampling algorithm of Paetkau et al. [32], (n = 1,000) to determine the critical value of the L_home/L_max likelihood ratio. Individuals were considered immigrants at two levels of significance, when the probability of being assigned to the reference population was lower than the commonly used 0.05 and lower than 0.01. To complement our results we also used the individual assignment test as implemented in ONCOR software [33].
Evidence of population bottleneck events was tested using two methods as implemented in the widely used program BOTTLENECK [34]. In the first method, the distribution of the heterozygosity expected from the observed number of alleles is calculated for each population and locus under the assumption of mutation-drift equilibrium. The program provides results under three possible mutation models; the Infinite Allele Model (IAM), the Stepwise Mutation Model (SMM) and the two-phase mutation model (TPM). The SMM is considered better suited for the microsatellite mutation process [35], although in practice, the mutation model that best describes microsatellite evolution varies among loci and falls on a range bordered by the IAM and SMM [34], which are considered the 2 extreme models of mutation [36]. As the two-phase model (TPM) [37] has been considered to better describe microsatellite data here we used both the TPM and the SMM models. Simulation of heterozygosity at mutation-drift equilibrium distributions for the TPM model assumed 70% single-step mutations and 30% of multiple-step mutations, as recommended for microsatellite loci [37]. Significance was assessed using Wilcoxon’s signed rank test, as recommended in the manual for less than 20 markers. The second method we implemented is based on the allele frequency distribution. The program tests whether the allele frequency distribution is approximately L-shaped, which is expected under mutation-drift equilibrium. A shift in the allele frequency distribution is indicative of a recent bottleneck [38]. The bottleneck analysis can only detect extreme reductions in population sizes that have occurred during the last 0.2–4.0 Ne generations [39]. Based on Ne estimations for Ae. aegypti populations [40–42] and assuming 10 generations per year [2,43,44] the method can detect bottleneck events up to ~50 years ago, but it strongly depends on the study population.
Bayesian computation methods (ABC) [45] as implemented by DIYABC v.2.0.4 [46] were used to infer the population history of Ae. aegypti in Brazil. For our analysis we used five regions (Venezuela, Dominica, USA, northern, and southern Brazil). Different combinations of these five regions lead to 120 scenarios that could be tested. However, testing all the possible scenarios makes the estimation computationally challenging. Thus, usually [47–49] a small number of scenarios based on historic or other source of data is tested. We chose our scenarios based on (i) the observed population structure, (ii) the results of Monteiro et al. [7], (iii) the historic/epidemiological data (e.g. the first dengue outbreak after eradication recorded in N. Brazil [6]) and (iv) choosing to focus on the origin of the Brazilian populations rather than on the relationships among all five regions. Thus, five possible scenarios were tested (Fig 2). Given that the Dominica population was represented by 48 individuals while the rest of the groups included more than 100 individuals, we randomly subsampled 48 individuals from each one of the remaining groups. Confidence in model choice (how the model fits the observed data) and the best scenario were evaluated as described by the authors [46]. Divergence times were estimated in generations (assuming 10 generations per year; see above) and were assumed to range between 10 and 500 generations (given that after the eradication, Ae. aegypti was recorded again in Brazil in the late 1970’s). A mutation rate ranging from 9x10-6 to 1x10-5 was used based on rates reported in the literature for other Diptera species [50,51]. Details on the effective population size and split time between regions used as priors for the ABC analysis are provided in S2 Table.
Sixty Fis values out of 558 (10.7%) population-by-locus comparisons deviated significantly from Hardy-Weinberg equilibrium (HWE) after sequential Bonferroni correction (S3 Table), a level common for microsatellites and most often due to rare null alleles [2,13]. Microchecker indicated that locus A9 has high probability of having null alleles (frequency ranging between 0.08 and 0.3) in 26 out of 48 populations tested. A result that is in agreement with previous findings [13]. Other loci with high probability of null alleles were AC5 and AG1 (with frequency 0.1–0.2), AC4 and AG2 (with frequency 0.06–0.3) in 8, 2, 9, and 7 populations respectively. The remaining loci also exhibited signs of having null alleles but their estimated frequency were lower than 0.16 (S4 Table). A total of 81 out of 2,974 (2.7%) locus-by-locus tests for linkage disequilibrium (LD) (S5 Table) remained significant (p<0.05) after Bonferroni correction for multiple tests, with no loci pair consistently correlated across many populations. This level of LD is low and unlikely to significantly influence methods that assume loci independence.
Population genetic statistics for each population are provided in Table 1. Allelic richness (AR) in Brazilian populations was similar to the corresponding values for non-Brazilian populations (Table 1). Private allelic richness (Np) was low (Np< = 0.08) in all cases with the exception of three Brazilian and two non-Brazilian populations (Table 1). Fst values showed moderate levels of population differentiation (considering all 48 populations; mean 0.17; range: 0.01–0.46) with the highest Fst values recorded in Coatzacoalcos (0.21–0.46) and Pance de Cali (0.14–0.46). The genetic differentiation between the Brazilian populations was slightly lower (mean 0.13; range: 0.01–0.29) and it is presented in S6 Table. In all pairwise Fst estimations, Arlequin indicated significant (p<0.05) differentiation.
We first performed a preliminary STRUCTURE analysis (Fig 1B) on the complete data set of all 48 populations. This analysis confirmed the presence of the two genetic clusters (Fig 1) and the approximate border between them (Fig 1A) which runs from east central Brazil (northern to Parnaiba (21)) through the region between Tocantins (30) and Maraba (15) and ends in North-West Brazil. The southern cluster included 23 Brazilian and one Dominican populations (here called “Cluster 1”). The northern cluster (“Cluster 2”) included 25 populations from the Caribbean, North/Central America, and the northern parts of South America, including North Brazilian populations. Three populations (Itacoatiara, Tocantins and Santos) located in central Brazil (forming a path from north-central to south-central) appeared admixed ancestry (Fig 1) since their Q values to their cluster were low (0.56–0.62) and many of the individuals were not assigned to the respective cluster (Santos; 33% of the samples had Q values <0.40 for Cluster 1, Itacoatiara; 40% of the samples had Q values <0.40 for Cluster 2, Tocantins; >40% of the samples had Q values <0.23 for Cluster 1).
We then carried out analyses on each of the two clusters separately. Fig 3A shows the result of the Structure analysis on Cluster 1. The Evanno et al. [24] method identified K = 2, as the most likely number of sub-clusters (sub-clusters 1A and 1B). The two genetic sub-clusters seemed to be well differentiated with little overlap between their DAPC defined clouds (S1 Fig). Sub-cluster 1A includes eastern and south-central populations and sub-cluster 1B includes the western Brazil populations and Dominica (Fig 3B). A STRUCTURE analysis on the two sub-clusters separately revealed further genetic structure (Fig 3A), with sub-cluster 1A consisting of three smaller genetic groups (1A1, 1A2, 1A3), and sub-cluster 1B consisting of two groups (1B1 and 1B2).
For Cluster 2 the Evanno et al. [24] method also identified K = 2 as the most likely number of clusters (Fig 4A): sub-cluster 2A to the South and sub-cluster 2B to the North (Fig 4B). These two groups were well differentiated and only slightly overlapped (S1 Fig) in their DAPC clouds. Given that the genetic substructure of North/Central America-Caribbean cluster has been discussed elsewhere [2], we focused our further analyses on sub-cluster 2A for which the Evanno et al. [24] supported the presence of three sub-groups (2A1, 2A2 and 2A3).
The assignment of the samples from each population to each one of the three DAPC-groups (S2 Fig) is presented in detail in S7 Table. Considering Cluster 1, the majority of the samples from sub-clusters 1A2 and 1A3 (south-eastern) were assigned to DAPC group1, from sub-cluster 1B1 (south-western) to group2 and from 1B2 (western) to group3 (S7 Table). The DAPC-groups for Cluster 2 (S2 Fig) confirmed the separation of North/Central America-Caribbean (DAPC-group2) from South American populations (DAPC-groups 1 and 3) (S2 Fig and S7 Table) with the exception of Coatzacoalcos (Mexico) population, which is included in DAPC-group1 instead of DAPC-group2 (S7 Table).
Mantel test on Cluster 1 dataset (S3 Fig) supported the presence of Isolation by Distance (IBD) between all pairs of populations in Cluster 1 (r = 0.55; p = 0.001) as well as for the populations within the sub-cluster 1B (r = 0.69; p = 0.008) but not within 1A (r = 0.12; p = 0.064). For Cluster 2, although the Mantel test supported (p<0.05) the presence of Isolation by Distance (IBD) for both Cluster 2 and the sub-cluster 2A (S3I Fig), the linear relationship between the genetic and the geographic distance was weak (r = 0.24 and r = 0.28 respectively).
Levels of genetic differentiation (Fst values) between all pairs of populations within Cluster 1 and Cluster 2 are presented in S6 Table and between broader regions (Caribbean, USA, Colombia, Venezuela, Mexico, Costa Rica and Brazilian Clusters 1 and 2) in S8 Table. Brazilian Cluster 1 and Cluster 2 populations showed low to moderate levels of genetic differentiation with Fst values, within each cluster, up to 0.21 and 0.22 for Cluster 1 and Cluster 2, respectively.
Pooling the samples within each genetic cluster or group of samples within a country (Caribbean, USA, Colombia, Venezuela, Mexico, Costa Rica and Brazilian Clusters 1 and 2), revealed high private allele (Np) richness, especially in Cluster 1 (S8 Table).
When we consider STRUCTURE Clusters 1 and 2 and the populations from the non-eradicated areas (Caribbean, Venezuela and southern part of North America) as three separate groups, ANOVA results (S9 Table) showed that the eradicated areas (Clusters 1 and 2 of Brazil) have significantly lower mean Ho levels (ANOVA; F2,38 = 12.5; p<0.001). Nevertheless, no significant difference was found in allelic richness levels (S9 Table).
S10 Table shows the results of the AMOVA analyses. Most of the genetic variation occurred within populations (81% and 71% for populations within Clusters 1 and 2, respectively), while variation among populations was less than 20% for Cluster 1 and less than 30% for Cluster 2. Considering the structure-defined sub-clusters instead of the original populations, the percentage of among groups variation decreased even more, depending on the number of genetic clusters considered (S10 Table).
S7 Table shows the results of the GeneClass2 assignments. Only 47.8% of the individuals in Cluster 1 were correctly assigned back to their population of origin when using the original populations as a reference. This percentage increased when the STRUCTURE inferred clusters, rather than the populations, were used as reference (76.9% for K = 2; 62.3% for K = 5). A larger percentage of individuals in Cluster 2 were correctly assigned back to their population of origin (55.4%) or to their cluster (88.6% for K = 2; 86.1% for K = 4).
S11 Table shows the number of individuals within each group of populations that could be first generation migrants according to the Geneclass2 results, at two levels of significance (p<0.01 and <0.05). Focusing on the two Brazilian clusters, the majority of the individuals that could be first generation migrants within Cluster 1 originated from Cluster 2. Moreover, most of them were from regions close to the border between the two Brazilian Clusters (Tocantins, Parnaiba) and Santos, which belongs to the admixed zone (Fig 1B). The majority of the individuals that could be first generation migrants within Cluster 2, and originated from Venezuela (S11 Table), were found in a single Brazilian population (Pacaraima; map code 20; Fig 1A and Fig 4B). The ONCOR results presented on the lower part of S11 Table are in agreement with the Geneclass2 results.
S12 Table shows the results of the bottleneck analyses. Using the TPM model the BOTTLENECK results were significant (p<0.05) for 21 populations (8 from Brazil). Using the SMM model only two non-Brazilian populations showed signs of significant demographic changes. Mode shift in allele frequency distributions was present in four non Brazilian populations (S12 Table).
Approximate Bayesian Computation (ABC) analysis provided strong support (PP = 0.845) for scenario 1 (Fig 2). In this scenario, Ae. aegypti from the non-eradicated populations from Venezuela invaded North Brazil (Cluster 2) and subsequently invaded the South of Brazil (Cluster 1). Except for scenario 2 (PP = 0.12), alternative scenarios tested were poorly supported (Fig 2; PP<0.022). In particular, the scenario favored by Monteiro et al. [7] (Scenario 3) has little support testifying to the importance of the additional information provided by the larger numbers of samples analyzed in this paper. S2 Table shows the posterior distribution of parameters for Approximate Bayesian Computation (ABC) analysis using the DIYABC software [46]. The estimated mutation rate under the best-fit scenario was 9.5x10-6, and falls within the range of microsatellite mutation rates estimated for other Diptera [50,51].
Our results show clear genetic differentiation between two groups of Brazilian Ae. aegypti populations (southern-Cluster 1 and northern-Cluster 2), that are roughly separated (except Rio Branco; map code 24; Fig 1A) by the Amazon forest. Our analyses confirmed previous findings [7,8,10,52,53] and revealed also an admixture zone between those clusters (Fig 1 and S7 Table). The admixed ancestry is expected for Santos, given that it is the principal port in Brazil as well as on a major trucking route.
Our results also provide insights into the patterns and levels of genetic connectivity between and within these two population clusters, revealing gene flow across the landscape. The detection of possible first generation migrants, the large number of Cluster 1 individuals that are assigned to Cluster 2 (S11 Table), and the moderate levels of genetic differentiation of these two clusters (S6 and S8 Tables) support the existence of genetic connectivity despite genetic distinctiveness (Fig 1B). This apparent contradiction can be resolved by noting that the majority of gene flow occurs in the areas close to the geographic borders of these two clusters rather than between distant localities. For instance, most of the possible first generation migrants detected in Cluster 1 coming from Cluster 2, are from sites in the admixture zone or close to the border between the two Clusters. Results from the Mantel test support the existence of a correlation between genetic and geographic distances (S3 Fig), which implies an ongoing gene exchange between geographically close samples. This gene exchange is less intense at larger geographic scales, where the existence of distinct genetic clusters implies the presence of limitations to gene flow.
Focusing on Cluster 1, two major genetic groups exist; the southeastern and the southwestern (Fig 3), a separation also supported by RAPD markers [8]. The strong geographic pattern is supported by the Isolation By Distance results (S3 Fig), a finding also reported by other studies [8,54] and by the DAPC analysis (S7 Table).
While genetically distinct, many populations, especially those in sub-cluster 1A, show signs of mixed ancestry (S7 Table). This is further corroborated by results of the DAPC analysis (S2 Fig) and the assignment tests, where a low percentage of individuals is assigned back to their population of origin (S7 Table). For example, Jacobina individuals have an admixed genetic profile, as suggested by their Q values (S7 Table), by the fact that they are found in all three DAPC-defined groups, and by their low percentage of correct assignment to their population of origin that is < 30% (S7 Table). Additionally, the private allele richness (Np) was low in almost all populations of Cluster 1, supporting the evidence of either admixed ancestry or gene flow among populations.
Within Cluster 1 we also found patterns of genetic connectivity that are not correlated with geographic distances, suggesting that the main driver of Ae. aegypti dispersal in these cases is human transport. This is especially true for sampling sites in the Rio Branco (see clustering in Fig 3), Foz do lguaçu, and Rio de Janeiro areas (see Fig 3 and Fst values in S6 Table). Foz do lguaçu samples have also a high proportion of private alleles (Np = 0.25, S6 Table), indicating limited gene flow. The high value of Np in Foz do lguaçu is possibly explained by the fact that it is located at the border between Brazil and Paraguay and Argentina (Fig 1A). The Rio de Janeiro samples (Fig 3) exhibit high levels of genetic variability, admixed ancestry, and no clear assignment to a DAPC group (S8 Table). One possible explanation is that this region is a cross-road, where mosquitoes from different places in Brazil and from abroad converge and then disperse again, due to the extensive road network that connects this area to all other large cities in Brazil and the heavy goods and tourist traffic of the area. Interestingly, this reasoning also explains the fact that Rio de Janeiro and its neighboring areas are the most frequent entry points for dengue into Brazil and that DENV-2 infection rates are very heterogeneous [54].
The second major genetic cluster in Brazil (sub-cluster 2A) is genetically close to Colombia, Venezuela, and Trinidad samples (Fig 4). The correlation between genetic and geographic distances is not as strong for this cluster as in Cluster 1 (S3 Fig). The low proportion of private alleles (Np; S6 Table) in Cluster 2 populations suggests that there is gene flow among them. Goncalves da Silva et al. [55] using mtDNA data also detected extensive gene flow among the major cities in this area. The occurrence of gene flow but with limited IBD may be explained by the different topography of the region. Most of the sampling sites in this study are connected by rivers including the Amazon (Fig 4B). Boat traffic along this water body may facilitate Ae. aegypti dispersal [56,57] in ways that will obscure patterns of correlation between genetic and geographic distances.
Our data show high levels of gene flow between Venezuela and Brazilian Cluster 2 samples. This is supported by the high number of possible first generation migrants detected in Brazilian Cluster 2 (S11 Table) and the moderate values of Fst (S6 Table) between these two areas. However, gene flow seems to be primarily among geographically close populations, since most of the possible first generation migrants detected in Brazilian Cluster 2 are found in Pacaraima and Boa Vista (Fig 1A), on the Brazilian border with Venezuela. There is intense traffic between Venezuela and these two cities. Also the genetic assignment of Boa Vista (with Bolivar (Venezuela); Fig 4) is particularly interesting because Boa Visa is considered an important entry point for new serotypes and genotypes of dengue virus into Brazil from the northern South American countries [54]. The genetic connection we found between North Brazil and Venezuela is consistent with other studies [54,55] and further support the hypothesis that Ae. aegypti populations from northern Brazil derive from Venezuela, where this species was never eradicated [7]. Boa Vista was the site of the first dengue (DENV-1 and DENV-4) outbreak after eradication (1981–1982), which did not spread to the rest of the country for several years; DENV-1 was first reported in Rio de Janeiro in 1986 [58]. In 2000, Boa Vista also reported DENV-3 about the same time DENV-3 was reported in Rio de Janeiro. Given that all dengue serotypes have been reported Venezuela, it is more likely that DENV-3 was introduced in Boa Vista from Venezuela and then spread south to Rio de Janeiro, rather than the other way around.
What were the origins of the two major genetic clusters in Brazil? Brazil, North America, and the Caribbean, have a long history of international trade and populations of Ae. aegypti with close genetic affinities [1,7,59]. Here the inclusion of three Caribbean populations reveals further complexity in the genetic affinities of Ae. aegypti in the Americas in agreement with previous findings [2]. For instance, samples from Dominica, Trinidad and Carriacou, although geographically close, are genetically distinct as they belong to three different groups (Trinidad groups with Venezuela-Colombia-northern Brazil, Dominica groups with southern Brazil and Carriacou groups with North/Central America; Fig 1 and Fig 4B).
To shed light on demographic dynamics, origin(s), and timing of Ae. aegypti re-infestation in the 1970s [6,7], we tested for the relative likelihoods of alternative colonization scenarios using Approximate Bayesian Computation, ABC (Fig 2). If we assume that Ae. aegypti survived in residual small populations, a genetic signature should be detectable, such as lower genetic diversity than samples from non-eradicated areas and signs of recent bottlenecks. Our data do not support this hypothesis; Brazilian samples have levels of genetic diversity comparable with that found in samples from countries where eradication never occurred (Table 1 and S9 Table). As far as the signs of bottleneck are concerned, although our analyses indicated 8 (one of them marginally significant) out of 31 Brazilian populations exhibiting signs of bottleneck (S12 Table), this was supported by only one method and one model. Stronger signs of bottleneck would be expected if small populations had survived the eradication in refugia, although the invasion hypothesis also assumes some level of bottleneck. Combining all the analyses we are in favor of the hypothesis supporting a re-invasion from the neighboring non-eradicated areas, although we cannot completely rule out the hypothesis of refugia or a combination of both.
The ABC analyses (Fig 2 and S2 Table), strongly supported Venezuela as the origin of the northern Brazilian samples, and followed by a southern expansion into Brazil. The divergence times estimates provided by the ABC analyses (S2 Table) are consistent with Ae. aegypti re-infesting northern Brazil in the 1970s [6] and spreading to the South during the late 1980s [6], as suggested by the timing the dengue outbreaks that occurred at those times. This scenario is also supported by the gene flow data between Venezuela and North Brazil and between North and South Brazil detected here (Fig 1, Fig 4 and S11 Table) and in other studies [7,55]. However, the ABC analysis did not support the suggestion from Monteiro et al. [7] that the southern Brazil Cluster originated from Dominica, instead suggesting northern Brazil as the most likely source (Fig 2) for the Southern Brazil cluster.
Consistent with a number of previous studies, there are two major genetic groups of Ae. aegypti populations in present day Brazil, a northeast set and south set (Fig 1). The most likely scenario of establishment of these groups after eradication ~60 years ago is recolonization from Venezuela where eradication was never achieved. This recolonization occurred in two waves, first in the north of Brazil then later in the south. The genetic differentiation of the two groups likely occurred when the first establishment in the south from the north occurred in a coastal city and then gradually expanded northward and inland. The present boundary between these groups may be due to environmental factors or simply an historical pattern that may disappear with time; hybridization in the border area indicates no barriers to gene flow. We confirm that the Caribbean island Dominica, is genetically closely related to the south Brazil genetic group, but contrary to a previous study, our more detailed analysis indicates that Dominica was likely established from Brazil.
Within each of the two major genetic groupings, further genetic subdivision is detectable. The level of gene flow among these subdivisions and individual populations is complex and varies by region. In some cases isolation by distance can be detected while in other areas not, likely due to whether migration is “natural” or human induced. Given the large area and heterogeneity of Brazil, this heterogeneity in genetic patterns and processes is to be expected.
While our present work establishes a reasonably good overview of history and patterns of genetic diversity of Ae. aegypti in Brazil, it is only the first step in fuller understanding of the population biology of this major vector in a country plagued for centuries by diseases it transmits. While fundamental, genetic data alone provide limited inferences of processes at work incorporating detailed information on landscape features such as highways, rivers, shipping routes, etc., overlaid with genetic data should provide more insights into processes. It is the understanding of contemporary processes, especially levels and patterns of movement among populations that are most useful in designing control measures.
|
10.1371/journal.pgen.1001335 | Role of the Drosophila Non-Visual ß-Arrestin Kurtz in Hedgehog Signalling | The non-visual ß-arrestins are cytosolic proteins highly conserved across species that participate in a variety of signalling events, including plasma membrane receptor degradation, recycling, and signalling, and that can also act as scaffolding for kinases such as MAPK and Akt/PI3K. In Drosophila melanogaster, there is only a single non-visual ß-arrestin, encoded by kurtz, whose function is essential for neuronal activity. We have addressed the participation of Kurtz in signalling during the development of the imaginal discs, epithelial tissues requiring the activity of the Hedgehog, Wingless, EGFR, Notch, Insulin, and TGFβ pathways. Surprisingly, we found that the complete elimination of kurtz by genetic techniques has no major consequences in imaginal cells. In contrast, the over-expression of Kurtz in the wing disc causes a phenotype identical to the loss of Hedgehog signalling and prevents the expression of Hedgehog targets in the corresponding wing discs. The mechanism by which Kurtz antagonises Hedgehog signalling is to promote Smoothened internalization and degradation in a clathrin- and proteosomal-dependent manner. Intriguingly, the effects of Kurtz on Smoothened are independent of Gprk2 activity and of the activation state of the receptor. Our results suggest fundamental differences in the molecular mechanisms regulating receptor turnover and signalling in vertebrates and invertebrates, and they could provide important insights into divergent evolution of Hedgehog signalling in these organisms.
| Non-visual β-arrestins are key proteins involved in plasma membrane receptor internalization, recycling, and signalling. The activity of β-arrestins is generally linked to seven-transmembrane receptors, but in vertebrates they can also participate in many other signalling pathways. Consistently, β-arrestins play important roles during vertebrate development and are implicated in a variety of human pathologies. Here we take advantage of the fruit fly model to analyse the genetic requirements of the unique fly non-visual β-arrestin (kurtz) in signalling during the development of imaginal epithelia. To our surprise, we find that the complete elimination of kurtz has no major consequences in imaginal cells. Our data suggest that insect epithelial cells have evolved arrestin-independent mechanisms to control receptor turnover and signalling, so arrestin function has become less critical. On the other hand, in contrast to previous reports in vertebrates, we find that the over-expression of Kurtz blocks Hedgehog signalling by promoting the internalization and degradation of the transductor Smoothened. We suggest that such differences are based on the specific requirement of the primary cilia for Hedgehog signalling in most vertebrates. These results could provide important insights into divergent modes of membrane receptor regulation and Hedgehog signalling in vertebrates and invertebrates.
| G-protein coupled receptors (GPCRs) are seven-transmembrane proteins that play critical roles during development and in the regulation of cellular physiology. GPCRs constitute the largest superfamily of cell membrane receptors [1]. The major GPCR regulatory pathway involves phosphorylation of agonist-activated receptors by G protein–coupled receptor kinases (GRKs), followed by binding of the cytosolic arrestin proteins [2]. This interaction prevents the receptor from activating additional G proteins in a process known as desensitization [3]. GRKs and ß-arrestins also participate in signal propagation by recruiting additional proteins to the receptor complex [4]–[6]. Thus, the GRK/ß-arrestin pathway facilitates receptor internalization from the cell surface through clathrin-coated pits, and this leads to numerous physiological outcomes, including receptor degradation, receptor recycling and the activation of distinct downstream signalling events [2], [7]–[10]. Finally, more recent evidence suggest a role for ß-arrestins in signalling by other families of cellular receptors, including receptor tyrosine kinase (RTKs), non-classical 7TMRs like Smoothened and Frizzled, Notch and TGFβ receptors, and also by downstream kinases such as MAPK and Akt/PI3K [5], [11]–[13].
The arrestin family is divided in two classes: the visual arrestins (arrestin 1 and 4), which are located almost exclusively in photoreceptor cells, and the non-visual β-arrestins 1 and 2 (also named arrestin 2 and 3, respectively), which are ubiquitously distributed [4]. These proteins are closely related and their sequence is highly conserved across species [14]. In Drosophila melanogaster there is only a single non-visual β-arrestin, encoded by kurtz (krz), which function is essential for development, survival and neural function [15]–[18]. In addition, the gene CG32683 encodes a related protein that presents some homology with β-arrestins, but lacks the clathrin-binding domain (see Figure S1). The GRK family includes seven members in humans (GRK1-7) and two components in flies (Gprk1 and Gprk2). Gprk1 modulates the amplitude of the visual response, acting as a Rhodopsin kinase, whereas Gprk2 regulates the level of cAMP during Drosophila oogenesis [19]. In addition, Gprk2 and Gprk1 play a key role in the regulation of the Hedgehog (Hh) signal transduction pathway [20]–[21], where they seem to phosphorylate and activate the seven-pass transmembrane protein Smoothened (Smo) [22]. The ß-arrestin Krz has also been involved in the regulation of Notch signalling, promoting the formation of a trimeric Notch-Deltex-Krz complex that mediates the degradation of the Notch receptor in an ubiquitination-dependent pathway [23], reminiscent of ß-arrestin-mediated ubiquitination of other canonical GPCRs [8]. More recently, Krz has also been implicated in the regulation of Smo accumulation [21] and ERK phosphorylation [24]. Because Krz is the unique ß-arrestin present in Drosophila, it is likely that the protein has additional functions in the modulation of other signalling pathways.
To address the participation of Krz in signalling events, we have analyzed its function during the development of the imaginal discs, the epithelial layers that give rise to the adult structures of the fly. Imaginal discs are very convenient model systems to study the activity of signalling pathways in vivo, because their development is under the regulation of the Hh, Wingless, EGFR, Notch, Insulin and TGFβ pathways [25]. In this manner, the response of these epithelia to the manipulation of Krz levels using genetic variants is a key diagnostic to identify the functional requirements of this protein in signalling during imaginal development. Surprisingly, considering the key roles identified for vertebrate non-visual arrestins, we find that the complete elimination of Krz in imaginal cells has no major consequences during imaginal development. Thus, and as claimed previously [15], krz mutant flies are morphologically normal. In contrast, the over-expression of Krz in the wing causes a phenotype identical to the loss of Hedgehog signalling. We find that excess of Krz inhibits Hh signalling by promoting Smo internalization and degradation in a clathrin- and proteosomal- dependent manner. Contrary to that observed in vertebrates, the effects of Krz on Smo are independent of Gprk2 activity and of the activation state of the receptor. We suggest that such differences in Hh signalling are based in the strict requirement of the primary cilia, a structure that is not present in fly epidermal cells, for Hh signalling in most vertebrates [8].
Krz is the Drosophila homologue of mammalian non-visual β-arrestins, and has all the molecular features of a canonical β-arrestin [15], [23] (Figure S1). The expression of krz occurs ubiquitously in all imaginal discs (Figure 1A and data not shown). To visualize the accumulation of the Krz protein, we generated an antibody against Krz. We found that the protein is localized in the cytoplasm of imaginal cells, being detected at higher levels close to the apical side of the epithelium (Figure 1B–1C, and 1D–1E, red). krz mRNA is also expressed in early blastoderms and during embryogenesis, mostly in the central nervous system and gut during stages 12–17, as assessed both by mRNA (Figure 1F–1H) and Krz protein (Figure 1I–1K) expression. The specificity of the antibody was confirmed by analysing the expression of Krz in loss-of-function conditions. Thus, Krz staining is lost in dorsal wing compartments expressing a krz interference RNA construct (Figure 1M–1M′), and is also absent in clones of cells homozygous for of a krz genetic deficiency (Figure 1L, 1N–1O′). The subcellular localization of the protein in wing discs over-expressing wild type Krz or different mutant forms (described below) is also in the cytoplasm, with higher levels at the apical side of the cells (Figure 1P–1S, red).
β-arrestin has widespread functions during mammalian development and cellular homeostasis [2], [8]. To identify the functional requirements of Krz during the development of the fly wing, we constructed flies with wings homozygous for a krz deficiency (Figure 1L). The elimination of Krz in the wing pouch (in 638-Gal4; FRT82 M(3)z/FRT82 Df(3R)krz; UAS-FLP/+ flies) produces a slightly folded wing of smaller than normal size but without any major defects in the pattern of veins or wing margin (Figure 2A–2B). This phenotype was also observed when the expression of krz was reduced in the entire wing blade (UAS-dicer/+; nub-Gal4/UAS-ikrz; Figure 2C). In wing discs of a similar genotype (638-Gal4/UAS-ikrz), we found a 70% reduction in krz mRNA levels (Figure S2). The very modest effects of krz elimination in the wing disc imply that the signalling pathways regulating wing patterning operate normally in the absence of Krz in imaginal cells. We also studied the consequences of krz elimination in the entire larva in Df(3R)krz homozygotes and in the Df(3R)krz/krz1 combination. These two genotypes survive until the third larval instar, where they became immobile and flaccid and form melanotic tumors, as described for krz1 homozygotes [16]. The imaginal discs of Df(3R)krz/Df(3R)krz and Df(3R)krz/krz1 larvae are very reduced in size, and express high levels of activated Caspase 3, indicating massive cell death in all imaginal tissues (Figure 2E). As expected, krz mRNA is absent in Df(3R)krz homozygous larva (Figure S2). We rescued the viability of Df(3R)krz/krz1 larvae by expressing Krz in the central nervous system in UAS-krz/wor-Gal4; Df(3R)krz/krz1 flies. The surviving flies display slightly folded wings that were very similar to those of flies where Krz levels are eliminated only in the wing (Figure 2D, compare with 2B and 2C). The corresponding wing discs have a normal appearance and the expression of signalling molecules occurs in the normal domains (Figure 2F for Smo and data not shown). These data confirm the essential function of Krz in the CNS for larval development [15]–[16] and indicate that the degeneration of imaginal tissues observed in Df(3R)krz/krz1 larvae is a consequence of the loss of krz in the CNS. Interestingly, the rescued flies, both males and females, albeit morphologically normal are sterile (data not shown), suggesting that krz is required during germ cell development.
The function of β-arrestin 2 [26]–[28] and Krz [23]–[24] has been related to the regulation of EGFR, Notch or Smo signalling in different experimental systems (reviewed in [8]). Therefore, and despite of the lack of a krz mutant phenotype suggestive of an alteration in any of these signalling pathways, we searched for genetic interactions between loss of krz and genetic variants affecting the efficiency of signalling by the Notch, EGFR or Hh pathways. We used the 638-Gal4/UAS-ikrz genotype as a background condition in which the levels of Notch, EGFR or Hh components were reduced. In these combinations, we only observed a genetic interaction between Notch and Krz, as the reduction of krz increases the phenotype of wing margin loss and thicker veins caused by loss of Notch (Figure 2G–2H). No effects of loss of krz were detected upon a reduction of EGFR (Figure 2I–2J) or Hh signalling (Figure 2K–2L).
Since the activities of β-arrestins have been mostly linked to mechanisms of receptor turnover and activation, we next studied the expression and subcellular localization of the EGFR, Notch and Smoothened proteins in krz mutant cells, as a second approach to identify Krz roles during imaginal development. We first looked at the expression of EGFR, Notch and Smo proteins in wing imaginal discs where krz levels are reduced only in the dorsal compartment (ap-Gal4/+; UAS-ikrz/+), because in these discs we can compare the dorsal (mutant) with the ventral (control) compartments in the same disc (Figure 3A–3D, 3F–3G and 3I–3I′). Only in the case of Notch did we observe a slight relative increase in the accumulation of Notch at the apical side of dorsal cells (Figure 3A–3D). No differences were detected in the expression of EGFR or Smo in dorsal versus ventral cells in ap-Gal4/+; UAS-ikrz/+ wing discs (Figure 3F–3G and 3I–3I′, respectively). To extend these results to krz null mutant cells, we generated clones of cells homozygous for the krz deficiency (Df(3R)krz) and for the krz1 allele in the wing imaginal disc. In both cases the clones were grown at 25°C and at 29°C, to determinate whether there are temperature-dependent effects of the loss of Krz, as described for Gprk2 [21]. The expression of Smo and EGFR in krz null cells is normal, and the subcellular localization of these receptors remains as in wild type cells (Figure 3H–3H′ and 3J–3J′). In the case of Notch, we could only observe a subtle increase of Notch accumulation in the apical membrane of some krz mutant cells (Figure 3E–3E′). This increase is not observed in all cells of the same clone, and most clones (60%) displayed a normal expression of Notch (Figure 3E–3E′). We noticed that some of the clones contained cells shorter than wild type cells (Figure 3E–3F). In these cases, the maximal expression of Notch is detected at a different focal plane of the epithelium because of the shortening of the cells in the apico-basal axis (Figure 3E–3E′, Figure S3 and Figure S4). Otherwise no major changes in Notch accumulation were observed in transversal sections of the disc (Figure 3E–3E′, Figure S3 and Figure S4). In summary, the analysis of krz loss-of-function conditions uncovered a modest requirement of this gene for Notch signalling, which was only observed upon a reduction of Notch levels of expression, and a variable effect of loss of krz on Notch protein levels. These results are in contrast to the key requirements of vertebrate Krz homologs in several signalling pathways described in cell cultures and in vivo systems (reviewed in [8]).
In Drosophila, besides the visual arrestins that are only expressed in the eye, there is another gene (CG32683) encoding a protein structurally related to Krz. It is unlikely that CG32683 is providing an arrestin-like function in the absence of Krz, because this protein lacks several conserved aminoacid motifs present in all members of the arrestin family (Figure S1). Furthermore, the expression of CG32683 is only observed during embryonic development (data not shown), and no transcripts are detected in the wing imaginal disc by RT-qPCR or in situ hybridization (Figure S2). As expected, the expression of interference RNA directed against CG32683, either alone or in combination with ikrz, does not cause any alteration in the wing (data not shown). Finally, the over-expression of CG32683-FLAG in the wing does not cause any mutant phenotype, although in this background (salEPv-Gal4/UAS-CG32383-FLAG) the protein is present at high levels in a pattern similar to that of Krz (Figure S2). In this manner, we conclude that CG32683 does not have any role during imaginal development, and that it cannot substitute for Krz in the absence of this gene.
The over-expression of β-arrestin is often sufficient to promote internalization of its agonist-activated GPCRs [28], [29]. In this manner, increasing the levels of Krz might reveal other activities of the protein not uncovered by the loss-of-function approach. To this purpose, we made several constructs to express under the UAS promoter the complete krz cDNA or different modified forms of the protein. When Krz is over-expressed in the wing blade (638-Gal4/+; UAS-krz/+), we obtained a variable phenotype of reduced wing size and changes in the pattern of veins (Figure 4A–4D and Figure S5). The strength of the phenotype depended on the transgenic UAS-krz line used in these combinations, and in the most severe cases the wing was very reduced in size and all longitudinal veins failed to differentiate (Figure 4D and Figure S5). These combinations were raised at 29°C, as we did not find any major pattern defects at 25°C. This is likely a consequence of insufficient levels of ectopic expression, because when we used two copies of the UAS-krz construct at 25°C we obtained similar results than with one copy of the UAS-krz at 29°C (Figure S6B–S6D). The most obvious phenotype of gain of Krz expression is the reduction of the L3/L4 intervein territory. This phenotype is caused by Krz over-expression in the anterior compartment, because when Krz is over-expressed only in anterior cells located in the central domain of the wing blade (dpp-Gal4/+; UAS-krz/+) the fusion of the L3 and L4 veins is also observed (Figure 4E). These veins and the L3/L4 intervein correspond to the territory specified by Hh signalling [30]. In fact, the observed phenotypes are very similar to those resulting from loss of Hh signalling, detected when, for example, Smo or hh expression is reduced or when Costal2 or Patched (Ptc) are over-expressed (Figure 4F–4H and data not shown). Furthermore, wings expressing lower levels of Hh have a stronger phenotype when Krz is over-expressed (638-Gal4/+; UAS-ihh/UAS-krz; Figure 4I, compare with 4B and 4H). These results indicate a negative effect of Krz on Hh signalling when Krz levels are higher than normal.
To demonstrate that increasing the level of Krz diminishes Hh signalling, we analyzed the expression of several Hh-target genes, such as Ptc and Engrailed (En), in Krz over-expression conditions. We found that the expression of Ptc and En is strongly reduced or absent in anterior cells of wing discs over-expressing Krz in the entire wing blade (638-Gal4/+, UAS-krz/+; Figure 4J–4K and 4N–4O), the dorsal compartment (ap-Gal4/+; UAS-krz/+; Figure 4L and 4P) or in clones of cells (hsFLP1.22; act<FRT>Gal4; UAS-GFP/UAS-krz; Figure 4M and 4Q). All together, these results indicate that Krz has the potential to antagonize Hh signalling, although this antagonism is only observed upon its over-expression.
It has been described that β-arrestin 2 interacts with Smo in cell cultures and promotes Shh signalling during Zebra fish development [8], [26]–[27]. Consequently, we analyzed the possible effects of Krz in the regulation of Smo accumulation in vivo. The expression of the smo gene occurs in all wing disc cells, but the protein is only detected in the cell membrane of posterior cells and of anterior cells where Hh signalling is more active [31]–[32]. We confirmed that the expression of Smo in the membrane of posterior compartment cells and at the A/P boundary is very much reduced in wing discs over-expressing Krz in the entire wing (638-Gal4/+; UAS-krz/+; Figure 5A–5B, see also [21]). The loss of Smo is also observed in clones of cells over-expressing Krz (hsFLP1.22; act<FRT>Gal4; UAS-GFP/UAS-krz; Figure 5C–5C′) and in dorsal cells of ap-Gal4/+; UAS-krz/+ genotype (Figure 5D). The resulting levels of Smo in Krz over-expressing cells are very similar to those of the anterior compartment, suggesting that Krz promotes Smo elimination. Interestingly, loss of Smo is also observed in wing discs raised at 25°C, even though the corresponding adult wings are almost normal (Figure S6 and [21]). These results suggest that Krz mostly promotes Smo elimination, and that only above a certain level of Smo reduction, Smo signalling is compromised. In addition, the effects produced by the ectopic expression of Krz on Smo accumulation appear to be very specific, because the localization of other membrane receptors, such as EGFR and Notch, is not modified by excess of Krz. Thus, these two proteins are expressed at normal levels in dorsal and ventral cells of ap-Gal4/+; UAS-krz/+ wing discs (Figure S8).
To further analyse the effects of Krz on Smo accumulation, we analysed the expression of Smo in S2 cells transiently transfected with Krz. To this purpose, we generated a stable cell line over-expressing myc-Smo and these cells were transfected with the pUASt-Krz and pActin-Gal4 vectors. Analysis of whole-cell lysates in Western blots revealed a decrease in Smo protein levels when Krz is over-expressed, both in the absence or presence of Hh in the medium (Figure 5E). These data are consistent with those obtained in the imaginal discs. To investigate how Krz reduces Smo levels, we performed assays in presence of the protein synthesis inhibitor cycloheximide and the proteosome-specific inhibitor MG132. The expression of Krz appears to favour Smo turnover either in the absence or presence of Hh (Figure 5E). In addition, proteosome inhibition inhibits the reduction of Smo levels, both in control (myc-Smo S2, mock transfected) and Krz over-expressing cells (myc-Smo S2, pUASt-Krz/pAct-Gal4 transfected) (Figure 5F). These results suggest that Krz enhances Smo degradation via the proteosomal pathway.
We identified in Krz several conserved aminoacids (Val94, Leu440/IsoLeu441/Leu443 and Ser427; see Figure S1) whose human counterparts are implicated in the targeting of GPCR to clathrin-coated pits without affecting receptor signalling (Val94) [33]–[34], in the binding of β-arrestin to clathrin (Leu440/IsoLeu441/Leu443) [34]–[35], or that reduce its ability to promote internalization of the β2-adrenergic receptor (Ser427) [36]–[37]. To explore whether the function of these residues is conserved in Krz, we made several constructs with mutant forms of Krz fused to the Flag tag (UAS-krzV94D-Flag, UAS-krzS427D-Flag, and UAS-krzLeu-Flag). As a control construct we used wild type Krz fused to Flag (UAS-krzWT-Flag). The expression of Krz-Flag in the dorsal compartment eliminates Smo from the cell membranes of dorsal cells (Figure 6A). In contrast, neither the over-expression of KrzV94D-Flag nor of KrzLeu-Flag affects the localization of Smo (Figure 6B and 6D). These results show that Val94 and the Leu440/IsoLeu441/Leu443 domain are conserved regions essential to the function of Krz, and suggest that Krz binds to Smo and internalizes it via clathrin-coated vesicles. The over-expression of KrzS427D-Flag causes the same reduction in Smo levels as the wild type form (Figure 6C), suggesting that modulation at Ser427 is not functional in Drosophila. Over-expression experiments using the wild-type and the mutant Flag-Krz forms in myc-Smo S2 cells were consistent with the in vivo data (Figure 6E). Co-immunoprecipitation studies showed that all mutant Krz forms interact with Smo (Figure 6F). Interestingly, KrzV94D and KrzLeu mutants co-immunoprecipitated higher levels of Smo (3 and 4 times over wild type Krz, respectively), consistent with an altered Smo internalization and degradation in such conditions. Overall, these results suggest that Krz promotes Smo internalization via clathrin vesicles, and that this step is relevant for its enhancing effect on Smo degradation.
The characteristic phenotype of krz over-expression, and the effects of Krz on Smo accumulation prompted us to study the possible interactions between Krz and Smo in wing discs. We found that the phenotype of reduced L3–L4 intervein in wings over-expressing Krz is rescued by the simultaneous over-expression of Smo, resulting in the formation of normal wings (Figure 7A and 7D). As expected, the corresponding wing imaginal discs express normal levels of Ptc and En (638-Gal4/+; UAS-smoWT-GFP/UAS-krz; Figure 7G–7H and 7K–7L), indicating normal Hh signalling. In this genotype, the over-expression of Krz reduces the level of ectopic Smo-GFP (Figure 7O–7P). The interaction of vertebrate β-arrestin-2 and Smo depends on Smo activation [26], [28]. Drosophila Smo is activated by phosphorylation [38]–[39], and consequently we studied the effects of Krz over-expression in the background of Smo mutant forms affecting its phosphorylation sites. The expression of Smo mutant forms lacking the CK1 and PKA phosphorylation sites (SmoCK1 and SmoPKA) causes a weak Hh loss-of-function phenotype (Figure 7B–7C), and the co-expression of Krz in these backgrounds (638-Gal4/+; UAS-smoCK1/UAS-krz and 638-Gal4/+; UAS-smoPKA/UAS-krz) strongly enhances these phenotypes, resulting in the loss of the entire wing (Figure 7E–7F). We also combined Krz with a Smo variant that mimics phosphorylation in the PKA and CK1 sites (SmoSD123; [38]). Discs expressing SmoSD123 are overgrown and show ectopic expression of Ptc and En in the anterior compartment (638-Gal4/+; UAS-SmoSD123/+; Figure 7I, 7M). The co-expression of Krz in this background reduces the levels of ectopic En and Ptc, and also the accumulation of SmoSD123 in the anterior compartment (Figure 7J and 7N and 7Q–7R). These results indicate that Krz is able to eliminate Smo independently of its phosphorylation state by the kinases CK1 and PKA. In agreement, the SmoPKA form is also eliminated from the cell surface by the over-expression of Krz (Figure S7).
Gprk2 has a positive role in Hh signalling [20]–[21], and its vertebrate homologues directly regulate Smo by phosphorylation, triggering ß-arrestin recruitment [26]. Although the loss of Gprk2 and the gain of Krz diminish Hh signalling, their effects on Smo accumulation are entirely different. Thus, the reduction in Gprk2 stabilizes inactive Smo in the cell membrane of anterior cells, whereas the over-expression of Krz induces internalization and degradation of Smo in both anterior and posterior cells, preventing its activity. To analyse the relationships between Gprk2 and Krz in the wing disc, we expressed simultaneously a Gprk2 interference RNA (iGprk2) and the UAS-krz in the same cells. In this background the formation of the wing fails entirely (Figure 8A–8D), suggesting that Hh signalling is severely compromised. When the miss-expression is directed to the dorsal compartment (ap-Gal4/UAS-iGprk2; UAS-GFP/UAS-krz), we find that the expression of Ptc and En is lost in anterior-dorsal cells, confirming a complete loss of Smo activity (Figure 8E–8F). Interestingly, the expression of Smo in this genetic background is absent in anterior and posterior dorsal cells (Figure 8H, compare to 8G). The same loss of Smo was obtained in cells homozygous for a Gprk2 deficiency (Df(3R)Gprk2) that simultaneously over-expressed Krz (Figure 8J–8J′, compare with 8I–8I′ and 8O). In this manner, it appears that Krz can internalize Smo independently of the activity of Gprk2. The efficiency of Krz to down-regulate Smo independently of Gprk2 activity is not modified when we over-expressed the phosphomimic form of Smo (SmoSD123). Thus, reducing Gprk2 together with an over-expression of Krz also eliminates SmoSD123 accumulation (Figure 8M–8N), and abolishes the ectopic expression of Ptc and En in the anterior compartment (Figure 8K–8L). Taken together, these results show that Krz promotes Smo degradation independently of the Smo phosphorylation state and of Gprk2 activity.
In this work we have analysed the requirement of krz during the development of the Drosophila wing disc. The wing disc is an epithelial tissue, and its patterning and growth depends on the activity of several conserved signalling pathways [25]. We therefore reasoned that any requirement of Krz in the regulation of these pathways should be uncovered by the phenotype of the complete genetic loss of krz in the disc. Surprisingly, we find that wing discs (and all other imaginal discs) can develop in an almost entirely normal manner in the total absence of Krz function (see also [15]). This finding implies that any role of Krz during normal development is dispensable for the regulation of the signalling pathways operating in the wing disc. We must emphasize that even small changes in the levels or domains of signalling by the Notch, EGFR and Hh/Smo pathways result in very characteristic and distinct phenotypes in the wing, and consequently we have to conclude that these pathways operate normally in the absence of Krz in the discs.
The function of Krz has been linked in imaginal discs with the regulation of Notch protein stability [23] and of MAPK phosphorylation [24]. These conclusions are base on sound biochemical data taken from cell culture experiments, and also on the analysis of genetic interactions evaluating the ability of krz mutations in heterozygosity to modify the phenotypes caused by Notch pathway components and MAPK alleles [23]–[24]. We also find that krz reduction enhances the phenotype of a Notch loss-of-function condition, but we never found any Notch-related phenotype in krz mutant wings. Furthermore, we only found changes in Notch accumulation in a small fraction of krz1 and Df(3R)krz mutant clones, in contrast to [23]. In this context, it is interesting to note that we were able to detect a robust accumulation of Notch when krz mutant cells over-express the Notch ligand Delta (data not shown), suggesting that the function of krz becomes critical to promote Notch turnover upon Notch-Delta interactions. In this manner, the implication of our analysis and of previous works is that Krz might be required to optimise some aspects of Notch degradation or MAPK phosphorylation, but that these processes can occur normally in the absence of Krz. It might well be that only upon particular alterations of Notch levels, or in sensitized genetic backgrounds, such as over-expressing a non-dephosphorylable form of MAPK, these fine-tuning aspects of Krz are manifested in phenotypic modifications. It is unlikely that the paucity of krz requirements during imaginal development was due to functional redundancy with other arrestin proteins, because the only Drosophila candidate, CG32683, is not expressed in imaginal discs and does not affect imaginal development when over-expressed.
The lack of a krz mutant phenotype in the discs is also surprising considering the multitude of roles assigned to its vertebrate counterparts in the Wnt, IGF, Notch, Smo and TGFβ signalling pathways and in ERK activation promoted by many GPCRs (reviewed in [8]). These roles rely both on the regulation by β-arrestins of receptor internalization and subcellular localization, and also on their functions as scaffold for a variety of proteins involved in cellular signalling. We have to postulate that insect epithelial cells have evolved arrestin-independent mechanisms to control receptor turnover and signalling, and consequently that arrestin function has become less relevant in these cells. This proposal is compatible with Krz retaining the capability to molecularly interact with similar proteins as its vertebrate counterparts, as Krz possesses both amino- and carboxy-terminal arrestin domains and is 72% similar to the mammalian ß-arrestin 2 and 74% similar to ß-arrestin 1 [15].
In contrast to the loss-of-function analysis of krz, the study of its over-expression offers clear-cut indications of its implication in regulating Smo internalization. Thus, over-expression of Krz causes a very specific phenotype of loss-of-Hh signalling, manifested in defects localised in the central part of the wing that in extreme cases lead to the total failure of wing development. These phenotypes are associated to the loss of expression of Hh target genes, confirming that they are caused by reduced Hh signalling. As previously described, increased levels of Krz are extremely effective in reducing Smo accumulation in the cell membrane ([21] and this work). This effect is observed with wild type forms of Smo, with Smo mutated in its phosphorylation sites and with a phospho-mimic Smo protein that is constitutively activated. The elimination of Smo is also observed in posterior cells, indicating that Krz promotes Smo elimination independently of Ptc, and also in anterior cells localised away from the source of Hh, suggesting that Krz affects Smo turnover in the absence of ligand. Finally, the elimination of Smo by excess of Krz is independent of Gprk2 activity, because it is still observed in cells deficient for the Gprk2 gene. Gprk2 is required for the transduction of Smo signal, and when Gprk2 levels are lowered, inactive Smo accumulates at the cell membrane [20]–[21]. In the double combination (excess of Krz plus loss of Gprk2), Smo is eliminated, suggesting that Smo unmodified by Gprk2 is still capable to interacting with Krz and being removed. The resulting flies show extreme hh loss-of-function phenotypes, likely the result of both loss of Gprk2-dependent Smo activation and increased, Krz-promoted, Smo turnover.
The ability of Krz to interact with Smo in the Drosophila wing is very specific, as we did not observe any other alterations in the localization and activity of other receptors, such as Notch or EGFR. In this context, it is intriguing that the function of vertebrate β-arrestins has also been linked to Smo signalling in several experimental settings. First, β-arrestin 2 promotes Smo signalling by translocating this protein to the primary cilium in mouse NIH-3T3 cells [28], [40]. Second, β-arrestin 2 promotes, upon GRK phosphorylation, the internalization of activated Smo in human embryonic kidney 293 cells [26]. Finally, β-arrestin 2 promotes Smo signalling in zebrafish embryos, and this seems to be a physiological function because it is detected in loss-of-function conditions [27]. In contrast, we only observe a clear antagonism of Krz on Smo signalling caused by Smo internalization and degradation promoted by excess of Krz, and this effect of Krz is independent of the Smo phosphorylation state and of Gprk2 activity.
One of the main differences in the Smo signalling pathway between vertebrates and Drosophila is the localization in vertebrates of active Smo to the primary cilium, a structure that is only present in the fly in sensory neurons [41]–[42]. We can only speculate that the necessity to translocate Smo complexes associated with the type II kinesin motor Kif3A to the cilium [28], a structure not present in fly epidermal cells, imposes a requirement for β-arrestins that is not observed in the fly. Nonetheless, our results show that the capability of Krz to interact with Smo is retained in Drosophila, and this is revealed upon the over-expression of Krz. Once Krz is bound to Smo it would trigger the formation of clathrin-coated pits that targets Smo for degradation in the proteasome, leading to the insufficiency of Hh signalling we observe. In this way, we propose that Krz has retained some of the molecular targets typical of vertebrate β-arrestins, but that these interactions might not occur at physiological levels of expression, or being redundant with other mechanisms of receptor trafficking and signalling.
We used the krz allele krz1 [15], and made a deficiency for the gene Df(3R)krz (see below). We also used the smo2 null mutation, the Gal4 lines 638-Gal4, nub-Gal4, ap-Gal4, wor-Gal4 and salEPv-Gal4 [43], and the UAS lines, UAS-EGFRDN, UAS-Nintra, UAS-iGprk2 [20], UAS-cos2, UAS-smoWT, UAS-smoCKI, UAS-smoPKA, UAS-smoSD123 [38], UAS-FLP and UAS-GFP. We generated the following lines: UAS-krz, UAS-krzWT-Flag, UAS-krzV94D-Flag, UAS-krzS427D-Flag, UAS-krzLeu-Flag, UAS-iCG32683, UAS-CG32683-Flag and UAS-ikrz (see below). We also used the RNA interference line 4637R2 (UAS-ihh from NIG-Fly, Japan). Lines not described in the text can be found in Flybase.
We used the Exelixis insertions e03507 and e00739, which are separated by 3.8 Kb of DNA including krz and the 5′ untranslated end of modulo (mod). Flipase (FLP)-induced recombination was induced by a daily 1 hour heat shock at 37°C to the progeny of hsFLP1.22/+; e03507/e00739 females and w; TM2/TM6b males. Thirty putative w; e03507-e00739/TM2 offspring males were individually crossed to w; TM2/TM6b females and after 3 days were used to extract genomic DNA to determinate by PCR the existence of FLP recombination. The position of the Exelixis flanking insertions e03507 and e00739 and the extent of the krz deficiency are described in Figure 1L.
We induced clones of cells expressing krz by a 12-min heat shock in larvae of hsFLP1.22; actin<FRT>Gal4/+; UAS-GFP/UAS-krz genotype. The elimination of the FRT cassette by FLP-mediated recombination allows the expression of Gal4 under the actin promoter. Clones were indentified by the expression of GFP. Wings homozygous for smo2 were generated in 638-Gal4/+; FRT42 smo2/FRT42 M(2)l2; UAS-FLP/+. Homozygous Df(3R)krz M+ clones and krz1 clones were induced in larvae of the following genotypes: hsFLP1.22; FRT82 Df(3R)krz/FRT82 M(3)w UbiGFP and hsFLP1.22; FRT82 krz1/FRT82 M(3)w UbiGFP, respectively. Homozygous Df(3R)krz or krz1 cells were recognized in the wing disc by the absence of GFP expression. Homozygous Df(3R)Gprk2 clones and homozygous Df(3R)Gprk2 clones over-expressing Krz were induced in larvae of the following genotypes: hsFLP1.22 actin-Gal4 UAS-GFP; FRT82 Df(3R)Gprk2/FRT82 tub-Gal80 and hsFLP1.22 actin-Gal4 UAS-GFP; FRT82 Df(3R)Gprk2/FRT82 tub-Gal80; UAS-krz/+.
Total RNA was prepared from a pool of 50 wing imaginal discs (both wild type and 638-Gal4/UAS-ikrz) and a pool of 30 larvae (both wild type and homozygous Df(3R)krz) using the TRIzol reagent protocol following Life Technologies (Grand Island, NY) instructions. Total RNA (0.7 µg) was used for a first round of reverse transcription employing the Gene Amp RNA PCR kit (Applied Biosystems). Quantitative PCR analysis was performed in a APRI PRISM 7900HT SDS (Applied Biosystems) using the TaqMan probes from Universal Probe Library (Roche) for Krz and CG32683. To normalize the results of the qPCR in the ikrz and CG32683 experiments we used probes for the genes Act42A, Tub84A and RPL32 and to normalize the results of the qPCR in the Df(3R)krz experiment we used a RNApol-II probe. Three independent experiments were done and the quantification of cDNA reduction was performed using Student's t-test. A p-value≤0.05 was considered to be statistically significant.
We used rabbit anti-activated Cas3 (Cell signalling) and anti-panArrestin (BD transduction) and rat anti-EGFR (a gift from B. Shilo), we also utilized anti-En, anti-Ptc, anti-Smo anti-Nintra and anti-FasIII mouse monoclonal antibodies from the Hybridoma Bank at University of Iowa (Iowa City, IA) and anti-FlagM2 mouse from SIGMA. Secondary antibodies (used at 1∶200 dilution) were from Jackson ImmunoResearch (West Grove, PA). To stain the nuclei we used TOPRO (Invitrogen). Imaginal wing discs and embryos were dissected, fixed, and stained as described in [44]. Confocal images were taken in a LSM510 confocal microscope (Zeiss). In situ hybridization with krz and CG32683 RNA probes were carried out as described [44]. We used the ESTs LD31082 and RH70434 as templates to synthesize krz and CG32683 probes, respectively.
|
10.1371/journal.pgen.1007094 | Post-guidance signaling by extracellular matrix-associated Slit/Slit-N maintains fasciculation and position of axon tracts in the nerve cord | Axon-guidance by Slit-Roundabout (Robo) signaling at the midline initially guides growth cones to synaptic targets and positions longitudinal axon tracts in discrete bundles on either side of the midline. Following the formation of commissural tracts, Slit is found also in tracts of the commissures and longitudinal connectives, the purpose of which is not clear. The Slit protein is processed into a larger N-terminal peptide and a smaller C-terminal peptide. Here, I show that Slit and Slit-N in tracts interact with Robo to maintain the fasciculation, the inter-tract spacing between tracts and their position relative to the midline. Thus, in the absence of Slit in post-guidance tracts, tracts de-fasciculate, merge with one another and shift their position towards the midline. The Slit protein is proposed to function as a gradient. However, I show that Slit and Slit-N are not freely present in the extracellular milieu but associated with the extracellular matrix (ECM) and both interact with Robo1. Slit-C is tightly associated with the ECM requiring collagenase treatment to release it, and it does not interact with Robo1. These results define a role for Slit and Slit-N in tracts for the maintenance and fasciculation of tracts, thus the maintenance of the hardwiring of the CNS.
| Early during embryogenesis, the Slit ligand is present only at the midline of the nerve cord. It binds its receptor Robo on growth cones of axons and guides axons such that they form a series of axon tracts called longitudinal tracts on either side of the midline. It has been proposed that Slit regulates axon guidance through a Slit gradient emanating from the midline. Mid-way through embryogenesis, a distinct set of axons begins to cross the midline, forming commissures. Slit from the midline travels along commissural tracts to the longitudinal connectives. The function of Slit in tracts is not known. The Slit protein is processed into a larger N-terminal peptide and a smaller C-terminal peptide. Here, I show that Slit/Slit-N interact with Robo in tracts to maintain the fasciculation and position of axon tracts following axon guidance. In the absence of Slit in mature tracts after their guidance, the tracts de-fasciculate and merge with one another and shift their position towards the midline. This work also shows that Slit and Slit-N are not freely present in the extracellular milieu but associated with the extracellular matrix and both interact with Robo1. Slit-C is bound tightly in the ECM and does not interact with Robo1. These results define a role for Slit in tracts for axon maintenance and fasciculation, thus the hardwiring of the CNS itself.
| In the Drosophila embryonic ventral nerve cord, about 20 longitudinal axon tracts traverse up and down the nerve cord to connect all hemisegments on either side of the midline. These tracts are inter-connected across the midline by the commissural tracts, which cross the midline only once and never re-cross. The longitudinal tracts, together with those commissural tracts that cross the midline, form the longitudinal connectives on either side of the midline. Pathfinding of longitudinal and commissural tracts have been studied in detail [1–11]. It is well documented that signaling pathways such as Slit-Robo [1–7] or Netrin-Frazzled [8–11], guide growth cones of these tracts to their synaptic targets. In the absence of these signaling cues, growth cones follow aberrant routes from the very beginning of their journey.
Slit-Robo signaling is the main system that mediates pathfinding of growth cones for the longitudinal tracts [3–7]. In Drosophila, the slit gene is transcribed in the midline glia, and the protein is present in the midline glia [1–3], whereas its receptors, Robo1, Robo2, and Robo3 are present in a combinatorial manner in axon growth cones [4–6]. The interaction between Slit and Robo mediates proper projection of growth cones on either side the midline parallel to each other. A loss of function for slit early during neurogenesis, for instance, causes the projection of pCC, a pioneering growth cone for the medial longitudinal tract, to head tangentially towards the midline [3, 7]. In older slit mutant embryos, all longitudinal tracts are collapsed at the midline. Loss of function for robo genes also causes collapsing of tracts at the midline or on to each other [4–7].
While the role of guidance molecules in axon pathfinding has been well-explored, it is less so whether these molecules are also required for maintaining the position of axon tracts. A previous study had explored the role of Slit on the fasciculation of tracts and their spacing in the mouse diaphragm [12], but not much is known if the position of tracts and their fasciculation are maintained within the CNS. By examining a mutation in the enzyme involved in the glycosylation of Slit, we have recently shown that Slit-Robo signaling contributes to the maintenance of axon tracts [13]. This issue of maintenance of tracts has major implications since it is essential for maintaining the integrity of the hardwiring of the nervous system, thus, neural function, or dysfunction later in development or in life.
While Slit is expressed in the midline glia [1–3], Slit is also found in tracts [7, 13, see also 1–3, 14]. We ruled out Slit in tracts as cross-reactivity to the antibody or a background staining and showed that tracts-Slit is transported from the midline to the longitudinal connectives along the commissural tracts [7, 13]. Since both Slit and Robo are present in tracts of the longitudinal connectives, it is most likely that they would interact with each other in tracts to mediate certain function.
The Slit protein is processed into a larger N-terminal peptide and a smaller C-terminal peptide. It has been suggested that the C-terminal peptide interacts with PlexinA1 to mediate commissural axon guidance, and the N-terminal peptide interacts with Robo to mediate guidance of longitudinal tracts [15, 16]. Because of a serendipitous discovery that the expression pattern of Slit is affected in late stage patched (ptc) mutant embryos, I sought to explore the post-guidance function of Slit in the Drosophila ventral nerve cord using mutations in ptc. Ptc was originally identified as a major segmentation gene, but it also regulates neurogenesis both in flies and vertebrates [17–21]. In this study, I report a novel effect of loss of function for ptc on Slit in the CNS of older stage embryos. This effect was accompanied by a novel nerve cord defect, which correlated with the changes in the expression of Slit and its localization in tracts. Ptc, together with mutations in another axon guidance molecule, Commissureless (Comm), which is involved in the down-regulation of Robo1 in commissural tracts at the midline [22–24], allowed me to examine the role of Slit in the maintenance of position and fasciculation of tracts. I also found that Robo1 binds to both full-length Slit and Slit-N in tracts but not to Slit-C. Slit and Slit-N are not freely found in the extracellular milieu, but are associated with the ECM. Compared to Slit or Slit-N, Slit-C appears to be more tightly associated with the ECM of axon tracts requiring collagenase treatment to release it. These results reveal novel insights into this important signaling system in the CNS.
Ptc is involved in multiple developmental and disease processes [17–21]. We have previously reported that misspecification of the identity of neurons that send pioneering axons is responsible for some of the axon guidance defects in ptc mutant embryos [20]. A closer examination of the ventral nerve cord in ptc loss of function embryos showed a novel nerve cord defect in embryos that were older than 13 hours post fertilization (hpf; at 22 0C). As shown in Fig 1, Fasciclin II (Fas II) and BP102 staining of mutant embryos showed that axon tracts progressively moved towards the midline in the anterior-posterior direction resulting in a funneling phenotype (panels B, D, F, see also Table 1). This funneling defect was seen in 82+/-3.6% (n = 30 embryos, N = 3 independent experiments). The difference in the distance between L-L tracts in ptc, but not in wild-type embryos, was statistically significant between the anterior and the posterior regions (P<0.001; S1 Data). This defect was not seen in embryos that were younger than 12–13 hpf (Fig 1A, 1C and 1E; Table 1), but was seen in embryos older than 13 hpf (Fig 1B, 1D and 1F; Table 1). While the fasciculation of longitudinal tracts was more or less normal in younger stage ptc mutant embryos (Fig 1E, upper images), in older embryos these tracts were frayed and de-fasciculated with no observable discreet bundles (Fig 1E, lower image, arrowhead). The defasciculation defects were much more pronounced in the posterior segments compared to the anterior segments (Fig 1G). These results suggest that in older ptc mutant embryos, tracts not only move towards the midline in the posterior region but also they de-fasciculate.
The slit gene is transcribed in the glial cells at the midline [1, 2; see Fig 2A, 2B and 2E). However, the protein is present in the midline glia as well as in axon tracts of the commissures and connectives (Fig 2A and 2B)[7, 13, see also ref. 1–3, 14]. The Slit in tracts appears to be transported along the commissural tracts, with the protein profile extending from the midline source cells to the longitudinal connectives along the newly forming commissural tracts (Fig 2A). This can be clearly seen with the ImageJ analysis of the staining. ImageJ analysis across an area that has the Slit-positive midline cells and the commissural tracts crossing these midline cells showed a midline peak that continues with a tiny dip before a smaller peak representing Slit in longitudinal tracts on either side of the midline (Fig 2A and 2B). A similar ImageJ analysis across the midline but between the anterior commissure (AC) and the posterior commissure (PC)(between neuromeres) also showed a midline peak and two smaller peaks corresponding to the Slit in longitudinal tracts, but the dip in between the midline and the longitudinal tracts peaks reached the baseline (Fig 2A and 2B). This dip was due to the absence of axon tracts and consequently the absence of any Slit in that region. ImageJ analysis of the slit-mRNA staining showed a single peak at the midline (Fig 2A).
In older ~15 hpf embryos, while the mRNA expression was restricted to the midline cells, a clear ladder-like Slit protein in tracts was seen (Fig 2B). The continuous domain of Slit along the midline now was resolved into two distinct domains, one located at the AC and the other at the PC in each neuromere/segment (Fig 2B). Consistent with Slit being spread to the longitudinal tracts along the AC and PC tracts, Slit was seen in AC and PC but not in between (Fig 2B). ImageJ analysis across the midline and at AC or PC showed a midline Slit-peak, and two peaks on either side of the midline corresponding to Slit in longitudinal tracts (Fig 2B). Whereas analysis across the midline but between neuromeres/segments, showed a dip at the midline because of lack of midline Slit, but only two Slit peaks in longitudinal tracts (Fig 2B). The spreading of Slit from the midline to the tracts along the commissural tracts can be seen with immunofluorescent labeling of Slit in ~14.5 hpf old embryos (Fig 2C). The Slit protein was seen strictly along the tracts but not in the areas between AC, PC, and LC, where no tracts are present. A gradient of Slit extending from the midline source to the periphery across the nerve cord was not observed. Staining embryos with an antibody against Slit-N also showed the presence of Slit in tracts and did not reveal any Slit-gradient (Fig 2D). These results show that outside of the midline, Slit is found only in tracts. Consistent with these findings, in embryos mutant for comm, where commissures are mostly absent or greatly reduced [23, 24, 7], Slit was present in the midline but mostly absent or greatly reduced in longitudinal connectives [7]. Thus, commissural tracts appear to play the conduit role in moving Slit from the midline to longitudinal connectives.
The expression of Slit in ptc mutant embryos was not affected during the early stages of axon guidance [20, see Fig 2E). However, in older ptc mutant embryos the expression decayed in a highly specific manner (Fig 2F). The mutant embryos that were 14 hpf or older had slit mRNA present in the anterior midline region of the nerve cord, but not in the posterior region (Fig 2F; 83%+/-11% of the embryos, n = 30 embryos/experiment, N = 3 separate experiments). Consistent with this pattern of slit RNA expression, in about the same percentage of embryos, the protein at the midline was restricted to the anterior region in these older ptc mutant embryos (Fig 2F). Moreover, Slit in tracts also followed the midline pattern with the protein present at higher levels in the anterior region and progressively decreased towards the posterior region (Fig 2F). In the remaining ~17% +/-11% of the embryos, a few slit-expressing midline cells could be found in the posterior region or interspersed along the midline (Fig 2G), and about the same percentage of embryos also had Slit protein distributed in a similar fashion in the midline as well as in tracts (Fig 2G). These results indicate that midline is the source of Slit in tracts and the presence of Slit in tracts mirrors Slit expression in the midline.
The staining of older stage ptc mutant embryos for Slit also revealed the A-P funneling defect of axon tracts in ptc mutant embryos (Fig 2F and 2G). In these embryos, the gradual disappearance of Slit followed a gradual funneling of tracts (Fig 2F). In those instances where the disappearance of Slit was dispersed or random, the narrowing of tracts followed the loss or reduction of Slit in the midline and in tracts (Fig 2G). These results suggest that Slit is essential for maintaining the position of tracts away from the midline in a parallel trajectory in older stage embryos. Unlike the loss of Slit expression, the abundance of Robo1 in tracts in ptc mutant embryos was uniform along the A-P axis of the nerve cord (Fig 3A; n = 30 embryos; N = 3 experiments), indicating that the limiting factor for the tracts positioning in ptc was Slit and not Robo. Since the Comm protein down-regulates Robo1 only in commissural tracts at the midline, the three Robo1-positive longitudinal tracts (Medial or M, Intermediate or I, and lateral or L tracts) that crossed the midline at AC and PC in ptc mutants had Robo1 in tracts at the midline in these regions. Note that the inappropriate midline crossing defects of longitudinal tracts in ptc mutants did not show any specific differences between anterior and posterior regions and were more or less the same in all segments. These results indicate that the funneling defect and the midline-crossing defect are independent of each other. It is also consistent with the fact that the midline-crossing defect is due to the misspecification of NBs and neurons and are ptc-dependent but slit-independent [20].
Next I sought to determine if the loss of Slit expression in older ptc mutant embryos is responsible for the funneling defect and separate it from axon guidance defect that occurs equally in all segments. A UAS-slit transgene and the midline driver single-minded (sim)-GAL4 were introduced to a ptc null mutant background and the slit transgene was expressed only in the midline at different time points during development. A transient expression of Slit in the midline was induced in embryos raised at 16.5 0C by shifting them to 22 0C (GAL4 does not or very weakly induces UAS-linked transgenes in 16.5 0C but induces at higher levels in 22 0C or above). Induction between 11 and 13 hpf (Fig 3E) resulted in Slit expression at the midline as well as its presence in axon tracts (Fig 3B). This was sufficient to rescue the funneling phenotype of longitudinal tracts in ptc mutant embryos (Fig 3B–3E). The rescue was seen in 81%+/-9% of embryos (n = 30 embryos per experiment; N = 3 experiments). A shift between 4–13 hpf also rescued the funneling phenotype (91%+/-12%, n = 20, N = 3), the later shift at 15 hpf, however, did not rescue it (n = 30; N = 3). Measurement of the distance between L-L tracts across the midline in rescued embryos showed that the distance was the same in the anterior and the posterior region. These results indicate that Slit has a direct role in maintaining the position of tracts along the midline, in addition to the initial growth cone guidance [see also ref. 13].
Moreover, the midline expression of the slit gene in ptc mutant embryos improved the fasciculation defects as well (Fig 3D). While the tracts still crossed the midline, which is Slit-independent, individual tracts could be seen in rescued embryos as opposed to in non-rescued ptc mutant embryos where the tracts are mostly de-fasciculated and frayed (Fig 3D). These results suggest that Slit in tracts maintains the fasciculation of axon bundles in longitudinal connectives.
A previous paper had reported that very few midline glial cells exist in older ptc mutant embryos [25]. However, we had previously found that ptc mutant embryos had midline cells even at 14 hpf [20]. A re-examination of ptc null embryos by staining with an antibody against Sim, a midline marker [26], showed that ptc mutant embryos had Sim-positive midline cells except for areas with small gaps and clustering of cells (Fig 3F). One explanation for the discrepancy between these results and that of Hummel et al [25] is that Hummel et al examined an unknown and uncharacterized enhancer-trap line that had failed to complement a ptc allele, unlike the results of Merianda et al [20] or shown here, where well-known and well-characterized ptc alleles were used. Moreover, the midline analysis in Hummel et al [25] used another uncharacterized enhancer-trap line with a midline expression, which itself might be regulated by Ptc. We used Sim, a well-known regulator of fates of all midline cells [26], in these studies.
While the midline expression of the slit transgene in ptc mutant embryos rescued the funneling phenotype, it did not rescue axons aberrantly crossing the midline (Fig 3C and 3D). This is because the midline-crossing phenotype is mainly due to the earlier events of misspecification of neurons that send out pioneering axons for these tracts [20] and are slit-independent. A midline expression of the slit transgene will not rescue this or any segmentation defects. These results argue that the gradual anterior-posterior narrowing of tracts in ptc is due to the corresponding gradual loss of Slit expression from the midline and in tracts.
I also examined if the loss of function for ptc progressively reduces the number of neurons in the anterior-posterior direction. Immunostaining of embryos with an antibody against the Elav protein, a pan-neural marker, showed that the number of neurons in ptc embryos is not reduced in the anterior-posterior direction (Fig 4A). However, the entire population of neurons in ptc embryos appeared to progressively move towards the midline with a tighter packing of neurons per hemisegment in the posterior region. This indicates that neurons, which are closely associated with axon tracts, also move towards the midline in ptc embryos. This is likely a secondary effect due to the movement of tracts towards the midline (see Discussion).
Embryos mutant for ptc were also examined with Repo, a glial marker. As shown in Fig 4B, there was no significant loss of glial cells, but the location of glial cells had shifted towards the midline in the posterior direction. Since glial cells are attached to the tracts, their movement appears to be the consequence of the movement of tracts. I also examined if a loss of neurons in the nerve cord could cause a shifting of tracts towards the midline. This was done by staining embryos that were mutant for the gene regulator of cyclin A1 (rca1). Rca1 regulates the expression of zygotic cyclin A and as in cyclin A mutants, in rca1 mutants also Ganglion Mother Cells (GMCs; these are secondary precursor cells for neurons) do not divide, but adopt the identity of one of their progeny neurons [27, 28]. Thus, their nerve cord possesses significantly fewer neurons and glia than wild-type. As shown in Fig 4C, the longitudinal tracts across the midline in rca1 were not any closer, instead, they were further away compared to wild-type. This is likely due to the decrease in the number of neurons in rca1. An anterior-posterior difference in the position of tracts was also not found in rca1 mutants. Finally, I examined mutants that appear to have the commissural defects similar to ptc to determine if commissural defects could cause the funneling defect. However, the funneling phenotype was not found in such mutants (Fig 4D)[see also ref. 23].
A loss of function for comm severely affects the formation of commissures, although they still have a few axons extending to the midline (see Fig 5A). This loss of most of the commissures in comm is due to the upregulation of Robo in commissural growth cones and therefore their inability to overcome the Slit barrier at the midline [24]. These embryos suffer from a significant loss of Slit in tracts (Fig 5B), indicating that proper commissural structures are required for Slit to travel to the longitudinal tracts [see also ref. 7]. Interestingly, we also noticed that in about 40% of comm mutant embryos (N = 60 embryos), Slit was present at higher concentrations in tracts in the anterior region compared to the posterior region (Fig 5C and 5D). This corresponded with a higher expression of Slit in the midline glial cells in the anterior region compared to the posterior region, where Slit-expression was lower or the slit-expressing cells were absent (Fig 5C and 5D). ImageJ analysis of the anterior versus posterior regions of comm embryos for Slit staining quantified and confirmed that there was a significant reduction/loss of Slit in longitudinal connectives from the anterior to the posterior region (Fig 5D; see also S1A Fig). As in ptc mutant embryos, in comm mutant embryos as well, a narrowing of tracts in the anterior-posterior direction was observed (Fig 5C and 5D). This defect corresponded with the gradual reduction in the levels of Slit in tracts.
Fas II staining of comm mutant embryos also revealed that while the longitudinal connectives/tracts were farther apart in comm mutant embryos, about twice the distance of wild-type (see Table 1; S1 Data), the tracts were funneling down towards the posterior end (Fig 5E; 52%+/-4%). This funneling defect was seen also with BP102 (Fig 5A) and Slit staining (Fig 5C and 5D). On the other hand, in about 50% of the embryos (N = 60) where the tracts had low levels of Slit all along the tracts, no recognizable funneling phenotype was seen (Fig 5F). These results, therefore, provide a second mutational data showing a similar funneling defect as in ptc embryos and its correlation with the decrease in the abundance of Slit in tracts.
Each of the longitudinal tracts occupies a specific position along the nerve cord. Slit in tracts appears to regulate the distance between these tracts. While Comm is present and is required in the midline and in commissural tracts spanning the midline, it is not present in longitudinal connectives [23, 24]. In comm mutants, however, the positioning of longitudinal tracts was aberrant with intermingling of tracts with each other. Additionally, the tracts were defasciculated (Fig 5E, 5F and 5G, see also S1B Fig). ImageJ analysis of such Fas II-stained nerve cord from comm mutant embryos illustrate the intermingling and defasciculation of tracts (Fig 5G; see also S1B Fig).
The longitudinal tracts in comm mutants are positioned farther away from the midline compared to wild-type. A loss of cohesion between hemineuromeres due to the absence of commissures could be the reason. However, it appears that the midline is severely affected in comm mutant embryos [29, 30]. Staining of comm mutant embryos with an antibody against Sim (Fig 6A), Fas II (Fig 6B) and sli RNA expression analysis (Fig 6C) showed that the midline was severely affected. The midline cells in comm failed to form a single line as in wild-type, but dispersed into two disorganized lines even at an early age (Fig 6A). This early origin of the midline defect in comm affecting the position of tracts was indicated by the finding that in comm the initial position of the medial tract was placed farther away compared to wild-type (Fig 6B, see also S2 Fig). This result argues against the possibility that the commissural positioning defect in comm is due to a loss of cohesion from the absence of commissural structures. Despite their aberrant position, tracts in comm indeed moved closer towards the midline in the posterior region, which strengthens the argument that Slit has a maintenance function, both the position of tracts as well as their fasciculation.
In ptc mutant embryos that were ~16 hpf or older did not have slit transcription or the Slit protein in the midline (Fig 7A). But, these embryos still had the Slit protein in axon tracts, with the highest amount in the anterior region of the nerve cord. The position of tracts in the anterior region was more or less normal (Fig 7A). With the Slit in tracts gradually decreasing towards the posterior region, the tracts were also getting progressively narrower (Fig 7A). By taking advantage of the expression pattern of Slit in these >16 hpf ptc mutant embryos, I sought to determine if the Slit in tracts physically interacts with the Robo in tracts, perhaps to regulate inter-tract spacing. If the Slit in tracts physically and locally interacts with the Robo1 in tracts, we should be able to pull down Robo1 with Slit in extracts from older stage ptc mutant embryos. Embryo extracts from >16 hpf old wild-type and ptc mutants were prepared and subjected to immunoprecipitation using an antibody against Slit-C (which recognizes the full-length Slit as well). The immunoprecipitated proteins were resolved on a polyacrylamide gel and probed for the presence of Robo1. If Robo1 is pulled down by Slit, given that Slit in these older stage ptc embryos is present only in tracts but not in the midline, the interaction between Slit and Robo1 must be occurring in tracts. As shown in Fig 7B, Robo1 was indeed pulled down by immunoprecipitation with anti-Slit in the extract from ~16 hpf ptc mutant embryos.
The Slit protein is processed into an N-terminal fragment of~150 kDa Slit-N and a C-terminal fragment of ~40 kDa Slit-C. It has been proposed that Slit-N interacts with Robo to guide longitudinal tracts, whereas Slit-C interacts with PlexinA1 to mediate commissural tracts [15, 16]. The results presented in Fig 7B show that an antibody raised against Slit-C pulls down Robo1. This would suggest that Slit-C interacts with Robo1. However, since this antibody against Slit-C also recognizes full-length Slit, it may be that the antibody pulls down Robo1 through full-length Slit. Therefore, I examined if the same immunoprecipitate complex also has the full-length Slit. As shown in Fig 7C, the immunoprecipitated complex contained full-length Slit, indicating that the full-length Slit interacts with Robo1 (see also Fig 8). However, no Slit-C was detected in the immunoprecipitate (Fig 7C; Slit-C migrates around 40 kDa, see also Fig 8).
I have recently developed a procedure to detect proteins that are secreted in vivo in embryos (see Materials and Methods). This method involves dissociating cells from embryos in M3 cell culture media with a Dounce homogenizer and analyzing the media and the cellular pellet for the protein in question using Western analysis. When the dissociation of cells was done using 6 strokes with the loose-fitting pestle in the Dounce homogenizer, the full-length Slit was readily recovered in the media (Fig 8A). With 7 strokes, there appeared to be cell-lysis, as indicated by the presence of Tubulin in the media (Fig 8A), although this Tubulin could also be from contaminating or broken axons. Dissociating cells with 4 strokes yielded no Slit in the media but only in the pellet (Fig 8A), indicating that Slit is not freely present in the milieu. The extra mechanical dissociation force caused by 6 strokes appears to release the externalized Slit protein from the ECM into the media.
The Slit-N peptide could also be readily detected in embryo extracts with an antibody raised against Slit-N (Fig 8B), indicating that Slit-N is also loosely associated with the ECM. However, Slit-C was only rarely detected in embryo extracts (Fig 8C) or in the media with 4–7 strokes (Fig 8A). Thus, Slit-C appears to be more tightly bound to ECM with its epitope perhaps buried within the ECM and not easily accessible/detectable. Only when the ECM somehow gets disrupted Slit-C could be detected in total extracts (Fig 8C).
To determine if Slit-C is tightly bound to ECM, I subjected the mechanically dissociated embryonic cells (6 strokes) to collagenase treatment (see Materials and Methods). Collagenase treatment is expected to disrupt the ECM. The supernatant was then subjected to Western analysis. I found that the treatment with collagenase consistently released Slit-C, and was readily detected in Western blots (Fig 8D). This result argues that disruption of the ECM with collagenase releases Slit-C. Since in older stage ptc mutant embryos (>15 hours old), the Slit protein is absent from the midline, but still present in tracts, I sought to determine if the tracts also contain Slit-C. Indeed, cells derived from >15 hours old ptc mutant embryos generated Slit-C but only when treated with collagenase (Fig 8E).
Anti-Slit-C pulls down Robo1 as well as Slit (Fig 7B and 7C) indicating that Slit-C pulls down the Slit-Robo1 complex. To further confirm this, I immunoprecipitated total cell extract from ~15 hours old wild-type embryos with anti-Robo1 antibody and determined if it pulls down full-length Slit. As shown in Fig 9A, anti-Robo1 pulled down full-length Slit, indicating that Robo1 physically interacts with full-length Slit. However, Slit-C was not detected in the immunoprecipitate (Fig 9A). That Robo1 also complexes with Slit-N is indicated by the result that anti-Robo1 pulls down Slit-N (Fig 9B). Since the absence of Slit-C in the immunoprecipitate could be due to the buried nature of Slit-C in the ECM, I treated total cell extracts with collagenase and then performed IP of this collagenase-treated extract with anti-Robo1. The IP was then analyzed by Western analysis with the anti-Slit-C antibody. As shown in Fig 9C, analysis of the IP with anti-Slit-C showed that Robo1 does not pull down Slit-C. Thus, Slit-C is unlikely to interact with Robo1, although it is possible that collagenase treatment somehow altered Slit-C such that it could not interact with Robo1.
The hardwiring of the nervous system, in general, constitutes axon pathfinding, axon fasciculation, dendritic arborization, and synaptic connectivity/plasticity. But, do organisms need to actively maintain hardwiring in the CNS, and if so, how do they accomplish this? Would it involve axon guidance molecules? The results described in this paper show that the position, as well as the fasciculation of tracts, are actively maintained within the CNS. Signaling by Slit-Robo appears to have a significant role in these events since loss of Slit later during neurogenesis causes loss of positioning and fasciculation of tracts [see also ref. 13]. These results also illuminate how Slit signaling could mediate these functions, which also sheds light on the significance of the presence of Slit in axon tracts. A combinatorial amount of Robo proteins interacting with Slit at the midline and then at the tracts appears to mediate the position and maintenance of tracts and their fasciculation (Fig 10).
The results shown here argue that Slit in connectives maintains the position of tracts in reference to the midline and axonal fasciculation within each tract-bundle. This appears to occur via Slit interacting with Robo proteins locally in the tracts (Fig 7B). These conclusions are reached based on the following findings. First, the pattern of decay of Slit expression and the funneling phenotype in ptc or comm embryos. A progressive loss of Slit in tracts in an anterior-posterior direction was accompanied by a progressive narrowing of axon tracts (Figs 1B, 1D, 2F, 5A, 5C, 5D, 5E and 7A). In mutant embryos where the loss of Slit was interspersed, the narrowing of tracts corresponded to those regions with loss or reduction of Slit in tracts (Figs 2G and 7A). Second, in ptc mutant embryos of ~16 hours of age or older, the midline expression of Slit was completely lost, but the anterior region of the nerve cord still had a high enough amounts of Slit in tracts (Fig 7A). In such embryos, the tracts were maintained at their more or less correct position in the anterior region, but not in the posterior region where the midline Slit was long gone and the abundance of Slit in tracts was much reduced. The position of tracts was also shifted progressively like a funnel in these embryos (Fig 7A). Third, while the tracts in comm mutant embryos were placed farther away from the midline compared to wild-type or ptc, which appears to be due to midline defects (Fig 6), whenever there was a loss of Slit in tracts, such embryos showed a narrowing of tracts (Fig 5). Thus, a continuous presence of Slit at the midline serves as a source for Slit in connectives (see Fig 10), and the Slit in connectives appears to be essential for maintaining the position of tracts. Since immunoprecipitation of extracts with anti-Slit-C from these older stage embryos pulled down Robo1, Slit and Robo1 must physically interact with one another in tracts of the connectives.
The funneling phenotype in ptc is unlikely due to a secondary, patterning defect or a general impact of defective commissures. Because, a loss of function for ptc affects patterning and other CNS defects (segmentation and NB specification defects) equally in all hemisegments, these defects are unlikely to cause the highly specific funneling phenotype. Would the aberrant commissures in ptc pull the longitudinal tracts mechanically closer to the midline? Again, all of the commissures are equally affected in ptc embryos and this occurs well before any defect in Slit expression occurs. The funneling phenotype is seen only in regions where Slit expression is lost (mostly towards the caudal end). Furthermore, mutations that affect commissures similar to the defects in ptc, do not cause the funneling phenotype seen in ptc (Fig 4D). There does not appear to be any loss of neurons in ptc embryos specific to the posterior region either, only that cells are more tightly packed towards the posterior end of the nerve cord (Fig 4A and 4B). Also, even a significant reduction in the number of neurons in the nerve cord does not cause longitudinal tracts to move towards the midline (Fig 4C). Finally, the rescue experiment shows that the funneling phenotype in ptc could be rescued without rescuing the guidance or commissural defects (Fig 3C, 3D and 3E), effectively separating the two and showing that axon guidance or commissural defects would not necessarily cause the funneling phenotype. This is consistent with our previous finding that axon guidance defects in ptc (midline-crossing of longitudinal tracts) are slit-independent but ptc-dependent, and mediated by the misspecification of neurons that pioneer these tracts [20]. Finally, a loss of Slit in tracts, but not in the midline in comm mutants causes longitudinal tracts to move towards the midline, towards each other and also become de-fasciculated (Fig 5), supporting the possibility that tracts Slit prevents both de-fasciculation and inappropriate movement of tracts towards the midline (see also S1 Fig).
In older ptc mutant embryos, both neurons and glia exhibited the funneling phenotype. Neurons and glia are an integral part of axon bundles, like grapes on a wine or beads on a rope, and keeping the tracts/bundles in a specific location within the nerve cord means keeping those neurons and the structure itself in place. The entire nerve cord in that sense is one continuous unit. Whatever that acts on tracts possibly affects the entire structure. Thus, the entire structure in ptc in the caudal region seems to move towards the midline, causing cells in the posterior region to pack more tightly. This phenomenon in ptc or comm may be analogous to the phenomenon of nerve cord retraction—the tracts and the entire nerve cord retracts and progressively gets shorter and shorter beginning with ~13 hours of development. This nerve cord retraction in all likelihood is mediated by shortening of tracts, pulling the entire structure towards the anterior end, thus leading to the condensation of the entire nerve cord.
Since Robo1 co-immunoprecipitates only Slit and Slit-N, but not Slit-C (B, C, Fig 9A and 9B), Robo1 must interact with Slit and Slit-N, but not with Slit-C. Also, Slit and Slit-N appear to be loosely associated with the ECM (Fig 8A and 8B), whereas Slit-C is likely buried or tightly bound to ECM, perhaps inaccessible to Robo. These results are consistent with the finding that while Slit-N interacts with Robo to guide longitudinal tracts Slit-C interacts with PlexinA1 to regulate commissural guidance [15, 16]. The results also show that the full-length Slit also interacts with Robo1, not just Slit-N, thus potentially mediate axon guidance. Whether Slit versus Slit-N further refines Slit-Robo signaling is not known. The presence of Slit-C in tracts in ptc mutant embryos likely reflects perhaps its role in regulating guidance and maintenance of commissural axons.
A following scenario emerges on how Slit-Robo combination could mediate axon guidance to define lateral positioning of longitudinal tracts, and later on regulate tracts position and fasciculation. Soon after its formation, a neuron generates neurites, which repeatedly extend and retract from the cell body. Out of several of these neurites, only one becomes an axon. The axonal growth cone starts to sample its environment while extending from the cell body towards the midline. In the fly embryonic nerve cord, the growth cones of longitudinal axons have Robo proteins, but the three Robo proteins are differentially expressed in growth cones of different tracts. Thus, growth cones of the medial tract have only Robo1, the intermediate tract have Robo1 and Robo3, and the lateral tract have Robo1, Robo2, and Robo3 (Fig 10A). Thus, the lateral tract growth cones have the highest amounts of Robo proteins, the intermediate tract has the next highest and the medial tract has the lowest Robo. During early neurogenesis, these growth cones will explore their environment and when they reach the midline, the interaction between Slit at the midline and the combined Robo in growth cones specifies the initial position of axons in a Robo-concentration dependent manner. Because the highest combined amounts of Robo is in lateral growth cones, they occupy the lateral-most position. The lowest amounts of Robo in the medial growth cones will specify their position closest to the midline. In this scenario, increasing the levels of Slit at the midline will not alter the eventual positioning of tracts, but the availability of Robo proteins/their saturation would. This was indicated by the previous findings that gain of function for Robo2 and Robo3 proteins in tracts shifted them away from the midline [4–6] but the over-expression of Slit in the midline had no effect [3].
During the maintenance phase (Fig 10B) when the growth cones of these tracts have successfully fasciculated with each other, a Slit-Robo interaction can occur only in tracts since growth cones are no longer exploring their environment but are stably fasciculated. But a Slit-Robo1 interaction indeed occurs in tracts (Fig 7B and 7C) where both are present. As shown previously [7, 13], and in this report, Slit from the midline gets transported to the longitudinal connectives along the commissural tracts (Fig 10B). The longitudinal connectives have both the longitudinal axon tracts as well as the commissural tracts that extend along the longitudinal tracts after crossing the midline (Fig 10B). The Slit in commissural tracts, possibly embedded in the ECM, must then interact with Robo in longitudinal tracts to maintain the fasciculation of individual axons within each tract, as well as their position between and from the midline (Fig 10B). The exact mechanism of Slit transport from the midline along the commissures to the tracts is not known. But, secretion of Slit is essential since loss of function for mummy, a gene that encodes an enzyme in the protein glycosylation pathway, and glycosylates Slit, prevents Slit secretion and causes an absence of Slit in tracts [13]. Consistent with a role for Slit in tracts, mutants in mummy show defasciculation and loss of distance between tracts and from the midline. Does Slit form a morphogen-gradient? Given our results, it appears unlikely, but perhaps a gradient of a modified Slit (and thus, unrecognized by the antibody) or some type of a functional gradient is still a possibility. Our result that Slit-Robo signaling contributes to the maintenance of hardwiring of the CNS has far-reaching implications in deciphering the hardwired neural circuitry networks and their functional aspects in normal and abnormal conditions across organisms.
Standard genetics were used [31]. The following fly stocks were used: ptcIN108, and ptcH84 which are phenotypically null alleles, ptcdeficiency [Df (2R) ED1742], slit2, robo14, robo1 deficiency [Df (2R) BSC786], a comm deficiency [Df(3L)BSC845], a deficiency for kuz [Df(2R)IR52a-d] and a deficiency for Sdc [Df(2R) 48] were from the Bloomington Stock center, comm5, comm6 were from Mark Seeger and Guy Tear, UAS-slit was from the Goodman lab, rca1 from Nipam Patel, and sim-GAL4 (on the 3rd chromosome) was from Steve Crews. For wild-type, Canton-S and Oregon-R flies were used. Mutant chromosomes were balanced using green fluorescent protein (GFP)-marked chromosomes (twi-GFP, CyO, Kr-GFP, CyO) to enable the selection of homozygous mutant embryos. Mutant embryos were further identified using their mutant phenotypes in other tissues and lineages.
To determine if the expression of the slit gene in the midline in ptc mutants restores the axon tract position defects in ptc mutants, UAS-slit was introduced to ptc mutant background and induced in the midline using sim-GAL4. The UASxGAL4 induction system is sensitive to temperature since the GAL4 protein is not active at lower temperatures, especially at 16.5 0C. Therefore, embryos from the above “rescue” cross were allowed to develop at 16.5 0C for various developmental time points (see Fig 3E) before shifting to 22 0C, where the embryos were allowed to develop until they were fixed for staining or down-shifted to 16.5 0C and kept until they were fixed. The embryos were then examined for the distribution of Slit and axon guidance defects. Embryos kept at 16.5 0C grow about 40% slower compared to embryos at 22 0C. The timings and stages were then compensated by taking this difference into account while representing the results in Fig 3E. Moreover, when the embryos were shifted to 22 0C, they were ascertained for their correct stages of development by visualizing a subset of them under the microscope after permeabilizing with Halocarbon oil [see ref. 31]. Thus, for example, an 11 hpf embryo shift would mean that embryos were collected for 15 min at 16.5 0C, they were then allowed to develop at 16.5 0C for an equivalent of 11 hours at 22 0C, which is about 15.5 hours of time at 16.5 0C. To positively ascertain the developmental stages of embryos before shifting, about 50 embryos were removed from the collection at 15.5 hpf, immersed in Halocarbon oil, which allows to monitor the stages of development clearly, and the stages were confirmed. To be certain, only those batches of embryos that showed the correct developmental stages corresponding to development at 22 0C were shifted to 22 0C.
Immunochemistry was performed as previously described [7, 13, 32]. Monoclonal and or polyclonal antibodies against the following proteins were used at the indicated concentrations: Fas II (1:20, mouse monoclonal 1D4, Developmental Studies Hybridoma Bank (DSHB), Slit-C (1:20, mouse, C555.6D, DHSB), Slit-N (1:4000), Robo1 (1:3, mouse, 13C9, DHSB), Elav (1:4, mouse, 9F8A9, DHSB), Repo (1:4, 8D12, DHSB). The monoclonal antibody BP102 recognizes an uncharacterized epitope on axons was used at 1:4 (AB_528099, DHSB). For confocal microscopy, secondary antibodies conjugated to Cy5 (rabbit, 1:400, Invitrogen, A10523), fluorescein isothiocyanate (mouse, 1:50, Invitrogen, 62–6511), Alexa Fluor 488 (rabbit or mouse, 1:300, Invitrogen, A-11008 or A-11001), or Alexa Fluor 647 (rabbit or mouse, 1:300, Invitrogen, A-21245 or A-21236) were used. For light microscopy, secondary antibodies conjugated to alkaline phosphatase (AP; rabbit, 1:200, Pierce, 31341) or horseradish peroxidase (HRP; rabbit, 1:200, Pierce, 31460) were used. Alkaline phosphatase was detected using 5-Bromo-4-chloro-3-indolyl-phosphate and nitro blue tetrazolium (Promega, S3771). HRP was detected with diamino-benzidine (Sigma, D4418). Whole-mount RNA in situ hybridization for slit was done as described previously using a Digoxigenin-labeled slit probe synthesized by PCR [20, 32] and the color reaction was developed by AP reaction. Different genotypes were identified using appropriate markers and phenotypes.
Western blot analysis was done as previously described [7, 13, 32]. Briefly, about 30 embryos of the specified age were collected and used for protein extraction. Embryos were collected on apple-juice agar plates, transferred to a mesh-wire basket, washed with running water, and the mutant embryos were selected (absence of green excitation for GFP) using a Zeiss microscope equipped with an Ultra-Violet (UV) light. The embryos were lysed in 40 μL extraction buffer (0.15 M NaCl, 0.02 M Tris pH = 7.5, 0.001M EDTA, 0.001 M MgCl2, 1% Triton-X-100 and 1X Protease Inhibitor Cocktail) using a sonicator for one minute on ice in a 1.5 mL Eppendorf vial. A hand-held, sonicator (Fisher Scientific) equipped with a disposable pestle (Fisher Scientific) was used for sonication. The lysates were centrifuged for 5 minutes at 13,000 rpm in a microfuge (Beckman). The supernatant was collected and 10 μL of 4X Laemelli buffer and 1.5 μL of the reducing agent (Invitrogen) were added, boiled in water for 10 minutes and cooled on ice. About 15 embryos-equivalent amounts were loaded per lane on a 4–12% pre-made SDS-PAGE gel (Invitrogen). Two different primary antibodies recognizing Slit were used: Slit-N, which recognizes the N-terminal portion (1: 50000) [7]; Slit-C, which recognizes the C-terminal portion (mouse, C555.6D, DHSB, 1:100) [3]. For Robo1 Westerns, mouse monoclonal 13C9 (DHSB) was used at 1:40 [32]. The chemiluminescent reaction kit (Millipore) was used to detect signals. The blots were re-probed with an antibody against Tubulin (1:4000, Abcam) to determine the loading control.
About 200 wild-type and ptc mutant embryos, aged 16 hours of development, were homogenized in 37.5 μL of ice-cold lysis buffer [50mM HEPES (pH 7.2), 100mM NaCl, 1mM MgCl2, 1mM CaCl2, and 1% NP-40]. The lysates were incubated on ice for 30 minutes, centrifuged at 15,000X g for 30 minutes at 4°C. 30 μL of the supernatant was used as starting material for each IP reaction using the Catch and Release v2.0 Reverse Immunoprecipitation System (Millipore #17500). The columns were washed with 1X Wash buffer (Millipore) thrice (2000X g, 20 seconds) and the IP reaction was set up by directly adding these ingredients to the column in the following order: 1X 435 μL of the wash buffer, 30 μL of the cell lysate, 25 μL of the antibody against Slit-C and 10 μL of the antibody capture affinity ligand. For immunoprecipitating with anti-Robo1, the IP reaction was set up as follows: 405 μL of the wash buffer, 30 μL of the cell lysate, 50 μL of the monoclonal antibody against Robo1 and 10 μL of the antibody capture affinity ligand. The columns were then incubated overnight at 4 0C. The flow through was collected and the columns were washed three times with 1X Wash buffer (2000Xg, 20 seconds), and eluted with 60 μL of PBS-based denaturing elution buffer (2000Xg, 20 seconds). For equalizing salt concentration between the lysate and the IP samples, 8 μL of the lysate was added to 8 μL of the denaturing elution buffer (Millipore), and 8 μL of the lysis buffer was added to 8 μL of the IP (which is in the denaturing elution buffer). The proteins were then separated on a 4–12% SDS-PAGE and immunoblotted with a monoclonal against Robo1 (1:40, DHSB), against Slit-C (1:100) or against Slit-N (1:40000). Signal detection was by a chemiluminescent ECL reaction kit. The blots were re-probed with an antibody against Tubulin (1:4000, Abcam) to determine the loading control.
I have recently developed a method by which one could detect secreted proteins within and outside of cells without having to culture cells. Briefly, about 75 embryos were collected in 500 μL of M3 insect cell culture medium. They were transferred to a 1.5 mL Dounce homogenizer. The cells from these embryos were dissociated using the looser fitting pestle and different number of strokes (without twisting the pestle) during homogenization. Cells quickly dissociate in M3 media, which is confirmed by visualizing an aliquot of the homogenate under a microscope. The homogenates were transferred to 1.5 mL Eppendorf tube and centrifuged at 4000x g for 5 min. The supernatant was collected into a Vivaspin 500 (Sartorius)(molecular weight cutoff 100 kDa) concentrator and microfuged at 15000x g for 17 min. The resulting ~30 μL of the media were then subjected to Western analysis for Slit. The pellet in the Eppendorf tube was washed once by gently re-suspending the cells in 500 μL of M3 media and microfuging at 4000x g for 5 min. The supernatant was discarded. The pellets (containing the cells) were then lysed using the Lysis buffer, and subjected to Western analysis for Slit.
About 75 wild-type or ptc mutant embryos were collected directly in 500 μL of M3 insect cell culture medium. The cells were dissociated in a Dounce homogenizer (using the looser fitting pestle) by using 6 strokes (without twisting the pestle) during homogenization (see Fig 8). The homogenates were transferred to 1.5 mL Eppendorf tube and centrifuged at 4000x g for 5 min. The supernatant was removed and the cell-pellet was re-suspended in 100 μL of PBS, centrifuged at 4000x g for 5 min. The supernatant was discarded, and the pellet was re-suspended in 20 μL of PBS. Collagenase type VII (2 μL of 10 mg/mL solution) was added to these cells and incubated at room temperature for 20 min. The cells were then centrifuged at 4000x g for 7 min, and the supernatant was collected for Western analysis. The cells-pellet was then re-suspended in lysis buffer, homogenized and then subjected to Western analysis for Slit. As a control, another batch of embryos was processed similarly but without adding collagenase. The pellets were then lysed using the Lysis buffer, and subjected to Western analysis for Slit.
About 75 wild-type embryos were collected directly in 500 μL of M3 insect cell culture medium. The cells were dissociated in a Dounce homogenizer by using 6 strokes. The homogenates were transferred to 1.5 mL Eppendorf tube and centrifuged at 4000x g for 5 min. The media was removed and concentrated using the Vivaspin concentrator (see above). The cells-pellet was re-suspended in 40 μL of the Lysis buffer (see above under Co-Immunoprecipitation experiment). Collagenase (4 μL of 10 mg/mL solution) was added to these cells and incubated at room temperature for 20 min. The cells were then centrifuged at 4000x g for 7 min, and the supernatant was collected and 30 μL of this supernatant was subjected to immunoprecipitation using anti-Robo1 antibody and the IP was processed as described above (under co-immunoprecipitation experiment). Western analysis was done using anti-Slit-C antibody.
|
10.1371/journal.pbio.1002021 | Perception of Odors Linked to Precise Timing in the Olfactory System | While the timing of neuronal activity in the olfactory bulb (OB) relative to sniffing has been the object of many studies, the behavioral relevance of timing information generated by patterned activation within the bulbar response has not been explored. Here we show, using sniff-triggered, dynamic, 2-D, optogenetic stimulation of mitral/tufted cells, that virtual odors that differ by as little as 13 ms are distinguishable by mice. Further, mice are capable of discriminating a virtual odor movie based on an optically imaged OB odor response versus the same virtual odor devoid of temporal dynamics—independently of the sniff-phase. Together with studies showing the behavioral relevance of graded glomerular responses and the response timing relative to odor sampling, these results imply that the mammalian olfactory system is capable of very high transient information transmission rates.
| Olfactory receptor neurons respond to odors in the olfactory epithelium located in the nasal cavity in mammals. Each olfactory receptor neuron expresses only one olfactory receptor, out of several hundred encoded in the mammalian genome. Olfactory receptor neurons expressing the same olfactory receptor are scattered throughout the olfactory epithelium; however, their axons converge in one of thousands of glomeruli in the olfactory bulb. The glomeruli are the first neural relay station in the olfactory system, where olfactory receptor neurons transmit olfactory information to mitral cells. It is well established that different odors evoke different spatial patterns across the glomeruli. It is believed that the more similar the patterns, the more similar the evoked odor perceptions. Glomeruli also are activated in odor-specific sequences in time. These dynamics could increase the amount of information about odors by immense amounts. We used transgenic mice, whose mitral cells were made responsive to light, and asked how well they could discriminate the temporal dynamics of simple spatial patterns of light presented to the olfactory bulb after each sniff. Mice could detect the presence of temporal dynamics down to 13 ms, which provides ample resolution for them to be able to detect the dynamics in response to actual odors. Mice could also discern whether virtual odors, based on actual olfactory bulb activity, were dynamic or static and did so without reference to exact sniff-time. We conclude that both the spatial glomerular activity patterns and the temporal dynamics thereof are used in the mammalian olfactory system to encode odors.
| Different odor stimuli are represented by different spatial patterns of activated olfactory glomeruli in the olfactory bulb (OB), as first shown by activity markers [1]–[3] and supported by the projection patterns of olfactory receptor cells [3]–[5]. Subsequent studies have suggested that these odor patterns are dynamic, evolving over time [6]–[9]. Temporal patterns of glomerular activation reliably differ across glomeruli and depend on the orthonasal odorant and its concentration in anesthetized mice [8],[10],[11] and have also been reported in awake mice [12],[13]. The unfolding of this dynamic odor map occurs by sequential activation of glomeruli at timescales of 10–200 milliseconds [8], and these temporal patterns of activation in the periphery can be read by downstream central brain areas, such as the piriform cortex [14].
The behavioral relevance of precise olfactory timing has been demonstrated [13],[15], relative to sniffing, in accord with the finding that mitral/tufted cell (MTC) activity relative to sniffs carry significant odor information [16]. These behavioral studies stimulated the olfactory epithelium (OE) with a single optical fiber. However, it remains unknown if sniffing is a necessary timing reference for precise temporal olfactory discriminations or if such discriminations can be performed independently of the sniff cycle using strictly across-glomerular "internal" timing. To assess this possibility, it is necessary to precisely control the spatial and temporal activity across the spatially convergent OE neural terminals at the olfactory bulb glomerular input or their MTC projections. We interrogated optogenetic mice with a novel, custom-designed light projector to afford this multidimensional control.
We used three paradigms to address the hypothesis that mice utilize spatial and temporal patterns of MTC activity to distinguish odors. We found that mice could discriminate between eight light spots that were projected either simultaneously or with internal delay (referenced to glomerular activity irrespective of exact sniff timing) onto the olfactory bulb. A single presentation per trial (Paradigm 1) yielded a delay detection threshold of 150 ms. Multiple sniff-triggered presentations (Paradigm 2) yielded a threshold of 13 ms. In Paradigm 3, mice successfully discriminated a dynamic virtual odor based on an optically imaged OB odor response from the same virtual odor devoid of dynamics, irrespective of the onset times' relation to the sniff-phase. Odors are hence not only encoded but can also be perceptually decoded in a spatiotemporal manner, both with and without reference to sniffing.
The experimental animals used were Thy-1 ChR2 mice, which express ChR2 in the MTCs of the OB (Fig. 1) [17],[18]. We opted for post-synaptic targets and, hence, for bypassing the inputs to the OB (and the sensory activation of processing in the glomerular layer), to definitively show that when we controlled the timing of MTCs directly, this timing information would be relayed to downstream targets, effectuating the behavioral decisions. Head-fixed Thy-1 ChR2 and control C57BL6 mice were first trained on a go/no-go task to distinguish between two odors (0.1% amyl acetate and 0.5% 2-hexanone) (Fig. 1). Mice were water restricted and rewarded with a drop of water for correctly licking for the S+ stimulus. An incorrect lick during a S- trial was punished with a drop of 1M NaCl. Mice usually took 1–3 d to acquire the odor discrimination task and performed at >80% accuracy (87.0%±1.8% n = 4) (Fig. 2B, inset). They were then switched to a go/no-go task with OB light patterns as the stimulus; i.e., they were required to discriminate between two light patterns that were spatially identical but differed temporally (i.e. temporal discrimination). The S- stimulus consisted of two sets of four ellipsoid bright spots, each spot mimicking clusters of glomeruli (see Methods for dimensions and S1 Text for further biomimetic design constraints). Four ellipses were presented at the rostral part of the dorsal OB (“A,” Fig. 2A), the other set at the caudal part (“B,” Fig. 2A). The S+ stimulus consisted of the identical spatial pattern, however the onset of the two caudal sets of four ellipses was delayed by a specific interval that could be varied manually or automatically (Fig. 2A). The duration of each spot was 250 ms. The overall intensity over time was therefore identical between S+ and S- stimuli, ensuring that whatever change was being detected by mice was purely due to timing differences.
Access to the OB was provided by creating an optical window by thinning the bone over the dorsal OB. Light patterns (3×4 mm) were projected over the OB using a Digital Light Processor (DLP) projector (Texas Instruments). The DLP enabled us to project high-resolution spatiotemporal movies at 1024×768 pixels (3.9×3.9 µm per pixel, thus a glomerulus spanning a diameter of approximately 25 pixels), 1,440 (binary) frames per second onto the mouse OB (Fig. 1).
In Paradigm 1, the mice had to discriminate between the dynamic S+ and static S- stimuli, wherein the S+ stimulus differed in that the onset of the posterior two sets of ellipses was delayed initially by 500 ms (Fig. 2A). A single stimulus was presented at the start of a trial, without respect to sniff times (i.e., not triggered by sniffing). Control wild-type mice were unable to perform this go/no-go task above chance performance (50.8±1.1% n = 3, mean ± SEM, p = 0.26, above 50%, one tailed unpaired t-test, n = 3, 10th session), while Thy1-ChR2 mice performed with an accuracy of over 85% (87.0%±1.8% n = 4, p<0.00001, one tailed unpaired t-test, n = 4, 11th session) (Fig. 2B). Thy1Chr2 mice were able to do the discrimination with over 75% accuracy within 7 d on average. Once the mice were able to discriminate at an accuracy of 80% for 20 trials (1 block) or 75% accuracy for 2 blocks (p<0.005, binomial statistics), the delay was manually decreased from 500 ms by 100–25 ms delays. We found that Thy1-ChR2 mice could successfully discriminate between the stimuli down to a threshold of 150 ms (79.4±3.9%, p<0.000001, above 50%, one tailed unpaired t-test, n = 4) with an accuracy of over 75% (Fig. 2C). Beyond 150 ms, their performance fell below 75% but was still significantly above chance at 50 ms (p<0.01). These results showed that Thy1-ChR2 mice could detect temporal differences in the singular (one stimulus per trial) activation of MTCs in the OB, with a temporal resolution of 150 ms.
The coupling of olfactory responses to respiration is a well-known phenomenon [19] and implies a possible role of the sniff cycle in odor coding. Work by Rinberg and colleagues [13],[15] showed that the timing of odor activation relative to the sniff cycle is an important cue used behaviorally by mice. Therefore, we next introduced the sniff-triggering of light patterns in Paradigm 2. Sniffing was measured using a non-invasive whole body plethysmograph [20], and the same model stimulus movies from the Paradigm 1 were presented approximately 10 ms after each sniff, now for a shorter, 100 ms duration (plus the automatically adjusted "A"–"B" delay, Fig. 2A). Fig. 3C shows an example sniff trace and sniff inhalation onsets.
As expected, mice were able to perform the task much more accurately with a temporal resolution down to13 ms (p<0.0001, n = 5 mice, seven to ten sessions per mouse) (Fig. 3A). On average, these mice performed above chance at 7 ms (p<0.05) and, more strictly, at 9 ms (p<0.001), and the average went above 75% (76.8±11.2%) at 13 ms. This suggests that the olfactory system is able to use timing information contained in the MTC response and can do so with a resolution of 13 milliseconds.
The discriminations under Paradigms 1 and 2 could be based on the difference between stimulus element onset times (A versus B latency) and/or overall stimulus duration (A duration + latency), but not overall activity (each stimulus has an identical Area Under the Curve [AUC]). Irrespective of the mechanism, our results point to a high temporal resolution of discriminability of activity among MTCs.
Wachowiak et al. [8],[21] showed that odor response latencies in the OB are regionally organized, with glomeruli in the caudo-lateral OB showing shorter latencies than those antero-medial. Reversing the order of the light patterns, so that the caudal ellipsoids preceded the rostral ones, did not produce any significant difference in mice behavior, showing that the order of activation did not affect their temporal resolution (Fig. 3B) (n = 3 mice, 3–12 sessions per mouse).
In Paradigm 3 we aimed to establish directly the biological relevance of this ability to detect timing differences. We hence moved from synthetic biomimetic maps to a biological dynamic odor map recorded from the OB of a transgenic mouse in response to 0.7% ethyl butyrate (EB). We used a spatiotemporal map obtained from a GCAMP3-EMX mouse as the stimulus (virtual odor) to be projected (60 fps grey scale) onto the Thy-1 ChR2 mouse dorsal OB (see Fig. 4A–4D, S1 Text, and S1 Video). We chose a GCaMP3-EMX mouse as it provided signals of unprecedented quality (see approximately 10% dF/F in Fig. 4C).
Mice were indeed able to distinguish between the S+ OB-response movie, which contained the biological temporal information, and the static S- movie of same duration (Fig. 4E), devoid of the timing information (Fig. 4E, 74.8±2.0%; p<0.0001, above 50%, unpaired one-sided t-test, n = 4 mice, five to nine sessions per mouse). Clearly, if odor maps were read as static snapshots mice would not be able to distinguish between the two stimuli, because the spatial pattern of activation was identical.
Since the stimuli in Paradigm 3 were all sniff-triggered, there was a remote possibility that mice may be detecting the timing of the delayed glomeruli of the S+ stimuli relative to sniffing, as had been seen in the work by Rinberg and colleagues [13],[15]. Therefore, to eliminate the possibility that mice may be using the timing relative to the sniff phase as a potential cue, we introduced a uniformly distributed random jitter of 0–50 ms at the start of both S+ and S- trials (Fig. 3C, blue versus red lines). The four mice were indeed able to do the playback task (Fig. 4E). Additionally, no significant difference was revealed in the performance before and after the introduction of jitter (72.5±2.8% versus 73.8±1.8%, mean ± SEM, p = 0.61, unpaired t-test, n = 2 mice).
To control for any possible visual discrimination of the light stimuli, we enucleated (removed the eyes from) the mice. After recovery, mice were still able to perform with an accuracy of 76.5±5.2%, which was not significantly different from their performance before enucleation (p = 0.28, unpaired t-test, n = 4 mice, average of eight sessions per mouse) (Fig. 4E), showing that stimulus discrimination was independent of any visual signal and solely due to MTC activation. We ensured that we light-activated MTC in a physiologically relevant way (i.e. evoked activity that did not saturate) by having recorded MTC single unit activity in response to light of identical spectrum and brightness (Fig. 1). This also confirmed work by others [17] that spike onsets were tightly controlled by light. We therefore conclude that odors are represented by spatiotemporal dynamic maps and the timing information contained within this bulbar response can be used to disambiguate information pertaining to odor quality and, importantly, do so independently of the sniff cycle.
The bulbar odor response is rich in temporal information, associated not only with the presynaptic response pertaining to ORN input [8],[11] but also seen with the post-synaptic output neuron (MTC) responses [10],[22]. Mechanisms for temporal processing within the olfactory bulb include inhibitory gating by granule cells of mitral cells [23], as well as inhibition of input [24],[25] and output [26] at the glomerular level.
While studies by Mouly and colleagues electrically stimulate spatial glomerular activity patterns in rats [27]–[29], temporal odor coding has not been investigated beyond the question of whether discrimination depends on the phase of the sniff-cycle in which the stimulation occurs [27]. More recently it has been shown that the timing of presynaptic odor activation relative to the sniff cycle can be detected by mice down to 10 ms [13],[15]. Here we show timing of similar accuracy of odor discrimination is possible even without reference to the sniff cycle.
We suggest that the earliest activated glomeruli and MTC serve as a time reference "internal" to the OB. Such internal sniff signal-independent reference has previously also been suggested in the form of the whole-population MTC activity [30]. This is however not to dismiss a significant role for sniffing as a temporal reference in the olfactory bulb, and we propose that both sniffing as well as internal relative glomerular and MTC dynamics contribute information about time to the animal.
Because animals in Paradigm 3 were able to discriminate dynamic versus static patterns of equal duration, this suggests that the mechanism involved in the discrimination of Paradigm 2 is feasible based on the relative onset times of the ellipsoidal spot only and not the overall stimulus duration.
A recent study by Haddad et al. [14] provides neurophysiological evidence for the transmission of bulbar timing information to the piriform cortex. It is therefore plausible that temporal latencies, notably the order of glomerular activation [31], as encoded in the periphery are also part of the coding scheme in the piriform cortex. Such temporal processing may occur in our discrimination task, as timing in reference to sniff cycle phase at the level of principal neurons of the OB is not necessary and such temporal coding may be processed elsewhere.
Studies have shown that the precise locking of MC firing to the sniff cycle can facilitate ensemble olfactory coding [13],[32] perhaps by enabling synchronization across neurons [33]. However, it is important to note that this was not used as a cue in our paradigm, as shown by the lack of the effect of the stimulus onset jitter relative to the sniff onset. This implies that timing relative to sniff phase detection and timing relative to activation of other MTCs might be distinct signals used by mice in odor encoding.
Our stimulation paradigm directly targets principal MTCs to examine how temporal coding in this layer of the OB is required for odor discrimination. Given the average brightness of 2–5 mW/mm2 of the stimuli that were projected onto the dorsal OB, at 1 mm depth into the OB this optical power should already be at the neural activation threshold (0.5 mW/mm2), given that 480 nm light power is reduced by 90% by brain optical scatter at 1 mm distance from a light source [17]. Anatomically, the only nearby non-OB brain tissue is the prefrontal cortex. This area is more than this threshold distance of 1 mm away from our excited region [34]. So, light passing beyond the OB is unlikely to stimulate neurons if they were to be thy1-ChR2 positive, including known centrifugal modulatory inputs. By our own findings (Fig. 1) and others [17],[18] thy-1 driven expression is predominantly in output cells. To the extent that there is ChR2 expression that is Mitral/Tufted cell-independent, if any at all [17],[18], they are both lower in number and expression levels, as well as deeper (granule cell layer [GC]) than MTC, all strongly suggesting only a relatively minor population effect, if any at all. Furthermore, we confirm that potential visual inputs are not interfering with this discrimination task by using both a head-mounted "light funnel" to prevent light from reaching their eyes externally, as well as bilateral enucleation. Considering the unlikely stimulation of non-MTCs and confirmation of no visual stimuli interference, we conclude that the behavioral discriminations of high temporal resolution reported here depended entirely on the OB, and likely solely on MTCs.
Paradigm 3 has several limitations. First, we do not know whether the replay of imaged bulbar dynamics in Paradigm 3 generated a percept identical to that of the actual EB odorant that was used to generate the optical stimulus. To demonstrate this would require a discrimination task of the actual odor versus light-based spatiotemporal pattern replay. We feel this identity is not a requirement, however, as we intended to demonstrate that optical stimulation based on actual odor-evoked neural activity, in contrast to the biologically inspired but not biologically recorded stimuli, could also be successfully discriminated. Second, Paradigm 3 used only a static control stimulus to establish that MTC dynamics are detectable. We hypothesize that mice can also discriminate among stimuli with different sequences of MTC activation, but this remains untested. Third, the time-integrated brightness of the S+ and S- stimulus are identical (S1 Figure), and for them to generate equal total spike counts requires linearity of the brightness-firing rate relationship. There is some data, c.f. Fig. 2A in [17] and Supplementary Fig. 1A in [35], showing a fairly linear relationship between light intensity and photocurrents in MTC. For faithful playback of the original MTC activity, we also assumed a roughly linear relationship between firing rate and GCaMP3 activity, which was demonstrated in hippocampal cells in (c.f. Fig. 3B in [36]). These relationships should be corroborated by future studies across a population of MTCs.
One of the surprising results of our work is just how temporally precise the olfactory system can be with regard to recognizing the temporal dynamics among glomerular post-synaptic neurons. The auditory system is considered the most sensitive, followed by the touch and visual system in humans, with weber fractions (detection time/stimulus duration time) at 10%, 16%, and 20% respectively [37]. Our data show that the mouse olfactory system, with a weber constant of 13% (13 ms for the 100 ms model movies of Paradigm 2), is comparable to the vibrotactile sense.
For another major component of the flavor system, the gustatory system, it is found that the temporal characteristics of taste responses convey information about the quality and intensity of a taste stimulus [38],[39] at timescales greater than 250 ms [40]. Rats can respond in a taste-specific manner depending solely on the temporal stimulus pattern in the nucleus of the solitary tract [41]. The flavor system hence employs temporal information, albeit with the olfactory system acting at an approximately 20 times finer timescale.
The fine temporal structure of the odor-evoked response at the level of the OB principal MTCs is functionally significant for odor perception. Our behavioral confirmation of a temporal dimension to the decoding of OB odor maps, combined with timing information related to the sniff phase and the importance of the graded nature of individual presynaptic glomerular responses [13],[15], implies a maximized transient information flow rate through the olfactory system.
All the animals were treated according to the guidelines established by the U.S. National Institutes of Health (2011), and the experimental protocols were approved by the Institutional Animal Care and Use Committee of the John B. Pierce Laboratory. The John B. Pierce Laboratory is AAALAC accredited. Behavioral performance data and stimulus data are available in a permanent repository (http://dx.doi.org/10.5061/dryad.01br7) [42]. Thy1-ChR2 heterozygous mice and wild-type were implanted with headbars for head fixation and the skull dorsal to the OBs was thinned and coated with super glue for optical transparency. After at least 5 d of recovery and 2 d of water regulation (15 min/day) mice were adapted to head fixation in the pletysmography box. Mice were then trained to perform a go/no-go task to (1) detect an odorant; (2) discriminate two odorants; (3) detect the optical dynamic S+ stimulus; and (4) discriminate the static versus dynamic optical stimuli of Paradigm 1 (250 ms duration per spot, single presentation per trial, delay time brought down to mice's delay threshold); followed by (5) discriminate the static versus dynamic sniff-triggered optical stimuli of Paradigm 2 (100 ms spot duration, down to mice's delay threshold); and finally (6) to discriminate the replayed biological dynamic versus static OB response projection pattern in Paradigm 3. These OB patterns were based on the optical imaging of awake heterozygous GCamP3-EMX mice. Mitral/tufted cell electrophysiology was performed in anesthetized Thy1-CHr2 mice.
Adult mice (60–400 d) were used in this study. Six C57BL/6 mice (Charles River, Wilmington) were used as controls. Our experimental group consisted of 10 Thy-1 ChR2 mice expressing channelrhodopsin in M/T cells of the OB [17]. Mice were water restricted for at least 2 d prior to the start of the training session. During water restriction, access to water was limited to 15 min every day. Food was available ad libitum. The weight of the mice was monitored daily. GCAMP3-EMX mice obtained by crossing a GCAMP3 reporter line (Jackson Laboratory, Maine) with EMX-CRE mice (donated by Drs. Robert Sachdev and David McCormick, Yale University, New Haven) were used for optical imaging experiments.
The mice were anesthetized with ketamine and dexdomitor (75–100 mg/kg and 0.5 mg/kg respectively, i.p.). Antisedan (0.5 mg/kg SC) was used for the reversal of the sedative effect. Toe-pinch reflex was checked before the start of the surgery as well as periodically during the surgery to ensure that the mouse was deeply anesthetized. The bone overlying the dorsal surface of the bulb was exposed, thinned and coated with cyanoacrylate glue to make the bone transparent. This yielded a ∼10 mm2 optical window which was clear for several months and was re-thinned when clarity was reduced. A head bolt for head fixation during the behavioral task was attached to the exposed skull using dental acrylic. Mice were allowed to recover for approximately 5 days before being put on water regulation in preparation for the start of training.
The mice were trained on a go/no-go task where the trial types (S+ or S-) were chosen randomly. Mice were trained for at least ten blocks; each block consisted of 20 trials. Each trial lasted for 5 s, with a tone indicating the start of a trial. The stimulus (odor or light pattern/movie) was presented after a 1.8 s delay. The reward for a correct S+ lick (11 µl water) was available 200 ms after the start of stimulus presentation. Incorrect licks of S- were punished with 1 M NaCl (11 µl), time-out of 3 s, as well as an incorrect lick tone. Six seconds separated each trial. A vacuum tube along the lick spout sucked up any residual fluid before the start of a new trial.
Initially, head-fixed mice were trained to lick the lick spout when presented with S+ odor alone. Once the mice were accustomed to licking the lick spout for reward, the S- odor was introduced randomly. Training started with no additional ITI or salt punishment, so as not to discourage the mouse. Both were introduced 1–2 d later. Mice usually took 1–3 d to acquire the odor discrimination task and perform at >80% accuracy. Once they reached this stage, the stimulus was changed to the light patterns and movies.
Mice were first trained to perform a detection task (S+ versus no light). Once the mice were able to perform this task with >85% accuracy (usually within 3–4 d), S- was introduced. After task acquisition, the brightness was titrated down (by 50%–80%, from 100 mW, being 11 mW/mm2) to the minimum level still allowing approximately 90% correct responses (approximately 2–5 mW/mm2). We used a calibrated Thorlabs PM100D with the S121C sensor, set to 480 nm, for all optical power measurements.
For discrimination of the light patterns, three sets of behavioral paradigms were followed. Initially mice in Paradigm 1 were trained on a biomimetic model stimulus where a single movie (S+ or S-) was presented during the trial. The rostral set of four ellipsoid spots was presented for 250 ms and after a specific delay a caudal set of four ellipsoids was also presented for 250 ms. Once the mice were able to discriminate at an accuracy of 80% for a single block or 75% for 2 blocks (p<0.005), the delay was manually decreased by a single frame (by 16.7 ms, 1/60th of a second). Each ellipsoid spot spanned 593 µm (a–p) ×445 µm (m-l) (152×114 pixels). All eight spots combined made up 14.1% of the total 4×3 mm projected frame area. The anterior set of four spots was shifted 1993 µm anterior to the posterior set (Fig. 2A). The more medial (and anterior) spots were 542 µm apart from each other (m–l, edge–edge) and the more lateral (posterior) spots were 1,541 µm apart. Within each set of four spots, the anterior spots were shifted 667 µm anterior to the posterior spots.
Mice in Paradigm 2 were trained on sniff-triggered movies where the same model stimulus was presented approximately 10 ms after each sniff detected during a trial. In this paradigm, the rostral and caudal sets of ellipsoids were each presented for a duration of 100 ms. An automated procedure decreased the delay from 250 ms down in 17 ms (1/60 s, one video frame) increments whenever the subject was able to respond correctly in eight consecutive trials (p = 0.004 by chance), or increased delay during eight consecutive errors. After mice progressed to single frame delay, subframe delay (i.e., at 1,440 fps instead of 60 fps) adjustments of 25% were made. Once mice got to a stage where they reached subframe delays (below 17 ms) reliably, all training days henceforth were included in our analysis. Occasionally, even after reaching the above criteria, mice failed to perform the task. Such days, identified by when they did not progress beyond discriminating a single frame (17 ms), were excluded from analysis.
In both groups, temporal discrimination threshold was defined as the minimum delay allowing 75% correct responses during a daily session.
In Paradigm 3, mice were trained to discriminate between a pre-recorded OB odor response movie as the S+ and a spatially identical S- movie, devoid of timing information (S1 Video). The movies were scaled to match the size of the dorsal OB window and presented at 7–10 mW overall brightness. A uniformly distributed random jitter of 0–50 ms was introduced at the start of both S+ and S- trials to eliminate any potential sniff timing cue. Once mice were reliably able to discriminate the playback odor movie with at least 70% accuracy for a minimum of 3 d, it was assumed that they had acquired the task. All sessions henceforth were used for data analysis. Any day when mice failed to lick for more than 10% throughout the training session (at least five blocks) was excluded from analysis, as it was assumed that mice were not motivated enough.
To ensure that discrimination was only via activation of channelrhodopsin in the MTCs and not due to visual detection of the light stimulus, we introduced the following measures. First, to mask any light that may reach the retina (only approximately 1 mm apart from the OB by the orbital bone) we presented intense blue 480 nm LED, located 4 mm lateral to each eye, starting 100 ms before OB stimulus onset, for the entire trial duration. This LED mask consisted of a constant brightness level (approximately 10 mW through mouse-pupil–sized pinhole at eye distance) summed with temporal white noise (0–10 mW). Second, a black ABS "light funnel" was implanted on their OB window to prevent light from reaching their eyes externally (Fig. 1). Third, at the end of Paradigm 3 mice were blinded by bilateral enucleation, thereby eliminating any visual cue. Mice were then once again evaluated on the light discrimination task. All data are reported ±SEM.
The stimulation setup was organized around a modified Olympus BX50WI microscope. The light source consisted of five 700 mW 455 nm lasers aimed at a 6 mm OD liquid light guide. The guide entered into the port of a custom-made DLP projector (Zinterscope, Guilford, CT), which contained the Texas Instruments D4100 0.7 XGA 1024×768 micro-mirror device (Fig. 1). A DVI to DMD (D2D) Interface board (Digital Light Innovations), supporting 24- bit binary expansion (60×24 = 1,440 fps, Paradigms 1 and 2) and 8-bit grey scale (60 fps, Paradigm 3), was plugged into the DLP board. Images were projected on to an Olympus UPlanFLN 4× n.a. 0.13 objective, yielding an image size of 3×4 mm that was focused onto the dorsal OB. Lasers were TTL-triggered via PWM with a 40 kHz cycle. The maximum brightness projected onto the OB, when lasers were driven at a 90% duty cycle and the entire DMD image turned on, was 100 mW. Frame timing was validated using a phototransistor and oscilloscope. For consistency across daily sessions, we centered the dorsal OB on a plus-shaped light pattern using an xy-stage onto which the mouse box was mounted.
Sniffing was measured with a whole-body plethysmograph [20], where the mouse was enclosed in an acrylic box (187 cm3). Bias airflow was provided by constant flow rate of 0.7 l/min house air and a Buxco vacuum (Wilmington, NC). The lid had a coverslip-covered cut-out above the OB and a black foam ring that sealed in the light entering the black OB funnel. The box pressure was transduced by a sensor (Buxco TRD5700), filtered 0.1–100 Hz, and amplified 100,000× (100× via a generic amplifier, 1,000× via a WPI DAM50). Licking was measured by a contact lickometer (MedAssociates ENV250). Two controller boards (NI USB-6259) interfaced the hardware to the PC Labview environment. For the sniff-triggered movie paradigm, Python was used to detect sniffs on one PC and trigger another ("movie server") PC over TCPIP to generate the movies with constant approximately 10 ms delay and an optional uniformly distributed random jitter of 0–50 ms (Fig. 3C). The movies, onset timing, and delay adjustment was controlled in Python, and to ensure that each movie was detected as a separate entity, triggers within 125 ms of the previous one were ignored. Labview was used for the overall go/no-go task control (Fig. 1).
Thy-1ChR2 mice were anesthetized with ketamine (100 mg/kg i.p.) and xylazine (10 mg/kg i.p.). The anesthesia was maintained with boosters as needed. Atropine (0.04 mg/kg i.p.) was administered every 2 h to improve breathing by reducing secretions in the respiratory tract. The animals' body temperature was maintained with a heating pad set at 37°C. Lidocaine was applied prior to incisions. Craniotomy was preformed over both olfactory bulbs. The bulbs were covered with a 2% agar and saline solution to minimize pulsations. In vivo extracellular recordings were made in the mitral cell layer. Extracellular electrodes in glass micropipettes (3–6 MΩ) containing 2.5 M potassium citrate were used. Recordings were collected with RZ5 Bioamp Processor and RA16PA 16 Channel Medusa Preamp amplifier (Tucker-Davis Technologies). Cells were stimulated optically using a Digital Micromirror Device (Texas Instruments) built into a custom projector (Zinterscope, CT). The light stimulus was a 300×300 pixel square that was centered on the recording site. The intensity of the light stimulus was controlled manually by adjusting the laser brightness to match intensities used in behavioral experiments.
Mice were killed and decapitated. The olfactory bulb was removed and kept in 16% paraformaldehyde overnight. 100 µm coronal sections were made on a vibratome (Leica) and were washed in 0.1% PBS. Some sections were incubated with primary antibodies mouse anti-GFP (Molecular Probes, OR) and rabbit anti-5HT2A (Abcam, MA). Secondary antibodies used were alexa fluor 555 goat antimouse and alexa fluor 488 goat antirabbit (Molecular Probes, OR). Sections were mounted on a slide with mounting medium containing DAPI (Vector Labs, CA). Sections were viewed on a three-channel laser scanning Zeiss confocal microscope 710.
Optical calcium signals from the dorsal OB were recorded using a CCD camera (Redshirt Imaging) with 256×256 pixel resolution and a frame rate of 25 Hz. The epifluorescence microscope used was a custom-made tandem-lens type with 1× magnification (F50/0.95). A high-power LED (Luxeon LXHL-PE09, Philips Lumileds) driven by a linear DC power supply acted as the light source. A custom-made DC amplifier (based on a linear Apex power operational amplifier; Cirrus Logic) powered a peltier (OT2.0-31-F1; Melcor) device, onto which the LED was glued. The LED-cooling peltier current was proportional to the LED current, yielding a stable illumination. The fluorescence filter set used was FF01-475/50-50 (excitation filter), LP515 (dichroic), and LP530 (emission filter; Semrock). This system provided fast imaging capabilities, a large field of view, and low noise.
Data were imported into Matlab R2013A. Raw images were converted to images representing the relative change in fluorescence (%ΔF/F) in each pixel and frame. Each trace was bandpass filtered (0.1–7.5 Hz, 4th order Butterworth) using. To get an estimate of the firing rate from the slow calcium signal, the ΔF/F signals were deconvolved using a time constant of 610 ms [43], using the following Matlab code: "kernel = exp((0:-1:-kernellength)/(tau * Sampling_Rate)" and "deconv_tr = deconv([filt_tr kernelpad], kernel)". Eleven frames (105–115) were selected as sniff-response for further preparation for playback (Fig. 4). The area outside the OB window was masked (set to 0). The 11 frames that were acquired at 25 fps were resampled to our projection rate of 60 fps yielding 27 frames, after subtracting out any offset present in the first frame. Values <0.01% dF/F were then set to 0. This S+ movie was next de-pixelated by convolving with a large spatial kernel. To determine the onset and offset times of the static S- movie we determined the frame at which the response rose to above 10% of peak and subsequent decline to below 10%, for all traces with a minimum response peak of 1.5% dF/F (n = 15,819 traces, 23% of 256×256 pixels). The onset frame was set as frame 2 (mean = 2.2, median = 2) and offset frame as frame 23 (mean = 22.2, median = 23). Each on-frame of S- movie was subsequently created as the frame based on the Area Under the Curve (AUC) of each S+ trace divided by 22 (number of on-frames). Thus, each pixel in the static S- spatial pattern had the same AUC as in the S+ movie (S1 Figure). Therefore the overall intensity over time was identical for both S+ and S-. Last, the movies were rotated and normalized between 0–255 by the min (0%) and max dF/F (5.5%) of the S+ movie (S1 Video).
|
10.1371/journal.pntd.0005803 | Assessment and optimization of Theileria parva sporozoite full-length p67 antigen expression in mammalian cells | Delivery of various forms of recombinant Theileria parva sporozoite antigen (p67) has been shown to elicit antibody responses in cattle capable of providing protection against East Coast fever, the clinical disease caused by T. parva. Previous formulations of full-length and shorter recombinant versions of p67 derived from bacteria, insect, and mammalian cell systems are expressed in non-native and highly unstable forms. The stable expression of full-length recombinant p67 in mammalian cells has never been described and has remained especially elusive. In this study, p67 was expressed in human-derived cells as a full-length, membrane-linked protein and as a secreted form by omission of the putative transmembrane domain. The recombinant protein expressed in this system yielded primarily two products based on Western immunoblot analysis, including one at the expected size of 67 kDa, and one with a higher than expected molecular weight. Through treatment with PNGase F, our data indicate that the larger product of this mammalian cell-expressed recombinant p67 cannot be attributed to glycosylation. By increasing the denaturing conditions, we determined that the larger sized mammalian cell-expressed recombinant p67 product is likely a dimeric aggregate of the protein. Both forms of this recombinant p67 reacted with a monoclonal antibody to the p67 molecule, which reacts with the native sporozoite. Additionally, through this work we developed multiple mammalian cell lines, including both human and bovine-derived cell lines, transduced by a lentiviral vector, that are constitutively able to express a stable, secreted form of p67 for use in immunization, diagnostics, or in vitro assays. The recombinant p67 developed in this system is immunogenic in goats and cattle based on ELISA and flow cytometric analysis. The development of a mammalian cell system that expresses full-length p67 in a stable form as described here is expected to optimize p67-based immunization.
| East Coast fever, caused by the tick-borne protozoan parasite Theileria parva, is a disease that results in significant bovine morbidity, mortality, and production losses in regions of sub-Saharan Africa. Susceptible cattle develop clinical signs within a 7–14 days of exposure, which often progress to severe pulmonary edema and death. Control of East Coast fever in affected regions of Africa is largely prohibited by the lack of an affordable and efficacious vaccine. Furthermore, pastoralist farmers in affected regions of Africa often lack resources to prevent losses due to East Coast fever, so these production losses play a significant role in food security and protein availability. Experimental immunization of cattle with a recombinant T. parva-derived antigen, p67, has shown promise in preventing East Coast fever, but this antigen is extremely difficult to produce in full-length in sufficient quantities, and results of immunization studies using truncated recombinant p67 products are highly inconsistent. In this study, p67 antigen production was optimized and produced for use in future immunization studies. Optimization of p67-based immunization strategies is an important step forward in the development of a sustainable, next-generation vaccine against T. parva, which is urgently needed to minimize losses associated with East Coast fever.
| Theileria parva (T. parva) is an intracellular protozoan belonging to the Theileria genus, order Piroplasmida and phylum Apicomplexa [1–3]. This parasite is the causative agent of East Cost fever (ECF), an acute and often lethal disease affecting cattle in different countries of eastern, central and south Africa [4]. T. parva is transmitted to cattle by Rhipicephalus appendiculatus ticks. Once within cattle, infectious sporozoites enter B and T lymphocytes and mature into schizonts [5,6]. Schizonts primarily induce T-cell transformation and proliferation [7–9], which is pharmacologically reversible using anti-Theileria drugs [9–11]. T. parva infection often results in pulmonary edema and death [12]. ECF is a leading cause of death in cattle in sub-Saharan Africa, resulting in approximately US$ 168 million in annual economic losses and death of over 1.1 million cattle [4].
The disease was conventionally controlled by acaricide use and chemotherapy. However, the rapid development of acaricide-resistance in tick populations and the high cost of veterinary care required for timely administration of chemotherapy limits the control of ECF. A mode of T. parva prevention is the infection and treatment method (ITM). ITM involves infection of cattle with live, T. parva sporozoites and concurrent treatment with a long-acting form of oxytetracycline. Although effective, production of ITM tabulates is extremely costly and inefficient, and the requirement of co-treatment with oxytetracycline makes this form of prevention too costly for many pastoralist farmers. Thus, new, safer and more economically sustainable methods of prevention, such as a next-generation vaccine, are urgently needed. [4,13].
During the last 20 years a strong endeavor has been made, with variable results, to search for an alternative vaccine to prevent ECF [13,14]. The majority of the work focused on the isolation and delivery of defined T. parva sporozoite and schizont antigens. The most protective T. parva sporozoite antigen identified to date is the surface protein, p67 [15]. p67 is recognized by neutralizing antibodies detected in immunized animals with T. parva sporozoites. Moreover, immunized mice with T. parva sporozoites produced neutralizing monoclonal antibodies and most of these antibodies recognized p67 on the sporozoites surface [15–17].p67 is essential for host cell recognition and sporozoite entry, and its expression is strictly limited to the sporozoite stage while the kinete, schizont, merozoite, and piroplasm stages of the parasite do not express p67 [18].
Several studies have been carried out using recombinant p67 expressed by different systems, administered by different adjuvants, and delivered by a variety of vectors [4,13]. Paradoxically, better results have been obtained using adjuvanted p67 protein expressed in E. coli or insect cells, rather than vector-delivered p67 [4,13,19]. This could be attributed to the low level of p67 stable form expression in mammalian cells. Although some papers reported the use of recombinant viruses to deliver the p67 ORF, these studies provided no data regarding the efficiency of p67 expression after cell transduction [19]. Vector-based delivery, and especially viral vector-based heterologous antigen delivery, needs careful regard considering that the immune system has evolved a sophisticated mechanisms array to both detect and eliminate invading viruses. Viral vectors also deliver the ORF antigen directly into the host cell, potentially conferring a high-level expression of the ORF antigen. Hence, expression cassette optimization represents a crucial step for a successful vector antigen construction.
In the present work, full-length p67 protein expression in mammalian cells has been achieved and optimized for the first time, paving the way for further p67 vectorialization for immunization studies and ECF vaccine development.
Bovine Bone Marrow Stromal Cells cell (BBMC), Goat Skin Stromal cells (GSSC), Swine Adipose Derived Stromal cells (SADSC), Equine Adipose Derived Stromal cells (EADSC) and Alpaca Skin Stromal cells (ASSC) were derived, immortalized and maintained as described in [20], [21], [22], [23] and [24]. HEK (Human Embryo Kidney) 293T (ATCC: CRL-11268), BBMC, GSSC, SADSC, EADSC and ASSC were cultured in Eagle's Minimal Essential Medium (EMEM, Gibco) containing 10% fetal bovine serum (FBS), 2 mM of L-glutamine (Gibco), 100 IU/mL of penicillin (Gibco), 100 μg/mL of streptomycin (SIGMA) and 0.25 μg/mL of amphotericin B (Gibco) and were incubated at 37°C, 5% CO2 in a humidified incubator.
The synthetic T. parva p67 ORF was excised from pEX-K4p67 (Eurofins, Genomics) via cutting with NheI and HindIII restriction enzymes. The 2246bp p67 fragment was then cloned into NheI/HindIII cut pEGFP-C1 shuttle vector (Clontech) to generate pCMV-p67.
The p67 secreted fragment (pCMV-p67ΔTM), without the trans-membrane domain, was obtained by PCR amplification from pCMV-p67 using NheI p67 sense (5’-cgtcagatccgctagcccaccatgcagatcacccagttcc -3’) and 685-SalI p67 antisense (5’-cccgtcgaccttcttcttcagcttctggatc-3’) primers. The PCR amplification reaction was carried out in a final volume of 50 μL, containing 1X Pfu buffer (20 mM Tris–hydrochloride pH 8.8, 10 mM (NH4)2SO4, 10 mM KCl, 100 ng/mL BSA, 0.1% TritonX-100, 2 mM MgSO₄, 10% Dimethyl Sulfoxide (DMSO)), 0.2 mM deoxynucleotide triphosphates, and 0.25 μM of each primer. One hundred nanograms of DNA were amplified over 35 cycles, each cycle consisting of 1 min of denaturation at 94°C, 1 min of primer annealing at 60°C and 2.30 min of chain elongation with 1U of Pfu DNA polymerase (Fermentas) at 72°C. The generated 2139bp p67ΔTM fragment was checked in 1% agarose gel and visualized after ethidium bromide staining in 1X TAE buffer (40 mM Tris-acetate, 1 mM EDTA). The amplified fragment was cut with NheI/SalI, ligated in NheI/SalI digested GFP (green fluorescent protein) ORF emptied pEGFP-C1 in order to obtain pCMV-p67ΔTM. p67 mutated protein, with the seven putative arginine glycosylation sites substituted with glutamine, was NheI/SmaI cut out from pEX-k4ΔGlyco (Eurofins, Genomics) and the 2152bp fragment was cloned into NheI/SmaI cut pINT2-EGFP [25] shuttle vector in order to obtain pCMV-p67ΔGlyco. A lentiviral transfer vector, pEF1α-p67ΔTM-iresGFP, delivering the p67 secreted form was obtained through ligation of the expression cassette, excised from blunt ended NheI/BamHI cut pCMV-p67ΔTM, into PmeI cut pWPI (addgene).
Briefly, HEK 293T cells were transfected in a T175 cm2 flask with 25 μg of transfer vector pEF1α-p67ΔTM-iresGFP, 13 μg of packaging vector p8.74, 10 μg of pseudotyping vector pMD2 and 10 μg of pREV using Polyethylenimine (PEI) transfection reagent (Polysciences, Inc.). Briefly, 58 μg of DNA were mixed with 116 μg of PEI (1mg/mL) (ratio 1:2 DNA-PEI) in 3 mL of Dulbecco’s modified essential medium (DMEM) high glucose (Euroclone) without serum. After 15 min at room temperature (RT), 14 mL of medium without serum were added and the transfection solution was transferred to the cells (monolayer) and left for 6 hours at 37°C with 5% CO2, in a humidified incubator. The transfection mixture was then replaced with 25 mL of fresh medium EMEM, with 10% FBS, 100 IU/mL of penicillin, 100 μg/mL of streptomycin and 0.25 μg/mL of amphotericin B and incubated for 24 hours at 37°C with 5% CO2. 48 hours after transfection, the flask was stored at -80°C and the lentivirus was obtained by freezing and thawing cells three times. Subsequently, the supernatant was first clarified at 3500rpm for 5 min at 4°C, filtered through a 0.45 μm filter (Millipore) and stored at -80°C. To obtain stably transduced HEK-p67ΔTM and BBMC-p67ΔTM cell lines, 1x105 cells were infected with 2x105 TU (transducing units) of viral reconstituted pEF1α-p67ΔTM-iresGFP. Twenty-four hours later, the culture medium was replaced with fresh medium supplemented with 10% of FBS and the cells were observed via fluorescence microscopy to monitor the transduction. GSSC, SADSC, EADSC and ASSC were similarly transduced.
HEK 293T cells were seeded into six-well plates (3x105 cells/well) and incubated at 37°C with 5% CO2. When cells were sub-confluent, the culture medium was removed and the cells were transfected with pCMV-p67, pCMV-p67ΔTM, pCMV-p67ΔGlyco, psecE2 [26,27] and pEGFP-C1 using PEI transfection reagent (Polysciences, Inc.). Briefly, 3 μg of DNA were mixed with 7.5 μg PEI (1mg/mL) (ratio 1:2.5 DNA-PEI) in 200 μL of Dulbecco’s modified essential medium (DMEM) high glucose (Euroclone) without serum. After 15 min at RT, 800 μL of medium without FBS were added and the transfection solution was transferred to the cells (monolayer) and left for 6 hours at 37°C with 5% CO2, in a humidified incubator. The transfection mixture was then replaced with fresh medium EMEM, with 10% FBS, 100 IU/mL of penicillin, 100 μg/mL of streptomycin and 0.25 μg/mL of amphotericin B and incubated for 24 hours at 37°C with 5% CO2. To test the supernatant protein expression, the transfection mixture was replaced with fresh medium DMEM/F12 (ratio 1:1) without serum and incubated for 48 hours at 37°C with 5% CO2.
Protein cell extracts were obtained from T25cm2 confluent flasks of transfected HEK 293T, HEK-p67ΔTM and BBMC-p67ΔTM by adding 100 μL of cell extraction buffer (50 mM Tris-HCl, 150 mM NaCl, and 1% NP-40; pH 8). Cell extracts were electrophoresed through 10% sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE). Different SDS-PAGE loading buffer denaturing conditions were also used to evaluate a possible p67 aggregation status: SDS concentration (0 to 5%) and heat treatment length (24 hours at 80°C). After protein transfer onto nylon membranes by electroblotting, membranes were incubated with Anti-p67 monoclonal antibody, AR22.7 [28], diluted 1:5.000 and then with a secondary antibody probed with horse radish peroxidase-labelled Anti-Mouse immunoglobulin (Sigma), diluted 1:10.000 to be visualized by enhanced chemiluminescence (ECL Kit, Pierce). Also cell supernatants, obtained from HEK 293T transfected with pCMV-p67ΔTM and HEK-p67ΔTM or BBMC-p67ΔTM, were collected at different time points (4, 8, 24 and 48 hours after serum free medium DMEM-F12 secretion condition) and analyzed through 10% SDS–PAGE. Protein loading was assessed by Commassie Brilliant Blue staining of the membrane as previously described [29].
One adult goat and two cattle were used for the in vivo immunization study. Animal Use Protocol 04596, entitled “Development of Bovine Herpesvirus-4 as a Vaccine Vector for Theileria parva in Cattle" was approved by the Washington State University Institutional Animal Care and Use Committee (IACUC) on 11/17/2014. Washington State University is a USDA registered research facility (43-R-011), is regularly inspected and files all required documentation, including an annual report. In addition, under the provisions of the Public Health Service Policy on the Humane Care and Use of Laboratory Animals, the University files required assurance documents to the Office of Laboratory Animal Welfare (OLAW). (OLAW Assurance Number A-3225-01, effective from March 4, 2013 through March 31, 2017). The Animal Care and Use Program at Washington State University is fully accredited by the Association for Assessment and Accreditation of Laboratory Animal Care, International (AAALACI), continuing accreditation notification July 18, 2012.
After pre-immune blood sample collection, both the goat and the cattle were immunized intramuscularly with 2 mL of p67ΔTM clarified (~14 μg) supernatant in addition to 2 mL of Squalene-based oil-in-water adjuvant (AddaVax InvivoGen). All animals were boosted 21 days after the first immunization with the same procedure. The last blood samples were collected 42 days after p67ΔTM supernatant immunization.
Serum samples were drawn before immunization and at scheduled times and processed for ELISA assays. Briefly, microplates (Microlon High Binding) were coated overnight at 4°C with 50μL/well p67ΔTM protein clarified supernatant obtained from T175 cm2 of pEF1α-p67ΔTM-iresGFP lentiviral vector stably transduced HEK 293T cells and diluted in 0.1 M carbonate/bicarbonate buffer pH 9.6. After blocking with 1% bovine serum albumin (BSA), 1:100 diluted serum samples were incubated for 1 hour at room temperature. After 3 washing steps in PBS+Tween 0.05%, 50 μL of diluted 1:20.000 rabbit Anti-Bovine immunoglobulin G-HRP (Sigma) was added to each well and the plate was incubated as above. Following the final washing step, the reaction was developed with 3,3′,5,5′-tetramethylbenzidine (TMB, IDEXX), stopped with 0.2 M H2SO4 and read at 450 nm.
To evaluate the presence of p67 protein on the cell surface and the presence of specific antibodies in the sera of immunized animals, a flow cytometry assay was performed on pCMV-p67 transfected HEK 293T cells expressing p67. The cells plated in a T75 cm2 flask were transfected with 22.5 μg of pCMV-p67 DNA, 67.5 μg of PEI (ratio 1:3 DNA-PEI) in 1.5 mL of Dulbecco’s modified essential medium (DMEM) high glucose (Euroclone) without serum. After 15 min at RT, 6 mL of medium without serum were added and the transfection solution was transferred to the cells (monolayer) and left for six hours at 37°C with 5% CO2, in a humidified incubator. After six hours, the transfection mixture was replaced with fresh EMEM with 10% FBS. The following day, the transfected cells were washed with sterile PBS to remove any traces of serum and subsequently detached with a PBS-EDTA solution (50 μL of EDTA Ethylenedinitrilotetraacetic acid 0.5 M in a final volume of 50mL).
2x105 re-suspended cells, for every sample (a total of 6 samples plus a control represented by cells only) were centrifuged at 1200 rpm for 4 min at RT. The pelleted cells were incubated at RT for 20 min with inactivated sera diluted 1:20 in 1mL PBS-FBS 2% final volume solution. Next, cells were centrifuged at 1200 rpm for 4 min at RT, washed with 1mL of PBS-FBS 2% solution, re-centrifuged as before to remove the wash buffer, and incubated with secondary Donkey Anti-Goat Fitc antibody (donkey Anti-Goat IgG-FITC: sc-2024 Santa Cruz Biotechnology, inc.) 1:200 diluted in a final volume of 200μL of PBS-FBS 2% and secondary Anti-Bovine IgG (whole molecule)–FITC antibody produced in rabbit (Sigma-Aldrich) 1:200 diluted in a final volume of 200μL of PBS-FBS 2% for goat and cattle sera respectively. As a negative control, the pre-immune sera were employed at the same dilution.
PNGase F was purchased from NEW ENGLAND BioLabs and tested as suggested by the company user manual. HEK-p67ΔTM and psecE2 transfected HEK cells serum free medium clarified supernatants, collected after 48 hours of secretion, were digested with PNGase F that cleaves between the innermost GlcNAc and asparagine residues (of high mannose, hybrid, and complex oligosaccharides) from N-linked glycoproteins. To optimize the electrophoretic migration according to the acrylamide gradient (4–15%), the supernatant samples were analyzed through BIO-RAD Criterion™ TGX™ (Tris-Glycine eXtended) precast gels for SDS-PAGE. p67 detection was performed by Western immunoblotting as described above.
Before attempting the generation of a suitable expression cassette for the T. parva p67 ORF gene in mammalian cells, the nucleotide composition of p67 gene was taken into account to prevent poor gene expression due to differences in codon usage between apicomplexan and mammalians cells. p67 codon usage was first adapted to the human genome codon usage using the Jcat codon adaptation tool (http://www.jcat.de/)). In general, the T parva genome has a low GC content of 34.1%, [30], and specifically, the p67 ORF has a GC content of 43% (S1A and S1C Fig). Adaptation to the human genome codon usage shifted the GC content from 43% to 68% (S1B and S1D Fig). Starting from the p67 human codon usage adapted ORF, a Kozak’s sequence (to improve the translation) and two restriction enzyme sites (to facilitate the sub-cloning in a suitable vector) were added at the ends of the ORF.
According to its amino acid sequence and as predicted by Phobius (http://phobius.sbc.su.se/) (Fig 1A), a server for prediction of transmembrane domains and signal peptides, and in agreement with a previously published paper [31], T. parva p67 seems to have an amino-terminal signal peptide (from aa1 to aa18), an extracellular domain (from aa19 to aa406) a hydrophobic region (from aa407 to aa425), a cytoplasmic domain (from aa426 to aa685) a transmembrane domain (from aa686 to aa708) and an extracellular single amino acid (aa709). Therefore, based on this prediction, p67 should be expressed in a eukaryotic expression vector as a full length membrane-linked protein. The synthetic p67 ORF was placed under transcriptional control of the CMV promoter and the bovine growth hormone polyadenylation signal to obtain the pCMV-p67 construct. Transiently pCMV-p67 transfected HEK 293T cells expressed p67, as shown by Western blotting (Fig 1B), which was displayed on the cell surface as shown by flow-cytometry (Fig 1C) using an immunized goat serum.
The presence of two hydrophobic regions, one corresponding to the putative transmembrane domain (from aa686 to aa708) and the other from aa407 to aa425, could give rise to two different potential topologies (Fig 2A and Fig 2B). Therefore to address this, it was assumed that the removal of the putative transmembrane domain would allow p67 to be secreted, giving credit to the protein topology designed in Fig 2A. A mammalian expression vector, pCMV-p67ΔTM, with the transmembrane domain deleted from the p67 ORF coding sequence was constructed. When HEK 293T cells were transfected with pCMV-p67ΔTM, p67 was secreted in the cell culture supernatant (Fig 2C) and reaching a concentration of ~10 μg/mL after 48 hours of incubation with serum free medium, thus confirming the presence of a single transmembrane domain and categorizing p67 as a Type I integral single–pass transmembrane protein when expressed in mammalian cells.
Both the secreted form and the membrane linked form of mammalian expressed p67, when loaded in SDS-PAGE and detected by Western blotting, migrated with a lower mobility than expected (Figs 1B and 2C). The possibility of p67 expression in mammalian cells as a glycosylated protein was considered. Analysis of p67 using three different glycosylation site prediction programs (Glyco EP, http://www.imtech.res.in/cgibin/glycoep/glyechk?job=932&tim=45; NetGlyc 1.0, http://www.cbs.dtu.dk/services/NetNGlyc/; Protter, http://wlab.ethz.ch/protter/#) yielded highly concordant results: all of the programs predicted the same seven putative N-linked glycosylation sites (S2 Fig) but no O-linked glycosylation sites. To validate this in silico prediction, pCMV-p67ΔTM transfected HEK 293T cells secreting p67 were treated with the glycosidases PNGase F (Fig 3A) but failed to reduce the molecular size of p67 when compared with undigested control p67. In order to eliminate any doubt regarding the glycosylation status of p67, a mutated form of p67, in which the seven potentially glycosylated asparagine residues were substituted with glutamine (S3 Fig) was constructed by gene synthesis pCMV-p67ΔGlyco and expressed in mammalian cells. Again, no reduction in molecular size between the mutated and non-mutated p67 was observed by Western immunoblotting (Fig 3B). This further corroborates the fact that p67 expressed by mammalian cells is not glycosylated. In light of these data, a possible state of p67 aggregation, indestructible by normal denaturing conditions of an SDS-PAGE, was hypothesized. In support of this hypothesis, by increasing the SDS-PAGE loading buffer denaturing conditions with higher SDS concentration (5%) and extending heat treatment length up to 24 hours at 80°C, it was possible to shift the p67 molecular size to ~67 kDa (Fig 3C).
A lentiviral transfer vector, pEF1α-p67ΔTM-iresGFP, delivering the secreted form of p67, under the transcriptional control of the elongation factor 1 alpha promoter (EF1α), followed by an internal ribosomal entry site (IRES), the GFP ORF and the woodchuck hepatitis virus post transcriptional regulatory element, was constructed (Fig 4A). After the reconstitution of replication incompetent lentiviral particles, BBMC and HEK 293T cells were transduced and cell lines constitutively secreting p67 antigen into the medium supernatant were obtained (BBMC-p67ΔTM and HEK-p67ΔTM) (Fig 4B and 4C). These cell lines represent a p67 antigen source to be employed for different purposes, such as immunization studies and as a diagnostic tool for anti-p67 antibody detection. In fact, p67 antigen collected from HEK-p67ΔTM serum free medium supernatant and then adjuvanted, was successfully employed to generate an anti-p67 antibody response in cattle, as detected by ELISA (Fig 5A and 5B) and flow cytometry (Fig 5C and 5D).
Different pEF1α-p67ΔTM-iresGFP transduced cell lines (Goat Skin Stromal cells (GSSC), Swine Adipose Derived Stromal cells (SADSC), Equine Adipose Derived Stromal cells (EADSC) and Alpaca Skin Stromal cells (ASSC)) originated from different animal species were further generated and all of them expressed p67 protein in stable and soluble form (S4 Fig).
Recent developments in artificial gene synthesis have enabled synthetic genes construction. De novo gene synthesis is a valuable synthetic biological tool for biotechnological studies which typically aim to improve tolerance to toxic molecules, retrofit existing biosynthetic pathways, design novel biosynthetic pathways and/or enhance heterologous protein production [32,33]. In the field of recombinant protein production, natural genes found in wild-type organisms are usually transformed into heterologous hosts for recombinant expression. This approach typically results in poorly expressed recombinant proteins since the wild-type foreign genes have not evolved for optimum expression in the host. Thus, it is highly desirable to harness the flexibility of synthetic biology to create customized artificial gene designs, optimal for heterologous protein expression. The degeneracy of the genetic code leads to a situation whereby most of the amino acids can be encoded by two to six synonymous codons. The synonymous codons are not equally utilized to encode the amino acids, thus resulting in phenomenon of codon usage bias. Importantly, codon usage bias has been shown to correlate with gene expression level, and it has been proposed as an important design parameter for enhancing recombinant protein production in heterologous host expression [34–36]. Based on this information, the popular web-based software, known as the Java Codon Adaptation Tool (JCat), allowed us to customize the T. parva p67 ORF for its expression in mammalian cells. Although we did not compare the adapted p67 ORF with the un-adapted one in terms of expression efficiency, the adapted p67 was expressed by mammalian cells both as membrane-linked and secreted form. Of note, the p67 ORF codon usage adaptation increased its GC content from 43 to 68%, which likely also influenced expression efficiency. Previous studies have shown that GC-rich genes in mammalian cells can be expressed 100-fold more efficiently than their GC-poor counterpart due to increased steady-state mRNA levels [37].
Based on its amino acids composition, p67 has a predicted molecular weight of ~67 kDa; however its migration through the denaturant SDS-PAGE was slower than predicted, at roughly twice the expected size, for both the membrane-linked and the secreted form. In silico analyses of the p67 amino acid composition using different softwares identified several N-linked glycosylation sites which could explain the unexpected electrophoretic migration. To test this hypothesis, the p67 supernatant was digested with PNGase F, and the p67 ORF was mutated by substituting glutamine for asparagine in the putative N-glycosylation sites. Glutamine was chosen because of its close structural similarity to asparagine, but found out to be unable of being linked to N-acetylglucosamine or fucose. However, none of these manipulations prevented the unexpected migration of p67 in SDS-PAGE.
A lower SDS-PAGE mobility of p67 was previously described by other researchers [28,38,39], during the attempt to express p67 as a full length in E. coli. It is very well known that E. coli is not able to glycosylate proteins, or at least recombinant proteins. The authors proposed that this anomalous mobility was due to the high serine/threonine (28%) and glutamate/glutamine (18%) protein composition [28,38,39]. When these observations were combined, glycosylation of p67 expressed by mammalian cells was excluded. In Western blots of the p67 membrane linked form (Fig 1B), two abundant bands, one corresponding to the p67 expected size and another one of larger size, were present, whereas in Western blots of the p67 secreted form, only the band with lower mobility was present (Fig 2C). Based on these observations, it was reasoned that at some stage during p67 translation, membrane sorting and secretion, the protein could form small soluble dimeric aggregates. Likely, this could happen inside the Golgi apparatus when the protein, after translation, is accumulated within the Golgi vesicles and reaches high local concentration that could allow its aggregation. This might explain why the secreted form is represented by a single band with low mobility. To investigate its presumed state of aggregation, p67 was subjected to more drastic denaturing conditions which returned the mobility to ~67 kDa.
Since cells transiently transfected with a plasmid delivering p67ΔTM expression cassette allowed us to collect p67 protein in the transfected cell medium, it was of interest to generate a cell line constitutively and stably secreting p67 to be employed for different purposes. Therefore, a third-generation, replication-incompetent lentiviral vector delivering p67ΔTM expression cassette was constructed and HEK 293T cells were successfully transduced with an efficiency close to 100% as measured by GFP expression. The GFP ORF was in a bicistronic form with p67ΔTM ORF by an IRES sequence [40,41], thus the level of GFP expression in the transduced cells should reflect the expression level of the ORF upstream to the IRES which, in this specific case, is p67. Although this was not investigated in this work, because the pool of transduced cells produced enough p67 in the medium of HEK-p67ΔTM, the amount of p67 could be strongly increased by simply sorting HEK-p67ΔTM cells with the highest GFP expression. Moreover, secreted p67 was highly soluble and purification was not needed since HEK-p67ΔTM could be maintained in serum-free medium, allowing the collection of supernatant almost free of nonspecific protein. This is a great advantage when a secreted protein needs to be used as an antigen for immunization purposes or for diagnosis. In fact, serum-free medium supernatant coming from HEK-p67ΔTM cells was successfully employed to immunize goats and cattle. The pEF1α-p67ΔTM-iresGFP lentiviral vector was used to generate many other cell lines stably expressing p67, coming from different animal species, including the bovine, which is T parva natural host. These cell lines can be used as p67 antigen cargos for cell-based immunization.
In the present piece of work the production of T parva p67 antigen, either as a membrane linked or as a secreted form was successfully achieved. The general work-flow we proposed here could be applied for the production of other Apicomplexan antigens to be delivered by mammalian expression vectors such as viral vectors, plasmid vector injection or gene gun, cell based immunization or simply as secreted antigens produced in mammalian cells.
|
10.1371/journal.pntd.0000732 | Serological Studies of Neurologic Helminthic Infections in Rural Areas of Southwest Cameroon: Toxocariasis, Cysticercosis and Paragonimiasis | Both epilepsy and paragonimiasis had been known to be endemic in Southwest Cameroon. A total of 188 people (168 and 20 with and without symptoms confirmed by clinicians, respectively, 84.6% under 20 years old) were selected on a voluntary basis. Among 14 people (8.3%) with history of epilepsy, only one suffered from paragonimiasis. Therefore, we challenged to check antibody responses to highly specific diagnostic recombinant antigens for two other helminthic diseases, cysticercosis and toxocariasis, expected to be involved in neurological diseases. Soil-transmitted helminthic infections were also examined.
Fecal samples were collected exclusively from the 168 people. Eggs of Ascaris lumbricoides, Trichuris trichiura and hookworms were found from 56 (33.3%), 72 (42.8%), and 19 (11.3%) persons, respectively. Serology revealed that 61 (36.3%), 25 (14.9%) and 2 (1.2%) of 168 persons showed specific antibody responses to toxocariasis, paragonimiasis and cysticercosis, respectively. By contrast, 20 people without any symptoms as well as additional 20 people from Japan showed no antibody responses. Among the 14 persons with epilepsy, 5 persons were seropositive to the antigen specific to Toxocara, and one of them was simultaneously positive to the antigens of Paragonimus. The fact that 2 children with no history of epilepsy were serologically confirmed to have cysticercosis strongly suggests that serological survey for cysticercosis in children is expected to be useful for early detection of asymptomatic cysticercosis in endemic areas.
Among persons surveyed, toxocariasis was more common than paragonimiasis, but cysticercosis was very rare. However, the fact that 2 children were serologically confirmed to have cysticercosis was very important, since it strongly suggests that serology for cysticercosis is useful and feasible for detection of asymptomatic cysticercotic children in endemic areas for the early treatment.
| A total of 188 people (168 and 20 with and without symptoms confirmed by clinicians, respectively, 84.6% under 20 years old) were selected on a voluntary basis in Cameroon. Soil transmitted helminthic infections were prevalent among persons surveyed as is common in developing countries, since eggs of Ascaris lumbricoides, Trichuris trichiura and hookworms were found from 56 (33.3%), 72 (42.8%) and 19 (11.3%) persons, respectively. Serological analyses revealed that 61 (36.3%), 25 (14.9%) and 2 (1.2%) persons were positive to the diagnostic antigens specific for toxocariasis, paragonimiasis and cysticercosis, respectively. Among 14 people with epilepsy, 5 persons were seropositive to the antigen of Toxocara and one of them was simultaneously positive to the antigens of Paragonimus. Serological confirmation of cysticercosis in two children is very important, and we suggest that further serologic surveys of cysticercosis be carried out in both children and adults in this area for the promotion of a better quality of life including control and early treatment.
| Parasitic infections are serious public health problems in many developing countries [1],[2]. These diseases can affect various tissues and organs including the brain leading to neurological dysfunction. Cysticercosis caused by Taenia solium metacestodes has been assumed to be the most common parasitic infection of the brain worldwide including Cameroon [3]–[5]. As cysticercosis is one of the major causative agents of the late-onset of epilepsy, the major work on cysticercosis has been carried out for adults but not for children in endemic areas, and other causative agents of epilepsy still remain unclear. Therefore, we were lead to obtain more information on the causative agents of epilepsy in developing countries, since many helminthic diseases including toxocariasis, paragonimiasis, onchocerciasis etc., and also protozoan diseases including malaria, toxoplasmosis and others may cause epilepsy [4]–[6]. Among these neglected helminthic diseases, toxocariasis is expected to have cosmopolitan distribution, since dogs and cats are companion animals with close contact with people in the world [7], [8]. Although there are no data on the prevalence of human toxocariasis in Cameroon, its prevalence in dogs in Cameroon is high [9]. Simultaneously, there is poor information on cysticercosis in children in Cameroon, although it seems to be rather common in the adult population [4], [5].
Tombel health district in South West Province in Cameroon (Figure 1) is known as an endemic focus of epilepsy and is also highly endemic for paragonimiasis [10],[11]. Our previous report in this area showed that 8.3% of enrolled people (14/168) suffered from epilepsy but only one of the epileptic patients simultaneously suffered from paragonimiasis [11]. Therefore, we concluded that paragonimiasis was not the major cause of epilepsy in children in this area.
In this study, we used the same 188 samples examined for paragonimiasis [11] and additional 20 samples from Japan, where cysticercosis and paragonimiasis have long been eradicated and toxocariasis is very rare [12], as healthy controls. We performed serosurveys using highly specific recombinant antigens for toxocariasis and cysticercosis, and simultaneously analyzed the unpublished data on microscopic observation of soil-transmitted helminthic (STH) infections. Serological data on paragonimiasis for this study were modified from published data [11]. Although onchocerciasis was known to be endemic in Cameroon and might be involved in neurological disorder, we could not examine simply because the lack of serological tools [13], [14].
Four villages in rural areas, Bulutu, Ebonji, Etam and Teke, were selected for this study. They are located in the Tombel Health District (50,000–100,000 inhabitants) in the rain forest zone about 40 km northwest of Kumba, Manengouba Department, South West Province of Cameroon (4°3′N, 9°3′W). The annual average temperature is 24°C and the relative humidity varies from 52% to 74%. Agriculture is the principal economic activity; hunting and fishing are also practiced (Figure 1).
The survey, approved by the National Ethics Committee of Cameroon, was conducted in the general population in January 2004 and February 2006 in villages mentioned above.
The chief of each village was informed about the study and participants or parents/guardians were asked to give informed consent for participation. A total of 188 people ranged in age from 4 to 78 years (14.9±7.8 years in males and 13.1±6.1 years in females) were examined by clinicians and were asked whether they had experienced symptoms such as cough, haemoptysis, headache, epilepsy, chest pain, and eye disorder and whether they consumed raw and/or undercooked fresh water crabs or pork. Our study population with symptoms ranged from 0–10 years (80 persons), 11–20 years (63 persons), and >21 years (25 persons). Following the questionnaire, serum, sputum and fecal samples were collected from 168 people who accepted to participate to the study voluntarily (28, 52, 55 and 33 from Bulutu, Ebonji, Etam, and Teke villages, respectively). By contrast, 20 healthy persons [5 persons from each village including 11 females and 9 males ranged from 6 to 34 years (13.0±3.7 in males and 15.1±7.5 in females)] confirmed by clinicians donated serum samples exclusively; these serum samples were used as expected healthy controls. An additional 20 serum samples from students at Asahikawa Medical College (AMC), Japan, were used as confirmed healthy controls. Sputum was examined for eggs of P. africanus [11]. Fecal samples were examined by flotation techniques for the presence of eggs to provide a diagnosis of helminthic infections.
A total of 208 serum samples were examined by ELISA. A recombinant antigen of T. canis second-stage larvae (0.5 µg/ml) was used for toxocaraisis [15]. Glycoproteins (GPs) (1.0 µg/ml) from T. solium cyst fluid purified by preparative isoelectric focusing (pH 9.2–9.6) were used for screening of cysticercosis by ELISA [16]. Immunoblot using a recombinant chimeric antigen, 100% specific to cysticercosis (0.5 µg/mini gel) was applied for serological confirmation of cysticercosis [16]–[19]. Somatic antigens of P. africanus adult worms (5µg/ml), which showed few cross reactivity with other parasitic infections were used for paragonimiasis [11]. Briefly, 96-well microtiter plates (Maxisorp; Nunc, Roskilde, Denmark) were coated with each of the antigens described above in PBS and incubated at 4°C overnight. The plates were probed with diluted serum samples. Serum dilutions were in 1∶200 with bicarbonate buffer for toxocariasis, and 1∶100 and 1∶200 with blocking buffer for cysticercosis and paragonimiasis, respectively, according to the original papers for these diseases described above. Peroxidase-conjugated rec-Protein G (Zymed, San Francisco, USA) diluted in 1∶1000 with blocking buffer was added into each well. Peroxidase activity was revealed by adding 0.4 mmol/l 2,2-azino-bis 3 ethybenz-thiazoline-6-sulphonic acid in 0.1 mol/l sodium citrate buffer, pH 4.7 containing 0.003% H2O2 at room temperature. The optical density (OD) was monitored at 405 nm on a microplate reader (ImmunoMini, model NJ-2300; Nalgene Nunc International, Tokyo, Japan). The cut-off value was calculated for each antigen based on the means+3SD of 40 healthy donors from the local areas in Cameroon (n = 20) and from Japan (n = 20).
To obtain adjusted odds ratios (ORs) of paragonimiasis and toxocariasis seropositivities for each symptom, we performed multivariate logistic regression analysis adjusted for age (−10, 11–20, 21year) and sex. Because the number of cysticercosis seropositivity was rather small (n = 3), we did not analyze the ORs of cysticercosis seropositivity. For all statistical analyses, a 5% level of significance was applied. All statistical analyses were conducted using SPSS for Windows version 18.0 (SPSS, Inc., Chicago, U.S.A.).
In this study, the samples used for paragonimiasis [11] were also tested for toxocariasis and cysticercosis and also the data of STHs were analyzed. The enrolled persons (168: 78 males and 90 females) were diagnosed suffering from cough (n = 135, 80.3%), haemoptysis (n = 18, 11.3%), chest pain (n = 80, 47.6%), epilepsy (n = 14, 8.3%), visual impairment (n = 30, 17.8%) and headache (n = 106, 63.0%) and had histories of eating raw or undercooked crabs (n = 137, 81.5%) or pork (n = 135, 80.3%). Microscopic examination revealed Paragonimus eggs in sputum from 16 (9.5%) persons but no eggs from feces [11], whereas A. lumbricoides, T. trichiura, and hookworms were found in feces from 56 [33.3%; 30 (53.5%) males and 26 (46.4%) females], 72 [42.8%; 38 (52.7%) males and 34 (47.2%) in females] and 19 [11.3%; 14 (73.6%) males and 5 (26.3%) females] persons, respectively. Among these helminthic infections, hookworm infection exclusively showed statistically significant difference between the genders (p<0.05). The difference in prevalence between males and females for hookworm infection may be due to the barefoot roaming behavior of males but further investigation of this topic is needed. The highest multiple infections were found in 3 kids infected with 3 STHs and were simultaneously seropositive for paragonimiasis and toxocariasis as well.
ELISA for diagnosis of toxocariasis, paragonimiasis and cysticercosis indicated that 61 [36.3%; 31 (50.8%) males and 30 (49.1%) females], 25 [14.9%; 10 (40%) males and 15 (60%) females] [11], and 3 [1.8%; 2 boys of 13 and 11 year-old, and one girl, 4 year-old] persons were positive (Figure 2). Persons with cough and haemoptysis were more likely to have paragonimiasis (Table 1, OR = 7.19 and 2.28 respectively, p<0.001), whereas there was a relative risk with other symptoms. As none of the symptoms were specific for toxocariasis, the probability to have the infection was equally likely in exposed and control group as OR values were close to 1 (Table 1). The likelihood for cysticercosis to occur was not included due to the low number of seropositive persons. Nonetheless, there were crucial differences in antibody responses between the two groups. Furthermore, there was no difference in OD values between healthy controls from endemic Cameroon and from non-endemic, Japan where we expected no positive samples from students at AMC. Therefore, we concluded that the serological findings indicated specific responses to these three helminthic infections. The ELISA system applied for paragonimiasis in this study was much more sensitive for diagnosis than detection of eggs as already shown (Figure 2) [11]. As it has already been shown that the ELISA for toxocariasis in this study showed no cross-reactions with ascariasis patients in Asia and Latin America [20], [21], we consider that it is highly specific to toxocariasis. As children are the most risky population for toxocariasis and the prevalence of T. canis in dogs in Cameroon was very high [9], we expected that 61 persons (36.3%) were really exposed to eggs of Toxocara [22], [23]. Among these seropositive persons, 11 persons were concluded to have dual infection of both Toxocara and Paragonimus.
Three children (1.7%) showing weak responses to the GPs of T. solium by ELISA (Figure 2) were further analyzed using the recombinant antigen for serological confirmation of cysticercosis, since there are no false positive antibody responses to the recombinant antigen by immunoblot [17]–[19]. Two of them showing higher OD values by ELISA (Figure 2) exhibited positive response with the recombinant antigen by immunoblot (Figure 3) [17], [18]. Therefore, these two cases are considered as asymptomatic cysticercosis and are important targets for cysticercosis studies in the future. We believe that further epidemiological surveys for neurocysticercosis in the adult population should be carried out in the same areas, since 1) the late-onset epilepsy due to cysticercosis is expected to be detectable more common from senior people [3]–[6], [24], [25], 2) cysticercosis prevalence in Cameroon ranges from 2.5% to 13% [4], [5] and 3) more than the half of epileptic adult patients show antibodies against cysticercosis in West and North West regions in Cameroon using the same serology [5].
In Papua, Indonesia, one of the most serious endemic areas of cysticercosis in the world, more than 80% and 70% of people over 18 years old, who had history of epileptic seizures with or without subcutaneous nodules, were confirmed as having cysticerci, respectively [26], [27]. Approximately 30% of asymptomatic healthy people were serologically identified as positive for cysticercosis and follow up investigations revealed that many of them had detectable subcutaneous nodules. Furthermore, the most recent retrospective study using molecular tools has revealed that a cysticercus of T. solium survived at least for 10 years in a patient's brain [28].
According to these data mentioned above, the most important implication on cysticercosis from this serological study is that asymptomatic cysticercosis can be detected from children in endemic areas. Therefore, introduction of serological screening of children becomes highly informative for detection of asymptomatic cases and for getting better and early treatment for them [29]. Follow-up studies on these 2 boys using neuroimaging tools are necessary for further evaluation. We recommend highly reliable serological screening for cysticercosis for all pupils in the primary school, if possible, or all teenagers at least in highly endemic areas. As risk factors associated with human cysticercosis include the occurrence of cysticercosis in pigs, detection of adult worm carriers should be investigated. For the future survey of taeniasis carriers, both copro-ELISA [30] and copro-DNA tests [31] are expected to be introduced in this area, Cameroon, and in any other areas where cysticercosis is highly endemic.
Participants in the study were selected on a voluntary basis and may not be representative for the population as the whole but the numbers of children younger than 20 years were approximately 84.6% of surveyed persons. Therefore, the results are highly informative as a preliminary study identifying areas for further investigation of all these helminthic infections in this area.
In conclusion, toxocariasis, paragonimiasis and cysticercosis have been serologically confirmed among surveyed persons. Five of 14 epilepsy cases were sero-positive for toxocariasis. Correlation between epilepsy and these helminthic infections should be further evaluated, since screening of children for these parasitic diseases may become more important and feasible for the early treatment and prevention of these infections and promotion of better quality of life in the future.
|
10.1371/journal.pcbi.1004584 | Computing the Local Field Potential (LFP) from Integrate-and-Fire Network Models | Leaky integrate-and-fire (LIF) network models are commonly used to study how the spiking dynamics of neural networks changes with stimuli, tasks or dynamic network states. However, neurophysiological studies in vivo often rather measure the mass activity of neuronal microcircuits with the local field potential (LFP). Given that LFPs are generated by spatially separated currents across the neuronal membrane, they cannot be computed directly from quantities defined in models of point-like LIF neurons. Here, we explore the best approximation for predicting the LFP based on standard output from point-neuron LIF networks. To search for this best “LFP proxy”, we compared LFP predictions from candidate proxies based on LIF network output (e.g, firing rates, membrane potentials, synaptic currents) with “ground-truth” LFP obtained when the LIF network synaptic input currents were injected into an analogous three-dimensional (3D) network model of multi-compartmental neurons with realistic morphology, spatial distributions of somata and synapses. We found that a specific fixed linear combination of the LIF synaptic currents provided an accurate LFP proxy, accounting for most of the variance of the LFP time course observed in the 3D network for all recording locations. This proxy performed well over a broad set of conditions, including substantial variations of the neuronal morphologies. Our results provide a simple formula for estimating the time course of the LFP from LIF network simulations in cases where a single pyramidal population dominates the LFP generation, and thereby facilitate quantitative comparison between computational models and experimental LFP recordings in vivo.
| Leaky integrate-and-fire (LIF) networks are often used to model neural network activity. The spike trains they produce, however, cannot be directly compared to the local field potentials (LFPs) that are measured by low-pass filtering the potential recorded from extracellular electrodes. This is because LFPs are generated by neurons with spatial extensions, while LIF networks typically consist of point neurons. In order to still be able to approximately predict LFPs from LIF network simulations, we here explore simple proxies for computing LFPs based on standard output from LIF network simulations. Predictions from the various LFP proxies were compared with “ground-truth” LFPs computed by means of well-established volume conduction theory where synaptic currents corresponding to the LIF network simulation were injected into populations of multi-compartmental neurons with realistic morphologies. We found that a simple weighted sum of the LIF synaptic currents with a single universally applicable set of weights excellently capture the time course of the LFP signal when the LFP predominantly is generated by a single population of pyramidal cells. Our study therefore provides a simple formula by which the LFP signal can be estimated directly from the LIF network activity, providing a missing quantitative link between simple neural models and LFP measures in vivo.
| Models of recurrently connected networks of leaky integrate-and-fire (LIF) neurons are well established tools for studying brain function [1,2]. The equations describing the single LIF neuron are simple and can be easily adapted to generate complex dynamics [3,4]. Despite their simplicity, LIF network models have proved able to describe a wide spectrum of different cortical dynamics and cortical functions, from the emergence of up and down states [5–7], working memory [8–10], attention [11,12], decision making [13], rhythmogenesis [14], and sensory information coding [11,15,16]. In some cases it is possible to describe the dynamics of LIF networks analytically [17,18], thus providing deeper insights into how spiking neuronal networks may implement the basic cerebral computational mechanisms [19].
Models can only be properly tested against experimental evidence when they can predict empirical measures quantitatively. Local cortical activity is often recorded in vivo or in vitro using the local field potential (LFP), a measure obtained by low-pass filtering (below a few hundred hertz) the electrical potential recorded from extracellular electrodes. The LFP signal reflects mass neural activity arising within a few hundred micrometers or more from the recording electrode [20–25]. This spatial scale is highly relevant for LIF network models, which typically aim to describe the activity of thousands or tens of thousands of cells. The recording of LFPs has a prominent role in systems neuroscience, and such recordings have been used extensively to investigate cortical network mechanisms involved in sensory processing [26], motor planning [27], and higher cognitive processes [28].
LFP is generated by transmembrane currents in the neurons in the vicinity of the recording electrode [23] and depends on morphological features of the contributing cells, the positioning of synapses, as well as the correlation level of synaptic inputs [20,21,29,30]. Under reasonable assumptions about the extracellular milieu the cellular LFP contributions can be computed as a weighted sum of the transmembrane currents in multi-compartment neuron models [31–34]. This allows for detailed numerical investigations of spatial, as well as spectral features of the LFP signals [35]. In particular, such simulations of large populations of morphologically detailed neurons have provided insight into how the neuronal activity at the population level influences the spatial reach and laminar variation of the LFP signal in vivo [20,21,33,34,36] the relative importance of active and passive currents [37], and the population LFP signal measured from cortical slices in microelectrode arrays (MEAs) [38,39].
However, it has been unclear how best to use LIF networks to model and provide understanding of LFP recordings. This is because extracellular potentials arise in biological tissue due to a spatial separation of inward (sinks) and outward (sources) transmembrane currents of the neurons, and neuron models used to compute an LFP signal must thus have a minimum of two spatially separated compartments in order to generate a potential [32]. In LIF models, however, a single compartment is typically used as an approximation of the entire neuron, including the spatially extended dendritic structure, and individual cells within a population are not assigned to a specific spatial position.
One possible way to compute LFPs from LIF network is to project the spike times generated by the LIF network under consideration onto morphologically detailed 3D neuron models and then compute the field that the currents flowing through these 3D networks generate. However, this approach would require the modeler to set up a cumbersome and computationally expensive network model based on multi-compartment model neuron. As a much simpler alternative, we here instead search for a general and easy-to-use proxy to predict the time course of the LFP based on variables available directly from the LIF network simulations. Here we investigate and evaluate different strategies to compute an LFP proxy directly from the output of standard LIF network simulations without the use of multi-compartment neuronal morphologies.
Our approach is as follows: we first simulate an LIF point-neuron network model and record the output spiking activity, membrane potentials, and synaptic currents. Next, we compute a realistic ground-truth estimate of the LFP that the same LIF network activity would generate. We do this by injecting distributed synaptic currents corresponding to the stored LIF synaptic events, onto a population of multi-compartment neurons with realistic distributions of dendrites and synapses (we call this population the “3D network”). We then compare this simulated ground-truth LFP signal to a number of LFP proxies computed directly from measures of activity of the point-neuron LIF network. These proxies include those previously proposed in the literature (e.g., the average firing rate [11,14,40], the average membrane potential [24,41–44], the sum of synaptic currents [7,45], and the sum of absolute values of synaptic currents [15]), as well as others proposed here. By separating the spiking dynamics generated by the LIF network from the LFP generated by the 3D network, we are also able to investigate how different assumptions regarding cell morphology, synaptic distributions and recording positions influence the accuracy of the different LFP proxies.
We find that a simple linear combination of excitatory (AMPA) and inhibitory (GABA) synaptic currents extracted from the point-neuron LIF network provides a proxy for the LFP that closely matches the temporal features of the signals resulting from the morphologically realistic LFP model generated by the 3D network. Even with a small set of fixed parameters this LFP proxy is able to account for the LFP signal with a high degree of precision under most investigated conditions.
Our goal was to understand how to compute a simple yet accurate approximation (denoted as “proxy” in the following) of the LFP that would be generated by the time series of synaptic activity of an LIF network if its neurons had a realistic spatial structure and arrangement. We therefore first simulated an LIF network (known to reproduce several features of cortical dynamics). Next, we injected the synaptic activity it generated into a synthetic three-dimensional network model (3D network) of a layer of a cortical column that employed multi-compartmental neurons with realistic morphology, spatial distributions of somata and synapses, and computed the extracellular potentials generated by this synaptic activity.
We selected an LIF network (adapted from [14] and refined in [15,16,46,47]) that has been shown to reproduce a number of important features of the dynamics of visual primary cortical neural population recorded in vivo during naturalistic sensory stimulation, including a realistic spectrum of cortical dynamics and of its modulation with the visual stimuli, including low-frequency (1–12 Hz) and gamma (50–100 Hz) oscillations [15,46]. Moreover, when using a simple proxy (which is demonstrated below to perform well) to compute an LFP from synaptic currents, this LIF network reproduced quantitatively several important properties of recorded extracellular potentials, including LFP power spectra and spectral information content [15], and cross-frequency and spike-field relationships [16,46]. Thus, the LIF network seemed to generate a sufficiently realistic dynamics to provide synaptic input for the generation of biologically plausible LFPs in the 3D network.
The LIF network model (Fig 1A) was composed of 4000 excitatory and 1000 inhibitory LIF neurons that were randomly connected with a pair-wise connection probability of 0.2 (for further details see Methods). The LIF network received two kinds of external inputs: a “thalamic” synaptic input thought to carry the information about the external stimuli and a stimulus-unrelated input representing slow ongoing fluctuations of activity. Synaptic dynamics and parameters are reported in Tables 1 and 2, and further details can be found in the Methods. Importantly, as is the case for most LIF network models to date, our LIF network did not have any spatial structure: the individual neurons were not assigned to a specific spatial position and consequently the connectivity had a random and sparse structure.
The LFP signal that would result from the time series of spikes generated by the LIF network provided the postsynaptic neurons had biologically plausible dendritic structures, was computed by injecting the LIF synaptic activity into a 3D network of morphologically detailed multi-compartmental model neurons (Fig 1B, see Methods). A summary of the properties of the 3D network is reported in Table 3, while the synaptic parameters are listed in Table 4 (see Methods for further details). In order to set up the 3D network we were required to make additional assumptions regarding the spatial positioning of cells, the shape and size of their dendritic structures, as well as the synaptic distributions. We focused on computing the LFP generated by one cortical layer (in terms of soma positions) that comprised both inhibitory and excitatory neurons. In our default setting, we assumed all neurons in the 3D network to be inside two cylinders with 250 μm radius and 250 μm height that were stacked one above the other to resemble the vertical structure of layer 2/3 (Fig 1B). Note that this spatial scale is similar to the size of the neuronal pool contributing to the recorded LFP, the so-called spatial reach, in the case of uncorrelated synaptic activity driving a neuronal population [20,21,35], and resulted in a neuronal density consistent with known estimates of 50000 neurons per mm3 in the cortex [48]. While our two model populations most directly resemble a pair of excitatory and inhibitory populations in cortical L2/3, we show in subsection “Dependency of the LFP signal on dendritic morphology” that our results also pertain to the LFP generated by neuron morphologies found in other cortical layers.
Given these geometrical constraints, we created the multi-compartmental cell models in the 3D network in the following way: soma locations for all cells were homogeneously distributed within the lower cylinder (Fig 1B). Next, we placed artificial straight axons that were distributed at random cortical depths and random orientations within both cylinders. They served as targets in an algorithmic generation of dendrites, through which pyramidal cell dendrites were connected to all axons within a specified reach distance while optimizing the following wiring conditions: short conductions times, short total cable length and synaptic democracy (i.e., equal impact of synaptic inputs at the site of dendritic integration [49,50]). This procedure has been shown previously to reproduce pyramidal-cell-like dendrites [51]. The number of axons and their length were set so that the resulting cell morphologies matched the membrane surface distribution of real cortical layer 2/3 pyramidal cell reconstructions [52] within the constraints of the simplified columnar arrangement that was chosen for this study. This procedure also provided good matches for total cable lengths and number of branch points (compare membrane surface distribution in S1 Fig and see Methods for more details). Note that the virtual axons used for the generation of the morphologies were subsequently discarded. Since the membrane area (and consequently the transmembrane current) of the axons is very small compared to the dendrites, we expected them to have a negligible contribution to the present 3D network LFP generation.
Stellate cell dendrites were generated in a similar manner, but were only connected to axons in the lower cylinder. This resulted in stellate cell morphologies with realistic bush-like dendrites. Fig 1C illustrates the overall structure of the resulting 3D network.
To further validate the simulation results obtained with these morphologies, we also built an alternative 3D network with anatomically reconstructed morphologies (see Methods) and checked that the results were essentially the same as for algorithmically grown morphologies (see subsection “Performance of LFP proxies in different dynamic network states”).
Finally, AMPA synapses were homogeneously distributed over the whole neuronal surface while GABA synapses were located only in the lower cylinder, closer to the soma (Fig 1B, see Methods for details). Alternative synaptic distributions are explored in the “Dependency of the LFP signal on the distribution of synapses” subsection.
For each neuron in the LIF network we randomly assigned a multi-compartmental neuron model with a unique dendritic structure in the 3D network. The connectivity of the LIF network determined which postsynaptic spikes in the LIF network simulation should serve as input spikes for each multi-compartment neuron. We then used these spike times together with the external input (see above) to activate synaptic currents in the 3D network (see Methods). In this way we assured that the synaptic input in a multi-compartment neuron was identical to its LIF neuron counterpart. The synaptic dynamics in the 3D network was identical to that in the LIF network. In a subsequent step, we took into account all transmembrane currents in the neurons of the 3D network to compute the LFP by means of well-established volume conduction theory and the so called line-source method [31,34] (see Methods).
Fig 1 shows a half-second excerpt of results for an example simulation using the spiking activity generated by the LIF network (Fig 1D) in response to a 1.5 spikes/ms stimulus (see Methods for details) to calculate the corresponding LFP signal along the vertical axis of the cylinder at different electrode depths from the 3D network (Fig 1E). The temporal fluctuations of the LIF signal were strongly correlated across depth, albeit with a sign shift around the depth just between the two cylinders (which we from now on will refer to as the inversion point). The sign of the baseline (DC) LFP was negative above the inversion point while it was positive below it. This reflects that the LFP was dominated by the perisomatic inhibitory synapses generating a net source current close to the soma and sink return currents in the apical branches. The excitatory synapses contributed less due to their homogeneous distribution (Fig 1B), giving only a weak current dipole [29], as will be discussed in more detail in the next sections.
We defined the amplitude of LFP fluctuations at each depth as the standard deviation of the signal over time, and further assigned it the same sign as the LFP baseline, i.e., negative/positive above/below the inversion point. The magnitude of the LFP amplitude was largest around the middle of each cylinder (Fig 2A), decreased steeply close to the inversion point and more smoothly beyond the vertical boundaries of the network. The decrease of the amplitude of the LFP fluctuations when the electrode was moved away from the center of the 3D network is shown in Fig 2B for all depths. This decrease in LFP power was consistent with results of [20]: inside the 3D network (X/R < 1, where X is the displacement of the electrode from the center and R is the radius of the cylinder) differences were small, but when the electrode was placed outside the 3D network (X/R > 1) the decrease was steep. Note that the region around the inversion point where the potential is very small, broadened with the distance from the center.
We observed that all power spectra recorded outside this noise-dominated region had similar shapes (Fig 2C and 2D), suggesting that LFP fluctuations could be roughly approximated by the same time series rescaled by the numerical value of the LFP amplitude shown in Fig 2B. The observation that LFPs recorded in different spatial positions had similar temporal behavior and differed mainly by a scaling factor, suggested that a single LFP proxy could work for recordings at different depths and positions in the horizontal plane, provided that it is properly scaled. Such a factorization of spatial and temporal dimensions can be expressed (see [30]) as
LFPproxy(r,d,t)=fproxy(r,d)*gproxy(t)
(1)
where d is the depth and r the distance from the population center. The term fproxy(r, d) then gives the amplitude of the signal as a function of the electrode position (as in Fig 2B) while the dimensionless gproxy(t) has variance equal to one and describes the temporal features of the LFP signal.
We first focused on finding the optimal gproxy(t) for an LFP signal recorded at selected depths along the central vertical axis (X/R = 0) of the 3D network. However, we found (see subsection “New class of LFP proxies”) that the identified optimal LFP proxy was applicable also to other depths and radial distances of the populations (given an appropriate overall scaling of the signal amplitudes, cf. Fig 2B).
The contribution to the LFP signal from synaptic inputs onto the interneurons (and their associated return currents) was negligible both in amplitude (Fig 3A) and in determining the LFP spectrum (Fig 3B). This was due to the different morphologies of the two types of neurons: consistently with what was shown previously for stellate cells with symmetrically placed synapses [20] (i.e., a so-called close-field arrangement [53]), the contribution from the interneurons to the LFP was negligible (Fig 3). Further, the associated power spectrum of this contribution was closer to colored noise and did not display gamma fluctuations. We investigate this in detail in the subsection “Dependency of the LFP signal on dendritic morphology”.
Since we obtained a very similar LFP when we only simulated the contribution from synaptic inputs onto the pyramidal neurons, all the results shown in the following will, unless otherwise stated, consider only the contributions from pyramidal neurons to LFP. Likewise, the LFP proxies will be based only on input onto excitatory neurons (as done previously [15]). However, the inhibitory neurons obviously play a key role (i) in generating the dynamics and (ii) in providing the GABA currents of synapses onto pyramidal neurons that contribute strongly to the LFP.
We first tested six LFP proxy candidates (Fig 4A): AMPA currents, GABA currents, the average firing rate FR, the average membrane potential Vm, and the sum of these synaptic currents ∑I as well as their absolute values ∑|I|. Note that the "AMPA currents" and "GABA currents" proxies are defined as the sum of the post-synaptic currents for each type of synapse over all pyramidal neurons (see Table 1G). These currents have depolarizing and hyperpolarizing effects, respectively, on the postsynaptic neurons. We thus here use the convention that assigns a positive sign to AMPA currents and a negative sign to GABA currents.
Because of the opposite signs assigned to the AMPA and GABA currents, the sum of the absolute values of the currents ∑|I| is equivalent to the difference between the currents. For several reasons, i.e., synaptic delay and dendritic filtering, we expected the best proxy for the LFP time course to possibly involve time-delayed measures of LIF network variables. To assess the best values of these delays we first computed the cross-correlation function between the ground-truth LFP and the considered LFP proxy obtained from the LIF network, and found the delay at which the absolute value of the correlation was largest (for half of the recording depths the correlation is negative due to LFP inversion). The LFP proxy that we chose was the z-scored (i.e., baseline-subtracted and normalized to have variance equal to one) and time-shifted LIF network variable that maximized the fraction of variance explained, R2. Finding the best delay and rescaling factor was done separately for each depth, but we found that the differences in the observed best values of the delay across depth, were minor (see S2B Fig). Fig 4B and 4C shows the comparison between the 3D network LFP signal at two different electrode depths and the LFP proxy given by the sum of absolute values of the synaptic currents ∑|I|, that given our sign convention simply becomes the difference between the currents, i.e.,
LFP∑|I|(r,d,t)=f∑|I|(r,d)*Norm[∑pyrAMPA(t−τ)−∑pyrGABA(t−τ)]
(2)
where Norm[] indicates the mean-subtracted, normalized version of the time series between square brackets. Fig 4D and 4E shows the cross correlation between the 3D network LFP signal and proxy for the two depths.
A comparison of the average fraction of variance explained by all the LFP proxies displayed in Fig 4A across different depths (Fig 4F and 4G) shows that the best one was the sum of absolute values of synaptic currents ∑|I| (<R2> = 0.83) followed by the negative of the GABA currents (<R2> = 0.81) and then the AMPA currents (<R2> = 0.78). The negative of the sum of synaptic currents ∑I and membrane potential Vm performed in a similar way (<R2> = 0.69), while the firing rate FR gave a poor fit (<R2> = 0.51). The R2 is slightly larger for depths about 100 μm from the inversion point, probably due to stronger synaptic and return currents.
We found two results to be of particular interest. The first was that a proxy based on GABA currents alone gave clearly a better match for the simulated LFP signal than the AMPA currents alone. The second was that the ∑|I| gives the best fit which suggests that the magnitude of the AMPA currents locally sums with the magnitude of the GABA return currents. Thus the two types of synaptic currents contribute to the LFP with the same sign. This feature is partly due to the fact that AMPA synapses are distributed over the whole surface of pyramidal neurons, while GABA synapses are located only in the lower cylinder close to the soma (Fig 1B). This will be further investigated in the “Dependency of the LFP signal on the distribution of synapses” subsection.
The fits above were computed by averaging the time-varying variables over the set of excitatory neurons in the LIF network. However, we also tested the quality of the fit obtained by averaging over all the neurons in the LIF network or only over inhibitory neurons. The results for each variable and depth are shown in S2A Fig, together with the associated optimal delays. The relative ranking of the candidate proxies remains unaltered. Further, proxies obtained by averaging the firing rate, the membrane potential, or the synaptic input currents over the excitatory neurons (as above) performed better than proxies obtained by averaging the same variables over the inhibitory neurons set and roughly the same as proxies obtained averaging over all neurons (S2A Fig).
Since AMPA and GABA currents contributed differently to the LFP signal we investigated a novel proxy, the weighted sum between AMPA and GABA currents (WS), that uses a linear combination of AMPA and GABA synaptic currents where we introduce a factor α describing the relative contribution of the two currents and a specific delay for each type of current:
LFPWS(r,d,t)=fWS(r,d)*Norm[∑pyrAMPA(t−τAMPA)−α(∑pyrGABA(t−τGABA))]
(3)
Note that the two proxies ∑|I| and ∑I are particular cases of the above equation in which the delays are the same, and α is equal to 1 and -1 respectively.
We first tested the WS proxy with the electrode located in the center of the 3D network for different depths. The optimal value of α was always positive, but varied across depths (Fig 5A). The optimal delays were always in the range [5–7] ms for τAMPA ms and in the range [-1 1] ms for τGABA. This implies that the optimal LFP proxy was achieved by subtracting the GABA PSCs (postsynaptic currents) from the AMPA PSCs occurring around 6 ms in the past. Performance was very high for all depths (up to 93% of variance explained, see Fig 5B). Since the optimal values of α, (Fig 5A) τAMPA and τGABA (S2B Fig) were relatively stable across depths, we defined a new proxy: the reference weighted sum LFP proxy (RWS). The structure of the RWS proxy is the same as the WS proxy but the variables are fixed: α is set to the average accross depths of the optimal values for WS (1.65, see Fig 5A) and the delays to τAMPA = 6 ms and τGABA = 0 (S2C Fig). This results in
LFPRWS(r,d,t)=fRWS(r,d)*Norm[∑pyrAMPA(t−6ms)−1.65(∑pyrGABA(t))]
(4)
We found that the performance of this proxy was almost indistinguishable from the single-depth optimized values across depths (Fig 5B) and largely outperformed all other proxies. Moreover, we found the performance of a proxy with α = 1.65 to be very good (>80% of variance explained) for a broad range of other AMPA- and GABA-current delays (S2C Fig).
We next tested the performance of the proxies for different distances of the electrode from the center of the 3D network: Fig 5C compares the fraction of variance explained by WS, RWS and the other proxies mentioned above for LFPs measured at different distances from the center of the 3D network. The depicted results are found from averaging across all depths. The Standard Error of the Mean of R2 across depths was <1% for all proxies and all lateral displacements and is not displayed in the figure since it would not be visible. Values for explained variance were very stable for different lateral electrode positions: in particular, for all lateral displacements RWS performances were similar to WS and outperformed all other proxies (Fig 5C). The average optimal value of α across depths was always close to the reference value 1.65 (Fig 5D). Given that the RWS proxy was much simpler than WS (see below) and able to explain more than 90% of the variance of the LFP time course at a wide range of electrode recording positions, we tentatively propose this as the best proxy for the LFP signal computable directly from LIF network variables.
The proxies given by the combination of two synaptic parameters (WS and RWS) have four free parameters (scale as described by the function f in Eq 1 and following, AMPA and GABA delays, relative amplitude of AMPA and GABA contribution) while the other proxies have only two free parameters (scale, delay). We assessed by means of the Bayesian Information Criterion (BIC, [54], see Methods for details) whether the benefit in terms of improved performance of the models based on the linear combinations of synaptic currents was worth the increase in model complexity due to the higher number of parameters. We found that, according to this model selection criterion, RWS outperforms all previous proxies and WS outperforms RWS and all other proxies (S3 Fig), demonstrating the power of the RWS and WS models. However, the optimal WS parameters are by construction different for each recording position and, as we will see in the following, for various network structures and states. Thus comparison of LFP predictions from use of the WS proxy requires detailed knowledge about recording position as well as the characteristics of the underlying network, and will thus have limited practical use. On the other hand, as the parameters of the RWS proxy are fixed, it can be used directly for all locations in space. As seen in the following, the RWS proxy performs well for a broad set of conditions (input intensity, neuron morphology, synaptic distribution), and this means crucially that the proxy can be used also under weak assumptions about the spatial structure of the underlying network. We thus conclude that RWS is the best LFP proxy based on LIF network variables. In the following we will test its robustness for different dynamic network states, spatial architectures and synaptic properties.
So far we investigated the LFP proxies using LIF networks in a state exhibiting weakly synchronized oscillations in the spiking dynamics, stimulating the LIF network at a relatively low intensity (1.5 spikes/ms). However, LIF networks can generate a variety of different dynamic network states when the frequency of external inputs is varied [14,15,18]. In order to test LFP proxies in different dynamic network states, we stimulated the LIF network with a wide range of input intensities, covering both much higher and much lower intensities than the one tested in above.
Fig 6A shows, from left to right, a raster plot of a subset of neurons in the LIF network for a low-intensity input (0.5 spikes/ms), the default input level (1.5 spikes/ms), and a high-intensity input (6 spikes/ms). Shown below (Fig 6B) is the LFP signal generated in the 3D network at the reference depth of 100 μm for these three cases together with their corresponding WS fits. For external stimulation with 0.5 spikes/ms, recurrent activity in the LIF network was almost absent, with all pyramidal neurons and most interneurons being silent. The LFP amplitude was very small and the signal very noisy. For an input of 1.5 spikes/ms, firing was sparse with coexisting slow and high-frequency LFP fluctuations, and for 6 spikes/ms the dynamics were dominated by high-frequency LFP gamma oscillations also visible in the LIF network spiking activity.
With an input frequency of 0.5 spikes/ms, none of the candidate proxies was able to account for the LFP (Fig 6C). This was presumably because in these low-firing conditions, randomly occurring, uncorrelated synaptic inputs onto the neurons close to the electrode dominated the LFP signal. Such activity does not give a strong dipolar LFP pattern [21] and is apparently more difficult to capture with the global LIF network variables considered in the proxies. For the larger inputs ranging from 1 to 6 spikes/ms, however, the WS proxy was able to explain more than 91% of the variance. RWS was able to explain 88–91% of the variance between 1 to 3 spikes/ms with a small decrease to 87% for an input of 6 spikes/ms (Fig 6C). For inputs of 1 spikes/ms or more the sum of the absolute values of the synaptic currents explained 81–85% of the variance, the membrane potential 70–79%, the sum of the currents 70–79%, and the firing rate only 51–60%. Overall, the ranking of the proxies regarding their R2 values remained the same for all dynamic network states and the RWS provided an excellent proxy in all cases. As shown in Fig 6D the relative weighting between AMPA and GABA currents as given by the parameter α for the WS proxy was stable and close to the reference values 1.65 chosen for RWS for input stimulus intensities, except for the case of very low-intensity input in which the LFP signal is almost absent and the fit is poor.
A key property of the LIF network is that it exhibits a prominent gamma-band activity (30–100 Hz) in the overall firing activity when the input intensity is increased as indicated by an increased peak in the power spectral density (PSD) [15]. We therefore investigated how this is reflected in our simulated LFP signal and how well the LFP proxies capture these properties of the LFP signal. Fig 7A shows the power spectra for three different input frequencies. All proxies except for the membrane potential tended to underestimate the low frequency LFP fluctuations and to overestimate frequencies in the gamma range. WS and RWS proxies both produced a nearly perfect fit of the LFP spectrum in the gamma-band range while exhibiting the smallest error in the low frequency components among all proxies. In the 1–3 spikes/ms input range the modulation of the LFP gamma power was well approximated by all proxies, while for 6 spikes/ms input, WS and RWS underestimated it (Fig 7B). All proxies essentially predicted the correct peak LFP gamma frequency (Fig 7C) for all input levels above 1 spikes/ms.
We hypothesized that the negligible contribution of inhibitory neurons was due to the weak dipole moment created by the symmetrically placed synapses on the dendrites of stellate cells [29]. To test this hypothesis we investigated in the following the effect of neuron shape on the LFP generation by systematically altering the morphology of the interneuron population while keeping its inputs fixed. This manipulation also tested the robustness of the LFP proxies to the specific choice of the neuronal morphology. We started with two overlapping cylinders (distance = 0 μm) describing the stellate cell morphology. Then we progressively increased their “pyramidalness”, i.e., the distance between the two dendritic bushes and generated a new interneuron population for each cylinder distance (Fig 8A; see Methods for details). The generated morphologies ranged from pure stellate cells (the interneuron used in the reference case), to cells corresponding to layer 2/3 pyramidal cells where the two cylinders were juxtaposed (the pyramidal neuron used in the reference case), to cells where the two areas were parted by several hundred micrometers (as in layer 5 pyramidal neurons). In all cases GABA synapses were distributed only on dendrites located inside the lower cylinder, while AMPA synapses were distributed over the entire dendritic tree (Fig 1B).
We found that the LFP signal from the 1000 interneurons was very weak for cylinder distances less than about 100 μm, corresponding to a 40% overlap between the two cylinders (see Fig 8B and 8C). The amplitude of the LFP signal increased with the cylinder distance together with the current dipole moment (Fig 8C and 8D; see Methods). The “transition distance” of about 100 μm is seen to be associated with the appearance of an inversion point in the LFP (Fig 8C) and with the establishment of a sizable dipole moment (Fig 8D). Above this transition distance the LFP became larger with larger cylinder separations, yet saturating somewhat for distances above about 250 μm, corresponding to our reference model of layer 2/3 pyramidal cell. This demonstrates that the lack of a sizable contribution to the overall LFP from our interneurons in the reference model was due to their stellate morphologies.
Below the inter-cylinder transition distance all proxies performed poorly with average fraction of variance explained across depths smaller than 70% (for 100 μm the range was <R2> between 0.37 and 0.64), but <R2> quickly saturated as soon as the dipole appeared (Fig 8E). <R2> was smaller for all proxies compared to the reference case (since the noise was larger due to the smaller number of neurons, i.e., 1000 neurons versus 4000 neurons for the reference case), but the ranking of performances for different proxies remained roughly the same: above the transition distance the fraction of variance explained by WS was 83%, RWS and the sum of absolute values of currents both explained 80%, the membrane potential and sum of synaptic currents 59%, while firing rate explained only 47% of the variance. Note that for inter-cylinder distances above the transition distance, the stable performance of the proxies were accompanied by stable values of the optimal coefficient α (Fig 8F). This result implies that the RWS we have found for populations of layer-2/3-like pyramidal cells, likely also can be applied to pyramidal cell populations with different morphologies, as long as they produce a dipolar LFP.
In order to verify that the assumptions we made to algorithmically construct the neuronal morphologies in the 3D network did not bias the results, we also did simulations using realistic morphologies obtained from anatomical reconstructions (see Methods). The spatiotemporal dynamics of these LFP signals was found to be qualitatively very similar to the one previously shown, and the agreement with proxies was even higher, with RWS reaching R2 = 0.95 (S4 Fig). This result indicates that our conclusions are not strongly dependent on the detailed branching patterns within the basal and apical dendritic bushes.
In the reference case (Fig 1B) GABA synapses were distributed only in the lower cylinder while AMPA synapses were distributed homogeneously across all dendrites. In order to test how our results depended on this distribution we therefore evaluated all LFP proxies for a variety of synaptic distribution patterns. Fig 9A illustrates the three main different synaptic distributions tested: (1) a case where all synapses were distributed homogeneously, (Hom.) (2) the reference case (Ref.), and (3) a case where AMPA synapses were located only in the upper cylinder (AM Up), leading to a complete separation between AMPA and GABA synapses. We further considered two conditions where (4) AMPA synapses were located only in the lower bush leaving the upper bush empty (AM down) and where (5) AMPA cortical synapses were located in the upper bush while thalamic AMPA inputs were distributed homogeneously (AMr Up).
Even though the parameters in the LIF network and thus the output activity remained precisely the same as before in these different situations, the corresponding LFP signal was dramatically altered by the choices of synaptic distributions (Fig 9B). The amplitude of the fluctuations was strongly affected, while the spatiotemporal features were only moderately altered. Note, however, that the position of the thalamic synapses only marginally affected the LFP fluctuations, and only the mean value of the LFP was affected. As a rule of thumb, we found that the more spatially segregated AMPA and GABA synapses are, the larger are the LFP fluctuations (Fig 9C). We further observed that the variation of the LFP amplitude on the synaptic distribution directly reflected changes in the magnitude of the current dipole moment (Fig 9D).
The individual contributions to the LFP from AMPA and GABA synapses were strongly dependent on the spatial distributions (Fig 9E): when synapses were distributed homogeneously, the contribution of their currents to the LFP signal was small as compared to when the synapses were segregated. Moreover, the AMPA contribution was larger when synapses were confined to the upper than to the lower cylinder. When the synapses were distributed homogeneously, the LFP signal was very weak resulting in poor performances for all LFP proxies (Fig 9F). When the cortical AMPA synapses were confined to the upper bush, the performance of the WS proxy was not affected, but a small decrease of 0.07 in the <R2> value was observed for both RWS and the sum of the absolute values of synaptic currents. For the same situation there was a larger decrease of 0.17 in the <R2> value to a global value of only 0.51 for both the membrane potential and the sum of synaptic currents. However, in the configuration in which AMPA synapses were confined to the lower bush and the LFP amplitude was small, the <R2> for the membrane potential and the sum of synaptic currents rose to 0.81 and 0.79 respectively, a value comparable to results for the WS and RWS proxies (0.80 and 0.78). This suggests that the advantage of using the WS and RWS proxies over, e.g., a membrane-potential proxy is particularly large when the AMPA and GABA synapses are spatially separated so that a large current dipole moment and a large amplitude LFP is generated (Figs 8C and 9D).
The best coefficients for WS strongly depended on the synaptic distribution (Fig 9G): When AMPA synapses were confined to the upper cylinder forming a strong current dipole moment, the optimal AMPA coefficients became larger than the GABA ones. Therefore, although the R2 value of RWS was still 0.82 under these conditions, a better result could be achieved with a proper tuning of the coefficients.
To keep the consistency with the LIF network in which the synapses were current-based (see Methods), all LFP simulations considered until now were done using current-based synapses in the 3D network. However, some neuronal features may be better approximated by conductance-based synaptic models in which the postsynaptic currents (PSCs) depend on the local membrane potential and do not have a fixed shape as in the case of current-based synapses. To test this situation, we repeated our simulations by introducing conductance-based synapses in the 3D network. Synaptic time constants were left unchanged, while the peak conductance values were scaled to obtain PSC amplitudes equivalent to current-based synapses for the reference stimulus intensity 1.5 spikes/ms [47].
While the simulated LFP amplitude was smaller when using conductance-based instead of current-based synapses (compare the three panels in Fig 10A with the three panels in Fig 6B and note the different y-axis scales), the time course was similar. We found that the explanatory power of the proxies was similar or better in all cases compared to the situation with LFPs computed with current-based synapses (Fig 10A): the R2 values for the RWS were in the range 0.91–0.93 for inputs between 1 and 3 spikes/ms, and 0.88 for 6 spikes/ms. We hypothesize that the main reason for the increase in performance was that the LFP contributions from different neurons were more correlated when synapses were conductance-based [47]. Note that in the case with conductance-based synapses, the performance of the membrane potential proxy is in the very low 0.5–0.6 range for R2 for all stimuli above 1 spikes/ms. This can be understood given that the membrane potential no longer depends linearly on synaptic input currents as in the case with current-based synapses.
The WS proxy coefficients for 1 spikes/ms inputs were rather similar to the current-based case, but when the input frequency was increased, the optimized value of the coefficient α, describing the ration of GABA to AMPA currents in the WS proxy, increased (Fig 10C). This likely reflects that for stronger stimuli the neurons were more depolarized, so that the average membrane potential was closer to the AMPA reversal potential and further away from GABA reversal potential. Consequently, the GABA versus AMPA PSC-amplitude ratio increased. Nevertheless, the RWS still performed well for all inputs (Fig 10B).
The main aim of this work was to develop an accurate, robust and an easy-to-use method to synthesize the LFP signal from output from a model network of LIF neurons. We simulated a biophysically realistic LFP signal using a population of morphologically detailed multi-compartmental neuron models and compared this LFP signal with several LFP proxy candidates extracted from the LIF network simulations. We found that a linear combination of summed and time-shifted AMPA and GABA currents in the LIF network explained a large fraction of the variance of the LFP of the 3D morphologically accurate network in nearly all conditions considered. Specifically, we identified a specific set of parameters (Eq 4), the so-called reference weighted sum LFP proxy (RWS), which accurately predicted the LFP time course for all considered electrode positions (Fig 5), and across different dynamic network states (Fig 6).
The fraction of LFP variance explained by the RWS proxy was only moderately affected by changes in neural morphology (Fig 8, S4 Fig), in synaptic distribution (Fig 9), or in synaptic dynamics (Fig 10). This LFP proxy was found to be very accurate for every condition considered in which the dipole generated by the synaptic currents was sizable and hence the amplitude of the LFP substantial. This LFP proxy only performed poorly in situations where the amplitude of the LFP signal itself was very small, i.e., at the inversion point or when the resulting current dipole moments from synaptic activation are small (homogeneous synaptic distributions (Fig 9), low activity (Fig 6)). Furthermore, we showed that despite the complexity of our LFP simulation setup (with 5000 different morphologies with realistic dendritic structures) the temporal evolution of the LFP was well captured by the RWS proxy based on synaptic currents with adjustment of only three parameters, the relative weight of the contributions from the two synaptic currents and the two synaptic delays. Our results further suggested that the RWS proxy can be used for a wide range of LIF network models and pyramidal-neuron morphologies to synthesize biophysically plausible LFP signals that can be compared with experimental LFP recordings. Table 5 describes how to properly use the proxy in a variety of conditions and modeling approaches.
Thanks to its robustness, our proxy can expectedly be applied to models of any brain area in which the LFP is likely to be generated by one dominant population (as in the hippocampus with a single population of pyramidal cells). When there are two or more populations giving a significant contribution to the LFP (as is likely in whole cortical column model taking into account several pyramidal neuron populations, e.g., layer 2/3, layer 5, layer 6), the total LFP can be approximated as a suitable linear combination of individual contributions if information on the depth positions of the populations relative to the recording electrode is available. Comparison of the model LFP with experimental results might then be used to estimate the relative weights of the LFP contributions from the different populations.
A major difference between the accurate LFP proxies using synaptic currents (sum of currents, WS, RWS) compared to the less accurate proxy based on firing rates is that a spike is a very local event in time, while the postsynaptic current following after a spike (as well as the contribution to the LFP) lasts for many milliseconds. So an instantaneous firing rate proxy like the ones we are considering based on firing rates cannot be expected to perform well (even with a fixed delay). In laminar population analysis (LPA, [30]) the LFP time course was rather assumed to be given by the measured firing rates convolved with a suitable (i.e., delayed exponential) kernel, the rationale being that spikes causally drive synaptic currents which in turn set up the LFP. The present RWS proxy is similarly constructed, effectively corresponding to a suitable weighted sum of exponentially convolved presynaptic spike rates corresponding to excitatory and inhibitory synaptic currents. The postsynaptic soma membrane potentials following presynaptic spiking is more low-pass filtered than the synaptic currents (and also the transmembrane return currents in the case of multicompartmental models) [29], and LFP proxies based on this dynamical variable will generally fail to predict the most rapid temporal changes in the LFP.
An interesting result is that all the proxies tested here displayed largely the same modulation of the LFP gamma power as a function of input intensity, both in terms of relative power modulation and peak frequency (Fig 7D and 7E). This is encouraging since we did not specifically aim to find a good prediction of the power spectrum when constructing the LFP proxies and estimating their parameters. We note however that no proxy is fully able to account for the low-frequency end of the spectrum (Fig 7A and 7B), which is overestimated by the membrane potential proxy and underestimated by the other proxies. If one is interested in a highly detailed reproduction of the whole LFP spectrum, preliminary results hint to the possibility of designing a WS fit optimized to match the spectrum instead than the spatiotemporal features and to define an LFP proxy that slightly differs from the RWS discussed above. However, the fraction of spectral variance explained by the RWS is already 0.91 (average over all stimulus intensities above 0.5 spikes/ms, standard morphology and synaptic condition) which likely is sufficient for most purposes.
In the present work we have focused on how the relationship between LIF variables and ground-truth LFP change when the 3D model features change, keeping the LIF model fixed. While different LIF networks would generate different activity and hence different synaptic currents, we expect roughly the same relationship between these synaptic currents and the generated LFP. Therefore, for any LIF network generating enough correlated activity to result in a sizeable LFP, we expect RWS to be a good proxy.
Our strategy had the advantage that we could vary the assumptions in the LFP-generating model, e.g., the distribution of synapses or neuronal morphologies, without affecting the spiking dynamics. The disadvantage of this approach is, however, that the 3D network does not match the LIF network in every respect; for instance, even though the synaptic input currents were identical in the two models, the resulting soma potentials in the multi-compartmental neurons were not identical to those in the LIF neurons (due to passive dendritic filtering). It is, however, unlikely that imposing identical somatic potentials, or identical currents entering the soma, in the two models would result in a more realistic LFP since large synaptic currents would be needed to overcome the passive filtering for distant synapses. Instead one could consider changing the synaptic weight distribution in the LIF network simulation to make the two models match better. Our focus here was to use LIF models as commonly used in the literature (typically using homogeneous weight distributions), but it would be an interesting topic for future studies to extract effective point-neuron synaptic weight distributions from the multi-compartmental population and use these in the LIF network simulations in order to make the two simulation environments even more similar.
We did not test different LIF network architectures or sizes, but we expect the RWS proxy to be applicable as long as the network displays a sufficient level of correlation. We have found in previous modeling studies [20,21] that correlated synaptic activity is necessary to create a sizable LFP signal, and in this case all cells in the dominant LFP-generating population will contribute. Making the network size larger or altering its connectivity would therefore likely not qualitatively change the form of the best LFP proxy (as long as a sufficient level of spiking correlations is maintained in the network). The LFP generated by larger populations, however, should be tested in further studies taking into account the summed effect of several cortical populations, across layers as well as heterogeneous spatial structure in the horizontal direction.
A limitation of the presented simulation setup is that it models only AMPA and GABA synapse contributions. However, most of our results pertaining to the proxy do not depend on the particular feature of the synapses and are therefore likely to extend to different synapses as well. For instance, it should not matter for the quality of our suggested proxy whether or not the synaptic weights are changing due to plasticity since the weight changes will be reflected in the synaptic currents extracted from the LIF network as well. Including slower synapses, such as NMDA synapses in the model setup, will on the other hand affect the LFP frequency content, particularly at low frequencies. This effect could be captured by a proxy including NMDA in the sum of synaptic currents with a weight depending on the number and spatial distribution of NMDA synapses. As with the synaptic weight distributions discussed above, the inclusion of NMDA synapses when computing the LFP proxy presupposes that it is also included in the LIF network model (which was beyond the scope of this study). Moreover, we did not model subthreshold active dendritic conductance [55], nor the active channels behind spike generation. The contributions from the latter is expectedly negligible for at least the low frequencies of the LFP [56] (but see [57–59]), while the effect of the former should be explored in future projects.
The present suggested proxy assumes the LFP contribution following spikes to be spatiotemporally separable, i.e., factorizable into a product of a function of time with a function of space [30]. Due to, for example, the intrinsic filtering effect [29,36,60] this is not strictly true as the spatial distribution of the transmembrane currents setting up the LFP depends to some extent on the frequency. However, if warranted the present proxy can be extended, for example by assuming a more detailed proxy consisting of a sum of such spatiotemporally separable kernels.
Recently, we presented an analytical method to estimate the LFP spectrum from the dynamics of a LIF network [61] using as LFP proxy the sum of the absolute values of synaptic currents. By fitting a recurrent excitatory-inhibitory LIF network model to LFP recordings from monkeys presented with visual stimuli, we were able to estimate the LIF model that best fitted the observed LFP, and to predict at least in part the observed firing rate and some of the visual features in the receptive field that elicited the observed neural activity. In this recent work [61], the time evolution of the LFP was computed analytically from the LIF network as a function of the external input by applying linear response theory to the mean-field approximations of each kind of synaptic current separately and then summing their absolute values over pyramidal neurons (as in [15] and in Eq 2). In principle, it is possible to extend this analytical calculation by using the more efficient proxy presented here by simply changing the coefficients in the final sum of the synaptic currents. This paves the work for obtaining realistic analytical estimations of LFPs from recurrent LIF networks. As discussed in [35], an efficient analytical approach could be at the heart of the development of model-based analysis methods for performing inferential statistics of network models on LFPs, analogous to the role played by Dynamic Causal Modelling [62,63] in the analysis of EEG and fMRI recordings.
Here we studied proxies for the LFP produced by a local 3D network, corresponding to a single cortical layer. Experimentally recorded LFPs, however, are most likely containing contributions from several layers [20]. Therefore, a natural extension of this work would be to study the LFP generated by several connected 3D networks forming a full cortical columns [64,65] and determine how LFP proxies should be designed in this context. Since electrical potentials in the nervous tissue are assumed to add linearly, we expect LFP proxies to be constructed in largely the same manner as presented here, by summing synaptic contributions from different cortical layers, possibly with a weighting depending on the recording depths. Constructing the LFP signal from a full cortical column model [65] is the topic for a separate ongoing project [66].
We expect our proxy to also work well for other brain structures where pyramidal neurons are elongated and arranged in an almost parallel way, such as the CA1 and CA2 regions of the hippocampus. On the other hand, many subcortical structures have a neuronal architecture so different from the cortex that we that we cannot a priori expect the present rules of LFP prediction to be applicable. A possible future line of research will be to apply the combination of LIF dynamics and 3D morphology we used in this work to investigate such areas to find a compact way to study the mechanisms generating the LFP observed there.
We focused in the present study on the LFP signal, but finding good models for relating activity in spiking network models and experimentally measured signals is relevant also for other types of commonly recorded signals such as the EEG, MEG and VSD. Since the biophysical mechanisms generating these signals are in principle known, we believe our framework could be extended to also study other measurement modalities in the future.
We summarize here the structure of the LIF network that generated the spiking dynamics. We refer to [15,46] for full details. The network was composed of LIF neurons with current-based synapses whose time evolution was modeled as difference between exponentials (see below), fixed threshold, fixed refractory time [67], and fixed conduction delay of 1 ms. Subthreshold dynamics for each neuron were given by
τmV˙m(t)=−Vm(t)+∑PSCsyn(t)
(5)
where τm corresponded to the membrane time constant due to the leak, Vm was the membrane potential, and PSC were the occurring synaptic events as a function of time t. When the membrane potential Vm crossed a threshold value of 18 mV above resting potential, a spike occurred, the potential dropped to a reset value of 11 mV above the reset potential and no spike could be emitted for a refractory time of 2 ms.
Post-synaptic currents (PSCs) were determined by the spikes emitted by the pre-synaptic neurons in the LIF network as well as by the external inputs. The time course of PSCs was described by the difference of two exponentials simulating the opening and closing process of the synapse. The equation can be written with two first order differential equations introducing the auxiliary variable x:
τdsynPSC˙(t)=−PSC(t)+x(t)
(6)
τrsynx˙(t)=−x(t)+τm(Jsyn∑synδ(t−tsyn−τl))
(7)
where τr/dsyn indicate the rise and decay times of the synapses, and Jsyn indicates the synaptic strength. The latency time of the synapses τl was set to 1 ms. Compound synaptic currents were the linear sum of contributions induced by single pre-synaptic spikes occurring at time tsyn. We included two types of synapses: AMPA and GABA. Pyramidal neurons had AMPA-like synapses, and interneurons had GABA-like synapses. Moreover, each neuron received excitatory external drive from (1) a long range cortico-cortical input activating AMPA synapses identical to those of the recurrent connections and (2) a thalamic input activating AMPA synapses with timescales and strengths resembling those of thalamocortical synapses. Synaptic parameters such as rise time, decay time, and amplitude depended on the type of synapse and the category of the post-synaptic neuron. All simulation parameters were in the range of the values reported in the literature [68–70] and are listed in Table 2. We verified that modifying these values did not affect the results qualitatively [15,46].
The default network was composed of 4000 pyramidal neurons and 1000 interneurons (Fig 1A). The LIF network connectivity was random and sparse, with a directed connection probability of 0.2 between any pair of cells. This resulted in an inhomogeneous connectivity with an average of 1000 pre- and post- synaptic connections for each cell. Each neuron received inhibitory and excitatory inputs from the neurons in the network, and also cortico-cortical and thalamic excitatory drives as described above. The long-range cortico-cortical drive represented the ongoing activity and the global contributions from other areas of cortex. Since ongoing cortical activity has most power for slow frequencies, this external drive was generated by an Ornstein-Uhlenbeck process with a low pass cut-off frequency of 10 Hz and a 0.25 mV standard deviation. Thalamic inputs were time-invariant in this set of simulations. Synapses carrying both types of external inputs were activated by random Poisson spike trains, with time-varying rates identical for all neurons. Details can be found in Table 1 and 2.
Simulations were computed with time steps of 0.05 ms and lasted 10.1 seconds, with the first 100 ms removed to limit the analysis to the network steady state.
Current based and conductance based LIF model source codes are identical to those used in [47] and are already available on the ModelDB sharing repository (http://senselab.med.yale.edu/ModelDB/ShowModel.asp?model=152539) with accession number 152539.
In order to compute the transmembrane currents that lead to an LFP signal, we constructed 3D morphological neuron models that captured the main morphological features of the cortical network described by point neurons in the LIF model. The algorithm used to construct the model morphologies was based on the fact that dendrites connect to their presynaptic partners in a manner minimizing their total length and conduction times from all synapses to the soma [71]. In such a framework, pyramidal cell dendrites can be seen as tree structures connecting as directly as possible to axons that are distributed in two distinct layers [51]. The generation of synthetic trees and subsequent analysis were performed using the TREES toolbox [71,72] [http://www.treestoolbox.org]. Two cylinders (250 μm radius and 250 μm height each) were therefore stacked to form a cylindrical column (Fig 1B). Somata of all cells were homogeneously distributed in the lower cylinder for both cell types. Axons were distributed isotropically within planes perpendicular to the cortical depth at random depth values. Pyramidal cells were connected first to the axons in the upper cylinder and then to the axons of the lower cylinder, this resulted in characteristic apical and basal dendritic trees. Stellate cells were only connected to the axons in the lower cylinder. Using 160 axons in each layer and a maximal reach distance of 150 μm for any dendrite to an input axon, resulted in realistic membrane surfaces, cable lengths and branch point number distributions (see S1 Fig). Diameter taper was selected to equalize synaptic democracy [73] and yielded good fits to the real counterparts with similar parameters for interneurons and pyramidal cells. The resulting pyramidal cell somatic input resistance was about 200 MΩ with specific membrane resistances Rm = 20000 Ωcm2 and axial resistances Ra = 150 Ωcm. The stellate cell input resistance was 175 MΩ with specific membrane resistances Rm = 10000 Ωcm2 and axial resistances Ra = 150 Ωcm. In order to spatially embed the simplified LIF network model, 4000 such pyramidal cells and 1000 interneurons were generated to populate the simplified columnar architecture. The resulting morphologies were then exported to NEURON [74,75] using the TREES toolbox functions.
In order to continuously alter the “pyramidalness” of cortical neurons as in Fig 8 we simply modulated the distance between the two cylinders corresponding to the two layers. With a distance of 0 μm, a perfect overlap of both cylinders, the resulting shape was symmetric as for the stellate cell. As the distance was increased between the two cylinders, the shape of the cortical cell traversed the shape of layer 2/3 pyramidal cells (distance of 250 μm), layer 4 pyramidal cells (distance of 350 μm) and layer 5 pyramidal cells (distance larger than 500 μm). The corresponding validation of morphological features compared with real dendrite reconstructions as can be observed in S1 Fig.
As a control for use of algorithmically constructed morphologies we derived an alternative model using multiple copies of real reconstructions distributed within the columnar arrangement. We used reconstructions from NeuroMorpho.org [76], made available by the group of Markram [76], of both stellate cells and layer 2/3 pyramidal cells in young rat somatosensory cortex. Since only 4 stellate cells and 36 layer 2/3 pyramidal cell morphologies were available, we reached the number of 1000 interneurons and 4000 pyramidal neurons by randomly selecting copies of the smaller sample and distributing them within the simplified columnar geometry. Cell body locations were chosen to preserve a fairly homogeneous distribution of membrane throughout the cylinders. This alternative model was then injected with the same synaptic current stimuli as the original model based on algorithmically developed morphologies, and yielded similar results (compare Fig 6 and S4 Fig).
Spike trains generated by the LIF network were used as input in the 3D network model used for LFP generation. Each multi-compartmental neuron model in the 3D network was associated with a given point neuron in the LIF network. To make sure the total synaptic currents in each cell were identical in the two simulation environments, we used the connectivity structure of the LIF network to determine the presynaptic LIF neurons for each postsynaptic multi-compartmental neuron in the 3D network. We triggered the synaptic currents in the multi-compartmental neurons of the 3D network at the precise times given by the spike trains generated by the presynaptic cells during LIF network simulations. Note that we did not take into account synaptic latency time. In the 3D network we associated with each presynaptic cell a single specific synapse in the postsynaptic cell. Synaptic dynamics in the 3D network was identical to the one in the LIF network (Eqs 4 and 5). In addition we recreated the external inputs (“Thalamic” and “Cortical”, see Fig 1A) used in the LIF network simulations and injected the same patterns of external spike trains in specific AMPA synapses in the 3D network neurons.
Since the LIF neuron model used in the LIF network simulations lacked spatial structure, we needed to make additional assumptions regarding the synapse placement when simulating the multi-compartmental neurons in the 3D network. The cylinders that were used to create the morphologies of the multi-compartmental models (see above) were also used to broadly define the synaptic regions. Our default setting was to place GABA synapses only in the lower cylinder, while AMPA synapses were placed in both cylinders. We tested also other scenarios in the “Dependency of the LFP signal on the distribution of synapses” subsection of Results (Fig 9). We randomly chose the detailed spatial position on the dendritic structure for each synapse, with the probability for a section to be selected being proportional to its membrane area, such that the resulting synaptic density was homogeneous within the selected cylinder
We calculated the model LFP signal from the transmembrane currents in the multi-compartmental neuron populations based on volume conduction theory and the line-source approximation implemented in the Python package LFPy (http://lfpy.github.io/) [34]. We first simulated transmembrane currents resulting from synaptic activity using the NEURON simulation environment [74,75] after which extracellular potentials were calculated as a weighted sum of those transmembrane currents [31,32,34]. The extracellular potentials were computed for 32 equispaced vertically aligned points in space (simulating a laminar multielectrode), set at 25 μm intervals along the central vertical axis of the 3D network cylinder (Fig 1B). For the analysis illustrated in the subsection “Spatial distribution of simulated LFP signal” the recording locations were set at different distances from the vertical axis of the 3D network cylinder. To directly match the LIF network simulations, morphological neurons used current synapses in the reference case, except in the simulation discussed in the subsection “Difference between current-based and conductance-based synapses for the LFP signal” were conductance synapses were adopted (Table 3E). The calculations of transmembrane currents in the morphological model were performed using passive neuron models with the parameters listed above (Table 4). Following volume conductor theory, the model neurons were assumed to be surrounded by an infinitely sized extracellular medium with conductivity assumed to be real, scalar (the same in all directions) and homogeneous (the same everywhere) with σ = 0.3 S/m [77]. For further discussion on these assumptions see [32].
The Python codes we used to generate LFP from artificial morphologies injected with LIF spike dynamics are available on the LFPy official site (http://lfpy.github.io/).
We tested several simple models to match the LFP simulation based on the different variables describing the activity in the LIF network: firing rate, membrane potential, AMPA and GABA synaptic currents. We considered variables computed over the set of all pyramidal neurons, of all interneurons or both populations. We considered proxies based on these variables and on the simple sum or the sum of absolute values of synaptic currents as in [15,46]. Then we considered linear combinations of synaptic currents with different time delays. We tested the accuracy of the proxy in describing the time evolution of the LFP given by the morphological model by using the mean of squared values of the correlation coefficient R (which is equivalent to the fraction of variance explained). The quality of the proxy was tested separately for each depth. We computed the cross-correlation between the simulated LFP signal and the corresponding proxy and we determined the delay as the lag of the cross-correlation peak (see Fig 4). For this delay we determined the best linear fit using the Matlab function polyfit for single regressors and the Matlab function regress for regressor combinations. We estimated the quality of the proxy as the squared correlation coefficient between the best fit and the LFP. The proxy for each depth is defined by the optimal delays and the coefficients of the different components for regressor combinations. To compare the performance of the different proxies taking into account the different number of free parameters between WS, RWS and all the other proxies, we used the Bayesian Information Criterion (BIC, [54,78])
BIC=−2l+Klogn
(8)
where l is the optimized loglikelihood function, K the number of estimable parameters and n the sample size. Under the assumption of Gaussian noise, −2l can be approximated as constant+nlogRSSn [79] where RSS is the sum of the residual squares, so the BIC criterion becomes
BIC=nlogRSSn+Klogn
(9)
which is the criterion we adopted in the manuscript.
|
10.1371/journal.pgen.1006131 | Identification of Conserved MEL-28/ELYS Domains with Essential Roles in Nuclear Assembly and Chromosome Segregation | Nucleoporins are the constituents of nuclear pore complexes (NPCs) and are essential regulators of nucleocytoplasmic transport, gene expression and genome stability. The nucleoporin MEL-28/ELYS plays a critical role in post-mitotic NPC reassembly through recruitment of the NUP107-160 subcomplex, and is required for correct segregation of mitotic chromosomes. Here we present a systematic functional and structural analysis of MEL-28 in C. elegans early development and human ELYS in cultured cells. We have identified functional domains responsible for nuclear envelope and kinetochore localization, chromatin binding, mitotic spindle matrix association and chromosome segregation. Surprisingly, we found that perturbations to MEL-28’s conserved AT-hook domain do not affect MEL-28 localization although they disrupt MEL-28 function and delay cell cycle progression in a DNA damage checkpoint-dependent manner. Our analyses also uncover a novel meiotic role of MEL-28. Together, these results show that MEL-28 has conserved structural domains that are essential for its fundamental roles in NPC assembly and chromosome segregation.
| Most animal cells have a nucleus that contains the genetic material: the chromosomes. The nucleus is enclosed by the nuclear envelope, which provides a physical barrier between the chromosomes and the surrounding cytoplasm, and enables precisely controlled transport of proteins into and out of the nucleus. Transport occurs through nuclear pore complexes, which consist of multiple copies of ~30 different proteins called nucleoporins. Although the composition of nuclear pore complexes is known, the mechanisms of their assembly and function are still unclear. We have analyzed the nucleoporin MEL-28/ELYS through a systematic dissection of functional domains both in the nematode Caenorhabditis elegans and in human cells. Interestingly, MEL-28/ELYS localizes not only to nuclear pore complexes, but is also associated with chromosomal structures known as kinetochores during cell division. Our studies have revealed that even small perturbations in MEL-28/ELYS can have dramatic consequences on nuclear pore complex assembly as well as on separation of chromosomes during cell division. Surprisingly, inhibition of MEL-28/ELYS causes cell-cycle delay, suggesting activation of a cellular surveillance system for chromosomal damages. Finally, we conclude that the structural domains of MEL-28/ELYS are conserved from nematodes to humans.
| Metazoans have an open mitosis, in which the nuclear envelope (NE) disassembles during prophase to allow chromosome segregation and then reassembles around condensing chromosomes at anaphase [1]. During this process, the nuclear pore complexes (NPCs) are disassembled then rapidly reconstructed. ELYS, a large AT-hook domain protein, is essential for the late-mitosis rebuilding of the NPC [2]. ELYS is the first NPC component to associate with chromatin at the end of mitosis [3, 4] and this association is required for the recruitment of the NUP107-160 subcomplex of the NPC, which in turn recruits vesicles containing the membrane-bound nucleoporins POM121 and NDC1 [4]. Thus ELYS binding to chromatin represents the first step in the post-mitotic building of the pore, and all other steps in its manufacture are dependent on this ELYS/chromatin interaction.
ELYS was originally identified in a cDNA subtraction screen seeking genes expressed at high levels in the mouse embryonic sac [5]. Mouse elys knockouts die in the preimplantation stage because of cell death within the inner cell mass [6]. ELYS function is essential in all metazoa and is particularly important in rapidly dividing cells [7, 8]. In C. elegans, the orthologous MEL-28 protein dynamically localizes to the nucleoplasm and NPC at interphase and then at the kinetochore and spindle at metaphase [9, 10]. Consistent with its localization pattern, embryos that lack mel-28 function have severe defects with NE function, mitotic spindle assembly and chromosome segregation and are unviable.
The ELYS/chromatin interaction has been studied extensively in vitro using Xenopus cell extracts. ELYS binds to chromatin during interphase but not at metaphase [11], when it instead associates with the spindle and kinetochore [12]. Chromatin immobilization assays have shown that the most C-terminal fragment of ELYS, corresponding to amino acids (aa.) 2281–2408, is sufficient for chromatin binding. This region includes the AT hook, a motif that binds to AT-rich DNA. However the aa. 2281–2408 fragment with a mutated AT hook and a C-terminal fragment that excludes the AT hook (aa. 2359–2408) also bound to chromatin [4]. A nucleosome binding assay showed that a large C-terminal fragment that includes the AT hook (aa. 2281–2408) was sufficient to bind to nucleosomes, whereas a piece that includes just the AT hook (aa. 2281–2358) or just the region C-terminal to the AT hook (aa. 2359–2408) could not bind to nucleosomes [13]. Additionally, incubation of Xenopus extracts with the C-terminal 208-aa. fragment of ELYS prevented native ELYS from binding to sperm chromatin and also prevented the recruitment of other nucleoporins to the nuclear rim, phenocopying the elys loss-of-function phenotype [11]. However, introducing a C-terminal fragment with a mutated AT hook does not disrupt nuclear pore assembly and is less effective at outcompeting the endogenous ELYS from binding to chromatin [4]. These in vitro experiments suggest that both the AT hook and other domains of the C terminus are important for the ELYS/chromatin interaction and the subsequent rebuilding of the NPC.
The ELYS/chromatin association has also been studied using mouse in vitro fertilization. During fertilization in mice, sperm chromatin is rebuilt de novo using histones present in the oocyte. Experiments using in vitro fertilized mouse oocytes depleted of histones showed that ELYS does not localize to the NE of the sperm pronucleus in the absence of histones, which in turn prevents the recruitment of other nucleoporins [14]. ELYS can be artificially targeted to the NE in the absence of histones by fusing it with a domain from an inner NE protein. This chimeric ELYS protein not only localizes to the NE but also recruits the other nucleoporins. This suggests that ELYS binding to chromatin is required for its localization to the nuclear rim, which in turn allows the remainder of the nuclear pore to be built.
The overall architecture of MEL-28/ELYS is similar throughout the metazoa (see schematic representations in Figs 2C and 7B). All metazoan MEL-28/ELYS homologs include an N-terminal β-propeller domain, a central α-helical domain, and a C-terminal domain that includes at least one AT hook. Crystal structure determination of the N-terminal domain of mammalian ELYS showed that it forms a seven bladed β-propeller structure with an extra loop decorating each of the propeller blades [15]. In human cells, the N-terminal 1018 amino acids of ELYS (which includes the β-propeller domain and the central α-helical domain but not the C-terminal AT hook) is sufficient to localize the protein to NPCs [15]. Mutational disruption of the conserved loop on blade 6 of the β-propeller domain (“loop2”) prevents the 1–1018 aa. fragment from localizing to the nuclear rim.
Despite the interest in defining the functional domains of MEL-28/ELYS, until now there have been no studies in which the phenotypic consequences of disrupting specific domains have been studied in developing animals. In this work, we have dissected the MEL-28 protein and studied its localization and function in live C. elegans embryos. We have identified regions of MEL-28 required for its roles in meiosis as well as in chromatin binding and post-mitotic nuclear pore construction. Our parallel studies in HeLa cells show that the domains required for proper localization in C. elegans are conserved in human ELYS, suggesting that conclusions from functional analyses of MEL-28 in C. elegans are broadly applicable to vertebrate ELYS.
We previously reported that C. elegans MEL-28 is broadly expressed [10]. However, a promoter study of 127 genes in C. elegans embryos suggested that MEL-28 is highly enriched in the intestinal E lineage ~200 min after fertilization [16]. We therefore revisited MEL-28 expression to analyze it in greater detail. Immunofluorescence analysis detected similar levels of MEL-28 in nuclei of all embryonic cells (S1A Fig) and all postembryonic tissues (S1B Fig). Next, using CRISPR-Cas9 technology [17], we generated a GFP knock-in mel-28 allele to analyze the expression of endogenous MEL-28 by live microscopy. Similar to the observations with antibodies against MEL-28, GFP::MEL-28 localized to the NE in all cell types during embryonic and larval development and in adults (S1C Fig). Thus, we conclude that MEL-28 is ubiquitously expressed throughout C. elegans development.
MEL-28 strongly accumulated on condensed oocyte chromosomes (S1C Fig; [9, 18]). Moreover, we noted during our initial studies of mel-28 mutant or RNAi-treated embryos that formation and migration of the maternal pronucleus was often more severely affected than the paternal pronucleus [9, 10]. Based on these observations we speculated that MEL-28 might have important functions in meiosis. C. elegans oocytes are arranged in a linear fashion in the proximal part of the gonad, where each oocyte is numbered relative to the spermatheca (-1, -2, -3, etc.) [19]. The -1 oocyte completes maturation including germinal vesicle breakdown immediately before ovulation and fertilization triggers rapid progression through meiosis I and II. To examine these processes we performed live in utero recordings of animals expressing GFP::MEL-28 and mCherry::HisH2B. In the -4 oocyte, MEL-28 localized to the NE and was absent from condensed chromosomes (Fig 1A). In the -3 and -2 oocytes MEL-28 gradually moved away from the NE and accumulated uniformly on meiotic chromosomes. Later, in the -1 oocyte MEL-28 redistributed to cover the surface of meiotic chromosomes (Fig 1A; S1 Video), in some cases completely enclosing the chromosomes and in other cases similar to the “cup-shaped” localization of kinetochore proteins, such as KNL-1 and KNL-3 [20]. The association of MEL-28 with chromosomes persisted throughout meiosis I and II until pronuclear formation ~30 minutes after germinal vesicle breakdown (Fig 1B; S1 Video). The localization pattern of MEL-28 suggested a possible role during segregation of meiotic chromosomes, similar to the situation in mitosis [9, 10]. We therefore analyzed mel-28(t1684) embryos expressing GFP::β-tubulin and mCherry::HisH2B. mel-28(t1684) encodes a premature termination codon at aa. 766 and behaves like a strong loss-of-function of MEL-28, presumably due to nonsense-mediated mRNA decay [10]. Maternal contribution enables homozygous mel-28(t1684) hermaphrodites to develop until adulthood but they produce only unviable embryos (hereafter referred to as mel-28 embryos, whereas embryos produced by heterozygous siblings are referred to as control or mel-28/+ embryos) with severe NE assembly defects [10]. Strikingly, in mel-28 embryos chromosomes failed to segregate in anaphase I (n = 5/6 embryos) and anaphase II (n = 4/6) and, consequently, mel-28 embryos had either no (n = 4/6) or a single (n = 2/6) polar body, whereas control embryos had two polar bodies (n = 6/6; Fig 1C; S2 Video). In addition, chromosomes in mel-28 embryos were not organized in a pronucleus but appeared scattered in the cytoplasm (Fig 1C; 36:00). To our knowledge, this is the first report describing the involvement of MEL-28/ELYS in meiosis, expanding previously described MEL-28 functions and establishing an important role in chromosome segregation during both meiosis and mitosis.
To characterize which regions of MEL-28 are required for its different functions, we examined full-length and truncated versions of MEL-28 fused to GFP and tracked their localization in live C. elegans embryos. While most transgenes are expressed (S2 Fig; S4 Fig), some exhibit localization patterns distinct from full-length MEL-28 (see below). During interphase full-length MEL-28 was mainly localized to the NE but was also found in the nucleoplasm (Fig 2A; S3 Video; S9 Video). In prophase and prometaphase, MEL-28 left the NE before complete NE breakdown and associated to the condensing chromosomes. By metaphase, MEL-28 appeared as two lines parallel to the metaphase plate, resembling the characteristic pattern of holocentric kinetochore proteins, and less abundantly to the area of the mitotic spindle (Fig 2A–2D). During anaphase, MEL-28 associated to decondensing chromosomes, and re-localized to reforming NE in telophase (Fig 2A; S3 Video).
We next analyzed a putative coiled-coil domain placed in the central part of the protein and which might be engaged in protein—protein interactions. However, GFP::MEL-28 lacking aa. 1140–1186 localized similarly to full-length MEL-28 (Fig 2C; S3 Video). During interphase MEL-28Δ1140–1186 was enriched at the NE and shuttled to kinetochores in mitosis whereas reduced signal was observed at the mitotic spindle (Fig 2D). Moreover, expression of GFP::MEL-28Δ1140–1186 completely rescued the embryonic lethality of mel-28 mutant embryos (Table 1). This demonstrated that the putative coiled-coil domain as well as enrichment at the mitotic spindle is dispensable for MEL-28 function.
Recently, Bilokapic and Schwartz found that the N-terminal half of ELYS containing the β-propeller and α-helical domains localized to the NE in HeLa cells [15]. However, the relevance of these domains has not been analyzed in the context of full-length MEL-28/ELYS. We first deleted the β-propeller and most of the α-helical domain (GFP::MEL-28826-1784) and found that both NE localization during interphase and kinetochore localization in mitosis were abrogated (Fig 2C). Instead, the truncated protein was found in the nucleoplasm and weakly associated with chromosomes during interphase and metaphase, respectively (note that kinetochore localization appears as two parallel lines whereas a single line reflects more uniform chromosome association). Similar mis-localization was observed on deletion of aa. 1–507 (GFP::MEL-28508-1784) or aa. 498–956 (GFP::MEL-28Δ498–956), whereas deletion of aa. 566–778 (GFP::MEL-28Δ566–778) also abolished the weak association to mitotic chromosomes (Fig 2C). Together, these results demonstrate that both the β-propeller and the α-helical domain are required for targeting MEL-28 to NPCs and to kinetochores. All four N-terminally truncated MEL-28 proteins accumulated in the nucleus in interphase, suggesting that the C-terminal unstructured domain of MEL-28 contains one or more nuclear localization signals (NLS’s; see below).
Finally, we assessed whether the truncations in the β-propeller and α-helical domains interfered with MEL-28 function. As expected from the severe mis-localization, ectopic expression of any of the four MEL-28 truncations failed to restore viability of mel-28 embryos (Table 1), suggesting that the localization of MEL-28 to NPCs and kinetochores is essential to MEL-28 function. We conclude from these experiments that the N terminus of MEL-28 is required for proper MEL-28 localization and functions. Whereas its importance for NPC localization is concordant with data on ELYS our experiments revealed a novel role in kinetochore association.
Bilokapic and Schwartz identified through protein crystallization and sequence alignments two conserved loops (loop1 and loop2) on the surface of the β-propeller of ELYS [15]. When they substituted 5 aa. within loop2 the structural fold of the β-propeller was maintained but NPC localization of the N-terminal half of ELYS (aa. 1–1018) fused to GFP was abrogated in HeLa cells. To test the relevance of loop2 in the context of full-length protein we introduced the equivalent aa. substitutions in MEL-28 (D409S/Y412S/R415A/V416S/P417G; MEL-28loop2mut; Fig 3A). In mel-28/+ embryos MEL-28loop2mut::GFP localized normally during interphase and mitosis (Fig 3A, left panels; compare with wild type GFP::MEL-28 in Fig 2A; S4 Video; S3 Fig), suggesting that loop2 residues are not essential for association of full-length MEL-28 with NPCs or kinetochores. However, MEL-28loop2mut::GFP was not able to substitute for endogenous MEL-28: mel-28 embryos expressing MEL-28loop2mut::GFP were unviable (Table 1) and had frequent meiosis defects as evidenced by failure in polar body extrusion and presence of multiple female pronuclei (Fig 3A, right panels; S4 Video; Fig 3B). Moreover, pronuclei were abnormally small, contained less MEL-28loop2mut::GFP and did not position properly. In 83% of mel-28; MEL-28loop2mut::GFP embryos (n = 10/12) female and male pronuclei did not meet before the first mitotic division. Instead, only the male pronucleus was positioned between the centrosomes, whereas female pronuclei exhibited shorter migration and remained in the anterior of the embryo. During mitosis chromosomes failed to congress to the metaphase plate (Fig 3A; 0:00) and severe segregation defects were observed (Fig 3A; 20:00–31:45). We also noticed alterations in cell cycle timing, in particular for the posterior P1 blastomere at the two-cell stage. In mel-28; GFP::MEL-28 and mel-28/+; MEL-28loop2mut::GFP embryos the cell cycle of P1 lasted ~1075 sec, whereas it lasted ~1513 sec (41% delay) in mel-28 embryos expressing MEL-28loop2mut::GFP (Fig 3C). Other frequent defects included cleavage furrow regression (37%; n = 6/16) and abnormal positioning of cells within the eggshell (53%; n = 8/15).
To analyze if the conserved loop2 is required for MEL-28’s role in NPC assembly we performed immunofluorescence on mel-28; MEL-28loop2mut::GFP embryos and compared them with wild type, mel-28, and mel-28; GFP::MEL-28 embryos. One-cell and four-cell stage embryos were analyzed for meiotic and mitotic defects, respectively, using mAb414 to visualize multiple Nups and specific antibodies against NPP-10C/NUP96, which is a component of the NUP107 complex [21]. Uniform peripheral signal was observed at pronuclei of wild type and mel-28; GFP::MEL-28 one-cell stage embryos, whereas fragmented pronuclei with inconsistent Nup signal was detected in mel-28; MEL-28loop2mut::GFP and mel-28 embryos (Fig 4A). Analysis of four-cell stage mel-28; MEL-28loop2mut::GFP embryos confirmed the defects in chromosome segregation observed by live imaging and revealed that although nuclei with peripheral Nup localization are formed, these are smaller than in wild type and mel-28; GFP::MEL-28 embryos (Fig 4B). The NE phenotypes in mel-28; MEL-28loop2mut::GFP embryos were less severe when compared to mel-28 embryos. As previously reported, nuclear reformation and NPC assembly was strongly inhibited in mel-28 embryos although a few cells had larger nuclei with irregular NE-structure (Fig 4B; bottom mel-28 embryo).
From these data we conclude that MEL-28’s loop2 is essential for correct chromosome segregation both in meiosis and mitosis but not strictly required for post mitotic NPC assembly, nor for incorporation into the NE.
The observation that perturbations in MEL-28’s N-terminal half do not prevent nuclear accumulation of MEL-28 prompted us to analyze the C-terminus for functional domains. We first expressed GFP::MEL-281-1744, which lacks 40 aa. from the C-terminal end including one of the two AT-hook motifs. This short truncation did not interfere with MEL-28 localization in interphase nor during mitosis (Fig 5; S5 Video). However, expression of GFP::MEL-281-1744 rescued lethality in only ~35% of mel-28 embryos (Table 1), indicating that the C-terminal AT hook of MEL-28 contributed significantly to MEL-28 activity. Next, we deleted aa. 1239–1728, including the other AT-hook motif. This reduced slightly the NE accumulation at interphase (Fig 5; GFP::MEL-28Δ1239–1728; S6 Video). Importantly, expression of GFP::MEL-28Δ1239–1728 was not able to rescue the embryonic lethality of mel-28 embryos (Table 1), which suggests that there are domains within this region required for MEL-28 function. Despite several attempts, we were unable to express a MEL-28 aa. 1–956 fragment consisting of wild type β-propeller and α-helical domains (S4 Fig). In contrast, a similar fragment, but with the five aa. substitutions in loop2 described above was efficiently expressed (MEL-281-956_l2m::GFP; S7 Video). MEL-281-956_l2m::GFP localized to the cytoplasm and NE, but its relative NE accumulation compared to kinetochore localization was dramatically reduced (S3 Fig). As expected, expression of MEL-281-956_l2m::GFP did not rescue the embryonic lethality of mel-28 embryos (Table 1). Taken together with the results presented in Fig 2, we conclude that although the N-terminal β-propeller and α-helical domains are the main determinants for NPC and kinetochore localization, the C-terminal portion of MEL-28 also contributes significantly.
A divergent ~300 aa. MEL -28/ELYS homolog termed ELY5 was recently identified in several fungi [22, 23]. Although our experiments presented above would suggest that the part of MEL-28 equivalent to ELY5 (identified as aa. 696–927 by [24]) does not contain the domains required for NPC localization we nevertheless expressed a fragment containing aa. 681–929 fused to GFP. As expected, this fragment did not localize to the NE or to kinetochores but showed instead diffuse cytoplasmic signal throughout the cell cycle (Fig 5; GFP::MEL-28681-929; S2B Fig).
We next expressed a series of overlapping fragments from aa. 681 to the C-terminal end. All fragments that contained aa. 846–1071 accumulated efficiently in the nucleus (Fig 5; GFP::MEL-28681-1350, GFP::MEL-28846-1071, GFP::MEL-28846-1350, and GFP::MEL-28846-1601; S4A Fig; GFP::MEL-28846-1167; S8 Video). A shorter fragment consisting of aa. 846–956 behaved similarly to free GFP (S4A Fig; GFP::MEL-28846-956). Nuclear accumulation was also detected for GFP::MEL-281188-1784, but not for GFP::MEL-281161-1601 or GFP::MEL-281239-1601 (Fig 5; S4A Fig). These observations are consistent with MEL-28 having at least two NLS’s mapping to the regions 846–1071 and 1601–1784. Moreover, using the NLS prediction software “cNLS Mapper” [25] we identified several putative mono- and bipartite NLSs in these regions: two in the central region (aa. 942–970 and 1033–1062 with scores 5.9 and 5.2, respectively) and three in the C-terminal region (aa. 1606–1636, 1682–1709 and 1741–1773 with scores 5.7, 7.4 and 5.3, respectively). Analysis of these C-terminal fragments also revealed that aa. 1239–1601 confer strong chromatin binding during mitosis (Fig 5).
Comparing the behavior of GFP::MEL-281239-1601 and GFP::MEL-281188-1784 indicated that MEL-28’s two AT hooks are not required for chromatin association, at least during mitosis (Fig 5). Moreover, in vitro binding experiments found no difference in chromatin affinity between recombinant peptides that contained either the C-terminal 128 aa of Xenopus ELYS including the single ELYS AT hook or a variant with mutated AT hook although the former was more efficient in competition assays [4]. In agreement with the competition assay, it was independently demonstrated that the same 128-aa. peptide efficiently binds nucleosome beads but not when the AT hook is mutated [13]. However, both studies concluded that the 128-aa. peptide contains residues outside the AT hook important for chromatin and nucleosome interaction. We attempted to address this in further detail, but we were unable to detect expression of a construct encoding the C-terminal 161 aa. of MEL-28 fused to GFP (S4B Fig; GFP::MEL-281624-1784). A shorter 48-aa. fragment containing a single AT hook localized similarly to free GFP (S4A Fig GFP::MEL-281740-1784). As a complementary approach, we examined the consequences of deleting the AT hooks from full-length MEL-28. We first compared mel-28/+ embryos expressing GFP::MEL-281-1629 (GFP::MEL-28ΔAT) with mel-28 embryos expressing full-length MEL-28 fused to GFP. Time-lapse confocal microscopy demonstrated that the mel-28/+; GFP::MEL-281-1629 embryos developed normally and the fluorescent protein localized similarly to GFP::MEL-28 (Fig 6A; compare left and middle panels; S9 and S10 Videos). In the absence of endogenous MEL-28, GFP::MEL-281-1629 still accumulated at the periphery of interphase nuclei and to kinetochores of mitotic chromosomes (Fig 6A; right panels; S10 Video). This was in contrast to the severe phenotypes observed in MEL-28loop2mut::GFP embryos (Fig 3A) and suggested that MEL-28’s function in post-mitotic nuclear assembly is not strictly dependent on the AT hook domain. However, mel-28; GFP::MEL-281-1629 embryos were unviable (Table 1) and displayed several defects. Most prominently, daughter nuclei were often (n = 5/7) trapped at the cleavage furrow during cytokinesis of the anterior AB blastomere of two-cell stage embryos (Fig 6A, right panels; 27:31–34:30). More direct evidence for chromosome segregation failure was obtained by immunofluorescence analysis of four-cell stage embryos, which also demonstrated that NPP-10C/NUP96 and other Nups accumulated at the NE of mel-28; GFP::MEL-281-1629 embryos, albeit in an irregular pattern (Fig 6E). In addition, nuclear growth was significantly reduced in GFP::MEL-281-1629 embryos (Fig 6A, third row; Fig 6B), consistent with defects in NPC-mediated nucleocytoplasmic transport [26]. While nuclei from mel-28; GFP::MEL-28 and mel-28/+; GFP::MEL-281-1629 grew to the same size (363.8 ± 19 μm3 and 363.3 ± 63 μm3; respectively), the maximum volume of P1 nuclei was reduced by 32% in mel-28; GFP::MEL-281-1629 embryos (346.6 ± 44 μm3). We also noticed that the nucleoplasmic pool of GFP::MEL-281-1629 was strongly diminished in mel-28 embryos compared to GFP::MEL-28 in mel-28 embryos and GFP::MEL-281-1629 in mel-28/+ embryos (Fig 6A and 6C). Whereas the ratio between nucleoplasmic and cytoplasmic GFP signal was similar between mel-28; GFP::MEL-28 and mel-28/+; GFP::MEL-281-1629 embryos (5.60 ± 1.29 and 4.72 ± 0.99; respectively), the ratio was 87% lower in mel-28; GFP::MEL-281-1629 embryos (0.76 ± 0.18). These data are compatible with a model in which GFP::MEL-281-1629 has reduced affinity for interphase chromatin and therefore accumulates at NPCs: in mel-28/+ embryos interaction of GFP::MEL-281-1629 with endogenous MEL-28 accumulates the former in the nucleoplasm, potentially interacting with chromatin.
During time-lapse recordings of 2-cell stage mel-28 embryos, we realized that division of the P1 blastomere was much delayed relatively to the AB division. In wild-type embryos the P1 cell division is delayed by ~2.5 min compared to AB division. This P1 delay is dependent on checkpoint proteins and is thought to have evolved to protect the germ-line lineage from aneuploidy. Thus, inhibition of DNA replication or induction of DNA damage is typically associated with extended P1 delay. When we compared embryos expressing GFP::MEL-281-1629 an increase in P1 delays by 176% was observed in mel-28 versus mel-28/+ embryos (423.5 ± 61.9 sec versus 154.1 ± 59.2 sec; Table 2; Fig 6D). The presence of chromatin bridges in mel-28; GFP::MEL-281-1629 embryos (Fig 6E) suggested that chromosomes might be entangled, potentially as consequence of stalled replication and/or double-stranded DNA breaks. To address if the DNA damage checkpoint indeed is involved in the extended P1 delay in mel-28; GFP::MEL-281-1629 embryos, we depleted ATL-1, the C. elegans homolog of ATR by RNAi [27]. This mitigated the P1 delay (285.7 ± 67.9 sec), which suggested that removal of the AT-hook domain from MEL-28 activates DNA damage and thereby an exaggerated delay of P1 cell division. However, depletion of ATL-1 did not fully rescue P1 cell-cycle timing, which suggests that other checkpoints are also activated in mel-28; GFP::MEL-281-1629 embryos. In conclusion, although GFP::MEL-281-1629 localizes properly to the NE and kinetochores, depletion of MEL-28’s AT-hook domain causes reduced nuclear growth, mis-segregation of chromosomes and activates the ATR DNA damage checkpoint.
To explore the degree of conservation of localization domains we expressed human full-length ELYS (ELYS1-2275) and 14 ELYS truncations fused to GFP in HeLa cells. As reported, ELYS1-2275 was enriched at the NE in interphase and in a pattern coincident with kinetochores in metaphase (Fig 7A; S5 Fig; punctate localization on metaphase chromosomes was observed in single confocal sections as well as in maximum intensity projections). Two fragments containing the entire β-propeller and α-helical domains (ELYS1-1101 and ELYS1-1700) still accumulated at the NE but had increased cytoplasmic signal, suggesting that, like for MEL-28, sequences outside the β-propeller and α-helical domains contribute to efficient NPC targeting (Fig 7A; S6 Fig). In contrast, all truncations from the N-terminal end abolished NE signal, including a deletion of ELYS aa. 1–178 (ELYS179-2275), indicating that the β-propeller is critically required for incorporation of ELYS into the NE. A short N-terminal fragment, ELYS1-329, was also not detected at the NE, which implies that although the first 178 aa. of ELYS are needed for NPC localization, they are not sufficient.
Two internal fragments, ELYS600-1101 and ELYS600-1700, were nuclear in interphase whereas ELYS1430-1700 was mostly cytoplasmic (Fig 7). This suggests that both MEL-28 (Fig 5; MEL-28846-1071) and ELYS have at least one NLS at equivalent locations within the central region of the protein. Nuclear accumulation was also observed for two non-overlapping C-terminal fragments, ELYS1851-2034 and ELYS2034-2275. In agreement with earlier predictions [5], this suggests the presence of NLS’s in the AT-hook-containing last 425 aa. of ELYS, similar to our mapping of a potential NLS to the AT-hook domain of MEL-28 (Fig 5; MEL-281188-1784) and would represent another functional conservation between ELYS and MEL-28. We also noted that the shortest C-terminal ELYS fragments were enriched in nucleoli, whereas longer fragments (e.g. ELYS179-2275, ELYS476-2275, and ELYS600-2275) were excluded from these compartments (Fig 7).
Interestingly, all 14 ELYS truncations localized differently from full-length ELYS during metaphase. The three N-terminal fragments (ELYS1-329, ELYS1-1101, and ELYS1-1700) and the three internal fragments (ELYS600-1101, ELYS600-1700, and ELYS1430-1700) were not detected on mitotic chromosomes (Fig 7). In contrast, truncations from the N-terminal end increased the abundance of ELYS on chromosomes aligned on the metaphase plate. Importantly, the pattern was more diffuse on the chromosomes compared to the punctate pattern of full-length ELYS (S5 Fig). This was particularly prominent for ELYS1700-2275 and ELYS1851-2275, but was also observed for the longer ELYS476-2275, ELYS600-2275, and ELYS1430-2275 fragments. These results suggest that the C-terminus of ELYS has affinity for chromatin but that the ability to interact with chromosomes is reduced in the context of full-length ELYS, which specifically localizes to kinetochores. Thus, we conclude that association with mitotic chromosomes is also conserved from C. elegans to humans. Because of the similarity between MEL-28 and ELYS in terms of structural organization despite low primary sequence homology, we propose that the functional assignments for MEL-28 domains presented in this work are likely to be relevant in more complex animals, including humans.
C. elegans MEL-28 and human ELYS have divergent amino acid sequences, with at best 23% sequence identity [10]. In spite of this, we report that the functional domains of invertebrate and vertebrate orthologs are remarkably well conserved (Fig 8).
Previous work demonstrated that MEL-28/ELYS is essential for mitotic chromosome segregation in C. elegans and vertebrates [9, 10, 28]. Here we show that MEL-28 is also required for meiotic chromosome segregation in C. elegans oogenesis. In C. elegans, chromosome segregation during female meiosis is kinetochore-independent, and instead depends on microtubule growth in the region between separating chromosomes and lateral microtubule attachments to the separating chromosomes [20, 29]. It may be that these lateral attachments to chromosomes are less stable in the absence of MEL-28, leading to failure of chromosome segregation. Alternatively there could be defects to the architecture of the meiotic spindle when MEL-28 is disrupted, as has been shown for the mitotic spindle in mel-28 RNAi-treated embryos [9, 21]. It is important to note that the cell cycle proceeds in mel-28 embryos despite the penetrant failure in meiotic chromosome segregation, which suggests that mel-28 does not affect the anaphase-promoting complex [30, 31].
In both C. elegans and HeLa cells, full-length MEL-28/ELYS localizes to the nucleoplasm and NPCs at interphase and to the kinetochore at mitosis [9, 10, 12, 28]. Here we observed that in C. elegans, localization to NPCs and the kinetochore is dependent on both the N-terminal β-propeller domain and the central α-helical domain, corresponding to the N-terminal 956 aa. residues. Mammalian ELYS NPC localization also requires the β-propeller and α-helical domains [15] and here we have shown that these domains are also necessary for the localization of ELYS to kinetochores at metaphase. Similar to previous studies of human ELYS [15], we have found that the conserved loop decorating blade 6 (“loop2”) is structurally conserved amongst the vertebrate and invertebrate MEL-28/ELYS homologs. When loop2 was disrupted by five substitution mutations in mouse ELYS, this prevented a 1018-aa. N-terminal ELYS fragment (corresponding to the β-propeller and α-helical domains) from localizing properly to the NE [15]. We found disruption of loop2 within an equivalent N-terminal fragment of MEL-28 (aa.1-956) caused a reduction of localization at the NPC and nucleoplasm, with a corresponding increase in cytoplasmic fluorescence. Interestingly, the full-length MEL-28 fusion with the loop2 defect had the wild-type localization pattern, suggesting that domains in the C terminus contribute to nuclear rim localization. Even so, mutations of loop2 severely disrupted MEL-28 function and caused cell cycle delay, nuclear expansion defects, problems with chromosome segregation during mitosis and meiosis, and ultimately embryonic inviability. However, NPC components were recruited to the reforming nuclei relatively efficiently. This suggests that the chromosomal functions of MEL-28 are more sensitive to defects to loop2 than the nuclear pore functions of MEL-28.
In vitro analyses studying the C-terminal domain of ELYS using Xenopus extracts have suggested that there are at least two domains, including the AT hook, required for chromatin binding [4, 11, 13]. Our results studying the C terminus of human ELYS are consistent with this. We identified at least two domains needed for metaphase chromatin localization. The C-terminal end of ELYS corresponding to aa. 1851–2275 bound to metaphase chromatin. However the aa. 1851–2034 fragment (which includes the AT hook) and a smaller aa. 2034–2275 C-terminal fragment were both excluded from metaphase chromatin, suggesting that both the AT hook and the domain C-terminal to the AT hook are required for metaphase chromatin binding.
The C. elegans MEL-28 data also suggest that both the AT hooks and other C-terminal domains are involved in chromatin binding. C. elegans mel-28(t1684) embryos expressing GFP::MEL-281-1629 had reduced fluorescence in the nucleoplasm at interphase, consistent with an inefficient chromatin binding. These embryos also showed defects in recruitment of NPC components that would be expected if MEL-28 could not effectively bind to chromatin [3, 4]. We studied multiple C-terminal fragments of MEL-28 (that also lacked the N-terminal β-propeller and the central α-helical domains). Such fragments that include aa. 1239–1601 localized to the metaphase chromatin, but fragments lacking this domain were excluded from metaphase chromatin. This suggests that aa. 1239–1601, just N-terminal to the AT hooks in MEL-28, comprise a chromatin-binding domain. Notably, MEL-28 fragments with an intact N terminus (including the β-propeller domain and the central α-helical domain) localized to the kinetochore regardless of the presence of aa. 1239–1601, showing that metaphase kinetochore localization does not require this domain. With human ELYS, in contrast, fragments were completely excluded from the chromatin and kinetochores unless they contained the C terminal domain including aa. 1851–2275.
In contrast to the behavior of C-terminal MEL-28 and ELYS fragments, full-length C. elegans and human proteins were enriched at kinetochores with no apparent affinity for other parts of the metaphase chromosomes. Moreover, disruption of kinetochores blocks recruitment of MEL-28 to mitotic chromosomes [9]. However, several observations indicate that full-length MEL-28 and ELYS also interact with chromatin. Firstly, ELYS bound to chromatin in interphase Xenopus egg extracts [4, 11, 13]. Secondly, DamID experiments in C. elegans adults showed specific interaction of MEL-28 throughout all chromosomes [32]. As a possible explanation for the different behavior at interphase and mitosis we speculate that MEL-28 and ELYS might undergo conformational changes in mitosis that lower their affinity for chromatin. Upon deletion of N-terminal regions, the chromatin association domain(s) in the C-terminus of MEL-28 and ELYS become more accessible and confer binding to metaphase chromosomes. Such a “shielding” mechanism is concordant with the gradual increase in association to metaphase chromosomes as more residues are deleted from the N-terminus of ELYS. Alternatively, or in combination with conformational changes of MEL-28 and ELYS, condensed mitotic chromosomes might provide a less favorable binding site for MEL-28/ELYS.
MEL-28 is efficiently targeted to the NPC and the kinetochore even without AT hooks. However, the ΔAT-hooks version of MEL-28 clearly lacks MEL-28 function; mel-28(t1684) embryos expressing MEL-281-1629 were defective in NPC assembly and nearly all died before hatching. This shows that having MEL-28 placed at the NE is not sufficient for efficient recruitment of the remaining components of the NPC but that this depends on the AT-hook domain. In addition, these embryos show chromatin bridges and activate a checkpoint associated with DNA breakage. Previous work has suggested a role for MEL-28 in chromosome congression and segregation [9, 10], and our observations suggest that these functions require the AT hooks.
The second, or most C-terminal, of the two predicted AT hooks clustered at the C terminus is a canonical AT hook whereas the penultimate is less well conserved [10]. Interestingly, the MEL-28 fusion missing its last AT hook retained some MEL-28 function, as mel-28(t1684) animals expressing this fusion showed partial penetrance embryonic lethality, with over one third of the embryos surviving (Table 1). Since removal of both AT hooks causes 99% embryonic lethality, either the penultimate AT hook or the short domain between the AT hooks must contribute to MEL-28 function. In either case, most mel-28(t1684) embryos expressing the version lacking the last AT hook are unviable, so the last AT hook is clearly needed for full MEL-28 function.
In conclusion, human ELYS and C. elegans MEL-28 have similar functional domains. Both orthologs depend on an intact β-propeller domain and central α-helical domains for NPC and kinetochore organization. The β-propeller domain contains several loops, and our work has demonstrated that loop2, a region that contributes to ELYS localization in mammals [15], is also critical for MEL-28 function. Both MEL-28 and ELYS also have several putative NLS’s traversing the central and C terminal regions of the protein and a C-terminal chromatin-binding domain. One major difference between MEL-28 and ELYS is that chromatin and kinetochore binding is strictly dependent on the C-terminal chromatin-binding domain in ELYS. In contrast, MEL-28 fragments lacking the C terminus are still delivered to the kinetochore as long as the N terminus is intact although in a more irregular manner. It is possible that MEL-28 kinetochore localization is more robust to perturbation because of the unique holocentric structure of the kinetochore in C. elegans.
DNA fragments to express MEL-28 full length and truncations were generated by PCR amplification (KAPA HiFi; KAPA Biosystems, Wilmington, USA) or restriction enzyme digestion and inserted into appropriate cloning vectors. In all cases, mel-28 introns were maintained. Plasmid details are listed in S1 Table.
To construct GFP-human ELYS (NCBI accession number: NP_056261.4), total RNAs from HeLa, K562 and WI-38 cells were isolated by FastPure RNA kit (TaKaRa Bio Inc., Shiga, Japan), and then cDNAs were generated by using SuperScript III First-Strand synthesis system (Invitrogen, Waltham, MA) according to manufacturer’s protocol. The coding region of ELYS was PCR-amplified using primers listed in S2 Table and inserted into the pEGFP-C1 vector (Clontech Laboratories, Palo Alto, CA) at the XhoI site by In-Fusion reaction (Clontech). Other ELYS fragments were amplified by PCR using the plasmid harboring full-length ELYS as a template and inserted into the pEGFP-C1 vector as describe above. DNA sequencing of all ELYS fusion plasmids was outsourced to the TaKaRa Bio Inc. Compared to the database sequence, 5 out of 5, 6 out of 7 and 2 out of 2 clones from HeLa, K562 and WI-38 cells, respectively, contained a mutation from A to G at position 2648, resulting in an amino acid substitution from N to S at the position 883. Since the mutation was predominant in three different cell lines, we decided to use this ELYS sequence in this report.
The wild type strain used was the C. elegans Bristol strain N2. Transgenic strains were generated by any of three different methods: MosSCI [33], CRISPR-Cas9 [17] or microparticle bombardment [34]. GE2633 (mel-28(t1684)) was obtained from the Caenorhabditis Genetic Centers. Other strains are listed in S3 Table. Strains were cultured at 15–25°C using standard C. elegans methods [35].
Rescue experiments were performed according to the promoter used to express the different MEL-28 fragments. For constitutive promoters homozygous L4 larvae were placed on individual plates to develop and lay eggs for 24 h at 20°C. Then, the adults were removed and the number of eggs was determined. Twenty four hours later embryonic lethality was calculated by counting unhatched embryos. For constructs with the hsp-16.41 heat shock inducible promoter, young gravid adults were incubated for 1 h at 32°C and allowed to recover and lay eggs for 24 h at 20°C. The adults were then removed and rescue of embryonic lethality was determined by the presence of viable offspring after 24 h at 20°C.
We carried out RNAi as described [36] with minor adaptations. In total, 10–15 synchronized L4 hermaphrodites were placed on NGM plates (+ 1 mM IPTG + 100 μg/ml ampicillin) seeded with E. coli producing double-stranded RNA (alt-1 RNAi clone sjj_T06E4.3 from [37]) and incubated for 20-24h at 20°C before analysis of cell cycle timing by live DIC microscopy.
HeLa cells were a gift from Dr. Hiroshi Kimura (see [38] for the cell origin). WI-38 cells were purchased from ATCC (Manassas, VA, USA). These cells were maintained in DME medium containing 10% fetal bovine serum (FBS) at 37°C in a humidified 5% CO2. K562 cells were obtained from the Riken Cell Bank (Tsukuba, Japan) and maintained in RPMI1640 medium containing 10% FBS. HeLa cells were grown in a glass-bottom culture dish (MatTech, USA). GFP fusion plasmids (1 μg) were transfected into the cells with Lipofectamine 2000 (Invitrogen) according to manufacturer’s protocol. After 24 hours transfection, the cells were fixed with 4% formaldehyde for 10 min, permeabilized with 0.1% Triton X-100 in PBS for 5 min. For immunostaining, the cells were blocked by blocking buffer (PBS containing 10% Blocking One (Nacalai tesque, Japan) and 0.1% Triton X-100), and then probed with anti-CENP-A antibody (generous gift from Dr. Tatsuo Fukagawa (Osaka University), [39]), followed by Alexa Fluor 568-conjugated anti-mouse IgG secondary antibody (1:500, Lifetechnologies, USA). The cells were stained with 4′,6-diamidino-2-phenylindole (DAPI) at 100 ng/ml for 10 min at room temperature. After washing 3-times with 0.1% Triton X-100 in PBS, the cells were mounted on ProLong Diamond antifade mountant (Molecular Probes, Carlsbad, CA). The cells were observed by confocal microscopy (LSM510META and LSM780; Zeiss; operated by built-in software) equipped with a C-Apo 40x NA 1.2 water immersion lens.
C. elegans embryos and larvae were collected and processed by freeze cracking and methanol fixation as described [40]. The following primary antibodies were used: mouse monoclonal antibody (mAb) 414 (Covance, Princeton, NJ, USA,1:250), mouse monoclonal antibody MH27 (1:50; [41], provided by the Developmental Studies Hybridoma Bank), rabbit polyclonal α-HCP-3 antiserum MH3N (1:200; generous gift from Dr. Mark Roth [42]), rabbit polyclonal α-NPP10-C/NUP96 antiserum GBLC (1:300; [21]), rabbit polyclonal α-MEL-28 antiserum BUD3 (1:200–250; [10]). Secondary antibodies were Alexa Fluor 546-conjugated goat anti-mouse antibodies (Invitrogen, 1:1000), Alexa Fluor 488- and Alexa Fluor 633-conjugated goat anti-rabbit antibodies (Invitrogen, 1:1000). For DNA staining, Hoechst 33258 (Hoechst) was used at 5 μg/ml. Confocal images for S1A Fig were obtained with a Nikon A1R microscope through a Plan Apo VC 60x/1.4 objective (Nikon, Tokyo, Japan) using a pinhole of 1 airy unit. All other immunofluorescence images were acquired with a confocal Leica SPE microscope equipped with an ACS APO 636/ 1.3 objective (Leica, Wetzlar, Germany) using a pinhole of 1 airy unit.
C. elegans samples were mounted between a coverslip and a 2% agarose pad; embryos were released by dissecting young adult hermaphrodites and mounted in 3 μL M9 buffer, whereas larvae and adults were mounted in 3 μL 10 mM levamisole HCl (Sigma-Aldrich, St. Louis, MI, USA). For in utero imaging of oocytes and newly fertilized embryos, young adult hermaphrodites were anesthetized in 20 μL 5 mM ethyl 3-aminobenzoate methanesulfonate (aka Tricaine; Sigma-Aldrich), 0.5 mM levamisole HCl, 0.5x M9 for 15–20 minutes prior to mounting in 3 μL of the same buffer on 2% agarose pads. Vaseline was added between the slide and the coverslip to avoid compression of the animals and melted VALAP (1:1:1 mixture of Vaseline, lanolin, and paraffin) was used to seal the cover slip. Confocal epifluorescence and DIC images were recorded at 22–24°C with a Nikon A1R microscope through a Plan Apo VC 60x/1.4 objective (Nikon, Tokyo, Japan) using a pinhole of 1.2–1.4 airy unit.
For preparation of Fig panels images were processed with FIJI (fiji.sc/Fiji) and Adobe Photoshop CS5 or CS6 (Adobe, San Jose, CA, USA). Identical adjustment of brightness and contrast was applied to all comparable panels within each Fig without changing gamma. Quantification of fluorescence signal at the NE, cytoplasm and nucleoplasm was performed on raw 12 bit images. Fluorescence intensity was normalized by background subtraction; for C. elegans, images of wild type embryos acquired with identical microscope settings were used, with exception of S2B Fig
Statistical analysis was performed with Origin 8.0 (OriginLab, Northampton, MA, USA), Microsoft Excel (Microsoft, Redmond, WA, USA) and online Graphpad tools (http://graphpad.com).
|
10.1371/journal.pgen.1008162 | MNM and SNM maintain but do not establish achiasmate homolog conjunction during Drosophila male meiosis | The first meiotic division reduces genome ploidy. This requires pairing of homologous chromosomes into bivalents that can be bi-oriented within the spindle during prometaphase I. Thereafter, pairing is abolished during late metaphase I, and univalents are segregated apart onto opposite spindle poles during anaphase I. In contrast to canonical meiosis, homologous chromosome pairing does not include the formation of a synaptonemal complex and of cross-overs in spermatocytes of Drosophila melanogaster. The alternative pairing mode in these cells depends on mnm and snm. These genes are required exclusively in spermatocytes specifically for successful conjunction of chromosomes into bivalents. Available evidence suggests that MNM and SNM might be part of a physical linkage that directly conjoins chromosomes. Here this notion was analyzed further. Temporal variation in delivery of mnm and snm function was realized by combining various transgenes with null mutant backgrounds. The observed phenotypic consequences provide strong evidence that MNM and SNM contribute directly to chromosome linkage. Premature elimination of these proteins results in precocious bivalent splitting. Delayed provision results in partial conjunction defects that are more pronounced in autosomal bivalents compared to the sex chromosome bivalent. Overall, our findings suggest that MNM and SNM cannot re-establish pairing of chromosomes into bivalents if provided after a chromosome-specific time point of no return. When delivered before this time point, they fortify preformed linkages in order to preclude premature bivalent splitting by the disruptive forces that drive chromosome territory formation during spermatocyte maturation and chromosome condensation during entry into meiosis I.
| Meiosis is a special cell division that occurs in two steps, meiosis I and II. It converts diploid into haploid cells which can be used as gametes for sexual reproduction where two gametes from opposite sexes, a sperm and an oocyte, fuse to generate the zygote, the first diploid cell of the next generation. Before the first meiotic division, the two parental copies of each chromosome need to be paired. The resulting bivalents are then integrated in a bipolar fashion into the spindle which separates the chromosome pairs apart and distributes the copies regularly onto opposite spindle poles during the first meiotic division. In males of the fly Drosophila melanogaster, the pairing of homologous chromosomes into bivalents is strikingly distinct from the canonical mode, as it does not include formation of a synaptonemal complex and of cross-overs. The proteins MNM and SNM are known to be required specifically for the alternative mode of homolog conjunction in D. melanogaster spermatocytes. Here we address important questions concerning MNM/SNM function. Are these proteins indeed components of a chromosome glue? If so, how is this glue applied so that exclusively appropriate partner chromosomes are linked? By analyzing the consequences of experimental changes in the program of mnm and snm expression during spermatocyte maturation, we arrive at a proposal suggesting that MNM and SNM might be part of an indiscriminate glue which is applied in a temporally controlled manner to prevent inappropriate chromosome linkages on the one hand and premature bivalent separation on the other hand.
| In preparation for the first meiotic division, homologous chromosomes are paired up into bivalent chromosomes. Pairing into bivalents is required for their bi-polar orientation within the spindle during prometaphase of meiosis I (MI). However, once all bivalents have reached bi-orientation, the ties between homologous chromosomes need to be severed during late metaphase I for reductional homolog segregation onto opposite spindle poles in anaphase I.
How the initial contacts between homologs are established before MI remains poorly understood. During canonical meiosis, pairing culminates with formation of a synaptonemal complex (SC), a conspicuous regular structure that crosslinks homologous chromosomes. As the SC is only a transient structure, additional linkage is required for maintenance of homologs in pairs until onset of anaphase I. These back-up ties result from cross-overs (COs), which are generated by developmentally programmed recombination between homologous sister chromatids, in combination with sister chromatid cohesion within the chromosome arm regions distal from the CO sites. Interestingly, beyond this canonical pairing mode, strikingly different alternatives have evolved. In Drosophila melanogaster and other higher dipteran flies, for example, homolog pairing before MI does not include formation of SCs and COs in males [1, 2], while pairing is canonical during female meiosis in these species [3]. Achiasmate meiosis appears to have evolved independently at least 25 times, and different types have been described in diverse evolutionary lineages [4, 5].
Homolog pairing during the achiasmate meiosis in D. melanogaster spermatocytes is known to involve an alternative homolog conjunction (AHC) system. Genetic approaches have led to the identification of three genes, modifier of mdg4 in meiosis (mnm), stromalin in meiosis/SA-2 (snm) and teflon (tef), that are specifically required for AHC in spermatocytes [6–8]. Loss of function mutations in these genes result in chromosome missegregation during MI exclusively in males. In mnm and snm mutant males, both sex chromosomes and autosomes are distributed randomly during MI [6]. In contrast, only autosomes are missegregated in tef mutant males [7].
The tef gene is predicted to encode a protein with three C2H2-type zinc fingers [8]. While transgenes coding for a TEF-EGFP fusion proteins were shown to restore normal MI segregation in tef mutants, it has not been possible to detect the TEF-EGFP product expressed from these transgenes [8]. Therefore, the dynamics of TEF expression and its intracellular localization during spermatogenesis have yet to be clarified.
SNM is a distant relative of the stromalins (SCC3/SA/STAG protein family) [6]. Stromalins usually function as subunits of cohesin complexes which make crucial contributions to chromosome organization during interphase and M phases [9]. However, absence of co-localization with core cohesin components has indicated that SNM does not function as a cohesin subunit [6]. MNM is translated from a specific transcript of the highly complex mod(mdg4) locus [6, 10]. MNM has an N-terminal BTB/POZ domain that is shared among almost all of the 31 distinct protein products predicted to be expressed from the various mod(mdg4) transcripts [10, 11]. In addition, MNM has a unique C-terminal Zn-finger domain of the FLYWCH type. Both domains within MNM are predicted to mediate protein-protein interactions [12]. MNM and SNM accumulate in early spermatocytes where they are strongly enriched in multiple subnucleolar foci [6]. At the start of MI, these foci coalesce into a single prominent spot on the sex chromosome bivalent [6]. Although the X and Y chromosomes are strongly heteromorphic in D. melanogaster, they both harbor rDNA gene clusters which function as sex chromosome pairing centers during male MI [13, 14]. Immunolabeling combined with fluorescent in situ hybridization (FISH) has clearly demonstrated that the prominent MNM/SNM spot observed on the XY bivalent during MI is closely associated with the rDNA loci of the sex chromosomes [6, 14]. Apart from the strong dot on the XY bivalent, far weaker MNM/SNM signals were observed on autosomal bivalents [6] which rely on euchromatic homology for pairing [2, 15]. Interestingly, the association of MNM and SNM with all bivalents is rapidly lost at the onset of anaphase I in a separase-dependent manner [6, 16]. While present evidence is consistent with the notion that MNM and SNM function as proteinaceous glue that conjoins chromosomes into bivalents, this possibility is far from proven and many crucial questions remain to be answered.
Some of the open questions are accentuated by the dynamics of chromosome pairing during D. melanogaster spermatogenesis [2]. Spermatogenesis starts with an asymmetric division of a germ line stem cell. The differentiating daughter cell progresses through four transit-amplifying cell cycles with incomplete cytokinesis, resulting in a cyst of 16 interconnected spermatocytes. Spermatocytes grow and mature during progression through the stages S1—S6 [17] before entering into the meiotic divisions. Chromosome pairing into bivalents is completed rapidly in early spermatocytes, as revealed by analyses with a lacO/lacI-GFP system [18]. With this system, a single lacI-GFP dot indicates homolog pairing in cells homozygous for an autosomal lacO array. While only around 50% of the cells displayed homolog pairing during the gonial amplification cycles before S1, about 95% of the cells had paired homologous lacO arrays during S1/S2a [18]. Strikingly, a few hours later around the S2b/S3 transition, pairing was no longer observed for any of 14 distinct euchromatic lacO array loci analyzed [18]. Moreover, beyond the loss of homolog pairing, even sister chromatid cohesion was no longer detectable except in centromeric regions [18, 19]. A similar dynamic of transient homolog pairing was also observed with most FISH probes targeting heterochromatic satellites, including several within pericentromeric regions [19]. The eventual separation of homologous satellite regions was also accompanied by sister chromatid separation in several cases [19].
The dramatic loss of homolog pairing and sister cohesion that starts during stage S2b is accompanied by the process of chromosome territory formation, where the XY bivalent as well as the bivalents formed by the two large autosomes, chromosome 2 (chr2) and chromosome 3 (chr3), are separated apart from each other within the spermatocyte interphase nucleus in a condensin II-dependent manner [2, 20]. The main purpose of this spatial chromatin re-organization into chromosome territories is presumably the breaking up of associations between non-homologous chromosomes. Non-homologous associations that persist until prometaphase I compromise can give rise to missegregation.
Non-homologous associations are prominent in D. melanogaster cells. The large centromere-proximal heterochromatin regions of all chromosomes, which are brought into close proximity during anaphase in mitotically proliferating cells, usually remain tightly associated within a common chromocenter throughout interphase. During progression through mitotic division cycles, the chromocenter is dissociated when chromosomes condense at the start of M phase. Beyond chromocenter dissociation, chromosome condensation at the start of mitotic divisions also resolves homolog pairing. Somatic pairing of homologous euchromatic chromosome arm regions during interphase is actually extensive in D. melanogaster cells [21–23]. For the success of meiosis, spermatocytes have to resolve non-homologous associations in a way that does not also disrupt all homologous associations in parallel. Accordingly, the AHC proteins might act to limit collateral damage on homolog pairing and sister chromatid cohesion, which evidently accompanies the disruption of non-homologous associations during chromosome territory formation. In addition, these proteins might also be required at the start of the first meiotic division for inhibition of premature bivalent splitting by chromosome condensation and spindle forces. In mnm and snm mutants, initial homolog pairing as well as territory formation does not appear to be affected [6]. However, an abnormal expansion of chromosome territories was noted well before the onset of the first meiotic division during which premature bivalent separation into univalents is plainly apparent in these mutants [6, 24].
Here, for an analysis of the temporal phases during which MNM and SNM need to be present in spermatocytes for successful meiosis, we have altered their expression program. By introducing appropriate transgenes into mnm and snm mutant backgrounds, we have generated genotypes expressing these AHC proteins during either only the early or only the late stages of spermatocyte maturation, or also continuously as in wild-type. We report that early provision of MNM or SNM until chromosome territories have formed is not sufficient. Univalents instead of bivalents are present at MI onset when MNM and SNM are not present during the late stages. Interestingly, late provision of MNM and SNM after chromosome territories have formed is partially sufficient with a pronounced chromosome-specific bias. Compared to the large autosomal bivalents, the sex chromosome bivalent is less dependent on an early presence of MNM and SNM.
To manipulate the temporal program of mnm and snm expression during spermatocyte maturation we made use of the GAL4/UAS system. We generated lines with UASt transgenes allowing expression of MNM or SNM with and without EGFP extensions (UASt-mnm, UASt-EGFP-mnm, UASt-mnm-EGFP, UASt-snm, UASt-EGFP-snm, and UASt-snm-EGFP). To assess functionality we expressed the UASt transgenes in flies trans-heterozygous for mutations in mnm or snm, respectively. The selected mutant mnm and snm alleles are null alleles based on genetic tests [6]. bamP-GAL4-VP16 was used to drive germline-specific UASt transgene expression. Moreover, a dominantly marked Y chromosome was crossed into the males, permitting an analysis of irregularities in sex chromosome transmission onto the next generation. As expected [6], sex chromosome segregation occurred randomly in mnm and snm mutant males. But bamP-GAL4-VP16-driven expression of the UASt transgenes in these mutants prevented sex chromosome missegregation largely or even completely (S1 Table). Transgenes driving C-terminally EGFP-tagged versions, UASt-mnm-EGFP and UASt-snm-EGFP, that restored sex chromosome missegregation in the mutants back to wild-type level (Fig 1A) were selected for further experiments.
To characterize the expression pattern resulting from bamP-GAL4-VP16-driven expression of UASt-mnm-EGFP and UASt-snm-EGFP, we analyzed whole mount and squash preparations of testes (Fig 1B and 1C). As expected based on the known pattern of bamP-GAL4-VP16 expression [25], GFP expression was not observed in somatic cells. In the germline, GFP signals were also not yet detectable in stem cells and gonial cells except weakly during the last gonial division cycle. GFP signal intensities increased strongly during the initial spermatocyte stage (Fig 1C). In early S1 spermatocytes [17], MNM-EGFP was detected mainly in one to a few strong dots that were within the nucleus but apparently not associated with chromatin (Fig 1C). In contrast, the initial accumulation of SNM-EGFP was less apparent and occurred diffusely throughout the nucleolus (Fig 1C). During the later stages MNM-EGFP also shifted into the nucleolus. The initial diffuse nucleolar localization of both MNM-EGFP and SNM-EGFP was increasingly transformed into distinct subnucleolar foci (Fig 1C). At the S6 stage, where chromosome territories start to condense and the nucleolus is disassembled in preparation for the first meiotic divisions, the subnucleolar foci of MNM-EGFP and SNM-EGFP started to coalesce into a strong single dot marking the sex chromosome bivalent until onset of anaphase I (Fig 1C). After the meiotic divisions, EGFP signals were no longer detectable within the nuclei of early round spermatids (Fig 1C).
The observed pattern of UASt-mnm-EGFP and UASt-snm-EGFP expression driven by bamP-GAL4-VP16 (Fig 1C) appeared to be similar to the endogenous mnm and snm expression pattern described previously [6]. For further comparison of endogenous and transgenic expression, we performed immunofluorescent labeling with antibodies against MNM. In control testes, these antibodies reveal the expression from the endogenous locus (Fig 1D). In mnm mutant testes, these antibodies no longer generate specific signals [6]. Therefore, these antibodies presumably detect exclusively transgene-derived protein in mnm mutant testis with bamP-GAL4-VP16 and UASt-mnm-EGFP (bamP>mnm-EGFP) (Fig 1D). Quantitative comparison of signal intensities during prometaphase I, a short and unequivocally identifiable stage with a compact dot-like signal on the sex chromosome bivalent, suggested that the level of MNM-EGFP observed in mnm mutants with bamP>mnm-EGFP was about threefold higher than the level of MNM in wild-type (Fig 1D). In case of SNM, we were unable to obtain immunofluorescent signals that were sufficiently above background for a robust quantification. In the following, the mnm null mutants rescued by bamP>mnm-EGFP will be designated as mnm(e+l) since MNM-EGFP is present from the early until the late spermatocyte stages. Analogously, snm(e+l) will be used for snm null mutants rescued by bamP>snm-EGFP.
Apart from the strong dots on sex chromosome bivalents, far fainter and smaller EGFP specs were detectable on autosome bivalents in mnm(e+l) and snm(e+l) during meiosis I until anaphase onset, as also reported previously after analyses of the endogenous mnm and snm expression with antibodies or with transgenes expressing EGFP fusions under control of other promoters [6]. The following descriptions will not comment on these far weaker signals.
To confine bamP-GAL4-VP16 driven UASt-mnm-EGFP expression to the early stages of spermatocyte maturation we combined it with deGradFP [26] for depletion of MNM-EGFP during the late stages. Protein depletion by deGradFP is achieved by expression of an Nslmb-vhhGFP4 fusion protein which results in polyubiquitination and consequential proteasomal degradation of GFP-tagged proteins. To express Nslmb-vhhGFP4 exclusively during late stages of spermatocyte maturation, we generated a transgene under control of the betaTub85D cis-regulatory region [27]. This betaTub85DP-Nslmb-vhhGFP4 transgene was combined with bamP-GAL4-VP16 and UASt-mnm-EGFP in the transheterozygous mnm mutant background, resulting in a genotype designated as mnm(e) in the following.
We analyzed testis preparations to assess whether MNM-EGFP is indeed present exclusively during the early spermatocyte stages in mnm(e) males. As expected, EGFP signals were clearly present in early spermatocytes (Fig 2A and 2B). However, compared to mnm(e+l) these signals were weaker (Fig 2A and 2B), presumably reflecting low level expression of betaTub85DP-Nslmb-vhhGFP4 already in early spermatocytes. EGFP signal quantification indicated that MNM-EGFP is around 3–4 fold lower in mnm(e) compared to mnm(e+l). Considering the estimated level of overexpression in mnm(e+l) (Fig 1C), the levels of MNM-EGFP present in early mnm(e) spermatocytes should be comparable to the MNM levels in wild-type. At the S3 stage, where chromosome territories are already clearly recognizable, MNM-EGFP was readily detectable also in mnm(e) spermatocytes (Fig 2B). Importantly, MNM-EGFP signals dropped sharply thereafter in mnm(e) testis. During the S5 stage (Fig 2C), as well as the subsequent stages S6 (Fig 2D) and prometaphase I (Fig 2E), MNM-EGFP was no longer detectable in mnm(e), while in mnm(e+l) it persisted within strong subnucleolar foci and later in the characteristic strong dot on the sex chromosome bivalent during prometaphase I.
DNA labeling revealed that chromosome territories became abnormal in mnm(e) spermatocytes in parallel with the disappearance of MNM-EGFP. In S5 spermatocytes, territories in mnm(e) were not as confined as in mnm(e+l) (Fig 2C). The increasing condensation of chromosomes during the S6 stage (Fig 2D) and in prometaphase I (Fig 2E) exposed the territory abnormalities in mnm(e) spermatocytes further. Prometaphase I cells were identified after double labeling of spindles with anti-tubulin. During normal prometaphase I, where chromosomes are maximally condensed, three large bivalents (those of chrXY, chr2 and chr3) and a small bivalent (chr4) can ususally be distinguished. In mnm(e+l), we observed the characteristic normal number and pattern of bivalents (Fig 2E). In contrast, an increased number of smaller DNA blobs were observed in mnm(e) (Fig 2E). This phenotype was indistinguishable from that observed in mnm null mutants (Fig 2D and 2E) [6].
The obvious defect in homolog conjunction observed in mnm(e) during prometaphase I is predicted to cause chromosome missegregation during MI. To assess the extent of chromosome missegregation, we performed FISH with a red fluorescent chrX probe and a green fluorescent chrY probe. After normal segregation of chrX and chrY during MI, spermatid nuclei are expected to have either a red or a green signal. In contrast, missegregation of the sex chromosomes will result in nuclei with either both a red and a green signal or no signal. In early spermatid cysts from mnm(e+l) males, we observed the expected normal pattern (Fig 2F). In contrast, in mnm(e), nuclei with a pattern of FISH signals indicating missegregation were frequent (Fig 2F). Quantification of the fraction of spermatids with normal FISH signals (either red or green) or abnormal signals (either none or both red and green) revealed minimal sex chromosome missegregation in control and mnm(e+l) (Fig 2F), consistent with our initial genetic analyses (Fig 1A). In contrast, sex chromosome segregation was found to be random in mnm(e), as also in mnm null mutants (Fig 2F). Random sex chromosome segregation in mnm null mutants had already been established earlier by genetic analyses (Fig 1A) [6]. Beyond the pattern of FISH signals, the striking size variation among the nuclei present in spermatid cysts of mnm(e) (Fig 2F) and mnm null provided further evidence of chromosome missegregation during meiosis.
Overall, the results of our phenotypic analyses with mnm(e) males indicate that a provision of MNM exclusively during the early spermatocyte stages is not sufficient for normal chromosome conjunction and segregation during MI. Apparently MNM needs to be present after chromosome territories have formed until the late spermatocyte stages for normal chromosome segregation during MI.
The deGradFP method was also applied for the elimination of SNM-EGFP during the late spermatocyte stages. The genotype resulting from combining betaTub85DP-Nslmb-vhhGFP4 with bamP-GAL4-VP16 and UASt-snm-EGFP in the transheterozygous snm mutant background will be designated as snm(e) in the following. Microscopic analyses were used to confirm SNM-EGFP elimination in snm(e) (Fig 3). In early snm(e) spermatocytes, SNM-EGFP signals were present and comparable to those observed in snm(e+l) (Fig 3A). However, in late snm(e) spermatocytes SNM-EGFP signals were far lower compared to snm(e+l) (Fig 3B and 3C). Therefore, SNM-EGFP elimination by deGradFP was clearly successful, but less efficient than that of MNM-EGFP, as indicated by the following observations. In case of mnm(e), MNM-EGFP signals were noticeably decreased already at the onset of expression and absent in prometaphase I (Fig 2). In contrast, in snm(e), SNM-EGFP signals were not obviously reduced already in early spermatocytes and often still detectable during prometaphase I although only very weakly (Fig 3C). Variable deGradFP efficiency with different GFP target proteins, as in case of MNM-EGFP and SNM-EGFP, has been observed before [26]. However, despite some residual SNM-EGFP in snm(e), chromosome conjunction and segregation during MI were clearly defective. As revealed by DNA staining at the late S6 stage (Fig 3B) and during prometaphase I (Fig 3C), chromosomes were more numerous and smaller in snm(e) compared to snm(e+l) where the normal number of three large and one small bivalent was present. The residual low SNM-EGFP signals that were still present during prometaphase I in snm(e) were maximal in two dots associated with two distinct chromosomes (Fig 3C), i.e., most likely the unconjoined chrX and chrY. In contrast, a single strong SNM-EGFP dot was present on the XY bivalent during prometaphase I in snm(e+l) (Fig 3C), as observed with anti-SNM in wild-type testis [6]. The chromosome abnormalities observed in snm(e) at the onset of MI were comparable to those observed in snm null mutants (Fig 3B and 3C). Quantitative analysis of meiotic sex chromosome segregation by FISH (Fig 3D) revealed random segregation in snm(e) and snm null, while marginal missegregation was detected in snm(e+l) and control. We conclude that as in case of MNM, provision of SNM exclusively during the early spermatocyte stages is not sufficient for normal chromosome conjunction and segregation during MI.
To delay expression of MNM-EGFP until the late stages of spermatocyte maturation, we generated a betaTub85DP-mnm-EGFP transgene and crossed it into the transheterozygous mnm mutant background. The resulting genotype will be designated as mnm(l). Microscopic analyses were performed to determine the temporal program of MNM-EGFP expression in mnm(l) testis. At the S3 stage, where chromosome territories have already formed, MNM-EGFP was not yet detectable in mnm(l), while it was clearly present in mnm(e+l) (Fig 4A). Subsequently MNM-EGFP accumulation started in mnm(l) in the nucleolus. Eventually some MNM-EGFP dots also appeared outside of the nucleolus, dispersed within the cytoplasm. At the early S6 stage (Fig 4B) such cytoplasmic MNM-EGFP dots were already detectable in mnm(l) apart from the stronger nucleolar signals. In contrast, cytoplasmic MNM-EGFP dots were not apparent in mnm(e+l) (Fig 4B). Later in mnm(l) during prometaphase I (Fig 4C) dispersed MNM-EGFP dots without chromosome association were even more numerous and stronger. However, these dots were usually still weaker than the most intense MNM-EGFP dot which was closely associated with a bivalent, presumably the sex chromosome bivalent (Fig 4C). The intensity of this bright chromosomal MNM-EGFP dot was variable between spermatocytes, reaching levels above that of the dot on the chrXY bivalent in mnm(e+l). We assume that the non-chromosomal MNM-EGFP dots within the cytoplasm of mnm(l) arise because the betaTub85D regulatory region drives very strong expression during the final spermatocyte stages.
The analysis of chromosome number and size in prometaphase I (Fig 4C) revealed considerable phenotypic variability in mnm(l). While some cysts had spermatocytes displaying the normal number of four masses of DNA staining (three large and one small bivalent), other cysts had cells with clearly too many distinct chromosomal blobs (Fig 4C). In prometaphase I cells with a normal pattern of bivalents, the most prominent MNM-EGFP dot was on a large chromosomal DNA mass (Fig 4C), most likely representing a normally conjoined chrXY bivalent. Interestingly, such an apparently normal chrXY bivalent characterized by the most prominent MNM-EGFP dot was also present in the large majority of prometaphase I cells with an abnormal chromosome pattern, raising the possibility that late provision of MNM-EGFP is more detrimental for autosomal bivalents compared to sex bivalents. Only a minority of prometaphase I cells had two prominent MNM-EGFP dots that were on two distinct DNA blobs, indicating an occasional failure of sex chromosome conjunction.
A quantitative analysis of meiotic sex chromosome segregation by XY FISH clearly confirmed the phenotypic variability between mnm(l) cysts (Fig 4D). Among the eight early postmeiotic cysts analyzed, the frequency of chrXY missegregation ranged from 50% (i.e., random segregation as in mnm null mutants) to zero (as in wild-type control).
For further characterization of MI chromosome segregation in mnm(l), we applied time lapse imaging using spermatocytes expressing histone H2Av-mRFP (His2Av-mRFP) and Cenp-A/Cid-EGFP for labeling of chromosomes and centromeres, respectively. Analogous analyses in wild-type and mnm null mutant spermatocytes have been described recently [24]. In wild-type MI, chromosome condensation around nuclear envelope breakdown (NEBD) converts the bivalents into compact blobs. The His2Av-mRFP marker reveals the large bivalents (chrXY, chr2 and chr3) readily but not that formed by the small dot chromosome (chr4) [24]. Rapid saltatory movements during prometaphase I accompany the bi-polar integration of bivalents into a compact metaphase I plate that remains stable for 15–20 minutes until bivalents split in anaphase I [24]. In contrast, in mnm null mutants, bivalents are separated prematurely into univalents. While some bivalents were still intact at NEBD in mnm null mutants, these were all very rapidly converted into univalents as soon as spindle forces started to act on kinetochores during prometaphase I [24]. After a temporally extended phase with saltatory movements in mnm null mutants, most univalents eventually reached stable positions preferentially near the poles, followed by anaphase onset and exit from MI [24].
Compared to wild type and mnm null mutants [24], the mnm(l) phenotype was observed to be intermediate and more variable (Fig 4E–4G). The mnm(l) cells, which expressed the fluorescent markers His2Av-mRFP and Cid-EGFP as in our previous analyses [24], produced MNM-EGFP in addition. This MNM-EGFP expression compromised centromere detection to some extent. MNM-EGFP generated an increased diffuse nucleoplasmic signal, as well as some dispersed non-chromosomal dots and usually a very strong dot on the chrXY bivalent. In Fig 4E–4G, the centromere signals are therefore not apparent (except for Fig 4F inset) because their visualization requires display settings resulting in excessive saturation of the MNM-EGFP dot on the chrXY bivalent. However, in Fig 4F and 4G, we have marked centromere positions by small colored spheres. Progression through MI was analyzed in a total of 15 mnm(l) cells from five different cysts. In eight of the 15 mnm(l) cells, an apparently normal MI was observed (Fig 4E, S1 Movie). His2Av-mRFP signals revealed the presence of normal large bivalents congressing into a stable metaphase plate and splitting at the onset of anaphase (Fig 4E, S1 Movie). The intensity of the very strong MNM-EGFP dot on the chrXY bivalent decreased rapidly during anaphase (Fig 4E, S1 Movie). However, segregation of chrX and chrY towards opposite poles started already when considerable amounts of the overexpressed MNM-EGFP were still associated with the sex chromosomes, resulting in stretching of the MNM-EGFP dot and a slight lag of sex chromosome separation (Fig 4E, S1 Movie). In the remaining seven of the analyzed mnm(l) cells that progressed through MI, one of the large autosomal bivalents (Fig 4F) or all autosomal bivalents (Fig 4G, S2 Movie) were separated prematurely into univalents. The premature bivalent splitting occurred after NEBD but well ahead of the metaphase to anaphase I transition, presumably as a result of pulling forces ensuing from interactions with spindle microtubules. Interestingly, in all mnm(l) cells with premature splitting of autosomal bivalents, the sex chromosome bivalent displayed a normal behavior, i.e., stable biorientation within the equatorial plane during metaphase followed by separation at anaphase onset (Fig 4F and 4G, S2 Movie). Time lapse imaging therefore suggested that delayed MNM-EGFP expression maintains chromosome conjunction in the chrXY bivalent more effectively than in autosomal bivalents, as indicated by the earlier analysis of fixed cells. As some chrXY missegregation was clearly revealed in mnm(l) by fixed cell analyses, the failure to detect abnormal chrXY behavior by time lapse imaging presumably reflects the far lower number of cells analyzed by this latter, more demanding method.
In conclusion, our phenotypic analyses of mnm(l) spermatocytes demonstrate that a delayed provision of MNM-EGFP, starting well after the formation of chromosome territories, restores homolog conjunction as well as faithful chromosome segregation during MI in mnm null mutants to a substantial extent but not completely. Moreover, conjunction and segregation of the sex chromosome bivalent is more normal in mnm(l) than that of the autosomes.
To assess whether delayed expression restores chromosome conjunction and segregation during MI also in case of snm, a betaTub85DP-snm-EGFP transgene was made and introduced into the snm null mutant background. The resulting genotype will be designated as snm(l) in the following. Microscopic analyses confirmed that SNM-EGFP expression in snm(l) occurred with the expected delay in comparison to snm(e+l). At the S3 stage (Fig 5A), where chromosome territories were already formed, SNM-EGFP was not yet detectable in snm(l), while it was clearly present well before the S3 stage in snm(e+l). At later stages, SNM-EGFP became detectable also in snm(l) spermatocytes (Fig 5B). However, its localization within the nucleolus was entirely diffuse, lacking the discrete subnucleolar foci of maximal signal intensity that were apparent in snm(e+l) (Fig 5B). In snm(e+l) spermatocytes, these SNM-EGFP foci coalesced into a single large dot on the chrXY bivalent during S6 (Fig 5C), when the nucleolus disintegrates and condensation of chromosome territories starts before the onset of the first meiotic divisions [17]. At the corresponding stage in snm(l), SNM-EGFP was dispersed throughout the nucleus (Fig 5C). Similarly, during prometaphase I (Fig 5D), the characteristic strong SNM-EGFP dot on the chrXY bivalent was present in snm(e+l) cells but not in snm(l). Prometaphase I spermatocytes with betaTub85DP-snm-EGFP in a snm+ background (rather than in an snm null background as in snm(l)) clearly displayed the prominent SNM-EGFP dot on the chrXY bivalent (Fig 5D), indicating that SNM-EGFP expressed by this particular transgene can localize normally in principle.
Apart from the absence of normal SNM-EGFP dots on the chrXY bivalent during prometaphase I in snm(l), chromosome conjunction was observed to be completely defective in this genotype. In late spermatocytes, chromosome territories were either too many (Fig 5B and 5C) or they had a pronounced split appearance. During prometaphase I (Fig 5D), there were too many chromosome masses of smaller size compared to controls. These abnormalities in snm(l) were comparable to those displayed in snm null mutants (Fig 3C) [6]. In addition, quantitative analyses by XY FISH indicated that sex chromosome segregation during MI was random in snm(l) comparable to snm null mutants (Fig 5E). In conclusion, a delayed provision of SNM-EGFP is entirely insufficient for normal chromosome conjunction and segregation during MI, in contrast to our findings with MNM-EGFP (Fig 4) where substantial rescue had resulted after delayed expression.
Consideration of several additional findings and their interpretation suggested a potential explanation for the discrepant extent of rescue observed in mnm(l) and snm(l). The original characterization of mnm and snm had already revealed interdependencies [6]. In mnm null spermatocytes, SNM was detected within the nucleolus as in wild-type, although it failed to form the strong dot on the sex chromosome bivalent eventually after disassembly of the nucleolus during entry into the first meiotic division [6]. In contrast, MNM could not be detected at any stage in snm null mutants [6]. Consistent with this latter finding, anti-MNM immunolabeling, which generates a characteristic dot-like signal readily detected on the chrXY bivalent during normal prometaphase I, failed to produce these signals, not only in snm null [6], but also in snm(e) and snm(l) mutants (S1 Fig). In snm(e), the disappearance of anti-MNM signals occurred in parallel with SNM-EGFP degradation during the late spermatocyte stages, consistent with the notion that MNM protein is stabilized by SNM.
Additional analyses after transgenic mnm-EGFP expression confirmed this notion that snm+ gene function is required for MNM accumulation because SNM protein stabilizes MNM protein (rather than boosting mnm transcript levels). After bamP-GAL4-VP16 driven UASt-mnm-EGFP expression in snm null mutants, MNM-EGFP was detected only very transiently in early spermatocytes outside of the nucleolus (S1 Fig). In contrast, after analogous bamP-GAL4-VP16 driven UASt-mnm-EGFP expression in heterozygous siblings with a functional snm+ copy, MNM-EGFP translocated into the nucleolus and persisted there throughout spermatocyte maturation instead of disappearing rapidly after initial expression (S1 Fig).
Our finding that MNM did not detectably accumulate in snm(l) in parallel with SNM-EGFP during the late stages (S1 Fig) suggested that endogenous mnm transcripts might not be present any longer in late spermatocytes. However, if MNM synthesis normally occurs only transiently in early spermatocytes, the MNM detected during the late spermatocyte/MI stages would have to be protein resulting from early production and surviving owing to stabilization by SNM. Our experiments with bamP-GAL4-VP16 driven UASt-mnm-EGFP expression actually indicated that MNM-EGFP protein can indeed stably perdure after early synthesis. bamP-GAL4-VP16 is known to induce only a transient pulse of UASt transgene transcription in early spermatocytes, similar to the endogenous bam transcription pattern although with some delay known to be inherent to the GAL4/UAS system [28, 29]. The transient pulse of synthesis driven by bamP-GAL4-VP16 in early spermatocytes is reported by MNM-EGFP generated after UASt-mnm-EGFP expression in snm mutants, where MNM-EGFP is highly unstable (S1 Fig). However, this transiently synthesized MNM-EGFP protein clearly perdures until MI after bamP-GAL4-VP16 driven UASt-mnm-EGFP expression in the snm+ back ground (S1 Fig). Moreover, the complete rescue observed after bamP-GAL4-VP16 driven UASt-mnm-EGFP in mnm mutants (i.e., mnm(e+l)) indicates that a transient pulse of MNM-EGFP synthesis in early spermatocytes is entirely sufficient for normal MI, arguing against occurrence and need for continuous MNM synthesis and exchange throughout spermatocyte maturation.
Based on the above considerations, it is hardly surprising that the delayed provision of SNM-EGFP in snm(l) does not rescue. When SNM-EGFP eventually accumulates in snm(l), all endogenous MNM protein made in early spermatocytes is long degraded and endogenous mnm transcripts for a late re-accumulation of MNM appear to be absent. Without MNM, however, the SNM-EGFP which eventually accumulates in snm(l) cannot provide its function. This interpretation predicts that rescue of normal MI in snm null mutants by delayed provision of SNM-EGFP might succeed, if supported by concomitant delayed MNM-EGFP expression. To evaluate this possibility, we crossed both betaTub85DP-mnm-EGFP and betaTub85DP-snm-EGFP into snm null mutants. This genotype will be designated as snm(l_s+m). As expected, EGFP signals were absent in in early snm(l_s+m) spermatocytes and EGFP accumulation started during the S4 stage primarily within the nucleolus (Fig 6A). Importantly, XY FISH analyses of early spermatid cysts clearly revealed that this delayed co-expression of both SNM-EGFP and MNM-EGFP in the snm(l_s+m) genotype reduced meiotic sex chromosome missegregation significantly in comparison to snm null and snm(l) (Fig 6B). Cytological analyses of spermatocytes provided further confirmation that the abnormalities in snm(l_s+m) were less severe than in snm null and in snm(l). At the late S6 stage and during prometaphase I, each snm(l_s+m) spermatocyte displayed a single most prominent cluster of EGFP dots on one of the chromosome masses, i.e., the sex chromosome bivalent in all likelihood (Fig 6A). Additional weaker EGFP dots were present in these cells, and at least some of these were not associated with chromosome masses, presumably reflecting aggregates formed as a result of overexpression. Overall, our observations indicate that sex chromosome conjunction and segregation is largely rescued in snm(l_s+m). In contrast, in snm(l_m), a snm null genotype with delayed provision of only MNM-EGFP (but not SNM-EGFP), MNM-EGFP accumulation was actually not detectable and chromosome conjunction was completely defective during S6 and prometaphase I (Fig 6C). Therefore, rescue of sex chromosome conjunction in snm null mutants after delayed provision of conjunction proteins occurs only when SNM-EGFP and MNM-EGFP are co-expressed.
In contrast to sex chromosome conjunction, that of autosomes was still severely defective in the snm(l_s+m) spermatocytes. Already when EGFP accumulation started during the S4 stage, chromosome territories had an abnormal fragmented appearance (Fig 6A). Chromosome condensation during the S6 stage and during entry into MI exposed the defect in autosomal homolog conjunction very clearly (Fig 6A). The number of DNA masses in these late snm(l_s+m) spermatocytes was clearly increased compared to controls and their size was smaller (Fig 6A). This defect in autosomal homolog conjunction in snm(l_s+m) appeared to be just as severe as in snm null mutants (Fig 3B and 3C) or in snm(l) (Fig 5C and 5D).
Overall, our findings in snm(l_s+m) demonstrate that absence of SNM and MNM during the early spermatocyte stages followed by delayed provision of these proteins compromises autosomal conjunction more than sex chromosome conjunction. The snm(l_s+m) phenotype is therefore similar to the mnm(l) phenotype where autosomal conjunction was also observed to be more sensitive compared to sex chromosome conjunction.
For additional assessment of chromosome missegregation during meiosis in the different genotypes, we quantified nuclear DNA signals with image stacks of early spermatid cysts. In control testis, the 64 haploid nuclei within a given cyst are expected to have an almost identical DNA content given the negligible estimated size difference between chrX and chrY of around 0–10% of the total genome size. However, after random segregation of chromosomes during MI, as in mnm or snm null mutants, the DNA content of early spermatid nuclei is predicted to be far more variable. For an estimation of the DNA content in spermatid nuclei, we applied semi-automatic image analysis for quantification of the DNA staining intensity (Fig 6D). As predicted, the DNA signal intensities varied far less among early spermatid nuclei within control cysts compared to mnm and snm null mutant cysts (Fig 6D and S2 Fig). Therefore, as a measure of overall meiotic chromosome missegregation, we determined the coefficient of variation of nuclear DNA signal intensities for each cyst and calculated an average after analysis of at least 5 cysts per genotype. The comparison among the different genotypes (Fig 6E) confirmed that meiotic chromosome segregation was close to normal in mnm(e+l) and snm(e+l). Severe defects comparable to those in mnm and snm null mutants were apparent in mnm(e), snm(e) and snm(l). In mnm(l), chromosome segregation was less defective than in mnm null mutants (p < 0.004, t test) even though it was clearly not normal. In snm(l_s+m), the overall chromosome missegregation appeared to be marginally less defective compared to snm null mutants (p < 0.17, t test), consistent with our results from cytological analysis of spermatocytes and XY FISH with spermatids in this genotype. The significant suppression of random segregation of sex chromosomes but not of autosomes results only in a subtle change in the variation of DNA content among spermatids in snm(l_s+m) compared to genotypes where missegregation affects all chromosomes.
The phenotypic consequences of a loss of MNM and SNM have clearly demonstrated that these proteins are required for normal conjunction of chromosomes into bivalents and hence for regular reductional segregation during MI in Drosophila spermatocytes [6]. These proteins might function directly as part of the physical linkage between chromosomes in bivalents. MNM and SNM are co-localized within a single prominent dot at the rDNA loci of the X and Y chromosomes [6], the known meiotic pairing sites of these otherwise highly heteromorphic sex chromosomes [13]. MNM and SNM disappear rapidly from the sex chromosome bivalent in a separase-dependent manner just before separation of the X and Y chromosomes to opposite spindle poles during the metaphase to anaphase transition of MI [6, 16, 24]. On autosomal bivalents, these proteins are more difficult to detect and the targeted chromosomal loci are unknown, but all phenotypic analyses have clearly argued for an analogous function in reductional autosome segregation during MI [6]. Here we provide further support that MNM and SNM are indeed part of the physical linkage between partner chromosomes within bivalents. These proteins are normally present throughout spermatocyte maturation during the stages S1-S6, as also the EGFP tagged versions expressed from UASt transgenes with the bamP-GAL4-VP16 driver, which can replace the endogenous conjunction proteins functionally. Premature degradation after territory formation of either MNM-EGFP or SNM-EGFP in spermatocytes lacking the corresponding endogenous gene function results in an absence of conjunction and random chromosome segregation during MI. Conversely, delayed provision of these conjunction proteins after territory formation (under control of the betaTub85D regulatory region) is largely sufficient to restore conjunction and MI segregation of sex chromosomes, while rescue in case of autosomes is far less efficient.
Premature removal of the EGFP tagged conjunction proteins was achieved with the help of deGradFP [26]. This method is known to have variable efficiency with different GFP fusion proteins [26]. The method appears to degrade MNM-EGFP more efficiently than SNM-EGFP. The former but not the latter protein was lowered by our deGradFP transgene (betaTub85DP-Nslmb-vhhGFP4) already in early spermatocytes, presumably as a result of premature low level basal deGradFP expression. Moreover, MNM-EGFP could also no longer be detected during S6 and MI, while SNM-EGFP was still detectable during MI, although only at very low levels. While deGradFP reduced MNM-EGFP already during the early stages, compensating overexpression driven by bamP-GAL4-VP16 from UASt-mnm-EGFP resulted in a level of remaining MNM-EGFP in mnm(e) that was comparable during the early stages to the level of endogenous MNM in wild type. Accordingly, the mnm null phenotype observed in mnm(e) indicates a requirement for MNM protein persistence until onset of anaphase I rather than a consequence of insufficient levels during the early stages. We acknowledge that this conclusion rests on comparisons of expression levels based on our microscopic quantification of signal intensities, an approach not free of pitfalls. In case of snm(e), however, early reduction of SNM-EGFP did not occur. The very low SNM-EGFP levels still detectable during prometaphase I in snm(e) did not result in any rescue of homolog conjunction and regular MI segregation, indicating that normal MI in males clearly requires persistence of the SNM conjunction protein at high levels beyond territory formation.
Delayed provision of MNM-EGFP and SNM-EGFP in the corresponding null mutants was used for further delineation of the critical time period during which MNM and SNM have to be present for normal chromosome conjunction and regular MI segregation. In the relevant genotypes, snm(l) and mnm(l), accumulation of the EGFP tagged conjunction proteins occurred after chromosome territory formation during the post-S3 stages. The resulting phenotypes indicate the critical role of temporal control of chromosome conjunction during spermatocyte maturation.
Time lapse imaging with mnm(l) spermatocytes indicated that delayed provision of MNM-EGFP is sufficient for normal chromosome conjunction and regular MI segregation. However, complete rescue was not observed consistently. Extensive phenotypic variability was also observed in fixed mnm(l) spermatocytes. Variability was observed between different cysts and to a lower degree also between spermatocytes within a cyst. Rescue of meiotic abnormalities varied from complete to none in mnm(l). Moreover, a chromosome-specific difference in rescue efficiency was observed. Overall, conjunction and segregation of sex chromosomes was rescued more efficiently than that of the large autosomes. While we do not understand the cause of this variability, we propose the following explanation.
The relative temporal dynamics of MNM-EGFP accumulation driven by the betaTub85D regulatory region and of the forces responsible for chromosome territory formation might vary between spermatocyte cysts and lesser also within cysts. The molecular mechanisms that drive chromosome territory formation and control its temporal dynamics are still poorly understood. Condensin II activity is clearly required [30], but whether and how its activity might be regulated during spermatocyte maturation has not yet been studied. Interestingly, the reported effects of condensin II activity on polytene chromosomes in nurse cells and salivary glands [20, 31] indicate that it is likely responsible not only for the disruption of the non-homologous associations of pericentomeric hetereochromatin (i.e., the chromocenter) but also for the extensive unpairing of homologous euchromatic arms and of sister chromatids that accompanies chromosome territory formation during the S2b/S3 stage. Condensin II activity is likely to persist beyond this stage, as suggested by the observed increase of unpairing at some pericentromeric satellite loci and of homologous centromeres from S3 to S5 [19, 32]. We propose that MNM and SNM need to provide physical chromosome linkage throughout these stages in order to prevent condensin II activity from separating homologs completely. Accordingly, the chromosome territory expansion phenotype that develops in mnm and snm during the late spermatocyte stages [6] might result from the postulated continuing unpairing activity of condensin II. Delivery of MNM and SNM after a time point, at which homologs have separated completely, cannot re-establish pairing. In case of the rDNA loci containing sex chromosome bivalent, we propose that the forces which drive nucleolar assembly [33] provide independent resistance against unpairing by condensin II activity beyond MNM and SNM mediated conjunction. The latter might therefore become crucial once nucleolar disassembly starts during the late S5 stage [17]in preparation for entry into the first meiotic division [17]. Accordingly, the point of no return, after which provision of MNM-EGFP and SNM-EGFP can no longer achieve rescue, would be later in case of the sex chromosome bivalent compared to autosomal bivalents. Of course, alternative explanations for the observed higher rescue efficiency of sex chromosome conjunction in mnm(l) are not excluded. Overall, our observations suggest that MNM/SNM mediated linkage cannot establish initial chromosome pairing de novo. It can only fortify and maintain previously established associations and protect these against unpairing forces. Beyond the forces that drive territory formation (presumably condensin II), those that achieve chromosome condensation later during entry into the first meiotic division (presumably condensin I) and the spindle forces during bivalent bi-orientation during prometaphase I are additional unpairing forces which need to be counteracted to prevent premature bivalent splitting.
The phenotype observed in snm(l_s+m) provides additional support for the above proposal. This phenotype was similar but not identical to that of mnm(l). While sex chromosome conjunction and regular MI segregation was rescued even somewhat better in snm(l_s+m) compared to mnm(l), the opposite was recorded in case of the large autosomal bivalents. According to our proposal above, MNM and SNM mediated linkage appears to develop later in snm(l_s+m) than in mnm(l), after the large autosomal time point of no return. The postulated delay might be linked to the program of mnm transcription and to MNM protein instability in the absence of SNM, as indicated by our analyses. Absence of MNM in snm mutants has been reported before [6]. Our results with MNM-EGFP expressed from transgenes argue that snm+ function promotes MNM accumulation by stabilizing MNM protein. The bamP-GAL4-VP16 driver that we have used drives transient transcription of UASt transgenes restricted to the initial spermatocyte stages. Therefore, after UASt-mnm-EGFP expression with this GAL4 driver, MNM-EGFP is detectable only very transiently in early spermatocytes if driver and target genes are in a snm null mutant background. In contrast, MNM-EGFP is present throughout spermatocyte maturation, if the same transgenes are in a snm+ background. As snm+ effects on the transcription program of the transgenes appear highly improbable, SNM protein seems to stabilize MNM protein. Consistent with this proposal, MNM was observed to disappear in parallel with SNM-EGFP in snm(e). However, late provision of SNM-EGFP in snm(l) was not paralleled by MNM accumulation, indicating an absence of endogenous mnm transcripts in late spermatocytes. Therefore, in snm(l_s+m), both SNM-EGFP and MNM-EGFP have to accumulate to effective concentrations without support by endogenous MNM before conjoining activity develops. In contrast, as endogenous SNM protein is present in mnm mutants[6] throughout the spermatocyte stages [6], conjoining activity by cooperation with the pre-existing endogenous SNM might develop more rapidly after late MNM-EGFP accumulation in mnm(l), at least in some of the cysts where rescue of autosomal conjunction is observed.
While bamP-GAL4-VP16 mediated UASt-mnm-EGFP transcription in the mnm(e+l) genotype is transient, MNM-EGFP is present throughout spermatocyte maturation, relying on stabilization by endogenous SNM. As the rescue of chromosome conjunction and regular MI segregation is essentially complete in mnm(e+l), it follows that normal MI does not require continuous production of MNM throughout spermatocyte maturation. In wild-type, such a continuous MNM production is actually unlikely to occur, as endogenous mnm transcripts do not appear to be present in late spermatocytes. Therefore, we propose that MNM and SNM protein are integrated early into stable physical linker complexes that keep chromosomes paired.
Overall, our findings provide further support for the proposal that MNM/SNM mediated chromosome linkage provides a function comparable to cross-overs (COs) during canonical meiosis [2, 6]. After an initial homolog pairing that might rely on identical mechanisms in somatic and pre-meiotic cells in Drosophila, COs maintain this pairing during the canonical female meiosis and MNM/SNM mediated conjunction during the achiasmate male meiosis. However, a major difference between COs and MNM/SNM mediated conjunction presumably concerns linkage specificity. COs are generated by homologous recombination and therefore they are established exclusively between homologous chromosomes. In contrast, it is rather difficult to imagine how MNM/SNM-dependent linkage might discriminate accurately between homologous and non-homologous chromosome associations and develop specifically between the former, in particular in case of the autosomes. In the sex chromosomes, the rDNA loci function as pairing centers [34]. DNA sequences, perhaps via specific proteins that are present exclusively within the rDNA chromatin, might recruit MNM and SNM and thereby establish an appropriate linkage. However, the regions promoting autosomal pairing in spermatocytes appear to be distributed throughout the euchromatic arms and MNM/SNM appear to associate at a lower level at more than one site within autosomal territories. Even if MNM/SNM were recruited to specific loci (for which there is no evidence so far) what might prevent them from linking MNM/SNM recruiting positions on non-homologous chromosomes? In principle, the spermatocyte-specific process of territory formation, which separates the different bivalents apart, in combination with a temporally controlled activation of MNM/SNM-dependent linkage after territory formation (but not after the time point of no return), provides a solution compatible with the use of a non-discriminate linker. The essential temporal control of MNM/SNM-dependent linkage in this scenario cannot be explained by the pattern of mnm and snm expression. As in snm(e+l) and mnm(e+l), endogenous MNM and SNM accumulation starts before territory formation [6]. An indiscriminate and effective MNM/SNM mediated chromosome linkage already during these early stages would be expected to inhibit the dissociation of the extensive non-homologous associations within the chromocenter by the process of chromosome territory formation. While MNM-EGFP and SNM-EGFP are clearly detectable before territory formation in mnm(e+l) and snm(e+l) spermatocytes, respectively, their initial subcellular localization is distinct from that observed during the later stages. Subnucleolar foci are absent initially; they form at around stage S2b and persist until they coalesce into a single sex chromosome bivalent associated dot in parallel with disassembly of the nucleolus and chromosome condensation during late S5 and S6. The formation of subnucleolar foci might therefore indicate when MNM/SNM-dependent stable linkage is activated.
In conclusion, the standard organization of chromosomes during interphase in Drosophila comprises the clustering of most pericentromeric heterochromatin into a single chromocenter in combination with isolation of paired homologous euchromatic regions into separate domains with minimal inter-homologous mingling [22]. Subsequently, during mitosis both the clustering of pericentromeric heterochromatin, as well as homolog pairing within the euchromatic regions, get disrupted by the processes of chromosome condensation and chromatid individualization, which are largely driven by condensin I [35], and by the spindle forces that interact with chromosomes for their eventual bi-orientation within the metaphase plate. To support achiasmate male meiosis, a mechanism seems to have evolved which preserves the homologous associations within the euchromatic regions in spermatocytes until after bi-orientation in metaphase I, but not the non-homologous associations within the pericentromeric heterochromatin. While our work effectively constrains the possibilities of how MNM and SNM contribute to this mechanism, various aspects of their proposed regulation and function remain speculative. The predictions made by our proposals will hopefully promote a successful future clarification.
The following previously characterized mutant alleles and transgene insertions were used: mnmZ3-3298, mnmZ3-5578, snmZ3-0317, snmZ3-2138, P{ry+, hsp70-mnm-EGFP} [6]; P{w+, bamP-GAL4-VP16}III [25], P{w+, His2Av-mRFP}II.2 and P{w+, gCid-EGFP-Cid}II.1 [36]. The marked Y chromosome (BSYy+) that was used for the genetic analysis of sex chromosome missegregation was obtained from +/ BSYy+; bw; mnmZ3-5578/TM3, Sb, a stock kindly provided by Bruce McKee (University of Tennessee, Knoxville, TN, USA). Lines with the following transgenes were generated with the plasmid constructs described further below: UASt-mnm, UASt-EGFP-mnm, UASt-mnm-EGFP, UASt-snm, UASt-EGFP-snm, UASt-snm-EGFP, betaTub85DP-mnm-EGFP, betaTub85DP-snm-EGFP, and betaTub85DP-Nslmb-vhhGFP4. All transgenes under control of the cis-regulatory regions from betaTub85D were integrated into the attP40 landing site on chromosome 2L (25C6). Lines with combinations of mutant alleles and transgenes were generated by standard crosses. For all experiments, flies were cultured at 25°C. Detailed genotypes of the flies analyzed are provided in the supplemental material (S2 Table).
For the production of transgenic lines allowing GAL4-dependent expression of mnm and snm with or without EGFP extensions we generated derivatives of pUASt [37], pUASt-EGFP-mcs or pUASt-mcs-EGFP [38]. The sequences of primers used for enzymatic amplification of insert fragments are provided in the supplemental material (S3 Table).
The inserts for pUASt-mnm, pUASt-EGFP-mnm and pUASt-mnm-EGFP were amplified from genomic DNA isolated from a ry506; P{ry+, hsp70-mnm-EGFP}/CyO male fly. The primer pair AB91/AB95 was used for pUASt-mnm and pUASt-EGFP-mnm; AB91/AB106 for pUASt-mnm-EGFP. After digestion with NotI, the insert fragments were cloned into the corresponding restriction site of the target vectors.
The inserts for pUASt-snm, pUASt-EGFP-snm and pUASt-snm-EGFP were amplified from a plasmid containing a full length snm cDNA (pGBD-SNM; kindly provided by Bruce McKee, University of Tennessee, Knoxville, TN, USA). The primer pair AB93/AB96 was used for pUASt-snm and pUASt-EGFP-snm; AB93/AB107 for pUASt-snm-EGFP. After digestion with NotI, the insert fragments were cloned into the corresponding restriction site of the target vectors.
For the production of transgenic lines that express the GFP-specific recombinant F box protein Nslmb-vhh4-GFP4 [26] under control of the cis-regulatory sequences of the spermatocyte-specific betaTub85D gene, we generated pattB-Nslmb-vhh4-GFP4, a pattB derivative [39]. Both 5’ and 3’ regions flanking the betaTub85D coding region (978 bp and 477 bp, respectively) were amplified from w1 genomic DNA and inserted into pattB. The region coding for Nslmb-vhh4-GFP4 was amplified from pUASt-Nslmb-vhh4-GFP4 [26] and inserted in between the betaTub85D 5’ and 3’ flanking regions. The same betaTub85D cis-regulatory region was also used for the cloning of the pattB-betaTub85DP-mnm-EGFP and pattB-betaTub85DP-snm-EGFP constructs.
Males of the genotype w/ BSYy+; UASt-xy/ +; allele-1/ allele-2 ± bamP-GAL4-VP16 were crossed to w virgin females. For the analyses where UASt-xy was UASt-mnm II.1, UASt-EGFP-mnm II.1, UASt-mnm-EGFP II.1, or UASt-mnm-EGFP II.2, allele-1 and allele-2 were mnmZ3-5578 and mnmZ3-3298, respectively. For the analyses where UASt-xy was UASt-snm II.1, UASt-EGFP-snm II.1, or UASt-snm-EGFP II.1, allele-1 and allele-2 were snmZ3-2138 and snmZ3-0317, respectively. Normal segregation of the sex chromosomes during male meiosis results in regular sperm with an X or a Y chromosome, while missegregation generates irregular sperm with either both an X and a Y chromosome or neither. The BS marker allowed identification of progeny fathered by regular or irregular sperm, respectively. Fertilization of w oocytes with regular sperm results in XY males with Bar-eyes and XX females with normal eyes. In contrast, fertilization of w oocytes with irregular sperm results in X0 males with normal eyes or XXY females with Bar-eyes.
For whole mount testis preparations, dissection was performed in testis buffer (183 mM KCl, 47 mM NaCl, 10 mM Tris-HCl, pH 6.8). Testes were fixed in depression slides for 10 minutes in phosphate buffered saline (PBS) containing 4% formaldehyde and 0.1% Triton X-100. For DNA staining, testes were incubated for 10 minutes in PBS, 0.1% Triton X-100 (PBTx) containing 1 μg/ml Hoechst 33258. After three washes with PBS, testes were transferred into a drop of mounting medium (70% glycerol, 1% n-propyl gallate, 0.05% p-phenylenediamine, 50 mM Tris-HCl pH 8.5) on a slide before adding a cover slip.
Testis squash preparations were made and stained essentially as described previously [40], according to protocol 3.3.2, except that we used the mounting medium described above. For immunolabeling, mouse monoclonal anti-α-tubulin DM1A (Sigma) was used at 1:10000 and the rabbit antiserum against ModC [11] at 1:4000. This latter antibody recognizes the N-terminal region that is present in all of the many different isoforms expressed by the mod(mdg4) locus. However, as MNM appears to be the only mod(mdg4) protein product expressed in spermatocytes [10], the designation anti-MNM is used here. Secondary antibodies were Alexa568-conjugated goat antibodies against mouse or rabbit IgG diluted 1:1000.
For immuno-FISH, testes were dissected and fixed with 4% formaldehyde in PBS, followed by permeabilization with PBS containing 0.3% Triton X‐100 and 0.3% sodium deoxycholate. Anti-α-tubulin staining was done as described above except that Cy5-conjugated goat anti-mouse IgG diluted 1:1000 was used as secondary antibody. Ethanol incubations and dehydration with a formamide series were also done as described (immuno-FISH protocol 3.2, steps 10–26) [41]. An oligonucleotide (5'-TTTTCCAAATTTCGGTCATCAAATAATCAT-3') with Atto-565 on 5’ and 3’ end (Integrated DNA Technologies, Leuven, Belgium) was used for detection of the X-specific 359 bp repeats at a concentration of 1 ng/μl in hybridization buffer. An oligonucleotide (5'-AATACAATACAATACAATACAATACAATAC-3') with Alexa-488 on 5’ and 3’ end (Integrated DNA Technologies, Leuven, Belgium) was used for detection of the Y-specific AATAC satellite repeats at a concentration of 2 ng/μl in hybridization buffer. The denaturation step was performed at 98°C for 6 min, and hybridization over night at 18°C. Slides were washed twice for ten minutes each time in 50% formamide, 2x SSCT at 18°C. Thereafter, additional washes for ten minutes each time were performed at room temperature, first once in 25% formamide, 2x SSCT and then three times in 2x SSCT. DNA was stained with 1 μg/ml Hoechst 33258 for 10 minutes and slides were washed twice in PBS for 5 minutes before mounting.
Preparations were analyzed with a Zeiss Cell Observer HS microscope. For the quantification of DNA signal intensities in nuclei within early spermatid cysts, a 40×/0.75 oil immersion objective was used for acquisition of image stacks with 280 nm separation between focal planes. For high resolution images of spermatocytes, stacks with 250 nm spacing between focal planes were acquired using a 100×/1.4 oil immersion objective. The images displayed in the figures represent maximum intensity projections. The data used for statistical analyses of a particular genotype was obtained from multiple slides and each slide was prepared with about ten dissected testes.
The quantification of the intensity of the dot signals associated with the sex chromosome bivalent during prometaphase I after staining with anti-Mod(C) or resulting from MNM-EGFP was done using ImageJ software and subtraction of local background as described previously [32]. To minimize variability affecting quantification of anti-Mod(C) signal intensities, testes dissected from control and mnm(e+l) were combined onto the same slide for fixation and staining. During microscopic analysis, genotypes were assigned based on presence or absence of MNM-EGFP signals.
The quantification of the intensity of the DNA signal in nuclei within early spermatid cysts was performed with the help of a CellProfiler pipeline [42]. Within a maximum intensity projection of the acquired image stacks, the region containing nuclei of an early spermatid cyst at the onion stage was outlined manually. These regions did not necessarily contain all 64 spermatid nuclei of a given cyst but usually at least 50%. Nuclei of cysts cells within these regions were marked manually and eliminated from the analyses. For the plots documenting the variation of DNA signal intensity among the nuclei in early spermatid cysts (Fig 6D and S2 Fig), the DNA signal intensities of all the nuclei within the image stack obtained of a particular cyst were averaged. The resulting cyst average was then used for normalization of the DNA signal intensity values of the nuclei from this cyst. Thereafter, the standard deviation of these values for the cyst was determined. For the comparison between the different genotypes, the standard deviations of all the analyzed cysts of a given genotype were plotted (Fig 6E). Source data (including raw integrated intensity per spermatid nucleus) is provided in S4 Table.
Time lapse imaging of progression through meiosis was performed as recently described [24]. In brief, testes from pupal or young adult males were dissected in Schneider’s Drosophila Medium (Invitrogen, #21720) supplemented with 10% fetal bovine serum (Invitrogen) and 1% penicillin/streptomycin (Invitrogen, #15140). The dissected testes were transferred into 40 μl of medium in a 35 mm glass bottom dish (MatTek Corporation, #P35G-1.5-14-C) and opened with fine tungsten needles to release the cysts. To reduce sample movements, 15 μl of 1% w/v methylcellulose (Sigma, #M0387) was added. A wet filter paper was placed inside along the dish wall before sealing the lid with parafilm. Imaging was performed at 25°C in a room with temperature control using a spinning disc confocal microscope (VisiScope with a Yokogawa CSU-X1 unit combined with an Olympus IX83 inverted stand and a Photometrics evolve EM 512 EMCCD camera, equipped for red/green dual channel fluorescence observation; Visitron systems, Puchheim, Germany) using a 60×/1.4 oil immersion objective. Image stacks with 24–30 focal planes spaced by 500 nm were acquired with a time interval of 10 or 20 seconds. Precise numbers are specified in the legends of S1 Movie and S2 Movie, respectively. Imaris software (Bitplane) was used to track centromere signals and for production of avi files from maximum intensity projections, as well as for exporting still frames, which were assembled using Photoshop (Adobe).
|
10.1371/journal.pgen.1004549 | Transcriptome Sequencing from Diverse Human Populations Reveals Differentiated Regulatory Architecture | Large-scale sequencing efforts have documented extensive genetic variation within the human genome. However, our understanding of the origins, global distribution, and functional consequences of this variation is far from complete. While regulatory variation influencing gene expression has been studied within a handful of populations, the breadth of transcriptome differences across diverse human populations has not been systematically analyzed. To better understand the spectrum of gene expression variation, alternative splicing, and the population genetics of regulatory variation in humans, we have sequenced the genomes, exomes, and transcriptomes of EBV transformed lymphoblastoid cell lines derived from 45 individuals in the Human Genome Diversity Panel (HGDP). The populations sampled span the geographic breadth of human migration history and include Namibian San, Mbuti Pygmies of the Democratic Republic of Congo, Algerian Mozabites, Pathan of Pakistan, Cambodians of East Asia, Yakut of Siberia, and Mayans of Mexico. We discover that approximately 25.0% of the variation in gene expression found amongst individuals can be attributed to population differences. However, we find few genes that are systematically differentially expressed among populations. Of this population-specific variation, 75.5% is due to expression rather than splicing variability, and we find few genes with strong evidence for differential splicing across populations. Allelic expression analyses indicate that previously mapped common regulatory variants identified in eight populations from the International Haplotype Map Phase 3 project have similar effects in our seven sampled HGDP populations, suggesting that the cellular effects of common variants are shared across diverse populations. Together, these results provide a resource for studies analyzing functional differences across populations by estimating the degree of shared gene expression, alternative splicing, and regulatory genetics across populations from the broadest points of human migration history yet sampled.
| Previous gene expression studies have identified factors influencing population-level variation in gene regulation. However, these efforts have been limited to a small set of well-studied populations. By leveraging the high resolution of RNA sequencing and broad population sampling, we survey the landscape of transcriptome variation across a globally distributed set of seven populations that span a breadth of human genetic variation and major dispersal events. We assess differences in gene expression, transcript structure, and regulatory variation. We find only 44 transcripts that show significant differences in expression, likely as a result of the small sample size, but we find that 25% of the variance in gene expression is due to population differences. This is a larger fraction than previously observed, and it is likely due to the greater breadth of human diversity assayed in this study. We also find that population-specific variance is mostly due to transcription variability rather than the configuration of expressed gene products. Additionally, known common regulatory variants have similar effects across populations including those we study here. These data and results serve as a resource cataloging the wide array of gene expression regulation affecting population variation among diverse groups, improving our understanding of transcriptional diversity.
| A central challenge in modern medical and population genomics is identifying trait-disposing genetic variants, interpreting their molecular consequences, and determining the transferability of their functional roles across individuals and populations. While genome-wide association studies (GWAS) have correlated an abundance of common and (increasingly) rare variants with disease, far fewer studies have pinpointed causal variants, discovered the biological mechanism of the association, or replicated their findings in different populations. Here, we build upon previous work using transcript abundance and splicing as model systems for understanding how population substructure can impact the genetic architecture of biomedical traits [1]–[4]. In particular, we focus on a set of populations that span the “Out-of-Africa” migration of anatomically modern humans using CEPH Human Genome Diversity Panel cell lines, for which we have collected an extensive ‘omics profile described below.
Genetic studies of microsatellites panels and single nucleotide polymorphisms (SNPs) have shown a decrease in genetic diversity as a function of a population's geographic distance from eastern or southern Africa [5]–[7]. This pattern fits a serial founder effect model, but it remains unclear whether transcriptome variation follows this pattern and how closely genetic effects on regulation mirror human migration history. Previous work has shown that population bottlenecks reduce heterozygosity and are associated with an accumulation of damaging and loss-of-function variation which can impact gene expression [8], [9]. However, further molecular work is needed to settle the controversy regarding demography and its impacts on the distribution of functional genetic variation among populations.
Gene expression studies within and between well-studied populations have been transformative in cataloging gene expression differences, expression quantitative trait loci (eQTLs) with different types of regulatory variants, as well as allele-specific expression (ASE) that underlie many disease associations [3], [10]–[16]. Technological advances in RNA sequencing and transcript assembly have also enabled analysis of variation in transcript structure and regulation of alternative splicing. For example, splicing ratios can differ between distant populations even in the absence of expression differences, and some population-specific splicing differences are involved in known disease-susceptibility genes that correspond with differences in prevalence [4], [17]. Additionally, thousands of unannotated transcripts have been identified within populations [18], [19], highlighting the difficulty in distinguishing population-specific transcripts that are functionally relevant versus those that simply arise from noisy splicing [20]. Elucidating how gene expression regulation and splicing are impacted by historical human migrations will aid functional interpretation of the genome and improve our understanding of the transferability and evolution of genetic regulation across populations.
This study aims to characterize regulatory, splicing, and expression differences via RNA sequencing across a global sampling of seven populations from the HGDP. We have also performed medium pass genome (∼8X) and high coverage (∼96X) exome sequencing of these individuals, enabling us to characterize genetic effects on transcriptome variation. These integrated DNA and RNA sequencing datasets are generated from the broadest points of human migration history yet sampled, and serve as a resource for future studies analyzing functional differences across populations.
To assess the molecular underpinnings of population level transcriptome diversity, we have sequenced the DNA and mRNA fractions of 45 lymphoblastoid cell lines (LCLs) from seven populations in the Human Genome Diversity Panel [21]: Namibian San, Mbuti Pygmies of the Democratic Republic of Congo, Algerian Mozabites, Pathan of Pakistan, Cambodians of East Asia, Yakut of Siberia, and Mayans of Mexico (Figure 1). Five of these groups are descended from the ancient human dispersals out of Africa associated with serial founder effects [5]. The populations in this study capture important differences in human genetic diversity resulting from early subdivision within Africa and subsequent serial founder effects into the Near East, back to North Africa [22], southern and eastern Asia and Central America.
DNA sequencing was performed via paired-end 101-base pair Illumina sequencing (Methods). Total coverage per individual genome and exome was 8.1±3.3X and 96.5±11.0X (mean ± standard deviation), respectively. Additionally, 15.4M±0.5M read pairs per sample were generated via transcriptome sequencing performed on lymphoblastoid polyA-selected mRNA, and an average of 10.8±4.6 million read pairs per sample were properly mapped to the hg19 transcriptome (Table 1). Gene quantification performed through Cufflinks [23] detects an average of 9,141 known genes expressed per each individual cell line, which is consistent with previous observations [12].
We randomized library preparations and sequencing across populations, including approximately one individual per population in each lane of sequencing in order to ensure that expression differences were due to biological rather than technical variation. We also sequenced technical replicates for each sample by sequencing each library preparation twice per individual. We assessed the correlation between replicates and identified problematic samples as previously described [24]. Briefly, we applied an optimal power space (OPS) transformation to expressed gene and transcript quantifications to ensure that all data points contributed equally to correlation measures, eliminating bias by low and high FPKM values. Pearson correlations between technical replicates were high (r = 0.915±0.034 (mean ± sd) for genes (Figure S1), r = 0.641±0.167 for transcripts). Higher correlations between replicates for gene versus transcript quantifications likely reflect the greater uncertainty in the deconvolution of the relative abundance of transcripts within a gene. Because reproducibility between replicates was high, we pooled reads across replicates and reassessed gene and transcript quantifications with Cufflinks. For each sample, we determined the median Pearson correlations (D-statistics) with all other samples. D-statistics were high overall (median D-statistic = 0.948 for genes, median D-statistic = 0.862 for transcripts, Figure S2). We identified two outliers, both within the San population (HGDP01029 and HGDP00992), and we removed these samples as well as the two remaining San samples from all downstream analyses.
To compare gene expression patterns across individuals, we first normalized our data. Exon and gene counts were quantified over regions annotated in UCSC known gene tables. Previous work has shown that the sample preparation protocol for RNA-seq introduces nonlinear, sample-specific effects that explain more than 50% of the variation in expression data [25], [26]. These nonlinear effects can manifest as sequence-specific biases [13], which we accounted for via conditional quantile normalization (CQN) [27]. This normalization strategy removed large distributional outliers (Figure S4) by accounting for non-linear guanine-cytosine (GC) content and feature length effects.
As previously observed, genetic variation clearly differentiates globally diverse populations [28], [29] (Figure 2A–D). A tree generated via hierarchical clustering of FST distances (Figure 2A–B) shows a clear separation of sub-Saharan African populations and out-of-Africa populations. Additionally, principal component analysis (PCA) of autosomal single nucleotide polymorphisms (SNPs) in the HGDP dataset (Figure 2C–D) shows population-specific clustering [29] with these seven global populations separating within the first four PCs. Despite clear clustering among the selected populations at the genetic level, PCA of gene expression levels assessed via Cufflinks reflects high individual expression variability and shows no clear population clustering (Figure 2E–F). A formal test of this hypothesis is presented in the last subsection of the Results section, “Variability in expression and alternative splicing ratios,” which also considers the impact of population labeling as a factor in gene expression differences among individuals.
We next sought to identify individual exons and genes that show strong evidence of differential expression (DE) among populations. We used a negative binomial model for gene expression analyses (Methods) and incorporated a normalization offset term from CQN via edgeR [30] (Figure S3); we find that our model provides a good fit to the data (Methods, Figure S4). We identified 251 DE exons via generalized linear model with a false discovery rate (FDR) of less than 5% when comparing all populations (Table S1). Two examples of genes containing highly DE exons are shown in Figure 3 (expression of all individuals shown in Figure S5), both of which are involved in immune function and have some previous evidence for population-specific effects [31], [32]. Figure 3A shows the expression of MX1 colored by population (FDR = 1.57%). MX1 is known to affect the immune response to influenza, the West Nile Virus, the avian flu, and other DNA and RNA viruses [33], [34]. Additionally, LSP1 (lymphocyte-specific protein 1, Figure 3B, FDR = 0.87%) has been associated with breast cancer risk in Europeans. Interestingly, this signal did not replicate using admixture mapping in Latina women, perhaps due to differences in allele frequency among the GWAS and attempted replication populations [32]. We also identified 44 differentially expressed transcripts at ≤5% FDR (Table S2). We used gene set enrichment analysis (GSEA) of ranked p-values to detect functional enrichment of differentially expressed transcripts [35]. The following categories were enriched with a FDR≤5%: RXR and RAR heterodimerization with other nuclear receptors (q = 0.007, canonical pathway), IL 2 signaling pathway (q = 0.015, BioCarta), and Top 40 genes from cluster 7 of acute myeloid leukemia (AML) expression profile (q = 0.018, chemical and genetic perturbations) (Figure S6).
Allele-specific expression (ASE) can be detected as a read imbalance at a given heterozygous site; it has previously been shown to tag regulatory variants [12]. To identify the degree to which allelic effects on expression vary, we compared ASE sharing among individuals for variants in the high coverage exomes. We define normalized ASE sharing as the number of shared significant ASE events (p<0.05) with at least 30 reads, normalized by sharing of SNPs that are heterozygous with at least 30 reads, regardless of presence or absence of a significant allelic imbalance. Reads were sampled to have equal counts in order to account for expression variability. There is a rapid reduction in normalized ASE sharing as the number of individuals in the comparison set increases (Figure S7A). That is, even when heterozygous sites are shared, most allelic imbalances are private to an individual. Some allelic imbalances are shared by pairs of individuals; rarely do three individuals in the set share an imbalance and very little sharing occurs across more than four individuals. We compared normalized ASE sharing across individual pairs and found similar levels of sharing within and between populations (Figure S7B). A potential explanation for this lack of ASE sharing among individuals is that the allelic state of the underlying causal regulatory variant tagged by the ASE exome site is acting in cis but in weak linkage disequilibrium, potentially with a rare regulatory variant.
In a previous study, Stranger et al. (2012) mapped eQTLs in eight populations from the HapMap3 dataset. To determine if the effects of these previously identified cis-regulatory variants can be captured in our more diverse HGDP populations, we compared ASE events in our dataset to previously discovered eQTLs [3] across populations. We hypothesized that if an individual is heterozygous for a previously discovered cis eQTL SNP (eSNP), and a significant ASE signal exists in the associated gene, then the allelic imbalance is more likely to be driven by the eQTL (see Figure S8 for a graphical representation of the model). We assessed the HGDP genotypes of eSNPs identified in HapMap3 and determined that there is a significant ASE enrichment within eQTLs associated with heterozygous versus homozygous eSNPs (p<2.2×10−16, Figure S9). This finding is consistent with our model and previous studies [12] and indicates that our measures of ASE are tagging shared regulatory variation between these studies. We also calculated an enrichment score similar to an odds ratio to determine how often ASE events are found in heterozygous versus homozygous eQTLs compared to the number of measured sites (Methods) for each HGDP and HapMap3 population. We observe an enrichment of ASE events in heterozygous eQTLs versus homozygous eQTLs consistently in all populations, but we do not observe a signal showing stronger effects in HGDP populations that are more closely related to the eQTL discovery population (Figure S10). This supports the previous notion that the effects of common regulatory variation are largely shared across populations with taggability depending on patterns of shared LD [3].
We next sought to determine whether regulatory events discovered within populations replicate more consistently in more closely related populations. Because of the limited sample size and structured populations in this study, de novo eQTL discovery is infeasible. We therefore assessed cross-population regulatory sharing using previously discovered eQTLs [3]. We compared Spearman's rank correlation coefficient ρ2 values, a measure akin to variance explained, between our dataset and the HapMap3 study and find consistency between the associations (r = 0.22, p<2.2 * 10−16). The −log10(p) values across studies were also significantly correlated (r = 0.14, p<2.2 * 10−16). We next measured the associations between eQTLs identified in each population. We find that the effect sizes of eQTLs are significantly associated across most pairwise populations (Figure 4), independent of genetic divergence. The reproducibility of eQTLs is similar across populations, indicating that previously discovered common eQTLs reflect either the true causal SNPs or tag the causal eQTL due to similar LD at the locus (Figure S11). We also assessed the impact of similarity in allele frequencies between studies on the ρ2 values and find that eQTLs with similar minor allele frequencies (MAFs) between studies replicate better than eQTLs with different minor allele frequencies. As expected, eQTLs with high MAFs in one study and low MAFs in another study replicate poorly (Figure S12).
Using the genome, exome, and RNA-seq resource described above, we characterize the completeness of current gene annotations as previously described [13]. By pooling our dataset of 1.7 billion paired reads, we identify regions of novel transcription that lie outside of previously characterized gene structures. By calculating per-base global sequencing coverage and merging together continuous transcribed regions above our cutoff filters, we identified 445,091 total regions of putative transcription in our LCLs, 384,285 (∼86%) of which corresponded to annotated exons in Refseq, Ensembl, UCSC, or Gencode databases (Methods, Figure S13). Conversely, 34,555 regions (∼7%) meeting our minimum expression threshold did not overlap with known annotations (Figure S14).
When we filter regions expressed in at least one individual per population at greater than or equal to 1 RPKM, there are only a few hundred of these 34,555 regions expressed across all individuals in that population (Table S3). Additionally, we see that every novel transcribed region expressed ubiquitously in one population is also present in at least one other individual of another population. This result suggests that the vast majority of novel transcribed regions are not population specific, but can be found across multiple diverse human groups.
Previous work indicates that exonic splicing may vary significantly more than gene expression variability across species within the same tissue [36], [37]. The majority of previous human transcriptome work has focused on expression and regulatory variability, leaving the degree of alternative splicing variation across diverse human populations relatively unexplored. To understand expression and splicing relationships within and between human populations, we measured the coefficient of variation, cv, in gene expression (standard deviation divided by the mean) and the variability in alternative splicing ratios (Hellinger distance to the centroid of the splicing ratios of each gene across all individuals in the population, ) using methods developed previously [4]. We find that the cv and values for genes are highly correlated between pairwise populations (cv correlations are, on average, within [0.44, 0.67] between pairwise populations, p<2.2×10−16 for each comparison (Figure S15), and correlations are on average within [0.64, 0.82] between pairwise populations. p<2.2×10−16 for each comparison (Figure S16)). The relationships overall between cv and values do not reflect the genetic divergences seen between pairwise populations (Mantel test with 1,000 Monte Carlo repetitions between cv Spearman rank correlation distance matrix and FST gives ρ = 0.38, p = 0.16, and the same test between and FST gives ρ = 0.44, p = 0.14).
We next used established methods to assess the proportion of gene expression variation among individuals attributable to population identity [1]. We find that population label, on average, explains 25.0% of the variation in gene expression among individuals (Figure 5A) for all genes expressing at least two transcripts. To assess significance for each gene, we used a permutation test reshuffling population labels among individuals and find that the p-value distribution is heavily skewed towards low p-values compared to the expected uniform distribution (Figure 5B). This genome-scale level of population stratification for gene expression is higher than previously seen by the GEUVADIS consortium [1], which reported ∼3% of the variance attributable to population label as a factor when considering populations of mostly European descent in the 1,000 Genomes Project. These results are perhaps expected given that the populations in our study span a greater breadth of human genetic diversity. We repeated this analysis comparing each population to all other populations and find that a smaller proportion of the variation is due to population-specific differences and that these differences do not follow the pattern expected by population divergence (Figure S17). We also decomposed population-specific variability into variability in overall expression levels as opposed to splicing variability via multiplicative model, which, as previously demonstrated [1], [4], accounts for differences in scales and units between expression and splicing metrics. We find that on average, variation in gene expression explains the majority (75.5%±22.3% (mean ± sd), Figure 5C) of population-specific variation, indicating that alternative splicing generally makes up the minority of population-specific variation within humans. We repeated this analysis comparing each population to all other populations and find consistent results (Figure S18). We next assessed differential splicing between pairwise populations. In Figure 6, we show a sashimi plot of a gene (ENSG00000183291.11, SEP15) with substantial differential splicing across all pairwise populations. Overall, we do not see evidence for differential splicing patterns consistent with population genetic divergence (Figure S19); this result is consistent with a minority of population-specific variance in gene expression levels explained by splicing variability.
We have analyzed the transcriptome landscape from populations spanning the breadth of human genomic diversity. While other studies have characterized variation within and among populations [12], [13], this study provides a unique opportunity to discover regulatory drivers of expression diversity in serially bottlenecked populations throughout human migration history. The HGDP populations in this study were explicitly chosen to encompass a large geographic range that experienced varied demographic histories, and thus they provide unique insight into global variation in transcription. In addition to gaining an understanding of transcriptome variation in diverse populations, this study also enables the discovery of novel gene structures and provides a public resource for analyses of diverse human transcriptomes.
In this study, we have assessed population-specific expression variability, alternative splicing, and regulatory variation. We account for technical artifacts in our analyses, including GC content and feature length effects, which otherwise add nonlinear systematic noise to expression data. We show that we substantially reduce technical sources of variation from these effects in our data and obtain high reproducibility between sequencing replicates. We detect few differentially expressed exons, which is likely affected by the fact that we analyze cultured cell lines grown in a highly homogenous environment. Further, given our sample size per population, we are only powered to detect very dramatic differences in expression among populations. Using variance decomposition methods developed previously, we find that 25.0% of transcription variability can be attributed to population differences among the six we study here. A previous study that sought to detect expression differences between the CEU and YRI estimated that ∼17% of genes were differentially expressed across these populations [38]. This estimate is quite comparable to ours. However, the estimates from both studies are substantially larger than those reported by the GEUVADIS consortium, which found that population labels accounted for only ∼3% of transcription differences among 462 individuals sampled from the European populations in the 1000 Genomes Project as well as Yorubans. One potential reason why our analysis produced estimates larger than GEUVADIS is that the European populations sampled there are more closely related to each other than the breadth of populations studied here.
Immunity genes as a whole are overrepresented in the set of differentially expressed genes across populations. This is highly consistent with the immune role of LCLs we study here. This finding is also consistent with previous work showing that natural selection may have favored different alleles in certain immune genes across human populations and that differences in autoimmune disease risks may be a side consequence of differences in these evolutionary histories [39], [40]. The increased expression of immune genes in LCLs also improves our power to detect differences with respect to most other gene functions. Potential mechanisms for differential expression across populations include variation in cis and trans eQTL allele frequencies, environmental differences, and epigenetic differences.
We also measured the population-specific variance attributable to expression versus splicing and find that on average, 75.5% is due to gene expression differences. This result is consistent with previous findings in humans and indicates that, within tissues, splicing differentiates populations less than expression. While this finding is consistent with previous human studies [1], [4], it appears to be inconsistent with other cross-species work [36], [37]. This suggests that splicing potentially plays a greater role on longer evolutionary time-scales. Additionally, the methodology used to assess splicing varies substantially between these studies; in this study, we have used variance decomposition methods relying on gene and transcript annotation data, which is more limited in many other species. In the cross-species studies, exonic splicing was measured via “percent spliced in” (Ψ), which may be affected by expression variability or other forms of transcript differences, such as those arising from alternative start sites. Further work on the efficacy of alternative splicing quantification methodologies would benefit future studies.
We also show that eQTLs that were previously identified across a wide range of human populations show allelic imbalances and replicate consistently across populations, but this replication is dependent on minor allele frequencies. Our results suggest that rare eQTLs within a population that are common in another population will likely have differing effect sizes. Given that the ∼1.2 million SNPs assayed in HapMap3 are common and therefore largely shared globally, we have only limited power to assess the effects of rare regulatory variants. As more transcriptomes are sequenced across diverse populations, we expect that rarer eQTLs identified in large population-based genome- and RNA-sequencing studies will identify more population-specific enrichment patterns.
This study provides the first analyses of transcriptome diversity from serially bottlenecked populations spanning the breadth of human migration history. In this study, we integrated genome, exome, and transcriptome sequencing data from LCLs that are part of the HGDP. This enabled us to assess regulatory drivers of global expression variation in serially bottlenecked populations across a large geographic range and different demographic histories. We find that population of origin accounts for ∼25% of variation in transcription. While we are powered to detect only large differences in expression among populations, genes involved in immunity are overrepresented in this set. Of the 25% difference in transcription explained by population of origin, expression differences accounts for three-fold more of the effect than do splicing differences. Further, the common regulatory variants we replicate here impact expression across broad geographic groups relatively uniformly and do not correlate with the degree of genetic divergence among populations. We look forward to larger studies spanning the breadth of human diversity that are better powered to detect additional population-specific effects and cellular mechanisms of global expression variation. Here, we analyze the total variance in expression and splicing explained by global populations, which, together with other studies, suggests a complex genetic mechanism for population level variation in transcription.
Total RNA was extracted from lymphoblastoid cell lines in four San, seven Mbuti Pygmies, seven Mozabites, six Pathan, seven Cambodians, seven Yakut, and seven Mayans from the Human Genome Diversity Panel using an RNeasy Mini Kit (Qiagen). mRNAs were purified using magnetic oligo-dT beads and randomly fragmented to 300–400 nucleotides in length. First-strand cDNA synthesis was performed using random hexamers and reverse transcriptase. This was followed by second-strand cDNA synthesis with dUTP via the dUTP strand-marking protocol [41]. Illumina TruSeq adaptors were ligated to the ends of the double-stranded cDNA fragments followed by digestion with uracil N-glycosylase (UNG) to remove second strand cDNA. A 300–400 bp size-selection of the final product was performed by gel-excision, following the Illumina-recommended protocol.
Each individual was sequenced in a 7-plex library on an Illumina HiSeq 2000 producing 101-bp paired end reads. Lanes were assessed for multiple quality metrics including number of reads, read quality, and reads mapping to the human genome. Two San individuals failed sequencing quality control and so all four San individuals were excluded from further analysis.
Sample genomic DNA was extracted from lymphoblastoid cell lines. Exonic regions were enriched using an Agilent SureSelect XT 44 Mb All-Exon Capture Kit (v2) and sequenced on Illumina HiSeq machines.
Illumina sequencing reads were mapped to the human reference genome (hg19) using a standard pipeline informed by the 1000 Genomes Project [42]. Briefly, reads were mapped and paired using bwa v0.5.9 [43]. Duplicate read pairs were identified using Picard (http://picard.sourceforge.net/). Base qualities were empirically recalibrated, indels were realigned, and variants were called using the Genome Analysis Tool Kit (GATK) v1.6 [44]. SNP calls that failed the Variant Quality Score Recalibration (VQSR) step were filtered out.
Exonic SNPs were annotated using the RefSeq database to identify synonymous coding variants. High confidence and high coverage synonymous variants were used to compute Weir & Cockerham FST values [45] for each pairwise population using vcftools (v0.1.11) [46].
Reads were mapped to the human reference genome (hg19) with bowtie-2.0.0 and tophat-2.0.4 split read mapping algorithms using the “-b2-very-sensitive” parameters [46]. Reads were subsequently filtered to include only properly paired reads. This yielded between 12.1 and 44.8 million reads per individual (29.3 mean±7.9 s.d. million reads), which corresponds to 62.17±13.79% of the total reads per individual.
Exon and gene count estimates were created by using bedtools to count read overlap with known genes and exons from the UCSC “knownGene” table file downloaded on July 17th, 2012 for differential expression analysis. Raw exon and gene read counts were normalized through conditional quantile normalization, which reduces expression outliers by accounting for feature level GC nucleotide content and overall feature length [27].
UCSC knownGene tables were also used for novel transcript structure analysis because a larger collection of gene structures have been catalogued in this annotation set. For all other analyses, gencode v13 annotations were used, because they give one-to-one correspondence of transcript to gene annotation, enabling the Gonzalez-Porta methods to be used as they were developed.
Transcript level quantification was performed with cufflinks-2.0.2 and produced FPKM (fragments per kilobase of exon per million) estimates per transcript. Cufflinks uses a generative statistical model of paired-end sequencing experiments to derive a likelihood for the abundances of a set of transcripts given a set of fragments. The likelihood function can be shown to have a unique maximum, which Cufflinks finds using a numerical optimization algorithm. The program then multiplies these probabilities to compute the overall likelihood that one would observe the fragments in the experiment, given the proposed abundances on the transcripts [23].
In order to compare expression levels in this dataset with those identified in Stranger et al [3], we reran Cufflinks (v2.1.1) using the Gencode v13 annotations to get both gene and transcript quantifications. These expression abundances were subsequently used to quantify the relative importance of variability in gene expression and variability in alternative splicing to individual transcript variability.
Sequencing variants called from the differentially expressed and differentially spliced regions were annotated for a series of functional predictions, conversation scores, and RefSeq database annotations as described below. This was done in order to better assess the significance of genetic variants present in the data and their potential contribution or involvement in modulating gene expression, transcript splicing, and phenotypic variability. General annotations include information from: the NHLBI Exome Sequence Project allele frequencies; 1000 Genomes Project allele frequencies; publically available Complete Genomics sample allele frequencies; region and exonic annotations from both Ensembl and RefGene; and information about protein structure and function from the UNIPROT and INTERPRO databases. Conservation scores were also produced from the following algorithms: GERP++, SLR, SIFT, LRT, PHYLOP, and SiPhy based on 29 mammalian genomes [47]–[51]. Lastly, functional prediction annotations were produced from the following sources: FATHMM, MutationTaster, Mutation Assessor, LRT, PolyPhen2, and the RefSeq RefGene database [50], [52].
Methods to characterize regions of previous unannotated transcription closely followed previously described work [13] (Figure S14). In brief, for each base of the genome we calculated global sequencing coverage and split the genome into continuous transcribed regions. Expression of a region was defined as the maximum per base coverage of bases in the region. As in previous studies, we chose a threshold of an average expression level of 5×10∧-8 (or 0.05 reads/million) to consider a region expressed and merged together regions separated by less than 15 bp [13]. Sample specific expression of these novel regions was then quantified by calculating RPKM of each region for each individual. For these analyses, we ran Cufflinks (v2.0.2) using the UCSC KnownGene tables downloaded on July 16, 2012 because there were fuller annotations than in Gencode v13.
ASE was determined as previously [12]. Briefly, variants were called for all HGDP individuals in this project using high coverage, high quality exome variant calls generated according to the GATK best practices. Samtools was used to determine the number of reads that matched the reference and non-reference allele. Imbalance reference allele mapping bias was compensated using the per individual overall reference ratio within the binomial test.
We used conditional quantile normalization for all exons and genes with unique start and stop positions, accounting for GC content and length as covariates, and generated an offset term per gene or exon and individual. We filtered to exons or genes where the standard FPKM expression was > = 2 and the length was at least 100 bp, which left 207,180 of all UCSC knownGene annotated exons (29.7%) and 72,931 of all annotated genes (26.8%). Then, we used the following negative binomial model to detect differential expression:Here, y is the count at gene g in individual i, β is the vector of population effects, x is the population label, o is the offset term from conditional quantile normalization, and ε is the error term. We perform an analysis of variance (ANOVA) comparing the null hypothesis of β = 0 to the alternative hypothesis of β≠0. In pairwise population comparisons, we computed genewise exact tests for differences in the means between the two groups of negative-binomially distributed counts.
eQTLs discovered in the HapMap3 populations were replicated in our HGDP dataset using genotypes derived from the exome sequencing variants and preliminary results for the full genomic variants (Henn & Botigue et al, unpublished data) for eQTLs outside the exome (Data Access).
The SRA accession number for the genome and exome sequence data reported in this paper is SRP036155. The GEO accession number containing the RNA-Seq data and gene/transcript expression matrices reported in this paper is GSE54308. Links to additional data (exome variant files, eQTL SNP data, FST matrices, gene/transcript expression quantifications, ASE tables, and eQTL data) and scripts are provided on an FTP site by the Stanford Center for Genomics and Personalized Medicine computing cluster located here: http://bustamantelab.stanford.edu/datasets.html.
|
10.1371/journal.ppat.1002081 | Cross-Neutralizing Antibodies to Pandemic 2009 H1N1 and Recent Seasonal H1N1 Influenza A Strains Influenced by a Mutation in Hemagglutinin Subunit 2 | Pandemic 2009 H1N1 influenza A virus (2009 H1N1) differs from H1N1 strains that circulated in the past 50 years, but resembles the A/New Jersey/1976 H1N1 strain used in the 1976 swine influenza vaccine. We investigated whether sera from persons immunized with the 1976 swine influenza or recent seasonal influenza vaccines, or both, neutralize 2009 H1N1. Using retroviral pseudovirions bearing hemagglutinins on their surface (HA-pseudotypes), we found that 77% of the sera collected in 1976 after immunization with the A/New Jersey/1976 H1N1 swine influenza vaccine neutralized 2009 H1N1. Forty five percent also neutralized A/New Caledonia/20/1999 H1N1, a strain used in seasonal influenza vaccines during the 2000/01–2006/07 seasons. Among adults aged 48–64 who received the swine influenza vaccine in 1976 and recent seasonal influenza vaccines during the 2004/05–2008/09 seasons, 83% had sera that neutralized 2009 H1N1. However, 68% of age-matched subjects who received the same seasonal influenza vaccines, but did not receive the 1976 swine influenza vaccine, also had sera that neutralized 2009 H1N1. Sera from both 1976 and contemporary cohorts frequently had cross-neutralizing antibodies to 2009 H1N1 and A/New Caledonia/20/1999 that mapped to hemagglutinin subunit 2 (HA2). A conservative mutation in HA2 corresponding to a residue in the A/Solomon Islands/3/2006 and A/Brisbane/59/2007 H1N1 strains that circulated in the 2006/07 and 2007/08 influenza seasons, respectively, abrogated this neutralization. These findings highlight a cross-neutralization determinant influenced by a point mutation in HA2 and suggest that HA2 may be evolving under direct or indirect immune pressure.
| Influenza A viruses mutate to escape neutralization by antibodies. These mutations predominantly occur in the globular head of the hemagglutinin protein, while the stalk is more conserved. Pandemic 2009 H1N1 influenza virus differs from seasonal H1N1 strains that circulated in the past 50 years and resembles a strain that did not circulate but was used in the 1976 swine influenza vaccine. We investigated whether persons immunized with either the 1976 swine influenza or recent seasonal influenza vaccines, or both, have antibodies that cross-neutralize pandemic 2009 H1N1. Sera from 1976 swine influenza vaccine trials cross-neutralized pandemic 2009 H1N1 and to a lesser extent the A/New Caledonia/20/1999 H1N1 strain that was used in vaccines during the 2000/01–2006/07 influenza seasons. Sera from persons who received several seasonal influenza vaccines containing A/New Caledonia/20/1999 H1N1 cross-neutralized pandemic 2009 H1N1, regardless of whether they received the 1976 swine influenza vaccine. We found that cross-neutralization between 2009 H1N1 and A/New Caledonia/20/1999 frequently mapped to the hemagglutinin stalk. A mutation in the stalk of strains circulating during the 2007/08–2008/09 seasons abrogates this neutralization. These findings highlight a cross-neutralization determinant influenced by a point mutation in the hemagglutinin stalk and suggest that the stalk may be evolving under direct or indirect immune pressure.
| In June 2009 the World Health Organization declared a new influenza pandemic due to sustained human to human transmission in several geographic regions of the novel swine-origin influenza A H1N1 virus, which was first identified in April by the Centers for Disease Control and Prevention (CDC) of the United States of America [1]. This novel H1N1 virus, referred to as pandemic 2009 H1N1 virus (2009 H1N1), has a hemagglutinin (HA) of classical swine lineage viruses that have circulated in the swine population for decades with little change in HA antigenicity [2]. The 2009 H1N1 HA is antigenically different from those of recent human seasonal influenza H1N1 viruses, but is closely related to A/New Jersey/1976 (NJ/76) influenza virus (Figure 1), a strain used in 1976 to immunize approximately 45 million people in the US during the swine influenza vaccination campaign after a localized outbreak [3]. However, NJ/76 influenza virus did not circulate. Emergence of the novel pandemic 2009 H1N1 virus raised questions about whether immunization with the 1976 swine or recent seasonal influenza vaccines could confer any protection. Several groups have reported that older persons may have substantial cross-immunity to the 2009 H1N1, though the literature is mixed on the degree of cross-immunity induced by prior seasonal influenza vaccines [4]–[9].
Influenza virus surface glycoprotein HA mediates virus entry and is the most important target of antibody-mediated protection. Cellular proteases cleave the HA precursor (HA0) to generate the HA1 surface subunit that mediates the binding to cell surface sialic acid receptors and the HA2 transmembrane subunit that mediates membrane fusion between viral and endosomal membranes after endocytosis (reviewed in [10], [11]). During infection and vaccination, HA elicits neutralizing antibodies. Antigenic maps of HA show that HA1 is the major target of neutralizing antibodies that inhibit virus binding to target cells [12], [13] and are classically detected by the hemagglutination inhibition (HI) assay. However, HA2 is more conserved than HA1. Neutralizing antibodies that bind to the stalk region of HA2 have been shown to confer broadly cross-neutralizing activity against several subtypes of viruses across clades but within a group [14]–[20] and to provide protection in animal models [16], [18]–[20]. These antibodies typically do not have HI activity and appear to neutralize virus by interfering with HA-mediated conformational changes required for virus entry [14], [17], [18].
Using lentiviral pseudovirions bearing HA on their surface (HA-pseudotypes) [21], we investigated whether persons immunized in 1976 with the NJ/76 swine influenza vaccine or more recently with seasonal influenza vaccines produced neutralizing antibodies to 2009 H1N1. Both sera from the 1976 swine influenza vaccine trials and contemporary sera from a cohort of subjects who received recent seasonal influenza vaccines, regardless of whether they received the 1976 swine influenza vaccine or not, often contained cross-neutralizing activity to 2009 H1N1. Some of this cross-neutralizing activity was dependent on the HA2 subunit and surprisingly was sensitive to a naturally-occurring variant at position 89 in HA2 that emerged in recent years. The implications of these findings for potential immune escape are discussed.
Because HA from A/New Jersey/1976 (NJ/76) and 2009 H1N1 influenza viruses are highly related (Figure 1), we first asked whether immunization in 1976 with the NJ/76 swine influenza vaccine could provide any immunity against the 2009 H1N1 influenza virus. Sixty five pre- and post-vaccination sera archived from the NJ/76 swine influenza vaccine trials conducted in 1976 [22] were evaluated for neutralizing activity to either NJ/76 or 2009 H1N1 A/Mexico/4108/2009 (Mex/4108/09) using HA-pseudotypes. Previously, we showed that HA-pseudotype neutralization titers using 95% inhibitory concentration (IC95) correlate well with conventional microneutralization titers using replicating influenza virus [23] and that HA-pseudotype neutralization is specific [21], [24]. Microneutralization titers >160 and a 4-fold increase after vaccination in assays involving replicating influenza virus have been proposed as correlates of seroprotection [4], but protective titers for HA-pseudotype neutralization have not yet been established. Positive control sera from 2009 H1N1 influenza virus infected ferrets typically have titers >10,000 [24]. Sera from the NJ/76 swine influenza vaccine trial were then tested and showed that NJ/76 vaccination generated neutralizing antibodies (titers >160 and a 4-fold increase after vaccination) in 85% and 77% of subjects against NJ/76 and Mex/4108/09 HA-pseudotypes, respectively (Table 1 and Figure 2A), consistent with the high degree of relatedness between the viruses and other recent reports [4], [8]. The neutralizing antibody titers to NJ/76 (GMT 597) and Mex/4108/09 (GMT 573) were also similar and correlated (Figure 2B). Most importantly, all sera with neutralization activity to NJ/76 showed significant neutralization activity to Mex/4108/09 (Figure 2B). Pre-vaccination sera did not exhibit significant neutralizing activity to HA-pseudotypes for either influenza virus, though titers against Mex/4108/09 (GMT 60) were higher than those against NJ/76 (GMT 3), suggesting that influenza viruses with shared epitopes to Mex/4108/09 influenza virus may have circulated previously.
We next asked whether subjects with a history of NJ/76 vaccination have significant neutralization titers to 2009 H1N1 today. Accordingly, we analyzed sera from a contemporary cohort of 23 subjects who had a history of NJ/76 vaccination and 19 aged-matched control subjects who did not. As shown in Table 2 and Figure 3A, sera from those who received the NJ/76 vaccine more than 30 years ago showed significant neutralization titers to NJ/76 (GMT 181), with 52% having neutralization titers >160. Sera from subjects who did not receive the NJ/76 vaccine had a GMT of only 44 to NJ/76, although a few individuals showed significant neutralization titers (>160). We note that the neutralization titers to NJ/76 in sera from subjects who did not receive the NJ/76 vaccine in this contemporary cohort were higher than the pre-vaccination sera in NJ/76 trials, suggesting that natural infection and/or vaccination with seasonal influenza strains during the period 1977–2009 provided a low level of cross-neutralization to NJ/76. Thus there appears to be residual immunity to NJ/76 in a majority of persons who were previously immunized with NJ/76 vaccine, or immunity may have been boosted by exposures during intervening years.
We next assessed cross-neutralization to 2009 H1N1 HA-pseudotypes. Unexpectedly, titers among those immunized with the NJ/76 vaccine were higher against Mex/4108/09 (GMT 331) compared to NJ/76 (GMT 181), with 83% having neutralizing antibody titers ranging from 161–1456 (GMT 469). There was a significant correlation between neutralization titers to NJ/76 and Mex/4108/09 (Figure 3B). However, sera from subjects without a history of NJ/76 vaccination had similar cross-neutralization titers to Mex/4108/09 (GMT 305), with 68% having neutralization titers >160 (Table 2 and Figure 3A). The substantial neutralizing titers to Mex/4108/09 found in a high proportion of subjects in this contemporary cohort, regardless of their vaccination history to NJ/76, indicated that their cumulative history of influenza infections and vaccinations have involved strains that share neutralizing epitopes with the 2009 H1N1 influenza virus.
All 45 subjects in the contemporary cohort received all annual seasonal influenza vaccines for at least the past five years (2004/05–2008/09 seasons). To investigate potential correlations between neutralizing activity to recent seasonal H1N1 influenza and the 2009 H1N1 viruses, we tested all sera for HA-pseudotype neutralizing activity against the three recent seasonal H1N1 influenza strains, A/New Caledonia/20/1999 (NCD/20/99), A/Solomon Islands/3/2006 (SI/03/06), and A/Brisbane/59/2007 (Bris/59/07). NCD/20/99 was used 7 times in influenza vaccines during the 2000/01 to 2006/07 seasons. SI/03/06 and Bris/59/07 were used in the 2006/07 and 2008/09 seasonal influenza vaccines, respectively (www.fludb.org/brc/vaccineRecommend.do?decorator=influenza). Neutralization of HA-pseudotypes corresponding to each of these strains is specific, as shown in Table S1. Using these HA-pseudotypes, 100% of subjects showed significant neutralization titers against NCD/20/99 (GMT 1237) (Table 3 and Figure 3C), consistent with the repeated use of the NCD/20/99 strain in recent seasonal influenza vaccines. Only 49% and 60% had neutralization titers >160 against Bris/59/07 and SI/03/06, respectively (Table 3 and Figure 3C). By comparison, the GMT of neutralizing titers to 2009 H1N1 is 319, with 76% having neutralization titers >160, regardless of vaccination history to NJ/76 (Figure 3C). The neutralization titers to Mex/4108/09 did not correlate with the titers to Bris/59/07 and SI/03/06 (data not shown), but subjects with higher neutralization titers (>600) to NCD/20/99 showed higher cross-neutralization titers to Mex/4108/09 (p<0.05) (Figure 3D), suggesting that there may be shared neutralization epitopes between NCD/20/99 and Mex/4108/09.
To look for cross-neutralization between 2009 H1N1 and NCD/20/99, we analyzed sera collected in 1976 from the NJ/76 vaccine trials for neutralizing activity to NCD/20/99 HA-pseudotypes. Since persons participating in the NJ/76 swine influenza vaccine trial were presumably not previously exposed to NCD/20/99 through natural infection or by vaccination, we considered the presence of neutralizing activity to NCD/20/99 in these sera to be due to cross-neutralizing antibodies. We found that the post NJ/76 vaccination sera had significant cross-neutralization activity to NCD/20/99 (GMT 320) with 45% having neutralization titers >160 and a 4-fold increase over pre-immunization titers, while only 12% of the pre NJ/76 vaccination sera had significant neutralization titers (Table 1 and Figure 2A). The reason that several pre NJ/76 vaccination sera have significant neutralizing activity to NCD/20/99 may be due to prior infections with related viruses.
To determine whether NJ/76 vaccination elicits cross-neutralizing activity to other recent seasonal H1N1 viruses, we analyzed the sera for the presence of neutralizing antibodies to Bris/59/07. Neutralization of Bris/59/07 HA-pseudotypes was seen in only 17% sera with titers >160 and a 4-fold increase over pre-immunization titers. Although the titers to Bris/59/07 were low (GMT 52) after vaccination with NJ/76, they were significantly higher than the titers in the pre-vaccination group (GMT 8) (Table 1 and Figure 2A). However, NJ/76 vaccination elicited much less cross-neutralization to Bris/59/07 than to NCD/20/99.
The cross-neutralization activity seen in sera after immunization with NJ/76 and seasonal influenza vaccines suggested the presence of shared neutralization epitopes between 2009 H1N1 and NCD/20/99. Since neutralizing antibodies can target either HA1 or HA2, we next investigated which subunit of HA accounts for the majority of the cross-neutralization between 2009 H1N1 and NCD/20/99 observed in our sera.
First we analyzed the sera from the NJ/76 vaccination trials. The sera with neutralization titers <160 to Bris/59/07 HA-pseudotypes were considered negative for neutralization to either HA1 or HA2 of Bris/59/07 HA. Twenty-one out of 65 post NJ/76 vaccination sera without neutralization activity to Bris/59/07, but with neutralization titers >160 and a 4-fold increase over pre-immunization titers to NCD/20/99 (neutralization titers <160 before vaccination), were identified (Table S2) and used for mapping. Chimeric HA involving the NCD/20/99 HA1 and Bris/59/07 HA2 subunits (NCD.HA1-Bris.HA2), as well the Bris/59/07 HA1 and NCD/20/99 HA2 subunits (Bris.HA1-NCD HA2) were constructed and used for making HA-pseudotypes. The infectivity and amount of HA in these chimeric HA-pseudotypes were comparable to the wild-type HA-pseudotypes (Figure S1). The chimeric HA-pseudotypes showed that: HA1 was responsible for most of the NCD/20/99 cross-neutralization in 2 out of 21 sera (e.g. 2S5H and 2S5A); HA2 was responsible for most of the NCD/20/99 cross-neutralization in 9 out of 21 sera (e.g. 2S5G, 2S5F, 2S5B, 2S4H, 2S3D, 2S2E, 2S1A, 1S2B and 1S1B); and both HA1 and HA2 were responsible for much of the NCD/20/99 cross-neutralization in 10 out of 21 sera (e.g. 2S6E, 2S6B, 2S5C, 2S4G, 2S4F, 2S4B, 2S3E, 2S3C, 2S3B and 1S2A) (Figure 4). In many cases, cross-neutralization titers to NCD/20/99 did not simply reflect the sum of the individual neutralization titers to each of the chimeras containing either NCD HA1 or HA2 subunits (e.g. 2S6B, 2S4G, 2S4F, 2S4B, 2S3E, 2S3D and 2S3C), indicating that HA1-HA2 interactions affected neutralization. These data suggested that there may be several targets for cross-neutralization. Nonetheless, the neutralization activity frequently mapped to the HA2 subunit, and in many cases, HA2 appeared to be the major determinant for cross-neutralization.
Next we analyzed the sera from the contemporary cohort. Sera with cross-neutralization titers (>160) to Mex/4108/09, but without neutralization titers (<160) to Bris/59/07 were identified (Table S3) and used for evaluating neutralizing antibodies that may be directed to Mex/4108/09 HA1 and/or HA2 subunits. HA-pseudotypes carrying the chimeric HA consisting of Bris/59/07 HA1 and Mex/4108/09 HA2 (Bris.HA1-Mex.HA2) showed that neutralization titers to Mex/4108/09 HA and Bris.HA1-Mex.HA2 were similar in all comparisons (samples S1, S7, S24, S31, S42, S44, S45, S58 and S59) (Figure 5A), suggesting that cross-neutralization to Mex/4108/09 involves the Mex/4108/09 HA2 subunit. Curiously, the chimeric Mex.HA1-Bris.HA2 HA-pseudotypes did not have high enough infectivity for neutralization studies, despite good HA incorporation and cleavage of HA0 in the HA-pseudotypes (Figure S1). Therefore, we could not directly assess the contributions of the Mex/4108/09 HA1 subunit to cross-neutralization.
To confirm the reliability of the cross-neutralizing data involving chimeric HA-pseudotypes with the Mex/4108/09 HA2 subunit, we identified sera with neutralization titers (>160) to NCD/20/99, but without cross-neutralization titers (<160) to Mex/4108/09 (Table S4). For these sera (samples S3, S25, S39, S43, S54, S56 and S201), neutralization titers for HA-pseudotypes carrying the chimeric NCD/20/99 HA1 and Mex/4108/09 HA2 (NCD.HA1-Mex.HA2) or NCD/20/99 HA were similar (Figure 5B), indicating that neutralization antibodies were directed to the NCD/20/99 HA1 subunit. Therefore, the presence of the Mex/4108/09 HA2 subunit in chimeric HA does not apparently give spurious neutralization results. Again, we were unable to assess neutralization of the complementary chimeric HA-pseudotypes containing Mex/4108/09 HA1 (Mex.HA1-NCD.HA2) due to the poor infectivity of this chimera, despite good incorporation of mature chimeric HA into the HA-pseudotypes (Figure S1). The difficulties in generating functional chimeric HA involving Mex/4108/09 HA1 further suggests that there are interactions between the Mex/4108/09 HA1 and HA2 subunits that are not present in recent seasonal H1N1 HA.
In 1993 [14] and again in a number of recent studies [15]–[20], neutralizing monoclonal antibodies that are broadly active against many influenza subtypes have been identified and mapped to epitopes in the stalk regions of the HA2 subunit [14], [16]–[20]. Although some of the cross-neutralization that we observed in our sera appears to map to the HA2 subunit, our data indicated that this cross-neutralization may be strain specific. As shown in Figure 4, Figure 5A and Figure 6, we found that sera with cross-neutralization to NCD/20/99 and Mex/4108/09 HA2 did not neutralize Bris/59/07. Significantly, there are only two amino acid differences in HA2, at the positions 89 (415 in full HA) and 146 (472 in full HA) between NCD/20/99 and Bris/59/07 HA2 (Figure 6A), suggesting that these two amino acids could influence HA2 antigenicity. When a leucine at residue 89 in HA2 (89L) or an asparagine at position 146 in HA2 (146N) corresponding to NCD/20/99 HA2 were introduced into Bris/59/07 HA2, the sera without cross-neutralization to Bris/59/07 HA showed neutralization to Bris/59/07 HA2-89L, but not to Bris/59/07 HA2-146N, with titers similar to NCD/20/99 HA and Bris.HA1-Mex.HA2 (Figure 6B and 6C). When both 89L and 146N were presented in Bris/59/07 HA2, serum titers were the same as those to Bris.HA1-NCD.HA2 in Figure 4 (data not shown). These results demonstrated that the neutralization epitopes in HA2 were influenced by residue 89 in HA2 (415 in full HA).
We then reviewed human H1N1 influenza virus HA sequences (www.fludb.org/brc/home.do?decorator=influenza) and noted that leucine at position 89 in HA2 has been maintained in seasonal H1N1 influenza viruses from at least 1918 to 2005 (Table 4). During this period, there are only two exceptions: A/Denver/1/1957 from North America has a methionine and A/Canterbury/106/2004 from Oceania has an isoleucine at position 89 of HA2. The change of leucine to isoleucine at position 89 of HA2 appeared frequently in 2006 with about 37.8% strains containing isoleucine, and the change of leucine to isoleucine continued in 2007 with about 34.1% strains containing isoleucine. However, by 2008, isoleucine completely replaced leucine at position 89 in HA2, raising the possibility that this change may reflect immune escape.
The 2009 H1N1 HA diverges considerably from recent seasonal H1N1 HA and is more closely related to the NJ/76 HA (Figure 1), raising doubts about the extent of protection that could be afforded by vaccination with recent seasonal influenza vaccines. Our studies show that sera from the NJ/76 swine influenza vaccine trials and contemporary sera from subjects who received recent seasonal influenza vaccines, regardless of whether they had been immunized with the NJ/76 swine influenza vaccine, frequently have cross-neutralizing activity to the 2009 H1N1. Further, these sera revealed one or more cross-neutralization epitopes that were sensitive to a conservative amino acid change in position 89 in the HA2 subunit, corresponding to a naturally-occurring amino acid variant that emerged in seasonal H1N1 influenza viruses in recent years.
Several groups have reported that prior infections or vaccinations can confer some immunity to 2009 H1N1, though findings vary. There is agreement that individuals >65 years have substantial cross-reactive antibodies to the 2009 H1N1, consistent with the epidemiology of the 2009 H1N1 pandemic showing that younger age groups were disproportionately affected [4], but the extent of cross-immunity induced by recent seasonal influenza vaccines is more ambiguous [4]–[9], [25]–[29]. Differences in methodologies and history of vaccination or infection with NCD/20/99 may have affected the outcomes. Our results involving persons aged 48–64 years (Table S5) extend other reports showing that older persons generally have some pre-existing immunity to the 2009 H1N1, but more significantly highlight the presence of cross-neutralizing antibodies between 2009 H1N1 and NCD/20/99. Because all subjects in our contemporary cohort received yearly seasonal influenza vaccines for at least the past five years, and NCD/20/99 was repeatedly used in seasonal vaccines during the 2000/01–2006/07 influenza seasons, we cannot determine the extent to which influenza vaccinations and/or natural infections contributed to the generation of cross-neutralizing antibodies to 2009 H1N1 and NCD/20/99.
To investigate potential cross-neutralizing determinants in NCD/20/99 and 2009 H1N1, we used chimeric HA-pseudotypes involving HA1 and HA2 subunits of NCD/20/99 and Bris/59/07 and sera that lacked neutralization to Bris/59/07 (Table S2). Both contemporary and archived sera from the NJ/76 swine influenza vaccine trials contained cross-neutralizing antibodies that depended on the HA2 subunit (Figure 4 and 5). Most remarkable, we found that the cross-neutralization was influenced by a single conservative amino acid change at position 89 in HA2, which differed between NCD/20/99 and Bris/59/07 (Figure 6). Thus, these data reveal a new determinant in the C helix region of the HA2 stalk that modified sensitivity to cross-neutralizing antibodies present in human sera from two different cohorts separated by more than three decades.
Growing interest in the generation of broadly neutralizing influenza antibodies has led to the discovery of several new monoclonal antibodies that bind to HA2 [14]–[20], [30], [31]. The first reported heterosubtypic neutralizing antibody, C179, derived from a mouse immunized with the A/Okuda/57 H2N2 strain, was found to be directed to a conformational epitope involving the A helix in the HA2 stalk (Figure 7A) and a region in HA1 [14]. More recently, several other HA2 heterosubtypic neutralizing monoclonal antibodies that are potent against strains from H1 and H5 subtype (Group 1) influenza viruses have been isolated using various methods. Some of these antibodies have been also shown to make contacts with the A helix of HA2 [16], [17], [30] (Figure 7A). Other HA2 monoclonal antibodies have been shown to bind to a highly conserved pocket in the stalk region containing the fusion peptide [18] or undetermined regions of the HA2 stalk [19]. Another potent broadly neutralizing monoclonal antibody against H3N2 (Group 2) but not H1N1 (Group 1) strains was shown to bind to a peptide corresponding to the C helix region in the HA2 stalk [20].
The HA2 monoclonal antibodies bind to regions in the HA2 stalk and interfere with conformational changes that are needed for virus entry [32], but they do not block HA attachment to receptors. These HA2 antibodies lack HI activity and were discovered using neutralization assays that sometimes involved HA-pseudotypes [18]–[20]. We [21], [23] and others [33], [34] have shown that HA-pseudotypes neutralization titers are highly correlated with microneutralization titers for replicating influenza virus, but the correlate of protection using HA-pseudotype neutralization titers has not been determined. Also, glycoproteins on the surface of HIV-based retroviral particle may be less densely packed and more exposed compared to HA on the surface of influenza viral particles, perhaps making them more susceptible to HA2-directed neutralization compared to influenza virus, as suggested in some studies [18], [19]. While sensitive screening assays have allowed many groups to fish out broadly neutralizing antibodies, it is generally believed that HA2 heterosubtypic neutralizing antibodies are present at relatively low concentrations, as compared with antibodies directed to HA1 [19], [35]. The need to change annual seasonal influenza vaccines to match dominant circulating strains indicates that such HA2 cross-neutralizing antibodies may not be present at high enough titers to provide robust protection. It is therefore difficult to discern the degree to which HA2 antibodies in our sera samples could contribute to protection to 2009 H1N1 virus. However, studies in animal models have provided proof of concept that induction [16], [18]–[20] or passive transfer of HA2 antibodies alone [20], [36] can provide protection. Appropriately designed vaccines may be able to induce robust immune responses to conserved neutralizing epitopes in HA2 [35], [37]. Recent examples involving several approaches are showing promise [20], [38], [39].
Our finding that the conservative substitution of isoleucine for 89 L reduced sensitivity to cross-neutralizing antibodies present in our sera was surprising. The crystal structure of the A/Cal/04/2009 HA [40] shows that 89 L packs tightly into a poorly exposed crevice underneath the HA1 crown (Figure 7A), making intimate contact with HA1 through a lysine and tyrosine at residues 310 and 308, respectively (Figure 7B). Substitution of 89 L with isoleucine may cause the interactions between HA1 and HA2 in this region to shift in order to accommodate the alternate side chain (Figure S2), and in doing so, could directly alter exposure or conformation of the antibody binding site. Alternatively, residue 89 may be evolving in response to immune pressure at distant sites. For example, 89I may reflect an adaptive change in HA2 resulting from direct immune pressure on epitopes in HA1. The 89I substitution could also impose allosteric changes on nearby or more distant neutralizing epitopes in either HA1 or HA2. The observation that Bris/59/07 was less sensitive to neutralization by an HA2 antibody compared to NCD/20/99 is consistent with the notion that this residue could influence neutralization by HA2 antibodies [19]. We also note that 89L is not near any of the contact residues for the recently described HA2 monoclonals specific for Group 1 HAs, although it is located on the C helix region of the HA2 stalk that has recently been suggested to contain an epitope for the 12D1 monoclonal antibody that binds H3 strains from Group 2.
Review of the database of human H1N1 HA also offers intriguing clues about the potential significance of the change of leucine to isoleucine at position 89 in HA2. We note that 89L has been maintained in seasonal H1N1 influenza viruses from at least 1918 until 2006 when it started to change to isoleucine, and 89L disappeared in 2008 (Table 4). It is tempting to speculate that this change could reflect immune escape. We also note that H3 strains from Group 2 influenza viruses generally have an isoleucine at the corresponding position in HA2. Interestingly, unlike Group 1 H1N1 HA, a carbohydrate can be seen in the H3N2 HA crystal structure extending in the vicinity of the isoleucine (coming from N285) (PDB 3HMG) [41], [42], which could conceivably have evolved to shield it from neutralizing antibodies. These observations offer a cautionary note that antigenic drift in this region may arise under strong selection pressure. Nonetheless, the viable substitutions may be limited due to the fact that residue 89 and others in the stalk regions make important contacts in both the native and low pH structures of HA, consistent with the difficulties in generating escape mutants with some of the HA monoclonal antibodies [18], [20]. Perhaps this explains why H3N2 strains have incorporated a carbohydrate in the vicinity of this region.
In summary, our studies showed that cross-neutralizing antibodies to 2009 H1N1 influenza that involve the HA2 subunit could be detected in sera collected in 1976 from NJ/76 swine influenza vaccine trials and sera from persons aged 48–64 who received annual influenza vaccines for at least the past five years. A conservative substitution at position 89 in HA2, found in drifted seasonal influenza virus variants from the 2006/07 and 2007/08 influenza seasons, abrogated this neutralization. Future studies involving vaccines that elicit strong antibody responses to HA2 will reveal the extent to which mutations can lead to immune escape.
Full-length HA ORF with Q223R mutation from A/Mexico/4108/2009 (GenBank GQ223112) and full-length wild type HA ORFs from A/Solomon Islands/3/2006 (GenBank EU100724), A/New Caledonia/20/1999 (GenBank AY289929), and A/Brisbane/59/2007 (GenBank CY058487) were amplified from viruses by reverse transcription-polymerase chain reaction (RT-PCR). Full-length wild type NA ORF from A/California/04/2009 (GenBank FJ966084) was also amplified from virus by RT-PCR. Full-length wild type HA ORF of A/New Jersey/1976 (GenBank CY021957) was chemically synthesized by GenScript (Piscataway, NJ). Chimeric HA carrying HA1 and HA2 from different strains were constructed by ligation of PCR fragments of HA1 and HA2. The HA and NA ORFs were then placed into the pCMV/R expression plasmid obtained from Dr. Gary J. Nabel (National Institutes of Health (NIH), Bethesda, MD), as described previously [21]. Full-length wild type M2 ORF of A/Puerto Rico/8/1934 (GenBank EF467824) was chemically synthesized by Integrated DNA Technologies (Coralville, IA) and placed into pCDNA 3.1(+) (Invitrogen, Carlsbad, CA). Codon-optimized human airway trypsin-like protease (HAT) gene expression construct (pCAGGS-HATcop) was described before [23]. The HIV gag/pol (pCMVΔR8.2) and Luc reporter (pHR'CMV-Luc) constructs were described previously [43], [44] and obtained from Dr. Gary J. Nabel (NIH, Bethesda, MD).
293T cells were cultured in Dulbecco's modified eagle medium (DMEM) with high glucose, L-Glutamine, MEM non-essential amino acids, penicillin/streptomycin and 10% fetal calf serum.
Ethics approval by the Research Involving Human Subjects Committee (RIHSC) at the US Food and Drug Administration was obtained for use of the sera involved in this study. Under 45 CFR 46.101 (b) (4), the sera from the 1976 swine influenza trial was included in the category of exempt research because the study used only existing sera, and information was recorded in such a manner that subjects can not be identified, either directly or through identifiers (RIHSC Protocol #09-043B). The sera from the contemporary cohort were obtained with written informed consent from all participants (RIHSC Protocol #09-110B).
Two groups of human sera were used in this study. The sera in group one included frozen samples retrieved from storage at FDA/CBER involving 65 pre-vaccination and post-vaccination sera from A/New Jersey/1976 swine influenza vaccine trials conducted in 1976 [22]. The sera in group two were collected in September-December of 2009 from 45 volunteers aged 48–64 years, without a history of vaccinations or influenza symptoms or exposures in 2009. All subjects in group two received at least five year (2004/05 to 2008/09) annual seasonal influenza vaccines including A/New Caledonia/20/1999, A/Solomon Islands/3/2006 and A/Brisbane/59/2007 used for the seasons from 2000/01 to 2008/09, and 23 subjects among them also received the A/New Jersey/1976 swine influenza vaccine (Table S5). Sera were heat inactivated by incubation at 56°C for 30 minutes prior to use in neutralization assays. Sera were assessed for neutralizing antibodies to 2009 H1N1 (A/Mexico/4108/2009) and the 2000/09 seasonal H1N1 influenza viruses (A/New Caledonia/20/1999, A/Solomon Islands/3/2006, A/Brisbane/59/2007) using an HA-pseudotype neutralization assay, as described below.
HA-pseudotypes carrying a luciferase (Luc) reporter gene were produced in 293T cells as described previously [21]. 2.5 µg of HAT, 2.5 µg of A/Puerto Rico/8/1934 M2, and 4 µg of A/California/04/2009 NA expression plasmids were included in the transfection. HA-pseudotypes were collected 48 hr post-transfection, filtered through a 0.45-µm low protein binding filter, and used immediately or stored at −80°C. HA-pseudotype titers were measured by infecting 293T cells with HA-pseudotypes for 48 hr prior to measuring luciferase activity in infected cells (luciferase assay reagent, Promega) as described previously [21]. HA-pseudotype titers were expressed as relative luminescence unit per milliliter of HA-pseudotype supernatants (RLU/ml).
As previously described [23], [24], HA-pseudotypes containing approximately 15 ng/ml of p24 antigen and 12 ng/ml of HA were incubated with heat-inactivated serum samples for 1 hr at 37°C, then 100 µl of HA-pseudotypes and serum mixtures were inoculated onto 96-well plates that were seeded with 2 x 104 293T cells/well one day prior to infection. HA-pseudotype infectivity was evaluated 48 hr later by luciferase assay, as previously described [21]. The serum dilution causing a 95% reduction of RLU compared to control (IC95-neutralizing antibody titer) was used as the neutralization endpoint titer [23]. IC95 was calculated using Graphpad Prism software. Data reported were from at least two independent experiments, with each serum sample run in duplicate.
To evaluate vaccination responses and potential cross-protection, sera with neutralization titers over 160 that inhibited 95% infectivity were considered highly significant [4], [23]. The neutralization titers were analyzed with nonlinear regression using GraphPad Prism software. The correlation of neutralization titers was evaluated with Spearman's p, a test for nonparametric correlation. t-test, geometric mean titer (GMT) with 95% confidence intervals and corresponding P value were analyzed using GraphPad Prism software. P values <0.05 were considered statistically significant.
|
10.1371/journal.pbio.0050044 | Surviving Endoplasmic Reticulum Stress Is Coupled to Altered Chondrocyte Differentiation and Function | In protein folding and secretion disorders, activation of endoplasmic reticulum (ER) stress signaling (ERSS) protects cells, alleviating stress that would otherwise trigger apoptosis. Whether the stress-surviving cells resume normal function is not known. We studied the in vivo impact of ER stress in terminally differentiating hypertrophic chondrocytes (HCs) during endochondral bone formation. In transgenic mice expressing mutant collagen X as a consequence of a 13-base pair deletion in Col10a1 (13del), misfolded α1(X) chains accumulate in HCs and elicit ERSS. Histological and gene expression analyses showed that these chondrocytes survived ER stress, but terminal differentiation is interrupted, and endochondral bone formation is delayed, producing a chondrodysplasia phenotype. This altered differentiation involves cell-cycle re-entry, the re-expression of genes characteristic of a prehypertrophic-like state, and is cell-autonomous. Concomitantly, expression of Col10a1 and 13del mRNAs are reduced, and ER stress is alleviated. ERSS, abnormal chondrocyte differentiation, and altered growth plate architecture also occur in mice expressing mutant collagen II and aggrecan. Alteration of the differentiation program in chondrocytes expressing unfolded or misfolded proteins may be part of an adaptive response that facilitates survival and recovery from the ensuing ER stress. However, the altered differentiation disrupts the highly coordinated events of endochondral ossification culminating in chondrodysplasia.
| The assembly and folding of secreted proteins in the endoplasmic reticulum (ER) is exquisitely regulated by a complex mechanism that maintains an equilibrium between folded and unfolded proteins. Perturbation of this homeostasis induces ER stress, which, if not alleviated through ER stress signaling (ERSS), ultimately triggers cell death. Normal bone growth occurs through a highly coordinated differentiation program that yields specialized cartilage cells (chondrocytes); when this program is disrupted, chondrodysplasia, or malformed skeletons, can result. Chondrodysplasias caused by mutations that affect protein assembly and secretion are characterized by a disorganization of bony growth plates and distension of the ER. We tested whether these chondrodysplasia characteristics were linked to ERSS. By investigating the impact of ER stress on the cell fate of hypertrophic chondrocytes (HCs) in transgenic mice expressing mutations in collagen that prevent proper folding, we revealed a novel adaptive mechanism that helps alleviate the unfolded protein load. Instead of undergoing apoptosis, the HCs undergoing ER stress adapt, re-enter the cell cycle, and revert to a less-mature state in which expression of the mutant collagen is reduced. Our findings have broad implications for adaptive mechanisms to ER stress in vivo and for the pathophysiology underlying chondrodysplasias caused by mutations that impact on protein assembly and secretion.
| Development and growth require the ability to detect, respond to, and survive stresses that compromise the normal state. Accumulation of misfolded or unfolded mutant proteins in the endoplasmic reticulum (ER) induces ER stress and may seriously affect the viability of cells. To cope with ER stress, ER-resident sensors detect misfolded or unfolded proteins and elicit the ER stress signaling (ERSS), which includes the induction of the highly conserved “unfolded protein response” (UPR). ERSS may lead to cytoprotection or death, depending on the nature of the stress and the cellular context.
ERSS involves the activation of at least three independent ER stress sensors: inositol-requiring 1 (IRE1), PKR-like ER kinase (PERK), and membrane-tethered activating transcription factor 6 (ATF6) [1]. Their activation represses protein synthesis via phosphorylation of the translation initiation factor eIF2α and activates signaling pathways that up-regulate the expression of ER-resident molecular chaperones and translation regulators. Activation of IRE1, PERK, and ATF6 depends on their dissociation from the molecular chaperone, binding Ig protein (BiP), a master regulator of ERSS. BiP ensures high-fidelity protein folding and prevents the accumulation of unfolded or aggregated proteins. Upon stress, unfolded proteins bind BiP and sequester it from interaction with IRE1, PERK, and ATF6. The released ATF6 and IRE1 activate the transcription factor XBP1 via production of its spliced form, XBP1s [2,3]. In an auto-regulatory loop, BiP activity increases further, since BiP up-regulation is partially dependent on XBP1s [4]. Induction of ERSS means that the amount of new protein translocated into the ER lumen is reduced, degradation of ER-localized proteins increases, and protein-folding capacity is enhanced.
ERSS is triggered in a range of pathogenic conditions, e.g., nutrient deprivation, viral infection, and expression of mutant secretory or membrane-bound proteins, that saturate the folding machinery, leading to overload of the ER (reviewed in [1]). It has been suggested that physiological ER load and ERSS components regulate cellular differentiation and developmental decisions: e.g., terminal plasma cell differentiation (reviewed in [5]) and bone and cartilage development [6]. ER stress has been implicated in many diseases, such as neurodegenerative disorders (Pelizaeus-Merzbacher disease [7] and amyotrophic lateral sclerosis [8]), osteogenesis imperfecta [9], and leukemia [10].
Despite the detailed description of the molecular pathways in ERSS, many questions remain regarding its impact in vivo. If ER stress is not alleviated, an apoptotic program is initiated [11], but how cells alleviate ER stress in vivo is not well understood. We addressed these questions by investigating the in vivo impact of ER stress on a well-defined and clinically important developmental pathway, the terminal differentiation of chondrocytes during endochondral bone formation.
This depends on a highly coordinated program of differentiation, proliferation, maturation by permanent withdrawal from the cell cycle, hypertrophy, and terminal differentiation of chondrocytes within the mammalian growth plate. A snapshot of this differentiation program is captured in histological sections taken through the developing growth plate in which the differentiation stages are reflected by morphologically distinct subpopulations of chondrocytes organized in a spatial and columnar pattern and in defined relative proportions (reviewed in [12,13]). Recognizable subpopulations of chondrocytes are round resting/reserve cells; flattened proliferating cells organized into columns; larger, non-dividing (mature) prehypertrophic cells (preHCs), and the even larger terminally differentiated hypertrophic chondrocytes (HCs), which undergo apoptosis as the cartilage matrix mineralizes and is being replaced with bone. This program is regulated by a complex network of molecules such as Indian hedgehog (IHH), parathyroid hormone-related peptide (PTHrP), bone morphogenetic proteins, fibroblast growth factors (FGFs), their respective receptors, and interaction between the cells and the extracellular matrix (ECM) [12,13].
Perturbation of this differentiation program results in a malformed skeleton (chondrodysplasia). Distension of the ER is a hallmark of cells undergoing ER stress [14,15]. Disorganization of growth plate chondrocytes and distended ER are characteristics of several chondrodysplasias [16–18], many of which are caused by mutations in genes encoding ECM proteins, such as collagens II, IX, XI, cartilage oligomeric matrix protein (COMP), and aggrecan (reviewed in [19]) and that result in the synthesis of abnormal proteins. Cultured pseudochondroplastic chondrocytes or cells transfected with mutant COMP retain ECM proteins and chaperones intracellularly and have an increased capacity for apoptosis [14,16,20]. Collagen X is an ECM protein synthesized specifically by HCs. Mutations in the human COL10A1 gene interfere with collagen X trimer assembly and secretion, with retention of mutant chains in the ER [21–23], resulting in metaphyseal chondrodysplasia type Schmid (MCDS) (reviewed in [24,25]). Abnormal organization of chondrocytes and an expanded hypertrophic zone (HZ) have been observed in the growth plate of a swine model for MCDS [26].
These observations raise the possibility that in chondrodysplasias caused by mutations that affect protein assembly and secretion, ERSS disrupts chondrocyte differentiation and thereby chondrocyte organization in the growth plate. In this study, we present evidence that when unfolded collagen X encoded by a murine Col10a1-equivalent of an MCDS mutation, a 13-base pair (bp) deletion within the NC1-encoding domain [27], is expressed in transgenic mice (13del), ERSS is triggered in the growth plate. Concomitantly, HC fate and terminal differentiation are perturbed, but apoptosis is not increased. The normal terminal differentiation program is disrupted. The 13del HCs in the lower part of the HZ re-enter the cell cycle and express markers more typical of preHCs, accompanied by down-regulation of Col10a1-13del gene (hereafter referred to as 13del) expression, suggesting an alteration in their cellular identity. This alteration of 13del HCs is cell autonomous, implying an association with activation of the ERSS. These findings reveal for the first time that HCs undergoing ER stress in vivo adapt, altering their differentiation status to a less-mature state in which expression of 13del is reduced, thereby alleviating the unfolded protein load.
Trimer assembly of collagen X α-chains via the NC1 domain is critical for triple helix formation and for secretion [24]. We tested the effect of the 13del mutation on collagen X trimer assembly in vitro by cell-free translation of wild-type (wt) and 13del mRNAs. Consistent with previous findings [21,22], the mutation resulted in collagen X α-chains that were unable to assemble into trimers (Figure 1A). Co-translation of wt Col10a1 and 13del mRNAs resulted in fewer collagen X trimers, suggesting 13del α-chains reduced trimer assembly (Figure 1A). Thus, this mutation would be expected to impair collagen X folding/assembly in vivo.
To assess the impact of unfolded collagen X α-chains on the differentiation of the chondrocyte layers in vivo, we generated transgenic mice carrying the 13del (Figure 1B). Five independent lines carrying the 13del transgene all exhibited disproportionate dwarfism with shorter limbs (Figure 1C). Histologic and morphometric analyses revealed altered proportional organization of the growth plate with a significant expansion specifically of the HZ (Figure 1D). This expansion was detectable in 13del growth plates at late fetal stage, peaking on about day 10 (p = 0.015, n = 6), and diminishing by 4 wk (p < 0.001, n = 13). By 10 wk, the height was normal and corresponded to the cessation of transgene expression (see below). Longitudinal bone growth as measured by calcein labeling was significantly reduced in 13del mice at 5- to 10-d-old (Figure 1E). At 10 wk, 13del tibiae were 15% shorter than that of the wt (n = 12, p = 4 × 10−14). These results indicated that the phenotypic changes were associated with disturbed endochondral ossification.
Transgenic mice overexpressing wt Col10a1 in HCs under the identical regulatory sequences are phenotypically normal (unpublished data). Hence, the observed expansion in the HZ is consistent with a defect induced by the expression of the 13del transgene. RNase protection assays confirmed the expression of the 13del transgene (Figure 1F). The expansion of the HZ was relatively greater in another 13del line showing higher transgene expression and was less in yet another line with lower 13del expression (Figure S1), indicating a dosage-dependent effect. In situ hybridization using Col10a1- and 13del-specific riboprobes revealed co-expression of 13del mRNA with endogenous Col10a1 specifically in the 13del HCs (Figure 2A). In wt littermates, Col10a1 expression was largely restricted to, and maintained at a high level in the HZ (Figure 2A). By contrast, the expression of both Col10a1 and the 13del transgene in 13del mice was down-regulated toward the lower portion of the hypertrophic zone (LHZ) (Figure 2A), indicating an alteration in the differentiation program of HCs.
Immunostaining with a 13del-specific antibody showed 13del proteins were restricted to the HCs in the growth plate. The 13del proteins were intracellular, with little or no staining in the ECM (Figure 2B, 13del panels), and co-localized with concanavalin A, which binds to carbohydrate moieties in the ER (Figure 2B, middle panels). Endogenous wt collagen X was secreted to the ECM in mutant as in wt mice (Figure 2B, insets). The 13del protein level varied both temporally and spatially: just before birth at 18.5 d post coitum (dpc), when the HZ expansion had not yet reached its maximum, mutant proteins appeared to be evenly present in the HZ (Figure 2C). By contrast, there was reduced amounts of mutant protein in the LHZ of 3-wk-old 13del mice (Figure 2C), a time point at which the degree of expansion of the HZ relative to wt was less than for 10-d-old mice.
Ultrastructural analysis revealed distended and fragmented ER in 13del HCs, but not in other growth plate zones (Figure 3A), raising the possibility that they were experiencing ER stress. We tested for expression of alternatively spliced Xbp1 mRNA (Xbp1s), a major transducer of the ER stress signal. Both the spliced and unspliced isoforms of Xbp1 were expressed in growth plate cartilage mRNA from 13del mice (Figure 3B), whereas only the unspliced form was found in wt littermates. Immunostaining revealed a strong induction of XBP1s protein in HCs expressing 13del protein in 13del upper HZ (UHZ) compared with wt littermates (Figure 3C, Figure S3A). Expression of Edem, a direct downstream target gene of XBP1s [28,29], was also induced in 13del HZ, but not in the wt littermates (Figure S2).
The mRNA for the molecular chaperone BiP is strongly expressed in the UHZ in 13del mice (Figure 3D), concomitant with the expression of the 13del transgene (Figure 2A), and gradually less so in cells toward the LHZ. This is consistent with the higher level of unfolded proteins and induction of ERSS in 13del UHZ. Immunostaining revealed markedly higher levels of BiP in HCs of the LHZ than in wt littermates (Figure 3E). Immunoblot analysis of proteins extracted from hypertrophic cartilage confirmed this up-regulation of BiP in 13del mice (unpublished data).
Another bZIP transcription factor, ATF4, regulates ER stress by inducing the apoptosis-associated gene Chop [30]. Quantitative RT-PCR showed marked up-regulation of Chop transcript (unpublished data), and immunostaining showed elevation of CHOP protein in 13del HCs (Figure 3F) compared to wt mice, but not of apoptosis as determined by terminal transferase dUTP nick end labeling (TUNEL) assays (Figure S4). Cytoplasmic localization of p53 is a molecular consequence of preventing apoptosis during ER stress [31], and we observed this in 13del HCs in the LHZ (Figure 3G). The ER-stressed HCs survived, which is consistent with an expansion of the HZ.
To determine whether the accumulation of HCs was due to increased rate of chondrocyte hypertrophy, we performed in vivo pulse-labeling experiments using 5-bromo-2′-deoxyuridine (BrdU). Two hours after BrdU injection, only proliferating chondrocytes were labeled in both 13del and wt mice (Figure 4A). There was no difference between wt and 13del in the proportion of labeled proliferating cells in the proliferating zone (PZ) (11.17% and 10.65%; p = 0.116; n = 3 for each genotype). After a 48-h chase, the position of the most distally located BrdU-labeled HCs relative to the PH was similar in 13del and wt mice, indicating that the transition to hypertrophy was unaffected (Figure 4A). The percentage of BrdU-labeled HCs relative to total number of BrdU-labeled chondrocytes in the PZ and HZ was also similar (p = 0.53) in 13del (31.9 ± 3.8%; n = 4) and wt mice (30.7 ± 4.4%; n = 4), indicating that the rate of chondrocyte progression to the hypertrophic state is normal in 13del. This suggests that the 13del-induced defect arises within the HZ.
p57Kip2 regulates cell-cycle exit and postmitotic hypertrophic differentiation in chondrocytes [32,33]. Expression of p57Kip2 in the UHZ was up-regulated to a similar degree in 13del and wt mice (Figure 4B), which indicates that 13del cells had exited the cell cycle and entered the hypertrophic program. However, p57Kip2 was significantly down-regulated in the mid to lower 13del HZ (Figure 4B), which suggests that the cells could have re-entered the cell cycle.
The 13del expressing HCs in the mid to lower HZ, but not wt cells, were also found to express cyclin D1, a marker for the G1 phase (Figure 4C, Figure S3B). Proliferating cell nuclear antigen (PCNA), a marker for proliferating cells, was also expressed specifically by cells of the 13del HZ, but not wt (Figure 4D). Together, these data suggest that in 13del mice, hypertrophic differentiation was initiated, but HCs in the mid to lower HZ re-entered the cell cycle. However, given that BrdU-labeled HCs were not detectable in the 13del HZ after 2 h (Figure 4A), 13del HCs did not appear to have progressed through the G1/S checkpoint. Thus, expansion of the HZ cannot be attributed to the proliferation of HCs by mitotic division.
We observed that some chondrocytes within the 13del LHZ acquired a more flattened and elongated cell shape with condensed nuclei (Figure 4B–4D). Moreover, we found that there were increased numbers of smaller chondrocytes in the 13del LHZ close to the chondro-osseous junction (29.5% ± 6.42%) compared to wt LHZ (17.8% ± 7.49%) (p = 0.04 by t-test; see Materials and Methods), indicating a change in identity. Thus, we hypothesized that the differentiation status of HCs in the HZ had been altered, contributing to the expansion of the HZ. The different subpopulations of chondrocytes display characteristic expression profiles of many genes encoding transcription factors (e.g., Sox9), signaling/growth factors (e.g., Ihh, Pthrp, and Igf2), receptors (e.g., patched [Ptc] and PTH/PTHrP receptor [Ppr]), cell-cycle regulators (e.g., p57Kip2 and cyclin D1), and ECM components (Col2a1, Col10a1, and Agc1).
As wt proliferating chondrocytes undergo hypertrophy, several genes are down-regulated in preparation for replacement of cartilage by bone. Col2a1 is normally expressed throughout the growth plate with the highest levels in the resting zone (RZ), PZ, and PH, and the lowest in the HZ (Figure 5A). Sox9 expression parallels that of its transcriptional target Col2a1, except in the HZ, where it is absent (Figure S5A). Igf2 is expressed at high levels in proliferating chondrocytes and at lower levels in preHCs, and is down-regulated in HCs (Figure S5B). Consistent with the finding that entry into the hypertrophic phase was not affected in 13del mice, the expression of Col2a1, Sox9, and Igf2 was down-regulated in the UHZ. However, 13del chondrocytes in the mid- and lower portions of the HZ re-expressed Sox9, Col2a1, and Igf2 (Figures 5A, S5A, and S5B). The α1-chain of collagen I (Col1a1), a marker of osteoblasts, was not expressed in 13del HCs (Figure S5C). Because HCs in the LHZ have normally progressed to the terminal stages of differentiation, our data indicate 13del LHZ chondrocytes had reverted to a “preHC-like” status.
To further characterize the abnormality in terminal chondrocyte differentiation, we focused on two pathways, IHH/PTC and PTHrP/PPR, which cooperate to regulate chondrocyte proliferation and hypertrophy [34]. In 10-d-old wt mice, expression of Ppr (encoding PTH/PTHrP receptor) and Ihh was found predominately in both preHCs and upper HCs (Figure 5B and 5C), and Ihh mRNA was diminished in the LHZ and absent from cells bordering the chondro-osseous junction. Expression of Ptc, which encodes for IHH receptor and a target gene of IHH per se, flanked the Ihh-expressing domain proximally and distally, just adjacent to the preHCs and within the osteogenic cells at the chondro-osseous junction (Figure 5D). In 10-d-old mice, Ihh, Ptc, and Ppr were expressed in similar regions in both 13del and wt; but in 13del mice, these genes were re-expressed in some cells in the LHZ (Figure 5B–5D). The strong activation of Ptc transcription and re-expression of Ppr indicate that cells in the 13del LHZ are receiving and responding to IHH signal. Overall, 13del cells in the LHZ expressed markers that are more characteristic of preHCs.
To confirm that 13del HCs had initiated hypertrophic differentiation normally, we created Col10a1-Cre/ROSA26 CRE reporter mice that expressed CRE recombinase and β-galactosidase specifically in HCs and crossed them with 13del mice. In the compound mutant mice with or without 13del transgene, all the HCs expressed β-galactosidase as shown by X-gal staining (Figure 5E). Some 13del HCs in the mid-lower HZ exhibited both Ppr-re-expression and positive X-gal staining (Figure 5E, inset in 13del panel). Because the Cre gene had been inserted into the Col10a1 gene by homologous recombination, its expression is under the control of the endogenous Col10a1 promoter. Therefore, the induction of β-galactosidase expression indicated that differentiation of 13del HCs was disrupted after they have initiated hypertrophy.
As HCs attain the terminal stages of differentiation at the chondro-osseous junction, they express osteopontin (Opn) and matrix metalloproteinase 13 (Mmp13). In wt mice, these genes were expressed exclusively in HCs at the chondro-osseous junction, whereas in 13del mice, expression of Opn and Mmp13 (Figure 5F and Figure S5D) were delayed and scattered throughout the LHZ. In situ hybridization of consecutive sections through 13del growth plates revealed that some ectopic Opn-expressing HCs also expressed Ppr, manifesting the characteristics of both preHC and terminally differentiated chondrocytes (Figure 5G). This co-expression of Opn and Ppr was not observed in wt growth plates (Figure 5G).
To determine whether the altered differentiation program was intrinsic to 13del chondrocytes, we created mouse chimeras by aggregating morulae from 13del mice and from mice ubiquitously expressing the enhanced green fluorescent protein (EGFP). Wt cells were detected by immunostaining for EGFP (Figure 6A–6C); 13del cells were detected by in situ hybridization using the 13del-specific riboprobe (Figure 6D–6F). Mice with different degrees of EGFP/13del chimerism, as well as EGFP/wt chimeras, were studied and expression of BiP (Figure 6G–6I), p57Kip2 (Figure 6J–6L) and Ppr (Figure 6M–6O) were analyzed.
As anticipated, the growth plates of EGFP/wt chimeras were indistinguishable from wt (Figure 6A). In EGFP/13del chimeras, the most noticeable changes were variations in the height of the HZ and an undulating chondro-osseous junction in the growth plate (Figure 6B). Where the contribution of 13del cells was higher, the degree of expansion of HCs was likewise higher and the undulation of the chondro-osseous junction generally correlated well with the relative contribution of 13del cells. This correlation suggests that the defect in 13del HCs is a cell-autonomous one, since their abnormal differentiation could not be overcome by the presence of wt HCs.
Key marker for ERSS (BiP) (Figure 6H), for cell-cycle progression (p57Kip2) (Figure 6K), and for chondrocyte differentiation (Ppr) (Figure 6N) were expressed abnormally in regions containing 13del cells. 13del HCs in the LHZ of chimeras re-expressed Ppr (Figure 6N). Thus, the presence of wt cells did not “rescue” the 13del cells, and the signal for altering differentiation program is most likely cell autonomous.
To understand the relationship between 13del transgene expression, ERSS, and the alteration in differentiation in fetal and postnatal growth, expression of the 13del transgene, the endogenous Col10a1 gene, BiP, and Ppr was followed over fetal and postnatal stages. Interestingly, at 14.5 dpc, although expression of endogenous Col10a1 (Figure 7A1) can be seen in the HZ of 13del mice, little or no 13del transgene expression was detected (Figure 7A2), and BiP expression was not induced in the HZ (Figure 7A3). Similar to wt mice, expression of Ppr was restricted to the PH and upper HZ in 13del mice and no expansion of HZ was detected (Figure 7A4 and 7A4′). In addition, Opn expression was restricted to the last layer of HCs and the primary ossification center in both genotypes (Figure 7A5 and 7A5′). 13del transgene expression in HCs could be detected at 15.5 dpc (Figure 7B2), with a concomitant induction of strong expression of BiP in the HZ (Figure 7B3; in contrast to Figure 7B3′). At this stage, the HZ in 13del was not expanded, and Ppr and Opn expression patterns were normal (Figure 7B4 and 7B4′, and 7B5 and 7B5′). At 17.5 dpc, the expression pattern of these key genes remained similar; however, a slight expansion of the HZ and punctate Opn expression in the LHZ could be detected in 13del mice (Figure 7C1-7C5). Re-expression of Ppr in the LHZ was detectable at 18.5 dpc (unpublished data), and most obvious at 10 d old (Figure 8A4) that correlated with the greatest HZ expansion (Figure 8A1-8A5) and induction of BiP expression (Figure 8A3). The 13del transgene, Col10a1, and BiP expression were down-regulated in the LHZ of 13del mice at 10 day old, when Ppr was re-expressed (Figure 8A1-8A5) and Opn showed punctate expression (Figure 8A1-8A5). A similar pattern for these markers was also observed in 3-wk-old 13del mice, although the degree of HZ expansion was reduced in terms of the number of cell layers in the HZ (Figure 8B1-8B5).
In 4-wk-old 13del mice, expression of the 13del transgene was relatively weaker than endogenous Col10a1 and was distributed through the middle to lower HZ (Figure 8C2 and 8C2′). Expansion of HZ was further diminished; strong expression of BiP persisted only in the middle to lower HZ (Figure 8C3), and there were much fewer cells expressing Ppr in the LHZ (Figure 8C4). By 6 wk and 10 wk, 13del transgene expression continued to diminish, being undetectable by 10 wk (Figure 8D2 and unpublished data), although robust levels of the endogenous Col10a1 gene were found (Figure 8D1). Induction of BiP was barely detectable at 6 wk and was absent at 10 wk (Figure 8D3 and unpublished data). These were accompanied with return to normal height of HZ and normal Ppr and Opn expression pattern similar to wt (Figure 8D4 and 8D5 and unpublished data).
The close correlation between Col10a1 and 13del expression with BiP was further shown by quantitative RT-PCR using serial transverse sections of the tibial growth plates. In the wt, levels of Col10a1 were high throughout the HZ, decreasing sharply across the chondro-osseous junction (Figure 9A, fraction 19–18). This reduction in expression is normally accompanied by an up-regulation of Mmp9, a marker for terminally differentiated HCs (Figure 9A, fraction 19–18). BiP expression was low throughout, and the levels showed only mild fluctuation. In the 13del HZ, Col10a1 and 13del levels were high in the UHZ. The up-regulation of Col10a1 and 13del expression was closely correlated with strong induction of BiP and Chop expression (Figure 9B, Fraction 34–30; unpublished data for Chop). In the 13del LHZ, Col10a1 and 13del expression was down-regulated some distance away from the chondro-osseous junction, which is marked by Mmp9 up-regulation (Figure 9B, fraction 31–29). This down-regulation was accompanied by reduced BiP and Chop expression in the LHZ, indicating ER stress was alleviated (Figure 9B, fraction 31–30). These data suggest that ER stress and its alleviation are closely associated with the altered terminal differentiation in 13del HCs.
Together, these data demonstrated that in 13del mice, transgene expression activated ERSS in the first instance, followed by Ppr re-expression in the LHZ, leading to an expanded HZ. From weaning age (3 wk) onwards, transgene expression level steadily diminished together with attenuation of ERSS.
To assess whether the ERSS can be induced in chondrocytes at a different stage of differentiation and is, therefore, potentially implicated in other forms of chondrodysplasia, we tested whether ER stress was triggered in the chondrocytes of growth plates of two other mouse models of chondrodysplasia. cmd/cmd mutants express a severely truncated aggrecan core protein because of a premature stop codon in exon 5 [35]. Col2a1G904C transgenic mice express procollagen II with a glycine to cysteine mutation (G904C) [36]. Both mutants show a fragmented ER in chondrocytes (Col2a1G904C, Figure S6; cmd/cmd mice [37]). The normal chondrocyte columns were disorganized, and immunostaining for BiP was enhanced in both mutants, indicating induction of ERSS (Figure 10A, Figure S6). There was no distinct zone of proliferating chondrocytes in the cmd/cmd mutant, with only a very small portion of cells progressing to hypertrophy, indicated by the region of the growth plate stained for collagen X (Figure 10A). CHOP expression was elevated, but there was no increase in apoptotic cells (unpublished data). There was also increased ectopic expression of cell-cycle markers such as p57Kip2 and PCNA (Figure 10A), indicating these cells are trapped in an abnormal state of cell-cycle progression.
Many studies have shown that factors influencing cell fate and/or differentiation are activated in ERSS, but how such changes impact differentiation programs in vivo is poorly understood. We have shown that HCs in the murine growth plate expressing unfolded collagen X experience ER stress but do not undergo apoptosis. The normal terminal differentiation process is interrupted; HCs adapt and survive via cell-autonomous reversion to a preHC-like state that results in delayed endochondral ossification and chondrodysplasia.
In vitro translation assays suggest that 13del mutant collagen X proteins are unable to assemble into trimers, and thus are poorly secreted. The accumulation of the 13del proteins in the ER and the low to undetectable level in the ECM of the HZs of 13del mice confirmed the secretion defect. Poor secretion is also seen with collagen X containing other MCDS mutations that prohibit subunit folding and assembly [23,38].
The 13del mice reveal that misfolding of mutant collagen X occurs in vivo. High-level expression of Col10a1 is characteristic of chondrocyte hypertrophy. 13del HCs transcribed both endogenous Col10a1 and 13del transgene. Intracellular accumulation of 13del proteins suggested that the endogenous degradation machinery could not clear all the mutant protein. The distended, fragmented ER indicates that ER stress was induced in HCs expressing 13del proteins, leading to activation of ERSS. Up-regulation of BiP, CHOP, expression of Xbp1s, Edem, and cytoplasmic localization of p53 in 13del HCs are all indicators that ERSS was triggered. The co-expression of both XBP1s protein and 13del in the same cells suggests that expression of the mutant collagen directly induced ERSS.
Interestingly, in contrast to the expanded HZ in 13del mice, expression of chicken collagen X chains containing a deletion within the helical domain in transgenic mice results in chondrodysplasia and a compressed HZ [39]. The underlying molecular mechanism is not clear, and there are no mutations reported in the helical domain of collagen X in humans for comparison. Trimerization of the mutant chicken collagen X was not affected and homo- and hetero-trimers formed efficiently in vitro [40]. Furthermore, mutant collagen X is detected in the extracellular matrix in growth plate cartilage of the transgenic mice, with little evidence for intracellular accumulation [40]. Thus, the molecular consequence to 13del mice is likely to be different, and the compression may be related to the presence of mutant chicken collagen X mixed with wt mouse collagen X or hybrid mouse–mutant chicken collagen X in the growth plate matrix, impairing the supramolecular structure of extracellular collagen X and hence tissue integrity.
A single histological section of the growth plate provides a continuous snapshot of the different phases of chondrocyte differentiation as they progress from a resting state to the proliferative state, become prehypertrophic, and then enter the final stages of hypertrophy and terminal differentiation at the chondro-osseous junction. Thus, our analyses of molecular changes in 13del chondrocytes using longitudinal sections taken through the growth plate provide an in vivo picture of ER stress induction. The response of the HCs to ER stress, indicated by induction of BiP expression, can then be monitored as the HCs approach the chondro-osseous junction. In cultured cells, induction of ERSS is characterized by a rapid increase of BiP mRNAs [41,42].
In 13del, strong induction of BiP mRNA in the UHZ was observed in pre-weaning age, suggesting that HCs are experiencing ER stress soon after hypertrophy. In contrast, by 4 wk postpartum, in UHZ chondrocytes, 13del transgene expression was diminishing, whereas BiP mRNA was more abundant in the LHZ where cells were expressing a higher level of the transgene and ER stress is maintained in these cells. Therefore, the stress level in 13del HCs varied during growth according to transgene expression level, and the stress response is dosage dependent.
It is interesting to note that the 13del transgene did not fully recapitulate the expression of the endogenous Col10a1 gene in fetal and postnatal growth. It is turned on later (between 14.5 and 15.5 dpc) and turned off earlier (between 6–10 wk postpartum) when compared to the endogenous Col10a1, probably because the transgene did not contain the full complement of Col10a1 regulatory elements; for example, the lack of a conserved enhancer element upstream of the transgene ([43] and unpublished data). When normal Col10a1 alone was expressed (14.5 dpc and at 10 wk postpartum), induction of BiP expression was not detected. This asynchrony in timing of expression between 13del and the endogenous Col10a1 and the qRT-PCR data serves to highlight the close association between 13del expression and BiP up-regulation, further supporting the notion that 13del proteins induced ERSS directly.
ERSS is primarily a response to relieve ER stress for cell survival. However, ER stress can lead to cell death as shown in various systems. In the Akita diabetic mouse, ER stress led to apoptosis via CHOP induction. In the absence of CHOP, apoptosis was reduced and the onset of diabetes was delayed [15,44]. CHOP is also implicated for anti-apoptotic activity. In a mouse model of human Pelizaeus-Merzbacher disease experiencing ER stress, CHOP protects oligodendrocytes from apoptosis [7]. These contradictory results may be explained by the dependence of concurrent signaling events during ER stress. Indeed, signals both upstream and downstream of CHOP (i.e., the PERK signaling cascade) have been shown to have opposite effects on cell survival [45–47]. In 13del, despite raised CHOP expression in the UHZ, apoptosis was not triggered and the HCs survived. In this situation, ERSS may play a cytoprotective role. It is known that growth plate chondrocytes are destined to survive hypoxic stress during development [48]. This implies that they are preconditioned to survive hypoxic conditions before becoming terminally differentiated, probably through the cytoprotective actions of HIF-1α PERK, and eIF2α [6,49,50]. Chondrocytes may have additional adaptive mechanisms that protect against stress-induced apoptosis or have a high stress tolerance or threshold before cell death can be triggered. One may speculate that cells that are not preconditioned to survive stress (e.g., neurons) may be much more susceptible to ER stress-induced death. Paradoxically, the cytoprotective effect of ERSS may interfere with the normal apoptotic fate of HCs, contributing to the abnormal terminal differentiation of 13del HCs.
Survival of 13del HCs was associated with signs of stress alleviation. The diminished expression of Col10a1/13del in the LHZ indicates that production of the mutant collagen X chains in those chondrocytes was reduced, and ER stress was correspondingly alleviated, as revealed by the down-regulation of BiP and Chop transcript in the LHZ. The outcome of alleviation of ER stress is the reduction in synthesis of 13del proteins. In 13del, a reduction in Col10a1 and 13del mRNAs occurred that must contribute towards alleviation of ER stress. Such reduction could occur by nonsense-mediated decay, as suggested for MCDS caused by the nonsense mutations [51,52] or by IRE1-mediated mRNA degradation recently identified to occur during the UPR [53] or by regulating transcription.
It is notable that the reduction in Col10a1/13del expression also correlated with the change in cell-cycle control and differentiation state of the chondrocytes in the LHZ (summarized in Figure 10B). These changes point to more complex modes of alleviating ER stress. In the 13del UHZ, the strong transcription of both wt and 13del Col10a1, up-regulated expression of p57Kip2, and extent of BrdU labeling in pulse-chase experiments indicated that 13del preHCs had exited the cell cycle and progressed into hypertrophy normally. Normal entry into hypertrophy is further confirmed by the expression of Col10a1-Cre and β-galactosidase activity in all HCs in the 13del expanded HZ. However, down-regulation of both Col10a1 and 13del transcripts in advance of expression of Mmp9, and delayed expression of Mmp13 and Opn (relative to the onset of hypertrophy) in the 13del LHZ, indicated that terminal differentiation was disrupted.
In the 13del LHZ chondrocytes, re-expression of genes (Ihh, Ppr, Ptc, Igf2, Sox9, and Col2a1) characteristic of proliferating chondrocytes/preHCs indicated a switch to preHC-like status. In addition, the down-regulation of p57Kip2 and the up-regulation of PCNA and cyclin D1 are consistent with cell-cycle re-entry. This is a novel finding since HCs are terminally differentiated and are not expected to return to the cell cycle. PTHrP suppresses p57Kip2 expression [33], IGF2 antagonizes p57Kip2 [54], and cyclin D1 is a target of IHH [55] and IGF2 [56] signaling. Igf2, Ppr, and Ihh show highest expression level in wt proliferating chondrocytes or preHCs, and are down-regulated in HZ. Their re-expression in the 13del LHZ suggests a link to the observed cell-cycle re-entry. Activated PTHrP and IGF2 signaling may down-regulate p57Kip2 expression, whereas activated IHH and IGF2 signaling could up-regulate cyclin D1 expression, stimulating cell-cycle re-entry. Cell-cycle re-entry for 13del LHZ chondrocytes is most likely a secondary effect due to the re-activation of signaling pathways that regulate chondrocyte proliferation and hypertrophy.
Cells in the 13del LHZ were asynchronized in terms of their differentiation state: some of them expressed preHC markers (e.g., Ppr), some expressed markers for terminally differentiated HCs (e.g., Opn), and some expressed both Ppr and Opn. The latter can be interpreted as cells captured in a “reprogramming” process. Because, as indicated in wt mice, expression of Opn marks HCs that have progressed to the terminal status, and for Ppr to be expressed in the same 13del HCs suggests a reversion process has occurred. However, the reversal of differentiation is not absolute, as endochondral ossification does occur, albeit delayed. Therefore, the co-expression of Ppr and Opn could also indicate stress-alleviated HCs resuming terminal differentiation. These cells may bypass the intermediate hypertrophic state to avoid expressing high level of mutant collagen X.
The inability of wt HCs to rescue the delayed terminal differentiation, and the altered gene expression of 13del cells in chimeras, suggest that these changes are dominant and cell autonomous. The normal terminal differentiation of HCs in collagen X null mice [57] and identical expansion of HZ in compound 13del; Col10a1 null mutants (unpublished data) are consistent with such a dominant effect. Given that there is normal entry into hypertrophy and a clear demarcation region of fully differentiated HCs in the UHZ, as shown by the β-galactosidase activity in all HCs in Col10a1-Cre–Rosa 26 reporter mice, the reversion of cells in the LHZ to a preHC-like status must be due to altered terminal differentiation of the stressed HCs.
The concomitant onset of ERSS in the 13del UHZ with disruption of terminal differentiation provides visual evidence for a possible link between these two processes, and is consistent with the latter occurring as a consequence of ERSS. The correlation between levels of 13del expression with degree of HZ expansion in independent lines of mice suggests a direct link between level of ER stress and the altered differentiation. In addition, the gradual loss of preHC-like cells after weaning age correlates with the down-regulation of 13del and delayed BiP induction, suggesting a causal relationship between ER stress and altered HC differentiation. Co-expression in the same cells of 13del with XBP1s and with cyclin D1 proteins provides molecular evidence for a direct link.
Reverting to a preHC-like state may be an adaptive or fortuitous response by which the 13del HCs alleviate and survive ER stress. This is interesting, since by reverting to a preHC-like state, both wt and 13del Col10a1 transcription would be down-regulated, thereby reducing the source of the ER stress (13del proteins) to a more acceptable level. Consistent with this hypothesis, expression of 13del, Col10a1, and BiP was reduced in the LHZ at 10 d and 3 wk postpartum.
Chondrocytes in culture are able to re-differentiate after dedifferentiation to a fibroblastic/osteoblastic state [58,59]. This plasticity involves changes in transcriptional profile and may be retained in HCs. The altered terminal differentiation of 13del HCs is the first in vivo demonstration of the phenotypic plasticity and capacity to revert to a preHC-like state. Unlike in cell culture, none of the 13del HCs expressed Col1a1, the osteoblast marker, suggesting that reprogrammed differentiation to a preHC-like state occurred rather than transdifferentiation to osteoblasts. This inherent plasticity may be exploited by HCs to adapt and survive ER stress in vivo.
Our findings provide novel mechanistic insight into how chondrocytes overcome ER stress in vivo. Until now, it is generally accepted from in vitro studies that cells respond to ER stress either by undergoing apoptosis or by a general down-regulation of protein synthesis and up-regulation of protein-folding/degradation capacity and thereby relieve the load of unfolded proteins in the ER. However, there is no information on the impact of ER stress on the differentiation program of chondrocytes either in vitro or in vivo.
We propose a model (Figure 10C) in which, in addition to the alleviation mechanisms mediated by the UPR, 13del HCs cope with ER stress through a “reprogram, recover, and survive” adaptive mechanism. Initially, 13del protein expression induces ERSS, but not apoptosis. The differentiation program of the HCs is altered in which they “revert” or “reprogram” to a preHC-like state in which collagen X expression is reduced. The “reprogramming” involves re-activation of signaling pathways that may stimulate re-entry into the cell cycle. However, p57Kip2 restricts cell-cycle progression. By reprogramming, the 13del HCs finally down-regulate endogenous Col10a1 and 13del mRNA levels, thereby alleviating the load of mutant protein and ER stress, providing the means to survive and complete differentiation, albeit with delayed endochondral ossification.
The exact mechanisms that mediate the reversion of the differentiation pathway in 13del HCs are not known. It is conceivable that the ER stress response takes control over gene expression and protein metabolism to such a significant extent that it interferes with normal differentiation processes, which may involve complex alteration in signal transduction and transcription factors recruitment and interaction, as well as epigenetic changes upon ER stress [60]. Identifying and understanding the underlying mechanisms are important and issues for further study.
Our data have broad implications for the mechanism of disrupted chondrocyte differentiation in MCDS and other chondrodysplasias caused by mutations that impair protein assembly and secretion. Although ER stress-induced apoptosis may provide a major route for pathogenesis, our study raises the possibility that chondrocytes can survive ER stress in a process that changes the normal differentiation program. Changes in the pattern of markers that characterize the differentiation status of chondrocytes have been noted in chondrodysplastic models. Mutations in collagen II that interfere with assembly and secretion, leading to chondrodysplasia, are associated with distension of the ER [61,62]. In Col2a1G904C mice, the fragmented ER, as well as the increased levels of BiP and CHOP in growth plate chondrocytes, indicate that ERSS is induced; abnormal terminal differentiation of HCs is indicated by the up-regulation of Col2a1 and Agc1 expression [36]. Transgenic mice expressing collagen II with a 36 amino acid deletion showed disorganized chondrocytes and fragmented ER, and there was loss of expression of the cell-cycle regulator Cdkn1a and the key differentiation markers Ihh, Col10a1, and Fgfr3 [62]. In cmd/cmd mice, the distributions and co-expression patterns of several chondrocyte differentiation markers [63] and regulators of the cell cycle (p57Kip2 and PCNA) were altered. The abnormal gene expression in these models and 13del suggest that alteration of the normal progression of differentiation, cell-cycle control, and fate change may be a common consequence of ER stress in chondrocytes. However, the phenotypic consequence in terms of differentiation pathway and growth plate architecture may differ depending on the nature of the mutation and the expressing cell type.
The 13del mouse has also provided insight into the adaptive mechanisms that facilitate survival of chondrocytes in vivo in the face of ER stress. But survival is not without cost; the changes in HC differentiation do result in delayed endochondral ossification. We postulate that the change in differentiation program contributes to the disorganization of chondrocytes in the growth plate that occurs in many chondrodysplasias. Whether this strategy is peculiar to chondrocytes or can be adopted by other cell types affected in protein-folding disorders is an important issue to address in the future.
Thirteen nucleotides (residues 6058–6070) of the murine Col10a1 gene were excised by overlapping PCR [22]. The resultant PCR product was cloned to generate the Col10a1-13del transgene. The transgene is a 10.5-kilobase (kb) fragment of the murine Col10a1 gene containing 2 kb of the 5′ and 1.3 kb of 3′ flanking sequence after exon 3 (Figure 1B).
13del transgenic mouse founders were generated by pronuclear injection into one-cell CBA/C57BL6 F1-hybrid zygotes. Analyses were performed on 10-d-old littermates unless otherwise stated. Mice were genotyped by PCR using primers (5′-CCCAGGCATATACTATTTCTC-3′ and 5′-TAGCCTTTGCTGTACTCATC-3′) flanking the 13-bp deletion.
Wt and 13del full-length cDNA constructs in pBluescript II SK(−) (Stratagene, La Jolla, California, United States) were transcribed and translated using the TNT-T3 polymerase-coupled transcription and translation system (Promega, Madison, Wisconsin, United States), supplemented with canine microsomal membrane vesicles (Promega) as described previously [22]. For heterotrimer assembly, equal amounts of each of the normal and mutant plasmids were co-translated. Collagen X chains were analyzed on 7.5% (w/v) SDS-polyacrylamide gels.
Limbs were fixed in 4% paraformaldehyde, and if necessary, were demineralized in 0.5M EDTA (pH 8.0) containing 0.2% formaldehyde prior to embedding in paraffin. Immunohistochemistry was performed using antibodies for p57Kip2, p53, cyclin D1, CHOP, PCNA (Santa Cruz Biotechnology, Santa Cruz, California, United States), XBP1s (BioLegend, San Diego, California, United States), BiP (Stressgen, San Diego, California, United States), and green fluorescent protein (Abcam, Cambridge, United Kingdom). Signals were detected using the avidin-biotin-complex system (ABC; Vector Laboratories, Burlingame, California, United States) for p57Kip2, p53, and PCNA antibodies, or using the secondary antibody-HRP-conjugated polymer system (EnVision+; Dako, Glostrup, Denmark) for 13del, CHOP, BiP, cyclin D1; and green fluorescent protein antibodies. For XBP1s and cyclin D1 antibody, signals were further amplified with biotinylated tyramide (PerkinElmer, Wellesley, Massachusetts, United States).
Immunostaining for collagen X was performed as described [57]. The 13del antibody was raised in rabbits against a synthetic peptide, AYTPLSMSTPLSQDS, which corresponds to the C-terminus of the 13del protein. Goat anti-rabbit Alexa 488 (Molecular Probes, Eugene, Oregon, United States) was used for immunofluorescence on cryosections. Concanavalin A conjugated with Alexa 594 (Molecular Probes) was used to visualize the ER.
The vertical height of RZ, PZ, and HZ in the tibial growth plate was measured on tissue sections using a test line grid as described previously [64]. Sizes of cells within the tibial LHZ were measured in wt and 13del mice as described [65]. Measurements were made in cells in which the plane of section was through the nucleus to avoid bias. Cells were defined as being “smaller” if the maximal height was equal to or less than that of the maximal height of cells in the PZ of the same section. At least five sections from each animal for three wt and three 13del mice were analyzed. For electron microscopy, growth plate cartilage was embedded in Epon 812 and ultra-thin (60 nm) sections were prepared for electron microscopy analysis as previously described [66].
Mice were injected intraperitoneally with 200 μg of BrdU per gram of body weight in either single dose (and sacrificed 2 h later) or two doses 6 h apart (and sacrificed 48 h after the first injection). Following fixation, BrdU in paraffin sections of limbs was detected using a BrdU Staining Kit (Zymed Laboratories, South San Francisco, California, United States). To determine the rate of hypertrophy, digital photographs were taken, and the number of BrdU-labeled chondrocytes in the PZ and HZ of the growth plate was counted. Cells in three consecutive sections from four 13del and four wt mice were scored; differences were assessed using the Student t-test.
Total RNA was extracted from 10-d-old wt and 13del growth plate cartilage using Trizol reagent (Invitrogen, Carlsbad, California, United States), and RNase protection assays were performed using a 354-base [α-33P]UTP-labeled cRNA generated from the plasmid pWF36 (Figure 1B). Protected fragments were analyzed using 6% (w/v) polyacrylamide gel. RT-PCR was performed to amplify Xbp1 mRNA residues 430–898 with specific primers. cDNA was synthesized from mRNA using SuperScript II (Invitrogen) primed with an antisense oligonucleotide (5′-GAGGTGCTTCCTCAATTTTCA-3′) against Xbp1, and used as a template in PCR for the amplification of Xbp1 and a spliced variant, Xbp1s, using a sense primer 5′-GCTGGATCCTGACGAGGTT-3′ and the aforementioned antisense primer.
For quantitative RT-PCR, long bones from wt and 13del mice were collected and snap frozen in liquid nitrogen. The ends of the long bones were mounted in Tissue Freezing Medium (Jung, Nussloch, Germany) and growth plates fractionated by collecting 5-μm sections, 20 sections per fraction, on a Zeiss cryostat (Carl Zeiss, Oberkochen, Germany). In between each fraction, a single section was mounted and subjected to von Kossa and Safranin-O staining to determine cell morphology of the tissue comprising the adjacent fractions. Total RNA was prepared from growth plate fractions using the Trizol reagent. cDNA was prepared from fractions by reverse transcription of 1 μg of total RNA using Superscript II reverse transcriptase and random hexamers. Quantitative PCR was carried out on an ABI 7700 real-time thermal cycler using a SYBR green qPCR kit from ABGene. Specific PCR primers designed with a melting temperature (Tm) of approximately 60 °C and to span at least one intron were used to determine gene expression profiles. The primers used are as follows: Col10a1, 5′-TTCATCCCATACGCCATAAAG and 5′-AGCTGGGCCAATATCTCCTT; BiP, 5′-TGAAACTGTGGGAGGAGTCA and 5′-TTCAGCTGTCACTCGGAGAA; Mmp9, 5′-TTCGCGTGGATAAGGAGTTC and 5′-CCTCCACTCCTTCCCAGTCT; and Actinb, 5′-GACGGCCAGGTCATCACTAT and 5′-GTACTTGCGCTCAGGAGGAG. Dissociation curves were collected for all PCR reactions to ensure specificity. Actinb was used as an internal control for normalization. The highest expression level for a particular transcript was designated arbitrarily as 1 for comparison.
In situ hybridization was performed as previously described [63], using [35S]UTP-labeled ribopobes for Ihh (from A. McMahon), PTHrP receptor (Ppr), Ptc, Opn, and Mmp13 (from H. Kronenberg), Igf2 (from A. Ferguson-Smith), and Edem (from K. Nagata). To detect wt and 13del Col10a1 mRNA, a 77-bp (nucleotides 1,824–1,899) wt-specific short probe and a 64-bp 13del-specific short probe (same as for the wt-specific probe, but with the 13-bp deleted) were generated. The probe for BiP corresponds to position 9–1,991 in mouse BiP mRNA. BiP cDNA was synthesized by RT-PCR and cloned into pBluescript II.
Marker analyses (with antibodies and/or riboprobes) were performed on sections from at least three 13del mice, and data shown is representative of consistent results.
To express Cre recombinase specifically in HCs, a replacement gene-targeting vector was generated that contained Col10a1 sequence extending from −2,070 to +7,680. This targeting vector was modified with Cre recombinase inserted at the ATG codon in exon 2 followed by frt-flanked neomycin resistance gene (PGKneo), such that the translated CRE proteins do not have a signal peptide and are not secreted. Gene targeting was carried out as described using R1 embryonic stem (ES) cells (gift of Andras Nagy) [67] Targeted ES clones were used to generate chimeras by blastocyst injection. Upon germline transmission of the Col10a1-cre gene, the PGKneo selection cassette was removed by crossing to β-actin-flp mice (gift of Susan Dymecki). To detect CRE expression and activity, Col10a1-Cre mice were bred to ROSA26 CRE reporter mice [68] and 13del mice. Tibiae of the compound mutant mice were collected for X-gal staining to detect β-galactosidase expression in mutant mice [68].
Homozygous albino (ICR) mice ubiquitously expressing EGFP were generated by crossing β-actin Cre transgenic mice (gift from Gail Martin) with the CRE reporter mouse, Z/EG [69]. EGFP/13del chimeras were created by aggregating morulae from the β-actin EGFP and 13del mice (CBA/C57BL6). The chimeras (white and agouti/black) generated were genotyped for the Col10a1-13del allele by PCR.
Fluorescence images were captured on an Axiovert 135 microscope (Carl Zeiss) using the Bio-Rad MRC-1024 laser scanning confocal imaging system (Bio-Rad, Hercules, California, United States). Pseudo-colored in situ hybridization images were created by overlaying bright field (blue filter) and dark field (red filter) images captured on a Axioplan 2 microscope (Carl Zeiss,) using a DKC-ST5 digital camera (Sony, Tokyo, Japan). The images were enhanced by adjusting the contrast with Photoshop 6.0 (Adobe Systems, San Jose, California, United States), and overlaid using the “Screen” blend mode of the “Layer” function to produce the final pseudo-colored image. For immunohistochemistry, images were captured on the same microscope with some images enhanced with the “Auto Levels” function in Photoshop 6.0 that automatically adjusts the tonal range to improve color contrast in each of the RGB channels with a default clip values of 0.5%.
The GenBank (http://www.ncbi.nlm.nih.gov/Genbank) accession numbers for the genes and gene products discussed in this paper are Actinb (NM_007393), BiP (NM_022310), BiP mRNA (AJ002387), Col10a1 (NM_009925), Mmp9 (NM_013599), and Xbp1 mRNA (NM_013842).
|
10.1371/journal.pntd.0005003 | The World Health Organization Recommendations for Trachoma Surveillance, Experience in Nepal and Added Benefit of Testing for Antibodies to Chlamydia trachomatis pgp3 Protein: NESTS Study | The World Health Organization (WHO) now requires a second surveillance survey for trachoma after an impact assessment has found follicular trachoma (TF) <5% to determine if re-emergence has occurred. Using new WHO guidelines, we undertook surveillance surveys, and determined the prevalence of infection and antibody positivity, in two districts in Nepal.
20 clusters were randomly selected within each district, 15 were randomly selected for antibody testing. In each cluster, we randomly selected 50 children ages 1–9 years and 100 adults ≥15 years. TF and trachomatous trichiasis (TT) were evaluated. Conjunctival swabs to test for chlamydial infection using GenXpert platform were obtained, and dried blood spots were collected to test for antibodies to Chlamydia Trachomatis pgp3 using the Luminex platform.
3 cases of TF were found in the two districts, and one case of infection. Pgp3 antibody positivity was 2·4% (95% confidence interval: 1·4%, 3·7%), and did not increase with age (P = 0.24). No clustering of antibody positivity within communities was found. TT prevalence was <1/1,000 population.
The surveillance surveys, as proposed by WHO, showed no evidence for re-emergence of trachoma in two districts of Nepal. The low level and no significant increase by age in seroprevalence of antibodies to C trachomatis pgp3 antigen deserve further investigation as a marker of interruption of transmission.
| Once districts have shown that the prevalence of follicular trachoma (TF) in children ages 1–9 years is below 5%, they must monitor for re-emergence. The World Health Organization (WHO) recommends a second surveillance or “pre-validation” survey to determine if re-emergence has occurred. Using new WHO guidelines, we undertook surveillance surveys, and determined the prevalence of antibody positivity, in two districts in Nepal. 20 clusters were randomly selected within each district, 15 were randomly selected for antibody testing. In each cluster, 50 children ages 1–9 years and 100 adults ≥15 years were randomly selected. Follicular trachoma (TF) was evaluated, conjunctival swabs to test for chlamydial infection were obtained, and dried blood spots were collected to test for antibodies to pgp3 in children. Adults were evaluated for trachomatous trichiasis (TT). Only 3 cases of TF were found in the two districts, and one case of infection. Overall Pgp3 antibody positivity was low (2·4%), it increase with age, and no clustering within communities was found. TT prevalence was <1/1,000 population. Our surveillance surveys, as proposed by WHO, showed no evidence for re-emergence of trachoma in two districts of Nepal. The low level and not significant increase by age in children with positive antibodies to Chlamydia trachomatis deserve further investigation as a marker of interruption of transmission.
| Trachoma, a chronic conjunctivitis caused by repeated episodes of Chlamydia trachomatis, is still the leading infectious cause of blindness worldwide[1]. The World Health Organization (WHO) recommends a multi-faceted strategy, the SAFE strategy, for trachoma control (SAFE is Surgery for trichiasis, Antibiotics to reduce infection, Facial hygiene, and Environmental change for sustainable interruption of transmission)[1–3]. Country programs are encouraged to map suspected trachoma endemic districts to determine the prevalence of follicular trachoma (TF) and trichiasis (TT), and institute SAFE where trachoma is more than 5% in children ages 1–9 years and TT is 1/1,000 total population or more[1–4].
The goals are sustainable reduction in prevalence of TF in children ages 1–9 years of <5%, and the reduction of TT unknown to the health system of <1/1,000 population. After 3–5 years of SAFE implementation, impact assessments are carried out to determine progress, and if the impact assessments show achievement of goals, then vertical program activities, particularly around mass treatment with antibiotics, can cease. However, to ensure longer term, sustainable, achievement of goals, post-MDA surveillance in formerly endemic districts should be carried out. The approach to surveillance activities has varied widely by country, for example there is a National Trachoma Surveillance unit in Australia that reports annually from aboriginal at-risk communities[5] while in Oman, active trachoma was integrated into the surveillance of other communicable disease and special trichiasis screening was done in formerly endemic regions[6,7]. In September 2014, the WHO recommended the surveillance survey consist of a population-based prevalence survey at least 2 years post MDA, measuring again TF and TT[8]. There is only one publication describing the results using the new WHO guidelines, from a district in Tanzania where four years after cessation of MDA, trachoma did not re-emerge[9].
There is concern that when TF declines to such low levels there is the risk of overcalling by graders (due to the challenge of standardized training), or that follicular disease may be due to other causes, resulting in ongoing or re-starting interventions that are inappropriate and unnecessary and may waste resources that could be allocated to other areas. Additional data provided by a test for presence of infection, or a test for antibody positivity to C. trachomatis antigens, could be important additions to these surveillance surveys. However, there are insufficient data on infection and a test for antibody positivity to determine their usefulness in that scenario. A district wide surveillance survey in Tanzania found prevalence of TF of 0·4% was associated with a similarly low rate of infection, 1%, and overall rate of antibody to pgp3 positivity of 7·5%[6]. In that study, the TF prevalence in 1–3 year children, born after the trachoma program in that district had ceased, was 5%, and in more than half the communities, no child of in that age group had antibodies to pgp3. A significant increase by age in seroprevalence was observed, but not as pronounced as in other, currently endemic settings[10,11]. These data were suggestive of an important role for a test for chlamydial antibodies, which could be a marker of cumulative exposure to trachoma, but more data are clearly needed.
Nepal has reached its program goals in all of its formerly endemic districts, and is embarking now on surveillance surveys using the new WHO guidelines. We had the unique opportunity to add into these surveys a test for infection and a test for antibody positivity to C. trachomatis antigen pgp3. The goal of this study was twofold: first, using the new WHO surveillance recommendations, to determine the prevalence of TF and TT in two districts in Nepal which had ceased mass drug distribution for trachoma 2 to 4 years previously (continuing trichiasis case finding and treatment). Secondly, to determine the prevalence of chlamydial infection, and antibody positivity. This is the first country to fully implement and report data from the new WHO surveillance guidelines, which will enable better understanding of the timeframe for surveillance and the relationship between TF and potential ancillary tests for potential use in the future.
This study was approved by the Johns Hopkins Institutional Review Board, the Nepal Netra Jyoti Sangh and the Nepal Health Research Council (238/2014). It followed the tenets of the Declaration of Helsinki. Written informed consent was obtained from the guardians of each child in the research project. All adults provided written consent for their examination and participation of themselves and their children participating in our surveys.
Nepal is divided into 75 districts of which 20 are formerly endemic for trachoma and four are considered “high risk districts” because of residual TF (but <5%) after the last impact survey. The 20 districts contain villages called Village Development Committees (VDC) which is an administrative unit that can vary from 300 persons to 35,000. The VDC are further divided into wards, of which there are at least 9 per VDC.
Two districts were chosen for this study, Dang and Dailekh, where residual TF at 1·1% was found at the last impact survey conducted 2 and 4 years ago and surveillance surveys were planned for early 2015. They each had four rounds of Mass Drug Administration before program activities ceased.
Within each district, 20 clusters (see below) for the surveillance survey were randomly selected with probability proportional to size. Fifteen were randomly assigned to have a test of infection and test for antibodies. The remaining five had the clinical survey alone.
The steps for the selection of the clusters are described below:
All residents of the geographic cluster were invited to the survey. Community volunteer went house to house after a random start and registered up to 50 children ages 1–9 years and 100 adults ≥15 years old for the survey.
Fifty children ages 1–9 year each from the 15 research VDC/district were included in our study of infection (1500 children) and within the 1500, only children ages 1–4 and 9 years were included in the study on antibodies. Data from 100 adults in each of the 20 VDCs/ district were included (4000 adults).
The survey involved three components: bilateral grading for TF, conjunctival swab of the right eye for testing of C. trachomatis, and a finger prick for collection of blood spot for antibody testing. NNJS provided azithromycin treatment for TF cases.
A GTMP certified grader conducted the examination for TF, using the WHO simplified grading scheme[13], a torch and 2·5 loupe. All cases were re-affirmed by a second grader (AZ). The right upper eye lid was everted and a dry swab taken of the upper conjunctiva. Strict adherence to protocol was observed to avoid field contamination, and 3 control swabs per cluster were taken in the field to monitor possible contamination. These were labeled and analyzed in an identical fashion to true specimens. The swabs were placed in a Cepheid transport media tube (Cepheid, San Jose, CA), kept cold in the field and processed the next day by a trained technician in Nepal using the Cepheid GeneXpert platform. Results were reported as positive or negative. Known positive and negative controls (Zeptometrix Corporation, Buffalo, NY) were run weekly to check the machine. Blood was collected by finger prick from each child onto filter papers with six circular extensions, each calibrated to collect 10 μl of whole blood (TropBio Pty Ltd, Townsville, Queensland, Australia). These were dried and shipped to the Johns Hopkins University. The blood spots were analyzed for antibody to chlamydial antigen pgp3 as previously reported[10], using a multiplex bead assay on a Luminex 100 platform. The results are reported as median fluorescence intensity minus background (MFI-BG, where background is the signal from beads with buffer only) and the positivity cut-off was as determined by receiver operator characteristics (ROC) analyses[7].
The overall district prevalence of antibody was estimated as the mean of the individual cluster prevalence. Since there were so few cases of TF and infection, we just report the number of cases. The rate and the 95% confidence intervals using exact estimates for a binomial proportion are presented for antibody positivity and presence of TT. The proportion of children positive for antibodies against pgp3 is presented for each age category, and then for the 9 year olds. We compute confidence intervals around the proportion assuming a poison distribution. A test for trend (Mantel-Haenszel Chi-Square (1df)) was used to test for increase antibody positivity with age. The magnitude of the clustering of antibody positivity within sampled cluster was assessed using the intra-class correlation coefficient, the point estimate and 95% confidence interval is reported.
In adults, bilateral examination of TT was conducted; if identified, we everted eyelids for evaluation of scarring to relate the trichiasis to evidence of trachoma, and exclude other causes of trichiasis. Contact with healthcare system was determined by asking if they had had surgery before, or had been offered surgery before.
Role of Funding Source: Funding source/s had no role in the design or implementation of this study.
A total of 2021 children were enrolled from the two districts in the surveillance survey. The prevalence of TF was 0·1% in Dang (95% CI = .03%, 0.55%) and 0·2% in Dailekh (95% CI = .02%, 0.72%). 1511 children from both districts were enrolled from the 15 clusters in the study, of whom 52% were male. One case of infection was detected in Dailekh district. No control swabs were positive.
Within the 15 clusters, we tested 794 children for antibodies to pgp3 (Table 1). The prevalence of antibody positivity was low in both districts, 2% in 1–4 year olds. Among the nine year olds, the prevalence of antibody positivity was higher, 4% and 3% respectively, but the differences were not statistically significant (Fischer’s exact test P >0·05). There was no evidence of an increase by age in seroprevalence among the 1–4 year olds either (Table 1). Fig 1 shows the values by age of antibody positivity.
A total of 4,000 adults were examined in the surveillance survey, 2000 per district. 11 cases of trichiasis were found, of whom 4 were non trachomatous trichiasis (no evidence of scarring). Of the remaining 7 cases, 5 had been approached for surgery before and had refused (and refused again) so were already known to the health system. Two were new cases in Dang, who were referred for surgery (Table 2). The prevalence of TT in the total population was zero in Dailekh and 0·5/1,000 in Dang, indicating having achieved the goals for TT during the surveillance period.
In this study from a country implementing the new WHO recommendations for post MDA surveillance in formerly endemic districts, we found no evidence of re-emergence of trachoma or infection in districts two and four years from MDA program cessation. Moreover, there was no evidence of exceeding the guidelines of one or more cases of TT unknown to the health system/1,000 population, in a setting with ongoing case finding and surgery that is part of the Nepal health care system.
Ideally, surveillance for an infectious disease should demonstrate ongoing interruption of transmission. However, we have no transmission markers for trachoma, and no agreed upon definition of interruption of transmission. The level of infection below which transmission cannot be sustained is unknown. In this study, we found only one case of infection in 1500 samples, whereas in a Tanzania setting where, after four years post MDA, clinical trachoma rates were still less than 1% TF, infection rates were 1·1%[9]. In neither site had trachoma specific activities in Mass Drug Administration, or environmental change been implemented in the four years (two years for Dailekh) preceding the surveillance surveys. A promising marker is antibodies to Chlamydial antigens, as they may show cumulative exposure to infection, and, if were very low or zero in children born during the surveillance period, could indicate interruption of transmission[9,10]. In Tanzanian communities with ongoing infection transmission, there was a sharp increase in the age seroprevalences of antibody to pgp3[10], such that by age 6 years, 100% of children had antibodies. In the current study, we observed a much lower prevalence of antibody positivity, 2%, and a not significant age specific increase. The low age specific distribution may reflect an absence of exposure and/or a waning of antibodies over time in the older age group in the absence of ongoing stimulation by infection. From longitudinal studies, it appears the antibodies are not reduced by treatment and are present for at least six months, but this was in the context of high rates of trachoma[11]. The rates of trachoma when the program started, as reported by the surveys done in Nepal, was 13.2% in Dang in 2003, and 11.6% in Dialekh in 2007, which were relatively low to begin with. It is possible that the low rate in the nine year olds in 2015 reflects very low to absent exposure growing up. Another study from 1997 in Nepal, in a neighboring district to Dialekh, found low trachoma rates of 6% but when testing 125 children for infection, none were positive[14].While this study does not have the power to detect infection levels at less than 1%, it suggests a long history of low trachoma in a neighboring district. We also recognize that there has been population movement into the Terai region of Nepal, which includes Dang, from north-eastern India where the rates of trachoma are unknown. As with any cross-sectional survey, the population evaluated were those currently in the area, but the antibody history may reflect exposure from outside Nepal.
Nevertheless, the absence of an increase by age in seroprevalence of antibodies to pgp3 antigens may also be a marker for interruption of transmission. In a study of a single sub-village in Tanzania where there was no infection, antibody positivity rate in the 200 children ages 1–9 years was 3·5%, compared to our finding of 2%[15]. In this sub-village, trachoma was 6·5%, whereas our trachoma rates were 3/1500 (0·2%). In a survey done as an impact assessment in Achham district in Nepal, low rates of infection and trachoma were also found, although too few children were tested for antibodies to determine a reliable rate of antibody positivity[16]. In the only other district surveillance prevalence survey, antibody prevalence in 30 communities in Kilosa district Tanzania was 7·5% and increased significantly with age from 5% in 1–3 year olds, to 9% in 7–9 year olds[9]. The higher rate in Kilosa may reflect the higher infection rate in this district, 1·1%, although the survey was done there 4 years after program cessation with no evidence of re- emergence of trachoma.
Another important component for surveillance is confirming a low rate of blinding TT in adults. Trichiasis due to trachoma will continue to manifest several years after active disease has been eliminated, and countries need to provide assurances that services for trachomatous trichiasis continue to be offered in formerly endemic districts. Ideally, as in Nepal, these services are integrated into existing programs. In Nepal, female community volunteers who carry out a variety of maternal and child health projects have been trained to screen for trichiasis and to refer cases to the district eye hospital. We found that it is important to confirm trachoma as the likely cause of trichiasis by looking for trachomatous scarring (TS) in the conjunctiva. In our survey we were able to characterize 4 of the 10 trichiasis cases in Dang district as unlikely due to trachoma and, while they were still offered services appropriate to their condition, they would not prevent the national program from reaching its elimination goal. While it may be that sub clinical scarring is associated with an aberrant lash[17], it is also likely that other causes of trichiasis could be the culprit and it seems unreasonable to attribute all trichiasis to trachoma and thus potentially prevent countries from reaching their elimination goals[18]. It is also unclear what the long term risk of vision loss is from non-scarring related trichiasis, since the overall goal is the elimination of blinding trachoma. We also found that the specific questions of being offered surgery before and refusing before, and again in this survey, helped in identifying trichiasis cases who are refusals and known to the health system. These cases again should not count against the trachoma trichiasis surgery elimination target, as patients do have a right to refuse services.
There are limitations to our study on antibody testing. Ideally, the determination of antibody status at the time of the impact survey immediately post-MDA would have enabled us to follow the trajectory of antibody responses over the time of surveillance. While we did not include the 5–8 year olds due to cost considerations, it is interesting that we found a similar seroprevalence rate in the 9 year olds as we did in the 1–4 year olds. A trajectory study would have enabled us to determine if this represents waning of antibodies in the older group, or a consistent, low level, exposure across all age groups. At present, the antibody test run on the Luminex platform is not readily available and technically complicated, but the designers are working on an ELISA and a field test version for pgp3 antibodies, which would bring it within the possibility of more widespread use.
Finally, the surveillance survey was powered to detect low prevalences of active disease at the district level, but the confidence intervals around the detection of TT at <1/1,000 are quite broad. In this study, the interval was 0·1 to 1·2%. There is also the concern that the TT cases may be more likely to be home on the day of the survey, and may preferentially be included in the random sample, inflating the estimate. With two thousand adults over age 14 and older in the district surveys, just 4 cases of TT might be sufficient to exceed the target of <1/1,000 total population, depending on the age distribution. Larger surveys of adults would address the precision issue, but come at a cost.
In summary, the new WHO surveillance surveys, carried out at 2 and 4 years post cessation of MDA in two districts in Nepal, showed no evidence for re-emergence of trachoma. The lack of significant age specific increase in seroprevalence of antibodies to C. trachomatis, and the low sero- prevalence, deserves further investigation as a marker of interruption of transmission. The survey also showed attainment of a goal of TT less than 1/1,000 population, but in one district with the addition of questions to determine previous surgical refusal, and flipping the lid to assess scarring and thus trachoma as the likely determinate of the trichiasis.
|
10.1371/journal.pcbi.1006915 | Ten quick tips for creating an effective lesson | We present 10 tips for building effective lessons that are grounded in empirical research on pedagogy and cognitive psychology and that we have found to be practically useful in both classroom and free-range settings
| As a species, we know as much about teaching and learning as we do about public health, but most people who teach at the postsecondary level are never introduced to even the basics of evidence-based pedagogy. Knowing just a few key facts will help you build more effective lessons in less time and with less pain and will also make those lessons easier for your peers to find and reuse. This paper presents 10 tips that you can apply immediately and explains why they work.
| There are many kinds of lessons, both formal and informal, from seconds long to lifelong. Most people have sat (or suffered) through hundreds of these but have never been shown how to design ones that are effective. These 10 simple tips for creating lessons are
The key insight that underpins all of these tips is that learning is both a cognitive and a social activity. On the cognitive side, incoming information (the lesson) passes through a “sensory register” that has physically separate channels for visual and auditory information and is stored in short-term memory, where it is used to construct a “verbal model” (sometimes also called a “linguistic model”) and a separate “visual model” [12]. These are then integrated and stored in long-term memory as facts and relationships. If those facts and relationships are strengthened by use, they can later be recalled and applied, and we say that learning has taken place.
One key feature of this model is that short-term memory is very limited: [13] famously estimated its size as 7 ± 2 items, and more recent studies place the figure closer to 4. If too much information is presented too quickly, material spills out of short-term memory before it can be integrated and stored, and learning does not occur.
A second key feature is that the brain's processing power is also very limited. Effort spent identifying key facts or reconciling the linguistic and visual input streams reduces the power available for organizing new information and connecting it to what's already present.
Learning is also a social activity. Learners who feel motivated will learn more; learners who feel that they may not be judged on their merits or who have experienced unequal treatment in the past will learn less (see the tip "Motivate and avoid demotivating"). In [14], e.g., Kenneth Wesson wrote, "If poor inner-city children consistently outscored children from wealthy suburban homes on standardized tests, is anyone naive enough to believe that we would still insist on using these tests as indicators of success?" Lesson designers must take the social aspects of learning into account if they are to create effective lessons; we discuss this further in the final tip ("Make lessons inclusive").
The first step in creating a good lesson is figuring out who the audience is. One way to do this is to make up biographies of two or three target learners. This technique is borrowed from user interface designers, who create short profiles of typical users to help them think about their audience.
These profiles are called “personas” and have five parts:
A learner persona for a weekend introduction to programming aimed at college students might be as follows:
Rather than writing new personas for every lesson or course, instructors often create and share a handful that cover everyone they hope to teach, then pick a few from that set to describe who particular material is intended for. Used this way, personas become a convenient shorthand for design issues: when speaking with each other, teachers can say, "Would Jorge understand why we're doing this?" or, "What installation problems would Jorge face?"
Personas help you remember one of the most important tips of teaching: you are not your learners. The people you teach will almost always have different backgrounds, different capabilities, and different ambitions than you; personas help you keep your lessons focused on what they need rather than on what your younger self might have wanted.
Some learning strategies are provably more effective than others [15, 16, 17], so lessons should be designed to encourage their use. As summarized in [18, 19], the six most important are as follows:
Different subsystems in our brains handle and store linguistic and visual information, and if complementary information is presented through both channels, then they can reinforce one another. However, learning is more effective when the same information is not presented simultaneously in two different channels [23, 12] because then the brain has to expend effort to check the channels against each other. This is one of the many reasons that reading slides verbatim is ineffective: not only is the reader not adding value, they are actually adding to the load on learners whose brains are trying to check that the spoken and written inputs are consistent.
“Summative assessment” is something done at the end of a lesson to tell whether the desired learning has taken place: a driving test, performance of a piece of music, a written examination, or something else of that kind. Summative assessments are usually used as gates (e.g., "Is it now safe for this person to drive on their own?"), but they are also a good way to clarify the learning objectives for a lesson. "Understand linear regression" is hopelessly vague; a much better way to set the goal for that lesson would be to define an exercise, such as the following:
This is better because it gives the lesson author a concrete goal to work toward: nothing goes in the lesson except what is needed to complete the summative assessments. This helps reduce content bloat and also tells the author when the lesson is done.
Writing summative assessments early in the lesson design process also helps ensure that outcomes are actually checkable. Since telepathy is not yet widely available, it is impossible for instructors to know what learners do and don't understand. Instead, we must ask them to demonstrate that they're able to do something that they couldn't do without the desired understanding.
Finally, creating summative assessments early can help authors stay connected to their learners' goals. Each summative assessment should embody an “authentic task,” i.e., something that an actual learner actually wants to do. Early on, authentic tasks should be learners' own goals; as they advance and are able to make sense of generalizations, these tasks may be extensions or generalizations of earlier solutions.
Continuing with the statistical example above, calculating a regression coefficient may be an authentic task for someone who already knows enough statistics to understand what such coefficients are good for. If the intended learners are not yet that experienced, this exercise could be extended to have them make some sort of judgment based on the regression coefficients to exercise higher-order thinking.
The counterpoint to summative assessment is “formative assessment,” which is checks that are used while learning is taking place to form (or shape) the teaching. Asking learners for questions is a common, but relatively ineffective, kind of formative assessment. What works better is to give them a short problem—one that can be done in 1–2 minutes so as not to derail the flow of the lesson and that will help them uncover and confront their misconceptions about the topic being taught.
Checking in with learners this way every 10–15 minutes accomplishes several things:
[24, 25, 26] offer inspiration for a wide variety of different kinds of summative and formative assessment exercises.
Research by Mayer and colleagues on the split-attention effect is closely related to cognitive load theory [23]. As described in the introduction, linguistic and visual input are processed by different parts of the human brain, and linguistic and visual memories are stored separately as well. This means that correlating linguistic and visual streams of information takes cognitive effort: when someone reads something while hearing it spoken aloud, their brain can't help but check that it's getting the same information on both channels.
Learning is therefore more effective when information is presented simultaneously in two different channels, but when that information is complementary rather than redundant. People generally find it harder, e.g., to learn from a video that has both narration and on-screen captions than from one that has either the narration or the captions but not both because some of their attention has to be devoted to checking that the narration and the captions agree with each other. Two notable exceptions to this are people who do not yet speak the language well and people with hearing exercises or other special needs, both of whom may find that the extra effort is a net benefit.
This is why it's more effective to draw a diagram piece by piece while teaching rather than to present the whole thing at once. If parts of the diagram appear at the same time as things are being said, the two will be correlated in the learner's memory. Pointing at part of the diagram later is then more likely to trigger recall of what was being said when that part was being drawn.
The split-attention effect does not mean that learners shouldn't try to reconcile multiple incoming streams of information—after all, this is something they have to do in the real world [27]. Instead, it means that instruction shouldn't require it while people are mastering unit skills; instead, using multiple sources of information simultaneously should be treated as a separate learning task.
No matter how good a teacher is, she can only say one thing at a time. How then can she clear up many different misconceptions in a reasonable time? The best solution developed so far is peer instruction. Originally created by Eric Mazur at Harvard [28], it has been studied extensively in a wide variety of contexts (e.g., [29, 30]).
Peer instruction is essentially a scalable way to provide one-to-one mentorship. It interleaves formative assessment with student discussion as follows:
The questions posed to learners don't have to be MCQs: matching terms to definitions can be equally effective, as can Parsons Problems (in which they are given the jumbled parts of a solution and must put them in the right order [31]). Whatever mix is used, the lesson must build toward them, and the question must probe for conceptual understanding and misconceptions (rather than check simple factual knowledge).
Group discussion significantly improves students' understanding because it forces them to clarify their thinking, which can be enough to call out gaps in reasoning. Repolling the class then lets the teacher know whether they can move on or whether further explanation is necessary. A final round of additional explanation and discussion after the correct answer is presented gives students one more chance to solidify their understanding.
But could this be a false positive? Are results improving because of increased understanding during discussion or simply from a follow-the-leader effect? [32] tested this by following the first question with a second one that students answer individually and found that peer discussion actually does enhance understanding, even when none of the students in a discussion group originally knew the correct answer.
It is important to have learners vote publicly so that they can't change their minds afterwards and rationalize it by making excuses to themselves like “I just misread the question.” Some of the value of peer instruction comes from having their answer be wrong and having to think through the reasons why. This is called the “hypercorrection effect” [33]. Most people don't like to be told they're wrong, so it's reasonable to assume that the more confident someone is that the answer they've given in a test is correct, the harder it is to change their mind if they were actually wrong. However, it turns out that the opposite is true: the more confident someone is that they were right, the more likely they are not to repeat the error if they are corrected.
A worked example is a step-by-step demonstration of how to solve a problem or do some task. By giving the steps in order, the instructor reduces the learner's cognitive load, which accelerates learning [27, 34].
However, worked examples become less effective as learners acquire more expertise [35, 36], a phenomenon known as the “expertise reversal effect.” In brief, as learners build their own mental models of what to do and how to do it, the detailed step-by-step breakdown of a worked example starts to get in the way. This is why tutorials and manual pages both need to exist: what's appropriate for a newcomer is frustrating for an expert, while what jogs an expert's memory may be incomprehensible to a novice.
One powerful way to use worked examples is to present a series of “faded examples” [37]. The first example in the series is a complete use of a problem-solving strategy; each subsequent example gives the learner more blanks to fill in. The material that isn't blank is often referred to as scaffolding since it serves the same purpose as the scaffolding set up temporarily at a building site.
Faded examples can be used in almost every kind of teaching, from sports and music to contract law. Someone teaching high school algebra might use them by first solving this equation for x:
and then asking learners to fill in the blanks in this:
The next problem might be this:
Learners would finally be asked to solve an equation entirely on their own:
(2x+7)/4=1
At each step, learners have a slightly larger problem to solve, which is less intimidating than a blank screen or a blank sheet of paper. Faded examples also encourage learners (and instructors) to think about the similarities and differences between various approaches.
Worked examples are themselves an example of “concreteness fading” [38, 39], which describes the process of starting lessons with things that are specific or tangible and then explicitly and gradually transitioning to more abstract and general concepts. Concreteness fading
One way to remember this strategy is the acronym PETE (Problem, Explanation, Theory, Example), which encourages instructors to
It is almost oxymoronic to say that learners spend a lot of their time trying to figure out what they've done wrong and fixing it: after all, if they knew and they had, they would already have moved on to the next subject. Most lessons devote little time to detecting, diagnosing, and correcting common mistakes, but doing this will accelerate learning—not least by reducing the time that learners spend feeling lost and frustrated.
In Carroll and colleagues’ “minimal manual” approach to training materials, every topic is accompanied by descriptions of symptoms learners might see, their causes, and how to correct them [40]. When studying second language acquisition, [41] identified six ways in which instructors can correct learners' mistakes:
All of these can be used preemptively during the design of lessons. An introduction to chemical reactions, e.g., could present an incomplete calculation of enthalpy and ask the learner to fill it in (elicitation) or present the complete calculation with errors, then draw attention to those errors and correct them one by one (recasting). All of these strategies provide retrieval practice by requiring learners to use what they have just learned and encourage metacognition by requiring them to reflect on the limits and applicability of that knowledge.
One of the strongest predictors of whether people learn something is their “intrinsic motivation,” i.e., their innate desire to master the material. The term is used in contract with “extrinsic motivation,” which refers to behavior driven by rewards such as money, fame, and grades. As [42] describes, the biggest motivators for adult learners are their sense of agency (i.e., the degree to which they feel that they're in control of their lives), the utility or usefulness of what they're learning, and whether their peers are learning the same things. Letting people go through lessons at the time of their own choosing, using authentic tasks, and working in small groups speak to each of these factors.
Conversely, it is very easy for educators to demotivate their learners by being unpredictable, unfair, or indifferent. If there is no reliable relationship between effort and result, learners stop trying (a particular case of a broader phenomenon called “learned helplessness”). If the learning environment is slanted to advantage some people at the expense of others, everyone will do less well on average [43], and if the lessons make it clear that the teacher doesn't care if people learn things or not, learners will mirror that indifference.
One way to tell if learners are motivated or not is to look at the incidence of cheating. In classrooms, it is usually not a symptom of moral failing but a rational response to poorly designed incentives. As reported in [44], some things that educators do that unintentionally encourage cheating include
Eliminating these from lessons doesn't guarantee that learners won't cheat but does reduce the incidence. (And, despite what many educators believe, cheating is no more likely online than in person [45].)
“Inclusivity” is a policy of including people who might otherwise be excluded. In STEM education, it means making a positive effort to be more welcoming to women, under-represented racial or ethnic groups, people with various sexual orientations, the elderly, the physically challenged, the economically disadvantaged, and others.
The most important step is to stop thinking in terms of a “deficit model,” i.e., to stop thinking that the members of marginalized groups lack something and are therefore responsible for not getting ahead. Believing that puts the burden on people who already have to work harder because of the inequities they face and (not coincidentally) gives those who benefit from the current arrangements an excuse not to look at themselves too closely.
One axis of inclusive lesson design is physical: provide descriptive text for images and videos to help the visually challenged, closed captions for videos to help those with hearing challenges, and so on. Another axis is social:
Committing fully to inclusive teaching may mean fundamentally rethinking content. [46], e.g., explored two strategies for making computing education more culturally inclusive, each of which has its own traps for the unwary. The first strategy, community representation, highlights students' social identities, histories, and community networks using afterschool mentors or role models from students' neighborhoods or activities that use community narratives and histories as a foundation for a computing project. The major risk is shallowness, e.g., using computers to build slideshows rather than do any real computing.
The second strategy, computational integration, incorporates ideas from the learner's community, e.g., by reverse engineering indigenous graphic designs in a visual programming environment. The major risk here is cultural appropriation, e.g., using practices without acknowledging origins. No matter which strategy is chosen, the first steps should always be to ask your learners and members of their community what they think you ought to do and to give them control over content and direction.
Following the 10 tips laid out above doesn't guarantee that your lessons will be great, but it will help ensure that they aren't bad. When it comes time to put them into practice, we recommend following something like the reverse design process developed independently by [47, 48, 49]:
We also recommend that lessons be designed for sharing with other instructors. Instructors often scour the web for ideas, and it's common for people to inherit courses from previous instructors. What is far less common is collaborative lesson construction, i.e., people taking material, improving it, and then offering their changes back to the community. This model has served the open source software community well, and as [9] describes, it works equally well for lessons—provided that materials are designed to make fine-grained collaboration easy. Unfortunately, widely-used systems like Git are designed to handle text files and struggle with structured document formats like Microsoft Word or PowerPoint. In addition, their learning curve is very steep and deters many potential users who have deadlines to meet or would rather think about engaging exercises than try to make sense of obscure error messages.
One key enabler of collaborative lesson construction is licensing. We strongly recommend using one of the Creative Commons family of licenses since they have been carefully vetted and are widely understood.
|
10.1371/journal.ppat.1003069 | Hsp90 Orchestrates Transcriptional Regulation by Hsf1 and Cell Wall Remodelling by MAPK Signalling during Thermal Adaptation in a Pathogenic Yeast | Thermal adaptation is essential in all organisms. In yeasts, the heat shock response is commanded by the heat shock transcription factor Hsf1. Here we have integrated unbiased genetic screens with directed molecular dissection to demonstrate that multiple signalling cascades contribute to thermal adaptation in the pathogenic yeast Candida albicans. We show that the molecular chaperone heat shock protein 90 (Hsp90) interacts with and down-regulates Hsf1 thereby modulating short term thermal adaptation. In the longer term, thermal adaptation depends on key MAP kinase signalling pathways that are associated with cell wall remodelling: the Hog1, Mkc1 and Cek1 pathways. We demonstrate that these pathways are differentially activated and display cross talk during heat shock. As a result ambient temperature significantly affects the resistance of C. albicans cells to cell wall stresses (Calcofluor White and Congo Red), but not osmotic stress (NaCl). We also show that the inactivation of MAP kinase signalling disrupts this cross talk between thermal and cell wall adaptation. Critically, Hsp90 coordinates this cross talk. Genetic and pharmacological inhibition of Hsp90 disrupts the Hsf1-Hsp90 regulatory circuit thereby disturbing HSP gene regulation and reducing the resistance of C. albicans to proteotoxic stresses. Hsp90 depletion also affects cell wall biogenesis by impairing the activation of its client proteins Mkc1 and Hog1, as well as Cek1, which we implicate as a new Hsp90 client in this study. Therefore Hsp90 modulates the short term Hsf1-mediated activation of the classic heat shock response, coordinating this response with long term thermal adaptation via Mkc1- Hog1- and Cek1-mediated cell wall remodelling.
| Candida albicans is one of the most persistent yeast pathogens known to man, causing frequent mucosal infections (thrush) in otherwise healthy individuals, and potentially fatal bloodstream infections in immunocompromised patients. C. albicans colonises warm-blooded animals and occupies thermally buffered niches. Yet during its evolution this pathogen has retained the classic heat shock response whilst other stress responses have diverged significantly. We have established that the essential, evolutionarily conserved molecular chaperone, Hsp90, coordinates thermal adaptation. Hsp90 interacts with and modulates the activity of the heat shock transcription factor, Hsf1, thereby controlling the expression of heat shock proteins required for the clearance of proteins damaged by proteotoxic stresses. In addition, Hsp90 modulates the activities of key MAP kinase signalling pathways that mediate cell wall remodelling and long term adaptation to heat shock. Loss of any of these factors results in a significant reduction in thermotolerance.
| Microorganisms inhabit dynamic environments and are continually challenged with environmental stimuli and stresses. Microbial survival depends upon effective environmental response strategies that have been elaborated over evolutionary time. These cellular strategies have been intensively studied in various contemporary model organisms [1], [2], [3], [4]. The emergent paradigm is that cells react to environmental changes via a sense and respond logic: they continuously monitor their environment, and upon encountering a stimulus, mount a cellular response [5]. This is achieved through diverse signalling pathways that drive physiological adaptation to a myriad of environmental stresses that include temperature fluctuations, osmotic, oxidative and weak acid stresses, as well as nutrient limitation [6], [7].
Fungal pathogens have evolved robust stress responses that enable them to counteract the antimicrobial defences of their host, thereby promoting the colonisation of specific niches. The major fungal pathogen of humans, Candida albicans, is an opportunistic pathogen that has evolved as a relatively harmless commensal of the mucous membranes and digestive tracts of healthy individuals [8], [9]. C. albicans is a common cause of mucosal infections (thrush) and when antimicrobial defences become compromised this yeast can cause life-threatening systemic infections [8], [10]. Stress responses are critical for survival of C. albicans inside the human body, and genetic inactivation of these responses attenuates virulence of this pathogen [11], [12], [13]. However, the regulation of these stress signalling mechanisms has diverged significantly in C. albicans compared with other yeasts [14]. For example, unlike Saccharomyces cerevisiae, Schizosaccharomyces pombe or Candida glabrata [2], [15], [16], C. albicans does not activate a large core transcriptional response [3]. The core transcriptional responses of S. cerevisiae, S. pombe and C. glabrata involve the activation of common sets of stress genes by one particular stress that promote cross-protection to diverse stresses [2], [15], [16]. In S. cerevisiae and C. glabrata, this core transcriptional response and hence stress cross-protection are dependent on the transcription factors Msn2 and Msn4, which activate target genes via stress response elements (STRE) in their promoters [17]. In S. pombe, the core stress response is driven largely by the Sty1 stress activated protein kinase (SAPK: the orthologue of Hog1 in other yeasts) [15]. In contrast, C. albicans does not mount a broad core transcriptional response to stress, there is limited stress cross-protection in this yeast, and the roles of Hog1 and Msn2/Msn4-like transcription factors have diverged in this pathogen [2], [3], [18], [19], [20]. Whilst, C. albicans does appear to activate a relatively specialised core transcriptional response to osmotic, oxidative and heavy metal stresses [19], the consensus view is that this pathogen mounts relatively specific responses to particular environmental challenges. This involves activation of the corresponding signal transduction pathway, subsequent activation of the relevant set of stress genes and requisite changes in cell physiology, morphology and adherence [21], [22], [23].
A number of stress regulatory modules have been conserved between C. albicans and other yeasts. For example, the AP1-like transcription factors S. cerevisiae Yap1 [24], S. pombe Pap1 [25], C. glabrata CgYap1 [26], [27] and C. albicans Cap1 [28] play analogous roles in the activation of transcriptional responses to oxidative stress. Also, the Hog1/Sty1 SAPK is conserved in these yeasts, although the orthologues have diverged with respect to the stress responses they regulate. S. cerevisiae Hog1 is primarily involved in responses to osmotic stress, whereas C. albicans Hog1 and S. pombe Sty1 contribute to a diverse range of stress responses [29], [30], [31]. Additional mitogen activated protein kinase (MAPK) cascades have been conserved between S. cerevisiae and C. albicans. These include the cell wall integrity Mpk1/Slt2 pathway [32] and the cell wall, morphogenesis and pheromone signalling pathways involving the Cek1 and Cek2 MAPKs [33], [34]. These MAP kinase pathways contribute to thermotolerance in S. cerevisiae [35], [36], [37], although the mechanisms by which they do so remain obscure. These MAP kinase pathways are also important for virulence, as MAP kinase defective C. albicans mutants display attenuated virulence in infection models [11], [13], [33], [38].
The heat shock response is among the most fundamentally important and ubiquitous stress responses in nature. The heat shock transcription factor (Hsf1) which drives this response, is conserved from yeasts to humans [39], [40]. Indeed, the Hsf1 module and the heat shock response are even conserved in C. albicans, an organism that is obligately associated with warm blooded mammals and hence occupies thermally buffered niches [41]. Furthermore Hsf1 is essential for viability in C. albicans [42] and other yeasts [43]. These observations reflect the fundamental importance of heat shock adaptation in all organisms. Even in the absence of stress, Hsf1 binds as a trimer to canonical heat shock elements (HSEs) in the promoters of target heat shock protein (HSP) genes [44], [45], [46]. When S. cerevisiae or C. albicans cells are exposed to an acute heat shock, Hsf1 becomes hyper-phosphorylated and activated, leading to the transcriptional induction of these target HSP genes, thereby promoting cellular adaptation to the thermal insult [39], [47].
Many HSPs are molecular chaperones that promote the folding, assembly, or cellular localisation of client proteins [48]. They also minimise the aggregation of unfolded or damaged proteins and often target such proteins for degradation [48]. HSPs are critical for the survival of eukaryotic cells under normal conditions as well as following exposure to an acute heat shock. Indeed our recent exploration of the dynamic regulation of Hsf1 during thermal adaptation has suggested that the Hsf1-HSE regulon is activated even during slow thermal transitions such as the increases in temperature suffered by febrile patients [49]. This explains why the Hsf1-HSE regulon is active in C. albicans cells infecting the mammalian kidney, and why activation of this regulon is essential for virulence of C. albicans [42]. Clearly the Hsf1-HSE regulon is critical for the maintenance of thermal homeostasis, not merely for adaptation to acute heat shocks.
Hsp90 has been suggested to play a critical role in regulation of the Hsf1-HSE regulon, contributing to an autoregulatory circuit involving Hsp90 and Hsf1 [49]. In the absence of stress, Hsp90 is generally expressed at relatively high levels [50], and is thought to repress Hsf1 [49]. However, thermal and other proteotoxic stresses can induce global problems in protein folding that overwhelm the functional capacity of Hsp90 [50]. Under these conditions the repression of Hsf1 by Hsp90 was proposed to be released, allowing Hsf1 to become activated leading to increased HSP90 expression [49].
The repression of Hsf1 by Hsp90 was suggested by the observation that pharmacological inhibition of Hsp90 correlates with HSF1 activation in mammalian cells [51], [52]. Zou and colleagues demonstrated that HSF1 can be cross-linked to Hsp90 in unstressed HeLa cells, suggesting that HSF1 might interact directly with Hsp90 [52]. Additionally, the trimeric form of human HSF1 has been shown to associate with an Hsp90-immunophilin-p23 complex, and this is thought to repress HSF1 transcriptional activity [53]. Furthermore, HSP90 modulates HSF1 regulation in Xenopus oocytes [54]. Hence HSF1 is known to be a client protein of Hsp90 in metazoan cells. However, although mutations that interfere with Hsp90 function have been shown to derepress the expression of Hsf1-dependent reporter genes in S. cerevisiae [55], no physical interaction between Hsf1 and Hsp90 has been demonstrated in the fungal kingdom.
In this study, we explore the regulatory control of cellular circuitry governing the response to temperature stress through an integrated approach involving specific hypotheses and unbiased screens in C. albicans. We determined that Hsf1 is a client protein of Hsp90, establishing for the first time in the fungal kingdom that the Hsf1-Hsp90 interaction is critical for the regulation of short term adaptive responses to heat shock. We questioned how cells adapt to heat stress in the longer term by investigating which other signalling pathways contribute to thermotolerance and executing genetic screens for protein kinase mutations that confer temperature sensitivity. This revealed that the Hog1, Mkc1 and Cek1 MAP kinase pathways contribute to thermotolerance, as does casein kinase signalling. We show that these MAP kinase pathways are not essential for Hsf1 activation. Rather, they contribute to thermal adaptation in the longer term via cell wall remodelling. Hog1, Mkc1 and Cek1 are client proteins of Hsp90, and genetic depletion of Hsp90 affects this cell wall remodelling. Therefore, Hsp90 integrates the short term and long term molecular responses that underpin thermotolerance.
All strains are listed in Table 1, with the exception of the library of C. albicans transposon mutants [56], [57], [58]. Strains were grown in YPD (1% yeast extract, 2% bactopeptone, 2% glucose) [59]. To impose an instant heat shock of 30°C–42°C, cells were grown in YPD at 30°C to exponential phase, and mixed with an equal volume of medium that has been pre-warmed at 54°C in flasks which had been pre-warmed at 42°C. Cells were grown at 42°C for the times indicated. Doxycycline was added to YPD medium at a concentration of 20 µg/ml. Geldanamycin was added at 10 µM (A.G. Scientific, Inc., San Diego, USA), and radicicol at 20 µM (A.G. Scientific, Inc.).
For co-immunoprecipitation of Hsf1 and Hsp90, Hsf1 was tagged with FLAG and Hsp90 was tagged with the tandem affinity purification (TAP) tag. The plasmid pACT1pHSF1, containing the ACT1p-3xFLAG-HSF1 construct [41] was linearized with StuI and transformed into the strains SN95 (wild type) or CaLC501 (HSP90-TAP) (Table 1) using published procedures [60]. Nourseothricin (NAT) resistant transformants were selected on YPD containing 150 µg/mL NAT, and insertion of the ACT1p-3xFLAG-HSF1 cassette confirmed by diagnostic PCR using the primers oLC1117/oLC1628 (Table S1).
Localisation of Hsp90 was achieved by 3′-tagging of one HSP90 allele with GFP in the wild type strain SN95 (creating CaLC1855, Table 1). The GFP-NAT cassette was amplified using primers oLC1616/1617 (Table S1) and transformed into SN95. Proper integration of the cassette in NAT resistant transformants was confirmed by PCR using primer pairs oLC600/756 (Table S1).
To determine Hsf1 phosphorylation status, the pACT1pHSF1, containing the ACT1p-3xFLAG-HSF1 construct [41] was linearized with StuI and transformed into mkc1, hog1, cek1, cka1, cka1, ckb1, ckb2 mutants (Table 1) [30], [32], [33], [61]. NAT resistant transformants were selected and confirmed by diagnostic PCR and expression of FLAG-Hsf1 in western blots.
Cek1 was tagged with the TAP tag at its C-terminus in the wild type strain SN95 (creating CaLC2287, Table 1) and its derivative CaLC1411 (creating CaLC2288, Table 1) using a PCR based strategy as described previously [62]. Briefly, the tag and a selectable marker (ARG4) were PCR amplified from pLC573 (pFA-TAP-ARG4 [63]) using oligos oLC2292/2251 (Table S1). 200 µl of PCR product was run through a PCR clean-up and dissolved in 50 µl sterile water and transformed into C. albicans. Correct genomic integration was verified using appropriate primer pairs that anneal ∼500 bp up (oLC2252) or downstream (oLC2253) from both insertion junctions together with oLC1593 (TAP-R) and oLC1594 (ARG4-F) that target the TAP and the selectable marker (Table S1).
Total soluble protein was extracted and subjected to western blotting using published protocols [64], [65]. Briefly, mid-log cells were pelleted by centrifugation, washed with sterile water, and resuspended in lysis buffer (50 mM HEPES, pH 7.5, 150 mM NaCl, 5 mM EDTA, 1% Triton X-100). An equal volume of 0.5-mm acid-washed beads was added to each tube. Cells were mechanically disrupted on a BioSpec (Bartlesville, OK) mini-bead-beater for six 30 second intervals, with 1 minute on ice between each cycle. The beads and cell debris were pelleted by high-speed centrifugation and the supernatant removed for analysis. Protein concentration was determined using a Bradford reagent (Sigma-Aldrich) assay. Protein samples were mixed with one-sixth volume of 6× sample buffer containing 0.35 M Tris-HCl, 10% (w/w) SDS, 36% glycerol, 5% β-mercaptoethanol, and 0.012% bromophenol blue. Between 2 µg and 30 µg of protein was loaded in wells of an 8% SDS–PAGE gel. Separated proteins were transferred to a PVDF membrane for 1 hour at 100 V at 4°C. Membranes were blocked in 5% milk or 5% bovine serum albumin (BSA) in TBS or PBS containing 0.1% Tween-20 at room temperature for 1 hour and subsequently incubated in primary antibody as follows. All primary antibodies (except those against p38 and p44/42 MAPK) were left on the membrane for one hour at room temperature. The p38 MAPK and p44/42 MAPK antibodies were incubated overnight at 4°C. Membranes were washed with 1×PBS-T or TBS-T and probed for one hour with secondary antibody dissolved in 1×PBS-T and 5% milk or BSA. Membranes were washed in PBS-T and signals detected using an ECL western blotting kit as per the manufacturer's instructions (Pierce).
To detect FLAG-Hsf1, a 1∶25000 dilution of anti-FLAG HRP conjugated antibody (Sigma, A8592) was used in PBS-T+5% milk [PBS 0.1% Tween-20, 5% (w/v) milk]. To detect Act1, an anti-Act1 antibody was used (Santa Cruz Biotechnology, sc47778) at a 1∶1000 dilution in PBS-T+5% milk. To detect Hsp90, a 1∶10000 dilution of anti-Hsp90 antibody was used (courtesy of Bryan Larson) in PBS-T+5% milk. To detect Hsp70, a 1∶1000 dilution of anti-Hsp70 antibody (Enzo Life Sciences, ADI-SPA-822) was used in PBS-T+5% milk.
To detect Mkc1 and Cek1 phosphorylation [22], [66], a 1∶2000 dilution of phospho-p44/42 MAPK (Erk1/2) (Thr202/Tyr204) Rabbit mAb was used (New England Biolabs, Hitchin, Hertfordshire, UK, #4370) in TBS-T+5% BSA [TBS 0.1% Tween-20, 5% (w/v) BSA]. For Hog1 phosphorylation [30], a 1∶2000 dilution of phospho-p38 MAPK (Thr180/Tyr182) rabbit mAB was used in TBS-T+5% BSA (New England Biolabs, Hitchin, Hertfordshire, UK, #9211).
To detect Mkc1-6XHis-FLAG, the anti-FLAG-HRP antibody was used as above. To detect total Hog1 an anti-Hog1 antibody (Santa Cruz Biotechnology, y-215) was diluted 1∶1000 in PBS-T+5% milk. TAP-tagged Cek1 was detected using a 1∶5000 dilution of anti-TAP tag rabbit polyclonal antibody (Thermoscientific, CAB1001) in PBS-T+5% milk.
C. albicans cultures were grown to mid-log phase (OD600 = 0.5), cells harvested, washed with sterile H20 and resuspended in 1 ml of lysis buffer (20 mM Tris pH 7.5, 100 mM KCl, 5 mM MgCl and 20% glycerol, with one protease inhibitor cocktail per 50 ml (complete, EDTA-free tablet, Roche Diagnostics, Indianapolis, IN, USA), 1 mM PMSF (EMD Chemicals, Gibbstown, NJ, USA) and 20 mM sodium molybdate (Sigma Aldrich Co., St Louis, MO, USA)). Cells were then disrupted by bead beating twice for 4 minutes with a 7 minute break on ice between cycles. Lysates were centrifuged at 1300×g for three 5-minute cycles, recovering the supernatants at each stage. The combined lysate was then cleared by centrifugation at 21,000×g for 10 minutes at 4°C and protein concentrations determined using the Bradford assay [67].
Anti-FLAG immunoprecipitations were performed by diluting protein samples to 2 mg/ml in lysis buffer containing 20 mM sodium molybdate and 0.2% Tween, and incubating with anti-FLAG M2 affinity agarose (Sigma Aldrich) at 4°C overnight as per the manufacturer's specifications. Unbound material was discarded, the beads washed five times with 1 ml lysis buffer containing 0.1% Tween, and the bound proteins eluted by boiling in one volume of 2× sample buffer (125 mM Tris-HCl, pH 6.8, 5% glycerol, 2.5% SDS, 2.5% beta-mecarptoethanol, dH2O, bromophenol blue). Anti-IgG immunoprecipitations were performed using the same approach, but using rabbit IgG agarose (Sigma Aldrich) as per the manufacturer's specifications.
Protein samples were then electrophoresed on 8% SDS-PAGE gels. Proteins were then electrotransferred to PVDF membranes (Bio-Rad Laboratories, Inc., Hercules, CA, USA) and blocked with PBS-T+5% milk. Blots were incubated with antibodies against CaHsp90 (courtesy of Bryan Larsen) (1∶10000 dilution, [68]), or FLAG (1∶10000, Sigma Aldrich Co.).
To monitor gene expression changes in response to tetO-HSP90 depletion, strains SN95 and CaLC1411 were grown overnight at 30°C in YPD while shaking at 200 rpm. Stationary phase cultures were split, adjusted to an OD600 of 0.04 where one culture was treated with doxycycline (BD Biosciences), while the other was left untreated. Cells were grown for 7 hours at 30°C. To monitor gene expression changes in response to heat shock, wild type and SN95 cells were grown to mid-log phase, subjected to a 30°C–42°C heat shock and 50 ml was harvested from each culture at the specified time, centrifuged at 3000 rpm for 2 minutes at 4°C, washed once with dH2O before being frozen at −80°C. RNA was then isolated using the QIAGEN RNeasy kit and cDNA synthesis was performed using the AffinityScript cDNA synthesis kit (Stratagene). PCR was carried out using the SYBR Green JumpStart Taq ReadyMix (Sigma-Aldrich) with the following cycle conditions: 94°C for 2 minutes, and 94°C for 15 seconds, 60°C for 1 minute, 72°C for 1 minute, for 40 cycles. All reactions were done in triplicate using the following primer pairs: HSP104 (oLC1620/1621), HSP90 (oLC754/755), PGA13 (oLC2256/2257), PMT4 (oLC2262/2263), RHR2 (oLC2266/2267). Transcript levels were normalised to ACT1 (oLC2285/2286) (Table S1). Data were analysed in the StepOne analysis software (Applied Biosystems).
The C. albicans transposon insertion mutant library was generously provided by Aaron Mitchell (Carnegie Mellon University) [56], [58]. Strains were inoculated in 100 µl YPD in 96 well plates and incubated overnight at 30°C with shaking at 200 rpm. Cells were then diluted 1∶10 in YPD and incubated at 30°C with shaking at 200 rpm for 4 hours. After 4 hours of exponential growth, cells were then exposed to a one hour 30°C–42°C heat shock by addition of 100 µl pre-warmed YPD at 54°C and incubating at 42°C, and the growth of each culture monitored continuously for 6 hours by measuring the OD600. Non-heat shocked control cells received 100 µl YPD at 30°C and were incubated at 30°C. This screen was repeated independently three times, and only those mutants that displayed consistent phenotypes were taken for further analysis. The validity of key transposon mutants highlighted by this screen was then confirmed by subjecting corresponding homozygous null mutants to the same screen.
Unless otherwise stated, the susceptibilities of strains were determined with the following stressors: heat stress (42°C heat shock), Calcofluor White (CFW: 100 µg/ml), Congo Red (CR: 100 µg/ml), H2O2 (5 mM) and NaCl (1 M). All stress assays were performed in YPD.
MIC assays were performed in flat bottom, 96-well microtiter plates (Corning Costar). Briefly, assays were set up in 0.2 ml/well, with 2× concentrations of NaCl, CFW and CR prepared in 100 µl YPD. Final concentrations of NaCl were 0, 0.25, 0.5, 1.0, 1.5 and 2 M; and for CFW and CR were 0, 25, 50, 100, 150 and 200 µg/ml. Cell densities of overnight cultures were determined and dilutions were prepared in YPD such that ∼103 cells were inoculated into each well. Plates were placed statically at either 25°C, 30°C, 37°C or 42°C for 48 hours, after which plates were sealed and cells resuspended by agitation. Absorbance was determined at 600 nm using a spectrophotometer (VERSA max, Molecular Devices), and was corrected for background from the corresponding medium. Every strain was tested in duplicate on three separate occasions. MIC data were quantitatively displayed with colour using the program Java TreeView 1.1.6 (http://jtreeview.sourceforge.net).
For stress cross-protection assays, cells were grown overnight in YPD at 30°C with shaking at 200 rpm. These were diluted to an OD600 = 0.2 in fresh YPD, and grown for a further 4 hours at 30°C. Cells were then subjected to a 30 minute heat shock at 42°C, incubated at 30°C and stressed for one hour with CFW, CR, H2O2 or NaCl. Cells were diluted, plated onto YPD and viability determined (CFUs). Control cells were not subjected to the prior heat shock. In other experiments cells were exposed to a prior CFW, CR, H2O2 or NaCl stress for one hour at 30°C before being heat shocked at 42°C for 30 min, and then cell viability determined.
To test stress sensitivity following Hsp90 depletion, C. albicans tetO-HSP90 cells (CaLC1411: Table 1) were incubated for 7 hours in YPD containing 20 µg/ml doxycycline at a starting OD600 of 0.04. These cells were left untreated or stressed for one hour with CFW, CR, a 30°C–42°C heat shock, H2O2 or NaCl at the concentrations specified above, and CFUs determined. Controls included CaLC1411 cells that were not treated with doxycycline, and parental SN95 cells that were subjected to the same treatments. Growth curves of CaLC1411 (tetO-HSP90/hsp90) were determined in the absence and presence of doxycycline to ensure adequate viability of this strain (Supplementary Figure S1C).
To determine kinase activation in response to Hsp90 depletion, wild type SN95 and tetO-HSP90 (CaLC1411) were grown overnight in YPD at 30°C. Cells were diluted in YPD with or without doxycycline at a starting OD600 of 0.04 and incubated for 7 hours. Cells were then left untreated or stressed with H2O2 for 10 minutes and NaCl for 12 minutes to determine Hog1 activation. For Mkc1 and Cek1 activation, tagged versions of these proteins in SN95 and tetO-HSP90 (CaLC1411) were grown as above and stressed with CFW for 30 minutes or a 30°C–42°C heat shock for 30 minutes. Mkc1 total levels were assayed using Mkc1-6XHis-FLAG tagged in SN95 and tetO-HSP90 (CaLC681 and CaLC648 respectively, Table 1). Total Cek1 levels were assayed using Cek1-TAP tagged in SN95 and tetO-HSP90 (CaLC2287 and CaLC2288 respectively, Table 1).
The chitin content of cells was measured as described previously [69]. Briefly, formalin fixed cells were stained with 25 µg/ml Calcofluor White and fluorescence was preserved with Vectashield mounting medium (Vector Laboratories, Peterborough, United Kingdom). All samples were examined by differential interference contrast (DIC) and fluorescence microscopy (456 nm) with a Zeiss Axioplan 2 microscope. Images were captured using a C4742-95 digital camera (Hamamatsu Photonics, Hamamatsu, Japan) and analysed using Openlab software (version 4.04: Improvision, Coventry, United Kingdom). CFW fluorescence was quantified for 50 individual yeast cells from each sample, using region-of-interest measurements. Mean fluorescence intensities were then calculated and expressed as arbitrary units.
Exponentially growing Hsp90-GFP (CaLC1855, Table 1) cells were heat shocked as described above, and 1 ml of cells were removed at 0, 10, 60 and 120 minutes post-heat shock. Cells were harvested and washed in 1 ml of 42°C pre-warmed 1×PBS. Supernatant was removed and cells were resuspended in the remaining 50 µl. Of this, 5 µl was placed on a slide, and cells were heat fixed by placing the slide on a 70°C hot plate for 1 minute. Slides were cooled for a few seconds before 4 µl of 1 µg/ml DAPI (Fluke, Sigma-Aldrich) were added. Imaging was performed on a Zeiss Imager M1 upright microscope and AxioCam MRm with AxioVision 4.7 software. An X-Cite series120 light source with ET green fluorescent protein (GFP) and 4′,6-diamidino-2-phenylindole (DAPI) hybrid filter sets from ChromaTechnology (Bellows Falls, VT) were used for fluorescence microscopy. DAPI fluorescence was viewed under the DAPI hybrid filter and GFP-tagged proteins under the GFP filter.
For high pressure freezing transmission electron microscopy (HPF-TEM), cells were prepared by high-pressure freezing with a Leica EM PACT2 (Leica Microsystems (UK) Ltd, Milton Keynes). After freezing, cells were freeze-substituted in substitution reagent (1% OsO4/0.1% uranyl acetate in acetone) with a Leica EM AFS2. Samples were encapsulated in 3% (w/v) low melting point agarose prior to processing to Spurr resin. Additional infiltration was provided under vacuum at 60°C before embedding in TAAB capsules and polymerizing at 60°C for 48 h. Semi-thin survey sections of 0.5 µM thickness were stained with 1% toluidine blue to identify areas of best cell density. Ultrathin sections (60 nm) were prepared with a Diatome diamond knife on a Leica UC6 ultramicrotome, and stained with uranyl acetate and lead citrate for examination with a Philips CM10 transmission microscope (FEI UK Ltd, Cambridge, UK) and imaging with a Gatan Bioscan 792 (Gatan UK, Abingdon, UK).
An autoregulatory loop, whereby Hsf1 activates HSP90 expression and Hsp90 interacts with and down-regulates Hsf1, is thought to lie at the heart of heat shock adaptation in fungi. This presumption provided the basis for mathematical modelling of thermal adaptation in C. albicans [49], but had not been confirmed experimentally. If this presumption is true, one would expect that inhibition of Hsp90, or Hsp90 depletion would lead to Hsf1 activation [49]. To test this, we examined the impact of the Hsp90 inhibitors, radicicol and geldanamycin [70], [71], upon Hsf1 phosphorylation. C. albicans cells (ML250: Table 1) were incubated with radicicol or geldanamycin for up to one hour, and Hsf1 phosphorylation was monitored via the resultant band shift revealed by western blot analysis, as described previously [41] (Figure 1A). These Hsp90 inhibitors induced Hsf1 phosphorylation after one hour, as confirmed by the band shift observed after controlled dephosphorylation with lambda phosphatase. To exclude the possibility that this Hsf1 phosphorylation was induced by a general effect of the drugs upon protein folding, we examined the impact of dithiothreitol and tunicamycin, known inducers of the unfolded protein response in C. albicans [72]. Hsf1 was not activated following treatment with 5 mM dithiothreitol or 4.73 µM tunicamycin for 1 hour (data not shown), suggesting that the induction of Hsf1 phosphorylation by radicicol and geldanamycin related to Hsp90 function rather than some general effect upon protein folding. Therefore, Hsp90 inhibits Hsf1, as predicted.
Next, we validated our pharmacological findings using a genetic approach. A doxycycline conditional C. albicans HSP90 mutant (tetO-HSP90) [73], in which HSP90 expression is independent of Hsf1, was used to ectopically down-regulate Hsp90 levels (Figure 1B and S1A). Doxycycline treatment led to Hsf1 phosphorylation after approximately 6 hours, at which point Hsp90 levels were reduced by 50% (Figure 1B and S1A). Therefore, ectopic down-regulation of HSP90 caused Hsf1 phosphorylation even in the absence of a heat shock, further reinforcing the hypothesis that Hsp90 inhibits Hsf1.
A key finding from our modelling of thermal adaptation was that this system displays perfect adaptation: i.e. Hsf1 activation returns to basal levels within two hours once cells have adapted to their new ambient temperature [49]. If Hsp90 down-regulates the heat shock response, then one would expect this perfect adaptation to be dependent on Hsp90. Also, if new Hsp90 synthesis is inhibited after a heat shock, Hsf1 would remain phosphorylated and the system would not adapt to this stress. We tested this by examining Hsf1 phosphorylation levels in doxycycline-treated C. albicans tetO-HSP90 cells after a 30°C–42°C heat shock. As predicted, these Hsp90-depleted cells were unable to recover, as revealed by the maintenance of Hsf1 phosphorylation four hours after the heat shock (Figure 1C). Therefore, perfect thermal adaptation is dependent upon Hsp90.
To further test the impact of depleting Hsp90 on the heat shock response, we looked at induction of HSP104, a known target of Hsf1 [49]. In wild type cells (SN95: Table 1) HSP104 was up-regulated approximately 10-fold in response to a 30 minute 30°C–42°C heat shock (Figure 1D), which was consistent with previous findings [49], and HSP104 expression was not affected by doxycycline treatment. HSP104 mRNA levels were elevated in tetO-HSP90 cells even in the absence of doxycycline, presumably because Hsp90 levels are significantly reduced under these conditions (Figure S1B). An additional increase in HSP104 expression was observed after doxycycline treatment when Hsp90 levels were reduced further (Figure S1B). Taken together, these data strongly imply that Hsp90 is a master regulator of the heat shock response.
The proposed Hsf1-Hsp90 autoregulatory loop [49] suggests a physical interaction between Hsf1 and Hsp90. There is some evidence for this in mammalian cells [52], [53], but none in fungal systems. Therefore we tested this experimentally by co-immunoprecipitation. First, proteins were extracted from C. albicans cells expressing a FLAG-tagged Hsf1 protein (CaLC1819), and from control cells in which Hsf1 was not FLAG-tagged (WT, SN95). Protein extracts were incubated with anti-FLAG beads, and the resulting immunoprecipitates probed for Hsp90 on western blots. Hsp90 was observed reproducibly in immunoprecipitates from the FLAG-Hsf1 expressing strain, but not in those from the untagged control strain (Figure 2A). Reciprocal immunoprecipitations were then performed to test the validity of this apparent Hsf1-Hsp90 interaction. C. albicans cells expressing both TAP-tagged Hsp90 and FLAG-tagged Hsf1 (CaLC1875) were used in these experiments, and cells lacking the FLAG-tagged Hsf1 (CaLC501: Table 1) were used as a control. Hsp90 was immunoprecipitated using IgG beads, which bind the Protein A in the TAP tag. These immunoprecipitates were then probed for the FLAG-tagged Hsf1, revealing a band of the appropriate mass from cells expressing TAP-tagged Hsp90, but not from the controls (Figure 2B). This confirmed that Hsp90 interacts physically with Hsf1. This is the first demonstration of a physical interaction between Hsf1 and Hsp90 in any yeast.
An Hsf1-Hsp90 autoregulatory loop, as inferred by the model [49], [74], predicts dynamic changes in the Hsf1-Hsp90 interaction during a heat shock. The model predicts that Hsp90 releases Hsf1 following a heat shock, rebinding Hsf1 as the response is down-regulated. We tested this hypothesis by co-immunoprecipitation, examining Hsp90-Hsf1 interactions over a 60 minute period following heat shock. Rather than decreasing when Hsf1 becomes activated (as was predicted), the Hsp90-Hsf1 interaction increased over 60 minutes (Figure 2C) during the period when Hsf1 activation is maximal [49]. Therefore, Hsf1 is not released from Hsp90 after heat shock, and this mechanism cannot account for Hsf1 activation. We examined the Hsf1-Hsp90 interaction two hours after heat shock, when the response is down-regulated [49], and found that the interaction was still increased when compared to untreated samples or a 10 minute heat shock (Figure S2B, bottom panel). This increase in the Hsf1-Hsp90 interaction after a prolonged heat shock (Figure 2B), accompanies the decline in Hsf1 activity and the down-regulation of the heat shock response [49].
To determine whether other components of the Hsp90 chaperone machinery might also interact with Hsf1, we focused on Hsp70, which operates within the Hsp90 chaperone system [74], and has been implicated in Hsf1 regulation. Indeed, the deletion of SSA1 and SSA2, which encode cytosolic isoforms of Hsp70 derepresses Hsf1 transcriptional activity in S. cerevisiae [75]. However, a physical interaction between Hsp70 and Hsf1 has not been reported in any yeast. Therefore, we re-probed our FLAG-Hsf1 immunoprecipitations with an antibody against Hsp70. We found that Hsp70 interacts with Hsf1, but only in response to a prolonged heat shock (Figure 2C, bottom left panel, and Figure S2A). We also re-probed our Hsp90-TAP immunoprecipitations for Hsp70. Our data suggest that Hsp90 and Hsp70 interact in response to prolonged heat shock (Figure 2C, bottom right panel and Figure S2B, top panel).
Given our findings that the Hsf1-Hsp90 interaction strengthens upon heat shock, and that Hsf1 is thought to bind DNA constitutively [44], one might predict that Hsp90 localises to the nucleus upon heat shock. Indeed, a recent study by Lamoth and colleagues shows Hsp90 nuclear localisation upon a 55°C heat shock in Aspergillus fumigatus [76]. Therefore we followed the localisation of Hsp90-GFP in C. albicans in response to a heat shock (CaLC1855, Table 1). Under steady state conditions, Hsp90-GFP was distributed throughout the cell, with no obvious localisation (Figure 2D), and this was also the case 10 minutes after a 42°C heat shock. However, 60 minutes post-heat shock, nuclear accumulation of Hsp90-GFP was clearly evident (Figure 2D), and Hsp90-GFP remained in the nucleus 120 minutes post heat shock (Figure S2C). Therefore the dynamics of the nuclear accumulation of Hsp90-GFP correlated with the dynamics of the Hsf1-Hsp90 interaction. These data reinforce the view that Hsp90 and the Hsp90 chaperone machine plays an important role in the down-regulation of Hsf1 and the heat shock response.
The above observations indicate that while Hsp90 down-regulates Hsf1, Hsf1 activation is mediated by Hsp90-independent mechanisms. How then is Hsf1 activated? C. albicans Hsf1 is activated by phosphorylation [41], but the protein kinase responsible for this in yeast remains unknown [52], [53]. To determine which kinase is responsible for Hsf1 phosphorylation in C. albicans, we exploited the recent availability of the C. albicans transposon insertion kinase mutant collection which was kindly provided by Aaron Mitchell [58]. This collection of mutants which comprises homozygous insertion or deletion mutations in 67 protein kinase genes and 13 protein kinase-related genes, were screened for temperature sensitivity by monitoring their growth following a one hour 30°C–42°C heat shock. Mutants that consistently displayed a >10% growth defect relative to the wild-type controls in three independent screens were considered temperature sensitive, and those that consistently displayed >10% growth acceleration were considered temperature resistant (Figure 3). The tetO-HSF1 mutant (CLM60-1: Table 1) was always temperature sensitive, and the known temperature sensitivity of mkc1 mutants [32] was consistently recapitulated. (Mkc1 is the orthologue of S. cerevisiae Slt2 [32].) These observations leant weight to the validity of the output from this screen. In this study we focussed on those kinases whose inactivation confers temperature sensitivity.
Numerous kinase mutants displayed significant and reproducible temperature sensitivity in our screen, notably the casein kinase subunits as well as components of key MAP kinase pathways such as the cell wall integrity pathway (Mkc1, Pkc1), the osmotic stress pathway (Pbs2) and the starvation and cell wall stress pathway (Ste11) (Figure 3). Several downstream kinases of these pathways were absent from the mutant collection, prompting us to perform a secondary screen of null mutants. This secondary screen confirmed the temperature sensitivity of all of the transposon mutants tested from the primary screen (Figure 3), including the casein kinase subunits (Cka1/2 and Ckb1/2) and Mkc1. Furthermore, the secondary screen tested the MAP kinases Cek1 and Hog1 (which were missing from the collection of transposon mutants), thereby confirming the involvement of these pathways in C. albicans thermotolerance (Figure 3).
Our next aim was to test whether any of the kinases identified in the above screen are responsible for Hsf1 phosphorylation in response to heat shock. Hsf1 was FLAG3-tagged at its N-terminus in CAI4 (MLC67) and in each of the following temperature sensitive null mutants: mkc1 (MLC30), hog1 (MLC15), cek1 (MLC21), cka1 (MLC24), cka2 (MLC27), ckb1 (CaLC2259) and ckb2 (CaLC2261) (Table 1). Each of these mutants was then subjected to a 30°C–42°C heat shock, and Hsf1 phosphorylation assayed at 0, 10, 30 and 60 minutes post-heat shock (Figure S3). All mutants displayed similar Hsf1 phosphorylation dynamics to wild-type cells, indicating that none of the kinases alone is essential for Hsf1 phosphorylation. Therefore, there might be functional redundancy with respect to Hsf1 phosphorylation during heat shock. Alternatively, Hsf1 might be phosphorylated by an essential kinase that was not represented in the kinase mutant collection. These observations also suggest that the MAP kinase pathways might contribute to thermal adaptation in C. albicans by mechanisms other than via Hsf1 phosphorylation and the activation of the heat shock regulon.
Next we explored how the signalling pathways contribute to thermotolerance. A recent and elegant study by Diezmann and colleagues [62] demonstrated that casein kinase 2 (CK2) regulates Hsp90 phosphorylation and activity. Therefore, the mutations in CK2 subunits (cka1, cka2, ckb1 and ckb2) probably reduce thermotolerance by interfering with Hsp90 function. In addition to regulating Hsf1 (Figures 1 and 2), which is critical for thermal adaptation [49], Hsp90 has numerous other client proteins, some of which may contribute to thermotolerance [62].
We therefore focussed on the roles of the three MAP kinase pathways that were highlighted by our screen: the cell wall integrity Mkc1 pathway, the osmolarity/oxidative stress Hog1 pathway and the starvation/cell wall response Cek1 pathway. First we monitored the activation of each MAP kinase during heat shock. Wild-type CAI4 cells were subjected to a 30°C–42°C heat shock, proteins extracted at 0, 10, 30 and 60 minutes, and MAP kinase phosphorylation probed by western blotting. Each MAP kinase responded differently to the thermal upshift (Figure 4A). Mkc1 was rapidly dephosphorylated before being re-phosphorylated in the longer term. Cek1 phosphorylation levels remained relatively stable. Hog1 was rapidly dephosphorylated, and was not reactivated during the one hour period examined (Figure 4A). These data were entirely consistent with previous studies that have reported effects of temperature on Hog1, Cek1 and Mkc1 phosphorylation [22], [30], [77], [78]. Significantly, total kinase levels remain unchanged during the 60 minute heat shock.
To further validate the effects of heat shock upon these signalling pathways, we examined the expression of key MAP kinase targets: PGA13 is an Mkc1 target [78], PMT4 is a Cek1 target [79] and RHR2 is a Hog1 target [30]. RNA was extracted from cells 0, 10, 30 and 60 minutes after a 30°C–42°C heat shock, and transcript levels assayed by qRT-PCR relative to the internal ACT1 mRNA control. The levels of these targets reflected the activation profiles of the corresponding MAP kinase (Figure 4B).
MAP kinase signalling is often represented in terms of linear pathways. However, in C. albicans, as in other organisms there is emerging evidence for cross-talk between these pathways [22], [80], [81], [82]. Therefore we tested whether they interact during thermal adaptation by comparing the phosphorylation status of the terminal kinases in wild type and MAP kinase mutants following a 30°C–42°C heat shock (Figure 5). First, we examined Cek1 and Hog1 activation in an mkc1 mutant (Figure 5A). Mkc1 inactivation did not affect the responses of either Hog1 or Cek1. Second, we monitored Hog1 and Mkc1 phosphorylation in a cek1 mutant (Figure 5B). After Cek1 inactivation, the transient dephosphorylation of Mkc1 was inhibited. Hog1 phosphorylation levels still declined over the duration of the heat shock. Third, we tested the effects of Hog1 inactivation upon Mkc1 and Cek1 (Figure 5C). Mkc1 phosphorylation was minimal and remained low when hog1 cells were exposed to the heat shock. In contrast, we observed that Cek1 was hyperphosphorylated in hog1 cells, as reported previously [22], [81], [83]. Our data reveal that there are significant interactions between the MAP kinase signalling pathways during thermal adaptation.
Given this cross-talk and the modulation of MAP kinase activities after thermal up-shifts, we reasoned that ambient temperature is likely to influence the resistance of C. albicans cells to those stresses normally associated with these signalling pathways. For example, Hog1 signalling mediates osmotic stress adaptation [84], and the Mkc1 and Cek1 pathways promote resistance to the cell wall stresses Calcofluor White and Congo Red [22]. Therefore we performed MICs to test the effects of ambient temperature upon the resistance of wild-type (NGY152: Table 1), hog1, cek1 and mkc1 cells to these stresses (Figure 6). The mkc1 cells were temperature sensitive, as reported [32], [84]. Also, as reported previously, hog1 cells were sensitive to NaCl [84]. Furthermore, mkc1 and cek1 cells displayed sensitivity to Calcofluor White [22], [81], [83]. These controls faithfully replicated previous observations.
We observed that ambient temperature significantly influences the sensitivity of C. albicans cells to cell wall, but not osmotic stresses (Figure 6). Firstly, wild-type cells were more sensitive to Calcofluor White at lower temperatures (25°C and 30°C). Secondly, hog1 cells were relatively resistant to Calcofluor White at all temperatures tested, and phenocopied wild type cells on Congo Red. Thirdly, cek1 cells were resistant to Congo Red at most temperatures, but sensitive at 42°C. Clearly ambient temperature significantly affects cell wall stress resistance. These data reinforce the notion of cross-talk between thermal and cell wall stress signalling pathways.
We reasoned that the Calcofluor resistance of hog1 cells at low temperatures (Figure 6) might be Cek1 dependent. This is because inactivation of Hog1 led to elevated Cek1 phosphorylation levels (Figure 5C), and Cek1 promotes Calcofluor White resistance (Figure 6). Hog1 and cek1 mutations are synthetically lethal [81], and hence we could not examine a hog1 cek1 double mutant. Therefore, instead we tested the phenotype of a hog1/hog1 hst7/hst7 double mutant, in which Cek1 signalling is blocked [81]. As predicted, the inactivation of Cek1 signalling attenuated the Calcofluor White resistance of hog1 cells at low temperatures (Figure 6). This indicates that Hog1 inactivation promotes cell wall stress resistance at low temperatures via Cek1 signalling. These data reinforce the importance of cross-talk between the MAP kinase signalling pathways and highlight the relevance of this cross-talk for thermal adaptation.
In some yeasts, the core transcriptional response to stress underpins the phenomenon of stress cross-protection, whereby exposure to one stress protects the cell against subsequent exposure to an alternative type of stress via the up-regulation of key stress response genes [2], [4], [15], [16]. The core stress response is limited in C. albicans [19]. Nevertheless, it remained conceivable that the effects of ambient temperature upon the resistance of C. albicans to certain stresses might be mediated through stress cross-protection. To test this, mid-exponential C. albicans cells (NGY152: Table 1) were subjected to a 30 minute 30°C–42°C heat shock and subsequently exposed to a cell wall stress (Congo Red or Calcofluor White), osmotic stress (NaCl), or oxidative stress (hydrogen peroxide). Cell wall or osmotic stress resistance was not enhanced by prior exposure to heat shock (Figure 7A). Furthermore, prior exposure to cell wall or osmotic stress resistance did not enhance resistance to a subsequent heat shock (Figure 7B). This was consistent with our observation that ambient temperature does not significantly affect osmotic stress resistance (Figure 6), and indicated that the influence of ambient temperature upon Calcofluor White resistance is not mediated by stress cross-protection. This was consistent with the divergent core stress response in C. albicans [3], [19].
We included oxidative stress as a control in the above experiments because a prior heat shock has been reported to protect C. albicans against peroxide stress [3]. The mechanisms by which a heat shock protects cells against a subsequent oxidative stress have not been elucidated. We noted that two uncharacterised genes that are induced by oxidative stress are also up-regulated by heat shock: orf19.7882 and orf19.7085 [3], [41], [85]. Both genes are induced in response to oxidative stress in a Cap1-dependent fashion, and are down-regulated by Hog1. Therefore we tested whether Hog1 and Cap1 are required for the observed stress cross-protection (Figure 7C). The hog1 mutant (JC50) displayed a comparable increase in survival to wild type cells when cells were pre-treated with a 30°C–42°C heat shock and then exposed to hydrogen peroxide. In contrast, cap1 cells (JC128: Table 1) lost this stress cross-protection. Therefore, the acquired resistance to hydrogen peroxide after exposure to heat shock is dependent on Cap1. This probably occurs through the Cap1 dependent up-regulation of oxidative stress genes such as orf19.7882 and orf19.7085 in response to heat shock.
What mechanisms are responsible for the cross-talk between thermotolerance and stress adaptation if this is not mediated by stress cross-protection? Hsp90 is a key regulator of thermal adaptation, regulating its own expression via Hsf1 (Figure 1). In addition, Hsp90 modulates the activities of multifarious client proteins [62]. This list of Hsp90 interactors in C. albicans includes CK2 subunits [62], Mkc1, a well-defined Hsp90 protein client in C. albicans [86], and Hog1 [62]. Mkc1 and Hog1 orthologues are also known Hsp90 client proteins in S. cerevisiae [87], [88]. As these protein kinases were identified in our screen as being important for thermotolerance, we reasoned that Hsp90 might play a significant role in coordinating thermal adaptation in C. albicans. According to this hypothesis, changes in ambient temperature are expected to influence Hsp90 availability [88] and this in turn modulates the activity of its client proteins [49]. These client proteins include Hog1 and Mkc1 which, when activated, induce expression of target genes that promote cellular adaptation to elevated temperatures and other stresses.
A clear prediction of this hypothesis is that Hsp90 depletion should attenuate the cell's ability to withstand specific stresses. To test this, tetO-HSP90 cells were treated with 20 µg/ml doxycycline for 7 hours, by which point Hsp90 levels were significantly reduced (Figure S1A and S1B) and growth was beginning to slow (Figure S1C). These cells were then stressed for one hour with Calcofluor White, Congo Red, a 30°C–42°C heat shock, hydrogen peroxide or NaCl (Materials and Methods). As predicted, Hsp90 depletion significantly attenuated cellular resistance to all of the stresses tested except NaCl when compared to wild type or tetO-HSP90 cells not treated with doxycycline (Figure 8A).
To test this further, we examined the effects of the Hsp90 inhibitor geldanamycin upon stress resistance. Although the differences were not as dramatic as for genetic depletion of Hsp90 (Figure 8A), similar effects were observed following geldanamycin treatment (Figure 8B). The fact that Hsp90 depletion did not affect osmotic stress resistance was entirely consistent with our previous findings that ambient temperature did not influence osmotic stress resistance (Figure 6), and that there was no stress cross-protection for thermal and osmotic stresses (Figure 7).
Clearly Hsp90 influences cellular responses to a range of stresses, not only to heat shock. To determine whether these effects are mediated through its client proteins we assessed the impact of Hsp90 depletion upon the activation of known client proteins, Mkc1 and Hog1, and the potential client protein, Cek1 (Figure 9). The basal activation of each MAP kinase was examined in wild type and tetO-HSP90 (SN95 and CaLC1411: Table 1) in the absence of stress, and following the imposition of stress. Mkc1 activation levels were attenuated following Hsp90 depletion. This was the case in the absence of stress and following heat shock or Calcofluor White treatment (Figures 9A and 9B). This corresponds with a decrease in total Mkc1 kinase levels following Hsp90 depletion. In contrast, Hsp90 depletion had no effect on the levels of Cek1 activation in the absence of stress, but led to an increase in Cek1 phosphorylation following Calcofluor White treatment (Figures 9A and 9B). With regard to Hog1, the basal levels of phosphorylation were maintained following Hsp90 depletion in the absence of stress (Figure 9C), and Hog1 activation was not attenuated in response to osmotic stress although total Hog1 levels decreased (Figure 9D). This was entirely consistent with our other findings, whereby ambient temperature did not affect osmotic tress resistance (Figure 6) and a prior heat shock did not protect cells against a subsequent osmotic stress (Figure 7). However, Hsp90 depletion blocked Hog1 activation following exposure to hydrogen peroxide (Figure 9C). We conclude that Hsp90 depletion exerts differential effects upon Hog1, Mkc1 and Cek1. Furthermore the data are consistent with our prediction that Hsp90 modulates the activities of these MAP kinases.
It was conceivable that Cek1 is an Hsp90 client protein. To test this we determined the impact of Hsp90 depletion on Cek1 stability as Hsp90 client proteins are generally destabilised in the absence of Hsp90 [62], [86], [89]. Cek1 was TAP-tagged at its C-terminus and the specificity of this tagging was verified by western blotting alongside untagged SN95 and CaLC1411 controls (Figure S4). The levels of this Cek1-TAP protein were then monitored in the tetO-HSP90 strain CaLC1411 (CaLC2288, Table 1) and in wild-type SN95 (CaLC2287, Table 1) cells (Figure 10). Cek1-TAP protein levels decreased in response to Hsp90 depletion in the absence of stress, as well as in response to heat shock and Calcofluor White treatment (Figure 10). This suggested that Cek1 is destabilised by Hsp90 depletion and that Cek1 is a client protein of Hsp90. Therefore the contribution of Cek1 to thermal adaptation appears to be modulated by Hsp90.
Several observations infer a link between stress adaptation and cell wall architecture in C. albicans. For example, the Pkc1/Mkc1 cell wall salvage pathway is activated by certain stresses [58], [66], [90]. Also, the Hog1 stress pathway has been implicated in cell wall biosynthesis [81], partly by regulating chitin synthesis [66], [69]. If Hsp90 depletion modulates Hog1, Mkc1 and Cek1 signalling (Figure 9), and these pathways contribute to cell wall biogenesis, we reasoned that Hsp90 could regulate cell wall architecture. We took two approaches to test this hypothesis. First, we tested the effects of Hsp90 depletion on chitin levels by Calcofluor White staining. Chitin content increased more than two-fold following doxycycline treatment of tetO-HSP90 cells compared to control cells (Figures 11A and 11B). Second, we examined the impact of Hsp90 depletion upon cell wall architecture by transmission electron microscopy (Figure 11C). Cell wall thickness increased two-fold after Hsp90 depletion compared to the controls. Therefore, Hsp90 is essential for normal cell wall structure, providing the first ever link between Hsp90 and cell wall architecture.
Our results illuminate novel functional connections between key cellular regulators required for thermotolerance, and establish distinct roles for Hsp90 in orchestrating short term versus long term mechanisms of thermal adaptation. We provide evidence that Hsf1 is a client protein of Hsp90 for the first time in any fungus, demonstrating that Hsp90 contributes to the short term regulation of thermal adaptation. Furthermore, we identified several key signalling pathways that contribute to thermotolerance in C. albicans. These include the Mkc1, Hog1 and Cek1 pathways, each of which plays a role in cell wall integrity. These pathways contribute to thermal adaptation in the longer term via cell wall remodelling, and Hsp90 links many of these pathways, as critical components of these pathways are Hsp90 client proteins. Finally, we establish a role for Hsp90 in cell wall biogenesis for the first time in any organism. Overall, we see that Hsp90 drives short term thermal adaptation via down-regulation of Hsf1, and longer term adaptation through modulation of other client proteins, leading to a more robust cell wall.
Hsf1 is known to activate HSP90 expression [41] and Hsp90 has been predicted to regulate Hsf1 [49]. Here we demonstrate that inhibiting Hsp90 in C. albicans, either pharmacologically or genetically, derepresses Hsf1 (Figure 1), indicating that Hsp90 down-regulates Hsf1. Furthermore, Hsf1 physically interacts with Hsp90 under steady state conditions (Figure 2A), confirming for the first time in any fungus that Hsf1 is an Hsp90 client. What's more, we have shown that this interaction increases during heat shock, and also leads to the recruitment of Hsp70, which binds Hsf1 and Hsp90 during a prolonged heat shock (Figures 2B and S1). This increased interaction correlates with an increase in the levels of both Hsp90 and Hsf1 that occurs during heat shock. Consistent with these findings, we found that Hsp90 accumulates in the nucleus upon a prolonged heat shock (Figure 2C and Figure S1). Similarly, the S. cerevisiae Hsp70 chaperone Ssa1 has been shown to enter the nucleus one hour after a 42°C heat shock [91]. This is consistent with our observation that Hsp70 binds Hsf1 and Hsp90 one hour after a 37°C–42°C heat shock. Therefore short-term thermal adaptation involves the activation of Hsf1 by Hsp90-independent mechanisms. Hsf1 induces the expression of Hsp90 and other chaperones that promote protein folding and repair proteotoxic damage [39], [41]. Hsp90 then down-regulates Hsf1 thereby dampening the heat shock response once adaptation is achieved. This regulatory circuit is central to thermal adaptation in C. albicans [41], [49] and may be conserved across the eukaryotic kingdom as pharmacological inhibition of Hsp90 derepresses Hsf1 orthologues in S. cerevisiae and mammalian systems [52], [55].
With a view to identifying the kinase responsible for phosphorylating Hsf1 in C. albicans we performed a screen for protein kinase mutants that are temperature sensitive (Figure 3). This highlighted several critical regulators on key MAP kinase pathways, including the Mkc1, Hog1 and Cek1 pathways. None of these pathways are essential for Hsf1 phosphorylation (Figure S3), suggesting that there is functional redundancy with respect to Hsf1 activation during heat shock, or that these pathways act independently of Hsf1 in promoting thermotolerance. These pathways are differentially activated during heat shock (Figure 4), and there is cross-talk between these pathways under these conditions (Figure 5). Furthermore, ambient temperature significantly affects the resistance of C. albicans cells to cell wall stresses, and these effects are influenced by Cek1, Hog1 and Mkc1 (Figure 6). Each of these MAP kinase pathways is known to contribute to cell wall remodelling [32], [81], [83], [90], and mutations that interfere with cell wall synthesis are known to confer temperature sensitivity upon C. albicans. For example, the inactivation of certain protein mannosyltransferases of the PMT family, or the deletion of OCH1 can confer temperature sensitivity [92], [93]. Additionally, deletion of SSR1, a GPI-anchored cell wall protein causes sensitivity to elevated temperatures [94]. These data strongly suggest that Mkc1, Hog1 and Cek1 signalling promotes longer term thermotolerance via the maintenance of a robust cell wall (Figure 12).
As an environmentally contingent hub of protein homeostasis and regulatory circuitry, Hsp90 has profound effects on biology, disease, and evolution. Hsp90 modulates the phenotypic effects of genetic variation in an environmentally responsive manner [95], [96], [97], [98], influencing approximately 20% of observed natural genetic variation and serving to maintain phenotypic robustness and promote diversification [96]. Mkc1 was defined as a protein client of Hsp90 after Mkc1 signalling was shown to contribute to antifungal drug tolerance in C. albicans [86]. Hog1 was subsequently shown to be an Hsp90 client protein after Diezmann and co-workers identified this MAPK in a chemogenetic screen of the C. albicans Hsp90 interactome [62]. However, Cek1 was not highlighted in this screen. We demonstrate here that, like Mkc1 and Hog1, Cek1 is an Hsp90 client protein (Figure 10). This is in keeping with a recent study by Taipale and colleagues which demonstrates that Hsp90 binds about 60% of mammalian kinases [99]. Therefore we tested whether Mkc1, Hog1 and Cek1 signalling is influenced by Hsp90 during thermal upshifts. We found that Hsp90 does influence the activation of these kinases during heat shock and in response to their respective stresses (Figure 9). Furthermore, Hsp90 depletion influenced the sensitivity of C. albicans cells to specific stresses such as cell wall and oxidative stress, as well as to heat shock (Figure 8). Clearly Hsp90 modulates Mkc1, Hog1 and Cek1 signalling and their outputs, and these pathways are known to contribute to cell wall architecture. Therefore, we reasoned that Hsp90 might, in part, influence cell wall remodelling (Figure 12). We confirmed this hypothesis by demonstrating that Hsp90 depletion significantly increases the chitin content and thickness of C. albicans cell walls (Figure 11). We note that cell wall robustness does not correlate with cell wall thickness. Indeed Ene and co-workers have recently shown that cell wall architecture is altered by growth on different carbon sources, yielding thinner cell walls that are more robust, leading to increased stress resistance [100]. Therefore, Hsp90 coordinates both short term thermal adaptation (via Hsf1 down-regulation), and long term thermal adaptation (via its client proteins Mkc1, Hog1 and Cek1) (Figure 12).
Additional pathways that were not highlighted in our screen might contribute to thermotolerance. For example some protein kinases that encode essential functions were missing from the transposon library. Indeed, the protein kinase responsible for Hsf1 phosphorylation remains obscure. It is worth noting that as Hsf1 is activated in response to Hsp90 depletion, and as such it is unlikely that the Hsf1 kinase requires stabilisation by Hsp90. One must also note that Hsf1 might be phosphorylated by multiple protein kinases, and hence that there may be functional redundancy with respect to Hsf1 phosphorylation. Indeed, NetPhos2.0 analysis of Hsf1 suggests multiple phosphorylation sites for multiple kinases.
Clearly ambient temperature plays a major role in fungal pathogenicity [42]. Furthermore our data indicate that ambient temperature strongly influences physiological attributes that contribute to fungal pathogenicity, such as a robust cell wall and effective stress adaptation [11], [13], [42], [49]. In addition we show that increases in ambient temperature lead to elevated oxidative stress resistance, but that the reverse is not true (Figure 7). This observation is consistent with the idea of “asymmetric adaptive prediction”, whereby microbes appear to have “learned” over evolutionary timescales that exposure to one stress is likely to be followed by exposure to a second unrelated stress [101]. As a result, exposure to the first stress results in the activation of an adaptive response that prepares the cell for exposure to the second stress [101]. With respect to C. albicans, the elevated temperatures associated with localised inflammation, appear to protect the fungal cells against the imminent exposure to oxidative stress that will follow exposure to macrophages and neutrophils.
In conclusion this study reveals new Hsp90 client proteins that play central roles in the control of cellular adaptation: Hsf1 and Cek1. Furthermore, this work provides important new insights into the mechanisms by which Hsp90 coordinates short and long term mechanisms that contribute to thermotolerance in a major fungal pathogen.
|
10.1371/journal.pntd.0002045 | Drivers of Echinococcus multilocularis Transmission in China: Small Mammal Diversity, Landscape or Climate? | Human alveolar echinococcocosis (AE) is a highly pathogenic zoonotic disease caused by the larval stage of the cestode E. multilocularis. Its life-cycle includes more than 40 species of small mammal intermediate hosts. Therefore, host biodiversity losses could be expected to alter transmission. Climate may also have possible impacts on E. multilocularis egg survival. We examined the distribution of human AE across two spatial scales, (i) for continental China and (ii) over the eastern edge of the Tibetan plateau. We tested the hypotheses that human disease distribution can be explained by either the biodiversity of small mammal intermediate host species, or by environmental factors such as climate or landscape characteristics.
The distributions of 274 small mammal species were mapped to 967 point locations on a grid covering continental China. Land cover, elevation, monthly rainfall and temperature were mapped using remotely sensed imagery and compared to the distribution of human AE disease at continental scale and over the eastern Tibetan plateau. Infection status of 17,589 people screened by abdominal ultrasound in 2002–2008 in 94 villages of Tibetan areas of western Sichuan and Qinghai provinces was analyzed using generalized additive mixed models and related to epidemiological and environmental covariates. We found that human AE was not directly correlated with small mammal reservoir host species richness, but rather was spatially correlated with landscape features and climate which could confirm and predict human disease hotspots over a 200,000 km2 region.
E. multilocularis transmission and resultant human disease risk was better predicted from landscape features that could support increases of small mammal host species prone to population outbreaks, rather than host species richness. We anticipate that our study may be a starting point for further research wherein landscape management could be used to predict human disease risk and for controlling this zoonotic helminthic.
| The loss or gain of certain host species may either dilute or amplify the risk of pathogen and parasite infection through direct or indirect effects. The relative contribution of host communities combined with climate and landscape characteristics on non-vector-borne parasite transmission to humans has been a relatively neglected area of investigation. Here we show that zoonotic transmission of the cestode E. multilocularis in China was not directly correlated with small mammal host species richness, but spatially correlated with alpine meadows, forest characteristics and rainfall that confirmed hotspots of human disease in a 200,000 km2 region of the eastern Tibetan plateau. Our findings indicate more intensive transmission in homogeneous landscapes with larger areas of optimal habitats for one or some host species in low diversity small mammal communities, making multi-annual population outbreaks more likely. Landscape features that could support large population outbreaks of small mammal host species were better predictors of E. multilocularis transmission to humans than indices of host species richness per se. Our results support the notion that landscape, small mammal host biodiversity and their population dynamics may protect humans from zoonotic parasite transmission where they prevent population outbreaks of a few specific small mammal host species.
| Ecologic systems are nested within one another. This well-known fundamental hierarchical organization [1] is easy to detect in nature but has been generally undervalued as a source of influence on the structure and development of pathogen transmission patterns, and also as a means of understanding the crucial connections between local processes and large-scale distribution patterns. At a community level, Guernier et al. [2] explored the worldwide distribution of human parasitic and infectious diseases (PID) and found that, after correcting for cofactors, PID richness (as for free-living species), was strongly correlated with latitude: PID species diversity decreased as one moved from the equator, and the strongly nested pattern of their global distribution was confirmed. They also pointed out how, along such gradient, the maximum range of precipitation and monthly temperature might be intimately connected in generating the observed pattern of disease diversity. This similarity in the diversity patterns of free living organisms and PIDs suggests that common processes are at work which might be explained at large by the climatically based energy hypotheses (energy availability generates and maintains species richness gradients) [2]. However, for a given PID species distribution patterns at regional scales may be more complex. Transmission may depend not only on species richness but also on host assemblage composition [3]. For instance, reducing host diversity can increase disease transmission when the lost species are either not hosts or suboptimal for the pathogen, especially when population size of optimal hosts are inversely correlated to species richness. On the other hand, a large number of competent host species may provide a much more stable transmission system for transmission, that is robust against environmental disturbances, anthropogenic or natural, that temporarily decrease the density of some host populations. However, empirical examples of the relationships between host biodiversity and parasite transmission are still relatively rare, in part because suitable datasets that may allow comparisons are deficient [4].
Further complexities are encountered when the distribution range of a pathogen covers a large number of host communities and climatic zones. Such a wide-extent distribution-range consists of a nested hierarchy of transmission systems that can be inter-connected in space and time via dispersion [5]. At these scales, datasets with high resolution and precision, describing hosts, disease distribution and environmental factors (such as climate and land cover) are most often heterogeneous.
Human alveolar echinococcocosis (AE) is a highly pathogenic parasitic disease caused by the larval stage of the cestode Echinococcus. multilocularis, which usually results in a slow-growing multivesicualted tumor-like lesion in the liver of cases. The parasite's life-cycle can exploit a large number of small mammal intermediate hosts (>40 species known to date) and several definitive host species (e.g. foxes, coyotes, wolf, dog, etc.). Human infection arises from accidental ingestion of E. multilocularis eggs from direct contact, or via food contaminated by carnivore definitive host faeces. Although patchy, its distribution range covers the Northern hemisphere from the Arctic to the 28th parallel on the Tibetan plateau. In Eurasia, although other carnivores can theoretically sustain the transmission of E. multilocularis, the whole range of the parasite is actually included in the range of its main definitive host, the red fox, Vulpes vulpes, except on the Tibetan plateau where the cestode circulates through the Tibetan fox, Vulpes ferrilata [6]. In contrast, throughout Eurasia, E. multilocularis transmission is sustained by a large variety of different small mammal communities [7]. Within Eurasia, continental China stretches from Siberia in North Xinjiang, Inner Mongolia and Heilongjiang to tropical rain forest in Yunnan, and includes high altitude areas such as the Tibetan plateau, several deserts (some of them below sea level), coastal and agricultural areas (Figure 1). Based on mammal and plant species comparisons a total of 25 biogeographical regions and 77 sub-regions are defined [8]. Transmission of E. multilocularis is sustained in diverse small mammal communities of western and northern China. While some of these communities have been investigated [5], [9] many remain unexplored.
Here we examine the distribution of human AE disease in China on two spatial scales (continental and regional) and test the hypotheses that human disease distribution can be explained by the distribution of intermediate host species richness (i.e. host biodiversity) or by environmental factors such as climate and land cover or an interaction between them. Our central hypothesis is that human AE distribution is best explained by considering the impact of landscape on low species-rich communities prone to population outbreaks, rather than host diversity per se. In that case, for endemic areas, basic information on land cover and climate could be used as a proxy to predict high risk transmission systems for this pathogenic helminthic zoonosis.
Continental maps of China for distribution of rainfall, altitude and temperature can be found in Supplementary Material. In summary these abiotics factors did not correlate well to human AE disease distribution. Furthermore, the continental distribution of human AE was not clearly correlated to small mammal species richness, even considering the distribution and number of species that are known to be potential intermediate hosts for E. multilocularis or pest species (Figure 2). However, the overlap between the spatial distribution of human AE and the spatial distribution of a combination of two Global Land Cover 2000 categories, i.e. ‘meadows’ and ‘alpine and subalpine meadow’ was very clear. The main endemic areas correspond to the central and eastern Tibetan meadows, those of the Tien Shan and Altai Mountains and northern Inner Mongolia (Figure 3a). On the Tibetan plateau, the human AE endemic foci corresponds to the Global land cover category ‘alpine and subalpine meadow’, characterized by alpine meadows densely covered with thick perennial sedges (Kobresia spp) and various forbs, lying generally below 4500 m as described by Schaller [47]. In the western part of the human AE focus, alpine meadow becomes largely riparian with streams, seepages, swamps and lakes. The area is characteristically composed of a 10–40 cm thick moisture retaining sod layer providing a long growing season. Livestock tend to concentrate on grassy habitat (28–70 animals/km2) and continual grazing and trampling coupled with solifluxion, cause extensive erosion. This anthropogenic activity helps to maintain extensive open habitats favorable to the population surges of the plateau pika (Ochotona curzoniae), a known intermediate hosts of E. multilocularis [48]. By contrast, human AE distribution did not correlate with the Global Land Cover 2000 category ‘alpine and sub-alpine plain grass’ (Figure 3b). This category, lying generally between 4500 and 5000 m, corresponds to an Alpine steppe, dry, cold and windy. Plant coverage is sparse (Stipa spp, Festuca spp, Poa spp, Carex moorcroftii) seldom more than 30%, and soil is poor without a sod layer [47].
Despite its great size (several 100,000 km2), the Eastern Tibetan plateau focus of human AE disease correlates well with Alpine meadows. The apparent absence of human AE cases on the western and north western Tibetan plateau may simply be due to the fact that human population density decreases from east to west to low population areas of high altitude desert (i.e. Chang Tang area). From east to west on the Tibetan plateau, climatic conditions get drier and colder, which impacts grass growth and yak/goat/sheep survival on which human populations depend. This aridity can also be expected to affect E. multilocularis egg survival. On the other hand, AE cases may possibly just be undetected in small discrete nomadic Tibetan populations of the western Tibetan plateau, since they are typically isolated from public health facilities and no mass screening has occurred to date west of Naqu in Tibet Autonomous Region. We return to this issue in the following section.
Based on this China-wide continental scale analysis, some interesting contrasting results were apparent. Regional areas of south Gansu province (i.e. Zhang-Puma counties), and south Ningxia region (i.e. Xiji-Guyuan counties, close to the North Liu Pan Mountains) were not expected to have large number of human AE cases. However, local foci over an area of 400–2500 km2 were confirmed to have significant AE prevalence in humans reaching an average of 4.1% and 3.0% respectively. These largely montane agricultural zones are considered to be significantly influenced by anthropogenic landscape disturbance caused by deforestation [9], [49], [50]. This process led to the regional opening of forest areas to agriculture, with transitional stages of grassland and pastures triggering small mammal population outbreaks, thus potentially fostering transmission of E. multilocularis locally over a relatively short time-span (10–20 years) when optimal small mammal habitats become temporarily available. Thus, in both the provincial Gansu and Ningxia endemic zones, risk of human AE prevalence increased during the 1970s–90s in response to anthropogenic local landscape changes. However these landscape changes cannot be seen on the coarse resolution land use map.
A total of 17,589 people were screened for hepatic echinococcosis in 94 villages and the overall prevalence of alveolar echinococcosis was 3.28% (3.02–3.56, 95%CI). Despite substantial sample sizes, no cases were detected to the west of Qinghai Lake (Figure 4). People screened in this area (n = 1,975) were 95.5% Tibetan (71.7% herdsmen, 25.6% children). Geographically isolated from the southern hotspot by the lower Qaidam Basin and the Chaka Yanhu depression, this area was not considered for further analysis (see study area subset Figure 4). Thus, the resulting filtered data set included 15,614 people and 81 villages, with a raw prevalence of 3.7% (3.41–4.01, 95%CI) (Table 1). Univariate analysis detected significantly higher AE prevalence among females than among males (X2 = 11.2, df = 1, p = 0.0008) and among Tibetan than among Han ethnic groups (X2 = 6.05, df = 1, p = 0.01). Evidence of occupation differences was found (X2 = 129, df = 5, p<0.000001), with AE prevalence greater in herdsmen compared to a pool of other categories (X2 = 29.5, df = 1, p<0.000001) and lower in employee (X2 = 5.57, df = 1, p = 0.02), student and children (X2 = 41.9, df = 1, p<0.000001), semi-herdsman (X2 = 7.02, df = 1, p = 0.008) and farmers (X2 = 32.1, df = 1, p<0.000001). Table 2 shows how different occupations were represented between ethnic groups. The link between age categories and some occupations (e.g. ‘student and children’) is obvious. A non-linear relationship of human AE prevalence to age was found (Figure 5).
The buffer radius maximizing the likelihood of the GAMM was found to be 100 km for all land cover variables, altitude, rainfall and temperature. Figure 5 shows the relationships between AE prevalence in humans and environmental variables. AE prevalence was found to increase exponentially with the ratio of Alpine meadows and to decrease with the ratio of forest (corresponding to a linear relationship on the linear predictor – data not shown). Human AE prevalence increased with the percentage of Alpine steppe to a maximum at 15.6% of total land cover, and then decreased to low prevalence. Similar patterns were obtained for rainfall, altitude, and temperature with maximum at 564 mm, 4385 m and −5.62°C respectively. This suggests an increased risk for human AE in areas with a larger percentage of alpine grassland at altitudes ranging between 4200–4600 m. In comparison, landscapes dominated by alpine steppe at higher altitude, mostly observed in the western part of the study area, appeared to be clearly sub-optimal.
We included in models either altitude as a proxy for other ecological parameters (such as rainfall, temperature and other unknown factors) or rainfall and temperature as potential ecological factors impacting directly e.g. Echinococcus egg survival. Models containing non-linear effects on all three variables frequently gave rise to computational difficulties and are not reported here. Models including the spatial random effect consistently returned lower DICs than their non-spatial counterpart (see Table S1 in Supplementary material for the list of models). Evidence for the importance of a non-spatial (pixel) random effect was less overwhelming. DIC comparisons show that 5 models among the 33 fitted were quasi-equivalent with a DIC difference lower than 2. All of them included all variables related to human populations (ethnicity, gender, age, occupation), all landscape variables and at least one variable related to climate or altitude. The mean and 95% credibility intervals of posterior samples for regression coefficients, and the probability that the coefficient be ≤0 (Tibetan, female, herders, meadows) or ≥0 (forest), are shown in Table 3. The proportion of positive coefficient samples were > = 0.98 for Tibetan, > = 0.99 for female and < = 0.03 for forest in all models. 95%CIs for meadow coefficients did not exclude zero or negative values when forest was included, however, >95% of posterior samples for that coefficient were positive. Although the coefficient for herdsmen was greater than zero in less than 90% of samples, dropping this term from models largely increased DIC.
For prediction of human AE disease risk, model 5 was selected in order to avoid altitude (a proxy variable whose importance may change with latitude outside of the study area), and in order to maintain the ecologically meaningful variables such as rainfall and temperature which appear not to lead to excessive over-parameterization, even keeping a pixel random effect. Posterior means and standard deviations of the variance parameters for the P-splines and Markov random field were 0.0147 (sd 0.02306) for age, 0.3831 (sd 0.6105) for rainfall, 0.1468 (sd 0.5690) for temperature, 1.2036 (sd 0.5164) for pixel spatial random effect, and 0.0586 (sd 0.0963) for pixel random effect. Figure 6a presents the predicted prevalence for a hypothetical 31.6 year old male, non-Tibetan, non-herdsman (corresponding to the mean age of the sampled population and to zero values for co-factors). The range of the 95% credibility intervals for each pixel of the spatial random effect is presented in Figure 6b. As a result of this analysis, one large hotspot of human AE disease was indicated in an area at the south-east border of Qinghai Province and at the north-west of Sichuan Province. Over a total area of 290,400 km2, an endemic area of 193,600 km2 was calculated to have a human AE prevalence higher than 1 per thousand; for human AE prevalence greater than 1% the predicted transmission zone was 67,200 km2.
Predictions were extrapolated across a larger area where rainfall and temperature data remained within the same ranges as the area used to train the model (Figure 7). Over a total area of 902,800 km2, human AE prevalence was predicted to be higher than 1 per thousand over 664,000 km2, and greater than 1% over 210,000 km2. Three other hotspots of human AE disease were predicted from the landscape model. Two were located in the Tibetan Autonomous Region (TAR), one region at the north-west of Naqu and the other to the south of Dingqing. The south Dingqing putative focus was predicted to be connected to the Qinghai-Sichuan hotspot by a crescent shape corridor of elevated prevalence. This appears to be confirmed because a recent initial screening study (n = 232 persons) in Dingqing found a very high AE prevalence of 4.7% (Feng X and Craig PS, unpublished observations). Another hotspot was predicted in Gansu at the north-east limit of the transmission area. Actually, the Gansu hotspot was the first confirmed in the late 1980s [51] (see discussion). Furthermore, the area west of Qinghai Lake was predicted as a low prevalence area, which was confirmed by the present study (this was the subset of 1975 people – 13 villages - where no AE cases could be detected, thus excluded from model fitting).
Zoonotic pathogens are of growing concern and the role of animal host biodiversity is both of theoretical and practical consideration regarding pathogen stability and the effect of anthropogenic perturbations on the transmission ecosystem [4], [52]. Human alveolar echinococcosis (AE) is a chronic potentially fatal hepatic infection, for which the causative pathogen Echinococcus multilocularis, is transmitted in indirect life-cycles involving mammalian wildlife predator-prey interactions between fox definitive hosts and small mammal intermediate hosts [6], [7]. This cestode zoonosis is known to be highly endemic in parts of Eurasia including China, and provides an excellent model to investigate the role of reservoir host diversity, landscape characters and climate for a non-vector borne zoonotic macro-parasite.
For continental China, we found that the spatial distribution of human AE disease was not clearly correlated to small mammal species richness, even when limiting consideration to the distribution and number of species that are known to be both potential intermediate hosts and/or agricultural/grassland pests. There was however a clear spatial overlap between the distribution of certain grassland types, and the spatial distribution of human AE. The main endemic areas corresponded to the central and eastern Tibetan meadows, those of the Tien Shan and Altai Mountains and of northern Inner Mongolia. Rainfall, altitude and temperature did not directly correlate with human AE disease distribution at continental scale.
Human AE prevalence was found to exponentially increase with the ratio of alpine meadows and decrease with the ratio of forests in our regional analysis. The resolution of small mammal atlases in China did not facilitate inclusion of biodiversity indices in regional models. However, the Qinghai-Sichuan human AE disease hotspot lies in one of the areas with the lowest regional biodiversity of small mammals in China (Figure 1). A comprehensive small mammal survey carried out in Shiqu county (see location Figure 6), in the middle of the Qinghai-Sichuan human AE hotspot, recorded 6 species only of which Microtus spp and Ochotona curzoniae were classified as pests [48]. By contrast, 15 small mammal species were found in the Rangtang/Maerkang locality of west Sichuan, in the more forested part of the study area, among which a maximum of 10 species were present in some forest habitats [53]. Some of those species potentially outbreaking were shared with the east Tibetan Shiqu area (i.e. Ochotona cansus, Microtus irene, M. limnophilus), but no indications of small mammal population surges were found from the survey and from local farmer interviews. Moreover human AE prevalence in these areas was lower (1.5% versus 6% in Shiqu county) [28].
Evidence of interactions between landscape and arvicolid vole population dynamics at various spatial scales has been provided in earlier studies in Western Europe. On the regional scale (area of about 2500 km2) larger variations in vole population densities occur where permanent grassland cover exceeded 50% and 85% of the total land for M. arvalis and A. terrestris respectively [16], [17]. In complex ecosystems, the population dynamics of small mammals is regulated by both top-down (predation, parasitism) and bottom-up (resources) forces in a multivariate context [54]–[57]. In general, this means that population surges are less likely in biodiverse communities of small mammals, in regions characterized by heterogeneous landscapes (e.g. forest mosaics). Towards the far west and north of the Tibetan plateau, the increasing aridity of Alpine steppes and semi-deserts and the more patchy distribution of Alpine meadows decrease both primary production and connectivity of optimal habitats, thus the probability of large scale small mammal outbreaks. By contrast extensive grasslands with higher grass production (Alpine ‘meadows’) of the Eastern Tibetan plateau forms optimal conditions on a regional scale for surges of potentially cyclic small mammal species such as the plateau pika (Ochotona curzoniae), several species of Microtus (M. leucurus, M. irene, M. limnophilus), and probably hamsters (Cricetulus sp.). In such contexts, definitive hosts, foxes, take advantage of the most abundant and accessible resources and specialize on them [58]–[60]. Raoul et al. [61] have shown that infection of foxes with E. multilocularis responds quickly and asymptotically to small increases in the densities of favored prey species. This may cause intense environmental contamination and human exposure, directly from fox feces or more likely via dogs infected from preying upon abundant (infectious) small mammal intermediate host reservoirs [62]. Such system processes may explain why, on the Tibetan plateau, regional landscape variables were found to help to predict human AE distribution.
The greatest difficulty in interpreting these results arises from the fact that in such regional systems, explanatory variables which may determine transmission, directly or not, are not independent. For instance, grass productivity and forest cover are correlated with rainfall and temperature, which are correlated (locally) with altitude. This may explain why apparent discrepancies were observed between univariate and multivariate statistics. For instance, most Tibetans were also livestock herders, which may have nullified the occupation variable in the multivariate analysis. Furthermore, human AE disease was found to be correlated with Alpine meadow cover in univariate statistics, but in multivariate statistics effect size of alpine meadow was smaller when the forest term was included in models.. However perfect colinearity between forest and meadow aerial covers was ruled out by examining covariation matrices (data not shown) and because discarding one of the two variables in multivariate models led to increased DICs. The spatial random effect may also have nullified other effects suggesting spatial heterogeneity in effect size or the role of other unmeasured factors (e.g. socio-economics, etc.).
Large unexplained differences between neighboring villages were observed on the Tibetan plateau. Such differences are commonly reported in most other studies in endemic AE areas, for instance in Gansu [27], [49], Ningxia [50] and France [9]. Our study shows that the Zhang county local AE focus (about 400 km2) in south Gansu Province lies in an area where regionally, rainfall and temperature were in the range of those found in the Qinghai-Plateau hotspot, although at a much lower altitude (2000–2800 m). The Zhang focus, first discovered in the late 1980s [51] was comprehensively described in studies carried out in the late 90s [9], [49], [63]. An overall human AE prevalence of 4.1% was reported there with some villages reaching more than 10%. Some authors [9], [27], [64] had previously provided indications that E. multilocularis transmission in Zhang was the consequence of a transient augmentation in shrub and grass cover in the 1980s, generated by successional growth following deforestation and triggering population outbreaks of the vole M. limnophilus and the hamster C. longicaudatus. Following 20–30 years of deforestation the parasite life-cycle is no longer being maintained in the current farmland landscape due to lack of both suitable intermediate and definitive hosts [5]. This suggest that in areas where climatic conditions are expected to be favorable, but landscape unfavorable, fine grain landscape alteration (here of anthropogenic origin) may create transitory local foci of E. multilocularis transmission that are prone to extinction. How the parasite may colonize such areas from larger and more stable regional foci remains open to question (mainland/island dynamics through fox movements and/or dog trade?) [5]. The present study however shows that even those risk areas may be predicted from climate and landscape analysis at medium (20 km×20 km) resolution.
In conclusion, our results indicate that the prevalence of the cestdode zoonosis, human alveolar echinococcosis, was higher in areas of relatively low small mammal biodiversity than in more diverse host communities within which they were nested. In low diversity small mammal communities, potential intermediate host species prone to population outbreaks (e.g. rodents and lagomorphs) can reach higher densities and enhance transmission. This can be a natural occurrence, as on the homogeneous meadows of the eastern Tibetan plateau where rainfall maintains relatively higher productivity than is possible on the western half of the Tibetan plateau. Conversely, it can also be the result of anthropogenic driven landscape alteration, as in south Gansu where deforestation under favorable climatic conditions led to a transitory decrease of small mammal local biodiversity, and an augmentation of habitats and landscapes favorable to one or several cyclic species of those low diversity communities. Our results support the notion that landscape, small mammal host biodiversity and their population dynamics may protect humans from E. multilocularis transmission by preventing population outbreaks of specific small mammal host species with subsequent consequences on prey/predator relationships and associated host/parasite transmission [65]. This mechanism is clearly different to the dilution and zooprophylaxis effects that have been described and debated elsewhere as potential utilitarian advantages of biodiversity conservation [4]
|
10.1371/journal.pgen.1005492 | The Intolerance of Regulatory Sequence to Genetic Variation Predicts Gene Dosage Sensitivity | Noncoding sequence contains pathogenic mutations. Yet, compared with mutations in protein-coding sequence, pathogenic regulatory mutations are notoriously difficult to recognize. Most fundamentally, we are not yet adept at recognizing the sequence stretches in the human genome that are most important in regulating the expression of genes. For this reason, it is difficult to apply to the regulatory regions the same kinds of analytical paradigms that are being successfully applied to identify mutations among protein-coding regions that influence risk. To determine whether dosage sensitive genes have distinct patterns among their noncoding sequence, we present two primary approaches that focus solely on a gene’s proximal noncoding regulatory sequence. The first approach is a regulatory sequence analogue of the recently introduced residual variation intolerance score (RVIS), termed noncoding RVIS, or ncRVIS. The ncRVIS compares observed and predicted levels of standing variation in the regulatory sequence of human genes. The second approach, termed ncGERP, reflects the phylogenetic conservation of a gene’s regulatory sequence using GERP++. We assess how well these two approaches correlate with four gene lists that use different ways to identify genes known or likely to cause disease through changes in expression: 1) genes that are known to cause disease through haploinsufficiency, 2) genes curated as dosage sensitive in ClinGen’s Genome Dosage Map, 3) genes judged likely to be under purifying selection for mutations that change expression levels because they are statistically depleted of loss-of-function variants in the general population, and 4) genes judged unlikely to cause disease based on the presence of copy number variants in the general population. We find that both noncoding scores are highly predictive of dosage sensitivity using any of these criteria. In a similar way to ncGERP, we assess two ensemble-based predictors of regional noncoding importance, ncCADD and ncGWAVA, and find both scores are significantly predictive of human dosage sensitive genes and appear to carry information beyond conservation, as assessed by ncGERP. These results highlight that the intolerance of noncoding sequence stretches in the human genome can provide a critical complementary tool to other genome annotation approaches to help identify the parts of the human genome increasingly likely to harbor mutations that influence risk of disease.
| Mutations in noncoding sequence can cause disease but are very difficult to recognize. Here, we present two approaches intended to help identify noncoding regions of the genome that may carry mutations influencing disease. The first approach is based on comparing observed and predicted levels of standing human variation in the noncoding exome sequence of a gene. The second approach is based on the phylogenetic conservation of a gene’s noncoding exome sequence using GERP++. We find that both approaches can predict genes known to cause disease through changes in expression level, genes depleted of loss-of-function alleles in the general population, and genes permissive of copy number variants in the general population. We find that both scores aid in interpreting loss-of-function mutations and in defining regions of noncoding sequence that are more likely to harbor mutations that influence risk of disease.
| Despite strong evidence that regulatory regions can be affected by pathogenic mutations, such as in fragile-X syndrome, β-thalassemia, Charcot-Marie-Tooth neuropathy, breast cancer and others [1–5], little has been done to quantify stretches of regulatory sequence in the context of both phylogenetic conservation and human-specific intolerance to variation, and then correlate it back to disease causing potential. While methods to assess phylogenetic conservation at a single site are established, such as GERP++ [6,7], purely phylogenetic approaches are at a risk of ignoring human specific regulatory sequence [8,9]. Furthermore, while efforts have been made to create predictors that seek to identify variants in noncoding sequence that might influence expression or have higher chance of causing disease [10–13], no framework has been introduced that focuses on standing variation in the human population to estimate the relative intolerance of a gene’s noncoding exome sequence to genetic variation. Since this regional-based approach proved effective for protein coding genes, it is natural to assess its application to noncoding exome sequence.
To assess whether noncoding sequence can predict genes that cause human disease through gene dosage aberration, we derive two measures: a phylogenetic conservation based score and a score reflecting intolerance to standing variation in a human population. To permit an unambiguous comparison to gene lists, we concentrate on each gene’s proximal regulatory regions: 5’ UTR, 3’ UTR, and the 250bp upstream of the transcription start site; recognizing that these three regions are only a subset of the relevant regulatory sequence for protein-coding genes. We generate a GERP++ region-based conservation score to assess the overall conservation of each gene’s proximal noncoding sequence [6,7]. To capture regulatory function that might be human-specific we formulate a novel human population genetic approach (ncRVIS). We then assess each gene’s proximal noncoding region for phylogenetic conservation and intolerance to genetic variation in the human population, and tie these scores back to genes known to cause disease due to a gene dosage aberration. An important clarification is that the current RVIS framework is a regional-based measure of intolerance to variation, and as such is complementary to traditional variant-level predictions. More recent ensemble-based predictors, such as CADD [12] and GWAVA [13] leverage multiple features including phylogenetic conservation to make predictions of functionality even for noncoding variants. To assess the levels of contribution from information beyond conservation, we adapted CADD and GWAVA into regionalized scores in a way analogous to ncGERP by taking the average CADD and subsequently the average GWAVA score across a gene’s noncoding proximal regulatory sequence as its ncCADD and ncGWAVA score, respectively (Methods).
Our results show that it is possible to use a combination of phylogenetic and human standing variation to identify regions of noncoding sequence that associate with gene-dosage sensitivity. Beyond the immediate noncoding flanking sequence of protein-coding genes, the framework introduced in this paper can be elaborated to include stretches of regulatory sequence beyond UTRs. Another important goal of this work is to illustrate that in addition to traditional phylogenetic signatures of important noncoding sequence, we can use signatures from human standing variation to help define boundaries of noncoding sequence that when considered as a unit might show an excess of mutations identified in cases compared to controls—similar to what is currently done in exome-sequencing studies where we assess excess mutations per each protein-coding gene.[14]
To evaluate whether a gene’s regulatory sequence can predict dosage sensitivity, we took four gene lists derived from independent sources. The first list contained OMIM disease-associated genes previously characterized as “haploinsufficient” [15]. The second list took a set of genes curated as dosage sensitive in ClinGen’s Genome Dosage Map (http://www.ncbi.nlm.nih.gov/projects/dbvar/clingen/). The third list—a novel list introduced here—relies solely on human polymorphism data from the 6503 whole exome sequences made available by the NHLBI Exome Sequencing Project (ESP) [16] to identify genes where, based on the sequence context and mutability, we observed fewer loss-of-function variants than we expected to observe. Finally, to look at the opposite end of the dosage sensitivity spectrum, we identified genes that are tolerant to copy number variations (CNVs) based on the CNV data from two large Database of Genomic Variants (DGV) studies [17–19].
We used pre-calculated hg19 GERP++ scores (accessed January 2014) to calculate a single average GERP++ score across a gene’s noncoding sequence (3’ UTR, 5’ UTR and immediate promoter region). We refer to this score as the noncoding GERP (ncGERP) score. We then constructed a protein-coding conservation based score for each gene, pcGERP, by using the same methodology across the gene’s protein-coding sequence. As described in the relevant papers, GERP++ provides a score per nucleotide base, which has been shown to reflect a base’s conservation across the mammalian lineage [6,7].
A limitation of phylogenetic approaches is that they are unable to capture sequence with human specific function. To address this, we used the pattern of standing genetic variation in a human population. This approach is a noncoding formulation of the Residual Variance Intolerance Score (RVIS), a regression framework we recently developed to score the protein-coding sequence of genes in terms of their tolerance to functional genetic variation. We showed in Petrovski et al (2013) that this approach provides significant information for which genes are likely to carry protein-coding pathogenic mutations [15]. To adapt this approach to noncoding sequence, however, several changes are needed (Methods). First, instead of using the total number of observed variants to predict the expected number of common variants, we used the estimated mutation rate to reflect the mutability of the noncoding sequence. Second, since we cannot reliably distinguish functional and non-functional UTR variation we compared the prediction to all possible common noncoding variants. Finally, because most currently available exome kits do not provide sufficient coverage of UTRs, we relied on whole-genome sequence (WGS) data from 690 samples generated at the Duke Center for Human Genome Variation (CHGV (S1 Table). We first demonstrated that the RVIS itself when applied to protein-coding sequence of genes still has predictive utility when each of these adjustments are made, suggesting that a similar approach is possible for regulatory sequence (S2 and S3 Tables). For comparisons to our previously published protein-coding RVIS, we also generated RVIS-CHGV, a score that is the exact formulation of the published RVIS [15], but is dependent on the 690 CHGV whole-genome sequenced samples used to construct the ncRVIS score and similarly to ncRVIS, adopts the mutation rate of the effectively sequenced sites (Methods). We found that the noncoding ncRVIS and protein-coding RVIS-CHGV scores are weakly correlated (Pearson’s r2 correlation of 0.04, S1A Fig). The ncRVIS (Fig 1), RVIS-CHGV and ncGERP scores and their corresponding genome-wide percentile scores can be found in S1 Data and at http://igm.cumc.columbia.edu/GenicIntolerance/.
To evaluate whether the ncGERP and ncRVIS scores correlate with known disease genes, we used the same gene lists as previously described [15]. We found that, using a logistic regression model, RVIS-CHGV, ncRVIS and ncGERP significantly predict OMIM haploinsufficient genes that have been linked through de novo mutations: p = 4.7x10-21 (AUC = 0.75), p = 2.4x10-7 (AUC = 0.63) and p = 2.7x10-24 (AUC = 0.78), respectively. A joint model of the three scores achieved an AUC of 0.816 when predicting OMIM haploinsufficient genes that have been linked through de novo mutations (Table 1). However, based on the other OMIM gene sets it does not appear that the noncoding sequence of genes can currently distinguish the broader set of OMIM disease genes, indicating that the patterns within the noncoding sequence are likely to be for the most part specific to diseases linked to haploinsufficiency (Table 1).
ClinGen’s dosage sensitivity map is another growing resource for genes that are curated by experts as being haploinsufficient or triplosensitive (http://www.ncbi.nlm.nih.gov/projects/dbvar/clingen/). As of the 1st of May 2015, 263 genes had been annotated as having either “Some evidence for dosage pathogenicity” or “Sufficient evidence for dosage pathogenicity” (S2 Data). We repeated the gene dosage sensitivity assessment using this better curated set of 263 genes. We again observed that both protein and noncoding RVIS and GERP scores were significantly associated (p < 1x10-16) with genes curated to be dosage sensitive in ClinGen (Fig 2). ncGERP had the highest AUC (0.78) among the set of scores (Table 1 and Fig 2). Based on the curated ClinGen list this indicates that genes with highly conserved noncoding sequence relative to the rest of the genome are strongly correlated with gene dosage sensitivity.
Additional noncoding scores were constructed for both CADD [12] and GWAVA [13], by taking the average of the nucleotide-level score across a gene’s noncoding sequence (Methods). These are designated ncCADD and ncGWAVA, respectively. These scores were assessed against ClinGen’s dosage sensitivity genes as well; both ncCADD (p = 1.5x10-12; AUC = 0.63) and ncGWAVA (p = 4.7x10-17; AUC = 0.66) were also found to be significantly predictive of ClinGen’s dosage sensitivity genes (Fig 2).
We performed a joint logistic regression model to investigate performance in predicting ClinGen dosage sensitive genes from the genome-wide background using six features: the two RVIS and two GERP scores supplemented with two additional noncoding scores derived from ncCADD and ncGWAVA (S4 Table, Fig 2). There was modest improvement (AUC = 0.83) compared to a joint model using three features: ncGERP, ncRVIS and RVIS-CHGV (AUC = 0.82, Table 1).
The haploinsufficiency gene lists in the previous section relied on known Mendelian gene-disease relationships. Another way to identify a list of genes sensitive to gene dosage is to use the absence, where expected, of protein-coding loss-of-function (LoF) variants in a large human population (Methods). Such a population-based LoF deficient gene list highlights genes where changes in expression levels could be selected against, yet are independent of known gene-disease associations.
Using the standing variation from the ESP6500SI reference population we identified 1,235 LoF deficient genes (FDR < 1%) and 1,762 LoF control genes that had both an ncRVIS and ncGERP score assigned (Methods). We found that 2.3% of the 1,235 LoF deficient genes overlap with known OMIM “haploinsufficient” genes compared to one (0.06%) of the 1,762 control genes (Fisher’s Exact test, two-tailed p = 2.4x10-10). Given that the construction of the LoF deficient gene list is independent of gene-disease databases, this list could include genes where haploinsufficiency might be incompatible with life (non-viable), genes that are yet to be associated to disease through haploinsufficiency, or genes that cause disease through mechanisms other than haploinsufficiency. The overlaps of the 1,235 LoF deficient genes and the 1,762 control genes with the OMIM disease gene list (minus haploinsufficiency genes) were 26.6% and 13.2%, respectively (Fisher’s Exact two-tail p = 3.1x10-20).
We find that the median ncRVIS of the collective 2,997 genes is 50.8%. By comparing the distribution of ncRVIS scores between the 1,235 LoF deficient genes and the 1,762 LoF control genes we demonstrated that LoF deficient genes have significantly more intolerant noncoding sequence (median 37.9% vs. 58.1%; Mann-Whitney U test, p = 7.1x10-34, Fig 3A and S2A Fig).
We repeated the LoF deficient assessment with ncGERP, which showed that LoF deficient protein-coding genes preferentially have a more phylogenetically conserved regulatory sequence (median ncGERP 23.4% vs. 64.5%; Mann-Whitney U test, p = 3.4x10-171, S2B Fig. To understand whether information can be gained from combining ncRVIS and ncGERP scores, we used a multivariate logistic regression model, which showed that ncRVIS (p = 5.4x10-6) maintains a significant signal for predicting LoF deficient genes. This supports the expectation that regulatory functions specific to humans may not always be captured by ncGERP, while ncRVIS is likely picking up such patterns of human-specific selection within the regulatory sequence of genes where regulated dosage is critical to normal function. An investigation of alternative noncoding scores showed that both ncCADD and ncGWAVA were also significantly associated with LoF deficient genes (Fig 3B and S2 Fig).
Taken together, these data advocate prioritizing coding LoF mutations and potentially the regulatory region mutations among LoF deficient genes that have conserved or intolerant noncoding sequence (Fig 3B). This conclusion is corroborated by earlier results showing that ncRVIS and ncGERP are both significantly predictive of OMIM and ClinGen disease genes with a primary mechanism of haploinsufficiency.
Individual CNVs distorting single or contiguous gene dosage have been linked to human diseases [20]. Inversely, genes that tolerate CNVs in the general population are unlikely to be dosage sensitive [21]. In this section we extended our assessment of ncRVIS and ncGERP to CNVs by asking whether a relationship exists between genes that have been shown to overlap (≥50% of the consensus coding sequence [CCDS]) with a deletion/duplication based on two study populations from Database of Genome Variation (DGV) [18]: Conrad et al (2010) and the 1K Genomes Project (2012) [17,19]. These two studies amass 1,602 individuals with comprehensive CNV data across 14,714 assessable CCDS genes. Of these assessable genes, 861 genes were found to have at least one CNV overlap among the combined population of 1,602 samples.
Genic tolerance to CNVs shows a clear relationship with the genes whose regulatory sequence also tolerates variation. We found that, on average, the 861 genes with a CNV overlap in these public databases have significantly higher ncRVIS (p = 2.3x10-28; AUC = 0.61) and ncGERP percentile scores (p = 9.2x10-31; AUC = 0.62) than the 13,853 genes without a reported CNV overlap in those data. Moreover, in a multivariate logistic regression model, RVIS (p = 3.1x10-26), ncRVIS (p = 1.9x10-9) and ncGERP (p = 8.9x10-12) each individually contribute to an improved overall prediction of genes that tolerate CNVs (AUC = 0.68).
The current data indicates that genes tolerating CNVs in the general population are also more likely to tolerate variation in their noncoding regulatory sequence. With 13,853 genes reporting no CNV overlap in this CNV dataset, much larger populations of high-quality, genome-wide CNV data are required to appropriately assess the question of whether intolerance in the regulatory sequence of a gene can strongly predict intolerance to specifically CNV deletions.
The correlation between RVIS-CHGV and ncRVIS is r2 = 0.04 (S1A Fig). We included RVIS-CHGV and ncRVIS in a multivariate logistic regression model and found that the signals from RVIS-CHGV and ncRVIS provided significant independent information in predicting OMIM haploinsufficiency genes annotated as carrying de novo pathogenic mutations. This multivariate logistic regression achieves an AUC estimate of 0.77; higher than each of the RVIS-CHGV (AUC = 0.75) and ncRVIS (AUC = 0.63) models. Next, we generated two additional scores for each gene (Fig 4A). The first was a combined genic intolerance assessment that considers the sum of the regulatory and protein-coding sequence by summing the values corresponding to a gene’s RVIS-CHGV and ncRVIS genome-wide percentiles, termed “RVIS-sum.” Using the list of OMIM haploinsufficient genes, we found that 84% of genes are in the lower 50th percentile of RVIS-sum scores (Fig 4B). The second score is meant to reflect the extent to which these two measures diverge, which we term “RVIS-diff.” A positive RVIS-diff score indicates that the noncoding regulatory sequence of the gene is ranked as more intolerant than the protein-coding sequence of the same gene (Fig 4A).
To assess how ncRVIS may be useful in interpreting mutations among patients, we specifically evaluated ncRVIS in the context of loss-of-function (LoF) de novo mutations reported across cohorts of individuals ascertained for the presence[22–33] and absence[30–34] of neuropsychiatric disorders. Here, loss-of-function de novo mutations were defined as nonsense, canonical splice and protein-coding indels that occurred within CCDS sequence and were absent in the ESP6500SI database. Firstly, among controls, we identified 180 LoF de novo mutations and the median ncRVIS percentile score of the genes those mutations were found in was 45.8%. When we considered the 494 LoF DNMs identified across cohorts of simplex trios ascertained for various neuropsychiatric disorders, we found that the LoF DNMs preferentially occurred among noncoding intolerant genes, with the median ncRVIS being 36.2%. No single LoF de novo mutation was observed twice among controls. Among cases, a SCN1A splice-donor de novo mutation was found among two probands, both ascertained for an epileptic encephalopathy [28]. Taking the combination of ncRVIS and protein-coding RVIS, the RVIS-sum vector, we found that among controls the median RVIS-sum was 85.91, while among neuro-ascertained cases it was 70.30 (Mann-Whitney U 2-tail test p = 0.001, S3A Fig). The significance remained even after excluding 19 loss-of-function de novo mutations among six previously known disease genes: CDKL5, NRXN1, SCN1A, SCN2A, STXBP1 and SYNGAP1 (Mann-Whitney U 2-tail test p = 0.008).
A similar assessment is to use the information from a gene’s noncoding and protein-coding percentiles to calculate a single metric that reflects Euclidean distance from the most intolerant coordinate (0,0). Genes close to the (0,0) coordinate are characterized as having both the most intolerant noncoding and protein-coding sequence. We found that loss-of-function de novo mutations among cases preferentially occurred among genes closer to (0,0) with a median Euclidean distance for case-ascertained LoF DNMs of 0.588 compared to 0.698 for control LoF DNMs (Mann Whitney U test, p = 0.0035). A logistic regression model regressing case/control LoF DNM assignment on the Euclidean distance achieved an AUC of 0.58 (S3B Fig). We then combined the genic information from the Euclidean distance metric with the previously defined loss-of-function deficiency bioinformatics signature. We took only the LoF de novo mutations that fell in genes with a Euclidean distance ≤0.4 and also occurred in loss-of-function deficient genes with no more than a single LoF variant reported among the Exome Variant Server (EVS) [16] (S5 Table). This identified nine observations among the controls—corresponding to 5.0% of LoF DNMs—and 70 observations among cases, corresponding to 14.2% of all LoF de novo mutations among cases (Fisher’s Exact test two-tail p = 6.5x10-4; odds ratio of 3.2); a modest boost to what we got when we relied solely on the loss-of-function deficient bioinformatics signature (Fisher’s Exact two-tail p = 9.5x10-4; odds ratio of 2.4).
The list of genes carrying one of the 70 case loss-of-function de novo mutations includes established genes: NRXN1, SCN1A and SCN2A. The list also includes recently implicated genes: CHD2 [35], CHD8 [36], KMT2E [37], MBD5 [35], SETD5 [38], and WDFY3 [39]. It is important to note that among the cases, the above six loss-of-function mediated pathogenic genes were of unknown significance when the de novo mutation data were first reported. This helps highlight the utility of this loss-of-function bioinformatics signature. The remaining case loss-of-function de novo mutations include some Mendelian disease genes with an existing neurological association, such as NIPBL, which is known to cause Cornelia de Lange syndrome [40] and KMT2A, which is known to cause Wiedemann-Steiner Syndrome [41]. The remaining genes with the same bioinformatics signature as the above established genes are: ANK2, ARHGAP5, ASH1L, BRD4, BTAF1, DLL1, DNAJC6, DOT1L, EPHB2, FAM91A1, GIGYF2, INTS6, ITGA5, KIAA1429, LARP4B, MED13, MED13L, NCKAP1, NOTCH1, PHF3, POGZ, RALGAPA1, RALGAPB, RANBP2, RB1CC1, SPAG9, STAG1, UBN2, UBR5, ZC3H4 and ZNF292 (S5 Table). It is unclear which of these genes could have their gene-disease association confirmed in the coming years; however, five of these candidates already have multiple LoF de novo mutation observations across neuropsychiatric ascertained patients: ANK2, MED13L, NCKAP1, POGZ and ZNF292.
Developing methods to recognize functional mutations in the regulatory part of the human genome is widely recognized as one of the central challenges facing modern human genetics. The difficulty is well illustrated by the results of the ENCODE project. Considerable effort and progress has been made in identifying parts of the genome with clear regulatory potential based on experimentally confirmed transcription factor binding sites and related approaches. However, since much of the genome is currently assigned a possible regulatory role it is difficult to use only those data to prioritize mutations in the study of human disease. Here, we show that population genetic and phylogenetic approaches can help fill this gap by adding further information about the possible functional role of a noncoding stretch of sequence. Integrating these approaches with the sequence regions identified by ENCODE [42] and related studies may ultimately prove to be the most effective approach. There are many additional regulatory sequences that can be included using the framework described here. Examples include distal enhancers, noncoding RNAs and larger promoter regions. However, correctly and unambiguously associating distal regulatory elements to the genes they regulate requires highly curated data, which is not yet straightforward to acquire. Therefore, here we focus only on regulatory sequences that can be unambiguously associated with specific genes in order to test the ability of the noncoding exome sequence to predict genes that cause human disease via gene dosage aberrations.
Using multiple resources, we show that dosage sensitive genes have distinct patterns of genetic variation in their proximal noncoding regulatory sequence. To the extent that more distant regulatory sequences may also carry variants that influence expression, we may expect a correlation between the intolerance patterns of a gene’s proximal and distal regulatory sequence. This possibility suggests that a sliding window of intolerance data throughout the human genome may provide a valuable new tool for identifying important regulatory sequence. Interpreting genome wide patterns of intolerance and relating those patterns to genes will not be a trivial task, but our results imply that genome wide patterns of intolerance have the potential to provide an important complement to other tools [42] used to identify important regulatory parts of the genome.
ncRVIS is a ‘regional-based’ guide to patterns of standing variation in the proximal noncoding sequence of a gene in the human population (Fig 1). It leverages the collective information from the standing variation in a stretch of noncoding sequence to assess whether that stretch of noncoding sequence has more or less polymorphic variation than expected. This is distinct from variant based scores that look at individual variants. By identifying stretches of noncoding sequence with preferential depletion of standing variation we are hypothesizing that in many cases this is driven by purifying selection among the human population acting against variation in that noncoding region as a whole, rather than at an individual variant site.
We and others have previously found that for the protein-coding sequence, RVIS and other estimates of human constraint are more indicative of disease causing genes than mammalian conservation [15,43]. However, in its current formulation, ncGERP outperforms ncRVIS in all current assessments. There can be a few explanations for this. Firstly, it is possible that the coding region is highly conserved throughout the genome to the point that there is limited allowance for big enough deviations between genic conservation in order to create an informative ranking. However, the noncoding regions may be more prone to allow such deviations. Secondly, the current ncRVIS formulation is based on a comparatively modest cohort (n = 690 samples).
There are two reasons we think ncRVIS remains important in light of the stronger signal observed from ncGERP. First, as we have shown throughout this work, the two scores are only weakly correlated (r2 = 0.06, S1K Fig) and ncRVIS can add information beyond ncGERP. This is evidenced by the various dosage sensitive gene list assessments including the ClinGen assessment where in a joint logistic regression model of just ncRVIS and ncGERP, ncRVIS had a significant contribution (p = 7.2x10-7). This likely occurs, at least in part, for the interesting reason that there are genomic regions that have important functions only in humans. Evolutionary conservation will miss these regions, population genetic approaches will not. Second, the performance of ncGERP is close to its limit, as we already have a fairly good assessment of which sites are phylogenetically constrained, and which are not. ncRVIS, however, we anticipate will increase in predictive value as sample sizes of sequenced genomes grow, and thus a more extensive dataset of noncoding standing variation is available.
Alternative noncoding predictors of dosage sensitive genes, which take the overall propensity for a gene’s proximal noncoding sequence to score as more ‘functional’ based on the average nucleotide-level CADD or GWAVA scores, suggest that nucleotide-level predictors of noncoding functionality do appear to detect additional signatures of regulatory function beyond conservation. We observe correlation between a gene’s ncGERP and ncCADD score (r2 = 0.32, S1V Fig), and to a lesser degree its ncGWAVA score (r2 = 0.06, S1AA Fig), as a result of their dependence on conservation-based signals in their construction. In a joint model, however, we found that both ncCADD and ncGWAVA provide signal independent of ncGERP and ncRVIS when predicting human dosage sensitive genes (Fig 2). This suggests that even though conservation is a major component of their predictive signal for ClinGen’s dosage sensitive genes, additional information not directly captured by conservation might be captured by these two ensemble predictors (S4 Table).
Currently, the basic paradigm to analyze protein-coding sequence is to use aggregate statistics that integrate the effect of different rare mutations affecting the same functional unit, often defined as the protein-coding sequence of a single human gene. This has proven effective in whole-exome sequence data because we know the protein-coding sequence boundaries we need to consider in order to effectively aggregate variants that affect the same functional unit [14]. In order to effectively interpret whole-genome regulatory sequence data, and find the noncoding regions that harbor risk-influencing mutations, we need to learn to recognize the functional noncoding stretches of sequences that affect gene expression. Current annotations lack specificity to define truly functional noncoding regions. Here, we have shown that a phylogenetic and population genetic framework can help define and prioritize the functional noncoding regions, and this is expected to improve when combined with information about sequences with regulatory potential from ENCODE [42] and related resources. Here, we also explore additional signals beyond conservation and human standing variation by assessing the dosage sensitivity predictive value of ncCADD and ncGWAVA scores, two nucleotide-level scoring frameworks that in addition to capturing signals of conservation, leverage other features and annotations from the noncoding sequence. Such an integrated framework will enable the definition of intolerant noncoding regulatory regions that have been under both strong evolutionary (ncGERP) and human population (ncRVIS) constraint. For these reasons, ncRVIS and related approaches are likely to play a key role in the development of a statistical genetic framework to support the interpretation of large scale whole genome sequence data that will soon emerge, for example through the recently announced National Human Genome Research Institute (NHGRI) call for genomics of common disease centers. In this context, it is essential to appreciate that the resolution of the ncRVIS approach depends critically on the total number of individuals that have been sequenced, and therefore its value is expected to continue to increase as whole-genome sequenced sample sizes increase.
Eleven data sources were used to develop and assess noncoding RVIS (ncRVIS) and noncoding GERP (ncGERP). As exome sequencing kits only capture a fraction of the untranslated region (UTR) sequence of genes, we utilized human whole-genome sequenced samples from the Institute for Genomic Medicine, Columbia University database (formerly Center for Human Genome Variation (CHGV), Duke University) to assess noncoding intolerance. We used Consensus Coding Sequence (CCDS) release 14 as the set of protein-coding genes of interest for scoring noncoding intolerance [44]. We used Ensembl 73 to define the UTR sequence of CCDS genes that did not overlap with CCDS protein coding regions of the same or overlapping genes [45]. We extracted gene-lists from OMIM database to reflect differing genetic models. We extracted a heavily curated list of haploinsufficient or triplosensitive genes from ClinGen’s Genome Dosage Map (http://www.ncbi.nlm.nih.gov/projects/dbvar/clingen/). For copy number variants (CNVs), we identified a set of deletions and duplications reported across two published studies: The 1K Genomes Project and Conrad et al. (2010) [17,19]. We used the GERP++ database to derive noncoding and protein-coding regional GERP scores to compare to phylogenetic conservation at the genic level [6,7]. We also used two noncoding ensemble nucleotide-level predictors, CADD [12] and GWAVA [13], to derive noncoding regional scores for each gene’s noncoding sequence as done for GERP++. Finally, we relied on the ESP6500SI [16] database to extrapolate a loss-of-function (LoF) deficient gene list, based on observing less than expected LoF variants in a gene.
To define the noncoding sequence for each gene, we relied on the Ensembl 73 noncoding annotation from that gene’s canonical transcript (downloaded 19th September 2013). We refer to noncoding exonic sequence of genes as the collection of 5'-UTR, 3'-UTR and an additional non-exonic 250bp upstream of transcription start site (TSS). The 5’ and 3’ UTR are derived based on the canonical transcript annotation. The additional 250bp upstream of the TSS is defined as the 250 bases upstream of the initial exon junction, taking into consideration whether the transcript lies on the (+/-) strand. For three Ensembl genes (PKD1L2, SPIB, and UGT2A1) that had multiple canonical transcripts, we took the larger of the two canonical transcripts.
Given the challenge of defining the upstream promoter region, we opted to choose a relatively small number of bases immediately adjacent to the TSS, and this was set at 250 for all genes. Defining different sized promoters per gene guided by the distribution of conservation (e.g., GERP++ scores) or human polymorphism density would create a situation where we specifically define the promoter region of the gene we are assessing based on the more intolerant or more conserved set of bases upstream of the TSS. Evidently, this could create a bias towards intolerant or conserved promoters in our score, and therefore we prefer for this formulation to define the upstream promoter regions agnostic to the data we use for the assessments.
The initial dataset was comprised of 56,715 noncoding units. These 56,715 units resulted in 19,563 unique Ensembl genes. We found that 18,507 genes had a 5’ UTR (average = 260 bases, median = 182 bases). 18,638 genes had a 3’ UTR. And, by design, all 19,563 genes had a 250bp promoter region. Of the 19,563 genes, 18,148 (92.77%) had all three noncoding units represented. 849 had only two units represented, whereas 566 were based solely on their promoter unit, with no UTR boundaries defined.
The overall genomic noncoding sequence comprised of these 19,563 unique Ensembl genes is 34,065,650 bases. These reflect the number of sites prior to exclusion of inadequately covered sites and sites that overlap with protein-coding position among other genes, as discussed below.
We found that whole-exome sequencing is inadequate for capturing the noncoding exonic sequence of protein-coding genes. To derive a noncoding RVIS, and to generate a comparative protein-coding RVIS based on the same subset of samples, we selected 690 internally sequenced (CHGV) control-approved whole genomes (78% Caucasian ancestry). For these 690 whole-genome sequenced samples, an average of 92.7% sites were covered, with at least 10-fold coverage across the 34,065,650 Ensembl defined noncoding sites. Similarly, relying on CCDS release 14 for the protein-coding sequence, we observed that these 690 whole-genome sequenced samples covered on average 94.6% of the 33,266,994 protein-coding sites in CCDS release 14 with at least 10-fold coverage.
The set of phenotypes contributing to the whole-genome sequenced set of 690 cases is summarized in S1 Table.
It is our experience that sites sequenced with consistently good coverage represent sites with more reliable alignment and variant calling than sites where coverage is sparse and inconsistently represented among a population. S4A Fig (blue curve) represents the number of our 690 whole-genome sequenced samples that had at least 10-fold coverage (Y-axis) versus the cumulative percentage of the 34.1Mbp Ensembl-defined UTR noncoding sites (X-axis). The intersection between the blue curve and green line (an illustrative cutoff) indicates that at this point approximately 92% of samples have at least 10-fold read coverage at approximately 83% of the Ensembl noncoding sites. Together, the green threshold line and the blue sample-site coverage profile partition the space into four regions. Region II and region III represent the overall heterogeneity of coverage and the amount of noncoding sequence pruned from analysis, respectively. Shifting the green line left retains noncoding sequence (smaller region III) at a cost of increased coverage heterogeneity (larger region II). Moving the threshold right reduces the noncoding sequence used in the analysis, but also reduces the noise from coverage heterogeneity. There are multiple ways to select a cutoff from these data; however, a balanced approach is to choose a cutoff that ensures region II and region III are as close as possible in terms of area.
To evaluate the area for region II and region III, we first smooth the sample-coverage profile (blue curve) by fitting smooth spines, as illustrated by S4B Fig where the blue dots represent the original profile, while the red curve represents the smoothed splines. The smoothed curve traces the original data well. We use the smoothed curve to compute the areas for region II and region III (through numeric integrations) for a selection of evenly spaced cutoff values. The areas for region II and region III for different cutoff values is shown in S4C Fig, with red and blue curves representing region II and region III, respectively. We choose the balanced cutoff to be the point where the curves intersect, representing a balance between loss of data (noncoding sequence sites) and variability from coverage heterogeneity. The method yields an optimal value of 0.074 based on the noncoding sequence data. This suggests that removing the 7.4% most inconsistently covered noncoding sites corresponds to requiring noncoding sites to have at least 67% of samples with at least 10-fold read coverage. We selected 70% for the manuscript, corresponding to removing the 8% ‘noisiest’ noncoding sites with respect to inadequate coverage at the population level (S4D Fig). We performed a sensitivity test varying the 70% threshold to a threshold of 60% (r2 = 0.986) or 80% (r2 = 0.971) and show that the final ncRVIS score is not highly sensitive to varying this threshold (S5 Fig).
Requiring ≥70% of the 690 samples have at least 10-fold coverage at a site prunes the noncoding sequence down to 31,355,520 (92.0%) of the initial 34,065,650 Ensembl-defined UTR noncoding sites. For the CCDS release 14 protein-coding sites, we found that this pruning process retained 31,528,600 (94.8%) of 33,266,994 CCDS sites.
Although it is expected that some variants in protein-coding sequence will affect gene regulation and that these would be easily associated with the genes they fall in, we excluded all protein-coding regions in order not to confound the ncRVIS score with protein-coding sequence. Through this additional step, we ultimately retained 31,112,586 (91.3%) of the 34,065,650 noncoding sites. We find that on average each of the 690 genomes has at least 10-fold coverage across 97.8% of the 31.1Mbps of noncoding sequence used to derive ncRVIS. Overall, the GC content of the 5’ UTR sequence is 61% in comparison to the GC content of the 3’ UTR sequence which is 42.5%.
Combining the three noncoding components into a single genic noncoding unit resulted in 19,484 (99.6%) of the 19,563 Ensembl genes retaining at least one noncoding component. The average length of noncoding sequence across the 19,484 Ensembl 73 genes was 1,597 (median = 1,096 sites).
Finally, we defined ncRVIS “assessable” genes as Ensembl genes not located on the Y chromosome, and with at least 70% of their noncoding sequence surviving the aforementioned filters. Through this, we retained 16,273 CCDS release 14 protein-coding genes that fulfilled the coverage requirements of having at least 10-fold coverage of at least 70% of the gene protein-coding sites across at least 70% of the CHGV whole-genome sequenced samples. The overlap between CHGV-derived RVIS and ncRVIS indicates that 15,471 genes were “assessable” for both coding and noncoding RVIS (S1 Data).
To accommodate the uncertainty surrounding the percentage of noncoding sequence sites that are neutral, we used an alternative metric to reflect mutability of a given sequence context in our ncRVIS and RVIS-CHGV adaptations. For the sites reflecting a genic unit (noncoding or coding) we use an in-house script developed by Dr Yujun Han. This script leverages the DNA tri-mer mutation rate matrix (kindly provided by Drs. Shamil Sunyaev and Paz Polak of The Broad Institute of MIT and Harvard, Cambridge) to generate a mutation rate for a given genic unit, which is calculated for each gene by summing the point mutation rates across the effectively captured sequence [28].
The mutation rate model provides an estimated rate of mutation per base. The rate is based solely on three bases: the interrogated base, the base immediately before, and the base immediately after the interrogated base. The model is based on human, chimpanzee and baboon genomic sequences [46]. The mutation rate model does not currently account for effects of larger sequence context or biological processes that affect mutation rate, such as background selection, distance to CpG islands, or replication timing. At the level of the gene, like others [43], we find very high correlation (r2 = 0.95) between gene coding length and mutation rate. While the high correlation suggests it is possible to use gene size as a proxy, we prefer leveraging the mutation rate to accommodate for some additional information that is likely lost when using gene size. The source code can be found in S3 Data.
All sequencing was performed on the Illumina HiSeq2000 platform (Illumina, San Diego, CA) in the Genomic Analysis Facility in the Center for Human Genome Variation (CHGV) at Duke University. After sequencing, reads were aligned to Genome Reference Consortium Human Genome build 37 (GRCh37) using the Burrows-Wheeler Alignment Tool (BWA)[47] and PCR duplicates were removed using Picard software (http://picard.sourceforge.net). The reference sequence we used is identical to the 1000 Genomes Phase II reference and it consists of chromosomes 1–22, X, Y, MT, unplaced and unlocalized contigs, the human herpesvirus 4 type 1 (NC_007605), and decoy sequences (hs37d5) derived from HuRef, Human Bac and Fosmid clones and NA12878. Variants were called using the Genome Analysis Toolkit[48]. SnpEff was used to annotate the variants[49].
To construct the ncRVIS score, we defined the minor allele frequency threshold dividing “common” and “rare” variants as ρ. To identify the number of variants with a MAF > ρ in the noncoding region of a gene, we use an in-house package, Analysis Tool for Annotated Variants (ATAV). ATAV communicates with our in-house relational database that houses all the variant call (and non-carrier) information for all sites across each of the 690 whole-genome sequenced samples. Additional filtering consisted of excluding indel calls and requiring a minimum of 10-fold coverage to call a variant (or be confident that a variant wasn’t present in a non-carrier sample). To increase confidence in called variants the following additional filters were applied: relying on GATK VQSLOD “pass” and “intermediate tranches,” requiring a QUAL score of at least 30, a QD (quality by depth) score of at least 2, a genotyping quality (GQ) score of at least 20, and a fisher strand bias (FS) score of less than 60. For noncoding regions, we considered all common variants residing in the noncoding sequence as contributors to (Y), the number of common variants.
For the CHGV-based protein-coding RVIS score (based upon the same 690 whole-genome sequenced samples as ncRVIS), we adopted the same criteria as in our earlier work introducing RVIS. That is, synonymous protein-coding variants did not contribute to the number of common ‘functional’ variants when deriving the CHGV protein-coding RVIS score. However, we did assess a secondary score (RVIS-Yall) for comparison purposes. RVIS-Yall considered all protein-coding variants as eligible to contribute to (Y), including the putatively neutral, synonymous coding variants.
We defined Y as the total number of common (Minor Allele Frequency [MAF]>ρ) SNVs in the noncoding sequence of a gene, and X as the effective mutation rate of the noncoding sequence of the gene, using the mutation matrix described previously. We then regressed Y on X and took the studentized residual as the noncoding Residual Variation Intolerance Score (ncRVIS). The raw residual was divided by an estimate of its standard deviation to account for differences in variability that come with differing mutational burdens. The ncRVIS then provides a measure of the departure from the (genome-wide) average number of common variants found in the noncoding sequence of genes with a similar amount of noncoding mutational burden. When S = 0, the gene has the average number of common noncoding variants given its total mutational burden; when S<0, the gene has fewer common noncoding variation than predicted; when S>0, it has more. Each ncRVIS is then translated to a corresponding percentile to reflect the relative position of that gene on the genome-wide spectrum of ncRVIS based on the relative intolerance of that gene’s noncoding sequence. S1 Data contains the X and Y estimates used to construct ncRVIS. The R code to reproduce ncRVIS relies on the MASS package [50]: studres(glm(Y~X)).
As we only had 690 whole-genome sequenced samples available, we chose to adopt a MAF threshold ρ of 1% for the noncoding RVIS and RVIS-CHGV. We had in our previous publication explored the behavior of the original RVIS for ρ of 0.01% and 1%, and found both of these to be strongly correlated with ρ = 0.1% (Pearson's r = 0.849 and Pearson's r = 0.813, respectively).
The collection of genomes used to derive ncRVIS includes various sample ascertainments (S1 Table). Given that we use the mutation rate matrix to define the underlying mutation rate (X), and only consider variants with a MAF>1% when determining (Y), we consider it highly unlikely that case-ascertained variants could be systematically influencing the current ncRVIS or RVIS-CHGV scores. We highlight F8 as the single gene that might require careful interpretation due to our collection of WGS samples that were ascertained for haemophilia.
Under the residual variation intolerance framework, ncRVIS will not correlate with either the noncoding mutability or noncoding sequence length. To confirm this, we find that the correlation between ncRVIS and the corresponding mutability or size of the effective noncoding sequence to be r2 = 3.0x10-8 and r2 = 2.0x10-5, respectively (S1B and S1C Fig). We further confirmed that the ncRVIS is not strongly correlated to the corresponding genes ‘protein-coding’ sequence size or ‘protein-coding’ mutability: r2 = 0.0031 and r2 = 0.0026, respectively (S1D and S1E Fig). We do note, however, that there is high correlation between a gene’s noncoding sequence length (number of bases) and its derived mutability using the mutation rate matrix (r2 = 0.9493, S1F Fig).
We first assessed the likely impact of using the estimated mutation rate instead of the observed variation by comparing two formulations of the original RVIS. To construct RVIS-mut we replaced the observed variation among the EVS population with the estimated mutation rate for that gene to represent (X) and kept the original Y variable from RVIS. Reassuringly, we find that RVIS-mut, using the estimated mutation rate, correlates highly (Pearson’s r2 = 0.83) with that using the total number of variants in each gene (RVIS) (S2 Table and S1G Fig).
We next evaluated the effect of not being able to identify functional mutations by comparing RVIS to a third formulation (RVIS-YALL). For RVIS-YALL we again use the effective mutation rate to represent X; however, we now permit all common protein-coding variants (including synonymous variants) for the Y variable. We find that RVIS-YALL remains highly correlated with the original RVIS (Pearson’s r2 = 0.59, S1H Fig); more importantly, it remains predictive of genes causing Mendelian disease (S2 Table).
Finally, we show that a fourth formulation of RVIS, using an independent set of 690 whole-genome samples that were sequenced at the CHGV (RVIS-CHGV), remains highly correlated with the original RVIS (Pearson’s r2 of 0.63, S1I Fig) despite a decreased sample size, and continues to be significantly predictive of the disease gene lists, with the exception of genes causing recessive disease (S2 Table).
These comparisons suggest that, in principle, the ncRVIS formulation should work similarly to RVIS when regulatory sequences are subject to purifying selection.
In our original RVIS paper we used omega (ω) as the phylogenetic approach to compare non-synonymous substitutions per non-synonymous site (dN) to the synonymous substitutions per synonymous site (dS) (aka Ka/Ks, dN/dS). Given we are now interested in noncoding sequence, we have generated an alternative estimate to assess whether correlation exists between the ncRVIS and that of possible phylogenetic conservation at noncoding sites. For each gene we constructed two conservation vectors: one reflecting the noncoding sequence of a gene after excluding protein-coding overlapping sites (ncGERP), and the other reflecting the protein-coding sequence of a gene (pcGERP). Both conservation vectors were based on the average GERP++ score [6] of the qualifying chromosomal sites within the defined sequence.
We found that ncGERP and pcGERP were moderately correlated to each other (r2 = 0.30). Compared to ncRVIS, both ncGERP and pcGERP had low correlation: r2 = 0.06 and r2 = 0.04, respectively. Likewise, compared to previously described RVIS [15], both ncGERP and pcGERP had relatively low correlation r2 = 0.06 and r2 = 0.15, respectively. These five correlation tests were performed based on the 14,998 genes with the corresponding scatterplots available in S1J–S1N Fig.
Interestingly, we found that pcGERP was inferior to ncGERP when comparing the 1,235 LoF deficient genes to the 1,762 LoF control genes, as described above (median pcGERP 32.38% vs. 65.57%; Mann-Whitney U test, p = 5.6x10-141; in comparison to median ncGERP 23.39% vs. 64.49%; Mann-Whitney U test, p = 3.4x10-171). While protein-coding genes are generally fairly phylogenetically conserved overall, there is variability inside the protein-coding genes in the phylogenetic conservation that correlates with whether a site causes disease or not. Overall, however, the majority of protein-coding genes are reasonably conserved across species. This leaves less scope for pcGERP variability among genes that can then be related to disease gene status (Fig 5). This is less true for the regulatory regions, where single-site variation is unlikely to systematically experience the same overall constraint as sites coding for structural components of the proteins. As a consequence of this, there is more scope for variability among the average ncGERP across the genome-wide spectrum of genes (Fig 5).
Literature includes alternatives to GERP++ for quantifying the degree of importance (sometimes referred to as functionality) of noncoding sequence in the human genome. Unlike GERP++, which is a direct measure of the phylogenetic conservation of a single site or a stretch of sequence, more recent alternatives include ensemble based predictors that leverage many features beyond conservation.
Although we recognize that nucleotide-level scores were constructed specifically for variant-level assessments; we nonetheless investigate whether a regionalized version of these scores could add information to predicting dosage-sensitive gene lists as well or better than ncRVIS or ncGERP. To this end, we calculated noncoding regional scores based on two popular nucleotide-level scoring frameworks: CADD [12] and GWAVA [13]. Using the same coordinates as ncGERP, we took the average CADD and GWAVA scores across the defined noncoding regions as a gene’s noncoding score.
To calculate regional noncoding CADD scores, referred to as ncCADD, we used the scaled C-scores from CADD version 1.0 [12]. In a regionalized form, ncCADD reflects the average CADD score for all possible single nucleotide substitutions across a gene’s defined noncoding sequence.
For regionalized noncoding GWAVA, referred to here as ncGWAVA, we downloaded the required training data and scripts from (ftp://ftp.sanger.ac.uk/pub/resources/software/gwava/) and followed instructions given by the developers to generate the site-specific scores for all noncoding exome sites. We were advised that for UTR sequence the TSS-distance matched training set would be optimal (personal communication with Dr. Graham Ritchie). Using the TSS-distance matched training set we derived the GWAVA score for each noncoding nucleotide site in a gene’s defined noncoding exome sequence and took the average to be the gene’s ncGWAVA score.
Neither CADD nor GWAVA were specifically developed to be interpreted as regional assessments. However, understanding the overall importance of a gene’s noncoding sequence as inferred from CADD or GWAVA could still be of interest. Both noncoding scores are provided in S1 Data. Scatterplots assessing correlations with other scores (including ncGERP) are available in S1S–S1AA Fig.
To assess the possible contributions of each ncRVIS subunit, we generated an ncRVIS score for promoter regions, 5’ UTR regions, and 3’ UTR regions for the set of 10,726 genes that had “assessable” sequence across all three distinct noncoding subunits. To permit comparisons with the original RVIS score, we further restricted comparison to the 9,644 distinct genes that also had a published RVIS score (Petrovski et al. 2013 [15]), an assessable ncRVIS score. We find that the highest correlation with the ncRVIS score comes from the 3’ UTR ncRVIS (r2 = 0.79), compared to promoter and 5’ UTR regions, which had r2 correlation of 0.25 and 0.20, respectively (S3 Table).
To generate a loss-of-function (LoF) deficient gene list, we take the five distinct mutation rates provided per gene by Samocha et al. (2014)[43] and calculate the expected frequency of protein-coding loss-of-function variants for each assessable consensus coding sequence (CCDS) gene (Ps). We achieve this by first summing the mutation rates corresponding to the three loss-of-function variant effect classes (nonsense, splice and frameshift) and dividing that by the total sum of the mutation rates of every possible mutation effect in the gene. We then use the resulting rate to determine the percentage of variants in a gene that we expect to result in a LoF effect, accommodating for the mutation rate. Based on the above, the average percentage of possible protein-coding mutations in a gene that are expected to result in a loss-of-function annotated variant (whether it is subsequently selected against or not) is ~9.22% of the sum of all possible protein-coding and canonical splice site mutation events. We then use the ESP6500SI database (accessed 20th March 2013) to extract for each gene both the total number of observed unique variants (n) and specifically the number of observed unique loss-of-function variants reported in the CCDS of each gene (x). This gives us our observed rate of LoF variants given all the protein-coding variation identified in the gene. Taking a gene’s expected percentage of unique loss-of-function variation under neutrality as calculated by (Ps), a subset of 1,235 genes with ncRVIS and ncGERP scores were identified as being significantly deficient of loss-of-function variants using a one-sided binomial exact test with Benjamini & Hochberg false discovery rate multiple-testing correction (FDR = 1%)[51] (S4 Data). As a comparative group, we identified a set of 1,762 ‘control’ genes where we observe greater than the expected number of loss-of-function variants. While this list of LoF control genes cannot be interpreted as significantly LoF tolerant, we consider the list a useful comparative group to the genes found to be significantly LoF deficient. It is clear that we are currently missing some true LoF intolerant genes due to insufficient resolution (power) from the EVS reference cohort. The result of this reduced power is that the majority of the exome is considered non-informative for LoF deficiency. Larger cohorts will enable better discrimination of truly LoF deficient genes. However, even though it is currently an incomplete list, the list of genes that are already significantly LoF deficient is already a valuable resource.
Finally, to illustrate that this list is robust to false positives driven by how much of the gene has been effectively sequenced, we repeated the exact implementation only this time asking whether any genes were significantly deficient of synonymous (presumed neutral) variation. In comparison to the LoF assessment where we identified 1,235 genes with an FDR < 1%, genome-wide the lowest FDR among the synonymous assessment was an FDR of 61%, with no other gene achieving an FDR < 99.99% for the synonymous assessment. This further highlights the integrity of this approach to detect LoF deficient genes in the human genome.
|
10.1371/journal.pntd.0005938 | Lymphatic filariasis transmission on Mafia Islands, Tanzania: Evidence from xenomonitoring in mosquito vectors | Lymphatic filariasis (LF) is a chronic nematode infection transmitted by mosquitoes and in sub-Saharan Africa it is caused by Wuchereria bancrofti. The disease was targeted for global elimination by 2020 using repeated community-wide mass drug administration (MDA) distributed in endemic areas. However, recently, there has been a growing recognition of the potential role of including vector control as a supplement to MDA to achieve elimination goal. This study was carried out to determine mosquito abundance and transmission of bancroftian filariasis on Mafia Islands in Tanzania as a prerequisite for a search for appropriate vector control methods to complement the ongoing MDA campaign.
Mosquitoes were collected indoor and outdoor using Centre for Disease Control (CDC) light and gravid traps, respectively. Collected mosquitoes were identified based on their differential morphological features and Anopheles gambiae complex and An. funestus group were further identified to their respective sibling species by polymerase chain reaction (PCR). Filarial mosquito vectors were then examined for infection with Wuchereria bancrofti by microscopy and PCR technique.
Overall, a total of 35,534 filarial mosquito vectors were collected, of which Anopheles gambiae complex, An. funestus group and Culex quinquefasciatus Say accounted for 1.3, 0.5 and 98.2%, respectively. Based on PCR identification, An. gambiae sensu stricto (s.s) and An. funestus s.s sibling species accounted for 88.3% and 99.1% of the identified members of the An. gambiae complex and An. funestus group, respectively. A total of 7,936 mosquitoes were examined for infection with W. bancrofti by microscopy. The infection and infectivity rates were 0.25% and 0.08%, respectively. Using pool screen PCR technique, analysis of 324 mosquito pools (each with 25 mosquitoes) resulted to an estimated infection rate of 1.7%.
The study has shown that Cx. quinquefasciatus is the dominant mosquito on Mafia Islands. By using mosquito infectivity as proxy to human infection, the study indicates that W. bancrofti transmission is still ongoing on Mafia Islands after more than a decade of control activities based on MDA.
| Lymphatic filariasis is a chronic human disease caused by parasitic worms and transmitted by mosquitoes. The disease is targeted for elimination by 2020 through the treatment of the entire population at risk in endemic areas using a mass drug administration (MDA) strategy. After several years of MDA, there is now growing interest in including vector control as a supplement to MDA to achieve elimination goal. This study was carried out to determine mosquito abundance and transmission of lymphatic filariasis on Mafia Islands in Tanzania after nine rounds of MDA. Mosquitoes were collected indoor and outdoor using Centre for Disease Control (CDC) light and gravid traps, respectively. Filarial mosquito vectors were examined for infection with Wuchereria bancrofti by microscopy and PCR technique. A total of 35,534 filarial mosquito vectors were collected, of which Anopheles gambiae complex, An. funestus group and Culex quinquefasciatus Say accounted for 1.3, 0.5 and 98.2%, respectively. Using PCR, An. gambiae sensu stricto (s.s) and An. funestus s.s sibling species accounted for 88.3% and 99.1% of the identified members of the An. gambiae complex and An. funestus group, respectively. A total of 7,936 mosquitoes were examined for infection with W. bancrofti by microscopy. The infection and infectivity rates were 0.25% and 0.08%, respectively. Using PCR technique, of 324 mosquito pools (each with 25 mosquitoes) tested, 115 were found to be infected with at least a larval stage of W. bancrofti. The study concludes that Cx. quinquefasciatus is the dominant mosquito on Mafia Islands and that W. bancrofti transmission is still ongoing on Mafia Islands after a decade of control activities based on MDA.
| Lymphatic filariasis (LF) is a chronic infection with serious physical, mental and socio- economic consequences to the affected individuals, and ranked as one the leading causes of long-term disability in the world [1, 2]. In Sub-Saharan Africa, LF is caused by the filarial nematode Wuchereria bancrofti and transmitted mainly by Anopheles and Culex mosquitoes [3]. Globally, it has been estimated that more than one billion people live in endemic areas and are at risk of infection, and more than one third of these are in Sub-Saharan Africa [4]. In Tanzania, it has been estimated that 34 million people are at risk of LF infection and about 6 million live with debilitating manifestations of the disease [5].
LF was considered eradicable and the World Health Organization (WHO) launched a Global Programme to Eliminate Lymphatic Filariasis (GPELF) by year 2020 [6]. The principal elimination strategy in endemic countries is based on yearly community-wide mass drug administration (MDA) with ivermectin or diethyl-carbamazine in combination with albendazole [6, 7]. The drugs mainly kill microfilariae and it is assumed that the reduction of microfilarial load in endemic communities will lead to reduction or even elimination of transmission [8]. Since the inception of GPELF, countries have initiated their local control programmes and encouraging reduction in disease prevalence as a result of MDA have been reported elsewhere [9, 10]. In Tanzania, MDA intervention was launched on Mafia Islands in the year 2000 and geographical coverage has been expanded in most of the endemic districts [5, 11].
Recently, there is growing recognition of the potential role of inclusion of vector control to achieve interruption of LF transmission in different epidemiological settings [3, 12]. In line with this assumption, studies have indicated that use of insecticide treated bed nets (ITNs) resulted in reduction in prevalence and transmission of LF [13–17]. However, insecticide based mosquito vector control interventions are threatened by development of insecticide resistance [18] and change in behaviour or shift of mosquito vectors species [19, 20]. Other studies have shown that Cx. quinquefasciatus, an important filarial vector is relatively tolerant to insecticides used for ITNs and IRS interventions [13, 21]. Thus, to expedite LF elimination efforts, novel control methods are needed to tackle the growing population of insecticide tolerant Cx. quinquefasciatus which is responsible for most of LF transmission in Tanzania as previously reported [19].
This study was carried out to determine mosquito abundance and transmission of bancroftian filariasis on Mafia Islands in Tanzania as a prerequisite for a search for appropriate vector control method to complement the ongoing MDA campaign. Xenomonitoring in filarial vectors has been considered as an integral component of monitoring the impact of MDA and has been reported to provide real time information on LF transmission [22, 23]. For mosquito surveys, both Centre for Disease Control (CDC) light and gravid traps have been found to be useful tools for collection of filarial mosquito vectors [24, 25]. Dissections of the vectors and molecular tests based on polymerase chain reaction (PCR) have proved useful in detection of W. bancrofti in mosquitoes [19, 26, 27]. The potential of PCR to screen large number of mosquitoes relatively quickly with high precision are requirements when infection rates in mosquitoes decrease after repeated MDA cycles. By using xenomonitoring as a proxy to human infection, this study reports W. bancrofti transmission on Mafia Islands 15 years after the launching of MDA campaign by the Tanzanian National Lymphatic Filariasis Elimination Programme.
The study was conducted on Mafia Islands (07°91554’S, 39°65529’E) in the Indian Ocean, off shore of the Pwani Region at about 195 km south-east of Dar es Salaam, Tanzania. A distance of about 40 km separates the islands from the mainland Tanzania (Fig 1). Mafia is an archipelago of islands, with the main island surrounded by seven small islets. Of these, five islets namely, Mafia (main islet), Bwejuu, Jibondo, Juani and Chole are inhabited. Mafia Islands have an estimated population of 50,167 people [28], of which 92.6% live in the main island of Mafia. The inhabitants of Mafia islands are subsistence farmers of coconuts and rice, and some are fishermen. The islets receive two rain seasons, long rains in March to June and short rains in October to December. As in many coastal areas of Tanzania, LF is an important mosquito borne diseases on the Mafia Islands. Before the start of MDA campaign in 2000, the baseline prevalence of W. bancrofti circulating filarial antigens (CFA) on the Mafia Islands was 49% and declined to 4% in 2006 [29]. This drop of prevalence indicates that the MDA rounds had marked impacts on the prevalence of W. bancrofti [30], but not yet reached the elimination threshold.
Three villages, Kilindoni and Kiegeani (from the main Mafia Island) and Chole islet were purposely selected for the study. Other islets were excluded due to transport-related challenges and very low filarial vectors collected during preliminary surveys. All hamlets in Kiegeani and Chole villages (3 hamlets each) and an equal number of hamlets were selected from Kilindoni village in a non-random fashion to increase the odds of catching mosquitoes. All households in the selected hamlets were mapped using hand held Global Positioning System (GPS) device (Garmin etrex Legend H, Garmin ltd, USA). Three households in each hamlet were randomly selected for indoor mosquito collections using Centers for Disease Control (CDC) light traps (John W Hock Co, Gainesville, FL, USA). Light trapping was conducted as previously described [31] and mosquitoes were collected in each of the selected households every other day from 22nd January to 10th March 2014, resulting in a total of 22 light trap catch nights. In brief light traps were set in the evening between 17.00 to 18.00 hours and retrieved from 06.00 to 8:00 hours the following morning. Caught mosquitoes were transferred from the traps to labelled paper cups covered with netting material and transported to the field laboratory for identification and processing.
Moreover, two households were selected from each of the 9 study hamlets for outdoor mosquito collection using CDC gravid traps (John W. Hock Co., Gainesville FL). Gravid traps were set in peri-domestic areas and trapping was conducted as described previously [25, 32]. Traps were set in the evening between 17:00 and 18:00 hours, and retrieved the following morning between 06:00 and 08:00 hours in alternating days from 26th January to 7th March 2014. At each household the traps were ran for 18 nights. Collected mosquitoes were treated as described for light trap catch. Upon arrival in the field laboratory live mosquitoes were knocked down with chloroform and both (live and dead) were identified using morphological criteria [33,34]. In the field laboratory, freshly killed Cx. quinquefasciatus, Anopheles gambiae complex and An. funestus group were processed for W. bancrofti detection by microscopy. The rest were stored in Eppendorf tubes containing silica gel desiccants for later identification of sibling species of An. gambiae complex, An. funestus group and detection of W. bancrofti by PCR technique.
Members of the An. gambiae complex were identified by PCR based on method previously described to identify An. gambiae sensu stricto (s.s), An. arabiensis, An. quadriannulatus, An. melas and An. merus [35, 36]. In brief, DNA was extracted by using Bender buffer method [36, 37] that involved homogenizing individual mosquito and precipitating extracted DNA using potassium acetate and ethanol. PCR reactions were conducted in a final volume of 20μl consisting of 0.25μM of each of the five primers, 1:1 TEMPase Hot Start polymerase master mix (Ampliqon III, VWR-Bie Berntsen, Denmark) and 2μl of DNA extract. The samples were amplified in GeneAmp PCR Systems 9700 (Applied Biosystems, USA) and cycling conditions were 95°C for 15 minutes followed by 35 cycles of denaturation at 94°C for 30 seconds, annealing at 50°C for 30 seconds, extension at 72°C for 30 seconds and final extension at 72°C for ten minutes.
On the other hand, sibling species of the An. funestus group were identified based on species-specific primers in the ITS2 region on the rDNA genes, a method previously described to identify An. funestus, An. vaneedeni, An. rivulorum, An. leesoni and An. parensis [38, 39]. DNA was extracted as described previously for sibling species of the An. gambiae complex. Each PCR run was conducted in a final volume of 25 μl consisting of 0.5 μM of each of the six primers, 1:1 TEMPase Hot Start polymerase master mix and 3 μl of DNA extract. The samples were amplified in GeneAmp PCR Systems 9700 and cycling conditions were 94°C for 15 minutes followed by 45 cycles of denaturation at 94°C for 30 seconds, annealing at 50°C for 30 seconds, extension at 72°C for 40 seconds and final extension at 72°C for ten minutes.
Freshly killed An. gambiae s.l., An. funestus group and Cx. quinquefasciatus from both light and gravid traps were dissected and examined under microscopy for the first, second and human infective third stage larvae of W. bancrofti as previously described [40]. The required sample size for filarial vectors (mainly Cx. quinquefasciatus) examined by microscopy was estimated based on thresholds criteria outlined by the World Health Organization [41]. Moreover, using PCR technique, a separate sample (proportionally equal to dissected specimens) of randomly selected filarial mosquito vectors were pooled (25 mosquitoes in each reaction tube) and examined for presence of W. bancrofti infection as previously described [39, 42]. DNA was extracted from the pooled mosquitoes in the same way as explained for identification of sibling species of An. gambiae s.l. and An. funestus group. Extracted DNA was examined for presence of W. bancrofti by PCR targeting a highly repeated DNA sequences (the SspI repeat) found in W. bancrofti. In the reaction mixture, each of the 20 μl of PCR consisted of 0.25μM of each of the two primers (NV1&NV2), 1:1 Hot-Start TEMPase polymerase master mix and 2 μl of DNA extract. PCR thermal cycling conditions were 95°C for 15 minutes followed by 54°C for 5 minutes: then 35 cycles of denaturation at 94°C for 20 seconds, annealing at 54°C for 30 seconds, extension at 72°C for 30 seconds and final extension at 72°C for 5 minutes.
The amplified DNA for both sibling species and W. bancrofti specimens were separated based on their fragment size by gel electrophoresis and visualized under ultra violet light as previously described [35, 38].
Data were entered in Excel and later transferred to STATA 12 (Stata Corp, College Station, Tx, USA) for analysis. The "infectivity rate" of the dissected mosquitoes was calculated as the percent of mosquitoes infected with infective larvae (L3) and the "infection rate" as the percent of mosquitoes infected with any stage of the parasite (L1, L2 and/or L3). For the PCR technique used for pooled mosquitoes, the probability that any one mosquito is infected with any stage of the W. bancrofti parasite were calculated using Poolscreen 2.02 software, providing maximum likelihood estimates for the rate of infection [43]. The 324 mosquito pools screened for W. bancrofti by PCR were randomly selected from a total of 563 pools made using random number generator programme in Microsoft Excel 2007. Mosquito infection and infectivity rates were compared using two sample test of proportions and p-value ≤ 0.05 was considered statistically significant.
The study received ethical approval from the Medical Research Coordinating Committee of the National Institute for Medical Research, Tanzania (Ref: NIMR/HQ/R.8a/VOL. 9/1616). Before data collection, meetings were held with the district and respective village leaders to inform them about the study and to obtain their cooperation. Written informed consent was obtained from the heads of households before commencing mosquito collection in their respective houses or peri-domestic areas.
A total of 38,505 mosquitoes were collected in the three villages of Chole, Kiegeani and Kilindoni during the study period. CDC light and gravid traps collected 17,831 (46.3%) and 20,674 (53.7%) mosquitoes, respectively. Out of the collected mosquitoes, 35,534 (92.3%) were filarial vectors belonging to members of the An. gambiae complex (1.3%), An. funestus group (0.5%) and Cx. quinquefasciatus (98.2%). All members of the An. funestus group and 99.8% of the members of An. gambiae complex were collected with light trap method. On the other hand, of 34,899 collected Cx. quinquefasciatus, 57.8% were collected using gravid traps. Majority (72.8%) of the filarial mosquito vectors were collected in Kiegeani village (Table 1).
Of the collected Anopheles, 270 members An. gambiae complex and 114 An. funestus group were processed for sibling species identity using PCR technique. An. gambiae sensu stricto (s.s) sibling species accounted for 88.3% of the analysed members of the An. gambiae complex. Other members of the An. gambiae complex identified were An. arabiensis, An. quadriannulatus and An. merus. On the other hand, An. funestus s.s was the majority (99.1%) of the identified sibling species in the An. funestus group (Table 2).
A total of 3,866 filarial mosquito vectors collected with CDC light traps were dissected and examined for infection and infectivity with W. bancrofti. Nine (0.23%) Cx. quinquefasciatus were found to be infected with any of the three larval stages (L1, L2 and /or L3) of W. bancrofti and three mosquitoes (0.08%) were infective. None of the dissected members of the An. gambiae s.l. and An. funestus were found to carry W. bancrofti larvae of any stage. On the other hand, a total of 4,070 Cx. quinquefasciatus mosquitoes collected with CDC gravid trap were dissected and examined for infection and infectivity with W. bancrofti. Eleven (0.27%) Cx. quinquefasciatus were found to be infected with any of the three larval stages (L1, L2 and /or L3) of W. bancrofti and three (0.07%) were infective. Mosquito infection and infectivity rates between the two trap types were not significantly different (Table 3).
Using PCR technique, of 324 mosquito pools (each with 25 mosquitoes) tested, 115 were found to be infected with at least a larval stage of W. bancrofti. Analysis by trap type revealed that of 163 gravid trap mosquito pools processed, 70 were infected whilst out of 161 light trap pools processed, 45 pools were infected. The infection rates between the two trapping methods were not significantly different (two sample test of proportions, p>0.05). On the other hand, of 6 Anopheles pools processed, only one (belonging to An. funestus group) was infected. For both trap types and species, the probability that any one mosquito in the pool was infected with any stage of the W. bancrofti parasite was estimated at 1.7%. Comparison of mosquito infection rates as measured by the two xenomonitoring methods have shown that PCR estimate seven-fold higher infection rate than dissection (Table 3).
Mosquitoes belonging to the An. gambiae s.l., An. funestus and Cx quinquefasciatus are the vectors of Wuchereria bancrofti in Tanzania as well as in many other parts of Sub-Saharan Africa [3, 40, 44, 45]. In their review, Bockarie and colleagues [3] examined the potential role of vector control as a supplementary component of MDA based strategies in LF elimination in different epidemiological settings. Inclusion of vector control was predicted to lower the number of MDA cycles even in areas with less than optimal treatment coverage [3]. Of particular relevance, inclusion of vector control has been considered crucial in LF elimination where Culex and Aedes mosquitoes are involved in the transmission [3].
Studies have documented an increased potential of Cx. quinquefasciatus as vector due to its expanding population and its inherent efficiency in LF transmission as the prevalence of the disease falls [3, 46, 47]. The current study searched for potential LF vectors on Mafia Islands in an attempt to validate and deploy vector control method based on "lure and kill" [48] to accelerate LF elimination efforts.
Previous studies in north eastern Tanzania documented principal vectors of W. bancrofti in order of decreasing importance to be members of An. funestus group, An. gambiae complex and Cx. quinquefasciatus [40, 44, 49, 50]. In the current study, Cx. quinquefasciatus accounted for 98.2% of the filarial mosquito vectors caught and W. bancrofti infection and infectivity was confined to this vector. The findings of the current study are supported by studies conducted recently in north-eastern Tanzania indicating a shift in filarial vector from transmission by anophelines to Cx. quinquefasciatus [19, 25]. Cx. quinquefasciatus has been described to be the predominant vector of lymphatic filariasis in urban areas of the neighbouring Islands of Zanzibar [51]. Previously, Cx. quinquefasciatus was considered an urban vector but it has become successful in establishing itself in the rural areas possibly due to adoption of urban life in rural areas [46,47]. It is likely that Cx. quinquefasciatus will remain an important LF vector in many parts of coastal Tanzania following the reported decline in anopheline vectors [45].
In mosquito surveys, CDC light trap has been considered as an important tool as it collects a proportion of mosquitoes involved in the transmission (host seeking) and compares fairly well with a standard method based on human landing catch [52]. On the other hand, light traps do collect both anopheline and culicine mosquitoes, both of which are filarial vectors [25, 44, 45]. Other studies have suggested that by collecting population of mosquitoes that have taken at least one blood meal, gravid traps are ideal for xenomonitoring [53,54]. However, a study comparing the CDC light and gravid traps as tool for xenomonitoring concluded that gravid traps may be useful where Cx. quinquefasciatus is the only vector [25]. The current study has shown that W. bancrofti infection and infectivity rates detected by microscopy from mosquitoes collected with CDC light and gravid traps were not significantly different. However, due to the fact that, the former target host seeking while the later collect preferentially gravid Cx. quinquefasciatus, the use of any trap type should be adapted to the prevailing local LF vectors. Based on our findings, in areas where Cx. quinquefasciatus is the main vector, either of the traps is likely to provide accurate information on ongoing LF transmission in xenomonitoring. However, in areas where LF is anopheline transmitted, or both vectors prevail, light trap is an ideal tool for xenomonitoring [25].
Dissection of vectors to detect W. bancrofti infection in mosquitoes has been considered a gold standard method for LF xenomonitoring [19, 40]. However, studies have shown that, as the prevalence of LF decrease following repeated rounds of MDA, molecular based technique with high throughput and precision are ideal for xenomonitoring [26, 27]. The findings of the current study have shown that PCR was able to detect more filarial infection in mosquitoes compared to dissection. It was moreover evident that W. bancrofti infection rates detected by PCR form mosquitoes collected by CDC light and gravid traps were not significantly different. However, it should be noted that W. bancrofti parasites detected by PCR in the mosquitoes included all the vector-borne stages, since the PCR test used was not designed to discriminate between infective and non-infective stages of the parasite.
While detection of infected mosquitoes is an indication of the existence of a reservoir of microfilaraemia in human population, presence of infective mosquitoes harbouring L3 stages of W. bancrofti signifies ongoing transmission. The findings of this study provide an indication of potential on-going transmission of W. bancrofti on Mafia Islands. In a neighbouring district of Rufiji, the prevalence of W. bancrofti circulating filarial antigens (CFA) among schoolchildren was recently reported at 14.4% suggesting that transmission of LF has continued in the area despite nine rounds of MDA [30]. Mosquito infectivity rate reported in the current study was lower than that reported prior to start of MDA campaign in Tanzania [19, 40] and comparable to the situation after 5 rounds of MDA in north-eastern Tanzania [45]. It was evident that Cx. quinquefasciatus was the main filarial vector on Mafia Islands, that worth to be targeted with a vector control intervention as supplement to the ongoing MDA to accelerate LF elimination efforts. With these findings, there is an urgent need to assess the extent of on-going transmission through doing a follow-up survey on people including the use of the novel antibody test for W. bancrofti L3 larvae antigens [55].
The study has shown that Cx. quinquefasciatus was the dominant man-biting mosquito on Mafia Islands and W. bancrofti infection is confined to this vector group. Both CDC light and gravid traps were found useful for mosquito vector surveillance. Moreover, it was found out that molecular method based on PCR was seven fold more sensitive than dissection in detecting W. bancrofti infection in mosquitoes. By using xenomonitoring as proxy to human infection, the study indicated that W. bancrofti transmission was still ongoing on Mafia Islands after more than a decade of control activities based on MDA. Our findings suggest that inclusion of mosquito control method that target Cx. quinquefasciatus will accelerate LF elimination on Mafia Islands and other coastal areas of Tanzania.
|
10.1371/journal.pcbi.1004776 | Power-Law Dynamics of Membrane Conductances Increase Spiking Diversity in a Hodgkin-Huxley Model | We studied the effects of non-Markovian power-law voltage dependent conductances on the generation of action potentials and spiking patterns in a Hodgkin-Huxley model. To implement slow-adapting power-law dynamics of the gating variables of the potassium, n, and sodium, m and h, conductances we used fractional derivatives of order η≤1. The fractional derivatives were used to solve the kinetic equations of each gate. We systematically classified the properties of each gate as a function of η. We then tested if the full model could generate action potentials with the different power-law behaving gates. Finally, we studied the patterns of action potential that emerged in each case. Our results show the model produces a wide range of action potential shapes and spiking patterns in response to constant current stimulation as a function of η. In comparison with the classical model, the action potential shapes for power-law behaving potassium conductance (n gate) showed a longer peak and shallow hyperpolarization; for power-law activation of the sodium conductance (m gate), the action potentials had a sharp rise time; and for power-law inactivation of the sodium conductance (h gate) the spikes had wider peak that for low values of η replicated pituitary- and cardiac-type action potentials. With all physiological parameters fixed a wide range of spiking patterns emerged as a function of the value of the constant input current and η, such as square wave bursting, mixed mode oscillations, and pseudo-plateau potentials. Our analyses show that the intrinsic memory trace of the fractional derivative provides a negative feedback mechanism between the voltage trace and the activity of the power-law behaving gate variable. As a consequence, power-law behaving conductances result in an increase in the number of spiking patterns a neuron can generate and, we propose, expand the computational capacity of the neuron.
| There is increasing evidence that the activity of individual membrane ion channels, conductances, and the firing rate of neurons are history dependent. In this work we studied how history dependent activation of membrane conductances affect the action potential activity of the Hodgkin-Huxley model, a widely used model of action potential generation. In order to implement history dependent activation, we made use of fractional order differential equations. This type of history dependent differential equations are increasingly being used in biomedical sciences to simulate complex phenomena. We use fractional order derivatives to model the kinetic dynamics of the gate variables for the potassium and sodium conductances of the Hodgkin-Huxley model. Our results show that power-law dynamics of the different gate variables result in a wide range of action potential shapes and spiking patterns, even in the case where the model was stimulated with constant current. As a consequence, power-law behaving conductances result in an increase in the number of spiking patterns a neuron can generate and, we propose, expand the computational capacity of the neuron.
| The large majority of conductance based neuronal models assume that the membrane voltage and conductances follow a Markov process [1, 2]. As such, the value of each of these variables in the next time point is dependent exclusively on its present state [3]. Increasing evidence shows that this assumption is not applicable all the time. The distribution of closed states of single channels [4, 5], the recovery time from inactivation of individual conductances [6, 7], and the spiking patterns generated over prolonged periods of time [8] show history dependence. If a conductance’s response to a voltage clamp command follows Markov dynamics then the time adaptation of the conductance is described with an exponential function. In contrast, if the adaptation of the conductance is history dependent then its response is usually described with a power-law. The power-law response could be due to the cumulative effect of multiple exponential processes with time constants distributed over a wide range of scales [9]. However, power-laws also arise when the fundamental Markovian assumptions break down with no single time constant describing the behavior of the system and possibly reflecting strong, allosteric, interactions among internal states of the channels [10]. Under such conditions the transitions between states depend on the history of the activity of the channel. While many of the studies on power-law dynamics in single neurons have centered on action potential rates [11–13] and membrane voltage [14–17] little is known of how a power-law behaving conductance could affect the spike generation properties of a neuron.
The natural mathematical tool to implement history dependent power-law dynamics is the fractional order differential equation [18]. For processes that show slow adaptation the order of the fractional derivative (η) is less than 1. The value of the fractional order corresponds to the power-law exponent of the process being modeled. We recently introduced the fractional leaky integrate-and-fire model (LIF) [14], which we have used to replicate the firing rate activity of adapting cortical neurons. We have also developed tools to efficiently integrate such equations [19]. Other groups have used the fractional derivative of the voltage to study the Hodgkin-Huxley [15–17] model or to model the power-law firing rate adaptation observed in cortical and brain stem neurons [12, 13]. Fractional order dynamics is being increasingly used through computational biology sub-disciplines to model complex systems that show history dependence and power-law dynamics [20].
Here we study the effects of power-law behaving conductances in a biophysical model of spiking activity, the Hodgkin-Huxley model. We systematically modified the dynamics of the gating variables of the potassium (n) and sodium (m and h) conductances to generate power-law history dependent activity. Our results show the emergence of a wide range of spiking behaviors in response to constant stimulation as a function of the fractional order in the different activation/inactivation variables. In the case of the n gate, the neuron shows reduction of spiking response and emergence of sub-threshold oscillations. While power-law behavior in the h gate results in bursting activity and pseudo-plateau potentials. This emergent richness in spiking activity, while only modeling two conductances, allows to study the effects of power-law behavior in neuronal activity. Computationally, we suggest that power-law conductance behavior allows neurons to increase their coding capacity.
The Hodgkin and Huxley model is [1]
CdVdt=−(gm(V−El)+gK¯n4(V−EK)+gNa¯m3h(V−ENa))+I
(1)
where C is the membrane capacitance; V is the membrane voltage; gm is the passive conductance; El is the leak reversal potential; gK¯ and gNa¯ are the maximum potassium and sodium conductances, respectively; EK and ENa are their reversal potentials, and I is the input current. The gating variables n, m, and h are defined by the general equation
dxdt=αx(V)(1−x)−βx(V)x
(2)
where x = [n, m, h], the function α is the forward rate, and β is the backward rate. The gating variables n and m are known as activation variables while h is an inactivation variable. The functional forms of n, m, and h are [1]:
αn(V)=0.1−0.01(V−V0)e1−0.1(V+V0))−1
(3)
βn(V)=0.125e−(V−V0)/80
(4)
αm(V)=2.5−0.1(V−V0)e2.5−0.1(V−V0))−1
(5)
βm(V)=4e−(V−V0)/18
(6)
αh(V)=0.07e−(V−V0)/20
(7)
βh(V)=11+e(3−0.1(V−V0)))
(8)
In this work we systematically study the effects on the spiking activity of the Hodgkin-Huxley model to the implementation of fractional dynamics on each of the gating variables:
dηxdtη=αx(V)(1−x)−βx(V)x
(9)
where we use the Caputo definition [21] of the fractional derivative for η<1
dηfdtη=1Γ(1−η)∫0tf′(t)(t−u)ηdu
(10)
where Γ is the Gamma function. The fractional derivative value is the result of integrating the activity of the function over all past activities weighted by a function that follows a power-law. The weighted past values are called the memory trace. As opposed to the first derivative, the fractional derivative provides information over all past activity. We numerically integrate the fractional derivative using the L1 scheme [22],
dηx(tN)dtη≈(dt)−ηΓ(2−η)[∑k=0N−1[x(tk+1)−x(tk)][(N−k)1−η−(N−1−k)1−η]]
(11)
where 0<η≤1, tk = k dt, N = tN/dt, and dt = 0.001 ms. By combining this equation and the gating dynamic equation and solving for x at time tN we obtain the equation that we use to integrate the function
x(tN)≈dtηΓ(2−η)[αx(V,tN−1)(1−x(tN−1))−βx(V,tN−1)x(tN−1)]+x(tN−1)−[∑k=0N−2[x(tk+1)−x(tk)][(N−k)1−η−(N−1−k)1−η]]
(12)
Where, again, x = [n, m, h]. The first two components of the right hand side of the equation are the solution of the classical differential equation. The last component of the equation is the memory trace. We have recently developed efficient ways to computationally solve these equations [19]. The memory trace is the last part of Eq 12
−[∑k=0N−2[x(tk+1)−x(tk)][(N−k)1−η−(N−1−k)1−η]]
(13)
The large number of simulations performed for this study were managed using our recently developed simulator workflow manager (NeuroManager) [23]. In brief, NeuroManager is an object-oriented application written in MATLAB (Natick, MA) that automates the workflow of submitting neuroscience simulations. The simulations in this paper were run by NeuroManager using a heterogeneous set of resources ranging from local UNIX servers (multi-core XEON processors), institutional clusters (Cheetah cluster at the UTSA Computational Biology Initiative, www.cbi.utsa.edu), and national resources (Stampede Cluster at the Texas Advanced Computing Center, www.tacc.utexas.edu). NeuroManager allows the user to isolate the free parameters of the simulations and define them as an Input Parameter Vector and organizes the results and products of each simulation. All code is available at GitHub (https://github.com/SantamariaLab/PowerLawHH), and the ModelDB database (https://senselab.med.yale.edu/ModelDB accession number 187600).Unless otherwise indicated the simulations use the following parameter values assuming 1 cm2 of membrane: C = 1 μF, gNa¯ = 120 mS, gk¯ = 36 mS, gm¯ = 0.3 mS, ENa = 50 mV, EK = -77 mV, and EL = -54 mV. For all the simulations we used the same initial conditions: m = 0.0529; h = 0.5960; n = 0.3177; and V0 = -65 mV, which produced a zero change in voltage in the classic case. To calculate the value of the Mittag-Leffler function (see below) we used the algorithm developed by I. Podlubny and M. Kacenak (www.mathworks.com/matlabcentral/fileexchange/8738-mittag-leffler-function).The value of the power-law behaving gate was calculated using Eq 12 and the value of all other variables in Eq 1 were calculated using a Runge-Kutta method of 4th order.
Our goal was to determine the effects of power-law activation of membrane conductances on spiking activity. The natural mathematical way to implement power-law dynamics is by using fractional order differential equations. We modified a Hodgkin-Huxley model to incorporate fractional order gating variables. First, we provide a theoretical justification of the model and then describe the effects of having fractional order dynamics on the individual gates. The analyses of the gate variables provide a method to determine whether an experimentally measured conductance is following a power-law process.
Traditionally, a single ion channel is described to have an open and closed states. The closed state can be composed of multiple ‘hidden’ states. Under Markovian assumptions states are independent and their residence times follow exponential dynamics. To produce power-law dynamics of the open-close transitions one can assume the existence of a large number of hidden states. Under such a model the state of the channel can be described as a diffusion process over a large number of traps. These types of models are well known to produce anomalous diffusion, a power-law behavior [24] and have been shown to replicate single channel dynamics [25]. It is also possible that the residence times do not follow exponential dynamics, due to internal state interactions or temporal correlations [25].
A purely power-law process does not have a mean residence time [26]. This would result in the absence of a stationary response. Since it is possible to get stationary responses when measuring conductance dynamics, it is necessary to assume that a channel can have a normal and power-law transitions. As such, we develop our model by expanding the Hodgkin-Huxley gating dynamics (Eq 2) to have both classical and power-law components
dxdt=r0[αx(V)(1−x)−βx(V)x]+∑i=1mrid1−ηidt1−ηi[αx(V)(1−x)−βx(V)x]
(14)
The sum on the right hand side of the equation describes multiple gating processes with different fractional order dynamics that describe memory dependent activity. We chose to use the same reaction rates (αx(V) and βx(V)) for simplicity and then scale them with the factors ri, i = 0 to m. This is similar to the fractional relaxation equation of [26]. This full model describes a system that has a finite mean residence time (classical component) with the perturbation from power-law processes. We can write the same equation in a compact form
dxdt=∑i=0mrid1−ηidt1−ηi[αx(V)(1−x)−βx(V)x]
(15)
We define η0 = 1 and r0 = 1 so in the case when m = 0 the model reduces to Eq 2. For m = 1 the system models a mixture of the classical and a single fractional order process. In our case, we assume that the rate of transition of the classical model is much smaller than the rate of the fractional model (r0 ≪ r1). This means that the fractional dynamics occur much faster than the classical process. Thus we can approximate the dynamics as (r1→1)
dxdt≈d1−ηdt1−η[αx(V)(1−x)−βx(V)x]
(16)
Re-arranging the fractional order operator yields our model
d−1+ηdt−1+ηdxdt=[αx(V)(1−x)−βx(V)x]
(17)
dηxdtη=[αx(V)(1−x)−βx(V)x]
(18)
The solution of this linear fractional differential equation can be obtained using the Laplace transform technique (see also [15])
ℒ(dηxdtη)=ℒ[x∞(V)−xτx(V)]
(19)
Where x∞(V) = αx(V)/(αx(V)+βx(V)), τx(V) = 1/(αx(V)+βx(V)). Resulting in:
sηX(s)˜−sη−1x(0)=x∞(V)τx(V)s−X(s)˜τx(V)
(20)
Where X(s)~ is the Laplace transform of x and s the Laplace space variable. Re-arranging
X(s)˜=x∞(V)τx(V)s(sη+1τx(V))+sη−1x(0)sη+1τx(V)
(21)
Using the method of partial fractions and re-arranging:
X(s)˜=x∞(V)s+[x(0)−x∞(V)]sη−1sη+1τx(V)
(22)
Note that the inverse Laplace transform of
ℒ−1(sη−1sη−1τx(V))=∑n=0∞znΓ(ηn+1)=Eη(z)
(23)
Eη is the Mittag-Leffler function or the generalized exponential function [27]. Therefore, taking the inverse Laplace transform of the entire equation results in [17]
x(t)=x∞(V)+[x(0)−x∞(V)]Eη(−[tητx(V)])
(24)
We characterized the response of each one of the power-law behaving activation gates (Eq 18 with x = [n, m, h]) to fixed voltage step commands. The simulations consisted of a period of 20 to 30 ms at a voltage V = 0 followed by the target voltage for up to 100 ms, with target voltages varying from -100 to 120 mV. For a given value of the input voltage command we varied η from 0.2 to 1.0. We compared the results of the numerical (Eq 12, Fig 1A dotted line) and analytical (Eq 24, Fig 1A solid line) solutions for all the traces, values of η, and voltage commands. The average mean squared error (m.s.e.) between the numerical and analytical solutions for the n gate was 8.2x10-7, for the m gate was 2.7x10-4, and for the h gate was 9.2x10-7. The relatively higher m.s.e. in the m gate traces could be due to the very fast kinetics of this variable which results in deviations from the analytical solution at very short periods of time. In fact, the simulations were unstable for the power-law m gate for values of η≤0.2, even when using time steps as small as 10−5 ms. In any case, for the large majority of cases our numerical integrations are well matched by the analytical solutions.
In order to quantify the effect of power-law dynamics on each gate we calculated the instantaneous long term response function (x∞η, with x = [n, m, h], see definition in explanation of Eq 19). The values of x∞η were obtained from the responses of the respective gates to all combinations of voltage commands and values of η. Specifically, to calculate x∞η we measured the value of the power-law behaving gates at t = 90 ms for the n gate, 40 ms for the m gate, and 110 ms for the h gate. These times were chosen because the value of the traces changed by less than 0.01% from the previous millisecond. For η = 1 the n, m, and h gates reproduced the classic Hodgkin-Huxley sigmoidal functions (Fig 1B). However, as the value of η decreases the slope at the inflection point of the n and h gates become shallower, but not for the m gate (arrows in Fig 1B). Comparing the values of x∞η using our numerical (dotted) and analytical (solid) models shows a very good match. Therefore, power-law dynamics affects the long term response of the n and h gates but has little effect on the fast activating m gate.
A hallmark of a power-law process is that the temporal response of the system cannot be characterized with a single time constant. To illustrate this property we fitted a dual-exponential process to the temporal response of each power-law gate over time windows of up to 100 ms. This fitting process resulted in the calculation of a fast and slow time constant (τxη, x = [n, m, h], see explanation of Eq 19 for a definition). For all the gates when η = 1 the τxη were identical for the fast and slow time constants and to the classic Hodgkin-Huxley model. For the n and h gates as η decreases the fast time constant accelerates while the slow time constant slows down, consistent with power-law dynamics. In comparison, the effect of the fractional order derivative on the m gate was fitted with a single exponential process that decreased with lower values of η. This suggested that the fast m gate kinetics were only affected over very short periods of time. In summary, power-law dynamics have a strong effect on τxη and x∞η for the n and h gates, while only having an effect on the fast time constant of the m gate.
The shapes of the kinetic curves for each of the gate variables as a function of η do not allow to predict whether the complete Hodgkin-Huxley model could produce spikes. In order to test this hypothesis we implemented a full Hodgkin-Huxley model in which a gating variable is governed by fractional dynamics while the other two remained normal. In all simulations we injected a constant current step, from 1 to 24 nA, for 500 ms. We found that action potentials were generated for all values of η for each one of the power-law dynamic gates. As is well known, the classical Hodgkin-Huxley model can respond with a single spike before it generates a sustained train of action potentials, with this first shape of the spike being slightly different than the rest [28]. For this reason, we characterized the second generated spike at the minimum input current to elicit spiking for the different values of η for each of the activation gates (Fig 2A). In the case of power-law n as η decreased the width at half-height of the action potential broadened, from 1.18 ms for η = 1.0 to 1.86 ms for η = 0.2. There was also a decrease in the minimum value of the repolarization. A similar analysis for the m gate shows that for lower values of η the action potential narrows (Fig 2A, m gate). The effect of power-law behavior on the h gate shows a strong effect on the repolarization phase of the action potential (Fig 2A, h gate). As the value of η decreases the spike width increases. For η = 0.2 the voltage seems to reach a fixed steady state, known as depolarization block. However, as we will show later, this is not the case. Instead, the spiking activity transitions to a pseudo-plateau action potential. Using the same data we calculated the current threshold to generate at least one action potential (Fig 2B). This analysis shows that for power-law dynamics in the n gate the current threshold initially increases and then decreases as a function of decreasing η. In contrast, for both power-law dynamic m and h, the current threshold increases. Overall, this analysis shows that fractional order dynamics of the individual gating variables results in the generation of action potentials. Depending on the gate being modified the current threshold of the action potential changes with respect to the classic Hodgkin-Huxley model.
We performed a phase plane study of the action potentials generated at the current threshold. The phase plane analysis is commonly used in experimental work to determine changes in intrinsic excitability [29, 30]. In the case of implementing power-law dynamics in the n gate the overall trajectory of the action potential remains intact with the largest change being the repolarization phase (Fig 2C, n gate). A similar analysis when the m gate has power-law dynamic shows that the speed of the action potential increases as a function of η [30, 31] (Fig 2C, m gate). Similar to the n gate, the effect of power-law dynamics on the h gate affects the repolarization phase of the action potentials (Fig 2C, h gate). Phase plane plots are also used in experimental work to determine the voltage threshold by determining the voltage when the speed of the voltage crosses a determined value [30]. In our case we determined the voltage threshold as the value of the voltage when dv/dt>20 mV/ms. This analysis shows that when n has power-law dynamics the voltage threshold increases up to 2.14 mV. In contrast, when the power-law dynamics is in the m gate the threshold decreases by 1.68 mV. As expected from its kinetic properties, power-law dynamics in the h gate has no effect on the voltage threshold (Fig 2D).
The overall analysis of single action potentials shows that spikes can be generated with a wide range of values of η. Whenever an action potential is generated the amplitude is similar to the classical Hodgkin-Huxley model. The types of action potentials generated in all cases resemble various types of spikes reported in the literature [29, 32–35], including those from non-neuronal cells [36–38]. Thus, conductances with power-law properties can generate a wide range of action potentials shapes observed in multiple cell types.
After analyzing the effects of power-law dynamics on individual gates and on the shape of single action potentials we characterized the spiking patterns that emerge from this process. For this purpose we simulated the response of the full model to constant current injection for periods of time between 1,500 to 3,000 ms. For the different combinations of values of η and injected current the model showed multiple spiking patterns. For example, for a constant input current of 18 nA we varied the power-law dynamics of the n gate while keeping the m and h gates normal. For η = 1.0 the model generated the typical repetitive spiking pattern with a constant firing rate of 84 Hz (Fig 3A). For a value of η = 0.8 the number of spikes decreased by almost half and resulted in an average firing rate of 43 Hz. However, the spiking pattern transitioned from repetitive to increasing inter-spike intervals (Fig 3B). For η = 0.6 the firing rate dropped to 13 Hz with sub-threshold oscillation between each spike (Fig 3C). Further decrease to η = 0.4 also showed sub-threshold oscillations and an increasing inter-spike interval with an average firing rate of 28 Hz (Fig 3D). Another example shows that the effect of power-law dynamics on the h gate also changes the spiking patterns generated by the model in response to constant input. In this case, for a fixed input current of 11 nA and values of η ≤ 0.6 the model generates bursts of action potential and sub-threshold oscillations (Fig 3E–3G). These examples show that the power-law behaving conductances results in complex spiking patterns that evolve over time.
We classified the spiking patterns generated by the effect of implementing power-law dynamics in individual gates. Since the models could produce non-stationary patterns we decided to classify the spiking activity based on their short (<500 ms) and long term (>1000 ms) responses. We classified the spiking responses as: resting state (RS), no spikes or only one spike at the onset of the stimulus; tonic spiking (TS, Fig 4A); phasic spiking (PS), a few spikes within the first 500 ms (Fig 4B); mixed-mode oscillations (MMO), single spikes surrounded by sub-threshold oscillations (Fig 4C); square-wave bursting (SWB), a group of spikes surrounded by sub-threshold oscillations (Fig 4D); and pseudo-plateau bursting (PPB), long lasting spikes more commonly seen in non-neuronal cells (Fig 4E and 4F) [36–38].
We manually classified the spiking patterns generated by the model for a range of input currents from 0–20 nA and η = 0.2–1.0. We then produced a spiking pattern phase transition diagram for each of the power-law behaving gating variables (Fig 5). In the case of modeling power-law activation of the potassium channel the phase diagram shows that the spiking activity transitions from RS→ PS → MMO → TS for η = 0.3–0.8 (Fig 5A). In all cases, when large input current is applied to the model, this overcomes the dynamics imposed by the fractional derivative and recovers the repetitive firing of the Hodgkin-Huxley model.
The same analysis applied to the activation and inactivation variables of the sodium channel results in very different behaviors. The spiking activity of the model to fractional dynamics of the activation variable, m, results in increased threshold as η decreases. After the threshold is crossed tonic spiking results for the duration of the simulation (Fig 5B). When power-law dynamics is applied to the inactivation variable, h, there are multiple spiking patterns that emerge. After the spiking threshold is crossed and for values of η <0.8 the system presents SWB and PPB (Fig 5C). For very strong input the neuron spikes regularly (TS) except for values of η ≤ 0.2.
In summary, the presence of power-law activation dynamics results in an increase in the diversity of spiking patterns, from tonic spiking to mixed mode oscillations and bursting.
The numerical solution of the fractional derivative (Eq 12) can be described as a negative feedback mechanism to the value of the gate being computed. The value of the gate at time t is equal to the normal integration of the equation of differences plus a factor that is called the memory trace (Eq 13). When the power-law dynamics of a gate is integrated into the entire Hodgkin-Huxley model then the memory trace acts as a balance between gate activation and action potential generation. To illustrate this point we analyzed the membrane voltage, gate values, and memory traces of several simulations when they generated different spiking patterns.
As shown before, the MMO patterns are obtained when implementing power-law dynamics in the n gate. We compared the voltage trace of the power-law n, with η = 0.7, (Fig 6A, black line) and classic (Fig 6A, gray line) models under the same current input conditions. This shows that the sub-threshold oscillations are not just a process in which the action potential threshold of the classic model is not reached, but that affects the underlying firing rate and spike shape (Fig 6A, right). The memory trace of the n gate shows a negative contribution to the activation of the gate during the action potential depolarization and positive during the repolarization phase (Fig 6B). The negative feedback effect during the generation of the action potential results in a peak value of n smaller than in the classic Hodgkin-Huxley (Fig 6C). As a result the dynamics of the normally activated m and h gates are also modified (Fig 6D and 6E). As shown in Fig 1, the time constant of the potassium conductance decreases over short periods of time. This is due to the positive feedback contribution of the memory trace as the action potential repolarizes. Then this conductance compensates faster for the influx of sodium current, thus blocking the generation of an action potential, instead, producing a sub-threshold oscillation. As the effect of the memory trace vanishes on the n gate then the two currents behave closer to the classical case and an action potential is produced. This dynamics is better understood with a phase plane of the currents involved (Fig 6F–6H). We plotted the value of the sodium current (INa) versus Iw = potassium + leak + input currents (Fig 6F, black line). We compared this phase plot to the classic Hodgkin-Huxley model under the same conditions (Fig 6F, gray line). As a reference, we plotted the balanced current between INa and Iw (Fig 6F, red line). Trajectories above this line tend to generate an action potential, while trajectories under this line show that the repolarizing currents are stronger than the INa. At the base of the phase plot we found an attractor that corresponded to the sub-threshold oscillations (red square in Fig 6F and 6G). This attractor has a trajectory around the line of balanced current. To better visualize the attractor we plotted the value of the imbalance current (INa+Iw) vs Iw (Fig 6H). This plot shows that the balance point is around -10 nA. After an action potential is generated then the Iw is faster to compensate for Ina (* in Fig 6H), bringing the trajectory close to the center of the attractor and oscillate outwards until the potassium conductance returns to a normal state, which then allows the generation of a new action potential.
We performed a similar analysis of the PS spiking pattern (Fig 7 with the corresponding voltage trace in Fig 4B). As in the MMO spike pattern the phase plane plot of the INa vs Iw also shows the presence of an attractor at the base of the trajectory (Fig 7A, red square). The current balance point between INa and Iw is close to -6 nA (Fig 7B). As the model generates spikes (S1 to S4 in Fig 7B) the positive imbalance current decreases until the model generates a first sub-threshold oscillation (labeled missed spike in the figure), then a forth spike (S4) is generated, then the trajectory settles into the attractor (RS in the figure). For the duration of this simulation (1,500 ms) no more action potentials were generated; however, it is possible that after the effect of the memory trace on the n gate vanishes the model could start spiking again. The attractors for the MMO and PS patterns are very similar (Figs 6H and 7B). In both cases, the generation of a new action potential is suppressed by a faster compensation of the INa by the potassium current, which is consistent with an acceleration of the time constant due to power-law dynamics.
Power-law dynamics in the h gate can generate SWB and PPB spiking patterns (Fig 8A) depending on the combination of input currents and values of η (see Fig 5C). There are two types of PPB patterns produced by the model. The first one resembles pituitary cell action potentials, which are characterized by a spike followed by high voltage oscillations [36]. The second PPB spiking pattern resembles cardiac myocyte action potentials with a sharp spike followed by a high voltage plateau [37]. Pituitary-type action potentials were generated with higher input currents than cardiac-type action potentials (Fig 8A). In all cases, including the SWB, the amplitude of the memory trace was more than an order of magnitude larger than in the case of the power-law n gate (Fig 8B). In the case of the SWB pattern the spiking activity is slowed down and, as in the case of power-law n gate dynamics, the sub-threshold oscillation do not correspond to just missing spikes from the classic model (Fig 8A, square wave bursting column, black and gay plots, respectively). The effect of the memory trace on the activation of the h gate is to slow down its response when compared to the classic model (Fig 8B and 8C). This slowdown allows the action potential to broaden (cf Fig 2A) and, as a consequence, the maximum value of the n gate is higher than in the classic model (Fig 8D), with the m gate not being affected (Fig 8E). As the effect of the memory trace vanishes from the dynamics of the h gate then the system can again generate a series of action potentials.
In the case of pituitary-type PPB patterns (Fig 8A pituitary-type column) the memory gate also results in a slower activation of the h gate. In this case, this allows the sodium current to remain open for longer periods of time, which compensates for the potassium current, causing an oscillation at a voltage higher than the action potential threshold (Fig 8B–8E Pituitary-type column). As mentioned above, the cardiac-type PPB patterns are generated with lower input currents than the pituitary-type (Fig 8A cardiac-type column). This results in a sharper initial spike and avoids the oscillatory behavior seen for the pituitary-type spiking (Fig 8B–8E cardiac type column). Note that the voltage traces of the pituitary- and cardiac-type spiking patterns show oscillations in different parts of the action penitential. While the pituitary-type has the oscillations in the decaying supra-threshold section of the action potential the cardiac type show sub-threshold oscillations.
The phase plane analysis of the SWB and PPB spiking patterns confirms that attractors generated by the power-law h gate can appear in different sections of the action potential. In all cases the amplitude of the current generated by the power-law model was larger than in the classic case (Fig 9A). In each one of the trajectories generated we identified the location of the attractors (red boxes). Analyzing the imbalance current phase plane shows that for the SWB pattern the activity is similar to the one of the PS pattern, in which the action potentials during a burst decrease their positive current until the trajectory enters close to the attractor and then spirals out until generating another burst of action potentials. In contrast, in the pituitary-style pattern the attractor is located in the early repolarization of the action potential. Finally, the cardiac-type has a similar trajectory to the SWB and PS patterns, except that the time course of the action potential spreads over a long window of time.
In summary, the effect of the negative feedback of the memory trace on each of the gates variables of the Hodgkin-Huxley model results in the emergence of temporal attractors that balance the depolarizing and repolarizing currents. As a results power-law dynamics of membrane conductances can give rise to a wide range of spiking patterns.
We used fractional order derivatives to study the effects of power-law behaving conductances on the generation of action potentials in the Hodgkin-Huxley model. The fractional order of the derivative provides a memory trace to the past activity of the gate. Our a priori hypothesis was that the history dependence on the potassium channel would cause this conductance to have a stronger activation than in the original model and no action potentials would be generated. Similarly, we expected that for power-law behaving sodium activation the model would show depolarization block. However, our systematic computational analysis showed that for a wide range of values of η, the model produces spikes with similar amplitude to the classic model. The resulting spike shapes resembled action potentials found in multiple neuronal and non-neuronal cells. The spiking patterns generated in response to constant stimulation also showed an increase in the diversity of responses, such as TS, MMO, SWB, and PPB. Together, our results suggest that power-law behaving conductances can increase the diversity of spike shapes and patterns. We propose that power-law behaving conductances increase the information coding capacity of neurons.
The standard model of a membrane conductance is based on the independence of the open, closed, and inactive states. This assumption is based on a Markov model of protein function. The rate at which a state changes is determined by the voltage and temperature, but not by the previous history of the channel. At the stochastic level this implies that the probability of transition between states depends exclusively on the present state of the system. As a result, the dynamics of the conductance is characterized with an integer order differential equation (η = 1).
The Markov model of a voltage or calcium activated conductance is represented by a single open state and multiple closed or inactive states. Power-law activation of such channels can emerge when the number of closed/inactive states is large [5]. In those cases, the state of the channel is assumed to diffuse over the multiple closed states. The time between open episodes depends on the trajectories through the close/inactive states. Under conditions in which the probability of staying in the same state is similar across all states (trapping probability) then the open states follow a power-law distribution. This behavior is equivalent to a random walk with random waiting times, which results in anomalous diffusion, a well-known power-law process [39]. Under this model, each closed/inactive state is still independent and, formally, the process is memory-less. While the transition between states only depends on the present state the emergent behavior is imposed by the complex interactions of a large number of closed states. Thus, the memory trace from the fractional derivative represents the complexity of the distribution in internal states of the channel.
An alternative mechanism to generate a power-law behavior is that there is a small number of internal states that interact with each other. In this case, the transition rates between states not only depend on the present state but on some memory of where the state has been in the past, such as in allosteric processes [40]. At the stochastic level this would mean that the probability of transition changes depending on the previous trajectory of the state. A transition state going from C2→C1→0 with a rate between C1→0 of x would be different if the trajectory were C3→C1→0. The slow power-law activation (η < 1) emerges because a state that is closed increases the probability of the next state to remain closed, slowing down the opening of the channels. The memory trace of the fractional derivative represents then how much internal states influence each other, thus deviating from classical Markovian dynamics. Power-law voltage dynamics could also be possible without the sum of multiple membrane conductances but because of actually having fractional order capacitance properties [41]. Thus, a neuron could have independent sources of power-law dynamical properties in the voltage and membrane conductances.
While only using a sodium and potassium conductances our power-law conductance models replicate action potential shapes and activity patterns of multiple cell types. However, some of these patterns are generated by the combination of several conductances. In this context the effect of the power-law dynamics captures the combination of multiple conductances or the different expression of sub-units, which could provide more internal-states or states that interact more strongly.
Our results suggest that it is the potassium or inactivating variables that provide the increase in spiking shape and pattern richness, which is consistent with recent experimental results. For example, different potassium sub-units allow cortical cells to generate firing rate adaptation [13, 42, 43], which we have suggested follows power-law dynamics [14]; the recovery from inactivation of some calcium and sodium channels has been shown to be history dependent [6, 7]; and extended recordings of neurons also show history dependence [8, 44].
In our previous work we implemented power-law dynamics in the membrane voltage of a LIF model. In this model our aim was to replicate the firing rate adaptation reported in multiple types of cortical cells. Instead of increasing the complexity of the model by adding different types of conductances operating in different time domains we proposed that their cumulative effect results in power-law behavior. We showed that with fixed parameters (threshold and membrane resistance) our model replicated a wide array of experimental results by only changing the input current and the value of η. Most experiments were replicated with values of η < 0.2 [14]. In the present study, the power-law dynamics of the sodium and potassium conductances resulted in changes of the spike shape and spiking patterns that again only depended on the input current and the order of the fractional derivative. The model was consistent with experimental results that suggest that it is the potassium conductances and the recovery from inactivation that allows neurons to generate complex spiking patterns [6, 7, 13, 42, 43]. As such, fractional derivatives can capture the complexity of the combination of multiple conductances or the intrinsic dynamics of individual channels.
A recent study, analyzed the spiking and network properties of a fractional order voltage dynamics Hodgkin-Huxley model [15]. This work showed that applying the fractional order derivative to the voltage reproduces spiking properties not seen in the original model, such as the fast time-to-peak and spike time adaptation. However, this model did not generate complex patterns such as MMO or SWB. This could be due to the effect of the memory trance only on the membrane voltage without affecting the kinetics of the gating variables. In this study it was also found that the range of current inputs that elicit spiking is reduced as a function of the value of decreasing η. Although, we find that in our model the threshold to generate spiking varies we found spiking over the entire range of tested values of η. Furthermore, whenever action potentials were generated their amplitude was very similar to the classic model.
There are two studies close to our work in which the authors generalized the Hodgkin-Huxley model by applying fractional order dynamics to all the gates [16, 17]. However, these studies were more focused on the application of fractional dynamic analytical and numerical techniques and only analyzed the generation of a single action potential over a narrow range of parameters and values of η > 0.65. In contrast, our work systematically studied the response of the model to individual changes of each gate to power-law dynamics over a broad range of input currents and values of η. In any case, the numerical techniques used in these and our studies could be incorporated into standard neuronal simulation packages [45].
The detection of power-law dynamics is a topic of growing interest across the biological sciences [46]. While in stochastic processes detection of a power-law could be complicated by noise, in mesoscopic phenomena, such as in ionic currents in neurons, the measurements can be done more easily; however, experiments have to be designed to be able to detect the existence of power-laws. Isolating single conductances in neurons is experimentally challenging, thus there has to be combination of steps to conclude the existence of power-law behavior:
The number of spiking patterns a neuron can generate in relation to its input determines its information capacity [48]. In a Markov process, the spiking activity of a neuron is history dependent as a function of its slowest time constant. This implies that the spiking response, such as firing rate, measures the amplitude or timing of the input. However, if a neuron is constantly integrating inputs and its condition reflects the integration over temporal scales then the spiking activity can vary. Our results show that if conductances follow power-law dynamics then the spiking activity of the neuron will reflect not only the amplitude of the input but how long this input has been delivered, as this would be reflected in the changing spiking pattern. Thus, power-law adaptation increases the computational capacity of neurons. Taking our previous and present results together suggest that power-law dynamics in the voltage or membrane conductances increases the spiking repertoire of a neuron and provides constant adaptation to encode information even in the case of having a small number of conductances.
|
10.1371/journal.ppat.1002629 | Necrotrophism Is a Quorum-Sensing-Regulated Lifestyle in Bacillus thuringiensis | How pathogenic bacteria infect and kill their host is currently widely investigated. In comparison, the fate of pathogens after the death of their host receives less attention. We studied Bacillus thuringiensis (Bt) infection of an insect host, and show that NprR, a quorum sensor, is active after death of the insect and allows Bt to survive in the cadavers as vegetative cells. Transcriptomic analysis revealed that NprR regulates at least 41 genes, including many encoding degradative enzymes or proteins involved in the synthesis of a nonribosomal peptide named kurstakin. These degradative enzymes are essential in vitro to degrade several substrates and are specifically expressed after host death suggesting that Bt has an active necrotrophic lifestyle in the cadaver. We show that kurstakin is essential for Bt survival during necrotrophic development. It is required for swarming mobility and biofilm formation, presumably through a pore forming activity. A nprR deficient mutant does not develop necrotrophically and does not sporulate efficiently in the cadaver. We report that necrotrophism is a highly regulated mechanism essential for the Bt infectious cycle, contributing to spore spreading.
| Bacillus thuringiensis (Bt) is a well known entomopathogenic bacterium successfully used as a biopesticide for fifty years. The insecticidal properties of Bt are mainly due to specific toxins forming a crystal inclusion associated with the spore. After ingestion by susceptible insect larvae, toxins could induce favorable conditions for spore germination. The bacteria multiply in the insect and coordinate their behavior using signaling molecules involved in quorum sensing. The activation of the quorum sensor PlcR leads to the production of virulence factors allowing the bacteria to kill the insect host. Here we show that, in the cadaver, Bt shifts from a virulent to a necrotrophic lifestyle during which a second quorum sensor (NprR) becomes functional. NprR activates genes encoding degradative enzymes (proteases, lipases and chitinases) and a lipopeptide (kurstakin) involved in swarming and biofilm formation. The kurstakin is also essential for the survival of Bt after insect death. This suggests that NprR allows the bacteria to survive and eventually to sporulate in the host cadaver, thus improving their ability to disseminate in the environment. Altogether these results show that the pathogenic and necrotrophic lifestyles of Bt are tightly controlled by two quorum-sensing systems acting sequentially during the infection process.
| Saprophytism, probably one of the most common lifestyle for micro-organisms, involves living in dead or decaying organic matter. For most pathogens, saprophytism is limited to necrotrophism (the use of the host cadaver). This step of the infection process is essential for the proliferation and horizontal transmission of these microorganisms (transfer of infection within a single generation) [1]. However, there have been very few studies addressing this major issue. The transition from a pathogenic to a necrotrophic lifestyle implies substantial metabolic changes for microorganisms [2]. The death of the host is a critical event which compels the micro-organisms to cope with a new series of challenges: competition with the commensal organisms and opportunistic incomers, stress, and nutrient deficiencies. Therefore, necrotrophism is likely to be highly regulated.
The insect pathogen Bacillus thuringiensis (Bt) is a suitable model for studying the time course of the infection process, including necrotrophism in the insect cadaver. Bt is an ubiquitous spore-forming bacterium belonging to the Bacillus cereus (Bc) group [3]. Its spores are found in a large variety of environments, such as soils, dead and living insects and plant phylloplane [4]. However, Bt probably does not grow in soil and reports of natural epizootic episodes are very rare [5], [6]. Unlike soil bacteria, such as Streptomyces spp and B. subtilis, Bc group genomes contain a large number of genes involved in nitrogen metabolism [3]. It is therefore likely that Bt multiplies in the host cadaver [1], [6].
Bt carries plasmids encoding specific insecticidal toxins responsible for their insecticidal properties [7]. Bt spores and toxins are ingested by larvae, and the toxins bind to specific receptors on the midgut epithelial cells, inducing cell lysis and creating favorable conditions for the development of the bacteria [8]. The vegetative bacteria multiply in the insect hemocoel and cause septicemia [1], [9]. Bt also harbors genes encoding exported virulence factors including enterotoxins, hemolysins, phospholipases and proteases [10]. The transcription of most of these virulence genes in bacteria growing in a rich medium is activated at the onset of stationary phase by the quorum-sensing system PlcR-PapR [11], [12]. PlcR-regulated factors account for about 80% of the extracellular proteome of Bt during early stationary phase in rich medium [13]. In sharp contrast, the expression of the PlcR-regulated genes is repressed when the bacteria enter sporulation [14] and the stationary phase secretome of Bt and B. anthracis (Ba) growing in a sporulation medium is mainly composed of the metalloprotease NprA [15], [16]. NprA (also designated NprB and Npr599 in Ba) cleaves tissue components such as fibronectin, laminin and collagen, thus displaying characteristics of pathogenic factors [17]. Transcription of nprA is activated during the late stationary phase by the regulator NprR [16]. NprR is a quorum sensor activated by its cognate signaling peptide, NprX. NprR-NprX functions as a typical Gram-positive quorum-sensing system: the pro-signaling peptide NprX is exported from the cell, and after being processed to its active form is reimported, and binds to NprR allowing the recognition of its DNA target and the activation of nprA transcription [16].
The first stages of Bt infection are relatively well documented, but the fate of the bacteria after death of the host remains unclear. Here, we report evidence that the necrotrophic lifestyle of Bt is a specific and highly regulated process. The quorum-sensing system NprR-NprX controls at least 41 genes some of which are required for Bt to survive in the insect cadaver and to complete its development in vivo ending with the production of spores.
We tested whether NprR, the activator of nprA transcription [16], is involved in the pathogenicity of Bt. The LD50 s of the Bt 407 Cry− (wt) strain and of the nprR-deficient (ΔRX) strain in the insect model Galleria mellonella were measured in two ways: by feeding larvae with spores mixed with the insecticidal toxin Cry1C and by injection of vegetative bacteria into the insect hemocoel (Table S1). The LD50 s of two strains did not differ significantly in either of the two conditions indicating that NprR was not required for pathogenicity. Consistently, an nprA-deficient strain was similarly found not to be affected in pathogenicity (not shown).
We investigated the involvement of NprR in the infection process by comparing, in vivo, the expression kinetics of nprA with that of the protease gene mpbE, reflecting the transcriptional activities of NprR and PlcR, respectively [16], [18]. The reporter strains grew similarly in insect larvae and a constitutively expressed PaphA3-lacZ fusion was used as the reference standard (Figure 1A and Figure S1A). Transcription of mpbE increased between 0 h and 24 h after injection and gradually decreased thereafter. In contrast, nprA transcription was low between 0 h and 24 h, increased between 24 h and 48 h and then decreased sharply (Figure 1B). Thus, NprR is active later in the infection process than PlcR, and after the death of the host.
To investigate the role of NprR during the late stage of infection, we compared the growth of the wt and ΔRX strains in insect larvae (Figure 2A). The total population of the two strains increased between 0 h and 24 h to reach about 1×108 cfu/mL. From 24 h to 96 h, the population of the wt strain remained stable, whereas the population of the ΔRX strain decreased sharply: 96 h post infection, the total population of the ΔRX strain was 6-log lower than that of the wt strain. Complementation of the ΔRX strain by pHT304-RX restored the wt phenotype. These findings indicate that NprR substantially improves the survival of Bt in insect cadavers.
In sporulating microorganisms, sporulation is generally regarded as the key process ensuring survival in unfavorable conditions. We therefore investigated i) whether NprR was involved in the sporulation process of Bt in the insect cadaver, and ii) whether sporulation is responsible for the survival of the bacteria in the insect cadaver. We compared the sporulation efficiencies of the wt and ΔRX strains in both LB and sporulation-specific medium (HCT) (Table S2). In HCT, the sporulation efficiencies of the two strains were similar. However, in LB medium, the total number of viable spores of the ΔRX strain was half that for the wt strain (8.30×107 vs 1.58×108), suggesting that NprR is involved in the sporulation of Bt in rich medium. Next, we monitored the counts of wt and ΔRX strain spores in insect larvae over 96 h (Figure 2B and Table S2). For the wt strain, heat-resistant spores were detected 24 h after injection and their number increased until 48 h. From 48 h to 96 h, the number of spores remained stable and represented one third of the total bacterial population. The large number of non sporulated bacteria 96 h after the death of the insect suggests that sporulation was not the main mechanism allowing Bt to survive. For the ΔRX strain, less than one percent of the bacterial population was heat-resistant spores throughout the infection process. The decrease in the number of heat-resistant spores from 48 h to 72 h is likely due to the germination of the spores. We suspected that the low number of spores is not a cause but a consequence of the inability of the ΔRX strain to survive in the insect cadaver. To test this idea, we tested the survival of a sigK-deficient strain (Figure 2C): SigK is a sigma factor involved in the transcription of late sporulation genes in the mother cell, and sigK-deficient strains are not able to form viable spores [19], [20]. The total population of the sigK strain in the insect cadaver was similar to the total population of the wt strain, indicating that NprR ensures the survival of Bt by a process independent of sporulation.
The only gene described as being controlled by NprR was nprA. Therefore, we monitored the survival of a ΔnprA strain in infected larvae (Figure S2). The survival of the wt and ΔnprA strains was similar throughout the experiment, suggesting that other NprR-regulated genes are involved in bacterial survival. Microarray analysis was used to identify other NprR-regulated genes. Gene expression ratios between the wt and the ΔRX strains were determined 3 h after the onset of stationary phase (t3), when nprA transcription increases sharply [16]. For 107 genes, this expression ratio was greater than 2 (p<0.05) (http://www.ebi.ac.uk/arrayexpress/experiments/E-TABM-790), suggesting that NprR has a direct or indirect effect on their transcription. Thirty-nine genes, with a relative expression ratio greater than 4, and a significance value (p) smaller than 0.01, were considered for subsequent analysis. The genes matching probes for BC2622, a macrolide glycosyltransferase, and BC3725, an exochitinase, were also investigated due to their functional similarity to the genes fulfilling these criteria. Quantitative RT-PCR confirmed that these 41 genes were at least four times up- or downregulated. Fusions to lacZ were constructed for nine of these genes and used to confirm that they are differentially regulated in the ΔRX mutant and wt strains (Figure S3). The expression kinetics of these genes were similar, with a sharp increase of expression after the onset of stationary phase. The final list of NprR-regulated genes is presented in Table S3. Of the 41 genes directly or indirectly regulated by NprR, 37 were down-regulated in the ΔRX strain, suggesting that NprR primarily acts as a transcriptional activator. The subcellular localizations of the products of these genes were assessed: 46% were cytoplasmic, 20% were associated with the membrane and 34% were extracellular or associated with the cell wall. The NprR-regulated genes can be distributed into four functional groups. The first group is composed of genes encoding proteins potentially involved in stress resistance: they include the genes for cytochrome P450 (BC2613), cysteine dioxygenase (BC2617), and Transporter Drug/Metabolite exporter family members (BC1063). The second group is a four-gene locus encoding the oligopeptide permease system Opp required for the import of small peptides into the cell. The third group is a five-gene locus encoding a nonribosomal peptide synthesis (NRPS) system showing similarities with the systems involved in the synthesis of secreted factors like toxins and antibiotics. The last group codes for degradative enzymes (metalloproteases, esterases and chitinases) and for proteins which can bind organic material (chitin-binding protein and collagen adhesion protein). The role of NprR in the degradation of lipids, proteins and chitin was analyzed by growing the ΔRX and wt strains on specific culture media (Figure 3A). The lipolytic, proteolytic and chitinolytic activities of the ΔRX strain were significantly lower than those of the wt strain. We monitored the expression kinetics in infected insect larvae of two NprR-regulated genes encoding degradative enzymes (BC0429 and BC2167) (Figure 3B). The two genes were specifically expressed after the insect death, from 24 h to 96 h, suggesting that Bt displays a necrotrophic lifestyle in the insect cadaver.
To identify the NprR-dependent survival factor we first tested whether this putative factor was secreted. Insect larvae were co-infected with two different ratios of wt and ΔRX strains: 90% of wt bacteria with 10% of ΔRX bacteria (90∶10), and 10% of wt bacteria with 90% of ΔRX bacteria (10∶90) (Figure 4A and Figure 4B). In insects infected with the ratio 10∶90, the total population of the wt and the ΔRX strains decreased after 24 h. This may result from the ΔRX strain capturing NprX without being able to express NprR-regulated genes, but nevertheless removing the peptide from the environment. Consequently, the amount of signaling peptide was insufficient to activate NprR-regulated genes in the wt, resulting in clearance of both populations. Co-infection with the ratio 90∶10 led to the survival of the two subpopulations during the 96 h of the experiment. In this condition, the concentration of NprX in the host was presumably sufficient to maintain the expression of the NprR-regulated genes in the wt subpopulation, and this expression allowed the survival of the ΔRX population. Therefore, the wt strain may produce a secreted factor that enables the ΔRX strain to survive in the insect cadaver.
NprR-dependent extracellular factors are degradative enzymes and the factor synthesized by the NRPS system. NprA, the major degradative enzyme produced during late stationary phase, is not required for bacterial survival in insect cadaver (Figure S2). The NRPS locus consists of seven open reading frames annotated BC2450 to BC2456 in the genome of the strain Bc ATCC 14579 used for designing the microarrays [3]. In silico analysis of all available sequenced Bt and Bc genomes, including that of strain Bt 407 used in this study, reveals that in all cases, this locus includes only four genes (http://www.ncbi.nlm.nih.gov/bioproject/29717). Several studies suggest that these four genes (designated krsA,B,C,E; Figure 5A) are involved in the production of the lipopeptide kurstakin [21], [22], [23]. KrsE is a presumed efflux protein and KrsA, B, C are the peptide synthetase subunits. The genes krsA, krsB and krsC were deleted from a wt strain and the survival of the mutant (ΔkrsABC) in insects was monitored for 96 h (Figure 5B). The total population of the ΔkrsABC strain declined from 2.107 cfu/ml at 24 h falling to 1.102 cfu/ml at 96 h. To test whether this effect was specifically dependent on the krsABC genes, we introduced a constitutive promoter upstream from these genes in the ΔRX strain. This NprR-independent expression of krsABC partially and significantly restored the survival of the ΔRX strain in the insect cadaver. These observations implicate the krsABC genes in the necrotrophic properties of Bt. We used MALDI-ToF-MS analysis to determine whether the krsABC genes are responsible for the production of kurstakin. Peaks characteristic of kurstakin were found for whole cells of the wt strain and not for those of the ΔkrsABC mutant (Figure 5C). This confirms that the krsABC genes are involved in kurstakin synthesis.
We compared properties of the wt and ΔkrsABC strains on swimming plates (LB Agar 0.3%) and on swarming plates (LB Agar 0.7% and EPS Agar 0.7%) (Figure 6A). The wt and the ΔkrsABC strains grown on LB 0.3% agar covered the plates, indicating that both strains were swimming proficient. However, unlike the wt, the ΔkrsABC strain was unable to swarm or to form dendrites indicative of swarming mobility [24], [25]. Lipopeptides are known to enhance biofilm formation [26] and it has been shown that the Bt 407 strain forms a thick biofilm at the air / liquid interface in glass tubes [27]. We tested the ability of the two strains to form biofilm in glass tubes (Figure 6A). The wt strain produced a significant ring at the air / liquid interface, whereas biofilm formation was abolished for the ΔkrsABC strain. Kurstakin is therefore necessary for swarming and biofilm formation.
Lopez and coll. have shown that swarming mobility in B. subtilis is triggered by surfactin, a lipopeptide, which acts as a pore-forming molecule causing potassium leakage across the cytoplasmic membrane [26]. We tested the swarming mobility of the ΔkrsABC strain on swarming plates with nystatin (a pore-forming molecule) and with nystatin plus K2HPO4 (Figure 6B). Nystatin restored the swarming mobility of the ΔkrsABC strain and the addition of K2HPO4 reversed this phenotype. These results suggest that kurstakin is a pore forming molecule causing a potassium leakage across the cytoplasmic membrane of Bt.
PlcR is the main virulence regulator in Bt and Bc [10], [12] and is required for the early steps of the infection process [9], [28]. We show here that another quorum sensor, NprR, is active after host death and is necessary for Bt to survive in the insect cadaver. NprR is a pleiotropic regulator directly or indirectly affecting the expression of at least 41 genes during the stationary phase. About 30% of the NprR-regulated genes encode extracellular or cell wall-associated proteins involved in the degradation of proteins, lipids and chitin. We report that nprA and two other NprR-regulated genes encoding degradative enzymes were expressed after death of the host. Therefore, it is likely that these enzymes allow Bt to use the content of the host, indicating that Bt displays a necrotrophic lifestyle in the insect cadaver. This nutrient acquisition may support the developmental program of Bt until sporulation. The degradative enzymes may also have other functions. Insect cuticles are made of chitin filaments arranged within a protein matrix which constitutes a physical barrier to the outside environment. Degradative enzymes may degrade this barrier and facilitate spore and toxin release into the environment. Degradative enzymes may also participate in cell protection against competitors. For example, the endochitinase ChiCW was reported as having antifungal properties [29], and InhA3 (BC2984) is a member of the Immune Inhibitor A metalloprotease family, which plays a key role in the resistance to the host immune defenses by degrading antimicrobial peptides [30], [31], [32]. In addition, two substrate-binding proteins (BC2827 and the BC3526) may increase the efficiency of these enzymes.
A large locus of three NprR-regulated genes (krsABC) codes for a NRPS system involved in the synthesis of a secreted lipopeptide called kurstakin. At least three suggestions could explain the important and surprising function of kurstakin:
Some NprR-regulated genes encoding cytoplasmic or membrane-associated proteins may participate in the necrotrophic development of Bt. A putative efflux system (BC1063), two macrolide glycosyl transferases (BC2066 and BC2622) and a N-hydroxyarylamine O-acetyltransferase could be involved in resistance to antimicrobial molecules, and cytochrome P450 (BC2613) may be involved in resistance to reactive oxygen species. The membrane-associated proteins are mainly components of an oligopeptide permease system (Opp) involved in the uptake of PapR, the signaling peptide required for PlcR activation [36]. The operon encoding this Opp system is downregulated by NprR suggesting that NprR controls PlcR expression negatively through the Opp system. Interestingly, nprA expression was only slightly reduced in a oppB mutant strain suggesting that another oligopeptide permease system is involved for the uptake of NprX (Figure S4). Consequently, a down-regulation of the opp genes could not have a significant effect on the expression of the NprR-regulated genes. Possibly, the extracellular concentration of NprX is a signal triggering the transition from a pathogenic to a necrotrophic lifestyle. During the early stage of infection, the PlcR regulon is expressed and the extracellular concentration of NprX might be low. Indeed, recent results indicate that nprX transcription starts in late stationary phase (TD, SP, DL, unpublished data). In view of these data, we hypothesize that after the host death, the extracellular concentration of NprX increases, leading to the expression of NprR-regulated genes and repression of the PlcR regulon via the Opp transporter. These various observations indicate that the necrotrophic lifestyle of Bt is a complex developmental stage, not limited to simple feeding on the host contents. They also imply that the transition from a pathogenic to a necrotrophic lifestyle is associated with significant metabolic changes.
It is becoming clear that the infectious cycle of Bt can be divided into four distinct and sequential phases starting with toxemia caused by the Cry proteins, followed by the action of PlcR in virulence, necrotrophism and the completion of the sporulation process involving NprR, and finally the dissemination of spores (Figure 7). We describe a nprR-nprX mutant that does not develop necrotrophically and is unable to sporulate efficiently, demonstrating that the necrotrophic properties of Bt are essential for horizontal transmission. It is remarkable that the developmental process through the complete infection cycle in the insect host is coordinated by three regulator-signaling peptide cell-cell communication systems: PlcR-PapR, NprR-NprX and the Rap-Phr complexes which control the phosphorylation of Spo0A [37], [38], [39].
Members of the Bc group may be able to follow two different life cycles: an infectious cycle (described above) and an endosymbiotic cycle in which the bacteria live in a symbiotic relation with their invertebrate hosts [6], [40]. Here, we report that Bt multiplies efficiently in the insect cadaver and has a genetic developmental program to live in this biotope. Consequently, we propose the existence of a strictly necrotrophic life cycle in which Bt colonizes a wide variety of dead insects, and uses the cadaver as a bioreactor to multiply and to produce spores and toxins.
In silico analysis reveals that nprR is found in all strains of the Bc group, except that in Bc ATCC14579 nprR is disrupted by a transposon [3]. The published genome sequences of Ba and Bc strains provide no evidence for any loss of genetic determinants, which might be crucial for saprophytic survival [41]. Therefore, the function of NprR is probably conserved in the Bc group, and it would be interesting to determine whether the necrotrophic development of Bc and Ba in their mammalian hosts requires the same quorum-sensing regulated network. It is also important to characterize kurstakin to determine how it promotes survival in insect cadavers. Moreover, the properties of this molecule may indicate possibilities for the development of phytosanitary products or adjuvants to improve both the ecological fitness and the efficacy of biopesticides.
The Bt strain 407 Cry− is an acrystalliferous strain cured of its cry plasmid [42]. This strain shares high phylogenic similarity with Bc [43]. Bacillus 407 oppB::tet, Bacillus 407 sigK::aphA3, Bacillus 407 nprRX::tet (ΔRX), Bacillus 407 nprA::lacZ and Bacillus 407 nprRX::tet nprA::lacZ mutant strains were described previously [16], [20], [36]. Escherichia coli K-12 strain TG1 was used as host for the construction of plasmids and cloning experiments. Plasmid DNA for Bacillus electroporation was prepared from the Dam− Dcm− E. coli strain ET12567 (Stratagene, La Jolla, CA, USA). E. coli and Bt cells were transformed by electroporation as described previously [42], [44]. E. coli strains were grown at 37°C in Luria Broth (LB). Bacillus strains were grown at 30 or 37°C in LB or in HCT, a sporulation-specific medium [45]. The following concentrations of antibiotic were used for bacterial selection: 100 µg/ml ampicillin for E. coli; 200 µg/ml kanamycin, 10 µg/ml tetracycline, 200 µg/ml spectinomycin and 10 µg/ml erythromycin for Bacillus. Numbers of viable cells were counted as total colony-forming units (cfu) on LB plates. Numbers of spores were determined as heat-resistant (80°C for 12 min) cfu on LB plates.
Force-feeding and intrahemocelic injection experiments with G. mellonella were carried out as described previously [9]. LD50 data were analyzed using the program StatPlus 2007 of Analysoft. Bt cells in living and dead insects were counted as follows. For each strain, each larva was injected with 2.104 bacteria and kept at 30°C for 96 h; 24 h after injection, surviving insects were eliminated. At the injection time and every 24 h for the 96 h of the experiment, two larvae were crushed and homogenized in 10 ml of physiological water and dilutions were plated onto LB agar plates containing appropriate antibiotics. To follow the spore population, bacterial colony-forming units were determined before and after treatment of the insect homogenate for 12 min at 80°C. At least four independent replicates were performed for each time and for each strain tested. In vivo ß-galactosidase activity was assayed from 2 ml aliquots of the insect homogenate as described previously [16]. At least three independent measurements were performed for each time and for each transcriptional fusion tested.
Chromosomal DNA was extracted from Bt cells using the Puregene Yeast/Bact. Kit B (QIAgen, France). Plasmid DNA was extracted from E. coli using QIAprep spin columns (QIAgen, France). Restriction enzymes (New England Biolabs, USA) and T4 DNA ligase (New England Biolabs, USA) were used in accordance with the manufacturer's recommendations. Oligonucleotide primers (Table S4) were synthesized by Sigma-Proligo (Paris, France). PCRs were performed in a Applied Biosystem 2720 Thermak cycler (Applied Biosystem, USA). Amplified fragments were purified using the QIAquick PCR purification Kit (QIAgen, France). Digested DNA fragments were separated on 1% (w/V) agarose gels after digestion and extracted from gels using the QIAquick gel extraction Kit (QIAgen, France). Nucleotide sequences were determined by Beckman Coulter Genomics (Takeley, UK)
The plasmid pRN5101 [46] was used for homologous recombination. The low-copy-number plasmid pHT304 was used for complementation experiments with wild-type nprR-nprX genes under their own promoters [16]. Transcriptional fusions were constructed in pHT304-18Z [47]. All the plasmids used in this study are described in Table S5.
The krsABC genes were disrupted by inserting a spectinomycin resistance gene into the coding sequence. The thermosensitive plasmid pRN5101ΩkrsABC::spc was used to disrupt the chromosomal wild-type copy of the krsABC genes in the Bacillus 407 wt strain by homologous recombination as described previously [46]. The recombinant strain, designated Bacillus 407 ΔkrsABC, was resistant to spectinomycin and sensitive to erythromycin. The thermosensitive plasmid pRN5101ΩPkrsABC::aphA3 was used to replace the natural promoter region of the krsABC genes in the Bacillus 407 ΔRX strain by aphA3 and its constitutive promoter. In the resulting Bacillus recombinant strain, the krsABC genes were transcribed from the aphA3 promoter; it was designated Bacillus 407 ΔRX PaphA3-krsABC, and was resistant to kanamycin and sensitive to erythromycin.
The methods used to study the proteolitic activity, the chitinolytic activity and the lipolytic activity have been described previously [32], [48]. Swimming and swarming were evaluated using LB 0.3% agar plates and LB 0.7% agar plates, respectively. Biofilm formation was assayed in LB medium and in glass tubes as described previously [27]. Dendrite formation was evaluated on EPS 0.7% agar. Strains were cultured in LB medium at 37°C until the beginning of stationary phase and 2.106 bacteria were spotted onto the center of the agar plate. Plates were incubated at 37°C for 24 h to 96 h.
For in vitro ß-galactosidase activity measurements, Bt cells containing lacZ transcriptional fusions were cultured in LB medium at 37°C. In vivo ß-galactosidase activity was assayed from 2 ml aliquots of insect homogenate (see in vivo experiments). ß-Galactosidase activities were measured as described previously [49]. The specific activities are expressed in units of ß-galactosidase per milligram of protein (Miller units).
Prewarmed 500 ml baffled erlenmeyer flasks with 50 ml LB medium were inoculated with 1 ml overnight cultures of Bacillus 407 nprA::lacZ or Bacillus 407 ΔRX nprA::lacZ, and incubated at 37°C and 250 rpm. Samples for microarray analysis were taken three (t3) hours after the onset of the stationary phase. Samples were harvested as described previously [12] mixed with RLT buffer from the RNeasy midi kit (Qiagen, France) and frozen at −70°C. After thawing samples at 37°C for 15 min, RNA isolation, cDNA synthesis, labeling and purification were performed as described [12].
The microarray slides were printed, prehybridized and hybridized as described previously [12], except that hybridization was extended to 17 hours. The slides were scanned on an Axon 4000B scanner (Molecular Devices). Gridding, spot annotation and calculation of raw spot intensities was done with the GenePix Pro 6.1 software (Molecular Devices). The LIMMA package [50], [51], [52] on the R 2.7.1 platform [53] was used for filtering, normalization and further analysis. The raw data were filtered and weighted by quality [54], and the four technical replicates on each slide were averaged to increase robustness. P-values were computed using a false discovery rate of 0.05. The analysis was based on hybridization to three slides, all employing biological replicates.
Gene expression was investigated in Bacillus 407 nprA::lacZ and Bacillus 407 ΔRX nprA::lacZ. Reverse transcription was performed according to the SuperScript III reverse transcriptase protocol from Invitrogen, but RNaseOUT was replaced with 0.1 µl SUPERase-In (Ambion). A negative control without reverse transcriptase was included. In all samples, the reaction volume was adjusted to 20 µl with DEPC-treated water (Ambion) before reverse transcription. The reaction product was diluted (1 µl in 39 µl) with water, and 8 µl applied to each well (2 µl for 5 s rRNA samples). Primers were added to a final concentration of 0.56 µM. A volume of 9 µl LightCycler 480 DNA SYBR Green I Master (Roche) was added, and the volume was adjusted to 18 µl. Primers (available on request) were designed to give PCR products of around 100 bp. The reference genes, gatB (BC4306) and 5 s rRNA, were included on every plate. The samples were analyzed on a Roche Lightcycler 480 (Roche Diagnostics GmbH, Mannheim, Germany). Cycling conditions were 95°C for 5 minutes followed by 45 cycles at 95°C for 10 seconds, 58°C for 10 seconds, and 72°C for 8 seconds. Cp values were determined using 2nd derivative max, and are averages of two technical replicates. The results were calculated by the delta-delta Ct approximation. The log2 expression ratios of Bacillus 407 ΔRX nprA::lacZ over Bacillus 407 nprA::lacZ in Table S3 are averages for three biological replicates.
Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry (MALDI-ToF MS) was used to screen kurstakin production from whole bacterial cells on solid media. Cultures were performed on AK agar plates incubated at 30°C for 24 h or 48 h. A saturated solution of α-cyano-4-hydroxy-cinnamic acid was prepared in 1∶2 (v/v) solution of CH3CN and H2O containing 0.1% TFA. Measurement was performed using UV laser MALDI-ToF spectrometer (Bruker UltraFlex TOF; Bruker Daltonics) equipped with a pulsed nitrogen laser (λ = 337 nm). The ions were extracted from the ionization source with an acceleration voltage of 20 kV. Samples were measured in the reflector mode, positive mode. The equivalent of about 1 µl of cell material was picked from agar plates with an automatic pipette. The tip with the culture was deposited in an eppendorf tube. 20 µl of matrix solution (saturated solution of α-cyano-4-hydroxycinnamic acid in a 1∶2 v/v solution of CH3CN/H2O with 0.1% TFA) are added. The eppendorf with the tip and the matrix solution was vortexed for 30 seconds. 1 µl of this sample solution was deposited on the MALDI target and let dry at room temperature. The spectrum was obtained with 5×30 shots on the sample. Analyses were performed on two different samples.
|
10.1371/journal.ppat.1000698 | Protein C Inhibitor—A Novel Antimicrobial Agent | Protein C inhibitor (PCI) is a heparin-binding serine proteinase inhibitor belonging to the family of serpin proteins. Here we describe that PCI exerts broad antimicrobial activity against bacterial pathogens. This ability is mediated by the interaction of PCI with lipid membranes, which subsequently leads to their permeabilization. As shown by negative staining electron microscopy, treatment of Escherichia coli or Streptococcus pyogenes bacteria with PCI triggers membrane disruption followed by the efflux of bacterial cytosolic contents and bacterial killing. The antimicrobial activity of PCI is located to the heparin-binding site of the protein and a peptide spanning this region was found to mimic the antimicrobial activity of PCI, without causing lysis or membrane destruction of eukaryotic cells. Finally, we show that platelets can assemble PCI on their surface upon activation. As platelets are recruited to the site of a bacterial infection, these results may explain our finding that PCI levels are increased in tissue biopsies from patients suffering from necrotizing fasciitis caused by S. pyogenes. Taken together, our data describe a new function for PCI in innate immunity.
| The innate immune system is an integral part of our battle against an invading pathogen. Antimicrobial peptides and proteins partake in this fight due to their ability to perforate the bacterial cell wall, which eventually will cause the efflux of bacterial cytosolic content and efficient bacterial killing. Protein C inhibitor (PCI) is a multifunctional heparin-binding serpin which has been implicated in a number of pathological conditions, including severe infectious diseases. Here we show that PCI is a potent antimicrobial agent that is able to destroy the bacterial cell wall and thereby cause death of the bacteria. Our study also shows that in contrast to many other antimicrobial peptides, processing of PCI is not required since the full length protein exerts its antimicrobial activity, and we present data demonstrating that PCI is enriched at the infected site of patients suffering from severe streptococcal infection.
| Our early response to an invading pathogen relies to a major part on our innate immune system. In order to sense and fight an infection, the human host has developed an arsenal of pattern recognition proteins that interact with so-called pathogen associated molecular patterns or PAMPs (for a review see [1]). Pattern recognition proteins have two major tasks. Some, like toll-like receptors, evoke an inflammatory response, such as the induction of proinflammatory cytokines [2], while others are involved in the direct killing of the pathogen. For instance, there are scavenger receptors that can act as phagocytic receptors mediating direct non-opsonic uptake of pathogenic microbes and/or their products [3]. However, there are also pattern recognition proteins, such as complement, and antimicrobial peptides that fall into both categories. For example, the anaphylatoxin peptide C3a is a potent chemoattractant for phagocytes, but also has a direct antimicrobial effect [4]; other examples include chemotactic chemokines and neuropeptides [5],[6]. The mode of the antimicrobial action of these substances is often based on their ability to penetrate the cell wall of the pathogen, which eventually leads to membrane disruption followed by cytosolic leakage and ultimately to the death of the targeted organism.
The number of antimicrobial peptides/proteins (AMPs) is constantly increasing and today more than 880 have been described [7]. In order to display their activity, many AMPs must first be released from their precursor molecules. Probably one of the best-studied mechanisms is release of LL-37 from cathelicidin hCAP-18 by the action of proteinase 3 [8]. Notably, in some cases an entire protein can exploit its antimicrobial activity without any prior processing. Thus, proteins such as bactericidal/permeability increasing protein, azurocidin and histidine-rich glycoprotein have been reported to function as antimicrobial agents (for reviews see [9],[10],[11]). It is noteworthy that many of these proteins have an affinity for heparin.
Protein C inhibitor (PCI) is a heparin-binding serine proteinase inhibitor [12]. As indicated by its name, PCI was originally reported as an inhibitor of activated protein C, a blood coagulation factor. Later it was reported that PCI is also found, apart from plasma, in tears, saliva, cerebral spinal fluid, breast milk, seminal plasma, and amniotic fluid (for a review see [13]). Recently, it was described that human PCI is efficiently internalized by neutrophils and targeted to the nucleus [14]. Interestingly, the authors also found that internalized PCI promotes phagocytosis of bacteria. As PCI apparently has an affinity for lipids, we set about to analyze its interaction with bacterial membranes. To this end we performed a number of experiments demonstrating for the first time that human PCI is a potent antibacterial reagent.
Previous work has shown that SEK20, a peptide derived from PCI (SEKTLRKWLK MFKKRQLELY), and LL-37 (LLGDFFRKSK EKIGKEFKRI VQRIKDFLRN LVPRTES), have a broad antimicrobial activity against pathogens such as Candida albicans, Enterococcus faecalis, Escherichia coli, Proteus mirabilis, and Pseudomonas aeruginosa [15]. In addition to these pathogens we find in the present study that SEK20 is also able to kill Bacillus subtilis, Staphylococcus aureus, and Streptococcus pyogenes. In concordance with the previous report, the antimicrobial activity of SEK20 was as efficient as that of LL-37 (Table 1). Figure S1 shows the effect of SEK20, LL-37, and GDK25 (a control peptide derived from human high molecular weight kininogen [16]) on E. coli and S. pyogenes bacteria which were used throughout this study. Both pathogens are frequently isolated from patients suffering from severe acute infectious diseases. The broad antimicrobial activity of SEK20 and its positive net-charge (pI = 10.3) [15], suggest that the peptide does not interact with species-specific surface proteins of these pathogens, but rather targets their cell membranes, which is also the point of attack for many other antimicrobial peptides (for a review see [17]). We therefore tested the effect of SEK20 in a permeabilization assay by employing unilamellar anionic liposomes [18]. To this end, liposomes were treated with SEK20 and LL-37 and the subsequent release of carboxyfluorescein was monitored. Figure 1A shows that SEK20 like LL-37 permeabilizes liposomes, suggesting that SEK20 has membrane lytic activity.
A negative side effect of some antimicrobial peptides is that they not only act on bacterial or fungal surfaces, but also lyse and ultimately kill eukaryotic cells. We therefore tested the effect of SEK20 on human erythrocytes and found that SEK20, in contrast to LL-37, had no hemolytic activity (Figure 1B). Similar results were obtained when measuring the LDH release from HaCaT keratinocytes, where LL-37 demonstrated significant release at higher concentrations, but SEK20 did not (Figure 1C). Taken together, the results show that SEK20 is a potent antimicrobial peptide with a broad specificity, but less toxicity for eukaryotic cells than LL-37.
In order to become active, many antimicrobial peptides such as LL-37 must be released from a precursor molecule [19]. In some cases, however, this processing is not required and the entire protein is antimicrobial by itself, most likely because the antimicrobial region is surface exposed as is the case for histidine-rich glycoprotein and azurocidin [20],[21]. The three-dimensional structure of PCI has been resolved and a closer examination revealed that a region spanning the SEK20 sequence forms a hairpin loop sticking out at the amino terminal part of the protein [22]. It was therefore tempting to speculate that PCI is by itself antibacterial. To test this, the effect of PCI on Gram-negative (E. coli) and Gram-positive (S. pyogenes) bacteria was investigated in viable count assays. Figure 2A shows that PCI kills E. coli bacteria very efficiently, while its antimicrobial activity towards S. pyogenes bacteria is slightly reduced when compared with SEK20 or LL-37 (Figure 2C). Notably, the antimicrobial effect of PCI was dose-dependent in both cases (Figure 2B and 2D). Next, we treated PCI with several proteinases (activated human protein C, factor Xa, plasma kallikrein, thrombin, elastase, cathepsin G, and proteinase 3) in order to exclude the possibility that the activity of PCI was achieved upon proteolytic processing of the protein and subsequent release of a SEK20-containing peptide. To this end, Western blot experiments with antibodies against PCI and SEK20 revealed that the protein is resistant to proteolytic degradation and a fragment spanning the SEK20 peptide has not been released when incubated with these enzymes (data not shown). Additional analysis by negative staining electron microscopy showed that protease treatment did not affect PCI's antimicrobial activity (Figure S2). These findings are in line with reports showing that also other antimicrobial proteins such as azurocidin are resistant to proteolysis [23]. Finally, we performed binding assays with radiolabeled PCI in order to investigate the interaction between the entire PCI molecule and the bacterial surface. We found that the two bacteria strains tested, E. coli (12% binding of added radio-labeled protein) and S. pyogenes (27% binding of added radio-labeled protein), were able to assemble PCI on their surface. When 125I-PCI bound to streptococci was eluted from the bacterial surface and run on SDS-PAGE followed by auto-radiographic analysis, we did not find any signs of degradation (data not shown), implying that the interaction of PCI with bacteria does not trigger truncation or processing of the protein. Taken together our findings show that the entire PCI molecule has antimicrobial activity and no cleavage of the protein is needed to generate this effect.
In the next series of experiments we wished to test whether PCI is able to permeabilize bacteria by the same mechanism as SEK20. Thus, liposomes were incubated with PCI and the release of carboxyfluorescein was recorded. The results show that PCI, like SEK20, has the ability cause a concentration dependent release of carboxyfluorescein (Figure 2E). To investigate the effect of PCI on the cell wall of E. coli and S. pyogenes, bacteria were treated with PCI and analyzed by negative staining microscopy. As seen in Figure 3, this treatment evoked significant membrane destruction, which was followed by the extravasation of cytosolic content as detected by the release of oligonucleotides (Figure 3I). An antimicrobial effect was also seen when PCI was used at 100 nM which reflects its concentration in human plasma (Figure S3). Thus, our results show that PCI mediates its antimicrobial activity by perforating the bacterial cell membrane followed by the efflux of intracellular material and subsequent death of the bacteria.
In humans, PCI is found in many fluids and secretions including plasma, seminal plasma, urine, sweat, saliva, tears, milk, and cerebrospinal fluid (for a review see [24]). Many antimicrobial peptides/proteins do not display their full activity in a physiological environment and require special conditions such as low salt concentration or low pH. To investigate whether this also applies for PCI, we studied the interaction of PCI with AP1 bacteria in human plasma. In contrast to E. coli, S. pyogenes bacteria are not phagocytozed in human blood and therefore we focused on the Streptococci only throughout the rest of this study. In a first series of experiments, we tested whether AP1 bacteria can absorb PCI from human plasma. To this end, bacteria and normal human plasma were incubated for 1 h at room temperature. After a centrifugation step to separate plasma and bacteria and a washing step, bacteria-bound plasma proteins were recovered by an acid wash and subjected to Western blot analysis with anti PCI antibodies. Figure 4 shows that PCI was recruited onto the surface of AP1 bacteria and a depletion of the protein from human plasma was also recorded. Based on these findings we wanted to explore whether PCI can exert its antimicrobial activity in plasma. In order to do so, we compared the growth of AP1 bacteria in normal and PCI-deficient plasma. As seen in Figure 5, bacterial proliferation is significantly accelerated when PCI has been removed from human plasma, suggesting that PCI is a relevant antimicrobial agent in human blood.
PCI is contained within the alpha-granules of platelets and may be released on activation [25]. It has previously been reported that M1 protein from Streptococcus pyogenes can stimulate platelet activation and that activated platelets are present at the site of streptococcal infection [26]. We therefore set about to determine whether PCI can be localized on platelets in response to the physiological activation (ADP) or bacterial activation (M1 protein). Unstimulated platelets had background levels of PCI on their surface. Following ADP activation, 11% of the platelet population had PCI on their surface and this was further increased to 17% on treatment with M1 protein (Figure 6). A proportion of PCI is likely to be released directly into the plasma, however our attempts to quantify platelet derived PCI in plasma failed due to technical limitations. We can therefore not differentiate between PCI released from platelets and PCI acquired from plasma and subsequently bound to platelets. These results do however demonstrate that PCI can be accumulated on the platelet surface during streptococcal infection and this may give rise to an increased PCI concentration at the local site of infection.
To test whether PCI levels increase at an infectious site, we analyzed biopsies derived from four patients with necrotizing fasciitis caused by S. pyogenes and a healthy control by immunohistochemistry. Bacterial colonization was demonstrated by employing an antibody against S. pyogenes, which demonstrates the presence of streptococci in the biopsy from the patient with necrotizing fasciitis, but not in the healthy control (Figure 7). When biopsies were immunostained for PCI, it was found that the protein was enriched at the infectious site, while it was not detectable in the control biopsy. PCI recruitment to the site of infection was localized in cellular infiltrates especially in areas with a lot of bacteria. To investigate these areas by confocal microscopy, biopsies were immunostained with antibodies against S. pyogenes and PCI. Once again the micrographs show that PCI and Streptococci are distributed throughout the cellular infiltrate (Figure 8A–C), and are most commonly co-localized (Figure 8D).
In the present study we show that PCI has a broad antibacterial activity towards many Gram negative and Gram positive microorganisms. Importantly, proteolytic processing of PCI is not required, since the protein is released into plasma in its antibacterial active form. This is in contrast to many other antimicrobial peptides/proteins that have to be generated from a precursor by the action of proteolytic active enzymes. Indeed, our data show that treatment of PCI with a panel of human proteinases including neutrophil-borne elastase, proteinases 3 or cathepsin G, does not diminish the antimicrobial activity of PCI. This feature has a major advantage, since due to neutrophil recruitment and their subsequent degranulation, neutrophil-derived proteinases are enriched at the infected site. Notably, especially in deep tissue infections such as necrotizing fasciitis with severe tissue degradation, extremely high proteolytic activity is recorded which is not only caused by host proteinases, but also by bacterial virulence factors (for a review see [27]). The increased protease activity at the site of infection may lead to an inactivation of antibacterial peptides that are not proteolytic resistant (for a review see [28]). This has been shown for instance for LL-37, which is probably cleaved by a streptococcal cysteine proteinase at the infected site in patients with severe tissue infections and completely broken down in wound fluids from patients with chronically infected venous ulcers [29],[30]. Our preliminary results with wound fluids from patients with chronically infected venous ulcers show that unlike LL-37, PCI is protected from degradation in this proteolytically potent environment (Malmström, Schmidtchen, and Herwald, unpublished results), suggesting that PCI is not only resistant to degradation by the host, but also by bacterial proteinases.
Analysis of biopsies from patients suffering from streptococcal necrotizing fasciitis reveal an accumulation of PCI at the infectious site and confocal microscopy studies show that most of the PCI is found co-localized with streptococci. These data confirm our in vitro and ex vivo results showing that PCI has an affinity for bacterial surfaces and they also allow the assumption that the protein exerts antibacterial activity at an infectious site. As PCI is found at low concentrations in plasma (4 to 6 µg/ml) an active transport of the proteins to or its up-regulation at the site of infection is required. Previous work has shown that platelets can infiltrate the infected site of patients suffering from soft tissue infections caused by S. pyogenes [26]. As the alpha-granules of platelets constitute a storage of PCI [25], we propose a model where PCI is released from platelets that are recruited to the site of infection. Unfortunately, we were not able to distinguish whether PCI was released from the α-granules or from the surface of platelets that have absorbed PCI from plasma. However, considering that platelets contain very little PCI (160 ng PCI/2×109 cells) [25], it seems more likely that they absorbed it from plasma. At this point we cannot exclude that PCI synthesis is induced at the infectious site and therefore, in ongoing studies we address whether PCI generation can be triggered in different cell lines, including HepG2 cells, upon stimulation with inflammatory substances, such as IL-1β, IL-6, or TNFα.
The affinity of PCI for negatively charged lipids may help explain the mechanism by which the bacterial cell wall is disrupted. As visualized by negative staining electron microscopy, the interaction of PCI with bacteria leads to destruction of the bacterial cell wall and the release of cytosolic content, eventually leading to bacterial killing. Recently, Baumgärtner et al. reported that PCI is avidly internalized by human polymorph nuclear neutrophils (PMNs), and this involves phosphatidyl-ethanolamines [14]. The authors also reported that this interaction enhances the uptake of E. coli bacteria and, thus they suggest an important role for PCI in innate immunity [14]. Considering that the PCI-evoked release of bacterial cytosolic content will cause additionally inflammatory reactions at the infected site, it seems plausible that the host has developed a counteracting mechanism. Uptake of PCI-opsonized bacteria by PMNs before their destruction may lead to decreased inflammatory reactions, while still guaranteeing an efficient killing of the pathogen. Thus a synergistic effect of PCI resulting in bacterial recognition by PCI and their subsequent uptake by phagocytic cells followed by intracellular killing, is an attractive concept that would lead to a clearance of the infection and a dampening of inflammatory responses.
Taken together, our studies show a novel function for PCI as an antimicrobial agent against a broad arsenal of bacterial pathogens. This is mediated by the ability of PCI to interact with lipids leading to the efflux of bacterial cytosolic content. When analyzing tissue biopsies we find an accumulation of PCI at the infectious site. These findings suggest an important and novel role of PCI in innate immunity.
The Human Subjects Review Committee of the University of Toronto and of Lund University approved the studies, and written, informed consent from the patients and volunteers was received.
Fresh frozen plasma from healthy individuals were obtained from the blood bank at Lund University Hospital, Lund, Sweden, and kept frozen at −80°C until use. Protein C Inhibitor deficient plasma was prepared as described [31]. M1 protein was purified from the supernatant of S. pyogenes MC25, as previously described [32].
SEK20 (SEKTLRKWLKMFKKRQLELYL) and LL-37 (LLGDFFRKSK EKIGKEFKRI VQRIKDFLRN LVPRTES) were synthesized by Innovagen AB, Lund, Sweden. The purity (>95%) and molecular weight of these peptides was confirmed by mass spectral analysis (MALDI.TOF Voyager). Recombinant protein C inhibitor was purified as previously described [33].
Escherichia coli 37.4 and ATCC25922, Pseudomonas aeruginosa ATCC27853, Staphylococcus aureus ATCC29213, Bacillus subtilis ATCC6633 bacterial isolates, and the fungal isolate Candida albicans ATCC90028 were grown as described elsewhere [34]. The Streptococcus pyogenes AP1 (40/58) strain of the M1 serotype was provided by the WHO (World Health Organization) Streptococcal Reference Laboratory in Prague, Czech Republic and cultured as previously described [35].
Radial diffusion assays were performed as described previously [36]. Briefly, bacteria were grown to mid-logarithmic phase in 10 ml full strength 3% (w/v) tryptic soy broth (TSB) (Becton Dickinson, Franklin Lakes, NJ, USA). The bacteria were washed once in 10 mM Tris, pH 7.4 and then 2×106 CFU were added to 5 ml of the underlay agarose gel consisting of 0.03% (w/v) TSB, 1% (w/v) low-electroendosmosistype (Low-EEO) agarose (Sigma) and a final concentration of 0.02% (v/v) Tween-20. The underlay was poured into a Petri dish. After the agarose had solidified, 4 mm diameter wells were punched and 6 µl of test samples were added to each well. Samples were allowed to diffuse into the gel for 3 h in 37°C and then the underlay gel was covered with 5 ml overlay (6% TSB, 1% Low-EEO agarose). Antibacterial activity was visualized as a clear zone around each well after overnight incubation at 37°C of the plate. The activities of the peptides are presented as diameter of clear zone-well diameter.
EDTA-blood was centrifuged at 800 g for 10 min and plasma and buffy coat removed. Erythrocytes were washed three times and resuspended in 5% PBS, pH 7.4. The cells were incubated with end-over-end rotation for 1 h at 37°C in the presence of SEK20 or LL-37 (3–60 µM). 2% Triton X-100 (Sigma-Aldrich) served as positive control. The samples were then centrifuged at 800 g for 10 min. Hemoglobin release was measured as the absorbance at λ 540 nm and the values are expressed as % of TritonX-100 induced hemolysis.
Dry lipid films were prepared by dissolving dioleoylphosphatidylcholine (Avanti Polar Lipids, Alabaster, AL) (30 mol%), dioloeolphosphatidic acid (30 mol %) and cholesterol (Sigma, St Louis, MO) (40 mol%) in chloroform, and removing the solvent by evaporation under vacuum overnight. Subsequently, buffer solution containing 10 mM Tris, pH 7.4, was added together with 0.1 M carboxyfluorescein (CF) (Sigma, St Louis, MO). After hydration, the lipid mixture was subjected to eight freeze-thaw cycles consisting of freezing in liquid nitrogen and heating to 60°C. Unilamellar liposomes with a diameter of about 140 nm (as found with cryo-TEM and dynamic light scattering; results not shown) were generated by multiple extrusions through polycarbonate filters (pore size 100 nm) mounted in a LipoFast miniextruder (Avestin, Ottawa, Canada). Untrapped carboxyfluorescein was then removed by filtration through two subsequent Sephadex G-50 columns with the relevant Tris buffer as eluent. Both extrusion and filtration was performed at 22°C. In the liposome leakage assay, the well known self-quenching of CF was used. Thus, at 100 mM CF is self-quenched, and the recorded fluorescence intensity from liposomes with entrapped CF is low. On leakage from the liposomes, released CF is dequenched, and hence fluoresces. The CF release was determined by monitoring the emitted fluorescence at 520 nm from a liposome dispersion (10 mM lipid in 10 mM Tris pH 7.4). An absolute leakage scale is obtained by disrupting the liposomes at the end of the experiment through addition of 0.8 mM Triton X100 (Sigma, St Louis, MO), thereby causing 100% release and dequenching of CF. A SPEX-fluorolog 1650 0.22-m double spectrometer (SPEX Industries, Edison, NJ) was used for the liposome leakage assay.
HaCaT keratinocytes were grown to confluence in 96 well plates (3000 cells/well) in DMEM, 10% FCS. The medium was removed and the cells were subsequently washed with 100 µl DMEM. 100 µl of SEK20 or LL-37 (0, 3, 6, 30, 60 µM) diluted in DMEM were added in triplicates. The LDH based TOX-7 kit (Sigma-Aldrich) was used to measure the viability of the cells.
Bacteria were cultivated overnight in Todd-Hewitt medium (TH; Difco) at 37°C. 250 µl were transferred to 10 ml TH-medium and grown to mid-log phase (A620≈0.4). The bacterial solution was washed 3 times in 10 mM Tris, 5 mM glucose, pH 7.4 (Tris-HCl buffer). 5 µl (2×106 cfu/ml) was added to the respective antimicrobial substance at a total volume of 17 µl and incubated for 1 h at 37°C. After the incubation 500 µl Tris buffer was added to the mixture and 100 µl was transferred to Todd-Hewitt broth agar plate to determine the bacterial growth. Plates were incubated overnight at 37°C and the number of colony forming units (cfu) was determined.
The antimicrobial effect of PCI against E. coli and S. pyogenes was analyzed by negative staining and electron microscopy as previously described [37]. Bacteria were diluted to a 1% solution with TBST (20 mM Tris, 150 mM NaCl, 0.05% Tween, pH 7.4) and 10 µl incubated with PCI at a concentration of 4 µM for 90 min at 37°C. 5 µl aliquots were adsorbed onto carbon-coated grids for 1 min, washed with two drops of water, and stained on two drops of 0.75% uranyl formate. The grids were rendered hydrophilic by glow discharge at low pressure in air. Specimens were observed in a Jeol JEM 1230 electron microscope operated at 60 kV accelerating voltage. Images were recorded with a Gatan Multiscan 791 CCD camera.
Bacteria, grown to mid-exponential growth phase were washed and resuspended in PBST (PBS+0.05% Tween-20). 250 µl of the bacterial solution (2×109 bacteria/ml) was incubated with 1.5 ml citrate plasma for 1 h at 37°C. The bacterial cells were collected, washed with PBST including 0.5 M NaCl and bound proteins eluted with 0.1 M glycine–HCl, pH 2.0. The pH of the eluted material was raised to 7.5 by addition of 1 M Tris. Precipitated material was dissolved in SDS sample buffer and subjected to Tricine-SDS gel electrophoresis and Western blot analysis.
Samples were boiled for 5 min in an equal volume of sample buffer containing 2% SDS and 5% 2-mercaptoethanol and run on SDS-PAGE. Bio-rad kaleidoscope prestained standards were used as molecular weight markers. Separated proteins were transferred to polyvinylidene difluoride (PVDF) membranes (Amersham Biosciences). Membranes were blocked with PBST (PBS+0.05% Tween-20) containing 5% dry milk powder (blocking buffer) overnight at 4°C and then incubated with primary antibodies (rabbit anti-PCI K88032 1∶1000) in blocking buffer for 1 h at 37°C. After a washing step with PBST with 0.35 M NaCl, the membranes were incubated with HRP-conjugated secondary antibodies (goat anti-rabbit IgG 1∶10000) in blocking buffer for 1 h at 37°C. The membranes were washed and bound antibodies detected by chemiluminescence.
S. pyogenes was cultivated in human plasma and PCI-deficient plasma at 37°C. Bacterial suspensions were diluted 10000 times in 10 mM Tris, containing 5 mM glucose (pH 7.4) and transferred to TH-agar plates at different time points (0, 4 and 8 h) to monitor bacterial growth. Plates were incubated overnight at 37°C and the number of bacteria determined.
Blood samples were collected from healthy donors who had not taken antiplatelet medication in the previous ten days. Five ml of blood was collected into citrated vacuum tubes. Platelet-rich plasma (PRP) was prepared by centrifugation at 150×g for 10 minutes. Twenty µl of PRP was incubated at room temperature with 25 µl of HEPES buffer pH 7.4, either in the presence or absence of 5 µM adenosine diphosphate (ADP) or M1 protein (1 µg/ml). After 5 minutes, primary antibodies were added (rabbit anti-PCI K88032 1∶100) and incubated for 5 minutes. Five µl of fluorochrome conjugated secondary antibody (anti Rabbit FITC) was then added and after 5 minutes the incubation was stopped by addition of 0.5% formaldehyde in ice cold PBS. Samples were analysed using a FACSCalibur flow cytometer in logarithmic mode with a gate setting on the platelet population. 50,000 cells were acquired and analysed using Cell Quest software (Becton Dickinson).
A snap-frozen tissue biopsy collected from patients with necrotizing fasciitis caused by group A streptococcus (GAS) was stained and compared with a snap-frozen punch biopsy taken from a healthy volunteer. The biopsies were cryostat-sectioned to 8 µm, fixed in 2% freshly prepared formaldehyde in PBS and stained. Tissue sections were initially blocked with 10% fetal calf serum in Earl's balanced salt solution (BSS) with 0.1% saponin for 30 minutes at room temperature, followed by additional blocking with 1% H2O2 in BSS-saponin and an avidin and biotin blocking (Vector laboratories). Primary antibodies were diluted in BSS solution containing saponin and 0.02% NaN3 and incubated over night at room temperature. S. pyogenes bacteria were identified with a polyclonal rabbit antiserum specific for the Lancefield group A carbohydrate (diluted 1∶10,000; Difco), while PCI was identified with a polyclonal rabbit antiserum (1 µg/ml) K88032. After incubation, tissue sections were washed and blocked with 1% normal goat serum in BSS-saponin before addition of biotinylated goat anti-rabbit IgG (diluted 1∶500, Vector laboratories) diluted in 1% normal goat serum in BSS-saponin. Avidin-peroxidase solution was added (Vectastain-Elite; Vector Laboratories) and the color reaction developed by the addition of 3,3-diaminobenzidine (Vector Laboratories) followed by counterstaining with hematoxylin. PCI and streptococci were also visualized with immunoflourescence stainings. The tissue was initially blocked with an avidin/biotin blocking kit (Vector laboratories) whereafter PCI was identified with a monoclonal mouse antibody (5 µg/ml) API-93 followed by a blocking step with 1% normal goat serum. This was followed by incubation with a biotinylated antibody against mouse IgG (diluted 1∶600, Dako), and subsequently with a streptavidin-conjugated fluorophor (Alexa 594, diluted 1∶500, Molecular Probes). After yet another round of blocking with avidin/biotin streptococci were detected using a biotinylated polyclonal rabbit antiserum specific for the Lancefield group A carbohydrate (16 µg/ml, Difco) followed by incubation with a second streptavidin-conjugated fluorophore (Alexa 488, diluted 1∶600, Dako). All antibodies and fluorochromes were diluted in PBS-saponin-BSA-c, while washes were done with PBS-saponin. The immunofluorescence stainings were evaluated by a Leica confocal scanner TCP SP II coupled to a Leica DMR microscope.
Statistical analysis was performed using GraphPadPrism 4.00. For viable count data the p value was determined using a one sample T test with a theoretical mean set as 100% survival based on untreated samples. For flow cytometry data the p value was determined using students T test.
|
10.1371/journal.pntd.0007425 | Impairing the maintenance of germinative cells in Echinococcus multilocularis by targeting Aurora kinase | The tumor-like growth of the metacestode larvae of the tapeworm E. multilocularis causes human alveolar echinococcosis, a severe disease mainly affecting the liver. The germinative cells, a population of adult stem cells, are crucial for the larval growth and development of the parasite within the hosts. Maintenance of the germinative cell pools relies on their abilities of extensive proliferation and self-renewal, which requires accurate control of the cell division cycle. Targeting regulators of the cell division progression may impair germinative cell populations, leading to impeded parasite growth.
In this study, we describe the characterization of EmAURKA and EmAURKB, which display significant similarity to the members of Aurora kinases that are essential mitotic kinases and play key roles in cell division. Our data suggest that EmAURKA and EmAURKB are actively expressed in the germinative cells of E. multilocularis. Treatment with low concentrations of MLN8237, a dual inhibitor of Aurora A and B, resulted in chromosomal defects in the germinative cells during mitosis, while higher concentrations of MLN8237 caused a failure in cytokinesis of the germinative cells, leading to multinucleated cells. Inhibition of the activities of Aurora kinases eventually resulted in depletion of the germinative cell populations in E. multilocularis, which in turn caused larval growth inhibition of the parasite.
Our data demonstrate the vital roles of Aurora kinases in the regulation of mitotic progression and maintenance of the germinative cells in E. multilocularis, and suggest Aurora kinases as promising druggable targets for the development of novel chemotherapeutics against human alveolar echinococcosis.
| Alveolar echinococcosis (AE), caused by infection with the metacestode larvae of the tapeworm E. multilocularis, is a lethal disease in humans. A population of adult stem cells, called germinative cells, drive the cancer-like growth of the parasite within their host and are considered responsible for disease recurrence after therapy termination. Nevertheless, benzimidazoles, the current drugs of choice against AE, show limited effects on killing these cells. Here, we describe EmAURKA and EmAURKB, two Aurora kinase members that play essential roles in regulating E. multilocularis germinative cell mitosis, as promising drug targets for eliminating the population of germinative cells. We show that targeting E. multilocularis Aurora kinases by small molecular inhibitor MLN8237 causes severe mitotic defects and eventually impairs the viability of germinative cells, leading to larval growth inhibition of the parasite in vitro. Our study suggests that targeting mitosis by MLN8237 or related compounds offers possibilities for germinative cell killing and we hope this will help in exploring novel therapeutic strategies against the disease.
| Alveolar echinococcosis (AE), caused by the larval stage of the cestode Echinococcus multilocularis, is considered as the most lethal helminthiasis. Humans become infected by accidentally ingesting infectious eggs, which contain oncospheres and then develop into cyst-like metacestode vesicles mainly in the liver. The metacestode larvae grow multivesicularly and infiltratively like tumors in host tissue, eventually leading to organ failure. The protoscoleces are generated in the metacestode vesicles through asexual multiplication, and then either mature into adult tapeworms if ingested by the definitive host (canids) or develop into metacestode vesicles when distributed in the intermediate host [1].
The ideal option for human AE treatment to date is surgery, which is always accompanied by chemotherapy. In cases where surgery is not possible, chemotherapy remains the only option [2]. Current anti-AE chemotherapy mainly relies on the benzimidazole carbamate derivatives albendazole and mebendazole. However, these drugs are only parasitostatic rather than parasiticidal, and the treatment usually needs years or even the life-long uptake of drugs. In addition, severe adverse side effects and the intolerance of benzimidazoles for a number of patients also limit their use for AE treatment. Therefore, novel chemotherapeutic options against AE have to be pursued [3–4].
It has been demonstrated that E. multilocularis possesses a population of pluripotent stem cells, called germinative cells. These cells are the only type of cells capable of proliferation and they give rise to all differentiated cells in the parasite. Hence, germinative cells are decisive for the tumor-like, infiltrative growth of E. multilocularis larvae within host organs, and should also be responsible for parasite recurrence upon discontinuation of chemotherapy [5–6]. Due to its fundamental roles in the asexual multiplication of E. multilocularis metacestode, the population of germinative cells has emerged as a crucial target to be considered for the development of chemotherapeutics against AE [6].
Aurora kinases (AURK), a family of serine/threonine kinases, play pivotal roles in the control of cell division via regulating mitosis especially chromosomal segregation and cytokinesis [7–10]. They have been described in various organisms, and their structure and function are well conserved through evolution. Yeast just has one Aurora kinase, while metazoans generally have two, named Aurora A and Aurora B. A third family member, Aurora C, is unique for mammals [11–13]. Although the N-terminus of AURK is variable among organisms, the C-terminal catalytic domain that contains the activation loop (T-loop) and the degradation box (D-box) is highly conserved within the family. Despite significant sequence homology, the localization and function of AURK members are largely distinct from one another. In mitotic cells, Aurora A localizes to the centrosomes and spindle microtubules, and functions in centrosome maturation, mitotic entry, and spindle assembly. Aurora B localizes to the inner centromere and spindle midzone, and is mainly involved in spindle assembly checkpoint, kinetochore attachment, and cytokinesis. Aurora C is expressed in testis, where it exhibits tissue-specific functions [7, 9, 11–13].
Dysfunction of Aurora kinases causes mitotic errors, which leads to genetic instability and chromosomal aneuploidy. Increasing evidence has shown that Aurora A and B are deregulated and/or overexpressed in many kinds of human tumors, and that inhibition of Aurora kinase results in cancer cell mitotic arrest and cell death [7, 8]. Aurora kinases have therefor emerged as attractive targets for cancer therapy, and a number of Aurora kinase inhibitors are developed and approved for various stages of clinical testing [10, 14, 15]. Strikingly, Aurora kinases have been identified in protozoan parasites (e.g. Trypanosoma brucei, Leishmania major and Plasmodium falciparum) [16–19], and human Aurora inhibitors have been shown to effectively inhibit the proliferation of these parasites, leading to Aurora kinase as the promising anti-protozoan drug targets [20–23].
In this study we identified two members of Aurora kinase in E. multilocularis, EmAURKA and EmAURKB, and show that they are actively expressed in the germinative cells. Targeting E. multilocularis Aurora kinases by MLN8237 causes severe mitotic defects and impairs the maintenance of germinative cell populations that leads to larval growth inhibition of the parasite, suggesting Aurora kinases as druggable targets for the development of chemotherapeutics against AE.
All animal experiments were conducted in strict accordance with China regulations on the protection of experimental animals (Regulations for the Administration of Affairs Concerning Experimental Animals, version from July-18-2013) and specifically approved by the Institutional Animal Care and Use Committee of Xiamen University (Permit Number: 2013–0053).
Published sequences of the Aurora kinase members of human, Xenopus, zebrafish, Drosophila and C. elegans were used as queries to blast the E. multilocularis genome database [24] available at http://www.sanger.ac.uk/resources/downloads/helminths/echinococcusmultilocularis. Only two gene loci (EmuJ_001059700 (EmAurka) and EmuJ_000891900 (EmAurkb)) encoding the members of Aurora kinase family were identified and their full coding sequences were then amplified from the cDNA preparations as described before [25]. 5’-rapid amplification of cDNA ends (RACE) was performed using the SMART RACE cDNA Amplification Kit (Clontech) according to the manufacturer’s instructions. Kinase domain was determined using the online software SMART (http://smart.embl-heidelberg.de/). Primers for amplification of the full coding sequences of EmAurka and EmAurkb were used as follows: EmAurka-F (5’-ATG CGT ATT ATG GAC GAC TCT GCT TTT CCC GAT-3’), EmAurka-R (5’-TTA AGT TCT TGT CGA GCT GGG GGT GGA GGC-3’), EmAurkb-F (5’-ATG AGT TCC TTG ATC GAA TAC GGT ACC CCT TC-3’) and EmAurkb-R (5’- TCA TGG TGG TGG CTT GCC CCT TTC GG-3’).
Parasite was maintained by in vivo propagation of the parasite material in mice (supplied by Xiamen University Laboratory Animals Center, XMULAC). Mature and developing protoscoleces were collected from parasite material, manually picked under the microscope, and then immediately used for RNA isolation or EdU labeling. In vitro cultivation of metacestode vesicles was performed using host cell conditioned medium as previously described [26]. The growth of metacestode vesicles and the process of vesicle formation from protoscoleces were examined after 21 days and 14 days of culture, respectively as described by Cheng et al. [27].
Aurora inhibitors (MLN8237, MLN8054, MK-5108 and AZD1152-HQPA), nocodazole and hydroxyurea were supplied by Selleck Chemicals. Drugs were added into the culture medium at a final concentration as indicated. All of the drug experiments were carried out under axenic culture conditions as described before [26]. For longer periods of treatment, experiments were performed with exchange of the medium containing the same ingredients every three days.
Total RNA was extracted from different larval stages of the parasite or the in vitro-cultivated metacestode vesicles treated with 40 mM of hydroxyurea or not. RNA was purified after DNase treatment and then reverse transcribed into cDNA. cDNAs were processed for real-time quantitative PCR (qPCR) analysis using the primers: 59700-qF (5’- TAA AGC GAG TGT TGG AAA -3’) and 59700-qR (5’- GCA GGC TGA CAT GAA AGT-3’) for EmAurka; 891900-qF (5’- GTC GGA GTT TTG TGC T-3’) and 891900-qR (5’- GAT CTT CGA AAT CAG GTC-3’) for EmAurkb; ELP-DW (5’-CAG GAT CTC TTC GAT CAA GTG-3’) and ELP-UP (5’-CCT GTG TTG CCA AGT ATG GTC-3’) for the constitutively expressed gene elp as the internal control [28].
The inactive mutants EmAURKAT208A and EmAURKBT184A were generated by substituting alanine for threonine in the conserved RxT motif within the Aurora kinase domain. pcDNA3.3-HA plasmids (gifts from Prof. Han Jiahuai, Xiamen University, China) containing the reading frame of EmAurka or EmAurkb were used as the templates, and the mutants were obtained by mutagenesis PCR as previously described [29] using the following primers: T208A-F (5’-CCA GGT TCT AGG CGT GCT GCT GTT TGG-3’) and T208A-R (5’- CAC GCC TAG AAC CTG GGC TGT GGA CCG CAC-3’) for EmAURKAT208A; T184A-F (5’-CCA GGT TCT AGG CGT GCT GCT GTT TGG GGC G-3’) and T184A-R (5’-CAC GCC TAG AAC CTG GGC TGT GGA CCG CAC-3’) for EmAURKBT184A. Plasmids containing the wild-type or the mutant coding sequence of the kinases were then used for the transfection of human 293T cells.
In vitro kinase assay was performed as previously described [30]. Briefly, lysates of the 293T cells expressing HA-tagged EmAURKA, EmAURKB or their inactive mutants were incubated with anti-HA tag antibody conjugated-sepharose beads overnight at 4°C. The bead pellets were washed five times with cell lysis buffer, and then washed twice with kinase buffer (25 mM Tris-HCl at pH 7.5, 5 mM beta-glycerophosphate, 2 mM dithiothreitol, 0.1 mM Na3VO4, and 10 mM MgCl2). The pellets were then incubated with full length recombinant human Histone H3 as the substrate at 30°C for 30 minutes in kinase buffer supplemented with 200 μM ATP. For inhibitor assays, kinase reactions were performed in the presence or absence of 1 μM of MLN8237. Kinases and phosphorylated substrates were then detected by western blot using the anti-HA tag antibody and anti-phospho-Histone H3 Ser10 antibody, respectively. All primary antibodies were supplied by CST and used according to the manufacturer’s instructions.
For EdU labeling, in vitro-cultivated vesicles were treated with drugs or not, and then incubated with 50 μM of EdU for 4 hours and whole-mount prepared as described before [31]. Click-iT EdU Alexa Fluor 555 Imaging Kit (Life Technologies) was used for detection of EdU. Immunofluorescence was performed using the whole-mount prepared metacestode vesicles. Phosphorylation of Histone H3 was detected using anti-phospho-Histone H3 Ser10 or Ser28 antibodies (CST). For all immunofluorescence experiments, an Alexa 488-conjugated second antibody (Life Technologies) was used and DNA was counterstained with 4’, 6-diamidino-2-phenylindole (DAPI). For the quantification of EdU+, pH3+ or the multinucleated cells, at least 12 random microscopic fields from 4–6 vesicles were captured and the positive cells were counted. At least 3 labeling experiments were performed and analyzed.
Data are shown as mean±SD or mean±SEM as indicated in the respective figure legend. Data within experiments were compared, and the significance was determined using two-tailed Student’s t-test.
By searching the E. multilocularis genome database, only two gene loci encoding members of Aurora kinase family, EmuJ_000891900 (annotated as serine/threonine protein kinase 12, STK12/Aurora kinase B) and EmuJ_001059700 (annotated as Aurora kinase A), were identified on chromosome 1 and 2, respectively. This is consistent with previous findings that mammals uniquely have three types of Aurora kinases (Aurora A, B and C), whereas other metazoans, including zebrafish, Xenopus, Drosophila and C. elegans, only Aurora A and Aurora B kinases are known [11]. Consistent with the annotation of Echinococcus genome project, we then named EmuJ_001059700 and EmuJ_000891900 as EmAurka and EmAurkb, respectively.
Informed by their genomic sequence, the entire reading frames of EmAurka and EmAurkb were PCR-amplified from metacestode cDNA preparations, and then cloned and sequenced. Our results show that the full length EmAurka coding sequence comprises 1050 bp, which is 198 bp longer than that determined by the genome project, resulting in a protein 66 amino acid residues longer. EmAurka gene spans a genomic region of 1.979 kb and comprises 7 exons, separated by 6 introns. Two additional short introns and one exon were identified in intron 1 of the predicted gene. Moreover, 57 of the 66 amino acid residues, which are absent in the protein determined by the genome project, are actually parts of the kinase domain of Aurora kinases (S1 Fig).
The coding sequence of EmAurkb we obtained is identical as determined by the E. multilocularis genome project. However, when we analyzed the sequence of the deduced protein, we found that the kinase domain is incomplete and the protein lacks N-terminal amino acids which are necessary for the cellular localization of Aurora kinases [32]. We then performed 5’ RACE and finally obtained an 873 bp-long reading frame encoding a protein 69 amino acids longer at the N-terminus of the deduced protein of EmuJ_000891900 (S2 Fig).
Comparison of their protein sequences with the characterized Aurora kinases from other organisms shows that E. multilocularis Aurora homologs contain the highly conserved catalytic kinase domain, including the Aurora signature motif within the activation loop and the destruction box (D-box) near the C-terminus (Fig 1). In mammals, the full activation of Aurora kinase and proper organization of activation loop requires phosphorylation on the threonine residue within the conserved RxT motif (e.g. Thr288 in human Aurora A and Thr232 in human Aurora B, respectively) [7], and this residue is also conserved in both of the E. multilocularis Aurora kinases (Thr208 in EmAURKA and Thr184 in EmAURKB, respectively). Although EmAURKA and EmAURKB share comparatively lower homologies with each other in the kinase domain (41% identical residues), they display more significant homologies to their mammalian homologues (49% identical residues between EmAURKA and human Aurora A; 61% identical residues between EmAURKB and human Aurora B).
It has been demonstrated that all Aurora A kinases in model organisms share a common feature in that the amino acid sequence corresponding to human Aurora A residue 198 is glycine or alanine, which has a short and hydrophobic side chain. On the contrary, all Aurora B kinases share an amino acid with a long and hydrophilic side chain such as asparagine or threonine at this position [32–35]. Consistent with that, the residue corresponding to human Aurora A G198 is glycine in EmAURKA and threonine in EmAURKB, receptively (Fig 1).
Given that the expression and activity of Aurora A and Aurora B increase from late G2 through the M phase in mammalian cells [10, 35] and that the germinative cells represent the only proliferative cells in E. multilocularis, we speculated that EmAurka and EmAurkb are predominantly expressed in the germinative cells that actively proliferate throughout the whole developmental stages of the parasite. We then analyzed their expressions in four larval stages of E. multilocularis: mature protoscoleces, developing protoscoleces, protoscoleces undergoing microcyst formation and metacestode vesicles.
As shown in Fig 2A, mRNA levels of both EmAurka and EmAurkb were much lower in the mature protoscoleces compared to the other three developmental stages, and the most prominent expression was observed in the developing protoscoleces. Previous studies have shown that the germinative cells are abundant in the developing protoscoleces and actively proliferate, whereas in the mature protoscoleces these cells remain in a quiescent state or with slow cell-cycle kinetics [5]. The protoscoleces undergoing microcyst formation and the metacestode vesicles also have a large population of proliferating germinative cells. These results may suggest that EmAurka and EmAurkb are specifically expressed in the proliferating germinative cells. We then analyzed their mRNA levels in metacestode vesicles treated with hydroxyurea, which could specifically deplete the germinative cell populations in E. multilocularis [5]. As expected, mRNA expression of both Aurora kinases was greatly downregulated after hydroxyurea treatment (Fig 2B). These results may suggest that EmAurka and EmAurkb are actively expressed in the germinative cells of E. multilocularis.
Histone H3 is one of the most important substrates of Aurora B during mitosis and it can be phosphorylated by Aurora A in vitro [36, 37]. To explore if EmAURKA and EmAURKB possess enzymatical activities, we performed in vitro kinase assay using Histone H3 as the substrate. As shown in Fig 3, both EmAURKA and EmAURKB could phosphorylate human Histone H3. The kinase activity of Aurora A and B is regulated by phosphorylation of the threonine in the RxT motif within the activation loop, and mutations of this residue eliminate the kinase activity almost completely [38, 39]. We then generated inactive forms of both kinases by substituting alanine for threonine (T208A for EmAURKA and T184A for EmAURKB, respectively), and found that EmAURKAT208A and EmAURKBT184A had greatly reduced in vitro kinase activity. These results demonstrate that E. multilocularis Aurora kinases possess enzymatical activities.
Considering the similarities in the Aurora kinase domain shared by E. multilocularis and human and the evidence that the inhibitors originally designed to target human AURKs also have impact on the AURK homologues in invertebrates [40–42], we then tested the effects of various AURK inhibitors on E. multilocularis.
We found that MLN8237, a potent inhibitor for human Aurora A which can also inhibit Aurora B activity at higher concentrations [37, 43–45], reduced the in vitro kinase activity of EmAURKA and EmAURKB (S3 Fig). We then treated in vitro-cultivated metacestode vesicles with different concentrations of MLN8237. As shown in Fig 4A, after the treatment of 1 μM MLN8237 for 24 h, about 20% of the mitotic cells (21.8 ±1.9%, three separate experiments) displayed misaligned or lagging chromosomes, which is indicative of Aurora A inhibition [46, 47].
Although Aurora A can phosphorylate Histone H3 in vitro, phosphorylation of Histone H3 during mitosis is mainly regulated by Aurora B rather than Aurora A in vivo [35–37]. We treated metacestode vesicles with 1 μM of MLN8237 for 24 h, and found no obvious changes in the level of phosphorylation of Histone H3 in the germinative mitotic cells, suggesting unimpaired activity of Aurora B (Fig 4A). However, we observed that the number of pSer10-Histone H3 positive cells was increased about 3–4 folds after the treatment of MLN8237 (3.8 ± 1.5 ‰ and 13.7 ± 4.0 ‰ of total cells for the control and MLN8237-treated groups, respectively) (Fig 4B), suggesting an elevated proportion of cells at G2/M phase, a phenotype caused by specific inhibition of Aurora A without affecting Aurora B activity [37, 48, 49]. These results indicate that MLN8237 can inhibit the activity of Aurora A in the germinative cells of E. multilocularis.
Aurora B phosphorylates Histone H3 at Ser10 and Ser28 in the N-terminal tail during mitosis, and phosphorylation of Histone H3 is considered as a robust and reproducible cellular readout for Aurora B kinase activity [37]. E. multilocularis Histone H3 exhibits significant homology to their mammalian relatives, and the two phosphorylation sites are also well conserved (S4A Fig). Our results indicate that phosphorylations of Histone H3 at Ser10 and Ser28 were greatly decreased in the mitotic germinative cells upon the treatment of 5 μM MLN8237 for 24 h (Fig 4C and S4B Fig), suggesting that MLN8237 can also inhibit Aurora B activity in E. multilocularis.
We also treated metacestode vesicles with nocodazole, a compound capable of arresting cells at G2/M by inhibiting microtubule polymerization [50, 51], to allow mitotic cells to accumulate. We found that pSer10-Histone H3 positive cells were apparently induced upon the treatment of nocodazole, whereas they were still hardly detected in the vesicles co-incubated with 5 μM of MLN8237 (S5 Fig). These data suggest that MLN8237-induced inhibition of Aurora B impairs the phosphorylation of Histone H3 in the mitotic cells of E. multilocularis.
Altogether, these results suggest that MLN8237 can inhibit the activities of both Aurora kinases in E. multilocularis, which may in turn cause severe impact on the mitotic progression of the germinative cells in E. multilocularis.
We found that 5 μM of MLN8237 also induced multinucleated cells in the germinal layer of the vesicles, a hallmark for Aurora B inhibition [49, 52, 53]. Formation of multinuclei or a single large nucleus could be observed as early as day 1 during treatment, and the number of multinucleated cells greatly increased on day 3 (Fig 5A and S6A Fig). The phenotype of multinucleation should only be existing in the germinative cells, the only cells capable of proliferation and division in E. multilocularis. These multinucleated cells could enter and exit mitosis but failed in completing cytokinesis due to the inhibition of Aurora B.
We then incubated MLN8237-treated vesicles with EdU, an analogue of thymidine which can be incorporated by the germinative cells at S phase [5, 27], and found that over 60% of the multinucleated cells were EdU+ after 3 days of MLN8237 treatment. On the other hand, almost all of the EdU+ cells exhibited multinucleation phenotype during the whole time of treatment (Fig 5B and 5D and S6B Fig), suggesting that MLN8237 displays specific effects on the germinative cells.
After 9 days of treatment, the multinucleated cells exhibited apparently impaired ability of incorporating EdU, and about 70% of the multinucleated cells were not labeled with EdU (Fig 5C and 5D and S6B and S6C Fig), probably because these germinative cells displayed a delayed cell cycle or could no longer continue to go through the cell cycle. In addition, after 6 and 9 days of treatment with 5 μM MLN8237, we could hardly detect cells exhibiting the M-phase typical DAPI-staining of condensed chromosomes, suggestive of severe mitotic errors.
We also tested the effects of MK-5108 (Aurora A-selective inhibitor), AZD1152-HQPA (Aurora B-selective inhibitor) and MLN8054 (the predecessor of MLN8237) [49, 54, 55], and found no severe defects in the germinative cells upon the treatment of MK-5108 or AZD1152-HQPA, e.g. chromosome misalignment, abnormal multinucleated cells and impaired phosphorylation of Histone H3. However, MLN8054 exhibited very similar effects as MLN8237, despite higher concentrations needed to give rise to the phenotypes of Aurora A/B inhibition (S7 Fig), confirming our observations about the effects of MLN8237 on the germinative cells of E. multilocularis.
We then prolonged the time of MLN8237 treatment to 14 days. Strikingly, the number of the multinucleated cells was decreased to only about 0.5% of total cells, and EdU was hardly detected in the remaining multinucleated cells (Fig 6A and 6B). We also allowed vesicles to recover for 3 days in drug-free media after 14 days of treatment and then performed EdU labeling, and EdU+ cells were still hardly detected (S8 Fig). These results indicate that inhibition of the Aurora kinases eliminates the germinative cells in E. multilocularis metacestode vesicles.
When exposed to MLN8237 for 21 days, the in vitro-cultivated metacestode vesicles did not display structural disintegration or collapse, however, they could no longer grow. In addition, the initial process of vesicle formation from protoscoleces was also greatly inhibited by MLN8237 (Fig 6C). These results suggest that Aurora inhibition impairs the maintenance of germinative cell populations, which in turn leads to the impaired larval growth and development of E. multilocularis.
The current chemotherapy for human AE mainly relies on benzimidazoles, which do not, however, act parasiticidally in vivo. It has been shown that benzimidazoles have limited effects on killing the germinative cells of E. multilocularis, probably because these cells express specific β-tubulin isoforms which are resistant to inhibition by benzimidazoles [6]. The stem cell-like germinative cells drive the larval growth and development of E. multilocularis within the hosts, and are considered the main cause of disease recurrence. Therefore, germinative cells are suggested as crucial targets for anti-echinococcosis drug development [6]. Recently, multiple lines of evidence have shown that drugs which impact the function of the germinative cells could efficiently inhibit, at least in vitro, the growth of E. multilocularis larvae [27, 29, 56].
As the only proliferative cells in E. multilocularis, germinative cells differentiate into other types of cells and also undergo extensive proliferation and self-renewal, maintaining their populations in the parasite [5]. Cell proliferation needs accurate control of the cell division cycle, which includes interphase, mitotic phase, and cytokinesis in eukaryotes. In the case of human cancer, therapeutic strategies have been proposed for targeting cell division, and targeting the mitotic progression is considered to offer possibilities for killing cancer cells [57]. Strikingly, these strategies have become referential for anti-human protozoan drug discovery, and an increasing number of investigations have focused on the regulatory components of protozoan cell division [22–23]. As concerning E. multilocularis, Schubert et al. have recently shown that inhibition of the Polo-like kinase EmPlk1 by the compound BI2536 could prevent the larval growth of the parasite, possibly by specifically inducing mitotic arrest and cell death of the germinative cells [29].
The conserved Aurora family of protein kinases are crucial regulators of essential processes ranging from mitotic entry to cytokinesis. Dysfunction of Aurora kinases causes aberrant mitosis, aneuploid or chromosome instability, which may in turn lead to cell senescence and death [7, 10]. We herein describe the identification of two druggable Aurora homologues in E. multilocularis, which play vital roles in regulating germinative cell mitosis. We show that both Aurora kinases, EmAURKA and EmAURKB are structurally homologous to the mammalian orthologs and contain conserved residues at corresponding positions which are essential for the kinase activity (Figs 1 and 3).
Activated Aurora A phosphorylates, and thus activates, its substrate PLK1 at T210 during mitosis in mammalian cells [7, 35]. The E. multilocularis PLK1 homologue, EmPLK1, has been identified and the corresponding threonine residue (T179) is also conserved in EmPLK1 [29], implicating a similar activation mechanism in E. multilocularis mitotic germinative cells. The transcription factor p53 is another prominent substrate involved in the checkpoint and apoptotic responses regulated by Aurora kinases [7, 10]. We have previously characterized the p53 homologue Emp53 in E. multilocularis [58]. Our recent studies found that Emp53 has two putative Aurora regulated-serine phosphorylation sites within the RxS motifs. It would be tempting to explore the functional significance of Aurora-p53 interactions in the regulation of E. multilocularis germinative cells in our future work.
Our data demonstrate that EmAurka and EmAurkb are highly expressed in the larval stages which have a large population of germinative cells extensively proliferating (Fig 2A). In addition, the expression of both kinases is significantly decreased upon the specific depletion of germinative cell populations by hydroxyurea treatment (Fig 2B). Since the germinative cells are the only cells capable of proliferation and division in E. multilocularis, these results may suggest that E. multilocularis Aurora kinases are specifically expressed in the germinative cells.
Due to their crucial roles in regulating mitosis, Aurora kinases have emerged as attractive drug targets in cancer and have become the focus of intense drug discovery efforts [14, 15]. A number of Aurora kinase inhibitors are developed, and some of them have been shown to also inhibit the Aurora kinases in protozoan, C. elegans and Drosophila [19–22, 40–42]. Our findings indicate that MLN8237 and its predecessor MLN8054 display significant effects on the mitotic germinative cells of E. multilocularis metacestode larvae (Fig 4, Fig 5 and S7 Fig). MLN8237 was initially designed as an Aurora A inhibitor, but also inhibits Aurora B at higher concentrations. In proliferation inhibition studies, MLN8237 exhibits activities against human tumor cell lines with IC50 values ranging from 15 to 469 nM [43]. In phenotype-based cellular assays, the considerable concentrations for selective inhibition of Aurora A range from 0.1 to 0.5 μM depending on the cell lines used, whereas 1 μM and higher concentrations of MLN8237 are necessary for Aurora B inhibition [37, 43, 45, 59]. Studies using animal models showed that the corresponding plasma concentration needed to inhibit Aurora A in vivo is approximately 1 to 2 μM [43]. In this study, we found that 0.5 μM of MLN8237 could slightly inhibit Aurora A in the in vitro-cultivated E. multilocularis metacestode larvae, resulting in chromosome misalignment in a few of mitotic cells. Further experiments demonstrated phenotypes of Aurora A inhibition by using 1 μM of MLN8237, which caused severe chromosomal defects in the mitotic germinative cells and increased the proportion of cells at G2/M phase (Fig 4A and 4B).
Previous studies have shown that the specificity window for MLN8237 against Aurora A and B is narrow and it varies in different tumor cell lines, making it difficult to fully inhibit Aurora A without affecting Aurora B [37]. In E. multilocularis, although a 24 h-treatment of 1 μM MLN8237 gave rise to phenotypes of specific inhibition of Aurora A with little effects on Aurora B activity (Fig 4A and 4B), multinucleated cells could also be observed upon the treatment with 1 or 2 μM of MLN8237 for longer time in our studies. We therefore hypothesize that MLN8237 exhibits weak specificity for the inhibition of Aurora A/B in the germinative cells of E. multilocularis.
We show that the treatment with higher concentration of MLN8237 apparently gave rise to phenotypes of Aurora B inhibition in the germinative cells, e.g. impaired phosphorylation of Histone H3 (Fig 4C, S4 Fig and S5 Fig) and multinucleation (Fig 5A and S6A Fig). The EdU+ cells always exhibited multinuclei, suggestive of specific effects of MLN8237 on the mitotic germinative cells (Fig 5B and 5D and S6B Fig). Importantly, the numbers of EdU+ cells were gradually reduced since day 3 of the treatment, and almost all of the multinucleated cells were vanished after 14 days of treatment (Fig 5E, Fig 6A and 6B), suggestive of impaired viability of the germinative cells.
MLN8237 can inhibit the larval growth and development of E. multilocularis (Fig 6C). The metacestode vesicles could not grow but remained structurally intact upon the treatment of MLN8237 for weeks, similar to the vesicles treated with hydroxyurea or BI2536, both of which are considered to cause specific depletion of the germinative cell populations [5, 29].
Taken together, we propose that inhibition of E. multilocularis Aurora kinases by MLN8237 specifically causes germinative cell killing, resulting in impaired maintenance of the germinative cell populations and in turn larval growth inhibition of the parasite.
In conclusion, we herein present the Aurora kinases of E. multilocularis as promising druggable targets for eliminating germinative cells in the parasite. MLN8237 or other related Aurora inhibitors may serve as lead compounds for the development of novel drugs that target the mitotic progression of Echinococcus germinative cells and would be considered as alternative or complementary chemotherapeutics of benzimidazoles against human echinococcosis. On the other hand, our studies reveal Aurora kinases implicated in maintaining the germinative cell populations of E. multilocularis, offering a solid basis for further explorations of Aurora kinase-relevant mitotic regulators and mechanisms governing the unique stem cell system of the parasite in the future.
|
10.1371/journal.pntd.0003283 | Forecasting Temporal Dynamics of Cutaneous Leishmaniasis in Northeast Brazil | Cutaneous leishmaniasis (CL) is a vector-borne disease of increasing importance in northeastern Brazil. It is known that sandflies, which spread the causative parasites, have weather-dependent population dynamics. Routinely-gathered weather data may be useful for anticipating disease risk and planning interventions.
We fit time series models using meteorological covariates to predict CL cases in a rural region of Bahía, Brazil from 1994 to 2004. We used the models to forecast CL cases for the period 2005 to 2008. Models accounting for meteorological predictors reduced mean squared error in one, two, and three month-ahead forecasts by up to 16% relative to forecasts from a null model accounting only for temporal autocorrelation.
These outcomes suggest CL risk in northeastern Brazil might be partially dependent on weather. Responses to forecasted CL epidemics may include bolstering clinical capacity and disease surveillance in at-risk areas. Ecological mechanisms by which weather influences CL risk merit future research attention as public health intervention targets.
| Cutaneous leishmaniasis (CL) is a disease resulting from infection by the Leishmania parasites, which humans may acquire when bitten by an infected sandfly. From a public health standpoint, it is important to identify cases early and monitor patients' clinical outcomes because unsuccessfully-treated patients are at risk for severe complications. Since weather conditions affect survival and reproduction of sandflies that transmit Leishmania, routinely-gathered weather and climate data may be useful for anticipating CL outbreaks, bolstering clinical capacity for high-risk periods, and initiating interventions such as active case-finding during these periods to limit disease burden. Here we assessed whether the number of CL cases occurring per month in a rural region of Bahía, Brazil was associated temperature, humidity, precipitation, and El Niño sea surface temperature oscillation patterns observed during preceding seasons. We formulated models that improved accuracy of one, two, and three month-ahead CL predictions by accounting for weather. Forecasts of this nature can contribute to reducing CL burden by informing resource allocation and intervention planning in preparation for epidemics.
| Diseases caused by the Leishmania parasites, including cutaneous leishmaniasis (CL), are important in tropical and subtropical areas worldwide, causing over one million cases per year [1]. Although the burden of leishmaniasis in the Americas has reportedly decreased [2], areas of northeastern Brazil, where Leishmania (Viannia) braziliensis is endemic, have seen increasing CL case notifications in recent decades [3], [4]. Recurring epidemics in this region comprise an increasing component of overall CL burden in Brazil [5], [6]. The endemic area is additionally expanding eastward from its historical center in the interior cerrado uplands toward coastal Atlantic forests [7], [8].
The increase in CL incidence and geographic range expansion by L. (V.) braziliensis are significant public health concerns. While CL does not cause death in the absence of complications, the disease causes debilitating and stigmatizing lesions and may progress to dangerous manifestations including mucosal or disseminated infection if treatment is not initiated early in the clinical course [9]–[11]. Individuals infected with L. (V.) braziliensis are more likely than other CL victims to experience such complications, which have been observed with increasing frequency in northeastern Brazil over the last three decades [3], [7], [9]. These trends are problematic because current chemotherapeutic regimens for CL have limited efficacy and because an increasing proportion of L. (V). braziliensis infections are resistant to first-line antimonial treatment [7], [12]–[14].
Forecasting CL epidemics could aid the allocation of public health resources in advance of high-risk periods [15]. Poor understanding of L. (V.) braziliensis has historically hindered efforts to anticipate CL risk in Brazil [16]–[18]. However, as for other vector-borne infections, variations in rainfall and temperature might be associated with outbreaks [15], [19]–[22]. Seasonal and weather-dependent population dynamics of insect vectors that transmit CL in South America motivate consideration of climatic and meteorological factors that may drive disease incidence [23]–[29]. Recent studies have demonstrated that local meteorological observations and global climate patterns such as the El Niño Southern Oscillation improve CL forecasting in Costa Rica [15], [19], [22], [30]. Although correlations between weather and CL [31] or visceral leishmaniasis (VL) [32] have been documented elsewhere, these observations have not yet led to the development disease forecasting systems serving most populations at risk [15]. In this study, we sought to identify potential associations between weather and CL risk and used these findings to develop model-based early warning systems for CL in a region of Northeast Brazil with endemic L. (V.) braziliensis transmission.
The Corte de Pedra health post in Presidente Tancredo Neves, Bahía, Brazil maintained paper records for leishmaniasis cases treated from 1988 onward. The health post treats over 90% of CL cases from surrounding municipalities; although the area has historically supported L. amazonensis, only L. (V.) braziliensis has been isolated from CL patients in the past two decades [7], [8], [14], [33], [34]. We used an aggregated time series comprising 10% of leishmaniasis cases identified at the health post; the dataset, and epidemiologic and clinical summaries of the cases, are described in a previous article [7]. Institutional review boards of the Federal University of Bahia and Weill Cornell Medical College approved the human subject protocol for the original study. We considered only CL cases to avoid double-counting CL patients progressing to disseminated or mucosal infection after initial treatment and to minimize heterogeneity in latent and pre-treatment periods.
We obtained daily ground-surface meteorological observations from all weather stations within a 500 km radius of the health post, as reported through the historical databank of the Instituto Nacional de Meterologia (INMET; http://www.inmet.gov.br/portal/). Daily meteorological data were available from 11 weather stations in and adjacent to Bahía as listed in the supplement (Table S1). Data from the weather stations were sparse prior to 1992. To allow consideration of lags up to 24 months in length between weather exposures and disease outcomes, we considered only cases presenting for treatment from 1994 onward. To monitor ENSO variations, we used the monthly Multivariate ENSO Index (MEI) [35], which quantifies meteorological anomalies related to variations in sea surface temperature in Niño Region 3.4 of the Pacific Ocean (5°N–5°S, 120°–170°W). Since MEI is computed as a two-month running average, we matched the disease cases in the current month with the MEI that covered the current and previous month.
Because the location of the weather stations does not necessarily match the study area, we interpolated [36] the time series of meteorological data for the study location based on the surrounding weather stations. We describe the interpolation procedure in detail in the supplemental methods (Text S1). Using these interpolated time series, we calculated the expected mean noontime temperature (°C), relative humidity (%), days with rainfall (%), and total daily rainfall (cm) within each municipality in the study area for each month over the period 1992–2008. To aggregate values at the regional level, we took the mean interpolated value for each month across all municipalities.
We normalized the time series of monthly CL cases by taking the square root. We identified an autoregressive integrated moving average (ARIMA) or seasonal ARIMA (SARIMA) specification for a null model describing temporal dependence in the transformed case series. Formal descriptions of the ARIMA and SARIMA frameworks, and procedures for model identification, are presented elsewhere [37], [38]. We determined an appropriate order for non-seasonal and seasonal autoregressive, integrated, and moving-average parameters in the null model according to three factors: (1) we identified significant lags in the autocorrelation and partial autocorrelation functions computed from the time series (Figure 1); (2) we ensured residuals from the null models did not retain significant temporal autocorrelation based on the Ljung-Box test [39] and inspection of the autocorrelation and partial autocorrelation functions computed from the residuals; and (3) we investigated potential overfitting relative to simpler order specifications according to the Akaike and Bayesian Information Criteria (AIC and BIC) [40], [41].
We used a common pre-whitening approach to select lags of the predictors to be used as covariates in forecasting models [37], [42]. The first step involved fitting a unique (S)ARIMA model to each predictor variable (Xi) on the basis of the variable's autocorrelation and partial autocorrelation functions, reducing the residuals of the Xi input to white noise. We used the fitted models for the predictors to filter the transformed case series (Y). We computed the cross-correlation function (CCF) between the residuals of the Y and Xi series and tested for significance at the 95% confidence level (cut-off at 1.96n−1/2, where n was the length of the time series in months). We considered as covariates all lags of the Xi variables where the absolute value of the CCF between the filtered series exceeded the cut-off.
We partitioned the data into an initial “training” period comprising observations for the interval ending in 2004 (132 months), and a “validation” period for the remaining 48 months from 2005 to 2008. The data from the training period served as a basis for estimating the initial autocorrelation and partial autocorrelations to be used for time-series modeling and lag filtering. We parameterized models to fit the training data and used the fitted models to forecast the number of cases in future time periods. The model fit was updated iteratively with the next most recent month, and new forecasts were generated based on the updated models. We generated forecasts at predictive horizons ranging from one month to the maximum number of months ahead that would be possible to predict from incoming data; the shortest significant lag in the CCFs thus specified the maximum forecast horizon (3 months). We centered and scaled all covariates prior to modeling by subtracting their means and dividing by their standard deviations; this allows parameters to be interpreted in terms of covariate standard deviation units to facilitate comparison of effect sizes [43], [44]. As a linear transformation of the covariates, this maintains a linear functional form relating measured predictors to square root-transformed cases. Models predicting square root-transformed CL cases using linear and non-linear relations to meteorological covariates have been compared in previous studies [15]. We ensured via the Ljung-Box test, and by checking autocorrelation and partial autocorrelation functions computed from model residuals, that introducing covariates did not induce temporal dependence in model residuals.
We considered several potential forecasting models for CL. First, we generated a null (S)ARIMA model predicting the transformed case series on the basis of its temporal dependence patterns alone. We additionally generated regression models considering all possible combinations of covariates, and fit each model with the null (S)ARIMA error specification determined from the ACF and PACF of the transformed case series. Last, we used Bayesian model averaging [41] to pool parameter estimates from the fitted models and formulate a global model. We calculated model weights (posterior probabilities for each fitted model) via the AIC, AICc, and BIC and used the weights to pool parameter, variance, and covariance estimates, as described elsewhere [41]. In addition to providing parameter estimates, the model averaging approach can be used to calculate the posterior probability that each covariate is useful in predicting monthly CL cases; this value is given as the sum of posterior model probabilities for models that included the covariate (we refer to parameter posterior probability as PPP henceforward). For model averaging, we updated posterior weights at each time point as models were re-fitted to incoming data. We conducted sensitivity analyses without updating of weights to verify certainty in the results.
We evaluated models' predictive accuracy on the basis of MSE in predictions; we computed this value by comparing model forecasts to the square root-transformed cases observed during the validation period. We compared predictive accuracy for models with covariates relative to the null model to ascertain improvements in forecasting.
The dataset included 1,209 leishmaniasis cases treated at the Corte de Pedra health post between 1988 and 2008. We identified 853 cases without disseminated or mucosal infection presenting for care between 1994 and 2008. Of these, 586 occurred in the initial training period (1994–2004) and 267 occurred in the validation period (2005–2008). The most notable epidemic appeared in 1999–2000 (Figure 1). The majority of cases occurred among adult male agricultural workers. The median age at symptom onset was 22, and the age distribution was heavily skewed toward younger ages. Further epidemiologic and clinical details about the cases are available elsewhere [7].
The transformed case series had a stationary mean indicating differencing was not required. The autocorrelation function showed significant dependence extending to a four-month lag, while significant partial autocorrelation cut off after a two-month lag (Figure 1). We identified no evidence for recurring seasonal patterns in the autocorrelation and partial autocorrelation functions. AIC and BIC scores indicated that accounting for autoregressive or moving average dependence at four-month lags resulted in model overfitting, as did incorporating a 12-month autoregressive term in a SARIMA framework. According to these observations and on the basis of eliminating autocorrelation in the residuals as detected by the Ljung-Box test and residual series' autocorrelation and partial autocorrelation functions, we selected an ARIMA(2,0,3) framework for the null model.
We identified significant cross-correlations between the case series and all predictors except temperature (Figure 2). The three-month lag at which relative humidity and CL cases were significantly correlated provided the maximum forecast horizon. We identified significant, negative-valued cross correlations linking pre-whitened CL cases to relative humidity and rainfall frequency at lags between three and five months (Table 1). We identified significant, positive cross-correlations with MEI (22-month lag) and total rainfall (10- and 21-month lags, respectively).
For the multivariate models, each covariate had the binary option of being included or not included. Since we identified six significant cross correlations, we fit 26 = 64 models in total. The best-fitting model according to BIC weights accounted only for a negative association between cases and five month-lagged relative humidity. The best-fitting model by AIC and AICc included a negative association with rainfall frequency at the five-month lag and total rainfall at 10- and 21-month lags. Averaging across all models did not reveal noteworthy differences in variables' contributions to model fit, as evidenced by similarity in PPP values among covariates under each averaging scheme. Parameter estimates averaged according to AIC and AICc weights differed by less than 10−4 and are consequently presented together as a single averaged model. Under the BIC and AIC/AICc weighting strategies, the models with the greatest posterior probabilities accounted for relative humidity and rainfall frequency at five-month lags, and total rainfall at 10- and 21-month lags. Meteorological parameter estimates differed across models, leading to averaged 95% confidence intervals including zero in all cases. The BIC-averaged model offered more conservative estimates and narrower confidence intervals for all meteorological parameters than the AIC/AICc-averaged model. Using model weights computed from the training period only rather than monthly-updated model weights did not lead to numerical changes in parameter estimates or PPPs greater than 10−4. Variable selection for the best-fitting models by AIC/AICc and BIC did not change as we updated the models.
We compared out-of-fit prediction accuracy for the null model with the best-fit models and the averaged models, which accounted for meteorological covariates (Table 2). The best-fit and averaged models reduced the MSE relative to the null model at all prediction lengths. Improvements in MSE relative to the null model were greatest at the three-month horizon and smallest at the one-month horizon for all models considered. The best-fitting model by BIC produced one, two, and three month-ahead forecasts with 10.6%, 12.8%, and 15.7% lower MSE than the null model, respectively (Figure 3, Figure S1, Figure S2). This model provided the most accurate forecasts at all prediction lengths; two month-ahead forecasts were the most accurate in terms of minimizing MSE. The averaged model constructed according to BIC offered smaller marginal reductions in MSE than the averaged model constructed according to AIC/AICc weights for all but the one month-ahead predictive horizon. Marginal reductions in prediction MSE were poorest from the best-fitting model selected according to AIC/AICc weights.
The best-fitting model by BIC offered one-, two-, and three month-ahead predictions with on average 6.0%, 7.3%, and 8.0% lower variance than the null model, respectively. These improvements in precision did not incur penalties to forecast accuracy. Observed cases exceeded the upper limits of the 95% confidence envelopes from all models in May of 2006 and May of 2008, at the peaks of epidemics during those years. One- and three month-ahead predictions from all models additionally under-estimated a secondary peak in July of 2008 (Figure S1, Figure S2). Adjusting models to include a seasonal autoregressive for the twelve-month period term did not improve forecasting of the May epidemic peaks, which became a regular feature in the data only from 2005 onward. Residuals from models incorporating covariates did not show significant temporal dependence via the Ljung-Box and test or in their autocorrelation and partial autocorrelation functions.
In this study we found that accounting for meteorological and climatic factors improved accuracy and precision of CL forecasts in a region of endemic L. (V.) braziliensis transmission in Northeast Brazil. Notably, dry conditions with respect to relative humidity and precipitation were significantly associated with CL case notifications three to five months later. Our results are consistent with the view that CL is sensitive to meteorological and climatic forcing [19], [22], [30]–.
Differences in out-of-fit predictive accuracy among models likely indicate where models may be overfit to within-sample data. The model with the best predictive accuracy at all horizons was selected by BIC and accounted only for five month-lagged relative humidity as a meteorological covariate. AIC and AICc have a lower penalty than BIC for potential overfitting [40], [41], and in the present analysis selected a model with more covariates, including covariates operating at longer (10- and 21-month) lags. Temporally remote effects of this nature may be difficult to identify and use for prediction due to heterogeneity in CL incubation periods [47], in the time individuals take to seek medical attention, and in ecological pathways connecting weather to disease risk. These factors contribute to uncertainty when forecasting with case notification data [48].
Although our analysis was not suited for identification of causal effects, numerous biological mechanisms may support associations between weather and CL epidemics. Inverse correlations between precipitation and humidity variables at lags between three and five months in particular demonstrate excess cases closely follow dry periods. Ecological sampling studies have indicated population densities of Lutzomyia sandflies in CL-endemic areas of Brazil and South America to be inversely correlated with relative humidity and rainfall in recent months [49]–[51]. While present year-round, dominant vectors for CL in the study region (including Lu. whitmani and Lu. migonei) are particularly abundant during the warm, dry season [23], [50]. The near-term moisture effects we identify may thus result from environmental conditions conducive to vector survival, reproduction, or feeding behavior. Mechanisms connecting weather and CL risk at longer lags are more likely mediated by the ecology of vertebrate L. (V.) braziliensis host species than by sandflies, whose life cycles span only one to several months. For instance, the positive cross-correlations with rainfall and MEI at 21- and 22-month lags most probably relate to longer-term effects of moisture surpluses on biological productivity necessary to sustain large populations of mammalian reservoirs [25], [52].
Our analyses have several limitations. Having fit models to a 10% subset of total reported cases treated at the health post, our estimates are sensitive to small month-to-month variations that may be less pronounced in a dataset providing complete case records. While forecasts succeeded in predicting overall epidemic patterns exhibited from 2005 to 2008, a notable weakness was the poor prediction of the size and duration of the 2008 epidemic. The low number of cases appearing in June of that year, mid-way through the epidemic, may be an artifact of the reduced dataset and likely contributed to this shortcoming. The passive surveillance system at the Corte de Pedra health post additionally provided an incomplete sample of total CL cases within the area, and may in particular under-represent persons unlikely to seek care. For instance, other analyses of the data presented here showed that many agricultural workers delay pursuit of therapy until onset of complications including mucosal or disseminated infection [7]. If long or heterogeneous gaps separate timing of infection, disease onset, and care-seeking, case notification data may not indicate sharp peaks in CL following important weather events. Such bias obscures potentially meaningful meteorological associations with CL risk.
While the Corte de Pedra health post remained the primary center for CL diagnosis and treatment throughout the study period [7], [33], [34], there were almost certainly long-term changes among the population at risk with respect to size, demographics, access to health services, and potential environmental exposures. Our analysis could not address these factors as potential controls or effect modifiers because the health post serves small rural communities not tabulated by the Brazilian census. Ongoing deforestation in the region, including conversion of cacao plantations to cattle ranches, likely caused temporal variation in habitat suitability for vectors and hosts and thus mediated disease risk. In addition, Northeast Brazil experienced secular rural-to-urban migration during the study period, likely offsetting natural increase within the population. Individual risk factors likewise predict variation within the population with respect to opportunities for exposure to L. (V.) braziliensis in domestic, peridomestic, and sylvatic environments [7]. Consequently, individual factors not considered here may mediate temporal patterns and weather sensitivity of CL risk among patients [23], [25], [53]. In view of these limitations to the present study, implications of population- and individual-level factors for CL transmission require future research attention to inform interventions reducing disease risk in northeastern Brazil.
Although the identified associations aided forecasting, inferential gains are limited by poor understanding of CL eco-epidemiology, including the fact that the local animal reservoir for L. (V.) braziliensis is unknown. Ecological sampling studies in the state of Pernambuco, near the study region, suggest several species of mice and rats may contribute to transmission [18], however the parasite is known to infect other rodents as well as dogs, cats, and equines [54]. Potential pathways by which weather affects ecological dynamics in American CL have been discussed extensively in previous work [55], [56], and are likely geographically heterogeneous. Meteorological and climatic sensitivity of Leishmania spp. transmission cycles can be anticipated to vary spatially according to species compositions, contact rates and competence among local vectors and hosts, and ecological sensitivity to weather and other environmental stressors; additionally, individually- and regionally-varying social factors influence human exposure to primarily-enzootic transmission cycles, and vulnerability to weather-related health risks [22], [57]. It is known, for instance, that seasonal dynamics of L. (V.) braziliensis and its vectors differ across Brazil, where predominantly sylvatic, peridomestic, or domestic transmission pathways in endemic foci reflect divergent underpinnings of CL eco-epidemiology [26], [29], [58]. For this reason, developing similar model-based early warning systems at fine geographic resolutions remains an important objective for other endemic settings within Brazil and Latin America.
As CL continues to expand in parts of Brazil, developing capacity to forecast epidemics will facilitate public health responses. Using model-based predictions to anticipate disease risk and expanding clinical capacity to address excess CL cases may constitute an important operational strategy for alleviating burden of disease. For example, understanding the timing of epidemics will enable implementation of enhanced case detection in advance of and during high-risk periods, limiting lesion size at the time patients are identified and reducing patients' risk for treatment failure and metastatic complications. This can be accomplished in part by ensuring adequate clinical and laboratory personnel and diagnostic reagents or microscopy resources are available to identify CL patients during high-demand periods. Furthermore, since procurement and delivery of first-line pentavalent antimonial agents to endemic regions requires significant lead-time, acquiring and distributing these drugs preemptively in response to model-based predictions may ensure that treatment centers are adequately stocked for epidemics. This is also critical with respect to maintaining supplies of difficult-to-procure alternative therapies such as liposomal amphotericin B [59], which may need to be considered as L. (V.) braziliensis strains resistant to conventional treatments continue to emerge [7], [12]–[14]. Spatial and population-based criteria merit consideration in service delivery so that clinical resources and surveillance attention can be targeted focally towards vicinities or persons known to be at high risk for infection [60], [61]; in the study area, this population primarily includes young men who work or live in agricultural settings [7], [8], [14].
One key question with respect to the application of model-based forecasting to improve responses to CL is the definition of epidemic thresholds. The limited capacities of local and national leishmaniasis control programs in resource-poor settings contribute to difficulty identifying alert and response priorities for early warning systems [62], particularly with respect to defining meaningful epidemic thresholds. Choice of such thresholds may be arbitrary in practice [63]. WHO standards for initiating alerts following months when incidence has been twice its monthly average are likely sub-optimal for settings with highly variable incidence rates, such as Northeast Brazil [64], [65], where doubling relative to previous monthly averages may not be an adequate basis for identifying an epidemic and anticipating whether it will continue. More meaningful intervention criteria in endemic regions may be based on model-predicted probabilities for incidence to exceed a level at which clinical resources are likely to be strained; probabilistic alert systems of this nature are increasingly recognized for their compatibility with model-based epidemic projections, and interpretable implications for policy responses [66]. Operational research is needed to assess how clinical capacity and resilience to epidemics vary across endemic settings, as a basis for setting alert thresholds informed by risk for shortcomings in service delivery. Notwithstanding these limitations to operationalizing early warning systems in Brazil, our outcomes suggest that incoming weather data improves CL forecasts at a sufficient predictive horizon to facilitate intervention planning. Best practices for integrating predictive models into planning for responses to CL epidemics merit research attention and consideration from public health authorities in CL-endemic areas [15], [19], [67], [68].
|
10.1371/journal.pntd.0006027 | Presentation of life-threatening invasive nontyphoidal Salmonella disease in Malawian children: A prospective observational study | Nontyphoidal Salmonellae commonly cause invasive disease in African children that is often fatal. The clinical diagnosis of these infections is hampered by the absence of a clear clinical syndrome. Drug resistance means that empirical antibiotic therapy is often ineffective and currently no vaccine is available. The study objective was to identify risk factors for mortality among children presenting to hospital with invasive Salmonella disease in Africa. We conducted a prospective study enrolling consecutive children with microbiologically-confirmed invasive Salmonella disease admitted to Queen Elizabeth Central Hospital, Blantyre, in 2006. Data on clinical presentation, co-morbidities and outcome were used to identify children at risk of inpatient mortality through logistic-regression modeling. Over one calendar year, 263 consecutive children presented with invasive Salmonella disease. Median age was 16 months (range 0–15 years) and 52/256 children (20%; 95%CI 15–25%) died. Nontyphoidal serovars caused 248/263 (94%) of cases. 211/259 (81%) of isolates were multi-drug resistant. 251/263 children presented with bacteremia, 6 with meningitis and 6 with both. Respiratory symptoms were present in 184/240 (77%; 95%CI 71–82%), 123/240 (51%; 95%CI 45–58%) had gastrointestinal symptoms and 101/240 (42%; 95%CI 36–49%) had an overlapping clinical syndrome. Presentation at <7 months (OR 10.0; 95%CI 2.8–35.1), dyspnea (OR 4.2; 95%CI 1.5–12.0) and HIV infection (OR 3.3; 95%CI 1.1–10.2) were independent risk factors for inpatient mortality. Invasive Salmonella disease in Malawi is characterized by high mortality and prevalence of multi-drug resistant isolates, along with non-specific presentation. Young infants, children with dyspnea and HIV-infected children bear a disproportionate burden of the Salmonella-associated mortality in Malawi. Strategies to improve prevention, diagnosis and management of invasive Salmonella disease should be targeted at these children.
| Nontyphoidal Salmonellae are a major, yet often neglected, cause of fatal invasive disease among young African children, responsible for over 100,000 deaths a year. There is currently a lack of prospective studies to understand the clinical presentation and course of this disease. Diagnosis can be confusing, antibiotic resistance is an increasing problem and no vaccine is available. Studying 263 consecutive cases of invasive Salmonella disease over a one year period, we observed disparate clinical presentations, precluding the possibility of making a reliable clinical diagnosis in cases of this disease. We confirmed high mortality rates, finding that young age, dyspnea and HIV infection are independent risk factors for death. These findings support both the need for improved measures for prevention, diagnosis and management of invasive nontyphoidal Salmonella disease and indicate which children with this disease are at most need of intensive clinical care.
| Invasive bacterial disease is a major cause of mortality among African children [1–4]. Prospective studies from rural-based district hospitals in Kenya and Mozambique have found community-acquired bacteremia to be responsible for 26% [1] and 21% [3] of pediatric inpatient deaths. Pneumococcus and nontyphoidal Salmonellae (NTS) are the most common causes of bacteremia among African children [1–5]. While the burden of disease due to pneumococcus should fall with implementation of pneumococcal conjugate vaccines, no vaccine is available or in clinical trials against invasive nontyphoidal Salmonella (iNTS) disease [6, 7].
iNTS disease among African children is caused primarily by Salmonella enterica serovars Typhimurium and Enteritidis [8–12], and has an associated case fatality of around 20–25% for bacteremia [8, 9, 11, 13, 14] and 52% for meningitis [15]. These findings contrast with Salmonella disease in industrialized nations which typically presents as self-limiting enterocolitis, rarely requiring hospitalization [16]. Despite reports of declining iNTS disease with falling prevalence of malaria from specific African sites [17–19], recently published data from multiple countries across sub-Saharan Africa indicate that NTS continues to be a major cause of childhood bacteremia in west [20,21], central [22], east [23,24] and southern Africa [25,26]. The Phase 3 RTS,S/AS01 malaria vaccine trial conducted in eleven sites across sub-Saharan Africa, agnostic to levels of Salmonella bacteremia, found an incidence of approximately 500 cases of Salmonella sepsis/100,000 children/year among children under two years [27]. Recent data from the Typhoid Surveillance in Africa Program across a further seven sub-Saharan African sites confirm that iNTS disease continues to be a major public health problem in the region [28].
The clinical presentation of iNTS disease is often a non-specific febrile illness [7–10, 14]. Further diagnostic challenges result from the association of iNTS disease with other co-morbidities [7–10, 14]. Among African children, iNTS disease often occurs with malaria [29–31], HIV [26, 31], malnutrition [32] and anemia [33]. In adults, there is a strong association with HIV infection [26, 34]. Increasing multi-drug resistance among iNTS isolates in Africa adds to the high attrition from iNTS disease [8, 10–12]. For Malawian isolates, 90% have been reported as resistant to ampicillin, chloramphenicol and cotrimoxazole [35].
We set out to provide data to guide improved management of children with iNTS disease. Consecutive microbiologically-confirmed cases admitted to a large government hospital in Malawi were studied for the relationship between clinical presentation and outcome.
Queen Elizabeth Central Hospital (QECH) is the largest hospital in Malawi. It is situated in Blantyre, one of the two main cities in Malawi, and serves a combined urban and peri-urban rural population of around 1 million. The hospital has approximately 1000 beds and admits around 10,000 adults and 30,000 children (<16 years) annually. Since 1996, the Malawi-Liverpool-Wellcome Trust Clinical Research Programme has performed blood and cerebrospinal fluid (CSF) cultures from patients admitted with suspected sepsis and meningitis.
Eligible participants were children age ≤ 14 years admitted to the Pediatric Department of QECH with isolation of Salmonella from blood and/or CSF between January 1st and December 31st 2006. Subjects not meeting all three eligibility criteria were excluded. In order to reduce bias due to early death following admission, children were approached for recruitment on the day that Gram-negative bacteria were first detected in the blood or CSF, either on initial Gram stain or following positive culture.
First-line antimicrobial therapy for suspected sepsis in children at the time of the study was intramuscular chloramphenicol and gentamicin, or penicillin and gentamicin. The great majority of Gram-negative bacteraemias detected were due to NTS, and these bacteria were almost all resistant to chloramphenicol and penicillin. Therefore, children were started on oral ciprofloxacin and/or intravenous ceftriaxone as soon as Gram-negative bacteria were first detected. Once the organism and antibiotic sensitivity profile were known, treatment would be amended as necessary.
First line antimicrobial therapy for pediatric meningitis was intravenous ceftriaxone. Demographic and clinical data collected on admission were recorded on standard forms, along with weight and height or length. Clinical progress was recorded daily through to discharge from hospital or inpatient death.
Respiratory distress was defined as the presence of a tracheal tug, intercostal or subcostal recession, head-bobbing or nasal flaring. Tachypnea and tachycardia on admission were defined as rates greater than the 90th centile for age [36]. Children with an admission axillary temperature >37.5°C were classified as febrile. Children were considered to have gastroenteritis if diarrhea (≥3 loose stools per day) or vomiting were present, and respiratory disease if there was shortness of breath or respiratory distress. Fever without focus was defined as fever in the absence of localizing clinical signs.
Severe malnutrition in children 6–60 months was defined as a weight-for-height greater than 3 z-scores below the WHO median, or bilateral pedal edema (kwashiorkor). In children over 60 months, severe malnutrition was defined as a body mass index (BMI)-for-age greater than 3 z-scores below the WHO median. Severe anemia was defined as a packed cell volume (PCV) <15% or a hemoglobin concentration <5.0 g/dl. Hypoglycemia was defined as a glucose concentration on admission <3.0 mmol/L.
Blood and CSF cultures were performed using a standard aerobic bottle (BacT/Alert, bioMérieux, France) using 1–2 ml of blood and 0.5–1 ml of CSF. Following detection of Gram-negative bacteria, Salmonellae were confirmed by biochemical testing with API 20E kits (bioMérieux) and serovar was determined using agglutinating antisera (Prolab Diagnostics). Antimicrobial susceptibility testing was performed by disc diffusion using ampicillin, chloramphenicol, cotrimoxazole, gentamicin, ceftriaxone and ciprofloxacin discs according to British Society of Antimicrobial Chemotherapy methods and breakpoints. The laboratory participates in the UK National External Quality Assurance Scheme. All children admitted to QECH have a malaria parasite slide and PCV performed. Full blood counts were determined using a HMX (Becton Coulter). Tests for HIV infection were with Determine (Abbot Laboratories) and UniGold (Trinity Biotech) rapid tests, following national Voluntary Counseling and Testing guidelines. For discordant results and children under 18 months with positive results, the diagnosis was confirmed by polymerase chain reaction for proviral DNA.
Monthly rainfall data through 2006 were collected from the meteorological station at Chileka Airport, Blantyre.
Data were entered into a Microsoft Access database and analysis was performed using R (http://www.R-project.org/). Variation with age and season, of clinical indices, co-morbidity and outcome, were calculated as Mantel-Haenszel odds ratios. To facilitate this, age was converted into four ordinal categories (0–6 months, 7–12 months, 1–2 years, >2 years). Presentation season was grouped as January-March, April-June, July-September and October-December and ranked by mean rainfall. Odds ratios of case fatality given the presence of clinical indices or co-morbidity adjusted for age and season of presentation were calculated by logistic regression. Finally, a model of case fatality was constructed and tested by multivariate logistic regression. Covariates were chosen for the first fit of the model if they predicted mortality with a p value <0.1 along with age and sex. To minimize the number of covariate patterns, age was collapsed into a binary variable (children 0–6 months and children 7 months and over) for the purposes of the model. Covariates with the largest remaining p value (but retaining age and sex) were sequentially removed from the model and the model re-evaluated for goodness-of-fit and distribution of residuals at each step. The model was also evaluated for two-way interactions between significantly-associated terms. Missing data were addressed by providing denominator information where applicable.
After ensuring that each child was on appropriate antimicrobial therapy, the study was explained to the parents or guardians and informed written consent obtained. Ethical approval for the study was granted by the College of Medicine Research and Ethics Committee, College of Medicine, University of Malawi.
In total, 4535 blood cultures and 1823 CSF cultures were performed on children admitted to QECH over one year in 2006. Gram-negative organisms were detected in 395/4535 (9%) blood cultures and 34/1823 (2%) CSF cultures. Of these, 257/395 (65%) and 12/34 (35%), respectively, were identified as Salmonellae (Fig 1A). Salmonellae were isolated from both blood and CSF of six children, giving a total of 263 episodes of invasive Salmonella disease. The majority of Salmonellae 234/263 (89%) were serovar Typhimurium, while 14/263 (5%) were serovar Enteritidis and only 8/263 (3%) Typhi. 7/263 (3%) could not be typed with locally available antisera. Nine children had either a single (n = 5) or double (n = 4) recurrence of NTS bacteremia. NTS bacteraemia occurred in two sets of twins.
Of the 263 children with microbiologically-confirmed invasive Salmonella disease, 35 (13%) had died and 23 (9%) been discharged prior to recruitment, leaving 205 (78%) whose parents and guardians were approached for consent. Consent was obtained for 178 (87%) out of the 205 children seen (Fig 1B). Median age of children was 16 months (range 0 months to 15 years) with 213/263 (81%) under 3 years (Table 1 and Fig 2A). The incidence of disease was associated significantly with rainfall (linear regression coefficient 4.70, 95% CI 2.48–6.92, p = 0.001), with 152/263 (58%) presenting between January and March during the rainy season (which last from November to April), and 30/263 from June to September in the dry season (11%) (Fig 2B). 7/263 (3%) children self-discharged or were not admitted.
Clinical features and comorbidities of children with invasive Salmonella disease are shown in Table 2 and Fig 2. Most children presented with a history of fever and were febrile on admission. Cough was significantly more common as a presenting symptom than either diarrhea (p<0.001) or vomiting (p<0.001), with signs of respiratory distress in 72/166 (43%) of children at admission. Older children (≥7 months) tended to present more often with fever in isolation than younger children (<7 months; OR = 1.26, 95% CI 1.02–2.20). Neither the proportions of respiratory nor gastrointestinal presentations varied with age (Fig 2E), and clinical presentation did not vary significantly with season of presentation (Fig 2F).
45/180 (25%) of patients were severely malnourished. Median PCV on admission was 25%, and 47/244 (19%) of children were severely anemic. 38/249 (15%) had concurrent Plasmodium falciparum parasitemia. Of children tested, 70/162 (43%) were HIV-infected. Significantly more older children (≥7 months) were HIV-infected than younger children (<7 months, OR = 1.55; 95% CI 1.15–2.09). Malaria and malnutrition did not vary significantly with age (Fig 2C). Children diagnosed with iNTS disease during the months with high rainfall were less likely to be HIV-infected than those admitted in the dry season (OR = 0.67; 95% CI 0.49–0.89). The rates of malaria and malnutrition did not vary significantly with season (Fig 2D).
Most strains were resistant to ampicillin, cotrimoxazole and chloramphenicol (multi-drug resistant; 211/259, 81.5%). None were resistant to ciprofloxacin or ceftriaxone. Only 15/244 (6%) of children received first-line antimicrobials on admission to which their Salmonella isolate was susceptible in vitro. Conversion to either ceftriaxone or ciprofloxacin occurred with a median delay of 3 days (range 1–12 days).
Inpatient mortality was 52/256 (20%) (Fig 1C), highest among infants of 0 to 6 months inclusive (53%) (Fig 2A), and declined with increasing age (OR = 0.56, 95%CI 0.42–0.75; age in this model was divided into four ordinal categories, children 0–6 months, 7–12 years, 13–24 months, and >24 months). Mortality did not vary significantly with season (Fig 2B). Death occurred on the calendar day of admission in 8/52 (15%) of children who died, with a median time from admission to death of 3 days (range 0–18 days). Adjusting for age and season, significant risk factors for mortality were: a history of dyspnea, absence of fever, presence of respiratory distress or hypoglycemia at presentation, and HIV infection (Table 2). Cotrimoxazole preventive therapy (CPT) was not associated with mortality in HIV-infected children (OR = 2.17, 95%CI = 0.54–10.88; p = 0.30). 147/204 (72%) of children surviving their acute admission were followed up four to six weeks post-discharge from hospital (median time to follow up: 43 days), with no reported deaths in the intervening time. Only one out of the nine children with recurrent NTS bacteremia died.
A logistic regression model predicting mortality including age, sex, HIV status and a history of dyspnea was shown to be statistically significant (likelihood ratio test χ2 = 21.69, p = 0.0002) with a pseudo-R2 of 0.179. The Hosmer-Lemeshow goodness-of-fit test for the model provides no evidence to reject the model (p = 0.73) and the distribution and influence of the residuals (S1 Fig) demonstrate that the model is consistent with the data. In this model, age less than 7 months (OR = 10.0; 95% CI 2.8–35.1), HIV infection (OR = 3.3, 95% CI 1.1–10.2) and a history of dyspnea (OR = 4.2; 95% CI 1.5–12.0) were significant independent risk factors for death. Inpatient survival in children with and without each of these risk factors for mortality is presented in Fig 3.
Salmonellae are a common cause of invasive disease in African children that is often fatal. The clinical features and high mortality in the current study are unchanged from a retrospective study from the same hospital a decade earlier [37]. Almost all patients had nontyphoidal serovars, most commonly Typhimurium, and the majority of isolates were MDR. In some settings, particularly urban centers, typhoid fever is more common than iNTS disease [12,24].
Invasive Salmonella disease occurred mainly in children under three years of age and, as previously reported, was more common in the rainy season [8, 9, 21, 35]. In 206/240 (86%) of children, the disease presented with fever together with respiratory symptoms, gastrointestinal symptoms or both of these (Fig 4A). Since a clinical diagnosis of invasive Salmonella disease is not reliable, laboratory detection of Salmonellae from blood or CSF by microbiological culture is required. Facilities for this are uncommon in African hospitals and, even where present, a diagnosis usually takes a minimum of 48 hours. Despite NTS being primarily considered an enteric pathogen, the majority of children presented with cough or shortness of breath, commonly in the absence of enteric symptoms, as previously observed [8–10, 38].
This partly explains why the large majority of children with iNTS disease did not receive empirical antimicrobials with good anti-Salmonella activity on admission. Conversion to an antimicrobial regimen with adequate Salmonella cover occurred with a median delay of 3 days from presentation, by which time half of the observed deaths had occurred. Therefore, in settings where NTS are responsible for a large proportion of invasive pediatric bacterial infections, empirical antimicrobial therapy for suspected bacteremia and sepsis should take into account local antimicrobial sensitivity of NTS. There has been a decline in antimicrobial resistance among iNTS isolates in Malawi since this study took place, but the majority of S. Typhimurium were still multidrug resistance in 2014. A small number of Salmonella isolates with altered susceptibility to ciprofloxacin and 3rd-generation cephalosporins have been reported in recent years [39].
The rapid demise of Malawian children with iNTS disease is similar to findings from Kenya [9]. This is a major challenge for the management of children presenting with a syndrome indistinguishable from severe pneumonia, for which WHO guidelines recommend the use of ampicillin and chloramphenicol [38]. Early deaths also increase the difficulty of studying iNTS disease, as children will often die before a microbiological diagnosis of Gram-negative bacteremia or meningitis is possible. This potential confounder can only be overcome by recruiting every child at the time of admission. With up to 400 pediatric admissions a day at QECH, this was not a feasible option in our setting.
Even in clinical facilities with diagnostic laboratories and appropriate antimicrobial therapy, the burden of iNTS disease remains high. Until a vaccine against iNTS disease becomes available and widespread immunization of children across sub-Saharan Africa is implemented, development of a rapid diagnostic that can identify children with iNTS disease in a timely manner should remain a public health priority. First-generation tests would be unlikely to provide information on antimicrobial resistance. Multidrug resistance (defined as resistance to ampicillin, chloramphenicol and cotrimoxazole) is common among NTS blood culture isolates across Africa [40, 41]. Therefore, in countries where conventional microbiological surveillance and antimicrobial resistance testing are lacking, treatment of NTS in the absence of information on antimicrobial resistance should avoid these antibiotics.
The possible emergence of new antimicrobial resistance patterns underlies the ongoing need for blood culture surveillance data with antimicrobial resistance testing. Owing to the ability of Salmonella to sequester in the intracellular niche within macrophages and cause latent/recrudescent infection, antibiotics with good intracellular penetration, such as ciprofloxacin, are advisable. The lack of reported deaths in the four to six week follow-up period emphasizes the importance and value of providing intensive clinical care to children with iNTS disease through their acute admission.
Comorbidity was common, with 86/139 (62%) of children presenting with at least one of: P. falciparum parasitemia, severe malnutrition or HIV infection (Fig 4B). Only 38/249 (15%) had concurrent parasitemia, fewer than has been reported previously [8, 9]. In contrast to previous reports [21], this proportion did not vary significantly with season, suggesting other reasons for seasonal fluctuation in iNTS disease cases, such as worsening sanitation during the rainy season facilitating transmission. We did not test for recent malaria, e.g. by detection of HRP2 in plasma. Recent malaria can be more strongly associated with iNTS disease in children than current malaria [9]. Hemolysis-mediated immunological defects [42], which predispose children with malaria to iNTS disease, persist for several weeks following parasite clearance [43].
The dominant co-morbidity was HIV infection, affecting 70/162 (43%) of the children in the study. This greater proportion than has been reported previously [8, 9], likely reflects the systematic testing for HIV infection in the study. In other studies, HIV testing has often only been performed on a subset of children where HIV/AIDS has been suspected clinically (for example [1]). The significance of HIV infection as a risk factor for iNTS disease may vary across Africa, being more important in Southern and Eastern Africa compared with Western Africa owing to regional differences in HIV prevalence.
Background prevalence of HIV infection among children in Malawi at the time of the study was not known. However, available data of antenatal HIV prevalence and the numbers of children living with HIV in Malawi at the time of the study, suggest that the prevalence for the group under-five years was less than 2% [44]. Data on HIV status were only available for 161/263 (61%) of the children, so these findings should be viewed with some caution. Nevertheless, proportionately more children who died lacked HIV data (29/52, 56%), as compared to those who survived (66/204, 32%; p = 0.003 for comparison of proportions), and so the association of HIV with mortality may underestimate the prevalence of HIV co-infection.
This study aimed to identify the children in whom iNTS disease is most likely to be fatal. Children with HIV infection, those with a history of dyspnea, and children under 7 months of age are all at significantly increased risk of dying. The odds of dying with iNTS disease are 10 times greater in children under 7 months than they are in older children. This substantially-increased risk of mortality in young infants is particularly striking (Fig 3A) and is observed in spite of a significantly lower prevalence of HIV infection in this age group. Immune naivety is the most likely reason. Infants are at particular risk of iNTS disease after maternally-acquired antibody is lost and before they produce their own NTS-specific antibodies [45–47].
The dyspnea associated with increased risk of dying is most likely due to metabolic acidosis resulting from Salmonella sepsis, or to bona fide Salmonella respiratory tract infection. Arterial blood gas and anion gap measurements were not available, and chest radiographs not commonly performed in these children. Hence, it was not possible to distinguish the underlying pathological mechanism. A history of shortness of breath often results in a child being misclassified as suffering from pneumonia in resource-poor healthcare settings [38], increasing the chance of children in this high risk subgroup receiving inappropriate empirical antimicrobials.
Although the association between HIV and iNTS disease is well established, the underlying mechanisms of HIV infection as a determinant of mortality in children with iNTS disease has not been well explored. Immunological defects in HIV-infected adults leading to susceptibility to iNTS disease include impaired gut mucosal immunity [48], and dysregulated cellular [49] and humoral immunity [50, 51]. The mechanisms that contribute to increased mortality in children with iNTS disease and HIV provide important questions for future research, which should explore the key interaction of HIV infection and malnutrition [31].
Only three children at presentation were receiving anti-retroviral therapy (ART), while 37 were on CPT. CPT has been shown to protect against iNTS disease in HIV-infected African adults prior to the use of ART [52] and subsequent studies have shown benefit of CPT in protecting against invasive bacterial disease in communities with high levels of in vitro cotrimoxazole resistance [53]. However, CPT did not affect outcome in our study among HIV-infected children with iNTS disease. Recent work has indicated an important role for the introduction of ART among HIV-infected African children in reducing the incidence of iNTS disease [26, 31], but has yet to show that ART improves outcome among HIV-infected children with iNTS disease [31].
The main limitation of the study is that these data were collected from one site during one period in time, the calendar year 2006. New prospective studies from other sites in sub-Saharan Africa would be valuable for confirming the reproducibility of our findings across the region and their ongoing applicability to iNTS disease in Africa. In Blantyre, the epidemiology of invasive Salmonella disease changed markedly in 2011 with the start of an epidemic of typhoid fever. By 2012 S. Typhi was a more common blood culture isolate than NTS [39], indicating the fluidity of the epidemiology of invasive Salmonella disease in one site.
Additionally, iNTS disease has declined in Blantyre, accompanied by reductions in malaria, HIV (following the successful implementation of an antiretroviral therapy program beginning in 2005) and acute malnutrition [31]. Nevertheless, across sub-Saharan Africa, iNTS disease remains the commonest presentation of invasive Salmonella disease and a major cause of pediatric bacteremia [2,8,27].
This prospective study provides additional evidence that iNTS disease is common and frequently fatal among children in sub-Saharan Africa, and particularly highlights the major diagnostic and management challenges associated with iNTS disease [7]. iNTS disease does not present with a readily-identifiable clinical syndrome and current empirical antimicrobials do not provide effective treatment for Salmonella infections. Empirical antimicrobial treatment for potential Salmonella infection should be considered for children presenting with a clinical syndrome compatible with systemic infection. This is especially true for children with HIV infection and young infants, and particularly applies to presentations suggestive of community-acquired pneumonia in regions where invasive Salmonella disease is common.
The development of cheap rapid diagnostic tests for iNTS disease in children should be a priority. The poor effectiveness of antimicrobials means that other approaches to treatment are required. Epidemiological studies provide strong evidence that antibodies bactericidal to iNTS in the presence of complement protect against these infections [45, 46]. The development and use of a vaccine that induces protective antibodies in young infants may provide effective protection [6, 7].
|
10.1371/journal.pcbi.0030207 | Empirical Multiscale Networks of Cellular Regulation | Grouping genes by similarity of expression across multiple cellular conditions enables the identification of cellular modules. The known functions of genes enable the characterization of the aggregate biological functions of these modules. In this paper, we use a high-throughput approach to identify the effective mutual regulatory interactions between modules composed of mouse genes from the Alliance for Cell Signaling (AfCS) murine B-lymphocyte database which tracks the response of ∼15,000 genes following chemokine perturbation. This analysis reveals principles of cellular organization that we discuss along four conceptual axes. (1) Regulatory implications: the derived collection of influences between any two modules quantifies intuitive as well as unexpected regulatory interactions. (2) Behavior across scales: trends across global networks of varying resolution (composed of various numbers of modules) reveal principles of assembly of high-level behaviors from smaller components. (3) Temporal behavior: tracking the mutual module influences over different time intervals provides features of regulation dynamics such as duration, persistence, and periodicity. (4) Gene Ontology correspondence: the association of modules to known biological roles of individual genes describes the organization of functions within coexpressed modules of various sizes. We present key specific results in each of these four areas, as well as derive general principles of cellular organization. At the coarsest scale, the entire transcriptional network contains five divisions: two divisions devoted to ATP production/biosynthesis and DNA replication that activate all other divisions, an “extracellular interaction” division that represses all other divisions, and two divisions (proliferation/differentiation and membrane infrastructure) that activate and repress other divisions in specific ways consistent with cell cycle control.
| In a eukaryotic organism such as the mouse, the complete transcriptional network contains ∼15,000 genes and up to 225 million regulatory relationships between pairs of genes. Determining all of these relationships is currently intractable using traditional experimental techniques, and, thus, a comprehensive description of the entire mouse transcriptional network is elusive. Alternatively, one can apply the limited amount of experimental data to determine the entire transcriptional network at a less detailed, higher level. This is analogous to considering a map of the world resolved to the kilometer rather than to the millimeter. Here, we derive from mouse microarray data several high-scale transcriptional networks by determining the mutual effective regulatory influences of large modules of genes. In particular, global transcriptional networks containing 12 to 72 modules are derived, and analysis of these multiscale networks reveals properties of the transcriptional network that are universal at all scales (e.g., maintenance of homeostasis) and properties that vary as a function of scale (e.g., the fractions of module pairs that exert mutual regulation). In addition, we describe how cellular functions associated with large modules (those containing many genes) are composed of more specific functions associated with smaller modules.
| The importance of modular organization in biology is widely appreciated [1–6] and is manifested in conserved gene modules across species [7–9]. High-throughput data has yielded progress in molecular-level descriptions of interactions of genes, proteins, and metabolites [10–14]; however, understanding an entire cell or its major components from genetic information is a major methodological challenge [15]. Here, we use genome-wide expression Alliance for Cell Signaling (AfCS) data to first empirically obtain modular functions and then empirically obtain the effective inhibitory and activating regulatory influences between these modules at many scales of resolution (see Figure 1). This approach yields copious results about effective regulatory interactions so that a complete discussion is not possible in a single manuscript. Thus, we approached the results in the manner of other high-throughput and genome-wide analyses, presenting general principles that apply across all the data as well as a selection of individual observations that are discussed in greater detail in Text S1. The distinct approaches we use to analyze the results are diagrammed in Figure 1A.
The technique used to infer these regulatory influences relies on the correlation of the expression levels of transcriptional regulators at one time with the expression levels of their regulatory targets a fixed interval of time later. This correlative analysis aggregates direct and indirect causal influences, and co-occurring behaviors. Still, the transition matrix obtained can be used to predict [15] the transcriptional level changes of large cellular modules over fixed time intervals with surprising accuracy (r > 0.95). This approach generates many specific results, each of which is the strength and polarity (activating or inhibiting) effective regulatory influence of one functional module on another. The results are derived directly from experimental data and are statistically validated.
This multiscale analysis yields a description of cell behavior in terms of traditional biological concepts (i.e., cellular or physiological systems such as “respiration” and “mitosis”), identifying the genes whose collective behavior they comprise. At all scales, new network models of regulatory interactions among modules encompassing the behavior of the entire cell are presented. Previous studies have considered genome-wide multiscale groupings of genes according to their expression behavior [16–18]. This work extends the paradigm of multiscale gene grouping by determining for the first time, at multiple scales, the network of mutual regulation of groups of genes on groups of genes, an approach that is analogous to having geographic maps of varying resolution.
Our analysis of attributes of these novel cellular-level regulatory networks reveals principles of organization, such as scale-dependent homeostatic feedback and target specificity, and asymmetric restrictions on the number of ingoing and outgoing regulatory influences. Knowledge gained that is unique to multiscale analysis includes how functions of smaller modules contribute to the aggregate function of larger modules, which is analogous to how physiological systems are composed of organs, and organs out of tissues.
We provide all of the networks and Gene Ontology (GO) data in the supporting information, as well as a discussion of the statistical methods used to identify them. Specific questions about interactions between cellular modules can be addressed with these databases, as well as general insights or quantitative models of cellular response to perturbations. In Text S1, we discuss in detail samples of (1) randomly chosen and (2) particularly intriguing regulatory relationships inferred from our analysis. These examples can be considered at length, as they provide a large number of specific insights into the complex biological functioning of the cell, manifesting the ability of our methodology to extract them, and the informational value of the large AfCS datasets. Given the high complexity of cellular function, it should not be expected that a simple summary of modular interactions would serve as a sufficient description of the large number of results obtained. In the Results section, we focus on a selection of results that demonstrate the variety of interesting results that were found and general principles that have been abstracted from them.
The mutual regulatory influences for networks comprising n = 12, 20, 42, and 72 cellular modules are shown in Figure 2 for the 1.5-h time-interval (Table S3). These regulatory networks reveal a wealth of information about regulation at the cellular level. For the n = 12 scale, we have previously reported a number of key results [15]. Considering the new results at the n = 20 scale, module 3 is a global (pleiotropic) activator. Appropriately, module 3 contains a significant overabundance of genes involved in aerobic respiration (Table S4). Since these genes contribute to increased cellular energy, it is not surprising that module 3 is a ubiquitous activator of transcription. Module 9 is activated by module 3, and has a statistically significant association only with mitosis. Cell proliferation is known to suppress transcription [19], so it is appropriate that module 9 is a repressor of many modules, including itself. Its targets include module 3, one of many examples of negative/homeostatic feedback, consistent with the known coupling of respiration and the cell cycle [20]. Some modules have no regulatory outputs in these networks, e.g., module 15, which has an overabundance of genes involved in nucleosome assembly. This is surprising because increased DNA binding by nucleosomes correlates with lower transcriptional levels [21,22]. Examples of positive autoregulation can also be found; module 16 is associated with oxygen regulation and is self-activating. The specific strengths of these and all other effective influences are given in Table S3, and Text S1 provides a discussion of many additional specific regulatory interactions, including intuitive and surprising examples.
We calculated the average of the influence outputs (Figure 2B, 2F, 2J, and 2N) of each module on its targets, as well as the average input to each module (Figure 2C, 2G, 2K, and 2O), shown in Figure 2 in self-organizing map (SOM) array order (i.e., modules with similar expression are proximal). It is striking, particularly for n > 12, that while every module has several inputs, many modules have no outputs (shown in gray). These correspond to modules that are nonregulatory. Not surprisingly, similar expression responses (adjacency in the SOM arrays) more often correspond to input similarity than to output similarity.
To convey the distribution of all interactions, from strongly activating to strongly inhibiting, we cluster-ordered [23] the rows and columns of the n × n transition matrices at each scale (Figure 2, right panels). Rows were ordered by similarity in functional input and columns by similarity in functional output, since the value at row i, column j is the effect of module j on module i. Vertical bands in the matrices imply that modules tend to have a uniform output, a property absent in randomized controls (unpublished data). These global influences may reflect a restricted energy economy in which consumption or production transcriptionally inhibits or activates all other cellular activities [15].
A goal of developing larger-scale models is to relate genetic function to conceptually accessible models of cellular function. Still, even at the largest scale given above, with 12 modules and 144 potential interactions, it is hard to develop a complete mental picture of the behavior of the cell. We therefore developed an even more accessible, larger-scale summary of cellular function, which can serve as a first guide to the understanding of cell behavior at all finer levels of organization. Inspecting the regulatory effects of the groups reveals that the cell transcription network at n = 12 can be partitioned into five functional divisions: (1) energy and component production, (2) proliferation and differentiation, (3) extracellular interaction, (4) membrane infrastructure, and (5) DNA replication. Groups 0, 1, and 4 comprise the first division. They are all enriched for genes involved in ATP synthesis and the production of nucleic acids and proteins, and are appropriately all global activators of transcription over short time-scales (1.5 h). Each group has unique sub-behaviors, with group 0 involved in endocytosis and group 1 in apoptosis and protein folding. Group 4 is involved in cell-cycle regulation (group 4 is also a global activator over 1 h), providing a connection between energy production and proliferation.
Groups 2 and 3 belong to the division contributing to proliferation and differentiation. They are both enriched for genes involved in small-molecule metabolism, and unlike the previous division, they are not global activators of transcription. Instead, they activate their own division over all time-scales and inhibit the DNA replication division (below), particularly after 1 and 3 h. This periodic repression of DNA replication by the proliferation division may provide for the timing of S-phase during the cell cycle. Group 2 shares some functions with the first division, containing genes involved in translation and transcription, but has no role in ATP synthesis. Like group 4, it is involved in regulating the cell cycle. Group 3 has specific sub-functions related to the immune response, cell adhesion, and, more generally, behaviors unique to particular cell types.
Groups 5, 8, and 9 are unified by gene content related to interactions between the cell and its external environment (division 3). They are respectively enriched for genes involved in cell matrix/adhesion and endocytosis; oxygen transport and exocytosis; and oxygen transport, chemotaxis, and the immune response. Functionally, all of these genes are global repressors of transcription after 1.5 h, with regulatory influences that are in opposition to the energy and component production division. Group 5 is also a weak early (0.5–1 h) activator of global transcription.
Groups 6 and 7 comprise the membrane infrastructure division, with both enriched for genes involved in the physical production and maintenance of the cell membrane, such as lipid metabolism, lipid catabolism, and cholesterol metabolism. These groups are generally self-activating, but do not exert strong global transcriptional influences on other groups over any time interval, consistent with their infrastructural role. Unlike other divisions, these groups are virtually indistinguishable in terms of gene function.
Last, groups 10 and 11 form the DNA replication division. They are enriched for nucleotide synthesis, nucleosome assembly, and regulators of DNA methylation. They are both strong global activators of transcription 2 h after they are activated, and while group 11 is at other times a weak global activator of transcription, group 10 is essentially not globally regulatory over other intervals. Group 10 is also uniquely enriched for genes promoting and suppressing apoptosis, and, like groups 8 and 9, has role in oxygen regulation.
We analyzed trends in the networks to determine which cellular properties hold across scales of observation and which vary with scale (Figure 3). The sparseness (identified interactions/total possible interactions; Figure 2, second column of panels) follows power-law scaling (Figure 3B). Specifically, while the number of possible edges grows as n2, the number of inferred influences grows linearly. This leads to progressively sparser and sparser matrices. For our four scales, this trend was very closely fit (r2 = 0.9991) by a power-law with exponent −0.92. Extrapolating this trend gives very rough predictions of ∼39,250 regulating interactions in the 6,000-gene yeast genome and ∼197,000 in mammals. The former falls between the number expected for genetic interactions (97,000 [24] and 100,000 [25]) and the number of expected physical interactions (8,300 [26] and 6,300 [27]). Another study [28] gives an estimate of roughly 340,000 physical interactions in mammals.
At all scales the distribution of the number of inputs to each module is Gaussian (Figure 3B), while the number of outputs is better fit by a power-law (Figure 3C). This is particularly true as we consider finer scales (Figure 3D and 3E). We suspect this reflects an inherent limitation—while a module can broadcast regulation over a large number of target modules (i.e., the overrepresentation of high numbers of outputs in the power-law distribution), each module is limited in the number of inputs it can usefully accept. These properties are also seen in influence networks of engineering projects [29]. How this limitation is mechanistically imposed on a large module of genes is an interesting open question.
At all scales, the number of modules with only activating outputs is nearly identical to the number of modules with only inhibiting outputs (Figure 3F). The magnitudes of influences appear exponentially distributed (Figure 3G) at all scales, implying a functional cutoff in influence strength. Given that the interaction of two modules is composed of the interactions of many pairs of constituent genes, this functional cutoff could be explained by assuming that individual gene interactions have a characteristic strength and occur effectively randomly. However, these assumptions may be too strong, and there may be selective advantages to bounding interaction strengths, such as limiting total module expression. We also considered the relationship between the average input and output of each module. For all n, these measures were anticorrelated (Figure 3H).
At the finest scale of n = 72, the targets of a particular module output tended to have similar expression profiles. This was manifested in the frequent adjacency of modules' targets in the SOM array (Figure 4A). We would expect this result at finer scales, since modules with similar expression patterns should have similar regulators at fine scales. The modules were hierarchically clustered according to correlation in their effects on target modules using the Fitch-Margoliash algorithm (Figure 4B–4E). The coarser trees (n = 12 and 20) only show variation along a single dimension ranging from all-activating to all-inhibiting outputs, without sub-branches. This implies that outputs of each module tend to be uniform across all targets. More specific targeting at smaller scales appears as sub-trees branching off the linear portion of the tree (Figure 4D and 4E insets) composed of modules that activate some targets but inhibit others. This trend is quantified in Figure 4F, which shows the fraction of nodes in the tree that are on sub-branches. The shape of this curve suggests a transition from universal to target specificity at finer scales.
In addition to the 1.5-h influences, we determined the transition matrices for all other time intervals (0.5 h [i.e., the transition between 0.5 h and 1 h], 1 h, 2 h, 3 h, and 3.5 h) in the AfCS data at each scale (available in Table S5). We calculated the average output influence of each module as a function of time (Figure S1); modules with similar regulation under all assayed conditions (such as putative housekeeping modules and constitutively repressed modules) have lower-magnitude outputs than modules that are regulated in a situation-dependent manner. Modules with weak outputs tend to occur around the periphery of the SOM array, while modules with the greatest variance are in the interior. “Peripheral modules” tend to have monotonically increasing or decreasing responses to all perturbations, whereas the interior modules' responses are more complex.
Averaging the magnitudes of the mean outputs of each module over all the modules reveals which time intervals mediate the greatest changes in expression. At all scales the average influence magnitude varies periodically with time, with greater frequency at finer scales (i.e., for n = 12, the most potent influences occur over t = 1.5 h, and the least over t = 3 h, whereas for n = 42, those times are 1 h and 2 h, respectively; see Figure S1C and S1D). Moreover, the magnitude of influences decreases at finer scales. This supports the idea that smaller modules bring about smaller transcriptional changes over shorter times [6].
We next considered the mapping of ontological terms for gene function between scales. To analyze how cellular functions are composed of sub-functions, we identified the mappings by which larger scale modules are composed of finer-scale modules. These mappings are shown in Figure 5A–5C, where the sources of modules at larger scales, in terms of the modules at the next finer scale, are shown by color-coding. The module partitioning is not strictly hierarchical, since the boundaries between modules at one scale need not align with the boundaries between modules at a different scale. Thus, a set of genes in a single module at smaller scales may be found across more than one module at larger scales. Moreover, the nonhierarchical organization of genes into behaviorally related modules is an observation derived from the data. If gene behavior were hierarchically organized, the SOM algorithm would have found fine-scale modules that were strict subsets of large-scale modules.
These mappings also show how ontological functions are distributed across modules at various levels as illustrated for four GO categories in Figure 5D. For example, in the n = 12 case, the “intracellular protein transport” function is coassociated with either ”protein biosynthesis” (in module 0) or “glycolysis” (in module 1), respectively, but at the n = 20 scale, “intracellular protein transport” occurs independently in modules 1 and 6, and in conjunction with “protein synthesis” in module 5 and “glycolysis” in module 2. In addition, the dissociation of the two modules labeled “intracellular protein transport” at n = 12 into four modules at n = 20 also illustrates how higher-specificity functions are aggregated to form larger-scale functions of lower specificity.
Finally, we examined to what extent the distribution of sub-functions across the multiscale SOM groupings can predict relatedness of GO function. Cluster analysis of a GO label's abundance similarity across the n = 12 and n = 20 SOM groupings (Figure 5E–5G) revealed that more closely related functions tend to have similar distributions, and are therefore proximal in the generated cluster tree; e.g., in Figure 5E, transcription elongation and initiation are adjacent in the tree, as are the Pol III–related functions. The deepest distinctions in the tree are based on differences in distribution at the n = 12 scale, while the shallowest branches are determined by the n = 20 scale. The clustering indicates that the hierarchy of GO functions (in an abstract functional sense) can be inferred solely from the distributions of genes across the SOM groupings. That the association between GO groups and multiscale SOM gene modules can be used to partially reconstruct the GO hierarchy implies that SOM module identity predicts a gene's function in terms of familiar large-scale cellular behaviors. This indicates that SOM grouping across levels provides an additional tool for identifying gene function.
We have determined networks of regulatory influences exerted by large groups of similarly behaving genes on other such large groups across multiple scales of resolution. These effective regulatory influences between modules are composed of direct and indirect causal mechanisms as well as temporally correlated effects that are seen across all 33 perturbations. Given a perturbation of gene expression, all of these components contribute to a prediction of the transcriptional state at later times [15]. Since the effective regulatory interactions accurately predict the transcriptional state, they capture almost all of the biologically relevant causal regulation occurring within that time interval.
We determined these effective regulatory interactions between gene modules at different scales of observations comprising between five and 72 components. The gene composition of these modules is not strictly hierarchical; i.e., two genes in the same fine-scale group may not belong to the same large-scale group. This is a natural consequence of imposing discrete classification categories onto systems that need not be hierarchically structured across scales. For example, if one were to classify visible colors into six categories, they might very well comprise red, orange, yellow, green, blue, and indigo. The hues “yellow-orange” and “yellow-green” might reasonably fall into the yellow category of this six-group partitioning. However, if one divides the same colors into three higher-scale categories—red, green, and blue—those two same hues would fall into separate categories (red and green, respectively). Similar considerations apply to physiological and metabolic categories. Therefore, our a priori expectation should be that gene modules would be nonhierarchical across multiple scales, as is observed in results of the SOM partitioning.
From these groups and regulatory influences, we derived many results comprising a first multiscale analysis of global gene-regulatory influences. We consistently observed mechanisms consistent with the maintenance of homeostatic equilibrium across the modules, particularly at higher scales. For example, the apparent regulatory dissimilarity of modules with similar expression patterns (Figure 2B, 2F, 2J, and 2N) likely reflects a homeostatic mechanism in which it is unfavorable to have all coactivated modules as either strong activators or strong inhibitors. This pattern is found at all scales analyzed. The near parity between the number of activating modules and inhibiting modules in networks of all analyzed scales (Figure 3F) could reflect a homeostatic requirement that the total rate of transcription remain roughly constant (provided regulatory strength and transcriptional rate are uncorrelated). We observe that the net input and average output of a module are negatively correlated, particularly at the highest scales. This can be understood as reflecting another homeostatic mechanism. If a module is on average an activator (inhibitor), its input tends to be inhibitory (activating) to avoid positive feedback that causes large fluctuations from equilibrium in the total transcriptional level. Interestingly, at finer scales, there is increasing correlation between average module input and output, reflecting the increasing possibility of positive feedback. Positive feedback often leads to bistability or multistability [30], a property required for developmental differentiation. Consistent with our finding of positive feedback at small scales, cell-fate decisions are typically controlled by small circuits of mutually regulatory “master genes” [31,32]. In contrast, homeostatic regulation (e.g., of basic metabolic states) involves large-scale biochemical networks where robustness to fluctuations is necessary for overall stability of cell function. Positive feedback would therefore be excluded at this highest scale to avoid unsustainable abrupt genome-wide changes in gene expression.
A multiscale approach is conceptually essential given the organization of living systems into structures at many scales, and is critical given the staggering challenge of obtaining a complete description of pairwise gene interactions. Still, in view of the complexity of biological function, there is a large amount of information that arises from a multiscale analysis. In this sense, our analysis can be considered as foundational to the development of many other results. It is a high-throughput analysis methodology analogous to high-throughput experimental methods of genome sequencing or gene expression data collection; through our approach, a seemingly overwhelming amount of data is generated by high-throughput consideration of the large number of regulatory interactions of modules across multiple scales. Our analysis of these results has been correspondingly multiscale. First, we identified global principles, such as the many facets of homeostasis and universality of regulatory effects at larger scales. Second, we found new patterns of multiscale organization, such as the dichotomous distributions of the number of regulatory inputs and outputs at various scales, the increased target specificity and speed of regulation at finer scales, and the aggregation of sub-module functions into collective larger-scale functions. Last, we provide detailed discussion of many specific regulatory relationships in Text S1. The diversity of analysis points the way to many new lines of investigation, in particular experimentally testable hypotheses at large scales of cellular organization.
To separate genes into modules [1–9] and determine their mutual regulation, we used the AfCS murine B-lymphocyte perturbation expression data [33] tracking the response of ∼15,000 genes to 33 perturbations at four time points (0.5 h, 1 h, 2 h, and 4 h; see Figure 1B). These expression levels were aggregated to yield a ∼15,000 (genes) × 132 (conditions and times) dataset. Genes were categorized by similarity of expression changes across all perturbations into n different modules, where n was taken to have the values 12, 20, 42, and 72 (see Tables S1 and S2), using the SOM algorithm. This categorization occurs without any a priori assumptions about the distribution of the data, and thus the SOM groups convey the full diversity of expression profiles.
The SOM process organizes the modules into a 2-D array according to the relatedness of their average changes in expression [34] such that modules that are adjacent in the SOM array have more similar expression responses across all conditions. Generally, genes that had monotonic responses to many perturbations (i.e., always being activated or repressed) tended to be placed in the corner positions of this array. These groupings were performed using the GEDI software [35].
Varying n allowed us to consider global sets of modules at various scales of description. Low n yields large-scale modules with many genes in each module; higher n yields small-scale modules with fewer genes. A representative profile for each module was used to represent the modules' behaviors, and was determined as the centroid of the expression profiles of all genes composing the module. From the n by 132 = 33 × 4 “module transcriptional profile” datasets, we obtained the effective regulatory interactions as an n × n transition matrix (M), where M × Xt = X t+k, and Xi is the n × 1 transcriptional state at time i. If the matrices were dense, the greatest mathematically solvable n would be the number of perturbations, 33. However, the matrices are sparse, and we used a bootstrapping technique to obtain transition matrices as large as 72 × 72. This was done by randomly choosing 12 out of n modules, solving for their mutual interactions, and repeating this process until each of the n2 interactions was estimated many times in different 12 × 12 sub-matrices. We constructed our regulatory networks out of only those interactions that were statistically reliable across perturbation and transcription contexts, using a signal to noise analysis (Protocol S1). The bootstrapping was performed using custom written C++ code, and the linear systems were solved using Mathematica (Wolfram Research, http://www.wolfram.com). Clustering trees were all generated using the Fitch-Margoliash method as implemented in the Phylip program [36]. |
10.1371/journal.pgen.1005932 | Mutational History of a Human Cell Lineage from Somatic to Induced Pluripotent Stem Cells | The accuracy of replicating the genetic code is fundamental. DNA repair mechanisms protect the fidelity of the genome ensuring a low error rate between generations. This sustains the similarity of individuals whilst providing a repertoire of variants for evolution. The mutation rate in the human genome has recently been measured to be 50–70 de novo single nucleotide variants (SNVs) between generations. During development mutations accumulate in somatic cells so that an organism is a mosaic. However, variation within a tissue and between tissues has not been analysed. By reprogramming somatic cells into induced pluripotent stem cells (iPSCs), their genomes and the associated mutational history are captured. By sequencing the genomes of polyclonal and monoclonal somatic cells and derived iPSCs we have determined the mutation rates and show how the patterns change from a somatic lineage in vivo through to iPSCs. Somatic cells have a mutation rate of 14 SNVs per cell per generation while iPSCs exhibited a ten-fold lower rate. Analyses of mutational signatures suggested that deamination of methylated cytosine may be the major mutagenic source in vivo, whilst oxidative DNA damage becomes dominant in vitro. Our results provide insights for better understanding of mutational processes and lineage relationships between human somatic cells. Furthermore it provides a foundation for interpretation of elevated mutation rates and patterns in cancer.
| The mutation load of human tissues is unknown and represents the genetic divergence from the fertilised egg. Reprogramming of somatic cells generates induced pluripotent stem cells (iPSCs), a cell type being considered for clinical applications. We generated iPSCs from tissues of healthy individuals and used whole genome sequencing to identify in vivo mutations accrued in a somatic cell during the lifetime of the individual. Next we identified in vitro mutations introduced during reprogramming and cell culture. Each has a unique mutation signature suggesting different mutagenic processes. Our study demonstrates the use of reprogramming as a tool to elucidate mutational processes within normal cells and highlights the importance of genetic characterisation of iPSCs prior to clinical translation.
| From the moment of fertilisation, as each cell divides random mutations occur which are fixed and inherited by daughter cells. Most of these variants have little, if any, physiological consequence but contribute to genetic diversity within tissues. A small proportion will contribute to pathogenic processes such as cancer [1]. Whole genome sequence analysis of cancer genomes has revealed their mutational landscape [1–4]. Cancers are clonally heterogeneous, like the somatic tissues from which they originate, and arise through a series of clonal expansions over decades often acquiring aberrant DNA repair processes [3,5,6]. Thus, the extent to which mutational signatures in human cancers reflect normal non-pathological mutational patterns that have arisen in their normal non-cancerous somatic ancestors is obscure. The mutations that have arisen in somatic cells throughout development and tissue homeostasis are generally difficult to identify in tissue biopsies because these are composed of heterogeneous polyclonal populations of cells.
To describe the landscape of mutations in normal somatic tissues, we sought to resolve the underlying heterogeneity of somatic tissues by reprograming the constituent cells into induced pluripotent stem cells (iPSCs) [7], a process of single cell cloning that facilitates subsequent expansion. Each clonal iPSC line generated from a heterogeneous polyclonal pool will carry a constellation of mutations reflecting both somatic and culture-induced mutations. Indeed previous work has suggested that a proportion of iPSC mutations originate from the founder somatic cell [8,9]. However although genome sequence analysis of these clones will reveal their mutational burden, it is not possible to definitively resolve the mutations which arose in vivo from those which arose during in vitro culture and reprogramming (Fig 1A). To confidently classify the origin of the mutations, we derived iPSC lines using monoclonal derived endothelial progenitor cells (EPCs) [10]. The iPSCs isolated from a monoclonal source would share the mutations of the founder cell (in vivo acquired somatic mutations) and in addition carry culture-induced mutations as unique private mutations. Sequencing of these iPSCs would allow interrogation of the number and pattern of somatic mutations present in vivo (Fig 1A).
Fibroblasts and/or monoclonal EPC lines were derived from three individuals: a 65-year old alpha-1 antitrypsin deficiency male (patient AATD [12]), a 22-year old healthy male (S2 [13]) and a 57-year old healthy male (S7 [13]), which were reprogrammed into iPSCs. The iPSC lines were initially screened using array-based comparative genomic hybridization (CGH) to select lines with the smallest number of copy number aberrations (S1 Table). In addition none of the lines selected had large scale loss of heterozygosity (LOH) through error-prone break recombination (S1 Fig [14]). Next we sequenced the protein-coding exons of these iPSC lines to determine the number and genomic location of their somatic mutations (Fig 1B–1E and S11–S14 Tables). Fibroblast-derived iPSCs from both individuals carried similar numbers of coding mutations, ranging between 14 and 28 single nucleotide variants (SNV) per line (Fig 1B and 1C). Consistent with a polyclonal origin, these SNVs were unique to each line and no shared SNVs were identified between lines from the same individual (Fig 1B and 1C). In contrast, monoclonal EPC-derived iPSC lines (iPSC-2, 3, 4 and 5 from AATD and iPSC-RE2, RE9, RE14, RE17 and RE19 from S7) carried fewer mutations, of which a subset was shared between them as well as with EPCs from the same individual. None of the shared SNVs were detected in the corresponding fibroblasts or whole blood, indicating that these SNVs were somatically acquired by the EPCs in vivo (Fig 1D and 1E). In addition, private SNVs were detected which were unique to each monoclonal-derived iPSC line and these were not found in EPCs or the individual’s reference genome. Deep sequencing of the donor EPC genome revealed that some of the mutations detected in the iPSCs were in fact present in the EPCs but at very low frequencies (Fig 1D and 1E, orange boxes; S7 and S8 Tables), suggesting that these mutations were acquired by the EPCs during the in vitro expansion process, prior to reprogramming. Notably no known driver mutations (using COSMIC database), which could confer a selective advantage, were identified in any of the iPSC lines. These results demonstrate that iPSCs derived from monoclonal somatic cells can be used to identify in vivo acquired somatic mutations.
The mutational burden of iPSCs reflects mutations accumulated in vivo in the ancestral somatic cell lineages and mutations acquired during in vitro cell culture and subsequent reprogramming. The iPSCs from heterogeneous somatic cells usually do not share any mutations but the exome sequencing data demonstrated that by using monoclonal cell sources it is possible to resolve mutations acquired in vivo from those arising during in vitro cell culture. Furthermore, identifying shared mutations in somatic cell lineages could be used to construct a cellular phylogenetic tree. We therefore performed whole genome sequencing on the S7-derived monoclonal EPCs, 3 iPSC lines (RE2, RE11 and RE14) and fibroblasts, which were used as the reference genome (S9 Table). The total number of mapped bases obtained per sample was 108.1–122.8Gb with 33 – 37X sequence coverage. We identified 463 SNVs in the monoclonal EPCs and 933, 1119 and 840 in the iPSCs, respectively (Fig 2A). A proportion of the putative SNVs were validated using PCR amplicon re-sequencing. This analysis revealed that we were able to detect SNVs with mutant allele frequencies of less than 30% with high specificity (S10 Table), which most likely represent mutations acquired during the first few divisions after founder cells started dividing (Fig 2B).
Amongst the SNVs called, 391 mutations were shared by all the iPSC lines and the monoclonal EPCs at a mutant allele frequency of approximately 50%, which is consistent with clonal mutations (heterozygous SNVs in diploid chromosomes). Therefore these 391 SNVs reflect the in vivo genetic divergence of the single EPC from fertilisation through development and adulthood. Some SNVs were shared between the EPCs and only a subset of the lines (Fig 2A), revealing the emergence of genetic differences during in vitro EPC culture. The remaining SNVs were unique to each iPSC line and not present in the EPCs at a detectable frequency. These private mutations in RE2 (506 SNVs), RE17 (419 SNVs) and RE14 (719 SNVs) represent in vitro SNVs acquired in the EPC culture and/or during reprogramming (S2–S6 Tables).
The SNVs detected in the EPCs and iPSCs are a historical record of the phylogenetic lineage of the cells (Fig 2C). For the individual S7, in the 57 years from fertilization to the point of derivation of the single EPC, 391 mutations had accumulated in vivo. The single EPC was then expanded in vitro prior to reprogramming. Following the first cell division of the EPC, one daughter cell (A) acquired at least 29 mutations and the other daughter cell (B) at least 9 mutations. After daughter cell A divides, two further branches appear resulting in at least 7 mutations in one granddaughter cell (A-1) and at least 1 mutation in the other (A-2). The progeny of daughter cells A-1, A-2 and B were the eventual substrates for the derived iPSC lines S7-RE2, S7-RE17 and S7-RE14, respectively.
The detailed mutation analysis we performed enabled us to estimate the in vitro mutation rate of the EPCs. Apart from the 391 in vivo mutations, the clonal SNVs detected in the iPSCs were acquired during the EPC expansion and reprogramming and thus should be present in parental EPCs. We sought to detect these sub-clonal mutations that are present in EPCs by deep sequencing and calculate a mutation rate during in vitro EPC expansion using a statistical model (See Materials and Methods). First, in order to ensure accuracy especially at the lower bound of allele frequencies, we investigated sequencing error rates. Eight genomic regions (S15 Table and S2 Fig) were PCR-amplified from the AATD iPSC-B cells and sequenced on a MiSeq instrument. Median error rates were 0.042–0.144% and 0.053–0.320% for the first and second reads respectively when the first and second reads were analysed separately. However, median error rates were substantially improved (0.016–0.025%) when consensus sequences were first generated from the first and second reads and then bases were counted (S2 Fig). We used this approach to accurately identify low-frequency subclonal mutations.
We amplified approximately 40% of the in vitro SNVs from genomic DNA derived from the S7 EPCs and performed deep sequence analysis. Of this subset, we detected 60, 51 and 58 SNVs in S7-RE2, S7-RE14, and S7-RE17 respectively to be present in the EPCs at allele frequencies between 41% and 0.05% (Table 1). The sub-clonal SNVs in the EPCs were then used to calculate the mutation rate during in vitro culture, resulting in an estimated mutation rate of 14.0 ± 2.0 SNVs per cell per generation or 2.1 x 10−9 per nucleotide per generation (see Materials and Methods).
Clinical use of iPSCs requires not only generation but also maintenance of iPSCs in cell culture. We therefore sought to measure the rate of single nucleotide mutagenesis in iPSCs. In order to calculate this precisely, we sub-cloned iPSCs from individuals S7 and S4 (a 61-year old healthy female) as well as H9 human embryonic stem (ES) cells [15] and grew these continuously for 60 divisions. At the end of the expansion period, we sampled the population from each cell line by sequencing single cell sub-clones that had been expanded to provide an adequate DNA sample for whole genome sequencing. Comparison of the DNA sequence from these sub-clones to its immediate parental population identified in vitro mutations acquired during 60 divisions. All three lines had a similarly low mutation rate of 0.8–1.7 SNVs per cell per generation or 1.8 x 10−10 per nucleotide per generation (Fig 3A and 3B). Intriguingly, although both EPCs and pluripotent stem cells have a similar cell cycle time, the mutation rate in pluripotent stem cells was approximately tenfold lower than that in EPCs during in vitro culture.
Next, we sought to understand whether the patterns of the mutations could inform us of the mutagenic processes involved both in vivo and during in vitro cell culture. We separated the S7 mutations into three groups that represented the continuous cellular lineage for this 57-year old man, from fertilisation to isolation of the single EPC (in vivo), expansion of the EPCs and reprogramming (in vitro somatic cells) and finally maintenance of the iPSCs (in vitro iPSCs) (Fig 4A). Using a Bayesian Dirichlet process [16,17] we were able to model clusters of clonal and subclonal (generated after the 1st cell division; <30% MAF) SNVs for each cell population. We explored the types of base substitutions seen in these groups of mutations and found variation in the overall mutation spectra (Fig 4B). There is a preponderance of C:G>T:A transitions in vivo and early in the cellular lineage. In contrast, in vitro and later in the cellular lineage, there is a preponderance of C:G>A:T transversions.
To explore mutational processes in more detail, we conducted Non Negative Matrix Factorization (NNMF) analysis [4]. Firstly, we found that the clonal mutations in S7-EPCs, representing somatic substitutions acquired in vivo, are associated with a signature that has been attributed to deamination of methylated cytosines, a process thought to occur in all cells. This signature is similar to the mutations observed in germ cells, another example of in vivo mutations in normal cells (Fig 4C). Secondly, the mutation signatures acquired by the EPC population in vitro (clonal S7REs) were composed of a combination of deamination and C>A transversions. We speculate that this latterly acquired signature represents damage accrued during culture and may be due to oxidative DNA damage [19]. Thirdly, we detected a sharp increase in the proportion of mutations associated with C>A transversions in sub-clonal mutations in the iPSCs (subclonal S7REs). These sub-clonal mutations detected in iPSCs arise in the first few cell cycles after a clonal cell line appears. Cells during this period are thought to be undergoing reprogramming, suggesting that iPSC reprogramming may stimulate a mutational process associated with C>A transversions. Finally, the in vitro mutations of iPSCs (maintenance cell culture) were associated with both deamination of methylated cytosines and the C>A transversions, reinforcing the suggestion that it is a putative imprint of culture-related/oxidative damage in vitro.
We have extensively analysed a series of normal single-cell derived clones by whole genome and exome sequencing. We report for the first time the number and characteristics of the acquired mutations in a monoclonal cell isolated from a healthy individual and subsequently derived iPSCs. From this data we are able to reconstruct the mutational history of a cell beginning from the fertilised egg through to adulthood, then to reprogramming and maintenance of iPSCs in long-term culture, demonstrating how mutagenic processes evolve through that cellular lineage. During first in vivo then in vitro cell divisions, there is a change in the mutation signatures, suggesting a proportional reduction in the contribution of deamination of methylated cytosines and a proportional increase in oxidative stress and DNA damage. Finally, consistent with the expectation that an organism should protect its stem cells, we observed a ten-fold reduction in mutation rate in iPSCs, which mirrored that in human ES cells, which have not been subjected to reprogramming.
We find that reprogramming is mutagenic at the nucleotide level and, similar to previous reports [20,21], not at the chromosomal level. The nucleotide-level mutations are associated with a sharp increase in the proportion of mutations associated with oxidative DNA damage. However established iPSCs seem to be substantially protected from DNA damage by their pluripotent state. The increased DNA replication fidelity of iPSCs and ES cells may be due to the activity of homologous recombination throughout the cell cycle in pluripotent cells, whereas in somatic cells it is restricted to the stages of the cell cycle in which there is presence of replicated chromatin [22,23]. Although in vitro culture of iPSCs has a reassuringly low mutation rate, the culture systems used altered the mutational spectrum, which shifted from predominantly C>T transitions to C>A transversions. Over the relatively few generations we studied, we could not find any evidence of a selection sweep within the culture. Notably we did not find any driver mutations in our analyses. Understanding how mutations accrue through iPSC reprogramming and during maintenance cell culture is paramount to developing safe clinical therapies. Furthermore the mutational signatures underlying normal development and tissue homeostasis provide insights into the biological processes occurring in normal cells.
Primary tissue samples and blood were obtained from a patient with alpha-1 antitrypsin deficiency (patient 2) under the ethics approval REC No. 08/H0311/201 or adult cadaveric organ transplant donors referred to the Eastern Organ Donation Services Team (part of NHS Blood and Transplant). Ethics approval for the latter was obtained from Cambridgeshire Research Ethics Committee 3 (REC No. 09/H306/73). All laboratory procedures were performed according to Standard Operating Protocols and safety assessments.
For each subject included in this study, around 3cm of skin was excised from the midline surgical incision. The fat and dermal layers of the skin sample were removed and the skin was cut into approximately 1mm3 pieces. These were dispersed evenly on a 10cm plate (maximum 20 pieces) and incubated with fibroblast growth media (Knockout DMEM with 20% FBS). At 21 days the fibroblasts were harvested using trypsin.
For each derivation, 100mL of blood was taken from the patient into two 50mL Falcon tubes each containing 5mL of 10% sodium citrate. The sample was mixed by inversion and transporting to the laboratory on ice. The blood samples were diluted 1:1 with Ca2+ and Mg2+ free PBS and 20mL was layered gently onto 15mL of Ficoll Paque Plus (GE Healthcare) and centrifuged at 400g for 35min. The buffy coat containing the mononuclear cells was transferred into a new Falcon tube, diluted 1:1 with PBS and the cells were pelleted by centrifugation at 300g for 20min. Cell pellets were re-suspended in 15mL of EPC media: EGM-2MV supplemented with growth factors (Lonza) supplemented with 20% FCS (HyClone), and plated onto collagen coated T-75ml flasks (BD Biosciences) [10]. The media was changed every 2 days and colonies started appearing from Day 10. After 21 days the EPCs were passaged using trypsin and re-plated into a new T-75 flask (without collagen). The cells were expanded through sequential passages in 1:3 ratios.
H9 hESCs were obtained from WiCell Research Institute. Human iPSCs and ES cells were maintained as described previously [11,15]. Briefly, the cells were cultured on irradiated mouse embryonic fibroblast (MEF) feeder layers in iPSC medium (termed KSR + FGF-2): Advanced DMEM/F12 (Invitrogen) supplemented with 20% Knockout Serum Replacement (Invitrogen), 2mM L-glutamine (Invitrogen), 0.1mM β-mercaptoethanol (Sigma-Aldrich) and 4ng/mL of recombinant human basic Fibroblast Growth Factor-2 (R&D systems). Medium was changed daily and the cells were passaged every 5–10 days depending on the confluence of the plates. To split iPSCs and ES cells, the plates were washed in PBS and 3mL of each of collagenase and dispase was added (Collagenase IV 1mg/mL, Invitrogen; Dispase 1mg/mL, Invitrogen).
For retroviral reprogramming, four pseudo-typed Moloney murine leukaemia retroviruses containing the coding sequences of each of human POU5F1, SOX2, KLF4 and MYC were obtained from Vectalys. For each iPSC derivation, 1 x 105 primary cells (fibroblasts or EPCs) were plated one day before transduction. The 4 viruses were added at a multiplicity of infection of 10 along with 10 μg/mL of polybrene (Millipore). The following day residual virus was washed off with PBS and the cells were re-fed with the fresh medium. On day 5 after infection, the cells were re-plated using trypsin onto a 10cm dish of fresh MEF feeders and 2 days later, the medium was changed from primary cell-specific media to the iPSC medium (KSR + FGF-2). The medium was changed every 2 days until colonies emerged after which the medium was changed daily. For Sendai virus-mediated reprogramming, four viruses containing the coding sequences of human POU5F1, SOX2, KLF4 and MYC were obtained from DNAVec. The protocol for reprogramming was identical to that of retroviruses except that 5 x 105 fibroblasts were used at a multiplicity of infection of three and polybrene was omitted.
The iPSC colonies were identified by their morphology and picked once they had reached sufficient size, typically from day 25 following transduction. Each colony was first detached from the surrounding feeders by scoring around the circumference. The colony was then split into quarters or eighths and the segments gently lifted off the plate and transferred to one well of a 12 well plate of fresh MEF feeders containing iPSC media (KSR + FGF2) supplemented with ROCK inhibitor (Y-27632, Sigma) [24]. The majority of the iPSCs used in this study have been previously characterised in other publications [12,13].
This was performed as described previously [11].
Genomic DNA was extracted from cell pellets using the DNeasy Blood and Tissue kit (Qiagen). Short-insert 500bp whole genome libraries were constructed, flowcells prepared and sequencing clusters generated according to the manufacturer’s protocols and sequenced using the Illumina HiSeq2000 platform (100bp paired-end). Short-insert paired-end reads were aligned to the reference human genome (GRCh37/hg19) using the Burrows-Wheeler Aligner (BWA) [25], duplicates removed. The average sequence coverage was 34-fold.
Somatic base substitution mutations were called using CaVEMan (Cancer Variants Through Expectation Maximization: http://cancerit.github.io/CaVEMan/) which provides a probabilistic estimate of a variant being a somatic mutation. Only variants with likelihoods of 95% and above were included. Post-hoc filters (previously trained on 21 WGS cancers [3]) that sought to remove systematic sequencing artifacts as well as artifacts that arise from mapping errors, were applied to reduce the false positive rate.
SNVs, for which PCR primers could be designed, were all analyzed by amplicon re-sequencing. PCR primers were designed using BatchPrimer3 to amplify regions spanning SNVs. PCR was performed with 5ng of genomic DNA (Fibroblasts, EPCs and iPSCs) used as a template with Phusion Hot Start DNA Polymerase with GC buffer in the following conditions: 98°C for 1 min, 35 cycles of 98°C for 15 sec, 58°C for 15 sec and 72°C for 30 sec, followed by the final extension, 72°C for 5 min. PCR products were first pooled by sample and then purified with QIAquick PCR Purification Kit (Qiagen). Purified PCR products from A1ATD patient B-derived EPCs were converted to a 454 library by emulsion-PCR and sequenced using the 454 Titanium platform according to the manufacturer’s instruction. Purified PCR products from the other samples were converted to an Illumina library by adaptor ligation and sequenced on either the MiSeq (150bp, paired end) or the HiSeq2000 (100bp, paired end) platforms. Reads from the 454 platform were aligned to a reference constructed from PCR-amplified regions. Paired end reads from the MiSeq or HiSeq2000 were first used to generate consensus sequences between each pair and then these were aligned to a reference using BWA SW [25]. The number of reads reporting each of the four bases was counted using Samtool.
PCR primers were designed in a way that each SNV was located in a region where both Illumina reads could reach. PCR and Illumina sequencing were performed as described above. Fastq files (1.fq and 2.fq) were first merged to generate consensus sequence reads. In this process, base calls were accepted only when a sum of Q scores from both reads was higher than 40 and both reads reported the same base. Reads were discarded if an overlapping region exhibited more than 10% mismatches between the two reads. Consensus reads were subsequently mapped onto the reference sequence using BWA SW and the number of reads reporting each of the four bases was counted using Samtool. Two-way contingency Chi-square tests were performed between the reads reporting reference and mutant variants and between fibroblasts and EPCs. Multiple test correction was performed using the Bonferroni correction. SNVs whose mutant read was significantly higher in EPCs were counted as subclonal mutations. Analyses on the subclonal SNVs with less than 0.1% were shown in S16 Table.
It is not possible to subclone and serially expand EPCs therefore a statistical model was used to estimate the SNV mutation rate in EPCs. We obtained 13.5 x 106 cells at the end of S7-EPC expansion, which represents that a single EPC underwent approximately 24 cell divisions. When 5ng (approximately 750 cells or 1,500 molecules) were used as a template for each PCR, assuming that the sampling of DNA molecule follows the Poisson distribution, probability of sampling k number of DNA molecules carrying each SNV introduced at generation n is therefore given by
Pn(X=k)=λnkexp(−λn)k!,
where λn (= 1500/2n+1) represents the mean molecule number of each mutation introduced at generation n in the 5ng DNA. The total number of mutations that can be detected with amplicon re-sequencing is
∑n=024Pn(X>0)Mave=9.88Mave,
where Mave is the average mutation rate, assuming that the mutation rate is similar throughout EPC culture. Taking into account the numbers of sub-clonal EPC mutations detected (SNVs detected in EPCs by deep sequencing; Table 1) and the 40% sampling for deep sequence analysis, we estimated mutation rate of 14.0 ± 2.0 SNVs per cell per generation or 2.1 x 10−9 per nucleotide per generation.
All work performed as part of this project was approved by an ethics committee under the REC Nos. 09/H306/73 and 08/H0311/201.
The aCGH data has been deposited with the ArrayExpress under the accession number, E-MTAB-1319. Whole genome sequence data have been deposited with the European Genome-phenome Archive under the accession number EGAS00001000231 and exome data under the accession number EGAS00001000492.
|
10.1371/journal.pgen.1000045 | Global Chromatin Domain Organization of the Drosophila Genome | In eukaryotes, neighboring genes can be packaged together in specific chromatin structures that ensure their coordinated expression. Examples of such multi-gene chromatin domains are well-documented, but a global view of the chromatin organization of eukaryotic genomes is lacking. To systematically identify multi-gene chromatin domains, we constructed a compendium of genome-scale binding maps for a broad panel of chromatin-associated proteins in Drosophila melanogaster. Next, we computationally analyzed this compendium for evidence of multi-gene chromatin domains using a novel statistical segmentation algorithm. We find that at least 50% of all fly genes are organized into chromatin domains, which often consist of dozens of genes. The domains are characterized by various known and novel combinations of chromatin proteins. The genes in many of the domains are coregulated during development and tend to have similar biological functions. Furthermore, during evolution fewer chromosomal rearrangements occur inside chromatin domains than outside domains. Our results indicate that a substantial portion of the Drosophila genome is packaged into functionally coherent, multi-gene chromatin domains. This has broad mechanistic implications for gene regulation and genome evolution.
| Genes are packaged into chromatin by a variety of specialized proteins. Many different types of chromatin exist, and each may regulate gene expression in different ways. It was previously observed that neighboring genes are sometimes packaged together into a single type of chromatin, which can facilitate their coordinated regulation. However, it has been unclear whether such multi-gene chromatin domains are exceptional, or may occur more frequently. Here, we report a systematic analysis of genome-wide binding patterns of a large set of chromatin components in the fruit fly Drosophila melanogaster. Strikingly, we find that at least 50% of all genes in this organism are packaged together with several of their neighboring genes into a single type of chromatin. Each chromatin domain can include dozens of genes and can be made up of different combinations of chromatin proteins. We show that genes in each domain often have similar functions and are coordinately expressed during development. Moreover, we find that many of these multi-gene domains have been kept intact during evolution, indicating that they are important functional units. In summary, multi-gene chromatin domains are much more common than previously thought, and they are likely to play important roles in the orchestration of gene expression.
| It is becoming increasingly clear that the ordering of genes in metazoan genomes is non-random [1],[2]. Functionally related genes are often located next to one another in the linear genome, and this proximity can be essential for their coordinated regulation during development [3]. Well-studied examples of this are the β-globin gene locus [4] and the hox gene clusters [5],[6]. Genome-scale studies point at the existence of many more clusters of functionally related genes [7]–[9]. In addition, analysis of transcriptome datasets has shown that genes with a similar expression pattern are frequently located in clusters in the genome. For example, testis- and sperm-specific genes in Drosophila melanogaster [10],[11] and muscle-specific genes in Caenorhabditis elegans [12] are significantly clustered. Analysis of genome-wide expression profiles during Drosophila development has identified many clusters of coexpressed neighboring genes, ranging from 10 to 30 genes in size [13]. Furthermore, the human genome shows large regions in which most genes are expressed at high levels, alternating with regions that contain predominantly lowly expressed genes [14],[15]. These observations strongly suggest that juxtaposition of genes in the linear genome can facilitate their coordinated regulation. However, the underlying molecular mechanisms are poorly understood.
Chromatin is a principal orchestrator of transcription. Neighboring genes can be packaged together into a single chromatin domain that may act as a regulatory unit [2],[3],[16],[17]. Several chromatin domains have been characterized in detail in a variety of species [18]–[23]. However, it remains unclear whether such domains are relatively rare, or represent a general principle of genome organization. Here, we present a systematic survey of chromatin domain organization of the D. melanogaster genome by computational analysis of a broad panel of genome-wide chromatin protein binding maps. Our results demonstrate that at least half of the Drosophila genome consists of multi-gene chromatin domains. Strikingly, these domains can be very large and include dozens of genes. We provide evidence that most of the newly identified domains are of functional relevance.
To systematically identify chromatin domains, we assembled a compendium of genome-scale binding maps of 29 broadly selected Drosophila chromatin components (Dataset S1). We included previously published DamID and ChIP-on-chip datasets [21], [22], [24]–[28] as well as newly generated DamID maps for 11 proteins (see Methods). The full list consists of heterochromatin proteins, Polycomb group proteins, chromatin remodeling proteins, high mobility group (HMG) proteins, various DNA binding factors, histone modifications, and specialized histones (Table 1). Most binding maps were obtained in the Kc167 cell line, which provides a homogeneous cell population. Only the map of the variant histone H3.3 was derived from the S2 cell line, and the maps of eve and Prospero from Drosophila embryos. At present, this is the most extensive collection of genome-scale chromatin protein binding maps in a metazoan.
The definition of a multi-gene chromatin domain is not trivial. Intuitively, it might be defined as a set of adjacent genes that are all bound by a chromatin protein X. However, it is conceivable that one or more genes loop out from a domain and do not bind X. In this case, the domain would consist of two or more sub-domains, and it is not obvious whether one should regard it as a single larger domain or as multiple smaller domains. Both views may in fact be correct; for example, the larger domain may determine the overall expression pattern of the included genes, while the sub-domains may act as separate fine-tuning units, and the intervening gene(s) may separate the units. This is just one theoretical example of a possible configuration; many different types of domain structures may exist [2],[3],[16],[17].
To obviate the need for detailed models, we took an unbiased statistical approach. We defined chromatin domains as regions of local enrichment in occupancy by a specific chromatin component over multiple adjacent genes. We require that this local enrichment is statistically significant, i.e., it must not be explainable by random fluctuations. Practically, this means that this local enrichment should not be observed when the order of genes in the genome is randomly permuted. To detect and visualize regions of local enrichment in our protein binding maps, we modified and extended a previously reported sliding window method [15] (see Methods). For each window of w consecutive genes, we tested whether the distribution of protein occupancy values differs from what is expected by chance, by comparing it to a null model in which the linear order of genes in the genome is randomly permuted. For each possible window position along each chromosome arm, and each possible window size, we accordingly computed a P-value representing the probability of observing the same or a larger degree of linear clustering by chance. Note that because all possible window sizes are analyzed, this approach allows for the identification of hierarchical structures of domains within domains. We emphasize that this approach does not require any pre-defined threshold for the level of protein occupancy, which would be arbitrary in the absence of objective criteria for choosing such a threshold.
To visualize the P-values that quantify the local enrichment of protein occupancy in multi-gene regions at all possible spatial scales for each chromosome, we use a triangular graph we call “domainogram”, in which window position is indicated on the horizontal axis, window size on the vertical axis, and P-value by a color scale. Fig. 1A shows a domainogram of the binding of Heterochromatin Protein 1 (HP1) on chromosome arm 2R. This graph reveals that a few large chromosomal regions are significantly enriched for HP1 binding (bright purple and red colors). The pericentromeric region shows strong enrichment of HP1, consistent with previous reports [29],[30]. In addition, a telomere-proximal region of highly significant enrichment is identified that was not previously known. Interestingly, this region displays a nested organization: two smaller regions of enrichment at ∼18 and 20 Mb together are part of a substantially larger region. No enrichments are seen after random permutation of the gene order (Fig. 1C,D), underscoring that our statistical criterion for spatial clustering is valid.
We systematically generated domainograms for all proteins in the compendium (Fig. 2 and Supplementary Fig. S1). Strikingly, nearly all proteins exhibit non-random enrichment at multiple sites in the genome. In some cases, such as for Lamin (Lam; Fig. 2A) and Polycomb (Pc; Fig. 2D) this is consistent with previously reported evidence for clustering of target genes [21],[22],[31]. For many other proteins, such as the HMG protein D1 and the transcription factor Mnt (Fig. 2B and C), the non-random genomic distribution has not been reported before. In several instances, the patterns of enrichment suggest a nested architecture, with larger domains subdivided into two or more smaller regions of enrichment (e.g., Fig. 2A–C). More complex enrichment patterns, sometimes covering a substantial part of a chromosome arm, can also be seen (e.g., D1 on chromosome 2L, Fig. 2B). Taken together, these results indicate that most chromatin components are highly non-randomly distributed along the Drosophila genome.
Most of the maps used for this analysis were obtained using cDNA arrays to detect protein binding. This means that genes are the units of mapping, and only protein binding at or in the flanking regions (about 1–2 kb on either side) of genes is detected [32]. To test whether this restriction might affect the identification of regions of enrichment, we also constructed domainograms of high-resolution tiling array DamID data of HP1. Comparison showed that cDNA array data yielded essentially the same enrichment patterns as tiling array data, although the latter provide a more fine-grained view (Supplementary Fig. S2). To rule out the possibility that the observed patterns of enrichment are the result of an experimental bias of the DamID technique, we compared DamID data for Pc with ChIP data for H3K27me3, the histone modification that forms the primary docking site for Pc [33] (Fig. 2D). Reassuringly, the domainograms are very similar.
We were surprised to find that 9 out of 29 proteins displayed moderate but significant enrichment along the entire X chromosome (note the purple or red colors in the top parts of the X chromosome domainograms in Fig. 2C and Supplementary Fig. S1). For histone H3.3 in male S2 cells this was previously reported and attributed to the dosage compensation mechanism [27], which ensures ∼2-fold increased expression of most genes on the single male X chromosome [34],[35]. The global X-enrichment of several other proteins (Bicoid, brahma, eve, Groucho, HP1, HP6, MBD-like, Mnt, Trl) in female Kc167 cells is surprising, but may be linked to the observation that X-linked genes in females also display slightly but significantly enhanced gene expression levels compared to autosomal genes [36].
To assess whether domains of enrichment are stable or dynamic entities, we compared HP1 binding patterns in Kc167 cells under two different culturing conditions, viz. medium with serum (BPYE) and without serum (HyQ). While some HP1 domains (e.g., in pericentric regions) remain constant under these two conditions, other domains appear to be dynamic (Fig. 2E and Supplementary Fig. S1). For example, the large telomere-proximal region of enrichment on chromosome 2R is only observed when the cells are grown in BPYE, and is completely absent in HyQ (Fig. 2E). This indicates that this region on 2R consists of a large cluster of conditional HP1 target genes that bind HP1 simultaneously upon an (yet unknown) intracellular signaling event that is triggered by serum. We have also studied the dynamics of chromatin domains formed by the protein HP6 by interfering with its interaction partner HP1 (Fig. 2F; data from ref. [24]). After knock-down of HP1, the formation of a prominent chromatin domain of HP6 binding is observed around position ∼10 Mb on chromosome 2L, a region that is also enriched in binding of Mnt (Fig. 2C) and several other proteins (see below). These results show that external signals or perturbation of chromatin complex composition can influence the formation of chromatin domains.
While the domainograms are useful for visualizing regions of local spatial enrichment, they do not provide precise domain boundaries, as would be desirable for subsequent functional analyses (see below). To this end, we developed a dynamic programming algorithm that for each protein identifies the optimal genomic partitioning into discrete domains. To capture potentially nested domain structures, we performed this procedure iteratively using a maximum domain size constraint, and combined results for all possible values of this maximum domain size. As a result, the domainogram is simplified to a set of partially overlapping discrete domains of enrichment. For a detailed description of our algorithm, see Methods. We refer to the discrete domains identified by the partitioning algorithm as Blocks of Regulators In Chromosomal Kontext (BRICKs). We note that whereas some chromatin domains may be discrete in reality, others may have less sharply defined borders. In the latter case the discretization into BRICKs represents an oversimplification for practical purposes.
Fig. 3A shows the BRICKs identified for HMG protein D1 on chromosome arm 2L. When tested on simulated data that consist of various discrete domains placed in a noisy background, our algorithm identifies most domains correctly, with a low false-positive rate (Supplementary Fig. S3). Parameters were chosen such that for randomly permuted datasets the algorithm discovers ∼40 times fewer discrete domains than in the actual biological binding maps, i.e., the estimated false discovery rate (FDR) is ∼2.5%. Importantly, our algorithm was designed to discover the intrinsic size of the binding domains: a larger region containing two or more smaller BRICKs will only be parsed as a BRICK itself if increased binding also occurs in the regions in between the smaller BRICKs (Fig. 3B). Therefore, the nested domain structure that can be observed between 14–18 Mb in Fig. 3A presumably reflects a complex chromatin domain structure. Consistently, in computer simulations of chromosomes with simple discrete domains, our algorithm typically does not find nested or overlapping BRICK patterns (Supplementary Fig. S3).
To compare the spatial binding patterns of the 29 tested proteins and histone marks, we used a visual representation in which their respective BRICKs are stacked, providing a compact simultaneous view of their chromosomal domain structure (Fig. 4A and Supplementary Fig. S4). This revealed that several proteins have strongly overlapping BRICKs, suggesting that these proteins may act together to form a distinct chromatin domain. As expected, heterochromatin components HP1, Su(var)3-9, HP3/Lhr, HP4, HP5 and HP6 colocalize in BRICKS in pericentric regions (Supplementary Fig. S4) and can also be seen to form a small consistent domain at position ∼8 Mb on chromosome 2L (Fig. 4A). Likewise, the BRICK structures of the Polycomb Group complex components Pc, Sce, esc and H3K27me3 are highly similar. Other combinations of proteins are more surprising. For example, the BRICKs for Mnt, H3K4me2, Sin3A, and eve overlap strongly on chromosome 2L around ∼10 Mb (Fig. 4A). BRICKs of Lamin, His1, D1, and SuUR also overlap, between ∼14 and ∼18 Mb on chromosome 2L. Some proteins can be part of different types of domains: In pericentric regions, D1 shares BRICKs with HP1 and other heterochromatin components, but at other sites D1 is found in various combinations with Lam, SuUR and His1 (Supplementary Fig. S4). Similarly, Sin3A forms different combinations with Sir2, H3K4me2, and Mnt (Fig. 4A and Supplementary Fig. S4), and also with H3.3 and eve (with the caveat that the latter profiles were not obtained in the Kc167 cell line). These results are suggestive of a combinatorial “chromatin code” that marks specific domains.
A merged overview of BRICKs in all chromosomes (Fig. 4B) reveals that a substantial part of the Drosophila genome is organized into chromatin domains. When BRICKs are limited to a maximum of 100 consecutive genes, 50% of the genome, corresponding to 54% of all genes, is covered by at least one BRICK. These results demonstrate a strikingly high degree of non-random organization of genes into chromatin domains.
BRICKs typically show average protein binding log-ratios ranging from ∼0.4–3 (Supplementary Fig. S5), which corresponds to ∼1.3–8 fold enrichments of a chromatin component in each BRICK relative to the genome-wide median value. Even subtle modulations of protein-genome interactions may have biologically relevant effects on gene regulation, but functional evidence is required to confirm this. To directly address whether BRICKs represent chromatin domains of functional importance, we performed three different analyses.
First, we hypothesized that genes may be packaged together in a BRICK to facilitate their synchronized expression during development. To test this, we determined the degree of developmental coexpression of genes within each BRICK, using a previously published Drosophila gene expression dataset [37]. Fig. 5A illustrates that a large fraction of the BRICKs indeed show substantial coregulation. Because neighboring genes are often coregulated [13],[37], we asked specifically whether genes within BRICKs display a higher degree of coexpression than genes in size-matched control windows located outside BRICKs. Statistical analysis of these data (Fig. 5B, see Text S1 and Supplementary Fig. S6) demonstrates that for about half of the investigated chromatin proteins the degree of coregulation is significantly higher within BRICKs than in control windows. This indicates that many BRICKs may be important for the developmental synchronization of gene sets. We note that this analysis is based on the assumption that chromatin domains remain unaltered between the cells in which the protein binding patterns were mapped (mostly Kc167 cells) and the developmental stages for which expression data was obtained (six different stages ranging from early embryos to adult flies [37]). While several reports indicate that some chromatin domains indeed are very similar in different cell types, tissues, and developmental stages [21],[22],[38], other domains are more plastic (e.g., Fig. 2E and [22]). Our coexpression analysis does not take into account such potential dynamics in domain structure, and therefore may be expected to underestimate the correlation between BRICK organization and coordinated gene expression.
Second, we asked whether genes within each BRICK have common functions. To this end, we tested for enrichment of specific Gene Ontology (GO) categories [39] within each BRICK (see Methods). Fig. 5C shows that, at an estimated FDR of 1% (Supplementary Fig. S7 and Text S1), roughly half of all BRICKs are enriched for one or more GO categories. This number is significantly higher than what is expected by chance, even if the known genomic clustering of GO categories [7] is taken into account (P = 0.017, based on 1,000 genome-wide circular permutations of the association between genes and GO categories). A striking example is a BRICK defined by the protein Prospero (Supplementary Fig. S8); in this BRICK many genes encode transcription regulators that are implicated in the Notch pathway. In total, we find 150 GO categories enriched in one or more BRICKs (data not shown). Fig. 5D summarizes the fraction of GO-enriched BRICKs for all proteins separately. In conclusion, the observation that BRICKs are frequently enriched for genes with related functions argues that they are likely to serve as functional modules. Comparison between Figures 5A and C shows that BRICKs enriched for GO annotation are often not enriched for coexpression, and vice versa.
Third, we reasoned that if BRICKs are functionally important, chromosomal rearrangements that disrupt the BRICK structure should be subject to negative selection during evolution. To test this, we analyzed the positions of synteny breakpoints in the genome of D. melanogaster relative to D. pseudoobscura [40]. These two species diverged about 25–50 million years ago [41]–[43]. Indeed, we find that synteny breakpoints are often located adjacent to BRICKs, rather than within BRICKs (Fig. 5E). Statistical analysis shows that BRICKS defined by His1, Prospero, Lamin, SuUR and D1, with sizes up to 40 probed genes, have significantly fewer synteny breaks (∼67% reduction) than expected based on the distribution of synteny breaks in the genome (Fig. 5F–G and Supplementary Fig. S9). Larger BRICKs typically do not show this reduction, possibly because their integrity as a single domain is less important, or because they cannot be preserved at the high overall rate of synteny breaks (on average one breakpoint per 15 genes, median is 8). While we cannot strictly rule out that syntenic breaks and chromatin domain boundaries have a common mechanistic origin, the apparent evolutionary selection against the break-up of chromatin domains suggests that many of them are functionally important.
Together, these three lines of evidence support the functional importance of BRICKs in the Drosophila genome.
Finally, we asked whether BRICKs represent regions with specific general sequence properties. First, we tested whether BRICKs are regions of unusual gene density. For the set of BRICKs of each protein we calculated the average gene density, and compared it with the genome-wide average gene density (Fig. 6A). This analysis shows that different sets of BRICKs vary substantially in gene spacing. For example, consistent with previous observations [22], genes within Lam BRICKs are relatively widely spaced. The same is true for BRICKs of other heterochromatin proteins, such as SuUR, esc and HP1. By contrast, genes within BRICKs of H3K4me3, Mnt, and Sir2 have very short intergenic regions. Also the lengths of genes within BRICKs can vary between proteins (Fig. 6B). In BRICKs associated with inactive chromatin (esc, Sce, Lam) genes tend to be longer than in BRICKs of active chromatin (H3K4me3, Mnt). Analysis of repeat content (Fig. 6C) showed that BRICKs formed by classical heterochromatin proteins such as HP1, Su(var)3-9 and HP1-associated proteins [24] are more repeat-rich than other BRICKs, which is consistent with previous analyses [29],[38]. BRICKs defined by individual proteins show only minor variation in G/C content (Fig. 6D). It is important to note that the combined BRICKs for all proteins do not show a systematic bias related to gene density, gene size, repeat density, or G/C content. Therefore it is unlikely that their detection is a systematic artifact of variation in any of these parameters along the genome.
The results presented here indicate that about half of the Drosophila genome is organized into large chromatin domains, most of which are functionally relevant. This estimate of the coverage of the genome by domains is likely to be an underestimate for three reasons. First, because the BRICK segmentation algorithm is computationally intensive, we restricted the BRICK sizes to a maximum of 100 genes. The domainograms however indicate that substantial non-random clustering also occurs above this limit. Second, even though our compendium of binding maps includes a wide range of known protein complexes, many other proteins must be mapped for a complete view. Third, we provided evidence that at least for some chromatin proteins the domain structure may depend on the cellular state. We predict therefore that maps of protein binding in various cell types will reveal additional, cell-type specific BRICKs. Taken these considerations into account, our estimate that approximately half of the fly genome is organized in chromatin domains is conservative.
Previous analyses of genome-wide expression data have revealed that there are domains of similarly expressed genes in the genome of Drosophila [Spellman, 2002, 12144710; Boutanev, 2002, 12478293; Stolc, 2004, 15499012]. Spellman and Rubin have shown that ∼20% of the genome can be found in coregulated domains ranging in size between 10 and 30 genes, with a median of 13. The BRICKs range in size between 2 and 100 with a median of 26. However, we stress that due to the very different nature of the methods that were employed in both studies this comparison should be interpreted with caution.
The domainograms and BRICK patterns suggest that chromatin domains can have a complex, nested structure. It is tempting to speculate that looping interactions take place in such nested regions. It is noteworthy that transcription factors such as Trl, bcd, and Jra also exhibit spatial clustering. These factors do not spread along the chromatin fiber but instead have focal binding sites [44]. BRICKs of transcription factors must therefore be interpreted as non-random clusters of focal binding sites. Genes in BRICKs defined by transcription factors generally do not show simple coexpression but tend to have common functions (Fig. 5B,D). This is reminiscent of the mammalian β-globin locus, in which functionally related genes are not coexpressed but instead are transcribed in a temporally defined order. Several transcription factors have multiple binding sites in the β-globin locus [45], and looping interactions play an important role [19]. We therefore speculate that some of the transcription factor BRICKs may be similar in structure to the β-globin locus. Our BRICK database (provided in GFF file format as Dataset S2) provides a rational starting point for the selection of loci to probe for looping interactions using the 3C/4C/5C technologies [46].
The surprisingly widespread occurrence of chromatin domains has two major implications. First, chromatin domains provide a plausible explanation for earlier observations that neighboring genes in eukaryotic genomes are often co-regulated [13],[37]. Our results suggest that chromatin domains may at least be partially responsible for the synchronized expression of neighboring genes, although it cannot be ruled out that in some instances the clustering of a chromatin mark may be the consequence rather than the cause of this synchronized expression. Second, our data suggest that chromatin domains impose considerable constraints on genome evolution. Most likely, this is due to negative selection of genome rearrangements that disrupt the integrity of chromatin domains, but it is also possible that chromatin domains stabilize the chromatin fiber and thereby physically prevent chromosome rearrangements. In summary, the widespread chromatin domain organization provides new clues towards the understanding of the mechanisms of transcription regulation as well as genome structure and evolution.
Table 1 summarizes the protein binding maps that we used. Published DamID and ChIP-on-chip profiles were taken from refs [21], [22], [24]–[28],[44]. In addition, we generated new DamID profiles for brm, Trl (GAGA factor), gro, Mnt, Sin3A and Sir2 using previously reported Dam-fusion expression vectors [44], [47]–[49], and for full-length D1, DSP1, His1, MBD-like, and Su(var)3-7 using newly constructed Dam-fusion vectors. These new profiles were generated in Kc167 grown in serum-containing medium as described [44]. The DamID profile of HP1 in Kc167 cells grown in serum-free Hyclone Insect-Xpress medium (“HyQ”) was not previously published but was generated in parallel with our already published profile of HP1 from cells in serum-containing medium [24], allowing for direct comparison. Plasmid sequences are available at http://research.nki.nl/vansteensellab. DamID experiments were performed as described previously [50]. Binding profiles represent the average of triplicate experiments, with one experiment in the reversed dye orientation. Log2 ratios were averaged across replicates. The raw data can be accessed via the Gene Expression Omnibus under accession number GSE10219; the combined binding data is also provided as Dataset S1, and the set of BRICKs is supplied in GFF format as Dataset S2. All data were generated in Kc167 cells, except for the maps of His3.3A, eve and Prospero. His3.3A data are from the S2 cell line [27]; Prospero [25] and eve [26] data are from stage 10–11 and stage 17 embryos, respectively.
Except for the eve and Prospero maps, all data were generated using 12k cDNA arrays. Each cDNA probe detects the binding at or in the vicinity (∼1–2 kb) of a gene [32]. Thus, genes are the units of our analysis. To ensure that each cDNA probe constitutes an independent datapoint, overlapping cDNA probes were removed, using the following rules: 1) if a probe overlapped with multiple other mutually non-overlapping probes we removed the former probe from the dataset, 2) if two probes overlapped more than 20%, the smaller of the two probes was removed.
Binding data of eve and Prospero were generated with genome-wide tiling arrays [25]. To allow for direct comparison with the cDNA array based data, we resampled the tiling array data, so that we had one datapoint per gene. For this we used the gene annotation from release 4.3 of the Flybase genome annotation (http://www.flybase.net). For every gene in the genome we calculated the average of all the probes encompassed by that gene. As with the cDNA data, when two genes overlapped more than 20%, the smallest gene was removed, with the exception of genes that overlapped with multiple non-overlapping genes, in which case the gene overlapping with multiple genes was removed. After removal of overlapping genes we are left with 12,821 genes for which we have reliable eve and Prospero data.
For the comparison of cDNA data to high-resolution data, the HP1 tiling array data was not resampled to one datapoint per gene. For the comparison we used the left arm of chromosome 2, which contains >222k probes (1 probe per 100 bp) [29]. Since for large numbers of probes the domainogram analyses become computationally intensive, the tiling array data was averaged into equal-sized bins of 3 kb. These data were used as input to the algorithm.
Because DamID and ChIP log-ratios for a specific protein are often not normally distributed (data not shown), we used a non-parametric approach to evaluate local enrichment. For each binding profile, probes are sorted in descending order according to their DamID or ChIP ratio and converted to single-gene quantile scores:Here N equals the total number of probes and ri is the rank for probe i = 1,…,N. To integrate evidence for protein occupancy across multiple adjacent probes for each window (i,w) of width w ending at probe i, we compute a multi-gene P-value, Piw, from the single-probe quantile scores (Qi−w+1, … , Qi), with i≥w. We define Piw so as to have a uniform distribution on the interval [0,1] if all the Qi values, which are uniformly distributed by construction, are independent random variables. To this end we use a transformation according to R.A. Fisher [51]: Given the product statistic can be computed using a χ2-distribution with 2w degrees of freedom:Note that for w = 1, we have Pi1 = Qi. The Piw can be visualized simultaneously in a triangular diagram (“domainogram”) using an approach similar to Versteeg et al. [15]. Image files were created using custom Perl scripts (available upon request).
To identify the most probable discrete domains of size >1 (BRICKs) from the Piw data structures, we used a dynamic programming algorithm [52]. We modified our scoring scheme so as to favor the “no-domains” segmentation consisting of only w = 1 windows by introducing a bias factor γ and defining:
Each possible segmentation of the genome into non-overlapping windows corresponds to a path {(i(k), w(k))} through the Piw triangle, where k = 1,...,K runs over all K windows in the segmentation (K≤N). Here i(k) denotes the last gene in the k-th window, while w(k) denotes the length of the k-th window. The optimal segmentation minimizes an objective function equal to the product over all windows constituting the path:This segmentation can be determined using the recursion relationand the initial condition V0 = 1. Backtracking starting from i = N according todefines the optimal segmentation.
To identify the nested structure present in the domain organization, we perform the previously discussed computation, with restriction of the maximum window size (wmax). This way the segmentation is restricted to smaller window sizes, which leads to the identification of smaller BRICKs. The analysis is iterated until the segmentation for all wmax>1 has been determined. See Text S1 for more detailed information on the BRICKs algorithm.
The Flybase Gene Ontology annotation version 1.92 was used to calculate the enrichment of GO categories. GO categories containing fewer than 5 genes were ignored. Enrichment of GO categories in each BRICK was determined using the cumulative hypergeometric distribution, accounting for multiple testing of all combinations of domains and GO categories. As both the BRICK structure and the GO dataset are hierarchically organized, we estimated the FDR using a Monte Carlo simulation in which all genes were randomly permuted while keeping the assignment to GO categories and BRICK structure intact. For each BRICK d, the P-value (cumulative hypergeometric distribution) for each GO category was determined, and the smallest of these was recorded as Pd. The false discovery rate for each BRICK is then given by FDR(Pd), where
In the denominator, D(P) represents the number of BRICKs with a minimal p-value smaller than P, while the numerator represents an average of the same quantity over random permutations. Fig. S6 shows the distribution of the actual p-values belonging to the BRICKs for all proteins combined and the p-value distribution from 10,000 random genome permutations.
We have also performed this analysis for BRICK sets defined by individual proteins. In this analysis the FDR cut-off was based on 1000 randomizations. We have used this per protein calculation of the FDR cut-off to determine the number of enriched BRICKs shown in Fig. 5D.
A circular permutation test was performed to account for possible uneven distribution of GO category members across the genome. In this analysis we circularly permuted the genes along the BRICK set. Using the above mentioned FDR(Pd) as a cut-off we determined the number of BRICKs (BRICKGO) that fell below this threshold. The distribution of BRICKGO of 1000 circular permutations is compared to the actual number of significantly enriched BRICKs to determine the P-value.
Release 4.3 of the D. melanogaster genome annotation (Berkeley Drosophila Genome Project) contains information on the start and end location of regions that are syntenic to genomic regions in D. pseudoobsura. These locations represent synteny breakpoints. Since it has been reported that the scaffolds from the dot chromosome (chromosome 4 in D. melanogaster and chromosome 5 in D. pseudoobscura) could not reliably ordered in D. pseudoobscura [40], we omitted chromosome 4 from our synteny calculations. On the other chromosome arms of D. melanogaster, the distribution of the synteny breakpoints is not significantly different from a uniform random distribution (Kolmogorov-Smirnov test, P = 0.6498; data not shown).
Synteny blocks spanning multiple genes sometimes contain insertions of a single gene from a different locus in D. pseudoobsura. In the genome annotation, these events are marked by two syntenic block entries. We decided that insertion of a single gene does not constitute a break in a synteny block, when it is embedded in a larger region of synteny. For the formation or break-up of chromatin domains, insertion of a single gene is likely a less deleterious event then an actual break in synteny.
Depletion of synteny breakpoints from BRICKs was determined as follows: Given the start and end position in a BRICK, we determined the genes that are encompassed by the BRICK. Since breaks in synteny almost exclusively occur in between genes, we counted the number of intergenic regions within all the BRICKs (n). Next we determined the number of synteny breakpoints within the BRICKs (k). Given that there are 955 synteny breakpoints (K) in the D. melanogaster genome and 14,351 intergenic regions (N), we can calculate a probability score using the cumulative hypergeometric distribution for k syntenic breakpoints in a BRICK containing n intergenic regions.
The median synteny block size is 8 genes, whereas some BRICKs are much larger (by definition up to 100 genes). Because of this partial discrepancy in scale, we performed the synteny analysis for subsets of BRICKs smaller than a given maximum size (BRICK size is the number of probed genes per BRICK). Plotting –log10(P-value) as a function of the maximum BRICK size visualizes the size-dependent depletion of synteny breakpoints from BRICKs (Fig. S8). Fig. 5G shows the P-values corresponding to the most significantly depleted BRICK size range for each protein. |
10.1371/journal.pmed.1002722 | Airway obstruction and bronchial reactivity from age 1 month until 13 years in children with asthma: A prospective birth cohort study | Studies have shown that airway obstruction and increased bronchial reactivity are present in early life in children developing asthma, which challenges the dogma that airway inflammation leads to low lung function. Further studies are needed to explore whether low lung function and bronchial hyperreactivity are inherent traits increasing the risk of developing airway inflammation and asthmatic symptoms in order to establish timely primary preventive initiatives.
We investigated 367 (89%) of the 411 children from the at-risk Copenhagen Prospective Studies on Asthma in Childhood 2000 (COPSAC2000) birth cohort born to mothers with asthma, who were assessed by spirometry and bronchial reactivity to methacholine from age 1 month, plethysmography and bronchial reversibility from age 3 years, cold dry air hyperventilation from age 4 years, and exercise challenge at age 7 years. The COPSAC pediatricians diagnosed and treated asthma based on symptom load, response to inhaled corticosteroid, and relapse after treatment withdrawal according to a standardized algorithm. Repeated measures mixed models were applied to analyze lung function trajectories in children with asthma ever or never at age 1 month to 13 years. The number of children ever versus never developing asthma in their first 13 years of life was 97 (27%) versus 270 (73%), respectively. Median age at diagnosis was 2.0 years (IQR 1.2–5.7), and median remission age was 6.2 years (IQR 4.2–7.8). Children with versus without asthma had reduced lung function (z-score difference, forced expiratory volume, −0.31 [95% CI −0.47; −0.15], p < 0.001), increased airway resistance (z-score difference, specific airway resistance, +0.40 [95% CI +0.24; +0.56], p < 0.001), increased bronchial reversibility (difference in change in forced expiratory volume in the first second [ΔFEV1], +3% [95% CI +2%; +4%], p < 0.001), increased reactivity to methacholine (z-score difference for provocative dose, −0.40 [95% CI −0.58; −0.22], p < 0.001), decreased forced expiratory volume at cold dry air challenge (ΔFEV1, −4% [95% CI −7%; −1%], p < 0.01), and decreased forced expiratory volume after exercise (ΔFEV1, −4% [95% CI −7%; −1%], p = 0.02). Both airway obstruction and bronchial hyperreactivity were present before symptom debut, independent of disease duration, and did not improve with symptom remission. The generalizability of these findings may be limited by the high-risk nature of the cohort (all mothers had a diagnosis of asthma), the modest study size, and limited ethnic variation.
Children with asthma at some point at age 1 month to 13 years had airway obstruction and bronchial hyperreactivity before symptom debut, which did not worsen with increased asthma symptom duration or attenuate with remission. This suggests that airway obstruction and bronchial hyperreactivity are stable traits of childhood asthma since neonatal life, implying that symptomatic disease may in part be a consequence of these traits but not their cause.
| It is believed that asthma develops from inflammation in the lungs that leads to loss of lung function, but low lung function may be an inherent trait in children at risk of asthma instead of a consequence of inflammation.
It is important to explore whether low lung function is an inherent trait that increases the risk of developing airway inflammation and asthma in order to establish primary preventive initiatives for low lung function.
Ninety-seven children who developed asthma and 270 children without asthma from the Danish COPSAC2000 birth cohort born to mothers with asthma were studied extensively with longitudinal lung function measurements from age 1 month to 13 years.
Lung function was measured by spirometry from age 1 month and plethysmography from age 3 years, including assessments of bronchial reversibility to inhaled β2-agonist from age 3 years. Bronchial reactivity was assessed by methacholine challenge from age 1 month, cold dry air hyperventilation from age 4 years, and exercise challenge at age 7 years.
Children developing asthma had reduced lung function from age 1 month throughout childhood compared to the children without asthma.
The lung function deficit was present before the children developed asthma, did not progress with symptoms, and remained even if symptoms ceased.
Low lung function appears to begin in early childhood in a group of children who will develop asthma. The lung function trait is established prior to development of airway inflammation and asthma, and does not worsen with increased duration of asthma symptoms. As airway obstruction and increased bronchial reactivity manifest as early as 1 month of age, it is possible that preventive measures undertaken during pregnancy will be most effective.
| The current paradigm of asthma pathophysiology suggests that an inflammatory process in the airways leads to progressive bronchial hyperreactivity and lung function deficits [1]. An alternative hypothesis is causality in the opposite direction, i.e., that reduced airway caliber and bronchial hyperreactivity are inherent and stable traits that increase the risk of asthmatic symptoms, exaggerated hyperreactivity, and intermittent airway obstruction from a superimposed inflammatory process. In support of this, anti-inflammatory inhaled corticosteroid (ICS) treatment does not affect the natural course of lung function in children [2–6]. Furthermore, reticular basement membrane and airway smooth muscle thickness are unrelated to inflammatory cell counts in bronchial biopsies [7]. This alternative causal direction from airway obstruction and bronchial hyperreactivity to asthma symptoms implies that prevention must take place before symptom debut and maybe even before birth [8,9].
We aimed to study this alternative paradigm with rigorous prospective assessment of disease duration and remission. Spirometry and bronchial reactivity to methacholine were assessed in neonates [10,11]. Neonatal spirometry was volume-anchored and therefore comparable to repeated assessments of spirometry and bronchial reactivity during childhood [12–15]. Effort-independent measures of airway resistance and measures of bronchial reactivity were recorded until age 13 years.
This study is reported as per the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines (S1 STROBE Checklist).
The study was nested in Copenhagen Prospective Studies on Asthma in Childhood 2000 (COPSAC2000): a Danish prospective birth cohort study of 411 infants born during 1998–2001 to mothers with a history of asthma [16]. At enrollment at age 1 month, we excluded any child with a history of symptoms of lower airway infection or neonatal mechanical ventilation, preterm birth (gestational age < 36 weeks), or any congenital abnormality or systemic illness. The children were examined at half-yearly scheduled visits until age 7 years, and again at age 13 years, including assessments of lung function 11 times during childhood. Thus, longitudinal lung function assessment during childhood was a strong and predefined focus of COPSAC2000, which is detailed in the cohort baseline paper [16], but there was no prospective analysis plan for how to model the data with respect to asthma development.
The local ethics committee (KF 01-289/96) and the Danish Data Protection Agency (2015-41-3696) approved the study. Both parents gave oral and written informed consent before enrollment.
Bronchial reversibility was measured at age 3, 5, 7, and 13 years by whole body plethysmography and at 5, 7, and 13 years by spirometry as the relative change from baseline 15 minutes after inhalation of β2-agonist. At age 3–7 years, terbutaline, 500 μg (2 × 250 μg) pMDI (Bricanyl, AstraZeneca, Cambridge, UK) was delivered via a metal spacer (Nebuchamber, AstraZeneca, Cambridge, UK). At 13 years, we used salbutamol, 400 μg (2 × 200 μg) from a discus DPI (Ventoline, GlaxoSmithKline Pharma, Middlesex, UK).
The parents were taught to recognize troublesome lung symptoms including noisy breathing (wheezing or whistling sounds), breathlessness, shortness of breath, and persistent coughing at a session at the research unit. They subsequently recorded their child’s troublesome lung symptoms, i.e., symptoms significantly affecting the well-being of their child, in a daily diary as a composite score (yes/no) from age 1 month to 7 years. They were requested to bring the child to the COPSAC clinic at each episode of 3 consecutive days with symptoms for examination and verification of the symptoms by the COPSAC pediatricians [2,22]. If the child was diagnosed with asthma, the diary recording of lung symptoms was continued beyond age 7.
Allergen sensitization towards 10 aeroallergens—birch, grass, mugwort, horse, dog, cat, Dermatophagoides pteronyssinus, D. farinae, Cladosporium herbarum, and Alternaria alternata—was determined at age 13 years by skin prick test [23] and by measuring blood levels of specific IgE [24]. Allergic sensitization was defined as any wheal > 3 mm and/or any specific IgE value ≥ 0.35 kU/A.
Description of baseline characteristics of the cohort, including pre-, peri- and postnatal factors is provided in S1 Text.
Baseline characteristics of included versus excluded children were compared using Fisher’s exact test, Student’s t test, and Wilcoxon 2-sample test.
The relationship between development of FEVz, MMEFz, sRawz, and PDz in the first 13 years of life and asthma status was analyzed with mixed models taking repeated participant measurements into account. The statistic extracted from these mixed models was the fixed effect of group, i.e., asthma versus no asthma. To test the effect of the normalization of the lung function measurements, which was done to enable comparison of measurements conducted at varying ages, we also ran the analyses with raw, untransformed data and subsequently investigated the normal distribution of the residuals from the different models with Q-Q plots.
The cold dry air challenge by spirometry (6 years) and exercise challenge (7 years) were only performed once for each child, and the cross-sectional difference in these measures between children with asthma and without asthma was analyzed by Student’s t test comparison of means.
We subsequently investigated whether development of FEVz, MMEFz, sRawz, and PDz prior to onset of asthma was different from their development in children never developing asthma. This was done in mixed models comparing FEVz, MMEFz, sRawz, and PDz measurements obtained before diagnosis in children developing asthma with measurements in children never developing asthma, evaluating the fixed effect of group, i.e., asthma versus no asthma. As an ancillary analysis, we also investigated by t tests whether neonatal FEV0.5, FEF50, and PD15 were different in neonates who subsequently developed asthma versus neonates who did not develop asthma.
The effect of duration of asthma in the first 13 years of life on lung function development, i.e., whether lung function declined over time with increasing disease length, was investigated by calculating the duration of symptomatic disease preceding each lung function measurement. The mixed models used for this analysis were adjusted for time since remission and estimated the slope of the lung function during the period with asthma. The statistic extracted from these mixed models was the fixed effect of time, i.e., disease length in the observation period. To further investigate whether asthma status affected measures of FEVz, MMEFz, sRawz, and PDz over time, age at measurement was included in the analyses as an interaction term.
Finally, lung function development after remission of asthma was analyzed in mixed models by evaluating the change over time in FEVz, MMEFz, sRawz, and PDz after remission, evaluating the fixed effect of time since remission.
As post hoc analyses, we investigated development of FEVz, MMEFz, sRawz, and PDz in children with remission of asthma, i.e., early-transient symptoms, compared to children with persistent symptoms and children never developing asthma. We also investigated development of lung function in children with asthma who had specific allergen sensitization at age 13 year compared to children with asthma without sensitization. These post hoc analyses were done to investigate whether there were specific lung function trajectories in different wheeze phenotypes and whether development of atopy influenced the trajectories. The analyses were done with mixed models, evaluating the fixed effect of wheeze phenotype and sensitization status.
The effect of missing observations for longitudinal values for FEVz, MMEFz, sRawz, and PDz was investigated by multiple imputation (for details see S1 Text).
All analyses were performed in R with the package lme4 (version 1.1.14) [25] for linear mixed models as well as the package mice (version 3.3.0). All estimates are presented with 95% confidence intervals; p-values < 0.05 were considered significant. The graphical presentations of the results were made with ggplot2 (version 3.0.0), and the longitudinal curves of FEVz, MMEFz, and sRawz development were smoothed with loess regression function.
The data are available in S1 Data.
Of the 411 children enrolled at 1 month of age in COPSAC2000, follow-up to age 13 was available for 367 (89%). The number of children ever versus never developing asthma in their first 13 years of life was 97 (27%) versus 270 (73%), respectively (Fig 1). A total of 54 (56%) children remitted before age 13 years; 14 (14%) remitted but relapsed later during childhood. Median age at diagnosis was 2.0 years (IQR 1.2–5.7), median remission age was 6.2 years (IQR 4.2–7.8), and median duration of asthma was 4.0 years (IQR 2.2–5.3). The mean age of the mother at the birth of the child was 30.0 years, and 39% of the children had older siblings. There were no significant differences in baseline characteristics between included and excluded children (Table 1).
The development of FEVz from age 1 month to 13 years in children ever versus never diagnosed with asthma in this period is depicted in Fig 2A, illustrating that children developing asthma already had reduced FEVz as neonates, which persisted until age 13 years as a stable trait without progression or attenuation during childhood (z-score difference, −0.31 [95% CI −0.47; −0.15], p < 0.001). The development of MMEFz was similar to that of FEVz (Fig 2B) (z-score difference, −0.44 [95% CI −0.60; −0.27], p < 0.001). sRawz measurements from age 3 to 13 years showed increased sRaw from age 3 years among children ever versus never developing asthma, which was sustained till age 13 years without progression or attenuation (Fig 2C) (z-score difference, +0.40 [95% CI +0.24; +0.56], p < 0.001). Finally, bronchial reactivity to methacholine (PDz) was persistently increased from age 1 month until age 13 years in children ever versus never diagnosed with asthma in that period (Fig 2D) (i.e., reduced PDz; z-score difference, −0.40 [95% CI −0.58; −0.22], p < 0.001) (Table 2). A sensitivity analysis utilizing the raw, untransformed data showed similar results (Table A in S1 Text). Furthermore, transforming the data led to more normalized residuals (Fig A in S1 Text). Finally, analyses using multiple imputation also showed the same differences in lung function development in children ever versus never diagnosed with asthma; although the effect estimates were slightly attenuated, the results remained significant (Table B in S1 Text).
In order to test whether the relationship between lung function development and asthma status varied with age, we introduced the interaction term asthma status × age at measurement in the models. This analysis showed that there was no interaction between asthma status and age for lung function measurement (Table C in S1 Text), suggesting that these are predetermined traits established in early life and stable throughout childhood. An ancillary analysis of neonatal FEV0.5, FEF50, and PD15 showed significantly decreased forced flows and increased reactivity to methacholine in neonates developing asthma within their first 13 years of life compared to neonates never diagnosed with asthma (Table D in S1 Text).
Children who developed asthma during the first 13 years of life compared to children never developing asthma also had increased bronchodilator response (absolute difference: FEV1, +3% [95% CI +2%; +4%], p < 0.001; MMEF, +6% [95% CI 0%; 12%], p = 0.04; sRaw, −4% [95% CI −6%; −2%], p < 0.001), reduced post-bronchodilator z-scores for FEV1 (−0.16 [95% CI −0.34; 0.01], p = 0.07) and MMEF (−0.35 [95% CI −0.54; −0.16], p < 0.001), and increased post-bronchodilator z-score for sRaw (+0.22 [95% CI +0.06; +0.38], p = 0.007).
Reactivity to cold dry air was increased among children ever versus never having asthma (absolute difference: FEV1, −4% [95% CI −7%; −1%], p = 0.007; MMEF, −8% [95% CI −15%; −1%], p = 0.03; and sRaw, +9% [95% CI +5%; +13%], p < 0.001). Additionally, children ever versus never diagnosed with asthma experienced a significantly larger drop in FEV1 after exercise challenge at age 7 years (absolute difference: FEV1, −4% [95% CI −7%; −1%], p = 0.02) (Table 2).
Children who developed asthma showed significantly increased airway obstruction and bronchial reactivity before symptom debut compared to children never developing asthma (FEVz, −0.29 [95% CI −0.49; −0.09], p = 0.004; MMEFz, −0.40 [95% CI −0.60; −0.20], p < 0.001; sRawz, +0.33 [95% CI +0.10; +0.57], p = 0.006; and PDz, −0.35 [95% CI −0.59; −0.12], p = 0.003).
The airway obstruction and increased bronchial reactivity in children developing asthma were independent of disease duration (change per year: FEVz, −0.01 [95% CI −0.07; +0.06], p = 0.82; MMEFz, −0.01 [95% CI −0.05; +0.03], p = 0.68; sRawz, −0.02 [95% CI −0.06; +0.03], p = 0.43; and PDz, −0.04 [95% CI −0.10; +0.03], p = 0.27). Furthermore, the airway obstruction and increased bronchial reactivity did not attenuate after remission (change per year: FEVz, +0.02 [95% CI −0.03; +0.06], p = 0.44; MMEFz, −0.00 [95% CI −0.04; +0.04], p = 0.98; sRawz, +0.01 [95% CI −0.03; +0.06], p = 0.54; and PDz, +0.00 [95% CI −0.07; +0.08], p = 0.92) (Table 3).
Among the 97 children who fulfilled the diagnostic criteria for asthma, 54 children had a transient phenotype and remitted before age 13 years, whereas the remaining 43 children had persistent asthmatic symptoms requiring continued ICS by age 13 years. Lung function development in children with transient asthmatic symptoms compared to children never diagnosed with asthma showed reduced FEVz (z-score difference, −0.30 [95% CI −0.51; −0.08], p = 0.008) and reduced MMEFZ (−0.36 [95% CI −0.59; −0.14], p = 0.002) from age 1 month to 13 years, and increased sRawz from age 3 years to 13 years (+0.27 [95% CI +0.06; +0.48], p = 0.01). PDz was reduced among transiently asthmatic versus healthy children, i.e., indicating increased airway reactivity, but this difference was not significant (−0.15 [95% CI −0.39; +0.09], p = 0.23).
There were no differences in development of FEVz, MMEFz, or sRawz among children with transient versus persistent asthmatic symptoms, whereas children with transient compared to persistent symptoms had less airway reactivity to methacholine, i.e., higher PDz (+0.48 [95% CI +0.15; +0.80], p = 0.004) (Fig 3; Table E in S1 Text).
At age 13 years, 157 (52%) of 304 tested children had allergic sensitization towards 1 or more of the tested allergens by either a positive skin prick test and/or elevated blood level of specific IgE. Sensitization was diagnosed in 109 (49%) of 223 children never developing asthma, 15 (36%) of 42 children with transient asthmatic symptoms, and in 33 (85%) of 39 children with persistent asthmatic symptoms. Thus, sensitization was more prevalent in children with persistent asthma versus children never developing asthma (chi-squared test, p < 0.001) and in children with persistent versus transient asthmatic symptoms (chi-squared test, p < 0.001).
There were no differences in development of FEVz, MMEFz, or sRawz among children who developed asthma with versus without allergen-specific sensitization to any of the tested allergens, whereas there was a pattern suggesting increased airway reactivity to methacholine among children with asthma who were sensitized by age 13 years, i.e., lower PDz (−0.34 [95% CI −0.70; +0.02], p = 0.06) (Table E in S1 Text). Stratifying the analyses on type of allergen as (1) house dust mite, (2) furred animal (dog, cat, horse), or (3) pollen (birch, grass, mugwort) showed similar results except that children with asthma and sensitization to pollen had significantly lower PDz (Table E in S1 Text).
Children developing asthma at any time during the first 13 years of life had airway obstruction and increased bronchial reactivity that was present at age 1 month without worsening with increased symptom duration and without improvement after remission. This suggests that these are inherent and stable traits not caused by inflammation during symptomatic periods but rather that predispose the child to develop asthmatic symptoms, exaggerated hyperreactivity, and intermittent airway obstruction.
The primary strength of this study is the thorough single-center close clinical surveillance of a cohort with a follow-up of 89% till age 13 years. The children were seen for clinical evaluation and lung function assessment at the age of 1 month and thereafter at least half-yearly until age 7 years and again at 13 years. Until age 7 years, the parents filled daily diary cards describing the child’s troublesome lung symptoms, thereby diminishing recall bias [26]. Asthma was diagnosed and treated by the COPSAC pediatricians only according to a predetermined algorithm with a conservative definition based on symptom load and response to and relapse after a standard dose and period of ICS [22], thus avoiding diagnostic heterogeneity. Children with asthma were seen for scheduled 3-monthly visits and for unscheduled acute visits during worsening of symptoms and continued daily symptom diary recording beyond age 7 years if symptoms persisted.
The diagnostic algorithm for asthma, which was based on quantitative symptom assessments verified at scheduled and acute care visits to the research unit and response to ICS treatment, could identify asthmatic children with transient, recurrent viral-induced wheeze and children with a persistent phenotype with or without atopy. This is apparent as approximately half of the children diagnosed according to the algorithm had transient symptoms and remitted before age 13 years. The median age at diagnosis was 2 years, implying that a large proportion of the cases were children whose initial episode of wheezing was related to an infection, but half of these children had ICS-dependent asthma by age 13 years. Importantly, not all children with asthma will respond to ICS initially, and because lack of ICS response cannot exclude asthma, we excluded other chronic lung diseases by means of chest X-ray and a sweat chloride test for cystic fibrosis.
A significant advantage of the study is the longitudinal repetitive lung function assessments performed at 11 time points during childhood by means of spirometry and plethysmography as well as bronchial reactivity to cold dry air, exercise, and methacholine challenge in accordance with recognized guidelines [17–20,27]. We did most measurements in the age range from 3 to 7 years; therefore, the shape of the curves is likely to be influenced by the readings at 1 month and 13 years. Still, the birth cohort design assured that lung function was assessed (1) before the debut of symptoms and asthma diagnosis, (2) repeatedly every 6 months in children with and without asthma, and (3) after remission in children outgrowing their asthma. This allowed us to scrutinize how lung function trajectories develop in children with asthma during childhood and how they are affected by duration and remission of symptoms. Our interaction analyses showed no evidence of age influence on the difference in lung function development in children with versus without asthma, but the low numbers in our cohort may preclude us from detecting subtle differences with age.
It is a strength of our study that neonatal spirometry was performed using the raised volume rapid thoracoabdominal compression technique, providing volume-anchored measurements [10], which makes its results comparable with the forced volumes obtained by spirometry later in childhood. This is an important difference compared to previous studies of neonatal lung function in relation to asthma and lung function development during childhood, which used non-volume-anchored methods [12–14,28]. Assessment of tidal breathing patterns is one such method, which is less sensitive, has high intra-individual variability [29,30], and yields results not associated with volume-anchored measurements later in childhood [13].
One previous study of 95 children investigated airway reactivity from age 1 month throughout childhood, but used a non-volume-anchored assessment of histamine challenge that did not correlate with the FEV1-based histamine challenge results at school age, highlighting the same problem of using different approaches to determine neonatal lung function and lung function later in childhood [12,31,32].
The limitations of our study are the relatively small sample size, the limited ethnic variation, and the high-risk nature of the COPSAC2000 cohort, which hamper the generalizability of the findings. However, mothers without asthma might be reluctant to have their neonates investigated so thoroughly by lung function testing during sedation. Children of asthmatic mothers are a priori at higher risk of asthma and may experience more severe symptoms and have poorer lung function. However, the children not developing asthma also had asthmatic mothers, and we do not expect the relationship between symptoms and lung function development to be different due to asthma predisposition.
We observed z-score differences in lung function trajectories with magnitudes between 0.3 and 0.4 in children who developed asthma compared to children not developing asthma. This overall difference in lung function development is not in the abnormal range, which would be less than −2 z-scores, but it should be kept in mind that we excluded measurements obtained during exacerbations, which may have diminished the overall difference between children with versus without asthma.
We found that airway obstruction and bronchial hyperreactivity related to asthma are fixed traits from age 1 month to age 13 years, without further deterioration from disease duration or improvement after symptom remission. These findings have important implications for our understanding of the underlying pathology, indicating that the symptomatic phases of childhood asthma are not causing the airway obstruction and bronchial hyperreactivity typical of asthma. Instead, we propose that airway obstruction and bronchial hyperreactivity are inherent traits that increase the risk of developing airway inflammation and asthmatic symptoms during childhood. This may explain the lack of effect from early intervention with ICS, which was unable to change the natural course of asthma with respect to both symptom burden and lung function development [2–6].
Our findings are consistent with a follow-up study of children diagnosed with asthma at age 9 years showing that individuals still experiencing symptoms at age 26 years had stable airway obstruction and bronchial reactivity without any progression from the assessments at age 9 years [33]. In addition, a 22-year follow-up of a cohort of 40-year-old adults also showed that reduced lung function at cohort inception remained stable over time [34]. Together with the present study, this suggests that airflow obstruction and bronchial hyperreactivity are inherent and stable deficits measurable already at age 1 month. Thus, even though lung function fluctuates with symptom load and anti-inflammatory treatment over time [35], the lung function trajectory is a static characteristic that might contribute to asthmatic symptoms, exaggerated bronchial reactivity, and intermittent airway obstruction.
Our findings contrast with those of a recent study investigating the development of lung function in children with asthma into adulthood [36], which suggested that the majority of children with asthma (75%) had an abnormal pattern of reduced growth and/or early decline in lung function, i.e., not a stable trait. This difference may be due to the fact that the study only enrolled 5- to 12-year-old children with uncontrolled chronic asthma (symptoms >2 days/week) and severe bronchial hyperreactivity at baseline (>20% drop in FEV1 after methacholine challenge), resembling a minority population of the most severely affected children. Even more important, the participants with reduced growth in lung function already had significantly lower lung function at enrollment, suggesting that their lung function trajectories may be a static characteristic established in early childhood. In line with our findings, another recent study demonstrated increased airway resistance among children with persistent wheeze, which tracked from early to late childhood [37].
We suggest that the propensity to develop asthmatic symptoms due to intrinsic and extrinsic factors is increased by underlying inherent airflow obstruction and bronchial hyperreactivity, which are stable traits without progression due to triggering factors or increased symptom duration. On the other hand, we and other groups have shown that allergic sensitization after the age of 6 years, but not earlier in childhood, is associated with an increased risk of developing asthma [38,39], with different risks for different allergens [40]. This raises the possibility that development of allergy may have a deteriorating effect on the lung function trajectory. However, our data in general argue against such a hypothesis as development of airflow obstruction as measured by FEVz, MMEFz, and sRawz among children with asthma was independent of allergic sensitization at age 13 years, whilst there was a non-significant suggestion of increased airway reactivity among sensitized asthmatic individuals that was significant in the subgroup with sensitization to pollens, which were the most common allergens in our cohort. These findings are in line with a study of 1,719 15-year-old participants in the German GINIplus birth cohort, which did not show any associations between sensitization and spirometric indices in children with asthma [41], but are in contrast to the American TENOR study of 1,261 children aged 6 to 17 years with severe or difficult-to-treat asthma, which showed an association between increased airflow limitation and higher IgE levels [42]. Importantly, both these studies were cross-sectional analyses; a longitudinal study showed that development of lung function from age 1 month until 11 years was unaffected by atopy [13].
Interestingly, we found that all children who fulfilled the diagnostic criteria for asthma compared to children never diagnosed with asthma had increased airway obstruction, i.e., reduced FEVz and MMEFz (age 1 month to 13 years) and increased sRawz (age 3 years to 13 years), irrespective of remission of symptoms or not. Thus, fixed airway obstruction was apparent in children with both a transient and a persistent phenotype and without differences in development of FEVz, MMEFz, and sRawz between the phenotypes, whereas bronchial reactivity to methacholine (PDz) was more pronounced in children with persistent as opposed to transient symptoms. This suggests that airway reactivity in children with transient asthmatic symptoms declines with airway growth through childhood, whereas reduced forced flows and increased airway resistance are fixed traits among children with the transient phenotype, irrespective of airway growth. These findings align with findings from longitudinal lung function measurements from birth until age 16 years in the Tucson Children’s Respiratory Study [43] and from age 3 years until age 11 years in the Manchester Asthma and Allergy Study [37], showing that children with persistent wheeze compared to never wheeze had increased and fixed airway obstruction, which was also apparent for children with early-transient viral-induced wheeze even though they outgrew their symptoms. Furthermore, a smaller Australian study from Perth also showed a suggestion of reduced lung function from birth until age 11 years in children with persistent wheeze, whereas no reduction of lung function was observed for other phenotypes of wheeze [12]. Overall, these studies together with our study suggest a common underlying lung function deficit driving early-transient, typically viral-induced symptoms and persistent symptoms, typically triggered by allergens, pollutants, or exercise.
In this study we observed that airway obstruction and increased bronchial reactivity associated with childhood asthma were established already at age 1 month, without further deterioration into early adolescence and without relation to asthma symptom duration or remission. This suggests that these chronic deficits are not the result of childhood asthma, but may instead contribute to asthma pathogenesis by increasing the risk of symptoms, exaggerated hyperreactivity, and airway obstruction, implying that preventive measures for improved lung health should focus on the pre- or perinatal period.
|
10.1371/journal.ppat.1004316 | Structure of CfaA Suggests a New Family of Chaperones Essential for Assembly of Class 5 Fimbriae | Adhesive pili on the surface of pathogenic bacteria comprise polymerized pilin subunits and are essential for initiation of infections. Pili assembled by the chaperone-usher pathway (CUP) require periplasmic chaperones that assist subunit folding, maintain their stability, and escort them to the site of bioassembly. Until now, CUP chaperones have been classified into two families, FGS and FGL, based on the short and long length of the subunit-interacting loops between its F1 and G1 β-strands, respectively. CfaA is the chaperone for assembly of colonization factor antigen I (CFA/I) pili of enterotoxigenic E. coli (ETEC), a cause of diarrhea in travelers and young children. Here, the crystal structure of CfaA along with sequence analyses reveals some unique structural and functional features, leading us to propose a separate family for CfaA and closely related chaperones. Phenotypic changes resulting from mutations in regions unique to this chaperone family provide insight into their function, consistent with involvement of these regions in interactions with cognate subunits and usher proteins during pilus assembly.
| Bacterial infection begins with microbial adhesion to host cells. For gram-negative bacteria, adhesion is often mediated by pili, proteinaceous polymers that protrude from the bacterial surface and recognize host receptors. During assembly, each pilus protein subunit is assisted in folding by a chaperone that shuttles the subunit to an outer membrane usher complex, which serves as assembly platform. There, the chaperone transfers its subunit cargo into the growing pilus polymer, which protrudes out the usher pore. Here, we present the crystal structure of CfaA, the chaperone protein of the CFA/I pilus. The CFA/I pilus is the archetypal colonization factor (CF) for enterotoxigenic Escherichia coli, a major cause of life-threatening, dehydrating diarrhea in young children of low-income countries and in travelers to these regions. This structure reveals unique features that allow us to define a new class of chaperones that assist pilus assembly in bacteria. Probing these unique features with site-direct mutagenesis, we were able to gain new insight into the mechanism of pilus assembly.
| Bacteria assemble filamentous projections on their surface to facilitate adhesion to other bacteria, eukaryotic cells and abiotic substrates. These macromolecular organelles are composed of protein polymers and can appear as regular, rod-like pili (or fimbriae), irregular, thin fibrils or indistinct structures. In gram-negative bacteria, many of these organelles are assembled by the chaperone-usher pathway (CUP). The three essential components of this pathway are one or more pilin subunits capable of polymerization, a periplasmic chaperone that catalyzes proper folding of the pilin subunits and shuttles them to the outer membrane for assembly, and an outer membrane usher that orchestrates ordered tip-to-base polymerization [1].
Extensive work on P pili and type 1 fimbriae from uropathogenic E. coli (UPEC) and related CUP fimbriae has yielded well-founded models of pilus bioassembly by the CUP [1]. Crystal structures of their evolutionarily related periplasmic chaperones, PapD and FimC, respectively, reveal two immunoglobulin (Ig)-like domains arranged in a boomerang shape [2]–[4]. Upon export of a nascent pilin subunit into the periplasm, a β-strand in the N-terminal domain of the chaperone fills a hydrophobic cleft in the pilin to provide the missing G strand in an otherwise incomplete Ig-like pilin subunit, a mechanism called donor-strand complementation (DSC) [3], [4]. The chaperone-pilin complex docks with the outer membrane usher and inserts a supernumerary N-terminal pilin β-strand into the hydrophobic groove of a foregoing pilin, thereby displacing the chaperone G1 strand from the latter by a ‘zip-in, zip-out’ process called donor strand exchange (DSE) [5]. Ordered iterations of this cycle drive pilus elongation and extrusion from the bacterial surface through the usher pore.
All chaperones of the CUP share certain structural motifs and highly conserved residues that are vital to its chaperone and transfer functions [6], [7]. This chaperone superfamily has been differentiated by sequence analysis into two subgroups, in which the loop between the F1 and G1 strand of the chaperone is either short (FGS) or long (FGL) [6]. The FGS family chaperones have a characteristic short subunit-interacting loop (on average 13 residues) between β-strands G1 and F1 in the central conserved β-sheet and are confined exclusively to the bioassembly of rod-like pili such as P pili or type 1 pili. By contrast, those in the FGL family feature a long interacting loop (on average 24 residues) between β-strands G1 and F1 and take part only in the assembly of atypical filaments, such as the F1 antigen of Yersinia pestis. The exact nature of chaperone-pilin interaction differs for these two groups, although both conform to the common mechanism of donor-strand complementation and exchange.
Another family of regular rod-like bacterial filaments, designated as Class 5 fimbriae [8] includes eight members that are produced by enterotoxigenic Escherichia coli (ETEC), a predominant cause of dehydrating diarrhea in travelers and young children in low-income countries. In studies of one such ETEC fimbria, CS1, the essential role of each of the four proteins encoded by the CS1 operon was experimentally defined [9]–[13]. These are a minor subunit (CooD) required for initiation of fimbrial assembly and adhesion, a major subunit (CooB) that is the primary antigenic determinant, a periplasmic chaperone (CooA) that stabilizes nascent structural subunits, and an outer membrane protein (CooC) presumed to serve an usher-like function. Thus, the CS1 bioassembly components have functional counterparts in the CUP, but none share any primary sequence similarity. This prompted speculation that Class 5 fimbriae evolved along a convergent evolutionary path [10] and evoked its designation as the ‘alternate’ chaperone pathway (ACP) [14]. Although none of the chaperones for Class 5 fimbriae were included in the study that led to the classification of FGL and FGS chaperones [6], the rod-like morphology of Class 5 fimbriae has presumptively suggested the association of all chaperones in this class with the family of FGS chaperones [15].
In more recent studies of CFA/I fimbriae, the archetypal Class 5 ETEC fimbria, the crystal structures of its minor (CfaE) and major (CfaB) structural components were solved [16], [17]. The lack of primary sequence similarity notwithstanding, each of these subunits generally conforms to the Ig-like domain structure of corresponding subunits of P pili and type 1 fimbriae. These and other studies clearly implicate the mechanism of donor-strand complementation and exchange in bioassembly of CFA/I fimbriae, suggesting that Class 5 fimbriae may actually have diverged from CUP fimbriae in the very distant past [18]. This view is substantiated in a more recent phylogenetic analysis of fimbrial usher protein sequences, which classified all Class 5 pili into a separate group or α clade that diverged from other CUP clades [19]. Interestingly, while this usher-based phylogeny categorizes all pili with FGL chaperones into a single clade (γ3 clade), those with FGS chaperones were grouped into several distinct clades (β-, γ1-, γ2-, γ4-, κ-, and π-fimbriae) that are not more closely related to each other than to the FGL systems, calling into question whether chaperones of rod-shaped Class 5 pili should all be placed into the FGS family.
In this report, we present the crystal structure of the CFA/I pilus chaperone protein CfaA. Structure-based sequence alignment indicates that chaperone proteins of Class 5 pili constitute a family that is distinct from the FGS and FGL families. Mutations in sequence motifs that are unique to the Class 5 chaperones result in measurable functional changes of CfaA consistent with our hypothesis and further suggest that the unique features in Class 5 pilus chaperones dictate their interactions with cognate subunits and usher proteins.
The full-length CfaA chaperone (residues 1–218) was expressed with a C-terminal hexahistidine tag and recovered from the periplasmic fraction post-cleavage of its 19-residue signal peptide. Mature CfaA was purified to homogeneity and crystallized. CfaA crystals diffracted X-rays well, revealing the symmetry of space group C2. Initial crystallographic phases were obtained experimentally by the method of multiple isomorphous replacement coupled with anomalous scattering (MIR/AS) using platinum and lead derivative data sets with an overall figure of merit of 0.48 (Table 1). The final atomic models were refined using either native or derivative data sets with the best resolution to 1.8 Å.
As with all CUP chaperone structures previously described [2], [20]–[24], the overall structure of CfaA adopts a boomerang shape. The N- (1–129) and C-terminal (130–218) domains form two lobes angled at 121 degrees, as measured along the longest inertial vectors for the two domains, with a deep interlobe cleft (Fig. 1A). Based on structural comparison of other chaperones in the presence and absence of bound subunit, this spatial arrangement of the two lobes is reportedly rigid [3], [5], [25], [26]. We predict that such structural rigidity is preserved in the CfaA structure due to the extensive interactions that exist between the two domains, including either water-mediated or direct hydrogen bonding interactions and van der Waals contacts represented by a buried interdomain surface area of 1191 Å2 (Fig. 1B and Tables S1 and S2).
Each domain is represented by a seven-stranded β-barrel with a typical immunoglobulin (Ig) fold (Figs. 1A and 1C). Despite an overall low average temperature factor (B factor) of 40.8 Å2, the N-terminal domain displays a significantly lower average B factor (26.8 Å2) than the C-terminal domain (59.9 Å2). This is due to the self-dimerization or self-capping of the N-terminal domain with the same domain of a neighboring molecule in the crystal (see below). Discontinuous electron densities were observed for residues 98–114 of the loop between the F1 and G1 strands of the N-terminal domain and for the loop (residue 203–209) between the F2 and G2 strands of the C-terminal domain, which were similarly observed in isolated PapD and SafB chaperone structures [5], [7].
In the absence of bound pilins, chaperone proteins have been shown to dimerize in order to protect their interactive surface from nonspecific aggregation. This has been called self-capping oligomerization in PapD and Caf1M chaperones [7], [23]. Although there is one CfaA molecule present in a crystallographic asymmetric unit, application of the crystallographic two-fold symmetry generates a dimer that is self-capped by two adjoining G1 strands, presenting a continuous β-sheet between the two subunits (Fig. S1).
CfaA and other chaperones of known ETEC Class 5 pili, all classified in the usher protein α clade [19], share high polypeptide sequence identity within this class (≥26%). By contrast, this group shares very low identities (≤15%, Table S3) with CUP chaperones of other fimbrial families, making accurate sequence alignment challenging. Availability of the atomic structures of chaperones from different clades enabled a structure-based sequence comparison. These structures include FaeE in κ [27], FimC and SfaE in γ1 [21], Caf1M and SafB in γ3 [5], [20], [23], CupB in γ4 [22], and PapD in π clade [2]. Using the CfaA structure reported here, a structure-based sequence alignment of Class 5 fimbrial chaperones with those of the other families (Figs. 2A and 3) reveals greater conservation in the N-terminal domain, which serves as the subunit-binding region and participates in subsequent donor-strand exchange, than the C-terminal domain, which is thought to be responsible for usher recognition [20].
In CfaA and all other Class 5 chaperones, two structural features are shared with the FGL chaperones (Table 2). First, the F1–G1 subunit-interacting loop is long, consisting on average of 20 residues, distinguishing it from the much shorter loops of the FGS chaperones (Fig. 3). Second, the subunit-binding motif immediately preceding the G1 strand features at least four candidate subunit-interacting hydrophobic residues (L/V114, V/F116, I/L118, Y/W120) rather than three in the FGS family (Fig. 3). This block of alternating hydrophobic-hydrophilic residues is, however, shifted by two residues towards the C-terminus in comparison to both FGL and FGS family chaperones. It is remarkable that Class 5 chaperones also share two features in common with FGS chaperones (Table 2). First, like FGS chaperones the Class 5 chaperones lack an N-terminal extension preceding the N-terminal A1 strand that is essential for subunit binding by FGL chaperones [28] (Fig. 3). Second, both the FGS and Class 5 chaperones lack the disulfide bridge that stabilizes the F1–G1 loop, which is conserved in the FGL chaperones (Fig. 3) and shown to be critical to formation of the FGL chaperone-subunit complex [29], [30].
Importantly, the Class 5 chaperones also possess several structural features that are absent in both the FGL and FGS chaperones (Table 2). They contain an insertion (D1′ insertion) that includes the D1′ β-strand and is rich in acidic residues (E45, E46, E48, D50 and D55) (Figs. 1A and inset), which form several pairs of salt bridges with contiguous basic residues (K36, R90 and R185). All Class 5 chaperones contain a long, very hydrophilic insertion in the C2–D2′ loop (K164 to N171, C2–D2′ insertion) that is stabilized by a unique disulfide bond (C163–C172) (Figs. 1A and 3). The linker between N- and C-terminal domains of Class 5 chaperones is considerably shorter than those for FGL and FGS chaperones (Fig. 3). In the Class 5 chaperones, there is no readily apparent proxy for a conserved N-terminal basic residue in the FGL (e.g., R20 in Caf1M) and FGS (e.g., R8 in PapD) chaperones that is required for anchoring of the cognate pilin subunit through interaction with its C-terminus [6], [31]. The side chain of the corresponding K9 residue in CfaA points away from the chaperone cleft, disfavoring potential contact with a bound subunit (Fig. 2C). In the two members of Class 5 chaperones not from ETEC, CblA and TcfA, the equivalent lysine residue is absent. Evidence is provided below to suggest that this anchoring function is served by R154 in CfaA, a residue that is conserved in all Class 5 and absent in FGS and FGL chaperones (Fig. 3). Given these multiple distinctions, we propose that the Class 5 chaperones be placed into a separate family distinct from the FGL or FGS chaperones.
Structure-based sequence alignment revealed a number of distinct features of Class 5 chaperones. To investigate the role of each of these unique structural attributes in subunit refolding, stabilization, escort function and usher interaction, mutations were introduced into each region with subsequent phenotypic analysis of the modified CfaA chaperone. While the ability of CfaA to stabilize the CfaB major subunit in an assembly-competent state was assessed using a pull-down assay and expressed as CfaA/CfaB ratio (Fig. 4), the assay that measures the amount of surface pili and the time-dependent mannose-resistant hemagglutination (MRHA) assay were used to reveal impairment of CfaA function in pilus assembly with respect to subunit transport and usher interaction (Fig. 5A). Accumulation of surface pili was determined after 30 minutes of induction by comparison of the amount of pili extracted from the bacterial surface by heat treatment (piliation at 30 minutes, p30) followed by SDS PAGE and anti-CfaB Western blot analysis (Fig. 5). As a control for periplasmic leakage of CfaB during heat extraction, anti-CfaA Western blots were also performed on these preparations with nominal detection of the periplasmic chaperone (data not shown). For recombinant E. coli containing the CFA/I operon with a native or modified CfaA gene, the functional pilus assembly rate (fprate) was determined by induction of CFA/I expression and performance of a semiquantitative MRHA assay at 15-minute intervals over an hour (Fig. 5).
Between F1 and G1 β-strands of all chaperones, there is a stretch of peptide with alternating hydrophobic-hydrophilic residues (Fig. 3). The FGS and FGL chaperones feature three and five hydrophobic residues, respectively. Each of these hydrophobic residues is assigned a position as P1, P2, P3, P4 or P5 based on its interaction site on the pilin subunit [32] (Figs. 2B and 3). Like FGL, Class 5 chaperones are predicted to have a minimum of four hydrophobic residues in the donor strand, but their positions are shifted compared to FGL chaperones based on the structure-based sequence alignment (Figs. 2A, 2B and 3). In keeping with the original convention [32], the hydrophobic residues L114, V116 and I118 would correspond to positions P3, P2, and P1, respectively, based on the alignment, leaving no assignment for Y120. Thus, we propose to assign Y120 the P0 position, which is a site unique to Class 5 chaperones as it relates to subunit interaction (see below). It should be noted that there is a hydrophilic residue (T112) at the P4 position, and a hydrophobic residue (L110) at the P5 position (Fig. 3). These two positions are not all conserved beyond the chaperones in the 5a and 5b subclasses (Fig. 3).
To assess the contribution of each of these residues to subunit binding, the four pilin-interacting, hydrophobic residues (L114, V116, I118 and Y120) in the donor strand preceding the G1 β-strand were each modified to alanine. Additionally, a T112A mutation was also made. Except for T112 at the P4 position, individual alanine mutations of all hydrophobic residues led to a marked reduction in the CfaA/CfaB ratio from 8.7% to 54.0% (Figs. 4B and 4C), indicating the importance of each of these residues in forming a stable complex. The P0, P1, and P2 CfaA mutations (i.e., Y120A, I118A, and V116A, respectively) were each also associated with reduced p30 and fprate in comparison with native CfaA with most dramatic reduction for the Y120A mutant, indicating impaired bacterial surface piliation (Figs. 5B and 5C). These results are consistent with the pull-down experiments and confirm the mechanism by which the subunit maintaining its competency in assembly is largely by the hydrophobic interactions between the donor strand from chaperone and the binding groove of the subunit. The L114A substitution in CfaA at the P3 position resulted in a clear reduction in the CfaA/CfaB ratio (Figs. 4B and 4C), but no detectable reduction in bacterial fimbriation as determined by p30 and fprate experiments, respectively (Fig. 5E), suggesting that a change at P3 alone is not rate limiting with respect to downstream pilus assembly. The T112A substitution at the P4 position in CfaA did not decrease the CfaA/CfaB ratio (Figs. 4B and 4C), but was associated with a marked decrease in p30 piliation and no detection of MRHA activity over time (Fig. 5B), suggesting that this mutation negatively impacts CFA/I assembly without apparent effect on major subunit binding.
The Class 5 chaperones feature two distinct sequence insertions: the D1′ insertion in the N-terminal lobe and the C2–D2′ insertion in the C-terminal lobe (Figs. 1A and 3, Table 2). The C2–D2′ insertion is additionally stabilized by a conserved disulfide bond between C163 and C172 (Fig. 1A). To probe function of the C2–D2′ insertion, alanine mutations were introduced to a block of eight residues (from K164 to N171) in the insertion loop. Moreover, the class-specific disulfide bond (C163 and C172) connecting the ends of the loop was also changed by mutating the two cysteine residues to serine residues. Both mutants showed similar decreases in CfaA/CfaB ratio of 54.7% and 55.2%, respectively, for K164-N171A and C163S/C172S (Fig. 4B), suggesting that neither of these motifs is critical to CfaA's ability to stabilize CfaB subunit. Correspondingly, the two mutants by fprate showed a right shift wherein MRHA activity was lower than wild-type CfaA at 30 minutes with catch-up to wild-type CfaA levels by 45–60 minutes (Fig. 5D), even though they displayed different p30 piliation levels. These results suggest a role for the C2–D2′ insertion, especially the disulfide linkage, in either the upstream subunit interaction or the down stream pilus assembly or both.
The introduction of three mutations in the middle of the acidic D1′ insertion (T44A/E45A/E46A) did not alter the CfaA/CfaB ratio as compared to the wild type (Fig. 4), but did affect p30 piliation as well as fprate levels (Fig. 5C). Thus, the unique D1′ insertion of CfaA plays a role in pilus assembly.
Structure-based sequence alignment indicated that K9 of CfaA is offset by one residue from the conserved N-terminal arginine in the FGL and FGS family chaperones (Fig. 3). Structure superposition between PapD and CfaA seems to suggest that the function of this conserved arginine in FGL and FGS chaperones is replaced by R154 in CfaA (Figs. 1B and 2C). In addition to the N-terminal arginine residue, a conserved lysine residue in the G1 strand of FGL and FGS chaperones (K112 in PapD and K139 in Caf1M) was shown to assist subunit binding [6], [31], [33]. The equivalent of this conserved lysine residue in CfaA is R125, which interestingly is also offset in the sequence alignment (Figs. 1B, 2A, 2B and 3). The conformation of these residues appears to be stabilized by salt bridges to another conserved glutamate residue (E86 in CfaA, E83 in PapD and E92 in Caf1M, Figs. 2 and 3). The offset in sequence alignment and lack of conservation in CblB and TcfA sequences indicate that K9 in CfaA may not perform the same function as anchoring residues for subunit binding, as demonstrated experimentally for FGL and FGS chaperones. To verify this hypothesis, a K9A mutation was introduced into CfaA, which had no apparent effect on the stability of the CfaA/CfaB complex (Figs. 4B and 4C). We also made an R125A mutant, which resulted in a decrease in the CfaA/CfaB ratio by the pull-down assay (Figs. 4B and 4C). Both mutations were associated with lowed p30 piliation level, while K9A was also associated with a delayed fprate (Fig. 5D), suggesting some degree of impedance of pilus bioassembly with each of these mutations.
Structure superposition between PapD and CfaA suggested that the function of the N-terminal conserved arginine in FGL and FGS chaperones may be replaced by R154 in CfaA, which is only conserved in Class 5 chaperones (Figs. 2C and 3) and is stabilized by residue E86 via a salt bridge (∼2.7 Å). In fact, E86 is conserved in all families of chaperones (Fig. 3). To confirm this hypothesis, alanine substitutions to R154 and E86 were introduced. Both mutations were associated with a reduction in the ability of CfaA to stabilize CfaB (Figs. 4B and 4C), while the only apparent defeat in piliation associated with either of these mutations was a lower p30 piliation level for E86A (Figs. 5D and 5E). The divergent findings in the binding and piliation assays may be consistent with the interpretation that the formation of CfaA/CfaB complex is a process that is not coupled tightly to that of assembly.
Chaperone-subunit complexes were among the first fimbrial components for which crystal structures were determined [3], [5], [26], [27], [32]–[34]. These structures elucidated the donor-strand complementation (DSC) and exchange (DSE) mechanism, integral to the subunit stabilization and pilus assembly of CUP pili. One of the most important, general features of these chaperones is the essential interactions between the G1 strand and the hydrophobic groove of pilus subunits [26]. Beyond the observed commonalities, sequence and structural differences have been recognized for chaperones of different pili, leading to the subdivision of FGL and FGS family chaperones [6], [29]. It was also recognized that FGL chaperones were found only in pili having thin, flexible morphology, whereas FGS chaperones appear to only assist assembly of rod-like pili [15], [35], [36]. In this work, the crystal structure was determined for the CfaA chaperone of CFA/I pili, which represents the first atomic resolution chaperone structure for the Class 5 pilus family. On the basis of structure-based sequence alignment with FGS and FGL chaperones, Class 5 chaperones, as represented by CfaA, display unique features distinguishing them from both FGL and FGS families. Given the historical assignment of Class 5 pili to the alternate-chaperone pathway for assembly, we propose the designation of FGA (F1–G1 Alternate) chaperones for this family.
FGA chaperones bear certain similarities to both FGL and FGS chaperones, but also possess several structural and functional features that make them unique. Similar to FGL chaperones, FGA chaperones have a long subunit-interacting loop harboring four hydrophobic residues for subunit interaction. FGA and FGS chaperones both lack an N-terminal extension and the disulfide bridge that stabilize the F1–G1 loop for FGL chaperones. Based on structure-based sequence alignment and the mutational analyses presented herein, there are unique structural features that are also important for FGA chaperone function (Table 2). First, the four subunit-interacting hydrophobic residues in the F1–G1 loop, designated as P0–P3, are shifted in position by two residues towards the C-terminus (Figs. 3 and 6). Second, CfaA appears to use a different set of residues (R154 and E86) to anchor the subunit into the binding cleft. Third, it features two insertions, a D1′ insertion in the N-terminal domain and a C2–D2′ insertion stabilized by a disulfide bridge in the C-terminal domain, which may play a role either in pilus bioassembly or in major pilin interaction (Figs. 4 & 5).
Supporting evidence for the designation of FGA family chaperones also comes from the sequence alignment from two FGA chaperones that are not part of Class 5 ETEC (Fig. 3). One is CblA from Cbl pili of Burkholderia cenocepacia and the other is TcfA of Tcf pili from Salmonella enterica. In these two sequences not only are all the unique features to FGA chaperones preserved but also the N-terminal SK motif is no longer present, whose function is, as proposed, replaced by R154 that indeed is conserved only in FGA family. Furthermore, the P0 position features an aromatic tryptophan residue for these two members of the FGA chaperone. Phylogenetic analyses of the usher proteins for CUP fimbriae found that all Class 5 pili fall into a single α-clade [19], corroborating their prior classification into the distinct group of pili assembled by the alternate chaperone pathway based on their genetically distinct chaperones [14].
In this work, mutations were introduced to residues and motifs of the CfaA chaperone, which are unique to the FGA family chaperones based on the structure-based sequence alignment. The effects of these mutations on CfaA function as it relates to stabilizing the major pilin subunit CfaB in an assembly-competent state and to pilus assembly were examined (Figs. 4 and 5, Table 3). Based on the pull-down assay, mutations in CfaA either dramatically reduced the CfaA/CfaB ratio (V116A, I118A and Y120A), showed no effect (for example K9A, T44A/E45A/E46A and T112A) or displayed moderate reduction in the CfaA/CfaB ratio (Figs. 4B and 4C). On the basis of their effects to pilus assembly, these mutations can also be categorized into four groups. One group contains mutations (T112A and Y120A) that showed little piliation and no detectable MRHA (Fig. 5B), while a second group (L114A and R154A) showed no effect in both (Fig. 5E). A third group (K9A, T44-E46A, V116A, and I118A) displayed a reduced p30 and, correspondingly, a significant delay in pilus assembly when compared to the wild-type CfaA (Fig. 5C). Finally, mutants (K164-N171A, R125A, C163/C172A, and E86A) in the fourth group exhibted equivocal results of mismatching p30 and fprate (Fig. 5D).
It should be noted that the pull-down assay (CfaA/CfaB ratio) measures only the stability of the CfaA/CfaB complex in solution; it does not provide information on how CfaA or its mutants interact with CfaE, the minor pilin subunit, nor CfaC, the usher. Piliation by p30 measures the amount of CfaB on the bacterial surface but is unable to differentiate between the wound and unwound forms of CFA/I pili [17]. The time-dependent MRHA (fprate) estimates the level of functional surface pili semiquantitatively. Not surprisingly, effects demonstrated by the pull-down and piliation assays are not necessarily correlated, suggesting the following possibilities: (1) Mutant CfaA altered interactions with the minor adhesin CfaE or the usher CfaC instead of with CfaB. (2) CfaA mutations could affect only the on-rate but not the off-rate of its interaction to CfaB. The on-rate is not measured by the pull-down assay because the dissociation of the CfaA/CfaB heterodimer is irreversible. And (3) the formation of CfaA/CfaB complex is a process that is not tightly coupled to the pilus assembly. In reality, each mutation in CfaA may contribute to all these possibilities. An example is the donor-strand T112A mutation that had no apparent effect on the stability of CfaA/CfaB complex but appeared to abolish piliation. A similar conclusion could be made for the L114A and R154A mutations that led to less stable CfaA/CfaB complex but wild type levels of piliation.
Previously, it was reported that besides the general hydrophobic interactions provided by the donor strand, all chaperones that assist pilus assembly have conserved “critical basic residues” in the substrate binding cleft, which interact with the C-terminal residue of a bound subunit, any mutations in those basic residues invariably affect pilus assembly [31]. Although CfaA and related FGA chaperones also have the pair of conserved basic residues, K9 and R125 in CfaA, corresponding to those in FGL and FGS chaperones, structure-based sequence alignment showed an offset in the alignment by one residue (Fig. 3). Moreover, in the CfaA structure the side chain of K9 points away from the cleft and is distant from R125, making it unlikely to interact with pilin subunit (Fig. 2C). Indeed, our mutational analyses support this conclusion.
Based on the crystal structure of CfaA, we suggest that R154, which is stabilized by the conserved E86, serves the anchoring function carried out by residue K9 in the FGS and FGL chaperones. Consistent with this hypothesis, the R154A mutation in CfaA results in a reduction in the stability of the CfaA/CfaB complex (Figs. 4B and 4C). However, both piliation assays, p30 and fprate, detected comparable amount of surface pili for the R154A mutant to that of wild type (Fig. 5C), suggesting that either R154A mutation alters the capture of CfaB by CfaA during CfaA-assisted subunit refolding in periplasm or the rate-limiting step in the pilus assembly is at the site of usher protein.
The observation that the FGA chaperones have donor strand residues (P0–P3) shifted in position by two residues suggests that the bound subunit may fit deeper into the chaperone cleft (Figs. 3 and 6), leading to the speculation that this altered pattern of interaction could be a source of specificity between cognate partners. The two hydrophilic residues flanking the hydrophobic stretch in donor strand (T112 and R125) are perhaps important for the donor-strand exchange function at the pilus assembly site [5], as mutations at these sites either destroyed or diminished piliation but had little impact to the stability of the chaperone-pilin complex in solution.
In summary, the elucidation of unique structural and functional features in the CfaA chaperone of CFA/I fimbriae provides a clear case for separating Class 5 chaperones into a distinct group of periplasmic chaperones, which are distinguished from those in the FGL and FGS families. Mutations introduced into these unique features of FGA chaperones produced effects that are indicative of their roles in cognate subunit recognition and in pilus assembly. The question remains unresolved as to how CfaA is able to recognize and interact with both the minor (CfaE) and the major (CfaB) CFA/I pilus subunits, which requires further structural and functional investigations.
The plasmid pNTP513 [37] was used as a template for PCR amplification of the coding regions of mature CfaB (residues 24–170), using primers containing NdeI and XhoI restriction sites at 5′- and 3′-end, respectively (Table S2). The digested PCR product was cloned into a pCDFDuet-1 vector (Novagen) with an added hexahisidine tag N-terminally to the mature CfaB to yield the plasmid pCDFDuet-1-(his)6cfaB. The CfaA gene was also amplified from pNTP513 and cloned into an expression vector pET24a (Novagen) with an added hexahistidine tag at C-terminus, yielding the vector pET24a-cfaA(his)6. CfaA (20–238) was also cloned into the pETDuet-1 vector (Novagen) without modification to yield the vector pETDuet-1-cfaA. The CFA/I operon (CfaABCE) expression plasmid pMAM2 construction has been described previously [16].
Site-specific mutations were introduced to pETDuet-1-cfaA and pMAM2 using site-directed mutagenesis kit (New England Biolab), yielding the following vectors: pETDuet-1-cfaA(K9A) and pMAM2(cfaA:K9A), pETDuet-1-cfaA(T44/E45A/E46A) and pMAM2(cfaA:T44/E45A/E46A), pETDuet-1-cfaA(E86A) and pMAM2(cfaA:E86A), pETDuet-1-CfaA(T112A) and pMAM2(cfaA:T112A), pETDuet-1-cfaA(L114A) and pMAM2(cfaA:L114A), pETDuet-1-cfaA(V116A) and pMAM2(cfaA:V116A), pETDuet-1-cfaA(I118A) and pMAM2(cfaA:I118A), pETDuet-1-cfaA(Y120A) and pMAM2(cfaA:Y120A), pETDuet-1-cfaA(R125A) and pMAM2(cfaA:R125A), pETDuet-1-cfaA(R154A) and pMAM2(cfaA:R154A), pETDuet-1-cfaA(K164-N171:A×8) and pMAM2(cfaA:K164-N171:A×8), and pETDuet-1-cfaA(C163S/C172S) and pMAM2(cfaA:C163S/C172S).
An inframe deletion of amino acids 15–222 of cfaA was introduced to pMAM2 using QuikChange II XL Site-Directed Mutagenesis Kit (Agilent Technologies), resulting in pMAM2(ΔcfaA).
To express hexahistidine-tagged CfaA(his)6, the expression plasmid pET24a-cfaA(his)6 was transformed into E. coli BL21(DE3) strain. E. coli cells were grown in terrific broth (Research Products International Corp.) in the presence of 50 µg/ml of kanamycin at 37°C. When cell density reached 0.8 at OD600, expression of recombinant proteins was induced by adding isopropyl β-D-1-thiogalactopyranoside (IPTG) to 0.8 mM. After a further 16 hours of incubation at 18°C, cells were collected by centrifugation.
Cell pellets were resuspended in a hypertonic buffer containing 60 mM Tris-HCl, pH 7.5 and 20% glucose for 10 minutes followed by another centrifugation. Periplasmic extracts were prepared by resuspending cell pellets in an ice cold hypotonic buffer consisting of 5 mM MgCl2, 5 mM CaCl2, 20 mM Tris-HCl, pH 7.5, and 50 mM NaCl followed by a high-speed centrifugation at 16,000× g for 30 min. The supernatant was loaded onto a Ni-NTA superflow column (Qiagen) pre-equilibrated with a binding buffer (20 mM Tris-HCl, pH 7.5, and 100 mM NaCl) plus 20 mM imidazole. After washing the resin with 5 column volumes of binding buffer plus 30 mM imidazole three times, CfaA(his)6 was eluted with the same binding buffer plus 300 mM imidazole. As a last step, size exclusion chromatography with a Superdex 200 column (GE Healthcare Life Science) was used to further purify CfaA(his)6 and the resulting protein was concentrated to 10 mg/ml for crystallization using an Amicon Ultra-15K with10 kDa MW cutoff concentrating device (Millipore).
E. coli BL21(DE3) was co-transformed with pCDFDuet-1-(his)6cfaB and one of the following additional plasmids: pETDuet-1 vector (negative control), pETDuet-1-cfaA (positive control), and each of the vectors above containing the specified mutation in cfaA. These co-transformants were grown at 37°C in LB media supplemented with 50 µg/ml each of streptomycin sulfate and ampicillin. When the culture reached an OD600 of 0.8, IPTG was added to a final concentration of 0.8 mM to induce expression, with subsequent incubation for 16 hours at 18°C, at which point cells were collected by centrifugation.
Periplasmic extract was prepared from each co-transformant in a manner identical to that described above for BL21(DE3)/pET24a-cfaA(his)6 and loaded onto a Ni-NTA superflow columns (Qiagen) pre-equilibrated with the binding buffer supplemented with 20 mM imidazole. The flow-through was collected for analysis of unbound CfaA. The columns were then washed 3 times with 5 column volumes of binding buffer supplemented with 30 mM imidazole, with subsequent elution with the binding buffer adjusted to an imidazole concentration of 300 mM. The eluate was analyzed for the presence of CfaA/(his)6CfaB complexes.
Flow-through and eluate samples were subjected to SDS-PAGE. Samples were heated to 70°C for 3 min, loaded and separated on 12% Bis-Tris polyacrylamide gels (Invitrogen). Eluate samples were analyzed after staining by coomassie blue. Recovered amounts of CfaA and (his)6CfaB for each of the co-transformants with modified CfaA were compared to the control co-transformant (unmodified CfaA) to determine the relative amount of CfaA bound to (his)6CfaB (complex formation). The flow-through samples were transferred to nitrocellulose for Western blot analysis using CfaA antiserum (1∶5000 dilution) to determine the relative amounts of expressed CfaA.
The pMAM2 parent plasmid and each of the derivatives bearing a modified CfaA gene were transformed to the E. coli host strain BL21-AI (Invitrogen), which places the CFA/I fimbrial operon under the control of an arabinose-inducible T7 promoter. These strains were grown in LB media with kanamycin (50 µg/ml) at 30°C. When the culture density reached an OD600 of ≥0.5, CFA/I fimbrial expression was induced with addition of arabinose to a final concentration of 0.2%. At 0, 15, 30, 45, and 60 minutes after induction at 30°C, cells were collected by centrifugation and resuspended in phosphate buffered saline with 0.5% D-mannose to a final OD650 of 40.
In a 12-well ceramic tile plate, 25 µl each of the bacterial suspension and 50 µl of a 1.5% bovine erythrocyte suspension were added to each well, and the plates were incubated with rocking on ice for 20 minutes. Positive mannose-resistant hemagglutination (MRHA) was determined visually by observation of any degree of erythrocyte clumping. For each bacterial preparation that gave a positive MRHA reaction with addition of the initial bacterial suspension (i.e., OD650 = 40), a two-fold dilution series was performed using PBS with D-mannose as the diluent, and the dilution series was assayed for MRHA. The highest bacterial dilution yielding a positive MRHA reaction was recorded as the MRHA titer. All bacterial samples were tested in 4–5 separate experiments on different days, and each experiment was performed in duplicate.
Quantitation of surface-localized fimbriae by heat extraction of bacteria was performed concomitantly with the aforementioned MRHA experiments. One ml of each concentrated suspension of bacteria (i.e., OD650 = 40) was removed at the 0, 30, and 60 min time points, pelleted by centrifugation and resuspended in 250 µl PBS. After incubation at 65°C for 25 min, cells were removed by centrifugation at 6,000× g for 30 min. These heat extract preparations were placed in sample buffer containing 1.5% SDS and placed at 100°C for 10 min just prior to separation by SDS-PAGE (15% polyacrylamide). After transfer to nitrocellulose, Western blot analysis was performed by chemiluminescence using mouse antiserum (at 1∶5,000,000 dilution) against recombinat CfaEB [17] and the SuperSignal West Femto Complete Mouse IgG Detection kit (Pierce). Western blot analyses were similarly performed using anti-CfaA antiserum (at 1∶1,000,000 dilution) to monitor for leakage from the periplasmic space.
Purified CfaA(his)6 was crystallized by the hanging drop vapor diffusion method at 293 K, mixing 2 µl of protein (10 mg/ml) with 2 µl of well solution containing 22% PEG3350, 0.2 M NaCl and 0.1 M MES pH 5.3. The platinum and lead derivatives were prepared by soaking native crystals in well buffer supplemented with 2 mM K2PtCl4 and 15 mM Pb(CH3COO)2, respectively, overnight. CfaA crystals were cross-linked using glutaraldehyde before flash-cooled in liquid propane in the presence of 30% glycerol [38].
Diffraction data sets were recorded at the SER-CAT BM beamline at the Advanced Photon Source (APS), Argonne National Laboratory (ANL) with a MAR-225 CCD detector. The data were integrated and scaled using the HKL2000 package [39]. The structure was solved by the multiple isomorphous replacement coupled with anomalous scattering (MIRAS) method using the program suite PHENIX [40].
An initial CfaA model generated from SOLVE/RESOLVE [41] was manually completed in Coot [42], and was refined against a 1.9 Å resolution data set using REFMAC5 [43] from the CCP4 suite [44]. Multiple structure-based alignments were done in O [45]. The structure was validated using Molprobity [46].
Atomic coordinates of the refined structures have been deposited in the Protein Data Bank (www.pdb.org) with the pdb code 4NCD for the structure of CfaA.
Proteins used in this study have the following accession numbers in the UniProtKB/SwissProt database: CfaA, E3PPC3; PapD, P15319; FimC, P31697 ; CooD, D7GKP2 ; CooB, P25731; CooA, P0ABW7; CooC, D7GKP1; CfaE, P25734; CfaB, E3PPC4; Caf1M, P26926; FaeE, P25401; SafB, Q93IN9; CupB, H3SUK7; CupB2, H3SUK8; SfaE, Q9EXJ6; PsaB, P69965; CsfB, Q93G70; CsuB, Q5SGF0; CosB, Q6R591; CsdB, Q5SGE5; CsbB, Q5SF91; CotB, Q47116; HifB, P45991; F17a, O30925; FasB, Q46992; CblB, B4ELG1; TcfA, S5GUW7.
|
10.1371/journal.ppat.1006444 | Endothelial cells are intrinsically defective in xenophagy of Streptococcus pyogenes | Group A Streptococcus (GAS) is deleterious pathogenic bacteria whose interaction with blood vessels leads to life-threatening bacteremia. Although xenophagy, a special form of autophagy, eliminates invading GAS in epithelial cells, we found that GAS could survive and multiply in endothelial cells. Endothelial cells were competent in starvation-induced autophagy, but failed to form double-membrane structures surrounding GAS, an essential step in xenophagy. This deficiency stemmed from reduced recruitment of ubiquitin and several core autophagy proteins in endothelial cells, as demonstrated by the fact that it could be rescued by exogenous coating of GAS with ubiquitin. The defect was associated with reduced NO-mediated ubiquitin signaling. Therefore, we propose that the lack of efficient clearance of GAS in endothelial cells is caused by their intrinsic inability to target GAS with ubiquitin to promote autophagosome biogenesis for xenophagy.
| Autophagy is an intracellular bulk degradation system to survive within starved condition, which is one of the most common threats limiting organism’s expansion. By the system, cells digest their own cytoplasmic compartments that are sequestered by double membrane structure called autophagosome. It is also utilized for selective targeting of unwanted materials inside the cells including invading bacteria. This system targeting bacteria is called xenophagy, and provides “non-specialists” with innate immune system. Xenophagy is well characterized in epithelial cells since they are primary targets for invading bacteria. However, even though bacterial penetration into blood vessel could cause severe symptoms, it remains unknown weather endothelial cells retain functional xenophagy fighting against them. In this report, we showed that endothelial cells fail to suppress group A Streptococcus (GAS) growth due to defect in xenophagy. It is due to endothelial cell’s defect in ubiquitination of GAS, which plays a key role in target recognition during selective autophagy. Because we confirmed that endothelial cells are proficient in canonical, non-selective autophagy, our findings illustrate intrinsic defect in xenophagy as unique but general character of endothelial cells, shedding light onto cell type specificity in selective autophagy.
| Streptococcus pyogenes, also known as group A Streptococcus (GAS), is a common human pathogen that causes a variety of illnesses, ranging from mild self-limiting infections to severe invasive diseases. Pathogenesis involves various virulence factors for adhesion, invasion, colonization, and defense against the immune system [1–4]. Although GAS is defined as an extracellular bacterium that is recognized by phagocytes through PRRs (pattern recognition receptors), triggering further immune responses [5], it can also invade eukaryotic cells, allowing the bacteria to escape from immune cell clearance and antibiotic killing [6–8]. Nonetheless, the host can eliminate internalized bacteria via xenophagy [9–11], a specialized form of the intracellular bulk degradation system known as autophagy. The xenophagy pathway is utilized not only in specialist immune cells, but also in other cell types such as epithelial cells.
Autophagy digests intracellular components to obtain minimum energy and basic building blocks to ensure cellular survival during starvation conditions. Following non-selective engulfment of cytoplasmic components by a double-membrane structure called the autophagosome, these compartments fuse with lysosomes, where the cargos are degraded by hydrolases [12,13]. Autophagy can also specifically target intracellular components including damaged organelles, protein aggregates, and invading bacteria in order to maintain cellular and systemic homeostasis. As a form of selective autophagy, xenophagy requires a mechanism for cargo recognition. In addition, xenophagy is distinguished from other forms of autophagy by its requirement for engulfment of bacteria. The GAS-containing autophagosome-like vacuoles (GcAVs), which trap invading GAS inside host cells, are often nearly 10 μm in diameter, and are thus capable of sequestering multiple bacteria; by contrast, regular autophagosomes range between 0.5 and 1.5 μm [11,14]. To build up this large structure, xenophagy requires Rab7, Rab9A, and Rab23 GTPases in addition to the core sets of autophagy genes [15,16].
The most significant difference between xenophagy and other forms of selective autophagy is that it targets intruders from outside the body. Thus, in order to understand the significance and mechanism of xenophagy, we must consider the penetration path and cell-type specificity of the invading pathogens. Most research in xenophagy has been focused on epithelial cells in the intestinal and respiratory tracts, because these cells are the primary targets of bacterial invasion into organisms. However, given that intrusion of bacteria into the cardiovascular system causes their dissemination throughout the body, leading to potentially fatal consequences, endothelial cells should also be investigated as important targets. Consistent with this idea, GAS can enter human umbilical vein endothelial cells (HUVEC) [17], and invasive GAS can survive in endothelial cells [18,19]. However, the details of bacterial fate after engulfment into endothelial cells remain poorly defined.
In this study, we found that GAS could survive and multiply in endothelial cells, whereas epithelial cells efficiently removed them via functional xenophagy. Invading GAS is decorated with the autophagosome marker LC3 in endothelial cells, but they could not be surrounded by the autophagic double-membrane structure, leading to fatal consequences. This intrinsic defect in endothelial xenophagy most likely stems from insufficient ubiquitination of invading GAS mediated by the nitric oxide (NO) pathway. Our findings regarding the cell-type specificity of xenophagy provide essential insights into the mechanisms of cellular defenses against bacterial invasion in vivo.
To determine the fate of GAS following internalization into endothelial cells, we compared the human microvascular endothelial cell line-1 (HMEC-1) with lung epithelial A549 cells. The efficiency of GAS (NZ131, type M49) invasion was 4–5 times higher in endothelial cells (10.6±2.031 x 104 cfu/ml) than epithelial cells (2.433±0.339 x104 cfu/ml), so we adjusted the ratio of the MOI between endothelial and epithelial cells to 1:5 in order to monitor GAS survival following engulfment of equal numbers of GAS into both cell types. Strikingly, a time-course growth analysis of intracellular GAS revealed that GAS multiplied in HMEC-1 cells, but remained suppressed in A549 cells (Fig 1A). GAS, detected as DAPI-stained dots in the cytoplasm, increased in abundance in a time-dependent manner (Fig 1A). Majority of endothelial cells with GAS was still viable at 6 hours post infection, but eventually resulted in necrotic cell death at 24 hours (S1A–S1D Fig).
To confirm that internalized GAS is capable of replication, we recovered bacteria inside the cells and performed colony-formation assays. The results revealed an increase in the number of colonies formed by GAS recovered from endothelial cells 6 h post-infection, whereas those from epithelial cells exhibited reduced viability (Fig 1B). This expansion was not specific to HMEC-1 cells, as we observed a similar phenotype even in untransformed, primary human umbilical vein endothelial cells (HUVEC) (Fig 1B), suggesting that the insufficient GAS clearance is not the artificial event specific to immortalized cells. Furthermore, in addition to A549 cells, GAS did not grow in two other epithelial cell types (HeLa and NRK). This endothelial defect in GAS growth suppression is not specific to the M49 serotype, because HMEC1 cells, but not control A549 cells, failed to suppress expansions M1 and M6 GAS strains as well (Fig 1C).
We assumed that bacterial virulence factors might be involved in this event, and thus examined the growth of GAS harboring a mutation in emm1 gene encoding a M protein, or hasA gene which is required for formation of hyaluronic acid capsule. We observed the expansion of GAS with either mutation in endothelial cells (Fig 1D and 1E), suggesting that GAS expansion in endothelial cells is not due to a lack of endothelial defense mechanism against those virulence factors. Moreover, we determined specie specificity for this defect. To this end, we infected cells with Salmonella and also S. aureus. Both two bacterial strains showed striking growth in endothelial cells but not in control epithelial cells, suggesting that this is not an event specific to GAS but rather a phenomenon generally observed (Fig 1F). Taken together, we concluded that endothelial cells are deficient in suppression of intracellular growth of GAS.
To obtain mechanistic insights into the failure of endothelial cells to suppress GAS growth, we studied the role of autophagy in GAS clearance in light of its significant role in GAS elimination in epithelial cells [11,15,16,20–22]. This idea is consistent with our observation that GAS with mutation in slo encoding Streptolysin O (SLO) did not expanded even in endothelial cells (Fig 1D), as GAS mutated in slo is degraded by a manner independent from autophagy [20]. To compare GAS autophagy between the two cell types, we first sought to exclude the possibility that endothelial cells have a defect in canonical autophagy induced by serum starvation. In response to starvation, endothelial cells are able to form LC3 puncta and exhibited autophagic flux at levels comparable to those in epithelial cells (S1E–S1G Fig), indicating that endothelial cells retain normal activity of canonical autophagy. Next, we focused on selective autophagy in response to GAS infection. We observed that GAS induced LC3 lipidation in a time- and infectious dose–dependent manner, as well as LC3 puncta formation in endothelial cells (S2A–S2C Fig), implying that autophagy could be induced by GAS invasion even in endothelial cells. However, using conventional EM, we rarely observed GAS surrounded by double-membrane structures in endothelial cells, whereas GAS in epithelial cells were positive for the structure (Fig 2A). To rule out the possibility that formation of the structure was simply delayed, we performed EM at later time points after infection. At these later times, we still found no isolation membrane surrounding the bacteria, and GAS continued growing in the cytoplasm of endothelial cells (S2D Fig). These results strongly suggest that endothelial cells are defective in sequestration of cytoplasmic GAS with autophagosome, and that the elevation in LC3 lipidation induced by GAS is unlikely to be related to the ability to suppress bacterial growth.
To investigate the molecular underpinnings of the less efficient xenophagy in endothelial cells, we asked whether endothelial cells were capable of detecting invading GAS. It has been suggested that invading bacteria can be recognized as invaders by the ruptured endosome surrounding them: luminal exposure of ruptured endosomes facilitates further recruitment of ubiquitin, which plays an essential role in xenophagy [23–25]. To explore this possibility, we measured GAS with Gal-3, a marker for ruptured endosome [24]. However, we observed no significant difference in the proportion of GAS with Gal-3 signals in endothelial versus epithelial cells (Fig 2B), suggesting that initial cue for xenophagy is triggered by GAS, even in endothelial cells. Moreover, we could observe overlap between the LC3 and Gal-3 signals on invading GAS, even in endothelial cells (Fig 2C). However, correlative light electron microscopy (CLEM) analysis clearly showed that those LC3 signals were not correlated with autophagosomal double membranes (Fig 2D and S3A–S3D Fig), again indicating that endothelial cells were defective in formation of autophagosomes surrounding invading GAS. Consistent with this, the number of LC3-positive GAS kept increasing over time in HMEC-1 cells, whereas their multiplication was suppressed in epithelial cells (S4A and S4B Fig), suggesting that the LC3 signals on GAS in endothelial cells did not indicate functional autophagosomes. A lack of autophagosome formation surrounding GAS is observed in primary HUVEC as well. Consistently, in HUVEC, GAS is able to be positive to both LC3 and Gal-3 signal but totally negative to autophagosomal double membrane structures (S3E Fig). Taken together, these findings indicate that endothelial cells are incompetent to suppress GAS growth due to impairment in formation of autophagosome membranes surrounding GAS with ruptured endosomes.
To determine the mechanism that gives rise to defective formation of autophagosomal membrane surrounding GAS, we focused on ubiquitination, which is an essential step for initiation of selective autophagy [26–28]. Consistent with this, deregulation of ubiquitination impairs recruitments of downstream components of xenophagy [29,30]. Strikingly, we found that recruitment of ubiquitin to GAS was reduced in endothelial cells in comparison with epithelial cells (Fig 3A), correlated with defective recruitment of downstream ATG proteins. The recruitments of ULK1, ATG14, and ATG9, all involved in formation of isolation membrane and xenophagy [31,32], were significantly reduced in endothelial cells (Fig 3B and S5A Fig). Not only for GAS, we confirmed that invading Salmonella is targeted by ATG9 at a lower level in endothelial cells than that in epithelial cells, together indicating that endothelial cells are compromised in early step of xenophagy (S5B Fig). As expected, GAS with LC3 signals were observed regardless of ubiquitination in endothelial cells, in contrast to the case of epithelial cells, where most LC3-positive GAS were associated with ubiquitin (Fig 3C and 3D). Accordingly, we hypothesized that ineffective ubiquitination on bacteria could be the major cause of the defect in xenophagy in endothelial cells.
To directly test this hypothesis, we pre-coated GAS with ubiquitin and asked whether they would be eliminated even in endothelial cells. To generate a physiologically relevant coat of ubiquitin, we used epithelial cells as ‘craftsmen’. Notably, we used ATG9-knockout (KO) HeLa epithelial cells (S6A–S6C Fig) for this purpose in order to avoid degradation of the product by the functional xenophagy system in wild-type cells. Using this system, we could successfully recover GAS with a ubiquitin coat from ATG9-KO HeLa cells, whose integrity of ubiquitination was confirmed by immunostaining (Fig 4A and 4B). To recover intact GAS with the coat, we treated infected HeLa cells with distilled water without any detergents so that the osmotic imbalance would disrupt the plasma membrane of the host cells, concurrently enabling us to exclude the possibility that harvested GAS were surrounded by intact endosome structure that might burst under such harsh condition. When we infected endothelial cells with naïve GAS or ubiquitin-coated GAS, we found that, even in endothelial cells, GAS were engulfed by autophagosomal double membrane, and efficiently eliminated if they were coated with ubiquitin (Fig 4C and 4D). This observation strongly supports the hypothesis that the defect in xenophagy in endothelial cells results from reduced ubiquitination activity on invading GAS, rather than a deficiency in autophagosome biogenesis itself. This is consistent with our observation showing that endothelial cells retained canonical autophagic activity, comparable to the level in epithelial cells (S1A–S1C and S2A–S2C Figs). The observed clearance of ubiquitin-coated bacteria in endothelial cells requires the intact autophagic machinery, because ATG9-KO endothelial cells (S6D–S6F Fig) failed to suppress the replication of GAS with a ubiquitin coat (Fig 4D). In addition, under naïve GAS infection, bacterial growth was slightly exacerbated in ATG9-KO endothelial cells in comparison with wild-type cells (Fig 4D and S6F Fig), suggesting that autophagic machinery does indeed help endothelial cells to defend against bacterial infection, even though the resultant suppression of GAS growth is far from sufficient.
To further explore these findings, we sought to determine whether the defect in ubiquitination was functionally associated with the nitric oxide (NO)-mediated signaling cascade. Recent work showed that this pathway facilitates ubiquitination and selective clearance of intracellular GAS in macrophages [33]. GAS invasion induces NO synthase (NOS)-mediated NO production, resulting in generation of 8-nitroguanosine 3′,5′-cyclic monophosphate (8-nitro-cGMP, an endogenous derivative of cGMP). It is followed by S-guanylation on cysteine residues of targets at the bacterial surface. S-guanylation promotes ubiquitination of the bacteria (summarized in S7A Fig). In this cascade, L-NMMA, a NOS inhibitor, or NaHS, an 8-nitro-cGMP eliminator, can inhibit ubiquitination and degradation of bacteria [33].
To determine whether the NOS-ubiquitin pathway is intact in endothelial cells, we measured endogenous 8-nitro-cGMP levels in these cells, and found that they had an intrinsically lower level of 8-nitro-cGMP than epithelial cells (Fig 5A). Additionally, GAS infection–induced 8-nitro-cGMP failed to compensate for this defect, whereas exogenous introduction of 8-nitro-cGMP increased its level inside the cell, even in endothelial cells. This observation indicates that endothelial cells are intrinsically deficient in both production and induction of 8-nitro-cGMP. This conclusion was further confirmed by treating cells with two inhibitors, L-NMMA and NaHS. These experiments showed that the poor ubiquitination of GAS in endothelial cells was not altered by the inhibitors, whereas ubiquitination in epithelial cells was significantly reduced (Fig 5B). Notably, this is not merely due to low levels of 8-nitro-cGMP in endothelial cells, because introduction of exogenous 8-nitro-cGMP failed to restore GAS ubiquitination in these cells, implying that, in addition to an intrinsically low level of 8-nitro-cGMP, endothelial cells lack a mechanism to facilitate ubiquitination of GAS mediated by this compound (Fig 5B). Consistent with this, endothelial cells did not exhibit any detectable increase in GAS growth upon treatment with L-NMMA or NaHS (Fig 5C and 5D, S7B–S7D Fig), whereas GAS replication was elevated in epithelial cells treated with those inhibitors or harboring silencing/knockout of autophagy genes, supporting the idea that endothelial cells are deficient in bacterial clearance via NOS–ubiquitin pathway.
Taken together, our data suggests that deficiency in the NOS–ubiquitin pathway in endothelial cells is at least partially responsible for their diminished ability to defend themselves against GAS. Additionally, our results also imply that endothelial cells not only fail to form GcAVs, but also cannot induce formation of regular autophagosomes in response to 8-nitro-cGMP. Formation of LC3 puncta formation could be induced by 8-nitro-cGMP in epithelial cells, but not in endothelial cells (Fig 5E), indicating that the lack of the NOS-dependent induction of autophagy in endothelial cells is linked to a wide range of physiological outcomes related to autophagy.
In this report, we showed that xenophagy is not equally efficient among different cell types, raising the question of why endothelial cells do not retain this vital ability to defend themselves against bacteria. We found that GAS efficiently replicates in endothelial cells at a rate comparable to that of in vitro GAS growth [34,35], indicating that the bacteria are essentially growing freely. We speculate that this situation evolved because robust activation of the NO pathway in endothelial cells could cause a lethal side effect on cardiovascular systems that would outweigh the beneficial effects of activating xenophagy. Essentially, constitutive releases of NO from endothelial cells via endothelial NOS (eNOS) serves a protective role in cardiovascular homeostasis by relaxing blood vessel pressure [36]. However, excess induction of NO, which activates the 8-nitro-cGMP pathway and xenophagy, could deregulate blood vessel pressure. In fact, robust NO induction by bacterial infection or LPS causes hypotension and sepsis, a phenomenon mediated by inducible NOS (iNOS) [37]. We found that endothelial cells have low levels of 8-nitro-cGMP and do not efficiently ubiquitinate invading GAS in response to 8-nitro-cGMP. This is consistent with the fact that NO production in endothelial cells is directly connected to that of NO in vascular muscle [36], creating a situation more sensitive than that in epithelium. Therefore, we speculate that, given the need for tight regulation of this pathway, endothelial cells had to abandon NO-mediated induction of xenophagy.
We found that exogenous coating of GAS with ubiquitin was sufficient to induce their clearance, even in endothelial cells, in a manner dependent on the autophagic machinery. This observation is in line with the recently established consensus that ubiquitination plays an essential role in cargo recognition in selective autophagy. Thus, endothelial cells lack a key ability to target invading bacteria. What, then, is the underlying mechanism explaining the failure of endothelial cells to perform this function? We suspect that this deficiency is not merely due to their poor NO-pathway response. We showed that in epithelial cells, NO pathway inhibitors only partially increased bacterial growth, to levels lower than those observed in ATG7-KO cells (Fig 5D). This was not simply because of an insufficient suppression of the NO-mediated pathway, as much higher doses of inhibitors did not further increase GAS growth (S7E Fig), It suggests that inhibition of epithelial NO-mediated ubiquitination pathway only partially recapitulates the xenophagy defect in endothelial cells, which is supposed to be caused by insufficiency in multiple systems. We assume that it is mediated by ubiquitin targeting system involving specific E3 ligase(s) [38–40]. Identification of these pathways in endothelial cells is a high priority because it may facilitate development of a novel approach to fighting GAS, in particular to alleviate their expansion in cardiovascular system and prevent life-threatening bacteremia, which is often exacerbated by antibiotic resistance or delay in a treatment.
In this study, we found that invading GAS is often positive for the autophagosome marker LC3. However, we found that these LC3 signals are not related to functional autophagy. Considering the poor ability of endothelial cells to eliminate GAS, this previously unknown autophagy-independent coating of GAS with LC3 may play only a minor role in innate immunity, whereas ATG9 knockout results in slightly exacerbated GAS growth, even in endothelial cells. Because LC3 lipidation can be induced by GAS infection in endothelial cells (S2A–S2C Fig), LC3 molecules on GAS are likely to be in the lipidated form. We speculate that the autophagic machinery facilitates LC3 lipidation for this event trying to limit their growth by autophagy-independent mechanism although it remains unclear how lipidated LC3 is recruited to invading GAS. It could be related to another autophagy-independent role for autophagy-related genes, as represented by LC3-associated phagocytosis (LAP), although it is less effective than LAP.
In summary, we showed that endothelial cells are not capable of carrying out xenophagy due to an intrinsic defect in ubiquitin-targeting system. Because ubiquitination is a universal key event in selective autophagy, further characterization of the mechanism may provide perspective and valuable insights into host defenses, as well as alleviate a wide range of diseases suppressed by selective autophagy.
Human microvascular endothelial cell line-1 (HMEC-1) [41] (obtained from the Centers for Disease Control and Prevention, USA) was cultured in endothelial cell growth medium M200 (Cascade Biologics) supplemented with 10% fetal bovine serum (FBS), 1 μg/ml hydrocortisone, 10 ng/ml epidermal growth factor, 3 ng/ml basic fibroblast growth factor, and 10 μg/ml heparin. Human umbilical vein endothelial cells (HUVEC, gift from Dr. H. Y. Lie) were maintained in M200 medium containing 10% FBS as HMEC-1 cells. A549 (laboratory stock), HeLa (laboratory stock), and NRK (normal rat kidney epithelial, laboratory stock) cells were maintained in Dulbecco’s modified Eagle’s medium (DMEM) supplemented with 10% FBS. Cells were cultured at 37°C in 5% CO2.
Streptococcus pyogenes strain NZ131 (type M49) is a gift from Dr. D. R. Martin (New Zealand Communicable Disease Center, Porirua). A20 (type M1), JRS4 (type M6) wild type, emm1 mutant, slo mutant, hasA mutant GAS strains and standard strain of Staphylococcus aureus (ATCC 25923) are provide from Dr. J. J. Wu. Salmonella enterica serovar Typhimurium SR-11 x3181 was used for infection. Bacteria were grown overnight at 37°C in tryptic soy broth with 0.5% yeast extract (TSBY) for GAS and S. aureus, and LB broth for Salmonella, and then transferred to fresh broth for 3 h. The culture was centrifuged and suspended in phosphate-buffered saline (PBS), followed by measurement of cell concentration as (0.2 OD600 = 1 × 108 cfu/ml, confirmed by plating). This procedure for bacterial preparations was used for all infectious experiments in this study. For heat inactivation, the suspended bacteria were treated at 70°C for 30 min.
A monolayer of cells was plated in 24-well or 6-well plates and incubated overnight. The prepared bacteria were directly added to the wells at a multiplicity of infection (MOI) of 1, 5, 10, or 25, and then centrifuged at 500 g for 5 min to ensure simultaneous infection of cells. In one set of experiments, the ratio of GAS and S. aureus infectious MOI between epithelial cells and endothelial cells was maintained at 5:1, due to the 5-fold higher internalization efficiency in endothelial cells relative to epithelial cells. Salmonella was used at MOI of 100 for both cell type infections. The cell and bacteria mixture was incubated at 15 min for Salmonella and 30 min for GAS and S. aureus. After incubation, the cell culture was washed twice with PBS to remove unattached bacteria, and then fresh medium containing 100 μg/ml gentamicin was added to kill the remained extracellular bacteria. After various time periods, cells were collected for individual experiments.
Cells seeded at 6 × 104/well in 24-well plates with cover glasses were cultured overnight and infected with GAS according to the infection protocol. Cover glasses were coated with cellular matrix (Cellmatrix type I-C, 100 μg/ml, 37°C, 30 min) in advance. At various time points post-infection, the cells were fixed with 4% paraformaldehyde (PFA), permeabilized with 50 μg/ml digitonin, and stained with anti-GAS (gift from Dr. J. J. Wu), anti-galectin-3 (M3/38, Santa Cruz Biotechnology), anti-LC3 (PM036, MBL), anti-FK2 (BML-PW8810, ENZO), anti-LAMP-1 (H4A3, Santa Cruz Biotechnology), or anti-8-nitro-cGMP (1G6) antibodies (gift from Dr. T. Akaike), followed by staining with secondary antibodies conjugated with Alexa Fluor 488 or 568 and imaging on a confocal microscope (FV1000; Olympus).
Cells at 5 × 104/well in 24-well plates were incubated overnight and transfected for 4 h with 1 μg of pEGFP-LC3 or GFP-ATGs (pEGFP-ULK-1, pEGFP-ATG14, pEGFP-ATG9) plasmid DNA using Lipofectamine 2000 reagent (Invitrogen, Carlsbad, CA, USA) in OPTI-MEM medium plus complete culture medium. After incubation at 37°C for 20 h, cells were prepared for infection.
Control and ATG13 siRNA (sense 5'- GAGUUUGGAUAUACCCUUUdTdT -3' and anti-sense 5'- AAAGGGUAUAUCCAAACUCdGdT -3') were purchased from Sigma-Aldrich. Cells for siRNA transfection were prepared at 1 × 104/well in 24-well plates and incubated overnight. The siRNAs (20 nM) were mixed with Lipofectamine RNAiMAX reagent (Invitrogen) in OPTI-MEM medium and added to the cell culture medium. After 4 h incubation, the medium was replaced with fresh medium. After 20 h culture, a second transfection was performed using the same protocol. Forty-eight hours after the second transfection, the cells were infected with GAS or collected for western blot assays to confirm efficient knockdown of ATG13 and measure autophagic flux.
To generate knock out cell lines of autophagy-related genes, we utilized the clustered regularly interspaced short palindromic repeats (CRISPR)-Cas9 system [42]. We designed RNA-guided Cas9 targeting the first exons of the human ATG7 and ATG9 genes. The specific recognition sequences of the 20 bp before the protospacer adjacent motif (PAM) in each construct were as follows: ATG7, 5’- CACCGAAATAATGGCGGCAGCTACG-3’; ATG9, 5’-CACCCCCTGGGGGTGAATCAC TAT-3’. These guide oligos, with BbsI restriction sites at both ends, were annealed with their anti-sense oligos and inserted downstream of U6 promoter in vector pSpCas9(BB)-2A-GFP (pX458) [43] (purchased from Addgene); the resultant plasmids were used for transfection. Single-cell sorting was performed after a 48-h transfection. After 1-week culture of single cells with antibiotics, fresh medium was added to support growth for 1 more week to allow colony formation. Genomic DNA of each clone was extracted, and the target gene was confirmed by sequencing using the following primers; ATG7, Fw 5’-GTCGACGTTCTGGAGATCTGTTTCACAACG-3’ and Re 5’-GAATTCTGGGATCAAAAAGTCAGGAAG-3’; ATG9, Fw 5’-ATATGTCGACCAGGATGAGCTCCATTCCCGT-3’ and Re 5’-ATATGAATTCCAGCCCCCAACAAAGGGACAG-3’.
Cells were seeded at 8 × 104 cells/well in 24-well plates, incubated, overnight, and then infected with GAS. At various times post-infection, cells were lysed with lysis buffer containing protease inhibitor mixture, followed by denaturation at 95°C in sample buffer, SDS-PAGE, and immunoblotting using rabbit anti-LC3 (PM036, MBL), anti-p62 (PM045, MBL), anti-ATG13 (SAB4200, Sigma-Aldrich), anti-ATG7 (013–22831, Wako), and mouse anti-GAPDH antibodies (Millipore).
Cells were infected with GAS at MOI of 25 and 5 for 1 h and Ub-coated GAS for 2 h. Cell pellets were gently collected by trypsinization and resuspended in phenol red–free medium containing 5% FBS and 40% Dextran T2000. Samples were kept on ice prior to high-pressure fixation using a High Pressure Freezer (Leica EM HPM100). After fixation, the specimens underwent freeze-substitution under low temperature and embedding in plastic (Epon812, TAAB Laboratories Equipment, Aldermaston, UK). Ultrathin sections (70 nm thick) were stained with saturated uranyl acetate and Reynolds lead citrate solution. Micrographs were acquired on a JEOL JEM-1011 transmission electron microscope (JEM-1011, JEOL, Tokyo, Japan).
Cells stably expressing GFP-LC3 and mStrawberry-galectin3 were cultured on glass-bottom dishes with a grid pattern (P35G-2-14-C-GRID; MatTek, Ashland, MA, USA) and infected for 1 h with GAS at an MOI of 25 for A549, and 5 for HMEC-1 and HUVEC cells. The cells were fixed with 4% formaldehyde in HEPES buffer (30 mM HEPES, 100 mM NaCl, 2 mM CaCl2, pH 7.4), and 1 μg/ml DAPI for 30 min at room temperature, washed in HEPES buffer, and observed using a confocal microscopy (FV1000; Olympus). After marking the locations of the target cells, the same specimens were further incubated with 2% formaldehyde and 2.5% glutaraldehyde in HEPES buffer at 4°C overnight. After three washes, the samples were post-fixed with 1% osmium tetroxide and 0.5% potassium ferrocyanide in HEPES buffer for 1 h, washed three times in distilled water, dehydrated in ethanol, and embedded in Epon812 (TAAB Laboratories Equipment, Aldermaston, UK). Ultrathin sections (70 nm thick) were stained with saturated uranyl acetate and Reynolds lead citrate solution. Micrographs were acquired on a JEOL JEM-1011 transmission electron microscope.
After overnight culture, ATG9-KO HeLa epithelial cells (4 × 105 cells/well in 6-well plates) were infected with GAS for 30 min at an MOI of 25, as described in the infection protocol. Gentamicin was added to the culture medium to kill extracellular bacteria. At 3 h post-infection, GAS replicated in the cytoplasm of ATG9-KO HeLa epithelial cells. To obtain intracellular GAS from infected host cells, the cells were treated with distilled water, causing an osmotic imbalance that disrupted the plasma membranes of host cells and the endosomal membranes surrounding the GAS. Bacterial number was determined before next infection (average ~107 cfu/ml). No centrifugation was performed in order to avoid aggregation between cell debris and Ub-coated GAS. For ubiquitin staining of GAS, the bacteria were attached to poly-L-Lysine–coated cover glasses and fixed with 4% PFA, and then subjected to immunofluorescence staining with anti-FK2 and anti-GAS antibodies.
For conventional EM sample, HMEC-1 cells were infected with Ub-coated GAS at an MOI of 5 (25 μl of the 1 ml lysate: expected bacterial number, 2.5x105 cfu) for 2 h. Samples were fixed and prepared for convention EM observation.
For the xenophagy rescue experiment, wild-type and ATG9-KO endothelial cells (1 × 105 cells/well, in 24-well dish) were infected with Ub-coated GAS at an MOI of 1 (10 μl of the 1 ml lysate: expected bacterial number, 105 cfu). GAS prepared in regular culture were used as controls. Colony formation assays were performed at 1 and 6 h post-infection. The fold increase in GAS number, reflective of replication, was calculated as the bacterial number at 6 h normalized against the number at 1 h.
Cells were infected with bacteria as described in the infection protocol. At the indicated time point post-infection, bacteria-infected cells were washed twice with PBS and lysed in sterile H2O, 1 ml/well (24-well plates). After serial dilution with PBS, the bacteria-containing PBS was plated on TSBY or LB agar plates. Colonies were counted after 24 or 48 h incubation at 37°C. Bacterial number determined 1 h post-infection was interpreted as the number of internalized GAS. The fold increase in bacterial number was calculated as the number at later time point normalized against the number of internalized bacteria. Each colony-forming assay was performed at least three times.
|
10.1371/journal.pcbi.0030201 | A Stochastic View of Spliceosome Assembly and Recycling in the Nucleus | How splicing factors are recruited to nascent transcripts in the nucleus in order to assemble spliceosomes on newly synthesised pre-mRNAs is unknown. To address this question, we compared the intranuclear trafficking kinetics of small nuclear ribonucleoprotein particles (snRNP) and non-snRNP proteins in the presence and absence of splicing activity. Photobleaching experiments clearly show that spliceosomal proteins move continuously throughout the entire nucleus independently of ongoing transcription or splicing. Using quantitative experimental data, a mathematical model was applied for spliceosome assembly and recycling in the nucleus. The model assumes that splicing proteins move by Brownian diffusion and interact stochastically with binding sites located at different subnuclear compartments. Inhibition of splicing, which reduces the number of pre-mRNA binding sites available for spliceosome assembly, was modeled as a decrease in the on-rate binding constant in the nucleoplasm. Simulation of microscopy experiments before and after splicing inhibition yielded results consistent with the experimental observations. Taken together, our data argue against the view that spliceosomal components are stored in nuclear speckles until a signal triggers their recruitment to nascent transcripts. Rather, the results suggest that splicing proteins are constantly diffusing throughout the entire nucleus and collide randomly and transiently with pre-mRNAs.
| Understanding the genomic program of an organism requires knowledge of how the information encoded in DNA is processed to generate messenger RNAs that can be translated into proteins. The initial products of gene transcription are extensively modified in the cell nucleus, and a major processing reaction consists of splicing of specific sequences from the middle of the primary transcripts. Splicing is catalyzed by the spliceosome, a large complex composed of five small RNAs and over 100 different proteins. Spliceosomes form anew on primary transcripts and disassemble after splicing, but what triggers the recruitment of individual spliceosomal components to selected gene products is unclear. Here, we have combined imaging and computational approaches to address this question. We obtained quantitative experimental data on the mobility and subnuclear distribution of splicing proteins before and after splicing inhibition, and we applied mathematical models to analyze and interpret the results. We conclude that spliceosomal components do not require a signal in order to be recruited to nascent transcripts. Our results favor the view that splicing proteins are constantly diffusing throughout the entire nucleus and collide randomly and transiently with primary gene products.
| The spliceosome is the multi-megadalton machine that catalyses pre-mRNA splicing. The building blocks of the spliceosome are uridine-rich small nuclear RNAs (U snRNAs) packaged as ribonucleoprotein particles (snRNPs) that function in conjunction with numerous non-snRNP proteins [1,2]. The major spliceosomal small nuclear ribonucleoprotein particles are the U1, U2, U5, and U4/U6 snRNPs. Each snRNP consists of one or two U snRNAs (U1, U2, U5, and U4/U6 snRNAs) bound by a protein complex that comprises seven common Sm proteins and one or more proteins specific to each snRNP [3]. The Sm proteins B/B′, D1, D2, D3, E, F, and G are common to all spliceosomal snRNPs, except U6, and are arranged into a ring structure around a highly conserved single-stranded uridine-rich sequence of the snRNA [4–6]. The biogenesis of spliceosomal snRNPs involves a sequence of reactions that take place at different compartments within the cell. With the exception of U6, which acquires a γ-monomethyl phosphate cap and is restricted to the nucleus, the snRNAs are transcribed as initial precursors that are rapidly exported to the cytoplasm where they associate with Sm proteins [7]. Although in vitro Sm cores assemble readily on uridine-rich RNAs, in cells this process involves the survival of motor neurons (SMN) complex [8]. Assembly of the Sm core is a prerequisite for removal of the snRNA 3′ extension present in the precursor forms and hypermethylation of the 5′ m7 G cap to m2,2,7 G (m3G or TMG) [3,9]. The assembled Sm core and the modified cap then function as independent nuclear localization signals (NLS) for subsequent reimport into the nucleus. The m3G cap is recognized by Snurportin1, an import adaptor that interacts with importin-β [10,11], whereas the Sm core–mediated transport is linked to the nuclear import of SMN [12].
Spliceosomes form anew on nascent pre-mRNAs and disassemble after introns are excised and exons ligated. Thus, spliceosomal snRNPs and non-snRNP proteins in the nucleus can be either actively engaged in splicing or waiting for the next chance to assemble a spliceosome. When the mammalian cell nucleus is viewed with the electron microscope, spliceosomal components are detected in morphologically distinct structures termed Cajal bodies (CBs), interchromatin granule clusters (IGCs), and perichromatin fibrils [13].
The CB is highly enriched in snRNPs but is devoid of non-snRNP splicing proteins. Direct visualization of snRNPs in living cells shows that after import into the nucleus, the newly synthesized particles accumulate first in CBs and are later detected in the speckles [14]. Several lines of evidence indicate that maturation of newly synthesized snRNPs is completed in CBs [15]. Additionally, CBs are likely to be sites where snRNPs are recycled after spliceosome disassembly [16,17].
The perichromatin fibrils correspond to nascent transcripts and appear scattered throughout the nucleoplasm, excluding regions of condensed chromatin [18]. Perichromatin fibrils are often closely associated with the periphery of IGCs, making it impossible to distinguish the two structures within the speckled pattern that characterizes the distribution of splicing factors observed by fluorescence microscopy. Whereas snRNPs and splicing proteins detected on perichromatin fibrils most likely correspond to active spliceosomes, a large body of evidence indicates that the spliceosomal components localized in IGCs are not primarily involved in splicing [19]. Upon activation of a gene, the spliceosome rapidly assembles on the nascent pre-mRNA [20,21]. Conversely, gene inactivation increases the pool of “reserve” splicing factors that accumulate within enlarged IGCs. Consequently, the organization of the speckled pattern observed by fluorescence microscopy is a reflection of the transcriptional and splicing activity of the cell [21,22].
Although recruitment of splicing snRNPs and non-snRNP proteins to nascent transcripts has been visualized in several systems, how spliceosomal components are targeted to IGCs/nuclear speckles and what triggers their subsequent release and recruitment to nascent transcripts remain a controversial issue. Several lines of recent evidence suggest that the formation and maintenance of nuclear structures involved in transcription, splicing, DNA replication, and repair is governed by self-organization principles [23–26]. To determine whether the concept of self-organization applies to spliceosome assembly and recycling, we have compared the kinetics of snRNP and non-snRNP spliceosomal proteins as they move throughout the nucleus in the presence or absence of splicing activity. The quantitative experimental data obtained was then used for mathematical modeling. Our results are consistent with the view that splicing proteins move by Brownian diffusion and bind with specific on- and off-rates to other nuclear components.
In the nucleus of mammalian cells, components of the spliceosome are found distributed throughout the nucleoplasm, excluding nucleoli, and concentrated in nuclear speckles or IGCs (Figure 1A); additionally, splicing snRNPs are detected highly enriched in CBs [19,27,28]. To visualize trafficking of spliceosomal components between subnuclear compartments, the green fluorescent protein (GFP) was fused in frame to the amino terminus of a number of splicing proteins, including SmE, U2AF65, U2AF35, SF1, SC35, and SF3a120. Western blot analysis confirmed the expression of fusion proteins with the expected molecular weight, and fluorescence microscopy showed that the GFP tag did not affect the localization of the proteins.
Fluorescence recovery after photobleaching (FRAP) was used to analyze the relative mobility of the splicing proteins in the nucleoplasm, nuclear speckles, and CBs. Nuclear speckles are identified as structures of heterogeneous size and shape with higher fluorescence intensity than the nucleoplasm, whereas CBs appear as brighter spherical foci approximately 0.5 μm in diameter [19,27,28]. The fluorescence of a small area located in each of these subnuclear compartments was irreversibly photobleached using a high-intensity laser, and subsequent recovery due to movement of non-bleached molecules into the bleached area was recorded by time-lapse imaging. All GFP-tagged splicing proteins were found to be mobile in each of the subnuclear compartments, with half-time fluorescence recoveries under 0.5 s. For all proteins, the recovery time was systematically lower in the nucleoplasm than in the speckles (Figure 1B and 1C). Quantification of FRAP recovery curves yielded effective diffusion coefficient values ranging from 0.70 to 1.84 μm2 s−1 in the nucleoplasm and 0.30 to 1.22 μm2 s−1 in the nuclear speckles. Some of the proteins investigated (GFP-tagged SmE, U2AF65, and SF3a120) also showed significant immobile fractions in the speckles. In the case of GFP-U2AF65, the apparent immobile fraction recovered completely when FRAP analysis was extended to longer periods of time (Figure S1), indicating that the protein is transiently immobilized in the speckles.
Next, we compared the mobility of spliceosomal components in the presence and in the absence of splicing activity. HeLa cells were treated with 5,6-dichlorobenzimidazole riboside (DRB), a drug that inhibits elongation, causing premature transcriptional termination [29]. DRB is a nucleoside analog that inhibits the protein kinases that phosphorylate the C-terminal domain (CTD) of the largest subunit of RNA polymerase II in vitro [30] and in vivo [31]. Time-lapse analysis of cells expressing GFP-U2AF65 revealed a rapid redistribution of molecules induced by DRB (Figure 2A and 2B, and see Video S1). In less than 10 min after addition of the drug to the medium, the GFP-U2AF65 fluorescence decreased in the nucleoplasm and accumulated in bigger and rounder nuclear speckles. The fluorescence intensities in the nuclear speckles and in the nucleoplasm were measured, and the corresponding ratio was calculated over time (Figure 2G). Whereas in untreated cells, the ratio was 1.27 ± 0.07 (n = 23 speckles, 5 cells), after DRB treatment the ratio increased to 1.42 ± 0.08 (n = 25 speckles, 4 cells). In parallel, the average area fold increase of each nuclear speckle was measured to be 2.05 ± 0.81 (n = 25 speckles, 4 cells; Figure 2I). This effect was completely reverted after removal of the drug (Figure 2D, 2E, and 2H, and see Video S2). Despite a decrease in the relative fluorescence intensity, splicing proteins were still significantly detected in the nucleoplasm of DRB-treated cells, indicating that spliceosomal components can localize to this compartment even when the bulk synthesis of mRNAs is inhibited. Our quantitative estimates further suggest that a larger pool of splicing proteins localizes to nuclear speckles in DRB-treated cells, consistent with the view that spliceosomal components are targeted to the speckles when not actively engaged in splicing. As more splicing proteins localize to nuclear speckles, the projected area of these structures become approximately 2-fold larger, whereas the relative concentration of the molecules within the compartment increases only by a factor of 1.12. Thus, the density of binding sites for splicing proteins at the nuclear speckles increases only marginally in response to DRB treatment.
What controls the trafficking of spliceosomal components in the nucleus is controversial. One possibility is that splicing proteins are constantly diffusing in and out of each subnuclear compartment; alternatively (or additionally), splicing proteins may receive signals to enter or leave a particular compartment. To start addressing this issue, we compared the kinetics of splicing proteins in the nucleus of HeLa cells that were either mock-treated or treated with DRB for 30 min. The results of FRAP experiments showed that none of the proteins tested was significantly immobilized in response to the drug; on the contrary, the recovery rate tended to be faster in DRB-treated cells (Figure 3A and 3B). It is noteworthy that the apparent immobile fraction of GFP-U2AF65 is no longer detected after DRB treatment (Figure 3B), suggesting that the transient immobilization of this splicing protein in the speckles requires ongoing transcription.
The observed FRAP results imply that, irrespective of the drug treatment, unbleached splicing proteins are constantly moving into the bleached speckles, replacing bleached molecules that moved out in the meantime. A more direct demonstration that splicing proteins continue to move out of the nuclear speckles in the absence of newly synthesized pre-mRNA was obtained by fluorescence loss in photobleaching (FLIP). A high-intensity laser was used to irreversibly destroy the GFP fluorescence in an area that corresponded to half of the cell nucleus (Figure 3C). The same area was repeatedly bleached while the loss of fluorescence in a non-bleached speckle was monitored. Experiments performed on HeLa cells treated with DRB and expressing GFP-U2AF65, GFP-U2AF35, and GFP-SF1 yielded a faster fluorescence loss in the speckles when compared to untreated cells, and quantification of fluorescence intensities in nuclear speckles over time consistently revealed significantly faster kinetics (Figure 3D–3F). We therefore conclude that shuttling of spliceosomal components between the nuclear speckles and the nucleoplasm is independent from ongoing pre-mRNA synthesis. Furthermore, the faster fluorescence recovery and decay detected by FRAP and FLIP experiments in cells treated with DRB suggests that splicing factors move more freely throughout the nucleus in the absence of transcription and splicing. Possibly, this is because the proteins spend less time bound to spliceosomes.
Because drugs such as DRB can cause multiple effects on cells, we thought to use an alternative approach to inhibit splicing activity. We took advantage of snurportin1 (SPN1), a nuclear import adaptor that recognizes the 2,2,7-trimethylguanosine (m3G) cap of spliceosomal snRNAs [10,11,32]. SPN1 is composed of two domains, an N-terminal domain required for binding to the import receptor importin-β, and a C-terminal m3G-cap binding region. The importin-β binding (IBB) domain comprises amino acids 1–65, and a deletion mutant lacking these residues (SPN1ΔN) retains full m3G-cap binding activity, but slows down or blocks the nuclear import of snRNAs [10]. Thus, SPN1ΔN appears to compete efficiently with endogenous SPN1 for binding to the m3G cap of the spliceosomal snRNPs. Deletion of amino acids 1–65 further prevents binding of the export receptor CRM1 to SPN1ΔN [33]. As binding of SPN1 to either CRM1 or m3G cap is mutually exclusive [33], the SPN1ΔN mutant most likely binds irreversibly to spliceosomal snRNPs in a dominant-negative way. To test this possibility, we fused either GFP or the cyan fluorescent protein (CFP) in frame to the amino terminus of wild-type (wt) SPN1 and SPN1ΔN. Although SPN1 is constantly shuttling between the nucleus and the cytoplasm, at steady state, the protein was predominantly detected in the cytoplasm (Figure 4A and 4B). In contrast, SPN1ΔN appeared concentrated in the nucleus (Figure 4E and 4F). A deletion mutant of snurportin1 lacking the residues 286–360 (SPN1ΔC) showed the same distribution as the wt protein (Figure 4I and 4J), arguing that the changes observed for SPN1ΔN distribution were not a consequence of the smaller size of this protein when compared to wt SPN1. Double-labeling experiments further revealed that in cells expressing wt SPN1, the Sm proteins were normally detected throughout the nucleoplasm, in nuclear speckles, and in CBs (Figure 4C), whereas in cells expressing SPN1ΔN, the Sm proteins accumulated in enlarged speckles and no longer concentrated in CBs (Figure 4G). A similar result was obtained in cells expressing GFP-SmE (Figure 5A and 5B), and after immunostaining with an antibody against the U2 snRNP protein B′′ (Figure 5C and 5D). As shown in Figures 4G, 5B, and 5D, the SPN1ΔN mutant colocalized with snRNPs in the nucleoplasm and in enlarged speckles, consistent with the assumption that it binds irreversibly to the m3G cap of snRNAs and fails to be recycled to the cytoplasm. Failure of SPN1ΔN to shuttle to the cytoplasm was indeed confirmed by lack of fluorescence loss in the nucleus after repeated bleaching of the cytoplasm (Figure S2).
As snRNP proteins were no longer concentrated at CBs in cells that expressed the SPN1ΔN variant, immunofluorescence was performed using antibodies against coilin. In non-transfected cells or in cells expressing wt SPN1, coilin was preferentially detected as bright foci that correspond to CBs (Figure 5E, blue foci); although CBs contain fibrillarin, this protein was observed mostly enriched at the dense fibrillar component of the nucleolus (Figure 5E, red structures). Expression of SPN1ΔN caused a major relocalization of coilin, which was no longer detected as bright foci, but rather accumulated in the nucleolus in association with fibrillarin (note superimposition of blue and red staining at nucleoli in Figure 5F). Another major component of CBs in normal cells is the SMN protein. As shown in Figure 5G, double-labeling of HeLa cells expressing wt SPN1 revealed complete colocalization of SMN and coilin at CBs (indicated by arrows; note additional minor coilin bodies that are apparently devoid of SMN, arrowheads). Following expression of SPN1ΔN, SMN dissociated from coilin (Figure 5H); SMN was still detected in foci (Figure 5H, arrows) and was never observed in nucleoli.
In conclusion, the results show that SPN1ΔN induces a redistribution of spliceosomal components similar to that observed when cells are treated with transcription inhibitors. To determine whether the mutant affected transcriptional activity, cells expressing SPN1ΔN were incubated with 5-fluorouridine (FU) for 15 min. Living cells incorporate FU into nascent RNA rapidly and specifically [34]. As previously described, nascent RNA was detected as small foci throughout the nucleoplasm and as larger structures in the nucleolus. Similar results were observed in cells transfected with either wt SPN1 or SPN1ΔN (Figure S3), indicating that the mutant does not inhibit bulk transcriptional activity.
To examine the effect of SPN1ΔN expression on splicing activity, we have used a reporter minigene transiently expressed in HeLa cells. We have shown previously that transcripts from this minigene are efficiently spliced in vivo [35]. Cells were cotransfected with SPN1ΔN constructs together with the reporter plasmid, and the transcripts derived from the reporter were analyzed 24 h post-transfection by reverse transcription followed by PCR (RT-PCR). The control cells showed three bands of approximately 375, 257, and 129 bp, as previously described [35]. The 375-bp band corresponds to the unprocessed primary transcript; the 257-bp and the 129-bp bands result from the two alternatively spliced transcripts obtained. The expression of wt SPN1 or SPN1ΔC does not affect splicing of the reporter primary transcript (Figure S4, lanes 6 and 8). By contrast, in cells expressing the variant SPN1ΔN, there is a significant reduction of spliced forms relative to unprocessed primary transcript (Figure S4, lane 4). Taken together, these results indicate that the dominant-negative SPN1ΔN mutant impairs splicing without affecting transcription.
Having established that expression of SPN1ΔN inhibits splicing in vivo, we next analyzed the mobility of splicing proteins in cells that express this dominant-negative peptide. HeLa cells were cotransfected with CFP fused to either wt SPN1 or SPN1ΔN and GFP-tagged splicing proteins. FLIP experiments were performed by repeatedly bleaching approximately half of the cell nucleus and monitoring the loss of fluorescence in nuclear speckles over time. The results show that the fluorescence decay curves were significantly faster (p < 0.0001) in cells expressing SPN1ΔN (Figure 6), similar to what was observed in cells treated with DRB (Figure 3). Taken together, the results obtained by two independent approaches show that the mobility of splicing proteins in the nucleus increases when splicing is inhibited.
Previous studies proposed that splicing proteins move within the cell nucleus by simple diffusive processes [36,37], and that transient interactions determine the steady-state subnuclear distribution of these molecules [38]. We applied a mathematical model to test whether such a combination of diffusion and binding events could explain our experimental results. In the proposed model, we assume that splicing factors constantly roam the nucleus and make transient interactions with immobile targets. In the nucleoplasm, we considered that the major binding targets are the intron-containing nascent transcripts, whereas in the speckles, the molecular nature of binding sites remains unclear.
Quantitative FRAP analysis of GFP-U2AF65 in HeLa cells treated with DRB revealed a diffusion rate of 1.58 μm2 s−1. Assuming that in DRB-treated cells, splicing proteins no longer assemble into spliceosomes (due to lack of newly synthesized pre-mRNA), we considered this value as representative of the effective diffusion coefficient of a splicing factor largely unaffected by binding to nascent transcripts.
Binding reactions with either nascent pre-mRNA molecules or nuclear speckles slow down the apparent diffusion rate of splicing proteins by a factor 1+
/koff,nuc [39], where
is the pseudo on-rate constant in the nucleoplasm, and koff,nuc the off-rate constant for binding sites in the nucleoplasm. The pseudo on-rate is by definition
= kon S, where kon is the second-order association constant for the binding reaction and S is the concentration of vacant binding sites [39], which is assumed to remain constant. Because
and koff,nuc cannot be directly estimated from photobleaching experiments, we empirically selected values that resulted in FRAP and FLIP simulations consistent with the experimental data. For untreated cells, we used koff,nuc = 10 s−1 and
= 3.28 s−1. As FRAP analysis of GFP-U2AF65 showed a slower recovery of the fluorescence in nuclear speckles compared to the nucleoplasm (Figure 1B), the koff,spk (i.e., the off-rate constant for binding sites in the speckles) was set to 0.066 s−1 to achieve similar recovery rates in FRAP simulations (∼15 s to recover 90% of the initial fluorescence). The kon,spk value was then chosen to achieve a ratio between steady-state concentration of splicing proteins in the nucleoplasm and nuclear speckles similar to the experimental data (see Figure 2). As a result, the affinity (the ratio between the pseudo on- and off-rates) of splicing proteins to nuclear speckles was assigned a higher value than the affinity to nascent transcripts in the nucleoplasm (i.e., koff,spkn/
).
To test the simulations, we further generated pure-diffusion FRAP curves, from circular bleaching regions, with different diffusion coefficients. The radial fluorescence profiles from the first post-bleach images were obtained and analyzed as previously described [40], yielding values for the effective diffusion coefficient and immobile fraction. The estimated diffusion coefficients were in good agreement with the original parameters defined for the simulations, showing that the Brownian motion algorithm was correctly implemented.
A reduction of the concentration of available binding sites results in a lower probability for the binding reaction to occur. Such a reduction occurs in the nucleoplasm when splicing is inhibited. Therefore, to model splicing inhibition, we decreased
while maintaining all other parameters unaltered. This change was sufficient to cause an increase in the concentration of splicing proteins at nuclear speckles, as observed experimentally (Figure 7). The increase in concentration of splicing proteins at nuclear speckles increases with decreasing
values, stabilizing below a certain value (Figure 8A). For the parameters used in the simulations, maximum concentrations were obtained with, at least, a 100-fold decrease in
.
Theoretically, an increase in the number of splicing proteins that at steady state localize in nuclear speckles would also be expected if the affinity of these molecules to the speckles increased in response to splicing inhibition. This was in fact observed by plotting the calculated ratio between the steady-state concentration of splicing factors in the speckles and in the nucleoplasm for increasing values of
(Figure 8B).
To further test which parameter best describes the observed kinetics after splicing inhibition (i.e., decreased
, increased
, or a combination of both), we performed FLIP simulations (Figure 9). The rate of fluorescent decay under normal conditions was very similar to the experimental data, as expected, taking into account that the simulation parameters were chosen to be consistent with the microscopic observations (Figure 3). Introducing a decrease in
resulted in increased rates of fluorescence loss from unbleached speckles (Figure 9B), whereas increasing the
had the opposite effect (Figure 9D). Thus, decreasing the number of nucleoplasmic binding sites in the model parameters is sufficient to reproduce the faster kinetics of splicing proteins observed in cells when splicing is inhibited. Furthermore, our analysis argues against the view that splicing inhibition leads to an increased affinity of splicing proteins for the nuclear speckles, as would be expected if the proteins were kept in the speckles until a signal triggered their recruitment to nascent transcripts.
In this report, we show that spliceosomal snRNP and non-snRNP proteins are constantly roaming the entire nucleus, moving in and out of the speckles independently of splicing activity. Our quantitative photobleaching analysis of GFP-tagged splicing proteins in living cells, and our mathematical interpretation of the data, argue against the view that spliceosomal components are stored at nuclear speckles until a signal triggers their transit to nascent transcripts. Rather, we propose that spliceosome assembly on pre-mRNAs relies on a combination of continuous diffusion and transient interactions.
To study how spliceosomal components are recruited to newly synthesized pre-mRNAs in the nucleus, we determined the mobility kinetics of splicing proteins in the presence or absence of splicing activity. FRAP and FLIP experiments were performed using a number of GFP-tagged proteins. Previous studies confirmed that chimeras of GFP fused to ASF/SF2 [21], snRNP proteins [16,41], and SC35 [42] behave similarly to the native proteins.
A classical approach to block splicing activity in vivo consists in using drugs such as actinomycin D, α-amanitin, or DRB, which primarily inhibit transcription. Here, in addition to treating cells with DRB, we thought to inhibit splicing activity by an independent mechanism. We show that expression of a dominant-negative deletion mutant of the snRNP-specific nuclear import adaptor snurportin1 (SPN1) specifically prevents splicing of a reporter minigene. SPN1 is an adaptor protein that binds simultaneously to the m3G-cap structure of spliceosomal snRNAs and to the nuclear import receptor importin-β [10,11,32]. Like other adaptor proteins, SPN1 shuttles continuously between the nucleus and the cytoplasm, binding cargo (snRNPs) in the cytoplasm, releasing the cargo in the nucleus, and recycling back to the cytoplasm without cargo. At steady state, SPN1 was predominantly detected in the cytoplasm (Figure 4A and 4B), indicating that the protein exits rapidly from the nucleus. SPN1 is transported out of the nucleus by the export receptor CRM1, and binding of SPN1 to either CRM1 or m3G cap is mutually exclusive [33]. Thus, CRM1 only exports SPN1 molecules that have already released their snRNP cargo in the nucleus. A SPN1 deletion mutant that lacks amino acids 1–65 (SPN1ΔN) retains the capacity to bind the m3G cap but blocks nuclear import of snRNPs because it lacks the IBB domain required for binding to and import by importin-β [10]. Moreover, deletion of amino acids 1–65 prevents binding of CRM1 to SPN1 [33] and consequently impairs export of SPN1ΔN from the nucleus (Figure S2). Because SPN1 binds very tightly to the m3G cap, in the absence of the competing CRM1 interaction, snRNPs are expected to remain bound to SPN1. In good agreement with this prediction, we observe a perfect colocalization of snRNPs and SPN1ΔN (Figure 4E–4G). SPN1ΔN is a small protein (<45 kDa [10]) capable of diffusing through the nuclear pore complex [43]. In the nucleus, SPN1ΔN most probably binds tightly to the m3G cap of snRNPs, thereby blocking spliceosome assembly. Consistent with this view, we observe that splicing of a reporter minigene is blocked in cells that express SPN1ΔN, whereas splicing is unaffected by expression of SPN1ΔC, a deletion variant that lacks the m3G-cap binding domain (Figure S4). Expression of SPN1ΔN further induces an accumulation of snRNPs and non-snRNP splicing proteins in enlarged nuclear speckles, similar to what is observed following splicing inhibition by treatment with transcription inhibitors [13,19] or microinjection of oligonucleotides or antibodies targeted to disrupt splicing [22]. Potential indirect effects caused by drug treatment have been excluded by showing that α-amanitin does not alter the distribution of snRNPs in the nucleus of cells that express an α-amanitin–resistant form of RNA polymerase II [44]. Thus, it is generally accepted that the accumulation of spliceosomal components in enlarged nuclear speckles occurs as a consequence of splicing inhibition.
We also show here that expression of SPN1ΔN causes the disappearance of CBs with redistribution of coilin to the nucleolus (Figure 5). Because the SPN1ΔN deletion mutant blocks nuclear import of newly synthesized snRNPs [10], this finding strengthens the view that CBs form as a result of ongoing snRNP biogenesis in the nucleus. Indeed, several recent studies reported the disappearance of CBs upon depletion of SMN, which disrupted Sm core assembly; PHAX, which blocked specifically the nuclear export of newly synthesized U snRNAs; or hTGS1, which impaired m7G-cap methylation [45–47]. Additionally, CBs disassemble when splicing is inhibited by treating cells with transcription inhibitors [48]. This further suggests that CBs are transient compartments, the maintenance of which requires both ongoing biogenesis of new snRNPs and continuous recycling of pre-existing snRNPs after each round of spliceosome assembly.
Previous FRAP studies using GFP-tagged ASF/SF2 and SC35 revealed that these splicing proteins are in constant flux and move throughout the entire nucleus, regardless of their initial location [37,49]. Our results show that GFP-tagged versions of SC35, SF3a120, SF1, U2AF65, and U2AF35 have diffusion rates in the nucleoplasm ranging from 1.2 to 1.4 μm2/s. By contrast, GFP-SmE diffuses at significantly lower rate (0.7 μm2/s). All proteins recovered faster in the nucleoplasm than in the speckles, but the difference is most striking for SmE, SF3a120, and U2AF65, which also showed apparent immobile fractions. We further show that the mobility of SmE is significantly lower in CBs than in the nucleoplasm and similar to the mobility observed in nuclear speckles (Figure 1B and 1C). The dynamic exchange of several CB components has been previously demonstrated in both mammalian cells [14,50,51] and in Xenopus germinal vesicles [52]. In agreement with our results, the spliceosomal snRNP core proteins SmB and SmD1 were estimated to reside in CBs for several seconds [51]. In contrast to snRNP proteins, coilin and SMN have residence times in CBs on the order of minutes, whereas the U4/U6 snRNP assembly factor SART3 dissociates from CBs after just a few seconds [51].
We observed that all spliceosomal proteins are continuously shuttling between the nucleoplasm and nuclear speckles, or between CBs, nucleoplasm, and nuclear speckles, and the exchange process occurs on the order of seconds. According to our previous estimates, GFP (27 kDa) diffuses in cells at 33 μm2 s−1 [40], and an unbound GFP-fusion protein with approximately 60 KDa is expected to diffuse at approximately 25.3 μm2 s−1 [53]. The significantly lower diffusion rates estimated for splicing proteins (Figure 1) suggest that these molecules are slowed down by interactions with less-mobile nuclear components. Furthermore, the slower recovery observed in nuclear speckles and CBs relative to the nucleoplasm could be due to lower mobility of splicing proteins inside these compartments, transient immobilization caused by binding to fixed structures, or a combination of both. How long a splicing protein resides in a particular compartment depends on its binding affinity to interacting molecules located in that compartment, and the binding kinetics may change over time in response to specific signals. According to the latter view, the accumulation of spliceosomal components in enlarged nuclear speckles following transcription and/or splicing inhibition could result from a longer retention at the speckle compartment. However, the results of photobleaching experiments depicted in Figures 3 and 6 argue against that possibility. Our FLIP data clearly demonstrate that spliceosomal proteins are constantly diffusing away from nuclear speckles independently of whether splicing is active or inactive, and the rate of fluorescence loss from the speckles increased after splicing inhibition. In agreement with these results, it was previously reported that the nuclear mobility of GFP-ASF increased after treating the cells with transcriptional inhibitors [37].
The finding that inhibition of splicing activity leads to concentration of spliceosomal proteins in enlarged nuclear speckles and yet the proteins move away from the speckles at faster rates is counterintuitive. To address this apparent paradox, we performed mathematical simulations using a model that relies exclusively on stochastic processes. In our model, splicing proteins were considered as particles moving by Brownian diffusion. The nucleoplasm and the nuclear speckles were considered as regions that provide binding sites for splicing proteins. The on- and off-rates for the binding were considered to differ in the nucleoplasm and the nuclear speckles to be consistent with the FRAP and FLIP experimental data. Inhibition of splicing, which reduces the number of pre-mRNA binding sites available for spliceosome assembly, was modeled as a decrease in the on-rate constant in the nucleoplasm. Simulation of time-lapse microscopy and FLIP experiments before and after splicing inhibition yielded results consistent with experimental observations. Indeed, the model reproduced both the accumulation of particles in speckles and the faster mobility of particles. The model assumes that under normal conditions, a diffusing particle (representing a spliceosomal protein) can either bind to a spliceosome in the nucleoplasm or to unknown partners in the speckles. In a simulated steady-state situation, this results in 11% of the particles being bound to the speckles while 22% are bound to spliceosomes in the nucleoplasm. The remaining 67% are freely diffusing in the nucleus. By decreasing the number of binding sites in the nucleoplasm, splicing inhibition reduces the binding competition between speckles and nucleoplasm. Consequently, a particle has higher chances of binding to a speckle, becoming temporarily part of it. As more particles bind simultaneously to nuclear speckles, their concentration increases and the compartment enlarges. In parallel, there are more particles diffusing because they are no longer retained by spliceosomes in the nucleoplasm. In the simulations, the percentage of diffusing particles increases to 86% after the number of binding sites in the nucleoplasm is reduced. Because there are more particles diffusing, there is a higher chance that they will reach the bleach region, resulting in faster loss of fluorescence.
In conclusion, we show that the changes in kinetic behavior observed for spliceosomal proteins following inhibition of splicing can be reproduced in a stochastic model by simply reducing the on-rate for binding to spliceosomes in the nucleoplasm. Our simulations further demonstrate that increasing the on-rate for binding to the speckles would decrease the rate of fluorescence loss from that compartment, as opposed to the FLIP results obtained experimentally. Thus, it is unlikely that spliceosomal components require a splicing-dependent signal in order to leave the nuclear speckles. Rather, we favor the view that splicing proteins are constantly diffusing throughout the entire nucleus and collide randomly and transiently with either pre-mRNAs or nuclear speckle components.
Human HeLa cells (ECACC 93021013) were grown as monolayers in minimum essential medium with Earle's salts (DMEM) supplemented with 10% (v/v) fetal calf serum, 1% (v/v) nonessential amino acids (Gibco, Invitrogen), and 2 mM L-glutamine (Gibco, Invitrogen). For live imaging, the cells were plated in glass-bottom chambers (MatTek), and the medium was changed to D-MEM/F-12 without phenol red, supplemented with 15 mM HEPES buffer (Invitrogen). Subconfluent cells were transfected with FuGENE6 reagent (Roche Biochemicals) and analyzed at 16–24 h after transfection. The transcription inhibitor DRB (Sigma) was used at 75 μM from a stock solution of 11 mM in ethanol.
For in vivo analysis of transcriptional activity, cells were incubated for 15 min in cell culture medium supplemented with 2 mM 5′-fluoruridine (5'-FU; Sigma-Aldrich). The incorporated 5'-FU residues were detected with a mouse monoclonal antibody anti-BrdU (clone BU-33; Sigma). For splicing analysis, we used the reporter plasmid IgM-Minx, which is a chimera of IgM and AdML splicing substrates, as previously described [35]. HeLa cells growing on 35-mm Petri dishes were cotransfected with 1 μg of GFP-SPN1 constructs plus 200 ng of the reporter plasmid.
For indirect immunofluorescence, cells were washed twice in PBS, fixed with 3.7% formaldehyde in PBS for 10 min at room temperature, and subsequently permeabilized with 0.5% Triton X-100 in PBS for 15 min at room temperature. The cells were then rinsed in PBS containing 0.05% Tween-20 (PBS-Tw), incubated for 60 min with primary antibodies diluted in PBS, washed in PBS-Tw, and incubated for 30 min with the appropriate secondary antibodies conjugated to fluorescein (FITC), indocarbocyanine (Cy3), or indodicarbocyanine (Cy5) (Jackson ImmunoResearch Laboratories). Finally, the coverslips were mounted in VectaShield (Vector Laboratories) and sealed with nail polish.
We used antibodies directed against the following proteins: Sm (mAb Y12; [54]), U2 snRNP protein B′′ (mAb 4G3; [55]), coilin (rabbit serum 204.3, kindly provided by Professor A. Lamond, University of Dundee, United Kingdom), and fibrillarin (mAb 72B9; [56]) and SMN (mAb 2B1, a gift from Professor G. Dreyfuss, University of Pennsylvania, Philadelphia, Pennsylvania, United States).
For western blotting analysis, protein extracts were prepared by scraping the cells into SDS-PAGE buffer (40 mM Tris-HCl [pH 6.8], 8% glycerol, 2.4% SDS, 75 mM DTT, 0.01% bromophenol blue) with 200 U/ml benzonzse (Sigma-Aldrich), incubating for 10 min at room temperature and then boiling for 5 min. Volumes of total protein extract equivalent to 5 × 105 cells were separated on 10% SDS-polyacrylamide gels and transferred to nitrocellulose membranes. Western blotting was carried out using a semidry electrophoretic transfer cell. Blots were probed with anti-GFP monoclonal antibody (Roche Applied Sciences) in 2.5% milk-PBS and developed using peroxidase-conjugated goat anti-mouse IgG (BioRad Laboratories). Bands were visualized using ECL (Amersham Biosciences).
The following GFP-tagged proteins were used: GFP-SmE (kindly provided by Professor A. Lamond), GFP-SC35 (a gift from Dr. Jan-Peter Kreivi, Uppsala University, Sweden), GFP-U2AF65 and GFP-U2AF35 [57], GFP-SF3a120 (a gift from Professor Angela Krämer, University of Geneva, Switzerland), and GFP-SF1. The cDNA of SF1 (Y08766) was obtained from pGEM/SF1 (a gift from Professor Angela Krämer) and cloned in the Bam HI site of pEGFP-C1 (Clontech). The pET28 snurportin1 constructs [10] were digested with Nco I, followed by a fill-in reaction, digestion with Bam HI, and cloning into the Sma I and Bam HI sites of pECFP-C1, pEGFP-C1, and pEGFP-N3 vectors. All constructs were purified using plasmid DNA midi-prep kit (QIAGEN) and sequenced.
Live cells were imaged at 37 °C maintained by a heating/cooling frame (LaCon,) in conjunction with an objective heater (PeCon). Images were acquired on a Zeiss LSM 510 confocal microscope (Carl Zeiss) using a PlanApochromat 63×/1.4 objective. GFP fluorescence was detected using the 488-nm laser line of an Ar laser (25 mW nominal output) and a LP 505 filter. Time-lapse 3D imaging of selected cells was performed on the confocal microscope immediately after DRB treatment and/or DRB removal. For this, a total of up to 200 z-stack series were acquired over time for each cell, each z-stack having between 15 and 20 images and with 0.60 μm of distance between each image in the stack. Image size was 512 × 512 pixels, and the pixel width was 72 nm. The time between each z-stack acquisition depended on its number of images, and varied between 20 s and 60 s. Maximum projection images were generated from each z-stack and processed with ImageJ (http://rsb.info.nih.gov/ij/) using a rigid body registration algorithm to correct for cell displacement during image acquisition. Movies of cells after treatment or removal of DRB were then generated and time-annotated. Fluorescence intensity values in nuclear speckles and nucleoplasmic regions were measured over time in registered projection images also using ImageJ.
In each FLIP experiment, cells were repeatedly bleached in a region of interest (ROI) that corresponded to half of the total nuclear area, and imaged between bleach pulses. Bleaching was performed by scanning the defined ROI with three iterations of the 488-nm laser line, at maximum intensity. Bleach pulse duration ranged from 2.2 to 3.1 s, depending on the size of the bleached region. Repetitive bleach pulses were achieved using the FLIP Macro for LSM software release 2.8, developed by Gwénaël Rabut, at the EMBL (http://www.embl-heidelberg.de/ExternalInfo/ellenberg/homepage/macros.html). Image size was 512 × 512 pixels, and the pixel width was 48 nm. For imaging, the laser power was attenuated to 0.1%–0.2% of the bleach intensity. Images were background subtracted and registered to correct for cell displacement during image acquisition using ImageJ. Fluorescence intensity values in nuclear speckles and nucleoplasmic regions were measured over time in registered projection images using ImageJ. The data were then normalized to correct for loss of fluorescence due to image acquisition, using non-bleached cells to estimate imaging bleach kinetics. Loss of fluorescence due to imaging could reach 10%–20% over the time course of the experiment.
FRAP experiments were performed essentially as described [40]. Each FRAP experiment started with three image scans followed by a bleach pulse of 110 ms on a spot with a diameter of 25 pixels (0.59-μm radius). A series of 97 single-section images (of size 512 × 50 and pixel width 48 nm) was then collected at intervals of 78.40 ms, again with the first image acquired 2 ms after the end of bleaching. For FRAP performed during a longer time, all parameters were kept the same, except the number of images, which was increased to 997 (total duration of acquisition thus increased to ∼80 s). For imaging, the laser power was attenuated to 1% of the bleach intensity. FRAP time series were analyzed as described [40]. All fitting procedures were performed using the NonLinearRegress function of Mathematica 5.0 (Wolfram Research).
In our model, the nucleoplasm and nuclear speckles were defined as circular regions with radii 8 μm and 0.7 μm, respectively, and the number of speckles was ns = 14. The Brownian motion algorithm was based on a modification of the Box-Müller algorithm [58] to generate random deviates with Gaussian distribution and standard deviation equal to
in each spatial direction. Typically Δt (the time between each simulation step) was set to 0.01 s, which yields a standard deviation of approximately 0.17 μm, less than the size of a nuclear speckle. The effective diffusion coefficient was set to Dfree = 1.58 μm2 s−1 based on the value estimated by FRAP experiments for GFP-U2AF65 in DRB-treated cells. Binding reactions slow the apparent diffusion rate of molecules by a factor 1+
/koff,nuc [39]. The probability for the binding reaction to occur is pbind = 1 − exp
, whereas the probability for a bound molecule to break its interaction is punbind = 1 − exp(−koffΔt). These probabilities are position-dependent, being different for the nucleoplasm and the speckles. Within the nucleoplasm and the nuclear speckles, we assumed that binding sites were distributed homogeneously. The values for kon and koff were empirically selected from FRAP and FLIP simulations.
FRAP and FLIP simulations were generated using a Monte Carlo approach. At least 105 molecules were simulated. The probability that a fluorescent molecule is bleached is pbleach = 1 − exp(−KBΔt) inside the bleaching region. For FRAP simulations, the bleach region consisted of a circular area (0.7-μm radius) positioned either in the nucleoplasm or in a nuclear speckle, and for FLIP simulations, half of the circle representing the nucleus was bleached. In a simulation step, for each molecule, a random number α with uniform probability density was generated in the interval [0,1]. The random number generator used is based on an implementation (http://fmg-www.cs.ucla.edu/fmg-members/geoff/mtwist.html) of the Mersenne-Twister algorithm [59], which generates deviates with uniform distribution. For a non-bound molecule, the binding reaction occurs if α < pBind. If α > pBind, then the molecule moves to another random location according to the Brownian motion algorithm. Molecules are constrained to move inside the nucleus. Bound molecules maintain their current coordinates and are freed from their sites only if α < punbind. Fluorescent molecules are bleached if the particle is located inside the bleach region and if a random number β in the interval [0,1] is smaller than pbleach. FRAP and FLIP curves were generated by counting at defined time intervals the number of fluorescent molecules inside either the bleached or unbleached regions, respectively. |
10.1371/journal.ppat.1004422 | Cyclophilin A Associates with Enterovirus-71 Virus Capsid and Plays an Essential Role in Viral Infection as an Uncoating Regulator | Viruses utilize host factors for their efficient proliferation. By evaluating the inhibitory effects of compounds in our library, we identified inhibitors of cyclophilin A (CypA), a known immunosuppressor with peptidyl-prolyl cis-trans isomerase activity, can significantly attenuate EV71 proliferation. We demonstrated that CypA played an essential role in EV71 entry and that the RNA interference-mediated reduction of endogenous CypA expression led to decreased EV71 multiplication. We further revealed that CypA directly interacted with and modified the conformation of H-I loop of the VP1 protein in EV71 capsid, and thus regulated the uncoating process of EV71 entry step in a pH-dependent manner. Our results aid in the understanding of how host factors influence EV71 life cycle and provide new potential targets for developing antiviral agents against EV71 infection.
| Enterovirus 71 (EV71) is the major causative agent of hand-foot-and-mouth disease (HFMD) in Asia-Pacific region and caused over one million infection cases and nine hundred deaths in the year of 2010 in China mainland. EV71 is known to infect the young children for the sake of their undeveloped immune system. Unlike other Enterovirus (e.g. coxsackievirus), EV71 could cause severe aseptic meningitis, encephalitis, myocarditis, and acute flaccid paralysis, thus leading to high fatality rates. There is no clinically applied therapeutics. In this work, we used CypA inhibitors as bioprobes to show that CypA played an essential role in EV71 proliferation. We also elucidated the mechanism by which CypA interacted with the EV71 VP1 H-I loop and functioned as an uncoating regulator in EV71 entry step. Since there are several non-immunosuppressive CypA inhibitors, e.g. NIM-811 and Debio-025, have been reported to show antiviral potency, our results provide a potential way to discover clinical therapeutics against EV71 infection.
| Cyclophilins (Cyps) are key cellular factors that function in numerous cellular processes, including transcriptional regulation, immune response, protein secretion, and mitochondrial function [1]. Cyps possess peptidyl-prolyl cis-trans isomerase activity and have high affinity for the immunosuppressant cyclosporine A (CsA). Cyclophilin A (CypA) is a key member of the Cyp family and was first shown to mediate the immunosuppressive function of CsA through the formation of a CsA-CypA complex. This complex binds to and inhibits the function of the phosphatase calcineurin, which normally functions to dephosphorylate NF-AT, a transcription factor important for T cell activation [1].
CypA is also known to play critical roles in the proliferation of a number of viruses, including human immunodeficiency virus type 1 (HIV-1), influenza virus, hepatitis C virus (HCV), vesicular stomatitis virus (VSV), vaccinia virus, severe acute respiratory syndrome coronavirus (SARS-CoV), rotavirus (RV) and human papillomavirus (HPV), by interacting with viral proteins or facilitating IFN-β production [2], [3]. CypA was first shown to be incorporated into HIV-1 virions through its interaction with the capsid protein (CA), and the interaction between newly synthesized HIV-1 CA and CypA is required for HIV-1 to induce dendritic cell maturation [4], [5]. CypA also interacts with other HIV-1 proteins, such as Vpr and p6, to regulate HIV infection [6], [7]. CypA was further revealed to interact with extracellular CD147, which is the main receptor for CypA on the cell membrane of human leukocytes, and this interaction can induce the phosphorylation of HIV-1 matrix protein to regulate the liberation of the reverse transcriptase complex into cytoplasm during an early stage of HIV-1 infection or can function in HIV-1 attachment to host cells [8]. But a recent research showed that CypA stabilized the HIV-1 capsid and antagonizes HIV-1 uncoating in vitro, indicating the versatile roles of CypA in HIV-1 infection [9]. Moreover, several lines of evidences revealed that Cyps play crucial roles in HCV life cycle. CypB was first reported to be important for HCV replication [10], but later studies showed that CypA, but not CypB, was required for HCV infection in vitro [11]–[14]. CypA was reported to function in the replication of HCV by increasing the affinity of the HCV polymerase NS5B for viral RNA to enhance HCV replication [13], or by binding to the HCV NS5A protein to aid in viral replication [15], [16]. Furthermore, Cyps were demonstrated to play an essential role in HPV infection by facilitating conformational changes in capsid proteins of HPV, resulting in exposure of the N-terminus of L2 protein, and the dissociation of L1 pentamers from recombinant HPV11 L1/L2 complexes in a pH-dependent manner [3], [17].
Enterovirus-71 (EV71), a member of the Picornaviridae family, is one of the major causative agents of hand-foot-and-mouth disease (HFMD) in pan Asia-Pacific region and results over eight millions of infections and three thousands of dead cases since 2008 [18], [19]. The genome of EV71 contains a single-stranded, positive-sense RNA (+ssRNA) and encodes a polypeptide with a molecular weight of approximate 250 kDa [20]. This polyprotein is initially processed into one structural (P1) and two non-structural (P2 and P3) regions and then undergoes proteolytic cleavage into various precursors, ultimately resulting in 11 mature proteins. Among them, P1 is further proteolyzed into VP1 to VP4 to form the viral capsid, while P2 and P3 are processed into replicase proteins. For a productive infection, virions must uncoat and release viral genome into host cytoplasm, following the successful bindings with functional receptors. Enteroviral uncoating process involves sequential capsid alterations by conformational changes [21]. During uncoating, mature particles with sediment coefficient of 160S are converted to the uncoating intermediate A particles with sediment coefficient of 135S, and subsequent empty 80S particles representing the final production of the entry process [22]. The 80S particles are empty particles that have shed genomic RNA, whereas the 135S particles retain the full complement of genomic RNA but lack some or all of their content of VP4 and have externalized most of the N-terminal extension of VP1 that is normally inside the virions [22].
The involvement of host cellular factors plays essential roles in virus proliferation. However, the knowledge of how EV71 utilizes host factors in its life cycle is limited. Only two extracellular membrane proteins, human P-selectin glycoprotein ligand-1 (PSGL-1) [23] and scavenger receptor B2 (SCARB2) [24], [25], as well as heparan sulfate (HS) [26], were recently identified as functional receptors for EV71 infection. Another result suggests that the binding of EV71 to human annexin II on the cell surface enhanced viral entry and infectivity, especially at a low infective dose [27]. Interestingly, SCARB2 was reported to be the exclusive uncoating receptor to trigger conversion of 160S particles to other forms during uncoating process at acidic condition, resulting in the releasing of viral genome [21]. Here we used CypA inhibitors as bioprobes to show that CypA played an essential role in EV71 proliferation. We also elucidated the mechanism by which CypA interacted with and modified the conformation of EV71 VP1 H-I loop, and thus regulated the uncoating process of EV71 entry. This CypA-EV71 capsid functional association not only provides information to understand the cellular factors used in EV71 infection, but also presents a new promising potential for the development of antiviral therapeutics.
Our compound collection, which includes 950 chemically synthesized compounds, was screened by using rhabdomyosarcoma (RD) cells infected with the EV71 virus strain AnHui1. This screen identified compound HL051001P2 (Figs. 1A) as a potent inhibitor of viral proliferation, with an EC50 value of 780 nM, by measuring EV71 virus RNA through quantitative RT-PCR (qRT-PCR) (Fig. 1C). No significant cytotoxicity was observed from compound HL051001P2 at concentrations below 20 µM, as demonstrated by the WST-1-based assay (Fig. 1D), indicating that the inhibition of EV71 proliferation was specific.
Because compound HL051001P2 was previously reported to function as a CypA inhibitor [28], we next selected CsA (Fig. 1B), a well-known Cyp inhibitor and clinical immunosuppressant drug with antiviral effects, to suppress HIV-1 and HCV replication and to check whether other CypA inhibitor can also inhibit EV71 replication. The results revealed that CsA clearly impaired EV71 proliferation with an EC50 value of 3.5 µM (Fig. 1C); however, CsA had a slightly higher cytotoxicity than HL051001P2 (Fig. 1D). Because Cyps are known to be involved in the viral life cycle, our interest in identifying host factors in the EV71 life cycle and antiviral agents prompted us to initiate further investigations to study the working mechanism of CypA in the EV71 life cycle and the inhibitory mechanism by which Cyp inhibitors block EV71 replication.
Over ten subfamilies have been identified in the Cyp family to date, among which CypA and CypB are the most abundant subtypes [10]. To clarify which type of Cyp is most essential for EV71 proliferation, we next used the RNA interference (RNAi) method to investigate the impact of CypA or CypB on EV71 virus proliferation.
We first introduced short hairpin RNAs (shRNAs) that were designed to recognize the 3′ non-coding region of CypA (sh-CypA) or CypB (sh-CypB) by lentiviral vectors into RD cells to downregulate endogenous CypA and CypB expression [12]. The expression of glyceraldehyde-3-phosphate dehydrogenase (GAPDH), a housekeeping gene used as an internal control, was not downregulated (Fig. 2A). We obtained stable knockdown cell lines through resistance gene screening and then infected these shRNA-RD cells by applying an EV71-GFP virus with a multiplicity of infection (MOI) of 0.5. The results of the flow cytometric studies revealed that 10.98% of the RD cells with negative control shRNA (RD-sh-control) were infected with the EV71-GFP virus, and the infection ratio was decreased to 3.31% in the RD-sh-CypA cells. However, the infection rate remained at 7.41% in the RD-sh-CypB cells, suggesting that CypA dominantly impacted EV71 infection (Fig. 2B).
Moreover, when infected with EV71 at a multiplicity of infection (MOI) of 1, the EV71 RNA level in the CypA knockdown cells was diminished to approximately 20% of the levels observed in the RD-sh-control cells, whereas the CypB knockdown did not result in this reduction (Fig. 2C). We also infected the RD-sh-CypA and RD-sh-CypB cells with EV71 at MOIs of 0.1 and 50, respectively, and the results revealed a similar finding as the one caused by EV71 infection at an MOI of 1 (Fig. 2D). Furthermore, the expression level of EV71 VP1 protein, which is the major component of the EV71 capsid [18], [29], was significantly reduced by the knockdown of CypA, but not CypB, which is consistent with the impact of CypA reduction on the EV71 RNA level (Fig. 2E, left half). An interesting observation is that the CypB knockdown resulted in a small increase in the EV71 RNA replication and VP1 expression (Figs. 2C and 2E).
To verify the impact of CypA in the different cell lines used for EV71 infection, we generated an additional stable CypA knockdown Huh7.5.1 cell line (Huh7.5.1-sh-CypA) and observed the EV71 infections (at MOI of 10) in Huh7.5.1-sh-CypA and Huh7.5.1 cells with the negative control shRNA (Huh7.5.1-sh-control). The results demonstrated that the decreased EV71 RNA replication (Fig. 2F) and VP1 expression (Fig. 2G) were similar to those in the RD cell lines. Taken together, all these data suggested that the loss of CypA function is specifically associated with the inhibition of EV71 proliferation.
Previous studies suggested that CypA was upregulated during virus infection and is correlated with the final results of the infection [2], [30], [31]. Similarly, the expression of host cell CypA was upregulated in the RD cells following EV71 infection (Fig. 2H). We also found that EV71 infection led a very minor alteration of secreted CypA in the supernatant of RD-sh-control cells, and very little CypA could be detected in the RD-sh-CypA culture with or without EV71 infection (Fig. 2I). This finding indicated that CypA was present in host cells, but not those that were secreted in the supernatant, and CypA is upregulated following EV71 infection.
CypA was found to interact with different viral proteins and affect different stages of the viral life cycle through distinct mechanisms [2]. To define the working target of CypA in the EV71 virus, we generated an EV71 virus that was resistant to a CypA inhibitor through multiple cell culture passages in the presence of compound HL051001P2.
Sequence analyses of the entire genome of multiple resistant viruses identified only a single T-to-C mutation at nucleotide position 3,164 of the EV71 genome (Table S1). This mutation translated into a single amino acid substitution of a serine to a proline at residue VP1-243 (all residue numbers correspond to the residue in the sequence of the VP1 protein, not in the polypeptide), which is located in the H-I loop of VP1 [32].
To confirm that the VP1-S243P mutant was the mutation that contributed to the resistant phenotype of the selected mutant virus, we engineered a recombinant mutant EV71 virus (S243P-EV71) from the wild type EV71 (wt-EV71) strain AnHui1 through the introduction of a single serine-to-proline substitution at the VP1-243 position and investigated the sensitivity of the S243P-EV71 virus to compound HL051001P2 or CsA in comparison with wt-EV71. The EC50 value of compound HL051001P2 on S243P-EV71 proliferation (3.56 µM) was approximately 5-fold higher than the EC50 value for wt-EV71, and the EC50 value of CsA was 2-fold higher for the S243P mutated virus (Table S2).
Moreover, following an infection with the drug-resistant S243P-EV71 recombinant virus, the EV71 RNA level in the RD-sh-CypA cells was approximately 70% of the levels in the RD-sh-control cells. This value was much higher than the values observed in RD-sh-CypA cells that were infected with wt-EV71 virus (approximately 25%), suggesting that the VP1-S243P mutant rescued EV71 replication. The expression level of EV71 VP1 protein also consistently recovered to normal levels following infection with the S243P-EV71 virus (Fig. 2E, right half). These data again revealed that the VP1-S243P mutant is specifically associated with resistance to the CypA inhibitor.
CypA has peptidyl-prolyl cis-trans isomerase activity to facilitate the conformational modification of proline residue. By examining the protein sequence of EV71 VP1, we found that there is only one proline residue located close to the VP1-S243 position, i.e., VP1-P246. We hypothesized that the resistant mutation from a serine residue to a proline residue at the VP1-243 position may increase the binding affinity of VP1 to CypA and help the virus escape from drug treatment and the depletion of the endogenous CypA. To clarify the mechanisms underlying the CypA regulation of the EV71 life cycle, we analyzed the molecular interaction of CypA with EV71 virions or the VP1 H-I loop by using a GST pull-down assay.
We first checked whether recombinant CypA protein could associate with the EV71 virions (Fig. 3). By using a GST-tagged CypA as a probe (Fig. 3A), we demonstrated that recombinant CypA protein was clearly bound to wt-EV71, and the interaction of CypA with the EV71 virion was increased by substituting with a proline residue at VP1-S243 (Fig. 3B, upper panel). However, when we replaced both S243 and P246 with alanine residues, the mutated virus, i.e., S243A/P246A-EV71, cannot bind with CypA (Fig. 3B, bottom panel). This interaction between CypA and the EV71 virion was also reduced in a dose-dependent manner after treating with CsA and can almost be abolished at a concentration of 4 µM CsA (Fig. 3C, upper panel). Additionally, the S243P mutant rescued the interaction between CypA and EV71 under CsA treatment (Fig. 3C, bottom panel). We further demonstrated that recombinant CypA protein not only bound to wt-EV71 strain AnHui1 but also to other strains of the wt-EV71 virus (Fig. 3D).
We next fused the peptide of the H-I loop (239-GSSKSKYPL-247) (GST-1S) and the H-I loop with the S243P substitution (239-GSSKPKYPL-247) (GST-1P) to the GST tag as probes (Fig. 3A) to test whether CypA can directly bind to the VP1 H-I loop in vitro. The result demonstrated that CypA directly interacted with the H-I loop of EV71 VP1, and the mutation of the serine at the VP1-243 position to proline can clearly increase the binding affinity of the H-I loop for CypA (Fig. 3E). We also used NMR spectra to demonstrate that recombinant CypA binding to chemically synthesized EV71 VP1 H-I loop peptides caused chemical shift changes, suggesting that CypA catalyzed the correct cis-trans reaction of the VP1 H-I loop (Fig. S1). Moreover, a reported catalytic-defective mutant of CypA called H126Q [33] eliminated the interaction between CypA and EV71 virions (Fig. 3F). A similar attenuation of the interaction between the CypA H126Q mutant and virions was also reported in HIV-1 and HCV [12], [34]. Taken together, all these results revealed that CypA functioned directly at the H-I loop of EV71 VP1, and the replacement of serine with proline at the VP1-S243 position could increase the binding affinity of CypA with EV71 virions.
S243 is located in the H-I loop of the VP1 protein of the EV71 virus, and several loop regions of the VP1 protein are known to play critical roles in the entry step during picornavirus infection [35]. Therefore, we hypothesized that CypA may also act in the entry step of the EV71 life cycle.
To verify this hypothesis, we first infected RD cells with EV71 and treated them with 5 µM compound HL051001P2 at −6, −4, −2, 0, 2, 4, 6 and 8 h post-infection (hpi), in which 0 hpi indicates the supply of a virus infection inhibitor. The results showed that the inhibition of EV71 by HL051001P2 represented a clear dependence on the treatment time. The HL051001P2 treatments at −6 to 0 hpi showed the inhibition of EV71 replication, whereas the anti-EV71 effect of the treatments after 2 hpi was significantly attenuated (Fig. 4A). We further transfected an EV71 subgenomic replicon RNA lacking the P1 region in the RD-sh-control and RD-sh-CypA cells and found that the EV71 RNA replication inside the host cells was not affected by the downregulation of CypA (Fig. 4B). These results indicated that CypA affected the early step, but not genome replication, during EV71 infection.
By detecting viral RNA at different time points in RD cells that had been infected with the EV71 virus, we found that the amount of EV71 RNA in the RD-sh-CypA cells decreased to less than 50% of that in the RD-sh-control cells at 1 hpi (Fig. 4C, start point), and this reduction was reversed when the RD-sh-CypA cells were infected with S243P-EV71 virus (Fig. 4D). When we detected EV71 VP1 expression, we found that VP1 expression can be clearly and consistently attenuated in EV71-infected RD-sh-CypA cells in comparison with EV71-infected RD-sh-control cells from 10 hpi (Fig. 4E). Moreover, the augmentation of EV71 virus RNA in RD-sh-control cells indicated that the replication of EV71 RNA began at 4 hpi (Fig. 4C, black line); by contrast, this stage was obviously delayed to 8 hpi in the RD-sh-CypA cells (Fig. 4C, red line). However, when we infected RD or RD-sh-CypA cells with S243P-EV71, the growth curves revealed a similar curve, indicating that the VP1-S243P mutant confers resistance to CypA depletion (Fig. 4D). When we checked the viral titers in the culture, we found that the viral titers in the supernatant were not clearly altered in EV71-infected RD-sh-CypA and RD-sh-control cells (Fig. 4F). We also infected RD cells by using wt-EV71 with a 5 µM HL051001P2 treatment and measured the viral titers in the supernatant at different hpis (Fig. 4A). The results showed that infectious viral production was affected by the inhibitor at -6, -4 and -2 hpi, but not as significantly as the impact on the intracellular viral genome at the same hpi. We thus speculated that CypA depletion blocked viral entry during re-infection and left more viruses in the culture.
We further examined the internalization of EV71 by using immunofluorescence (Fig. 4G). The RD-sh-control and RD-sh-CypA cells were infected with wt- and S243P-EV71, and endogenous CypA and EV71 VP1 proteins were analyzed by immunofluorescence. In the RD-sh-control cells infected with wt-EV71, EV71 VP1 was distributed throughout the cytoplasm at 2 hpi, which was indicative that the virus particle internalization and localization with CypA was random (Fig. 4G, panel a-c). The downregulation of CypA in the RD-sh-CypA cells was first confirmed (Fig. 2A). In RD-sh-CypA cells infected with wt-EV71, the localization of wt-EV71 was restricted to the cytoplasm of the perimembrane region at 2 hpi (Fig. 4G, panel d-f). We can also observe the colocalization of EV71 VP1 with CypA, suggesting that the knockdown of CypA inhibited the internalization of EV71 and CypA was accumulated around EV71 virions. By contrast, the internalization of EV71 was rescued by the S243P-EV71 mutant (Fig. 4G, panel g-i). A similar observation was also reported in the HPV16 pseudovirus in the presence of the CypA inhibitor [12]. These data indicated that CypA depletion inhibited the internalization of EV71 into the host cells.
The entry of EV71 can be further divided into the following two processes: 1) receptor binding and 2) uncoating to release the viral genome [35]. To demonstrate the exact function of CypA in EV71 entry, we first checked the effect of CypA downregulation in the binding of EV71 virions to host cells. The results showed that EV71 binding to host cells was attenuated by CypA knockdown (Fig. 5A) and could be rescued by the substitution of S243 with a proline residue (from 50% attenuation to 90% attenuation) (Fig. 5B). We then checked the binding affinity of three reported EV71 functional receptors, i.e., SCARB2, PSGL-1 and HS, for the wt-EV71 virions without or with CypA treatment. The results revealed that CypA treatment did not lead to obvious upregulation in the binding with EV71 functional receptors (Figs. 5C-5E). Together with the result showing that CypA directly interacts with the EV71 virion, the CypA treatment is not likely to enhance the binding to all reported receptors, and the attenuation of EV71 virions that are binding to RD-sh-CypA and compensation by S243P mutation are likely to resulted in an interaction change between the virions and CypA located at the cell membrane.
The conversion from EV71 160S particles to 135S particles can be induced by uncoating SCARB2 under an acidic condition [21]. However, recent biochemical and structural studies have suggested that simply heating the 160S particles near 60°C induces virus expansion and RNA genome release, and heating over 65°C leads to the subsequent protein melting of virions during uncoating [36]. We next used a previously reported virion flotation assay [21], which is used to detect the conversion of 160S particles into other forms during viral uncoating, to check whether the uncoating process of EV71 entry could be affected by CypA. To be consistent, EV71 virions that were treated at 61°C exhibited a smaller shift (Fig. 5F, blue line) from the native peak (160S) (Fig. 5F, black line) after ultracentrifugation in a 1.1–1.5 g/ml discontinuous CsCl gradient, whereas the virions that were treated at 68°C exhibited a much larger shift (Fig. 5F, red line).
We next incubated 160S virions with 20 µg of recombinant CypA followed by incubation at 37°C for 4 h at pH 5.5 and pH 6.5, respectively, before subjecting them to ultracentrifugation at 41,000 rpm for 10 h at 4°C. The results revealed that CypA cannot trigger the conversion of 160S particles at pH 5.5 and pH 6.5 (Fig. 5G). On the contrary, when a mixture of 160S particles and CypA was incubated at 37°C at pH 6.0 for 4 h, the shift from 160S particles was distinct (Fig. 5H, black and red lines). Moreover, the catalytic-defective mutant of CypA, namely H126Q, was incubated with 160S particles at 37°C under pH 6.0 for 4 h, the shift in virions was completely eliminated (Fig. 5H, blue line). All these results support the idea that CypA can regulate the uncoating process of EV71 entry in a pH-dependent manner, which plays a similar role as the only EV71 uncoating receptor, or SCARB2 [21].
To study the fitness of the mutation in the VP1 H-I loop, we transfected RD cells with RNA transcripts of EV71 recombinants containing the 5 coding mutations and generated recombinant viruses, which were designated as S243P-EV71, S243A-EV71, P246A-EV71, S243P/P246A-EV71, and S243A/P246A-EV71 (Fig. 6). In comparison with the wt-EV71, S240A-EV71 and S240P/P246A-EV71 had almost equal supernatant EV71 infectivity titers (P = 0.698 or P = 0.106, respectively). P246A-EV71 and S243A/P243A-EV71 had slightly lower EV71 infectivity titers (P = 0.030 or P = 0.038, respectively) (Fig. 6A). This finding indicated that a proline residue located in the H-I loop acts in the interaction of CypA and EV71, leading to the correct conformation of viral capsid and further virus uncoating.
It is notable that the supernatant infectivity titers of S243P-EV71 were 1.75 log10 lower than those of wt-EV71 (P = 0.01, Fig. 6A). The intracellular growth curve showed that S243P-EV71 growth is also slower than that of wt-EV71 (Fig. 6C). However, when we infected RD cells with S243P-EV71 and wt-EV71 viruses under a 5 µM HL051001P2 treatment, we found that the growth of S243P-EV71 was much better than that of wt-EV71 (Fig. 6D). This finding is consistent with the results of the infection by wt-EV71 or S243P-EV71 in RD-sh-CypA cells (Figs. 2C and 2D). Together, these results suggested that replacing S243-VP1 with a proline residue decreased viral fitness but conferred resistance to CypA inhibitors or caused a CypA loss of function.
The results we report here demonstrate that the CypA host factor played a crucial role in the uncoating process during the entry step of EV71 infection, and the action site of CypA was mapped to the H-I loop of capsid protein VP1. An analysis of all EV71 sequences in GenBank showed that the action position of CypA in the EV71 VP1 H-I loop was strictly conserved in all EV71 genotypes and stains (Fig. 7A), either in the protein sequence or the gene codon. However, a comparison of several representative strains of Coxsackie virus (CV), poliovirus (PV) and EV suggest that this position is not conserved among EV71, CVA16, CVB3 and PV (Fig. 7A). The dependence of the proliferation of other enteroviruses or picornaviruses on the host factors must be further defined.
The crystal structure of mature EV71 particles [32] revealed that the VP1 H-I loop is a mostly solvent-exposed region at the surface of the virus particle (Fig. 7B) and usually functions in receptor binding or uncoating. Among all reported EV71 functional receptors, SCARB2 is the only one that can mediate both attachment to the host cell and uncoating [21]. PSGL-1 cannot induce the conversion from mature 160S particles to other forms during the viral uncoating process [21]. In a recent result, Nishimur et al. reported that the H-I loop of VP1 plays an essential role in EV71 recognition through one of its functional receptors, namely PSGL-1, and they demonstrated that the substitution of VP1-K242 and K244, which are located in the VP1 H-I loop, significantly attenuated virus binding to PSGL-1 [37]. They also indicated that the VP1 E145 residue modulates the orientation of VP1 K244 and thus regulates the exposure of the positively charged lysine side chain, which in turn regulates receptor binding [37]. Moreover, Tan et al. showed that EV71 binds to heparan sulfate on the cell surface, and they suggested that heparan sulfate may bind to the positively charged amino acids (including VP1-K242, K244, and R161) that form a cluster around the five-fold symmetry axis [26]. These findings suggested that the lysine residues at the VP1-242 and 244 positions play essential roles in the binding of the EV71 virus to variable receptors. Interestingly, these two lysine residues are surprisingly very close to the CypA action site in the VP1 H-I loop, which is VP1-S243 (Fig. 7B). In a very recent result, Lee et al. reported an anti-EV71 neutralizing antibody called MA28-7, which has epitopes at the fivefold vertex that cover the VP1 H-I loop [38]; this study supports the critical role of the correct H-I loop conformation in the EV71 entry step. By contrast, the binding site of the EV71 virion to SCARB2 was mapped at a canyon of VP1 around residue Q172 [21], which is far away from S243-VP1. We observed that EV71 virion binding to SCARB2 was not enhanced, but slightly decreased by CypA treatment, suggesting that the CypA function in SCARB2-mediated EV71 entry could be more complicated. Taken together, we propose that CypA plays a role as an uncoating regulator by altering the conformation of the H-I loop in VP1 during the EV71 entry through the PSGL-1 or HS-mediated pathway, and CypA has different impacts on the entry of EV71 through various functional receptors.
Another interesting observation is that CypA mediated EV71 uncoating most distinctly at pH 6.0, but not at pH 5.5 and 6.0. During virus internalization, endosome acidification increases during maturation, at values ranging from pH 6.8 to 6.1 in early endosomes to pH 6.0 to 4.8 in late endosomes [39], [40]. Because SCARB2 was previously shown to mediate EV71 uncoating most efficiently at pH 5.6 [21], we propose that CypA acts to mediate EV71 uncoating before SCARB2 during the maturation of late endosomes. With the increasing maturation of late endosomes and acidification, the EV71 uncoating regulator transfers from CypA to the next one, namely SCARB2. A similar observation was also found for the CypA study in HPV entry. CypA treatment induced the release of capsid protein L1 from L2 in a pH-dependent manner, in which L1 dissociation from L2 was most efficient at pH 6.0, less efficient at pH 7.4, and undetectable at pH 5.5 and 8.0 [17], suggesting a complicated process during virus uncoating in endosomes.
Furthermore, CypA showed complicated impacts on the HIV-1 life cycle; CypA is necessary for HIV-1 infection [1], [41], [42] but also blocks HIV-1 uncoating [43], as revealed in previous reports. We cannot simply exclude the possibility that CypA may not only be associated with EV71 entry but might also affect other intracellular steps of EV71 protein translation, assembly or secretion in addition to its effects on the entry step. In our results, we actually found that the S243P-EV71 virus proliferated more slowly than the wt-EV71 without CypA inhibitor treatment, although the S243P mutant in VP1 can enhance the interaction between virions and CypA. Moreover, when we infected RD-sh-CypA cells with wt-EV71, although the intracellular genome RNA and VP1 protein level were much lower than that of wt-EV71-infected RD-sh-control cells, the supernatant virus titer exhibited no significant difference. Interestingly, the downregulation of CypB did not attenuate but actually increased EV71 RNA replication and VP1 expression (Figs. 2C and 2D), and the expression of endogenous CypB was also upregulated by EV71 infection (Fig. 2H). Moreover, the S243P mutant recovered both the RNA replication and protein expression of EV71 in RD-sh-CypA cells, but presented discrepancies in RD-sh-CypB cells, i.e., slightly increased RNA replication but decreased protein expression (Figs. 2C and 2E). Interestingly, two recent works revealed that Cyps inhibited the proliferation of HIV-1 virus, which is in opposition to the previously identified positive function of Cyps in the HIV-1 life cycle [9], [44], [45]. All these findings indicated that Cyps may have multiple functions in the EV71 life cycle and may have additional (or opposing) effects on viral assembly and secretion. This finding requires further validation.
The work we describe here highlights the new function of CypA as an uncoating regulator for EV71 proliferation by facilitating the conformational shift of the VP1 H-I loop. Our results significantly increase our understanding of virus-host interactions and provide an additional target of action for CsA-derived antivirals without immunosuppressive activity that are currently in clinical trials for treating EV71-infection.
RD cells (a human embryonal rhabdomyosarcoma cell line) were purchased from ATCC and the Huh7.5.1 cells were kindly given by Jin Zhong (Institute Pasteur of Shanghai, Chinese Academy of Science). The cells were grown in Dulbecco's modified Eagle's medium (DMEM) (GIBCO) supplemented with 10% fetal bovine serum (FBS) (GIBCO) at 37°C in a humidified incubator with 5% CO2. The plasmids containing human EV71 strain AnHui1 (GQ994988.1) and BrCr (U22521) were kindly provided by Prof. Bo Zhang from the Wuhan Institute of Virology. Plasmids containing human EV71 strain SK-EV006 (AB469182.1) and EV71-GFP, which contains a GFP reporter gene that is inserted into the SK-EV006 genome, were donated by Prof. Satoshi Koike (Tokyo Metropolitan Institute of Medical Science). The plasmid with EV71 subgenomic replicon RNA was given by Prof. Wenhui Li (National Institute of Biological Sciences [21]). The pNL4-3 plasmid was given by Prof. Linqi Zhang (School of Medicine, Tsinghua University). The EV71 viruses were amplified in RD cells, quantified by making a determination of the 50% tissue culture infective dose (TCID50) per 1 ml in RD cells as previously described [46], and used for all experiments.
A mouse anti-EV71 monoclonal antibody against VP1 (Abcam, 10F0, cat #ab36367) was used to detect the virus in all experiments. Rabbit anti-CypA monoclonal antibody (cat #ab41684-100) and rabbit anti-CypB monoclonal antibody (cat #ab16045) were purchased from Abcam. The anti-GAPDH monoclonal antibody and anti-GST monoclonal antibody were purchased from JiaMei, China. The secondary antibodies used for western blot analysis and immunofluorescence were purchased from Southern Biotech (HRP-conjugated goat anti-mouse IgG(H+L)), CoWin Bioscience (HRP-conjugated goat anti-rabbit IgG), Santa Cruz (PE-conjugated goat anti-mouse IgG), and Life Technologies (donkey anti-rat IgG (H+L)).
The cyclophilin A inhibitor known as CsA was purchased from Sigma, and HL051001P2 compound was generously provided by Prof. Jian Li [28]. The inhibitors were initially dissolved in DMSO, and stock solutions were stored at −20°C. Immediately before addition, these compounds were diluted to the desired concentrations in DMEM with 10% FBS.
TRIzol reagent and a Super Script III First-strand Synthesis System for RT-PCR kit were purchased from Invitrogen. A MEGA script T7 High Yield Transcription kit was purchased from Ambion. A QuantiTect SYBR Green RT-PCR kit was purchased from Qiagen. A cell viability and proliferation assay (WST-1) was purchased from Roche.
RNA transcripts and the EV71 subgenomic replicon were obtained by using the MEGA script T7 High Yield Transcription kit (Ambion), and the DNA that was linearized by SalI or XbalI (NEB) digestion was used as a template according to the manufacturer's protocol. In vitro transcribed RNA was transfected into RD cell monolayers in 100 mm × 20 mm dishes with Lipofectamine 2000 (Invitrogen), and the cells were then incubated at 37°C in 10 ml DMEM containing 10% FBS per dish. The cytopathic effects (CPE) of RD cells were observed at 24 h post transfection. When 90% of the cells exhibited CPE, the cell supernatants were then collected by centrifugation at 4,000 rpm for 5 min, and the target viruses were stored at -80°C.
The virus titers were determined by using endpoint dilution assays (EPDA), with focus-forming units (ffu) as the read-out [47]. In brief, the measurement was performed by seeding 1×104 RD cells per well in 96-well microtiter plates. After overnight culture, the EV71 viruses were serially diluted 10-fold with DMEM containing 10% FBS (10−1- to 10−8-fold dilutions) and added to RD cell. The plates were then incubated at 37°C in 5% CO2. CPE was observed under the microscope after 3 to 4 days post infection or the GFP expression level was monitored under a fluorescence microscope after 3 days post infection. The virus titer, which was expressed as the TCID50, was determined by EPDA.
Total cellular RNA was isolated with TRIzol reagent according to standard protocols. The following primer sequences were used for qRT-PCR: GAPDH, forward primer 5′-CCCACTCCTCCACCTTTGACG-3′, reverse primer 5′-CACCACCCTGTTGCTGTAGCCA-3′, EV71 5′-UTR forward primer 5′-TGAATGCGGCTAATCCCAACT-3′, and reverse primer 5′-AAGAAACACGGACACCCAAA G- 3′. qRT-PCR was performed with a QuantiTect SYBR Green RT-PCR kit (Qiagen), and the EV71 and GAPDH transcript levels were determined by ΔΔCT methods.
To determine the amount of purified EV71 virions, viral RNA was extracted from 50 µl of PBS buffer containing EV71 virions by using TRIzol LS reagent (Invitrogen). To determine the amount of EV71 virions in the CsCl fractions, viral RNA was extracted from 125 µl of the CsCl fraction containing EV71 virions with an additional 125 µl of nuclease-free water by TRIzol LS reagent (Invitrogen). A pUC18-EV71AH1 plasmid was used as a standard sample to generate a standard curve ranging from 1011–103 copies/ml. EV71 RNA copies were quantified by using the QuantiTect SYBR Green RT-PCR kit (Qiagen).
The antiviral activities of the compounds were determined by using a qRT-PCR-based assay with the EV71 virus and RD cells. In brief, 100,000 RD cells were seeded in each well of the 24-well tissue culture plates and allowed to attach in complete culture medium overnight. The culture medium was replaced with medium containing serially diluted compounds in the presence of 10% FBS and 0.5% DMSO. After 6 h, the RD cells were infected with EV71 at the multiplicities of infection (MOIs) indicated in the figure legends, and the compounds were added at the indicated concentrations. Total cellular RNA was isolated by using TRIZOL reagent according to standard protocols at 24 hpi. The qRT-PCR assay was performed as described above. The EV71 and GAPDH transcript levels were determined by ΔΔCT method. The IC50 value represents the concentration of the compound at which the EV71 RNA level in the RD cells was reduced by 50%.
To monitor the cytotoxic effects of the compounds, the viability of the RD cells was determined after 24 h of compound treatment; the viability was determined in 96-well tissue culture plates by using cell proliferation reagent WST-1 (Roche). Each data point represents the average of three replicates. The EC50 and cytotoxicity values were plotted by using GraphPad Prism software.
RD cells were seeded at 5×105 cells/well in 6-well plates. On the following day, the medium was removed and replaced with DMEM containing 10% FBS and 11.4 µM HL051001P2; 0.5% DMSO was used as a control. After 6 h, EV71 strain AnHui1 was used to infect RD cells at an MOI of 0.1 in complete medium containing the inhibitors. Over the course of selection, the RD cells were split when they reached 70–90% confluence. Fresh complete medium containing inhibitors was added when the cell cultures were split. Viral replication in the presence of compound HL051001P2 was monitored by determining the cytopathic effects (CPE) at each passage. The viruses demonstrated apparent CPEs after approximately 5 to 7 days after EV71 infection in the medium containing inhibitors or after 2 days in the medium containing 0.5% DMSO. The cell supernatants were then collected following centrifugation at 4,000 g for 5 min and were stored at −80°C as EV71-P2 (passage 2) virus. The RD cells were then treated with cyclophilin A inhibitors for 6 h and were infected with the EV71-P2 virus under the same conditions described above. The experiment was repeated for 6 cycles, and the cell supernatants were collected as EV71-P6 (passage 6) virus. The RD cells were lysed with TRIzol reagent.
For the EV71 RNA resistance mutation analysis, cellular RNA extraction was performed by using TRIzol reagent (Invitrogen) according to the manufacturer's instructions. For reverse transcription PCR, first strand cDNA was synthesized with a gene-specific primer (5′- ACCCCCACCAGTCACATTCACG- 3′), and the Super Script III First-strand Synthesis System for RT-PCR kit (Invitrogen) was used according to the manufacturer's instructions. The EV71 protein coding region of the genome was amplified by PCR in 5 short fragments as follows: fragment 1 (EV71-718-sense 5′-ATCTTGACCCTTAACACAGC-3′, EV71-2046-anti 5′-GACCATTGGGTGTAGTACCC-3′), fragment 2 (EV71-1975-sense 5′-CGATCCTGGGCGAAGTGGAC-3′, EV71-3345-anti 5′-TGTTGTCCAAATTTCCCAAG-3′), fragment 3 (EV71-3248-sense 5′-TACCTATTCAAAGCCAACCC-3′, EV71-4643-anti 5′-ATAAAGACATATCCTTGCCG-3′), fragment 4 (EV71-4569-sense 5′-ACGGCTACAAGCAACAGGTG-3′, EV71-6044-anti 5′-TTCCCTCGAAGATATCATGG-3′), and fragment 5 (EV71-5972-sense 5′-GGAAGGCTCAACATCAATGG-3′, EV71-7345-anti 5′-GGGTTGAGGTGTGTATAGCC-3′).
The short RT-PCR products of the resistant EV71 virus or the control EV71 virus were ligated into the TA cloning vector PMD18-T (Takara). Multiple individual bacterial colonies were isolated for each time point, and the purified plasmid DNA was sequenced. The sequences were aligned with Sequencher 5.0 and BioEdit software.
The mutant EV71 recombinant virus clone was constructed on the basis of the pUC18-EV71AH1 plasmid, which contained the original full-length EV71 AnHui1 strain. Site-directed mutagenesis was performed with a QuikChange Lighting Site-Directed Mutagenesis kit (Stratagene). The mutagenic primers were designed as follows: S243P-EV71(5′-GTGGGGACCTCCAAGCCCAAGTACCCTTTAG- 3′), S243A-EV71(5′-GTGGGGACCTCCAAGGCCAAGTACCCTTTAG-3′), P246A-EV71(5′-GGACCTCCAAGTCCAAGTACGCTTTAGTGGTTAGAATTTACATG-3′), S243P/P246A-EV71(5′-GTGGGGACCTCCAAGCCCAAGTACGCTTTAGTGGTTAGA ATTTAC-3′), and S243A/P246A-EV71(5′-GTGGGGACCTCCAAGGCCAAGTACGCTTTA GTGGTTAGAATTTAC-3′). The constructs were confirmed by sequencing.
The CypA and CypB stable knockdown RD or Huh7.5.1 cell lines were produced as previously described, with some modifications [12]. The following shRNA sequences were used in this study: NC, 5′-TTCTCCGAACGTGTGTCACGTTTC-3′; CypA, 5′-CTGGATTGCAGAGTTAAGTTTA-3′; and CypB, 5′-GCCGGGTGATCTTTGGTCTCTT -3′. shRNA recombinant lentiviruses (LV2-NC, LV-CypA, LV-CypB) were produced by Shanghai GenePharma, and the virus titers were determined to be 1×108 TU/ml. RD or Huh7.5.1 cells were infected at an MOI of 10. The shRNA recombinant lentivirus was incubated with 5 µg/ml polybrene to enhance the lentivirus infection. All knockdown cell lines were confirmed at 72 h post infection by western blot analysis. For the stable knockdown cell lines, the RD or Huh7.5.1 cells were incubated in selection medium containing 5 µg/ml puromycin (Invitrogen) beginning 48 h after transduction, and the CypA and CypB knockdowns were stable after approximately two weeks of cell culture.
RD cells or CypA knockdown cells were grown on cover slips until the cells reached 50% confluence; the cells were then infected with the EV71 virus. The cells were washed with PBS at the indicated times post infection and fixed with 4% paraformaldehyde for 15 min at room temperature, washed, and permeabilized with 0.5% Triton X-100 in PBS for 10 min. The cells were then washed and blocked with 1% normal goat serum in PBS for 30 min, followed by a 1 h incubation with primary antibodies (1∶400 dilution) at room temperature. After three washes with PBS, the cells were incubated with FITC- or PE-conjugated secondary antibodies (a 1∶200 dilution) for 1 h. After extensive washing with PBS, the cell nuclei were stained with DAPI. Images were captured by using a confocal microscope (Olympus FluoView FV1000 Confocal Microscope operated by FluoView software). The same microscope settings and exposure times were used within the individual experiments.
The genes encoding the H-I loop of EV71 VP1 (residues 239-GSSKSKYPL-247), the H-I loop with S243P substitution (residues 239-GSSKPKYPL-247), human CypA and the catalytic-defective mutant CypA H126Q were cloned into the pGEX-6p-1 expression vector with a GST tag fused at the N-terminus according to a general protocol. The accuracy of the insert was verified by sequencing. The plasmids were transformed into E. coli BL21 (DE3) cells, and the transformed cells were cultured at 37°C in LB media containing 100 mg/L ampicillin. After the OD600 reached 0.5, the culture was cooled to 16°C, and recombinant protein expression was induced. After overnight induction, the cells were harvested by centrifugation. The pellets were then resuspended in lysis buffer containing 20 mM Tris-HCl (pH 7.5) and 150 mM NaCl, followed by homogenization using an ultra-high-pressure cell disrupter (JNBIO, Guangzhou, China) at 4°C. The insoluble material was removed by centrifugation at 20,000 g. The supernatant was then loaded twice onto a GST column pre-equilibrated with lysis buffer. After loading, the GST column was washed with at least 5 column volumes of lysis buffer to remove the unbound protein. The beads containing recombinant GST-1S, GST-1P, GST-CypA or GST protein were added to Eppendorf tubes and stored at −80°C until use. To obtain recombinant human CypA without a GST tag, the GST tag on CypA was removed by overnight incubation with PreScission Protease, and the target proteins were eluted with lysis buffer. The eluted target proteins were further purified by Superdex-75 gel filtration chromatography (GE Healthcare) to remove any contamination. The fractions were analyzed with SDS-PAGE, and the final purity was over 95%.
For the immunoblot analysis, the cells were lysed in a lysis buffer containing 50 mM Tris-HCl (pH 8.0), 150 mM NaCl, 1% Nonidet P-40, 0.1% SDS, 2 mM EDTA, and protease inhibitors; the protein concentrations of the lysates were determined with a spectrophotometer. The proteins were resolved by sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) and transferred to nitrocellulose membranes (Millipore). The membranes were blocked for 4 h with 5% nonfat dry milk solution in Tris-buffered saline. The membranes were then blotted with specific primary antibodies, followed by incubation with secondary antibodies conjugated to horseradish peroxidase. The proteins were visualized by chemiluminescence by using a Clarity Western ECL Substrate (BIO-RAD). To allow the pull-down assays to detect the interaction between CypA and the EV71 virion, we incubated 200 µl of the EV71 strain (AnHui1, SK-EV006, S243A/P246A-EV71 and BrCr) (2×107 TICD50) with 50 µl of glutathione-sepharose beads containing GST (200 µg) or GST-CypA (200 µg) in 300 µl of immunoprecipitation buffer (50 mM Tris-HCl, pH 8.0, 150 mM NaCl, 1% Nonidet P-40, 2 mM EDTA, and protease inhibitors) overnight at 4°C. The beads were then washed three times with PBS, and the complexes were eluted from the glutathione-sepharose beads with reduced glutathione (GSH) solution. We then moved the supernatant to new Eppendorf tubes and added SDS loading buffer. We then incubated the supernatants in boiling water for 5 min and subjected the samples to 12% SDS-PAGE followed by western blot analysis with mouse antibodies against EV71-VP1 or GST. To detect the interaction between CypA and GST-1S or GST-1P, recombinant human CypA was expressed, purified, and concentrated to 20 mg/ml. Recombinant human CypA (200 µg) was incubated with 50 µl of glutathione-sepharose beads containing GST (200 µg), GST-1S (200 µg), or GST-1P (200 µg) in 300 µl of immunoprecipitation buffer as described above, and the reactions were incubated overnight at 4°C. We then washed the beads three times with PBS and eluted the complexes from the glutathione-sepharose beads with reduced GSH solution. The samples were then subjected to western blot analysis, as described above, by using mouse antibodies against GST or CypA. The bands were quantified by ImageJ software.
The time of addition effect was examined for HL051001P2. RD cells (1.5×105 per well in 500 µl of 10% FBS-DMEM medium) were cultured at 37°C under 5% CO2 in 24-well plates overnight. The cells were subsequently treated with 5 µM HL051001P2 either concurrent with the wt-EV71 (0 h) at an MOI of 50 or at intervals of −6, −4, −2, 0, 4, 6, and 8 hpi. After incubating at 37°C for 12 h, the antiviral activity was determined by measuring the percentage of EV71 RNA from the untreated control cells, and the mRNA level of GAPDH was used as an internal control. The supernatant virus titer, which was expressed as the TCID50, was determined by EPDA.
The virus binding assay was performed by using a previously reported protocol with some modifications [48]. In brief, RD cells were seeded at 1×105 RD cells/well in 24-well plates. The following day, the culture medium was removed and the cells were washed once with cold phosphate-buffered saline (PBS). After that, 500 µl of binding buffer (PBS containing 1% BSA and 0.1% sodium azide) was added to the cells on ice and incubated for 10 min; the supernatant was subsequently removed from the cells. The EV71 stocks (Strain AnHui1) (108 TCID50/ml) were prepared as previously mentioned. The EV71 virus was diluted in 500 µl of DMEM complete medium (dilution fold = 1:10 [5×106 TCID50], or 1:20 [2.5×106 TCID50]) per well and added to the cells. After 1 h of incubation on ice, the unbound virus was removed by three wash steps with 500 µl of PBS, and the cells were lysed in the wells with 500 µl of TRIzol. Viral RNA was extracted and detected by qRT-PCR. The virus binding assays were systematically performed in duplicate, and two individual experiments were performed for each condition.
The virus binding assay was performed according to a previously reported protocol [23], [26]. To detect the influence of CypA on the HS and EV71 virus interaction, 4 ml of EV71 strain AnHui1 (4 ×108 TICD50) was incubated with or without CypA at 4°C for 2 h. The supernatant was added to a 1 ml HiTrap Heparin HP column (GE Healthcare, Sweden) that was previously equilibrated with binding buffer (0.02 M Tris-HCl and 0.14 M NaCl [pH 7.4]) at a flow rate of approximately 0.5 ml/min. After loading, the two columns were washed with at least 5 column volumes of binding buffer to remove the unbound virus. The bound viral particles were eluted by using elution buffer (0.02 M Tris-HCl and 2 M NaCl [pH 7.4]). Fractions of 1 ml were collected, and the EV71 RNA was isolated and quantified by using the qRT-PCR method described above. Purified CypA was also uploaded to the same HiTrap Heparin HP and did not show any detectable interaction between CypA and the column (data not shown).
To detect the CypA influence on the PSGL-1 and EV71 virus interaction, EV71 strain AnHui1 (2 ×107 TICD50) was incubated with or without recombinant human CypA (4 µg) in 200 µl of DMEM for 2 h at 4°C. Human PSGL-1-Fc (3 µg, R&D Systems) or human IgG VRC01 Fc (3 µg) (a control), were then added to the assays, and the reactions were incubated for 1 h at 4°C. Protein G-agarose (50 µl, Roche) was then added to the mixture, and the tubes were shaken overnight at 4°C.
To detect the CypA treatment effect of the SCARB2 and EV71 virus interaction, EV71 strain AnHui1 (2×107 TICD50) was incubated with or without recombinant human CypA (6 µg) in 200 µl of DMEM for 2 h at 4°C. SCARB2 at a 5 µg quantity was added to the assays, and the reactions were incubated for 1 h at 4°C. Ni-NTA beads (50 µl) were then added to the mixture, and the tubes were shaken overnight at 4°C. All the beads were washed three times with PBS and then eluted with 100 µl of elution buffer containing 20 mM Tris-HCl, pH 8.0, 500 mM NaCl and 1 M imidazole. The EV71 RNA in the supernatant was isolated by TRIzol LS reagent and quantified by using the qRT-PCR method described above.
The purification of wt-EV71 virions was performed by using a previously reported protocol with modifications [21]. In brief, RD cells in five T175 cell culture flasks were infected with EV71 (Anhui1 strain) at an MOI of 0.1 and cultured in 10% FBS. When 90% of the cells exhibited CPE, the supernatant was collected and concentrated by filtration through a 100 kDa-cutoff centrifugal filter (Millipore). The concentrated virus was mixed with 1.4 g/ml CsCl at a volume ration of 1∶4 and loaded on the middle of a CsCl gradient (1.1 g/ml, 1.2 g/ml, 1.3 g/ml, 1.4 g/ml, and 1.5 g/ml, discontinuously) followed by ultracentrifugation at 41,000 rpm for 10 h at 4°C in a Beckman SW41Ti rotor. After being dialyzed with PBS, the purified EV71 virus was quantified and stored at -80°C. The RNA copies of purified EV71 virus was quantified as previously described.
Fifty µl of purified EV71 virus (1×1010 genome copies) was incubated with 20 µg of purified wt CypA or catalytic-defective mutant CypA H126Q in PBS containing 0.5% BSA in a total volume of 200 µl. For the low pH treatments, HCl was added to the mixture to bring the pH values to 5.5, 6.0 and 6.5. The mixture was then incubated at 37°C for 4 h and subsequently applied to a 1.1-1.5 g/ml discontinuous CsCl gradient, which was then ultracentrifuged at 41,000 rpm for 10 h at 4°C in a Beckman SW41Ti rotor. The samples were then analyzed by qRT-PCR. The transition states of viral particles during uncoating was performed in vitro with native 160S virions by heating for 10 min in a low salt buffer containing 4 mM CaCl2, 20 mM HEPES, pH 7.4 at 61°C and 68°C.
|
10.1371/journal.ppat.1006354 | A second wave of Salmonella T3SS1 activity prolongs the lifespan of infected epithelial cells | Type III secretion system 1 (T3SS1) is used by the enteropathogen Salmonella enterica serovar Typhimurium to establish infection in the gut. Effector proteins translocated by this system across the plasma membrane facilitate invasion of intestinal epithelial cells. One such effector, the inositol phosphatase SopB, contributes to invasion and mediates activation of the pro-survival kinase Akt. Following internalization, some bacteria escape from the Salmonella-containing vacuole into the cytosol and there is evidence suggesting that T3SS1 is expressed in this subpopulation. Here, we investigated the post-invasion role of T3SS1, using SopB as a model effector. In cultured epithelial cells, SopB-dependent Akt phosphorylation was observed at two distinct stages of infection: during and immediately after invasion, and later during peak cytosolic replication. Single cell analysis revealed that cytosolic Salmonella deliver SopB via T3SS1. Although intracellular replication was unaffected in a SopB deletion mutant, cells infected with ΔsopB demonstrated a lack of Akt phosphorylation, earlier time to death, and increased lysis. When SopB expression was induced specifically in cytosolic Salmonella, these effects were restored to levels observed in WT infected cells, indicating that the second wave of SopB protects this infected population against cell death via Akt activation. Thus, T3SS1 has two, temporally distinct roles during epithelial cell colonization. Additionally, we found that delivery of SopB by cytosolic bacteria was translocon-independent, in contrast to canonical effector translocation across eukaryotic membranes, which requires formation of a translocon pore. This mechanism was also observed for another T3SS1 effector, SipA. These findings reveal the functional and mechanistic adaptability of a T3SS that can be harnessed in different microenvironments.
| Non-Typhoidal Salmonella are important agents of food borne disease worldwide. These facultative intracellular bacteria use a specialized Type III Secretion (T3SS1) system to invade intestinal epithelial cells. Effector proteins translocated by this system across the eukaryotic plasma membrane induce actin rearrangements and target signaling pathways. One such effector is SopB, which contributes to invasion and mediates activation of the pro-survival kinase Akt. Within epithelial cells, Salmonella survive and replicate within a modified phagosome, known as the Salmonella-containing vacuole, or the host cell cytosol. Here, we investigated the post-invasion role of T3SS1 in epithelial cells, using SopB as a model effector. SopB-dependent Akt phosphorylation was observed at two distinct stages of infection: during and immediately after invasion, and later during peak cytosolic replication. SopB delivery by cytosolic Salmonella required T3SS1 but was translocon-independent. This was also observed for another T3SS1 effector, SipA, indicating that T3SS1 effectors may be secreted directly into the cytosol. Infection with a SopB deletion mutant eliminated the induction of Akt phosphorylation and decreased the lifespan of infected cells. These effects were reversed by expressing SopB specifically in cytosolic bacteria, confirming a role for SopB and T3SS1 during the cytosolic stage of infection. Thus, T3SS1 has two temporally distinct roles during epithelial cell colonization.
| Type III Secretion Systems (T3SSs) are used by a variety of Gram-negative bacteria for interkingdom delivery of proteins (known as effectors) from the bacterial cytosol into eukaryotic cells [1]. For bacterial pathogens, such as Salmonella enterica, Yersinia spp, and pathogenic Escherichia coli, these molecular syringes are key virulence determinants essential for a variety of processes including: adherence; invasion; intracellular survival and cytotoxicity. This broad repertoire is due to the diversified nature of effectors rather than the mechanism of delivery, which is highly conserved [2,3]. T3SS delivery is a contact-dependent process characterized by the formation of a pore, or translocon, at the point of contact with the eukaryotic membrane and through which effectors are delivered into the host cell [4].
Salmonella enterica serovar Typhimurium (hereafter Salmonella), a leading cause of gastroenteritis, possesses two functionally distinct T3SSs, encoded on Salmonella Pathogenicity Islands 1 and 2 (SPI1 and SPI2) [5]. The SPI1-encoded T3SS1 triggers invasion of non-phagocytic cells, such as intestinal epithelial cells, following contact with the plasma membrane. A cohort of translocated effectors targets the actin network and membrane phospholipids to direct formation of membrane ruffles, leading to uptake of the bacterium into a modified phagosome known as the Salmonella Containing Vacuole (SCV) [6]. Within the SCV, the SPI1 regulon is rapidly down-regulated whereas the SPI2 regulon is induced. Consequently, SCV biogenesis is primarily determined by effectors translocated via T3SS2 [7].
In epithelial cells, some Salmonella escape from the SCV and can survive and replicate in the cytosol resulting in two distinct populations of intracellular bacteria [8,9]. Cytosolic Salmonella replicate faster than vacuolar bacteria [9,10] and this “hyper-replication” results in a subpopulation of infected cells that are filled with Salmonella, both in vitro and in vivo [8]. In cell culture models, such as HeLa and C2BBe1 cells, cytosolic replication occurs in a largely synchronous fashion starting at ~4 h post-infection (hpi) and continuing for several hours until the inflammasome mediated death of the host cell at ~8–10 hpi [8,9,11,12]. In contrast, the contribution from vacuolar bacteria to intracellular replication is primarily seen from 12 hpi onwards [9]. Thus, within a time frame of 4–10 hpi, cells containing cytosolic Salmonella can be distinguished from those containing only vacuolar Salmonella due to the higher bacterial numbers. Additionally, these populations can be differentiated using fluorescent transcriptional reporters for SPI1 and SPI2 genes during this time period. SPI2-induced bacteria are only observed in the vacuole whereas SPI1 induction has only been observed in cytosolic hyper-replicating bacteria [8,12].
Several lines of evidence suggest that the SPI1-encoded T3SS1 has post-invasion activities in addition to its well characterized role in invasion. For example, the T3SS1 effector SopB (SigD), contributes to actin remodeling and induces phosphorylation of the pro-survival kinase Akt during invasion of epithelial cells [13–16], but SopB-dependent Akt phosphorylation can be detected for several hours following invasion [15] and it has been implicated in intracellular replication of Salmonella [17]. It is not clear whether the late activity of SopB can be attributed solely to effector translocated by T3SS1 during or post-invasion, or if it can be translocated by T3SS2. An alternative possibility is that the SP1-induced cytosolic subpopulation of intracellular Salmonella delivers de novo synthesized SopB. However, it remains to be determined whether T3SS1 is functionally active in this intracellular population of bacteria.
Here, we investigated the post-invasion intracellular role of T3SS1 using SopB as a model effector and Akt phosphorylation as an indicator of effector activity. Our study reveals widespread expression and activity of T3SS1 in cytosolic Salmonella. This activity results in delivery of a second wave of SopB, leading to resurgence in Akt phosphorylation and ultimately prolonging the lifespan of this subpopulation of infected cells. Unexpectedly, effector delivery was not inhibited in the absence of the translocon, indicating that effector delivery by cytosolic Salmonella can occur via a non-canonical translocon-independent mechanism.
We previously reported that the T3SS1 effector SopB induces sustained Akt phosphorylation following T3SS1-mediated invasion of epithelial cells by Salmonella Typhimurium [14,15]. Akt phosphorylation is readily detected in infected HeLa cell lysates using antibodies that specifically recognize phosphorylated Akt. Although Akt phosphorylation peaks at 0.5–1 hpi, a low level of phosphorylation is sustained for at least 7 hpi (Fig 1A). We hypothesized that this persistent Akt phosphorylation could be due to either: a low level of phosphorylation in all infected cells; or a high level of phosphorylation in a subpopulation of infected cells. In particular, we wondered if Akt phosphorylation levels were different in cells containing cytosolic vs vacuolar Salmonella. These two distinct intracellular populations can be resolved in infected HeLa cells by a modified gentamicin protection assay, in which chloroquine is used to selectively kill vacuolar bacteria, or by fluorescence microscopy (Fig 1B). We focused these studies on the time period from 2–8 hpi, which encompasses the peak of cytosolic replication [9,12]. In order to synchronize invasion, and the subsequent intracellular processes, we grew the Salmonella under SPI1-inducing conditions and then used a high moi (approximately 50 bacteria/cell) and a relatively short infection time (10 min) as previously described [18,19]. Under these conditions, the number of bacteria internalized into each cell is controlled so that over 95% of infected cells contain 1–10 bacteria at 2 hpi (Fig 1B). Following the onset of intracellular replication, the number of bacteria per cell increases and by 8 hpi approximately 40% of infected cells contain >10 bacteria. Approximately 25% of these cells, or 10% of total infected cells, contain hyper-replicating cytosolic bacteria and can be readily identified due to the high number (>50 per cell) of intracellular bacteria [8,9,12]. To determine whether Akt phosphorylation is increased in cells containing cytosolic bacteria, we used fluorescence microscopy combined with in situ proximity ligation amplification (PLA), a sensitive and specific method to detect low levels of proteins [20]. In cells infected with WT Salmonella, confocal microscopy revealed high levels of phosphorylated Akt (pAkt) compared to uninfected cells (Fig 1C and 1D). At 1 hpi the pAkt signal was 20- to 30-fold higher in cells containing WT Salmonella compared to uninfected cells in the same monolayer (Fig 1D). Akt phosphorylation was similarly increased in cells infected with a SopB deletion mutant (ΔsopB) complemented with plasmid borne SopB (psopBWT) but not a catalytically inactive mutant (psopBC460S) [14,15]. This SopB-dependent Akt phosphorylation was transient since, by 3 hpi, pAkt levels in infected cells decreased to near background levels and were not significantly affected by the presence or absence of active SopB (Fig 1D). A resurgence of Akt phosphorylation was observed at 6 hpi, although only in a subset of infected cells. We observed a clear correlation between the number of intracellular bacteria and levels of pAkt, which was 20- to 30-fold higher than background in cells containing ≥20 bacteria but was at background levels in cells containing <20 bacteria (Fig 1C and 1D). This cut off was selected as we have previously shown that, within this time frame, cells with 20 or more bacteria contained predominantly cytosolic Salmonella [8]. As at 1 hpi the increase in pAkt was dependent on the catalytic activity of SopB (Fig 1C and 1D). Similar results were seen in C2BBe1 colorectal epithelial cells (Fig 1E). These results show that, at a single cell level, there are two distinct periods of SopB-dependent Akt phosphorylation. The first phase is widely induced during invasion and is largely depleted by 3 hpi, whereas the second phase is induced later (observed at 6 hpi) in the subpopulation of infected cells that contain greater than 20, likely cytosolic, bacteria.
Since SopB-dependent Akt phosphorylation was detected in cells containing >20 bacteria at 6 hpi we next wanted to: confirm the presence of SopB in this subpopulation; determine its subcellular localization; and show whether it correlates with the presence of cytosolic or vacuolar bacteria. Detection of T3SS effectors, such as SopB, in host cells is difficult due to the low amounts delivered as well as a lack of good antibodies. For these reasons, we used epitope tagged SopB for these experiments. Specifically, we used WT Salmonella expressing 3xFLAG-tagged SopB (SopB3xFLAG) from the sopB chromosomal locus and under the control of its native promoter (Salmonella sopB3xFLAG). Infected HeLa cells were fixed and stained with an anti-FLAG antibody. Under the conditions used here (paraformaldehyde fixation and permeabilization with saponin) antibodies cannot cross the bacterial cell wall so that intrabacterial SopB3xFLAG is not detected. At 1 hpi, ~20–30% of infected cells contained detectable levels of extrabacterial SopB3xFLAG (SopB+ cells) (Fig 2A and 2B). Thereafter, the number of SopB+ cells rapidly decreased so that by 2 hpi, less than 10% of infected cells were SopB+ and this did not change significantly up to 8 hpi. At 6 hpi, similar to pAkt, SopB3xFLAG was predominantly detected in cells containing >20 bacteria. In cells infected with the ΔsopB strain, no significant FLAG staining was observed at any time point even in cells containing hyper-replicating bacteria. Interestingly, SopB3xFLAG staining was predominantly detected in close proximity to individual bacteria whereas pAkt was distributed throughout the cytosol (compare Figs 1C and 2B).
In our experience, cytosolic bacteria are readily distinguished from vacuolar bacteria at later time points (4–8 hpi) due to their greater intracellular numbers. However, individual cells are often infected with more than one bacteria and can harbor both vacuolar and cytosolic bacteria. Therefore, in order to definitively determine whether SopB3xFLAG staining is associated with cytosolic or vacuolar bacteria, and how this changes with time, we used digitonin-based differential permeabilization (a.k.a. phagosomal protection assay) [8]. Selective permeabilization of the plasma membrane, while leaving the phagosome membrane intact, allows cytosolic bacteria to be selectively stained with antibodies. Cells were infected with Salmonella sopB3xFLAG, or ΔsopB strain as a control. Cells containing one or more bacteria and staining positive for SopB3xFLAG were categorized into two populations: 1) Infected cells containing only vacuolar bacteria, and 2) Infected cells containing any cytosolic bacteria. At 1 hpi, the T3SS1 effector was found in both populations (Fig 2C and 2D). In contrast, at 6 hpi, SopB3xFLAG staining was almost exclusively detected in cells containing cytosolic hyper-replicating bacteria. To determine whether this is specific to SopB we performed similar experiments with another T3SS1 effector, SipA, and obtained almost identical results (S1 Fig). Thus, at 6 hpi, the presence of T3SS1 effectors in infected cells strongly correlates with the presence of hyper-replicating cytosolic Salmonella reinforcing that it is this population, rather than vacuolar bacteria, which delivers the effectors into the host cell.
The above experiments revealed that, in HeLa cells, T3SS1 effectors are present in the subpopulation of cells that contain cytosolic hyper-replicating Salmonella. To investigate whether this phenotype occurs in vivo, we utilized a C57BL/6J mouse model in which Salmonella disseminates to various tissues, including the gallbladder. Cytosolic hyper-replication has been observed in gallbladder epithelial cells which are extruded into the lumen where they can be readily identified [8]. In order to detect the T3SS1 effectors, mice were infected with Salmonella expressing either SopB3xFLAG or SipA3xFLAG from their chromosomal loci. Gallbladders harvested 5 days pi were stained for Salmonella and FLAG-tagged effectors. Extruded epithelial cells containing cytosolic, hyper-replicating Salmonella were observed in the lumen and many of these cells stained for SopB3xFLAG or SipA3xFLAG (Fig 3A, 3B and S2 Fig). In contrast, there was no significant FLAG staining in cells containing hyper-replicating WT Salmonella (no FLAG-tagged effectors, Fig 3C). Thus, in vivo, SopB and SipA are present in extruded epithelial cells containing cytosolic Salmonella.
To separate the post-invasion role of SopB in the cytosolic population of Salmonella from its role in invasion we used an inducible gene expression system in the ΔsopB background. The hexose phosphate transporter promoter, PuhpT, responds to exogenous glucose-6-phosphate, a glucose metabolite that is exclusively found in the cytosol [21,22]. To first verify the fidelity of PuhpT in our system, we used a plasmid borne GFP transcriptional reporter, pPuhpT-gfp, in WT Salmonella. These bacteria were internalized into HeLa cells and then the differential permeabilization assay and confocal microscopy used to determine their intracellular localization and GFP status (S3 Fig). At all time points GFP+ bacteria were found exclusively in the cytosol confirming that PuhpT is specifically induced in this subpopulation of intracellular Salmonella. Next, we used a transcriptional fusion of this cytosol inducible promoter with HA-tagged sopB, PuhpT-sopB2XHA, in a ΔsopB background to assess the role of SopB in cytosolic bacteria. As expected, immunostaining for SopB2xHA in infected cells showed an expression pattern similar to GFP under PuhpT, confirming that the effector was expressed only in cytosolic Salmonella (Fig 4A). Using PLA to detect pAkt revealed a dramatic increase in pAkt in SopB2xHA-positive cells, particularly in cells with cytosolic hyper-replicating bacteria at 6 hpi. In contrast, infected cells lacking bacterial SopB2xHA expression were devoid of pAkt. To confirm that there was no SopB activity at early time points in this system, lysates prepared from cells infected with ΔsopB/pPuhpT-sopB2xHA were assessed by Western blotting (Fig 4B). At 0.5 hpi, the pAkt levels were similar to those infected with the ΔsopB strain and uninfected cells whereas at 3 and 6 hpi, pAkt levels were 2.5- and 2-fold higher than ΔsopB infected cells, respectively, and were comparable to WT infected cells. Increased Akt phosphorylation was SopB-dependent since there was no increase in cells infected with ΔsopB bearing a promoterless control plasmid (pPNULL-sopB2xHA) (Fig 4A and 4B). These results illustrate that post-invasion cytosolic induction of SopB expression is sufficient for late Akt phosphorylation in infected cells.
The above experiments showed that, in the absence of SopB during invasion, de novo production of SopB by cytosolic bacteria could account for SopB-dependent Akt phosphorylation at 6 hpi. However, we also wanted to know whether, in the absence of cytosolic SopB, persistence following invasion could also possibly contribute to Akt phosphorylation. For this, we used a strain (ΔsopB/pPBAD-sopB2xHA) with a plasmid borne arabinose inducible construct. Expression of the tagged effector (SopB2xHA) was induced immediately prior to invasion by addition of arabinose during growth of Salmonella under SPI1-inducing conditions. Following internalization in HeLa cells, SopB2xHA should be rapidly depleted due to the absence of arabinose in the intracellular environment. Immunofluorescence microscopy and Western blot analysis of HeLa cells infected with ΔsopB/pPBAD-sopB2xHA showed SopB2xHA expression and elevated pAkt only at 0.5 hpi, but not at 3 and 6 hpi (Fig 4A and 4C). Thus, SopB delivered during invasion does not persist and, therefore, does not sustain Akt phosphorylation. Altogether, these results indicate that SopB delivered by cytosolic bacteria post-invasion is both essential and sufficient for sustained Akt phosphorylation in infected HeLa cells.
We have previously found that SopB could protect infected HeLa cells from apoptosis [15], although at that time we assumed that intracellular replication of Salmonella occurred within the SCV. The data presented above suggests that the anti-apoptotic effect of SopB could be limited to cells containing cytosolic hyper-replicating Salmonella. To address this question, we measured release of the cytosolic enzyme lactate dehydrogenase (LDH) from monolayers of HeLa cells infected with WT or ΔsopB Salmonella at 6 hpi (Fig 5A and 5B). At this time point, cells infected with the WT strain had similar levels of lysis (3.5±0.9%) as uninfected cells whereas the cells infected with the ΔsopB strain showed a two-fold increase in cell lysis (8.6±0.6%), despite lower numbers of intracellular of ΔsopB relative to WT (Fig 5A and 5B). In contrast, no increase in lysis was observed in cells infected with the ΔsopB strain complemented with plasmid borne SopB, either under the control of its own promoter (psopBWT) or the uhpT promoter (pPuhpT-sopB-2xHA). A control “promoter-less” plasmid (pPNULL-sopB-2xHA) did not rescue this phenotype. Considering that only 10–15% of infected cells contain hyper-replicating cytosolic Salmonella (Fig 1 [8]) and that not all HeLa cells in the monolayer are infected, the relatively small total amount of lysis caused by strains lacking SopB is not unreasonable. The lower numbers of ΔsopB bacteria observed at 6 hpi (Fig 5B) as well as other studies suggested that SopB may contribute to intracellular replication of Salmonella [17]. However, we were unable to detect any defect in intracellular replication of the ΔsopB strain when the results were normalized to the number of intracellular bacteria at 2 hpi (Fig 5C and 5D). In order to gain a better understanding of the role of SopB in cells containing hyper-replicating cytosolic bacteria we focused our analysis on this specific sub-population of cells by using a live cell imaging approach previously used to compare the replication rates of cytosolic and vacuolar Salmonella [9]. In order to assess cytosolic replication we took advantage of the plasmid borne GFP transcriptional reporter, pPuhpT-gfp, described above (Fig 4). Infected HeLa cells were imaged on a spinning disc confocal system for up to 10 h pi (Fig 5E, 5F and 5G). As a rapid readout for cell death, propidium iodide was used. This red fluorescent nuclear and chromosome counterstain is not permeant to live cells and is commonly used to detect dead cells. Post acquisition analysis of time lapse movies showed that cells containing the ΔsopB strain died earlier (412 min pi) than those containing WT Salmonella (482 min pi) (Fig 5E and 5G). In contrast, there was no detectable difference in the doubling rates of the two strains (Fig 5F). As a control for doubling rate, we included a strain in which constitutive expression of red fluorescent protein (RFP) causes a defect in intracellular replication. Altogether these results show that SopB does not affect intracellular replication of Salmonella but rather promotes the survival of HeLa cells containing hyper-replicating cytosolic Salmonella.
Previous studies have shown that some, but not all, cytosolic hyper-replicating bacteria are SPI1 induced [8,12]. Since this would have significant implications for our findings that SopB-dependent Akt phosphorylation is widespread in this population of cells, we considered whether this was an accurate estimation of SPI1 induction. One possibility is that limitations in the sensitivity of the experimental systems could result in an underestimation of SPI1 induction. Specifically, HeLa cells were infected with Salmonella containing a GFP-prgH transcriptional reporter with destabilized GFP, GFP[LVA] (PprgH-gfp[LVA]) [8,12]. As shown in Fig 6A, this system reveals many SPI1-induced (GFP+) bacteria at 0.5 hpi and, thanks to the short half-life of the destabilized GFP[LVA], down-regulation of SPI1 results in a dramatic decrease in the number of GFP+ bacteria by 3 hpi. By 6 hpi, GFP fluorescence reappears, although only a fraction of hyper-replicating cytosolic bacteria are GFP+, suggesting that SPI1 induction is not universal in this population ([8] and Fig 6A). Nevertheless, we considered that, while the use of destabilized GFP[LVA] is critical for following down-regulation of the prgH promoter, it could also result in the under-detection of SPI1-induced bacteria. Therefore, to increase the sensitivity of detection without losing temporal fidelity, we used an anti-GFP antibody to amplify the fluorescent signal (Fig 6B). One caveat of this system is that, in order to get the antibodies into the bacteria, we had to permeabilize with methanol fixation, which resulted in denaturation of GFP and, consequently, loss of fluorescence. However, antibody recognition was not affected and thus the antibody-mediated amplification revealed a dramatic increase in the proportion of cytosolic hyper-replicating bacteria that were GFP+ at 6 hpi (Fig 6, compare A and B). The fidelity of the amplified system was confirmed by the lack of any detectable increase in GFP staining in intracellular populations of bacteria that are not SPI1 induced (see 3 h time point and vacuolar bacteria at 6 h, Fig 5B and 5C). Thus, by 6 hpi, T3SS1 is widely expressed in cytosolic hyper-replicating, but not vacuolar, Salmonella.
During invasion of epithelial cells, SPI1 effectors, including SopB and SipA, are translocated across the plasma membrane by the T3SS1. In the absence of a functional T3SS1, bacterial internalization into HeLa cells is almost completely abrogated, making it technically challenging to study the role of T3SS1 in post invasion events [23]. To circumvent this problem, we developed an inducible strain, which has a functional T3SS1 for invasion, but is essentially a T3SS1 defective mutant in the intracellular environment. To do this we used the arabinose-inducible promotor PBAD to control expression of the SPI1 gene invA, which encodes a conserved structural component of T3SS1 (Fig 7A) [24]. This T3SS1 arabinose-inducible strain, T3SS1IND+-sopB3xFLAG, was invasion competent when grown under SPI1-inducing conditions in the presence of arabinose (S4A Fig) but was unable to deliver detectable amounts of either of the T3SS1 effectors (SopB3xFLAG, SipA) in cells containing cytosolic hyper-replicating Salmonella (>50 bacteria/cell) (Fig 7). In these, and subsequent, experiments we used a higher stringency cut-off for cytosolic bacteria (>50 bacteria/cell) so as to avoid any possibility of contamination by cells containing only vacuolar bacteria. To confirm that the defect in effector delivery by T3SS1IND was not due to a lack of effector expression, we immunostained intracellular bacteria following methanol-permeabilization. Levels of intrabacterial effector staining for the T3SS1IND and WT strains were comparable (S4B–S4E Fig). We also tested whether the SPI2 encoded T3SS2 could be involved in SopB or SipA delivery since some T3SS1 effectors, including SopB, have the potential to be delivered by T3SS2 as well as T3SS1 [25,26]. However, a T3SS2 deficient strain (ΔT3SS2) delivered SopB3xFLAG and SipA at levels indistinguishable from Salmonella WT (Fig 7C and 7E). Similar results, showing a requirement for T3SS1 but not T3SS2, were obtained in C2BBe1 cells (Fig 7D and 7F). Thus, T3SS1, but not T3SS2, is required for delivery of the effector proteins SopB and SipA by cytosolic hyper-replicating Salmonella.
Canonical delivery of effectors into host cells via T3SSs requires contact with host membrane and subsequent formation of a translocon pore through which the effectors are delivered. For T3SS1, the translocator proteins, SipB and SipC, are required to establish the translocon pore and translocation does not occur in their absence (see illustration in Fig 7A) [27–29]. Immunostaining for SipB in WT Salmonella infected cells revealed this translocon component in association with cytosolic but not vacuolar bacteria at 6 hpi, corroborating the induction of T3SS1 in cytosolic Salmonella (S5 Fig). To examine whether delivery of SopB and/or SipA by cytosolic Salmonella requires the T3SS1 translocon, we took advantage of a translocation defective mutant (ΔsipB), which is unable to invade epithelial cells (S4F Fig) but retains the ability to secrete effectors in broth culture (S4G and S4H Fig). To generate an invasion-competent sipB mutant strain, we again used the PBAD inducible system so that sipB expression was induced when bacteria were grown under SPI1-inducing conditions in the presence of arabinose (SipBIND) (S4I Fig). Confocal microscopy was used to assess the ability of the SipBIND strain to deliver SopB3xFLAG and/or SipA3xFLAG in epithelial cells. At 6 hpi, effector staining in infected HeLa cells infected with WT or SipBIND bacteria revealed no difference in the numbers of cells containing cytosolic hyper-replicating Salmonella (>50 bacteria/cell) that were positive for effectors (~40–50%, Fig 8B and 8C). Immunostaining for SipB in SipBIND infected HeLa cells confirm expression at 0.5 hpi but not at 3 and 6 hpi, excluding the contribution of residual SipB following invasion (S5 Fig). Similar results were obtained in C2BBe1 cells (Fig 8D and 8E). Thus, the T3SS1 translocon pore is not required for SopB or SipA delivery by cytosolic bacteria.
The ability of Salmonella to actively invade and colonize epithelial cells is critical for pathogenesis. Following invasion, the bacteria can survive and replicate within the SCV, a modified phagosome, or in the cytosol. Effectors translocated by the SPI1-encoded T3SS1 play critical roles in invasion. Here, we show a novel role for T3SS1 in the cytosolic subpopulation of intracellular bacteria (See model in Fig 9). Delivery of the effector SopB, via a translocon-independent mechanism, leads to Akt phosphorylation and prolonged survival of epithelial cells containing cytosolic Salmonella.
In enteric infection, environmental signals in the lumen of the small intestine—though still not well understood—are believed to be essential for SPI1 induction [31,32]. Our findings show that induction can also occur in the intracellular environment, specifically the cytosol of infected epithelial cells. This must confer an advantage to the bacteria since expression of the SPI1-encoded T3SS1, and its associated effectors, comes at a cost [33]. The T3SS1 needle/apparatus is recognized in the cytosol of mammalian cells resulting in inflammasome activation and host cell death [34]. In epithelial cells, the SPI1-encoded T3SS1 is specifically expressed in the cytosolic subpopulation of bacteria [8], leading to cell death via activation of a non-canonical inflammasome [11,35]. Whether this is an advantage to the host or pathogen may depend on the context. Is SPI1-induction in these bacteria priming them for the extracellular environment, i.e. lumen of small intestine, or is there a specific function for T3SS1 in the cytosolic niche? Here, we show that the latter may be true since delivery of T3SS effectors delays the onset of cell death thus conferring an advantage to the intracellular pathogen.
The activity of Akt, a central regulator of eukaryotic cell death and survival, is regulated by phosphorylation. Salmonella uses SopB to target this pro-survival kinase suggesting an important role for T3SS1 in regulating host cell survival [14,15,36]. However, since SopB is not essential for intracellular replication, the role of SopB-dependent Akt phosphorylation in epithelial cells was still unclear. In order to resolve this disparity, we used a single-cell approach to re-examine the temporal relationship between SopB and Akt phosphorylation. We found that, at later time points, Akt phosphorylation was highly induced in the subpopulation of infected cells containing hyper-replicating cytosolic Salmonella. De novo T3SS1-dependent delivery of SopB by cytosolic bacteria, as opposed to persistence of the effector, was key to this second wave of Akt phosphorylation. As a master regulator, Akt affects diverse cellular processes and lies at the crossroads between cell survival and death [37]. Dysregulation has been implicated in many human cancers and recently, an interesting link between Salmonella, Akt and human cancer was described [38]. The incidence of gallbladder cancers is increased in areas where there is high incidence of Salmonella Typhi infection, and experiments in chronically infected mice showed that tumors are induced in a SopB-dependent manner, suggesting a causal relationship [39]. This is consistent with our finding showing that SopB-dependent Akt activitation does not directly affect the ability of Salmonella to replicate but rather delays host cell death [15,40–43]. This may be a more widespread strategy since the PI3K-Akt signaling cascade is also targeted by other bacterial pathogens, including Chlamydia trachomatis, Chlamydia pneumonia, Coxiella burnettii and Mycobacterium tuberculosis, to delay or prevent apoptosis in infected cells [44–47].
Previously, the continued presence of T3SS1 effectors post-invasion has been explained by effector persistence in the intracellular environment [48] or delivery by the T3SS2 [25,26,49]. The main reason that the role of T3SS1 post-invasion is not well understood is because mutants lacking T3SS1 activity are unable to infect host cells. To overcome this obstacle we developed a set of inducible mutants, which allowed us to specifically address the post-invasion roles of the T3SS1 and SopB. Using this approach yielded a more complete picture of the differences between the two intracellular populations of Salmonella and particularly the activity of T3SS1 in the cytosolic population. SopB delivery by cytosolic bacteria requires continued transcription and translation of the molecule by the bacterium post-invasion [50]. Furthermore, T3SS2 has no role in delivery of SopB or SipA to infected host cells, although that has previously been suggested as a mechanism for sustained delivery both in vitro and in vivo [25,26,49]. Using a murine systemic infection model, we confirmed that SPI1 effectors are present in cells containing large numbers of cytosolic bacteria in vivo. Altogether, our data show that T3SS1 can account for the delivery of T3SS1 effectors at later stages of infection.
To the best of our knowledge this is the first time that T3SS1 has been shown to use a translocon-independent mechanism of delivery. Cytosolic Salmonella are not contained within a membrane-bound compartment, have no obvious contact with a membrane and can deliver effectors in the absence of the translocator protein SipB. For both Salmonella T3SS1 and the Shigella flexneri Mxi-Spa T3SS, the translocon proteins are gatekeepers that effectively prevent secretion of effectors in the absence of a specific signal. Mutants lacking translocon proteins constitutively secrete effectors but are unable to invade host cells [51,52]. Interestingly, Shigella, which also replicates in the cytosol of epithelial cells, can uncouple translocation and secretion depending on environmental stimuli [53]. Whether the same is true for Salmonella is still to be determined. The Salmonella SPI1, Shigella Mxi-Spa and Yersinia Ysa T3SSs are members of the Inv/Mxi-Spa subfamily, and other members are found in a number of pathogens including, Burkholderia pseudomallei (Bsa) and Chromobacterium violaceum (Cpi-1/-1a) [1,54]. While these T3SSs generally have key roles in the invasion of non-phagocytic cells [55–58], several of them also have post-invasion activities that contribute to pathogenesis [34,59,60].
SPI1 is a paradigm for bacterial adaptation to the host environment [61]. Here, we identified a novel activity of the SPI1-encoded T3SS1 in the cytosol of mammalian epithelial cells. Thus, this highly regulated T3SS mediates host cell interactions in at least two vastly different environments: the lumen of the gut, and the cytosol of epithelial cells. Given the central role of T3SSs at the host-pathogen interface, our findings highlight the need to reassess the contributions of the Salmonella T3SS1 and its effectors in post-invasion events to fully understand their impact on bacterial pathogenesis.
Bacterial strains, plasmids and oligonucleotides used in this study can be found in the supporting tables document. All mutants are derivatives of the parental Salmonella Typhimurium strain SL1344 [62] and were constructed using the bacteriophage λ recombinase system [63]. Bacteria were cultured in Miller formulation lysogeny broth (LB-M) supplemented with appropriate antibiotic, where necessary.
HeLa cells (human cervical adenocarcinoma, ATCC CCL-2) and the Caco-2 subclone C2BBe1 (human colorectal adenocarcinoma, ATCC CRL-2102) were grown at 37°C in 5% CO2 in complete growth medium (CGM): HeLa—MEM supplemented with 10% (v/v) heat-inactivated fetal bovine serum (Invitrogen), 2 mM L-Glutamine and 1 mM sodium pyruvate; C2BBe1—DMEM supplemented with 10% (v/v) heat-inactivated fetal bovine serum, 4 mM L-Glutamine and human transferrin (10 μg/mL). Both cell lines were passaged as recommended by ATCC and used within 15 passages of receipt.
Plasmids are listed in S1 Table. Oligonucleotides are found in S2 Table. A low copy arabinose-inducible expression plasmid was constructed by excising the araC gene and promoter from pBAD18-Cm using ClaI and HindIII, and cloning into the same restriction sites of pMPMA3ΔPlac. A transcriptional terminator (Part Bba_B0015, parts.igem.org) was subsequently cloned in using the SphI and HindIII sites, resulting in the plasmid pMPMA3ΔPlac PBAD TT. invA or sipB was amplified from SL1344 genomic DNA using oligonucleotides containing a ribosomal binding site and cloned into pMPMA3ΔPlac PBAD TT with NheI and SphI, resulting in the plasmids pPBAD-invA, and pPBAD-sipB, respectively. The sopB-sigE operon was amplified from the plasmid pSopB2xHA in the same manner, for the resulting plasmid pPBAD-sopB-2HA.
A cytosolically induced gfp reporter was constructed by transcriptionally fusing the promoter of uhpT to gfp as follows. First, gfpmut3.1 was amplified from pFPV25.1 and cloned into the XhoI and KpnI sites of pMPMA3ΔPlac, resulting in plasmid pMPMA3ΔPlac-gfp. The uhpT promoter region was amplified from SL1344 genomic DNA and cloned into the NotI and BamHI sites of pMPMA3ΔPlac-gfp. Finally, part Bba_B0015 (see above) was cloned into ClaI and XhoI, resulting in the final plasmid pPuhpT-gfp. To construct pPuhpT-sopB-2xHA and the promoterless control plasmid pPNULL-sopB-2xHA, the sopB-2xHA sigE operon was amplified from psopB2xHA and cloned into SphI and HindIII sites of pPuhpT-gfp or into the BamHI and XhoI sites of pMPMA3ΔPlac-gfp, thus replacing the gfp gene in each plasmid.
Cells were passaged 16–18 h prior to infection into 24-well tissue culture treated plates. For C2BBe1 cells, coverslips were pre-coated with collagen type I (BD Biosciences). Bacteria were grown under conditions optimizing T3SS1-dependent invasion [18,19]: a 2 mL overnight culture was subcultured in 10 mL of LB-Miller broth (1:33 dilution, no antibiotics) with shaking at 37°C for 3.5 h. For strains harboring pPBAD-invA, 0.02% arabinose was added for the duration of the subculture; for pPBAD-sipB, 0.2% arabinose was added for the duration of the subculture; for ΔsopB harboring pPBAD-sopB-2xHA, 0.2% arabinose was added for the final 1 h of the subculture. Bacteria were pelleted (1 mL, 8,000 x g, 2 min) at room-temperature (RT) and re-suspended in an equal volume of Hanks’ Balanced Salt Solution (HBSS, Mediatech). Monolayers were infected at an MOI of ~50 for 10 min at 37°C in 5% CO2. Extracellular bacteria were removed by washing with HBSS (x2) and cells were incubated in antibiotic-free CGM until 30 min pi. Cells were then incubated for 1 h in CGM supplemented with L-Histidine (500 μg/mL) and gentamicin (50 μg/mL), followed by CGM supplemented with L-Histidine (500 μg/mL) and gentamicin (10 μg/mL) for the remainder of the infection.
HeLa cells were infected according to the ‘Bacterial Infection of Mammalian cells’ section. At the desired time points, wells were lysed in 0.2% (w/v) sodium deoxycholate to enumerate viable intracellular bacteria.
HeLa cells were infected according to the ‘Bacterial Infection of Mammalian cells’ section with the following modification: 1 h prior to sample collection, media in replicate wells were replaced with growth media containing gentamicin and histidine, with or without chloroquine (400 μM). Monolayers were lysed in 0.2% (w/v) sodium deoxycholate and serial dilutions plated.
Proximity Ligation Assays (PLA) were performed using the Red DuoLink In Situ PLA Kit (Sigma-Aldrich). Cells were serum starved (0% FBS) 3h prior to and for the duration of the infection. Infected cells were fixed in 2.5% w/v paraformaldehyde (PFA) for10 min at 37°C, washed with PBS and stained with Alexa Fluor 647-conjugated wheat germ agglutinin for 10 min at 37°C (Life Technologies). Cells were washed in PBS, fixed in PFA for 5 min at RT, and blocked and permeabilized in 0.2% (w/v) saponin and 10% (v/v) normal donkey serum in PBS (SS-PBS) for 30 min at RT. The PLA assay was subsequently performed according to the ‘custom solutions’ protocol of the DuoLink In Situ PLA kit. Antibodies (goat anti-CSA1, 1:300; KPL and rabbit phospho-Akt Ser473 (D9E), 1:400; Cell Signaling) and PLA probes were diluted in SS-PBS. For HeLa cells, phosphorylated Akt PLA signals were manually quantified from maximum intensity projections assembled from 20 slice stacks. For C2BBe1 cells, signals were quantified using CellProfiler (www.cellprofiler.org) [64] from maximum intensity projections assembled from 15 slice stacks. Our analysis pipeline involved image thresholding followed by nuclei detection in the DAPI channel. A perinuclear area defined by a 50-pixel extension of the identified nucleus object was used to define this region for PLA signal quantification. PLA signals were identified following image thresholding and related to their respective parent nucleus.
At the indicated time points, infected HeLa cells were washed three times with KHM buffer (110 mM potassium acetate, 20 mM HEPES, 2 mM MgCl2, pH 7.3), and the plasma membrane selectively permeabilized by incubation with digitonin (40 μg/mL in KHM buffer) for 1 min at RT, followed by three washes with KHM buffer. Cells were then incubated for 12 min at RT with rabbit anti-Calnexin (Stressgen, 1:250 in KHM), to label the cytosolic face of the endoplasmic reticulum in permeabilized cells, and goat anti-Salmonella CSA1 antibodies (KPL, 1:100 in KHM), to detect cytosolic bacteria. Cells were washed in PBS, fixed in PFA and all host cell membranes were permeabilized with SS-PBS. Antibodies delivered post-digitonin permeabilization were detected with Alexa Fluor 647-conjugated anti-rabbit and Alexa Fluor 568-conjugated anti-goat antibodies. After washes with PBS, SopB3xFLAG and SipA effectors were detected using mouse anti-FLAG M2 (Sigma-Aldrich, 1:250) and mouse anti-SipA (1:50), respectively. Cells were washed again in PBS and incubated with Alexa Fluor 488-conjugated anti-mouse to detect effector bound antibodies and Pacific Blue-conjugated goat anti-Salmonella CSA1 to label all intracellular bacteria. Coverslips were then washed sequentially with PBS and distilled water, and mounted on glass slides in a Mowiol solution.
Infected cells on coverslips were washed with PBS and fixed in either 2.5% PFA for 10 min at 37°C or 100% ice-cold methanol for 1 min. After three washes with PBS, cells were blocked and permeabilized with SS-PBS for 30 min at RT, incubated in primary antibodies diluted in SS-PBS for 1.5 h at RT, washed sequentially in PBS and saponin-PBS, incubated in Alexa Fluor-conjugated secondary antibodies diluted in SS-PBS for 45 min at RT and washed sequentially in PBS and distilled water. Coverslips were mounted onto glass slides in a Mowiol solution supplemented with 2.5% (w/v) DABCO. Samples were imaged on a Carl Zeiss LSM 710 confocal laser-scanning microscope equipped with a Plan APOCHROMAT 63X/1.4 N.A. objective and assembled into flat maximum-intensity projections using FIJI (NIH) or Zen 2012 SP1 software. Figs were assembled using Adobe Photoshop CC.
HeLa cells were plated onto 6-well tissue culture treated plates and infected as described above except the monolayers were infected at an MOI of ~100 and serum starved 3h prior to and for the duration of the infection. At the indicated times, monolayers were solubilized in boiling 1.5X Laemmli sample buffer and kept at -20°C until ready for analysis. Protein samples were boiled for 10 min at 95°C, separated by SDS-PAGE (10% v/v bis-acrylamide gels) and transferred to nitrocellulose (0.45μm; Bio-Rad Laboratories) at 100V for 1 h. Membranes were incubated in blocking buffer (5% w/v BSA in Tris-buffered saline/0.1% Tween 20; TBST) for 1 h at RT and incubated overnight at 4°C with primary antibody diluted as recommended by the manufacturer. Membranes were washed three times in TBST and incubated at room temperature for 1 h with appropriate HRP-linked IgG secondary antibody (Cell Signaling Technology; 1:20,000 diluted in blocking buffer). Membranes were washed three times in TBST and developed with SuperSignal West Femto Chemiluminescent Substrate (Thermo Fisher Scientific).
Seven-week old C57BL/6J mice (Jackson Laboratories) were infected by retro orbital i.v. injection with ~500 CFU of Salmonella Typhimurium SL1344 SopB3xFLAG or SipA3xFLAG (4 mice per strain). Mice were monitored daily for signs of clinical illness. On days 4 and 5, animals were anesthetized with isofluorane prior to transcardial perfusion with pharmaceutical grade heparin/saline (100 U/mL), followed by perfusion with 4% (w/v) PFA. Gallbladders were harvested and post-fixed in 4% (w/v) PFA for 4 h at RT, washed three times with PBS (30 min each) and cryopreserved overnight in 30% (w/v) sucrose/PBS at 4°C. The following day, gallbladders were incubated in ‘optimal cutting temperature’ compound (OCT; Sakura Finetek) for 15–30 min (RT) and 5 μm sections were prepared on Shanodon Positively Charged Superfrost slides (Thermo Scientific) using a Leica CM3050S Kryostat. Slides were air-dried and stored at -20°C until staining.
Cryosectioned slides were equilibrated to room temperature, the OCT layer removed and washed twice with PBS (5–10 min each). To quench free aldehyde groups, samples were incubated for 1 h with 0.3 M Glycine/PBS, rinsed once with PBS, incubated for 10 min with 50 mM NH4Cl/PBS and washed again in PBS. Samples were incubated in a humidity chamber for 30 min in block solution (2% donkey serum, 1% BSA, 0.5% Triton X-100, 0.95% Tween-20, PBS pH 7.2) prior to overnight incubation at 4°C with primary antibodies. Slides were washed twice in PBS (10 min each with rocking) and incubated with secondary antibody for 1 h at room temperature. Slides were finally washed twice in PBS and mounted in Prolong Gold Anti-fade reagent with DAPI (Life Technologies). Tile scans of murine gallbladder images were acquired at frame size of 3073 x 3072 pixels with 40X EC Plan-Neofluar 40x/1.30 N.A., 0.6X digital zoom. Antibodies used were as follows: rat anti-LAMP1 1D4B (1:100; Abcam), Alexa Fluor 488-conjugated goat anti-CSA1, Alexa Fluor 568-conjugated mouse anti-FLAG M2 (concentrations determined by user post-conjugation) and Alexa Fluor 647 goat anti-rat (1:400).
SPI1-induced cultures (10 mL) were prepared as detailed in the main Experimental Procedures. 1 mL of culture was pelleted by centrifugation (8,000 x g, 2 min, RT), supernatant removed and the pellet resuspended in 250 μL boiling 1.5x Laemmli SDS-PAGE sample buffer, boiled at 95°C for 10 min, and snap-frozen. In parallel, the remaining culture (~ 9 mL) was split between two pre-chilled thick-wall, polycarbonate ultra-centrifuge tubes (Beckman Coulter #355647) on ice and centrifuged at 30,000 x g in a MLA-80 rotor in an Optima Max Ultracentrifuge (Beckman Coulter; 20 min, 4°C). Supernatant was carefully removed, filtered through a 0.2 μm PES low-protein binding filter (GE Healthcare) and proteins were precipitated overnight at 4°C in 10% (v/v) trichloroacetic acid. Precipitated sample was divided into pre-chilled 2 mL snap-cap tubes and centrifuged for 20 min at 16,000 x g (4°C). Pellets were washed once with ice-cold acetone, centrifuged as above and allowed to dry for 5–10 min in a fume hood. Pellets were resuspended in a total volume of 200 μL pre-heated 1.5X Laemmli sample buffer (95°C), snap frozen and stored at -80°C until use. Aliquots of 10 μL were analyzed by SDS PAGE to determine secretion of relevant effector proteins. Antibodies used for protein detection were as follows: mouse anti-FLAG M2 (1:1000; Sigma-Aldrich) and mouse anti-DnaK (1:20,000; Enzo Lifesciences).
Cytotoxicity assays were performed using the colorimetric CytoTox 96 Non-Radioactive Cytotoxicity assay kit (Promega). HeLa cells were infected as described above except that cells were serum starved (0% FBS) 3 h prior to and for the duration of the infection. Incubations from 0.5 hpi were carried out with the addition of 0.2% BSA to minimize the cytotoxic effects of serum starvation. At 6 hpi, supernatants from sample wells were collected and assayed for lactate dehydrogenase release following manufacturer’s instructions. Absorbance at 492 nm was measured with a Tecan Infinite M200 Pro.
HeLa cells were plated and infected in black glass-bottom 24-well plates (Grenier BioOne) according to the ‘Bacterial Infection of Mammalian cells’ protocol. When appropriate, cells were treated with either 50 μg/mL AF 488-dextran (Invitrogen) 20h prior to infection or 250 ng/mL propidium iodide (ImmunoChemistry Technologies) 1.5h post-infection. Between 3 and 10 hpi, images were acquired every 5 minutes using a Nikon TiE spinning disc confocal microscope (CSU10 Yokogawa) with Perfect Focus, Cascade II CCD camera (Photometrics), and custom laser launch (Prairie Technologies). All imaging was preformed within a stage-top incubation chamber (Pathology Devices) at 37°C, 75% humidity, and 5% CO2. Wells were imaged using a Plan Fluor 40X 0.75 N.A. Ph2 air objective. All post-acquisition image analysis was done using ImageJ software (W.S. Rasband, National Institutes of Health, Bethesda, MD version 2.0.0) and Adobe Photoshop (CS5 v12.1 Adobe).
Unless otherwise stated in the figure legend, data were analyzed for statistical significance by a one-way analysis of variance (ANOVA) with Bonferroni’s post hoc test. A P-value of ≤ 0.05 was considered significant. * = P ≤ 0.05, ** = P ≤0.01, *** = P ≤0.001, ns = not-significant/ P > 0.05.
All animal work at Rocky Mountain Laboratories adhered to the U.S. Government Principles and applicable humane and ethical policies in accordance with the Public Health Service (PHS) policy, the Guide for Care and Use of Laboratory Animals and the Animal Welfare Regulations. The Rocky Mountain Laboratories ACUC reviewed and approved this research (ASP# 2014–028).
|
10.1371/journal.pbio.0060110 | The Parasexual Cycle in Candida albicans Provides an Alternative Pathway to Meiosis for the Formation of Recombinant Strains | Candida albicans has an elaborate, yet efficient, mating system that promotes conjugation between diploid a and α strains. The product of mating is a tetraploid a/α cell that must undergo a reductional division to return to the diploid state. Despite the presence of several “meiosis-specific” genes in the C. albicans genome, a meiotic program has not been observed. Instead, tetraploid products of mating can be induced to undergo efficient, random chromosome loss, often producing strains that are diploid, or close to diploid, in ploidy. Using SNP and comparative genome hybridization arrays we have now analyzed the genotypes of products from the C. albicans parasexual cycle. We show that the parasexual cycle generates progeny strains with shuffled combinations of the eight C. albicans chromosomes. In addition, several isolates had undergone extensive genetic recombination between homologous chromosomes, including multiple gene conversion events. Progeny strains exhibited altered colony morphologies on laboratory media, demonstrating that the parasexual cycle generates phenotypic variants of C. albicans. In several fungi, including Saccharomyces cerevisiae and Schizosaccharomyces pombe, the conserved Spo11 protein is integral to meiotic recombination, where it is required for the formation of DNA double-strand breaks. We show that deletion of SPO11 prevented genetic recombination between homologous chromosomes during the C. albicans parasexual cycle. These findings suggest that at least one meiosis-specific gene has been re-programmed to mediate genetic recombination during the alternative parasexual life cycle of C. albicans. We discuss, in light of the long association of C. albicans with warm-blooded animals, the potential advantages of a parasexual cycle over a conventional sexual cycle.
| Candida albicans is an important human fungal pathogen that has an unconventional sexual cycle. Efficient mating requires that diploid cells of opposite mating type first switch from the more common “white” phase to the “opaque” phase and then undergo cell fusion. The resulting tetraploid strains can return to the diploid state via a non-meiotic parasexual program of concerted chromosome loss. We used SNP and comparative genome hybridization to analyze the progeny resulting from this parasexual cycle and found a range of genetically diverse strains with altered phenotypes. In addition, in a subset of these strains, genetic recombination was found to have taken place between homologous chromosomes. This recombination was dependent on Spo11, a conserved protein required for the introduction of DNA double-strand breaks in the chromosomes of eukaryotes that undergo conventional meiosis. Thus, Spo11 is required for genetic recombination and the generation of increased genetic diversity during the C. albicans parasexual cycle.
| In most sexually reproducing eukaryotes, meiosis is used to precisely halve the DNA content in the cell, often for the formation of haploid gametes from diploid precursor cells. This specialized form of cell division involves one round of DNA replication followed by two successive rounds of DNA division. Each round of DNA division is unique. During the first meiotic division (meiosis I) extensive DNA recombination takes place between maternal and paternal homologous chromosomes, which then are segregated from one another. The second round of DNA division (meiosis II) more closely resembles normal mitotic DNA division, in which sister chromatids are segregated to opposite poles. In the case of spores in fungi and spermatozoa in animals, all four haploid nuclei form four different haploid cells, while in the female meioses of animals only one haploid nucleus survives and forms the mature oocyte.
The meiotic process has been studied extensively in the model fungi Saccharomyces cerevisiae and Schizosaccharomyces pombe. In S. cerevisiae, mating of haploid MATa and MATα cells normally generates a stable diploid a/α cell that replicates mitotically until subsequently induced to undergo meiosis under conditions of limiting nitrogen availability and the presence of a non-fermentable carbon source [1]. In S. pombe, mating also occurs between haploid cells but the diploid state is often transient, immediately undergoing meiosis to regenerate the haploid form. The sexual program in S. pombe is again controlled by nutritional cues, as mating and meiosis normally occur only under starvation conditions [2]. In both S. cerevisiae and S. pombe, meiosis generates four recombinant haploid spores held together in an ascus.
While S. cerevisiae and S. pombe are rarely pathogenic in humans, the related ascomycete C. albicans is an opportunistic pathogen capable of causing both debilitating mucosal infections and potentially life-threatening systemic infections [3]. C. albicans is normally a harmless commensal fungus, existing in the gastrointestinal tract of at least 70% of the healthy population [4]. However, C. albicans is also the most commonly isolated fungal pathogen, particularly targeting individuals with compromised immune systems and leading to death in up to 50% of patients with bloodstream infections [5–7].
Until recently, C. albicans was thought to be asexual, existing only as an obligate diploid organism and thus classified amongst the Fungi imperfecti [8]. However, a robust mating system has now been uncovered in this organism, in which mating occurs between diploid mating type-like (MTL) a and α strains to generate an a/α tetraploid strain. Mating occurs both under laboratory conditions and in different in vivo niches in a mammalian host [9–12]. Population studies of clinical isolates are also consistent with C. albicans strains undergoing genetic exchange in their natural environment, albeit at a limited rate [13].
While an efficient mating apparatus has now been identified in C. albicans, the mating cycle differs in several important respects from that of S. cerevisiae and other fungi. For example, mating in C. albicans is regulated by phenotypic switching; MTL homozygous C. albicans cells can reversibly switch between two heritable states termed white and opaque, and only the opaque form is competent for efficient mating [14]. This unusual mode of mating regulation is so far unique to C. albicans (and the very closely related yeast, Candida dubliniensis [15]) making it likely that this adaptation has evolved to regulate mating of C. albicans strains in their natural environment—that of a warm-blooded host. Completion of the mating cycle in C. albicans also seems to occur in an atypical manner. Although reductional DNA divisions by a meiotic program have not been observed, tetraploid strains of C. albicans have been shown to return to the diploid state via a parasexual mechanism. During this process, tetraploid cells exposed to certain laboratory media were induced to lose chromosomes in an apparently random, but concerted, fashion, thereby forming cells with a diploid, or very close to diploid, DNA content [16]. The genetic locus responsible for determining C. albicans mating type (the MTL locus) segregated randomly in these experiments so that many of the progeny cells were a and α diploid cells that were themselves mating competent. Mating of diploid cells to form tetraploid cells, followed by random chromosome loss to generate diploid progeny cells, thereby constitutes a parasexual mating cycle in C. albicans.
In this study, we examined the genetic profile of strains formed by the parasexual mating process in C. albicans using SNP and comparative genome hybridization (CGH) techniques. We observed extensive shuffling of the parental configurations of chromosomes by the parasexual cycle, giving rise to many types of recombinant C. albicans progeny. Many of the progeny strains are not true (euploid) diploids; rather, they are aneuploid strains that are often trisomic for one or more chromosomes. In addition, we provide the first evidence that tetraploid strains experiencing chromosome instability and subsequent chromosome loss also undergo genetic recombination between homologous chromosomes.
We also report that genetic recombination in C. albicans tetraploids was dependent on the presence of Spo11p, a conserved protein that in other eukaryotes initiates meiotic recombination by the introduction of double-strand breaks (DSBs) into the DNA [17]. These results suggest that the parasexual pathway in C. albicans has evolved as an alternative pathway to meiosis for promoting a reduction in cell ploidy, and furthermore, that at least one gene that normally functions in meiotic recombination has been co-opted for use in the parasexual mating cycle.
The parasexual cycle of C. albicans, as currently envisaged, is shown in Figure 1A. Note that no meiotic program has been observed in C. albicans, despite the presence of many genes in the genome whose homologues function specifically in meiosis in other fungi [18]. However, C. albicans strains have been found to undergo a parasexual cycle; tetraploid strains become genetically unstable when incubated on certain laboratory media, losing chromosomes and generating diploid (and aneuploid) progeny strains that are themselves mating competent. The chromosome loss process is concerted, with loss of one or more chromosomes predisposing the cell to lose additional chromosomes, and the diploid state being the final product [16]. While tetraploids are stable when grown on YPD medium at different temperatures, two culture conditions were identified that induced genetic instability in C. albicans: (i) growth of tetraploid strains on S. cerevisiae “pre-sporulation” (pre-spo) medium at 37 °C, and (ii) growth of tetraploid strains on medium containing L-sorbose at 30 °C. The latter condition was previously shown to also induce chromosome loss in diploid C. albicans strains [19]. More specifically, diploid strains were unable to grow on L-sorbose medium unless they first underwent loss of one copy of Chromosome (Chr) 5, becoming monosomic for this chromosome. In contrast, diploid strains were relatively stable when grown on pre-spo medium, indicating that diploid and tetraploid strains exhibit very different selective pressures when cultured on this medium.
To monitor changes in ploidy in tetraploid strains of C. albicans, we exploited a genetically marked tetraploid strain, RBY18, containing markers on Chr 1 and 5. The strain was constructed by mating a/Δα and Δa/α cell types, as shown in Figure 1B [16]. Strain RBY18 is heterozygous for the GAL1 gene 1 on Chr 1, which is counterselectable. Strains carrying wild-type GAL1 are unable to grow on medium containing 2-deoxygalactose (2-DOG) as the carbon source, while derivative strains that have lost both copies of the GAL1 gene are able to grow on 2-DOG medium [20]. In most cases, it is expected that loss of GAL1 function in RBY18 will occur by loss of both chromosomes carrying the GAL1 allele, although GAL1 function also can be lost by mutation or genetic recombination. The RBY18 tetraploid strain is also heterozygous for all four MTL alleles on Chr 5: WTa, WTα, Δa1/a2, and Δα1/α2, which are easily distinguishable using whole cell PCR and oligonucleotides specific to each MTL allele [14].
To generate progeny strains that have undergone the parasexual mating cycle, the marked tetraploid strain RBY18 was induced to undergo chromosome loss on either pre-spo or sorbose medium and gal1− strains were selected by growth on 2-DOG medium. These 2-DOG resistant (DOGR) strains were subsequently analyzed by PCR to confirm that loss of MTL alleles on Chr 5 had accompanied loss of GAL1 alleles on Chr 1, an indication that cells had undergone a reduction in overall cell ploidy (unpublished data). PCR of the MTL loci was also used to detect possible jackpot effects, where several gal1− progeny might have been derived from a single cell having undergone a chromosome loss event. Where possible, progeny cells with different combinations of MTL alleles were used for subsequent analysis. Selected progeny strains were grown in YPD medium at 30 °C and analyzed by flow cytometry to determine the overall ploidy of each strain, as shown in Figure 2. Flow cytometric analyses confirmed that each strain was diploid, or close to diploid, in DNA content, as judged by staining of the DNA with sytox green [9]. Seven strains (P1 to P7) were derived from RBY18 by growth on pre-spo medium, and six strains (S1 to S6) were derived from RBY18 by growth on sorbose medium (Figure 2). Subtle differences were observed in the flow cytometry DNA profiles between isolates, where distinct peaks were evident representing non-replicated (G1 phase) and replicated (G2 phase) DNA. In some strains the majority of the cells contained replicated DNA (e.g., S5 and S6, Figure 2, panels N and O), while others had an almost equal distribution of cells with unreplicated and replicated DNA (e.g., P4, panel F). However, there was no obvious correlation between DNA profiles analyzed by flow cytometry and cell growth rates.
To further characterize the strains generated by parasexual chromosome reduction, progeny were plated for single colonies on rich (YPD) medium to examine colony growth. After incubation at 30 °C for 7 d, colonies were compared for overall size and morphology (Figure 3). A wide range of phenotypes was observed, including smaller colony sizes relative to diploid and tetraploid parental strains and altered colony morphologies. Some of the isolates produced hyper-filamentous morphologies, as evidenced by increased surface wrinkling of the colonies (e.g., progeny strains P3, P4, and P6; Figure 3, panels E, F, and H). Normally, C. albicans cells grow as budding yeast, pseudohyphal, or true hyphal cells. Examination of cells from the wrinkled colonies by microscopy confirmed that these colonies contained many filamentous (pseudohyphal and true hyphal) cells, while the unwrinkled colonies (including control strains) contained very few filamentous cells (unpublished data). Some progeny strains also exhibited reduced filamentation on medium that normally induces hyphae formation (Spider medium and serum-containing medium, KA and RJB, unpublished data).
Thus, the parasexual cycle of C. albicans can generate variant strains with diverse colony morphologies. Changes in the ability to undergo the yeast-hyphal transition have been closely linked with the pathogenic potential of C. albicans strains [21–24]. It is therefore likely that many of these variant strains will exhibit reduced virulence in models of candidiasis; but it is also possible that some of these isolates could have increased fitness under particular selective conditions, leading to improved colonization of defined in vivo niches in the host.
SNP and CGH microarrays are powerful approaches for examining genetic recombination and genome structure in C. albicans [25–28]. SNP arrays were designed to exploit the sequence diversity between chromosome homologues in the diploid C. albicans genome. The genome-wide SNP arrays used here included 152 SNPs, distributed across all eight chromosomes of C. albicans. As each SNP is specific for one of the parental chromosome homologues, each homologue can be distinguished in progeny from the parasexual mating cycle. In addition, loss of heterozygosity (LOH) at SNPs on otherwise heterozygous chromosomes can be used as a marker for genetic recombination. Quantitative SNP analysis can also be used to determine the relative copy number of each homologue in a sample (see Materials and Methods).
CGH analysis provides a complementary approach to SNP arrays for the determination of the copy number of each gene on each chromosome in the sample. Labeled genomic DNA from experimental samples (Cy3 labeled) and labeled DNA from a reference diploid SC5314 strain (Cy5 labeled) were hybridized to whole genome arrays containing >6,000 C. albicans ORFs [27,28]. CGH data provides information on the copy number of every chromosome, as well as indicating large-scale aneuploidies. In this study, we used both CGH and SNP approaches to obtain a detailed picture of the products of the C. albicans parasexual cycle following concerted chromosome loss.
SNP and CGH arrays were first used to analyze RBY18 and the diploid parental strains that had been used to construct this tetraploid strain. SNP analysis confirmed that MTLa and MTLα parental diploid strains were heterozygous for most of the SNPs on the array, although in the parental MTLα strain Chr 2 was homozygous for all markers (Table S4). CGH array data confirmed that the parental strains were euploid diploids and RBY18 was a euploid tetraploid, as they contained two and four copies of each of the eight C. albicans chromosomes, respectively.
We then analyzed 13 progeny strains produced by concerted chromosome loss from RBY18 using SNP and CGH arrays (see Figures 4 and S4, and Tables S1 and S4). Only three of the 13 strains were true diploids (P2, P5, and P6). The majority (10/13) of the progeny strains contained at least one extra chromosome: four of the seven strains derived from growth of the tetraploid on pre-spo medium were trisomic for one to three chromosomes and all six strains derived from growth on sorbose were also trisomic for up to three of the eight C. albicans chromosomes (Figure 4). Thus, concerted chromosome loss was often incomplete and did not immediately result in true diploid strains.
Curiously, there was a strong bias towards trisomy of Chr 4 in the progeny strains; all strains carrying at least one trisomic chromosome (four pre-spo-selected strains and all sorbose-selected strains) were trisomic for Chr 4. Trisomies of Chr R, 2, 5, 6, or 7 were also detected in at least one of the progeny. As expected, Chr 1 was always present in the disomic parental configuration (one copy of each homologue) because selection of DOGR progeny requires that the strains lose both Chr 1 homologues from the MTLα mating parent (Figure 1B).
The most striking feature of the progeny genetic profiles was that three strains contained a number of short LOH tracts (six or seven LOH tracts were observed in each strain), evidence of multiple recombination events between homologous chromosomes. Isolates P1, S3, and S4 exhibited recombination events that included LOH at SNPs on multiple chromosomes (including Chr R, 1, 2, 4, 5, 6, and 7, see Figure 4). While selection on 2-DOG required inheritance of the gal1Δ alleles on Chr 1, the LOH events detected here are independent of the GAL1 locus. Moreover, these events did not involve homozygosis of all of Chr 5, which might be expected to occur in response to sorbose selection. Instead, the recombination events we observed appear to be selection independent. Overall, the appearance of multiple gene conversion tracts within several strains, and the general absence of gene conversion tracts in other strains, suggests that some cells become generally competent for recombination at more than one locus, while other strains do not undergo such recombination events at all.
In at least one example (Chr 2 in strain P1) one complete chromosome arm (Chr 2L) became homozygous (Figure 4). This recombination event may have arisen in one of two ways: (i) A cross-over between chromosomes led to reciprocal recombination between homologues, as commonly occurs during meiosis in other fungi. In this case, the partner DNA involved in the reciprocal exchange was lost during the process of concerted chromosome loss. (ii) A break-induced replication event occurred. In this case, a DSB in one chromosome was repaired by DNA replication that copied the template strand from the break near the centromere all the way to the telomere in the homologous chromosome. Break-induced replication is a non-reciprocal recombination event and in S. cerevisiae is often restricted to repair of DNA DSBs where only one end of the break shares homology with the template [29].
Potential hotspots for recombination were identified in the three strains that had undergone inter-homologue recombination. For example, SNPs HST3 and 2340/2493 on Chr 5 underwent LOH in P1, S3, and S4 recombinant strains. Additional experiments are necessary to fully document hotspots for recombination. However, our results indicate that recombination events are not uniform across the C. albicans genome during the parasexual cycle.
Natural isolates of C. albicans are diploid, and it has been proposed that haploid forms cannot exist because of the presence of recessive lethal alleles in the genome. Evidence supporting this idea came from classical mitotic recombination studies [30,31]; however, no systematic investigation of possible recessive lethal alleles in the C. albicans genome has been reported. Using the present dataset, we can rule out the presence of recessive lethal alleles on some chromosomes. For example, it was already known that Chr 5 does not harbor recessive lethal alleles: loss of either homologue can be induced in diploid cells by growth on sorbose medium [19]. The SNP data presented here supports this finding, as both AA and BB configurations of Chr 5 homologues were observed in the progeny strains P2 and P6, respectively (this nomenclature assigns the parental configuration of chromosome homologues as AB). Similarly, several other chromosomes did not carry recessive lethal alleles, as their homologues could be lost during the parasexual cycle. Chr R, 2, 3, 5, 6, and 7 were all found to be homozygous in at least one independent isolate. However, only one homozygous configuration was observed for each chromosome (either AA or BB), leaving open the possibility that the other chromosome homologue carries recessive lethal alleles. We will revisit the issue of recessive lethal alleles below.
The C. albicans parasexual cycle provides an alternative mechanism to meiosis for a reduction in cell ploidy. Although no experimental evidence for a meiotic pathway in C. albicans currently exists, the genome contains homologues of many genes that function specifically in meiosis in the related yeast S. cerevisiae [18]. Some of the meiosis genes from C. albicans even complement for meiotic function in S. cerevisiae, demonstrating they encode a conserved protein activity [32]. It seems likely that either (i) C. albicans has a cryptic meiotic program still to be discovered, or (ii) meiotic genes have been adapted to other processes in C. albicans, perhaps some in the parasexual pathway.
To address the latter possibility, we investigated the potential role of the Spo11 protein in genetic recombination during the parasexual cycle. In fungi such as S. cerevisiae and S. pombe and in higher eukaryotes, Spo11p makes meiosis-specific DSBs in DNA via a topoisomerase-like mechanism of DNA cleavage [33,34]. C. albicans ORF19.11071 on Chr 2 encodes a potential homolog of S. cerevisiae SPO11 (http://www.candidagenome.org). An alignment of this ORF with SPO11 genes from diverse species including S. pombe, S. cerevisiae, Kluyveromyces lactis, and Drosophila reveals that several of the critical conserved residues identified for DNA strand cleavage are present in the C. albicans sequence (Figure S1). In particular, the conserved active site tyrosine residue, required for breakage of the DNA and formation of a phosphotyrosine bond, is present in the C. albicans protein. Similarly, Glu-233 and Asp-288 residues that are required in S. cerevisiae Spo11p for meiotic recombination [35] are conserved in the C. albicans protein. ORF19.11071 is a homologue of the Spo11 family and will therefore be referred to as C. albicans Spo11p in the rest of this study. Attempts to complement S. cerevisiae Spo11 function with C. albicans Spo11p, as measured by rates of meiotic recombination in return-to-growth experiments, were unsuccessful (Table S2). This result is perhaps not surprising as SPO11 sequences from diverged species are poorly conserved outside of the core catalytic residues [36] (Figure S1). It is also worth noting that meiotic proteins in general are faster evolving than most cellular proteins [37,38], an issue that is taken up again in the Discussion.
To investigate whether C. albicans Spo11p is expressed in mitotically dividing cells, a Spo11-13myc fusion protein was constructed in diploid C. albicans strains. Western blots show that the Spo11-13myc protein was detectable in mitotic extracts of diploid cells grown in YPD medium, although the level of expression was relatively low (see comparison of protein levels with that of the mitotic spindle protein Kar3-13myc) (Figure 5). Thus, in C. albicans, the Spo11 protein is expressed in mitotically dividing cells.
The observation that C. albicans Spo11p is expressed during mitotic growth is consistent with it having a function outside of meiosis. To examine if C. albicans Spo11p is required for genetic recombination in the parasexual mating cycle, we deleted all four copies of the SPO11 gene in genetically marked tetraploid strains (RBY176/RBY177) that were heterozygous for GAL1 on Chr 1. The strains were induced to undergo concerted chromosome loss on pre-spo or sorbose medium and were then exposed to 2-DOG to select for strains that had lost both copies of GAL1. Eighteen DOGR colonies were selected from tetraploid growth on pre-spo (eight colonies) or sorbose (ten colonies) and subsequently analyzed by flow cytometry to determine if they were diploid, or near diploid, strains (Figure S2). Indeed, we detected diploid Δspo11 progeny strains, indicating that Spo11p is not necessary for the process of concerted chromosome loss in tetraploid C. albicans strains. We next analyzed the colony morphologies of the Δspo11 diploid progeny. As was seen with progeny from wild-type tetraploids (Figure 4), many of the Δspo11 progeny strains exhibited altered colony morphologies on YPD medium (Figure 6).
Genomic profiles of the Δspo11 diploid progeny (along with the parental diploid and tetraploid strains) were generated using SNP and CGH microarrays (see Figure 7, as well as Tables S5 and S6, and Figures S1 and S4). One of the diploid parents (RBY79, MTLα parent) was initially homozygous for Chr 2, and the other parent (RBY77, MTLa parent) carried a long tract of LOH on Chr 2 (Figure 7). This is reflected in the patterns of Chr 2 inheritance in the diploid progeny which either received only one type of Chr 2 homologue (Ps2, Ps3, Ps4, Ps5, Ps6, Ss1, Ss2, Ss3, Ss4, Ss8, and Ss10) or received two homologues that only differ near the Chr 2R telomere (Ps1, Ps7, Ps8, Ss5, Ss6, Ss7, and Ss9). Similarly, one of the gal1Δ Chr 1 homologues in the parental MTLa strain had undergone LOH of a single SNP near the telomere of Chr 1L and this LOH tract was retained in all of the progeny.
As in the wild-type (SPO11+) progeny that were close to diploid, a majority (11/18) of the strains carried at least one and up to three trisomies, and Chr 4 was often one of the trisomic chromosomes (5/11 strains). Other chromosomes that became trisomic were Chr R, Chr 1, Chr 2, Chr 5, Chr 6, and Chr 7. The only chromosome that did not become trisomic in these strains or in the wild-type diploid progeny strains was Chr 3. Concerted chromosome loss did result in homozygosis of Chr R in nine strains (and trisomy in one strain) with the same homologue always being retained (the blue-colored “A” homologue in Figure 7). Interestingly, while no trisomies of Chr 3 were found, Chr 3 underwent LOH in ten strains, with seven of them retaining homologue B (colored pink) and three retaining the A homologue (Figure 7).
The most striking feature of the Δspo11 progeny strains was that they did not undergo any detectable genetic recombination events. No single LOH events (gene conversion events) or chromosome crossing over events (long-range LOH) events were observed (although we note that if reciprocal recombination events occurred, in which both recombinant chromosomes were retained, these would not be detected by SNP analysis). In contrast, progeny derived from SPO11+ strains exhibited multiple recombination events in three out of 13 strains (Figure 4), a difference that is statistically significant (p < 0.05). Taken together, the SNP and CGH experiments indicate that genetic recombination takes place in wild-type cells during the parasexual mating cycle, generating recombinant C. albicans strains. These recombination events are dependent on Spo11p, a conserved protein that normally acts specifically in meiosis in a wide range of eukaryotes. We suggest that Spo11p function has been adapted in C. albicans for mediating genetic recombination in the alternative parasexual mating cycle.
The Spo11 experiment more than doubled the data on chromosome loss from C. albicans tetraploids, and we used this expanded dataset to re-evaluate patterns of chromosome loss during the parasexual cycle. We pooled genomic profiling data for all 31 progeny strains derived from tetraploids by concerted chromosome loss (13 from wild-type SPO11+ tetraploids and 18 from Δspo11 tetraploids) (Table 1). We determined, for each chromosome in each strain, whether they existed as the parental configuration of homologues (AB), a homozygous configuration (AA or BB), or a trisomic configuration (AAB or ABB). We excluded Chr 1 from this analysis, as selection for the loss of the GAL1 gene required the AB configuration be retained for Chr 1 in all isolates (see Figure 1B).
While the number of strains analyzed in this study is relatively small, several trends are apparent. First, if two of the four copies of each chromosome in tetraploid strains were lost with equal probability, it would be expected that 67% of chromosomes would consist of AB homologues, while 33% of chromosomes would exhibit either AA or BB configurations. Isolates selected from pre-spo medium contained a chromosomal distribution very close to this, with 72% of disomic chromosomes being AB homologues, and 28% of chromosomes being AA or BB homologues. In contrast, isolates derived from sorbose medium were biased towards a homozygous chromosome configuration (45% were AA or BB with only 55% exhibiting the AB configuration). Sorbose-selected strains were also more likely to contain trisomic chromosomes than were strains selected on pre-spo medium. Trisomic chromosomes were present for 24.3% of chromosomes selected on sorbose medium, while only 12.4% of chromosomes were trisomic in isolates selected on pre-spo medium. Both of these differences between pre-spo and sorbose media were significant (p < 0.05) and provide evidence that strains undergoing chromosome loss on these media either experience different patterns of chromosome loss or different selective pressures.
Previous studies have also observed differences between pre-spo and sorbose medium in that diploid C. albicans strains were stable on pre-spo medium but exhibited chromosome instability (particularly that of Chr 5 but also of other chromosomes) on sorbose medium [16,19,39]. One possibility for the higher fraction of homozygous AA/BB chromosomes in tetraploids exposed to sorbose medium is that these conditions generate monosomic chromosomes that then undergo re-duplication to form homozygous disomic chromosomes. At least for Chr 5, this possibility was ruled out by PCR typing of MTL alleles on this chromosome, as all four MTL alleles in the tetraploid are distinct (MTLa, MTLα, MTLΔa, MTLΔα; Figure 1B). PCR analysis revealed that strains that were homozygous for Chr 5 by SNP analysis always contained two distinct MTL alleles, indicating that monosomy and reduplication had not occurred (unpublished data). These experiments demonstrate that, at least for tetraploid strains, the formation of viable progeny on sorbose medium does not require monosomy of Chr 5 at any stage.
Trisomy was more common in strains derived from sorbose medium than pre-spo medium and could be due either to chromosome loss of one homologue from tetraploids or to re-duplication of one chromosome homologue in a disomic strain. Curiously, at least in a subset of cases, trisomy was a result of reduplication of one chromosome homologue in sorbose-derived strains. For example, three strains, S5, Ss1, and Ss9, were shown to be trisomic for Chr 5 by SNP analysis and yet each strain contained only two types of MTL allele by PCR genotyping. This indicates that trisomy of Chr 5 arose by re-duplication of one homologue of Chr 5 in a disomic strain. In addition, one isolate (strain Ss2 derived from sorbose medium) was trisomic for Chr 1, but was clearly gal1− by PCR (lacked the GAL1 ORF) and was also 2-DOG resistant. Thus, where trisomies can be distinguished in sorbose-derived strains, they were due to re-duplication of chromosome homologues for Chr 1 and Chr 5. In contrast, strain Ps8 derived from pre-spo medium was trisomic for Chr 5 and also tri-allelic at the MTL locus, indicating that trisomy occurred via loss of one homologue of Chr 5 and not chromosome re-duplication. Overall, our results suggest that growth of tetraploids on sorbose medium may apply more selective pressure to the cells than growth on pre-spo medium, causing them to produce progeny with increased trisomies by chromosome re-duplication and more bias in the distribution of whole chromosome LOH events.
A further conclusion from our analysis is that every chromosome (excluding Chr 1 because of the selection for gal1Δ/Δ) can exist in a homozygous form. Because of the limited number of strains analyzed, we only detected one homozygous configuration (either the AA or BB configuration) for most chromosomes, however both Chr 3 and Chr 5 were found in both the AA and BB configurations. This implies that Chr 3, like Chr 5, does not contain recessive lethal alleles on either chromosome homologue. One possible caveat to this conclusion is that undetected recombination events may have repaired recessive lethal alleles on these chromosome homologues. However, this seems unlikely given that the Δspo11 progeny produced a significant number of strains that were homozygous for both homologues of Chr 3; seven progeny were homozygous for the B homologue and three were homozygous for the A homologue.
We detected trisomies for all chromosomes except Chr 3 in at least one progeny strain. This suggests that one or more genes on Chr 3 may not be well tolerated at higher than euploid copy number under these conditions. Recent studies in S. cerevisiae have shown that increased copy numbers of certain chromosomes can be lethal, as haploid cells disomic for Chromosome VI were inviable [40]. However, at least in the majority of cases, C. albicans strains trisomic for one or more chromosomes were viable and produced apparently stable karyotypes.
Finally, a comparison of the growth rates of the progeny strains from the parasexual cycle was revealing. While most of the euploid progeny grew at rates very similar to that of a control diploid strain, aneuploid strains grew at increasingly slower rates as the number of trisomic chromosomes increased (Figure S3). Thus, euploid progeny grew on average 7.4% slower than a control SC5314 strain, while strains containing one trisomic chromosome grew 9.5% slower, strains containing two trisomies 16.3% slower, and strains with three trisomies 23.4% slower. Thus, as the number of additional chromosomes increased, so, in general, did the doubling time of the cell. In S. cerevisiae a similar observation has been made, where aneuploid chromosomes (disomies in haploids or trisomies in diploids) were found to cause a proliferative disadvantage, and this disadvantage generally increased as the number of extra chromosomes increased [40,41]. Aneuploidy therefore appears to confer a proliferative disadvantage in multiple yeast species.
Most sexually reproducing organisms use meiosis to reduce the chromosome number of the cell and to generate genetic diversity through recombination, typically for the formation of recombinant haploid progeny from diploid precursors. In C. albicans, a meiotic program has not been identified, despite an intact mating apparatus and the presence of many genes in the genome that function specifically in meiosis in related fungal species [18]. However, an alternative pathway has been described that through chromosome loss can complete a parasexual cycle. In contrast to the precision of the meiotic process, the parasexual pathway utilizes random, yet concerted, chromosome loss for the formation of diploid progeny from tetraploid cells [16]. In this paper, we used both SNP and CGH microarray analyses to reveal that the parasexual pathway generates highly divergent strains by three distinct mechanisms: (i) shuffling of whole chromosomes, leading to new combinations of homologues; (ii) formation of aneuploid strains, usually trisomic for one or more intact chromosomes; and (iii) accumulation of multiple recombination events between homologous chromosomes, in a process that is dependent on the conserved meiosis protein, Spo11p.
The parasexual process of concerted chromosome loss in C. albicans was previously suspected of yielding new combinations of homologues by random segregation of parental chromosomes [16,42]. Our work now confirms this idea. Strains from the parasexual cycle also often contained aneuploid chromosomes, as would be expected if the chromosome reduction process was not completed or if strains obtained a selective advantage from the presence of a particular aneuploidy (as suggested by the prevalence of Chr 4 trisomies in this study). Prior to the discovery of mating in C. albicans, classical experiments demonstrated that tetraploids could be formed by fusion of spheroplasted diploid cells [43,44], and that chromosome instability could be induced in these tetraploid cells by artificial means (e.g., heat shock, drug selection) [45,46]. The products of chromosome loss were also cells with a diploid, or close to diploid, DNA content, indicating that random segregation of chromosomes can occur in tetraploids generated either by mating or by fusion of spheroplasted cells.
Much less expected was our discovery that some strains undergoing the parasexual cycle underwent genetic recombination between homologous chromosomes. Even more surprising, these strains underwent recombination events at multiple different chromosomal loci. The observation that recombination was extensive in some strains but absent in others, may indicate that strains can exist in two alternative states; those that are primed to undergo genetic recombination during the parasexual cycle and those that are not. We note that it is unlikely that these recombinant strains were formed by a subset of tetraploids undergoing meiosis, as all three recombinants were aneuploid, being trisomic for at least one chromosome. In contrast, meiosis would be expected to produce primarily true diploid (euploid) strains without chromosomal aneuploidies. Genetic recombination is integral to meiosis in most fungi, where accurate segregation of chromosomes at the first meiotic division requires recombination between homologous chromosomes (for recent reviews see [17,47,48]). Meiotic recombination is initiated by the formation of DNA DSBs catalyzed by Spo11p, generating covalent protein-DNA intermediates that are subsequently processed by enzymes including homologs of bacterial RecA [49,50]. Meiotic recombination can lead either to the reciprocal exchange of DNA flanking the DSB (crossover events) or to events in which no exchange of the flanking DNA takes place (gene conversion or non-crossover events). However, at least in S. cerevisiae, Spo11 has not been observed to influence rates of mitotic recombination (C. Giroux, personal communication).
Recombination in strains undergoing the parasexual pathway in C. albicans was less frequent than that expected from a classical meiotic pathway. Significantly, however, deletion of C. albicans Spo11 function eliminated all recombination during the parasexual mating cycle (Figure 7 and Tables S5 and S6). This result suggests that C. albicans Spo11p is integral to the generation of genetic diversity during the parasexual cycle, and thereby enhances the degree of variability in the strains produced. In addition, in the absence of evidence for a functional meiosis in C. albicans, our findings suggest a role for the conservation of one meiotic gene in this organism. We propose that the Spo11 protein, which functions specifically in meiosis in other organisms, has been re-programmed in C. albicans to function during the parasexual pathway. Future experiments will determine if other meiosis-specific proteins function in the alternative parasexual process in C. albicans. We cannot rule out, however, the possibility that meiotic proteins have been retained to function in a cryptic meiotic pathway that remains to be discovered.
In S. cerevisiae, Spo11 functions together with a number of accessory proteins to introduce meiotic DSBs, including Ski8, Mer2, Mei4, Rec102, Rec104, and Rec114 [17]. However, with the exception of Ski8, homologues of these accessory factors are not recognizable in the C. albicans genome [18]. This may be due, at least in part, to the fact that many of the proteins involved in meiotic recombination are faster evolving than most other cellular proteins [37,38]. The limited conservation of meiotic factors may also account for the observation that C. albicans Spo11p did not complement an S. cerevisiae spo11 mutant for meiotic recombination. In fact, cross-complementation of Spo11p function between any two species has yet to be successfully demonstrated (S. Keeney, personal communication). Even in S. pombe, where a number of genes involved in meiotic DSB formation have been identified, most of these genes share either very limited or no sequence homology with genes in S. cerevisiae or any other organism. Thus, many of the proteins involved in DSB formation appear to have significantly diverged from one another. In addition, it appears that different biochemical functions are utilized in different organisms to initiate the formation of DSBs [17]. Clearly, it will be of significant interest to identify co-factors that act with Spo11 to mediate recombination during the parasexual cycle of C. albicans.
The products of the C. albicans parasexual cycle were diploid and aneuploid progeny that exhibited altered colony morphology phenotypes. In particular, many strains had an increased tendency to form hyphal filaments on solid medium, evident either by increased surface wrinkling of the colony or by increased peripheral filamentation at the edge of the colony. Since the yeast-hyphal transition is closely associated with virulence of C. albicans strains, it is likely that many of these progeny strains will show altered virulence in animal models of candidiasis. Several of the progeny strains exhibited growth defects relative to control diploid strains, to control tetraploid strains, and to other diploid progeny. In some cases this was likely due to chromosomal aneuploidies, as many of these strains carried extra copies of up to three of the eight chromosomes of C. albicans. Indeed, being trisomic for two or three chromosomes increased cell doubling times by 16% and 23%, respectively, over a diploid control strain. Recent studies in S. cerevisiae have found that aneuploidy due to the presence of one or more additional chromosomes resulted in compromised growth rates [40]. Aneuploidy of large chromosomes or of multiple chromosomes correlated with the most significant cell cycle delays in S. cerevisiae [40]. We observed the same phenomenon in C. albicans strains, that the more aneuploid chromosomes a strain carries, the greater the proliferative disadavantage.
Compromised growth was also observed in a subset of euploid progeny from the parasexual mating cycle. In general, it appeared that LOH on 1–2 chromosomes did not typically compromise growth rates, but that LOH across multiple chromosomes did (e.g., strains Ps7 and Ss10). In these cases, it is likely that LOH at multiple genes led to the reduced fitness of these strains. Consistent with this idea, most clinical isolates, including the SC5314 strain whose genome was sequenced, show extensive heterozygosity. Previous studies have shown that allelic differences between C. albicans genes from different chromosome homologues can result in altered protein expression and altered protein function [51–54]. In addition, a recent study analyzed Chr 5 heterozygosity in multiple clinical isolates and found that LOH at multiple genes along Chr 5 reduced the virulence of strains in a model of systemic candidiasis [55]. Our work is also consistent with the idea that heterozygosity of multiple chromosomes provides C. albicans strains with a fitness advantage, at least for growth on laboratory media. Taken together, these results indicate that being heterozygous for genes on multiple chromosomes can improve both the fitness of mitotically dividing cells in vitro and the virulence of strains in vivo. However, the parasexual cycle (including the recombination events described in this paper) generates a great deal of genetic diversity, and it seems likely that conditions exist where strains that show reduced fitness in the laboratory have a selective advantage elsewhere.
Recent work has revealed that C. albicans, like other prevalent human fungal pathogens such as Cryptococcus neoformans and Aspergillus fumigatus, has access to a sexual mating program, but that under most conditions it propagates primarily in an asexual manner [56–58]. Recent studies in the model yeast S. cerevisiae found that both asexual and sexual modes of reproduction can be advantageous under the right experimental conditions. Under constant environmental conditions, the asexual mode of propagation was favored, but under stressful conditions the sexual strain had the competitive advantage [59,60]. In the case of C. albicans strains, population genetics on clinical isolates first suggested that the predominant mode of reproduction was clonal, with only limited evidence for genetic recombination between strains [61–64]. A recent study, however, found evidence for a high frequency of recombination events amongst clinical isolates, consistent with C. albicans strains undergoing sexual or parasexual recombination in their natural environment [13]. These studies are also consistent with the results presented here: the parasexual mating cycle can generate variant genotypes, including a subset of strains that have undergone extensive genetic recombination between chromosomes.
For C. albicans, the parasexual mechanism may provide two significant benefits over a conventional sexual pathway. First, the parasexual mechanism is imprecise, generating many aneuploid strains as well as euploid progeny strains. Common aneuploidies included diploid strains harboring trisomic chromosomes, and this karyotypic variation led to greater genetic and phenotypic diversity in the progeny population. Consistent with this observation, changes in chromosome copy number have previously been linked to phenotypic changes in C. albicans, including increased resistance to antifungal azoles [28,65]. Thus, karyotypic variation appears to be an important mechanism utilized by C. albicans to regulate physiologically important genes [66]. In S. cerevisiae, aneuploid strains are similarly at a competitive disadvantage with euploid strains, unless there is a strong selective pressure that favors growth of the aneuploid form [40,67,68]. A second potential benefit of the parasexual cycle is that it bypasses the process of sporulation common to the sexual cycle of most ascomycetes. Ascospores are thought to be highly antigenic, and, given that C. albicans strains normally exist as commensals within warm-blooded hosts, the absence of spore formation may facilitate the generation of genetic diversity without compromising cell survival [56].
In summary, the parasexual cycle in C. albicans provides an alternative to a sexual reproductive cycle. Concerted chromosome loss reduces the ploidy of the cell from tetraploid to approximately diploid, generating recombinant progeny strains with variant phenotypes. Genetic recombination between homologous chromosomes, dependent on the Spo11 protein, takes place during these reductive mitotic divisions, further contributing to genetic diversity. We propose that at least some of the meiotic recombination machinery has been re-programmed to function in parasexual recombination in C. albicans. Finally, we note that as C. albicans thrives only in warm-blooded animals, the parasexual cycle provides a number of potential advantages over a conventional sexual cycle.
Standard laboratory media were prepared as previously described [69]. Construction of the genetically marked tetraploid strain, RBY18, was previously described [16]. A tetraploid Δspo11 strain was constructed by first deleting the SPO11 gene in the diploid strains RBY16 and CHY477 [16]. Both copies of SPO11 were sequentially disrupted using a modified Ura blaster method [70,71]. A SPO11 gene disruption construct was made by PCR amplifying the HisG-URA3-HisG cassette using oligonucleotides SPO11 KO-5′ and SPO11 KO-3′ from plasmid pDDB57 (see Figure S5) [71]. Heterozygous strains were then constructed by replacing SPO11 coding sequences with the URA3 selectable marker flanked by HisG repeats. Ura+ strains that were deleted for one copy of SPO11 were grown on nonselective medium and subsequently plated on SCD medium containing 5-fluoroorotic acid (5-FOA) and uridine medium to select for loss of the URA3 gene [70]. The HisG-URA3-HisG cassette was then used to delete the second copy of the SPO11 gene. The construction of Δspo11 mutants was confirmed using PCR to check both 5′ and 3′ junctions following integration of the Ura blaster cassette and also to confirm the loss of the SPO11 ORF following the second round of transformation. Deletion of SPO11 in the diploid strains RBY16 and CHY477 generated strains RBY77 and RBY79, respectively. The diploid strains were mated as previously described [16] to form the tetraploid Δspo11 strains RBY176 and RBY177.
To follow expression of the Spo11 protein the gene sequence was fused to that encoding a 13 × myc epitope tag. The Spo11 gene and promoter were first amplified by PCR using oligonucleotides Spo11(myc) for, 5′-cccaatatgaagcactaaactc-3′ and Spo11(myc) rev, 5′-ggcgcgcccggggatccgtttcgtatagctagccgttcc-3′. The amplified sequence was then digested with HindIII and SmaI enzymes and ligated into a pMYC-HIS1 vector. The resulting plasmid contains the SPO11 gene sequence fused to 13 copies of the myc epitope. The plasmid was then linearized by digestion with BstBI and used to transform strain RBY1118 (a diploid a-type mating strain) to generate CAY126. RBY1118 itself was derived from a/α strain SNY87 [72] by growth on sorbose medium to select for a and α derivatives, as previously described [19]. PCR was used to confirm that the vector had inserted at the endogenous SPO11 allele. To induce chromosome instability in tetraploid strains, the SPO11+ tetraploid strain RBY18, or Δspo11 tetraploid strains RBY176/177, were incubated on S. cerevisiae pre-sporulation (pre-spo) medium (0.8% yeast extract, 0.3% peptone, 10% dextrose, and 2% agar) at 37 °C for 10 d. Alternatively, tetraploid strains were incubated on L-sorbose medium (0.7% yeast nitrogen base (without amino acids), 2% L-sorbose, and 2% agar) at 30 °C for 10 d. Following incubation, cells that had undergone loss of Chromosome 1 and become gal1− were selected by growth on 2-deoxygalactose (2-DOG) medium for 2 d, as previously described [16]. 2-DOG+ colonies were patched onto YPD and subsequently frozen (in a 1:1 solution of 50% glycerol and YPD). Subsequent culturing of progeny strains was kept to a minimum (less than 1 wk).
We have not attempted to reintegrate SPO11 for three major reasons. First, the phenotype being tested is subtle; RBY18 (SPO11+) tetraploid strains exhibited recombination events in three out of 13 progeny, while Δspo11 mutants exhibited no observable recombination events. Second, because we are studying a phenomenon in tetraploid strains, it is not clear how many copies of the SPO11 gene would need to be reintegrated into the tetraploid to generate a significant difference from the mutant. And third, reconstituted strains often exhibit a range of complementation efficiencies, with multiple strains having to be analyzed to confirm restoration of the wild-type phenotype.
PCR analysis of the MTL alleles was used as an indicator of the copy number of Chr 5 in each sample. PCR primers unique to MTLa1, MTLα1, Δa1, and Δα2 were used to distinguish MTL alleles in tetraploid cells and progeny cells derived from tetraploids. The oligonucleotides used for MTL analysis have been previously described [14].
To generate cells for flow cytometric analysis, test strains were grown in YPD medium at 30 °C and harvested when the OD was between 1 and 2. Samples were then prepared for analysis as previously described [16].
Previously, we described the development of a SNP microarray to determine genotypes at 123 SNP loci across the genome of C. albicans (Forche et al., unpublished data.). For this study the microarray was expanded to include an additional 29 SNP loci, giving a total of 152 (Table S3). Fifteen of the 29 new SNP loci were adapted from Wu et al. [73] (Table S3). Since clinical isolates were used by Wu and co-workers and not derivatives of strain SC5314 (the SNP microarray is based on SNPs from SC5314), the presence of reported SNPs was confirmed by sequencing, as described previously [25]. New primer pairs were developed to allow for the amplification of small PCR products suitable for SNP microarray analysis. Design of allele-specific oligonucleotides, probe generation, slide preparation/hybridization, data analysis, and sequence confirmation of LOH events were conducted as described elsewhere ([26]; Forche et al., unpublished data). CGH that has been adapted for C. albicans was carried out as described previously [27].
A two-tailed t test was performed to indicate if changes in karyotype were statistically significant. A p-value of < 0.05 in the two-tailed t test was interpreted as a significant difference, while p-values >= 0.05 were insignificant.
Cultures of strains CAY126 (Spo11-13myc), RSY84 (Kar3-13myc), and the untagged RBY1118 strain were grown to logarithmic phase in YPD medium at 30 °C and cells harvested. Whole-cell extracts from these strains were prepared by resuspending cell pellets in lysis buffer (10 mM Tris-HCl [pH 7.5], 50 mM NaCl, 1 mM dithiothreitol) containing protease inhibitors (pepstatin A, leupeptin, phenylmethyl sulfonyl chloride, and aprotinin) and lysis achieved by bead beating for 12–15 cycles (30 s vortexing following by 30–60 s on ice). An aliquot from each sample was separated by SDS-PAGE and analyzed by western blotting. The myc-tagged proteins were detected using an anti-myc antibody at 1/2,000 dilution (4a6 antibody; Millipore) followed by an anti-mouse HRP (horseradish peroxidase)-conjugated antibody at 1/1,000 dilution (Jackson Laboratories). Antibody binding was visualized using the SuperSignal West Pico Chemiluminescent Substrate (Pierce) and exposure to autoradiography film.
|
10.1371/journal.pcbi.1006294 | The importance of mechanical constraints for proper polarization and psuedo-cleavage furrow generation in the early Caenorhabditis elegans embryo | Intracellular polarization, where a cell specifies a spatial axis by segregation of specific factors, is a fundamental biological process. In the early embryo of the nematode worm Caenorhabditis elegans (C. elegans), polarization is often accompanied by deformations of the cortex, a highly contractile structure consisting of actin filaments cross-linked by the motor protein myosin (actomyosin). It has been suggested that the eggshell surrounding the early embryo plays a role in polarization although its function is not understood. Here we develop a mathematical model which couples a reaction-diffusion model of actomyosin dynamics with a phase field model of the cell cortex to implicitly track cell shape changes in the early C. elegans embryo. We investigate the potential rigidity effect of the geometric constraint imposed by the presence and size of the eggshell on polarization dynamics. Our model suggests that the geometric constraint of the eggshell is essential for proper polarization and the size of the eggshell also affects the dynamics of polarization. Therefore, we conclude that geometric constraint on a cell might affect the dynamics of a biochemical process.
| Polarization, whereby molecules and proteins are asymmetrically distributed throughout the cell, is a vital process for many cellular functions. In the early C. elegans embryo the asymmetric distribution of cell cytoskeleton during the initiation of polarization leads to asymmetric contractions which are higher in the anterior and lower in the posterior of a cell. The C. elegans embryo is surrounded by a rigid body, the eggshell, which functions in numerous cell processes. We investigate the structural support of eggshell during the establishment phase by tracking the moving cell surface. We incorporate protein dynamics involved in polarization into the membrane evolution. We conclude that eggshell might have a role in cell polarization by preventing the distortion of cell surface.
| The geometry of a cell can have a profound influence on cell function and survival [1–4]. Cell geometry is generated by internal structures such as the cytoskeleton, and can also be externally imposed by interactions with neighboring cells or mechanical structures like an eggshell. Eggshells are critical for early development of the nematode worm C. elegans as it prevents multiple sperm from fertilizing a single egg, along with ensuring proper chromosome segregation during meiosis, and proper organization of membrane and cortical proteins [5–7].
Shortly after fertilization, the C. elegans embryo polarizes by asymmetrically localizing specific proteins, including actin, myosin and polarity determinants such as the Par proteins, in response to a cue from the sperm [8, 9]. Polarization of the embryo proceeds in two distinct phases: establishment and maintenance [10]. During the establishment phase, the developmental time frame we consider in this paper, the actomyosin cortex, a thin structure below the membrane consisting primarily of polymerized actin filaments and cross-linked by the motor protein myosin, is highly dynamic and contractile, creating small invaginations on the cell surface called ruffles [9, 11]. The cue locally relaxes the actomyosin cortex causing local loss of ruffles and initiation of cortical flow that transports the anterior Par proteins, PAR-3, PAR-6 and aPKC, towards the anterior pole. This allows the posterior Par proteins, PAR-1, PAR-2 and LGL, which are mutually antagonistic to the anterior Par proteins, to bind to the cleared area at the posterior pole [10, 12, 13]. During advection of the Par proteins and the actomyosin cap, a domain of high actomyosin density, towards the anterior pole, an invagination similar to the ruffles but much deeper, called the pseudocleavage furrow, forms and moves with the edge of the actomyosin cap and the interface between the anterior and posterior Par proteins [9, 10, 14]. At the end of the establishment phase, the pseudocleavage furrow along with the actomyosin cap and the Par protein interface reach the middle of the cell. The pseudocleavage furrow retracts and the segregated Par protein domains are held through the maintenance phase as the cell prepares for first division.
It has been demonstrated that the pseudocleavage furrow and ruffles are not essential for proper polarization. A maternal effect mutation nop-1(it142) eliminates cortical contractility during the establishment phase which results in the absence of the pseudocleavage furrow and ruffles but does not appear to interfere with proper development despite attenuation of actomyosin asymmetry and cortical flow [15, 16]. Although it is known that the pseudocleavage furrow and ruffles are generated as a result of contractility, and the pseudocleavage furrow appears at the boundary between high and low actomyosin concentrations, the mechanical generation of these invaginations is not fully understood.
The developing C. elegans embryo is surrounded by an eggshell, a rigid body containing the protein chitin, which is created by the embryo after fertilization and persists until hatching. When the eggshell is genetically or chemically manipulated prior to the two cell stage, the cells are highly mechanically sensitive and fail to properly extrude polar bodies at the beginning of the establishment phase, and do not properly polarize or form the pseudocleavage furrow [5–7]. Removal of the eggshell at or after the two cell stage does not interfere with development [17]. Since chitin is responsible for eggshell rigidity [5], this suggests that the eggshell may provide structural support during the asymmetric actomyosin contractions associated with polarization [5], although this role is not well understood.
In this paper, we develop a mathematical model to investigate the mechanical effect of the eggshell during establishment of polarization. This model allows us to modify or remove the eggshell completely to explore the role of the eggshell shape and rigidity on polarization. The model simulates the morphological changes of the membrane during establishment of polarization which includes movement of the pseudocleavage furrow. Reaction-diffusion equations describing the dynamics of Par proteins and actomyosin [1] are incorporated into the moving interface problem. Cell shape dynamics are tracked using the phase field method which is widely used for moving interface problems [18–20].
In the phase field method, the interface is not explicitly tracked, which avoids potential numerical difficulties associated with the classical Lagrangian method. Here the interface is the level set of an auxiliary field ϕ. ϕ takes on different constant values corresponding to inside and outside of the cell and these constant values are connected by a smooth interface. In our model, cortex is assumed to be a viscous material and the asymmetric actomyosin contractions in the cortex drives cell deformations. The interactions between membrane bending energy, surface tension, eggshell constraint and forces applied by the cortex determines the current state of the cell. Using numerical simulations we found that the eggshell rigidly supports the membrane and reinforces the polarization process. We also showed the importance of the size of the eggshell for proper polarization. Our results suggest that mechanical constraints on cells imposed by structures such as an eggshell might affect dynamics and localization of proteins.
In the previous work by Dawes et al. [1], a model of reaction-diffusion equations was developed to capture the dynamics of Par proteins and actomyosin on a fixed domain. In their model, cortical tension is assumed to be linearly proportional to actomyosin concentration and the advective speed of protein movement is linearly proportional to the gradient of cortical tension. The solution is shown to have a moving interface between high and low concentrations of the proteins, which represents the movement of the interface between the anterior and posterior domains during the protein movement.
While the model in [1] is capable of capturing the essential features of the protein dynamics with relatively simple equations, it does not include any change in cell shape and therefore is limited in providing information about the physics of the entire polarization process. Here we consider the same species and reactions, and along with protein dynamics, we include the formation and movement of the pseudocleavage furrow as well as the constraint of the eggshell. In our model, since the plasma membrane and cortex are mostly in contact and deforming together, we do not distinguish the plasma membrane and cortex but assume they are a single structure, and in the following we use membrane or cortex to refer to this structure.
Following the model in [1], we do not consider individual Par proteins but group these proteins into two modules by their localizations: the anterior and posterior Par proteins. The anterior Par protein module consists of PAR-3, PAR-6 and aPKC, and the posterior Par protein module consists of PAR-1, PAR-2 and LGL. Thus, the model contains the following species with the corresponding notations:
Am: Cortical anterior Par protein monomers, Asd: Cortical anterior singly bound dimers, Add: Cortical anterior Par protein doubly bound dimers, P: Cortical posterior Par proteins, M: Cortical actomyosin, Ay: Cytoplasmic anterior Par protein monomers, A2y: Cytoplasmic anterior Par protein dimers, Py: Cytoplasmic posterior Par proteins My: Cytoplasmic actomyosin.
We make the following key assumptions about the interactions as in [1, 21]:
The anterior Par proteins can dimerize and bind to the cortex, and the anterior and posterior Par proteins promote each other’s dissociation from the cortex via phosphorylation. The interactions between these species follow mass-action kinetics. Cytoplasmic Par proteins, Ay, A2y and Py, are assumed to be at quasi-steady state and can associate with the cortex, and proteins dissociated from the cortex return to the cytoplasmic pool. Cytoplasmic actomyosin My is assumed to be at quasi-steady state. Cytoplasmic actomyosin assembles into the cortex and this assembly is negatively regulated by the posterior Par proteins.
The interactions between these species take place on the cortex, and therefore the domain of interest is the evolving cortex. A schematic diagram of the protein interactions are depicted in Fig 1C, and the protein dynamics can be described by the following equations:
∂ [ A m ] ∂ t = D p a ∇ c 2 [ A m ] - ∇ c · ( v c [ A m ] ) + k on A A y - k off A [ A m ] - 2 k d + [ A m ] 2 + 2 k d - [ A d d ] - k d + A y [ A m ] + k d - [ A s d ] - r A [ P ] [ A m ] , (1) ∂ [ A s d ] ∂ t = D p a ∇ c 2 [ A s d ] - ∇ c · ( v c [ A s d ] ) + k on A s d A 2 y - k off A [ A s d ] + k d + A y [ A m ] - k d - [ A s d ] - k on A d d [ A s d ] + k off A [ A d d ] - r A [ P ] [ A s d ] , (2) ∂ [ A d d ] ∂ t = D p a ∇ c 2 [ A d d ] - ∇ c · ( v c [ A d d ] ) + k d + [ A m ] 2 - k d - [ A d d ] - k off A [ A d d ] + k on A d d [ A s d ] - 2 r A [ P ] [ A d d ] , (3) ∂ [ P ] ∂ t = D p p ∇ c 2 [ P ] - ∇ c · ( v c [ P ] ) + k on P P y - k off P [ P ] - r P ( [ A m ] + [ A s d ] + 2 [ A d d ] ) [ P ] , (4) ∂ [ M ] ∂ t = D m ∇ c 2 [ M ] - ∇ c · ( v c [ M ] ) + k on M M y k P k P + [ P ] - k off M [ M ] . (5)
In Eqs (1)–(5), ∇ c 2 stands for the Laplace-Beltrami operator and ∇c is the gradient operator on the membrane. The anterior Par proteins, posterior Par proteins and actomyosin diffuse on the cortex at the rates Dpa, Dpp and Dm, respectively. The protein movement induced by the initial cue which relaxes the cortex at the posterior pole drives the advection of anterior and posterior Par proteins. The advective velocities for all of the proteins, denoted by vc, are assumed to be linearly proportional to the gradient of cortical tension [1], which we assume to depend linearly on the actomyosin concentration, that is, vc = ν∇M with ν a constant.
The Par proteins move along with the cortex, and therefore the aforementioned protein dynamics occurs on a deforming interface. To model the evolution of the cortex, we use the phase field model which implicitly tracks moving interfaces. The choice of phase field model over other explicit interface tracking methods, such as a Lagrangian frame, is based on the ease of coupling the protein dynamics with the deforming cortex, while avoiding numerical difficulties that may arise due to the deep invagination of the pseudocleavage furrow.
Since we are interested in the dynamics at the cell cortex, we use a 2D cross section of the cell as our domain Ω ⊂ R 2, as shown in Fig 1A. We believe that this model, albeit simplified in geometry, will provide information on the possible mechanisms underlying cell-shape changes and pseudocleavage furrow formation. The phase field function ϕ(x) is defined on the entire domain Ω, where the region with ϕ = 0 represents the exterior of the cell, the region with ϕ = 1 represents the interior of the cell, and the region with 0 < ϕ < 1 is the “diffuse interface” including the level set ϕ = 0.5 that represents the cell membrane Ωs. The width of this diffuse interface is controlled by the transition parameter ϵ, which is taken to be a very small number. Note that the choice of ϵ depends on the spatial resolution: the spatial grid needs to be fine enough to resolve the diffuse interface. The evolution of the diffuse interface is governed by kinetic equations that is defined on the whole domain, and they will be described in the remainder of this section. Given a fixed membrane profile (i.e., a fixed ϕ), one can easily simulate the protein dynamics of Eqs (1)–(5) along the cell membrane by introducing G(ϕ) = 18ϕ2(ϕ − 1)2 in the equations. Note that G(ϕ), the double well potential, is nonzero only around the membrane, and therefore by multiplying G with the unknown variables or reaction terms, the protein dynamics are restricted to the neighborhood of the interface. In our numerical simulations, a non-dimensionalized version of model (1)–(5) was used. The non-dimensionalized model equations, coupled with G(ϕ), are displayed in Eqs. (S1)-(S5) of S1 Text.
In our model, the cell shape, i.e., the phase field function ϕ, is determined by the interactions between 1) the membrane bending energy, 2) membrane surface tension, 3) volume conservation, 4) eggshell constraint and 5) the force resulting from myosin contractility in aligned and cross-linked forms. Here we assume that that the cortex and cytoplasm are viscous with different viscosities, which is appropriate for long time scales [22–24]. The phase field function ϕ is governed by the following equation [19, 25]:
∂ ϕ ∂ t + u · ∇ ϕ = Γ ( ϵ ∇ 2 ϕ - G ′ ( ϕ ) / ϵ + c ϵ | ∇ ϕ | ) , (6)
where u is the velocity field driving the membrane-cortex deformation, Γ the relaxation coefficient and c = ∇ ⋅ (∇ϕ/|∇ϕ|) the local interface curvature, which is added to stabilize the phase-field interface [19], ϵ∇2 ϕ − G′(ϕ)/ϵ is boundary free energy, which guarantees the existence of two phases connected through a smooth interface. The velocity field u is generated by the forces on the membrane, and it is also associated with the viscosity of the cortex and cytoplasm. Assuming that viscosity forces dominate advective inertial forces [23], u is the solution of the Stokes equation:
∇ · [ η ( ϕ ) ( ∇ u + ∇ u T ) ] - ξ u + F m e m = 0 . (7)
In Eq (7), the first term describes the strain rate tensor where η(ϕ) is the viscosity. It is defined as η(ϕ) = ηm m4ϕ(1 − ϕ)+ ηc ϕ, where m is the non-dimensionalized variable for actomyosin concentration [M], 4ϕ(1 − ϕ) is approximately 1 in the interface where the cortex resides, ηm is the viscosity coefficient around the cortex region and ηc is the viscosity coefficient for the cytoplasm. We assume that the viscosity around the cortex is proportional to actomyosin concentration, and the viscosity coefficient in the cortex is greater than the one in the cytoplasm (ηm > ηc).
The second term, ξ u, is the hydrodynamic drag [25]. We consider the hydrodynamic drag force since the extra-embryonic matrix (EEM), the space between membrane and eggshell, is fluid-filled [5, 26].
The last term in Eq (7), Fmem, is the sum of forces by the membrane/cortex. Since we assume cortex and membrane to be a single entity, the forces resulting from membrane deformations and cortex contractions are considered together as follows:
F m e m = F t e n s i o n + F b e n d i n g + F e g g s h e l l + F v o l u m e + F a c t o m y o . (8)
In particular, the first four forces can be derived from the surface free energy E by taking the variational derivative of the energy functional - δ E ( ϕ ) δ ϕ. In the following we describe the individual forces in detail.
The domain of our model is taken to be [−45 μm, 45 μm] × [−45 μm, 45 μm] on the x − y plane. Considering the size of an early C. elegans embryo which is approximately 50 μm in length and 30 μm in diameter, the initial phase field function ϕ is taken to be an ellipse with radii 15 and 25, and is defined by:
ϕ ( x , y ) = 0 . 5 ( tanh ( 1 - ( x 15)2 + ( y 25 )2 0 . 025 ϵ ) + 1 ) , (9)
where ϵ is the parameter that scales the width of the interface in the phase field function ϕ, and is taken to be 2 in this work. The boundary conditions for ϕ are taken to be periodic boundary conditions for the ease of using Fourier transform in computation.
Initially, the concentration of anterior Par proteins is high in the anterior part of the cell and low in the posterior part, and the posterior Par proteins have a reciprocal distribution. To determine initial conditions of the species, the bistable steady-state solutions of model (1)–(5) with high and low concentrations are found for all species. The initial distributions of [Am], [Asd], [Add] and [P] are defined on the x − y plane and are of the form c 1 ( 0 . 5 - 0 . 5 tanh ( - y - K 0 0 . 5 ϵ ) ) + c 2, where K0 determines the location of the initial interface between the high and low Par protein concentrations, and c1 and c2 are the high (low) and low (high) base concentrations. We chose the initial interface between high and low anterior Par proteins to be close to the posterior domain to mimic the initial cue for polarity establishment. The values of K0, c1 and c2 for each species are given in S1 Table. The initial actomyosin [M] is assumed to be uniformly high. In this work, we take 0.7 for the initial actomyosin concentration. No-flux boundary conditions for these species are taken on the membrane.
The non-dimensionalized version of model (1)–(5) is used in the simulation, and the equations are shown in S1 Text, Eqs. (S1)-(S5). The associated parameter values listed in S1 Table. are chosen to produce desired model behavior. For the membrane evolution Eqs (6)–(8), the values of all of the parameters, their physical meanings and references are listed in S2 Table. While experimental measurements for the tension and forces are unavailable for this organism, we chose the parameters so that the results qualitatively reproduce the behavior of a pseudocleavage furrow in a wild type C. elegans embryo, which refers to a contracting anterior domain and an invaginating pseudocleavage furrow moving toward the anterior pole as the cell polarizes. To test how robust the model behavior is with respect to parameters, we have perturbed the some parameters in the phase field model by 20% and found that the results are very similar (see S1 Fig).
In the numerical simulations, Eqs. (S1)-(S5) are coupled to Eqs (6)–(8) alternately: given a fixed membrane profile, the reaction-diffusion equations are solved, followed by solving the phase field model using a fixed protein profile. The details of the implementation of the numerical algorithms are provided in S2 Text.
We first wished to determine if our model could reproduce wild type behavior of the embryo during the establishment phase, as described in the Introduction and Model setup and parameters. In particular, we wish to see if our model can produce a pseudocleavage furrow, an invagination that advects with the edge of the actomyosin cap and the interface between the anterior and posterior Par proteins. This process is illustrated in Fig 1A, and a DIC image of a typical wild type C. elegans embryo at the end of the establishment phase, when the pseudocleavage furrow has reached the middle of the cell, is shown in Fig 1B.
Numerical simulations of our model successfully reproduce the wild type behavior, by using the estimated parameters in S1 and S2 Tables. In Fig 2A, we show the distributions of actomyosin (m, non-dimensionalized [M]) along with the deforming membrane, and its relative position with respect to the eggshell. The overall profile of the total anterior Par proteins (a1+ a10+ a11, non-dimensionalized [Am]+ [Asd]+ [Add]) is almost identical to that of [M] qualitatively and therefore is not shown here. From Fig 2A, we observe the initial formation of a shallow invagination at the interface between anterior and posterior Par proteins occurs before T = 1600s (the time of the appearance of the furrow is related to the preset initial cue), and as it moves toward the anterior end, the pseudocleavage furrow deepens and reaches the middle of the cell around T = 4600.
Although we took a specific set of parameter in the simulation to demonstrate the wild-type behavior, our numerical experiments showed that it is possible to generate qualitatively similar behavior for a wider range of values for these parameters, which we discuss in The relationship between pseudocleavage furrow depth, time of polarity establishment and actomyosin related forces.
It is known that the eggshell provides structural support for the embryo, but details about the mechanical interactions between the eggshell and the embryo are still unclear. In the experiments of [28, 29], the authors showed that mutations causing a loss of peri-vitelline space or extra-embryonic matrix (EEM), which is the space between eggshell and membrane, results in embryos lacking a pseudocleavage furrow and unable to initiate cytokinesis. This led us to investigate with our computational model how cell polarization and formation of the pseudocleavage furrow changes under different eggshell-to-membrane distances. In the following, we denote the distance between the eggshell and the membrane by ed. The value of ed is constant throughout each simulation and does not vary as the cell undergoes shape changes. All parameters except ed remained the same as in the wild-type simulations of Fig 2A.
In our model, the key element that generates the cell shape changes is the actomyosin contractility forces Factomyo, which consists of two parts: cross-linked actomyosin contractility proportional to the actomyosin concentration (controlled by the parameter cm), and the aligned actomyosin contractility which is proportional to the gradient of the actomyosin concentration (controlled by cg). As described in Mathematical model, we differentiate these two forces by how the actomyosin are structured, although both forces are due to actomyosin contractility. We would like to investigate how modulating cg and cm gives rise to different membrane behaviors during the establishment phase. In particular, one of the aims is to find the ideal ranges for those parameters to generate wild-type behavior qualitatively and understand how these parameters affect the depth of the pseudocleavage furrow and the time of polarity establishment. In this section, we uniformly sampled 11 of cg values and 24 of cm values from the ranges cg ∈ [300, 800], cm ∈ [10, 240] and performed simulations for each set of (cg, cm).
We also wished to investigate how the eggshell affects Par protein polarization and cell morphology. This is difficult to study experimentally since the eggshell is essential for the survival of the early embryo. Fortunately, modeling has no such constraint.
Here we used the parameter set that produced the wild-type behavior in Fig 2 but take Ms, the coefficient of the eggshell force, to be zero to model the removal of eggshell. As shown in Fig 5A, we observed that without an eggshell, the asymmetric contraction of actomyosin, which is higher in the anterior domain, led to an expanded posterior domain and a small anterior domain as the pseudocleavage furrow moves to the anterior. Moreover, the pseudocleavage furrow reaches the middle of the cell in much shorter time (2100 s) compared to the wild-type case (4600 s). This suggests that the eggshell might be significant for proper morphology as well as timing of cell polarization.
In the above numerical experiment, it was assumed that the presence of the eggshell, a nonzero value of Ms, is the only parameter that is changed in the with/without eggshell cases. However, it is possible that the removal of the eggshell also leads to changes in the aligned actomyosin contractility (controlled by the parameter cg). It may be that compression between the membrane and eggshell exerts extra force, deepening the furrow ingression caused by the aligned actomyosin contractility. If this is the case, then the aligned actomyosin contractility (cg) will be smaller when the eggshell is removed. To test this scenario, we decrease cg from 500 (wild type) to 300 and take Ms = 0 (no eggshell), and the results are displayed in Fig 5B. Due to the reduced cg, the invagination at the anterior-posterior Par protein interface disappears, that is, there is no pseudocleavage furrow. Similar to Fig 5A, the cell expands in the posterior domain, and despite the loss of furrow, the change of curvature in the cell membrane is still visible because of the differential actomyosin contraction in the anterior and posterior domains. A systematic study of varying cg values in the absence of eggshell showed the continuous change in cell morphology (S2 Fig), and in all cases the time for the anterior-posterior Par protein interface to travel to the middle of the cell is significantly shorter than that for a wild-type cell.
Altogether, our simulations in Fig 5 suggest that if the eggshell is removed, the cell shape will be highly distorted, and in some cases, the pseudocleavage furrow may not form. These results suggest that the eggshell might be effective in reinforcing polarization and preserving cell morphology, and the presence of the rigid eggshell may also significantly slow down the polarization.
In this paper, we have used a mathematical model to investigate morphological changes of the C. elegans embryo during the establishment phase of its polarization process. In particular, we are interested in the role of the eggshell in formation of the pseudocleavage furrow, and the interaction between the eggshell and the asymmetric distribution of actomyosin concentration observed during polarization. Our model not only allowed us to qualitatively reproduce the experimentally observed wild-type membrane behavior and pseudocleavage furrow, but also provided biological insights into some scenarios that are unattainable with current experimental tools. Our model combines the well established phase field model to describe the morphogenesis of the cell membrane/cortex by incorporating force generated by several mechanisms: actomyosin contractility in cross-linked and aligned form, constraint from volume conservation, and constraint from the eggshell. The phase field model is coupled with protein dynamics on the cell membrane. Using our mathematical model, we have demonstrated that cell mechanics and geometry may affect protein dynamics on the cell membrane.
Previous experiments have shown that cells with mutations that eliminate the space between embryo and eggshell do not have pseudocleavage furrows and are defective in cytokinesis [28]. Our numerical simulations produced consistent results: when the space between the eggshell and membrane is eliminated, no pseudocleavage furrow was observed. Therefore the absence of a pseudocleavage furrow in mutants might have a mechanical explanation in that there is no place for a furrow ingression. The unsuccessful cytokinesis in those mutants can be explained by the lack of room for invagination of the membrane, similar to the reason for the absence of a pseudocleavage furrow. Our results also indicate that the size of the space between the eggshell and the membrane might affect the speed of protein polarization: if there is no space between eggshell and embryo, the speed of protein polarization is greatly attenuated, possibly due to the lack of a pseudocleavage furrow. If it would be experimentally possible to increase the space between the embryo and the eggshell, the model predicts that the cell will experience a shorter time to polarization and a deeper furrow relative to a wild type embryo. Our results also indicate a relationship between force generated by cross-linked actomyosin contractility and force generated by aligned actomyosin contractility, a potential area for further experimental investigation. In the numerical experiment in which the eggshell is removed, we observed that a highly asymmetric contraction of actomyosin leads to a distorted cell shape. This result suggests that the mechanical support of an eggshell plays an essential role in proper protein polarization and that if the eggshell can be removed while maintaining cell integrity, the model predicts large scale morphological changes and a small anterior domain relative to the posterior.
In Mayer et al. [30], the authors found that, in the anterior, the tension in the direction orthogonal to the anterior-posterior axis is different than the tension along the anterior-posterior axis. The anisotropy of tension increases the depth of the pseudocleavage furrow [27]. In this work, our aim was to investigate the qualitative behavior of the pseudocleavage furrow by taking advantage of a 2D model in the phase field context, therefore the simplest form of tension was considered. Another possible mechanism not being considered in this work is tension generated by compression in the tangential direction. It is possible that the eggshell increases compression, and beyond some point the cortex-membrane complex might generate the pseudocleavage furrow as a result of a buckling type of instability, leading to formation of a crease [31]. In this case, removal of the eggshell might reduce the compression, which will likely lead to the absence of a pseudocleavage furrow. However, to understand buckling behavior due to compression, we would need to include advection of the cortex in the tangential direction which cannot be tracked by the boundary tracking method we use.
In this investigation, we focussed on the large invagination formed by the pseudocleavage furrow. However, the early embryo also exhibits shallow, transient invaginations called ruffles during the establishment phase. The mechanisms behind ruffle formation are not clear. Limited change in area, contractility by actomyosin and the eggshell may have a combined effect leading to formation of creases on the surface, creating ruffles. In our model, we assumed that area can vary due to high tension in the anterior. Our model captures the overall area change in the anterior due to contraction without including the self-intersecting and invaginating area seen with ruffles. A more detailed model including cross-linking actomyosin foci might be needed to explain the mechanism behind the generation of both the pseudocleavage furrow and ruffles which we leave for future work.
|
10.1371/journal.ppat.1004752 | Decreased HIV-Specific T-Regulatory Responses Are Associated with Effective DC-Vaccine Induced Immunity | The role of regulatory T cells (Tregs) in vaccination has been poorly investigated. We have reported that vaccination with ex vivo-generated dendritic-cells (DC) loaded with HIV-lipopeptides (LIPO-5-DC vaccine) in HIV-infected patients was well tolerated and highly immunogenic. These responses and their relation to viral replication following analytical treatment interruption (ATI) were variable. Here, we investigated whether the presence of HIV-specific Tregs might explain these differences. Co-expression of CD25, CD134, CD39 and FoxP3 was used to delineate both antigen-specific Tregs and effectors T cells (Teffs). Median LIPO-5 specific-CD25+CD134+ polyfunctional T cells increased from 0.1% (IQR 0-0.3) before vaccination (week -4) to 2.1% (IQR 1.1-3.9) at week 16 following 4 immunizations (p=0.001) and were inversely correlated with maximum viral load following ATI (r=-0.77, p=0.001). Vaccinees who displayed lower levels of HIV-specific CD4+CD134+CD25+CD39+FoxP3+ Tregs responded better to the LIPO-5-DC vaccine. After vaccination, the frequency of HIV-specific Tregs decreased (from 69.3 at week -4 to 31.7% at week 16) and inversely correlated with HIV-specific IFN-γ-producing cells (r=-0.64, p=0.002). We show that therapeutic immunization skewed the HIV-specific response from regulatory to effector phenotype which impacts on the magnitude of viral replication following ATI.
| Highly active antiretroviral therapy (HAART) has considerably decreased AIDS-related mortality and morbidity in recent years. Nevertheless, the search for effective vaccine to combat HIV is in the limelight of modern medical research. In clinical trial settings, T-cell responses are routinely measured following vaccinations. However, the measurement of antigen-specific regulatory T-cell (Tregs) responses is omitted most of the time, since their detection is not possible with the use of standard assays. Following a phase I clinical trial in which autologous dendritic-cells pulsed with HIV-lipopeptides were used to induce T-cell responses, we used a novel assay to detect a whole range of T-helper responses, including Tregs. We report very high levels of HIV-specific Tregs responses in infected patients and interestingly, we observed that the dendritic cell-based vaccine shifted the responses from regulatory to effector phenotype, which impact on the magnitude of viral rebound after treatment interruption.
| AIDS-related mortality and morbidity have decreased considerably since the introduction of highly active antiretroviral therapy (HAART). Yet, HIV infection cannot be eradicated and lifelong HAART treatment is associated with several co-morbidities [1–3]. It is currently thought that the control of the HIV-1 epidemic will require both prophylactic and therapeutic vaccines. Despite considerable investments, potent HIV vaccines are not yet available [4,5]. Prophylactic vaccine development had mainly been focused on the induction of neutralizing humoral responses [6]. Several studies conducted in HIV-infected individuals or in Non-Human Primates have shown that vaccines which could induce HIV-specific T-cell responses may be effective against HIV replication [6–10].
Monocyte-derived dendritic cells (moDCs) pulsed ex vivo with tumor- or pathogen-derived antigens can induce T-cell responses in animal models [11,12]. This strategy has been used in the context of HIV infection in several studies [13,14]. We and others [15–18] have shown that DC-based vaccines were safe and efficient in inducing HIV-specific immune responses. Ex vivo generated autologous DCs loaded with HIV-derived long lipopeptides covering gag, nef and pol epitopes (LIPO-5-DC vaccine) were immunogenic in vivo. The induced polyfunctional HIV-specific responses were negatively correlated with the maximum viral load after HAART cessation [18]. In the present study, we have extended the characterization of vaccine-elicited T-cell responses to regulatory T-cell (Tregs) responses. Induction of Tregs by an HIV-vaccine is not a desired outcome as these cells can suppress HIV-specific effector T-cells (Teffs) responses [19].
Current assays used to evaluate antigen-specific responses, including effector cytokine or proliferative capacity measurements, are limited as they do not take into account antigen-specific Tregs because these cells are known to be anergic in vitro [20]. Moreover, detection of antigen-specific CD4+ T-cell responses by cytokine production (intracellular staining) after exposure to antigen can be misleading since the kinetics of cytokines secretion such as IFN-γ, IL-17, IL-2 or IL-10, is very variable. Therefore, we used here the “OX40 assay” [21] to simultaneously detect a full range of Th responses including antigen-specific Tregs responses [22]. CD134 (OX40) is an inducible co-stimulatory molecule from the TNFR superfamily. It is expressed on recently activated T cells and its interactions with its ligand promote survival, proliferation as well as cytokine production [23]. The coexpression of CD134 and CD25 along with Tregs-specific markers, FoxP3 and CD39, allowed the detection of both HIV-specific Tregs and cytokine-producing Teffs.
We report that HIV-infected individuals harbor high levels of HIV-specific Tregs at baseline. The LIPO-5-DC vaccine preferentially induces Teffs responses and shifts the HIV-specific Tregs:Teffs ratio towards polyfunctional effector responses that inversely correlate with maximum viral load rebound after treatment interruption. Interestingly, vaccinees who display lower levels of HIV-specific CD4+CD134+CD25+CD39+FoxP3+ Tregs, show better Teffs responses to the LIPO-5-DC vaccine.
Nineteen HIV-1 infected individuals under successful antiretroviral therapy have been included in this pilot study (Table 1) out of which we had access to frozen samples of 14 participants. Patients received LIPO-5-DC vaccine every 4 weeks during 16 week period. Blood was drawn 4 weeks prior to first vaccination (week -4) and 4 weeks after the last (week 16). Virological endpoints following analytical treatment interruption (ATI) starting at week 24, were defined at the study entry due to safety issues. Primary endpoint was the maximum viral load while predefined secondary virological endpoints were the time to viral rebound, the area under the curve of viral load, and the slope of the initial viral rebound [18].
We first determined both frequency and phenotype of CD4+ and CD8+ T-cell subsets ex-vivo to verify whether the vaccine influenced these parameters. A slight, although statistically significant increase in the CD4+/CD8+ T-cell ratio after vaccination (week 16) was observed (Table 2). No changes in CD8+ Tregs percentages or in activation (CD38/HLADR) and/or exhaustion (PD-1/2B4/Blimp-1) markers within the CD4+ and CD8+ T-cell compartments were found. Bulk CD4+CD25+CD127low Tregs fraction increased slightly after vaccination probably reflecting the increase in CD4+ T-cell compartment (Table 2).
We stratified (using symbols- square, triangle and circle) the patients according to the magnitude of maximum viral rebound following ATI. Thus, patients with good (squares), intermediate (triangles) and poor (circles) virological responses were defined according to the maximum viral load post-ATI (VL ATI <40x103, 40x103 <VL ATI <120x103 and VL ATI >120x103 copies/ml respectively). The three subgroups correspond to the tertiles of the VL distribution. We then compared the levels of antigen-specific CD4+ T cells measured using the “OX40 assay”, between these patient groups. PBMCs from before and after vaccination were stimulated with either HIV-derived peptide pools (gag p24), LIPO-5 vaccine (which is a pool of 5 lipopeptides, 2 gag, 2 nef and 1 pol) or CMV lysate for 44-hrs in vitro. A significant increase in both LIPO-5- and gag p24- specific responses (CD4+CD25+CD134+ cells) after vaccination was observed, while the responses to CMV remained unchanged (Fig. 1A-B upper panel). Good virological responders showed the greatest increase in immune responses (Fig. 1A-D). To check whether vaccine-induced immune responses and post-ATI viral load, was not driven by pre-HAART viral load levels, we performed additional analysis using the historical viral loads prior to any HAART. These analyses showed that the maximum viral load post-ATI in the trial was not associated with patient’s pre-HAART viral load (r = -0.03, p = 0.93).
The increase in activated LIPO-5-specific CD4+ T cells was accompanied by an increase in the frequency of cells expressing intracellular IFN-γ, TNF-α and IL-2 (Fig. 1C). Similar increases of cytokine-secreting cells were observed when gag p24, but not CMV (S1 Fig), was used as eliciting antigen. In 9 out of 14 patients from whom sufficient cell numbers were available, we confirmed the results by additional testing of HIV-peptide pools representing each of the individual immunogens in the LIPO-5 vaccine. Interestingly, there was a significant increase in pol-, nef- or gag p17-specific responses (CD4+CD25+CD134+) but not to gag p2-6 (Fig. 1B, lower panel) that was not contained in the LIPO-5 vaccine. The specificity of the CD4+CD25+CD134+ T cells was further demonstrated by the co-expression of CD154, a marker of recently-activated antigen-specific cells [24] (S2 Fig).
Antigen-specific CD4+CD25+CD134+ cells are heterogeneous and express a wide range of transcription factors such as Tbx21, Gata3, Rorc, Foxp3 and Bcl-6 [25]. They comprise Th1-like cells that are commonly measured in standard ICS protocols but also other Th subtypes. To evaluate the functional profile of HIV-specific responses, we measured by Luminex the cytokines in the supernatants collected from the “OX40 assays” described above (44-hrs post-culture). Increases in IFN-γ, IL-2, IL-4, IL-21, IL-17F, TNF-α, MIP-1β, IL-3, IL-5, IL-9, IL-10, IL-13, IL-27 and sCD40L (S3A Fig) were observed after the vaccination. Notably, the increased levels of cytokines correlated with the increase of antigen-specific CD4+CD25+CD134+ T cells, thus indicating their polyfunctionality (S3B Fig). Moreover, we calculated multivariate immune scores (see Statistical analysis in Methods) to summarize the data across several immune markers. Based on cytokine-producing CD4+CD25+CD134+ T cells as well as IFN-γ, IL-2, IL-13 and IL-21 secretion assessed by Luminex, the median immune score increased significantly from median -6 (IQR -10 to -4) to 9 (IQR 9 to 10) between baseline and the post-vaccination time point (p = 0.008). Consistent with our previous report [18], the post-vaccination immune score showed a significant negative correlation with the maximum viral load after ATI (r = -0.79; p = 0.010).
In addition, the relative increases in LIPO-5-specific cells inversely correlated with the maximum observed viral load rebound after ATI (Fig. 1D). As mentioned above, in this phase I trial, the follow up post-ATI was limited to a duration of 24 weeks (from wk24 to wk48) to ensure participants’ safety, therefore several patients did not reach stable levels of viral load within this short period. In order to verify our observations reported in Fig. 1D, we used average viral load levels after ATI (S4A-B Fig) as well as viral loads observed at the end of the follow up (week 48, 6 months post ATI except for two patients who resumed HAART prior to that time point, S4C Fig) and we have reached the same conclusions. The good virological responders (low maximum viral load after ATI) displayed the highest specific CD4+CD25+CD134+ T-cell responses. Similar inverse correlation was observed with gag p24 (S5 Fig), though the correlation was stronger for LIPO-5 than for gag p24 (r = -0.77, p = 0.001 for LIPO-5 vs r = -0.60, p = 0.026 for gag p24 pool). This suggests that the responses covering more than 1 peptide pool (breadth) might be more predictive of vaccine efficacy outcome. The graph showing the frequency of CD25+CD134+ T-cell specific responses for each peptide pool and for each patient, reveals that the good virological responders responded to more than one peptide pool, suggesting that vaccine efficacy is linked to the breadth of the response (Fig. 2A). Our functional assay allows us to further determine the strength of the HIV-specific responses. We gave empirical scores to the antigen-specific responses for each peptide pool from 1 to 4 based on the percentages of CD4+CD25+CD134+ antigen-specific cells measured at week 16 post-vaccination (S1 Table). Importantly, the overall strength of the response inversely correlated with the maximum of viral load after ATI (r = -0.78, p = 0.017) (Fig. 2B). In addition, patient N19 (black square), who did not experience viral rebound after ATI, showed the highest combination of breadth and strength of HIV-specific responses (S1 Table).
These data underline that LIPO-5-DC vaccination elicited a robust polyfunctional T-cell response which relies on both strength and breadth of the responses, a feature commonly desired for a functional HIV vaccine.
Antigen-specific CD4+ T cells include both CD25+CD134+CD39+FoxP3+ Tregs and CD25+CD134+CD39-FoxP3- Teffs that can produce IFN-γ, TNF-α and IL-2 (S6A Fig). CD25+ cells that have not upregulated CD134 post 44hrs stimulation, include ~90% of FoxP3+ positive cells. These cells produce no or very little IFN γ, TNF-α or IL-2 (S6B Fig).
We sought to determine the origin of the two antigen-specific CD4+CD25+CD134+CD39+FoxP3+ Tregs and CD4+CD25+CD134+CD39-FoxP3- Teffs subsets. CD4+ T cells were sorted based on their high, intermediate or low expression of CD25 (gating strategy on Fig. 3A) and then mixed with CD4neg cells (fraction 1 that includes all cells that are outside the CD4 T-cells gate) at 1:4 ratio. We used CMV lysate to stimulate the cells. Forty-four hours later, cells were stained for IFN-γ, FoxP3 and CD39. The results in Fig. 3B show that antigen-specific CD4+CD25+CD134+CD39+FoxP3+ Tregs originated from CD25hi cells that upregulated CD134 upon stimulation. These cells did not produce IFN-γ (Fig. 3B, right panel). In contrast, CD4+CD25+CD134+CD39-FoxP3- Teffs, secreting high levels of IFN-γ, originated from CD25lo cells. Finally, cells expressing intermediate levels of CD25 prior stimulation contained a mixture of antigen-specific Tregs and Teffs (Fig. 3B). To check whether CD4+CD25+CD134+CD39+FoxP3+ Tregs are thymically derived or induced in the periphery, we included an anti-Helios monoclonal antibody in our experiments. This molecule was recently proposed as a marker of thymically derived Tregs [26], although these studies are still quite controversial [27]. We observed that CD39+ Tregs, regardless of their antigen specificity, are Helios+ suggesting they might be of thymic origin (S7 Fig). This observation surely needs confirmation since more reliable markers will hopefully be available in the future. To fully define CD4+CD25+CD134+CD39+FoxP3+ cells as Tregs, we performed functional assays [28–30]. Depleting CD25hi Tregs [31] prior to stimulation led to an increase in antigen-specific IFN-γ-producing cells (Fig. 4A right panel) and a decrease in CD4+CD25+CD134+CD39+FoxP3+ T cells (Fig. 4A left panel). These results confirm that antigen-specific Tregs originate from CD25hi Tregs. As shown in Fig. 4B and C, CD25hi but not CD25lo cells suppressed CD4+ and CD8+ IFN-γ and TNF-α responses (ratio 1:2, Tregs:Teffs) after in vitro stimulation with a pool of gag p24 peptides. Due to the scarcity of the isolated Tregs, we could not test higher ratios (1:1, Tregs:Teffs), which can explain lower levels of suppression (30–35%) we detected in our experiments (Fig. 4C). As previously shown [32], likely a Treg:Teffs ratio of 1:1 would show a higher suppressive activity.
To investigate the influence of Tregs on the LIPO-5-DC-induced responses, we measured antigen-specific CD4+CD25+CD134+CD39+FoxP3+ Tregs in patients’ peripheral blood prior to and after vaccination.
The frequency of HIV-specific Tregs prior to vaccination was elevated, accounting for a median of 43.8% (IQR 24.3–61.2) of gag p24- and 69.3% (IQR 55.8–75.2) of LIPO-5-specific response (Fig. 5A). CMV-specific Tregs in the same patients accounted for 24.2% (IQR 14.3–41.4) of the total CMV-specific CD4+ T-cell response.
Following vaccination, proportions of HIV-specific Tregs significantly decreased (26.3% (IQR 20.2–48.5), p = 0.002, of gag p24- and 31.7% (IQR 22.1–38.2), p = 0.008, of LIPO-5-specific CD4+ T cells) and this was accompanied by an increase in IFN- γ-producing HIV-specific CD134+CD25+ CD4+ T cells: from median 0.0% to 5.6% (p = 0.009) among gag p24-specific and from median 0.0% to 4.6% (p = 0.001) among LIPO-5-specific CD4+ T cells. Thus, while Tregs responses were dominant (69.3%) over Teffs (30.7%) before vaccination (Fig. 5B), the balance shifted after vaccination and the proportion of Tregs decreased (31.7%) simultaneously with an increase in both IFN- γ-producing cells (4.6%) and in “other responses” (63.7%). These “other responses” that we have not determined yet are probably associated (directly or indirectly) with the significant production of IL-2, IL-4, IL-13, IL-17F, TNF-α, MIP-1β, IL-3, IL-5, IL-9, IL-10, IL-21, IL-27 and sCD40L, as measured in bulk PBMCs using Luminex technology (S3A-B Fig).
When patients were stratified according to the magnitude of maximum viral rebound following ATI, good ATI-responders showed decreased HIV-specific Tregs responses after vaccination as compared to poor ATI-responders (Fig. 5C-D). Fig. 5C (upper left and middle panels) illustrates the change in the flow cytometry plots from a representative good ATI responder (patient 11) showing the decrease in frequency of CD39+FoxP3+ specific Tregs within the CD134+CD25+ cells. In contrast, the lower panels (left and middle) illustrates the lack of change in the high frequency of CD39+FoxP3+ specific Tregs within the CD134+CD25+ cells from a representative poor ATI responder (patient 10). Right upper and lower panels in Fig. 5C show LIPO-5 specific IFN-γ responses for both patients. When these parameters were combined for all patients, we could see that majority of patients with high specific Tregs frequency and low IFN- γ levels are mainly poor ATI-responders (circles) and can be clustered together in Fig. 5D (right circle). Patients with low Tregs-specific responses (<40%) included mainly good and medium virological responders (squares and triangles in left circle, Fig. 5D) and showed medium to high IFN-γ responses (> 1%). Finally, CMV-specific responses, including Tregs and IFN-γ-producing cells, were unchanged before and after vaccination (Fig. 5A and S1 Fig).
We explored further the data and used the multivariate immune score (See Statistical Analysis in Methods) to assess correlations between CD39+FoxP3+ LIPO-5-specific Tregs and effector functions after vaccination. Although this did not reach statistical significance likely due to the small sample size and limited statistical power, we found a consistent signal for a negative correlation between baseline Tregs and post-vaccination immune score (Fig. 6A), as well as between Tregs after vaccination and the immune score (Fig. 6B).
Together, these data suggest that the low IFN-γ responses usually found in HIV+ patients might be due to the presence of high percentages of HIV-specific Tregs among HIV-specific cells that might not be detected with current assays.
Efficient vaccines are characterized by the establishment of long-lived immunity. CD4+ T cells play an important role and are necessary for the control of viremia either directly or by providing help to B and CD8+ T cells [33,34]. CD4+ T cells comprise diverse populations, namely Th1, Th2, Th17, Tregs, Tfh and probably others [35]. We and others have previously shown that DC-based vaccines for HIV are feasible, safe, and well tolerated [17,18]. Our vaccine induced polyfunctional CD4+ and CD8+ T-cell responses, with a more prominent CD4+ response, that resulted in partial control of the viral load [18]. We also observed an inverse correlation between HIV RNA values after HAART interruption and frequencies of polyfunctional HIV-specific CD4+ T-cell responses detected 16 weeks after the start of vaccination protocol. One of the caveats of our study design is the fact that safety requirements for this phase I trial did not allow longer follow up periods after ATI. This resulted in the fact that more reliable measurement of post-ATI viral load rebound, such as viral load setpoint could not be clearly established. Therefore, we decided (consensus meeting with experts) to use maximum viral load rebound as a primary virological endpoint. This parameter is considered to be relevant as it reflects the capacity of the immune responses to control viral replication. Also, to strengthen our findings, we show that the average viral load post-ATI, as well as the viral load at the end of the follow up, inversely correlates with vaccine elicited CD4+ T-cell responses. However, these findings will be further corroborated in phase II trial (DALIA II), in which we will further address the effectiveness of the vaccine.
In this study, we explored in depth the frequency and function of antigen-specific CD4+ T-cell responses that were induced by the vaccine using the “OX40 assay” that allows the measurement of a whole range of antigen-specific cells regardless of their functional profile. Notably, this assay is very useful as it is able to detect HIV-specific CD4+ Tregs along with Teffs [21,22].
The role of Tregs in HIV infection has been extensively studied [36]. These cells may play a dual role firstly by decreasing immune activation, which is beneficial for HIV-infected individuals, but also secondly by suppressing anti-HIV responses. Even though the induction of Tregs was assessed in cancer [37,38] as well as in HIV vaccine trials [39], the induction of HIV-specific Tregs following vaccination has not been studied before. Indeed, the lack of tools that one can easily use in clinical trials setting has been preventing the measurement of Tregs-specific responses. Angin et al., recently reported the presence of gag-specific Tregs in infected individuals [40] by using MHC Class II tetramer loaded with gag peptide. Although interesting, this approach is challenging in clinical trials due to the genetic variability of MHC Class II as well as the limited availability of Class II tetramers. Tregs could also have different affinity with MHC comparing to Teffs, which could lead to differential staining and probable under- or over- estimation of their frequencies.
We were able to circumvent all these issues by the use of the inductive expression of CD134 on antigen-specific Tregs following an in vitro stimulation. Using this approach, the first surprising observation was that, prior to vaccination, a large proportion of HIV-specific Tregs with an activated phenotype (CD4+CD25+CD134+CD39+FoxP3+) were found. Forty-four percent of gag p24- and 69.3% of LIPO-5-specific CD4+ T cells were Tregs, as compared to 24.2% of CMV-specific CD4+ T-cell response. Whether these high proportions of Tregs among antigen-specific cells are a peculiarity of HIV-specific responses is a question that is currently being studied in our laboratory. Chronic HIV infection is thought to induce higher proportions of Tregs as a mechanism preventing long-term damage caused by chronic immune activation [36]. On the other hand, these high levels of circulating Tregs could dampen Teffs responses and inadvertently help maintain viral persistence which, in turn, would lead to immune exhaustion. Therefore, the study of HIV-specific Tregs is a crucial aspect to consider in the quest for an efficient HIV-1 vaccine. The low levels suppression (30–35%) we obtained in our in vitro assays might not translate to what would have happened in vivo and more investigation using animal models would be more informative. Nevertheless, our point in this study was not to make a statement that the magnitude of Tregs’ suppression could be translated to a clinical impact but to show that these cells exert a suppressive effect.
When investigating whether vaccination shifted the balance of HIV-specific Tregs and Teffs, we found that the relative proportions of HIV-specific Tregs decreased significantly following vaccination. In contrast, Teffs increased in proportions, as measured by higher percentages of IFN-γ-, IL-2- and TNF-α- producing cells as well as increases in secretion of several other cytokines. Interestingly, the increase in these cytokines strongly correlated with the increase in LIPO-5-induced CD4+ specific responses. These results are in line with the fact that CD4+CD134+CD25+ antigen-specific cells contain several Th-subtype-defining transcription factors [25], and show that our vaccine indeed induced highly polyfunctional Th responses. In addition, we found that besides polyfunctionality, the breadth of the response is also an important predictive mark of vaccine effectiveness. Notably, patient N19, the only patient who did not experience viral rebound, responded strongly to all peptide pools after vaccination. These HIV-specific responses were not detected at entry prior to therapeutic immunization, thus suggesting that a shift to a less immunodominant response (such as the response to gag p17), could lead to a better distribution of the overall response and possibly a more effective viral control. This concept will be examined more in depth in our future trials. Of note, our vaccine contains palmitoyl-lysylamide lipid tail, known to signal through Toll-Like Receptor 2 and affect Tregs expansion and function in mouse studies [41,42]. Palmitoyl-lysylamide however may not have a similar role in human, as reflected by the decreased Tregs proportions observed after vaccination in our study.
An impact of HIV-specific Tregs on the elicited vaccine response was further supported by a consistent signal for an inverse correlation between both baseline and post-vaccination LIPO-5- specific Tregs, respectively, and post-vaccination immune scores. Although this did not reach statistical significance, as the analyses were likely underpowered due to the small sample size, these results suggest a negative role for Tregs in the induction of vaccine induced effector responses.
It would be of importance to know whether there is a clinical benefit in adding a Tregs blocker along with the vaccine in future studies. Outcomes from the cancer field clearly showed that Tregs suppress vaccine-induced immune responses and correlate with poor clinical benefit. In melanoma patients, reduction of suppressor cells by cyclophosphamide enhanced responses to vaccination [43]. Another study including patients with human papillomavirus type 16 (HPV16)-induced vulvar intraepithelial neoplasia, clearly showed that those with larger lesions mounted higher frequencies of HPV16-specific CD4+CD25+Foxp3+ T cells and displayed a lower HPV16-specific IFNγ/IL-10 ratio after vaccination [37], suggesting that high frequency of antigen-specific Tregs is predictive of poor clinical benefit. To circumvent the potential side-effects Tregs blocker could have on non-targeted immune responses, dendritic-cell based vaccination offers an interesting alternative. Pen et al. recently reported that multifunctional T cells could be induced without the induction of Tregs by vaccination with dendritic cells in which soluble PD1 or PD-L1 were induced by mRNA electroporation [44]. Also, with the future discovery of novel markers, we will be able to address the question of central versus peripheral origin of HIV-specific Tregs which could facilitate the in vivo targeting of these cells.
Another question that remains to be answered is whether effector specific-responses measured in patients after vaccination, were induced by naïve T cells priming or whether they originated from the preexisting pool of memory T cells. Although probably both priming of naïve cells and expansion of memory pool took place, we would need to use animal models to be able to track precursors and clearly address this question.
In addition, agonistic OX40 signaling itself could represent a good candidate for modulating vaccine responses towards a Th1 or Tregs in viral infections or autoimmune settings respectively. It was shown that when DCs were pulsed with KLH and injected to mice together with an anti-OX40 antibody, there was an increase in Th1 responses. In re-challenge experiments, OX40 stimulation led to the amplification of preexisting memory responses. These data suggest that skewing of the response based on OX40 ligation might be achieved only in unexposed individuals [23]. Of note, these findings need to be taken with caution as OX40, unlike in humans, is constitutively expressed on murine Tregs. Therefore, the modulation of the response by OX40 ligation in human and mouse is probably very different and needs further study. Nevertheless, this molecule may be an interesting target for future immunomodulation protocols, not only in HIV infection, but also in cancer and autoimmune settings.
In conclusion, we show here that the vaccination with DC-based vaccine pulsed with LIPO-5 construct, induced strong polyfunctional and polyspecific CD4+ T-cell responses. The strength of the induced responses inversely correlated with maximum viral load after antiretroviral treatment interruption. Importantly, the fact that we were able to measure Tregs and Teffs-specific cells in a single readout, gives our approach a significant advantage over other described approaches addressing the induction of CD4+ T-cell responses of different functional properties, especially in clinical trial settings.
Peripheral blood mononuclear cells (PBMCs) were obtained from healthy volunteers or vaccinees. Blood was collected in either heparin tubes or after apheresis. PBMCs were isolated from blood preparations by Ficoll density gradient centrifugation. All experiments were performed on freshly thawed cells that were left to rest for 5–6 hours in human serum-supplemented medium at 37°C.
ANRS/VRI DALIA 1, a phase I single-center study was performed at the North Texas Infectious Diseases Consultants in Dallas, TX. The study was sponsored by Baylor Institute for Immunology Research (BIIR) and the Agence Nationale de Recherches sur le SIDA et les hépatites virales (ANRS). DC-based vaccines were generated from blood monocytes by culturing with GM-CSF and IFN-α and additionally activated with LPS, as previously described [45]. Briefly, monocytes were obtained from the apheresis product of HAART-treated HIV-infected patients by elutriation and cultured in a closed system with GM-CSF/IFN-α for 3 days. Differentiating DCs were pulsed for the last 24 hours with the ANRS HIV LIPO-5 peptides: gag (17–35; 253–284); pol (325–355); and nef (66–97; 116–145). DCs were then activated with LPS (purified lipopolysaccharide prepared from Escherichia coli O:113; U.S. Standard Reference Endotoxin vialed under Good Manufacturing Practice guidelines) for 6 hours, harvested and frozen in autologous serum with a final concentration of 10% DMSO. After thawing, the DC vaccine cells suspended in 1 ml of freezing solution were diluted with 9 ml of saline to give a total volume for injection of 10 ml. Approximately 15x106 viable frozen-thawed HIV lipopeptide-loaded DCs were injected subcutaneously in 3 separate injection sites (3.3 ml per site) in the upper and lower extremities. Subsequent DC injections were rotated to different locations on the upper and lower extremities. The vaccine was administered 4 times, at 4-weekly intervals. The blood samples (apheresis) analyzed were from wk -4, corresponding to the blood draw 4 weeks prior to first vaccination and wk 16, corresponding to the blood draw 4 weeks after the last vaccine. Antiviral treatment was stopped at wk 24 and viral load was measured thereafter.
Ethical committee approval and written informed consent from all subjects, in accordance with the Declaration of Helsinki, were obtained prior to study initiation. Committee and institutional review board(s) of EFS and INSERM (REF: C CPSL UNT—N° 12/EFS/079 and Convention reference number: I/DAJ/C2675) approved our study.
The study was approved by the IRB of Baylor Research Institute (BRI) (Clinical Trials Registration Number NCT 00796770). All patients gave written informed consent.
All staining experiments were performed at 4°C for 30 minutes. Antibodies used were CD3-PerCPCy5.5, CD8-APCCy7, CD25-APC, CD134-PE, TNF-α-PECy7, CD154-APC ((Becton Dickinson (BD) Biosciences)), CD4-Alexa Fluor 700, IFN-γ-eFluor450, IL2-PerCPeFluor710, Streptavidin-Alexa Fluor 700 (eBioscience), FoxP3-Alexa Fluor 488, CD25-Brilliant Violet 421 (BioLegend), CD39-biotin, CD127-PE (Miltenyi biotec), Streptavidin-ECD, CD45RO-ECD (Beckman Coulter). LIVE/DEAD fixable aqua staining kit (Life technologies) was used to discriminate live and dead cells. For intracellular staining, FoxP3 buffer set (eBioscience) was used.
The “OX40 assay” is described in details elsewhere [21,22]. Briefly, two million PBMCs or Tregs-depleted cells were plated in 24-well plate and stimulated with 1μg/mL CMV lysate (Behring) or 2μg/mL of LIPO-5 or HIV peptide pools (192 peptides contained in 18 pools of 15-mers peptides (NeoMPS, Strasbourg, France) covering HIV-1 gag (G1 to G11 including 3 pools covering gag p17, 5 pools covering p24 and 3 pools gag p2/p6/p7), 4 pools of pol (RT12 to RT15) and 3 pools of nef (N16 to N18)) for 44 hours. In the last 6 hours, 1μg/mL of Brefeldin A (Sigma) was added to block the secretion of IFN-γ, IL-2 and TNF-α. Cells were then collected and stained for subsequent analysis by flow cytometry (BD LSR II).
Tregs-depleted PBMCs were obtained after efficient depletion of CD25+ Tregs as described previously [31]. The method comprised labeling total PBMCs with anti-CD25 beads (Miltenyi biotech) and one passage over LS columns. Briefly, 10 to 20 million PBMCs were used in all experiments. Ten microliters of anti-CD25 beads were added per 10x106 PBMCs resuspended in 90μL of cold MACS buffer. Cells were then incubated for 20 minutes at 4°C then washed with 2–3 mL of MACS buffer before their passage through an LS column which has been placed on a manual magnetic separator. Both flow-through (Tregs-depleted) and remaining (Tregs) fractions were collected for further analysis and functional studies.
Tregs obtained by the above method were used in suppression assays in Tregs:Tresp ratio of 1:2. Either Tregs or non-Tregs were mixed with responding cells and incubated overnight in the presence of 2μg/mL of gag p24 peptide pool, 1 μg/mL of αCD28 and αCD49d (both from BD biosciences) and 10μg/mL of Brefeldin A. Responding cells were discriminated from Tregs (or non-Tregs) by labeling with carboxyfluorescein succinimidyl ester (CFSE, Life technologies) at 0.025mM final concentration for 15 min at 37°C.
FACS sorting of CD25hi, CD25int and CD25lo fractions were performed using MoFlo (Beckman Coulter, Hialeah, FL, USA). These CD4+ T-cell populations were subsequently cultured with non-CD4+ T cells in 1:4 ratio to “reconstitute” the conditions as for a standard PBMC “OX40 assay”.
After 44 hours of stimulation with LIPO-5, 500μL of each supernatant was collected and frozen at -80°C. Cytokine secretion measurement for TGF-β1, TGF-β2, IL-17F, IL-17A, IL-21, IL-22, IL-27, IL-31, IFN-γ, IL-10, IL-12p40, IL-12p70, IL-13, sCD40L, IL-9, IL-1β, IL-2, IL-3, IL-4, IL-5, IL-6, IL-8, IP-10, MCP-1, MIP-1β and TNF-α was performed using Luminex multiplex bead-based technology and a Bio-Plex 200 instrument (BioRad), according to the manufacturer’s directions. Data were analyzed both in terms of fluorescence intensity (FI) and after transformation to concentration (pg/ml) by a 5-parameter logistic curve, according to the manufacturer’s directions.
Analyses of differences between pre- and post-vaccination time points were done by Wilcoxon matched-pairs signed rank test. Correlations were assessed by Spearman correlation coefficients.
To summarize the immune response to vaccination across several immune markers we used a method for multivariate ordinal data based on U-scores. Allowing for ties between variables, a partial ordering of the individuals is established based on their multivariate immunogenicity data, and an immune U-score for each individual is calculated by the difference in the numbers of individuals with superior versus inferior orders [46]. With this method we calculated a multivariate immune score across the following immune markers, best reflecting those correlated with maximum viral load post-ATI in the core trial analyses [18]: Luminex IL2, IL13, IL21 and IFN-γ after LIPO-5 stimulations of PBMC and % of IL-2, IFN-γ and TNF-α among CD134+CD25+ after LIPO-5 stimulation.
Prism 5.0, version 5.0d, (GraphPad Software, Inc.) and SAS V9.2 (SAS Institute, Cary, NC, USA) were used for statistical analyses. P values were considered significant when < 0.05, without adjustment for multiple testing in this exploratory study.
|
10.1371/journal.pgen.1000382 | Disease-Associated Mutant Ubiquitin Causes Proteasomal Impairment and Enhances the Toxicity of Protein Aggregates | Protein homeostasis is critical for cellular survival and its dysregulation has been implicated in Alzheimer's disease (AD) and other neurodegenerative disorders. Despite the growing appreciation of the pathogenic mechanisms involved in familial forms of AD, much less is known about the sporadic cases. Aggregates found in both familial and sporadic AD often include proteins other than those typically associated with the disease. One such protein is a mutant form of ubiquitin, UBB+1, a frameshift product generated by molecular misreading of a wild-type ubiquitin gene. UBB+1 has been associated with multiple disorders. UBB+1 cannot function as a ubiquitin molecule, and it is itself a substrate for degradation by the ubiquitin/proteasome system (UPS). Accumulation of UBB+1 impairs the proteasome system and enhances toxic protein aggregation, ultimately resulting in cell death. Here, we describe a novel model system to investigate how UBB+1 impairs UPS function and whether it plays a causal role in protein aggregation. We expressed a protein analogous to UBB+1 in yeast (Ubext) and demonstrated that it caused UPS impairment. Blocking ubiquitination of Ubext or weakening its interactions with other ubiquitin-processing proteins reduced the UPS impairment. Expression of Ubext altered the conjugation of wild-type ubiquitin to a UPS substrate. The expression of Ubext markedly enhanced cellular susceptibility to toxic protein aggregates but, surprisingly, did not induce or alter nontoxic protein aggregates in yeast. Taken together, these results suggest that Ubext interacts with more than one protein to elicit impairment of the UPS and affect protein aggregate toxicity. Furthermore, we suggest a model whereby chronic UPS impairment could inflict deleterious consequences on proper protein aggregate sequestration.
| The accumulation of cytotoxic protein aggregates occurs in many neurodegenerative diseases. It is difficult to determine if the protein aggregates found in these diseases represent a cause or consequence of the disorder. Degradation pathways, such as the ubiquitin/proteasome system (UPS), remove misfolded proteins that are prone to aggregate. The UPS involves many players that work in concert to target proteins for degradation by the proteasome. A mutant form of ubiquitin has been associated with many diseases, including Alzheimer's disease. We developed a yeast model of the mutant ubiquitin protein in order to investigate its effect on UPS function and protein aggregation. We demonstrate that this mutant ubiquitin causes impairment of the UPS and suggest that it does so by interacting with multiple components of the pathway. Using this model, we evaluated the effects of the mutant ubiquitin on nontoxic protein aggregates and found that they were unaltered by its presence. We demonstrate that the mutant ubiquitin acts as a modifier, which increases cellular susceptibility to the phenotypic effects of deleterious protein aggregates by altering UPS functionality and substrate ubiquitination. Furthermore, the system we developed can be utilized to further understand the complex interplay of proteasomal impairment and protein aggregate toxicity.
| As technology and medicine further extend the human lifespan, age-related diseases will become more prevalent. Alzheimer's disease (AD) is a neurodegenerative disorder that affects 20 million people worldwide and is the most common form of late-onset dementia [1]. The study of genetic mutations that cause early onset AD has provided insight into some of the factors involved, but most cases of AD are sporadic and of unknown origin. Uncovering the risk factors involved in any multi-factorial disease is challenging but vital for disease treatment and prevention. Many fundamental pathways, including the ubiquitin proteasome system (UPS), have been suggested to play a role in AD. Therefore, investigating the relationship between AD and the UPS could lead to new therapeutic targets.
The UPS is an evolutionarily conserved pathway that selectively eliminates short-lived and damaged proteins. A number of cellular processes, including the cell cycle, stress response, and DNA repair, require the UPS [2]. Protein degradation by the UPS involves a series of enzymes that ultimately attach ubiquitin, a small well-conserved protein, to an internal lysine residue in the target protein [3]–[5]. Multiple ubiquitin proteins can be connected to form a polyubiquitin chain which serves as a degradation signal recognized by the 26S proteasome. A series of events involving E1, E2 and E3 enzymes are required to attach ubiquitin via its C-terminal glycine residue to the target protein. The formation of polyubiquitin chains and the process of ubiquitin conjugation to protein targets displays exquisite specificity, in part by the multitude of E2 and E3 enzymes. Despite intensive study, the roles of many components of the UPS remain to be elucidated.
The importance of the UPS in cellular homeostasis is apparent not only by the redundancy and conservation of the components, but also by its role in disease [5],[6]. The complex interplay between protein aggregation and UPS function is easily appreciated, yet it is often difficult to determine the causal nature of the problem. UPS dysfunction can prevent the degradation of misfolded proteins, which can lead to aggregation. Conversely, protein aggregates can be challenging substrates for the UPS and can thus cause proteasomal impairment [7]. Protein aggregation is a hallmark of many neurodegenerative disorders [6]. In addition, mutations in ubiquitin processing enzymes, such as UCHL1 and Parkin, can lead to inherited forms of neurodegenerative diseases [8],[9]. Furthermore, many protein aggregates associated with disease show ubiquitin deposition [10], suggesting that dysfunctional UPS activity may contribute to pathogenesis. Understanding the interplay between protein aggregation and clearance is an active area of research, but most systems are complicated by cellular toxicity, which alone can have negative consequences on protein homeostasis.
A mutant form of ubiquitin was found associated with AD and other diseases and was proposed to act as a natural proteasome inhibitor [11]. The generation of this mutant ubiquitin protein is unusual - the mutation is found in the messenger RNA, but not in the DNA sequence of the ubiquitin-B gene. The mutant ubiquitin results from a dinucleotide deletion near the 3′ end of the mRNA transcript which shifts the reading frame for translation. The mutant protein has been named UBB+1 [12]. The dinucleotide deletion event in the mRNA has been termed “molecular misreading”, though the mechanism by which the deletion occurs remains elusive [13],[14]. Many human mRNA transcripts, including all copies of ubiquitin, contain potential sites for molecular misreading, since hotspots for these events are hypothesized to occur near simple repeat sequences (e.g. GAGAG) [15]. The best characterized +1 mutant ubiquitin protein has a short C-terminal extension, with the majority of the protein being identical to ubiquitin [12]. As such, the protein is presumably folded and recognized as ubiquitin, but the C-terminal glycine residue essential for conjugation to substrates is absent.
The accumulation of the UBB+1 protein in the neurological hallmarks of AD is curious, since the mutant cannot be conjugated to target proteins [12]. The presence of UBB+1 has been proposed to represent an endogenous readout of proteasomal dysfunction [16],[17]. Due to its association with protein aggregation, it was also suggested that UBB+1 could contribute to disease pathology [18]. UBB+1 protein accumulation has been documented in multiple disorders such as polyglutamine expansion diseases (including Huntington's disease), Pick's disease and even non-neuronal tissue diseases [11],[19]. However, the mechanism of UBB+1 action in these diseases remains unclear.
To evaluate the role of UBB+1 in disease, the effects of ectopic UBB+1 expression have been investigated in cultured mammalian cells. Although UBB+1 cannot be conjugated to target substrates, it can be ubiquitinated by wild type ubiquitin and degraded by the proteasome [20]. However, high levels of UBB+1 expression cause proteasomal impairment [16],[21],[22]. As a natural inhibitor of the UPS, UBB+1 could be another example whereby proteasomal impairment induces protein aggregation. Therefore, UBB+1 might act as a disease modifier. Recently, a UBB+1 transgenic mouse has been characterized [23]. UBB+1 expression resulted in constant UPS impairment that caused a minor learning deficit and caused changes in transcription profiles that mirror those found in brains of humans with AD [23]. The expression of UBB+1 in mammalian cells enhances the toxicity and aggregation of an expanded polyglutamine protein [24]. However, measuring changes in protein aggregation in cells that are dying from toxic protein aggregates is challenging. Hence, it remains to be determined if UBB+1 affects protein aggregation per se, or if it affects the ability of the cells to cope with the aggregates.
We developed a model system using Saccharomyces cerevisiae to evaluate the cellular effects of UBB+1. We expressed a mutant ubiquitin protein (Ubext) analogous to UBB+1 and found that it caused UPS impairment in yeast. Furthermore, we found that Ubext changed the ubiquitination pattern on a UPS substrate. Taking advantage of non-toxic protein aggregates in yeast, we demonstrated that the expression of Ubext neither induced nor changed these aggregates. However, Ubext did make cells more susceptible to toxic protein aggregates. We propose that Ubext does not cause protein aggregation, but rather acts as a phenotypic enhancer of deleterious aggregation. We present a model based on our work and other recent advances in the field to explain how this might occur.
The mechanism by which +1 proteins, such as UBB+1, are produced is currently unknown. To create a yeast model of UBB+1, we generated an expression vector containing the sequence of the first ubiquitin-coding region of the yeast tandem ubiquitin gene, UBI4, such that a dinucleotide deletion occurred near the carboxy terminus (Figure 1A). The deletion caused a frameshift in the coding sequence of ubiquitin and extended the open reading frame to the next stop codon (termed extended ubiquitin or Ubext). This construct mimics the generation of UBB+1 from the human tandem ubiquitin gene (ubiquitin-B). Constitutive expression of Ubext in log-phase yeast did not cause a growth defect when assessed in either liquid medium (data not shown) or on solid medium (Figure 1B). Wild type cells expressing Ubext did show a reduced growth rate after recovery from stationary phase (data not shown).
To evaluate the functionality of Ubext, we analyzed its ability to replace wild type ubiquitin. The stress-inducible UBI4 gene encodes a tandem array of five ubiquitin moieties that are separated post-translationally by deubiquitinating enzymes (DUBs) that cleave after the C-terminal glycine residue, G76 [25]. UBI4 is non-essential in vegetatively growing cells but is required for cells to recover from various stress conditions [26],[27]. We utilized a strain lacking UBI4 to evaluate the functionality of Ubext. Δubi4 cells were transformed with expression plasmids that contain wild type ubiquitin, Ubext or empty vector. The transformants were grown for two weeks to allow them to reach stationary phase and then plated again to evaluate their ability to recover. Only cells expressing extra wild type ubiquitin were rescued from the loss of UBI4 and could grow after this stress (Figure 1C). This demonstrates that Ubext is a non-functional ubiquitin, as expected due to the lack of the C-terminal glycine residue required for conjugation to target substrates.
If Ubext affects UPS functionality in yeast as UBB+1 does in mammals, then we hypothesized that Ubext would display synthetic lethality with a proteasome mutant. We evaluated the cellular viability of a temperature-sensitive catalytic proteasome mutant strain (pre1-1 pre2-2) [28] expressing Ubext. As predicted, Ubext-expressing pre1-1 pre2-2 cells were inviable at the restrictive temperature (Figure 2A). Wild type cells expressing Ubext grown at the restrictive temperature did not show a growth defect (Figure 2A). Next we evaluated another ubiquitination-dependent process to determine if Ubext effects are more widespread. We challenged Ubext-expressing cells to DNA damage induced by UV irradiation and found that they survived as well as the control cells (data not shown).
Ubext cannot be conjugated to target protein substrates, but can be recognized as a UPS substrate. Therefore, we assessed its ubiquitination. Protein lysate from Ubext-expressing cells and control cells were evaluated by SDS-PAGE and western blot. Cells expressing Ubext exhibited a unique band which represents the extended mutant ubiquitin protein (Figure 2B, grey arrow) which is larger than wild type ubiquitin (Figure 2B, arrowhead). Cells expressing Ubext also displayed a distinctive laddering pattern which suggests that Ubext is conjugated by wild type ubiquitin moieties (Figure 2B, black arrows). A similar laddering pattern was previously observed in cells expressing UbΔGG [29], a mutant ubiquitin protein lacking only the two C-terminal glycine residues, and we observed the same pattern when we expressed UbΔGG in yeast (data not shown). Additionally, a strain lacking the ubiquitin recycling DUB (Δubp14) accumulates free ubiquitin chains [29] and we also observed that Δubp14 cells show the same ubiquitin laddering pattern as cells expressing Ubext (data not shown).
The expression of Ubext also caused an increase in the level of unconjugated wild type ubiquitin, which was evident by the accumulation of the mono-ubiquitin band in the Ubext lane in comparison to the empty vector control lane (Figure 2B, black arrowhead). Further analysis by quantitative western blot showed approximately a 10-fold increase in wild type mono-ubiquitin in the presence of Ubext (data not shown). Transcriptional activity from the UBI4 promoter using a UBI4promoter-LacZ reporter in Ubext-expressing cells demonstrated a modest two-fold increase (data not shown), suggesting that UBI4-induced transcription may be one, but perhaps not the only source for the increased ubiquitin. Cells expressing Ubext also displayed an increase in the abundance of high molecular weight ubiquitin-conjugated proteins in comparison to the empty vector control (Figure 2B, compare left lane WT to right lane Ubext). The fact that Ubext caused lethality in the proteasome mutant strain and Ubext-expressing cells accumulated ubiquitinated-protein conjugates, suggests that it is affecting protein degradation. An accumulation of high molecular weight ubiquitinated proteins also occurred with the over expression of wild type ubiquitin (Figure 2B, middle lane). Most likely this occurs because of more ubiquitination of endogenous proteins due to an excess of functional ubiquitin provided by the over expression construct.
We tested the functionality of the UPS in cells expressing Ubext using two different proteasome reporters constructs: an N-end rule substrate and a ubiquitin fusion degradation (UFD) substrate [30]. These substrates are processed by the UPS using distinct enzymes [3],[31],[32]. The N-end rule substrate is a Ub-R-LacZ fusion. The ubiquitin moiety is efficiently cleaved by endogenous DUBs to expose the N-terminal amino acid (arginine) of β-galactosidase (βgal). According to the N-end rule, R-βgal is an unstable protein that is polyubiquitinated and rapidly degraded by the 26S proteasome [33]. The UFD reporter substrate is Ub-P-LacZ. In yeast, no DUB can cleave ubiquitin from βgal if the first amino acid after ubiquitin is proline. Because of the ubiquitin fusion, Ub-P-βgal is unstable and is rapidly degraded by the proteasome. These constructs, along with a stable LacZ control (Ub-M-LacZ), were transformed into cells expressing Ubext to assess UPS function by βgal activity assays. Cells expressing Ubext and either of the unstable proteasome reporters displayed higher levels of specific βgal activity (Figure 2C and 2D). Cells expressing extra wild type ubiquitin showed a slight increase in the stabilization of the reporter constructs. The expression of extra wild type ubiquitin also generated a large steady state population of ubiquitin-conjugated proteins (Figure 2B, middle lane), which could be taxing the degradation capacity of the proteasome. To evaluate if LacZ fusion expression was affected by Ubext, stable M-βgal activity was measured and showed no difference (data not shown). These results demonstrate that the expression of Ubext in yeast inhibits the degradation of two different UPS reporter substrates.
Such stabilization of the proteasome reporter constructs could be due to a lack of ubiquitination of the reporter, since the expression of Ubext also causes accumulation of unconjugated wild type ubiquitin. The reporter substrates (βgal protein) were immunoprecipitated from cells with and without the co-expression of Ubext. Western blot with an anti-βgal antibody revealed that more β-gal protein was precipitated in Ubext-expressing cells (Figure 2E, left). This result correlates with the higher levels of βgal activity measured in Ubext-expressing cells (Figure 2C and D). Analysis with an anti-ubiquitin antibody showed ubiquitin-conjugated R-βgal and Ub-P-βgal in cells expressing Ubext (Figure 2E, right). This data demonstrates that Ubext is not stabilizing these UPS substrates by blocking their ubiquitination.
Another plausible explanation for the UPS inhibition could be that Ubext binds to the proteasome and this interaction precludes other proteasome substrates from being efficiently degraded. Alternatively, Ubext could interact with other component(s) of the UPS and inhibit their function. To examine whether Ubext is clogging the proteasome, we took advantage of a ubiquitin-independent proteasome substrate. Ornithine decarboxylase (ODC) is an enzyme involved in polyamine biosynthesis [34],[35] and a short peptide from this protein serves as a ubiquitin-independent degradation signal (i.e. degron) [36]. Measuring the degradation of ODC reflects the functionality of the proteasome in a manner independent of the non-proteasomal components of the UPS cascade. A fusion of GFP with the degron of ODC (GFP-ODC) serves to target GFP to the proteasome where it is rapidly degraded [37]. A point mutation in the ODC degron (C441A) stabilizes the fusion protein by lowering its affinity for the proteasome [38],[39]. GFP-ODC fusions were transformed into cells expressing Ubext and the steady state level of GFP-ODC was evaluated by western blot (Figure 3A). Cells expressing Ubext were able to degrade the GFP-ODC protein while the stable GFP-ODCC441A protein accumulated (Figure 3A). Even prolonged exposure showed that the steady state level of GFP-ODC was approximately equal with or without Ubext expression (Figure 3B). Thus, Ubext permits the degradation of a ubiquitin-independent proteasome substrate, suggesting that the proteasomal degradation capacity is not significantly impaired in cells expressing Ubext.
We sought to determine how Ubext exerts its negative effects on the UPS pathway. We asked whether Ubext was sequestrating wild type ubiquitin proteins. Ubiquitinated-Ubext could be refractory to DUBs, thereby tying up ubiquitin, as suggested for UBB+1 [20]. To test this hypothesis, we expressed extra ubiquitin in the presence of Ubext and found that the UPS test substrates were still stabilized (data not shown). This result was not surprising since monomeric ubiquitin appears to be abundant in cells expressing Ubext (Figure 2B, arrowhead). This suggests that a lack of wild type ubiquitin is not the cause of the UPS impairment elicited by Ubext.
Ubext lacks the essential C-terminal glycine residues (G75 and G76) required for ubiquitin conjugation and these glycine residues are vital for many proteins to interact with ubiquitin [40]. We tested whether adding back two glycine residues to the C-terminal extension of Ubext (Ubext+GG) could restore these interactions and alleviate the proteasomal impairment. Cells expressing Ubext+GG still displayed proteasomal impairment (data not shown), indicating that the C-terminal extension plays a mechanistic role in the phenotype observed.
UPS-mediated protein degradation is a selective process and polyubiquitination is the signal which targets proteins to the proteasome for degradation [41],[42]. Therefore, we asked whether blocking the ubiquitination of Ubext would alleviate the associated UPS inhibition. Polyubiquitination can occur on multiple lysine residues of ubiquitin [43]. We mutated four of the lysine residues typically utilized for polyubiquitination by changing them to arginine (referred to as UbextKxR). Ubiquitin conjugation of Ubext was visualized by a distinct laddering pattern on a western blot (Figure 2B, black arrows). While none of the single point mutations prevented ubiquitination of Ubext, the double lysine mutant, UbextK29/48R, did prevent the conjugation (Figure 4A, black arrows).
We evaluated the degradation of the UPS substrates in the presence of the UbextKxR mutants. The expression of each single UbextKxR mutant stabilized the N-end rule substrate, R-βgal (Figure 4B). However, the expression of the UbextK29/48R double mutant allowed for better degradation of the reporter protein, suggesting that the ubiquitination of Ubext is necessary to impair the degradation of the N-end rule substrate. The steady state levels of βgal protein were detected by western blot and corroborated the result of the βgal activity assay (Figure 4B, lower).
Next, we evaluated the degradation of the UFD substrate in the presence of the UbextKxR mutants. Each UbextKxR mutant, including the double mutant (UbextK29/48R), impaired the degradation of the UFD reporter protein Ub-P-βgal (Figure 4C). Since these data contradict the effects of UbextK29/48R on N-end rule substrate stability (Figure 4B) and previously published results with UBB+1 [22], we evaluated another UFD substrate, a ubiquitin-GFP fusion (UbG76V-GFP). Western blot analysis revealed that this UFD substrate was also stabilized by Ubext as well as each UbextKxR mutant, including the double mutant (Figure 4D). Taken together, these data demonstrate that the conjugation of Ubext is necessary to cause impaired degradation of an N-end rule substrate, but mono-Ubext (i.e. UbextK29/48R) can still impair the degradation of UFD substrates. Based on these data, we suggest that ubiquitin conjugation to N-end rule substrates and UFD substrates is different. The degradation pathways utilized for these two reporters are distinct [3],[31],[32], however they typically report on the same degradation competence of the proteasome, although differences have been cited under certain circumstances [29],[44],[45]. The observed differences here could be explained if different proteins interact with the substrates to perform the ubiquitin conjugation. Perhaps preformed ubiquitin chains are conjugated en masse to N-end rule substrates but ubiquitin is added sequentially to UFD substrates. Thus, in the presence of UbextK29/48R the substrates would be affected differently. Furthermore, this emphasizes that the mode of ubiquitin conjugation, which remains somewhat of a mystery [46], may be an important factor in the differential ability of the cells to cope with one UPS substrate versus another.
Our data suggest that Ubext might be interacting with multiple components of the ubiquitin processing pathway, sequestering proteins required for efficient degradation of proteasome target substrates. Ubiquitin contains a hydrophobic patch (L8, I44 and V70) that is critical for its interaction with many other proteins and the proteasome [47],[48]. The ubiquitin mutation I44A disrupts the hydrophobic patch and this mutant fails to interact with some of its partner proteins [48]. We created a UbextI44A mutant and tested whether its expression caused UPS impairment. Cells expressing UbextI44A still stabilized the N-end rule substrate, R-βgal (Figure 5A). However, expression of UbextI44A resulted in a modest, yet reproducible, increase in the degradation of UFD substrate Ub-P-βgal (Figure 5B). This differential stabilization of the reporters did not occur with different type of mutant ubiquitin, UbΔGG I44A (data not shown). These data suggest that the interaction of Ubext with other proteins is partially disrupted by mutating the hydrophobic patch and further supports that Ubext may have multiple interacting partners to impose the UPS impairment.
The UPS is required for the removal of misfolded proteins. Failure to remove misfolded proteins can lead to aggregation and have detrimental phenotypic consequences. Since the expression of Ubext exacerbates UPS defects, we next analyzed whether the tolerance to misfolded proteins was decreased in cells expressing Ubext. Canavanine is an arginine analog which becomes incorporated into newly synthesized proteins and causes misfolding [49]. Serial dilutions of cells expressing Ubext were spotted onto solid medium containing canavanine. Ubext-expressing cells showed impaired growth on canavanine containing medium (Figure 6). This suggests that Ubext interferes with the ability of the UPS to degrade natural substrates and challenges cell viability when presented with misfolded proteins.
We next asked whether misfolded proteins that aggregate would present an additional challenge to cells expressing Ubext. Using tools and properties uniquely available in the yeast system, we sought to determine if Ubext affects protein aggregation by evaluating both toxic and non-toxic protein aggregates. Since cell death associated with toxic protein aggregates makes it difficult to evaluate the potential contribution of UPS dysfunction, the use of non-toxic aggregates in yeast could provide additional insight as to the direct effects of Ubext. UBB+1 enhanced the aggregation and toxicity of a polyglutamine-expanded protein in cultured mammalian cells [24]. To perform similar experiments in our yeast model, we used a galactose-inducible expanded Huntingtin (Htt) polyglutamine construct, TOXIC-Q103, which creates a toxic protein aggregate [50],[51]. Cells expressing Ubext could only tolerate a very low amount of TOXIC-Q103, and even with minimal induction, Ubext-expressing cells grew much worse in comparison to control cells (Figure 7A). Interestingly, the expression of UbextI44A did not result in the same enhanced protein aggregate toxicity (data not shown). Thus, partially alleviating the UPS impairment by altering Ubext protein interactions relieved the enhanced toxicity.
To determine whether Ubext expression might affect the aggregates themselves, we imaged a non-toxic version of a polyglutamine-expanded Htt protein fused to GFP (HttQ103-GFP) [52]. Evaluation of these protein aggregates eliminates the complication of cell death associated with toxic aggregates. Previous studies have demonstrated that genetic manipulations, such as altering chaperone levels, can change the abundance and pattern of polyglutamine-GFP aggregates in cells [53]. Thus, we tested whether UPS dysfunction caused by the expression of Ubext would change the aggregate distribution. Neither the abundance nor the pattern of HttQ103-GFP aggregates was altered in cells expressing Ubext (Figure 7B). Thus, although the expression of Ubext did enhance the cellular susceptibility to toxic aggregates, it did not grossly alter the formation or maintenance of non-toxic polyglutamine protein aggregates.
One mechanism by which Ubext could be enhancing the toxicity of TOXIC-Q103 could involve stabilization of the protein, as the level of expression directly correlates to the amount of toxicity. The stability of TOXIC-Q103 protein was evaluated from cells expressing Ubext after protein translation was inhibited by cycloheximide. No drastic stabilization of TOXIC-Q103 protein was apparent in cells expressing Ubext (Figure 7C).
We next asked whether the TOXIC-Q103 aggregates themselves caused UPS impairment. The stability of the UPS reporter protein, Ub-P-βgal, was monitored in cells containing TOXIC-Q103 aggregates in comparison to a non-pathological polyQ25 protein. No stabilization of the reporter was observed in cells harboring the toxic aggregates (Figure 7D). In addition, the UPS impairment caused by Ubext was not further increased by the presence of TOXIC-Q103 (Figure 7D). Thus, the enhanced toxicity of TOXIC-Q103 caused by Ubext is not due to additive effects on UPS impairment.
To evaluate the generality of the effects of Ubext on the phenotypic response to toxic protein aggregates, we used a yeast prion protein. Prion proteins in yeast form ordered aggregates that are not harmful to the cells [54]–[56]. Sup35p, an essential translation termination factor, is the protein determinant of the yeast prion [PSI+] [55]. The aggregated prion state of Sup35p, [PSI+], causes read through of stop codons in translated mRNAs (nonsense suppression). The percentage of read through is low and generally has no deleterious effects to cells grown in rich medium [54]. The presence of the [PSI+] prion can be monitored in a strain carrying an ade1-14 mutant allele with a premature stop codon [57]. In [psi−] cells, Sup35p is soluble and functional, and translation is terminated at the premature stop codon in ade1-14. Thus, [psi−] ade1-14 cells cannot grow on medium lacking adenine and when grown on rich medium they appear red due to the accumulation of an intermediate in the adenine biosynthetic pathway. Conversely, aggregated Sup35p in [PSI+] cells limits the amount of functional Sup35p, thereby causing nonsense suppression of the ade1-14 premature stop codon and translation of full-length Ade1 protein. These cells are adenine prototrophs and appear white on rich medium. As such, one can evaluate the functional state of Sup35p as it relates to protein aggregation by monitoring the color of the yeast colony. Cells can be maintained stably as [psi−], but they can be induced to become [PSI+] by over expressing the Sup35 protein.
The [PSI+] prion state is not toxic, however, over expression of Sup35p in [PSI+] cells inhibits cell growth due to the lack of sufficient translation termination [58]–[60]. As one would expect, the over expression of Sup35p is not toxic to [psi−] cells. Thus, the toxicity results from too much aggregation of Sup35p in the prion state. These toxic aggregates provide a means to assess the effects of aggregation of a protein of known function in combination with UPS dysfunction. Since most toxic protein aggregates cause cell death by unknown mechanisms, analyzing the Sup35p aggregates in [PSI+] cells provides a unique opportunity to dissect the contributions of the toxic protein aggregates and UPS dysfunction. To evaluate the effects of UPS dysfunction on toxic protein aggregates, [PSI+] cells harboring a copper-inducible SUP35 were transformed with Ubext and assayed for cell viability (Figure 8A). Ubext-expressing [PSI+] cells were more susceptible to the over expression of Sup35p (Figure 8A, red box). The expression of Ubext did not increase basal levels of Sup35p, as determined by SDS-PAGE and western blot analysis (data not shown). Intriguingly, the expression of a different mutant ubiquitin protein, which caused UPS impairment similar to Ubext (data not shown), UbΔGG, did not enhance the toxicity of Sup35p over expression to the same extent (Figure 8A, compare fourth row to sixth row). These results show that Ubext enhances the toxicity of protein aggregates by a mechanism that cannot be solely attributed to its effects on UPS impairment, since UbΔGG did not have the same effect. Furthermore, the hydrophobic domain mutant, UbextI44A, did not result in the same sensitivity to over expressed Sup35p in [PSI+] cells (Figure 8A). This suggests that the mechanism by which Ubext enhances the toxicity of protein aggregates requires interactions with other proteins via the hydrophobic domain.
We evaluated whether the aggregation of Sup35 is altered by the expression of Ubext. A previous study demonstrated that altering ubiquitin levels by either increasing the expression of ubiquitin or preventing its recycling caused an increase in the formation of the [PSI+] prion [61]. Furthermore, deletion of a ubiquitin conjugating enzyme also enhanced [PSI+] induction [62]. Thus, there is genetic precedence for perturbations of the UPS affecting prion protein aggregation. We asked whether the presence of Ubext would alter the spontaneous formation of aggregated Sup35p and change cells from [psi−] to [PSI+]. We did not observe a change in the spontaneous conversion rate (data not shown), which we have measured to be ∼1 in 105 in our strain [63]. We next evaluated the induction of the [PSI+] prion state in the presence and absence of Ubext by over expressing Sup35p in [psi−] cells. Since Ubext perturbs the UPS, one might predict an effect on the induction of protein aggregation. To the contrary, the expression of Ubext did not enhance the induction of [PSI+] (Figure 8B).
The enhanced toxicity of protein aggregates caused by Ubext could be the result of a general stress response elicited in cells expressing Ubext. The expression of a heat shock element (HSE)-LacZ reporter fusion was evaluated in Ubext-expressing cells and no increase in transcription from the HSE promoter at 30°C or at a sub-lethal heat stress of 37°C was observed (data not shown). We next asked whether the presence of Ubext increased the translation of a stress-inducible heat shock protein. Protein lysate from Ubext-expressing cells and control cells showed similar levels of Hsp104p (Figure 8C), a stress-responsive chaperone. Finally, we tested the tolerance of the cells to oxidative stress. Cells challenged with hydrogen peroxide showed no change in survival in the presence of Ubext (Figure 8D). These results suggest that Ubext expression in yeast neither induces a general stress response nor preconditions the yeast to exogenous insult. Therefore, the enhanced susceptibility of Ubext-expressing cells to toxic aggregates is not likely the result of Ubext inducing a general stress.
Overcoming the enhanced protein aggregate toxicity induced by Ubext expression could shed light on the mechanism by which Ubext exerts its affects. In attempts to alleviate the Ubext-enhanced aggregate toxicity we conducted a genomic over expression screen using the toxicity caused by over expression of Sup35p in [PSI+] cells. We uncovered two rescuing factors, HSP104 and SUP45. Both of these proteins alleviate the toxicity by affecting Sup35p aggregation and the associated phenotypic readout. Over expression of Hsp104p affects the Sup35p aggregates [64] and Sup45p can sequester Sup35p away from the aggregates [65]. To verify that the enhanced protein aggregate toxicity in the presence of Ubext can be overcome by altering nonsense suppression, we over expressed the C-terminal domain (CTD) of Sup35p, which is sufficient for translation termination but cannot aggregate and form or join the prion state [58],[66]. We found that the expression of the CTD not only restored translation termination of [PSI+] cells (Figure 8E, upper), but also alleviated the enhanced toxicity caused by the expression of Ubext (Figure 8E, lower). These results demonstrate that alleviating the primary deficit in the cells (i.e. the effects of [PSI+]) is sufficient to overcome toxicity even in the presence of a modifier (Ubext).
We next asked whether Ubext affected the toxic Sup35p aggregates, since the enhanced cellular toxicity caused by Ubext and excess Sup35p is [PSI+]-dependent. We assayed Sup35p aggregates by semi-denaturing detergent agarose gel electrophoresis (SDD-AGE) [67]. This technique allows large protein aggregates to migrate into the gel and can resolve aggregates of different sizes, as demonstrated by a strain variant of [PSI+] (weak [PSI+]), which harbors larger Sup35p aggregates than our [PSI+] starting strain (Figure 8F). We observed no change in the size of Sup35p aggregates from cells over expressing Sup35p in combination with Ubext or UbΔGG. One possible explanation for the enhanced toxicity in the presence of Ubext could relate to a change in the degradation of misfolded Sup35p. As such, we asked whether Ubext was promoting the accumulation of ubiquitinated-Sup35p. We reprobed the SDD-AGE membrane with an anti-ubiquitin antibody but did not find any ubiquitinated Sup35p by this approach. In additional attempts to look for ubiquitination of Sup35p, we purified Sup35 aggregates [68] but again were unable to detect any ubiquitinated Sup35 protein (data not shown). Other researchers have also noted an inability to identify ubiquitinated-Sup35p [61],[62]. Thus, we conclude that although Ubext affects the ability of cells to tolerate toxic Sup35p over expression, it is unlikely a direct consequence of blocking the ubiquitination and degradation of Sup35p.
We also evaluated whether the polyglutamine-expanded Htt proteins are ubiquitinated. We were unable to detect ubiquitinated polyglutamine protein in yeast by immunoprecipitation, SDD-AGE or immunofluorescence (data not shown). The inability to find ubiquitinated polyglutamine protein has also been noted previously [52],[69],[70]. Therefore, as with toxic Sup35p, Ubext is affecting the tolerance to TOXIC-Q103 aggregates by an indirect means.
How could Ubext be affecting the toxicity of protein aggregates if those proteins are not subject to ubiquitination and degradation by the UPS? One possible explanation of the effects of Ubext on protein aggregate toxicity could be due to a change in the ability to efficiently sequester the toxic proteins into large aggregates (Figure 9). A toxic polyglutamine protein expressed in yeast was rendered non-toxic when sequestered into a single, large aggresome-like structure [70]. Furthermore, a non-toxic polyglutamine protein, which localizes to an aggresome-like structure, became dispersed in ubiquitination-deficient cells. We hypothesize that Ubext alters the localization of toxic proteins into the large aggregate structures due to its effects on UPS function. The enhanced toxicity could be the consequence of a reduced ability to sequester toxic soluble oligomer species (Figure 9).
Based on our hypothesis, we predict that protein aggregate toxicity can be affected by perturbations in ubiquitination or by overwhelming the UPS in general. We took advantage of a temperature-sensitive ubiquitin activating enzyme (E1) mutant (uba1-204) [71] to evaluate the effect of an overall reduction in ubiquitination on the phenotypic response to TOXIC-Q103 aggregates. UBA1 is an essential gene responsible for the first step of the ubiquitination cascade. At the restrictive temperature, the uba1-204 mutant limits substrate ubiquitination. A recent study demonstrated that polyglutamine protein aggregate patterns were altered in cells expressing the uba1-204 mutant [70]. uba1-204 cells expressing TOXIC-Q103 or the control (Q25) were grown in inducing conditions at the permissive (30°C) and restrictive temperatures (32°C) and colony survival was measured (Figure 10A). Cells expressing TOXIC-Q103 showed approximately 50% survival in comparison to those expressing Q25, and this survival was further decreased in conditions of limiting ubiquitination (i.e. 32°C). To directly compare the affect of Ubext expression on the TOXIC-Q103 aggregates, we measured colony survival as performed above. Cells harboring TOXIC-Q103 aggregates in the presence of Ubext allowed for only a 7% survival in comparison to TOXIC-Q103 aggregates alone (56% survival). Thus, Ubext is a more potent modifier of toxic protein aggregates than perturbations in ubiquitination.
Since decreased ubiquitination had an affect on the protein aggregate toxicity, we asked if protein aggregate toxicity could also be enhanced by increasing the burden on the UPS. We measured the viability of cells expressing TOXIC-Q103 or over expressing Sup35p in the presence of canavanine. Serial dilutions of cells expressing Q25 and TOXIC-Q103 were spotted onto inducing media containing canavanine. The effects of the glutamine expansion on cell viability can be seen on inducing plates and in the presence of a UPS burden (canavanine) the toxicity is enhanced (Figure 10B). Over expressed Sup35p in [PSI+] cells also shows toxicity and in the presence of canavanine the toxicity is slightly enhanced (Figure 10C). However, canavanine is less potent at enhancing the toxicity of over expressed Sup35p in comparison to the effect of Ubext (Figure 8A). Nonetheless, perturbations to the UPS in general do appear to enhance protein aggregate toxicity. We propose that this is due to a change in efficient sequestration of toxic proteins into insoluble aggregates (Figure 9).
Since Ubext enhanced the toxicity of TOXIC-Q103, we tested whether Ubext-containing cells were compromised in their ability to sequester or retain TOXIC-Q103 in the insoluble aggregates. Protein lysates from Ubext and controls cells (EV) were subjected to high speed ultracentrifugation and analyzed to determine whether Ubext influences the amount of soluble TOXIC-Q103. Serial dilutions of the total and resulting soluble fraction were applied to PVDF and visualized by western blot. The amount of soluble protein as normalized to total protein was determined by densitometry (Figure 10D). The amount of soluble TOXIC-Q103 was higher in Ubext-expressing cells than wild type cells. Thus, the enhanced toxicity of TOXIC-Q103 in Ubext-expressing cells correlates to an increased pool of soluble protein and supports the model proposed in Figure 9.
Since altered ubiquitination affected the distribution of expanded polyglutamine proteins [70] and enhanced the cellular susceptibility to toxic polyglutamine aggregates (Figure 10A), we asked whether Ubext has a direct effect on the ubiquitination of proteasome substrates. In light of the fact that the toxic protein aggregates are not ubiquitinated, we evaluated the ubiquitination pattern of the UPS reporters. To compare the ubiquitination of these constructs with and without the expression of Ubext, we utilized a temperature-sensitive proteasome mutant strain (pre1-1 pre2-2) [28]. This strain is defective in proteolysis and when grown at the restrictive temperature, R-βgal and Ub-P-βgal accumulate (Figure 11A). Striking substrate ubiquitination can be observed in pre1-1 pre2-2 cells expressing Ubext and control cells after IP. When we compared the R-βgal substrate ubiquitination in EV and Ubext-containing cells, we did not discern any difference in the ubiquitination pattern (Figure 11A). However, a subtle yet reproducible ubiquitination pattern difference was seen with the Ub-P-βgal substrate (Figure 11B). Three independent IP experiments are shown and two ubiquitinated-βgal bands appear in control cells (EV) which are absent or greatly reduced in Ubext-expressing cells. The altered ubiquitination pattern of some UPS substrates in the presence of Ubext could change the ability of these proteins to be processed by the proteasome. Furthermore, such changes could be an important modifier of the cellular effects of toxic protein aggregates.
We created a novel model of UBB+1 by constitutively expressing an analogous mutant ubiquitin protein in yeast to investigate the causal relationship between this proteasomal inhibitor and protein aggregation. We demonstrated that the Ubext mutant was not functional as ubiquitin and was not deleterious to the cells. Importantly, the expression of Ubext in yeast caused impairment of the UPS. Since proteasome dysfunction can lead to protein aggregation, we were intrigued that the presence of Ubext served to neither induce nor alter non-toxic protein aggregates in yeast. However, the expression of Ubext rendered the cells more susceptible to toxic protein aggregates, and this could not be attributed to an increase in general stress elicited by Ubext. We propose that the reduced UPS functionality and altered ubiquitination of UPS substrates in Ubext-expressing cells creates an environment in which toxic amyloidogenic proteins either cannot join or are not maintained as large insoluble aggregates. As a result, protein aggregate toxicity is enhanced due to an increase in soluble or oligomeric toxic protein. Thus, this yeast model system revealed that Ubext is a phenotypic modifier of toxic protein aggregates. This genetically tractable model provides a platform to further dissect how UBB+1 affects the cellular tolerance to toxic protein aggregates.
The mechanism of UPS impairment caused by UBB+1 is not well understood. We asked whether Ubext causes a reduction in proteasome activity. Using an unstable ubiquitin-independent substrate (GFP-ODC) [37], we observed no significant change in the activity of the proteasome in Ubext-expressing cells. Based on this result, we suggest that Ubext is not clogging the core of the proteasome and propose that Ubext is interacting with other components of the ubiquitin processing cascade or with the regulatory cap of the proteasome. We hypothesized that disrupting the interaction of Ubext with component(s) of the ubiquitin processing pathway would alleviate the proteasomal impairment. Mutational analysis revealed that ubiquitin conjugation and the hydrophobic patch affect the extent to which Ubext causes UPS impairment. Interestingly, the effects were distinct with different substrates. This supports the idea that Ubext is interacting with multiple components of the UPS; reduction of its interaction via the hydrophobic patch or elimination of its ubiquitination weakened some of the observed effects but not others.
Previous studies have investigated the connection between UPS dysfunction and protein aggregation, especially in the context of protein conformational disorders [72]. It remains difficult, however, to discern the precise nature of the causal relationship between protein aggregation and proteasomal impairment. Evidence that UBB+1 and other disease-associated mutations in the UPS can cause proteasomal impairment and increase protein aggregation supports the idea that proteasome dysfunction plays a stimulatory role in protein aggregation. However, in some cases, such as that with mutant Parkin in familial Parkinson's Disease, decreased UPS function is not associated with protein aggregation [8]. Using non-toxic protein aggregates in yeast, we have demonstrated that a UBB+1-like protein, Ubext, neither induced nor changed protein aggregates. Our results provide evidence that a compromised UPS does not necessarily affect protein aggregation per se but can cause phenotypic effects by decreasing cellular tolerance to deleterious protein aggregates.
We hypothesize that Ubext is altering the sequestration of aggregated proteins (Figure 9). Due to the altered substrate ubiquitination and the general UPS impairment caused by Ubext, misfolded proteins are not efficiently degraded and somehow perturb the sequestration of amyloidogenic proteins into the insoluble aggregates which may have a protective function. How the UPS functionality plays a role in the ability of the cell to efficiently sequester non-ubiquitinated proteins remains to be elucidated. One recent study suggests that different cellular compartments retain aggregates of ubiquitinated and non-ubiquitinated proteins and a reduction in UPS activity can cause a change in this localization [69]. If proper localization of aggregated proteins protects the cell from smaller toxic oligomeric species [73],[74], then the inability of toxic oligomers to be efficiently sequestered would be deleterious (Figure 9). Indeed, the expression of Ubext resulted in an increase in the relative amount of soluble TOXIC-Q103 protein (Figure 10D) and the combination of Ubext and TOXIC-Q103 was more deleterious to cell survival (Figure 7A). Further evidence to support the idea that the redistribution of aggregates can lead to cell death comes from a recent report investigating the nature of the aggregates formed in response to the expression of expanded polyglutamine protein in yeast [70]. A single large aggregate, an aggresome-like structure, was formed by polyglutamine proteins that were not toxic to the cells. When the large aggregate was unable to form, multiple small aggregates were observed and the appearance of these correlated with toxicity. Thus, the single large aggregate appears to be protective against polyglutamine protein aggregate toxicity. Among the cellular factors found that could disrupt the formation of the single aggregate when mutated were two ubiquitin-associated proteins. Furthermore, limiting general cellular ubiquitination by the uba1-204 mutant also disrupted the formation of the large aggregate [70]. We show that uba1-204 enhanced the cellular toxicity of the toxic polyglutamine aggregates used in our study (Figure 10A). Taken together, the data support the proposed model of the effect of Ubext on protein aggregate toxicity (Figure 9).
Since Ubext causes UPS impairment and a change in ubiquitination of substrates, this could cause the mis-handling or redistribution of some ubiquitin-conjugated proteins and hinder toxic protein aggregates from being rapidly sequestered, resulting in enhanced cell death (Figure 9). Thus, even though the toxic protein aggregates may not be substrates of the UPS, perturbations in the processing of normal UPS substrates may affect cellular tolerance to toxic aggregates. Our data suggest that all perturbations in the UPS are not equally potent at altering the cellular tolerance to toxic aggregates. Therefore, we conclude that the magnitude of the enhanced protein aggregate toxicity in the presence of the extended mutant ubiquitin is exceptional. This is likely due to its interactions with other proteins and supports further that UBB+1 may be a potent disease modifier.
Since protein conformational disorders result from a combination of cellular perturbations, often including the unknown affects of aging, then eliminating individual modifiers or enhancers may prove useful for disease therapy. Obviously, alleviating the primary causative agent, when known, could prove to be the most beneficial. For example, when we used the Sup35p toxic aggregate model we were able to rescue the Ubext-enhanced toxicity by restoring the loss of function caused by Sup35p sequestration into aggregates. However, in many protein conformational diseases, the function of the proteins found in the aggregates and cellular toxicity is not understood. Therefore, investigating ways to alleviate the effects of known modifiers represents an important therapeutic avenue for disease treatment and prevention. The insight gained by developing a yeast model of UBB+1 has provided a means to further investigate the role of protein aggregate compartmentalization in toxicity, which may underlie some of the effects observed in cells or tissues experiencing chronic UPS impairment. The identification of UBB+1-interacting proteins may allow for the elucidation of the mechanism whereby a natural modifier of UPS function affects cellular tolerance to toxic protein aggregates.
Yeast strains were grown and manipulated by standard techniques [75]. Unless otherwise indicated, all yeast strains used in this study were derivatives of 74-D694 (MATa or MATα ade1-14 trp1-289 his3Δ-200 ura3-52 leu2-3,112) [64]. The Δubi4 strain was created by PCR amplification of the antibiotic resistance marker KanMX4 with primers A and B and subsequent transformation of the resulting product into 74-D694. For all primer sequences, see Table 1. The Δubp14 strain was created by PCR amplification of BY4741 Δubp14 genomic DNA with primers C and D and subsequent transformation of the resulting product into 74-D694. The proteasome mutant strain, WCG4-11/22a (MATa his3-11,15 leu2-3,112 ura3 pre1-1 pre2-2) and control strain, WCG4a (MATa his3-11,15 leu2-3,112 ura3) were a kind gift of P. Coffino [37]. The 74-D694 [PSI+]-inducible prion strain [psi−] [RNQ+] and the weak [PSI+] strain variant were a kind gift from S. Liebman [76]. A 74-D694 [PSI+] [RNQ+] strain was used in the PQ toxicity study. The uba1-204 strain was a kind gift from R. Deshaies [71].
All plasmids were created using standard molecular biology protocols [77] and verified by DNA sequencing. For primer sequences, refer to Table 1. Where appropriate, the enzyme used is listed parenthetically. To create p413TEFUbext, ubiquitin was PCR amplified from 74-D694 genomic DNA using primers E and F and cloned into p413TEF [78] at XbaI and BamHI. To create p413TEFUb, ubiquitin was PCR amplified from 74-D694 genomic DNA using primers G and H and cloned into p413TEF at BamHI and SalI. Ubext was subcloned from p413TEFUbext to p423TEF and p426TEF at SpeI and BamHI. Ubiquitin was subcloned from p413TEFUb to p423TEF and p426TEF at SalI and BamHI. All Ubext amino acid substitutions (p423TEFUbextK11R, UbextK29R, UbextK48R, UbextK63R, UbextK29/48R, UbextI44A) were created using either three-way ligation or bridge PCR into p423TEF using p423TEFUbext as a template (except for the p423TEFUbextK29/48R mutant which utilized p423TEFUbextK29R) and following standard molecular biology techniques [77]. p423TEFUbext+GG was created by PCR amplification of ubiquitin DNA with primers G and P and cloned into p423TEF at BamHI and SalI. p423TEFUbΔGG was created by PCR amplification of ubiquitin DNA with primers G and Q and cloned into p423TEF at BamHI and SalI. The 4xHSE-LacZ plasmid was a kind gift of S. Lindquist. In vivo UPS functionality was measured using Ub-X-LacZ reporters: pGal-Ub-M-LacZ, pGal-Ub-R-LacZ, and pGal-Ub-P-LacZ [30]. The ubiquitin-independent proteasome substrates, p416ADH1GFP-mODC and p416ADH1GFP-mODCC441A were a kind gift from P. Coffino [37]. The UBI4promoter-LacZ reporter was a kind gift from M. Altmann [79]. [PSI+] induction assays used the inducer plasmid pEMBL Sup2 (referred to as pSup35 in this manuscript) [58]. Non-toxic polyglutamine aggregation assays used p416GPD polyQ103-GFP [52], referred to as HttQ103-GFP in this manuscript. Toxic polyglutamine aggregation assays employed p416Gal FLAG103Q-CFP (referred to as TOXIC-Q103) and p416Gal FLAG25Q-CFP (referred to as Q25) (kind gift M. Duennwald) [50],[51]. For the toxicity assay in [PSI+] cells, Sup35p was over expressed from a copper inducible promoter. pRS315Cup-SUP35 was generated by cloning Cup1-SUP35 between XhoI and SacI. pRS316-TEF-CtermSup35 contains only the C-terminal domain (amino acids 254–685) of Sup35 and was created by subcloning TEF-CtermSup35 from pRS306TEF-CtermSup35 [80] at HindIII and SacI.
Protein lysates were analyzed by standard SDS-PAGE. Protein lysis followed the β-galactosidase assay (see below). The following antibodies were used: Ubiquitin (PD41) (Santa Cruz sc-8017), Hsp104 (kind gift of S. Lindquist), GFP (kind gift of M. Linder), β-galactosidase (Promega Z378A), Pgk1 (Molecular probes A6457), and Sup35 (kind gift of S. Lindquist) [81]. Large Sup35 protein aggregates were separated by SDD-AGE as previously described [82] with modifications previously described [63]. Sup35p over expression was achieved by growing the cultures in 50 µM copper sulfate overnight. Immunoprecipitations were carried out as previously described [83] using 5 µl of mouse anti-β-galactosidase. TOXIC-Q103 protein stabilization was measured after a six hour induction (2% galactose and 1% raffinose containing media) in the presence of 0.5 mg/ml cycloheximide in cultures with equal numbers of cells.
The relative amount of TOXIC-Q103 soluble protein was determined by slot blot. Cells containing TOXIC-Q103 and either EV or Ubext were grown overnight in selective medium, washed in inducing medium containing 2% galactose/1% raffinose and induced for 14–16 hours. Cells were harvested and lysed with glass beads in PEB (250 mM Tris HCl pH 7.5, 50 mM KCl, 10 mM MgCl2, 1 mM EDTA, 10% glycerol, 10 mM PMSF, 5 µg/ml Aprotinin, Roche Protease cocktail inhibitor (Roche)). Equal protein (100 µg) from EV and Ubext-containing cells was subjected to ultracentrifugation (80,000 rpm for 30 minutes at 4°C). Serial dilutions of the supernatant and total fractions (diluted 1/10) were applied to activated PVDF and probed with an anti-GFP antibody. The supernatant fraction and corresponding total fractions were quantified using Image J software and graphed as normalized arbitrary units.
UPS functionality was determined by the degradation of Ub-LacZ fusions [30] using Galacto-light™ (Applied Biosystems). Cells containing pGal-Ub-M-LacZ, pGal-Ub-R-LacZ and pGal-Ub-P-LacZ were grown in selective medium for 24 hours. The cultures were washed three times in selective medium containing 2% galactose / 1% raffinose and grown overnight in the 2% galactose / 1% raffinose. The cultures were harvested and lysed in Galacto-light Lysis Solution using glass beads. Cell lysate was pre-cleared for 30 seconds at 6,000 rpm at 4°C. In a flat bottom, black-sided 96-well dish, 70 µl of Galacto Reaction Buffer was added to 10 µl of protein lysate and incubated for 60 minutes at room temperature. Luminescence was read immediately after the addition of 100 µl of Light Emission Accelerator. Luminescence values were normalized to protein concentration as determined by Bradford reagent (BioRad). Error bars in all βgal activity assays represent the standard deviation from three independent cultures for each sample. The TOXIC-Q103 protein βgal activity assay was conducted as described above using a TRP1 version of pGal-Ub-P-LacZ (subcloned into p424Gal vector) with a 24 hour induction. All statistical analyses were conducted using Student's T-Test.
Polyglutamine aggregation was monitored by GFP fluorescence in a 74-D694 [PSI+] [RNQ+] strain background. Three independent samples of mid-log phase cells containing p416GPD polyQ103-GFP [52] and p423TEF EV or p423TEF Ubext were visualized. Individual fluorescent cells were evaluated for a single aggregate, few aggregates (2–3 per cell) or multiple aggregates (greater than 3 aggregates per cell) as previously described [53]. Approximately 200 cells were analyzed for each sample in triplicate. Error bars represent the standard deviation.
|
10.1371/journal.pgen.1000802 | Mis-Spliced Transcripts of Nicotinic Acetylcholine Receptor α6 Are Associated with Field Evolved Spinosad Resistance in Plutella xylostella (L.) | The evolution of insecticide resistance is a global constraint to agricultural production. Spinosad is a new, low-environmental-risk insecticide that primarily targets nicotinic acetylcholine receptors (nAChR) and is effective against a wide range of pest species. However, after only a few years of application, field evolved resistance emerged in the diamondback moth, Plutella xylostella, an important pest of brassica crops worldwide. Spinosad resistance in a Hawaiian population results from a single incompletely recessive and autosomal gene, and here we use AFLP linkage mapping to identify the chromosome controlling resistance in a backcross family. Recombinational mapping with more than 700 backcross progeny positioned a putative spinosad target, nAChR alpha 6 (Pxα6), at the resistance locus, PxSpinR. A mutation within the ninth intron splice junction of Pxα6 results in mis-splicing of transcripts, which produce a predicted protein truncated between the third and fourth transmembrane domains. Additional resistance-associated Pxα6 transcripts that excluded the mutation containing exon were detected, and these were also predicted to produce truncated proteins. Identification of the locus of resistance in this important crop pest will facilitate field monitoring of the spread of resistance and offer insights into the genetic basis of spinosad resistance in other species.
| Evolving resistance to control agents, such as antibiotics or insecticides, can have major costs to human health or agricultural food production. Once a genetic mechanism for resistance to a particular compound has been identified, other resistant species can be rapidly assessed to search for a parallel mechanism. Insecticides often target the insect nervous system as they can be toxic at low concentration and act rapidly. Here we report a genetic mutation in a global agricultural pest, diamondback moth, that is associated with resistance to the bioinsecticide spinosad. A mutation in an intron splice junction of nicotinic acetylcholine receptor (nAChR) alpha 6 causes mis-spliced mRNA transcripts that are predicted to produce truncated proteins lacking important functional domains. nAChRs require 5 subunits to function, and insects generally encode 10–12 subunit genes. Spinosad may therefore be targeting a redundant nAChR subunit not essential for survival in diamondback moth. Other insects that evolve field resistance to spinosad can now be tested to determine whether the same resistance mechanism is involved.
| Insecticide resistance has become one of the major driving forces altering the development of integrated pest management programs worldwide. The diamondback moth, Plutella xylostella, is a global agricultural pest of crucifers and commonly develops resistance to insecticides in the field [1]. Resistance, defined as a change in response to selection by toxicants [2], has been reported to a wide range of chemicals with different modes of action, including pyrethroids, carbamates and organophosphates [3] as well as biologically derived insecticides Bt [4] and spinosad [5]. Understanding the mode of action of insecticides, and identifying the genetic mechanisms and mutations that confer resistance, will ultimately enable early detection of resistance alleles in the field and improve management strategies.
Resistance to spinosad emerged in field populations of P. xylostella at a remarkably rapid rate. For example, after only ≈2.5 years of commercial application of spinosad in Hawaii, six of 12 field collected populations were highly resistant, with toxicity ratios of >100 relative to a susceptible control strain [5]. Spinosad resistance in diamondback moth has subsequently been reported in additional populations in the USA, Thailand and Malaysia [5]–[7]. Resistance to spinosad has also been selected in laboratory strains of Heliothis virescens [8], Musca domestica, [9] and Bactrocera dorsalis [10] and reported in western flower thrips, Frankliniella occidentalis, collected from greenhouses [11].
Since its introduction in 1997, spinosad has been approved in more than 30 countries for use on over 150 different crops [12]. The insecticide targets a range of lepidopteran and dipteran pests [13], yet is relatively safe to non-target organisms [14],[15]. The active ingredients of spinosad are macrocyclic lactones, spinosyn A (primary component) and spinosyn D, produced by the actinomycete Saccharopolyspora spinosa [16] during fermentation [17],[18]. Upon spinosad exposure, insects experience tremors and paralysis caused by neuromuscular fatigue as the insecticide interferes with the central nervous system, which ultimately leads to death [19].
Spinosad primarily targets the nicotinic acetylcholine receptor (nAChR) [20], which plays an essential role in excitatory synaptic transmissions of insect nervous systems [21],[22]. nAChRs consist of five subunits, with extracellular N-terminal domains that bind acetylcholine, and four transmembrane domains. Five insect genomes have been mined for nAChRs, with 12 identified from Tribolium castaneum [23] and Bombyx mori [24], 11 from Apis mellifera [25] and 10 from both Drosophila melanogaster [26] and Anopheles gambiae [27]. Although insects generally have fewer nAChRs than vertebrates, increased subunit diversity has been reported through alternate exon splicing, exon exclusion or A-to-I pre-mRNA editing. For example, it has been estimated nAChR Dα6 of D. melanogaster is theoretically capable of producing >30,000 different subunit variants [28] and there are at least 18 reported transcripts (8 of which include premature stop codons) in T. castaneum Tcasα6 [29].
It has already been demonstrated that a nAChR Dα6 deficiency strain of D. melanogaster with one chromosome carrying a deletion of Dα6 shows 1181 fold resistance to spinosad [30]. One of the breakpoints in the opposite balancer chromosome CyO occurs within an exon of Dα6, fusing it to another gene. Although this prematurely truncates the coding sequence, it confers resistance without being lethal, making this gene a prime candidate for field based resistance in insect pests. However, Gao et al. (2007) found no significant differences in sequence or expression of the Musca domestica orthologue, Mdα6 in a laboratory selected resistant strain (rspin) [31]. We have focused on field-based resistance to spinosad in a Plutella xylostella strain originally collected from Pearl City, Hawaii. Following further laboratory selection, resistance in the Pearl-Sel strain was shown to be a recessive and inherited as a single autosomal locus, and not due to metabolically mediated detoxification [5]. Crossing experiments have recently shown the same field evolved spinosad resistance mechanism is shared among populations isolated from Hawaii, California and Georgia [32]. Here we take a genetic linkage mapping approach to identify the chromosome carrying a field derived spinosad resistance mechanism. The nAChR Dα6 orthologue, Pxα6, was mapped to the resistance locus PxSpinR by recombinational mapping, and a mutation in the 5′ donor site of intron 9 was found to cause mRNA mis-splicing thereby introducing an additional 40 bases into the mRNA of the resistant strain. This mutation leads to a premature termination codon between transmembrane domains 3 and 4 and is the likely functional cause of resistance. Further analysis around this gene region revealed complex transcript splice patterns that result in multiple frame shift mutations in the resistant, but not susceptible strain.
Spinosad resistance in Plutella xylostella was predicted to be caused by a single, autosomal recessive gene [5]. We used biphasic linkage analysis, as previously employed in mapping Bt-resistance in P. xylostella [33], to identify the chromosome and localized region containing the resistance gene. Crosses were prepared between a spinosad susceptible Geneva 88 female and a spinosad resistant BCS3-Pearl male. Some F1 progeny were bio-assayed with a diagnostic dose of spinosad (10ppm), with no survival, demonstrating that resistance is recessive at this dosage. Single pair “female informative” backcrosses were established between an F1 female and a BCS3-Pearl male. The backcross progeny were expected to segregate 1∶1 for spinosad resistance or susceptibility. Approximately 70 sibling larvae were treated with 10 ppm spinosad to kill any heterozygous susceptible progeny, leaving 35 “bioassay survivors”, while 32 “untreated controls” were not exposed to insecticide. Bioassay survivors and untreated controls were reared to adults, and genomic DNA isolated for molecular analysis.
Female Lepidoptera do not undergo crossing over between chromatids during oogenesis [34]–[36]. Consequently, the chromosomes inherited from the mother are passed to the next generation as complete units. All genes and molecular markers on the same chromosome are therefore linked; and we used this property to identify the linkage group containing PxSpinR. AFLP genotyping was performed on a BCS3-Pearl grandfather, Geneva88 grandmother, F1 mother, BCS3-Pearl backcross father, 20 F2 untreated controls and an average of 19 F2 spinosad bioassay survivors. 146 variable AFLP markers inherited from the F1 mother were scored and assigned to 30 of the expected 31 linkage groups, each containing between 2 and 10 markers. The origin of each AFLP marker from the F1 mother could be associated with the resistant grandfather or susceptible grandmother. Following this, 2×2 χ2 tests were performed for each linkage group, comparing the number of susceptible and resistant AFLP genotypes inherited in the untreated controls with the spinosad bioassay survivors. A single linkage group was significantly associated with spinosad resistance, with all bioassay survivors inheriting the resistance derived LG01 (χ2 = 15.53, P>0.0001) (Figure 1).
A P. xylostella cDNA pool derived from egg and larval tissue was sequenced using 454-FLX sequencing technology (Roche). This provided transcriptome sequence to search for resistance candidate genes, however, nAChR Pxα6 was not present in this dataset. Consequently, PCR with degenerate primers was used to amplify a nAChR α6 gene fragment from larval cDNA (amino acids 105–304) with 92% identity to the Drosophila homologue Dα6. Species specific primers were designed for gene mapping, and Pxα6 genetically mapped to the spinosad resistance linkage group, LG01. All 35 backcross progeny that survived exposure to spinosad inherited the same BCS3-Pearl derived linkage group from the F1 mother, while 32 untreated controls segregated 15∶17 for the susceptible or resistant derived chromosome respectively.
As chromosomal crossing over occurs during spermatogenesis, distances between markers on the same chromosome can be estimated based on recombination rates using the progeny of male informative crosses (F1 male backcrossed to a female) in the second step of biphasic linkage analysis. Male informative mapping families were generated from 31 F1 brothers who were backcrossed to BCS3-Pearl females in single pair matings. Bioassays with 15 ppm spinosad were performed on 2315 backcross progeny, of which 884 survived (38% survival).
To determine whether nAChR Pxα6 mapped to the PxSpinR locus, DNA was extracted from 24 of the male informative backcrosses, totalling 734 bioassay survivors and 286 untreated controls. A genotyping assay using a polymorphism within intron 5 of Pxα6 showed that only 3/734 bioassay-survivors inherited the allele from the spinosad susceptible strain, compared to 48.9% of controls, demonstrating that this marker was tightly linked to the spinosad resistant mutation. At any polymorphism causally responsible for resistance, however, no susceptible alleles would be expected among survivors, since F1 heterozygotes cannot survive the concentration of spinosad used in the bioassay. To determine whether the resistance causing mutation was up- or down-stream of Pxα6 intron 5, candidate markers for genes flanking Pxα6 were identified from the genome of silkmoth Bombyx mori and BLASTed against P. xylostella 454 cDNA sequences. Genotyping assays were developed for flanking genes phosphatidylserine receptor (PPTSR) and arginine kinase (ArgKin). Genotyping in PPTSR identified 6/723 recombinants, including the same three individuals from nAChR Pxα6 intron 5, showing this was further from the resistance locus. Genotyping in arginine kinase had 16/536 recombinants, none of which were present at Pxα6 intron 5. Hence the spinosad resistance region mapped between Pxα6 intron 5 and arginine kinase. A second Pxα6 PCR genotyping assay spanning intron 11 of nAChR Pxα6 was performed on all recombinants and a subset of progeny that were nonrecombinant in this region. Here, all bioassay survivors had the same BCS3-Pearl derived resistant genotype showing complete linkage with the spinosad resistance locus, PxSpinR (Figure 2).
To identify predicted coding and intragenic sequence of Pxα6, a P. xylostella genomic BAC library was constructed using susceptible strain Geneva88, 23K clones printed to nylon membrane filters, then hybridised with a cDNA amplicon covering a portion of the Pxα6 coding sequence. Clone Px8d14 was identified, sequenced and assembled into 7 ordered fragments covering >126 kb. The predicted full length nAChR Pxα6 coding sequence was identified, based on homology with B. mori (GenBank ABV45518), spanning twelve exons plus the alternative exon versions 3a, 3b, 8b and 8c reported from other insects. The full-length gene from start methionine to stop codon spanned >75 kb of the 126 kb BAC clone (GenBank GU058050, Figure 3A). To verify the coding sequence annotation, primers were designed in predicted 5′ and 3′ untranslated regions and amplified from cDNA of a 4th instar Geneva88 larva using a proof reading polymerase (GenBank GU207835, Figure 3B). The predicted protein sequence of the full length product was 96%, 96% and 83% similar to nAChR α6 orthologues of B. mori (ABP96888), H. virescens (AAD32698) and D. melanogaster (NP_723494, isoform A) respectively.
Exons 2–12 of the Pxα6 were PCR amplified with gene specific primers using cDNA generated from total RNA of 4th instar spinosad susceptible (Geneva 88) or spinosad resistant (a backcross bioassay survivor) larvae. Products were excised from agarose gels (≈1500 bp), purified and reamplified with a nested reverse primer, (also within exon 12) and cloned. All 9 clones sequenced from Geneva88 (plus single clone sequenced from exons 1–12) contained the full complement of exons, and all 10 clones from BCS3-Pearl contained in addition, a frame-shifting 40 bp insertion between exons 9 and 10 creating a premature stop codon in resistant larvae (GenBank GU060294–GU060298). Genomic DNA of the BCS3-Pearl grandfather, used to generate the resistance-mapping crosses, was PCR amplified across the Pxα6 40 bp insertion, cloned and sequenced (GenBank GU060290). Intron 9 was approximately 6 kb shorter (1515bp in BCS3-Pearl compared to 7748 bp in Geneva88), and contained a point mutation at the 5′ donor site (GT changed to AT). Comparison with the BCS3-Pearl cDNA sequence indicated that intron splicing occurred after 40bp, at a second “GT” splice-site, not found in Geneva88 (Figure 4). This mutation has marked effects on the protein sequence and predicted transmembrane topology of the Pxα6 subunit. Although leaving the third transmembrane segment TM3 intact, it removes the 148-aa cytoplasmic loop and the 19-aa TM4 and short extracellular carboxy-terminus. No functional variants of nAChR subunits lacking the cytoplasmic loop or TM4 are known.
Considerable splice-form variation has been reported in nAChR α6 orthologues from other insect species, and this was further confirmed here for P. xylostella. Six out of 10 Geneva88 clones contained an additional 30 bp at the acceptor site of intron 10, which added 10 amino acids to the subunit, between TM3 and TM4. The identical 30 bp sequence was observed in BCS3-Pearl genomic DNA, but not in any of the sampled mRNA molecules. Geneva88 clones also incorporated either exon 3a or exon 3b (4 and 6 clones respectively), while all 10 BCS3-Pearl clones expressed exon 3a. Additional clone sequencing using primers positioned in the 5′ and 3′ untranslated regions confirmed the presence of exon 3b in resistant insects (GenBank GU207836). Thirty synonymous single nucleotide polymorphisms (SNPs) were identified within or between Geneva88 and the bioassay survivor (Table S1), excluding exon 3a and 3b splice variants and exon 5 A-to-I editing sites (see below). There was no clear correlation between the different splice variants described, either the additional 30 bp and exon 5 editing, the synonymous SNP variants or the alternative forms of exon 3 seen in Geneva88.
The observation of splicing mutations at intron 9 in the resistant strain and splicing variants of exon 11 in the susceptible strain prompted further investigation of transcripts of this specific gene region. cDNA from a resistant and a susceptible 4th instar were PCR amplified using primers in exons 6 and 12, products column purified, reamplified with exon 7 and 11 primers and products cloned. Colonies were picked and amplified directly then carefully chosen for sequencing based upon amplicon size differences. In the susceptible strain, one additional splice form lacking exon 8b was detected, removing transmembrane domain 2, without a change in reading frame. Three additional splice forms were identified in the resistant strain, all of which introduced in-frame premature stop codons including i) a 4 bp insertion following the intron 9 point mutation, ii) an exon 9 exclusion and iii) exclusion of exons 8b plus 9 (Figure 5). To compare these splice variants in a broader sample set, cDNA from 4th instar larvae of 12 resistant siblings from a backcross and 12 susceptible individuals were PCR amplified (as above) and products size separated using agarose gel electrophoresis. Diverse yet reproducible Pxα6 splice patterning was observed within both resistant and susceptible larvae, however amplicon sizes differed between these groups (GenBank GU060299–GU060305, Figure S1).
A-to-I mRNA editing in exon 5 of nAChR α6 has been reported to increase subunit diversity in many insects [28],[37]. To determine whether editing differences occur between spinosad resistant and susceptible strains, primers within exon 5 were designed for sequencing gDNA and cDNA from the same individual. Four editing sites were confirmed in both susceptible and resistant strains and, based upon the numbering system outlined in Jin et al. (2007), sites 5, 6 and 10 were conserved with H. virescens, B. mori and D. melanogaster while site 4 was in the same codon but edited a different non-synonymous base (Figure S2).
We used genetic mapping to identify for the first time, a locus underlying field evolved resistance to the widely used bioinsecticide spinosad, in the insect pest Plutella xylostella. A point mutation in the nAChR Pxα6 gene predicted to produce a truncated subunit was discovered in spinosad resistant individuals. As this mutation originated from the field and not from a laboratory selection experiment, this finding will enable field monitoring for a relevant resistance allele in this global insect pest of brassica crops, and also aid studies of resistance to spinosad in other insects. Convergent evolution of the genetic mechanisms controlling resistance to insecticides is common across insect orders because the same essential targets are involved. For example, resistance to cyclodienes has been associated with the same amino acid substitution in the GABA gated chloride ion channel in Diptera, Coleoptera and Dictyoptera [38] while laboratory selected resistance to Bt toxins in Lepidoptera can involve various mutations in a midgut cadherin-like protein [39]–[41]. Thus, molecular characterization of the mechanism of resistance to spinosad in diamondback moth provides strong candidates to search for similar mutations across other insect genera.
Insecticides have an essential role in controlling pests in modern agriculture, and management strategies to minimize the evolution of resistance can play a critical role in maintaining productivity. Identification of specific resistance mutations can enable screening assays to be developed for early monitoring of the spread of resistance alleles. This is particularly important for genetically recessive resistance alleles, such as that studied here, where the phenotypic expression of resistance is very rare when the alleles first arrive in a population. P. xylostella populations have typically developed resistance rapidly after sustained application of spinosad [7]. Crosses testing allelic complementation in field evolved resistant populations of P. xylostella have shown that allelic mutations in the same resistance gene are present in three US states, Hawaii, Georgia and California [32]. When any two genetically recessive spinosad resistant populations were crossed, F1 progeny were resistant, demonstrating a shared resistance gene. These crossing results and our molecular findings predict that mutations in the nAChR Pxα6 cause spinosad resistance in all these field evolved populations, however it remains to be seen whether the same intron 9 point mutation is present in every case. Genetic assays for monitoring the presence of resistance alleles, even in untreated populations, can be developed at the Pxα6 locus isolated here, similar to population screening approaches employed for cadherin mutations in Bt resistance [42],[43].
Several classes of insecticide target nAChR's including neonicotinoids and spinosad. Recently spinosyn A, the primary component of spinosad, was shown to act independently of known binding sites on nAChRs for other compounds, including the site for the neonicotinoid insecticide, imidacloprid [44]. Orr and colleagues conclude that a novel mode of action is responsible for spinosad toxicity that does not involve known ligand binding domains. The truncation of the Pxα6 coding sequence after exon 9 in the mutant may indicate that spinosad is interacting with the wild type nAChR molecule at the intracellular receptor loop between TM3 and TM4, which is removed by this truncation. These loops are thought to be involved with receptor biosynthesis and assembly, and can affects the rate at which current flows through the receptor's channel [45]. Alternatively, spinosad may interact with the extracellular carboxy-terminus of the protein, although this seems unlikely as only 8 amino acids are predicted outside the membrane. Both regions are also deleted in the Drosophila spinosad-resistant CyO allele of Dα6, as well as TM3, due to the occurrence of one of the CyO inversion breakpoints within exon 8b. Thus any protein expressed by this Drosophila strain would lack the TM3 and downstream domains. Alternatively, transcripts with truncated CDS may produce entirely non-functional proteins, or the transcripts may be degraded through non-sense mediated decay. Whatever the exact mechanism, the high levels of resistance conferred by both the resistance mutation identified here in Plutella, and the truncation mutation previously identified in Drosophila, indicates that the nAChR α6 subunit is the prime target of spinosad action.
Insect nAChR genes can exhibit extensive splice-form variation and other post-transcriptional modification. Notably, frameshifts caused by alternate exon splicing or incorrect intron splicing have been reported in nAChRs from T. castaneum, A. mellifera and D. melanogaster and Anopheles gambiae [27]–[29]. It is unclear whether these shortened fragments have a functional role, however they are likely to have a profound effect on channel properties [25]. It has been suggested that alternatively spliced products of nAChR genes may act as acetylcholine “sponges”, or influence expression of full-length transcripts [25],[28]. The presence of truncated protein molecules in wild-type genetic backgrounds may suggest that these are only mildly deleterious, and perhaps might indicate that the recessive resistance allele could have been present even before the advent of spinosad insecticides. This may explain the rapid appearance of resistance in Plutella. To search for additional mis-splice mutations, Pxα6 exons 7 to 11 were amplified. Multiple frameshift mutations were identified in a resistant larva due to the presence of the intron 9 point mutation or complete exclusion of the mutation containing exon. In contrast, all transcripts sequenced from susceptible larvae maintained the correct translational reading frame.
It is interesting to note, that in the housefly, sequence variation in subunit Mdα6 did not show an association with laboratory generated spinosad resistance. Nonetheless, a single Mdα6 clone showed a similar frameshift mutation, due to incorrect splicing of intron 9, a mutation in the same gene region as shown here in Pxα6 [31]. Whether this region of the gene is prone to mutations remains unclear, however, we speculate that similar resistance mechanisms as those described in Plutella could arise in other insects experiencing similar selective pressures.
Although there may be a fitness cost associated with resistance [46], full length transcripts of the α6 gene are apparently not necessary for survival. High levels of protein sequence identity across insect orders would seem to indicate strong stabilising selection on protein function. However, spinosad resistant strains of Plutella xylostella have survived under laboratory conditions for more than 7 years, although costs of resistance may not be fully expressed in laboratory conditions. Whatever is the case, knockout or truncation mutations are not particularly common causes of field evolved insecticide resistance, presumably as insecticide target molecules are generally, almost by definition, functionally important and therefore knockout mutations in target molecules will tend to be lethal. However, the existence of several genes encoding nAchR α-type subunits may allow for some functional redundancy, if another subunit can be recruited to substitute for a defective Pxα6 protein. It will clearly be interesting to further investigate how and when this truncation mutation in Pxα6 arose, its molecular mode of action in conferring resistance, and to identify any associated fitness costs. Identification of the molecular changes in the Pxα6 gene associated with resistance is a key step towards all of these goals.
The spinosad susceptible strain of P. xylostella, Geneva 88, was collected from Geneva, NY in 1988 and maintained on artificial diet without insecticide exposure. The spinosad resistant strain Pearl-Sel was collected from Oahu, Hawaii in 2001 and was 1080 fold resistant to spinosad at generation F5 [5]. Selection of Pearl-Sel with spinosad under laboratory conditions increased the toxicity ratio to 18,600 fold. Pearl-Sel was crossed to Geneva 88 for two generations, selected for survival on artificial diet for laboratory rearing, then backcrossed to Geneva 88 for three times and re-selected for spinosad resistance, resulting in BCS3-Pearl used in this study. Spinosad bioassays were prepared by soaking artificial diet in liquid spinosad (SpinTor 2 SC) for two hours, excess fluid drained, and residual droplets air dried. Second instar larvae were used in bioassays and reared on diet containing insecticide until pupation.
Prior to mapping crosses, BCS3-Pearl larvae were treated with a diagnostic dose (10 ppm) of spinosad. Single pair matings were established between a BCS3-Pearl male and Geneva 88 female. Some F1 individuals were bio-assayed to confirm that resistance was recessive. Single pair backcrosses were then established between a BCS3-Pearl male and F1 female. Some backcross progeny were reared to adult then 32 untreated controls frozen (−80°C) while ∼70 of the progeny were treated with a diagnostic dose of spinosad and 35 survivors frozen. A second series of crosses were established for male informative crosses for recombinational mapping. Male informative mapping families were generated from 31 F1 brothers who were backcrossed to BCS3-Pearl females in single pair matings. Bioassays were performed using 15 ppm spinosad, and produced 2315 survivors that were related by a single grandparental cross.
Genomic DNA extraction procedures were performed according to Zraket et al. (1990) [47]. Total larval RNA was extracted using RNeasy kit (Qiagen). Reverse transcription of total RNA was performed with BioScript (Bioline) using a random hexamer (0.2 µg).
AFLPs were performed on 100–200 ng of genomic DNA according to Vos et al. (1995) using 11 primer combinations with three selective bases (EcoANN-MseCNN) [48]. AFLP Eco primers were labelled with γ-32P or γ-33P and separated on 6% polyacrylamide gels and exposed on X-OMAT film (Kodak) for 1 to 7 days depending on the strength of the isotope. AFLP bands were analysed manually. MapMaker v2.0 was used to assemble raw AFLP data into linkage groups function with LOD ≥3.00 and θ≤0.40, using both genotype phases.
Specific primers were designed using Oligo 6.4 (Molecular Biology Insights) or Primer3 [49] (Table S2). PCR reaction volumes were between 10µl and 50µl using Taq polymerase (Bioline) with final reaction concentrations: buffer (1×), MgCl2 (2 mM), dNTP (0.1 mM), primer (0.2 mM), Taq polymerase (0.5 units). Extensor enzyme (Thermo Scientific) was used for genomic DNA and cDNA clone amplification. Template concentrations ranged from 3ng–100ng of genomic DNA and 1–2 µl of cDNA template generated from reverse transcription reactions. Clones were obtained by ligating PCR products into pGEM T-easy vector system (Promega, WI, USA) or CopyControl (cambio). DNA sequencing reactions were prepared using Big Dye 3.1 and sequenced using a 3730×l Capillary Sequencer (ABI). Sequence analysis was performed using CodonCode Aligner. Multiple cDNA clones were sequenced from single individuals to distinguished polymorphic sites from cloning errors. The sequences reported in this paper have been deposited in the GenBank database (GU058050, GU207835, GU207836, GU060290–GU060305).
Degenerate primers were designed by aligning nAChR α6 protein sequences with MacVector 7.0 (Accelrys) [H. virescens (AAD32698), D. melanogaster (Q86MN8), B. mori (ABV45518), A. gambiae (XP_308042)]. Genotyping was performed using PCR amplification and agarose gel electrophoresis for a female informative cross with PxDα6 primers Pxα6_ex7_F×Pxα6_ex8_R. In male informative crosses, Pxα6_Intron5F×Pxα6_Intron5R was digested with BsrG1 (NEB) and Pxα6_ex11_F×Pxα6_ex12_R digested with AluI (NEB). The location of nAChR a6 was identified in the genome of Bombyx mori (silkdb, nscaf2838) and flanking genes were BLAST against P. xylostella 454-ESTs to obtain gene specific sequence. PPTSR (GenBank GU060291) was amplified with PPTSR_F, PPTSR_R and digested MscI (NEB) and arginine kinase (GenBank GU060292) using ArgKin_F×ArgKin_R, digested with Taq alpha1 (NEB).
Messenger RNA was purified from Geneva 88 eggs and all larval stages using TRIzol reagent (Invitrogen) and larval midguts by the RNeasy MinElute Clean up Kit (Qiagen). Genomic DNA was removed by incubation with DNAse (TURBO DNAse, Ambion) for 30 min at 37°C. RNA integrity and quantity was verified on an Agilent 2100 Bioanalyzer using the RNA Nano chips (Agilent Technologies) and Nanodrop ND-1000 spectrophotometer. Full-length enriched, normalized cDNAs were generated from 2 µg of total RNA using the Creator SMART cDNA library construction kit (BD Clontech). Reverse transcription was performed with a mixture of several reverse transcription enzymes for 60 min at 42°C and 90 min at 50°C. Double-stranded cDNAs were normalized using the trimmer-direct cDNA normalization kit (Evrogen) to reduce abundant and increase rare transcripts. This normalized larval cDNA was used as a template for 454-FLX sequencing which resulted in a total of 68.9 Mb from 315367 reads, clustered into 19,309 contigs using Newbler software (Liverpool, UK).
A P. xylostella genomic BAC library was constructed using Geneva 88 after partial digestion with restriction endonuclease MboI and ligating into vector pIndigoBAC536 (Clemson University Genomics Institute). The average insert size was 109.4 kb which provided 7.6× genome coverage from 23,808 clones. A nAChR Pxα6 sequence amplified from cDNA (primers Pxα6_ex7_F×Pxα6_ex11_R) was 33P labelled using Prime-a-Gene labelling kit (Promega) and used to screen the library. Five clones were identified (Px7p6, Px8d14, Px10h8, Px14d18, Px17d20, where Px = Plutella xylostella, followed by plate number and grid position) and Px8d14 selected for sequencing (GenBank GU058050). Clone annotation was performed using the B. mori annotation program KAIKOGAAS (http://kaikogaas.dna.affrc.go.jp/) and BLASTn searching against P. xylostella 454-ESTs. The BCS3-Pearl grandfather used to produce all male informative mapping families was PCR amplified with primers Pxα6_ex9_F×Pxα6_ex10_R and Pxα6_ex10_F×Pxα6_ex12_R and assembled into a single sequence (GenBank GU060290).
PCR primers predicted to be within nAChR Pxα6 5′ and 3′ untranslated mRNA regions (Pxα6_5prime_F1×Pxα6_3primeR1) were used to amplify a product from Geneva 88 with Extensor polymerase (GenBank GU207835). SignalP 3.0 predicted the signal peptide cleavage site [50], transmembrane domains predicted with TMpred program (http://www.ch.embnet.org/software/TMPRED_form.html) and ProSite identified the neurotransmitter gated ion-channels signature [51] (Figure 3).
A single 4th instar backcross (R(RxS)) larvae that survived a spinosad bioassay and a single Geneva 88 4th instar larva were amplified with primers in exon 2 (Pxα6_ex2_F) and 12 (Pxα6_ex12_R3). Products were excised from 1.5% agarose gel and re-amplified with the same forward primer and slightly nested reverse primer, also in exon 12 (Pxα6_ex12_R2). dATP overhangs were added and products cloned into pGEM-t-Easy vector. Nine clones from G88 and 10 clones from BCS3-Pearl were amplified with proof-reading taq polymerase and sequenced with vector primers (T7 and SP6) plus one internal primer located within exon 6 (Pxα6_ex6_F) (GenBank GU060294–GU060298). nAChR Pxα6 was amplified from cDNA of multiple Geneva 88 and BCS3-Pearl larvae with exon 6 and 12 primers (Pxα6_ex6_F×Pxα6_ex12_R), products were purified using MinElute columns (Qiagen) then reamplified using exon 7 and 11 primers (Pxα6_ex7_F×Pxα6_ex11_R). One individual from each strain was cloned and sequenced (GenBank GU060299–GU060305), and remainder run on agarsoe gel (1.5%, 12 hour 50 volts).
|
10.1371/journal.pgen.1003239 | Structural Basis of a Histone H3 Lysine 4 Demethylase Required for Stem Elongation in Rice | Histone lysine methylation is an important epigenetic modification in regulating chromatin structure and gene expression. Histone H3 lysine 4 methylation (H3K4me), which can be in a mono-, di-, or trimethylated state, has been shown to play an important role in gene expression involved in plant developmental control and stress adaptation. However, the resetting mechanism of this epigenetic modification is not yet fully understood. In this work, we identified a JmjC domain-containing protein, JMJ703, as a histone lysine demethylase that specifically reverses all three forms of H3K4me in rice. Loss-of-function mutation of the gene affected stem elongation and plant growth, which may be related to increased expression of cytokinin oxidase genes in the mutant. Analysis of crystal structure of the catalytic core domain (c-JMJ703) of the protein revealed a general structural similarity with mammalian and yeast JMJD2 proteins that are H3K9 and H3K36 demethylases. However, several specific features were observed in the structure of c-JMJ703. Key residues that interact with cofactors Fe(II) and N-oxalylglycine and the methylated H3K4 substrate peptide were identified and were shown to be essential for the demethylase activity in vivo. Several key residues are specifically conserved in known H3K4 demethylases, suggesting that they may be involved in the specificity for H3K4 demethylation.
| Genomic DNA is associated with histone proteins to form the basic structure of chromatin. Lysine residues within the N-terminal end of histones H3 and H4 can be methylated, which may have a positive or a negative effect on the activity of associated DNA or genes, depending on the position of the lysines in the histones. Histone lysine methylation can be reversed by histone demethylases. However, it is not very clear how the specificity of histone demethylases to different histone lysines is determined. In this work we have identified a rice histone demethylase, namely JMJ703, which specifically demethylates methylated histone H3 lysine 4. We found that loss of the enzyme reduces cell division rate of the stem and the size of plant stature, indicating the importance of the protein in plant growth. The crystal structure of the catalytic domain of the protein shares a general similarity with that of mammalian and yeast proteins that demethylate methylated histone H3 lysine 9 and lysine 36, but displays several distinct structural features that are important for substrate and cofactor binding and enzymatic activity of the protein. We found that key amino acids involved in the specific structures are conserved within known H3 lysine 4 demethylases, which may be involved in the specificity to histone H3 lysine 4.
| Histone methylation is an important epigenetic modification for chromatin structure, genome function, and gene expression in eukaryotic cells [1]. Histone methylation can be reversed by histone demethylases [1]. Lysine Specific Demethylase 1 (LSD1) is the first identified histone demethylase characterized as a member of the flavin-dependent amine oxidase family [2]. The second class of histone demethylases featured with the jumonji C (JmjC) domain has been shown to catalyze histone lysine demethylation through ferrous ion (Fe(II)) and α-ketoglutaric acid (α-KG)-dependent oxidative reactions [3].
Structurally related JmjC domain-containing proteins are classified into 7 subgroups based on phylogenetic analysis of members from yeast and animal cells [4]. Among them, the JmjC domain-containing histone demethylase 1 (JHDM1) subgroup has been demonstrated to reverse mono- and dimethylated histone H3 lysine 36 (H3K36me1/me2) [3]. The JmjC domain-containing histone demethylase 2 (JHDM2) subgroup has been identified to reverse dimethylated histone H3 lysine 9 (H3K9me2) [5], whereas the JMJD2 (also called JHDM3) group specifically reverses methylated H3K9 and/or H3K36 [6]. UTX/UTY is involved in the reversal of histone H3 lysine 27 methylation (H3K27me) [7], whereas PHF8 which belongs to PHF2/PHF8 subfamily demethylates H3K9me1/2, H3K27me2 and monomethylated histone H4 lysine 20 (H4K20me1) [8]–[10]. Aside from these members, the JARID subgroup is responsible for H3K4 demethylation [11]. The JmjC domain-only subgroup has the smallest molecular architecture; members of which have been shown to catalyze divergent reactions, such as histone arginine demethylation [12] and asparagine protein hydroxylation [13]. More recently, JMJD6 is shown to catalyze protein lysyl-hydroxylation [14].
Plant JmjC proteins have been shown to play important roles in the regulation of epigenetic processes, growth and development [15]. Although conserved with yeast and animal homologues, plant JmjC proteins display several distinct features. For instance, the UTX subgroup proteins that exhibit H3K27 demethylase activity are not found in plants. Recent data show that RELATIVE OF EARLY FLOWERING 6 (REF6), a member of the JMJD2 subgroup, can demethylate H3K27 in Arabidopsis [16]. Conversely, there exists a subgroup of plant JmjC proteins that include additional protein modules that are missing from animal or yeast homologues [17]. The amino acid sequence of the JmjC domains of this subgroup is closely related to JARID, while the overall domain organization of the core protein is similar to that of the JMJD2 group [17], [18]. To study this plant specific group of JmjC proteins, we analyzed the developmental function, enzymatic activity, and crystal structure of JMJ703, a member of this subgroup in rice. Our results demonstrate that JMJ703 is essential for plant cell division and stem elongation and specifically demethylates mono-, di-, and trimethylated H3K4 in vivo and in vitro. The high resolution structure of the catalytic core of JMJ703 (c-JMJ703) in complex with cofactors and substrate peptide reveals that the overall folding of c-JMJ703 is similar to those defined in animal and yeast JMJD2 that are H3K9 and H3K36 demethylases. However, the crystal structure of c-JMJ703 displays a number of specific features in cofactor interaction and in substrate peptide binding. Substitution mutation analysis indicated that residues implicated in the specific structures are essential for the enzymatic activity, some of which are conserved within JARID proteins and plant H3K4 demethylases and may be involved in the specificity of this class of enzymes.
To investigate the developmental function of JMJ703, we characterized a T-DNA insertion mutant and generated several RNAi lines of the gene (Figure 1A). Both knockout and knockdown plants were semi-dwarf and produced smaller seeds (Figure 1B, Figure S1). The phenotypes co-segregated with the T-DNA insertion or the transgene (Table S1). Histological study of stem epidermal tissues revealed no difference in cell length between wild type and the mutant (Figure 1C), suggesting that the shorter stem phenotype may be caused by a slower cell division rate in the jmj703 mutant plants. Quantitative RT-PCR analysis revealed that the expression of several cell cycle-related genes was unaffected by the mutation (not shown). However, analysis of the cytokinin oxidase (CKX) gene family that reduces active cytokinin levels revealed that several members were highly induced in the young stem of mutant plants (Figure 1D). Chromatin immunoprecipitation assays revealed that H3K4me3 was clearly increased over the promoter region of the CKX genes in the mutants (Figure 1D). These data suggested that JMJ703 might regulate H3K4me3 on CKX genes and that the mutant phenotype might be due to cytokinin deficiency caused by increased H3K4me3 and increased expression of CKX genes.
JMJ703 protein contains several modules, including JmjN, JmjC, C5HC2 zinc finger, FYRN, and FYRC (Figure 2A). The JmjC domain is critical for histone demethylase activity [3]. To investigate the substrate specificity of JMJ703, the FLAG:HA-tagged JmjN-JmjC-zinc finger region (between amino acids 113 and 700, called FA-J3NCZ) of the protein was transiently over-expressed in tobacco leaves for in vivo histone demethylase assays. As shown in Figure 2B, nuclei expressing FA-J3NCZ showed a clear decrease of H3K4me1/2/3 compared to non-transfected nuclei, whereas no difference was observed for H3K27me3. FA-J3NCZ was then affinity-purified from transfected tobacco cells for in vitro histone demethylase assays. Consistent with the in vivo results, FA-J3NCZ could demethylate H3K4me1/2/3 in vitro (Figure 2C). By contrast, no activity of FA-J3NCZ to demethylate H3K9me1/2/3, or H3K36me1/2/3 was detected either in tobacco cells or in vitro (Figure S2A–S2B), indicating that JMJ703 is an H3K4-specific demethylase.
Although the structures of a number of animal and yeast JmjC proteins have been defined [19]–[21], the structure of an H3K4 demethylase has not yet been reported. To study the structure of JMJ703, we made many attempts, including N- and C-terminal truncations and limited proteolysis, to obtain JMJ703 crystals. Finally, the fragment spanning amino acids 139 to 498 (termed c-JMJ703), covering the JmjN and JmjC domains, was deemed to be suitable for crystallographic investigation. We first performed surface plasmon resonance (SPR) experiments to measure the binding affinities of c-JMJ703 (J3NC) to H3K4 peptides with mono-, di-, or trimethylation. SPR results indicated that c-JMJ703 (J3NC) bound to H3K4me1, H3K4me2, and H3K4me3 peptides with dissociation constant Kd values of 28.9 µM, 19.3 µM, and 30.1 µM, respectively (Figure 2D). In addition, c-JMJ703 (J3NC) could also bind to H3K4me0 peptide, but with a much lower Kd value (76.6 µM), while no binding activity to H3K9me0 and H3K36me0 peptide was observed (Figure 2D). Meanwhile, the fragment corresponding to the JmjN-JmjC-zinc finger region (J3NCZ) showed a higher affinity to H3K4me1/2/3 peptides with a Kd value of 12.6 µM, 15.1 µM and 15.9 µM, respectively (Figure S2C). However, neither c-JMJ703 nor J3NCZ bound to H3K9me3 peptide, suggesting that the zinc finger enhances the substrate binding affinity of JMJ703 but is not essential for the binding specificity (Figure S2D).
The crystal structure of c-JMJ703 alone or in complex with α-KG (termed as c-JMJ703-α-KG) or with NOG (N-oxalylglycine, a non-catalytic analog of α-KG) and H3K4me3 peptide (termed as c-JMJ703-NOG-H3K4me3) was determined via the molecular replacement method with the modified crystal structure of the core of human JMJD2A (PDB code 2OQ6) [22] as the initial searching model. The final model was best at a resolution of 2.35 Å with a final Rwork value of 19.3% (Rfree = 22.5%). The crystals belonged to the P63 space group with a slight change in unit cell parameters among the three crystals. One c-JMJ703 molecule was identified in the asymmetric unit with a Matthews coefficient of 2.3 Å3/Da (corresponding to 46% solvent content) [23]. There are five solvent-exposed regions in c-JMJ703 structure, including P195-K199, S224-R261, R288-S295, T329-Y349, and Q363-V377, which could not be built due to lack of interpretable electron density, suggesting their intrinsic structural flexibility. Moreover, although inter-molecular interactions were found in the c-JMJ703 crystal, gel filtration revealed that c-JMJ703 existed as a monomer in solution (Figure S3), suggesting that the monomer may be the biological unit.
The c-JMJ703 molecule presented a canonical overall folding of JMJD2 proteins and contained four of the five domains defined in the structure of c-JMJD2A: the JmjN domain (A139-K199), the long β-hairpin (D200-T271), the mixed domain (L272-V377) and the JmjC domain (L378-A498) (Figure 3A, Figure S4). The JmjC domain, sandwiched by the JmjN and the long β-hairpin with the mixed domain, adopted a jellyroll-like structure with two four-stranded β-sheets as a cupin fold (Figure 3A) [24].
Although c-JMJ703 shared low primary sequence similarity (less than 25% of the sequence identities) with the reported structural homologues [20], [22], the core portion, especially the JmjC domain and the catalytic center of c-JMJ703, presented a topology similar to that of JMJD2 proteins with root mean square derivations of 1.75 Å and 1.81 Å relative to c-JMJD2A and c-Rph1, respectively (Figure 3B–3E). Nevertheless, c-JMJ703 displayed several significant structural differences. First, the JmjN domain presented a number of distinct features (Figure 3B). The orientation of the N-terminus of c-JMJ703 was opposite to that of c-JMJD2A and c-Rph1 (Figure 3B). Second, the long β-hairpin domain contained two β-strands (β2 and β3, aa 209–215 and aa 267–270) and a short α-helix (α4, aa 216–219). The two β-strands of c-JMJ703 were shorter than that of c-JMJD2A and c-Rph1, but had an extra long insertion between them. However, except for the short α-helix (α4), this insertion was mostly invisible in the structure (Figure 3C, Figure S4A). The short α-helix (α4) represented a sharp difference between c-JMJ703 and c-JMJD2A/c-Rph1. Moreover, the mixed domain of c-JMJ703, composed of several different structural elements, also showed clear structural differences. The first α-helix (α5, aa 272–286) of the mixed domain was two turns longer than that of c-JMJD2A and c-Rph1 (Figure 3D). The residues Q363 to V377 in the mixed domain of c-JMJ703 were structurally disordered and presented an uninterpretable electron density, whereas the corresponding region of c-JMJD2A is an α-helix. An additional short helix (α7, aa 356–361) was observed in c-JMJ703, whereas a loop was present in c-JMJD2A (Figure 3D). Several distinct differences could also be observed in the JmjC domain of JMJ703 compared to mammalian and yeast homologues, particularly in the helix-rich region (α8 to α11, aa 419–459) (Figure 3E, Figure S4A). Residues W381, C392, F437, Q440, L443, H445, L447 and V448 are conserved in and specific to JARID and plant H3K4 demethylases (Figure 4A). Most of these residues were located in the helix-rich region (α8 to α11) except W381 and C392 (Figure 4B). A potential extension path of H3K4me peptide toward its C-terminal was proposed based on the locations of these H3K4 demethylase-specific residues (Figure 4B). Substitution mutations of W381 and L447 abolished the H3K4 demethylase activities (Table 1, Figure S6), indicating that these residues are essential for the demethylation function of the protein.
The unambiguous electron density denoted Fe(II) (determined by inductively coupled plasma mass spectrometry) bound to c-JMJ703 and identified the active site within the JmjC domain (Figure 5). Three key residues, H394, E396, and H482, are perfectly conserved in JMJD2 proteins. They chelated Fe(II) in the active site through their hydrophilic side chains. Fe(II) also interacted with the C-1 carboxyl and C-2 oxo groups of NOG (Figure 5). Substitution mutations H394A, E396A, and H482A abolished the activity of JMJ703 to demethylate H3K4me1/2/3 (Table 1, Figure S6), confirming their critical role in the enzymatic activity of the protein.
The electron density indicated the presence of substrate peptide in the c-JMJ703-NOG-H3K4me3 complex (Figure 3, Figure 6). Three of the ten residues in the H3K4me3 peptide (ARTKme3QTARKS) were visible in the c-JMJ703-NOG-H3K4me3 complex structure (Figure 3, Figure 5, Figure 6). This complex structure provided a view of the substrate peptide-binding mode of an H3K4 demethylase. Compared with the structure of c-JMJD2A in complex with methylated H3K9/H3K36 peptide, the methylated H3K4 and its two flanking residues adopted a different binding conformation to c-JMJ703. H3K9me3/H3K36me3 and their two flanking residues stretched along the long axis of α-KG in c-JMJD2A-α-KG-H3K9me3/c-JMJD2A-NOG-H3K36me3 structure (PDB code 2Q8C/2Q8E, Figure 7B–7C), whereas H3K4me3 and its two flanking residues stretched along the short axis of NOG and was almost perpendicular to H3K9me3/H3K36me3 and their flanking residues (Figure 7A).
The methyl group binding pocket of JmjC domain is unique among methylated peptide- binding proteins due to the polar rather than hydrophobic environment [25]. The methyl groups could not be defined properly in the c-JMJ703-NOG-H3K4me3 complex structure because of low occupancy as indicated by obviously higher B-factor for atoms in H3K4me3 peptide. Based on the crystal structure of c-JMJD2A in complex with H3K9me3 (PDB code 2Q8C) [26], four of the five residues that have been shown to be important in methyl group binding in c-JMJD2A [25] were conserved in c-JMJ703, namely, G376, Y383, E396, and N496. S288 in c-JMJD2A (S335 in Rph1) is substituted by an alanine (A494) in c-JMJ703. G376 had no electron density and was invisible in structure, whereas Y383, E396, A494, and N496 were well-defined and adopted very similar conformations as in c-JMJD2A [26]. Substitution mutations of these potential methyl group-binding residues (G376A, Y383A, E396A, N496A) generally impaired the H3K4 demethylase activity of JMJ703 in tobacco cells, with the exceptions of Y383A, which retained a residual activity to demethylate H3K4me2, and N496A, which was still active to demethylate H3K4me2/3 (Table 1, Figure S6).
In the crystal structure of c-JMJ703-NOG-H3K4me3, NOG formed three hydrogen bonds with the side chains of N404 and K412 (Figure 5) similar to the JMJD2 homologues. K412A mutation also abolished demethylation activity of H3K4 in all three methylation states (Table 1, Figure S6). Unexpectedly, the N404A mutation produced no clear effect on the demethylase activity of JMJ703, whereas the mutation of the counterpart in Rph1 abolished the H3K36me3 demethylase activity [20]. Moreover, NOG interacted indirectly with N490 through two water molecules (Figure 5), a finding that is not observed in mammalian and yeast homologues. Although most of the residues in α-KG/NOG stabilization showed high structural similarities with its homologues, several features, particularly those of Y321 and N404, distinguished JMJ703 from the JMJD2 members. Y321 and N404 showed significant conformational shifts compared with their counterparts in c-Rph1 and c-JMJD2A (Figure 8A–8C and Figure S5). Alternative conformation of the Tyr corresponding to Y321 in other JmjC proteins has been observed in JMJD2D (PDB code 3DXU). Interestingly, substitution of Y321 by alanine decreased H3K4me1 demethylase activity but did not affect that of H3K4me2 and H3K4me3 (Table 1, Figure S6), whereas substitution by alanine of the corresponding residue in Rph1 leads to loss of the H3K36me3 demethylase activity of the protein [20]. These data suggested a different NOG-binding mechanism between JMJ703 and Rph1.
In this work, we have provided evidence that JMJ703 is a histone demethylase specifically reversing the three forms of methylated H3K4. Arabidopsis JMJ14 that belongs to the same group as JMJ703 also has an H3K4 demethylase activity [27]. About two-thirds of plant genes contain at least one type of H3K4me, among which H3K4me3 is associated with gene activation in Arabidopsis and rice [28], [29]. Demethylation of the three forms of H3K4me implies that JMJ703 is important for rice gene expression. The phenotype caused by T-DNA insertion and RNAi and increased expression of CKX genes suggest that JMJ703 may regulate growth hormone metabolism in rice, which is different from JMJ14 that has functions in floral transition, DNA methylation, and RNA silencing in Arabidopsis [27], [30]–[33]. H3K4me3 on specific plant genes can be induced by external signals, such as light, drought, and submergence, among others [34]–[36]. H3K4me3 is suggested to be a mark of active genes and may play a role in plant adaptation to environmental cues. Whether JMJ703 is involved in resetting stress-induced gene expression requires further analysis.
Although JmjC domain of JMJ703 is closely related to that of the JARID proteins [17], the domain organization of the catalytic core of the enzyme (JmjN- long β-hairpin-mixed domain-JmjC) resembles that of JMJD2 members (c-JMJD2A and c-Rph1). However, our functional analysis has revealed that JMJ703 (i.e. FA-J3NCZ) was unable to demethylate H3K9me1/2/3 or H3K36me1/2/3 (Figure S2A–S2B). This suggests that the enzymatic specificity may be determined essentially in the JmjC domain. Our data indicate that the helix-rich region (α8–α11, aa 419–459) of the JmjC domain of JMJ703 is substantially divergent from that of JMJD2A and Rph1. For instance, K241 of c-JMJD2A, which is conserved in c-Rph1, is considered to be a key catalytic residue that recruits and positions O2 between Fe(II) and the methyl group to participate in the reaction [25]. Substitution mutation of K241L abolishes the activity of c-JMJD2A [25]. However, this position is replaced by a leucine in JMJ703 (L447) and in other plant homologues and JARID proteins (Figure 4). L447 was found to be essential for the H3K4 demethylase activity of JMJ703 (Table 1, Figure S6). This leucine together with the other residues that are specifically conserved in JARID/H3K4 demethylases and mostly found in the helix-rich region (α8–α11, aa 419–459) of c-JMJ703 (Table 1, Figure 4 and Figure S6), may be involved in the specificity of H3K4 demethylases.
Our data indicate that although most of the JMJ703 residues in α-KG/NOG stabilization showed a high structural similarity with JMJD2 proteins, the residues Y321 and N404 displayed specific features (Figure 8, Figure S5). The conformation of Y321 in c-JMJ703 indicates that the residue may not be in direct contact with α-KG or NOG. Substitution mutation of Y321 only affected H3K4me1 demethylase activity. In addition, N404A substitution did not clearly alter the H3K4 demethylase activity.
The methyl group binding pocket in c-JMJ703 differs also from that of c-JMJD2A and Rph1 in one key residue (A494 in JMJ703 compared to S288 in JMJD2A or S355 in Rph1). S288 of JMJD2A has been shown to be important to reverse di- and trimethylated H3K9 [25], [26], [37]. However, JMJD2D, which harbors an alanine in this position, is 7-fold and 60-fold more efficient than JMJD2A in the demethylation of H3K9me3 and H3K9me2, respectively. JMJD2D has residual activity to H3K9me1 that is absent from JMJD2A [26]. The presence of alanine at this position in JMJ703 (A494) supports the demethylation function of the protein of all three forms of methylated H3K4 (Figure 2). Substitution of this alanine by a serine abolished the demethylase activity of H3K4me1/2, but retained the activity toward H3K4me3 (Table 1, Figure S6). Interestingly, this alanine residue is conserved in most of the plant JMJD2 members (Figure S4), suggesting that these plant proteins may be able to demethylate the three methylated forms of relevant histone lysines.
In addition, the zinc finger that overlaps partially with the JmjC domain of JMJD2A is not found in JMJ703. Although the residues involved in the zinc finger formation in JMJD2A are conserved in Rph1, no zinc ion is observed in its structure and this domain is disordered and invisible in the c-Rph1 structure [20]. By contrast, JMJ703 contains a distal zinc finger motif relative to the JmjC domain (about 100 amino acids downstream the JmjC domain), which is present in Rph1, but absent from JMJD2A. The distal zinc finger is found in a number of other JmjC proteins and is shown to be involved in nuclear localization of JMJ706 [17]. These observations together with our data showing that the inclusion of the motif increased the substrate peptide (H3K4me1/2/3) binding activity to two-folds but not alter the binding specificity, suggest that this zinc finger may not be a component of the catalytic core of JmjC proteins, but is important for their enzymatic activity. Our data also revealed a number of other clear structural differences in the plant JmjC protein. The N-terminal end of c-JMJ703 adopted a completely different conformation from that of c-JMJD2A and c-Rph1. The long β-hairpin domain and mixed domain of c-JMJ703 also showed significant divergence at the amino acid sequence level compared to c-JMJD2A and c-Rph1 (Figure S4). However, the divergent regions are mostly invisible in c-JMJ703 and c-Rph1, indicating intrinsic disorder.
In summary, we have shown that JMJ703 is an H3K4 demethylase that is important for rice plant development. Although JMJ703 displays an overall structure similar to that of JMJD2 H3K9 and H3K36 demethylases, it exhibits a number of important structural features which are conserved within JARID and plant H3K4 demethylases. In particular, our data suggest that the helix-rich region (α8–α11, aa 419–459) of the catalytic JmjC domain of JMJ703 may be involved in the determination of substrate specificity of the H3K4 demethylase.
A T-DNA insertion line of JMJ703 (3A-00550) was obtained from the Postech rice mutant database (http://www.postech.ac.kr/life/pfg/risd/). Insertion was confirmed by PCR using the primers muJ3-F and muJ3-R and a T-DNA-specific primer 2715L1 (Table S2).
A cDNA fragment between nucleotides 2282 bp and 2834 bp relative to the translation start site was amplified using primers RiJ3-F and RiJ3-R and then cloned into T vector (Promega). The fragment was cloned into double strand RNAi vector pDS1301 [38]. Rice transformation with callus generated from the rice cultivar Zhonghua11 (ZH11) was performed as previously described [38].
The cDNA fragment of the J3NCZ region was amplified using primers FAJ3NCZ-F and FAJ3NCZ-R and then cloned into pFA121 vector modified from pBI121 (GenBank: AF485783.1), in which GUS was replaced by 2×FLAG∶2×HA tag. The pFA121-J3NCZ plasmid was transferred into Agrobacterium tumefaciens strain EHA105 cells for tobacco infection with the help of Agrobacterium cells harboring P19 plasmids [39]. For in vivo histone demethylation assay, tobacco infection and nuclei isolation were performed as previously described [27]. The immunostaining protocol was modified from http://sites.bio.indiana.edu/~pikaardlab. Briefly, the nucleus solution was placed on a poly-lysine coated slide, air dried, and then refixed with 4% formaldehyde in KPBS (1.28 M NaCl, 20 mM KCl, 80 mM Na2HPO4, 20 mM KH2PO4, pH7.2) containing 1% Triton X-100 for 20 min. After 3 washes with KPBS-1% Triton, slides were blocked with blocking solution (1% BSA in KPBS-1% Triton) and incubated at 37°C for 30 min. The blocking solution was washed off with KPBS-1% Triton and the slides were incubated with both anti-HA and histone méthylation specific antibodies diluted in blocking solution to 1∶300 at 4°C overnight. After washing with KPBS, slides were blocked and incubated with fluorescent-labeled antibodies at 37°C for 2 h. After washing with KPBS thrice, nuclei were stained with 5 µg/mL 4,6-diamidino-2-phenylindole (DAPI), coated with a drop of VECTASHIELD Mounting Medium (H-1000, Vector Laboratories), and then covered with a coverslip followed by confocal microscope (Leica) detection. In vitro histone demethylation assays were performed as previously described [6]. FA-J3NCZ was purified after overexpression with Anti-FLAG M2 magnetic beads according the manufacturer's instruction (Sigma).
Antibodies used in this study: anti-HA (M20003M, Ab-mart), anti-H3K4me1 (ab8895, Abcam), anti-H3K4me2 (04-790, Millipore), anti-H3K4me3 (07-473, Millipore), anti-H3K27me3 (ABE44, Millipore), anti-H3 (ab1791, Abcam), anti-H3K9me1 (ab9045, Abcam), anti-H3K9me2 (07-441, Millipore), anti-H3K9me3 (ab8898, Abcam), H3K36me1 (ab9048, Abcam), anti-H3K36me2 (ab9049, Abcam), anti-H3K36me3 (ab9050, Abcam), Goat Anti-Mouse secondary antibody (A-11029, Invitrogen), and Goat Anti-Rabbit secondary antibody (A-11036, Invitrogen).
Chromatin immunoprecipitation assays were performed as previously described [38]. Briefly, 2 g of 10 day-old rice seedlings were fixed with 1% formaldehyde, then chromatin was extracted and immunoprecipitated with anti-H3K4me3 (ab8580, Abcam) and anti-H3K4me2 (04-790, Millipore) antibodies. After reversing crosslink and protease K treatment, DNA was recovered for realtime PCR analysis with primers listed in Table S2.
DNA fragment encoding amino acids 139 to 498 of wild type JMJ703 was amplified by PCR, ligated into pET-28a expression vector using BamHI and XhoI and transformed into Escherichia coli Rosetta (DE3) cells. Transformed E. coli cells were cultured at 37°C in LB medium containing 50 mg/L kanamycin until the OD600 nm reached 0.8. The culture was cooled to 16°C and subsequently induced with 0.5 mM isopropyl β-D-1-thiogalactopyranoside. Cells were harvested after overnight induction by centrifugation at 5,000 g for 10 min at 4°C. The cell pellets were resuspended in lysis buffer containing 50 mM Tris (pH 8.0), 500 mM NaCl, and 10% (v/v) glycerol and then disrupted by an ultra-high pressure cell disrupter (JNBIO, Guangzhou, China) at low temperature. Cell debris was removed by centrifugation at 25,000 g for 30 min at 4°C. The 6×His tagged protein was purified by Ni-NTA affinity chromatography, cleaved with thrombin (Sigma) overnight at 4°C, and eluted with lysis buffer. The purified c-JMJ703 protein was concentrated and further purified by Hitrap Q (GE Healthcare) anion-exchange chromatography using a 0.05 M to 1 M NaCl gradient in 25 mM HEPES (pH 7.5). The target protein was confirmed to have purity over 95% by SDS-PAGE and concentrated to 10 mg/mL before crystal growth or storage.
Crystallization was performed at 18°C by the hanging-drop vapor-diffusion technique. Crystals were obtained by mixing 1 µL of the protein solution with an equal volume of a reservoir solution; the mixture drop was equilibrated against 500 µL of the reservoir solution. Crystals were obtained with a reservoir solution containing 0.05 M potassium phosphate monobasic, 25% (w/v) polyethylene glycol 8,000 and reached final dimensions of 50×50×100 µm3 within 2 days. Crystals of apo c-JMJ703 were initially obtained but showed poor diffraction at 3.0 Å resolution and poor reproducibility. Crystals of c-JMJ703 in complex with NOG displayed much better diffraction quality at 2.3 Å, indicating that NOG helps stabilize the conformation of the c-JMJ703 polypeptide. Crystals of the c-JMJ703 substrate complex were obtained by co-crystalizing c-JMJ703 with 10 mM peptide. Crystals were cryo-protected by soaking in a cryo-protectant consisting of the reservoir solution with additional 15% (v/v) glycol. Cryo-protected crystals were then flash-cooled in liquid nitrogen and transferred into a dry nitrogen stream at 100 K for X-ray data collection.
Diffraction data for apo c-JMJ703 crystals were collected at a resolution of 3.0 Å at 100 K using a MAResearch M165 CCD detector in beamline 1W2A at the Beijing Synchrotron Radiation Facility. The data set for c-JMJ703-α-KG and c-JMJ703-NOG-H3K4me3 were collected at 2.3 Å and 2.4 Å in beamline BL17A (Photon Factory, Japan) and BL17U1 (SSRF, Shanghai, China), respectively, with an ADSC Q315 CCD detector at the wavelength of 1.0000 Å. All data sets were indexed, integrated, and scaled using the HKL2000 package [40]. The crystals belonged to space group P63 with cell parameters a = 53.0 Å, b = 86.1 Å, c = 87.3 Å, and β = 90.7°. All crystals showed very small shifts in their cell parameters.
The molecular replacement method was used to calculate the phases using the PHASER program [41] and the modified crystal structure of a catalytic core of the human c-JMJD2A (PDB code 2OQ6) as the initial searching model. Manual model building and refinement were performed with the programs COOT [42] and PHENIX [43]. Solvent molecules were located from stereochemically reasonable peaks in the σA-weighted 2Fo–Fc difference Fourier electron density map (1.2 σ). Model geometry was verified using the program PROCHECK (Table S3) [44]. Coordinates have been deposited in PDB with accession 4IGP, 4IGO and 4IGQ for apo c-JMJ703, c-JMJ703-α-KG and c-JMJ703-NOG-H3K4me3, respectively.
Analyses were carried out at 25°C with the BIAcore 3000 system. Ten µg/mL c-JMJ703 in 10 mM sodium acetate buffer (pH 5.0) was covalently coupled to a CM5 chip (Biacore) using an Amine Coupling Kit (Biacore) according to the manufacturer's instructions. Using HEPES buffer (10 mM HEPES pH 7.5, 150 mM NaCl, 0.005% Tween20), various concentrations of the H3K4me1/2/3 peptide were injected through a flow cell that was not activated and then through another flow cell containing c-JMJ703 at a rate of 10 µL/min for 2 min. The c-JMJ703 surface was regenerated between two injections by running 15 µL of 5 mM NaOH twice through the flow cell at 30 µL/min. The sensorgram obtained for the inactivated flow cell was subtracted to correct for nonspecific binding and the bulk signal from the peptide in solution. Data were analyzed using BIAevaluation 4.1 software.
|
10.1371/journal.pgen.1007515 | Genes regulated by SATB2 during neurodevelopment contribute to schizophrenia and educational attainment | SATB2 is associated with schizophrenia and is an important transcription factor regulating neocortical organization and circuitry. Rare mutations in SATB2 cause a syndrome that includes developmental delay, and mouse studies identify an important role for SATB2 in learning and memory. Interacting partners BCL11B and GATAD2A are also schizophrenia risk genes indicating that other genes interacting with or are regulated by SATB2 are making a contribution to schizophrenia and cognition. We used data from Satb2 mouse models to generate three gene-sets that contain genes either functionally related to SATB2 or targeted by SATB2 at different stages of development. Each was tested for enrichment using the largest available genome-wide association studies (GWAS) datasets for schizophrenia and educational attainment (EA) and enrichment analysis was also performed for schizophrenia and other neurodevelopmental disorders using data from rare variant sequencing studies. These SATB2 gene-sets were enriched for genes containing common variants associated with schizophrenia and EA, and were enriched for genes containing rare variants reported in studies of schizophrenia, autism and intellectual disability. In the developing cortex, genes targeted by SATB2 based on ChIP-seq data, and functionally affected when SATB2 is not expressed based on differential expression analysis using RNA-seq data, show strong enrichment for genes associated with EA. For genes expressed in the hippocampus or at the synapse, those targeted by SATB2 are more strongly enriched for genes associated EA than gene-sets not targeted by SATB2. This study demonstrates that single gene findings from GWAS can provide important insights to pathobiological processes. In this case we find evidence that genes influenced by SATB2 and involved in synaptic transmission, axon guidance and formation of the corpus callosum are contributing to schizophrenia and cognition.
| Schizophrenia is a complex disorder caused by many genes. Using new gene discoveries to understand pathobiology is a foundation for development of new treatments. Current drugs for schizophrenia are only partially effective and do not treat cognitive deficits, which are key factors for explaining disability, leading to unemployment, homelessness and social isolation. Genome-wide association studies (GWAS) of schizophrenia have been effective at identifying individual SNPs and genes that contribute to risk but have struggled to immediately uncover the bigger picture of the underlying biology of the disorder. Here we take an individual gene identified in a schizophrenia GWAS called SATB2, which on its own is a very important regulator of brain development. We use functional genomics data from mouse studies to identify sets of others genes that are influenced by SATB2 during development. We show that these gene sets are enriched for common variants associated with schizophrenia and educational attainment (used as a proxy for cognition), and for rare variants that increase risk of various neurodevelopmental disorders. This study provides evidence that the molecular mechanisms that underpin schizophrenia and cognitive function include disruption of biological processes influenced by SATB2 as the brain is being organized and wired during development.
| Neocortical organization and circuitry requires the coordinated execution of a series of developmental processes, including the specification of neuronal identity, neuronal migration, and wiring of neural circuits [1]. Special AT-rich sequence-binding protein 2 (SATB2) and B-cell lymphoma/leukaemia 11B (BCL11B) are two of the several key transcription factors that control the projection identity of cortical neurons (subcortical vs. callosal) during cortical development[2]. SATB2 modifies higher-order chromatin structure and can mediate chromatin loop formation via self-association in order to regulate other genes [3–6]. De novo structural and point mutations in SATB2 result in SATB2 haploinsufficiency and SATB2-associated syndrome, which is characterised by developmental delay, mild to severe intellectual disability, speech and behavioural problems and abnormal craniofacial features [7].
During development, pyramidal neurons (excitatory projection neurons primarily found in the cerebral cortex [8]) project axons across multiple brain regions and to the corticospinal tract [9]. Based on their projections, pyramidal neurons can be divided into two groups; deep layer neurons (located in cortical layers 5 and 6) projecting to subcortical regions and upper layer neurons (located in cortical layers 2, 3 and 4) projecting across the corpus callosum to the contralateral hemisphere[10]. SATB2 is required for the projection of upper layer neurons and loss of SATB2 leads to upper layer neurons incorrectly projecting to subcortical regions [11,12]. In the adult CNS, SATB2 is critically important as a regulator of synaptic plasticity in the hippocampus that underlies memory functions [13,14].
SATB2 specifically mediates callosal projection identity by repressing the expression of BCL11B (also known as CTIP2), a zinc finger protein required for subcortical projection neuron identity [2,11]. SATB2 directly binds to the BCL11B locus and recruits the Ski protein and the nucleosome remodeling deacetylase (NuRD) complex to initiate chromatin modifications inhibiting BCL11B expression [12,15]. BCL11B is required for the postnatal development of the hippocampus and its loss leads to impaired hippocampal learning and memory in the adult brain [13,16]. GATA zinc finger domain containing 2A (GATAD2A; also known as P66-alpha), is a core component of the NuRD complex and mediates the interaction between histones and other core proteins [17,18]. GATAD2A plays a key role in memory preservation through activity-induced histone modifications[19].
Analysis of just genome-wide significant SNPs for SZ implicated SATB2, BCL11B and GATAD2A in the aetiology of this disorder as epigenetic regulators of neocortical development [20]. We hypothesized that variation in other genes that function with or are regulated by SATB2 are also contributing to SZ aetiology. Given the high polygenicity of SZ and the weak individual SNP effects detected in GWAS, we decided to move beyond individual SNP analysis and instead performed gene-set analysis (GSA) on three gene-sets that contain genes either functionally related to SATB2 or targeted by SATB2 at different stages of development. This makes it possible to detect the effects of multiple weaker associations that may be missed by individual SNP or gene based-analysis.
We tested these SATB2 gene-sets for a contribution to SZ using the largest available GWAS dataset that used 40,675 cases and 64,643 controls [21]. Given the genetic overlap between SZ and cognition [22], and the facts that SATB2 has an identified role in memory function[13,14] and that cognitive deficits are present in individuals with SATB2 syndrome[7], we investigated these gene-sets for a genetic contribution to cognition. We based this analysis on educational attainment (EA), a proxy for cognition based on measuring years of schooling, using the largest available EA GWAS results from 328,917 samples[23]. We also sought independent evidence that these gene-sets contribute to SZ and other neurodevelopmental disorders with cognitive deficits by testing these gene-sets for enrichment of genes that contain de novo variants and genes with an increased burden of ultra-rare protein altering variants in SZ cases.
We developed three different gene-sets containing genes that either function together with or are regulated by murine Satb2 at different stages of development. The first gene-set contains 127 genes (S1 Table), the majority of which (n = 117) are genes that have been reported as differentially expressed in the cortices of Satb2 mutant mice during neurodevelopment [11,24]. Additionally, the gene-set contains genes considered to be vital components of the NuRD complex [25] as it has been previously shown to facilitate Satb2-mediated repression of Bcl11b during development. This first gene-set is called SATB2+NuRD. The second gene-set is based on data from a single study that generated a dataset of 1,341 ChIP-seq peaks that map binding sites of SATB2 in cortices of wild type mice at embryonic day (E) 15.5 [24]. By mapping these ChIP-seq peaks to regulatory regions of genes, we generated a set of 778 genes that are targets of and potentially regulated by SATB2 during cortical development. This second gene-set is called SATB2_Cort (S2 Table). The third gene-set is based on data from a single study that generated a dataset of 5,027 ChIP-seq peaks that map binding sites of Satb2 in primary hippocampal cell cultures from wild type mice at postnatal day P0 to P1 [13]. We mapped these ChIP-seq peaks to identify 4,138 target genes and called this gene-set SATB2_Hipp (S3 Table). Full details on the generation of each gene-set are supplied in Materials and Methods.
The rationale for three rather than a single gene-set is as follows: SATB2+NuRD includes genes that were reported in a number of different studies that used different mouse models and study designs. SATB2_Cort and SATB2_Hipp are based on single studies each using material from different brain regions at different time points during development and we know that SATB2 has different functions at different stages of development. Combining the three into a single gene-set would miss the opportunity to test for enrichment in SZ and EA GWAS data in these spatially and temporally defined gene-sets that capture SATB2 function at important brain regions and different developmental time points. S1 Fig shows the overlap between the three gene-sets. Seven genes are common to all three gene-sets. Gene symbols from each overlapping category are listed in S4 Table.
We used MAGMA [26] for GSA to simultaneously study multiple genetic markers in order to determine their joint effect and test if the genes in SATB2+NuRD were more strongly associated with SZ or EA than other genes in the genome. MAGMA uses summary statistics (SNP P values) from GWAS and a significant enrichment within SATB2+NuRD points to variation across those genes influencing SZ and/or EA, and provides further evidence that biological functions related to SATB2 are part of disorder aetiology. The SATB2+NuRD gene-set (n = 127 genes) was enriched for SZ risk genes (P = 9.54x10-5) and for genes associated with EA (P = 0.0005). We knew SATB2+NuRD contained three genes associated with SZ (SATB2, BCL11B and GATAD2A). To test if these three genes were driving the significant enrichment in SZ, we removed them and re-ran the GSA in the SZ data using a smaller SATB2+NuRD gene-set (n = 124). We still detected enrichment of SZ (P = 0.001) indicating this gene-set contains multiple other genes associated with SZ.
Brain-expressed genes are a major contributor to SZ [27] and EA [23]. It is possible that the enrichment detected here could be due to the SATB2+NuRD gene-set representing a set of brain-expressed genes. However, the SATB2+NuRD enrichment was robust to the inclusion in the analyses of both ‘brain-expressed’ (n = 14,243) and ‘brain-elevated’ (n = 1,424) gene-sets as covariates (SZ: P = 0.0003 and P = 0.0005 respectively; EA: P = 0.0004 and P = 0.0007 respectively). To examine if the enrichment we detect for SZ and EA is a property of polygenic phenotypes in general, we obtained GWAS summary statistics for 10 phenotypes and we tested SATB2+NuRD for enrichment in each one. These were child-onset psychiatric disorders (attention deficient hyperactivity disorder (ADHD) and autism spectrum disorder (ASD)), adult-onset psychiatric disorders (bipolar disorder (BPD) and obsessive compulsive disorder (OCD)), other brain-related disorders (Alzheimer’s disease (AD) and stroke (STR)), and non-brain related diseases (cardiovascular disease (CAD), Crohn’s disease (CD), ulcerative colitis (UC) and type 2 diabetes (T2D)). SATB2+NuRD was not enriched for any of the 10 phenotypes (S2 Fig).
The SATB2_Cort gene-set (n = 778 genes) was enriched for EA genes (P = 0.0068) but not for SZ risk genes (P = 0.26). The enrichment in SATB2_Cort for EA was robust to the inclusion of both ‘brain-expressed’ and ‘brain-elevated’ gene-sets as covariates (P = 0.013 and P = 0.0077 respectively). When tested for enrichment in 10 other GWAS datasets, SATB2_Cort only showed one nominally significant enrichment (for ADHD; P = 0.021) but this did not survive multiple test correction (S3 Fig).
The study that reported the SATB2 ChIP-Seq data, which we used to map the SATB2 target genes in the SATB2_Cort gene-set, also reported 3,129 genes that were differentially expressed in P0 cortices of SATB2 wild-type (WT) v knock-out (KO) mice. We used these data to identify those SATB2 target genes that were differentially expressed and thus functionally impacted by the loss of SATB2. Fig 1 shows that the subset of genes within SATB2_Cort that are differentially expressed are making a stronger contribution to EA (n = 229 genes; P = 0.00016) than those genes that are not differentially expressed (n = 513 genes; P = 0.32). Thus, variation in genes that are both targeted by SATB2 and functionally affected when SATB2 is not expressed in the mouse cortex contributes to EA in the general population.
Given SATB2’s role in the formation and structural integrity of the cerebral cortex [28], we tested our SATB2_Cort gene-set for enrichment of genes against intracranial volume [29]. Intracranial volume was chosen because it is closely related to brain volume in early life as the brain develops after which it becomes stable when the brain has fully developed and it remains unaffected by later age-related changes [30,31]. SATB2_Cort was not enriched for genes associated with intracranial volume and no enrichment was observed for the subset of genes within SATB2_Cort that showed the strongest enrichment for EA, i.e. those genes that were differentially expressed in the mouse cortex upon SATB2 ablation (S5 Fig).
The SATB2_Hipp gene-set (n = 4,138 genes) was enriched for SZ risk genes (P = 0.0040) and for EA genes (P = 2.03x10-6). The enrichment in SATB2_Hipp for SZ did not remain significant when we conditioned on the set of ‘brain-expressed’ genes (P = 0.058). The enrichment in SATB2_Hipp for EA was robust to the inclusion of both ‘brain-expressed’ and ‘brain-elevated’ gene-sets as covariates (P = 3.77x10-5 and P = 5.74x10-6 respectively). When tested for enrichment in 10 other GWAS datasets, SATB2_Hipp only showed two nominally significant enrichments (for ASD (P = 0.028) and CAD (P = 0.040) but these did not survive multiple test correction (S4 Fig).
There was no gene expression data available from the primary hippocampal cell cultures to accompany the ChIP-Seq data used to generate the SATB2_Hipp gene-set. To further explore this gene-set, we investigated these SATB2 target genes using data on gene expression levels in (A) the brain, (B) the hippocampus, (C) neurons and (D) at the synapse. (A) Brain expressed genes are strongly enriched for genes associated with EA (P = 1.27x10-07) whereas non-brain expressed genes are not (P = 1; Fig 2). When we categorise brain expressed genes into those potentially targeted and regulated by Satb2 or not, i.e. those with or without an adjacent Satb2 binding peak (termed SATB2+ and SATB2-), we observed a much stronger enrichment for EA in the SATB2+ genes (P = 2.11x10-07) compared to the SATB2- genes (P = 0.35; Fig 2).
(B) We used data from human hippocampal samples (from the Brainspan Atlas of the Developing Human Brain) at an early stage of post-natal development (37 post conception weeks (pcw) to 1 year) to capture gene expression levels at an equivalent developmental time point to when the primary hippocampal cell cultures were generated and used in the ChIP-Seq analysis. We categorized genes as having a low, medium or high level of expression in the hippocampus. Genes expressed at a medium and high level in the hippocampus show enrichment for EA genes (P = 2.30x10-4 and P = 0.0012 respectively; Fig 3). We took these medium and high expressed neuronal genes and categorized them into SATB2+ and SATB2- genes. We observed stronger enrichment for SATB2+ genes compared to SATB2- genes in both the medium and high expressed hippocampal genes (Fig 3).
(C) SATB2 functions in neurons where sets of both medium and high expressed genes show enrichment for EA genes (P = 0.012 and P = 2.67x10-08 respectively; Fig 4). We took these medium and high expressed neuronal genes and categorized them into SATB2+ and SATB2- genes. We observed stronger enrichment for SATB2+ genes compared to SATB2- genes in both the medium and high expressed neuronal genes (Fig 4). (D) Given SATB2’s role in synaptic plasticity, we next investigated potentially synaptic genes expressed in neurons. For genes highly expressed in neurons, there was stronger enrichment for EA in those that are potentially synaptic (P = 2.95x10-06) compared to those that are not potentially synaptic (P = 0.0078; Fig 5). Categorizing the potentially synaptic genes as SATB2+ or SATB2-, there was stronger enrichment for EA in the SATB2+ genes (P = 2.53x10-5) in EA compared to SATB2- genes (P = 0.0071; Fig 5). Together, these data indicate that for genes expressed in the brain, hippocampus, neuron and genes encoding potentially synaptic proteins, those targeted and potentially regulated by Satb2 are contributing more to EA than genes not targeted by Satb2.
We tested SATB2_Hipp for enrichment of genes associated with hippocampal volume [32]. No significant enrichment was observed for SATB2_Hipp in the hippocampal volume GWAS data. Similarly, sets of SATB2+ hippocampal or synaptic expressed genes, which had showed enrichment for EA, did not show enrichment for genes associated with hippocampal volume (S6 Fig).
We studied genes harbouring de novo variants identified in patients with SZ, ASD, intellectual disability (ID) and in unaffected siblings and controls [33]. De novo variants were categorized as all, loss-of-function (LoF), non-synonymous (NS) and silent and the gene number within each group is detailed in Table 1. Each of the three SATB2 gene-sets contained a significant enrichment of genes containing de novo variants for at least one of SZ, ASD or ID, following Bonferroni correction (Table 1). The SATB2+NuRD gene-set was enriched for genes containing de novo mutations reported in ASD (both ASD_all, P = 7.00x10-06 and ASD_LoF, P = 7.25x10-04) and ID (ID_all, P = 5.6x10-05 and ID_LoF, P = 8.96x10-10). The SATB2_Cort gene-set was enriched for SZ (SCZ_all, P = 3.67x10-04), ASD (ASD_all, P = 2.03x10-04 and ASD_silent, P = 3.49 x10-04), and ID (ID_all, P = 5.08x10-04 and ID_LoF, P = 8.63x10-08). The SATB2_Hipp gene-set was enriched for SZ (SCZ_all, P = 6.75 x10-04) and ID (ID_all, P = 9.86 x10-04). Importantly, none of the gene-sets were enriched for genes harbouring de novo variants reported in the unaffected or control data. Each of the three gene-sets was significantly enriched for genes listed in the Sys ID database of ID genes (Table 1). Finally, the SATB2_Hipp gene-set was enriched for genes reported to have an excess of disruptive and damaging ultra-rare variants (dURVs) in SZ patients compared to controls (P = 0.0164) based on an exome sequencing study of 12,332 individuals [34].
S5 Table lists all genome-wide significant genes within the SATB2_NuRD (for SZ or EA), SATB2_Cort (EA only) and SATB2_Hipp (EA only) gene-sets. This is based on MAGMA gene analysis with Bonferroni correction for numbers of genes tested. For the smaller number of genes from SATB2_NuRD and SATB2_Cort, full gene names, their known biology and associated phenotypes are listed in S6 Table. As discussed below, many of these genes have known roles in brain development and are associated with a variety of neurodevelopmental disorders and neurocognitive functions. We performed gene ontology enrichment analysis of EA genes within the larger SATB2_Hipp gene-set (S7 Table). We detected significant enrichment for neuron development and axon guidance.
The SATB2+NuRD gene-set contains genes that were reported to be differentially expressed in the developing neocortex of Satb2 mutant mice, and genes encoding components of the NuRD complex. The data presented here show that the SATB2+NuRD gene-set is enriched for genes associated with SZ and with EA. It is also enriched for genes harbouring de novo variants that have been reported in ASD and ID and for ID genes as listed by SysID. Thus, both analyses of common and rare variants indicate that genes in this set are contributing to the aetiology of SZ, EA and neurodevelopment disorders that involve cognitive dysfunction.
The prenatally-derived SATB2_Cort gene-set is enriched for genes associated with EA. Importantly, the enrichment signal is being driven by those genes that are not only targeted by SATB2 but are also differentially expressed when SATB2 is knocked out. Thus, genes functionally impacted by SATB2 and by extension the processes regulated by SATB2 may represent the molecular mechanisms that underpin EA. Reviewing the genome-wide significant genes for SZ or EA within the SATB2_NuRD and SATB2_Cort gene-sets provides insights into the biological processes that are affected during brain development (S6 Table). A number of the risk genes have known roles in synaptic transmission (KCNN2 [35], SLC32A1 [36], EXOC4 [37]), axon guidance and formation of the corpus callosum (DCC [38], NFIB [39–41], BCL11B [11,16,38,42], TBR1 [2,43,44]), axon regeneration and neurite branching (KLF9[45]), neurite outgrowth and axonogenesis (FOXP2 [46,47], NEGR1 [48]), maturation and maintenance of upper-layer cortical neurons (ATXN1 [49]), cortical cell migration (AFF3 [50]) and the development of specific sensory circuits in the CNS (MEF2C [51], SEMA6D [52,53]). Many of these genes are the locations of rare causative mutations for neurodevelopment disorders and have also been associated with neurocognitive functions (S6 Table). This indicates that for these phenotypes, common variants of small effect and rare variants of large effect impact on some of the same genes involved in neocortical organization and circuitry.
Results for SATB2_Cort indicate this gene-set is enriched for genes associated with EA but not SZ. However, analysis of rare variant data shows that this gene-set is enriched for genes carrying de novo variants reported for SZ, as well as for ASD and ID. It is possible that there are molecular mechanisms here where common low effect variants are contributing to EA but rare higher effect variants contribute to SZ.
The postnatally-derived SATB2_Hipp gene-set is enriched for genes associated with EA. Further analyses using this gene-set identified a consistent phenomenon: For genes that are expressed in the brain, or hippocampus, or in neurons, or at the synapse, there is an enrichment of genes associated with EA but the effect is stronger for the subsets of genes that are targeted by SATB2. Again, these data indicate that the processes regulated by SATB2 may represent the molecular mechanisms that underpin EA.
There is relatively little overlap of SATB2 targets when comparing SATB2_Cort to SATB2_Hipp. Beyond the temporal and spatial differences in how the ChIP-seq data was produced, and the different experimental procedures used (S8 Table), this also reflects that the role of SATB2 in the postnatal brain differs greatly to its role in the prenatal brain. Compared to the prenatal brain, SATB2 expression extends from the cortex and into the hippocampus and hypothalamus of the adult brain[54]. It plays a crucial role in both long-term and working memory and mediates late long-term potentiation and synaptic plasticity in the postnatal hippocampus [13,14]. SATB2_Hipp is enriched for genes associated with EA, for genes harbouring de novo variants for SZ and ID and for genes containing dURVs in SZ, suggesting there is an active pathophysiology in the postnatal brain. At a molecular level, these SATB2 target genes may influence cognitive function via biological processes such as BDNF signalling, epigenetic chromatin modifications and miRNA dysregulation [13]. That impaired fear memory caused by deletion of Satb2 in pyramidal neurons was successfully rescued through restoration of Satb2 expression in mouse hippocampus [13] indicates that intervention to restore normal cognitive function may be possible if the molecular mechanisms can be targeted.
EA is a good, though not perfect, proxy for cognitive ability [55] and its specific utility for GWAS is that very large samples have been available for analysis. As new large GWAS for neurocognitive phenotypes, and for SZ, are produced, it will be important to determine if genetic variation within biological processes regulated by SATB2 influence specific traits or instead exert an influence across multiple behavioural and neuropsychiatric phenotypes.
SATB2 is required for the correct formation and structural integrity of regions in the brain such as the cerebral cortex [28], the corpus callosum [11] and the hippocampus [14]. Disrupted expression of SATB2 in these regions can result in anatomical and functional abnormalities associated with a range of behavioural phenotypes [13,14,56,57]. Our analysis of neuroimaging GWAS provided no evidence that SATB2 influences intracranial or hippocampal volume but this too needs further study in larger datasets to determine if SATB2’s influence on SZ risk or cognitive function is mediated via effects on brain structures.
In summary, we have built on single gene associations detected in GWAS of SZ to show that genes that are functionally related to SATB2 and the NuRD complex during neocortical development or are targeted by SATB2 in the pre- and postnatal brain are enriched for common variants associated with SZ and EA, and for rare variants that increase risk of SZ and other neurodevelopmental disorders. These findings are supported by the existing Satb2 mouse models demonstrating deficiency in long-term and working memory upon Satb2 ablation. Thus, this study provides evidence that the molecular mechanisms that underpin SZ and cognitive function include perturbations of the biological processes influenced by SATB2 in the brain.
Data were directly downloaded from published studies and no additional ethics approval was needed. Each study is referenced and details on ethics approval are available in each manuscript.
A study by Alcamo et al. [11] mapped Satb2 expression in developing cortex and showed that Satb2 mutant mice display altered expression of 28 genes associated with axon projection, including BCL11B at E18.5. A more recent study by McKenna et al. [24] performed RNA-seq analysis of cortices at postnatal day P0 to study differentially expressed genes (DEGs) between wild type and Satb2-deficient mice. This led to the identification of 74 DEGs in the deep layers and 15 DEGs in the upper layers of Satb2-deficient cortices. The list of genes from these two studies (n = 117) was increased using data from other studies of Satb2 mouse models [2,15,38]. We also included in this set genes considered to be vital components of the NuRD complex [25] as it has been previously shown to facilitate Satb2-mediated repression of Bcl11b during development [5,12]. Altogether, following conversion of murine gene IDs to orthologous human gene IDs, a total of 127 genes (including SATB2, BCL11B and GATAD2A) were included in this first gene-set named SATB2+NuRD (S1 Table).
The second gene-set was generated using a dataset of 1,341 ChIP-seq peaks (GEO accession: GSE68910) that map binding sites of SATB2 in cortices of wild type mice at E15.5 [24]. ChIP-seq reads were mapped against the mouse NCBI37/mm9 assembly. Functional annotation tool GREAT (http://bejerano.stanford.edu/great/public/html/index.php) was used to associate both proximal and distal input ChIP-seq peaks with their putative target genes and thereby identify genes that may be regulated by SATB2 [58]. We used the default basal plus extension approach within GREAT where each gene in the genome is assigned a basal regulatory domain of a minimum distance of 5kb upstream and 1kb downstream of the transcription start site of the canonical isoform of the gene (regardless of other nearby genes). The gene regulatory domain is extended in both directions to the nearest gene’s basal domain but no more than the maximum extension of 1,000kb in one direction. In addition, GREAT utilizes a set of literature curated regulatory domains that extend the regulatory domain for each gene to include its known regulatory element. GREAT mapped 1,341 ChIP-seq peaks to 1,800 unique gene IDs. For only 144 of these genes, the peak was located 5kb upstream or 1kb downstream of the gene. Given the large default extension region applied in GREAT, this may have led to a number of spurious results. We filtered the peaks mapping to the remaining 1,656 genes by overlapping them with defined enhancers from ENCODE (http://chromosome.sdsc.edu/mouse) to provide extra support for a potential regulatory role. A total of 452 peaks overlapped with mouse brain-specific enhancers (E14.5) and were mapped back to 712 of the 1,656 genes. This resulted in a final set of 856 mouse genes where a SATB2 ChIP-seq peak maps to regulatory regions of those genes. The Ensembl data-mining tool BioMart (http://www.ensembl.org/index.html) was then used to convert these mouse gene IDs to human gene IDs, which resulted in a final set of 778 human genes. This second gene-set was named SATB2_Cort (S2 Table).
The third gene-set was generated using a dataset of 5,027 ChIP-seq peaks (GEO accession: GSE GSE77005) that map binding sites of Satb2 in primary hippocampal cell cultures from wild type mice at postnatal day P0 to P1 [13]. This dataset represents the high-confidence peak list derived from two independent biological ChIP-seq replicates by using the MAnorm to filter out the inconsistent peaks (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3439967/). We used these ChIP peaks to identify 4,138 human gene targets using the same procedure as mentioned above. This third gene-set was named SATB2_Hipp (S3 Table).
There is a considerable difference in gene number between the SATB2_Cort and SATB2_Hipp gene-sets. Factors contributing to this difference are likely to include the different functions of SATB2 in the pre- and post-natal brain and that the ChIP-Seq data has been generated from different brain regions (cortex v hippocampus). In addition, the SATB2 ChIP-Seq data was generated under different experimental conditions (tissue v primary neuronal cultures) including use of different antibodies (anti-SATB2 v anti V5-tag antibody (ChIP-grade)). These details are supplied in S8 Table.
Sets of ‘brain-expressed’ genes (n = 14,243) and ‘brain-elevated’ genes (n = 1,424) were sourced from the Human Protein Atlas (https://www.proteinatlas.org/humanproteome/brain) and used as covariates in the GSA. Brain-elevated genes are those that show an elevated expression in brain compared to other tissue types.
Summary statistics from the most recent SZ GWAS [21] were obtained from the Walters group data repository on the MRC Centre for Neuropsychiatric Genetics and Genomics website (http://walters.psycm.cf.ac.uk/). This study included data on 40,675 cases and 64,643 controls. Summary statistics from the most recent EA GWAS [23] were obtained from the Social Science Genetic Association Consortium (SSDAG) website (http://ssgac.org/Data.php, Summary data file: EduYears_Main.txt—discovery and replication cohorts except 23andMe). This study reported results for 328,917 individuals. Summary statistics from a GWAS of hippocampal volume (n = 33,536; [32]) and a second GWAS of intracranial volume (n = 32,438 [29]) were obtained from the ENIGMA Consortium website (http://enigma.ini.usc.edu/). GWAS summary statistics were sourced for AD [59], ADHD (https://www.biorxiv.org/content/early/2017/06/03/145581), ASD (https://www.biorxiv.org/content/early/2017/11/27/224774), BPD [60], CAD [61], CD [62], OCD [63], STR [64], T2D [65] and UC [66].
A gene-set analysis (GSA) is a statistical method for simultaneously analysing multiple genetic markers in order to determine their joint effect. We performed GSA using MAGMA [26](http://ctg.cncr.nl/software/magma) and summary statistics from various GWAS. An analysis involved three steps. First, in the annotation step we mapped SNPs with available GWAS results on to genes (GRCh37/hg19 start-stop coordinates +/-20kb). Second, in the gene analysis step we computed gene P values for each GWAS dataset. This gene analysis is based on a multiple linear principal components regression model that accounts for linkage disequilibrium (LD) between SNPs. The European panel of the 1000 Genomes data was used as a reference panel for LD. Third, a competitive GSA based on the gene P values, also using a regression structure, was used to test if the genes in a gene-set were more strongly associated with either phenotype than other genes in the genome. The MHC region is strongly associated in the SZ GWAS data. This region contains high LD and the association signal has been attributed to just a small number of independent variants [67]. However, MAGMA still identifies a very large number of associated genes despite factoring in the LD information. Of 278 genes that map to chromosome 6 (25-35Mb), 130 genes were associated with SZ in our MAGMA analysis. To avoid the excessive number of associated genes biasing the MAGMA GSA, we excluded all genes within the MHC region from our GSA of SZ. MAGMA was chosen because it corrects for LD, gene size and gene density (potential confounders) and has significantly more power than other GSA tools [68]. Numerical data used for all figures displaying MAGMA results are provided in S9 Table.
A list of genes harbouring de novo variants identified in patients with SZ, autism spectrum disorder (ASD), intellectual disability (ID) and in unaffected siblings and controls were sourced from Fromer et al. [33]. We used the categories of variant as defined in that study (all, loss-of-function (LoF), non-synonymous (NS) and silent; gene number within each group is detailed in Table 1). We sourced a list primary ID genes (n = 960) from the curated SysID database of ID genes (http://sysid.cmbi.umcn.nl/) [69]. From an exome sequencing of 12,332 unrelated Swedish individuals (4,946 individuals with SZ), we sourced a list of 42 genes that had a significant excess of disruptive and damaging ultra-rare variants (dURVs) in SZ cases compared to controls [34]. We performed enrichment analysis of these gene lists with our gene-sets using 2x2 contingency tables with genes restricted to those annotated as protein coding using a background set of 19,424 genes (https://www.ncbi.nlm.nih.gov/). Bonferroni multiple test correction was performed separately for the tests of de novo variant genes (n = 48 tests), for the tests of SysID genes (n = 3) and for the tests of dURVs in SZ genes (n = 3).
Human brain expression data from the Protein Atlas (http://www.Proteinatlas.org/humanproteome/brain/) was used to filter the SATB2_Hipp gene-set to only include genes expressed in the brain. This dataset included 14,540 genes expressed in, but not unique to, the human brain. For filtering SATB2 gene-sets to include only neuron-expressed genes, we used an RNA-Seq transcriptome and splicing database of glia, neurons, and vascular cells of the cerebral cortex [70]. We used RNA-Seq data from mouse neurons (https://web.stanford.edu/group/barreslab/brainrnaseq.html) and separated genes into three categories; low, medium and high expressed. Low expressed genes were those with Fragments Per Kilobase of transcript per Million mapped reads (FPKM) values <2.0 (n = 12,161 genes). The median FPKM value for the remaining genes was 9.6, hence that was used to categorize medium and high expressed genes; medium (FPKM = 2.0–9.6; n = 5,107 genes) and high (FPKM>9.6; n = 5,189 genes). Mouse gene IDs were converted to human gene IDs using BioMart. For analysis of the SATB2_Hipp gene-set, we used expression data from the hippocampus from pcw 37 to 1 year (n = 4 samples) from the Brainspan Atlas of the Developing Human Brain (http://www.brainspan.org/). We calculated mean expression values and categorised genes as low (FPKM<2.0; n = 9,931), medium (FPKM = 2.0–7.45; n = 5,619) and high expressed (FPKM>7.45; n = 5,842 genes). We followed a method previously outlined [34] to identify potentially synaptic genes.
ConsensusPathDB-human (http://cpdb.molgen.mpg.de/) was used to perform overrepresentation analysis of gene-sets and we report on enriched gene ontology-based sets[71].
|
10.1371/journal.pgen.1002645 | Coordinate Regulation of Lipid Metabolism by Novel Nuclear Receptor Partnerships | Mammalian nuclear receptors broadly influence metabolic fitness and serve as popular targets for developing drugs to treat cardiovascular disease, obesity, and diabetes. However, the molecular mechanisms and regulatory pathways that govern lipid metabolism remain poorly understood. We previously found that the Caenorhabditis elegans nuclear hormone receptor NHR-49 regulates multiple genes in the fatty acid beta-oxidation and desaturation pathways. Here, we identify additional NHR-49 targets that include sphingolipid processing and lipid remodeling genes. We show that NHR-49 regulates distinct subsets of its target genes by partnering with at least two other distinct nuclear receptors. Gene expression profiles suggest that NHR-49 partners with NHR-66 to regulate sphingolipid and lipid remodeling genes and with NHR-80 to regulate genes involved in fatty acid desaturation. In addition, although we did not detect a direct physical interaction between NHR-49 and NHR-13, we demonstrate that NHR-13 also regulates genes involved in the desaturase pathway. Consistent with this, gene knockouts of these receptors display a host of phenotypes that reflect their gene expression profile. Our data suggest that NHR-80 and NHR-13's modulation of NHR-49 regulated fatty acid desaturase genes contribute to the shortened lifespan phenotype of nhr-49 deletion mutant animals. In addition, we observed that nhr-49 animals had significantly altered mitochondrial morphology and function, and that distinct aspects of this phenotype can be ascribed to defects in NHR-66– and NHR-80–mediated activities. Identification of NHR-49's binding partners facilitates a fine-scale dissection of its myriad regulatory roles in C. elegans. Our findings also provide further insights into the functions of the mammalian lipid-sensing nuclear receptors HNF4α and PPARα.
| Mammalian nuclear receptors are actively targeted for treatment of a range of cardiovascular diseases and obesity. However, effective drug development still depends on a more exhaustive characterization of how different nuclear receptors mediate their different physiological effects in vivo. Taking advantage of the roundworm Caenorhabditis elegans, we used a combination of genetic and biochemical approaches to characterize the gene network of the nuclear receptor NHR-49 and to explore the impact of the different target genes on physiology. This work has identified genes and pathways that were not previously known to be regulated by NHR-49. Importantly, we identified NHR-49 co-factors NHR-66 and NHR-80 that regulate specific subsets of NHR-49 target genes and that contribute to distinct phenotypes of nhr-49 animals. Taken together, our findings in C. elegans not only provide novel insights into how nuclear receptor transcriptional networks coordinate to regulate lipid metabolism, but also reveal the breadth of their influence on different aspects of animal physiology.
| Modern day lifestyle and diet dramatically increase the threat of chronic diseases including obesity, diabetes and atherosclerosis. These metabolic disorders have been consistently linked to the imbalance between energy consumption and expenditure. Growing evidence suggests that faulty regulation of fat metabolism promotes metabolic diseases [1]. The control of fat metabolism is often mediated by nuclear receptors (NR), which are ligand-regulated transcription factors that play a central role in the cell's ability to sense, transduce and respond to lipophilic signals by modulating the appropriate target genes [2]. Dissecting the role of nuclear receptors in fat metabolism is therefore essential to our understanding of how energy homeostasis is maintained in an organism.
Nuclear receptors typically exhibit highly conserved modular domains including a zinc-finger DNA binding domain (DBD) and a ligand-binding domain (LBD) [2]. Ligand binding affects nuclear receptor activity by inducing structural changes within the LBD, which then alters the receptor's affinity to different co-factor proteins such as co-regulators and binding partner(s). Co-regulators include both co-activators and co-repressors, and are critical in mediating transcriptional responses. Alteration in co-regulator–NR binding can thus lead to a change in the transcriptional response. For instance, co-repressors can be replaced with co-activators to promote transcription of target genes, or NRs can form distinct homo- or heterodimers to activate or repress a specific set of target genes [3]. Thus, binding of distinct co-factors determines how nuclear receptors influence different gene networks.
The Hepatocyte Nuclear Factor 4- alpha (HNF4α) is an example of a lipid sensing nuclear receptor in mammals. HNF4α orchestrates the regulation of a diverse range of target genes and is especially important in the control of genes involved in glucose and fatty acid homeostasis [4], and is mainly expressed in the liver, pancreas, kidney and small intestine [5]. Consistent with its role in metabolism, mutations in HNF4α are associated with both maturity onset diabetes of the young (MODY) and type 2 diabetes [6]–[9]. Although it is not entirely clear how HNF4α protects against diabetes, it has been reported that mutations in HNF4α can lead to early death of pancreatic beta-cells resulting in pancreatic dysfunction and a subsequent decrease in insulin production [10], [11]. In addition, knock-out of HNF4α in the adult mouse liver leads to increased lipid accumulation in hepatocytes and to the misregulation of genes involved in glucose and lipid metabolism [12], [13].
HNF4 receptors are evolutionarily conserved. Whereas mammals have two paralogs of this receptor, HNF4α and HNF4γ, the HNF4 family has expanded enormously in the nematode C. elegans to include 269 HNF4-like members [14], [15]. One of these 269 HNF4-like members is NHR-49, which is remarkably similar to the mammalian Peroxisome Proliferator-Activated Receptors (PPARs) in its overall biological effects on metabolism, fat storage, and life span [16]. The PPARs are global modulators of fat metabolism, controlling fat storage, expenditure, distribution and transport [17]. Perhaps most striking is the finding that NHR-49 and the PPARs, particularly PPARα and PPARδ, positively influence similar genes in multiple metabolic processes, including fatty acid β-oxidation, fatty acid desaturation, and fatty acid binding/transport [17], [18]. Moreover, knockout of PPARα or PPARδ can lead to high-fat phenotypes [18], [19] that are similar to those observed in nhr-49 animals, demonstrating the common physiological effects of these receptors on fat storage.
In this study, we set out to elucidate the genome-wide regulatory network of NHR-49 and to characterize its target genes to better understand the impact of NHR-49 mediated transcriptional regulation on worm physiology. We identified NHR-66 and NHR-80 as physical NHR-49 co-factors and were able to delineate their specific contribution to distinct phenotypes of nhr-49 mutants, namely selective effects of individual co-factors on life span and on mitochondrial function. We also uncovered novel roles for NHR-49 in the regulation of lipid metabolism including sphingolipid breakdown and lipid remodeling. Taken together, our findings support a model whereby NHR-49 heterodimerizes with other nuclear receptors to mediate the activation or repression of genes involved in distinct aspects of lipid metabolism.
We previously found that NHR-49 promotes two distinct aspects of lipid metabolism, fatty acid desaturation and fatty acid β-oxidation [16]. However, the complete list of NHR-49's regulatory targets was still not known. Thus, we used whole genome C. elegans oligonucleotide microarrays to define the transcriptional profiles in an nhr-49(nr2041) deletion strain (compared to N2 wild-type worms). Table 1 lists genes that exhibit statistically significant changes in expression with (|log2(ratio)|≥0.848 and p-value≤0.001), in nhr-49 animals ([20] and Materials and Methods). The genes with negative values of logFC (fold change) are down regulated in the nhr-49 mutant (i.e. activated by NHR-49), whereas the genes with positive values of logFC are up regulated in the nhr-49 mutant (repressed by NHR-49). The finding that acs-2, which participates in mitochondrial β-oxidation and was previously demonstrated to be an NHR-49 target gene [16] had reduced expression in nhr-49 mutants validated the experimental approach. However, some of the previously reported genes like fat-5, fat-6, fat-7, cpt-2 and ech-1[16], were not found to be significant in our microarray analysis. This is likely due to the cut-off that we employed. We also uncovered new targets that are repressed by NHR-49 that include genes involved in sphingolipid breakdown, lipid remodeling and xenobiotic detoxification.
To validate these candidate NHR-49 targets, we employed quantitative RT-PCR to analyze their mRNA levels (Figure 1). In nhr-49 mutants, the expression of the sphingolipid processing genes acid ceramidase, glycosyl hydrolase, sphingosine-phosphate lyase (SPL), as well as lipid remodeling genes like phospholipases, TAG lipase and O-acyltransferase, was up regulated, as expected (Figure 1A and Table S1). Consistent with previously published data, nhr-49 mutants also showed a decrease in the expression of the fatty acid desaturase genes fat-7, fat-5 and fat-6, and the fatty acid beta-oxidation genes acs-2, cpt-5 and ech-1 (Figure 1B). Together, the gene expression data confirm that NHR-49 activates fatty acid β-oxidation and desaturase genes, and for the first time identifies target genes repressed by NHR-49 that include those involved in sphingolipid processing and lipid remodeling.
To identify pathways and molecular functions common to the genes observed by microarray analysis, we employed the gene ontology (GO) enrichment analysis using GOrilla [21]. As expected due to NHR-49's known role in lipid biology, there was a significant overrepresentation of GO-terms for functions related to fat metabolism (Figure 2 and Table 2). We also found that pathways regulating protein processing, maturation and proteolysis were overrepresented.
Because NRs form homo and/or heterodimers and associate with co-regulators, we hypothesized that NHR-49 might differentially regulate its distinct target genes by interacting with specific transcriptional co-factors [22]. To identify such factors, we performed a yeast two-hybrid screen using the NHR-49-LBD as bait. In addition to the Mediator subunit MDT-15 [23], we identified six NHRs as candidate NHR-49-LBD partners (Table S2a). Notably, all NHR prey clones identified included their respective LBDs, suggesting that dimerization occurs via the LBD, as has been described for other NRs. We estimated the relative binding strength of these preys using a LacZ reporter, and found that five of the six NHRs interacted strongly and specifically with the NHR-49 LBD (Table S2b). In parallel, a large-scale yeast-two-hybrid screen identified a set of 13 additional candidates to interact with full-length NHR-49 (Table S2a) [24]. Because they were identified in both yeast-two-hybrid screens, we deemed NHR-13, MDT-15, NHR-22, NHR-66, and NHR-105 as most likely to represent NHR-49 cofactors.
To determine whether any of these cofactors regulate the newly identified NHR-49 pathways of sphingolipid processing, lipid remodeling or the β-oxidation and fatty acid desaturation pathways, we quantified the mRNA levels of the NHR-49 activated and repressed genes in these pathways (see primer pairs, Tables S3 and S4). We chose to analyze the nhr-13(gk796) and nhr-66(ok940) mutants, and also included nhr-80(tm1011) because it regulates the fatty acid desaturase genes [25]. Our qRT-PCR analyses revealed a striking separation of gene regulation by the different candidate co-factors. The deletion of nhr-66(ok940) resulted in the up regulation of most of the genes that are repressed by NHR-49, including the sphingolipid and lipid remodeling genes (Figure 1A and Table S1). The levels of derepression of these genes observed in nhr-66 mutants were comparable to that seen in nhr-49 animals, suggesting that the two NRs act together. In contrast, nhr-66 mutants did not show any change in the expression of nhr-49 activated genes, like those involved in β-oxidation and desaturation (Figure 1B and Table S1). These results strongly suggest that NHR-49 acts with NHR-66 to specifically repress the transcription of sphingolipid and lipid remodeling genes.
In contrast, nhr-80 and nhr-13 deletion mutants do not affect the sphingolipid, lipid remodeling, or β-oxidation genes. Instead, these mutants exhibited a decreased expression of the fatty acid desaturase genes fat-7, fat-5 and fat-6 (Figure 1B), confirming previous analyses of nhr80 mutants by the Watts laboratory [25]. The fat-5, fat-6 and fat-7 genes are members of the Δ9 fatty acid desaturases and are key enzymes in fatty acid metabolism [26]. Their function is to introduce a double bond in saturated fatty acid chains to generate monounsaturated fatty acids (MUFAs) that are important in membrane fluidity and energy storage [27]. These results suggest that NHR-49 associates with NHR-80 and NHR-13 to regulate the ratio of saturated and unsaturated fat in lipid membranes. However, the fold changes observed in fatty acid desaturation regulation by NHR-80 and NHR-13 did not parallel those observed in nhr-49 mutants, suggesting the involvement of other regulatory factors. Consistent with this, the C. elegans ortholog of the sterol-regulatory-element-binding protein (SREBP) transcription factor SBP-1 and the Mediator subunit MDT-15 are also implicated in the regulation of fatty acid desaturases [23], [28]–[30].
Our data strongly suggest that NHR-66 regulates genes in the sphingolipid and lipid remodeling pathways whereas NHR-80 regulates the fatty acid desaturase genes. To determine whether these NRs regulate other genes in addition to these NHR-49 targets, we performed a genome-wide microarray analysis on nhr-66 and nhr-80 mutants. The list of differentially regulated nhr-66 and nhr-80 genes is presented in tables S5 and S7, respectively, and the corresponding GO term analyses of NHR-66 and NHR-80 is presented in Tables S6 and S8, respectively. As expected, the microarray data confirmed that NHR-66 regulates several genes involved in the sphingolipid and lipid remodeling pathways, and that NHR-80 regulates genes involved in fatty acid desaturation. In addition, Figure 3A and Table S9 show the genes commonly regulated by NHR-49 & NHR-66 and NHR-49 & NHR-80, using our microarray analysis. We also found evidence that these nuclear receptors regulate additional target genes. For example, NHR-66 is involved in GDP-mannose metabolic processes and regulates gmd-2, a GDP-mannose dehydratase. The NHR-80 GO analysis indicates that it regulates genes involved in several different processes including embryonic development and cell death. In conclusion, our analyses suggest that NHR-66 and -80 are not only transcriptional partners required for NHR-49, but can independently act with other binding partners to regulate unique pathways (Figure 3A).
Our yeast-two-hybrid and gene expression data suggested that NHR-49 might directly interact with NHR-66, NHR-80 and NHR-13. To test this, we performed in vitro GST-NHR-49 pull-down assays with full-length NHR-66, NHR-80 and NHR-13 proteins. NHR-49 was a positive control since it exhibits a two-hybrid interaction with itself [24]. Purified glutathione-S-transferase (GST)-tagged NHR-49 was incubated with in vitro translated 35S-methionine-labeled NHR-49, NHR-66, NHR-80 and NHR-13. As expected, GST-NHR-49 successfully formed a homodimer with radiolabeled NHR-49 (Figure 3B). In addition, it was also able to interact directly with NHR-66 and NHR-80. NHR-13 did not bind to NHR-49 in vitro above background levels, suggesting that additional factors may contribute to an interaction with NHR-49 in vivo. Alternately, NHR-13 may regulate fatty acid desaturation via another unknown mechanism.
The physical interaction and functional studies support a model whereby the control of distinct NHR-49 regulatory modules is based on NHR-49's association with distinct partner proteins. In this case, mutation of individual NHR-49 partners should delineate the contributions of each co-factor to the phenotypes observed in nhr-49 mutants. For example, knocking out NHR-66 could reveal NHR-49-dependent sphingolipid and lipid remodeling gene mediated phenotypes, whereas knocking out NHR-80 and NHR-13 could reveal NHR-49 dependent fatty acid desaturase regulated phenotypes. We note that although we did not obtain clear evidence that NHR-13 is a direct physical partner of NHR-49, we continued to characterize it because its gene expression profile suggests it plays a role in fatty acid desaturation.
Among the many phenotypes exhibited by nhr-49 mutants, we chose to focus on its reduced lifespan. To determine what pathway of NHR-49 is important in regulating its lifespan, we analyzed the individual co-factor mutants for effects on lifespan. The nhr-66 mutant animals had a lifespan of 17.16+/−0.41 days compared to wild-type lifespan of 17.35+/−0.34 days, suggesting that sphingolipid and lipid remodeling genes do not play a role in the reduced lifespan phenotype of nhr-49 mutants (Figure 4). In contrast, at 20°C, nhr-80 and nhr-13 mutants had significantly shorter lifespans of 13.19+/−0.38 days and 14.17+/−0.4 days, respectively (Figures 4A and 4B and Table S10). Moreover, an nhr-80; nhr-13 double mutant had a lifespan of 12.29+/−0.37 days, which approaches the nhr-49 mutant lifespan of 9.52+/−0.23 days. Together, these results suggest that the reduced expression of fatty acid desaturases observed in the nhr-80 and nhr-13 mutants might contribute to the shortened lifespan of nhr-49 animals.
These observations are in accordance with previously published work demonstrating that shortened lifespan is correlated with lowered expression of the fat-7 SCD (Stearoyl CoA Desaturase) [16]. In addition, both nhr-49 mutants and worms where FAT-7 was knocked down using RNA-interference, exhibit a concomitant increase in the levels of saturated fat. More specifically, the reduction in desaturase levels strongly alter the ratio between stearic acid and oleic acid (C18:0/C18:1n9) to approximately 3.74+/−0.33 in nhr-49 animals (0.98+/−0.06 in wild-type animals). We thus quantified the abundance of individual fatty acids species in nhr-13, nhr-80, and nhr-80; nhr-13 mutants using Gas Chromatography-Mass Spectrometry (GC-MS). The C18:0/C18:1n9 ratio in nhr-80; nhr-13 double mutants (2.99+/−0.22) was similar to the ratio observed in nhr-49 mutants, and higher than the ratio in wild-type worms (0.98+/−0.06) (Figure 5A and Table S11). Strikingly, there is a strong inverse correlation (r2 = 0.86) between the level of saturated fat and the duration of mean worm lifespan (Figure 5B), supporting the notion that excess saturated fat plays a role in the early death of the nhr mutants. These results also suggest that NHR-49's interaction with NHR-80 and possibly NHR-13 contributes to the shortened lifespan phenotype that is observed in nhr-49 mutants.
To understand why the imbalance of lipid saturation observed in nhr-49 mutants could be detrimental to the animal, we hypothesized that the excess pool of saturated fat might get incorporated into membranes, thus affecting their function. Because NHR-49 functionally resembles the PPAR family of mammalian nuclear receptors that promote mitochondrial biogenesis [31], [32], we employed high pressure-transmission electron microscopy (HP-TEM) to visualize mitochondria in nhr-49 animals (Figure 6). This ultrastructural analysis revealed multiple morphological defects in the intestinal mitochondria of one-day old adult nhr-49 mutants (compared to wild-type worms), although mitochondria in nhr-49 mutants were comparable in size to wild-type mitochondria (Figure 7A). Strikingly, when we measured the average fractional area occupied by mitochondria per total intestinal area in nhr-49 mutants, we found that there was a considerable variation in the distribution, although the average was not statistically different from wild-type animals (Figure 7B). Moreover, about 25% of the intestinal mitochondria of the nhr-49 mutants were uncharacteristically irregular in shape and appeared to have more turns compared to wild-type animals (Figure 6A). The average number of turns exhibited by the intestinal mitochondria in nhr-49 animals was greater than the more rounded wild-type mitochondria (Figure 7C). Taken together, these results suggested that mitochondria in nhr-49 animals exhibit an altered shape when compared to those in wild-type animals of the same age.
We next asked if the NHR-49 partners NHR-66 and NHR-80 contribute to the observed NHR-49 mitochondrial abnormalities. In contrast to nhr-49 mutants, the mitochondria in one-day old adult nhr-66 and nhr-80 mutants were comparable in shape to those observed in wild-type worms (Figure 6B and 7A). However, the average fractional area occupied by the mitochondria in the intestine was significantly higher in nhr-66 animals and significantly lower in nhr-80 animals (compared to wild-type worms; Figure 7B). Lastly, even though the average size of the intestinal mitochondria in the nhr-66 animals was similar to the size of mitochondria in wild-type animals, the nhr-80 animals showed significantly smaller mitochondria (Figure 7C).
Because these data suggested that NHR-66 and NHR-80 contribute to mitochondrial morphology, we next examined intestinal mitochondria in one-day old adult nhr-66; nhr-80 double mutants. The fractional area of mitochondria in these animals was not statistically different from wild-type when averaged, but the distribution was much broader, similar to nhr-49 animals (Figure 7B). In addition, 11.4% of the intestinal mitochondria in the double mutants were highly irregular in shape, as was observed in 25% of the mitochondria in nhr-49 mutants (Figure 6C and Figure 7C). Although the mitochondrial phenotypes in nhr-66; nhr-80 mutants are not as strong as those in the nhr-49 animals, these data do suggest that NHR-66 and NHR-80 contribute to normal mitochondria morphology. Together, these data suggest that NHR-49 maintains mitochondrial morphology via multiple pathways, including NHR-66 and NHR-80 dependent regulation as well as additional, unknown mechanisms.
Because mitochondrial morphology was altered in the nhr mutants, we assayed mitochondrial function using two metabolic assays. First, we monitored the basal oxygen consumption rates in live animals (normalized to worm count). Consistent with the mitochondrial abnormalities observed by electron microscopy, nhr-49 animals consumed oxygen at significantly reduced rates of 5.22 pmoles/min/worm compared to 9.625 pmoles/min/worm in wild-type animals (Figure 8A). The single mutants nhr-66 and nhr-80 also had reduced basal oxygen consumption rates of 6.12 and 7.44 pmoles/min/worm, respectively. Interestingly, the respiration rate for the nhr-66; nhr-80 double mutant was lower than that of nhr-49 with a rate of 3.87 pmoles/min/worm but higher than the electron transport chain (ETC) complex I defective mutant gas-1(fc21), which showed rates of 2.55 pmoles/min/worm.
In a second assay for mitochondrial function, we indirectly measured β-oxidation by feeding the animals radiolabeled palmitic acid and then measuring the rates of production of an acid soluble metabolites, a byproduct of lipid oxidation. As the mitochondrial β-oxidation gene acs-2 is down regulated in nhr-49 mutants and contributes to their increased fat content, we used the acs-2(ok2457) mutant as a control [16]. Just like the acs-2 mutants, the nhr-49 animals also had a significant reduction in acid soluble metabolite production with a rate of 0.56 pmole/min/µg protein compared to 1.29 observed in wild-type animals (Figure 8B). Surprisingly, although the qRT-PCR analysis did not predict NHR-66 and NHR-80 to regulate genes in the β-oxidation pathway, the nhr-66 and nhr-80 single mutants also exhibited reduced oxidation rates (0.69 and 0.43 pmole/min/µg protein, respectively). This is consistent with our EM data that nhr-66 and nhr-80 mitochondria differ from wild-type, which may be explained by the reduced β-oxidation in these mutants. The β-oxidation rate for the nhr-66; nhr-80 double mutants was also significantly reduced relative to wild-type animals (0.43 pmole/min/ug protein), although the rate in the double mutant was identical to the rate in the single mutant with the stronger effect (nhr-80), suggesting that the two NRs may act in the same pathway. Together, our data show that altered mitochondrial morphology strongly correlates with aberrant mitochondrial physiology, and they further suggest that several NRs play a significant role in maintaining normal mitochondrial function.
NHR-49 regulates genes involved in fatty acid β-oxidation and fatty acid desaturation [16], but its full influence on lipid metabolism remained obscure. Here, we reveal a previously uncharacterized group of NHR-49 targets, namely genes involved in the repression of sphingolipid processing and lipid remodeling. We also identify several NHR-49 interacting partner receptors and show that at least two of these, NHR-66 and NHR-80 directly bind to NHR-49 and modulate separable NHR-49 dependent pathways. In characterizing NHR-49's transcriptional network and studying its influence on physiology, our findings inform us on the evolutionary history of HNF4-related receptors across species and on their conserved metabolic pathways and physiological functions.
HNF4α plays an important role in mammalian physiology and despite the identification of several HNF4 bound and regulated genes, it is still unclear what the relevant targets are in vivo. The number of genes estimated to be regulated by HNF4α varies greatly and depends on the experimental approaches [33], [34]. For example, Odom et al. performed ChIP on chip to identify promoters occupied by HNF4α in the human liver and pancreas and identified 1575 potential HNF4 target genes [35]. In contrast, gene expression analysis of pancreatic cells in an HNF4α conditional knockout model identified only 133 genes as HNF4α regulated [36]. This highlighted that revealing the regulatory targets and processes of metabolic nuclear receptors needed further characterization. We therefore performed genome-wide transcriptional profiling to identify the targets of the PPAR/HNF4 like nuclear receptor NHR-49 in C. elegans. Our analysis revealed a previously unrecognized role of NHR-49 in the regulation of genes involved in membrane lipid metabolism, particularly glycosphingolipid processing. It will be important to determine whether mammalian HNF4α also regulates these genes.
It has also been unclear what co-factors of mammalian HNF4α influence distinct aspects of gene expression. We took advantage of C. elegans genetics to identify multiple partners of NHR-49 and confirmed that NHR-66 and NHR-80 physically interact with NHR-49 using in vitro binding assays. We further characterized the transcript level changes observed for genes regulated by NHR-66, NHR-80 and NHR-13. Notably, knockout of nhr-66 affected only a subset of NHR-49 targets, sphingolipid and lipid remodeling target genes, whereas knockout of nhr-80 and nhr-13 affected the expression of NHR-49-regulated fatty acid desaturases. We therefore propose that NHR-49 and NHR-66 heterodimerize to regulate genes that mediate the breakdown of glycolipids and remodeling of lipid membranes, whereas NHR-49 cooperates with NHR-80 and possibly NHR-13 to sense and regulate the balance between saturated and unsaturated fat (Figure 9). Even though we did not detect a direct interaction between NHR-13 and NHR-49, NHR-13 clearly affects fatty acid desaturation. In the future, it will be important to determine if the different target genes are direct NR targets in vivo. To date, it has been difficult to identify a consensus binding sequence for NHR-49 and other NRs given the degeneracy of response elements often observed in C. elegans, and the lack of antibodies suitable for chromatin immunoprecipitation to define in vivo NR binding sites. GFP reporters of NHR-49/NHR-66 and NHR-49/NHR-80 regulated genes will be useful to determine whether these distinct dimers may selectively drive gene expression in individual tissues. Although HNF4 is not yet known to dimerize with other partners, it will be critical to determine if there are similar cofactors and/or it carries out similar mechanistic roles.
Another interesting finding of this study is the complexity of the NHR-49 regulatory network. For instance, both NHR-66 and NHR-80 are NHR-49 co-factors, but there are nuances in their co- regulation of NHR-49 genes. In the case of NHR-66, the fold changes observed in sphingolipid and lipid remodeling genes were comparable to those seen in nhr-49 animals. However, in the case of NHR-80 and NHR-13, the levels of activation in fatty acid desaturation regulation did not completely parallel the levels we observed in nhr-49 mutants. This suggests that additional regulation of these genes may involve NHR-49 hetero and/or homodimers or NHR-49 independent mechanisms, consistent with our observation that NHR-13 influences NHR-49 regulated desaturases but may not directly bind to NHR-49. Adding to the complexity, our gene expression data suggest that, in addition to the genes jointly regulated by NHR-49/NHR-66 and NHR-49/NHR-80, NHR-66 and NHR-80 regulate additional genes independently of NHR-49. In fact, our studies on mitochondrial morphology and function do not clearly differentiate between the possibilities that the observed defects are the result of NHR-49-dependent pathways or the result of multiple independent pathways regulated by NHR-66 and NHR-80. Our data also does not exclude the possibility that NHR-66 and NHR-80 act together for some functions. Future work will be needed to address these different scenarios and elucidate the complexity of this transcriptional network.
Our lifespan analysis revealed that NHR-80 and NHR-13 regulated pathways appear to contribute to the early death of nhr-49 deletion mutants. We propose that NHR-49's interaction with NHR-80 and possibly with NHR-13 modulates the conversion of saturated to unsaturated fat, and contributes to the shortened lifespan phenotype of nhr-49 mutants. Supporting this idea, there is a significant correlation between the C18:0/C18:1n9 ratio and mean lifespan. This fits well with a recent study that showed the correlation between several fatty acid metabolic parameters to longevity, including the ratio of C18 to C18:1n9 [37]. Our data my also provide insight into the mechanism by which mammalian HNF4α protects against diabetes. Both in vitro and in vivo studies showed that pancreatic β-cells are highly susceptible to saturated fat-induced apoptosis or lipotoxicity [38], [39], whereas overexpression of the fat-6/fat-7 homologue Stearoyl CoA Desaturase (SCD) protects against this lipotoxicity [40], [41]. It is intriguing to speculate that the shortened lifespan could be related to roles of HNF4 in protection against premature cell death.
Although NHR-49 is derived from the ancestral HNF4-like receptors [42], NHR-49 functionally resembles the PPARα. Our EM data suggest that mitochondrial morphology was abnormal in nhr-49 mutants, which led us to test mitochondrial function in these worms. We observed defects in oxygen consumption and fatty acid β-oxidation, which may be the result of altered membrane lipids affecting the function of intestinal mitochondria. Because the co-factor mutants also exhibit mitochondrial defects, we propose that multiple pathways regulate mitochondrial function.
It has been shown that PPARs regulate the density (number) of mitochondria. In fact, PPARγ and PPARδ promote mitochondrial biogenesis in a cell-type specific manner, specifically increasing biogenesis in white adipose tissue and skeletal muscle, respectively [31], [32]. PPARs have also been shown to influence mitochondrial function by mostly regulating genes encoding mitochondrial fatty acid β -oxidation enzymes and mitochondrial uncouplers [43]. Our data suggest that nhr-49 mutants differ from wild-type animals in the shape but not the fractional area (number) of their intestinal mitochondria. Given that some mitochondria in the nhr-49 mutants are irregular in shape, it would be interesting to determine if the mitochondria in mammalian PPAR mutant mice exhibit similar phenotypes. Alternatively, a set of genes not commonly regulated by NHR-49 and PPAR may contribute to the altered morphology of the mitochondria in nhr-49 mutants only. In line with this notion, our study revealed novel NHR-49 dependent targets and pathways, particularly glycosphingolipid processing and lipid remodeling, which mammalian PPARs are not known to regulate.
When HNF4 and HNF4-like NRs are compared across species (Table 3), a common theme that emerges is the conservation of their roles in lipid metabolism pathways. It is possible that one ancestral role of HNF4–the mobilization of stored energy during nutrient deprivation and the regulation of β-oxidation–was adopted by PPARα, whereas other functions of HNF4 were either retained or outsourced to other NRs. In C. elegans, HNF4-like NRs underwent a massive expansion and it is likely that various functions were allocated to different nuclear receptors [44]. HNF4 paralogs in nematodes could then have been selected for divergent functions that included sphingolipid metabolism and lipid remodeling.
Mammalian RXR serves as a heterodimer partner for numerous NRs, including PPARs [45]. We thus speculate that like PPAR and its heterodimeric partner, RXR adopted specific HNF4 roles in mammals. It is possible that NHR-49 heterodimerized with other binding partners to adopt individual tasks that encompassed the numerous functions of the precursor nematode HNF4. This is particularly intriguing given the striking absence of any C. elegans orthologs of RXR-like molecules that in other organisms heterodimerize with a range of interacting proteins. NHR-49's action is promiscuous as it is clearly assuming different function with different partners. NHR-49 may therefore serve as an ancestral binding partner reminiscent of the mammalian RXR. C. elegans thus represents a class of animals that switched from solely using HNF4 receptors to regulate lipid metabolism to those that began to employ different heterodimeric partners to regulate distinct metabolic pathways.
In closing, our study has identified distinct binding partners that modulate specific pathways of NHR-49 activity. Additionally, we have also shown that these NRs play a role in mitochondrial physiology and function. Given the conservation of HNF4 structure, function and regulatory modules across species, it is possible that mammalian HNF4α utilizes similar mechanisms as that of C. elegans NHR-49, thus informing us of ways to selectively modulate its activity. Exploring these possibilities can assist in pharmacological efforts to manipulate mammalian HNF4α for specific desired outcomes without any detrimental side-effects.
C. elegans strains N2 Bristol (wild-type), nhr-49(nr2041), nhr-66(ok940), nhr-80(tm1011), nhr-13(gk796), gas-1(fc21) and acs-2(ok2457) were grown at 20°C on high-growth plates seeded with OP50 bacteria and maintained as described [46]. The mutant strains were obtained from the CGC and were outcrossed at least five times with wild-type N2 worms. Single-worm PCR was used to determine the genotype of worms during crossing to generate nhr-80; nhr-13 and nhr-66; nhr-80 double-mutant lines. For mRNA and GC/MS analysis, worm embryos were allowed to hatch on unseeded nematode growth media (NGM)-lite plates overnight at 20°C. The next day, synchronized L1 larvae were plated onto NGM-lite plates seeded with Escherichia coli strain OP50. Worms were grown to early L4s at 20°C, harvested, washed three times with M9, and flash-frozen in liquid N2.
C. elegans strains N2-Bristol (wild-type), nhr-49(nr2041), nhr-66(ok940), nhr-80(tm1011) and nhr-13(gk796) were grown at 20°C on high-growth plates seeded with OP50 bacteria and maintained as described [46]. Gravid adults from 10 10-cm plates were bleached, and embryos were dispersed onto 15-cm nematode growth media (NGM)-lite plates seeded with OP50. Worms at L4 stage were harvested, washed twice with M9, and frozen in liquid nitrogen. For RNA preparation, worms were thawed at 65°C for 10 min, and RNA was isolated using the Tri-Reagent Kit (Molecular Research Center, Cincinnati, Ohio, United States). Isolated total RNA was subjected to DNAase treatment and further purification using RNAeasy (Qiagen, Valencia, California).
For the NHR-49-LBD yeast two-hybrid screen [23], the strain AH109/pGBK-Leu2-NHR-49-LBD was used to probe a C. elegans oligo(dT)-primed cDNA library cloned into pPC86 (kindly provided by the Vidal laboratory, Dana Farber Cancer Institute, Boston). In short, ∼4.3×106 independent transformants were screened for growth on medium-stringency plates (lacking tryptophan, leucine, histidine, and uracil containing 7.5 mM 3-amino-1,2,4-triazole (3-AT; Sigma, H-8056). Candidate clones were retested twice for their ability to grow on high-stringency medium (i.e. medium-stringency plates additionally lacking adenine), allowing recovery of 141 candidates. Following PCR and AluI digestion to identify redundant clones, 49 candidate plasmids were extracted and sequenced, yielding 24 independent cDNAs of 13 genes (Table S1c). To estimate the relative interaction strength of these candidate proteins with the NHR-49-LBD, plasmid pairs were transformed into strain Y187 (Clontech) and liquid β-galactosidase assays were performed as described in the manufacturer's protocol.
cDNA was prepared from 5 µg of total RNA in a 100-µl reaction using the Protoscript cDNA preparation kit (New England Biolabs, Beverly, Massachusetts, United States). Primer pairs were diluted into 96-well cell culture plates at a concentration of 3 µM. Next, 30-µl PCR reactions were prepared in 96-well plates. Each PCR reaction was carried out with TaqDNA Polymerase (Invitrogen, Carlsbad, California, United States) and consisted of the following reaction mixture: 0.3 µM primers, 1/500th of the cDNA reaction (corresponds to cDNA derived from 10 ng of total RNA), 125 µM dNTPs, 1.5 mM MgCl2, and 1× reaction buffer (20 mM Tris pH 8.4, 50 mM KCl), 0.15 µl (0.75 units) of TaqDNA Polymerase was used for each reaction. Formation of double-stranded DNA product was monitored using SYBR-Green (Molecular Probes, Eugene, Oregon, United States). Data were collected using RNA from at least three independent C. elegans growths. To determine the relationship between mRNA abundance and PCR cycle number, all primer sets were calibrated using serial dilutions of cDNA preparations. qRT-PCR primers were designed using Primer3 software [47]. qPCR was performed using a BioRad iCycler (MyiQ Single color).
For microarray analysis, total RNA was assayed for quality using an UV-Vis spectrophotometer and an Agilent 2100 Bioanalyzer (Agilent Technologies, Inc., Santa Clara, CA). Cy5- and Cy3-labeled cDNA targets were generated from 30 µg of total RNA using a reverse transcription, amino-allyl based labeling protocol [48]. Mutant and wild-type targets were co-hybridized to Washington University/Genome Sequencing Center C. elegans 23K spotted oligo arrays for 16 hours at 42°C, followed by a series of stringency washings at 42°C for 5 cycles in 2× SSC and 3 cycles in 0.1× SSC using an automated GeneTac Hybridization Station (Genomic Solutions, Ann Arbor, MI, USA). Post-hybridized arrays were scanned using a GenePix 4000B scanner (Axon Instruments, Union City, CA) and image analysis was performed using GenePix Pro software. Independent datasets were generated for each mutant vs. wild-type comparison: nhr-49 (n = 2, with dye swap), nhr-66 (n = 3), nhr-80 (n = 4).
Array data was pre-processed through a custom-built application used to filter data based on criteria associated with spot-level signal quality (e.g., signal-to-noise, lower bound intensity threshold, spot dimension). Each array was background subtracted and the ratios (mutant/wt) were log2 transformed. Intra-array normalization was performed using a loess algorithm to correct for intensity-dependent ratio biasing [49]. For each comparison, the dataset was filtered by applying a lower-bound signal intensity cutoff, followed by the application of a variance filter using the ‘shorth’ function in the Bioconductor package genefilter. Pair-wise significance testing (mutant vs. wild-type) was performed using the Bioconductor package limma [20] and p-values were initially corrected for multiple testing using the false discovery rate (FDR) method of Benjamini and Hochberg [50]. We attempted to define differential expression as |log2(ratio)|≥0.848 with the FDR set to 5%, however the multiple testing penalty was too high to produce a gene list in some cases and as such we slightly relaxed our criteria by employing |log2(ratio)|≥0.848 and p-value≤0.001.
Gene ontology (GO) enrichment analysis was performed using GOrilla [21]. Each list from the limma analysis was ranked from smallest to largest p-value and analyzed for enriched biological process ontology terms found near the top of the list. Functional classification summary for the nhr-49 mutant were presented as a scatter plot using the GO visualization tool REViGO [51].
Fatty acids were isolated from 10,000 L4 animals grown on a single 15-cm NGM-Lite plate. Total lipids were extracted and converted to fatty acid methyl esters (FAMEs) as described [52]. After incubation at 80°C for 1 hour the samples were cooled and the FAMEs were extracted by adding water and hexane. FAMEs were analyzed for fatty acid composition by gas chromatography/mass spectrometry (GC/MS) (Agilent 5975GC, 6920MS). Peaks were assigned using fatty acid standards.
The PCR products of the full length NHR-49, NHR-66, NHR-80 and NHR-13 were cloned into the expression vector pCS2+ that was generously provided by Dr. B. Eisenmann (Fred Hutchinson Cancer Research Center, Seattle). The PCR product for NHR-49 was flanked by BamH1/EcoR1, NHR-66 by Stu1/Xba1, NHR-80 by BamH1/EcoR1 and NHR-13 by BamH1/EcoR1 and then cloned into the corresponding sites in pCS2+. For bacterial expression, GST-NHR-49 was constructed by inserting BamH1-NHR-49 cDNA-EcoR1 into PGEX-2T vector. All clones were confirmed by sequence analysis. All plasmid requests should be directed to Stefan Taubert.
GST-fused to NHR-49 (GST-NHR-49) was induced with 1 mM IPTG for 2 hours at 23°C, expressed in the Escherichia coli BL21 (DE3) strain and purified using glutathione-Sepharose 4B beads. The IVTs were transcribed from the SP6 promoter and translated with the wheat germ system (Promega: Madison, WI), in the presence of unlabeled (Promega: Madison, WI) or 35S- labeled methionine (Perkin Elmer) according to the manufacturer's instructions. For the in vitro protein-protein interaction assays, the IVT protein was incubated with GST-NHR-49 for 90 minutes at 37°C and washed in binding buffer. The samples were then run on SDS-PAGE and the radiolabeled protein detected by autoradiography.
Approximately 30–40 L1 worms were transferred to 6-cm plates seeded with L4440 RNAi bacteria and life-span assays were carried out at 20°C as described previously [53]. The L4 stage was counted as day 0. Adults were transferred to new plates daily until progeny production had ceased. Animals were considered dead when they no longer responded to a gentle tap with a worm pick. Life span curves and statistical data including p-values from Log-rank (Mantel-Cox) test were generated using GraphPad Prism version 5 software (GraphPad Software, San Diego, CA).
Day 1 adults were placed into a 20% BSA/PBS buffer solution and prepared in a Leica-Impact-2 high-pressure freezer. In short, animals were kept for 60 hours in 100% acetone and uranyl acetate at −90°C. The temperature was then ramped from −90°C to −25°C over the course of 32.5 hours. Next, samples were incubated at −25°C for 13 hours. Finally, the temperature was brought from −25°C to 27°C in a 13 hour temperature ramp. Serial sections were post-stained in uranyl acetate followed by lead citrate. Thin cross sections were taken from resin-embedded clusters of young adults. Sections for adult animals were obtained from 3 different animals.
Ten micron transverse sections typically spanning the length of the worm from the pharynx to the vulva were examined per animal. Mitochondria from each section were individually outlined and the average measurements were obtained using Image J software. At least two animals were examined per mutant strain. Statistical data including p-values from unpaired t-tests using Welch's correction for unequal variance were generated using GraphPad Prism version 5 (GraphPad Software). Irregularity in mitochondrial shape was measured by counting the number of times the mitochondrial outer membrane changed from convex to concave. A turn was measured every time the mitochondria changed from convex to concave and vice versa.
The production of acid-soluble metabolites was used as an index of the β-oxidation of fatty acids based on an assay that was originally developed for cell lines [54]. We further modified the protocol for C. elegans based on the protocol reported by Mullaney et al, 2010 [55]. Synchronized L4 animals were washed off plates with 1× M9 medium and counted. The animals were then rinsed three times in 0.9% NaCl. An aliquot was stored at −80 C for subsequent protein determination. The worms were resuspended in S-Basal medium with 14C-palmitic acid (40–60 mCi/mmol) to a final concentration of 1 µCi /ml complexed to 25% fatty-acid-free BSA (all from GE Healthcare Life Sciences) per well. Samples were incubated on an orbital shaker for 2 hours at room temperature. Subsequently, 70% perchloric acid was added to precipitate the BSA-bound fatty acid. The samples were centrifuged for 10 minutes at 14,000 g and the radioactivity of the supernatant was determined by liquid scintillation. Samples without animals were included as background controls. These experiments were performed in triplicates two independent times. Statistical data was analyzed using one-way ANOVA and p-values were generated using GraphPad Prism version 5 (GraphPad Software).
Between 50 and 100 synchronized L4 worms were washed with 1XM9 and seeded into triplicates of the Seahorse XF-24 cell culture plates (Seahorse Bioscience, North Billerica, MA) in M9. Oxygen consumption rates were measured at least five times using the Seahorse XF-24 Analyzer (Seahorse Bioscience). Measurements were taken under basal conditions and were normalized to the number of worms counted per well. The Seahorse software was used to plot the results. The experiment was repeated two times under these conditions. Statistical data including p-values from unpaired t-test were generated using GraphPad Prism version 5 (GraphPad Software).
|
10.1371/journal.pntd.0001634 | Treatment for Schistosoma japonicum, Reduction of Intestinal Parasite Load, and Cognitive Test Score Improvements in School-Aged Children | To determine whether treatment of intestinal parasitic infections improves cognitive function in school-aged children, we examined changes in cognitive testscores over 18 months in relation to: (i) treatment-related Schistosoma japonicum intensity decline, (ii) spontaneous reduction of single soil-transmitted helminth (STH) species, and (iii) ≥2 STH infections among 253 S. japonicum-infected children.
Helminth infections were assessed at baseline and quarterly by the Kato-Katz method. S. japonicum infection was treated at baseline using praziquantel. An intensity-based indicator of lower vs. no change/higher infection was defined separately for each helminth species and joint intensity declines of ≥2 STH species. In addition, S. japonicum infection-free duration was defined in four categories based on time of schistosome re-infection: >18 (i.e. cured), >12 to ≤18, 6 to ≤12 and ≤6 (persistently infected) months. There was no baseline treatment for STHs but their intensity varied possibly due to spontaneous infection clearance/acquisition. Four cognitive tests were administered at baseline, 6, 12, and 18 months following S. japonicum treatment: learning and memory domains of Wide Range Assessment of Memory and Learning (WRAML), verbal fluency (VF), and Philippine nonverbal intelligence test (PNIT). Linear regression models were used to relate changes in respective infections to test performance with adjustment for sociodemographic confounders and coincident helminth infections.
Children cured (β = 5.8; P = 0.02) and those schistosome-free for >12 months (β = 1.5; P = 0.03) scored higher in WRAML memory and VF tests compared to persistently infected children independent of STH infections. A decline vs. no change/increase of any individual STH species (β:11.5–14.5; all P<0.01) and the joint decline of ≥2 STH (β = 13.1; P = 0.01) species were associated with higher scores in WRAML learning test independent of schistosome infection. Hookworm and Trichuris trichiura declines were independently associated with improvements in WRAML memory scores as was the joint decline in ≥2 STH species. Baseline coinfection by ≥2 STH species was associated with low PNIT scores (β = −1.9; P = 0.04).
Children cured/S. japonicum-free for >12 months post-treatment and those who experienced declines of ≥2 STH species scored higher in three of four cognitive tests. Our result suggests that sustained deworming and simultaneous control for schistosome and STH infections could improve children's ability to take advantage of educational opportunities in helminth-endemic regions.
| Parasitic worm infections are associated with cognitive impairment and lower academic achievement for infected relative to uninfected children. However, it is unclear whether curing or reducing worm infection intensity improves child cognitive function. We examined the independent associations between: (i) Schistosoma japonicum infection-free duration, (ii) declines in single helminth species, and (iii) joint declines of ≥2 soil-transmitted helminth (STH) infections and improvements in four cognitive tests during18 months of follow-up. Enrolled were schistosome-infected school-aged children among whom coinfection with STH was common. All children were treated for schistosome infection only at enrolment with praziquantel. Children cured or schistosome-free for >12 months scored higher in memory and verbal fluency tests compared to persistently infected children. Likewise, declines of single and polyparasitic STH infections predicted higher scores in three of four tests. We conclude that reducing the intensity of certain helminth species and the frequency of multi-species STH infections may have long-term benefits for affected children's cognitive performance. The rapidity of schistosome re-infection and the ubiquity of concurrent multi-species infection highlight the importance of sustained deworming for both schistosome and STH infections to enhance the learning and educational attainment of children in helminth-endemic settings.
| Many children in developing countries perform below academically desired levels [1]. Helminth infections are a pervasive part of children's environments in these settings that may contribute to poor educational outcomes through reduced iron status, inflammation, decreased macro-nutrient nutritional status, and distracting symptoms such as abdominal pain [2], [3].
Some epidemiologic studies have linked these infections to low academic achievement in resource-limited settings [4]–[7]. However, many of the studies did not control for important confounders or had methodological differences that made comparability of findings across studies difficult [8]. All but two prior studies [9], [10] examined associations between cognitive performance and single helminth species. Recently, polyparasitism, that is, the concurrent multi-species helminth infection, has been associated with childhood anemia and self-reported morbidity [11]–[13]. Its relationship to performance in cognitive tests deserves specific investigation [8].
An earlier cross-sectional study by our group found that moderate or higher intensity infection with Trichuris trichiura, Ascaris lumbricoides, and Schistosoma japonicum were, respectively, associated with low scores on tests of verbal fluency, and the memory and learning subscales of the Wide Range Assessment of Memory and Learning (WRAML) tests in school-aged children [14]. It is expected that treatment for parasitic helminth infections will confer a range of benefits to child health, including improvements in academic performance among heavily infected children [15]. However, empirical support for this claim is lacking [8]. Short follow-up periods for most randomized controlled trials, variability in prevalence and baseline intensities of helminth infections, and a background of high re-infection pressure could explain failure to consistently find treatment-associated score improvements.
The ambiguity in the literature justifies further exploration of this subject and motivates this longitudinal study to determine the relationship between cognitive testscore improvement and independent declines of schistosome and single soil-transmitted helminth (STH) infections, as well as the impact of concurrent declines of two or more STHs on changes in cognitive testscores. Specifically, we provide associations between cognitive testscore improvement and: (i) treatment-induced changes in S. japonicum intensity, (ii) non-treatment-related or natural declines in single STH infections, and (iii) joint infection decline for ≥2 STH species. We hypothesize that no or low level S. japonicum re-infection after praziquantel treatment, and clearance or intensity reductions for single and polyparasitic STH infections will predict improvements in cognitive testscores during follow-up among school-aged children living in a schistosome and STH co-endemic area of Leyte, The Philippines.
The parent study and the nested study reported here were approved by the Brown University, Lifespan, and Philippines Research Institute of Tropical Medicine Institutional Review Boards. Participants' aged ≥18 years provided written informed consent. In addition, all parents/guardians provided written informed consent on behalf of child participants, whereas children aged ≥8 years provided assent. All participants were S. japonicum infected and were treated with the anti-schistosomal drug praziquantel (60 mg/kg over 4 hours) at enrolment as part of the parent study. Only cognitive testing was conducted in a subset of 253 children, aged 7–19 years, as part of this nested observational study.
There was no baseline treatment for STH infections as large-scale helminth treatment campaigns were not available in The Philippines at the time this study was conducted. However, at the end of the study, children with STH infection were treated with albendazole and those that became re-infected with S. japonicum were treated with praziquantel. An approach that includes waiting to treat children infected with STH would not be taken today given more recent published findings regarding subtle morbidities related to STH infections.
This study was conducted in Macanip, a malaria-free rural rice farming village in Leyte, The Philippines, where S. japonicum and STH infections coexist with high prevalence. This is a nested prospective cohort study conducted in a subset of S. japonicu- infected Filipinos aged 7–30 years who were enrolled in a study of immune correlates of resistance to S. japonicum reinfection [16].
Eligibility criteria included: baseline S. japonicum infection, age 7–19 years at enrolment, provision of parental consent, and child assent for participation in this study. Exclusion criteria included pregnancy or lactation, severe malnutrition (weight-for-height z-score<−3), severe anemia (hemoglobin<7 g/dl), or the presence of a serious chronic disease determined by history, physical examination, or laboratory findings.
Four cognitive tests were administered, including the Philippine nonverbal intelligence test (PNIT), verbal fluency (VF), and two domains of the Wide Range Assessment of Learning and Memory (WRAML), namely verbal memory and learning. Tests were chosen based on their ability to capture a range of cognitive processes including fluid intelligence (PNIT), learning (WRAML), and memory (VF and WRAML) while being adaptable across different cultures. The PNIT is an intelligence test that measures concept recognition and abstract thinking [17]. VF test is thought to be a good measure of the central executive component of working memory. The WRAML assesses a child's ability to learn and recall new information. Specifically, the WRAML learning subtests evaluate a child's performance over trials on tasks using the free-recall paradigm, while the WRAML verbal memory subtests assess a child's memory capabilities on meaningful (i.e., stories) and meaningless material (i.e., strings of random digits and letters) [18]. Each of the domains assessed by the WRAML consists of three age-standardized subtests that are added together to derive a total age- and gender-scaled score per domain. Unlike the WRAML, neither the PNIT nor the VF are age standardized; therefore, these tests were adjusted for age variation using linear regression from which we calculated the error terms associated with each child's testscore. We then modeled as the dependent variable the error terms associated with performance in PNIT and VF tests.
All tests were translated, adapted for cultural appropriateness, and pilot tested among Filipino children from other S. japonicum-endemic villages near the study area. Testing was conducted in a designated room adjacent to the field laboratory with sufficient lighting and minimal external noises. Ambient temperature within the classroom was approximately 27°C. All children were provided a snack about 30 minutes prior to testing.
Joint inter-rater and test-retest reliability with a 6-week interval between tests were evaluated. Cronbach's alpha coefficient was used to assess the degree of internal consistency between tests in the WRAML learning (α = 0.54) and WRAML verbal memory (α = 0.81) domains. For all tests, higher scores correspond to better performance. Details of each test and its psychometric properties have been previously reported [14]. More details about the rationale for choosing specific tests and their respective properties are presented in Appendices S1 and S2.
Cognitive assessments were made at months 0, 6, 12, and 18. All infections were assessed at baseline and quarterly thereafter. We have previously reported on cross-sectional associations between helminth infections and performance in the aforementioned tests [14]. Here we determine associations between post-treatment testscores and: (i) post-treatment re-infection with S. japonicum and (ii) natural infection clearance/decline for STH infections. Only cognitive assessments at 6, 12, and 18 months are included in the outcome matrix to preserve temporal sequence between infections and testscore changes.
The origin of this prospective analysis is the cohort-wide interval of least infection intensity for all species (i.e., months 1–3). STH and schistosome infections were assessed at months 0, 3, 6, 9, 12, 15, and 18. For S. japonicum only, an additional assessment (one month post-treatment) was done to evaluate treatment efficacy.
The number of eggs per gram (EPG) of stool was determined via duplicate examination of three stool samples by the Kato-Katz method for all species [19]. EPGs were used to define none, low, moderate, or high intensity categories for each species using World Health Organization EPG thresholds [20]. For each individual helminth species, except hookworm, a separate dichotomous baseline intensity indicator was defined as: uninfected/low vs. moderate/high infection to accommodate the intensity distribution in this cohort. For hookworm infection only, baseline infection intensity was defined as none vs. any infection, since >40% of participants were hookworm-free at enrollment and those infected had predominantly low infections.
Children were initially grouped by the intensity of concurrent infection with hookworm, A. lumbricoides and T. trichiura as having: (i) one or zero low; (ii) two or three low; (iii) one moderate/high STH; (iv) two moderate/high; and (v) three moderate/high intensity coinfections [11]. These categories were further combined into one baseline polyparasitic STH indicator to distinguish children with ≥2 STH species at moderate/high intensity (which may include zero or one low infection of the third STH species) from those with at most one STH infection at moderate or higher intensity STH coinfection (other STHs are either absent or present at low intensity only).
Given our treatment-reinfection design and study inclusion predicated on S. japonicum infection, the most dynamic infection changes occurred with respect to S. japonicum during follow-up; however, STH infection intensity also varied over time. These non-treatment related changes in STH intensity may be due to one or more of the following factors: (i) natural changes in STH infections within individuals over time, (ii) the limited sensitivity of some STH species to praziquantel [21], [22], and (iii) lower diagnostic sensitivity for the Kato-Katz method especially when used for the simultaneous assessment of multiple STH species at low intensity in the same host [23]. We defined three post-treatment infection intervals: 1≤t1<6, 6≤t2≤12 and 12<t3≤18 months; to correspond with the three repeated cognitive assessments. For each STH, t1 infection value (I1) was the mean EPG at month three, whereas for S. japonicum I1 was the mean of EPGs at months one and three. T2 infection (I2) was the mean of EPGs at months six and nine, and t3 infection (I3) was the mean of EPGs at months 12, 15, and 18. Within respective intervals, intra-individual infection change scores (δit) were defined by species as follows: t2: δi2 = Ii2 - Ii1; and t3: δi3 = Ii3 - Ii1.
Hence, δit ranged from −∞ to +∞ and will be negative, zero, or positive for a given STH species if the child's infection was lower, equivalent to, or greater than their infection intensity at t1. For each species, separate δit values were defined and ultimately dichotomized into high vs. low categories as δit≥0 vs δit<0.
For S. japonicum only, infection-free duration was defined as a four level categorical variable that is: (i) 0 if not reinfected by month 18; (ii) 1 if reinfected between months 12 and 18; (iii) 2 if reinfected between months 6 and 12; and (iv) 3 if never cured or S. japonicum positive in t1, t2, and t3 (reference group). Children reinfected by 6, 12, or 18 months were compared to those not reinfected by study end.
We determined the number of concurrent STH declines as the sum of individual STH intensity declines using the previously described dichotomous infection decline variable based on δit. Possible values for polyparasitic STH declines were: 0 = no decline/increase STH species, 1 = any one STH, 2 = any two STH to 3 = all STH species intensity decline in a given interval. Using these values, polyparasitic STH decline within intervals was defined as: concurrent intensity decline of ≥2 vs. ≤1 of 3 STH species.
We considered an extensive array of potential confounding factors. Because exposure to helminth infection and cognitive testscores vary by age, sex, and socioeconomic status (SES), these factors were considered non-time varying potential confounders. SES measurements were based on baseline questionnaire data addressing four domains of social position; parental and child education, occupation, home/land ownership, and assets. The method used to derive and validate this measure of SES has been described elsewhere [14], [24]. The derived summary SES variable is divided into four ordinal categories by the quartiles of its distribution.
Anemia and nutritional status at baseline were considered potential confounders and/or mediators of low testscores. Anemia was defined on the basis of age- and sex-specific hemoglobin cutoffs recommended by the WHO [25]. Hemoglobin measurement was based on complete hemograms determined on a Serono Baker 9000 hematology analyzer (Serono Baker Diagnostics, Allentown, PA). Nutritional status was assessed using weight-for-age z-scores (WAZ) calculated using the National Center for Health Statistics year 2000 reference values in EpiInfo software (version 2000, Atlanta, GA). Normal and malnutrition status were defined by WAZ≥−2 and WAZ<−2, respectively.
Multivariable random effects regression models were fitted separately to each cognitive test without adjusting for testscore at study enrollment (month 0) given our observational study design [26]. We assumed an unstructured covariance matrix to account for non-independence of repeated cognitive tests within individuals and accounted for clustering of observations within households by including a random intercept for household. Empirical standard errors were used for all estimations to ensure that significance tests were robust against mis-specification of the covariance matrix.
In addition, we examined the relationship between test performance and S. japonicum-free duration in separate regression models. Sample regression models for estimation of associations between testscores and S. japonicum infection decline and S. japonicum infection free duration are provided in Appendix S3.
Finally, we examined the potential for modification in the association between infection change and testscore improvement by the following baseline factors: helminth infection intensity, underweight, and anemia. For example, to examine whether the relationship between hookworm infection decline and testscore improvement was heterogenious by hookworm baseline infection intensity, we introduced a three-way multiplicative interaction consisting of the dichotmous indicator of hookworm infection decline, time, and baseline hookworm intensity in a multivariate models that in addition to other confounders also adjust for the baseline intensity of A. lumbricoides, T. trichiura and S. japonicum as well as each of the three dichotmous indicators of change in these infections from the interval of lowest infection. We then examined the p-values associated with interaction terms and where P≤0.05, results are presented by strata of baseline hookworm intensity. The same approach was used to examine baseline underweight and baseline anemia as potential effect modifiers in separate multivariate regression models.
The prevalence of A. lumbricoides, T. trichiura and hookworm infections in this S. japonicum-infectected cohort at baseline were 79.9%, 95.6%, and 50.6%, respectively. Of the 253 children, 97% were concurrently infected by S. japonicum and at least one STH species, approximately 36% were anemic and 60% were underweight relative to U.S. children of the same age and sex (Table 1).
The lowest intensity of S. japonicum infection (mean = 6.8 EPG) occurred one month post-treatment at which 92% (n = 217) of the sample was infection-free. However, re-infection was rapid and increased steadily until the 12th month of follow-up, at which point 70.8% of participants were infected with S. japonicum. Only 25 (10.6%) of the re-examined children were free of S. japonicum infection at 18 months. Individual STH intensities also declined from enrollment with the lowest average infection for all STH species occurring at three months. Infection intensity stabilized near this level throughout follow-up for hookworm and T. trichiura infections. The cohort-wide, A. lumbricoides infection intensity by the 18th month was comparable to month zero despite the initial decline post-S. japonicum treatment (Figure 1).
From multivariable models adjusted for sociodemographic characteristics and the intensity of coincident S. japonicum and STH species, declines in the intensity of T. trichiura, hookworm, and polyparasitic STH infections were independently associated with higher average scores on the learning and verbal memory domains of WRAML tests during follow-up. Similarly, A. lumbricoides intensity decline was independently associated with higher scores in the learning sub-scale of WRAML. The intensity of individual infections at enrollment were generally not associated with performance on any of the tests employed, except for moderate/high intensity polyparasitic STH infection, which was associated with lower scores on the PNIT (Table 2).
A decline vs. no change or an increase in S. japonicum intensity from the interval of least infection was not independently associated with improvements in any tests over the study period (Table 2). We found no evidence that the relationship between S. japonicum infection decline and performance in respective tests differed within strata of S. japonicum intensity at enrollment (data not shown). However children who were S. japonicum free for ≥18 months or those who were S. japonicum infection free until 12 months post-treatment scored higher in all tests relative to rapidly re-infected or persistently infected children. The strength of association was generally attenuated in multi-variable models that controlled for several sociodemographic characteristics and coincident STH and the baseline intensity of S. japonicum infection. Nevertheless, never S. japonicum re-infected children and those S. japonicum infection-free for up to 12 months scored higher in the verbal memory sub-scale of WRAML and VF test, respectively (Table 3).
Anemia and underweight status at enrollment were not independently associated with performance in any tests. However, among children with anemia at enrollment, S. japonicum decline was associated with higher scores on WRAML learning subscale (mean = 10.5, 95% confidence interval (CI): 4.8–16.3). There was no association between S. japonicum infection decline and performance in WRAML learning subscale among children without anemia at enrollment (mean = −3.0, 95% CI: −6.4–0.4).
In this cohort of S. japonicum-infected children whose infections were treated at enrollment, we found positive associations between performance in the verbal memory subscale of WRAML and the test of verbal fluency and longer S. japonicum infection-free duration independent of concurrent STH infections. We also found parasite decline-associated improvements for scores in the learning and memory subscales of WRAML; specifically scores for these tests improved for children whose hookworm, T. trichiura, and polyparasitic STH infections declined relative to those who experienced no change or an increase in these infections from the interval of lowest infection. Further, declining A. lumbricoides was independently associated with superior testscores in the learning subscale of the WRAML. With the exception of baseline moderate or high intensity polyparasitic STH infection, which was associated with low PNIT scores, baseline helminth infection intensities were not generally associated with testscores over the study period.
Our finding that S. japonicum infection-free duration was associated with higher testscores in WRAML verbal memory subscale and verbal fluency test corroborates similar findings from a randomized controlled trial of S. japonicum using a different battery of tests in a subset of young children from the People's Republic of China [27]. Similarly, the positive associations between declines in polyparasitic STH and performance in the WRAML tests corroborates finding from two cross-sectional studies conducted among children from South Africa and Brazil [9], [10]. In the South African study, children with intestinal parasites and S. mansoni scored significantly lower on tests of sustained attention compared to uninfected children or children with single species infections [10]. More recently, Brazilian children concurrently infected with hookworm and A. lumbricoides scored lower on a different battery of cognitive tests relative to children with only single infections [9].
With respect to individual infections, we show that longer S. japonicum infection-free duration predicted significantly higher testscores in WRAML verbal memory independent of the intensity of coincident STH infections. Likewise declines in A. lumbricoides and T. trichiura intensities were independently associated with improvements WRAML learning score. These findings are congruent with our previously published cross-sectional findings in this cohort that these infections were associated with lower cross-sectional scores in both subscales of WRAML and the verbal fluency test [14]. In addition, our finding that decline in T. trichiura intensity over time was associated with significant elevation in both subscales of WRAML scores is in agreement with prior observations among helminth infected Ecuadorian [28] and East African [4] children. Our finding of positive associations between hookworm infection declines and improvements in both WRAML tests is supported by recent hookworm-associated cross-sectional report of lower concentration and information processing in Brazilian children [9].
In line with our hypothesis, baseline moderate/high intensity polyparasitic STH infections predicted lower average score on the PNIT. Further, a significant improvement in WRAML verbal memory testscores was evident for children with anemia but not for children who were not anemic at enrolment. These observations suggest that: (i) helminth infections combine with other infections, hematologic and nutritional risk factors to impair cognitive performance, and (ii) the cognitive benefit of declines in helminth infection intensity may be blunted in some subgroups depending on the extent of anemia, malnutrition and other infection they start out with. Nevertheless, we believe that all children in helminth endemic areas will likely benefit from a multi-pronged control strategy, including sustained deworming and improvement of nutritional status in the effort to counteract the effects of helminth infections on academic performance [29], [30]. These interactions may also explain some of the controversial findings in the literature as treatment benefits may be more profound in certain sub-groups, which, if not explored, may lead to different interpretations.
To put into context the improvements observed in this study with declines in various individual species or polyparasitic STH infections, and to evaluate their public health relevance, we compared our estimates to differences in cognitive test performance for children exposed to well known risk factors of cognitive impairment in children – including malaria [31] and fetal alcohol exposure [32]. Our estimated improvements in WRAML learning tests scores associated with single and polyparasitic STH intensity declines over the 18 months of this study is 6.3–7.8 and 7.1–8.9 times the improvement observed when African children without a history of hospitalization for severe or cerebral malaria were compared to children with severe or cerebral malaria infection using a different battery of tests [31]. Likewise, the WRAML verbal memory score differential for S. japonicum cured vs. never cured children is approximately 3.7 times the difference in performance reported 6–10 years later for a cohort of American children free of prenatal alcohol exposure relative to children exposed to these substances in-utero using the same tests [32]. Hence we conclude that the cognitive improvements noted with infection decline here are at least comparable to those associated with other well known important determinants of pediatric cognitive impairment and are therefore likely to be of clinical and public health relevance.
S. japonicum, A. lumbricoides, T. trichiura, and polyparasitic STH infections may impair children's performance in cognitive tests through adverse effects on iron and nutritional deficiencies associated with the presence of these parasites [3]. In addition, cytokines made in response to infection, particularly S. japonicum [33]–[36], which lives in the bloodstream, may have direct adverse effects on cognitive processing. Interferon gamma (IFN-gamma) and TNF-alpha, are thought to mediate “sickness behavior” [37], which refers to the behavioral, neurological, and cognitive alterations described in various infectious and inflammatory disease states [38]. Human studies have specifically related elevated levels of TNF-alpha and IFN-gamma to dysfunction in the memory domain [39], [40] and other work in this cohort suggests that anemia of inflammation may be an important contributor to congnitive impairment [41].
Given our observational study design, we cannot exclude residual confounding by unmeasured covariates as an alternative explanation for our findings. By comparing children present at 18-months with those present at baseline on key factors, children scoring in the highest tertile of WRAML verbal memory at baseline and girls were over-represented among those lost to follow-up; however, there was no difference in average hemoglobin, SES, baseline STH intensity and average scores in WRAML learning, PNIT, and VF. In addition, the Kato-Katz relative to other helminth diagnostic methods has been reported to be of lower sensitivity for detecting helminth eggs particularly for individuals with light infections [42] and those with concurrent multi-species infections [23]. We expect that our duplicate assessment of three separate stool samples for each child would have improved the accuracy of helminth diagnosis in this study; however, we are unable to rule out the possible impact of limited sensitivity for lightly infected children.
To our knowledge, this is the first longitudinal study to investigate the independent effect of schistosome and individual STH infections as well as that of polyparasitic STH infection decline on learning domains of cognitive function, which may better reflect children's ability to take advantage of limited educational opportunities. The prospective study design, control for coincident helminth infections and numerous other confounders, and the explicit exploration of baseline infection, anemia and nutritional statuses as potential mediators of observed associations are additional strengths of this study.
We observed notable fluctuations in T. trichiura and A. lumbricoides intensity in this study even though only S. japonicum infection was treated at enrolment. Praziquantel, however, has been shown to have some anti-hookworm activity [22]. Unlike prior investigations of this question, our analytic strategy highlights the cognitive performance deficits associated with S. japonicum rapid reinfection following treatment as well as the cognitive benefits of natural declines in STH infections among school-aged children. By modeling the relationship between helminth infections and cognitive testscores from the interval of least infection following S. japonicum treatment, we highlight the cognitive test performance advantage of sustained low level single and polyparasitic helminth infections that is derivable in the presence of systematic frequent deworming programs. This relationship may be blunted or lost in an environment characterized by infrequent deworming and high helminth reinfection pressure. Findings from this design and analytic strategy may be more generalizable to the actual implementation of deworming programs than randomized trials.
We conclude that declines in the burden of some helminth species and polyparasitic STH infections have beneficial long-term impacts on children's cognitive performance. Our results highlight the benefit of combined control for S. japonicum and STH infections; it further stresses the importance of sustained deworming for improving the learning, memory, and educational attainment of children in helminth-endemic settings. The benefit of combined treatment for these infections notwithstanding, deworming is only a necessary first step in the implementation of a comprehensive integrated helminth control program, which must be tailored to a given endemic setting and include provision of clean water and improved sanitation to mitigate the fundamental causes of these infections and their associated adverse health effects among the most vulnerable populations [43], [44].
|
10.1371/journal.pntd.0006786 | Genetic diversity, infection prevalence, and possible transmission routes of Bartonella spp. in vampire bats | Bartonella spp. are globally distributed bacteria that cause endocarditis in humans and domestic animals. Recent work has suggested bats as zoonotic reservoirs of some human Bartonella infections; however, the ecological and spatiotemporal patterns of infection in bats remain largely unknown. Here we studied the genetic diversity, prevalence of infection across seasons and years, individual risk factors, and possible transmission routes of Bartonella in populations of common vampire bats (Desmodus rotundus) in Peru and Belize, for which high infection prevalence has previously been reported. Phylogenetic analysis of the gltA gene for a subset of PCR-positive blood samples revealed sequences that were related to Bartonella described from vampire bats from Mexico, other Neotropical bat species, and streblid bat flies. Sequences associated with vampire bats clustered significantly by country but commonly spanned Central and South America, implying limited spatial structure. Stable and nonzero Bartonella prevalence between years supported endemic transmission in all sites. The odds of Bartonella infection for individual bats was unrelated to the intensity of bat flies ectoparasitism, but nearly all infected bats were infested, which precluded conclusive assessment of support for vector-borne transmission. While metagenomic sequencing found no strong evidence of Bartonella DNA in pooled bat saliva and fecal samples, we detected PCR positivity in individual saliva and feces, suggesting the potential for bacterial transmission through both direct contact (i.e., biting) and environmental (i.e., fecal) exposures. Further investigating the relative contributions of direct contact, environmental, and vector-borne transmission for bat Bartonella is an important next step to predict infection dynamics within bats and the risks of human and livestock exposures.
| Bartonella are globally distributed bacteria that can cause endocarditis in humans and domestic animals. Bats have been implicated as a likely reservoir host for these bacteria, but little is known about how prevalence varies over time, routes of transmission, and the genetic diversity of Bartonella in bats. We present results from a two-year, spatially replicated study of common vampire bats, which have previously shown high infection prevalence of Bartonella and could pose risks of cross-species transmission due to their diet of mammal blood. We found that vampire bat Bartonella is genetically diverse, geographically widespread and endemic, and that individual-level infection risk is highest for large, male, non-reproductive bats. Phylogenetic analysis supported vector-borne transmission, and we found support for potential transmission through direct contact and fecal exposures through PCR. Confirming whether arthropod vectors are the main route of transmission for bat Bartonella is needed for understanding infection dynamics in bats and for predicting risks of cross-species transmission to humans and livestock.
| Bats (Order: Chiroptera) serve as reservoir hosts for viruses of concern for human and animal health [1,2] including SARS coronavirus, rabies virus, filoviruses, and henipaviruses [3–6]. Bats can also harbor protozoa and bacteria of potential zoonotic relevance [7–9]. Bartonella spp. are of particular interest, as these Gram-negative bacteria cause bacteremia and endocarditis in both humans and livestock [10,11] and exhibit high genetic diversity in bats across multiple continents and species [12–17]. Moreover, phylogenetic analyses show bats are reservoirs of zoonotic Candidatus B. mayotimonensis [18–20], a causative agent of human endocarditis [21].
Given the zoonotic potential of bat-associated Bartonella, understanding transmission within bats is critical for understanding how Bartonella persists in bat populations and for assessing spillover risks [22,23]. Ectoparasites are frequently invoked as a transmission route [12,19,24], in part because vector-borne transmission occurs in other taxa [25,26] and because Bartonella has been identified in bat flies and ticks [27–29]. While some bat ticks can feed on humans [30], the high host specificity of bat flies [31,32] could limit opportunities for cross-species transmission through ectoparasites [31–33]. Transmission through close contact (e.g., biting) could occur given detection of Bartonella in dog and cat saliva [34,35] as well as human infection following scratches from dogs and cats [36]. Phylogenetic patterns of weak Bartonella host specificity in Neotropical bat communities could not only reflect transmission through close contacts between species in multi-species roosts, but could also stem from transmission through generalist vectors [15,24,37]. Bartonella might also be transmitted through exposure to feces between bats and to humans that enter roosts or to domestic animals exposed to bat feces [18,38].
In addition to the potential risks of cross-species transmission from bats to livestock and humans, the infection dynamics of Bartonella in bats are also uncertain. In rodents, Bartonella prevalence varies through time [39,40], but such patterns have not been well studied in bats [41]. Individual heterogeneities in infection by age and sex could also inform exposure patterns. Finally, global analyses suggest geographic structure in bat Bartonella genotypes, with notable differences in genotypes from Latin American and those from Africa, Europe, and Asia [42]. However, as such patterns appear driven by bat families restricted to different continents, analyses within narrower geographic and taxonomic ranges could inform the scale of Bartonella transmission and the role that dispersal plays in the spatial dynamics of this infection [43].
Common vampire bats (Desmodus rotundus) have high prevalence of Bartonella throughout their large geographic range in Latin America [15,16,24,44]. Vampire bats are of particular concern because they subsist on blood, which could create opportunities for Bartonella transmission to humans and livestock either from bites during blood feeding or through vector sharing facilitated by close proximity [45–48]. Here, we capitalize on a two-year, spatially replicated study of vampire bats to examine the genetic diversity and infection prevalence of Bartonella, including its geographic structure across the vampire bat range as well as individual and temporal correlates of infection status. To explore possible transmission routes of this bacterial pathogen, we also test for associations between bat fly infestation and Bartonella infection status, which would support vector-borne transmission, and by screening bat saliva and fecal samples for evidence of Bartonella DNA, which would support transmission through bites or grooming and environmental exposure to bacteria shed in feces, respectively.
Samples were collected as described in Becker et al. [49] in 2015 and 2016 across seven sites in Peru (Departments of Amazonas [AM], Cajamarca [CA], and Loreto [LR]) and two sites in Belize (Orange Walk [OW] District). We sampled sites one to two times annually, capturing one to 17 individuals per site and sampling interval (S1 Table). To screen for Bartonella by PCR, up to 30 μL blood was stored on Whatman FTA cards at room temperature. To assess the presence of Bartonella in saliva and feces, we collected oral and rectal swabs from vampire bats in Peru. Swabs were preserved in 2 mL RNAlater (Invitrogen) at –80°C until laboratory analyses. For Peru sites sampled in 2016, we also recorded the number of bat flies per vampire bat [32].
Field procedures were approved by the University of Georgia Animal Care and Use Committee (A2014 04-016-Y3-A5) and the University of Glasgow School of Medical Veterinary and Life Sciences Research Ethics Committee (Ref08a/15); all procedures were conducted in accordance with accepted guidelines for humane wildlife research as outlined by the American Society of Mammalogists [50]. Bat capture, sample collection, and exportation were authorized by the Belize Forest Department under permits CD/60/3/15(21) and WL/1/1/16(17) and by the Peruvian Government under permits RD-009-2015-SERFOR-DGGSPFFS, RD-264-2015-SERFOR-DGGSPFFS, and RD-142-2015-SERFOR-DGGSPFFS. Access to genetic resources from Peru was granted under permit RD-054-2016-SERFOR-DGGSPFFS.
We analyzed samples that were previously screened for the presence of Bartonella by Becker et al. [49] using nested PCR to amplify a region of the citrate synthase gene (gltA) [51]. Among the Bartonella-positive samples, we randomly selected 5–10 positive samples per site for Sanger sequencing (n = 51). PCR products were purified with DNA Clean & Concentrator Kits (Zymo Research) and sequenced in both directions at the Georgia Genomics Facility. Resulting chromatograms were checked for quality and trimmed using Geneious (Biomatters) [52]. Post-trimmed forward and reverse sequences were assembled to create 348 base pair (bp) consensus sequences for each sample (n = 35; the quality of 16 chromatograms was too low). Sequences were considered part of the same genotype if they had >96% identity in gltA, an established cut-off for Bartonella species identification [53]. Sequences with >99.7% similarity were considered the same genetic variant [54]. We used a Chi-squared test with the p value generated via a Monte Carlo procedure with 1000 simulations [55] to assess whether our defined Bartonella genotypes were associated with region (i.e., Belize, eastern Peruvian Amazon, western Peruvian Amazon).
Two datasets were created for phylogenetic analyses. Dataset 1 was designed to assess the spatial structure of vampire bat–associated Bartonella across Latin America and therefore included our new sequences plus all previously reported gltA sequences from Desmodus rotundus. Dataset 2 was designed to capture the relatedness of the new sequences to all previously described Bartonella spp. regardless of isolation source, which comprised sequences generated in this study plus sequences obtained by conducting a BLAST search of each new sequence against GenBank, selecting the top 10 hits, and removing duplicates. For both datasets, consensus sequences were aligned using MAFFT. Phylogenetic analyses were carried out in MrBayes using the GTR+gamma model suggested by jModeltest2 [56]. For dataset 1 (Desmodus-associated sequences), we fit a codon partitioned substitution model by linking rates in codon positions 1 and 2 separately from codon position 3. For dataset 2, we used a simpler non-partitioned model because the more complex codon-partitioned model failed to converge. Dataset 2 included one sequence from Brucella abortus (Genbank Locus: MIJI01000003.1) as an outgroup [13]. Both datasets were run for 2.5 million generations with convergence checked and burn-in periods selected by assessing posterior traces in Tracer [57]. With dataset 1, we analyzed spatial clustering of vampire bat Bartonella by country (Belize, Guatemala, Mexico, Peru) using Bayesian Tip Association Significance Testing (BaTS) [58]. We here selected 1,000 trees from the posterior distribution of the MrBayes run and compared the country-level clustering to a null distribution from 10,000 trees with swapped tip associations [58].
We analyzed 193 samples from Desmodus rotundus to test whether temporal variation (season and year) and individual risk factors (e.g., age, sex) explain differences in Bartonella infection, using generalized mixed effects models (GLMMs) with binomial errors and a logit link fit with the lme4 package in R [59,60]. We fit a single GLMM with an interaction between site and year to first test if prevalence varied over years across sampling locations; we excluded one site from this analysis (i.e., LR6) owing to sampling in only 2015. We included bat identification number (ID) as a random effect to account for multiple sampling of a small number of bats (n = 6). To assess seasonality in infection, we fit a separate GLMM with season (spring, summer, fall) as a predictor to data from two sites in Peru (AM1 and CA1) sampled across seasons (n = 63). We also fit a generalized additive model (GAM) with restricted maximum likelihood, binomial response, and a cyclic cubic regression spline for Julian date using the mgcv package [61]. We randomly selected repeatedly sampled bats, as including bat ID as a random effect here overfit the GAM.
To identify individual risk factors for Bartonella infection, we fit a single GLMM with bat age, forearm size, sex, and reproductive status; we also included interactions between sex and reproduction, sex and age, sex and forearm size, and reproduction and forearm size. We included categorical livestock biomass as a predictor in the GLMM to control for a previously observed negative association with Bartonella infection (121/173 positive bats) [49]. We fit this GLMM to a reduced dataset free of missing values (n = 189), included bat ID nested within site as a random effect, and calculated marginal R2 (R2m) to assess model fit [62]. Finally, for a data subset (n = 40 bats sampled in Peru in 2016), we fit two separate GLMs with bat fly intensity and presence as predictors to test whether ectoparasites explained Bartonella infection status. We fit a separate GLM with quasi-Poisson errors to test for sex and age differences in bat fly intensity.
To examine possible transmission of Bartonella through biting, grooming, blood sharing, or fecal–oral exposure, we used metagenomic data from a parallel study to screen vampire bat saliva and fecal samples for Bartonella DNA. Three saliva and three fecal pools were shotgun sequenced, each containing nucleic acid extractions from swabs collected from ten vampire bats from one to two colonies. Pooled samples contained individuals from the same colonies of bats tested for Bartonella in blood through PCR, though not necessarily the same individuals.
As described previously [8], total nucleic acid was extracted from swabs and pooled equally according to RNA concentration. Pooled samples were DNAse treated and ribosomal RNA depleted, then cDNA synthesis was performed. Libraries were prepared using a KAPA DNA Library Preparation Kit for Illumina (KAPA Biosystems) modified for low input samples, and were individually barcoded during the PCR reamplification step [10]. The libraries included in this study were combined in equimolar ratios with other metagenomic libraries for sequencing on an Illumina NextSeq500 at the University of Glasgow Centre for Virus Research.
Reads were demultiplexed according to barcode and quality filtered using TrimGalore [63,64] with a quality threshold of 25, minimum read length of 75 bp, and clipping the first 14 bp of the read. Low complexity reads were filtered out using the DUST method and PCR duplicates removed using PRINSEQ [65]. We screened cleaned reads for the Bartonella genotypes detected in this study using nucleotide BLAST [66] against a custom database composed of the PCR-generated Bartonella sequences from this study, retaining only the best alignment (the high-scoring segment pair with the lowest e-value) for a single query–subject pair. To investigate the presence of Bartonella species other than genotypes detected in blood samples from vampire bats, cleaned reads were de novo assembled into contigs using the assembly only function of SPAdes [67]. Individual reads and contigs were screened for sequences matching Bartonella using protein alignment in Diamond [68], and close matches at the protein level were further characterized by nucleotide BLAST against the Genbank nt database. As the gltA gene is not highly transcribed, we also tested sequences for matches to Bartonella DNA-directed RNA polymerase subunit B (rpoB). We selected two rpoB sequences (Genbank accessions KY629892 and KY629911) from a study of vampire bat Bartonella [16] for which the same individuals exhibited 100% identity in the gltA gene to our blood sequences, and we used Bowtie2 to map quality filtered reads and contigs to those sequences [69].
Lastly, because nucleic acid pools were DNase treated for metagenomic sequencing, potentially reducing detection sensitivity, we used the same nested PCR protocol as used for blood-derived DNA [51] to test for the presence of gltA in DNA from individual saliva and fecal swab samples that made up metagenomic pools (n = 58; 28 saliva and 30 feces). As with our blood samples, we randomly selected a subset of positive amplicons for Sanger sequencing.
Bartonella prevalence across the 193 vampire bats included in this study was 67%. Our phylogenetic analysis of 35 vampire bat Bartonella sequences showed 78.8–100% pairwise identity in gltA and revealed at least 11 paraphyletic genotypes (S2 Table). BaTS analysis of all Desmodus-associated Bartonella showed significant phylogenetic clustering by country (association index = 3.81, parsimony score = 31.51, p<0.001), although most vampire bat Bartonella genotypes were still widely distributed (Fig 1). For the 11 genotypes delineated from our 35 sequences, we observed no association with the geographic study region (χ2 = 23.3, p = 0.27). Genotypes 1 and 2 were detected across all regions, and genotypes 7–10 were detected within both Belize and Peru, highlighting the broad distribution of vampire bat Bartonella genotypes (Fig 2); however, genotype 3 was unique to both regions of Peru, genotypes 4–6 were unique to the western Peruvian Amazon, and genotype 11 was only detected in Belize.
We also assessed the phylogenetic position of our vampire bat Bartonella sequences among known Bartonella genotypes (Fig 3, S3 Table). Half of our Peruvian and Belizian sequences (18/35) were nearly identical (>99.7% identity) to Bartonella from common vampire bats (Desmodus rotundus) from Mexico (e.g., GenBank accession numbers KY629837 and MF467803), again confirming the wide geographic distribution of these genotypes. Other sequences(9/35) fell within the same clade (>96% pairwise identity) as Bartonella from bat flies (Strebla diaemi) in Panama (JX416251), from Parnell's mustached bat (Pteronotus parnellii) in Mexico (e.g., KY629828), from phytophagous bats in Peru (e.g., Carollia perspicillata; JQ071384) and Guatemala (e.g., Glossophaga soricina; HM597202), or from Mexican vampire bats as noted above. Eight sequences were novel (<96% identity to GenBank sequences) but were most similar to Bartonella from phytophagous bats in Costa Rica (e.g., 90–93% to KJ816666 [Anoura geoffroyi]) and from Mexican vampire bats (e.g., 93% to MF467776).
Other novel sequences were weakly related to B. bovis from livestock in Israel and Malaysia (e.g., 89–90% to KJ909844 and KR733183), to B. chomelii from cattle in Spain (e.g., 89% to KM215693), to B. capreoli from elk in the United States (e.g., 89% to HM167503), and to B. schoenbuchensis from roe deer in Germany (e.g., 89% to AJ278186); indeed, posterior support for a bat–ruminant clade was low (<50%; Fig 3). Our BLAST procedure also identified weakly related Bartonella from rodents (e.g., 90% to Rattus norvegicus from the United States [KC763951] and 92% to Apodemus agrarius from China [KX549996]) and from carnivores (e.g., 89% to Procyon lotor from the United States [CP019786]). However, these livestock, rodent, and carnivore sequences formed separate phylogenetic clades from bat- and bat fly–derived Bartonella sequences (Fig 3). Despite the geographic proximity of our field sites to Brazil, our BLAST procedure found no Bartonella seqeunces similar to those recently described in Brazilian bat or rodent species [70–72]. An additional phylogenetic tree that includes these recently identified Bartonella is provided in S1 Fig.
Bartonella was detected by PCR in all nine sites in each year, with prevalence ranging from 30–100% (Fig 4). Prevalence did not differ by year across all sites (χ2 = 3.13, p = 0.54) nor within individual sites (site*year; χ2 = 2.82, p = 0.90). The seasonality GLMM for the western Peruvian Amazon (n = 63) showed no difference in odds of infection between spring, summer, and fall (χ2 = 1.99, p = 0.37; S2 Fig). The GAM also showed no significant seasonal variation (χ2 = 0, p = 0.68; S2 Fig). Recaptures were rare (n = 6) but showed changes in infection from negative to positive (n = 2, 68–424 days) and from positive to negative (n = 2, 15–369 days; S3 Fig).
After controlling for site-level livestock biomass, vampire bat sex and forearm size were the strongest predictors of infection (Fig 5); no interactions were significant (all χ2≤1.18, p≥0.28) and were dropped from the final GLMM (R2m = 0.28). The odds of Bartonella infection were highest for vampire bats with larger forearms (OR = 1.2, p<0.001) and for males (OR = 5.41, p<0.01), were marginally higher for non-reproductive individuals (OR = 2.36, p = 0.10), and did not differ between subadult and adult bats (OR = 1.58, p = 0.38); our sample did not contain juveniles. Individual bat fly intensities were highly variable (0–28, median = 7.5) and showed overdispersion (ϕ = 5.08 in an intercept-only quasi-Poisson GLM). The bat fly GLMs showed that neither ectoparasite intensity (OR = 0.98, p = 0.81) nor ectoparasite presence (χ2 = 1.13, p = 0.29) were associated with Bartonella infection status. We note that the majority of infected bats in this sample were infested with at least one bat fly (31/36), limiting conclusive assessment of the ectoparasite–infection relationship. Our multivariable quasi-Poisson GLM showed that ectoparasite load did not vary by bat sex (χ2 = 0.86, p = 0.35) or bat age (χ2 = 0.09, p = 0.77).
There were no matches in any of the screened saliva and fecal metagenomic pools to the Bartonella gltA sequences detected in the blood or to previously published Bartonella rpoB sequences. The saliva pool from Amazonas had no matching Bartonella-like reads or contigs (S4 Table), while one read each from the Loreto and Cajamarca saliva pool was assigned as Bartonella by nucleotide BLAST; however, these reported species assignments should be interpreted cautiously as they are based on one read and percent identity was low. Pooled fecal samples from all departments of Peru contained Bartonella-like reads and contigs. Bartonella ancashensis, B. australis, and B. bacilliformis were all identified at both the read and contig level in fecal samples. However, because percent identity was relatively low, species assignments should again be interpreted cautiously. Subsequent BLAST hits following the top hit also frequently (though not always) matched to Bartonella, suggesting the presence of poorly characterized Bartonella species present or that these may be matches to other bacteria.
In contrast, nested PCR of individual swabs detected gltA in 21.4% of saliva samples (6/28) and 30% of fecal samples (9/30). For swab samples that were also assessed by PCR in blood (n = 15 for saliva, n = 28 for feces), both corresponding positive saliva samples were positive in blood; most positive fecal samples were also PCR positive in blood, although one fecal-positive sample was PCR negative in blood (S4 Fig). For our random subset of sequenced positive saliva (n = 4) and fecal (n = 5) samples, phylogenetic analyses suggested that all sequences shared a minimum of 97% identity to one or more of our 35 blood-derived Bartonella sequences (S5 Table, Fig 6). In many cases, saliva and fecal sequences were the same genotype as blood sequences derived from the same geographic region (e.g., the saliva sequence from D234 shared >96% identity to the blood sequence from D98, both from AM1). For the one case in which we sequenced positive samples from the same individual bat (i.e., D203), both the blood sequence and fecal sequence shared 100% identity (S5 Table, Fig 6). For the few sequences at the lower range of our similarity spectrum, BLAST still demonstrated that the closest relatives were all derived from vampire bats (i.e., 8368 from CA1 was identical to MF467797 from Mexico).
Despite an increasing focus on Bartonella genetic diversity and prevalence in bat communities, individual risk factors and transmission routes of this pathogen in bats remain largely unknown. For example, a survey of vampire bats within Guatemala found neither geographic, dietary, demographic, or viral coinfection correlates of Bartonella infection status [44]. Using a larger sample across a more diverse range of study sites and timepoints, we here show that Bartonella is genetically diverse, geographically widespread and endemic within vampire bat populations, and that individual-level odds of infection are highest for large, male, and non-reproductive bats. Furthermore, we use several approaches to suggest vector-borne transmission to be likely in addition to possible direct contact and environmental sources of Bartonella exposure in bats.
The Bartonella genotypes we identified were paraphyletic and closely related to those from other vampire bat populations, other Neotropical bat species, or bat flies. Although BLAST also identified Bartonella spp. sequenced from rodents, carnivores, and livestock within our hit selection criteria, these consistently formed separate phylogenetic clades that did not contain bat- or bat fly–derived Bartonella (Fig 3). These phylogenetic patterns indicate that Bartonella has commonly shifted between bat host species in the Americas but do not support frequent transmission between bats and other host groups. Our BaTS analysis also showed that vampire bat Bartonella sequences clustered by country more than expected by chance. However, given that several Bartonella genotypes were present in vampire bats from both Central and South America, we suspect this clustering mostly resulted from variation in locally abundant genotypes rather than true barriers to the spread of Bartonella. Because vampire bats are largely sedentary and non-migratory [45], dispersal of these Bartonella genotypes across large distances is unlikely to be attributable to bat movement alone. Bartonella genotypes may also have infected vampire bats over long evolutionary timescales, and thus the biogeography of the pathogen may have followed that of its host. Alternatively, Bartonella dispersal by other arthropod vectors (e.g., ticks) or other bat species that share Bartonella genotypes with vampire bats may be conceivable and could be resolved by further field surveys combined with population genetic analyses of alternative bat host species, arthropod vector species, and Bartonella genotypes.
Few studies have examined temporal patterns of bat Bartonella, emphasizing the general need for more longitudinal studies to understand how pathogens persist in bat populations [1,6]. Here, Bartonella was detected at relatively high prevalence across both study years within each sampling site, and neither year nor its interaction with site were predictive within our analyses. Similarly, no temporal patterns in Bartonella were observed for a limited sample of Myotis mystacinus, Pipistrellus spp., Myotis daubentonii, and Nyctalus noctula in the United Kingdom [41]. Such findings contrast with highly seasonal Bartonella infections in rodents, which show high prevalence in summer and fall due to seasonality in birth and ectoparasite intensity [39,40]. The lack of seasonality in our western Peruvian Amazon sample in particular could simply be due to low statistical power; alternatively, no seasonality in infection could also be explained by the non-seasonal or less-pronounced birth pulses observed for vampire bats (but see [73]). While high Bartonella prevalence in bats has been proposed to stem from persistent infection [15], this seems unlikely, as we observed possible clearance of infection in some recaptured bats. While this could also reflect bacteria DNA loads too low to be detected by PCR, infection risk did not increase with age, as would also be expected if bats could not clear infection [74]. However, we do note that our sample only contained adult (n = 162) and subadult (n = 28) bats, limiting more robust tests of age-dependent infection. Alternatively, Bartonella infections could be chronic and vary in infection intensity over time or could become latent (i.e., be undetectable in erythrocytes but persist in endothelial cells), particularly as infection does not appear to confer long-term immunity [36]. Such explanations could be confronted in future work with larger sample sizes of recaptured bats, multiple assessments of infection status over time, and quantitative PCR.
Bat forearm size, sex, and reproductive status were important predictors of Bartonella infection status, with odds of infection being higher in larger, male, and non-reproductive bats. While subadult status itself was not an important predictor of Bartonella infection, these findings could suggest higher risk in young male bats that are relatively large for their age. Our previous work has shown stronger innate immune defense (i.e., bacterial killing ability) in reproductive (mostly male) vampire bats, also suggesting greater susceptibility of non-reproductive hosts [49]. Similarly, subadults across a Mexican bat community also had higher odds of Bartonella infection [16], and young male vampire bats play key roles in the long-distance dispersal of rabies virus [43] and display higher rates of rabies exposure, possibly owing to more direct contacts during the first year of life [75]. Larger forearm size could also relate to direct contact if larger bats are more dominant and aggressive, as found in other phyllostomid bat species [76].
Although vector-borne transmission is generally assumed for Bartonella in other hosts [12,19,25,77], including some Neotropical bats [16,78], infection status in vampire bats was not associated with bat fly intensity. Further supporting this observation, male bats had higher odds of Bartonella infection but did not differ in their bat fly intensities compared to females. Weak correspondence between bat fly intensity at the time of sampling and Bartonella infection thus may cast doubt on bat flies as a primary transmission route. Time lags could provide one reason for this discrepancy, given that new Bartonella infections may take days or weeks to develop and become detectable and over which time ectoparasite load may have changed due to the mobile nature of bat flies [26,36,79]. On the other hand, it is possible that vector presence (rather than abundance) is a more important driver of transmission. Unfortunately, nearly all bats in this study had ectoparasites, so comparisons of Bartonella presence in bats with and without bat flies had little statistical power (31/36 Bartonella-positive bats were infested with at least one bat fly). Given that ectoparasitism predicted Bartonella infection more generally across a Mexian bat community [16], larger sample sizes with greater variation in bat fly intensity could provide better inference. However, our phylogenetic analysis does provide a tentative line of evidence supporting vector-borne transmission, as several of Bartonella genotypes fell within the same clade as Bartonella from streblid bat flies [29]. A recent survey of Mexican bats and their sympatric bat flies suggested that corresponding hosts and their bat flies had varied Bartonella genotypes, although one vampire bat did show complete sequence homology with the Bartonella from its paired bat flies [37]. As genetic similarity between Bartonella in bat flies and hosts has been interpreted as evidence of vector-borne transmission in other bat species [29,54], further assessments of Bartonella genotypes between vampire bats and their various ectoparasites (bat flies but also ticks) would shed additional light on possible routes of vector-borne transmission.
Lastly, we analyzed bat saliva and feces using metagenomics and PCR to explore alternative transmission routes, namely through close contact and fecal exposure. Metagenomics detected no Bartonella DNA matching to gltA or rpoB in either saliva or fecal pools. This absence could be explained in that the short sequences (345–425 bp) used as targets, and the large size of bacterial genomes together make the likelihood of detecting a specific gene low. However, the Bartonella-like reads and contigs recovered from saliva and feces were short fragments (51–258 bp) and showed low homology to known Bartonella from GenBank (S4 Table). Notably, we used a similar approach to search for other bacteria (i.e., hemoplasmas) and found clear evidence of their presence [8]. While this could suggest true absence of Bartonella from bat saliva and feces, our PCR found Bartonella in a subset of individual saliva and fecal samples. This discrepancy between methods could stem from treating saliva and fecal pools with DNase before metagenomic sequencing. Furthermore, phylogenetic analyses confirmed that these sequences were closely related to those identified in blood, which argues against these PCR positives only representing bacteria derived from environmental contamination or from feeding on prey. PCR results further showed strong correspondence between blood and saliva, suggesting that Bartonella infection may be systemic in vampire bats. While fecal and blood PCR results also mostly matched, we found one case where a bat was negative in blood but positive in feces. As consumption of ectoparasites during grooming has been observed in other bat families (e.g., Pteropodidae [80]), this discrepancy could suggest the incidental ingestion of ectoparasites during grooming and that this does not lead to systemic infection more generally indicated by the concordance between blood, saliva, and fecal positives and their close genetic similarity.
Similar prevalence of Bartonella in saliva and feces suggests that direct contact and environmental exposure could serve as complemenry transmission routes to arthropod vectors. The presence of Bartonella in saliva samples contrasts with previous work showing an absence of Bartonella in vampire bat saliva [44,81], providing evidence for possible direct transmission. Bartonella in fecal samples could also suggest environmental transmission between bats [18]. Both saliva-borne and fecal–oral transmission of vampire bat Bartonella could further pose potential risks to humans or livestock, either through bites during feeding or by environmental exposure of humans that enter roosts or to domestic animals exposed to bat feces [18,38,48,82]. For the former pathway, however, a recent survey of Bartonella in Mexican ruminants did not identify being bitten by vampire bats as a risk factor for infection [81], and our phylogenetic results provide relatively more support for the possibility of vector-borne transmission. Vector-borne transmission of vampire bat Bartonella might reduce their potential to infect humans or livestock, given the high host specificity of most bat flies [31,32]. However, ectoparasite transfer between individuals could still occur during pupal deposition and close contact [83], facilitating Bartonella transmission within vampire bat colonies and to other bat species. While our analyses of ectoparasitism only considered bat flies, we have observed heavy tick burdens of vampire bats in other field sites (e.g., Belize). Bartonella has been detected in ticks infesting other bats [28], and these ectoparasites could also be more likely to facilitate cross-species transmission [30]. Metagenomics also potentially identified Bartonella ancashensis and B. bacilliformis in vampire bat fecal samples, and these species cause notable infectious disease in humans likely through phlebotomine sand flies in Andean regions of Peru [84,85]. Controlled infection trials and more extensive phylogenetic analyses of Bartonella in vampire bats, their various ectoparasites, and sympatric prey are therefore needed to examine the contributions of different transmission routes for bacterial spread within vampire bats and to recipient prey and to confirm whether saliva and feces represent viable transmission routes. Given the high rates of bat bites and proximity to wildlife, humans, and domestic animals that define vampire bat ecology, such efforts to verify the possibility and frequency of oral and environmental exposures would elucidate Bartonella transmission dynamics in this common host species and the risks of cross-species transmission.
|
10.1371/journal.pbio.2003903 | Genome-wide maps of ribosomal occupancy provide insights into adaptive evolution and regulatory roles of uORFs during Drosophila development | Upstream open reading frames (uORFs) play important roles in regulating the main coding DNA sequences (CDSs) via translational repression. Despite their prevalence in the genomes, uORFs are overall discriminated against by natural selection. However, it remains unclear why in the genomes there are so many uORFs more conserved than expected under the assumption of neutral evolution. Here, we generated genome-wide maps of translational efficiency (TE) at the codon level throughout the life cycle of Drosophila melanogaster. We identified 35,735 uORFs that were expressed, and 32,224 (90.2%) of them showed evidence of ribosome occupancy during Drosophila development. The ribosome occupancy of uORFs is determined by genomic features, such as optimized sequence contexts around their start codons, a shorter distance to CDSs, and higher coding potentials. Our population genomic analysis suggests the segregating mutations that create or disrupt uORFs are overall deleterious in D. melanogaster. However, we found for the first time that many (68.3% of) newly fixed uORFs that are associated with ribosomes in D. melanogaster are driven by positive Darwinian selection. Our findings also suggest that uORFs play a vital role in controlling the translational program in Drosophila. Moreover, we found that many uORFs are transcribed or translated in a developmental stage-, sex-, or tissue-specific manner, suggesting that selective transcription or translation of uORFs could potentially modulate the TE of the downstream CDSs during Drosophila development.
| Upstream open reading frames (uORFs) in the 5′ untranslated regions (UTRs) of messenger RNAs can potentially inhibit translation of the downstream regions that encode proteins by sequestering protein-making machinery the ribosome. Moreover, mutations that destroy existing uORFs or create new ones are known to cause human disease. Although mutations that create new uORFs are generally deleterious and are selected against, many uORFs are evolutionarily conserved across eukaryotic species. To resolve this dilemma, we used extensive mRNA-Seq and ribosome profiling to generate high-resolution genome-wide maps of ribosome occupancy and translational efficiency (TE) during the life cycle of the fruit fly D. melanogaster. This allowed us to identify the sequence features of uORFs that influence their ability to associate with ribosomes. We demonstrate for the first time that the majority of the newly fixed uORFs in D. melanogaster, especially the translated ones, are under positive Darwinian selection. We also show that uORFs exert widespread repressive effects on the translation of the downstream protein-coding region. We find that many uORFs are transcribed or translated in a developmental stage-, sex-, or tissue-specific manner. Our results suggest that during Drosophila development, changes in the TE of uORFs, as well as the inclusion/exclusion of uORFs, are frequently exploited to inversely influence the translation of the downstream protein-coding regions. Our study provides novel insights into the molecular mechanisms and functional consequences of uORF-mediated regulation.
| Eukaryotic protein translation is highly regulated to ensure that proteins are produced from the coding DNA sequences (CDSs) in a controlled manner [1, 2]. In eukaryotic cap-dependent translation initiation, the 43S preinitiation complex (PIC) first binds near the 5′ cap of an mRNA, scans through the 5′ untranslated region (UTR), and associates with a 60S subunit to assemble into a ribosome to commence translation when the PIC encounters an AUG start codon [3]. Upstream open reading frames (uORFs), which are located in the 5′ UTRs and upstream of the AUG start codons of CDSs (cAUGs), are important in regulating translation initiations of CDSs [4–28]. When a PIC encounters a uORF, it either scans through or initiates translation of that uORF. Once initiating translation of a uORF, the PIC might drop off or stall at the stop codon of that uORF (both of which might trigger nonsense-mediated mRNA decay) [12, 15, 29]; alternatively, the PIC can reinitiate translation of the downstream CDS, and the reinitiation process reduces the translational rate (i.e., repress translation) of the CDS [11, 30–33]. The recently developed ribosome profiling (also known as Ribo-Seq) technique [13, 34] has further advanced our understanding of the regulatory roles of uORFs in translational regulation. Studies performed in yeasts, zebrafish, and mammals have systematically demonstrated how genomic features of uORFs, such as conservation levels and sequence contexts, affect the repressiveness of uORFs on the translation of CDSs [10, 13, 14, 20, 22, 34–39]. Overall, these studies have broadened our view of the genome-wide features of uORFs in modulating protein translation.
Although uORFs are prevalent in eukaryotic genomes [21, 40–42], the observed uORFs in the 5′ UTRs are significantly fewer than expected by chance in a wide range of species, presumably because new uORFs disturb normal protein translation and are hence selected against [41, 43–46]. On the other hand, the uORFs preserved in the genomes are usually evolutionarily more conserved than expected under the assumption of neutral evolution [10, 22, 43, 46, 47], suggesting those uORFs are maintained by functional constraints. The two different modes of purifying selection on uORFs are well manifested in human populations, in which both the mutations that create new uORFs or disrupt preexisting uORFs can cause diseases [15, 19, 21, 48, 49]. For example, a point mutation introducing a uORF in the 5′ UTR of CDKN2A decreases cyclin-dependent kinase inhibitor 2A (CDKN2A) protein level and causes melanoma [50, 51]. Similarly, creating a new uORF by a point mutation in SRY reduces translation of SRY mRNA and leads to gonadal dysgenesis [52]. On the other hand, eliminating a uORF in THPO mRNA increases translation of the downstream CDS and causes thrombocythemia [53]. In summary, these seemingly contradictory observations suggest further studies are needed to understand the evolutionary forces that have shaped uORFs at a genome-wide level.
If most new uORFs are deleterious and selected against, why are there so many uORFs maintained in the genomes by natural selection during evolution? Population genetics suggests that slightly deleterious mutations can be fixed due to genetic drift [54, 55]. Hence, many uORFs might be neutral (or slightly deleterious) but drift to fixation [56], given the weak repressive effects performed by uORFs on CDSs [10, 16, 21, 22]. However, if this hypothesis is correct, it is hard to explain why most uORFs are preserved by natural selection during evolution. Previous studies have proposed uORFs might serve adaptive functions by fine-tuning cellular or developmental processes [15, 27, 30, 34, 57–60]. Nevertheless, the evolutionary genetic evidence to support the adaptive evolution of uORFs is currently lacking. Therefore, many unaddressed gaps remain in our understanding of the evolutionary principles of uORFs. Since the functional uORFs might experience distinct evolutionary forces compared to the random (neutral) uORFs in the 5′ UTRs, to address these questions, we have to combine evolutionary analysis and the functional genomic studies.
In this study, we constructed high-resolution genome-wide maps of uORF ribosome occupancy in the major developmental stages of Drosophila melanogaster with extensive mRNA-Seq and Ribo-Seq experiments. These data, together with an analysis of the genomic features and evolutionary patterns, enable us to discover that many of the newly fixed uORFs in D. melanogaster are driven by positive selection, especially for those associated with ribosomes. We also present evidence that ribosome-associated uORFs exert widespread inhibitory effects on CDSs and modulate translation during Drosophila development.
We annotated the canonical uORFs (beginning with AUG and ending with a stop codon UAA/UAG/UGA) in the protein-coding genes of D. melanogaster and identified 37,619 candidate uORFs (with a median length of 39 nt) that were supported by the published mRNA-Seq and cap analysis of gene expression (CAGE)-Seq data from the modENCODE project [61–63] (S1 and S2 Tables; Materials and methods). Of these uORFs, 29,624 were detected if we only considered the longest transcript of each gene, significantly lower (P < 0.001) than the number of uORFs obtained by randomly shuffling the 5′ UTR sequences (the mean is 51,942 [95% CI 51,642–52,241], Materials and methods). This comparison is consistent with previous results that the uORFs are overall deleterious and selected against [41, 43–46]. To investigate how many uORFs show evidence of ribosome occupancy, we carried out mRNA-Seq (measuring mRNA abundances) and Ribo-Seq (measuring abundances of ribosome-protected mRNA fragments [RPFs]) experiments using samples from the ISO-1 strain of D. melanogaster at the following stages: embryos at 0–2 h, 2–6 h, 6–12 h, and 12–24 h old; third-instar larvae; stage P7–8 pupae; female heads; male heads; adult female bodies (heads removed); male bodies; and Drosophila S2 cells (Materials and methods). The Ribo-Seq procedures were performed following Dunn and colleagues [64], with modifications (see S1 Text, S3 Table, and S1 Fig for detailed information). To cover more developmental stages, we also analyzed the mRNA-Seq and Ribo-Seq data of mature oocytes of D. melanogaster generated in a previous study [65] (Table 1).
We mapped the RPFs (27–34 nt in length) to the reference genome, assigned each RPF read to its P-site (corresponding to the second binding site for a tRNA in the ribosome) as previously described [64], and calculated the density of RPFs (reads per kilobase of transcript per million mapped reads [RPKM]) for each feature (CDS or uORF, see Materials and methods). When the P-site of an RPF was located in multiple overlapping uORFs, it was assigned to all the overlapping uORFs, as previously described [10, 22]. For uORFs that were overlapping with CDSs, only the nonoverlapping regions of uORFs were used in calculating RPKM. We also mapped the mRNA-Seq reads and calculated the RPKM value for each feature (CDS or uORF) in each library (Materials and methods). We obtained 1,077 million reads in total (see Table 1 for sequencing summary and S4 Table for mapping statistics). We performed two biological replicates for both female bodies and male bodies and observed high correlations between the replicates (Pearson’s r2 > 0.972 and P < 10−307 in both mRNA-Seq and Ribo-Seq results of CDSs, S2A Fig), suggesting the high reproducibility of our experimental procedures. Statistically significant but lower r2 (ranging from 0.636 to 0.760) between the biological replicates was observed for uORFs in both the mRNA-Seq and Ribo-Seq experiments, presumably due to the larger sampling variance caused by the shorter length of uORFs than CDSs (S2B Fig). Indeed, if we calculated the RPKM in the 5′ region of each CDS with the same length of a uORF, we obtained r2 values comparable to those for the uORFs (S2C Fig). Moreover, when we modeled the read count Kij for a feature (CDS or uORF) i in biological replicate j (j = 1 or 2) as following a negative binomial distribution with mean μij and dispersion φi (the variability between replicates) as previously described [66–70] (Materials and methods), we found the estimated φi values of uORFs are significantly higher than those of the CDSs for both mRNA-Seq and Ribo-Seq counts (S3 Fig). Consistent with Dunn and colleagues [64], the phase of mapped RPF reads along CDS was compromised, owing to the cutting bias of micrococcal nuclease (MNase) (S4 Fig and S5 Fig). As observed in mammals [13, 37] and yeasts [34], the ribosome occupancy around the cAUGs or AUG start codons of uORFs (uAUGs) was considerably higher than that of the flanking triplets in each of the 12 D. melanogaster samples (S6 Fig). For each sample, we followed previous procedures [34, 64, 71, 72] and calculated the translational efficiency (TE) for each feature (CDS or uORF) to measure its translational rate, by contrasting the RPKM of Ribo-Seq versus mRNA-Seq for that feature (in each sample, the median TE value for a feature is around 1, S7 Fig). With mRNA-Seq RPKM ≥ 1 as an arbitrary cutoff, we identified 6,028 protein-coding genes that were constitutively expressed (CEGs) in all 12 samples and another 7,149 protein-coding genes that were nonconstitutively expressed (NCEGs) but detected in at least 1 sample (Fig 1A). With TECDS ≥ 0.1 as an arbitrary cutoff, 94.5%–99.9% of the CEGs showed evidence of translation, and a slightly lower percentage (92.1%–97.3%) of the NCEGs were translated in a sample (see Table 1 and S8 Fig for details). We still found 62.1%–88.6% of the expressed genes are translated if we increased the TECDS cutoff to 0.5 (Table 1). Overall, these results suggest that our Ribo-Seq data detected the genome-wide translational activities of CDSs with high sensitivity.
Next, we examined whether our Ribo-Seq experiments efficiently captured the translational signals of uORFs. We focused on the 35,735 uORFs that were annotated in the modENCODE mRNA-Seq and CAGE-Seq data and also expressed in at least 1 of the 12 samples we examined (mRNA-Seq RPKM ≥ 1). We found 32,224 (90.2%) and 28,952 (81.0%) of these uORFs showed evidence of translation at TEuORF ≥ 0.1 and ≥ 0.5 in at least 1 of the 12 samples, respectively (Fig 1B). In an individual sample, 72.8%–87.0% and 54.3%–75.4% were translated with TEuORF ≥ 0.1 and ≥ 0.5, respectively (Table 1). Overall, the number of ribosome-associated uORFs in a sample varied from 9,939 to 17,416 (Table 1), with pupae having the highest number of translated uORFs and mature oocytes having the lowest number of uORFs, evidenced by ribosome occupancy. The gene enrichment analysis suggests that genes lacking ribosome-associated uORFs were enriched in the pathways such as “cuticle structure,” “energy metabolism,” or “chromatin organization” (S9A Fig and S5 Table). The genes with ribosome-associated uORFs were significantly enriched for “regulation of transcription,” “protein kinase,” “axon guidance,” or receptor activities (S9B Fig and S5 Table), suggesting uORFs might play regulatory roles in these biological pathways.
Notably, 62.9%–65.5% of the expressed uORFs were overlapping with other features (uORFs or CDSs) in a sample (S6 Table). Although we only considered the nonoverlapping region of a uORF if it was overlapping with a CDS, it is possible that we might have overestimated the proportion of the translated uORFs by assigning a single RPF to multiple overlapping uORFs. To evaluate this possibility, in each sample, we separately considered the uORFs that overlapped with other features (overlapping) and those not overlapping with any other feature (nonoverlapping). At the cutoff of TEuORF ≥ 0.5, the percentages of the nonoverlapping and overlapping uORFs that showed evidence of translation in a sample were comparable (49.2%–75.2% versus 55.5%–75.5% for the former versus the latter, P = 0.24, Student paired t test; S6 Table). Moreover, even if we assigned an RPF to the longest uORF in case it was matched to multiple overlapping uORFs, the proportion of the translated uORFs in a sample was only modestly affected (S7 Table). Thus, our observation that most uORFs were translated might not be affected by the overlapping between uORFs.
Previous studies suggest that uAUGs are generally located in disfavored Kozak sequence contexts compared to cAUGs [16, 18, 22]. To examine whether TE is different between uORFs and CDSs, for a uORF i and its downstream CDS i, we denoted βi = TEuORF,i/TECDS,i, and tested whether log2(βi) = log2(TEuORF,i)–log2(TECDS,i) is significantly different from 0 in a sample (S10 Fig; Materials and methods). Note that for a feature (uORF or CDS), we assume its log2(TE) follows a normal distribution. We first estimated the standard error (SE) of log2(TE) based on the biological replicates of female and male bodies, grouped them according to increasing normalized mRNA counts and log2(TE), and then fitted the SE values against the mRNA counts and log2(TE) to obtain a smooth surface. For a feature in the samples without biological replicates, we estimated the SE of log2(TE) by subjecting the observed mRNA count and log2(TE) to the fitted surface obtained with the biological replicates of female and male bodies (Materials and methods). When we focused on the uORFs and CDSs that were well transcribed (RPKM ≥ 1 and normalized read counts ≥ 30 in mRNA-Seq), we found 27.3%–66.3% of the uORFs are significantly different from CDSs in TE (false discovery rate [FDR] < 0.05, Table 2 and S11 Fig). Although 7.2%–49.2% of the uORFs had higher TE than CDSs, significantly higher proportions (9.5%–54.3%) of uORFs have lower TE than CDSs (P = 0.031, Wilcoxon signed-rank test), suggesting uORFs are overall translated at lower efficiency than the downstream CDSs. Moreover, for a certain uORF, the β value (TEuORF/TECDS) often varies across samples (S11 Fig), suggesting a uORF might play a regulatory role in a stage- or tissue-specific manner.
The transcriptome of D. melanogaster is highly dynamic during development, with prevalent stage-, tissue-, or sex-specific gene expression, alternative transcription initiation, or splicing [61, 63]. Consistently, we found many CDSs are not constitutively transcribed or translated in all 12 samples (Fig 1A). To explore in depth the transcriptional dynamics of uORFs across samples, we examined the uORFs in the CEGs and NCEGs separately. At the mRNA RPKM ≥ 1, 13,230–16,005 uORFs in the CEGs were expressed in a sample, and 9,162 of these uORFs are constitutively expressed in all 12 samples. Due to the developmental stage- or tissue-specific expression of the NCEGs, the numbers of uORFs expressed in the NCEGs varied wildly across samples (ranging from 1,275 to 11,198), suggesting the widespread transcriptional dynamics of uORFs across samples. At TEuORF ≥ 0.5, 56.3%–78.3% of the expressed uORFs in CEGs and 42.0%–66.2% of the uORFs in NCEGs were evidenced with ribosomal P-site occupancy (S8 Fig), suggesting some uORFs might be selectively translated during Drosophila development, although the patterns might be different between uORFs in CEGs and NCEGs.
To quantitatively measure to what extent a feature (uORF or CDS) is expressed in a stage- or tissue-specific manner, we calculated the tissue specificity index Hg [73]. An Hg value closer to 0 indicates more restricted expression, while an Hg value closer to log2(N) means broader expression, where N is the number of samples. We found uORFs have significantly smaller Hg values than the corresponding downstream CDSs in the mRNA-Seq data (P < 10−307, Wilcoxon rank-sum test [WRST]; Fig 1C), and this pattern still held when we controlled for the difference in length between uORFs and CDSs (P < 10−307, WRST; S12 Fig). These results further support that many uORFs are transcribed in a stage- or tissue-specific manner. Our observation is also consistent with previous in silico studies that uORFs are significantly enriched in the alternatively rather than the constitutively expressed regions in 5′ UTRs of mammals [47]. Notably, the uORFs also had significantly smaller Hg values than the corresponding downstream CDSs in the Ribo-Seq data (P < 10−307, WRST; Fig 1C and S12 Fig), suggesting the transcription or translation of uORFs is more restricted than that of CDSs.
To examine in depth whether uORFs are selectively translated, we only focused on the uORFs in the genes that had the same dominant transcripts constitutively expressed in all the 12 samples. Briefly, we employed kallisto [74] to quantify the abundance of each mRNA transcript and detected the major (most abundant) transcript in each mRNA-Seq library (Materials and methods). The major isoforms that were 2-fold dominant [75] (expressed at least twice as much as the second most abundant isoform) in each of our mRNA-Seq libraries (ranging from 5,581 to 9,324) were well supported (85.5%–92.6%) by the mRNA-Seq data in the matched samples of the modENCODE project (S8 Table). By this way, the possible bias caused by the minor transcripts and selective transcription of the uORFs are well controlled. Among the 1,515 uORFs that were constantly expressed in these dominant isoforms across all 12 samples (mRNA RPKM ≥ 1 in each library), only 443 (29.2%) and 1,081 (71.4%) of them showed evidence of translation in all the samples under TEuORF ≥ 0.5 and TEuORF > 0, respectively. These results are in line with the notion that uORFs might be selectively translated during Drosophila development. Nevertheless, the translation of some uORFs might not be detected in our Ribo-Seq because of sampling errors, since uORFs are overall short and poorly translated, especially for the lowly expressed ones. Indeed, for the well-transcribed uORFs in each sample (RPKM ≥ 1 and normalized read counts ≥ 30 in mRNA-seq), only 1.1%–7.2% of them did not show any signal of translation (i.e., 0 RPFs, Table 2). To further evaluate the effect of sampling bias, we calculated Pm(R0), the probability of observing 0 RPF reads for a uORF with the observed mRNA read count in a sample m under the null hypothesis (H0(c)) that the TE of that uORF is the same as that of the downstream CDS (S13 Fig). Note that our calculation of Pm(R0) takes into account the possible sampling errors in mRNA and RPF read counts of both uORFs and CDSs (Materials and methods). At the FDR of 0.05, we found roughly 28.2%–76.6% of the well-transcribed uORFs that have 0 RPF reads detected might be truly not translated under H0(c) (Table 2). Moreover, for a uORF that had 0 RPFs detected in a sample m but showed evidence of translation in at least two other samples (the average TE was u), we calculated Pm(R0) under the null hypothesis that the TE for that uORF in sample m was u (H0(u)). Also, we calculated Pm(R0) by assuming the expected TE for that uORF was 0.1 (H0(0.1)). Not surprisingly, we found lower proportions of the well-transcribed uORFs that did not show evidence of translation might be truly untranslated under H0(u) or H0(0.1), because of overall TECDS > TEuORF > 0.1 (Table 2). These results reinforce the thesis that some uORFs are not translated although they are well transcribed.
In case we detected 0 RPF reads on a uORF in multiple samples in which it is well transcribed, we aggregated the P values using Fisher’s method [76] or calculated the P value after pooling the mRNA or RPF reads across those samples (Materials and methods). In total, there are 2,077 well-transcribed uORFs that had 0 RPFs in at least 1 sample. At the FDR of 0.05, 1,152 (55.5%, Fisher’s method) or 1,190 (57.3%, pooling method) of them might not be translated under H0(c) (S9 Table). Note that here we only focused on the well-transcribed uORFs and did not consider the lowly transcribed ones, because of limited statistical power. Overall, our deep sequencing results suggest that a large number of uORFs were transcribed and translated during Drosophila development, and many of these uORFs were not constitutively transcribed. Interestingly, even if the uORFs are transcribed, some of them might be selectively translated. Based on the ribosome occupancy patterns, we classified the 28,952 uORFs that showed strong evidence of translation (TEuORF ≥ 0.5) into 3 classes (Fig 1B). Class I contained 4,237 uORFs that were associated with RPFs in ≥ 11 out of 12 samples, Class II was comprised of 11,467 uORFs translated in 5–10 samples, and Class III consisted of 13,248 uORFs evidenced with ribosome occupancy in 1–4 samples. Class IV was made up of 8,667 uORFs, including 6,783 uORFs that were expressed with mRNA-Seq RPKM ≥ 1 in at least one of our samples but did not show evidence of translation at the cutoff of TEuORF ≥ 0.5, and 1,884 uORFs that were only expressed in the modENCODE mRNA-Seq data. Not surprisingly, we found the fraction of the well-transcribed uORFs that were not detected in the Ribo-Seq data in at least 1 sample increased in the order of the Class I, II, III, and IV (S9 Table). The difference in translational breadth (defined as the number of samples in which a uORF is translated) among the four classes of uORFs might cause them to show differences in genomic features and evolutionary patterns.
To provide further evidence that uORF-associated RPFs were generated as a result of those uORFs having undergone translation, we employed two different approaches: (1) contrasting the coverage of RPFs in the bona fide and hypothetical uORFs and (2) profiling translation initiation events in harringtonine-treated S2 cells.
First, we compared the proportion of the canonical uORFs (beginning with AUG) that were associated with RPFs to those of the hypothetical uORFs. Briefly, after masking the canonical uORFs in the 5′ UTRs, we assumed that each of the other 60 non-stop-codon triplets was the start codon of a hypothetical uORF that did not overlap with canonical uORFs. We then calculated the density of P-site coverage for that hypothetical uORF in each library, as performed for the canonical uORFs (Materials and methods). Compared to the canonical uORFs, significantly lower proportions of the hypothetical uORFs were associated with RPFs (TEuORF ≥ 0.5) in at least 11 out of the 12 libraries (P < 10−307 in each of the 60 comparisons, Fisher’s exact tests; Fig 2A). The average signal-to-noise (i.e., canonical to hypothetical uORFs) ratio for this analysis was 7.91, which might be conservative given that some hypothetical uORFs might be genuine (near-cognate uORFs) [13, 34, 77, 78]. The difference was more striking when we increased the stringency of ribosomal occupancy (Fig 2B). For example, the average signal-to-noise ratio was 1.90 and 15.3 when we set TEuORF ≥ 0.1 and ≥ 1.0, respectively (P < 5.6 × 10−158 in each of the 60 comparisons in both cases, Fig 2B). Thus, the uORF-associated RPFs detected in our Ribo-Seq data well reflect the translational events on the uORFs.
Second, we treated S2 cells with harringtonine and characterized the genome-wide translation initiation events of uORFs with Ribo-Seq (Materials and methods). It has been nicely demonstrated that harringtonine, which arrests ribosomes at the translation initiation sites [79], enhances ribosome occupancy around the genuine start codons in mammalian cells [13, 80, 81]. Our metagene density profiles revealed that, compared to dimethyl sulfoxide (DMSO) treatment, the ribosome occupancy around cAUGs (position 1) was considerably higher in the Ribo-Seq of harringtonine-treated S2 cells (30 min) (Fig 2C). Importantly, we also observed very similar patterns for uAUGs (Fig 2D), suggesting the translation initiation events of uORFs were efficiently captured in the harringtonine-treatment experiments.
RPF peaks around uAUG after harringtonine treatment usually indicate translation initiation of uORFs [37]. Here, we followed a previous study [37] and identified uAUGs or cAUGs with ribosome occupancy peaks by requiring that the ribosome occupancy at the +1 codon is larger than that at the +2 codon and greater than the summed occupancy of the −1 and −2 triplets [37]. We found that in the harringtonine-treated S2 cells, 63.4% of the cAUGs (only uORFs with Ribo-Seq RPKM > 10 in DMSO-treated S2 cells were considered) showed significant peaks of ribosome occupancy (Fig 2E). Moreover, CDSs with higher RPF densities in S2 cells tended to have higher proportions of cAUGs with ribosome occupancy peaks in the harringtonine-treated S2 cells (Spearman’s rho = 0.778, P = 0.008) when the CDSs were grouped into 10 equal-sized bins with increasing densities of RPFs. Using the same criteria, we found that 43.8% of uAUGs (Ribo-Seq RPKM > 10 in S2 cells) showed significant peaks compared to the flanking regions (Fig 2E). We also found that uORFs with higher RPF densities tended to have a higher proportion of uAUGs with ribosome occupancy peaks (Spearman’s rho = 0.976, P < 10−307), as observed for CDSs (Fig 2E). One should note that, overall, the proportions of uAUGs that show ribosome occupancy peaks were lower than those of cAUGs, presumably due to the disfavored sequence contexts around uAUGs compared to cAUGs [16, 18, 22].
Altogether, these results suggest that our Ribo-Seq experiments (without harringtonine pretreatment) satisfactorily captured the translation initiation events at uORFs. Our datasets can be used to detect uORF translation events in Drosophila with considerable sensitivity and with a high degree of accuracy.
It is well established that genomic features affect the TE of uORFs as well as their repression efficiencies on CDSs [10, 13, 14, 16, 18, 20, 22, 34–37]. Nevertheless, it is unclear whether the genomic features show differences among uORFs with different translational breadth. In vertebrates and yeasts, uAUGs are generally located in disfavored Kozak sequence contexts compared to cAUGs [16, 18, 22]. To test whether this pattern holds true in Drosophila, we first retrieved the −6 to 1 nucleotides around each cAUG [4, 82] and derived a position probability matrix (PPM) for Kozak sequence contexts for all the CDSs (S10 Table). Then, we calculated the Kozak score for each uAUG or cAUG using this PPM (a higher Kozak score means a more preferred context for translation initiation). As shown in other species [16, 18, 22], in Drosophila, uAUGs also have significantly lower Kozak scores (i.e., they are located in disfavored contexts) compared to cAUGs (P < 10−307, WRST). Notably, in all the samples, higher Kozak scores tend to cause the higher TE of uORFs (S14A Fig). Interestingly, the Kozak score for each of the four classes of uORFs (Classes I to IV) monotonically decreased (P < 0.038, WRSTs; Fig 3A), suggesting that uORFs with higher translational breadth tend to have more preferred sequence contexts around their start codons.
AUG triplets are overall selected against within a 500 nt distance of the cAUG, while outside this distance, the selective pressure against AUG triplets is relatively weak [44]. Since the 5′ UTR regions adjacent to the cAUGs are generally less structured [83–85], it is possible that uORFs closer to the cAUGs might have a higher tendency of ribosomal occupancy and thereby experienced stronger selective pressures. Although purifying selection might have effectively removed the deleterious uORFs that are highly translated and closer to cAUGs, it is equally possible that some of the highly translated uORFs are beneficial and preserved in the genomes. To distinguish between these two possibilities, we examined the relationship between the distance from the uAUGs to the cAUGs and the tendency of ribosomal occupancy of the uORFs. The uAUGs of Classes I, II, and III were significantly closer to cAUGs (in a monotonically increasing manner) than those of Class IV (P < 0.001, WRSTs; Fig 3B), and uORFs whose uAUGs were closer to cAUG generally had significantly higher TE in all the samples (S14B Fig), suggesting that uORFs adjacent to cAUGs are more likely to be translated and functional in D. melanogaster.
Given the widespread translational signals of uORFs in Drosophila, we questioned whether the ribosome-associated uORFs have coding potential. A recent study, which identified approximately 2,700 uORFs that were translated in S2 cells by Poly-Ribo-Seq [86], suggests that the translated uORFs could not be distinguished from intergenic or random sequences in the phastCons scores [87] or amino acid compositions. Here, we pursued this issue with PhyloCSF scores, which measure the coding potentials based on sequence alignments [88]. A positive PhyloCSF score indicates the alignment is likely to encode a functional protein, whereas a negative score means otherwise. After subjecting the uORF sequence alignments across 23 insect species to the phyloCSF analysis (Materials and methods), we found the mean phyloCSF score monotonically decreased in the four classes (Class I to IV) of uORFs (P < 1.3 × 10−12, WRST; Fig 3C). Furthermore, in each sample, the uORFs with higher phyloCSF scores showed a stronger tendency to be associated with ribosomes (Fig 3D). Thus, uORFs with higher translational breadth or enhanced TE, in general, are more similar to the canonical coding regions in substitution patterns during evolution. However, these results do not necessarily suggest the translation events of such uORFs would produce functional peptides, because 93.5% of the translated uORFs had negative phyloCSF scores that were below the threshold of coding sequences [88]. This conclusion is also supported by the comparison of codon usages in the uORFs and CDSs of D. melanogaster: the relative synonymous codon usage (RSCU) [89] of uORFs was more similar to the random trinucleotide frequencies in the 5′ UTRs than to the RSCU of CDSs (S15 Fig). Altogether, these results suggest that the sequence composition of uORFs might be optimized to effectively associate with ribosomes. However, the outcome of this process is more likely to efficiently repress translation of the downstream CDSs rather than to directly encode functional peptides.
Genes with different expression levels or different expression breadths show differences in evolutionary patterns [90–93]. Our phyloCSF analysis suggests that the nucleotide substitution patterns in the uORFs that had higher translational breadth are more similar to those in the canonical coding regions (Fig 3C and 3D). Since the uAUG is an essential defining feature of a uORF, here, we further asked whether uORFs with higher translational breadth are evolutionarily more conserved on the uAUGs. The phyloP scores [94], which measure sequence conservation levels based on multiple alignments, were significantly higher for uAUGs compared to their flanking (−3 to +3) triplets, suggesting uAUGs are evolutionarily more conserved. This pattern was consistently observed for the translated uORFs (P < 1.3 × 10−53 for each class, paired t tests; Fig 4A) and for the Class IV uORFs that showed little evidence of translation (P < 9.0 × 10−14, Fig 4A). Interestingly, for uAUGs and neighboring triplets, the phyloP score decreased with reduced translational breadth among the four classes of uORFs (Fig 4A). Moreover, in each of the 12 samples we surveyed, the uORFs with higher RPF densities tended to have higher phyloP scores (i.e., to be evolutionarily more conserved) at uAUGs (S16 Fig). Altogether, these results suggest that the uORFs with higher translational breadth across samples or highly translated in a sample have experienced stronger selective pressures to be preserved during Drosophila evolution.
Frequent gains and losses of uORFs have been observed in human populations, and some of these uORF-altering mutations are deleterious [15, 19, 21]. Here, we asked whether such a pattern exists in D. melanogaster. In the 84 strains of D. melanogaster sequenced in the Global Diversity Lines (GDL) project [95], we identified 4,263 and 2,498 SNPs that created or destroyed uAUGs, respectively (D. sechellia was used as an outgroup to polarize the mutations). Not surprisingly, the mutations that caused polymorphic uORFs associated with ribosomes (Class I, II, and III) had significantly lower derived allele frequency compared to Class IV uORFs (P = 0.006, WRST; Fig 4B), suggesting they are under stronger purifying selection. Compared to the mutations in positions 8–30 of short introns (≤65 nt), which evolve neutrally [96–99], both the AUG-creating and AUG-disrupting mutations had significantly lower derived allele frequencies (P < 4.4 × 10−79 in each comparison, WRST; Fig 4C). Similar results were obtained when we examined data from Drosophila Genetic Reference Panel (DGRP) [100, 101] of D. melanogaster (S17 Fig). In summary, our results suggest the segregating mutations in D. melanogaster that create new uORFs or destroy the existing ones are overall deleterious.
Given that uORF-creating mutations are selected against at the population level, one question that remains to be addressed is what shaped the current distribution of uORFs in the genomes. Here, we tested two possible hypotheses about the origin and subsequent evolution of uORFs. The null hypothesis is that many newly emerged uORFs might be neutral or slightly deleterious but become fixed in the populations of D. melanogaster due to genetic drift. The alternative hypothesis is that although many mutations that create uAUGs are deleterious, the (slightly) beneficial ones would be driven to fixation very rapidly by positive Darwinian selection.
To distinguish between these two hypotheses, we first identified the newly emerged uORFs in the lineage of D. melanogaster after it diverged from D. sechellia about 5.4 million years ago [102], with D. yakuba as the outgroup (Fig 4D). Based on the genome sequence alignments of the 3 Drosophila species (indels and repetitive sequences were excluded), we found that 2,198 uAUGs detected in D. melanogaster are not present in D. sechellia: 994 (45.2%) of these differences were caused by nucleotide changes that disrupted the uAUGs in D. sechellia, and 1,204 (54.8%) were caused by the creation of uAUGs in D. melanogaster. These results suggest (1) that uORFs have undergone frequent gains and losses during evolution and (2) that prevalent new uORFs emerged in the lineage of D. melanogaster after its divergence from D. sechellia.
To test whether the newly emerged uORFs in D. melanogaster bear signatures of positive selection, we conducted a generalized McDonald-Kreitman (MK) test [103, 104] by contrasting the newly fixed uAUGs in the D. melanogaster lineage and the polymorphic uAUGs in the GDL of D. melanogaster (Fig 4D). As the neutral controls, we counted the newly fixed and polymorphic AUG triplets in positions 8–30 nt of short introns (≤65 nt) (Materials and methods). Since the possible demographic histories and the (slightly) deleterious mutations in the polymorphic data would cause a bias in estimating α, which is the fraction of nucleotide substitutions that are driven to fixation by positive selection [105–107], we estimated α with 3 alternative approaches that account for these effects. First, we removed the polymorphic AUG triplets that had low minor allele frequency (MAF < 0.05) and conducted the MK test as previously described [105, 108, 109]. With this original approach, we estimated that at least 7.9% (αori) of the newly fixed uAUGs in D. melanogaster lineage were driven by positive Darwinian selection (Fig 4E).
Although we removed the low-frequency polymorphism (MAF < 0.05) in the above MK analysis (the “original” method), the estimation of α might still be biased, since some deleterious mutations might segregate at higher frequencies in the populations [110–113]. Thus, we also estimated α with the DFE-alpha method (αdfe), which analyzes the unfolded site frequency spectrum (SFS) and infers the distribution of fitness effects (DFE) for deleterious mutations and the prevalence and selective strength for advantageous substitutions [110, 111]. Also, the DFE-alpha method incorporates the demographic change that affects the fixation probability of selected alleles. The third method we used is AsymptoticMK, which first evaluates polymorphism levels for different mutation frequencies separately and then estimates α (αasym) by extrapolating a function fitted to the data [112, 114]. Since AsymptoticMK estimates αasym at different derived allele frequencies, the bias that distorts SFS due to demographic history, background selection, or genetic draft will cancel out. Previous results suggest that both DFE-alpha and AsymptoticMK are more powerful in detecting positive selection than the original MK test [110, 112]. Indeed, we found both αdfe and αasym are larger than αori in both the DGRP and GDL dataset: 25.2%–53.0% of the newly fixed mutations creating uAUG in the D. melanogaster lineage were under positive selection (Fig 4E and S18 Fig). These results suggest higher fractions of newly fixed uAUGs are under positive selection after controlling for the effects of slightly deleterious mutations, demographic changes, and epistasis. One caveat in the above analyses is that the α values might be biased when pooling loci from different genomic regions that differ in the effective population size [106, 115, 116]. Nevertheless, empirical data analysis suggests summing data across loci in the MK test would not cause severe biases of α estimation [110]. Indeed, we still detected strong signals of positive selection in the newly fixed uAUGs when we randomly sampled the uAUGs in the 5′ UTRs and the ATG triplets in positions 8–30 of the short introns (with replacement) and calculated the α values (Fig 4F, see Materials and methods for details).
Next, we questioned whether the prevalence and strength of positive selection were different for the newly fixed uAUGs whose uORFs were different in translational breadth or TE. Since our mRNA-Seq and Ribo-Seq experiments were primarily carried out with the ISO-1 strain of D. melanogaster, which was sequenced to assemble the reference genome of D. melanogaster [117], in the MK tests, we would only consider the mutations that were present in the ISO-1 strain. Since DFE-alpha relies on the full spectrum of site frequency [110, 111], and the results will be distorted if we only consider the mutations present in the ISO-1 strain, here, we estimated α primarily based on the original MK test and AsymptoticMK. With the GDL polymorphism data, for the newly fixed uORFs in Classes I+II (combined), III, and IV, the αori analysis suggests that 49.9%, 30.5%, and 20.4% of them, respectively, were under positive selection (Fig 4G, S11 Table). As expected, the AsymptoticMK analysis revealed an even higher α value (αasym = 68.3%) for all the translated uORFs (Classes I+II+III), and αasym was higher than αori for each class of translated uORFs (Fig 4H). Importantly, both the original MK test and AsymptoticMK revealed the strength of positive selection decreased in the order of I+II, III, and IV. It should be noted that here, in both methods, we only considered the mutations present in the ISO-1 strain. To evaluate whether this approach would cause a biased estimation of α, we randomly sampled 1,000 genes and performed the MK tests on all the nonsynonymous and synonymous mutations in the populations of D. melanogaster versus those only present in the ISO-1 strain (the simulations were performed for 1,000 replicates, see Materials and methods). Compared to the MK tests based on the polymorphic data in all the strains, αori was overestimated to 1.38 (95% CI 1.21–1.86) and αasym was overestimated to 1.03 (95% CI 0.91–1.26) folds of the original values when we only used the mutations present in the ISO-1 strain (S19 Fig). Although our analysis might exaggerate αori, such effects should exist for each class of uORFs and might not distort the relative strength of positive selection on different classes of translated uORFs. The AsymptoticMK analysis, which was only modestly affected when we used only the mutations present in the ISO-1 strain, suggests that αasym decreased in the order of Class I+II, III, and IV in both the GDL (Fig 4H) and the DGRP (S20 Fig) dataset. Moreover, among the newly fixed uORFs that were expressed in each developmental stage/tissue, those with higher TEuORF in general had a higher α value than those with lower TEuORF (S21 Fig). Overall, these results suggest that the newly fixed uORFs that show stronger signals of ribosome occupancy have experienced more substantial positive selection, presumably due to their more important regulatory roles across tissues or developmental stages. Notably, although many sites in the 3′ UTRs of D. melanogaster are under positive selection [104], we still detected prominent signals of positive selection in the newly fixed uORFs that were translated (αasym = 0.343 for GDL and αasym = 0.280 for DGRP data, S22 Fig) when we used the AUG-creating mutations in the 3′ UTRs as putatively neutral controls. Altogether, our results, to our knowledge, demonstrate for the first time that positive Darwinian selection is the driving force for the fixation of uORFs after their origins.
To detect whether the ribosome-associated uORFs affect the TE of the downstream CDSs, in each sample, we only focused on the major transcript for each gene and examined the relationship between the TE of the downstream CDS (TECDS) and the number of uORFs that were translated in that transcript. As expected [10, 13, 14, 20, 22], we found genes containing ribosome-associated uORFs (TEuORF > 0) have significantly lower TECDS compared to genes without ribosome-associated uORFs. Roughly speaking, TECDS was 8.38%–30.4% lower for genes containing 1 single translated uORF and 18.4%–60.7% lower for genes having multiple translated uORFs, except for the sample derived from 0–2 h embryos (Fig 5A; see S23 Fig for results with different cutoffs). Moreover, the number of translated uORFs showed significant negative correlation with TECDS in all the 12 samples (rho ranged from −0.360 to −0.027, P < 0.01, S24A Fig; and other TEuORF cutoff yields similar results, S24B and S24C Fig). Our results thus suggest that uORFs inhibit translation of the downstream CDSs, most likely by competing for ribosomes. Notably, the anticorrelation between TECDS and the number of translated uORFs was weak in the 0–2 h embryos. Ribosome profiling data of 0–2 h embryos generated in other studies [64, 65] show a similar pattern (S25 Fig). Since translation is predominately controlled by poly(A)-tail length in early embryos of Drosophila [118], the repressive effects of uORFs on TECDS in the 0–2 h embryos might be overwhelmed by the activating effects of the poly(A)-tails, which are overall longer for uORF-containing genes (S26 Fig).
Besides uORFs, many cis-regulatory elements (CREs) in 5′ UTRs also influence TECDS [11]. In nearly all the samples, TECDS was significantly correlated with features in its 5′ UTR (S1 Text), such as the length of the 5′ UTR (negative correlation, S27 Fig), the GC content (negative, S28 Fig), the Kozak context of the cAUG (positive, S29 Fig), the minimum free energy (MFE) of the secondary structure around the cAUG (positive, S30 Fig), the MFE of the secondary structure around the 5′ cap (positive or negative, S31 Fig), and the number of stable hairpin structures in the 5′ UTR (negative, S32 Fig). Nevertheless, our analysis on the relative importance of the aforementioned features (S12 Table; S1 Text) suggests that the number of ribosome-associated uORFs significantly contributes to the reduced TECDS after controlling other factors (Fig 5B).
Previous studies in yeasts and animals suggest that the repressiveness of a uORF on its downstream CDS is specified by its sequence contexts, including the Kozak score for uAUG, uORF length, distance from uAUG to 5′ cap, and distance from uORF stop codon to cAUG [10, 14, 21, 22]. Our analysis revealed similar patterns in Drosophila (Fig 5C and S33–S36 Fig). Moreover, we also found the extent to which TECDS was repressed was more or less affected by the evolutionary features of uORFs: phyloP for conservation level of uAUGs (S37 Fig), phyloCSF for potentials to encode conserved peptides (S38 Fig), and branch length score (BLS) [119] for uORF sequence conservation levels across 23 Drosophila species (S39 Fig). It is possible that these genomic or evolutionary features influence the translational efficacy of uORFs, which further affects their repression efficiency on the translation of downstream CDSs. After multiple regression analysis between TECDS and these uORF features (S1 Text), we found that optimized Kozak contexts around uAUGs, high conservation level of uAUGs, and long distance between uAUG and 5′ cap are the most important features of uORFs that determine the repressiveness of uORFs on the downstream CDSs (S13 Table, Fig 5D).
In summary, our results suggest that in Drosophila, the ribosome-associated uORFs exert widespread regulatory effects in modulating TE of CDSs, and the key features of uORFs that specify their repressiveness might be conserved across Drosophila, yeasts, and vertebrates.
Our analyses suggest that many uORFs might vary in TE across samples, even if they are constitutively expressed. Since uORFs impede translation of downstream CDSs by competing for ribosomes, we questioned whether the changes in TE of uORFs would impact TE of the downstream CDSs during Drosophila development. To this end, we focused on the genes that have the same dominant isoforms between two neighboring developmental stages as supported by the CAGE and mRNA-Seq data and examined the relationship between changes in TE of well-transcribed uORFs and their downstream CDSs. Notably, the changes in TEuORF were significantly positively correlated with changes in TECDS in all the pairs of samples we examined (S40 Fig), presumably due to the genewise trans-regulatory effects that were exerted on the translation of both the uORFs and their downstream CDSs. Nevertheless, the magnitude of changes in TECDS was generally less than that in TEuORF if TEuORF is increased and vice versa (S40 Fig), suggesting the magnitude of changes in TECDS is inversely affected by changes in TEuORF during development. To control for the stochastic sampling effect in this analysis, we first identified uORFs that showed statistically significant changes in TE between the two samples. Briefly, for each uORF that was expressed in both sample 1 and 2, we tested whether log2(βu) = log2(TEuORF,2)–log2(TEuORF,1) is significantly different from 0 (Materials and methods). We found 9.3%–55.8% of the well-transcribed uORFs we examined showed significant differences in TE (βu ≠ 1) between neighboring samples (Table 3). To quantitatively examine whether the changes in TEuORF between sample 1 and 2 would inversely impact the magnitude of changes in TECDS between these two samples, we defined γ = (TECDS,2 / TECDS,1) / (TEuORF,2 / TEuORF,1) and tested whether log2(γ) is significantly different from 0 (see S41 Fig for the scheme, Materials and methods). Among the well-transcribed uORFs that show βu ≠ 1 between neighboring samples, 37.4%–79.2% of them had downstream CDSs that showed γ ≠ 1 between the matched samples (Table 3). Strikingly, in each pair of samples, uORFs with log2(βu) > 0 were usually accompanied with log2(γ) < 0 and vice versa (P < 0.01 in each pair of samples, χ2 test). These results further support the notion that the magnitude of changes in TECDS is inversely affected by changes in TEuORF during development. For example, dPPP1R15 is a eukaryotic initiation factor 2 alpha (eIF2α) phosphatase that is important for Drosophila development [120]. dPPP1R15 has only one transcript, and the translation of its CDS is regulated by its uORFs [120]. Compared to in 12–24 h embryos, the TE of uORFs is considerably increased, and the TE of the CDS is remarkably reduced in larvae (Fig 6A). Altogether, our results suggest that changes in TE of uORFs might be important to modulate the translation of CDSs during Drosophila development.
Note that in the above analyses, we only focused on the impact of individual uORFs and did not consider the possible interactions between uORFs in the same mRNA. Interestingly, in each sample, the number of expressed uORFs in a gene was negatively correlated with the proportion of uORFs that were translated (TEuORF ≥ 0.1) in that gene. This pattern held for all the dominant transcripts that were constitutively expressed across the samples (P < 0.05 in each sample, S42 Fig) or for all the genes expressed in each sample (P < 5.6 × 10−7 in each sample, S43 Fig). Hence, it is possible that there is competition for ribosome occupancy between different uORFs in a gene, and some uORFs tended to have the stronger tendency of ribosome association. Therefore, we also pooled the mRNA or RPF reads of uORFs in the same mRNAs together and examined the relationship between TE changes in uORFs versus those in CDSs (Materials and methods). We still found the changes in TEuORF inversely affect the changes in TECDS (S14 Table). Taken together, these results suggest that uORFs can change their TE to inversely modulate the translation of the downstream CDSs during Drosophila development.
Next, we focused on the genes that switched their major transcripts between neighboring developmental stages to investigate whether the inclusion or exclusion of uORFs would impact the TE of CDSs (Materials and methods). To increase the accuracy in identifying such genes, we analyzed both our mRNA-Seq and the modENCODE mRNA-Seq data and required the same isoform switching events to be detected in both datasets. We found 36–144 (with a median of 83) genes switched the major transcripts, which caused the numbers of expressed uORFs (mRNA RPKM ≥ 1) to be changed between two samples (Fig 6B, Table 4). These results suggest that uORFs might be selectively transcribed during development to regulate the TE of CDSs. For example, genderblind (gb) encodes a glial glutamate transporter, and male flies with reduced gb show strong homosexual courtship [121]. We found a long isoform of gb that contains 4 uORFs predominates in female and male adult bodies, while a short isoform without uORFs, which has higher TE compared to the long isoform, is predominantly expressed in female and male adult heads (Fig 6C). The preferential expression of the short, uORF-free isoform in adult heads might maintain high levels of GB protein in the brain. Stage- or tissue-dependent expression of transcript isoforms with different number of uORFs and consistent CAGE signals was also observed for dichaete (S44 Fig)—which is a group B Sox-box transcription factor involved in embryo segmentation and nervous system development [122]—and glycerol kinase 2 (S45 Fig), which is required for glycerol utilization [123]. To systematically probe the regulatory function of selective transcription of uORFs, we investigated the relationship between changes in TECDS and the change in the number of expressed uORFs between two samples. Overall, in 9 out of 10 pairs of comparisons, the change in TECDS was negatively correlated with the change in uORF numbers (P < 0.05 in 4 comparisons, left panel of Fig 6D).
One caveat in this analysis is that the switches of major isoforms were heavily based on the gene models that were annotated in FlyBase. Although the high-throughput mRNA-Seq and CAGE-Seq data have been comprehensively incorporated in the genome annotation of D. melanogaster [124], we cannot exclude the possibility that some of the isoform switching events we detected were affected by the annotations of gene models. Therefore, we further validated the isoform switching events with the profiles of transcriptional start sites identified by the CAGE-Seq data from the modENCODE project. Overall, 92.6%–100% of the isoform switching events were supported by the CAGE signals when the CAGE-Seq data were available for both samples (Table 4). For example, the CAGE signals well supported the altered expression of uORFs in gb (S46 Fig), dichaete (S47 Fig), and glycerol kinase 2 (S48 Fig) across stages. Importantly, with only isoform switches that were consistent with CAGE data in each pair of samples, we still observed negative correlations between changes in TECDS and the change in the number of expressed uORFs in all of the nine pairs (P < 0.05 in 3 comparisons, right panel of Fig 6D). Given these observations, we propose that uORFs might be selectively expressed to regulate the translation of the downstream CDSs.
In summary, our results suggest that uORFs play important roles in shaping the translatomes during Drosophila development via selective expression or translation.
In this study, we generated genome-wide maps of ribosome occupancy and TE during the life cycle of D. melanogaster. Our data allowed us to distinguish the uORFs that show evidence of translation from the putative nonfunctional uORFs. By integrating functional genomic and evolutionary analyses, we for the first time demonstrated that the majority of the newly fixed uORFs in D. melanogaster were driven by positive Darwinian selection. Herein, we propose a unifying model to describe how natural selection has shaped uORFs during evolution (Fig 7): (1) Frequent nucleotide mutations generate AUG triplets in the 5′ UTRs, giving rise to new uORFs. A newly emerged uORF in the population might be deleterious, neutral, or advantageous. (2) The highly detrimental uORFs are removed by natural selection or persist in the population at low frequencies, whereas the neutral or slightly deleterious ones might randomly drift in the population. (3) The beneficial new uORFs, which often have a higher tendency to be associated with ribosomes, are favored by natural selection and become fixed in the population very rapidly. (4) The newly fixed uORFs, which regulate the translation of their downstream CDSs, are maintained by natural selection and very hard to be lost during evolution. Our model solves the dilemma that (1) uORFs are generally deleterious and selected against, and (2) many uORFs are highly conserved across divergent species.
The newly fixed uORFs with a stronger tendency of ribosome occupancy bear stronger signals of positive selection on the uAUGs. Nevertheless, we also detected signals of positive selection in the uAUGs of the uORFs that did not show compelling evidence of translation (Class IV). In addition, Class IV uORFs also had uAUGs more conserved than flanking triplets and neutral region. It is possible that many of the Class IV uORFs are translated at low levels, but they are beneficial to the hosts. For example, 44.3% (3,842 out of the 8,667) of the Class IV uORFs were associated with at least 1 RPF in our Ribo-Seq dataset, although none of them met the criteria of TEuORF ≥ 0.5. It is also possible that some of the Class IV uORFs are highly translated in other tissues or stages that were not covered in this present study. Moreover, the competition for ribosome association between different uORFs in the same 5′ UTR might cause some uORFs to be weakly translated, although they also contribute to the translational inhibition of the downstream CDSs. Supporting this notion, we found that the proportion of translated uORFs (TE ≥ 0.1) in a gene is negatively correlated with the total number of uORFs in this gene, suggesting that ribosome association at some uORFs will suppress the translation of downstream CDS as well as other uORFs in the same 5′ UTR. Altogether, our results suggest that many newly fixed uAUGs might be favored by natural selection, although the relevant uORFs do not show strong evidence of translation. The large effective population size of D. melanogaster, which makes natural selection more efficient [125, 126], is crucial for detecting selection signals on the uAUGs. In species with small effective population size, such as humans [125], it might be difficult for natural selection to detect the selective advantages of uORFs. However, human populations experienced frequent gains and losses of uORFs [15, 19, 21], and some uORF-altering mutations cause diseases. Therefore, newly created uORFs in human populations might undergo purifying or positive selection like D. melanogaster, but the detailed landscape remains to be further determined.
It is known that certain uORFs encode functional peptides [35, 127, 128], and some nascent uORF peptides can even interact with the translating ribosomes to cause ribosome stalling [15, 129]. However, our phyloCSF scores analysis (Fig 3C and 3D) and codon usage bias analysis (S15 Fig) suggest such uORFs are not likely to encode evolutionarily conserved peptides. Our results are consistent with a recent study that detected only about 50 potential coding ORFs in the 5′ UTRs of Drosophila [127]. Therefore, we propose that most translational events of uORFs are to compete for ribosomes to impede the translation initiation of the downstream CDSs but not to produce functional peptides.
The systematic characterization of uORFs in this study also allowed us to confirm that genes with ribosome-associated uORFs had reduced TE in Drosophila. Moreover, we also found sequence features and conservation patterns of uAUGs are associated with the ability of uORFs to repress translation. Recent studies have revealed that translation of mRNAs is modulated through uORFs in response to stresses [12, 130, 131] or immune induction [132, 133]. Here, we have furthered our understanding of the regulatory roles of uORFs by demonstrating that uORFs could perform regulatory functions in a stage- or tissue-specific manner: (1) changes in TEuORF would inversely influence the TE of the downstream CDSs, even if the dominant transcripts do not change, and (2) inclusion or exclusion of uORFs caused by isoform switching could also modulate the translation of the downstream CDSs. Although the gene expression change caused by uORFs might be weak, the small changes might make a big difference under certain environmental conditions and contribute to phenotypic evolution. Our result is also consistent with recent studies showing how the translation of CDSs is modulated by switching between alternative mRNA isoforms that differ in the content of uORFs through meiotic differentiation in budding yeasts [134–136]. Moreover, many trans-acting regulators such as microRNAs [137, 138] and RNA-binding proteins [139] also regulate translation, such as sex lethal (SXL) [140] and density-regulated protein (DENR)–multiple copies in T-cell lymphoma 1 (MCT-1) complex [18]. Recently, the tissue-specific or cell-specific ribosome-profiling technique has been developed [141], which might be helpful to investigate the possible interplays between uORFs and those trans-acting regulators in the future.
Taken together, this present study reveals positive Darwinian selection is the major evolutionary force that drives the newly emerged uORFs to fixation. Our functional genomic studies, combined with our evolutionary analyses, shed new light on the molecular mechanisms and functional consequences of uORF-mediated regulation.
The ISO-1 isogenic strain (y; cn bw sp) of D. melanogaster, which was sequenced to assemble the reference genome of D. melanogaster [117], was used to generate all the libraries in this study. Flies were grown in 12 h light: 12 h dark cycles at 25°C. The 0–2 h, 2–6 h, 6–12 h, and 12–24 h old embryos were collected following a standard protocol at 25°C. Wandering larvae were collected as third-instar larvae. Stage P7–8 pupae were collected approximately 2 d after pupation. The 1–10 d old adult flies were sexed, and the heads and bodies of each sex were separated using brass sieves in liquid nitrogen. The larvae, pupae, and adult heads and bodies were ground into fine powder in liquid nitrogen and then homogenized in the cold room.
The Ribo-Seq experiments for the 0–2 h, 2–6 h, 6–12 h, and 12–24 h old embryos and for the fine powder of third-instar larvae, pupae, heads, or bodies were performed according to a previous study [64], with some modifications (see S3 Table for key differences). The detailed experimental procedures for Ribo-Seq and for the high-throughput sequencing of mRNAs are fully described in the S1 Text. Two biological replicates of mRNA-Seq and Ribo-Seq (independent sample preparation, library construction, and sequencing under otherwise identical conditions) were prepared for 1–10 d old female bodies and male bodies.
Drosophila S2 cells were cultured in Schneider's Insect Medium (Sigma-Aldrich) containing 100 U/mL penicillin and 100 μg/ml streptomycin with 10% heat-inactivated fetal bovine serum. The cells were pretreated with 2 μg/ml harringtonine (Sigma-Aldrich, dissolved in DMSO) or DMSO (as control) for 30 min. Then, all cells were treated with 100 μg/ml CHX (cycloheximide, Sigma-Aldrich) for 5 min, washed twice with cold PBS containing 100 μg/ml CHX, and subsequently harvested. The subsequent mRNA-Seq and Ribo-Seq procedures for the S2 cells are presented in the S1 Text.
We identified all the possible ORFs (starting with AUG start codons and ending with UAA/UAG/UGA stop codons) in the mRNA sequences of D. melanogaster (FlyBase r6.04, http://www.flybase.org) and treated the ORFs with AUG start codons in the 5′ UTRs as uORFs. We did not restrict the length of the uORFs. If a uORF does not overlap with any other uORF on the same transcript, this uORF is classified as a nonoverlapping uORF. If a uORF is in-frame overlapping (i.e., the distance between the two uAUGs is a multiple of 3) or out-of-frame overlapping with at least 1 other uORF or the downstream CDS, this uORF is classified as an overlapping uORF.
We downloaded the mRNA-Seq and matched CAGE data for different developmental stages, tissues, and cells lines of D. melanogaster that were generated by the modENCODE project [61–63] from Sequence Read Archive (SRA) under accession SRP001602, SRP001065, SRP009459, and SRP000709 (S2 Table). The abundance of annotated transcripts (FlyBase r6.04) in each mRNA-Seq library was determined with kallisto 0.43.1 [74] using default parameters. The NGS reads in both CAGE libraries and mRNA-Seq libraries were mapped to the reference genome of D. melanogaster using STAR 2.4.2a [142]. We calculated the mRNA-Seq coverage for each nucleotide site as described previously [64] and then calculated the RPKM for a feature (mRNA or uORF) in a sample as ∑k=1Lck/(L∙N)×109, where L is the length (nucleotides) of that feature, ck is the mRNA coverage of position k, and library size N is the total number of mRNA reads uniquely mapped to the transcriptome. For each gene, the RPKM was calculated with the most abundant transcript isoform. The Bam files for alignment of CAGE tags were processed with CAGEr 1.18.1 [143] to identify CAGE tag starting sites (CTSS) in each sample, and CTSSs within 20 bp were merged into a single tag cluster. CAGE tag clusters with fewer than 5 raw reads at dominant CTSS were excluded. The boundary of a tag cluster was calculated as 10% and 90% quantile positions of the distribution of CAGE tags in this cluster. Each tag cluster was assigned to the nearest transcript within 500 bp with bedtools “closest” [144]. To identify uORFs expressed in each sample, we required that (1) the gene containing a uORF should be detected with mRNA-Seq RPKM ≥ 1; (2) the uORF itself should also have RPKM ≥ 1 in the mRNA-Seq data; (3) in case CAGE signals were detected for this gene, the uORF should be located in transcript isoform supported by CAGE tags and at downstream of 3′ boundary of the dominant CAGE tag cluster for this transcript. Under these criteria, we identified 37,619 uORFs that were expressed in at least 1 sample of modENCODE data among all the 41,483 putative uORFs.
To estimate the expected number of uORFs under the assumption of randomness, the 5′ UTR sequences of the longest transcripts of the protein-coding genes were randomly permutated while maintaining the same dinucleotide frequencies with uShuffle [145]. The permutation procedures were repeated for 1,000 replicates. The median, 2.5% and 97.5% quantiles of the numbers of uORFs in the shuffled sequences were tabulated.
After removing 3′ adaptors [146] and quality controls, the NGS reads of the mRNA-Seq and Ribo-Seq experiments were mapped to the reference genome of D. melanogaster (FlyBase, r6.04) using STAR 2.4.2a. In each sample, we assigned a mapped RPF (27–34 nt in length) to its P-site and calculated the RPKM values for a feature (CDS or uORF) with the mRNA or PPF data as previously described [64]. For uORFs that were overlapping with CDSs, only the nonoverlapping regions of the uORFs were used in calculating RPKM. The TE for a feature (CDS or uORF) was calculated as the ratio of RPF RPKM over mRNA RPKM [34, 147]. In each sample, the most abundant transcript in mRNA-Seq for each gene was inferred with kallisto 0.43.1, and only the genes with mRNA RPKM ≥ 1 in the CDS were considered, unless otherwise stated. The tissue specificity index Hg for a CDS or uORF was calculated as previously described [73]. For each mRNA-Seq or Ribo-Seq library, we followed a published procedure [37] to build the metagene profile around start codons by calculating the coverage of a 51-triplet window (including the start codon itself, 10 upstream triplets, and 40 downstream codons) for each cAUG or a 16-triplet window (including the start codon itself, 5 upstream triplets, and 10 downstream triplets) for each uAUG. The detailed analytical procedures are described in the S1 Text.
Basewise phyloP scores of D. melanogaster were downloaded from UCSC genome browser (genome.ucsc.edu) [148], and the phyloP score for each uAUG was extracted with bigWigAverageOverBed [149]. To calculate phyloCSF [88] and BLS [119] of uORFs, the 27-way multiple sequence alignments of D. melanogaster (dm6) against 26 insect species and the corresponding phylogenetic tree was downloaded from UCSC genome browser [148]. The alignments for uORFs among 23 Drosophila species were extracted and stitched together using custom scripts. The PhyloCSF software [88] was used to evaluate each alignment with the parameter “23flies --removeRefGaps --bls --ancComp --aa --files”. For the 13,282 protein-coding genes in D. melanogaster (the longest transcript isoform was used for each gene), we retrieved the −6 to 1 nucleotides around each cAUG and derived a PPM for Kozak sequence contexts (S10 Table). Then, we calculated the Kozak score for each uAUG or cAUG as a log-odds ratio [150]: Σi[log2(Pi,j/0.25)], where Pi,j is the probability of observing a nucleotide j (A, U, C, and G) at position i (−6 to 1) (S10 Table).
To find differences in AUG triplets in the 5′ UTRs and in positions 8–30 nt of short introns (≤65 nt) among D. melanogaster, D. sechellia, and D. yakuba, we extracted and stitched alignments of these regions from the 27-way multiple alignments, using custom scripts, and searched for differences in ATG triplets in regions of interest. The gains and losses of the AUG triplets were inferred using a parsimonious method based on the phylogenetic tree of the three species (Fig 4D).
We tabulated all the SNPs that cause AUG triplet differences in the genetic variation data from GDL [95] and DGRP2 [101]. We polarized each mutation in D. melanogaster by comparing the orthologous site in D. sechellia with LiftOver [151] based on the pairwise alignments of D. melanogaster and D. sechellia that were downloaded from the UCSC genome browser.
We investigated each AUG triplet in the reference genome of D. melanogaster to verify whether it was newly created in the D. melanogaster linage (fixed or polymorphic in the extant populations of D. melanogaster). This was done by comparing each AUG triplet with the orthologous sites in D. sechellia and D. yakuba (Fig 4D). Next, we masked the AUG triplets that were located in repetitive regions identified by RepeatMasker (http://www.repeatmasker.org) or that overlapped with CDS regions of other transcripts. For the remaining AUG triplets in 5′ UTRs (uORFs) or in positions 8–30 nt of short introns (neutral regions), we examined whether they were polymorphic in the GDL or DGRP2 databases (we required the polymorphic AUG triplets to have MAF of ≥0.05 as previously described [109]) or fixed in the D. melanogaster lineage. We employed Kimura’s 2-Parameter model [152] to correct for multiple substitutions for the fixed differences. The proportion of newly fixed uAUGs driven by positive selection was calculated as αori=1−DSI∙PRIPSI∙DRI, where D is the fixed difference, and P is the polymorphic difference. SI stands for 8–30 nt of short introns (≤65 nt), and RI stands for regions of interest [106].
We also used the AsymptoticMK (https://github.com/MesserLab/asymptoticMK) [114] to estimate αasym [112]. Briefly, the number of fixed and polymorphic sites was derived as described above. The polymorphic sites in neutral control regions were grouped into bins of equal size based on increasing derived allele frequency, and the same break points were used to divide the polymorphic sites in test region into different bins. Only bins whose derived allele frequencies were within 0.05 and 0.95 were used to estimate αasym as a function of derived allele frequency. In both the original and AsymptoticMK tests, we not only estimated the α values using all the polymorphic data meeting the cutoff criteria but also estimated the α values by requiring the mutations to be present in the ISO-1 strain of D. melanogaster. Both the original and AsymptoticMK tests were also applied to mutations that created uAUGs of Classes I, II, and III (combined) using AUGs in 3′ UTR as neutral controls.
To assess the influence of pooling loci from different genomic regions that differ in the effective population size, we randomly sampled the same number of newly fixed and polymorphic AUG triplets with replacement for newly fixed AUGs or polymorphic AUGs in 5′ UTRs or 8–30 nt of short introns, respectively. Then, we performed the original MK test and the AsymptoticMK analysis. This procedure was repeated for 1,000 replicates, and the median and the 95% CI of αori and αasym were estimated. To evaluate the effect of estimating α values by requiring the mutations to be present in the ISO-1 strain, we first followed the procedure described in [114] to estimate the α values on the fixed nonsynonymous mutations in 1,000 randomly selected genes of D. melanogaster, using the synonymous mutations as neutral controls, and then we conducted the same analysis except that we required the mutations to be present in the ISO-1 strain. This procedure was performed for 1,000 replicates to obtain CI.
We estimated αdfe [110] using the DFE-alpha program (http://www.homepages.ed.ac.uk/pkeightl/dfe_alpha/download-dfe-alpha.html, version 2.15). For the test and neutral regions, the number of triplets that could be mutated into an ATG triplet by a single point mutation was counted. These numbers were further adjusted for multiple hits in the same triplet based on the proportion of ATG-creating mutations that were newly fixed in D. melanogaster lineage to derive the number of background sites. The polymorphic sites with fewer than 150 alleles in GDL dataset (130 alleles for DGRP dataset) were excluded. For each of the remaining sites, 150 alleles were randomly sampled without replacement to calculate the unfolded SFS in the test and neutral regions, which were then used to estimate DFE with “est_dfe” program in DFE-alpha [110, 153]. A two-epoch model, in which the population size changed from N1 to N2 T2 generations ago, was used during estimation. While N1 was a fixed number, N2 and T2 were searched through maximum likelihood estimation. The αdfe was estimated based on the DFE using the “est_alpha_omega” program in DFE-alpha. The parameters “do_jukes_cantor” and “remove_poly” were set to 0, as the number of fixed sites was already corrected for multiple hits, and polymorphic sites had been removed from fixed sites as described above.
In each sample, we counted the mRNA-Seq reads that were overlapping with a feature (CDS or uORF) and calculated the RPF read count as ⌈∑k=1Lck⌉, where L is the length (nt) of that feature, and ck is the P-site coverage of RPFs at position k. For a sample, we used DESeq2 [66] to determine the size factors of the mRNA-Seq and Ribo-Seq libraries and normalized the mRNA or RPF read counts by dividing the raw counts with the corresponding size factors (S15 Table). The normalized read counts were used throughout the statistical modeling procedures.
We modeled the mRNA or RPF read count Kij for a feature (CDS or uORF) i in a biological replicate j (j = 1 or 2) with a negative binomial distribution with mean μij and dispersion φi as previously described [66–70]. Based on the two biological replicates in female bodies (or male bodies), for the mRNA or RPF data, we first used the “estimateDispersionsGeneEst” function in DESeq2 to estimate the featurewise φ values and then used the “estimateDispersionFit” function in DESeq2 to fit φ as a function of μ (i.e., φ(μ)) for each type of data (S3A Fig). Here, we only considered the well-transcribed features (RPKM ≥ 1 and normalized counts ≥ 30 in mRNA-Seq) in estimating dispersion. We also analyzed the features (CDSs or uORFs) that were expressed in both female and male bodies and estimated the overall dispersion trend of RPFs (φR) or mRNA reads (φM) while taking gender information into consideration (S3A Fig). Note that the dispersion trends are very similar when we considered female bodies and male bodies separately or jointly (S3A Fig).
For other samples that did not have biological replicates, we assumed the mRNA or RPF read count for a feature follows a negative binomial distribution with the same dispersion trend (φM or φR) that was estimated from the biological replicates of female and male bodies.
For a well-transcribed uORF i and its downstream CDS i in a sample, we denoted the ratio TEuORF,i/TECDS,i as βi and tested whether log2(βi) = log2(TEuORF,i)–log2(TECDS,i) is significantly different from 0 in a sample with Wald test. We assumed the log2(TE) value of a feature (CDS or uORF) follows a normal distribution, which well approximated the observed distribution of log2(TE) obtained with mRNA and RPF counts simulated with negative binomial distributions (S49 Fig). With the biological replicates in female bodies (or male bodies), we contrasted the RPF counts against mRNA-Seq read counts with DESeq2 to estimate the log2(TE) and SE of log2(TE) values for each feature. Then we fitted the SE values against the normalized mRNA counts and log2(TE), using the “gam” function (in the R package “mgcv”) with a log link to obtain a smooth surface (S10 Fig). For other samples that did not have biological replicates, we derived the SE of log2(TE) for a feature (CDS or uORF) by subjecting the observed mRNA counts and log2(TE) to the fitted surface obtained based on the biological replicates of female and male bodies. Once the SE values of the uORF i and the CDS i were estimated, the SE of log2(βi) can be derived as
SElog2(βi)=SElog2(TEuORF,i)2+SElog2(TECDS,i)2.
As the Wald statistic log2(βi)SElog2(βi) follows a standard normal distribution under the null hypothesis that log2(βi) = 0, we calculated the P value with 2∙(1−Φ(|log2(βi)SElog2(βi)|)). Note that occasionally the TE values we estimated based on the normalized counts of RPFs and mRNA reads are slightly different from those calculated using the RPKM method as previously described [34, 64, 71]. Throughout this study, we only considered the difference that showed the same trend in both methods when we compared the TE values of two features or compared the TE values of a feature in different samples.
For a uORF i that is expressed with Kim normalized mRNA reads but not covered by any RPF in a sample m, we calculated Pm(R0), the probability that this uORF is translated. Under the null hypothesis H0(c), we assumed the expected TE of this uORF (x) is the same as that of the downstream CDS (TECDS,i, see S13 Fig). We first estimated the prior distribution of x and Kim. By assuming the log2(TECDS,i) follows a normal distribution, we can obtain the prior distribution of x under H0(c) as
f(x)=fNorm(log2(x);log2(TECDS,i),2SElog2(TECDS,i)2)=12πSElog2(TECDS,i)e−(log2(x)−log2(TECDS,i))24SElog2(TECDS,i)2
where SElog2(TECDS,i) is the SE of log2(TECDS,i) and estimated as described above. Kim follows a negative binomial distribution with the formula
fNB(Kim;μim,φM(μim))=Γ(Kim+φM(μim)−1)Γ(Kim+1)Γ(φM(μim)−1)(11+φM(μim)μim)φM(μim)−1(φM(μim)μim1+φM(μim)μim)Kim,
where φM is the dispersion trend of mRNA read counts estimated above. By modeling the RPF count of the uORF using a negative binominal distribution with mean xKim at given x and Kim, we can derive the posterior probability of observing 0 RPF reads as
Pm(R0)=∫f(x)∙∑Kim≥0fNB(Kim;μim,φM(μim))fNB(0;xKim,φR(xKim))dx,
where φR is the dispersion trend of RPF counts estimated above. Using the similar approach, we also estimated Pm(R0) under two other null hypotheses: (1) H0(u): x is the average TE (u) of the uORF in at least 2 other samples in which the uORF is well expressed (≥30 normalized mRNA reads and ≥3 normalized RPF reads); and (2) H0(0.1): x has a fixed value of 0.1.
For a uORF and its downstream CDS in an mRNA that dominates in both sample 1 and 2, we denoted βu = TEuORF,2/TEuORF,1 and examined whether log2(βu) is significantly different from 0 using the Wald test as above described (S10 Fig). Then, we defined γ = (TECDS,2 / TECDS,1) / (TEuORF,2 / TEuORF,1) and tested whether log2(γ) is significantly different from 0 (S41 Fig). We modeled the log2(TEuORF,1), log2(TECDS,1), log2(TEuORF,2), and log2(TECDS,2) with normal distributions and estimated SElog2(TEuORF,1),SElog2(TECDS,1),SElog2(TEuORF,2), and SElog2(TECDS,2) with the biological replicates from female and male bodies. Thus, log2(γ) also follows a normal distribution with
SElog2(γ)=SElog2(TEuORF,1)2+SElog2(TECDS,1)2+SElog2(TEuORF,2)2+SElog2(TECDS,2)2.
Therefore, we calculated the P value under the null hypothesis log2(γ) = 0 with 2∙(1−Φ(|log2(γ)SElog2(γ)|)). For each sample, the same analysis was also performed after pooling mRNA or RPF reads of all the uORFs in the same dominant isoform.
For the mature oocytes and activated eggs of D. melanogaster, the raw sequencing data were downloaded from Gene Expression Omnibus (GEO) with accession number GSE52799 [65]. The Ribo-Seq of S2 cells at different ion concentrations and mRNA-Seq and Ribo-Seq of 0–2 h fly embryos were downloaded from GEO with GSE49197 [64].
All deep-sequencing data generated in this study were deposited in the Sequence Read Archive (SRA) under accession number SRP067542. The numeric values underlying the main figures and supplementary figures can be found in S1–S8 Data.
|
10.1371/journal.ppat.1002576 | Metabolism of Phosphatidylinositol 4-Kinase IIIα-Dependent PI4P Is Subverted by HCV and Is Targeted by a 4-Anilino Quinazoline with Antiviral Activity | 4-anilino quinazolines have been identified as inhibitors of HCV replication. The target of this class of compounds was proposed to be the viral protein NS5A, although unequivocal proof has never been presented. A 4-anilino quinazoline moiety is often found in kinase inhibitors, leading us to formulate the hypothesis that the anti-HCV activity displayed by these compounds might be due to inhibition of a cellular kinase. Type III phosphatidylinositol 4-kinase α (PI4KIIIα) has recently been identified as a host factor for HCV replication. We therefore evaluated AL-9, a compound prototypical of the 4-anilino quinazoline class, on selected phosphatidylinositol kinases. AL-9 inhibited purified PI4KIIIα and, to a lesser extent, PI4KIIIβ. In Huh7.5 cells, PI4KIIIα is responsible for the phosphatidylinositol-4 phosphate (PI4P) pool present in the plasma membrane. Accordingly, we observed a gradual decrease of PI4P in the plasma membrane upon incubation with AL-9, indicating that this agent inhibits PI4KIIIα also in living cells. Conversely, AL-9 did not affect the level of PI4P in the Golgi membrane, suggesting that the PI4KIIIβ isoform was not significantly inhibited under our experimental conditions. Incubation of cells expressing HCV proteins with AL-9 induced abnormally large clusters of NS5A, a phenomenon previously observed upon silencing PI4KIIIα by RNA interference. In light of our findings, we propose that the antiviral effect of 4-anilino quinazoline compounds is mediated by the inhibition of PI4KIIIα and the consequent depletion of PI4P required for the HCV membranous web. In addition, we noted that HCV has a profound effect on cellular PI4P distribution, causing significant enrichment of PI4P in the HCV-membranous web and a concomitant depletion of PI4P in the plasma membrane. This observation implies that HCV – by recruiting PI4KIIIα in the RNA replication complex – hijacks PI4P metabolism, ultimately resulting in a markedly altered subcellular distribution of the PI4KIIIα product.
| It is estimated that 3% of the world's population are chronically infected by the hepatitis C virus (HCV). Most infections become chronic and eventually evolve into cirrhosis and hepatocellular carcinoma. Host factors are interesting targets for anti-HCV therapies due to their inherent high genetic barrier to resistance. Recently, phosphatidylinositol 4-kinase α (PI4KIIIα) has been identified as a crucial host factor for HCV replication. Many different pathogens, including HCV, subvert components of the phosphatidylinositol-4 phosphate (PI4P) pathway to function in favor of their own life cycle. In this paper, we show that HCV dramatically alters cellular PI4P metabolism and distribution, resulting in the enrichment of PI4P in the membranous web required for viral replication with a concomitant decrease of PI4P in the plasma-membrane. Moreover, we demonstrate that 4-anilino quinazolines, antiviral agents previously believed to target HCV NS5A, do in fact inhibit PI4P formation by inhibition of PI4KIIIα. This compound class is a promising lead for the development of a novel antiviral therapy based on PI4KIIIα inhibition. Specific PI4KIIIα inhibitors would also be important research tools required for a deeper understanding of the functions and regulation of PI4P.
| Hepatitis C virus (HCV) is an enveloped, single-stranded RNA virus classified as member of the Hepacivirus genus within the Flaviviridae family. The 9.6 kb positive-sense RNA genome contains a single open reading frame encoding a polyprotein of about 3,000 amino acids, flanked by highly structured 5′ and 3′ untranslated (UTR) regions. Following its release into the cytoplasm of the host cell, viral RNA is translated via an internal ribosome entry site (IRES), giving rise to a single polypeptide that is cleaved into 10 different mature protein products: Core, gpE1, gpE2, p7, NS2, NS3, NS4A, NS4B, NS5A, and NS5B. HCV RNA replication takes place in the cytoplasm, in association with a virus-induced intracellular membrane structure termed “membranous web”, onto which NS proteins assemble to form the so-called RNA replication complexes.
It is estimated that 3% of the world's population are chronically infected by the hepatitis C virus (HCV). Most infections become chronic and over time evolve into chronic hepatitis. The most unwanted complication of chronic hepatitis is cirrhosis, a massive liver fibrosis, which can lead to liver failure and hepatocellular carcinoma.
Since the discovery of hepatitis C virus (HCV) in the late 1980's much progress has been made in the understanding of the viral life cycle of HCV. Nonetheless, to date no vaccines are available and the current standard of care, involving lengthy treatment with a combination of ribavirin and pegylated interferon-α (peg-IFN-α), eradicates the infection in half of treated patients. A large effort has been made in the past two decades in order to develop novel anti-HCV therapies with greater efficacy. Two oral direct-acting antiviral agents (DAA) targeting the HCV NS3/4 protease, boceprevir and telaprevir, have recently reached the market and more are being developed [1]. While the initial efforts to the discovery of DAA focused almost exclusively on the best characterized HCV enzymes required for viral replication – the NS3/4A protease and the NS5B polymerase – in the past few years the NS5A viral protein has been attracting more and more attention as a target for drug development [1], [2]. NS5A possesses no known enzymatic activity. It is a multifunctional non-structural protein important for viral replication [3]–[6] as well as viral assembly [7]–[9]. It is a phosphoprotein consisting of three domains [10]. Domain I is highly conserved and forms a dimeric structure [11], [12], whereas domains II and III are believed to adopt a “natively unfolded” conformation [13], [14].
In recent years, several anti-HCV compounds identified using cell-based replicon screens were indicated to target NS5A based on the analysis of the mutations associated with emergence of resistance in the replicon system [15]–[17].
The most studied series of these “NS5A inhibitors” is represented by BMS-790052, an agent that is leading the field, having demonstrated potent antiviral activity in clinical studies [18]. Compounds in this class are characterized by a complex, dimeric or pseudo-dimeric structure and a high molecular weight, when compared with conventional “drug-like” small molecules [17], [19]. Resistance mutations against these compounds emerge readily in domain I of NS5A [20], with the most recurrent of these changes corresponding to variant of tyrosine at position 93 [20]. Although direct interaction with purified NS5A has not been demonstrated, compelling reverse genetic experiments [20] as well as molecular models [15], [21] strongly support the notion that NS5A is the direct target of these compounds.
A less characterized series of compounds, belonging to a different chemical class, was also initially indicated to target NS5A on the basis of the mutation pattern observed in resistant replicons [21]. The common structural element of this latter class of inhibitors is a 4-anilino quinazoline core. A representative member of this class of compounds is A-831/AZD-2836, an experimental antiviral agent that entered clinical trials but was later discontinued due to the lack of adequate exposure [17]. For these agents, the mutations reported to be associated with resistance were found to be different from those expected for the NS5A inhibitor described above, pointing to a different mechanism of action: a few mutations were found at the C-terminal end of NS5A domain I (E212D, L199F and T200P), whereas most mutations occurred in NS5A domains II and III (P299L, S370P, V388D, V362A, S390G and S370P). Additional mutations were also found in NS4B (S258T) and NS5B (S76A) [17], [21], [22]. Reverse genetics studies in which these mutations were reintroduced in the replicon, however, did not recapitulate the resistant phenotype observed in the original cellular clones [17], leaving thus the possibility open that these compounds act through a different viral or cellular target.
Interestingly, many kinase inhibitors, including some approved antitumoral drugs (gefitinib, lapatinib, erlotinib) are 4-anilino quinazoline derivatives [23]–[25]. Altogether, these considerations led us to investigate whether the anti-HCV activity displayed by these compounds might be due to inhibition of a cellular kinase.
Recently, several small-interfering RNA (siRNA) screening campaigns have identified type III phosphatidylinositol 4-kinases (PI4K) as crucial host factors for HCV replication. In particular, PI4KIIIα was found to be required for HCV RNA replication in a cell line- and genotype-independent manner, whereas the requirement for the β isoform was observed to be less dramatic and limited to Con-1 (genotype 1b) replicons [26]–[29]. It was shown that the catalytic activity of PI4KIIIα is required to rescue HCV replication in cells with a stable knock-down of PI4KIIIα. In addition, it has been proposed that NS5A stimulates PI4KIIIα activity by direct interaction via domain I [30]–[32]. All these observations taken together made us consider the phosphatidylinositol 4-kinases a potential alternative candidate target for 4-anilino quinazoline inhibitors of HCV replication.
In this paper, we present evidence that AL-9, a member of this class of compounds previously reported to target NS5A, inhibits PI4P formation by direct inhibition of phosphatidylinositol 4-kinase IIIα (PI4KIIIα). In addition, we provide evidence that pharmacological inhibition of PI4KIIIα with AL-9 results in altered subcellular distribution of NS5A similar to that observed after RNAi knock-down of the PI4KIIIα mRNA, strongly supporting a mechanism of HCV inhibition mediated by the inhibition of PI4KIIIα. Moreover, we show that HCV subverts components of the phosphatidylinositol-4 phosphate (PI4P) pathway to function in favor of its own life cycle, thereby enriching the PI4P concentration in the membranous web while depleting the plasma membrane PI4P pool.
AL-9 is a member of 4-anilino quinazoline-containing HCV replication inhibitors described previously ([21]; Figure 1). In order to confirm its anti-HCV activity, we tested the effect of this compound on HCV replication in Huh7.5 cells stably expressing genotype 1b or 2a subgenomic replicons (Con1-SR and JFH-A4, respectively). The EC50 values, calculated by measuring viral RNA after incubation with AL-9 for three days, are reported in Table 1. Replicon EC50 values for AL-9 were found to be 0.29 µM and 0.75 µM for genotype 1b and 2a, respectively. In order to prove that AL-9 inhibits HCV replication not only in the context of a HCV subgenomic replicon, but also in the context of the complete viral life-cycle, we determined the inhibitory activity using the J6/JFH-1 HCV virus. In this case, the EC50 value was found to be 1.2 µM, a figure comparable with the result obtained with genotype 2a subgenomic replicon. CC50 values are shown for Con1-SR, JFH-A4 and Huh7.5 cells, respectively. In summary, AL-9 inhibits HCV across different genotypes with activity in the sub-micromolar to low micromolar range in the absence of significant cytotoxic effects.
In the following experiment, we investigated whether AL-9 inhibits the purified type III phosphatidylinositol 4-kinases PI4KIIIα and PI4KIIIβ (Figure 2). Both enzymes were inhibited by AL-9 with a five-fold preference for PI4KIIIα (IC50 of 0.57 µM and 3.08 µM, respectively). This result demonstrates that AL-9 inhibits type III PI4 kinases in vitro at concentrations similar to those required for its anti-HCV activity, displaying a moderate selectivity for the α over the β isoform. We also tested the activity of AL-9 on two class I PI3-kinases (p110α and p110β). While PI3-kinase p110α was inhibited with an IC50 of 1.1 µM, the potency of AL-9 for PI3-kinase p110β was significantly lower (40% inhibition @10 µM, data not shown).
Our hypothesis is that AL-9 inhibits HCV replication via inhibition of PI4KIIIα. Thus, we wanted to assess whether AL-9 also inhibited PI4KIIIα in living cells. To this aim, we needed to set up an assay that allowed us to monitor the activity of this kinase in intact cells. PI4KIIIα is primarily localized to the ER, whereas PI4KIIIβ is localized to the Golgi membranes [33]. It was shown that PI4KIIIβ contributes to the synthesis of PI4P at the Golgi membranes [34], [35]. Subcellular localization of the enzymes, however, does not always coincide with their function. Thus, PI4KIIIα, considered to be an ER-resident enzyme, has previously been shown to be critical for the generation and maintenance of the plasma membrane PI4P pool during phospholipase C activation and Ca2 signaling in HEK-293 or Cos-7 cells [35], [36] as well as in resting Cos-7 cells [37]. Whether PI4KIIIα is responsible for the maintenance of the plasma membrane PI4P pool under normal cell culture conditions in hepatoma cells is currently not known. Hammond et al [37] have developed immunocytochemical techniques that enable selective staining of the PI4P pool present in the plasma membrane (plasma membrane staining protocol) or in the intracellular membranes (Golgi staining protocol), respectively. We used this technique, in combination with RNA gene silencing or pharmacological inhibition, to decipher which of the type III enzymes participates in the synthesis of the Golgi- or plasma membrane PI4P-pools in Huh7.5 hepatoma cells.
To address which type III PI4 kinase is responsible for the synthesis of the different cellular PI4P pools, Huh7.5 cells were treated with siRNAs targeting PI4KIIIα, PI4KIIIβ or an unrelated siRNA (mock-siRNA) as described in the Materials and Methods section. Immunoblots assays show specific knockdown of PI4KIIIα or PI4KIIIβ by their corresponding siRNAs (Figure 3C). Three days after siRNA treatment, PI4P was revealed either by the plasma membrane staining protocol (Figure 3A, upper panel) or by the Golgi membrane staining protocol (Figure 3A, lower panel). In cells treated with the unrelated siRNA (mock-siRNA), PI4P was detected both in the plasma membrane and in intracellular membranes. Intracellular PI4P was localized primarily in the Golgi membranes, as judged by the colocalization with the Golgi marker giantin. Silencing of PI4KIIIα resulted in a significant decrease of the PI4P level in the plasma membrane. Concomitantly with the decrease in the plasma membrane PI4P levels, we consistently observed a pronounced increase of PI4P level in the Golgi membrane following PI4KIIIα knockdown. In the case of PI4KIIIβ knockdown, we observed a ∼30% decrease of Golgi membrane PI4P level, whereas the PI4P levels of the plasma membrane remained substantially unaffected (Figure 3B).
These results are in line with the previously reported role for PI4KIIIα in maintaining the PI4P plasma membrane pool [35]–[37] and confirm the importance of PI4KIIIβ for the synthesis of at least part of the Golgi membrane PI4P [34], [35]. We also observed that decreased expression of PI4KIIIα resulted in an unexpected increase in the level of the Golgi membrane pool (Figure 3A), suggesting a complex level of cross-talk between the cellular type III PI4 kinases in maintaining the physiological PI4P levels at the Golgi membrane, at least in our experimental model.
In order to confirm and extend the results described above, we utilized a known pharmacological inhibitor of the type III PI4 kinases. PIK93 was previously exploited to distinguish between the roles of the two PI4KIII isoforms [38], [39]. In particular, a concentration of 0.5 µM PIK93 is expected to affect only PI4KIIIβ, whereas 30 µM PIK93 should inhibit both PI4KIIIβ and PI4KIIIα. Thus, Huh7.5 cells were treated with 0.5 µM or 30 µM PIK93 or with DMSO as control. After two hours of incubation, PI4P was revealed either by the plasma membrane staining protocol (Figure 4A, upper panel) or by the Golgi staining protocol (Figure 4A, lower panel). PI4P levels associated with the Golgi membranes decreased by ∼25% after incubation with 0.5 µM PIK93 (Figure 4B). This is in line with PI4KIIIβ contributing to the production of PI4P present in the Golgi membranes (PI4KIIα, another contributor of Golgi-localized PI4P is not inhibited by PIK93 [38], [39]). Increasing PIK93 concentration to 30 µM further increased the inhibition of the intracellular membrane PI4P pool, to ∼65% (Figure 4B). This could be due to a more complete inhibition of PI4KIIIβ; however, based on this experiment, we cannot rule out a contribution of PI4KIIIα activity to the maintenance of the Golgi membrane PI4P pool. In contrast to what observed in the Golgi-associated membranes, the plasma membrane PI4P level was not significantly affected upon incubation with 0.5 µM PIK93, but decreased by nearly 50% after incubation with 30 µM of PIK93 (Figure 4B). Combined with the RNAi experiments described above, these results support the notion that, in Huh7.5 cells, PI4KIIIα is involved in the maintenance of the plasma membrane PI4P pool, whereas PI4KIIIβ is at least partly responsible for the maintenance of the Golgi membrane PI4P pool.
We then evaluated the PI4K inhibitory activity of AL-9 in Huh7.5 cells using the same methodology. Briefly, Huh7.5 cells were incubated either with DMSO or with increasing concentration of AL-9 (1, 2, 4 or 8 µM) for two hours (Figure 5A). Treatment with AL-9 gradually reduced the amount of PI4P in the plasma membrane (Figure 5B). Conversely, the concentration of PI4P in the Golgi-associated membranes remained substantially unaltered up to the highest AL-9 concentration used (Figure 5B).
In all, the results described above suggest that AL-9 inhibits PI4KIIIα also in living cells, while not appreciably affecting the activity of PI4KIIIβ. This is in line with the selectivity for PI4KIIIα over PI4KIIIβ observed in the biochemical assays.
Viral infection induces modification of intracellular membrane structures [40] and, for some RNA viruses including HCV, it has been shown that these induced membranous structures are highly enriched for PI4P [32], [41]. Before testing the activity of AL-9 in HCV-infected cells, we wanted to know what the impact of HCV on cellular membrane structures was, with special regard to the subcellular membrane distribution of PI4P.
Naïve Huh7.5 cells or cells actively replicating the genotype 2a or 1b HCV subgenomic replicon were investigated for their PI4P concentration in internal membranes or plasma membranes, respectively (Figure 6A). As previously shown, cells expressing the HCV replicon form a membranous web that is highly enriched for PI4P (Figure 6A, lower panel). The level of PI4P in these virus-specific membrane structures is markedly higher in JFH-A4 cells, containing the very efficient genotype 2a JFH-1 replicon, compared to the Con1-SR cells, which are based on the genotype 1b Con1 replicon, possibly mirroring the different RNA replication efficiency. It is well established that the kinase responsible for the production of the PI4P pool present in these structures is PI4KIIIα. In the current model, PI4KIIIα interacts with the viral protein NS5A, leading to up-regulation of the kinase activity and accumulation of PI4P in the virus-specific membranous web [30]–[32]. Conversely, the results shown in Figures 3–5 suggest that – in absence of viral replication – a major function of PI4KIIIα is the synthesis of the PI4P pool in the plasma membrane. We therefore asked ourselves whether the presence of HCV could not only influence distribution and enrichment of PI4P in internal membranes, but also alter the PI4P plasma membrane pool. In Figure 6A, we show that, concomitantly with the increase of PI4P in the internal membranes (lower panel), HCV replication promotes a marked decrease of PI4P concentration in the plasma membrane (upper panel). Relative quantification of the PI4P levels in the different experimental conditions is shown in Figure 6B. This experiment demonstrates that the presence of HCV causes a dramatic change of PI4P localization in cellular membranes, whereby the increase of PI4P concentration in the virus-specific membranous structures appears to be accompanied by a depletion of the PI4P pool normally present in the plasma membrane.
We next investigated whether HCV-associated changes in PI4P distribution could be reverted upon cure of the HCV replicon by specific inhibitors. We treated JFH-A4 cells for two weeks either with the HCV RdRP inhibitor HCV-796 or with the HCV NS3/4A protease inhibitor MK-5172 and followed PI4P localization in internal membranes and in the plasma membrane (Figure 7). Independent of the type of inhibitor used, the result shows that the HCV-induced PI4P-enriched membranous web in JFH-A4 cells disappeared upon suppression of HCV replication and that the intracellular PI4P localization returned to the Golgi-localization as observed in the naïve Huh7.5 cells (left column). In parallel, the plasma membrane concentration of PI4P increases to the levels observed in naïve cells (middle column). NS5A staining (right column) as well as real-time RT-PCR (not shown) indicated that the prolonged treatment with HCV-inhibitor led to complete and stable suppression of viral protein expression and undetectable level of HCV RNA. Thus, removal of HCV RNA brings PI4P synthesis and distribution back to a level comparable to naïve Huh7.5 cells. It is worth of note, however, that the previous presence of HCV replicons in the cured cells induced some irreversible morphological changes of unknown nature, such as a smaller cell size.
We have shown that PI4KIIIα is inhibited by AL-9 in naïve Huh7.5 cells. As discussed above, in HCV-replicating cells, the kinase activity of PI4KIIIα is up-regulated by a direct protein-protein interaction with the viral protein NS5A [30], [32]. In the following experiment (Figure 8), we explored whether AL-9 is able to inhibit PI4KIIIα also in this context. JFH-A4 cells were incubated with increasing concentration of AL-9 for 4 hours and PI4P concentration in the HCV membranous web was followed by immunostaining (Golgi staining protocol). Treatment of cells with AL-9 lead to clear inhibition of PI4P accumulation in the HCV membranous web. Incubation with 8 µM AL-9 depleted as much as 70% of the PI4P present in the intracellular membranes of replicon-containing cells. This result confirms that AL-9 inhibits PI4KIIIα independent of its membranous localization and suggests that this inhibition could be responsible for the observed antiviral effect. Since AL-9 has anti-HCV activity in the concentration range used here, longer incubation of HCV replicons with AL-9 results in inhibition of HCV RNA- and protein-synthesis. As a consequence, the PI4P-enriched HCV membranous web would disintegrate. In this case loss of PI4P in the internal membranes could be not a direct consequence of PI4KIIIα inhibition, but a consequence of disintegration of the HCV membranous web.
In order to rule out this possibility, we checked localization of NS5A, a presumed marker for HCV replication sites, after 4 hours of incubation with AL-9. Localization of NS5A does not change, suggesting that AL-9 does not significantly change the structure of the HCV membranous web upon 4 hours of treatment. Moreover, incubating the same replicon cells for 4 hours with HCV-796, an HCV polymerase inhibitor, did not lead to appreciable depletion of the membranous web PI4P pool indicating that the loss of PI4P in the HCV-induced intracellular membranes is the direct consequence of inhibition of PI4KIIIα, and not the consequence of inhibition of HCV replication. Additional evidence is provided in the experiment below, in which expression of the HCV polyprotein, and consequently formation of a membranous web, was driven by cDNA plasmid rather than by autonomously replicating HCV RNA.
It was previously shown that knock-down of PI4KIIIα expression by RNAi resulted in the production of large NS5A clusters. This was achieved in an experimental setting where the HCV polyprotein was expressed from DNA constructs, thus avoiding potential confounding effects due to inhibition of HCV RNA replication [27], [32]. We wanted to assess whether pharmacological inhibition of PI4KIIIα kinase activity would lead to similar effects on NS5A subcellular localization. Thus we followed the effect of AL-9 on NS5A localization after transient DNA transfection in Huh7-Lunet/T7 cells with a plasmid expressing genotype 2a nonstructural proteins NS3-NS5B under the control of a T7 promoter [42]. Cells were treated either with DMSO (upper panels) or with 8 µM AL-9 (lower panels) for 2, 8 or 16 hours and localization of NS5A as well as PI4P were followed by indirect fluorescence microscopy (Figure 9). Cells successfully transfected with the HCV polyprotein expressed NS5A and induced the PI4P-enriched membranous web. After 8 hours of treatment with AL-9, changes in NS5A localization in form of larger clusters become visible. At the same time, PI4P concentration in the membranous web started to decrease. After 16 hours of incubation with AL-9, NS5A was concentrated almost exclusively in large clusters. At this time-point, PI4P in the internal membranes had completely vanished.
In summary, this experiment shows that, in cells expressing the HCV polyprotein from cDNA, prolonged treatment with AL-9 results in a redistribution of NS5A into large clustered structures with high resemblance to the structures previously observed after silencing of the PI4KIIIα gene by RNAi [27], [32]. Concomitantly, we observed a depletion of the PI4P pool present in the HCV-induced membranous structures. These results indicate that the catalytic activity of PI4KIIIα is directly or indirectly required for the proper localization of HCV NS5A protein into the membranous web. Furthermore, the experiment just described lands additional support to the notion that the antiviral effect of AL-9 is mediated by the inhibition of PI4KIIIα.
In the present paper, we show that a compound belonging to the class of 4-anilino quinazoline inhibitors of HCV replication is an inhibitor of PI4KIIIα, a cellular lipid kinase required for viral replication.
PI4KIIIα belongs to the family of type III phosphatidylinositol 4-kinases, enzymes that catalyze the conversion of phosphatidylinositol to phosphatidylinositol 4-phosphate (PI4P). PI4P is the most abundant monophosphorylated inositol phospholipid in mammalian cells and the importance of this phospholipid is just started to be unraveled [43]. In addition to playing important roles in intracellular signaling and membrane trafficking, phosphatidylinositol lipids and their metabolizing enzymes are also exploited by many different viruses in order to transform cellular membranes in structures supporting their replication [40], [44], [45]. PI4KIIIβ was shown to be a host factor required for enterovirus replication [41], whereas several reports have demonstrated that PI4KIIIα is crucial for HCV replication [26]–[29]. Owing to the importance of this pathway, the need for specific inhibitors of PI4III kinases is increasing. Only recently, some enviroxime-like compounds with antiviral activity against enterovirus have been demonstrated to target PI4KIIIβ. One of these agents is a very specific inhibitor of the β-isoform of the type III PI4-kinases [46]. So far, no such compound exists for the PI4KIII-α isoform. A commonly used inhibitor for type III phosphatidylinositol 4-kinases is PIK93, which has originally been designed to inhibit class I PI3-kinases [38]. This compound allows differential inhibition of PI4KIIIβ alone or PI4KIIIα and PI4KIIIβ together depending on the concentration used. In this paper, we show that a 4-anilino quinazoline derivative, termed AL-9 (Figure 1 and Figure S1), is able to inhibit PI4KIIIα in a test tube as well as in living cells. AL-9 inhibited purified PI4KIIIα, with a moderate (∼5-fold) selectivity over the β isoform (Figure 2). In cell culture, we observed that treatment with AL-9 efficiently inhibits the maintenance of the plasma membrane PI4P pool in Huh7.5 cells while not significantly affecting the Golgi membrane pool at the highest concentration used (Figure 5). This finding is in line with the moderate selectivity observed in the biochemical assay. Thus, AL-9 represents a lead candidate for the development of more potent and more specific inhibitors of PI4KIIIα.
Anti-HCV compounds of the 4-anilino quinazoline class were previously assumed to exert their antiviral effect via inhibition of the viral protein NS5A. This conclusion rested on analysis of the mutations found in the HCV replicon in association with resistance to these agents [21]. Mutations generated against 4-anilino quinazolines were localized mainly in NS5A, in triplets that occurred all in NS5A or appeared concomitant with changed in NS4B or NS5B [17], [22] (see also Introduction). Reverse genetic experiments, in which these mutations were reintroduced in the replicon (single, double and triple combinations), however, did not support a role for these mutations in conferring resistance to 4-anilino quinazolines [17]. In order to assess whether the reported mutations conferred any level of resistance to AL-9, we independently performed reverse genetics studies in which selected mutations triplets, reported to be associated with the higher level of resistance, were reintroduced in a genotype 1b replicon with the same genetic background as the one reported in the original resistance study (Figure S2). These mutation triplets are: FAG: L199F+V362A+S390G (NS5A), DLD: E212D+P299L+V388D (NS5A), and PPA: T200P+S370P(NS5A)+S76A(NS5B). We observed that the replicon containing the first triplet lost the ability to replicate at significant level. For replicons containing the latter two combinations of mutations, RNA replication could be measured, although at a lower level compared to the parental construct (35% and 20%, respectively). These replicons, however, remained equally sensitive to AL-9 as the parental replicon (Figure S2), opening the question as to which really is the target of this compound class. We are currently trying to select HCV replicons resistant to AL-9. So far we were unable to identify mutations that confer resistance to AL-9.
Our new data on AL-9 suggest that inhibition of HCV replication by 4-anilino quinazoline compounds is a consequence of PI4KIIIα inhibition. Our conclusion rests on a number of experimental findings. First of all, we showed that AL-9 is an inhibitor of purified type III PI4 kinases. Furthermore, we clearly demonstrated that AL-9 inhibits PI4KIIIα both in naïve Huh7.5 cells (Figure 5, discussed above) as well in cells harboring actively replicating HCV RNA (Figure 8). In cells where HCV replication occurs, PI4KIIIα interacts physically with HCV NS5A. This interaction, in turn, leads to the stimulation of PI4P synthesis at the HCV replication sites [32]. Treatment of replicon-harboring cells with AL-9 leads to efficient suppression of the PI4P pool at the HCV replication sites and does so independently of inhibition of HCV replication. This indicates that – although the enzymatic activity of PI4KIIIα is modulated by the interaction with the HCV protein NS5A – it remains sensitive to the action of the 4-anilino quinazoline inhibitor.
We also investigated whether the dramatic changes observed in PI4P membrane levels by treatment with AL-9 could be associated with alteration in the subcellular distribution of type III PI4 kinases. To this aim, we analyzed the subcellular distribution of the type III PI4 kinases in Huh7.5 or Luc-A4 cells following incubation with AL-9 (Figure S3). We observed no major effect of AL-9 on the localization of either PI4KIIIα or PI4KIIIβ, in line with the notion that the observed effects are primarily due to the inhibition of the kinase activity rather than to an altered protein subcellular distribution.
In cells that express the HCV polyprotein from a trans-gene, knock-down of PI4KIIIα by RNAi was previously shown to cause a dramatic change in NS5A subcellular distribution, from a pattern consistent with localization in the membranous web replication complexes to abnormally large cytoplasmic clusters [27], [30], [32]. In Figure 9, we show that AL-9 treatment of cells ectopically expressing the HCV nonstructural proteins results in a time-dependent depletion of PI4P and a concomitant change of NS5A localization to the large-clustered structures discussed above, reinforcing the notion that the anti-HCV effect of AL-9 and related compounds are likely to be mediated by the inhibition of PI4KIIIα.
We also found that PI3K p110α is inhibited by AL-9 in vitro at concentration similar to those needed to inhibit type III PI4-kinases. However, no Class I PI3-kinase has been shown to influence HCV replication thus inhibition of HCV replication by AL-9 is not due to inhibition of Class I PI3-kinases. So far, the only PI3-kinase that resulted as positive hit for HCV replication inhibition in siRNA screens is PI3-kinase C2 gamma [28]. Future work will have to address whether AL-9 inhibits PI3KC2G in addition to Type III PI4-kinases.
During the characterization of AL-9 we focused our attention on various aspects of PI4P metabolism in Huh7.5 cells with and without replicating HCV. We observed a typical Golgi localization of PI4P in intracellular membranes of naïve Huh7.5 cells and confirmed a role for PI4KIIIβ in maintaining at least part of this pool. In order to get the complete picture we also investigated the PI4P pool present in the plasma membrane. In yeast, Stt4p, the ortholog to the mammalian PI4KIIIα, is localized at the plasma membrane and it is the major contributor for the synthesis of the plasma membrane-localized PI4P [43]. In mammalian cells, the role of PI4KIIIα for the maintenance of the plasma membrane PI4P pool has been demonstrated in HEK-293 and Cos-7 cells [35]–[37]. Here we demonstrate that liver-derived Huh7.5 cells are endowed with a rich PI4P pool in the plasma membrane and that the enzyme responsible for its maintenance is PI4KIIIα. In HCV-replicating cells, the subcellular PI4P distribution is profoundly altered. As already reported previously, the presence of HCV causes the induction of a membranous web highly enriched for PI4KIIIα-syntesized PI4P. In accordance, several reports demonstrate that NS5A recruits PI4KIIIα to the membranous web by direct protein-protein interaction, thereby stimulating its enzymatic activity [30]–[32]. Concomitantly with the induction of highly PI4P-enriched internal membranes, we observe a marked decrease of PI4P in the plasma membrane. One possible explanation could be that – by hijacking PIKIIIα – HCV might be able to enrich PI4P in the virus-induced membranous web not only by directly activating the enzymatic activity of PI4KIIIα recruited into the HCV RNA replication compartment, but also by preventing transport of the PI4KIIIα-synthesized PI4P from the synthesis site to the plasma membrane. How PI4KIIIα, localized at the ER, synthesizes the PI4P pool present in the plasma membrane it is still an enigma. This topological discrepancy can partially be resolved assuming that PI4KIIIα-dependent PI4P production occurs on ER-PM contact sites, that is, sites of close apposition between ER and PM. In yeast it has been demonstrated that a complex interplay between different proteins regulate the PI4P metabolism at the plasma membrane [47]. Among these proteins are Osh, the yeast ortholog of the human OSBP and the ER membrane VAP proteins Scs2 and Scs22, the yeast orthologs of human VAP proteins. Interestingly, h-VAP-33 and OSBP have been shown to be important for HCV replication [48]–[50]. It may be possible that recruitment of PI4KIIIα to the HCV membranous web through NS5A prevents interaction of PI4KIIIα with its cellular protein partners required to direct PI4P to the plasma membrane. Upon withdrawal of HCV from the cells (Figure 7) PI4KIIIα is again free for interaction with the adequate partners. A possible role of PI4KIIIα in PI4P trafficking between the plasma- and intracellular membranes is suggested by our finding that RNAi silencing of this PI4 kinase results in decreased concentration of PI4P in the plasma membrane with a concomitant increase in the level of PI4P in the endomembranes (Figure 3). Such a function of PI4KIIIα would have to be independent of the kinase activity, since pharmacological inhibition (with PIK93 or AL-9) does not recapitulate this phenomenon observed by knocking down the protein expression.
In summary, the presence of HCV may change PI4P metabolism not only by activating the catalytic activity of PI4KIIIα by NS5A but also by modulating the PI4P distribution between different membrane compartments. The net result is an enrichment of the PI4P pool in the HCV-induced membranous web with a concomitant depletion of the plasma membrane PI4P pool.
Concluding, in this paper we demonstrate that a class of HCV inhibitors originally proposed to target NS5A does in fact target the host factor PI4KIIIα. Compounds targeting host factors may have the general advantage of imposing a higher genetic barrier to the development of resistance. AL-9, a member of this class of compounds, inhibits PI4KIIIα and to our knowledge, it is the first compound with a clear preference for PI4KIIIα over PI4KIIIβ. For this reason, AL-9 offers a good candidate as lead compound for the development of more potent and specific pharmacological inhibitors of PI4KIIIα to be used both as important research tools as well as leads for initial drug discovery.
The HCV RNA polymerase inhibitor HCV-796 and the PI kinase inhibitor PIK93 were a gift from Arrow Pharmaceuticals. The HCV protease inhibitor MK-5172 was purchased from Selleck Chemicals. Nucleic acids were manipulated according to standard protocols. Plasmid FBac-His-CD-PI4KA was constructed as follows: the catalytic domain of PI4KIIIα was amplified by PCR using the oligonucleotides 5′-CACTGCGGATCCATAATGGGGATGATGCAGTGTGTGATTG-3′ (sense), 5′-CCTGCGAATTCTCAGTAGGGGATGTCATTC-3′ (antisense) and the plasmid pEF1A-PIK4CA untagged (a kind gift from G. Randall, Department of Microbiology, University of Chicago) as template. The resulting PCR fragment was subcloned into the vector pCR-Blunt II-Topo (Invitrogen) and finally cloned into the BamH1–XhoI cloning sites of the plasmid vector pFastBac THT-B. The resulting protein expressed from this plasmid contains an N-terminal hexa-histidine tag and starts at PI4KIIIα amino acid G873 (reference sequence NM_058004). pTM-NS3-5B expression vector expressing the HCV genotype 2a nonstructural proteins under the control of the T7 promoter was a generous gift from V. Lohmann (Department of Molecular Virology, University of Heidelberg) [42]. Synthesis of compound AL-9 is described in Supporting Information.
The human hepatoma-derived cell line Huh7.5 [51] were grown in Dulbecco's modified Eagle's medium (DMEM) supplemented with 10% fetal bovine serum, 100 U/ml penicillin, 100 µg/ml Streptomycin and 2 mM L-glutamine; G418 (0.8 mg/ml) was added to cell lines containing the HCV replicon. Stable cell lines expressing HCV genotype 1b or 2a subgenomic replicons were generated by electroporation of in vitro-transcribed RNA into Huh7.5 cells [52] and following selection with G418 (0.8 mg/ml) for three weeks. Con1-SR: Huh7.5 cells replicating the Con1 subgenomic replicon with the adaptive mutations E1202G in NS3 and S2204R in NS5A. JFH-A4: Huh7.5 cells replicating the JFH-1 subgenomic replicon together with the luciferase reporter gene constructed as described previously [53]. JFH-A4 cells were cured from the HCV replicon by two weeks of treatment with the protease inhibitor MK-5172 (0.2 µM) or the HCV RNA polymerase inhibitor HCV-796 (2 µM), respectively. Huh7-Lunet/T7 cells were a kind gift from V. Lohmann (Department of Molecular Virology, University of Heidelberg, Germany).
For replication assays, JFH-A4 or Con1-SR cells were plated at the density of 3×104 or 6×104 cells/well, respectively, in 24-well dishes the day before the experiment. Cells were treated with AL-9 resulting in a final concentration of 1% DMSO in the cell medium. After three days of treatment, RNA was extracted using the RNeasy Mini Kit (Qiagen) and HCV RNA was quantified by real time PCR using the following oligonucleotide and probe set designed for the HCV IRES as described previously [52]: sense (5′-GCGAAAGGCCTTGTGGTACT-3′), antisense (5′-CACGGTCTACGAGACCTCCC-3′), and probe (5′-CCTGATAGGGTGCTTGCGAGTGCC-3′, 5′ 6-carboxyfluorescein [FAM]/3′ 6-carboxytetramethylrhodamine [TAMRA]). GAPDH mRNA was used as internal control for data normalization.
Production of infectious virus was performed as follows: J6/JFH-1 chimeric RNA (1-846(J6CF)/847-3034(JFH1) was electroporated into Huh7.5 cells using the protocol described previously [52]. Briefly, 2×106 cells were electroporated with 10 µg of RNA in a final volume of 200 µl and 4×106 cells were plated in a T-75 flask. Three days post electroporation, medium was harvested and stored at −20°C in small aliquots. Calculation of EC50 of AL-9 using the infectious HCV virus was performed as follows: Huh7.5 cells were plated at 4×104 cells/well in 24-well plates the day before infection. Infection was started by addition of 10 µl of cell medium containing infectious virus (see above) at an MOI of 50 in a final volume of 400 µl. After 6 hours of incubation, medium was removed and replaced with 400 µl of fresh medium containing serial dilutions of AL-9. RNA was collected after 72 hours of incubation and quantified by real time PCR. Cell cytotoxicity (CC50) of AL-9 was calculated using the cell viability assay CellTiter-Blue (Promega). Huh7.5, JFH-A4, or Con1-SR cells (5×103 cells/well in 96-well dishes) were plated the day before treatment. AL-9 was added and cell viability was measured after four days of treatment.
Recombinant baculovirus was generated with the plasmid FBac-His-CD-PI4KA using the Bac-to-Bac system following the instructions of the manufacturer (Invitrogen). For protein expression, Sf9 cells were infected with recombinant baculovirus at a density of 2×106 Sf9 cells/ml for 3 days at 20°C. To prepare cell extract (1.5×108 cells), cells were incubated in hypotonic buffer (10 mM HEPES (pH 7.5), 10 mM NaCl, 1 mM Tris(2-carboxyethyl)phosphine (TCEP) and EDTA-free protease inhibitor cocktail (Complete, Roche) for 30 min in ice and mechanically broken by 20 strokes of a Dounce homogenizer. After homogenizing, cells were incubated in lysis buffer (50 mM HEPES (pH 7.5), 500 mM NaCl, 10% glycerol, 1% Triton-X100, 1 mM TCEP and EDTA-free protease inhibitor cocktail (Complete, Roche) for further 30 min in ice and cell extract was cleared by centrifugation for 45 min at 20.000 g. The cleared supernatant was incubated in batch with Ni-Sepharose High Performance (GE Healthcare) for 2 hours at 4°C with continuous shaking. The resin was first washed with 10 resin-volumes of wash buffer (50 mM HEPES (pH 7.5), 10% glycerol, 0.4% Triton X-100, 150 mM NaCl and 20 mM imidazol) followed by elution with wash buffer containing 250 mM imidazole. Active fraction (0.5 ml) were dialyzed against 50 mM HEPES (pH 7.5), 150 mM NaCl, 1 mM DTT, 0.4% Triton X-100 and 10% glycerol and stored at −80°C in small aliquots.
PI4K kinase activity was assayed with the ADP-Glo Kinase Assay (Promega), according to the manufacturer's instructions. Briefly, 0.5 µl of PI4KIIIα-CD or 0.05 µl PI4KIIIβ (32 ng, Invitrogen) were preincubated with DMSO or AL-9 in reaction buffer (20 mM Tris (pH 7.5), 5 mM MgCl2, 2 mM DTT, 0.5 mM EGTA, 0.4% Triton X-100) for 10 min at room temperature in a final volume of 8 µl. The reaction was started by addition of 2 µl of ATP and PI∶PS Lipid Kinase Substrate (Invitrogen) to give a final concentration of 100 µM and 150 µM, respectively. After 1 hour of incubation at room temperature the reaction was stopped and further processed as described by the manufacturer. In parallel the reaction was performed without PI∶PS substrate in order to detect contaminating ATPase activity present in the protein fractions. This activity was subtracted from the measured kinase activity. Kinase activity of PI3Kα (p110α/p85α) and PI3Kβ (p110β/p85α) was assayed as above using 5 ng or 20 ng, respectively (Millipore). Reaction buffer was changed to 50 mM HEPES pH 7.5, 10 mM MgCl2 and 1 mM DTT.
Cells were plated one day before the experiment in 24-well plates (5×104 cells/well for Huh7.5 and JFH-A4 cells, 7×104 cells/well for Con1-SR and 1×105 cells/well for cured JFH-A4 cells). Cells were either untreated or treated with compounds for the time as indicated in the figure legend. PI4P staining of the plasma membrane or internal membranes was performed exactly as described previously [37]. Primary antibodies used were: anti-PI4P IgM (Cat.No. Z-P004, 1∶300, Echelon), anti-Giantin antibody (Cat. No. PRB-114C-200 1∶1000, Covance), affinity-purified rabbit anti-NS5A antibody (1∶2000) [54], anti-PI4KIIIα kinase (Cat. No. 4902, 1∶50, Cell Signaling), anti-PI4KIIIβ kinase (Cat. No, 611817, 1∶500, BD Transduction). Secondary antibodies used were goat anti-mouse IgM Alexa Fluor 488 (Cat. No. A-21042, 1∶600, Invitrogen) and goat anti-rabbit Alexa Fluor 568 (Cat.No. A-11011, 1∶600, Invitrogen). For type III PI4K kinases or NS5A staining, all incubations were performed at room temperature. Cells were washed once with PBS and fixed with 300 µl of 4% PFA for 15 min. Cells were washed three times with PBS and permeabilized with 500 µl of 0.1% Triton X-100 (or 0.5% for PI4KIIIα kinase staining) in PBS for 10 min. Unspecific binding was blocked by incubation with 3% BSA in PBS (for PI4KIIIα staining no blocking was performed). After incubation with the primary antibody in blocking buffer, cell were washed with PBS and subsequently incubated with goat secondary antibodies conjugated to Alexa-Fluor 568, or Alexa-Fluor 488 at a dilution of 1∶600. Nuclei were stained with Hoechst dye 33342 (Sigma; 1∶4000). Slides were then mounted with 5 µl ProLong Gold Antifade (Invitrogen) and analyzed by using an inverted Leica TCS SP5 scanning laser confocal microscope. Digital images were taken using LAS AF software (Leica) and processed using Volocity software (Perkin Elmer). Quantification of fluorescence intensity was determined from multiple images using Volocity. Relative changes in fluorescence intensity mean values where obtained from four randomly picked fields for each condition (150∼300 cells). For plasma membrane staining, total PI4P fluorescence intensity obtained in each condition was normalized to the number of cells present in each field. For the quantification of relative PI4P levels in internal membranes, PI4P fluorescence intensity was normalized using the fluorescence intensity of the Golgi marker giantin. Quantitative immunofluorescence data are presented as means ± the standard error of the mean (SEM). For the calculation of statistical significance, a two-tailed, unpaired t-test was performed.
3×104 Huh7.5 cells/well were seeded in 24-well plates on microscope cover glasses and transfected with 50 nM of siRNAs in serum-free Opti-MEM (Invitrogen) using Lipofectamine RNAiMAX (Invitrogen), according to the manufacturer's protocol. For western blot analysis, the transfection reaction was proportionally scaled up to 6-well plates. In order to maximize the silencing efficiency, 24 hours after the first transfection, the cells were subjected to a second round of siRNA transfection. siRNA sequences were the following (5′→3′ sense strand): mock siRNA, 5′-GUAUGACCGACUACGCGUA[dT][dT]-3′ (custom, Sigma-Aldrich); PI4KIIIα siRNA, 5′-CCGCCAUGUUCUCAGAUAA[dT][dT]-3′ (custom, Sigma-Aldrich); and PI4KIIIβ siRNA, 5′-GCACUGUGCCCAACUAUGA[dT][dT]-3′ (Silencer Validated siRNA s10543; Ambion). Three days after the initial transfection, cells were stained for PI4P as described previously [37], or subjected to western blot analysis. For immunoblot analysis of protein expression, cells were harvested with TEN buffer (10 mM Tris/HCl pH 8.0, 1 mM EDTA, 100 mM NaCl), washed once with PBS and lysed with 2X protein sample buffer (125 mM Tris-HCl pH 6.8, 10 mM EDTA, 0,003 gr bromophenol blue, 20% glycerol, 4% SDS and 10% β-mercaptoethanol; 200 µl). The samples were then sonicated, heated at 95°C and loaded onto 7.5% polyacrylamide-SDS page (Criterion, Biorad). After electrophoresis proteins were transferred to a nitrocellulose membrane and unspecific binding was blocked by PBS supplemented with 0.5% Tween (PBS-T) and 5% milk. Membranes were then incubated overnight at 4°C with primary antibodies (anti-PI4KIIIα, cat no. 4902, 1∶250 Cell Signaling, anti-PI4KIIIβ, cat. No. 611817, 1∶3000 BD Transduction Laboratories, mouse anti-β-actin, cat. No. A1978, 1∶5000, Sigma). HRP-conjugated secondary antibodies (donkey anti-rabbit, Cat. No. 9341 and sheep anti-mouse, Cat. No. 9311, GE Healthcare) were incubated for 1 hour at room temperature and detection was performed using SuperSignal-Femto chemiluminescent substrate (Pierce-Thermo Scientific).
1.5×106 Huh7-Lunet/T7 cells/100 mm dish were transfected with 20 µg pTM-NS3-5B using the transfection reagent Lipofectamine 2000 (Invitrogen). Six hours after transfection, cells were seeded in 24-well plates on microscope cover glasses for indirect immunofluorescence. After 5 hours, cells were treated either with DMSO or with 8 µM AL-9 for 2, 8 or 16 hours and co-staining of NS5A and PI4P was performed using the Golgi staining protocol, as described previously [37].
|
10.1371/journal.pntd.0002643 | Clinical Associations of Human T-Lymphotropic Virus Type 1 Infection in an Indigenous Australian Population | In resource-poor areas, infectious diseases may be important causes of morbidity among individuals infected with the Human T-Lymphotropic Virus type 1 (HTLV-1). We report the clinical associations of HTLV-1 infection among socially disadvantaged Indigenous adults in central Australia.
HTLV-1 serological results for Indigenous adults admitted 1st January 2000 to 31st December 2010 were obtained from the Alice Springs Hospital pathology database. Infections, comorbid conditions and HTLV-1 related diseases were identified using ICD-10 AM discharge morbidity codes. Relevant pathology and imaging results were reviewed. Disease associations, admission rates and risk factors for death were compared according to HTLV-1 serostatus. HTLV-1 western blots were positive for 531 (33.3%) of 1595 Indigenous adults tested. Clinical associations of HTLV-1 infection included bronchiectasis (adjusted Risk Ratio, 1.35; 95% CI, 1.14–1.60), blood stream infections (BSI) with enteric organisms (aRR, 1.36; 95% CI, 1.05–1.77) and admission with strongyloidiasis (aRR 1.38; 95% CI, 1.16–1.64). After adjusting for covariates, HTLV-1 infection remained associated with increased numbers of BSI episodes (adjusted negative binomial regression, coefficient, 0.21; 95% CI, 0.02–0.41) and increased admission numbers with strongyloidiasis (coefficient, 0.563; 95% CI, 0.17–0.95) and respiratory conditions including asthma (coefficient, 0.99; 95% CI, 0.27–1.7), lower respiratory tract infections (coefficient, 0.19; 95% CI, 0.04–0.34) and bronchiectasis (coefficient, 0.60; 95% CI, 0.02–1.18). Two patients were admitted with adult T-cell Leukemia/Lymphoma, four with probable HTLV-1 associated myelopathy and another with infective dermatitis. Independent predictors of mortality included BSI with enteric organisms (aRR 1.78; 95% CI, 1.15–2.74) and bronchiectasis (aRR 2.07; 95% CI, 1.45–2.98).
HTLV-1 infection contributes to morbidity among socially disadvantaged Indigenous adults in central Australia. This is largely due to an increased risk of other infections and respiratory disease. The spectrum of HTLV-1 related diseases may vary according to the social circumstances of the affected population.
| The Human T-Lymphotropic Virus type 1 (HTLV-1) infects at least 5–10 million people worldwide. In developed countries, the most frequently reported HTLV-1 associated diseases include a fatal hematological malignancy, Adult T-cell Leukemia/Lymphoma (ATLL), and the neurological disorder, HTLV-1 associated myelopathy (HAM), which arise in <10% of HTLV-1 carriers during their lifetime. However, most HTLV-1 carriers live in resource-poor areas where infectious diseases, such as strongyloidiasis, could be more important causes of morbidity. Demonstrating such an effect is difficult due to the resource constraints experienced by developing countries in which populations with a substantial burden of infectious diseases reside in areas that are highly endemic for HTLV-1. This is not the case in HTLV-1 endemic central Australia where Indigenous Australians have, for example, among the highest reported blood stream infection rates worldwide in a setting in which sophisticated medical facilities are readily available. We report that bronchiectasis, blood stream infections and admissions with lower respiratory tract infections and strongyloidiasis are associated with HTLV-1 infection. These conditions were far more common than HTLV-1 associated malignancies or neurological conditions in this socially disadvantaged Indigenous population. The spectrum of HTLV-1 related diseases therefore varies according to the social circumstances of the affected population.
| The Human T Lymphotropic Virus type 1 (HTLV-1) is an oncogenic retrovirus that preferentially infects CD4+ T cells [1]. Worldwide, HTLV-1 infects at least 5–10 million people who predominantly dwell in areas of high endemicity in southern Japan, the Caribbean basin, parts of South America and inter-tropical Africa. A smaller endemic focus is present in central Australia [2] and we have recently shown this to be due to infection with the HTLV-1c subtype [3]. Epidemiological and clinical associations have been best described for populations in the Caribbean basin, South America and Japan [1]. A minority of HTLV-1 carriers experience clinically significant sequelae, including a rapidly progressive hematological malignancy, Adult T cell Leukemia/Lymphoma (ATLL) [4], [5], and inflammatory disorders, such as HTLV-1 associated myelopathy/tropical spastic paraparesis (HAM/TSP) [6]. A severe exudative eczema, infective dermatitis, predominantly affects children [7]. In Japan and the Caribbean, life-time risks range between 0.3–4% for HAM/TSP, 1–5% for ATL [1] and approach 10% for HTLV-1 associated malignancy or inflammatory diseases overall [1].
Infectious diseases also contribute to HTLV-1 related morbidity and mortality. Severe scabies [8], mycobacterial infections [9] and symptomatic infection with the nematode parasite Strongyloides stercoralis [10], [11] are all more frequent among HTLV-1 carriers. In areas endemic for HTLV-1 and S.stercoralis, HTLV-1 infection is the major risk factor for complicated strongyloidiasis or ‘hyperinfection’, which is associated with pulmonary involvement [12] and life-threatening sepsis due to enteric bacterial pathogens [13]. Infection with S.stercoralis may also reduce the latent period required for the development of ATLL [14]. HTLV-1 infection reduces clearance rates of hepatitis C virus and increases the risk of liver disease and liver disease-related deaths [15]. Whether the risk of chronic hepatitis B virus (HBV) infection is similarly affected is unknown. Interactions between HTLV-1 related inflammatory diseases and infection have also been demonstrated. Infective dermatitis, for example, typically affects HTLV-1 carriers from lower socio-economic backgrounds and predisposes to skin infections with bacterial pathogens [7], which may progress to life-threatening invasive disease [16]. Recently, we reported high rates of HTLV-1 infection among socially disadvantaged Indigenous adults with bronchiectasis in central Australia [17]. Clinically significant pulmonary disease is not a feature of HTLV-1 infection in other developed countries [18]–[20], and we suggested that recurrent lower respiratory tract infections (LRTI) might contribute to this risk in our study population. The spectrum of HTLV-1 related clinical diseases may therefore differ according to social status and the risk of environmental exposure to other pathogens. However, demonstrating such an effect requires diagnostic capabilities that may not be available in developing countries in which a heavy burden of infectious diseases affects a population with a high prevalence of HTLV-1 infection.
Central Australia is well placed to study the associations between poverty and infectious diseases [21]. HTLV-1 is endemic to this region and infects 7.2–13.9% [22], [23] of socially disadvantaged Indigenous adults. There has been no attempt to control HTLV-1 transmission among the Indigenous residents of central Australia, most of whom reside in isolated remote communities in conditions of considerable socio-economic disadvantage [21]. Those who live in the major regional center of Alice Springs dwell in either overcrowded ‘town camps’, which have poor amenities and limited refuse disposal, or are integrated with the majority of the non-Indigenous population within the township's suburbs [21]. Central Australia also has the highest reported blood stream infection (BSI) incidence rates [21] and the highest prevalence rate of adult bronchiectasis [17] worldwide. Prevalence rates of chronic HBV infection exceeded 20% in some communities prior to the introduction of vaccination [24]. Consequently, infection-related mortality rates approach those of some African countries prior to the current HIV pandemic [25]. A single well-resourced community-based hospital, Alice Springs Hospital (ASH), serves this region of 1,000,000 km2 (Fig. 1). Critically ill patients are retrieved by air to tertiary referral centers 1,500 km away. Medical services are provided without charge and, notwithstanding the poor social circumstances of the resident population, sophisticated radiological, microbiological and other diagnostic facilities are readily available. The present study describes the spectrum of HTLV-1 associated diseases that affects socially disadvantaged Indigenous adults in central Australia.
This study was approved by the Central Australian Human Research Ethics Committee, which is a regional committee supervised by the National Health and Medical Research Council of Australia.
All adults (age ≥15 years) admitted to ASH between 1st January 2000 and 31st December 2010 who had an HTLV-1 screening test performed were identified from the hospital pathology data-base. HTLV-1 testing at ASH is performed where there are clinical suspicions of HTLV-1 related diseases, including malignancy, neurological disease, strongyloidiasis and bronchiectasis. Demographic data including ethnicity, dates of birth and death, indigenous status and place of residence were obtained for all patients from the ASH patient management system. For each admission between 1st January 2005 and 31st December 2010 International Statistical Classification of Diseases and Related Health Problems, Tenth Revision, Australian Modification (ICD-10 AM) morbidity codes relating to non-communicable diseases, possible HTLV-1 related conditions and infectious diseases were also extracted (Table S1). Discharge morbidity codes for admissions prior to 2005 were not available and patients who died prior to this date were therefore excluded from statistical analysis. All data were de-identified prior to analysis. Infectious diseases were grouped according to ICD-10 AM codes; i) sepsis or bacterial infection for which a focus was not stated, ii) specified foci of infection and iii) strongyloidiasis (Table S1). HTLV-1 related conditions included ATLL, HAM/TSP, bronchiectasis and infective dermatitis. Cases of ATLL and HAM/TSP were also sought from specialist neurological and hematological units that provide tertiary level care to ASH patients. Case notes, microbiology, radiology and other relevant pathology results were reviewed for all patients with possible HTLV-1 related conditions including ATLL, neurological disorders, bronchiectasis and infective dermatitis.
Place of residence was categorized as i) remote (>80 km from Alice Springs), ii) Alice Springs town camp and iii) urban (resident in Alice Springs, but not in a town camp). Remote residence was further divided into quadrants (north, south, east and west) relative to Alice Springs.
Results for S.stercoralis serology, HBV serology and blood cultures were obtained from the ASH pathology data-base. During the study period, S.stercoralis serology was performed using an in-house enzyme-linked immunosorbent assay based on antigen extracts of Strongyloides ratti, which is highly sensitive and specific. A blood culture from which a pathogen was isolated defined a ‘BSI episode’. Repeated culture of the same organism from blood culture was regarded as a separate ‘episode’ only if blood samples were drawn more than one month apart. Blood stream infections excluded potential contaminants including coagulase negative staphylococci, bacillus spp., coryneforms and viridans streptococci unless grown from more than one BC in a 24 hour period and Acinetobacter spp in the absence of an identifiable focus. For statistical analysis, the major BSI pathogens were grouped according to their most likely origin: i) skin (Staphylococcus aureus and Streptococcus pyogenes), ii) respiratory (Streptococcus pneumoniae and Haemophilus influenzae), iii) urinary tract (Escherichia coli) and iv) gastrointestinal tract (Enterobacteriaceae other than E.coli). ‘Definite bronchiectasis’ was defined as an ICD-10 AM code for bronchiectasis that was confirmed by High Resolution Computed Tomography (HRCT) chest according to American College of Chest Physicians criteria. ‘Possible bronchiectasis’ was defined as an ICD-10 AM code for bronchiectasis in the absence of HRCT chest confirmation of this diagnosis. A diagnosis of ATLL [4] and HAM/TSP [26] was made using established criteria. Cases of HAM/TSP were categorized as ‘probable’ if the clinical presentation was consistent with HAM/TSP in the absence of confirmatory analysis of cerebrospinal fluid (CSF) [26].
Initial screening tests were performed using the Serodia HTLV-1 particle agglutination assay (Fujirebio, Japan) or Architect rHTLV-I/II assay at the Royal Darwin Hospital, Northern Territory of Australia, (1458) or the Institut Pasteur, Paris (156). Positive samples were again tested using both the Serodia HTLV-1 particle agglutination assay and Murex HTLV-I+II test kit (Murex Diagnostics, Dartford, UK)(National Serological Reference Laboratory, Melbourne) or an indirect immunofluorescence assay (IFA) using an HTLV-1-transformed human T cell line (MT2)(Institut Pasteur). HTLV-1 serostatus was then confirmed by Western blot (HTLV Blot 2.4, MP Diagnostics) using stringent criteria for all samples for which screening tests were positive.
Categorical variables were summarized using frequency and percentage and compared using a Chi-square test or, in the case of small numbers, a Fisher's Exact test. Multiple simultaneous comparisons were adjusted for using a Bonferroni correction. Continuous variables were assessed for significant departures from normality with normally distributed variables summarized using mean and standard deviation (SD) and compared using a t-test whilst skewed variables were summarized using median and inter-quartile range (IQR) and compared using a Wilcoxon rank-sum test.
Predictors of HTLV-1 seropositivity were examined using Poisson regression with robust standard errors. Strongyloides admissions (identified by ICD-10 AM codes), rather than serology, were included in the multivariable model because these are more likely to reflect symptomatic strongyloidiasis [10], [11], [27]. Direct modeling of relative risk (RR) using Poisson was preferred over Odds Ratios (OR) from logistic regression to estimate RR due to the frequency of the outcome studied. A link test was used to assess the model for specification error whilst overall goodness of fit was assessed using both visual examination of residuals coupled with a likelihood-ratio test and a Pearson goodness-of-fit test.
Incidence rates of admission count by diagnostic group were expressed as a proportion of the total number of HTLV-1 seropositive and seronegative patients respectively. Predictors of admission counts for a range of diagnostic groups according to HTLV-1 seropositivity were examined using negative binomial regression and are presented with their negative binomial 95% confidence intervals. Negative binomial modeling was preferred over straight Poisson regression due to over-dispersion in admission count outcome variables. The model coefficients represent the estimated change in admission counts for a particular level of a predictor variable. The influence of HTLV-1 seropositivity on admission count was adjusted for demography and comorbidities. In the case of admissions with asthma, LRTI, pneumonia and chronic obstructive pulmonary disease, the model was adjusted for both definite and possible bronchiectasis and tobacco smoking. A link test was used to assess the model for specification error whilst overall goodness of fit was assessed using both visual examination of residuals coupled with a likelihood-ratio test and a Pearson goodness-of-fit test.
Predictors of hepatitis B surface antigen (HBsAg) positivity were analysed using logistic regression. In this case, a logistic approach was preferred secondary to the rarity of the outcome. Overall model fit was assessed using a Hosmer & Lemeshow goodness-of-fit test.
Predictors of time to mortality were examined using Cox Proportional Hazards Regression. Analysis of scaled Schoenfeld residuals were used to assess compliance with the proportional hazards assumption. For this analysis patients with possible bronchiectasis were assumed not to have the condition.
All reported p-values are two-tailed and for each analysis p<0.05 was considered significant. All analyses were conducted using Stata version 12 (StataCorp, College Station, Texas).
HTLV-1 screening tests were performed for 1614 Indigenous adults and these were positive for 624 (38.7%) cases. Samples from 605 patients were referred for confirmatory Western blot tests. These were indeterminate in 73 cases (4.6%) and confirmed HTLV-1 infection for 531 patients (33.3%). Patients whose western blot results were indeterminate were excluded from further analysis, as were 74 patients (HTLV-1 seropositive, 24; HTLV-1 seronegative, 50) who died prior to 2005. The subsequent analysis therefore included 1451 Indigenous adults (HTLV-1 seropositive, 507; HTLV-1 seronegative, 944) who were admitted 115,919 times (HTLV-1 seropositive 39,967; HTLV-1 seronegative 75,952) during the study period.
HTLV-1 seropositivity rates among males increased significantly with age (<45 years, 106/329 (32.2%); ≥45 years, 135/319 (42.2%); p = 0.008). Rates were otherwise not significantly different between age groups or genders (Table 1). Seropositivity rates differed according to place and type of residence. Rates were lowest among residents of communities north of Alice Springs (14.6%) and highest among those from communities to the south (64.3%) and west (37.5%)(Fig. 1)(Table 1). Seropositivity rates were higher among town camp residents (42.6%) and lowest among those living elsewhere in the township (27.0%). Demographic risk factors for HTLV-1 infection after multivariable analysis included age (adjusted RR, 1.01 per year; 95% CI, 1.01–1.02; p = 0.000) and residence in communities to the south (aRR 3.83; 95% CI, 2.64–5.57; p = 0.000) and west (aRR 2.77; 95% CI, 1.54–3.37; p = <0.001) of Alice Springs relative to those in the north (Table 2).
Nearly 70% of patients (HTLV-1 seropositive, 391; HTLV-1 seronegative, 621) recorded at least one discharge code for sepsis with no focus specified during the study period. Although HTLV-1 carriers more often recorded discharge codes for sepsis with no focus specified (Table 3) and had higher admission numbers for this category (Table 4), these associations were lost after adjusting for covariates (Table 5). HTLV-1 carriers were also more likely to experience a BSI (Table 6) and had more BSI episodes after adjusting for covariates (Table 5). When analyzed according to the most likely origin of infection, BSI from a probable gastrointestinal source remained significantly associated with HTLV-1 infection in a multivariable model (aRR, 1.36; 95% CI, 1.05–1.77; p = 0.020) (Table 2).
Among 988 (68.1%) patients tested, 127 (12.9%) were HBsAg positive of whom 16 (12.6%) were also HBeAg positive (Table 6). The geographic distribution of HBsAg positivity was similar to that of HTLV-1 seropositivity. Risk was greatest among residents of remote communities to the south (unadjusted odds ratio (uOR), 3.98; 95% CI, 2.23–7.10) and west (uOR, 2.23; 95% CI, 1.25–3.99) compared with northern communities and was reduced for urban relative to remote residents (uOR, 0.30; 95% CI, 0.14–0.64). Although HTLV-1 infected patients were more likely to be HBsAg positive (HTLV-1 seropositive, 65/201 (32.3%); HTLV-1 seronegative, 62/338 (18.3%)(p = <0.001) (Table 6), exposure to HBV was more frequent among HTLV-1 seropositive patients (anti-HBc positive: HTLV-1 seropositive, 59.6%; HTLV-1 seronegative, 51.9%)(p = 0.021)(Table 6) and HBsAg positivity was not associated with HTLV-1 infection in a multivariable model (Table 2).
Among 338 deaths that occurred during 5,739 years of follow-up, 120 (23.7%) were HTLV-1 seropositive and 218 (23.1%) were HTLV-1 seronegative. There was no difference between HTLV-1 seropositive and seronegative patients in median age of death (HTLV-1 seropositive, 56.9 years; IQR, 46.2, 63.9); HTLV-1 seronegative, 53.2 years; IQR, 44.4, 62.5) (Table 1). Demographic risk factors for death included male gender and increasing age (Table 7).
Bronchiectasis (HR, 2.07; 95% CI, 1.45–2.98; p = 0.000) and BSI with Enterobacteriaceae other than E.coli (HR 1.78; 95% CI, 1.15–2.74; 0.009) remained significant predictors of death after multivariable analysis (Table 7). Other risk factors for death were S.pneumoniae BSI (HR, 1.70; 95% CI, 1.09–2.64; p = 0.018) and non-communicable diseases (chronic liver disease, diabetes and malignancy)(Table 7).
In a hospitalized cohort of Indigenous Australian adults, we found an HTLV-1 seropositivity rate (33.3%) that was approximately three times the estimated background rate in central Australia (7.2–13.9%) [22], [23]. This suggests that HTLV-1 associated morbidity in our study population may substantially exceed that resulting from the occasional cases of ATLL and HAM/TSP that are reported here. Consistent with its global epidemiology [2], HTLV-1 carriers were more likely to live in poverty in town camps or remote communities and more often had a history of harmful alcohol consumption. HTLV-1 infection was associated with strongyloidiasis and blood stream infections with enteric pathogens; however, respiratory diseases contributed most to HTLV-1 related morbidity in this socially disadvantaged Indigenous population. After adjusting for covariates, HTLV-1 infection was associated with bronchiectasis and with increased admission numbers for all respiratory conditions studied with the exception of chronic obstructive pulmonary disease.
Pulmonary involvement is common among HTLV-1 carriers elsewhere. Radiological abnormalities, for example, have been reported in 50% of Japanese patients with HAM/TSP and 30% of asymptomatic HTLV-1 carriers who were examined by chest X-ray [28] and chest CT [29], respectively. Airway involvement is frequent in this population; chest CT reveals bronchiolitis or bronchitis in 19% [30] and bronchiectasis in 18–26% [29], [30] of cases. Lymphocyte infiltration of bronchioles [31] and partial bronchiolar obstruction [31], [32] are the histopathological correlates of these radiological findings. Lymphocytes obtained from HTLV-1 carriers by bronchoalveolar lavage (BAL) have high HTLV-1 proviral loads [33], [34] and these are correlated with those of peripheral blood [31]. An inflammatory response to the HTLV-1 antigen load derived from infected lymphocytes is thought to be the major determinant of other HTLV-1 related inflammatory diseases [35]. Airway inflammation in response to HTLV-1 antigens, such as the immuno-dominant regulatory protein, Tax [30], may therefore provide the pathological basis for clinical associations with asthma and LRTI other than pneumonia in our Indigenous cohort and for the increased incidence of self-reported asthma among HTLV-1 carriers in the USA [20].
Nevertheless, clinically significant pulmonary disease is an uncommon feature of HTLV-1 infection in developed countries [18]–[20]. In contrast, HTLV-1 infection contributes to bronchiectasis prevalence rates among Indigenous adults in central Australia that are the highest reported worldwide [17]. In the present study, 142 cases of bronchiectasis were confirmed by HRCT and nearly 60% of these patients were HTLV-1 infected. Consistent with our previous study [17], bronchiectasis was associated with a very high early mortality. Previously we have shown that HTLV-1 infection is associated with more extensive bronchiectasis, more frequent right heart failure and with bronchiectasis-related deaths [17]. In a recent case-control study the mean HTLV-1 proviral load in peripheral blood lymphocytes was significantly higher among HTLV-1 infected patients with bronchiectasis [36]. An HTLV-1 mediated inflammatory process [35] may therefore underlie HTLV-1 associated pulmonary disease in our study population. Disease progression to multifocal bronchiectasis might then follow further pulmonary injury resulting from recurrent LRTI, which were more common among HTLV-1 carriers in the present study.
Consistent with the results of other studies [27], [37], HTLV-1 carriers in central Australia were not at increased risk of serologically defined strongyloidiasis. Nevertheless, HTLV-1 infection in other populations is associated with a higher larval burden and with increased risks of symptomatic, recurrent and complicated strongyloidiasis [10], [11], [27]. Our study design and the use of serological tests to diagnose strongyloidiasis preclude any assessment of disease severity. However, HTLV-1 carriers in the present study were more likely to be admitted with a diagnosis of strongyloidiasis and had higher admission numbers for this condition, findings that could result from a higher larval burden. Unfortunately, our analysis of admission numbers for strongyloidiasis might also be confounded by the acknowledged disease association with HTLV-1 infection, which may lower the clinical threshold for administering antihelminthics to HTLV-1 carriers and increase the likelihood that a Strongyloides-related ICD-10 AM code is recorded. The association between HTLV-1 infection and strongyloidiasis in central Australia therefore requires confirmation in a prospective study. Nevertheless, high rates of S.stercoralis infection were found among Indigenous adults in an arid region of Australia that would appear otherwise hostile to soil transmitted helminths. The presence of HTLV-1 infected ‘core transmitters’ who carry a high larval burden may be central to the survival of S.stercoralis in this environment and could increase the risk of S.stercoralis infection among other community members. Strongyloidiasis may also contribute to the very high BSI incidence rates that have been reported in central Australia [38]. Among Indigenous adults in this region, enteric gram-negative bacilli are the most common pathogens isolated from blood [38] and we have previously reported BSI-related deaths among patients with complicated strongyloidiasis [13].
In our Indigenous Australian cohort, respiratory and infection-related morbidity were increased among HTLV-1 carriers in the absence of an increased risk of death. However, an effect of HTLV-1 infection on mortality may be obscured by analysis according to HTLV-1 serological status rather than HTLV-1 proviral load, which is closely associated with HTLV-1 related diseases [1]. Certainly, the recent finding of higher HTLV-1 proviral loads among HTLV-1 carriers with bronchiectasis [36] suggests that stratifying mortality by HTLV-1 proviral load may more accurately reflect risk in our patient population. Interestingly, an increased risk of death among HTLV-1 carriers in Guinea-Bissau [39], [40] is associated with higher HTLV-1 proviral loads [41]. A modest increase in all-cause mortality has also been reported among HTLV-1 carriers in Japan [42]; however, no such association has been found for blood donors in the USA [43]. These geographic differences in HTLV-1 associated mortality might reflect environmental conditions in resource poor areas that predispose to recurrent respiratory tract infections and expose HTLV-1 carriers to other pathogens, such as Mycobacterium tuberculosis [39] and S.stercoralis [12].
The retrospective nature of this study results in a number of limitations. First, patients with HTLV-1 related diseases were identified from discharge morbidity codes. Attempts were made to identify other patients with these conditions by contacting specialist medical units to which such patients are referred; however, cases may have been missed if these were not coded or referred appropriately. Consequently, our data are likely to under-estimate the actual burden of HTLV-1 related diseases in this population. The risk of bronchiectasis attributable to HTLV-1 infection is also likely to be underestimated because individuals who had not received radiological confirmation of this diagnosis were assumed not to have the condition. Similarly, the effect of HTLV-1 infection on respiratory conditions may be underestimated because ‘possible’ bronchiectasis was included in the final model to account for the increased risk of respiratory infection resulting from structural lung disease. Determining the strength of other possible associations was dependent on the accuracy of discharge coding; however, with the possible exception of strongyloidiasis noted above, this is unlikely to vary according to HTLV-1 serological status. Finally, we assumed that HTLV-1 infection was acquired in most cases prior to the period in which ICD-10 AM codes were collected. The low annual incidence rate of HTLV-1 seroconversion among discordant couples [1] suggests that this is likely to be the case. Indeed, vertical transmission may be relatively more important in our study population due to the substantial risks posed by the custom of prolonged breast-feeding [44]. Strengths of the study are the large sample size, which included 10% of the region's Indigenous adult resident population, the presence of a single well-resourced hospital that serves this population and the use of data from different sources to study the HTLV-1 related associations reported here.
In a setting of overcrowded housing, inadequate health hardware and poor community hygiene [45], [46], HTLV-1 infection substantially increases respiratory and infection-related morbidity. Socially disadvantaged HTLV-1 carriers in our Indigenous Australian cohort experienced more BSI episodes and were more often admitted with respiratory conditions including LRTI and bronchiectasis, which was the major independent risk factor for death. In contrast to other developed countries [1], infection-related complications were more common than either ATLL or HAM/TSP. The spectrum of HTLV-1 related diseases is therefore likely to vary according to the social circumstances of the affected population. These findings have not been reported previously; however, access to the medical facilities required to confirm these diagnoses is limited in developing countries in which populations with a similar burden of disease exists. Clearly, the benefits accrued by controlling the vertical transmission of HTLV-1 in a resource poor setting must be considered relative to the capacity of the health care system to ensure the safety of alternative sources of infant nutrition. However, our data provides strong support for public health interventions, such as improvements to housing and community hygiene, that limit the exposure of HTLV-1 carriers to other pathogens.
|
10.1371/journal.ppat.1003255 | Rational Engineering of Recombinant Picornavirus Capsids to Produce Safe, Protective Vaccine Antigen | Foot-and-mouth disease remains a major plague of livestock and outbreaks are often economically catastrophic. Current inactivated virus vaccines require expensive high containment facilities for their production and maintenance of a cold-chain for their activity. We have addressed both of these major drawbacks. Firstly we have developed methods to efficiently express recombinant empty capsids. Expression constructs aimed at lowering the levels and activity of the viral protease required for the cleavage of the capsid protein precursor were used; this enabled the synthesis of empty A-serotype capsids in eukaryotic cells at levels potentially attractive to industry using both vaccinia virus and baculovirus driven expression. Secondly we have enhanced capsid stability by incorporating a rationally designed mutation, and shown by X-ray crystallography that stabilised and wild-type empty capsids have essentially the same structure as intact virus. Cattle vaccinated with recombinant capsids showed sustained virus neutralisation titres and protection from challenge 34 weeks after immunization. This approach to vaccine antigen production has several potential advantages over current technologies by reducing production costs, eliminating the risk of infectivity and enhancing the temperature stability of the product. Similar strategies that will optimize host cell viability during expression of a foreign toxic gene and/or improve capsid stability could allow the production of safe vaccines for other pathogenic picornaviruses of humans and animals.
| Picornaviruses are small RNA viruses, responsible for important human and animal diseases for example polio, some forms of the common cold and foot-and-mouth disease. Safe and effective picornavirus vaccines could in principle be produced from recombinant virus-like particles, which lack the viral genome and so cannot propagate. However the synthesis of stable forms of such particles at scale has proved very difficult. Two key problems have been that a protease required for the proper processing of the polyprotein precursor is toxic for host cells and the empty recombinant particles tend to be physically unstable in comparison to virus particles containing nucleic acid. This is particularly true in the case of Foot-and-Mouth Disease Virus (FMDV). Here we report the production and evaluation of a novel vaccine against FMDV that addresses both of these shortcomings. Importantly, the strategies we have devised to produce improved FMDV vaccines can be directly applied to viruses pathogenic for humans.
| Foot-and-mouth disease (FMD) is a highly contagious viral disease of cloven-hoofed animals including cattle, sheep and pigs. Infection spreads rapidly through susceptible populations and can give rise to large scale epidemics, causing debilitation, pain and loss of productivity. Outbreaks of FMD such as that in the UK in 2001, which resulted in the slaughter of over 6 million animals and cost in excess of £8 billion, highlight the need for vaccines that support a ‘vaccinate to live’ policy. Vaccination is currently reliant on the use of inactivated virus produced in large bioreactors in high containment facilities. This is unsatisfactory on several grounds: the set-up and running costs are very high, limiting global production capacity, storage and supply are constrained by the poor vaccine stability at ambient temperatures, and it can be difficult to distinguish vaccinated from infected animals. Thus, more options for FMD vaccine production are urgently required.
FMDV is a non-enveloped single-stranded RNA virus belonging to the family Picornaviridae. The capsid consists of 60 copies each of four structural proteins (VP1–VP4). During assembly a 95 kDa polyprotein (P1) is cleaved by the viral 3C protease to yield VP0 (36 kDa), VP1 (32 kDa) and VP3 (27 kDa) which self-assemble to form the capsid. Auto-catalytic cleavage of VP0 into VP2 (28 kDa) and VP4 (8 kDa) occurs during encapsidation of the viral genome to produce the mature virus [1], [2]. During infection empty particles (hereafter termed natural empty particles) may also be produced, which resemble the mature virus in structure and antigenicity, but are inherently less stable [1]. The structural proteins are arranged in an icosahedral lattice of 12 pentameric building blocks which are the major structural intermediates for FMDV assembly and disassembly. The capsids are held together by electrostatic interactions, hydrogen bonds and weak hydrophobic interactions between the inter-pentameric subunits [3], [4] and unlike enterovirus capsids which release RNA by receptor-mediated uncoating [5], [6], FMDV capsids appear to release their genome by dissociation into pentamers at pH<7.0 and elevated temperatures. This instability translates into vaccines with limited shelf-life, necessitating a cold chain in many parts of the world where they are distributed.
Attempts to produce alternative vaccines have shown that intact virus particles stimulate the best immune response [7]. Picornavirus capsids can be synthesized using recombinant techniques by expressing (minimally) the P1 structural protein precursor and the 3C protease that cleaves it, since the capsid proteins spontaneously assemble to produce empty virus-like-particles(VLPs) [8]. The inherent problem is balancing the expression of P1 and 3C, the latter being toxic to cells, especially in the case of FMDV [2], [9], [10]. We demonstrate here the expression of FMDV VLPs at levels that are potentially viable commercially. We report the production of wild-type as well as stabilized VLPs, their characterization by X-ray crystallography and their ability to induce a sustained protective immunity in cattle.
Three-dimensional structures for several serotypes of FMDV [2], [3], [11], [12] reveal the basis for the limited particle stability, highlighting specific atomic interactions along the interface between pentameric assemblies [4], [13]. This allowed us to pick histidine 93 of VP2, on the helix adjacent to the icosahedral 2-fold symmetry axis (Figure 1a) as a target for mutagenesis to a cysteine (H2093C) in order to form energetically favourable disulphide bonds across the inter-pentameric interface [14].
Initial production of empty capsids utilised infection of mammalian cells (RK13) with recombinant vaccinia viruses that encode a P1–3C expression cassette, where P1 (derived from FMDV A22) is either wild-type (vA22-wt) or carries the H2093C mutation in VP2 (vA22-H2093C). This cassette was flanked by 5′ and 3′ UTRs to permit translation from the FMDV IRES [15] (Figure S1 in Text S1). Expression of capped transcripts was driven by a T7 polymerase promoter [14]. Provision of the T7 polymerase by co-infection with vaccinia virus recombinant vTF7.3, effectively lowers the cytotoxicity due to 3C protease or its capsid product; use of an inducible promoter regulates the expression levels of the P1-2A-3C cassette compared to that of a constitutive promoter [9], [16]. Infected cells were pelleted, lysed and the extracts analysed using 15–45% sucrose gradient sedimentation. Gradient fractions were collected from the bottom of the tubes and analysed by western blot using anti-FMDV A22 serum. FMDV capsid proteins were detected in the bottom half of the gradients (fractions 4 in Figure 1b top panel), indicative of the ability of both the wild-type and mutant recombinant capsid proteins to assemble into empty particles [14].
To investigate the stability of the recombinant particles, aliquots (from fractions 4 of the wild-type and H2093C gradients shown in Figure 1b top panel) were subjected to either acidification or heating to 56°C for 2 h before repeat sedimentation on 15–45% sucrose gradients, with fractions analysed by western blot. Heat-treated A22-wt derived proteins remained near the top of the gradient in fraction 10 (Figure 1b, middle left panel) demonstrating dissociation. By contrast, proteins derived from heat-treated A22-H2093C were detected predominantly in fraction 4, showing that the mutant particles had withstood heating (Figure 1b, middle right panel). Acidification was to pH 5.2 with sodium acetate buffer. The samples were incubated for 15 min before sucrose gradient sedimentation. Analysis of gradient fractions by western blot showed that A22-wt capsids dissociated (Figure 1b, bottom left panel), whereas A22-H2093C capsids remained intact (Figure 1b, bottom right panel).
To develop a practical method for novel vaccine production we explored the baculovirus expression system [17]. The same P1-3C cassettes were inserted into a baculovirus-compatible transfer vector, pOPINE [18]. In recombinant baculoviruses (bA22-wt and bA22-H2093C), capsid expression is driven by the baculovirus promoter p10. Capsid expression was optimised by reducing the activity and expression of 3C protease and thereby its toxic effects on the cell: this was achieved by site-directed mutagenesis in the vicinity of the 3C protease active site to reduce its activity and by insertion of a frameshift sequence upstream of the 3C gene to down-regulate its expression [19]. Following infection of suspension cultures of Sf9 cells [19], capsids were purified by a procedure similar to that used for mammalian cells. FMDV capsids produced in insect cells sedimented at the same position on 15–45% sucrose gradients as those produced using vaccinia virus in mammalian cells (Figure 2a top panel). A second lower band was observed on gradients loaded with the extract from insect cells (Figure 2b top panel). SDS-PAGE confirmed that the upper band harboured FMDV capsid proteins whilst the lower band contained proteins from baculovirus nucleocapsids (Figure 2b bottom panel). Yields ranged from 0.8 µg/ml for A22-H2093C to 1.2 µg/ml for A22-wt. VP0 was cleaved into VP2 and VP4 to a similar extent in recombinant A22 wt and H2093C. This type of cleavage had already been observed with A22 empty particles arising during an FMDV infection and progressed to completion on short term storage [2].
In situ room temperature X-ray crystallography [20] was used to determine the structure of both wild-type and mutant capsids produced using vaccinia virus at 2.2 Å and 2.9 Å resolution respectively. The crystals were essentially isomorphous to those obtained for both A22 virus and A22 natural empty particles [2], [21], and refinement, using strict 15-fold non-crystallographic symmetry and real space averaging gave reliable maps and models for both structures. Diffraction from crystals for baculovirus expressed particles was indistinguishable, demonstrating that the particles from both expression systems are iso-structural (data not shown).
The structures of recombinant A22 empty capsids were very similar to those previously reported for A22 virus and its natural empty particles [2], [21]. However, one region on the surface of the particle showed a significant structural difference: residues 171–181 of the VP3 GH loop adopt essentially identical folds in the two natural particles, whereas in wt and H2093C recombinant particles their structure is more extended (Figure S2 in Text S1) and almost identical to that seen in another serotype A virus, A10 [11]. We have previously shown that, for serotype O viruses, the VP3 GH loop conformation is modulated by changes in the adjacent VP1 GH loop [21], so it is possible that one or more amino acid sequence changes occurred in the highly variable disordered VP1 GH loop during native A22 virus replication and account for the repacking of the VP3 loop (no sequence changes were detected in the ordered portions of the native A22 virus capsid) [2].
The electron density map for the A22-H2093C mutant particles showed that the disulphide bond across the 2-fold axis relating two pentamers was correctly formed whilst A22-wt showed the expected histidine side-chain density (Figure 3a). A difference electron density map between the wt and H2093C recombinant particles (Figure S3 in Text S1) revealed, apart from this mutated residue, no significant features on the particle surface. Changes are however apparent on the interior of the particles, with VP4 being similarly ordered in the recombinant wt particle and the A22 virus structure [2] whereas only two residues of VP4 were visualized in the electron density map for H2093C (Figure 3b). It is possible that the greater rigidity of the H2093C particle inhibits movements required for VP4 to fully settle into the structure seen in mature virus following cleavage of VP0. Structural superimposition gives rms deviations in Cαs of the native and mutant recombinant particles of 0.36 Å (661 equivalent residues) and 0.4 Å (609 equivalent residues) respectively compared to the A22 virus.
To characterize the protective immunity induced by recombinant capsids produced with baculovirus, we immunised two groups of four out-bred cattle each with 12 µg of highly purified capsids formulated in oil adjuvant. One group received A22-wt and the other A22-H2093C capsids. All animals were re-immunised after 3 weeks. There was a rapid induction of neutralising antibodies after primary immunisation. The mean antibody titre of both groups was greater than 5.5 (Log2) which is considered protective [22]. There was a significant increase in virus neutralising antibody titres (VNT) post-boost and antibodies were maintained at high titres, greater than 6 (Log2), up to 22 weeks post-immunisation, but had reduced to pre-boost levels after 34 weeks in both groups of animals (Figure 4). Throughout the experiment there were no significant (P>0.05) differences in titres between animals vaccinated with wild-type empty capsids and those vaccinated with mutated empty capsids.
At 34 weeks post immunisation the animals, plus two non-vaccinated control animals, were challenged by direct inoculation into the tongue with live A22 virus. Two of the four animals immunised with wild-type capsids and three of the four immunised with mutated capsids were fully protected using the criteria described in the European pharmacopeia [22]. The two control animals succumbed to full clinical signs (e.g. vesicular lesion of all four feet). Large quantities of viral genome were detected in their sera (greater than 6.5 log10 genomes/ml) for three or four days (Figure 5c). In contrast, lower quantities of viral genome were detected in the vaccinated animals (Figure 5a and 5b). The total amount of virus produced, estimated by computing the area under the curve (AUC) for copy number versus time, showed that there were no significant differences in AUC between animals vaccinated with A22-wt empty capsids and those vaccinated with A22-H2093C empty capsids (P = 0.23), whilst the AUC was significantly higher for non-vaccinated compared with vaccinated animals (P = 0.04).
Here we have demonstrated the production of safe, effective FMDV empty capsids that do not require bio-containment during manufacture. Furthermore, enhanced stability of the empty capsids will reduce losses during production, storage and transport whilst maintaining antigenic structure and immunogenicity. In addition the complete absence of FMDV non-structural proteins from the vaccine formulation will allow the development of diagnostic tests to discriminate between infected and vaccinated animals (DIVA).
Disulphide bonds are used to stabilise many extracellular proteins and also certain virus capsids [23]. Such covalent cross-links are more robust than the non-covalent interactions that generally hold protein assemblies together. Here we have rationally engineered a disulfide bond by mutating a single histidine residue at position 93 of VP2 located at the icosahedral 2-fold axis between adjacent pentamers [4]. Baculovirus expressed wild-type and stabilised capsids produced equivalent titres of neutralising antibodies, following a standard immunisation regimen, over a 34 week period post immunisation. These results inform the debate on the effect of increased antigen stability on immunogenicity. Delamarre et al [24] showed that for two proteins with the same T cell and B cell epitopes but with different susceptibilities to lysosomal proteolysis, mediated by single point mutations, less digestible forms induced more efficient T cell priming and antibody responses. In contrast, recent studies with a model antigen in mice suggested that enhanced conformational stability resulted in reduced antigenicity [25]. Although as yet we do not know if forming a disulphide bridge will be possible for all serotypes, especially in the baculovirus expression system, our results demonstrate that capsid stability can be augmented without compromising immunogenicity and this might be a general tactic for improving vaccine efficacy. The rational structure-based approach initiated here should in principle allow the tuning of these parameters to match the particular circumstances of different viruses. For instance, enhancing the stability of capsids for highly prevalent FMDV serotype O, which are more labile than those of A22 [26]. Recent work on EV71 has demonstrated that maintaining the proper positions of the 2-fold helices (which harbour the H2093C mutation in FMDV) is essential for maintaining native antigenicity [6], suggesting that the approach we have demonstrated here may be applicable across a wide range of human and animal picornaviruses, including polioviruses and coxsackieviruses.
An expression cassette based on the sequence of FMDV A22 Iraq was designed (Figure S1 in Text S1), synthesized de novo (Geneart) and cloned into the vaccinia virus transfer vector pBG200 [9] downstream of the T7 promoter. Substitution of a BstEII-SpeI fragment with a sequence encoding the H2093C mutation converted the pBG200-A22-wt plasmid to pBG200-A22-H2093C.
The recombinant viruses were made by transfecting pBG200-A22-wt and pBG200-A22–H2093C into CV-1 cells infected with vaccinia virus (VV) strain WR. Recombinant VVs (with an interrupted thymidine kinase gene) were selected in HuTK-143 cells using 5-bromo-2-deoxyuridine. Three rounds of plaque purification in conjunction with screening by PCR using FMDV-specific primers were carried out to get stable recombinant VVs. These were amplified in RK13 cells and virus stocks titrated by plaque assay on BS-C-1 cells.
All mammalian cells were grown in DMEM supplemented with 10% FCS and appropriate antibiotics at 37°C.
A single 175 cm2 flask of RK13 cells was dually infected with either vA22-wt or vA22-H2093C at an MOI 10 and vTF7.3 at an MOI 5. After 24 h cells were harvested by centrifugation at 2,000 g for 5 min at 4°C and the pellet resuspended in 1 ml 0.5% Ipegal (Sigma) in 40 mM sodium phosphate, 100 mM NaCl pH 7.6. The sample was incubated on ice for 20 min, clarified, loaded onto a 15–45% sucrose gradient and spun for 20 h at 22,000 rpm (SW41 rotor, Beckman) at 12°C. Each gradient was fractionated into 12 fractions of 1 ml and aliquots were analysed by western blotting.
A 200 µl aliquot of the empty capsid-containing fraction identified during the initial sedimentation experiment (see above), was diluted 1/3 either (i) with phosphate buffer pH 7.6 and incubated in a water bath at 56°C for 2 h or (ii) with 50 mM sodium acetate buffer pH 4.6, to give a final pH of 5.2 and incubated at room temperature for 15 min before neutralisation with NaOH. Treated samples were loaded onto 15–45% sucrose gradients, centrifuged and fractionated as above. Each fraction was precipitated with an equal volume of saturated ammonium sulphate overnight at 4°C. Precipitates were collected by centrifugation at 16,000 g for 15 min at 4°C and analysed by western blot.
HEK293 cells grown in 2×2125 cm2 roller flasks were dually infected as described above. After 20 h, cells were pelleted at 3,500 g for 30 min at 4°C. Pellets were resuspended in phosphate buffer and lysed with 0.5% Igepal on ice for 20 min. Lysates were clarified at 10,000 g for 20 min at 4°C and the resulting pellets resuspended in a small volume of buffer for re-extraction with 1 volume of chloroform. The aqueous phases were pooled with the clarified extracts and pelleted over 30% sucrose cushions at 105,000 g for 5 h at 12°C. Pellets were resuspended in a small volume of buffer, treated with 200 mg/ml RNAse A in the presence of 0.1% Igepal for 30 min on ice, clarified and loaded onto a 15–45% sucrose gradient. Following centrifugation at 54,000 g for 22 h at 12°C, the gradient was fractionated and fractions analysed by SDS-PAGE. Sucrose was removed by desalting with a spin column (Zeba, Pierce) and samples concentrated by ultrafiltration (Amicon).
pTri-EX-derived plasmid pOPINE was used for In-Fusion cloning [18] of the FMDV coding sequence from pBG200-A22-wt resulting in pOPINE-A22-wt [19]. An overlapping PCR procedure which exchanged a P1 region for a fragment bearing the H2093C mutation resulted in plasmid pOPINE-A22-H2093C. Subsequent alterations within the P1-2A-3C expression cassette in order to down-regulate the 3C protease were as described [19].
Sf9 cells were grown in Insect-XPRESS (Lonza) supplemented with 2% FCS and antibiotics at 27.5°C. Transfer vector and AcMNPV bacmid KO1629 (0.5 µg of each) were mixed in the presence of 3 µl Fugene (Roche) for 20 min at room temperature and used to transfect Sf9 cells at a density of 1.2×106/well in a 6-well plate. Since baculovirus DNA with gene knockout 1629 will not initiate an infection unless rescued by recombination with a baculovirus transfer vector, the AcMNPV harvested in the culture supernatant after 5 days was 100% recombinant virus [19]. Virus stocks were produced by infecting Sf9 cells at a confluence of 70% with 200 µl recombinant per 175 cm2 flask and harvested from culture supernatants after 5 days. For the expression of empty capsids, Sf9 cells at a density of 1–2 106/ml were infected with 1/10 volume of baculovirus stock. After 3 days virus extraction was as described for mammalian cells except that lysis was with 1% Triton X-100 in the presence of 5 µl/ml protease inhibitor cocktail (Sigma).
The crystal structures of FMDV serotypes A10 [11], A22 [2] and O1bfs [3] were inspected and amino acid residues involved in inter-pentameric interactions were identified. An energetically favourable disulphide bond was predicted by manually measuring the pair-wise Cαi-Cαj and corresponding Cβi-Cβj distances using COOT [27].
Crystals were grown by the sitting-drop vapour diffusion method in Crystalquick X plates (Greiner Bio-One) using 100 nl virus plus 100 nl precipitant dispensed with a Cartesian liquid dispensing robot as described previously [28]. Micro-crystals of A22-wt empty capsids (3 mg/ml) with average dimensions of 50×50×5 µm3 and A22-H2093C (2.3 mg/ml) with average dimensions of 30×30×5 µm3 grew within 1 week at 294K with 4 M ammonium acetate, 100 mM bis-Tris Propane, pH 7.0. Optimisation by varying the concentration of precipitant and pH around the initial condition produced sufficient crystals for structural solution. A 20×20 µm2 beam (λ = 0.9778 Å; I24 micro-focus beamline, Diamond), was used for in situ diffraction image collection [20] at 294 K on a Pilatus 6 M detector.
The structures of A22-wt and A22-H2093C were solved by molecular replacement. The orientation of the particles (obtained from a self-rotation function) was found to be the same as for the native A22 virus structure (PDB id: 4GH4). Hence the coordinates and non-crystallographic symmetry (NCS) operators from native virus were used for the refinement. Initial estimates of phases were obtained by rigid body refinement with CNS [29]. Iterative positional and B-factor refinement (via CNS) used strict NCS constraints (Table S1 in Text S1). Phases were further improved by 15-fold cyclic NCS averaging using the General Averaging Program (GAP, D Stuart and J. Grimes, unpublished). There was good agreement between the observed data and those calculated from the averaged electron density map of R = 10.4% and CC = 97% for the wild-type and R = 12.3% and CC = 95% for the mutant. Model building used COOT [27].
Two groups of four 100 to 150 kg Holstein Friesian calves were vaccinated with either A22-wt or A22-H2093C capsids. Each animal received 12 µg of purified capsid formulated in oil adjuvant (Seppic 206B) as an intramuscular injection on week 0 and week 3 of the study. All eight animals plus two non-vaccinated control animals were needle challenged intradermolingually with 1×105 TCID50 of cattle adapted FMDV A22 on week 34. Animals were examined clinically and blood sampled from the day of challenge until day 9. Cattle were considered protected if lesions could not be detected at sites distal from the inoculation point. Animal experimentation was approved by the Pirbright Institute (PI) ethical review board under the authority of a Home Office project licence in accordance to the Home Office Guidance on the Operation of the Animals (Scientific Procedures) Act 1986 and associated guidelines.
Sera from the 8 immunised cattle and control sera from 2 non-vaccinated animals were prepared from blood samples. Their neutralising activities were determined as reported previously; testing was in duplicate, in serial 2-fold dilutions and endpoints calculation was made as described [22]. Titres of FMDV-specific antibodies are expressed as the reciprocal value of the highest dilution giving ≥50% neutralisation of homologous virus growth.
Total nucleic acid was extracted from 200 µl of serum and automated reverse transcription procedures were performed incorporating homologous FMDV RNA standards. Real-time PCR amplification was performed using A22 specific primers and standard curves constructed to provide a measure of the number of FMDV genome copies [30].
Virus neutralisation titres were analysed using linear mixed models including time since vaccination and vaccine type (wt or H2093C empty capsids) as factors and animal as a random effect. Model selection proceeded by stepwise deletion of non-significant (P>0.05) terms, starting from a model including time since vaccination and vaccine and an interaction between them. The total amount of virus produced following challenge was estimated for each animal by computing the area under the curve (AUC) for copy number versus time using the trapezium rule. A Wilcoxon rank-sum test was used to check for significant (P<0.05) differences in AUCs, first between animals vaccinated with A22-wt or A22-H2093C empty capsids and second between vaccinated (with either capsid) and non-vaccinated animals.
|
10.1371/journal.pcbi.1004111 | Evolving Nutritional Strategies in the Presence of Competition: A Geometric Agent-Based Model | Access to nutrients is a key factor governing development, reproduction and ultimately fitness. Within social groups, contest-competition can fundamentally affect nutrient access, potentially leading to reproductive asymmetry among individuals. Previously, agent-based models have been combined with the Geometric Framework of nutrition to provide insight into how nutrition and social interactions affect one another. Here, we expand this modelling approach by incorporating evolutionary algorithms to explore how contest-competition over nutrient acquisition might affect the evolution of animal nutritional strategies. Specifically, we model tolerance of nutrient excesses and deficits when ingesting nutritionally imbalanced foods, which we term ‘nutritional latitude’; a higher degree of nutritional latitude constitutes a higher tolerance of nutritional excess and deficit. Our results indicate that a transition between two alternative strategies occurs at moderate to high levels of competition. When competition is low, individuals display a low level of nutritional latitude and regularly switch foods in search of an optimum. When food is scarce and contest-competition is intense, high nutritional latitude appears optimal, and individuals continue to consume an imbalanced food for longer periods before attempting to switch to an alternative. However, the relative balance of nutrients within available foods also strongly influences at what levels of competition, if any, transitions between these two strategies occur. Our models imply that competition combined with reproductive skew in social groups can play a role in the evolution of diet breadth. We discuss how the integration of agent-based, nutritional and evolutionary modelling may be applied in future studies to further understand the evolution of nutritional strategies across social and ecological contexts.
| Getting enough nutrients and at the right balance is among the primary challenges that an animal has to overcome. Animals that live in groups have the added complexity of competition among individuals over foods. We used an evolutionary simulation to explore how the intensity of such competition interacts with the composition of available foods to influence the strategies that an animal should use to meet its nutritional requirements. We found that two general strategies emerged. When competition was weak, animals that only locate and consume foods with an ideal balance of nutrients were favoured. However, when competition was strong, a strategy with which animals meet their nutritional requirements by eating large amounts of nutritionally imbalanced, but complementary, foods was optimal. These results implicate a role for competition for foods between animals within social groups in shaping dietary breadth. Evolutionary simulations such as those described here are important for understanding how different species evolve to meet their nutritional requirements in a range of ecological circumstances.
| Access to nutrients is one of the most influential factors affecting reproductive development and ultimately fitness (e.g. [1–4]). A range of factors can influence nutrient access, but for many organisms interactions with conspecifics are pivotal. Group living animals in particular face a complex trade-off between access to foods that provide them with a balanced diet, social interactions that enhance fitness via benefits of group cohesion, and competition [5]. Contest-competition, for example, where individuals directly engage one another for access to nutrients, is a source of inter-individual variance that can lead to clear dominance hierarchies [6,7]. In the extreme, contests may even lead to a reproductive division of labour, with only those individuals at the top of the hierarchy being able to access enough nutrients, and at the right balance, to reproduce [8–10].
The effects that competition over food access can have on inter-individual variation in reproduction are well known in arthropods. For example, colonies of social spiders (e.g. Stegodyphus sp.) tend to be characterised by body size asymmetries and reproductive skews [11–15]. As a result of contest-competition over food access, only larger females are able to attain enough of the right nutrients to reproduce [8,13]. It has even been proposed that the reproductive asymmetries that arise from competition over nutrients may constitute a reproductive division of labour, in which non-reproductive spiders provide alloparental care ([8,16] c.f. [14,17]). In the burying beetle, Nicrophorus vespilloides, females compete for access to carcasses, which in turn leads to a dominance hierarchy where reproduction is skewed in favour of the dominant female [18]. Experimental data indicate that access to appropriate nutrition is the main factor determining reproductive output and also impacts performance in dominance interactions [18,19]. Although there is no such direct evidence in cooperative breeding vertebrates, correlative studies in mongooses (Mungos mungo) and meerkats (Suricata suricatta) show that subordinate females breed more frequently in periods of food abundance [20,21]. These results highlight the importance of contest competition as a potentially major ecological factor shaping the evolution of nutritional and social traits in animal groups.
In recent years, a new understanding of interactions between an organism’s nutritional requirements and its environment has been gained using the state-space models of the Geometric Framework (GF) [22–24]. In the GF, nutrients are represented by a Cartesian coordinate system, which is referred to as the nutrient space [23]. In the simple case of a nutrient plane, the GF represents two food components (e.g., the macronutrients protein and carbohydrate) on x and y axes (Fig. 1). The optimal amount and blend of nutrients that the animal requires over a specified period in its life are represented by a coordinate or region within the nutrient space called the Intake Target (IT; [23]). Foods are represented by ‘food rails’, which are radials through the nutrient space with a slope that reflects the ratio of macronutrients present within the food (Fig. 1). As an individual eats, its nutritional state (x, y coordinate) moves through the nutrient space in parallel with the rail of the food it consumes. A high quality food may be considered one with a food rail that will guide an individual’s nutritional state to the IT from its current state (Fig. 1); i.e., one that is nutritionally balanced. When confined to nutritionally imbalanced foods, the animal needs to resolve the trade-off between over-ingesting some nutrients and under-ingesting others. The strategy that it adopts in this situation, known as the ‘rule of compromise’, is expected to vary within and between species depending on the relative costs of ingesting excesses and deficits of the different nutrients [23,25].
Recently, the GF has been combined with Agent-Based Models (ABMs; simulations representing each individual explicitly, sometimes named individual-based models in ecological fields [26]) to successfully demonstrate how social interactions and nutritional strategies affect one another [5]. With regard to the influence of competition on the emergence of reproductive asymmetries, Lihoreau et al. [5] link classic models of contest competition (outlined by Bonabeau et al. [27]) with the GF in an ABM. In that model, access to each food rail is limited, and individuals must displace competitors via dominance interactions before feeding. Performance in dominance interactions is a function of the individual’s fitness, which in turn is negatively correlated with the distance between an individual’s nutritional state and the IT. Reproductive asymmetry arises as individuals who ‘get lucky’ and are able to feed on high quality foods early experience a ‘winner effect’ (see [28]). That is, a loop of positive feedback ensues whereby better-nourished individuals continue to perform well in dominance interactions and monopolise high quality foods. Ultimately, only certain individuals attain enough nutrients at the right balance to breed, a model outcome that is consistent with observations of reproductive skew in some social animals (e.g. spiders [8,13] and burying beetles [18,19]). Interestingly, this model also clearly demonstrates how early stochasticity in nutrient access can lead to the emergence of a self-organised social structure from an initially homogeneous group [5,29].
The aforementioned mechanistic model, however, does not consider the optimal nutritional strategy that individuals should adopt. When feeding on a poor quality food, an individual may choose to stop eating and seek an alternative. However, the individual risks incurring costs; e.g., the time spent attempting, but ultimately failing, to gain access to alternative better foods. Under some circumstances it is thus conceivable that an individual could get its nutritional state closer to the IT by consuming a poor quality food, rather than by frequently searching for better balanced alternatives. Ultimately, the optimal strategy for leaving a suboptimal food may be dependent on the level of competition and the kinds of food in the environment.
The incorporation of evolutionary and genetic components into GF based ABMs has been identified as a promising method with which to understand how ecological factors interact with nutritional strategies [5,23,30]. Here, we present the first such model, which we used to explore how intra-specific competition might affect the evolution of animals’ nutritional strategies. In the model by Lihoreau et al. [5], an individual’s nutritional strategy was governed by the fixed global parameter K, which we refer to here as ‘nutritional latitude’. When eating a food that will not guide its nutritional state to the IT an individual has some probability of leaving, which is both a function of the balance of nutrients in the food being consumed, and K. Here, a high K means an individual is likely to consume the same imbalanced food until reaching a point of nutritional compromise (at which point it then seeks an alternative). In contrast, a low K corresponds to a low probability that an individual will continue feeding on a food rail that will not guide its nutritional state directly to the IT. Individuals with extremely high or low values of K may, thus, be thought of as nutrient generalists or specialists, respectively (sensu Raubenheimer and Simpson [31]; we note that K as we model it here is equivalent to 1—K in Lihoreau et al. [5]).
In this study, we couple nutritional latitude with an evolutionary algorithm, whereby an individual’s K is governed by an individual-level, heritable and mutable value. Each generation consists of 150 individuals that must attain a certain level of fitness (i.e., nutritional state) within a fixed number of model iterations for it to be considered fit enough to breed. Fitness-proportionate selection then operates among those individuals fit enough to breed, with proximity to the IT (optimal point of nutrient intake in the nutrient space) determining this fitness. We allowed K to evolve over 1000 generations under varying levels of competition and in differing nutritional environments (i.e., different abundance and nutritional compositions of food). In doing so, we aimed to explore the effects of contest competition and the number and composition of foods in the nutritional environment on the evolution of individual nutritional strategies.
We began by exploring the effect of intensity of competition on the evolution of nutritional latitude in 2- and 3-food environments. We performed 30 model runs under varying intensities of competition (c, which is bounded at 0 and 1; all parameters are outlined in Table 1, and their mode of action is described in Models). From each model run we recorded the population mean nutritional latitude (K, also bounded at 0 and 1) after 1000 generations.
We first looked at the effects of competition in environments containing one nutritionally balanced food, and two imbalanced but complementary foods (i.e., those which between them subtend a region of the nutrient space containing the IT). For the latter two complementary foods we varied the extent of their nutritional imbalance (Fig. 2). In these environments when c = 0, K was stable at a range of values (Fig. 2). The high variance in stable values of K suggests that no one level of nutritional latitude is optimal where competition is weak, but most low levels are equally fit. In the face of increasing c, K was relatively stable up to a point. With mildly imbalanced foods at c = 0.7, and with extremely imbalanced foods at c = 0.67, K increased sharply to above 0.91 (Fig. 2). In both 3-food environments, increases in K were accompanied by declines in the variance of evolved K (Fig. 2). For example, in the environment with mildly imbalanced foods the 2.5th and 97.5th percentiles of K were 0.14 and 0.47, respectively with c = 0, but were 0.69 and 0.88 when c = 0.73 (Fig. 2A). Thus, selection for a high level of nutritional latitude is strong at moderate to high c, with a further sharp decrease at extremely high levels of c (i.e., > 0.85), largely being driven by a change in the lower 2.5th percentile of K (Fig. 2). At very high c the population could not support itself as no individuals could fulfil the fitness requirements to be considered in breeding condition by the end of the simulation (Fig. 2).
We next considered competition in a 2-food environment, containing one balanced and one imbalanced food, the latter of which varied in the degree of nutritional imbalance (Fig. 3). With a mildly imbalanced food, absent or weak competition selected for a lower K (and lower variance; Fig. 3A) than in 3-food environments (Fig. 2). Thus, selection for low nutritional latitude was stronger in this 2-food environment than in the 3-food environments. That being said, in the 2-food environment with a mildly imbalanced food and low c, K was stable, before transitioning to high K under moderate to high c (Fig. 3A), as was the case in 3-food environments (Fig. 2). In the 2-food environment that contained a balanced and a severely imbalanced food, nutritional latitude showed a quite different profile from that previously observed. Increasing c in this environment selected for low nutritional latitude (and very low variance in K), reaching a minimum value of K = 0.06 at c = 0.625 (Fig. 3B).
These results clearly indicate the importance of access to a complementary food to correct the nutritional state associated with consuming large amounts of a severely nutritionally imbalanced food (see S1 File for additional discussion). As part of experiment 1, we also looked at environments containing two nutritionally imbalanced but complementary foods. In these environments, the response of K to increasing c resembled that in 3-food environments (see S1 File).
In our model, the parameter η (see Details in Models and Table 1) is the power of the relative nutritional states (fitness; F) of the ith and jth individuals to predict the outcome of a dominance interaction between these individuals. Given that we are largely concerned with species for which ability in dominance interactions is strongly correlated with nutritional state (see Lihoreau et al. [5]), in the above results η is assumed to be high (η = 25). We now explore the effects of the intensity of competition (c) in scenarios where there is greater stochasticity in the outcome of contests over food; η = 20 and η = 10.
Where η was set at a lower levels, increasing the intensity of competition had the same qualitative effect on the evolution of nutritional latitude (K) as described above; i.e. at low c a range of low levels of nutritional latitude appear optimal, but a transition to high K is favoured at c greater than 0.733 (Figs. 2 and 6). However, at lower levels of η the intensity of competition that lead to population extinction was decreased. With η = 10 the population could not consistently sustain itself above values of c of 0.833 (Fig. 6B). In an equivalent nutritional environment with η = 25 the population could not consistently sustain itself above values of c = 0.8667 (Fig. 2). These results indicate that having a stable dominance hierarchy, which is based on nutritional state can allow the population to better survive poor nutritional environments. We further discuss the biological implications of this finding below (see Future Directions in Discussion).
In the results described above, selection acts via two mechanisms. First, only those individuals able to attain fitness greater than 0.5 within 500 iterations are assumed to be in good enough condition to breed. Second, among those individuals fit enough to breed, fitness-proportionate selection operates [32]. The sensitivity of our results to this general selection mechanism was assessed by running the model with an alternative mechanism, truncated selection [29]. In this instance, the first 10% of the population to attain fitness over a cut-off are assigned as parents for the next generation. In our experiments cut-offs of 0.5 and 0.9 were assessed.
Under truncated selection extinctions do not occur as the population is given a flexible amount of time to reach the fitness cut-off. We evaluated the effects of truncated selection on the model’s output in a 3-food environment with severely imbalanced foods (such an environment produced results typical of most other environments; Fig. 2B). In the absence of competition (c = 0) there was little or no selection on nutritional latitude: mean K = 0.5 with large variance (Fig. 7). However, low and moderate levels of competition selected for very low nutritional latitude (Fig. 7). As was the case under our general selection mechanism, a transition to increased nutritional latitude was still favoured under moderate to high competition (Fig. 7). However, K was not increased to anywhere near as high a level as under an equivalent nutritional environment with our general selection mechanism (Figs. 2 and 7). Finally, at very high levels of competition a return to low K was favoured (Fig. 7). The contrasting results of experiments 1 and 4 clearly illustrate that the mode of selection affects how nutritional strategies respond to contest competition. The implications of this finding for the biological interpretation of our model are discussed below (see Future Directions in Discussion).
We developed an ABM that combines principles of the GF with an evolutionary algorithm to explore how contest competition may affect the evolution of animal nutritional strategies. Specifically, we modelled the extent to which individuals consume nutritionally imbalanced foods that will not guide them directly to their intake target (nutritional latitude, K). In most of the nutritional environments we modelled, no competition and weak to moderate competition favoured low consumption of a suboptimal food. However, given that we observed high variance in stable values of K, it seems likely that there is no single optimal strategy. Rather, any fairly low level of nutritional latitude performs well. In contrast, moderate to severe competition appears to favour the consumption of more of an imbalanced food before seeking an alternative, than when competition is weak (i.e., they evolve increased nutritional latitude), potentially even consuming that food until reaching the point of nutritional compromise (see [23]). The balance of nutrients in the foods available also influences the optimal level of nutritional latitude. For example, in a 2-food environment that contained one highly imbalanced and one balanced food, a very low level of nutritional latitude was favoured regardless of competition (Fig. 3B). Thus, considering the nutritional composition as well as the amount of available foods is essential if we are to understand the role of competitive interactions in shaping the evolution of nutritional strategies.
Our model suggests that in social groups where the availability of nutrients is highly variable, plastic nutritional latitude should be adaptive so that individuals can alter their strategy in response to the intensity of competition. Several biological systems are well suited to empirical exploration of this idea. In social spiders, experimental evidence suggests that access to lipids governs reproductive asymmetry [13]. The manipulation described by Salomon et al. [13] (creating prey that vary in lipid content) could be employed, and then the behaviour of marked individuals within these groups observed (such as described in Whitehouse and Lubin [12]). An alternative model is the house cricket (Acheta domesticus), a species with well-studied nutritional requirements [33–38]. Males are known to compete with one another for food; moreover, sexual selection likely results in reproductive asymmetry, with larger males most likely able to meet the energy requirements for intra-sexual competition [39–43]. Contest-competition and aggression over food and mate access are also observable phenomena in male fruit flies (Drosophila melanogaster; [44,45]). This species also offers numerous other advantages including, being a model organism in genetics, being well studied with regards to its nutritional requirements and fitness consequences of nutritional imbalance and being suitable for artificial selection [1,2,46–48]. Using our ABM it will be possible to generate predictions for any number of nutritional scenarios specific to the model organisms described above. For example, considering spiders one may wish to explore a situation in which as food becomes scarce (i.e., competition increases in intensity), certain food rails appear only sporadically [49,50].
At the inter-species level our model suggests a role for contest competition and reproductive skew in shaping the evolution of dietary breadth. Specifically, consistent intense competition for access to a food containing a limiting nutrient, which results in reproductive skew, can select for high nutritional latitude, hence contributing to nutritional generalism. This hypothesis could be tested in a comparative nutrition framework (e.g., [31]), focussing on the intensity of contest competition and reproductive skew within groups of social generalists and specialists. Such approaches have, in the past, proved useful for studying the evolutionary mechanisms underlying dietary breadth, specifically suggesting that nutritional heterogeneity may lead to the adoption of specialist/generalist-specific rules of compromise ([23]; further discussed in Future Directions.).
Additional to insights into evolving nutritional strategies, our model supports predictions that contest competition over foods can lead to dominance hierarchies and reproductive asymmetries in social groups, because dominant individuals monopolise key nutrients for reproduction [5,8,13]. Contests for limited food can cause between-individual variance in reproductive output, regardless of the level of nutritional latitude or the nutritional environment. Given an apparent link between limited resources and alloparental care [51], contest competition over nutrients may be a mechanism forcing groups of animals on to the continuum from cooperative breeding, where helpers occasionally provide care to the offspring of breeders, to eusociality, characterised by a complete division of labour [52]. We note that our models represent a scenario in which individuals are unable to leave the group, even when competition becomes strong, due to some unstated ecological constraint. If future models explicitly focus on how nutrition and contest competition contribute to the evolution of sociality, they will likely want to vary the strength of constraints that keep individuals within the group.
Our models highlight some interesting relationships between nutrition, individual-level fitness and mean population fitness. Specifically, these models show that where individual nutritional state is a strong predictor of performance in dominance interactions (here η) and in turn reproductive asymmetry (i.e. a high variance in fitness), the population is better able to survive when nutrients are severely limiting. Accordingly, previous theoretical and experimental studies in social spiders have also suggested a strong dominance hierarchy ensures that the colony is able to survive resource poor periods, as at least a few females are able to monopolise enough nutrients to breed [8,11]. We also note that the spread of high nutritional latitude under strong competition bears some similarities to an evolutionary “tragedy of the commons” [53], because once the strategy becomes highly prevalent the mean fitness of the population becomes depressed to a lower level than might be the case if all (or the vast majority of) individuals to maintain low nutritional latitude.
Our evolutionary model could be further expanded to give a more detailed representation of specific biological systems. First, we assumed that the fitness payoffs surrounding the IT are symmetrical. Geometric nutritional studies have shown that in some instances the fitness landscapes associated with the intake of nutrients may be asymmetrical [25]. A case in point is the predatory ground beetle (Anchomenus doralis), where a female’s egg production displays an asymmetrical response to protein and lipid intake when mapped as a response landscape onto a protein-lipid nutrient-space [54]. Models considering the effects that asymmetrical fitness landscapes have on the evolution of nutritional strategies themselves, and in turn the consequences for social structure, are particularly exciting avenues of investigation. Second, geometric nutritional studies also demonstrate that different species follow different rules of compromise (the extent to which they consume excesses of one nutrient relative to the IT to gain another which is limiting in the diet). The model described here conforms to what is known as the ‘nearest distance’ rule of compromise [23]: individuals seek to attain a nutritional state that minimises the Euclidean distance from the IT (see Models). Some species, such as the migratory locust (Locusta migratoria), appear to conform to such a rule of compromise when confined to a single food [31]. However, other rules of compromise are also followed. For example, the desert locust (Schistocera gregaria) follows what is known as an ‘equal distance’ rule of compromise, eating more of an imbalanced diet, over-consuming the excess nutrient to a greater degree (and under-consuming the deficient nutrient to a lesser degree) than L. migratoria, under the same no-choice experiment [31]. Evidence from this and other examples using the comparative approach suggests that the adoption of these two different rules of compromise closely associates with dietary breadth, with nutrient specialists adopting the nearest distance rule we implement here [23]. The study of the co-evolution of nutritional rules of compromise, dietary breadth, fitness-landscapes and other nutritional strategies (e.g. nutritional latitude) remains largely theoretical [23,25]. However, with the increasing application of nutritional geometry to a wider range of species, both in the lab and in the field, the comparative studies required to untangle the co-evolution between the aforementioned traits should soon be possible [23].
Within the selection mechanism implemented here, individuals must first attain a certain nutritional state to breed. Amongst those individuals with a high enough fitness to breed, relative fitness (determined by proximity to the IT) then governs overall representation in the subsequent generation (i.e., fitness proportionate selection; [32]). Thus, what we term the ‘general’ selection mechanism is most analogous to systems where reproductive asymmetries arise when resources become limiting. This mechanism of selection is typical of experimental outcomes in some social systems. For instance, female social spiders that do not attain enough nutrients (lipids) to reach a mature size at the end of the season are not capable of breeding, and larger individuals produce more offspring ([8,13,55–57] c.f. [15]). We also explored the effects of truncated selection on the model output. Whilst these two selection regimes produced some broadly similar results, there were also differences; namely, with truncated selection there was a lack of selection on nutritional latitude in the absence of competition, but low nutritional latitude was favoured under even weak competition. Truncated selection is most analogous to social systems, where dominance hierarchies and reproductive asymmetries are always present, regardless of food availability. For example, in eusocial wasps (Polistes; the inspiration for the original manifestation of the contest competition model we implement [27]) linear hierarchies form amongst females, with reproduction limited to the individual at the top (or the top few; [58]). For such species, where reproduction is always limited to a few individuals (perhaps those best able to track the IT), a model operating truncated selection may be most appropriate. Additionally, it occurs to us that artificial selection experiments can use truncated selection; i.e., the top few performing individuals are selected for breeding (e.g. [59]). In the future, geometric ABMs such as ours may be used to generate predictions for selection experiments on nutritional strategies. However, those models should explicitly incorporate truncated selection as other modes of selection may produce inaccurate predictions.
The models described here make simplifying assumptions about within population variation in nutritional requirements and the effects of nutritional state on fitness. For example, we only consider a single sex although sex differences in nutritional requirements may be ubiquitous (e.g. [60]). Such assumptions seem justifiable on the basis of the biological systems that we are interested in. Considering sex specifically, the relationship between contest competition, reproductive asymmetry and nutritional state is often only profound (or well understood) in one sex. For example, in populations of social spiders female sex ratio bias tends to be very strong and males seem largely absent [13,14]. Thus it seems reasonable to assume that males play a relatively minor role in competition over nutrient access. When modelling the relationship between nutritional state and other social phenomenon (e.g. collective behaviour and communal feeding [5]), however, it may be more realistic to model such variation. To incorporate this variation, rather than modelling a single intake target as we do here, one could include a bi-modal distribution of intake targets representing the differential requirements of each sex and individual heterogeneity simultaneously.
In this instance, the combination of evolutionary algorithms, ABMs and the GF has allowed us to produce testable experimental predictions for how intra-specific competition affects the evolution of nutritional latitude and dietary breadth. The wider application of this integrated approach could be applied to assess how other nutritional strategies and ecological factors interact [30]. For instance, Lihoreau et al. [5] explore how collective decision-making can optimise the nutritional decisions of entire groups, a phenomenon that may be applicable to animals exhibiting a range of social interactions [61–63]. By expanding that model with an evolutionary algorithm, it would be possible to generate predictions for how nutritional strategies and social phenomenon co-evolve. The next step in combining evolutionary algorithms with GF-based ABMs will be to use spatially explicit models [30]. In this way researchers will be able to model the evolution of nutritional strategies in complex environments that are closely representative of real world ecosystems.
Models were programmed in the software Netlogo [64]. Graphs were created and statistical calculations performed with R version 3.1.1 [65]. The model is described following the overview, design and details format of ABM description as widely recommended [66–68]. The code for the model can be found in S2 File.
|
10.1371/journal.pgen.1007930 | Developmental regulation of DNA cytosine methylation at the immunoglobulin heavy chain constant locus | DNA cytosine methylation is involved in the regulation of gene expression during development and its deregulation is often associated with disease. Mammalian genomes are predominantly methylated at CpG dinucleotides. Unmethylated CpGs are often associated with active regulatory sequences while methylated CpGs are often linked to transcriptional silencing. Previous studies on CpG methylation led to the notion that transcription initiation is more sensitive to CpG methylation than transcriptional elongation. The immunoglobulin heavy chain (IgH) constant locus comprises multiple inducible constant genes and is expressed exclusively in B lymphocytes. The developmental B cell stage at which methylation patterns of the IgH constant genes are established, and the role of CpG methylation in their expression, are unknown. Here, we find that methylation patterns at most cis-acting elements of the IgH constant genes are established and maintained independently of B cell activation or promoter activity. Moreover, one of the promoters, but not the enhancers, is hypomethylated in sperm and early embryonic cells, and is targeted by different demethylation pathways, including AID, UNG, and ATM pathways. Combined, the data suggest that, rather than being prominently involved in the regulation of the IgH constant locus expression, DNA methylation may primarily contribute to its epigenetic pre-marking.
| DNA methylation mainly occurs at CpG dinucleotides and strongly influences gene expression during development. Deregulation of DNA methylation is often associated with disease. In mammalian genomes, unmethylated CpG dinucleotides are generally associated with active regulatory sequences, while methylated CpGs are often associated with silent promoters. The immunoglobulin heavy chain constant locus comprises multiple inducible constant genes and is expressed exclusively in B lymphocytes. We show that methylation patterns of most of the locus cis-elements, including promoters, enhancers and insulators, are established and faithfully maintained independently of B cell activation or transcription initiation. Acquisition of DNA methylation by the constant genes exons occurs independently of transcriptional elongation. One late B cell specific promoter is hypomethylated early in ontogeny. Constant genes promoters recruit different demethylation pathways that become dispensable for the maintenance of the mark in the B cell lineage. The data suggest that, rather than playing a prominent role in transcriptional regulation, DNA methylation may contribute to the epigenetic pre-marking of the IgH constant locus.
| DNA methylation is a common epigenetic regulation mechanism in vertebrates and is involved in gene expression regulation during development and differentiation as well as in defense of the genome against transposable elements. DNA methylation provides a robust epigenetic mechanism for cell fate decisions, cell identity and tissue homeostasis. The importance of this epigenetic regulation is highlighted by the finding that its absence is lethal and aberrant DNA cytosine methylation is often associated with disease such as cancer [1].
Mammalian genomes are predominantly methylated at cytosines in the context of CpG dinucleotide. Mammalian genomes are mostly CpG-poor and these CpG motifs are globally methylated. However, a minority of CpGs occur in CpG-dense regions called CpG islands (CGIs) and are generally refractory to DNA methylation. While unmethylated CpG sites and CGIs are generally associated with active promoters, methylated CpGs (mCpGs) and mCGIs are closely associated with transcriptionally silent promoters. This pattern is less obvious when it comes to transcription elongation as mCpGs and mCGIs in gene body did not block elongation, leading to the notion that it is transcription initiation that is more sensitive to cytosine methylation [2–4].
B lymphocytes are derived from pluripotent hematopoietic stem cells and develop in fetal liver during embryonic development, then shift to the bone marrow around birth [5]. B cell development requires assembly of its antigen receptor loci through V(D)J recombination which occurs in developing B cells in fetal liver and bone marrow [6, 7]. Further development leads to migration to peripheral lymphoid organs such as the spleen where, upon antigen encounter, mature B cells can undergo another recombination process called class switch recombination (CSR). CSR enables IgM-expressing B cells to switch to the expression of other antibody classes, specified by different constant genes. Each constant gene is part of a transcription unit where transcription, termed germline (GL) transcription, initiates at an inducible promoter (called I promoter) and terminates downstream of the constant exons [8]. GL transcription is associated with various induced epigenetic changes (e.g. [9, 10]) and is controlled by different cis-regulatory elements including enhancers and insulators (e.g. [11–14]). In particular, the 3′ regulatory region (3′RR), which contains four enhancers located downstream of the IgH locus, effects a long-range enhancing activity on the multiple I promoters [15].
While V(D)J recombination targets all antigen receptor loci in B and T lymphocytes [7], CSR is strictly B-cell specific and targets exclusively the immunoglobulin heavy chain (IgH) locus [8]. This highly restricted targeting raises important developmental questions. For instance, it is still unknown whether all the epigenetic features of the IgH constant locus are acquired de novo in the B cell lineage and at the right B cell developmental stage, i.e. when GL transcription occurs, or whether the locus is at least in part epigenetically pre-marked.
Here, we focused on DNA methylation and used bisulphite sequencing to analyze the methylation profiles of multiple cis-acting elements at the IgH constant locus. We show that the methylation patterns of most cis-acting elements are established and faithfully maintained independently of B cell activation or GL transcription. Moreover, one I promoter, but not enhancers, was hypomethylated early during ontogeny and recruited different demethylation pathways.
Splenic B cells can be activated by various extracellular signals (mitogen, cytokines…). Each stimulation condition induces a specific (set of) I promoter(s) and directs CSR to the corresponding constant gene(s) [8]. We checked induction of GL transcription and as expected, RT-qPCR and FACS revealed high levels of GL transcripts and robust CSR upon appropriate stimulation (S1 Fig).
To analyze methylation profiles of I promoters, we used bisulphite sequencing. Because this technique does not discriminate 5-methylcytosine from 5-hydroxymethylcytosine, a fraction of methylated cytosines may include 5-hydroxymethylcytosines. Conversely, a fraction of unmethylated cytosines may include 5-carboxylcytosines and 5-formylcytosines. Throughout this study, we did not quantify the levels of the oxidized methylcytosines.
In order to determine if and how CpG methylation patterns are affected upon induction of GL transcription, we first compared the methylation state of all CpGs at I promoters and flanking sequences (Fig 1A), in resting and activated splenic B cells. We focused on the promoters’ CpGs to establish the link between DNA methylation and transcription initiation, but we also analyzed I exons and different constant exons as sites of transcriptional elongation. Analysis of some CpGs upstream of the promoters, located outside the transcription units and the known regulatory regions, served as “negative controls” as we anticipated them to be hypermethylated (S2 Fig and S3 Fig).
Inspection of the data revealed various unexpected aspects of CpG methylation in the IgH constant locus. In particular: Most CpGs upstream of the promoters were heavily methylated in resting B cells and remained so after activation (Fig 1B–1E and S3 Fig). Strikingly, some promoters’ CpGs, notably the unique CpG at Iγ3 (see discussion), three CpGs at Iγ2b, and one CpG at Iα promoters, were fully unmethylated in resting B cells (Fig 1B and 1E). At the promoters, there was no obvious correlation between promoter activation and CpG demethylation (Fig 1B–1E), except for the Iγ1 promoter’s unique CpG, which lost all methylation upon IL4 activation (Fig 1D). The nature of the stimulus did not alter the CpG methylation state of Iγ2b promoter as a similar pattern was observed following either LPS or TGFβ stimulation, which both activate this promoter (Fig 1B and 1E). A positive correlation between induction of GL transcription and CpG demethylation could be seen for specific, mostly proximal, CpGs at Iγ3, Iγ1, Iγ2b and Iγ2a exons (hereafter Iγ exons). In contrast, the CpGs of Iε and (more markedly) Iα exons remained hypermethylated (Fig 1B–1E). CpG methylation status of all constant exons studied (Cγ3, Cγ1, Cγ2b, and Cα) was unchanged upon appropriate activation (Fig 1B, 1D and 1E). The targeting of CpGs for (de)methylation is highly focused, i.e., there is no evidence for spreading of this epigenetic mark as best illustrated by the hypomethylated CpG of Iα promoter (Fig 1E and S3 Fig) (see below).
In order to determine whether CpG demethylation occurs as a consequence of B cell activation or whether it is a direct consequence of GL transcription per se, we investigated CpG methylation in genetic contexts where Iγ3 and Iγ2b promoters were silenced in activated B cells, or constitutively active in resting B cells. In ZILCR mouse line, the chicken β-globin core insulator was inserted upstream of the 3’RR, resulting in a complete silencing of Iγ3 and Iγ2b promoters upon LPS stimulation (Braikia and Khamlichi, in preparation). In the second mouse model, the 5’hs1RI CTCF insulator within the Cα constant gene was deleted, leading to constitutive activity of Iγ3 and Iγ2b promoters in resting B cells [13] (Fig 2A and 2B).
The unmethylated state of Iγ3 and Iγ2b promoters remained essentially unchanged in LPS-activated ZILCR B cells (Fig 2A), and in unstimulated 5’hs1RI splenic B cells (Fig 2B). In LPS-activated ZILCR B cells, the methylation pattern of Iγ3 and Iγ2b exons was comparable to that seen in WT resting B cells (Fig 1B and Fig 2A). When Iγ3 and Iγ2b promoters were active in the absence of B cell activation, a lack of methylation was seen at exons Iγ3 and Iγ2b that was globally similar to that in LPS-activated WT B cells (Fig 1B and Fig 2B).
Taken together, the data from WT and mutant splenic B cells demonstrate that the unmethylated state of Iγ3 and Iγ2b promoters is locally established prior to B cell activation and transcription induction, and is maintained independently of B cell activation and promoter activity. Additionally, insulation of the 3’RR does not affect the methylation pattern of Iγ3 and Iγ2b promoters. In contrast, the relative demethylation of Iγ3 and Iγ2b exons results from GL transcription and not from B cell activation.
Iγ3 and Iγ2b promoters were unmethylated prior to, and following B cell activation, reminiscent of Eμ enhancer and the 3’RR [16–19]. This led us to explore the methylation pattern of other cis-acting elements, with known or suspected regulatory function. We focused on three CpG-rich clusters at Cδ-Iγ3 intergenic region (3’δ1 to 3’δ3) (Fig 3A). Two clusters (3’δ1 and 3’δ2) flank a region that is highly enriched in transcription factors binding sites and may play a role in early B cell development [20]; the other, located further downstream, is used as a negative control. We also examined two DNase I hypersensitive sites within Cγ1-Iγ2b intergenic region (hereafter 3’γ1E and 5’γ2bE) that bind various transcriptional/architectural factors [21, 22] and are involved in long-range interactions with multiple regulatory elements of the IgH locus in early B cells [21]. Additionally, 3’γ1E displays enhancer activity in pro-B cells [22]. Finally, we analyzed the intragenic 5’hs1RI insulator region whose CTCF binding site does not contain any CpG but is flanked by two clusters of 3 and 11 CpGs [13].
The data showed distinct CpG methylation patterns: 3’γ1E was largely unmethylated, both in resting and LPS-activated splenic B cells (Fig 3A) and its pattern was unchanged upon insulation of the 3’RR or deletion of 5’hs1RI (Fig 3B). The 3’δ1–3, 5’γ2bE and 5’hs1RI elements were hypermethylated in resting B cells as well as after LPS activation (Fig 3A). 5’γ2bE CpGs were also methylated in TGFβ-activated splenic B cells (Fig 3C).
The finding that Iγ3 and Iγ2b promoters and 3’γ1E enhancer were essentially unmethylated in resting splenic B cells led us to investigate when their non-methylated state was established, and whether this state was B cell-specific. To this end, we analyzed CpG methylation in various tissues and cell types. As controls, we assayed the Eμ enhancer, known to undergo lymphoid-specific demethylation and to remain unmethylated throughout B cell development [17, 19], and 5 CpGs upstream of Iγ2b which were heavily methylated in splenic B cells (Figs 1 and 2).
Indeed, The 5 CpGs upstream of Iγ2b were hypermethylated regardless of the cell type analyzed (Fig 4A and 4B). In contrast, Eμ was only minimally methylated in CD4+ T cells (10% of mCpGs), and was fully unmethylated in WT fetal liver B cells and in pro-B cells derived from the bone marrow of Rag2-deficient mice (Fig 4B). However, Eμ was relatively more methylated in mature sperm (64%), in serum-grown embryonic stem cells (ESCs) (51%) and in the tail tissue of Rag2-deficient mice (76%) (Fig 4A). The 5’γ2bE was heavily methylated in all tissues and cell types analyzed except in ESCs where it was relatively less methylated (58%) (Fig 4A and 4B). Interestingly, 3’γ1E underwent a strict B cell-specific demethylation, contrasting with Eμ enhancer whose demethylation was more pronounced in T cells (Fig 4B). Importantly, Iγ3 promoter was markedly hypomethylated in sperm (31%) (Fig 4A), whereas Iγ2b promoter (69%) (Fig 4A) and Iγ1 (100%) and Iα (81%) promoters (S4 Fig) were heavily methylated. Importantly, Iγ3 promoter and, to lesser extent, Iγ2b promoter underwent further demethylation in ESCs (8% and 51% respectively) (Fig 4A). In non-B cells, compared to ESCs, Iγ3 and Iγ2b were more methylated in Rag2-/- tail (40% and 65% respectively) (Fig 4A), whereas in CD4+ T cells, Iγ3 underwent moderate methylation (31%) while Iγ2b was further demethylated (25%) (Fig 4B). Remarkably, in the B cell lineage, Iγ2b promoter was more demethylated than Iγ3 promoter in fetal liver (7% and 35% of mCpGs). Iγ3 promoter became fully unmethylated in pro-B cells of Rag2-deficient mice (Fig 4B and S5 Fig).
Altogether, the data revealed that, among the cis-acting elements analyzed, Iγ3 promoter was already hypomethylated in sperm and ESCs, and fully unmethylated in pro-B cells of adult mice. Iγ2b promoter, Eμ and 3’γ1E enhancers were hypermethylated in sperm but underwent massive demethylation in fetal liver B cells.
The above data showed that Iγ3 and Iγ2b promoters displayed different dynamic methylation patterns during embryonic development and cell differentiation, and that in sperm and ESCs, Iγ3 promoter was hypomethylated compared to Iγ2b. One possibility is that the two promoters are targeted by different demethylation machineries. In an attempt to identify the demethylation pathways involved, we assayed for CpG methylation at Iγ3 and Iγ2b promoters in mature sperm and resting splenic B cells of mice with Activation-induced cytidine deaminase (AID), Uracil DNA glycosylase (UNG), Ataxia telangiectasia mutated kinase (ATM), or the Tumor suppressor protein p53 deficiency (see discussion).
Strikingly, Iγ3 promoter displayed a hypermethylated pattern in AID-, UNG-, and ATM-deficient sperm compared to WT control (Fig 5A). In contrast, the methylation pattern of Iγ3 promoter did not significantly change in p53-deficient sperm (Fig 5A). The methylation pattern of Iγ2b promoter was not significantly affected regardless of the genetic deficiency (Fig 5A). For all deficiencies analyzed, the methylation pattern of Iγ3 and Iγ2b promoters was similar to WT in B cells (Fig 5B).
The data established that in mature sperm, Iγ3 and Iγ2b promoters displayed different methylation patterns, and that Iγ3 promoter was specifically hypermethylated in AID-, UNG-, and ATM-deficient sperm. In resting B cells however, the unmethylated profile of both promoters was essentially insensitive to AID, ATM, UNG, or p53 deficiency.
Four main conclusions emerge from this study. DNA methylation does not play a significant role in IgH constant genes expression. Acquisition of DNA methylation by the constant exons is not mediated by transcriptional elongation. The hypomethylated pattern of the late B cell-specific Iγ3 promoter was manifest in mature sperm and ESCs already, in contrast to Eμ and 3’γ1E enhancers and other I promoters. Iγ3 and Iγ2b promoters recruited different demethylation pathways.
Except for Iγ1, B cell activation and induction of GL transcription did not perturb the methylation patterns of I promoters. Explanations such as the nature of the stimulus or the number of promoter CpGs cannot explain these patterns. For instance, Iγ1 and Iε promoters are both induced by IL4 stimulation, but while Iγ1 underwent full demethylation, Iε did not. On the other hand, both Iγ3 and Iγ1 promoters contain a single CpG, but while Iγ3 was already unmethylated in resting B cells, Iγ1 became fully unmethylated only after induction. In this regard, various studies showed that methylation of a single CpG can have important functional or pathological consequences [23–26].
GL transcription at the IgH constant locus is largely controlled by the 3’RR [15], which was shown to engage in long-range interactions with I promoters through chromatin looping, in a stimulus-dependent manner [27–30]. Additionally, the 3’RR controls various active histone modifications at I promoter/exon regions [31]. Our findings strongly suggest that the formation of IgH loops and the set-up of active histone marks associated with I promoters activation do neither require nor induce demethylation of I promoters. Significantly, Iγ3 and Iγ2b promoters are the most sensitive to 3’RR mutations (e.g. [11, 12]). Nonetheless, their unmethylated pattern did not change upon insulation of the 3’RR, which fully repressed these promoters. The 3’RR thus controls Iγ3 and Iγ2b promoters through mechanisms that do not involve DNA methylation, contrasting in this regard with other Ig enhancers (e.g. [17, 19, 32]). It remains to be established whether the 3’RR displays a demethylating activity at earlier B cell developmental stages. Paradoxically, Iγ1, known to be relatively 3’RR-independent (e.g. [11, 12]), was the only I promoter whose demethylation was induced. This may relate to the presence of specific regulatory elements with demethylating activity such as the putative Iγ1 promoter-associated enhancer [33], and/or the 3’γ1E enhancer. Testing these hypotheses still awaits appropriate knock-out models.
Induction of GL transcription led to a moderate hypomethylation of essentially the most proximal CpGs of Iγ exons. This may be due to pausing of RNA pol II that takes place 30–60 nucleotides downstream of the transcription start site(s). Accordingly, high-levels of RNA pol II p-Ser5 were detected at Iγ3 exon upon LPS stimulation [9], which may protect some CpGs against methylation. Methylation of Iε and Iα exons, however, was not impacted by stimulation, suggesting that the mechanisms that underlie pausing at I exons may differ.
Seminal studies using transformed cell lines and methylation-sensitive restriction enzymes found a positive correlation between DNA hypomethylation and constant genes transcription [34–37]. However, this correlation was not observed in primary B cells [38]. Accordingly, we found that Cγ3, Cγ1, Cγ2b and Cα exons were already hypermethylated in unstimulated splenic B cells and remained so after induction of GL transcription, regardless of the nature of the stimulus. This indicates that transcriptional elongation across the chromatin of constant exons does not bring about any obvious change of their hypermethylated pattern. Interestingly, this hypermethylated pattern coincides with transcription-associated deposition of H3K36me3 at Cγ exons [9]. In genomic imprinting for instance, acquisition of DNA methylation through transcription-associated H3K36me3 has been demonstrated for some imprinting control regions. In this process, H3K4 methylation, which prevents the action of DNMT3A-DNMT3L de novo methyl-transferase complex, is first removed from chromatin, this enables transcription-associated H3K36me3 to recruit DNMT3A-DNMT3L complex that will methylate DNA [39]. This is clearly not the case for IgH constant exons which are likely methylated through a different mechanism.
Previous work indicated that H3K36me3 and intragenic DNA methylation contribute to the silencing of alternative, intragenic promoters [40, 41]. Low levels of antisense switch transcripts have been detected (e.g. [42, 43]), but antisense promoters have not been precisely defined. Intragenic methylation may contribute to down-regulation of the antisense promoters and/or other cryptic promoters. An attractive possibility could be that DNA hypermethylation and H3K36me3 across the constant exons protect these regions from AID attack by favoring a compacted chromatin structure after nucleosome displacement induced by RNA pol II passage. This chromatin-based protection mechanism is physiologically relevant as the constant exons are coding sequences whose reading frame must be preserved if the Ig heavy chain is to be produced. In this regard, the highly cytosine-rich, non-coding switch sequences, which are preferentially targeted by AID during CSR are strikingly poor in CpG [44] compared to constant exons.
Two major waves of DNA methylation reprogramming occur during development, shortly after fertilization and in primordial germ cells (PGCs). After the massive methylation erasure in PGCs, de novo DNA methylation is acquired in prenatal prospermatogonia before birth. The methylation patterns are fully established at birth and are maintained before the cells enter meiosis [45, 46], and it was shown that sperm cells display the highest global DNA methylation level [47]. In stark contrast to I promoters, and to Eμ and 3’γ1E enhancers, Iγ3 was already hypomethylated in mature sperm. Moreover, Iγ3 promoter underwent further demethylation in serum-grown ESCs despite the fact that ESCs grown in this condition display high DNA methylation levels [48], comparable to those of mature sperm [47]. These findings suggest that Iγ3 promoter is hypomethylated in pre-implanted embryo. Upon differentiation however, Iγ3 promoter moderately acquires DNA methylation and is fully unmethylated only in B cells of adult mice. Altogether, the above findings strongly suggest that the cis-acting elements analyzed are targeted by (de)methylating activities in a highly specific manner. The differential targeting is also evident from the patterns of Iγ3 and Iγ2b in sperm with AID, UNG, ATM or p53 deficiency.
The role of AID in DNA demethylation is still controversial (e.g. [45, 46, 49–51]). AID was implicated both in vitro and in vivo at various stages of mouse embryonic development [47, 52, 53]. Our data show that Iγ3, but not Iγ2b, is hypermethylated in AID-deficient sperm. This indicates that AID demethylation pathway is involved, and preferentially targets Iγ3 promoter. Whether it is AID itself, or a cofactor, that is directly implicated is presently unclear. Also, we do not infer that AID-mediated demethylation occurs in mature sperm. Demethylation may have occurred in PGCs, and the hypomethylated pattern subsequently maintained during the establishment of the male germ line. In this regard, low levels of AID expression were detected in PGCs but not in the germ line [52, 54].
Preferential targeting of Iγ3 promoter was also evident in UNG-deficient sperm. The base excision repair pathway [54], and in particular UNG which excises uracil from DNA, has been implicated in DNA demethylation in zygotes and PGCs [53, 55, 56]. In antibody diversification mechanisms in B cells, AID deaminates a non-methylated cytosine to generate a U:G mismatch that can be processed by UNG [57]. A somewhat analogous scenario has been proposed for cytosine demethylation in mouse zygotes [55]. Whether, similarly, UNG acts downstream of AID in PGCs is presently unclear. However, it is possible that UNG is involved through an AID-independent pathway.
ATM is a major component of the DNA damage response, and it has recently been implicated in the establishment of DNA methylation patterns during spermatogenesis, as global DNA methylation was reduced in ATM-deficient testis [58]. Based on this, we expected a hypomethylated pattern in ATM-deficient sperm. However, Iγ3 was hypermethylated while Iγ2b was unaffected. This suggests that ATM-mediated demethylation of Iγ3 implicates different, yet unknown mechanisms. In contrast, methylation patterns of Iγ3 and Iγ2b promoters did not significantly change in p53-deficient sperm. p53 has been shown to down-regulate the de novo DNMT3A and DNMT3B methyl-transferases and up-regulate TET1 and TET2 in naïve ESCs, whereas in differentiated cells, p53 became a repressor of Tet1 and Tet2 genes [59]. None of these modes of regulation seems to target Iγ3 and Iγ2b promoters although an effect of p53 at a discrete developmental stage can not be excluded.
Overall, the methylation pattern of Iγ3, but not of Iγ2b promoter, was perturbed in AID, UNG or ATM-deficient sperm. This suggests that different pathways somehow contribute to the setting of the methylation patterns of these promoters. Whether these pathways act at the same developmental stage and whether they interact with each other and/or with other pathways is presently unknown. In contrast, none of the pathways studied was significantly required for the maintenance of the unmethylated state of Iγ3 and Iγ2b promoters in resting splenic B cells. Thus, once the demethylation mark has been set up, the involved pathways seem dispensable for the maintenance of the mark at subsequent B cell developmental stages.
Though still debatable, accumulated evidence supports the notion that at least some of the epigenetic features that underlie tissue-specific expression are somehow stamped at earlier developmental stages, prior to the specification of the relevant lineage [39, 60, 61]. For instance, asynchronous replication, set up early during development, was suggested to epigenetically mark antigen receptor loci for mono-allelic recombination at the right developmental stage [62, 63]. Some, but not all, B cell-specific enhancers are primed in hematopoietic stem cells (e.g. [64–67]). Other tissue-specific genes are epigenetically marked in ESCs [68, 69]. Regarding DNA methylation specifically, different tissue-specific enhancers, but not promoters, displayed a subset of hypomethylated CpGs in ESCs [25, 70].
What could be the functional significance of the overall hypomethylated pattern of Iγ3 promoter? Splenic marginal zone B cells represent a special population of the adaptive immune system. These “innate-like” lymphocytes [71] play an important role in rapid protective responses against blood-borne antigens. They are in a state of active readiness and switch to IgG3 preferentially in response to T-cell-independent antigens [71]. We speculate that the early set-up of the hypomethylated pattern of Iγ3 may be part of an epigenetic programme that predisposes this promoter for fast activation in marginal zone B cells.
In conclusion, methylation patterns of IgH constant locus elements are essentially transcription-independent. The mature B cell-specific Iγ3 promoter is hypomethylated early during ontogeny. Iγ3 and Iγ2b promoters recruit different demethylation pathways that are dispensable for the maintenance of the demethylation mark once established in the B cell lineage. Further investigations are required to unravel the multiple facets of DNA methylation regulation at the IgH locus during development and to elucidate the mechanisms that control the process.
The experiments on mice were carried out according to the CNRS Ethical guidelines and were approved by the Regional Ethical Committee (Accreditation N° E31555005).
ESCs (CK35 line, of 129Sv background) were provided by Chantal Cress (Institut Pasteur, Paris, France). The WT and homozygous Rag2-/-, ZILCR, 5’hs1RIΔ/Δ were of 129Sv genetic background. AID-/-, ATM-/-, UNG-/-, and p53-/- mutant mice were enriched in 129Sv genetic background through at least 8 back-crosses, and both their chromosomes 12 (harbouring the IgH locus) were derived from 129Sv. All the mice used were 6–8 week-old. ATM-deficient mice were purchased from Jackson labs and p53-deficient mice were from the European Mutant Mouse Archives, Orléans, France. AID-deficient mice were provided by T. Honjo, through C-A. Reynaud and J-C. Weill. UNG-deficient mice were provided by T. Lindahl, through C. Rada and the late M.S. Neuberger.
Single cell suspensions from the bone marrows or spleens were obtained by standard techniques. Rag2-deficient pro-B cells (from the bone marrow of Rag2-/- mice) and WT fetal liver B cells (at day 14 post-coitum) were positively sorted by using B220- and CD19-magnetic microbeads and MS columns (Miltenyi). Splenic B cells were negatively sorted by using CD43-magnetic microbeads and LS columns (Miltenyi). Splenic CD4+ cells were sorted as B220-IgM-CD4+ population. ESCs cells were serum-grown in the presence of LIF (106 units/ml) throughout: first on mitomycin-treated feeder cells for 2 days, trypsinized and amplified for additional 2 days without feeders. After trypsinization, the cells were plated on gelatinized dishes for 2 hours, and the ESC-enriched supernatant carefully pipetted off and plated again for additional 2 hours in order to get rid of contaminating feeders. Sperm was collected from the cauda epididymis of adult males by the “swim-up” method [72].
To induce GL transcription, negatively sorted CD43- splenic B cells were cultured for 2 days, at a density of 5 x 105 cells per ml in the presence of LPS (25 μg/ml) + anti-IgD-dextran (3 ng/ml) (hereafter LPS stimulation), LPS (25 μg/ml) + anti-IgD-dextran (3 ng/ml) + IL4 (25 ng/ml) (IL4 stimulation), LPS (25 μg/ml) + anti-IgD-dextran (3 ng/ml) + IFNγ (20 ng/ml) (IFNγ stimulation) or LPS (25 μg/ml) + anti-IgD-dextran (3 ng/ml) + IL4 (10 ng/ml) + IL5 (5 ng/ml) + BLyS (5 ng/ml) + TGFβ (2 ng/ml) (TGFβ stimulation).
Genomic DNAs were purified from the following sources: sorted resting splenic B cells from WT, 5’hs1RIΔ/Δ, AID-/-, UNG-/-, ATM-/-, and p53-/- mutant mice; from WT, ZILCR, AID-/- splenic B cells at day 2 post-stimulation; from WT ESCs, resting splenic CD4+ T cells, and fetal liver B220+ cells; from pro-B cells or from the tail of Rag2-/- mice; from mature sperm of WT, UNG-/-, AID-/-, ATM-/-, and p53-/- mutant mice.
Purified genomic DNAs were assayed by sodium bisulphite sequencing by using a bisulphite conversion kit (Diagenode). Modified templates were amplified by PCR using converted primers listed in S1 Table. Converted primers were designed by using the public MethPrimer software. PCR products were separated by agarose gel electrophoresis, purified using QIAquick gel extraction kit (Qiagen), and cloned into pCR2.1-TOPO vector (Invitrogen). Transformed bacteria were plated immediately after transformation without pre-culture, and randomly picked clones were sequenced (Eurofins Genomics). Sequence analysis showed 99%-100% bisulphite modification efficiency.
Allophycocyanin (APC)-conjugated anti-B220, fluorescein isothiocyanate (FITC)-conjugated anti-IgG1, Phycoerythrin (PE)-conjugated anti-IgG2b, PE-conjugated anti-IgG2a, and PE-conjugated anti-CD4 antibodies were purchased from BioLegend. FITC-conjugated anti-IgG3 and FITC-conjugated anti-IgA were from BD-Pharmingen. LPS was purchased from Sigma, anti-IgD-dextran from Fina Biosolutions, TGFβ, B-LyS, IFNγ and IL5 from R&D, and IL4 from eBiosciences.
At day 4 post-stimulation, B cells were washed and stained with anti-B220-APC and either anti-IgG3-FITC, anti-IgG2b-PE, anti-IgG1-FITC, anti-IgG2a or anti-IgA-FITC. Activated B cells from AID-deficient mice (unable to initiate CSR) were included throughout as negative controls. Data were obtained on 5 x 105 viable cells by using a BD FACSCalibur flow cytometer.
Total RNAs were prepared from B cells at day 2 post-stimulation, reverse transcribed (Invitrogen) and subjected to qPCR using Sso Fast Eva Green (BioRad). Actin transcripts were used for normalization. The primers used have been described [13].
Results are expressed as mean ± SD (GraphPad Prism) and overall differences between values from day 0 and day 2 post-stimulation were evaluated by paired t-test, and from WT and AID-, UNG-, ATM- and p53-deficient sperm by unpaired t-test. The difference between means is significant if p value < 0.05 (*), very significant if p value < 0.01 (**), and extremely significant if p value < 0.001 (***).
|
10.1371/journal.pbio.3000343 | FishNET: An automated relational database for zebrafish colony management | The zebrafish Danio rerio is a powerful model system to study the genetics of development and disease. However, maintenance of zebrafish husbandry records is both time intensive and laborious, and a standardized way to manage and track the large amount of unique lines in a given laboratory or centralized facility has not been embraced by the field. Here, we present FishNET, an intuitive, open-source, relational database for managing data and information related to zebrafish husbandry and maintenance. By creating a “virtual facility,” FishNET enables users to remotely inspect the rooms, racks, tanks, and lines within a given facility. Importantly, FishNET scales from one laboratory to an entire facility with several laboratories to multiple facilities, generating a cohesive laboratory and community-based platform. Automated data entry eliminates confusion regarding line nomenclature and streamlines maintenance of individual lines, while flexible query forms allow researchers to retrieve database records based on user-defined criteria. FishNET also links associated embryonic and adult biological samples with data, such as genotyping results or confocal images, to enable robust and efficient colony management and storage of laboratory information. A shared calendar function with email notifications and automated reminders for line turnover, automated tank counts, and census reports promote communication with both end users and administrators. The expected benefits of FishNET are improved vivaria efficiency, increased quality control for experimental numbers, and flexible data reporting and retrieval. FishNET’s easy, intuitive record management and open-source, end-user–modifiable architecture provides an efficient solution to real-time zebrafish colony management for users throughout a facility and institution and, in some cases, across entire research hubs.
| FishNET facilitates remote tracking of individual zebrafish lines and links associated biological resources to enable robust and efficient colony management and storage of laboratory information related to zebrafish.
| The fecundity, rapid development, external fertilization, amenability to both forward [1–7] and reverse [8–11] genetic approaches, conservation of core vertebrate protein-coding genes [12], small size, and inexpensive husbandry costs make zebrafish a powerful vertebrate model for studying human physiology and disease [13, 14]. Moreover, their optical transparency [15–17] and readily available suite of genetic tools (Tol1 and 2 [transposable element] tranposase-mediated transgenesis, Gal4/UAS [yeast Gal4 transcriptional activator/upstream activation sequence] gene expression system, Cre/loxP [bacterial Cre recombinase gene/locus of X(cross)-over in P1] genetics, etc.) [18–27] and fluorescent reporters (e.g., gCaMP [green fluorescent protein, calmodulin, and M13 peptide], lifeact fluorescent proteins, etc.) [18, 28] have made zebrafish the premier model system for studying vertebrate biology in real time. These features, combined with their impressive regenerative capacity, also make zebrafish ideal for studying vertebrate tissue and organ regeneration [29, 30]. Given these advantages, it is no wonder that fields as diverse as developmental neurobiology [31] and cancer [32] have leveraged the embryonic and adult zebrafish, respectively, to make valuable biological insights. As more and more labs embrace this model system and shared-use zebrafish “core” facilities continue to be built across the world, there has been an explosion in the amount of transgenic and mutant lines generated by the community, creating a significant need for accurate, automated, real-time colony management tracking software. This need for a centralized database is not only desirable at the level of an individual laboratory group but in some cases is necessary throughout a facility and institution or even across an entire research hub as researchers desire to build a more cohesive zebrafish community.
Despite the importance of effective data management in animal research, many investigators employ handwritten notebooks or spreadsheet applications for managing small and medium-sized animal colonies. While these ad hoc data entry approaches offer the benefit of being simple to adopt, they do not scale with increased user numbers (even within a single laboratory), and they are not practical across an entire facility. Relying upon individuals to enter the correct line nomenclature (e.g., allele) and accurate information (e.g., sex, date of birth, etc.) unnecessarily exposes these “databases” to avoidable operator error. Additionally, paper records have the significant drawback that they cannot be accessed simultaneously by multiple users, they cannot be viewed remotely, and they can be misplaced or destroyed. On the other hand, electronic spreadsheet databases suffer from their own limitations, as multiple users cannot access them simultaneously in real-time. In addition, data stored in this format are challenging to mine; they typically lack a uniform/controlled lexicon, and best practice rules for data entry are absent, thus making them highly susceptible to operator error. Moreover, simple spreadsheets (e.g., Excel) cannot navigate complex data structures, such as breeding schemes or pedigrees, as required in a model colony management software.
Paraphrasing Silver [33] and as epitomized in the Jax Colony Management Software for mice [34], an ideal database would track (1) individual animals and their ancestors, (2) matings between animals, (3) progeny born from such matings (e.g., litters) and the individuals within litters that are used in experiments, and (4) experimental materials (tissues and DNA samples) obtained from individual animals. Such a database should also format records so that determination of relationships between any components of the colony is possible (e.g., which tissue came from which animal). Ideally, this platform would feature an intuitive graphical user interface, have a robust online community to support trouble shooting or modifications to the underlying architecture or functionality, and also be open source.
A handful of software solutions specifically designed for managing zebrafish animal husbandry exist. A summary of some of the currently available zebrafish colony management applications, both commercial and open source, is provided in Table 1, while their functionalities are summarized in Table 2. Available solutions for data management also vary significantly in terms of cost, implementation requirements, flexibility, and level of user support. Additionally, few of these options are open source, and even fewer also record phenotyping and experimental data. These drawbacks, along with the cost of commercial options or the potentially intimidating learning curve associated with adopting open-source options—such as creation and maintenance of structured query language (SQL) databases, along with knowledge of programming languages like Python or personal home page (PHP) to manage them—may explain the reluctance of the zebrafish community to embrace these solutions.
Here, we describe FishNET. FishNET is a comprehensive relational database application developed specifically to meet biomedical research community demands for a well-engineered, flexible database system supporting zebrafish animal husbandry and data management. FishNET meets the aforementioned criteria for an ideal record-keeping system with several added benefits, including a remotely accessible virtual facility view; a pedigree function; barcode-scannable labels for tanks, crosses, embryos, and fry; a calendar function that emails users for events such as line turnover or graduation of fry to the nursery; sick fish reports; records for genotyping protocols; and real-time records of all fish tanks, their use, and water quality in a facility for billing purposes and Institutional Animal Care and Use Committee (IACUC) reporting purposes.
FishNET runs directly on Macintosh or PC computers using the FileMaker Pro Advanced (FMPA) application. Hosted databases can also be viewed remotely via web browsers on PC, Macintosh, or Linux computers by incorporating FileMaker WebDirect. Furthermore, it can be accessed on iPad and iPhones through the FileMaker Go application in the App Store (https://apps.apple.com/us/app/filemaker-go-18/id1438460792?ls=1). The functional features of FishNET include support for controlled vocabularies (such as uniform allele nomenclature to avoid confusion regarding the origin of a transgenic or mutant line), multiuser capabilities, quick and accurate data reporting, pedigree tracking, animal husbandry workflow, sample tracking, and experimental data capture. The expected benefits of FishNET are improved vivaria efficiency, increased quality control for experimental numbers, and flexible data reporting and retrieval. FishNET is a freely available tool, and FileMaker software is a well-supported application with a large user base. Free trial versions of FMPA 17 are available at https://content.filemaker.com/filemaker-trial-en-rf. FileMaker Go (for iOS mobile devices) is available at the App Store (https://www.filemaker.com/products/filemaker-go/). A full database, as well as an empty shell, of FishNET is freely available at http://www.wythelab.com/wythe-lab-databases (or via S1 File).
Zebrafish protocols were approved by the IACUC at Baylor College of Medicine and the University of Texas MD Anderson Cancer Center.
FishNET employs a normalized relational database that can be run through FMPA. The relational data model that underlies FishNET minimizes data redundancy, enforces data integrity, leverages controlled vocabularies to ensure proper allele designations, and enables accurate data retrieval against large data sets. The user interface enables addition of new functionality and enhancement of existing functions without major modifications to the basic infrastructure, enabling easy adaptation to user-specific needs. FMPA ensures safe multiuser concurrent data entry and editing in real time. Additionally, FishNET features a built-in barcode generator and barcode reader, and when these features are combined with a validated printer (as we demonstrate herein), together, they provide instant recognition and tracking of all fish stock information and related actions on a mobile device.
An overview of the two primary configurations of FishNET is shown in Fig 1A. FishNET can be set up to run as a standalone database on a personal computer running a Windows or MacOS operating system. Alternatively, FishNET can be hosted on a central computer running a Macintosh or Windows operating system using FMPA Server software to support small- to medium-sized research groups in which FMPA client software is installed on all user desktop and laptop computers (e.g., 1 license of FMPA per accessing computer). This same FMPA Server configuration also supports use of FishNET through the FileMaker Go App on mobile handheld devices with barcode readers running iOS 11.2 or later (e.g., iPad, iPhone). Handheld computers that have been tested include iPad Air 2 as well as iPhone 6s and iPhone X. In the server configuration, FishNET can be accessed—with appropriate user authentication capabilities—on any internet-capable device running Safari, Chrome, Internet Explorer, or Microsoft Edge internet browsers using FileMaker WebDirect.
The cost of setting up and hosting FishNET will vary depending on the configuration. The number of users and their need to modify the underlying database architecture (e.g., do they need to function as an administrator or simply upload, access, and download data) will dictate whether a standalone license is sufficient or whether multiple licenses that each connect to an FMPA Server (hosted via an on-premise server) or FileMaker Cloud (hosted remotely via FileMaker, not described here) are required. At this time, an annual FMPA license costs $540 for 5 licenses per year (academic pricing) with FMPA Server included, while a permanent individual instance of FMPA costs $324 and FileMaker Cloud costs $400 (user licenses not included). The cost to host FMP Server will vary by institution and will also depend on whether the institution hosts it on their own server (which requires Windows Server 2016 or newer) or if it is instead hosted locally by the laboratory (for instance, on a computer running MacOS Sierra 10.12 [or newer] software). A guide to determining which license is appropriate for your group is provided in Fig 1B, as are images of the stationary and mobile stations in Fig 1C and 1D, respectively. A more detailed comparison between the costs of FMP Server and Cloud is provided in S1 Table.
The base hardware requirements of FMPA and FishNET are relatively modest. FishNET runs smoothly on a late 2014 Mac mini with a 2.6 GHz, dual-core Intel i5 (2278U) processor with 8 GB of 1,600 MHz LPDDR3 onboard memory, 3 MB virtual memory, and a 1-TB hard drive and 802.11ac Wi-Fi wireless networking and Bluetooth 4.0 wireless technology requiring 100–240V AC. While the database itself can be set up multiple ways, below, we describe two standard configurations. One option is an immobile station on a bench top within the facility room that requires a wireless barcode reader integrated with the computer (to read barcodes and enter data) as well as a label printer (for the racks, tanks, crosses, and fry) (Fig 1C). For multiple users, a more sensible configuration may be to locally host FishNET (this requires an FMPA Server license, which is included in the purchase of a team FMPA license—which covers 5 or more individual licenses). This set-up allows for simultaneous, multiuser access and data entry using either FMPA on the same network or through an internet web browser on the same network via FileMaker WebDirect. In this configuration, access on the same network could also be achieved on a handheld device using the FileMaker Go application (which is restricted to iPads and iPhones running iOS 11.2 or above). Individual licenses for FMPA would only be required for users that want to easily print labels with barcodes and run the automated genotyping annotation script (see below) or those that need to modify the underlying database architecture. The hardware and costs for this configuration are outlined in Table 3.
A second configuration is for a mobile station that can travel throughout an entire facility (Fig 1D). The costs and components of this set-up are shown in Table 4. A basic overview comparison between the computing software and hardware costs for all FishNET configurations is provided in Table 5.
Regardless of the hardware set-up, the main drawback of using a commercial relational database platform is that while it is open source, non-coding–based, and user modifiable, a facility or laboratory must purchase a permanent or annual license to use FMPA. Fortunately, several flexible options exist, as outlined below. Additionally, the FileMaker Go App is compatible with Apple mobile devices (e.g., iPhone and iPad). If FishNET will be hosted, then a computer with a Dual-Core CPU processor, 8 GB of RAM, and at least 80 GB of hard drive storage running Windows Server 2016 Standard Edition (with Desktop Experience), Windows Server 2012 R2 with Update, MacOS High Sierra 10.13, or MacOS Sierra 10.12 will be required. The FMP Server license is included when you obtain at least 5 licenses of FMPA, making this the logical choice for most user groups.
To protect the user’s data, FMPA Server offers AES-256 encryption. It also supports third-party secure sockets layer (SSL) certificates to establish secure links between the server and web browsers. Furthermore, using an institution’s wireless local area network (Wi-Fi) to transfer files from the FMPA Server to individual users adds another layer of security (provided that the institution requires user authentication to connect to the network/has a secure firewall).
A centralized database, such as FishNET, offers the advantage of simplifying data redundancy and mirroring (e.g., creating backups). When using a single instance of FMPA on a single computer, a user or lab can back up the entire contents of the hard drive, along with the FMPA database file(s), using a standard backup program (e.g., Time Machine, SuperDuper!, etc.), storing these copies on an external hard drive or remotely in the cloud. In the case of FMPA Server, the program by default creates a backup daily, storing the last seven backups (and successively rewriting over them from oldest to newest), with the option to keep specific backups at will. In this case, we suggest copying FishNET to an external drive.
We have developed a system for positional identification of tanks and lines based on user-defined “Facilities,” “Rooms,” “Racks,” “Tanks,” and “Lines.” The underlying infrastructure of this system is a relational database. A typical database is composed of tables of individual entries or “Records” (as they are referred to in FMPA) that contain data (in the case of FishNET, these data are information such as tank number, genotype, and date of birth). In a relational database, these records relate to one another through shared or common data (e.g., tank identifier number, genotype, etc.), and as such, these records (and all of their associated data) can be sorted, queried, and viewed independently or in a list format. Tables of records within FishNET can be broadly grouped into seven categories (or “layouts,” in FMPA terms): Tanks, Crosses, Lines, Harvests, Nursery, Labs, and Statistics. Information regarding the allele (Lines), parental strain(s)/mating history (Tanks and Crosses), progeny (Nursery), owner of the tank/IACUC number(s) associated with a tank (Labs and Tanks), usage (Harvests), and water quality/births/deaths (Statistics) are all stored in these various records, enabling robust and detailed records of zebrafish husbandry within a research group or across an entire facility. An eighth category, Virtual Facility, which contains unique identifiers for the facility, room, rack, row, and column of a given tank, creates a virtual facility containing room(s), rack(s), tank(s), and nursery of unique zebrafish lines, thus enabling users or administrators to view all occupied tanks within the facility (as well as the pertinent information for each individual tank: sex, age, genotype) and easily and efficiently track and locate tanks within a facility. A final category is the “Calendar” layout, which relates to all other tables (e.g., Crosses, Tanks, Nursery, etc.). This category has email functionality to create reminders for graduating fry to the main system, turning over lines, or any other user-defined event. Finally, each tank, cross, and fry/larvae, as well as each rack, is uniquely barcoded to enable, fast, reliable data entry and functionality (such as moving tanks within or across racks, setting up matings, recording dead fish, etc.).
Once you have determined the correct FMPA configuration for your needs (e.g., locally hosted server, cloud, etc. [Fig 1A and 1B]) and purchased and installed the software, visit http://www.wythelab.com/wythe-lab-databases and download the latest version of FishNET. If using a single standalone FMPA license, the user only needs to open the FishNETv1.fmp12 file and proceed with the set up. If using the FMPA Server, the downloaded file needs to be placed in the FMP Server database folder. In macOS, this folder is placed by default in /Library/FileMaker Server/Data/Databases, or in Windows, in C:/Program Files/FileMaker/FileMaker Server/Data/Databases. Below, we provide a logical, step-by-step guide for creating your virtual facility, entering in lines, and populating racks with these lines, an overview of which is provided in Fig 2A. An overview of how resources relate to one another within FishNET can be found in Fig 2B. In short, (1) a tank with adult zebrafish is given a tank unique identifier (TUID). If two adults are crossed to (2) produce offspring, a cross unique identifier (CUID) is assigned to the mating. Embryos resulting from this cross (3) are given a nursery unique identifier (NUID), and they can either be processed for experimental purposes (4) and be given a harvest unique identifier (HUID) or (1) graduated to the system and given a new TUID.
After depositing the downloaded file in the appropriate folder (as outline above), there needs to be a one-time activation in the FileMaker Server Admin console to bring FishNET online. This can be done by selecting the database and selecting “Open” (Fig 3A). In order to add the database to individual FMPA instances, users need to select “Add App From Hosts,” then add the host using the IP address of the server. This can be found in the FileMaker Server Admin console (Fig 3B).
Upon opening the software, a window asking for username and password will appear. Enter “admin” as the username and “admin1234” as the password to access the database. Upon selecting sign in, the landing page should be visible (Fig 3C). Here, you will see a directory below the main FishNET icon. You can also select from the pulldown “Layout” menu in the upper left-hand corner for navigation. From the “Landing Page,” or “Home,” you will first select File/Manage…/Security. By default, an “admin user” is set up. If the database will be shared between different laboratories, we recommend setting up a password to access the administrator account because this account can access all records in the database and can also change the underlying architecture of each layout, whereas the individual labs will only see and modify records that belong to their respective laboratories and are prevented from modifying database-wide records (see Video 1: https://youtu.be/BwCD6-r6MM4). Creating labs and individual users will be addressed later.
After generating an administrator profile, the next step is to configure the facility (or facilities). This functionality is restricted to the administrator of the database and not individual users. From the landing page, select the “Configuration” button. In this layout, in the first column, the name of the facility needs to be entered (Fig 4A). In the second column, the different types of tanks being used are specified by the size and number of spaces they occupy on the rack(s). Following the same nomenclature, indicate the different tank capacities (for instance, if you are using a 5-liter tank, enter “5L”) ordered from smallest (nursing tanks) to largest. The “Spaces Occupied” column is used as an approximation of the size of the tank in the zebrafish rack. Fig 4B shows an example set-up of a Tecniplast rack and the corresponding spaces occupied for tanks in each row. This information is used to create a customized virtual rack view. After this initial configuration has been created, the number of rooms within a facility must be specified. This can be done by selecting “Virtual Facilities” from the landing page; upon clicking the button, a list of facilities should be shown in a pop-up scroll-down menu (Fig 4C). Selecting a facility will direct the user to the “Facility View” layout. Here, the option to “Add Rooms” can be found (Fig 4D). Selecting “Virtual Facility” from the main menu bar can also direct the user to the rest of the facilities where rooms need to be set up. It is important to note that in order to use the Virtual Facility function, all Facilities and Rooms need to be properly labeled prior to inputting any records. This cannot be changed later without having to change all records linked to a Room or Facility. Finally, once the Facility and number of Rooms have been specified (and named), the number of racks per room must be entered by selecting the “View Room” button (which takes users to view an individual room) (Fig 4E). When viewing an individual Room, a user can select “Add Rack” to create digital racks that will reflect the physical room.
For each rack that is added, the administrator will choose from predefined rack configurations corresponding to the current major commercial aquatic habitat manufacturers (e.g., Techniplast, Aquaneering, and Pentair) (Fig 5A). Pictures of the different aquatic systems can be viewed in the “Rack Layouts” section. Selecting one of these options creates a default layout of tank spaces matching these normal vendor configurations (Fig 5B). Upon selecting a rack layout, a virtual view of the rack will be shown; in the Virtual Facility View, individual positions can be labeled with barcodes by selecting the “Print Rack Labels” function (Fig 5C, indicated with red arrow). If the facility has a unique or custom rack layout, then select the “Custom” option. To set up a custom rack configuration, select the number of tanks in each row (A, B, and so on) by entering in the top number, then specify the number of spaces they occupy by entering in the lower number (Fig 5D). Repeat this for each row on the rack. The “Facility,” “Room,” and “Rack” categories provide spatial organization to the database to create a “Virtual Facility” that can be explored by the administrator, animal husbandry staff, and scientific users alike (more functions within this framework will be discussed after lines and tanks have been introduced). The categories of “Lines” and “Tanks” will be superimposed upon this user-defined framework later (see Video 2: https://youtu.be/6aNV2MroMc0).
Next, individual laboratories and users within these laboratories will be entered into the database. Before beginning this section, have the IACUC/animal protocol number(s) for the lab as well as the email address, phone number, and complete name of each user within a laboratory group readily available. The contact information for labs and users will later be assigned to individual tanks on the racks, as well as all crosses and any fry to be raised, allowing for automated reminders about graduating fry to the nursery (or the main system), dead fish notifications, or providing details for contacting other users regarding available lines within the facility. To create a laboratory group, first select the “Labs” button from the landing page (or dropdown layout menu). Once you are in the “Laboratories” layout, click the “+ New Record” button at the top of the FileMaker task bar (Fig 6A). Add all of the pertinent information to this empty field to create a laboratory. Once the lab has been entered, you can then populate it with users by selecting the “Lab Members” button. Within the “Lab Members” layout, click the “New Record” button to add users (Fig 6B). This information will later be used to establish ownership of individual tanks, crosses, and embryos within a facility. Finally, each lab should enter in a valid IACUC protocol number and protocol duration. This information will be associated with all tanks, crosses, and actions of users within a laboratory (and present on all tank, cross, and nursery labels) (see Video 3: https://youtu.be/vfBigAefij4).
By default, all new laboratories have access to view and modify the complete database. Below are a series of permissions that an administrator can restrict if they wish to limit individual laboratory members’ access to certain features or functions within FishNET (otherwise, ignore the following steps). Specifically, the following commands will allow individual users to “See and Modify” all information (e.g., genotype, number, sex, location) for tanks owned by all users in a common laboratory, but tanks from other laboratories within the facility (including administrators’ stock tanks) are hidden and the records locked for modification. Administrators, such as animal facility technicians, can view all information and record mortalities and health problems and can work with any tank (e.g., to graduate fry or to retire a sick tank). For this type of access, first create a new user or group account (Laboratory) with restricted access to the database by selecting File/Manage/Security. Then, select “New Account” and make sure the option to authenticate via FileMaker File is confirmed (Fig 6C) (note that the “Account Name” must match the name of the laboratory within FishNet). Type a password for the new account, then select “Privilege Set” and choose “New Privilege Set.” A pop-up window will open, and within that window, change the privilege set name to the name of the laboratory. After the name is changed, make sure all the data access and design options are set to “All Modifiable,” then click Records and select “Custom Privileges” (Fig 6D). A new window will be opened where you can see all of the tables that store information in the database. Each table will have a different setting. Here, you can select “Fish Lines,” then change the “Delete” option to “No.” Select “Tanks” and change the “View” option to “limited,” then enter the following text into the pop-up window: Laboratories::Lab Name = "Wythe", replacing “Wythe” with the desired laboratory. Repeat this same operation for the “Delete” option. In order to complete the set-up for each new laboratory, the administrator must set up the appropriate permissions for all data tables. For a fully functioning lab, refer to Fig 6D and modify each field as shown. Follow the previous instructions for every instance of “limited” privileges, referring to Table 6 for necessary settings (see Video 4: https://youtu.be/NrI7TOiSCvs).
Lines, in this case, refers to distinct alleles that follow nomenclature established by the zebrafish information network (ZFIN) (https://wiki.zfin.org/display/general/ZFIN+Zebrafish+Nomenclature+Conventions). Novel lines, such as those generated by Clustered Regularly Interspaced Short Palindromic Repeats/CRISPR-associated protein-9 nuclease (CRISPER/Cas9) mutagenesis, can be also be entered in FishNET. To enter an allele, from the landing page, select the “Lines” tab (or select the “Fish Lines” layout from the Layout pulldown menu) (Fig 7A). If your database has no lines entered, you will be taken to a blank record. Under “Record View,” you will only be able to view one line or record (e.g., a unique allele) at a time. Use the forward and backward icons at the top upper left of the database window to scroll through the records manually (Fig 7B, indicated with blue arrow).
To create an allele in “Record View,” populate the following fields in the blank record: locus, allele name, nickname, line unique identifier (LUID), lab of origin, line ZFIN ID, originating strain, PUBMED ID, laboratory the line was received from, cloning notes, and general notes (Fig 7B). Importantly, whatever designation is used for the “Allele Name” field will be redeployed throughout the database in dropdown menu choices, ensuring uniform nomenclature within a research groups, across other labs, and throughout entire facility (see Video 5: https://youtu.be/ONgTj538AmU). Each line that is entered is assigned an LUID number (beginning with L). Each subsequent record is automatically assigned the next available LUID number (L + 1) (Fig 7B). FishNET also allows for users to upload and store attachments associated with each line, such as associated publications, representative images of example phenotypes, or expression patterns that can function as a reference for sorting offspring (Fig 7B, indicated by asterisks). Additionally, one can link genotyping protocols to individual lines such that the protocol can be accessed by selecting the “Genotyping Protocols” layout or button within the “Record View” field (these will be discussed in more detail in the following section).
While users or owners of the line are not defined when a line is created, later, if tanks are populated with a line and placed on a rack, they will be visible in the “Lines: Record View” window under the “Tanks Available” heading. Only live tanks will appear within this window. Clicking on the magnifying glass provides a detailed view of the tank, including its genotype, date of birth, sex, number, the owner of the line, its status (e.g., juvenile, adult), location in the facility, and the paternal and maternal animals that generated the tank (Fig 7B).
To view all lines that have been entered into a database, within the “Lines” layout, select “List View” (rather than the individual “Record View”) (Fig 7C). This generates a simple, scrollable list of every allele, including the locus, allele, and ZFIN ID. To define a new locus in the “List View” layout, simply input text into the space provided (or select “New Record”), and the new locus will be indexed and will appear as an option in any subsequent input. Enter the correct genetic manipulation, locus, allele, and ZFIN information, and the rest of the database will populate the allele name using this designation. For adding additional details, select the “Record View” layout and finish entering in all pertinent information.
To view all lines that are actually available within a facility or user group, select “Compact View.” Users can navigate this view using the “Sort By” button to segregate the animals by any of the following criteria: genotype, facility, lab, or user. This view will show users the TUID, facility, and lab that own any tank(s) of a given genotype. Additionally, users can click on the magnifying glass for a detailed view of that particular tank (location, parents, age, etc.) (Fig 7D).
To create a genotyping protocol, from the landing page, select “Genotyping Protocols” (Fig 8A, indicated with red arrow), then select the “New Record” button. Here, you will name the protocol and enter the ZFIN identification number and any notes, as well as PCR primers and thermocycler conditions, expected product size, and a representative agarose gel image, by dragging and dropping any .PNG or .JPEG file into the PCR example container (Fig 8B). For optimal viewing, gel images should be resized to 4 inches width and a resolution of 300 pixels/inch. To scroll through available protocols, use the FileMaker forward and backward keys available at the top of the screen or use the search window.
The underlying logic of FishNET that enables the tracking and searchable features relies upon a TUID that is assigned to every new tank. From the “Tank List” layout (Tanks > List View or Landing Page > Tanks), one can see a list of all tanks, each with a TUID, stored within the database (Fig 9A). All pertinent information (sex, genotype, age, owner, and location) are present in this view. To create a tank, click the “Detailed View” tab (or, from the main dropdown menu, select “Tanks” and go to “Tanks Detailed View”). In that layout, select “New Record.” Then, select the loci and the appropriate allele from the dropdown list (that should have been populated by following the instructions outlined in Entering Lines). After this, select the “User” of the tank (this will autopopulate the user’s lab, email address, institution, phone number, and IACUC information). If the TUID of the maternal or paternal parent tank is known, enter it now to establish a pedigree for this new tank. The TUIDs can be entered manually, or you can select the field(s) with your cursor and then scan a barcode for the animal of interest. Be sure to enter the date of birth, tank size, and number of fish for the TUID record. The status of the line will be color-coded according to the date of birth (fish younger than 2 weeks are white, fish older than 2 weeks but less than 3 months are yellow, fish older than 3 months but younger than 1 year are green, fish older than 1 year are red, and retired or euthanized tanks are black). Additionally, the database automatically creates a turnover date for 1 year after the date of birth. Users can choose to receive an automated email reminder for line turnover by selecting the “Set Reminder” calendar button above the “Turnover Date” field (but this must be done after establishing the User/Owner of the tank to set up an email address for the reminder). Finally, a user must assign a location for the tank by selecting the “Facility” dropdown menu and entering in the Rack, Row, and Column for the new tank. After entering that information, select the “Add to Rack” button to place the tank in the virtual facility. Alternatively, one can click the “Add to Rack” button in the header field and then scan in the barcode location on the rack followed by the barcode on the tank to establish the location of the tank (see Video 6: https://youtu.be/fp1ZA1NSeEI). Once the user adds the tank to a rack, selecting the “Locate Tank” button will show where the tank in question is located within the virtual rack window (Fig 9B).
Each mating generates a CUID number to track every mating and to generate pedigrees. To create a cross, select the “Crosses” button from the header or landing page menu (or select “Cross” from the pulldown menu). Then, in the header menu, select “Set Up Cross.” This option can also be found in the Tank Detailed View (Fig 10A, top). A pop-up window will appear asking for the paternal TUID and the maternal TUID (Fig 10A, bottom). These can be entered either manually by text or by reading the barcode of each parent tank. Next, a pop-up window will ask what type of mating is being setting up: a bulk cross, a trio mating (defined as one male crossed to two females), or a pair. The next window will ask how many of this type of mating are being set up (e.g., 8 pairs). After filling in this field, the application will show you a mating label while asking you if you want to repeat the cross. If you are only setting up one type of cross (e.g., just pairs) and selected the appropriate number, then chose “No.” If you wish to set up another type of mating from this cross, such as a few trio tanks, then select “Yes.” The cycle repeats until you chose to no longer repeat this cross. After choosing “No,” the screen will return you to the “Crosses” layout. To simplify the view, you can choose to “View Active Crosses” rather than “View All Crosses.” In the header field, you will now have the option to print mating labels for each of the individual mating tanks that you set up. These labels display the genotype, sex, TUID, and location of the parent tanks, as well as the owner of the cross. If additional mating labels are needed, select the “Print Additional Labels” field in the header. We also use these to label 10-cm plates of embryos from crosses (and thus print extra labels). Active crosses that are less than 1 day old will be labeled green in the “Crosses” layout, while active crosses more than 1 day old will be labeled red to indicate that the fish should be returned to the system (or fed). Retired crosses will be colored gray (and users should retire every cross once the mating pairs are taken down and returned to the system). This convenient color-coding system enables easy visual tracking of active crosses within a facility or user group (Fig 10B) (see Video 7: https://youtu.be/1EkTnV2AfdI).
FishNET also has the capacity to track all larvae that are destined to be graduated to the main system, enabling real-time tracking of all tanks on a nursery, as well as the success of fish survival rates, through the “Statistics” button within the “Virtual Facility” layout. To raise fry from a cross, select the “Raise Fry from Mating” button in the header menu (Fig 10B). A pop-up window will ask you for the date of birth for the fry, as well as the number of fry for a single tank to raise (this will help determine survival on the system, as discussed later). Within the “Nursery” layout, each tank of fry is assigned an NUID. After filling in this information, a new pop-up window will ask if you wish to raise more fry from this cross (e.g., additional plates/tanks).
At day 5, select the “Graduate to System” button in the top right of the header. Upon selecting this button (located in the header field of the “Nursery” layout), a pop-up window will ask the user to enter NUID(s) that will be used to form a new Tank (in the event that more than one dish of fry of the same genotype are used to create a new tank on the system) (Fig 10C, right). This automatically generates a new, unique TUID record that contains all of the previous NUID information (allele, owner, strain, etc.) (Fig 11A). Once a new TUID is created, a user may select the “Add to Rack” function, as described previously, to “place” the tank in the virtual main system (see Video 8: https://youtu.be/9wATt56mVsg).
The calendar layout/function (Fig 11B) enables users to access a facility-wide calendar with all related events (crosses set up; system reminders such as change baffle size, alternate food, graduate to main system; etc.). Selecting “Add Event” will take the user to a field where the title of the event, date, and facility are entered. Finally, the calendar function has an option for email reminders that automatically populates information using the laboratory user field(s). This function can also be used to copy administrators or animal husbandry staff on reminders.
Another novel, to our knowledge, feature of FishNET is the ability to track the pedigree of embryos or adults that are used for experiments (as well as all experimental conditions) in the “Harvests” layout. This layout generates a novel HUID that pulls from either the parental cross CUID (for embryos) or the TUID (for an adult) to create a unique record with the date of the harvest, experimental treatment, and number of fish collected (Fig 12A). Additionally, using the “Create Resources” tab, one can generate detailed records for every embryo or adult within a harvest (Fig 12B). While nonessential for facility management, many individual labs may find this functionality helpful for tracking embryos or adults that have been harvested and stored in the freezer and for keeping track of sections, mRNA, or protein samples. Tagging samples with a unique Resource ID and labeling a sample “H0001-1” is easier and clearer than writing out an entire genotype, sex, date, and treatment on one slide or an Eppendorf tube. Furthermore, there will be a permanent record of the parents, animal, genotype, and treatment associated with this HUID. Additionally, images (such as genotyping gels) and other relevant data can also be pasted within these “resource” pages (Fig 12C).
To automate annotation of genotyping gels, we created an Apple script (FishNET_Genotype.scpt file) that can take the record list from the harvest section of FMPA into Adobe Photoshop Creative Cloud 2017 to annotate images of PCR gels (this function is only available in Mac OS X). This requires a completed genotyping protocol (S1A Fig), which can be found under Lines/Genotyping Protocols. A special security permission is also needed to allow Apple scripts in FMPA: select File/Manage/Security… Select Extended Privileges and give full access to “fmextscriptaccess.” To start, download the script (S2 File). Open the script in Script Editor and follow the simple instructions to adapt it to your computer (S1B Fig). We recommended adding a shortcut for the script to your menu bar. From the script button on the menu bar, select “Open Scripts Folder/Open Users Script Folder” and deposit the downloaded script file there (now the script can be run from the Script menu in the menu bar). Before running the script, a genotyping gel should be open and resized in Photoshop to 4 inches width by 3 inches wide, with a resolution of 300 pixels/inch (S1C Fig). Run the script and enter the HUID number you want to annotate. Make sure all names match the correct bands on the image, then select “OK” in the pop-up window asking, “Are ready to add the gel to the database?” Select “Yes” to create a new genotyping record. Drop the new annotated gel image into the empty field and select the appropriate name of the genotyping protocol as seen in S1D Fig. The annotated image is now present in the detailed view of all records linked to the selected harvest ID (S1E Fig).
Because many laboratory groups use Excel spreadsheets to manage their existing colony, we provide a simple way to import this information into FishNET. Once the laboratory, members, facility, and rooms are set up in FishNET, one can import preexisting line data. In Excel, ensure that the following columns are populated: Locus (Tg, Et, etc), Allele Name, Lab of Origin, and General Notes (Fig 13A and S3 File). After all fields are entered, save the document in Tab-delimited text (txt) format. To import these data into FileMaker, go to the “Lines” tab and select File/Import Records/File. Next, select the .txt file where the data were saved. Once selected, the user has to match the source field from Excel to the target fields in FileMaker Pro. Activate field mapping and importing by selecting all fields that should be imported (the middle symbol between “Source Fields” and “Target Fields” should change to an arrow) (Fig 13B). “Add new records” should be selected, as well as “Don’t import first record (contains field names)” (Fig 13B). Similarly, it is possible to import data from a preexisting FileMaker database. In the original database, go to the layout you wish to export (Tank list, Lines, etc.) and select File/Export Records. Then, “save” the new file as an FMPA-type (fmp12) file. Next, select the fields to be exported and select “Export.” Finally, to import these data into FishNet, follow the above instructions, selecting the new .fmp12 file as the source of import.
We placed an emphasis on the reporting capabilities of the database, which allow for storing fish census and usage reports for the whole facility in an exportable, Excel-compatible format file. This graphical report provides an overview of all fish morbidities and mortalities, and individual fecundity records are maintained for each line across the entire facility. FishNET also tracks all animals, can generate user-defined fish use reports (as required for annual IACUC protocol reporting), and, as previously mentioned, generates graphical reports for water quality data. Within the “Virtual Facility” layout (Fig 14A), selecting the “View Stats” function (Fig 14B) will take users to a graphical report of the number of tanks and their status (i.e., adult mating age, juvenile, older than 1 year) and the number of total fish and tanks (Fig 14C, top). Because of limitations in graphing capabilities within FMPA, we have opted to generate reports using Google Charts Application Programming Interface (API), a third-party software (as outlined below in the Importing Water Quality section). Selecting the “Vitality Summary” button in this layout will display the number of births across a rack, room, or facility, with weekly, monthly, or yearly reports (Fig 14C, bottom). These graphical reports for facility stats and fish stats are both populated by user-entered data from dead fish reports, mating records, and tanks that are retired. In this “View Stats” layout, because different systems have unique headers and labels for data, we have simplified uploading external data. The management and partitioning of users and labs in FishNET, combined with barcode labeling, will allow the PIs and animal facility managers (or staff) to track animal usage and husbandry and enable accurate billing and IACUC reporting in real time. FishNET can also alert users if fish require attention or care.
A caretaker can create a follow-up report in a tank detailed view (Fig 15A). A list of all follow-up reports can be found in the tank list view by selecting fish reports (Fig 15B, indicated with red arrow). For laboratories running FishNET through FileMaker Server, it is possible to generate automated email reminders to users and/or facility administrators. If this feature is desired, the administrator must open the script workspace (S2A Fig, left red arrow). Here, find the script called “Send Follow-Up” (right red arrow), and within the script, select the configure button (yellow arrow). In the send mail option (S2B Fig), select “Specify” next to the simple mail transfer protocol (SMTP) Server option. A new window will pop up in which the user administrator must enter their lab or institutional SMTP Server information. For calendar reminders, this process needs to be repeated on the “Send Reminders” script (S2B Fig, right). Once that is finalized, the user needs to go to the Filemaker Server Admin Console (S2C Fig). Under Configuration/Script Schedules (red arrow), select Create Schedule (indicated with yellow arrow) and select Filemaker Script as Schedule Type and click on “Set Database” (S2D Fig, red arrow). In the new window, select FishNET, and then enter the administrator username and password (S2D Fig, right). From here, go to select script (S2E Fig) and pick “Send Follow-Up” from the list. Lastly, choose how frequently you would like to send facility users the emails. This process can be repeated for the calendar alerts by adding a new schedule and selecting the “send reminders” script.
A graphical overview can be found in the “Mortality Summary” in the “Stats” category in the Facility, Room, or Rack layout (Fig 15C, left). Once a report is generated in the mortality summary, three pie charts give an overview of the cause and symptoms present in the fish, followed by charts with the total number of dead fish. This report can be generated weekly, monthly, or yearly. A weekly report that can be printed can be found in the “Tank List” view by selecting “Fish Reports/Dead Fish Reports” (S3A Fig). The user can select the facility and the dates to be included in the report (S3B Fig). Furthermore, a complete list of dead fish can be found in the “Dead Fish List” in the “Virtual Facility Room” list.
We have enabled FishNet to generate water quality reports (e.g., conductance, pH, etc.) using the interactive Web service, Google Charts. Because of the limited options for data display in FileMaker, we have instead used Google Charts to generate graphical reports because it is more flexible in terms of graphing options, colors, and data input options. To generate these visual reports, export existing system water quality data as a .CSV file from a rack or facility of interest. Then, open FishNET and navigate to “Virtual Facility” and the specific “Room” containing the rack that the data correspond to. Next, select “Virtual View” for the individual rack of interest, then, in the next window, select “View Stats” (Fig 16A). Once in the individual rack stats view and in the present month of interest for the data, select “Water Quality Report” and “create new record” to import data (Fig 16B). Two different fields with either “Days” or “Values” will appear for each parameter field (conductivity, pH, temperature, nitrites, nitrates, and chlorine). From the .CSV file, simply copy the date of data acquisition (dd/mm/yy, or just the day number) in the first field and enter the values in the second field (making sure that the number of days and values are correctly related to one another). If there are multiple values recorded on one day, the system will display a bar graph with the average values and corresponding error bars.
During the course of optimizing FishNET, we tested numerous printer configurations to find a sturdy, waterproof readhesive label that is also barcode compatible in a size usable with both large and small aquarium tanks as well as 10-cm petri dishes for fry. After much trial and error, we recommend using a BBP33 Brady printer and permanent, barcoded polyester labels for each tank location label on a rack. Unfortunately, the BBP33 printer only supports Windows computers. While some third-party vendors have developed BBP33 drivers for Mac, those we tested were unreliable. Instead, as a workaround, we suggest installing Windows 10 using Parallels Desktop for Mac (https://www.parallels.com/). As a cheaper alternative, one may use a Dymo 450 LabelWriter Turbo printer (Berkeley, CA, USA), but note that the labels are permanent and their removal may require scratching the tanks (details available, as well as printer layouts, upon request). Printing layouts for various labels are provided within FishNET under the main layout dropdown menu under the heading “Printing Layouts.” From here, users can visualize all of the printer layouts for petri dishes (for embryos), fry on the nursery, small tanks, large tanks, racks, and crosses. Each of these layouts are modifiable, and the label size can be changed to one that best meets a user’s needs, including which information should or should not be present on the labels. To create barcodes in FishNET, the Code 39 Barcode Font from IDAutomation (IDAutomationHC39M Code 39 Barcode.ttf) is required in any computer printing barcodes. It can be downloaded for free here: https://www.dafont.com/idautomationhc39m.font. Once the font is properly installed, the user needs to go to each printing layout (S4A Fig) and select “Edit Layout.” In edit mode, select the barcode rectangle and change its font from Arial to IDAutomationHC39M (S4B Fig). A barcode should appear. Then “Exit” the layout and “Save” changes. Repeat this process with each printing template.
After configuring a facility, within the “Virtual Facility” layout, a user can select their “Facility,” then select an individual “Rack,” and within this layout, a button to “Print Coordinates Labels” is present in the upper right area of the header. Selecting this function will create a pop-up window asking, “Which Row do you want to Print?” After entering the desired row of the rack you are currently using (e.g., “A”), then selecting “OK,” the labels for one row will be printed. For labeling the tanks on the main system and nursery, as well as the petri dishes containing embryos, we prefer to use a removable label, which has the added benefit of reducing adhesive buildup on tanks while also eliminating errors in transferring information (such as date of birth, genotype, sex, etc.) that occur during manual transcribing of labels. For an example of a completely labeled rack and tanks, see S4C and S4D Fig. To make input of tank locations easy, a barcode-compatible function is ready using the “Add Multiple Tanks” function in the Virtual Rack template, in which a sequential series of pop-up messages will prompt the user to scan the tank barcode followed by the rack location barcode (printed above).
A common error in FMPA (at least within our team) is that after importing data or deleting a record, a user would like to reset the TUID, CUID, LUID, or NUID number to a previous value (to eliminate gaps in numbering and ensure consistency). To do so, select “File” in the FMPA menu, go to “Manage,” then select “Database,” and a pop-up window will appear. Within this pop-up window, go to the “Tables” section and double click the table you want to edit (e.g., “Fish Crosses”). Then, double click the unique identifier Field Name (e.g., “CUID”), and a pop-up window with options for the field will appear. Then, select the “Auto-Enter” section. Here, the “Serial Number” option will have a checkmark, and below this option, the next serial value is defined (e.g., “C0032” if you are in “CUID”). To change this next value, simply edit the number after the first character (e.g., change “C0032” to “C0031” if you needed to delete one erroneously created cross record/CUID). After the changes are made, click the “OK” button to close the “Field Options” pop-up window, then select “OK” again to close the “Manage Database” window, and then finally save the changes.
Despite the increasing demand to ensure rigor and reproducibility at each step of the research endeavor, a robust, affordable, and intuitive archival database for zebrafish animal husbandry records has not been developed and widely adopted by the zebrafish community. We have created a facile, network-accessible relational, open-source database that meets the needs of researchers, animal husbandry staff, and institutional animal oversight committee members alike that creates and preserves comprehensive, detailed records for an individual lab or entire zebrafish facility. Additionally, such centralized, comprehensive record-keeping and the data visualization tools contained within FishNET will limit unnecessary duplication of lines within and across laboratories, ensure timely line turnover, and should flag fish husbandry or facility-wide issues (e.g., water quality, decreased fecundity, etc.) in real time, allowing for institutions to reduce the overall number of animals required for experiments and save critical research dollars.
Using the open-source, non-coding-based FMPA platform, the underlying architecture of FishNET can be modified by any end user to meet their unique needs or directly expanded upon to increase database functionality. FishNET also scales according to user demand, as does the FMPA platform, enabling functionality for research groups as small as one laboratory or as large as an entire institution. Future updates to FishNET will be available at http://www.wythelab.com/wythe-lab-databases. In regard to migrating data to updated versions of FishNET, FileMaker Pro offers a cost-effective data migration script package (FileMaker Data Migration Tool), available with the FileMaker Developer Subscription (https://store.filemaker.com/product/FDS) for $99 a year. Additionally, several third-party solutions are available online as well, although we have not yet validated any such software solution. Continual upgrades, along with integration of improved FMPA software and computer hardware technologies, will ensure that this inventory system continually evolves to meet the needs of the zebrafish community.
|
10.1371/journal.pntd.0007202 | Virulence difference of five type I dengue viruses and the intrinsic molecular mechanism | Dengue virus (DENV) is the most important vector-borne virus globally. The safe and effective vaccines are still under development and there are no antiviral drugs for DENV induced diseases. In this study, we obtained five DENV1 isolates (DENV1 A to E) from the outbreak of dengue fever in 2014 of Guangzhou, China, and analyzed their replication efficiency and virulence in vitro and in vivo. The results suggested that among the five DENV1 strains, DENV1 B has the highest replication efficiency in both human and mosquito cells in vitro, also causes the highest mortality to suckling mice. Further study suggested that nonstructural proteins from DENV1B have higher capacity to suppress host interferon signaling. In addition, the NS2B3 protease from DENV1B has higher enzymatic activity compared with that from DENV1 E. Finally, we identified that the 64th amino acid of NS2A and the 55th amino acid of NS2B were two virulence determining sites for DENV1. This study provided new evidences of the molecular mechanisms of DENV virulence.
| Dengue is the most important vector-borne viral infection that endangers an estimated 2.5 billion people globally. The recently licensed dengue vaccine has major weaknesses and there are no antiviral drugs for the treatment of dengue related diseases. Identifying the virulence determinants is important for understanding the molecule bases of viral life cycle, also contributing to vaccine design and development. In this study, we analyzed the virulence differences among five DENV1 strains that obtained from the 2014 DENV outbreak in Guangzhou, China, and identified two novel virulence determining sites for DENV1. This study provides new ideas for investigation of DENV protein function, pathogenic mechanism and novel attenuated vaccine.
| Dengue virus (DENV) is currently the most popular mosquito-borne virus and widely spreads in tropical and subtropical regions[1]. A series of symptoms caused by DENV, such as dengue hemorrhagic fever/dengue shock syndrome (DHF/DSS), seriously threaten human health[2,3]. Dengue virus belongs to a single-stranded positive sense RNA virus and lacks of accurate replication correcting system. Virus nucleotide changes will eventually lead to stronger or weaker virus virulence during the long-term process of virus spread. Secondly, host response is induced upon virus infection and the interactions between host and virus also influence the virulence. These two aspects corporately influence the virus pathogenicity and severity of the diseases[4,5]. Therefore, it is of great significance to identify the sites within the virus genome that are associated with virulence and to investigate the interactions between virus and host.
DENV genome is an approximately 10.7-kb positive-sense RNA, encodes a single polyprotein that is cleaved posttranslationally by host and viral proteases into three structural proteins (capsid [C], premembrane [prM], and envelope [E]) and seven nonstructural proteins (NS1, NS2A, NS2B, NS3, NS4A, NS4B, and NS5). Structure proteins C, M and E are components of viral particles, and the viral nonstructural proteins are critical for viral genome transcription and replication[5]. At the same time, flavivirus nonstructural proteins are reported to attenuate host antiviral responses and facilitate viral survival[6]. For example, Dengue virus (DENV) NS4B and NS4A inhibited TBK1 phosphorylation thereby reducing the IFN production[7]. DENV NS2A, 2B, 4B and NS5 inhibit IFN-mediated JAK-STAT activation and impair interferon-stimulated gene (ISG) production[8–12]. DENV NS5 could inhibit STAT2 phosphorylation thereby blocking IFN downstream signaling[13]. The amino acid changes in the nonstructural proteins were believed to influence the activity of virus to antagonize host antiviral responses.
The variations in virus genome are closely related with the virulence. Lots of studies showed that the mutations in virus E protein resulted in the significant changes in virulence of flavivirus[14]. By using the reverse genetic approaches, Prestwood and collaborators found mutations at amino acid 124 and 128 in E protein increased the virulence of DENV[15]. Three substitutions in E protein (196Met→Val, 365Val→Ile,405Thr→Ile) and one in NS3 protein (435Leu→Ser) were reported to be associated with the pathogenesis of neurotoxicity of DENV[16,17]. As a multiple function protein, NS3 has serine protease activity in its N terminal domain, which is required for the cleavage of viral polyprotein and the maturation of viral particles. The NS3 protease cleavage ability will determine the assembling efficiency of viral particles[18,19]. Furthermore, the hypervariable regions of the 3’UTR are also believed to be the virulence determination sites, although the biological relevance is remained to be elucidated[20,21].
In the past 25 years, cases of dengue infection have been reported in Guangdong province of China every year[22–24]. The peak of infection happened in 2014, and DENV1 appeared as the major serotype at that year[23,25]. We acquired five DENV1 isolates with different genotypes from Guangdong province among the outbreak of dengue fever of 2014. The differences between these virus strains were studied in in vitro and in vivo models. To further understand the molecular mechanism underlying the virulence difference of five variants, we investigated the ability of the variants antagonizing host innate immune response and the functions of their NS2B3 protease.
Five different DENV1 isolates, named DENV1A to DENV1E in this study, were isolated from the DENV outbreak of 2014 in Guangdong Province, China. The nucleotide sequences of these five DENV1 strains were determined by high throughput sequencing/assembling approach and submitted to Genebank under the accession numbers MH271402 (DENV1A) to MH271406 (DENV1E).
To test the replication efficiency of these five isolates in mammalian cells, human 293T cells were infected with DENV1 A to E respectively at the same MOI of 0.5. Cells were harvested at 12, 24, 48 and 72 h post infections, and the viral replication efficiency were determined by measuring viral envelope (E) gene mRNA copies, then normalized to human β-actin gene. The results suggested that DENV1B and DENV1C had higher replication efficiency among the five virus strains in 293T cells (Fig 1A). Consistent with the intracellular viral RNA levels, the virus titers in supernatants from DENV1B and 1C infected cells were higher than those from DENV1D and 1E infected cells (Fig 1B).
To test the infection ability of these five strains to mosquito cells, C6/36 cells (origin from Aedes albopictus) were infected with DENV1 A to E. The results suggested that DENV1B also showed higher replication efficiency and produced more viral particles in mosquito cells when compared with other strains (Fig 1C and 1D).
Then we used a sucking mice infection model to test the virulence of these five DENV1 strains in vivo. Three- or four-day-old suckling mice were inoculated intracerebrally with 100 PFU of DENV1 A to E respectively, and the mortality rates were monitored daily. The results suggested that sucking mice started to die at 10 days post DENV1B infection, while this date was postponed to day 15 in case of DENV1E infection. 100% of DENV1B infected mice died at day 15 post infection, but 70% of DENV1E infected mice were survived from infection and recovered (Fig 1E). These data suggested that DENV1B is the most virulent virus, while DENV1E is the weakest. We also quantified the viral loads in infected mouse brains by qRT-PCR and plaque assays. Consistently, the viral loads in the brains of DENV1B infected mice were significantly higher than those from DENV1E infected mice (Fig 1F and 1G).
To further confirm that DENV1B is the most virulent virus while the DENV1E is the weakest, HUVEC and Huh7.0 cells were infected with DENV1B and E, respectively. The results suggested that DENV1B showed significantly higher replication levels at all time points post infection and produced more viral particles in these cells (Fig 1H to 1K). Taken together, these data suggested that DENV1B is the most virulent virus among the five DENV1 strains, while DENV1E is the weakest.
DENV-1 can be divided into 5 main genotypes. Genotype II and III have only a few early strains, and I, IV and V are the three major genotypes in circulation[26,27]. In order to understand the origin and relationships of the five DENV-1 viruses, molecular evolution analysis was performed using various bioinformatics approaches. 123 full length DENV1 sequences were downloaded from ViPR (www.viprbrc.org) database. The ORFs of these 123 viruses, together with DENV1 A to E from this study, were analyzed by Multiple alignment program Mafft (https://mafft.cbrc.jp/alignment/server/). The phylogenetic tree showed that DENV1A, B and C belong to genotype I of DENV1 (Fig 2A, and S1 Fig). DENV1A is closely related with the DENV1 viruses that are endemic in Zhongshan (China) (2013) and Shizuoka (Japan) (2014). DENV1 B and C are closely related to LC011948, which is endemic in Chiba, Japan in 2014. Then we speculated that the strains erupted in Guangdong and Japan in 2013–2015 probably came from the same ancestor. DENV1D and 1E are very similar to each other. They are located in a branch of DENV1-V genotype, share homology with DENV-1 (KX380801 and KX380796) isolated from Singapore in 2014 (Fig 2A, and S1 Fig). In addition, DENV1B and C share high similarities to a DENV1 strain (AB178040.1) isolated from Japan in 2004 (Fig 2B). Then, a DENV1 replicon, DGL2, origin from this DENV1 strain[28] was used as a reverse genetics approach to perform the single point mutations in DENV1B genomes.
Using MEGA software, the ancestral sequences for the five DENV1 viruses were analyzed and 72 amino acid variations between DENV1 A&B&C and DENV1 D&E were identified (S2 Table). At the same time, all the variations in NS2A, 2B, 4A and 4B among DENV1 A to E were showed in Fig 2C–2F.
DENV NS proteins were reported to have the ability to suppress IFN signaling, and this activity will contribute to its virulence in mammalian host. To test whether NS proteins from DENV1 A-E have different capacity against IFN signaling, NS2A, 2B, 4A and 4B from all these five strains were cloned and expressed in 293T cells. IFN-β-Luciferase reporter assay suggested that NS2A and 2B from DENV1B showed the highest inhibitory activity against RIG-I directed IFN activation, compared with NS2A/2B from other strains. NS4A and NS4B from DENV1 A&B&C also have higher inhibitory capacity to IFN signaling compared to those from DENV1 D&E (Fig 3A–3D). Consistent with these results, RIG-I induced IFNβ mRNA expression was also dramatically decreased in cells expressing NS proteins from DENV1B when compared with those from other strains (Fig 3E). Type one IFNs binds to interferon receptor and activates the transcription of genes containing an ISRE responsive element in their promoters. We also tested whether NS proteins from different DENV1 strains showed variable capacities to modulate the ISRE activation. The results suggested that NS proteins from DENV1B also showed the highest inhibitory activity on ISRE-Luc activity during RIG-I-N or IFNα stimulation (Fig 3F and 3G). Consistently, RIG-I-N or IFNα induced transcriptions of typical ISG genes, such as IFIT1 and Cig5, were significantly inhibited in DENV1B NS protein expressing cells (Fig 3H and 3I). In line with this, we also confirmed that DENV1B showed a better replication than DENV1E in 293T cells if we treated the cells with IFNα.
To confirm the functions of DENV1B NS2A, we constructed two mutant DENV1 replicon plasmids (NS2A A64V and F159L) based on the DGL2 replicon (Fig 3J) (The NS2A proteins of DGL2 are 100% identical with that of DENV1B). The 64th Ala (A) and 159th Phe (F) of NS2A from DENV1B were changed to Val (V) and Leu (L) (from DENV1E), respectively. DNA sequencing results indicated that the point mutation was successfully introduced into DGL2 replicon (Fig 3K). After transfecting these replicons into 293T cells, we found that NS2A A64V mutation significantly impaired the replication efficiency of DENV1 replicon, but F159L substitution only slightly influenced the replication (Fig 3L). These data suggested that NS2A amino acid position 64 is one of the important virulence determinants for DENV1.
Since NS proteins from DENV1B have higher capacity to inhibit IFN signaling, we wondered whether this is a major factor that determining the virulence of these five DENV1 strains. IFNAR1-/- mice were introduced to study the replication efficiency of DENV1 B and E in IFN non-responsive system. MEF cells from wild type and IFNAR1-/- mice were obtained and infected with DENV1 B and E, respectively. Surprisingly, DENV1B still has higher infection efficiency than DENV1E in IFNAR1-/- MEFs, just like what it does in wild type MEFs (Fig 4A–4D). To further confirm this, IFNAR1-/- mice were challenged with DENV1B and E via intraperitoneal infection for 3 days, then the viral loads in blood and spleens were tested by qRT-PCR and plaque assay. The results suggested that the viral load in blood and spleen samples from DENV1B infected IFNAR1-/- deficient mice were significantly higher than that from DENV1E infected mice (Fig 4E–4H). These data suggested that the difference in antagonizing IFN signaling is not the only determination factor for the virulence of these DENV1 strains.
The results above remind us that the amino acid variations may not only contribute to the difference in virus-host interaction, but also determine the replication ability of the virus itself. In DENV’s life cycle, NS2B forms a complex with NS3, and plays an important role in viral polyprotein procession. We then try to address whether amino acid changes in NS2B will directly influence viral protease function as well as viral replication. The mature forms of NS2B3 protease, which has a 48 amino acids NS2B co-factor domain (48–95 aa) and 180 amino acids NS3 protease domain, have been cloned and expressed in recombinant GST prokaryotic expression system (Fig 5A and 5B). The enzymatic assay suggested that NS2B3 protease from DENV1B showed higher substrate cleavage efficiency than NS2B3 from DENV1E (Fig 5C and 5D). Using site-directed single point mutation technology, we made a K55R mutation in NS2B3 of DENV1B, in which the 55th amino acid Lys (K) was changed to Arg (R) (which is from DENV1E NS2B), as well as a R55K mutation to NS2B3 of DENV1E (Fig 5E and 5F). The enzymatic test showed that DENV1B NS2B3-K55R protein had lower enzymatic activity than wild type NS2B3 from DENV1B, while DENV1E NS2B3-R55K showed higher cleavage activity than wild type DENV1E NS2B3 (Fig 5G). We then made a NS2B K55R mutation to DENV1 replicon DGL2 (Fig 5H), and tested its replication efficiency. NS2B-K55R DGL2 replicon showed significantly lower replication efficiency than WT replicon (Fig 5I). These results suggested that NS2B K55 is a virulence determinant that important for DENV1 NS2B3 protease activity and viral replication. While, there are also several amino acid differences in the NS3 1–180 protease domain between DENV1B and DENV1E. Our preliminary data suggested that these mutations may also slightly influence the activity of NS2B3 activity. Further study need be performed to characterize other potential virulence determinants in NS3 protease domain.
Viruses are small infectious agents that replicates only inside the living cells of other organisms. The viral genome only encodes a limited number of proteins which are necessary for viral structure and replication. Instead, viruses use the machinery and metabolism of a host cell to complete their life cycles. At the same time, viral infections provoke an immune response that usually eliminates the infecting virus. To counteract the host defense mechanism, many viruses have evolved suppressor proteins to overcome the antiviral responses. So that, the virulence of a virus will be determined by two aspects: one is the ability of virus utilizing or antagonizing host responses, the other is the essential functions of those viral proteins.
A number of studies have reported single mutations in flavivirus protein influence the viral-host interactions, thereby determining the virulence of distinct virus. Yuan L et al. reported that S139N mutation in preM protein significantly increased the neurovirulence of Zika virus (ZIKV), and this could be the reason why ZIKV caused more microcephaly since the outbreak of 2010s[29]. Xia H et al. identified the A188V substitution in ZIKV NS1, which enhancing its IFN antagonizing activity [30]. The N124D and K128E mutations in DENV2 E protein reduced its heparin sulfate binding activity, and weakened the infectivity of mutated viruses[15]. In our current study, we also noticed that DENV1B NS proteins have stronger inhibitory ability against host IFN signaling. The A64V mutation in NS2A impaired the replication of DENV1, suggesting that A64 NS2A is a novel virulence determinant that may influence virus-host interaction.
At the same time, other studies suggested that variations in viral proteins may directly influence the viral protein functions. Some of DENV virulence determinants have been described, most of which locate at the E protein[14]. For example, the N67Q mutation in DENV2 E protein decreased virus growth, and N67 was identified as an important N-glycosylation site for this protein which is critical for viral assemble and budding[31]. Some substitutions in NS1, NS4B, and NS5 proteins were evidenced to increase viral replicative fitness in native mosquitoes[32]. In this study, we also identified two novel virulence determinants in the genomes of DENV1. The K55R substitution in NS2B results in an impaired protease activity of NS2B3, thereby compromised the viral replication efficiency. The 64th amino acid of NS2A was also important for DENV1 replicon replication. The transmembrane topological structure of NS2A and NS2B[33–35], as well as the 3D crystal structure of NS2B3[36], were reported previously by several groups. By sequence alignment analysis, we found that 55th amino acid residue of NS2B was located near the end of the first β-strand structure of the NS2B cofactor domain, which could be critical for stabilization of NS3 protease. Mutations made to the 63-65th amino acids of NS2A displayed a lethal phenotype to DENV2 virus[35]. The 64th amino acid residue was located in the top of the hinge area of the third transmembrane helix domain which may influence the topology of NS2A. Beside of this, molecular evolution analysis also suggested an A9G variation in Capsid protein between DENV1 B and DENV1 E (S2 Table). In the ViPR database, almost all of the Capsid proteins from DENV1 strains are A9, and only 52 strains which are G9. This suggests that the 9th Alanine residue may be the dominant virulence loci. There are also a 20nt deletion in 3’UTR region of DENV1 D and E compared with DENV1 A-C, and this deletion may interfere with the stability of a SL1 loop in the 3’UTR, which is critical for the sfRNA generation[21]. Further study will be required to explore those potential virulence determinants for DENV1. We should also mention that even though we have confirmed that NS2A A64V and NS2B K55R mutations in replicons of DENV1B backbone have defect replication efficiencies than wild type replicon, the reverse mutations of these amino acids in a replicon with DENV1E backbone should also be important to support these findings. Further experiments will be performed to address this question.
Taken together, we compared the difference of replication efficiency and virulence of five DENV1 variants. We found that DENV1B is the most virulent virus, and DENV1E is the weakest. We further suggested that the 64th amino acid of NS2A and 55th amino acid of NS2B were potential virulence determinants of DENV1, which provided a theoretical basis for better understanding the molecular mechanisms of DENV virulence. It also provides new ideas for investigation of DENV protein function, pathogenic mechanism and novel attenuated vaccine.
The HUVEC (Human Umbilical Vascular Endothelium Cells) and PBMC (human Peripheral Blood Mononuclear Cells) were obtained from BeNa Culture Collection (Bejing, China). The projects using of human biological specimens were approved by an institutional review board (IRB) of Soochow University.
Animal experiments were conducted according to the Guide for the Care and Use of Medical Laboratory Animals (Ministry of Health, People’s Republic of China) and approved by the Animal Care and Use Committee as well as the Ethical Committee of Soochow University (No. SYSK-(S2012-0062)).
Five different DENV1 viruses, isolated from the DENV outbreak of 2014 in Guangdong, were obtained from CDC of Guangdong province. The viruses were propagated in mosquito C6/36 cells (ATCC CRL-1660). 293T, Huh7.0 and Vero cells were obtained from ATCC (Manassas, USA) and grown in DMEM (Life Technologies, Grand Island, USA) supplemented with 10% FBS and antibiotics/antimycotics. HUVECs were grown in 1640 (Life Technologies) supplemented with 10% FBS and antibiotics/antimycotics. Mouse embryonic fibroblasts (MEFs) were prepared from the mouse embryo using standard protocols [37]. Cells were infected with DENV at a multiplicity of infection (MOI) of 0.5, unless otherwise stated.
BABL/C and C57BL/6J mice were obtained from Shanghai Laboratory Animal Center (Shanghai, China). IFNAR1-/- mice (in a C57BL/6J background) were prepared by Institute of medical laboratory animal research, Chinese Academy of Medical Sciences (Beijing, China). All the animals were maintained in a biosafety level 2 animal facilities.
Total RNA from DENV infected cells were extracted using the total RNA kit I (OMEGA, USA) and reverse-transcribed using the PrimeScript Master Mix kit (TaKaRa, Japan). cDNAs were mixed with RT-PCR primers and SYBR Premix Ex Taq II (TaKaRa, Japan) and amplified for 40 cycles (95°C 15 s, 60°C 30 s, and 72°C 15s). The intracellular viral loads, in terms of transcript levels of the specific viral genes, were quantified through qRT-PCR and normalized to β-actin gene. The mRNA expression levels of human IFNβ1, IFIT1, and Cig5 genes were also determined via qRT-PCR. (Oligo-primer sequences for qRT-PCR of this study were shown in S1 Table).
The titers of DENV in cell-free supernatants or tissue extracts were determined with a median tissue culture infective dose (TCID50) assay and plaque assay according to protocols previously described [38,39], with slight modifications. Briefly, samples were serially diluted and inoculated into Vero cells in 96-well plates. After 5-day incubation, cells were fixed with 4% paraformaldehyde, stained with 10% crystal violet buffer, and examined for cytopathic effects (CPE) and plaque formation under a light microscope. The virus titer (TCID50/ml) was calculated using the Reed-Muench method. 1 TCID50/ml was equivalent to 0.69 pfu/ml[40].
DNA-based replicons (for DENV type 1) expressing secreted Gaussia luciferase (DGL2), were generously provided by Dr. Takayuki Hishiki (Kyoto University, Kyoto, Japan)[28]. The point mutations to the DGL2 replicon were obtained by using the QuickChange Site-directed Mutagenesis kit (Agilent Technologies, USA) according to manufacturer’s instructions.
For the Gaussia luciferase assay, 50 ng of DGL2 replicon plasmid was transfected into 293T cells in 96-well plates. Culture supernatants were collected at different time points and luciferase was measured using BioLux Gaussia Luciferase Assay Kit (New England Biolabs, UK) according to manufacturer’s instructions.
Each 3- or 4-day-old BABL/C suckling mouse was inoculated intracerebrally with 100 PFU of DENV1 A-E respectively as previously described[41]. Animals were monitored for 21 days to evaluate the morbidity and mortality. The DENV replication levels in cerebrum at day 4 were measured by qRT-PCR method described above.
For infection of IFNAR1-/- mice, 4–6 week-old IFNAR1-/- mice were infected with 1×107 PFU of DENV1B or DENV1E respectively by intraperitoneal injection. At day 3 post infection, mice were euthanized and the viral loads in whole blood cells and spleens were determined by qRT-PCR and plaque assays as described above.
The ORFs of NS2A, 2B, 4A and 4B from DENV1 A-E were subcloned into a eukaryotic expression vector pcDNA3.1A-His/Myc individually. The expressions of NS proteins were confirmed by western blot using anti-His Antibody (Sigma, USA). Prokaryotic expression plasmids for protease NS2B3 of DENV1 B and E (and NS2B3 mutants) were constructed as described previously[11,18,42]. Briefly, the coding region of NS2B enzyme co-factor domain (48–95 aa) and NS3 protease domain (1–180 aa) were amplified by nested PCR using the primers listed in the S1 Table, and cloned into the pGEX-6p2 bacterial expression vector. Then the pGEX-NS2B3 constructs were transformed into Escherichia coli strain BL21 (DE3) for protein expression. The GST-tagged recombinant proteins were induced with 0.1mM IPTG at 30°C for 4 h and purified using GST affinity agarose (GE Healthcare, Sweden). The GST tag was removed by Prescission Protease (Sigma, USA).
IFNβ- or ISRE-Luciferase reporter assay was performed as described previously[39,43]. Briefly, 293T cells were transfected with IFNβ- Luc (or ISRE-Luc) (Firefly luciferase, experimental reporter, 100 ng/well) and pRL-TK reporter (Renilla luciferase, internal control, 5 ng/well) plasmids (Clontech, USA), IFNβ activator RIG-I-N (the active caspase recruitment domain (CARD) containing form of RIG-I), together with individual NS proteins from DENV1 A-E or vector control. (For ISRE-Luc assay, cells were also treated with IFNα (1000 U/ml) for 6h instead of stimulation with RIG-I-N transfection.) 24 h post-transfection, cells were lysed and the luciferase activity was measured using a Dual Glow kit according to the manufacturer’s instructions (Promega, USA).
Various concentration of purified recombinant NS2B3 from DENV1B, E and 1B K55R (or 1E R55K) mutants were incubated with DENV1 substrate Ac-EVKKQR-pNA [42](GL Biochem, Shanghai, China) at 37°C for indicated time courses, respectively. Enzymatic assay were carried out with the following buffers: 50 mM Tris-HCl, 10mM NaCl , 20% glycerin, 1mM CHAPS, pH 9.2. The substrate cleavage efficiencies were analyzed by measuring the OD value at 405nm as described before[42].
Prism 7 software (GraphPad Software) was used for survival curves, charts and statistical analyses. The significance of results was analyzed using ANOVA followed by Tukey’s test for multiple comparisons, Student’s t-test (for comparisons between two groups) and Log-rank (Mantel-Cox) Test (for survival data), with a cutoff P value of 0.05.
|
10.1371/journal.pntd.0006516 | Identification of a host collagen inducing factor from the excretory secretory proteins of Trichinella spiralis | In a previous study, we found that Trichinella spiralis muscle larva excretory and secretory proteins (ES-P) most likely activate collagen synthesis via TGF-β/Smad signaling, and this event could influence collagen capsule formation.
In order to identify the specific collagen inducing factor, ES-P was fractionated by a Superdex 200 10/300 GL column. We obtained three large fractions, F1, F2, and F3, but only F3 had collagen gene inducing ability. After immunoscreening, 10 collagen inducing factor candidates were identified. Among them, TS 15–1 and TS 15–2 were identical to the putative trypsin of T. spiralis. The deduced TS 15–1 (M.W. = 72 kDa) had two conserved catalytic motifs, an N-terminal Tryp_SPc domain (TS 15-1n) and a C-terminal Tryp_SPc domain (TS 15-1c). To determine their collagen inducing ability, recombinant proteins (rTS 15-1n and rTS 15-1c) were produced using the pET-28a expression system. TS 15–1 is highly expressed during the muscle larval stage and has strong antigenicity. We determined that rTS 15-1c could elevate collagen I via activation of the TGF-β1 signaling pathway in vitro and in vivo.
In conclusion, we identified a host collagen inducing factor from T. spiralis ES-P using immunoscreening and demonstrated its molecular characteristics and functions.
| Trichinella spiralis can make collagen capsules in host muscle cells during its life cycle, which encapsulates muscle stage larvae. Many investigators have tried to reveal the complex mechanism behind this collagen capsule architecture, and it has been suggested that several serine proteases in excretory-secretory proteins of the parasite are potential collagen capsule inducing factors. In addition, collagen synthesis is activated through the TGF-β/Smad signaling pathway and these events are closely related with protease activated receptor 2 which was activated by various serine proteases. In this study, we isolated and characterized a collagen gene expression inducer from T. spiralis ES-P using immunoscreening and investigated the candidate protein for its usefulness as a wound healing therapeutic agent.
| Trichinella spiralis can make collagen capsules in host muscles during their life cycle that surround muscle stage larvae and might protect the larvae from the host immune system. This phenomenon can be understood as the parasite creating a simple structure to protect itself, but when examined closely, numerous different mechanisms are involved in this stage of the parasite’s life. Division of the host muscle cell nucleus, regulation of host cell cycling, huge elevation of host collagen gene expression, and generation of new blood vessels around the collagen capsule are observed during nurse cell formation by T. spiralis [1–4]. The process of nurse cell formation induces de-differentiation, cell cycle re-entry, arrest of infected muscle cells, and activation, proliferation, and differentiation of satellite cells. These events are very similar to those occurring during muscle cell regeneration and repair [2].
In a previous study, we found that T. spiralis excretory and secretory proteins (ES-P) most likely activate collagen synthesis via TGF-β/Smad signaling, and this event could influence collagen capsule formation [5]. These events were closely related with protease activated receptor 2 (PAR2), which was activated by various serine proteases [5]. However, the question of which protease in T. spiralis ES-P has a role in collagen gene expression of host muscle cells is still unanswered. The identification of a specific collagen gene inducer from T. spiralis could be exploited as a therapeutic and/or cosmetic agent. In this study, we isolated and characterized the collagen gene expression inducer from T. spiralis ES-P by immunoscreening and investigated the candidate for its usefulness as a wound healing therapeutic agent.
The T. spiralis strain (isolate code ISS623) used in this study has been maintained in our laboratory via serial passage in rats. For acquisition of muscle larva, eviscerated mouse carcasses were cut into pieces, followed by digestion in 1% pepsin 1% hydrochloride digestion fluid (artificial gastric juice) for 1 hr at 37°C with stirring. Larvae were collected manually from muscle digested solution under microscopy and washed 6 times with sterile PBS containing 100 μg/ml ampicillin, 5 μg/ml kanamycin and 50 μg/ml tetracyclin. After collection, in order to prevent contamination with the host material, worms were thoroughly and carefully washed several 3 times with PBS. Whole parasite proteins (total extract; TE) was obtained from muscle larva according to previous study [6]. In brief, muscle larva were rinsed in PBS and homogenized in 50 mM Tris–HCl (pH 7.5) with a glass homogenizer. The homogenates were briefly sonicated and then centrifuged for 30 min at 12,000 × g and 4°C. The supernatant (TE) was stored at -20°C.
Small intestines were removed on the day 6 after infection from infected rat, opened, sliced by 2 cm, washed with PBS, and incubated for 1 hr at 37°C in PBS containing antibiotics. Adult worms were collected on a PBS, washed 3 times with PBS containing antibiotics, and incubated for 24 hrs in serum-free RPMI 1640 medium containing antibiotics. After incubation, NBL were passed through 40 μl nylon mesh (BC falcon, USA) to be separated from adult worms.
Muscle larvae were isolated from T. spiralis infected mice (4 weeks after infection) and ES-P from cultured muscle larvae was obtained according to the previously reported method [5]. The ES-P was fractionated using gel filtration chromatography. ES-P (5 mg) in 10 ml PBS was applied to a Superdex 200 10/300 GL column (GE Healthcare, Uppsala, Sweden). The flow rate was 0.25 ml/min. Each 0.5 ml fraction was collected and protein quantity was measured by UV detection at 260 nm. Three big fractions, F1, F2, and F3, were acquired and used for collagen gene inducing experiments (Fig 3A).
Twenty female C57BL/6 mice at the age of 6 weeks and twenty female 14 week-old mice were purchased from Samtako Co. (Gyeonggi-do, Korea). The skin of the left ear of each mouse was treated with T. spiralis ES-P (30 μg) or rTS 15-1c (30 μg) in PBS (total volume 50 μl) every day for 14 days, and that of the right ear was treated with PBS (Figs 1A and 7A). The mice were housed in a specific pathogen-free facility at the Institute for Laboratory Animals of Pusan National University.
In order to compare the expression level of type I collagen and their signal pathway related genes, mouse fibroblast (MEF) cells were used in this study because type I collagen was preferentially synthesized by two cell types, the osteoblast and the fibroblast [7]. MEF cells were isolated from C57BL/6 mouse fetuses 10 days after fertilization [8]. MEF cells were incubated in DMEM (Difco) with 5% FBS and 5 × 105 cells were plated in 24-well plates and incubated overnight at 37°C in 5% CO2. The cells were treated with ES-P, boiled- ES-P, F1, F2, F3, boiled F3, TS 15-1c, and TS 15-1n (final conc. 1 μg/ml); ES-P with PMSF (serine protease inhibitor, final conc. 1 mM; Sigma-Aldrich, USA), F3 with PMSF for 2 hrs.
Gelatin-gel containing 0.2% gelatin was prepared from gelatin-stock solution. The proteins, T. spiralis ES proteins, TS 15-1c, and TS 15-1n were mixed with 2 × sample buffer (1 M Tris pH 6.8, 1% bromphenol blue, glycerol, β-mercaptoethanol), and the gel loaded with these proteins was run with 1 × Tris-Glycine SDS running buffer on 125 V for 2 hrs at 4°C. After running, the gel was washed to remove the SDS and re-natured proteinase activity with zymogram renaturing buffer (2.5% Triton X-100). The gel was developed with zymogram developing buffer (0.5 M Tris-HCl pH 7.6, 0.02 M NaCl, 0.5 mM CaCl2) for 30 min at room temperature. The gel was incubated with developing buffer at 37°C for 8 hrs. The gel was stained with Coomassie Blue R-250 for 30 min and distained with an appropriate destaining solution (Bio-Rad laboratories, Inc., USA).
Homogenized ear tissues were mixed with TRIzol (Invitrogen, Germany), and RNA extraction and cDNA synthesis (Invitrogen, Germany) was performed in accordance with the manufacturer’s protocols. Expression levels of several genes were determined with real-time RT-PCR using the iCycler (Bio-Rad laboratories Inc., USA) real-time PCR machine. Primer sequences for collagen I, TGF-β, smad2, smad3, and GAPDH, and PCR conditions were identical to those mentioned in the previous study [5]. To evaluate variation of Ts-15-1 gene expression during T. spiralis life cycle, total RNAs were extracted from new born larva, adult worm, muscle larva and T. spiralis infected mouse muscle (1, 2, and 4 weeks after infection) using TRIzol (Invitrogen, Germany), and cDNA synthesis (Invitrogen, Germany) was performed in accordance with the manufacturer’s protocols. Expression levels of several genes were determined with real-time RT-PCR using the iCycler (Bio-Rad laboratories Inc., USA) real-time PCR machine. The primer sequences for the putative trypsin (TS 15-1c), and T. spiralis GAPDH were 5′- TTG GAA TGA CGC TGA TTG -3′, 5′- GTG GCT TAT GAT GGT AGG AGA AT -3′ and 5′- CAG GTG CTG ATT ACG CTG TT -3′, R—5′- ACG CCA ATG CTT ACC AGA T -3′ respectively. Amplification of two genes was performed under the following conditions: 1 min 30 sec host start at 95°C, followed by denaturation at 95°C for 25 sec, primer annealing at 50 ~ 55°C for 20 sec, and elongation at 72°C and 30 sec for 40 cycles. Fluorescent DNA-binding dye SYBR was monitored after each cycle at 50 ~ 55°C. An iCycler multi-color real-time PCR detection system (Bio-Rad Laboratories) was used for estimation of expression levels. Then, using the Gene-x program (Bio-Rad Laboratories), relative expression of the gene was calculated as the ratio to a T. spiralis GAPDH gene.
A cDNA library generated from 60,000 plaques forming units of T. spiralis muscle larvae was screened with the α-TS F3 antibody. Immunoscreening was performed using the SMART cDNA Library Construction Kit (Clontech, USA) in accordance with the manufacturer’s protocols. Briefly, after primary and secondary screening, positive plaques were picked and the phagemids were prepared by in vivo excision. The phagemids were transformed into XL1-Blue MRF cells. Clones were selected based on blue-white color selection of the colonies grown on LB-ampicillin agar plates. The plasmid harboring the cDNA inserts were then extracted using a plasmid DNA purification system (Cosmogenetech, Seoul, Korea). The cDNA inserts were then sequenced using the primer for T3 promotor (Cosmogenetech, DNA sequencing service, Seoul, Korea) and compared against the GenBank database.
Following confirmation of the PCR product sequences, TS 15–1, The TS 15-1c (C-terminal serine protease domain) and TS 15-1n (N-terminal serine protease domain), the genes were ligated with pET-28a vector (Novagen, USA). After gene ligation, the constructed plasmids were expressed in Escherichia coli BL21 (DE3, Novagen, USA). Pre-cultured cells were inoculated into Luria-Bertani broth containing kanamycin, and the cells were grown at 37°C until an OD600 of 0.5–0.6 was reached. Recombinant TS 15-1N and TS 15-1C expressions were induced addition of 0.5 mM isopropyl β-D-1-thiogalactopyranoside (IPTG) at 25°C for 16 h. The cells were harvested by centrifugation and the cell pellets were resuspended in buffer. A consisting of 50 mM Tris–HCl pH 7.5 and 200 mM NaCl. Cell disruptions were lysed by sonication on ice and the crude extracts were centrifuged to remove the cell debris. Ts15-1 N and C pellets were then sonicated in buffer including 50 mM Tris–HCl pH 7.5, 200 mM NaCl, and 6 M Urea on ice. The clear supernatant of the lysate was subjected onto the Ni–NTA column which had been pre-equilibrated with buffer A. The column was subsequently washed with buffer A containing imidazole, after which the bound proteins were eluted by varying the imidazole concentration (20–400 mM). The eluted proteins were analyzed using 10% SDS-PAGE. However, we could not get recombinant protein of TS 15–1 because very poor expression level.
Female four-week-old Wistar rats were purchased from Samtako Co. (Gyeonggi-do, Korea). Rats were immunized subcutaneously with a 1:1 mixture of the 250 μg F3 fraction of TS 15-1c protein (in 0.5 ml PBS) and 0.5 ml Freund’s complete adjuvant (#F5881, Sigma-Aldrich, USA) at 0 week. At 2 weeks the rat was given additional infections of the 250 μg F3 fraction or TS 15-1c protein with Freund’s incomplete adjuvant (#F5506, Sigma-Aldrich, USA). One week after their final booster, rats were sacrificed and serum was obtained.
Homogenized ear tissues were mixed with TRIzol (Invitrogen, Germany), and total RNA extraction and cDNA synthesis (Invitrogen, Germany) was performed in accordance with the manufacturer’s protocols. Expression levels of several genes were determined with real-time RT-PCR using the iCycler (Bio-Rad laboratories Inc., USA) real-time PCR machine. Primer sequences for collagen I, TGF-β, smad2, smad3, and GAPDH, and PCR conditions were identical to those mentioned in the previous study [5]. Each gene expression levels were normalized with GAPDH gene expression.
Mice were killed at 0, 1, 2, and 4 weeks after T. spiralis infection and serum was obtained. Sera were stored at -20°C until used.
Ten μg each of ES-P and F3 fraction (Fig 3) or 10 μg ES-P and total extract from T. spiralis, TS 15-1c (Fig 5B) or 10 μg of purified TS 15-1c antibody (Fig 5C) or 15 μg of each ear tissue samples (Fig 7) were separated on 10% acrylamide SDS-PAGE gel at 100 V for 90 min. Sweden), The loaded proteins were transferred onto a nitrocellulose membrane (Amersham Biosciences, Little Chalfont, UK) and blocked with 5% skim milk in TBST at 4°C overnight. Then, the membrane was incubated with primary antibody (polyclonal α-F3, α-TS 15-1c (1:500); time-course sera (1:1,000), α-TGF-β1 (1:1000; abcam, Carlsbad, CA, USA),;p-Smad2/3 (1:1000; Thermofisher science, Waltham, MC, USA),;α-mouse type I collagen (1:1000; abcam), and actin (1:5000, abcam)) in 5% skim milk in TBST for 2 hrs at room temperature. The secondary antibody, α-mouse or α-rat IgG-HRP conjugate (Sigma, Seoul, Korea) was used at 1:5,000 dilution for 1 hr at room temperature. HRP was detected using an ECL substrate (Amersham Biosciences, Uppsala, Sweden), analyzed using the LAS 3000 machine. (Areas of the detected bands were determined and compared by Image J software).
Paraffin-embedded T. spiralis infected or non-infected mouse muscle tissue were de-paraffinized and hydrated. For antigen retrieval, slides were immersed in citrate buffer (0.01 M, pH 6.0) and heated twice in a microwave (700 W or ‘high’) for 5 min. Slides were then quenched with endogenous peroxidase by incubation in a 3% hydrogen peroxide solution for 5 min and were washed three times in PBS for 5 min each. Slides were immuno-stained with primary antibody (α-TS 15-1c antibody that was produced according to the polyclonal antisera method; 1:500 dilution) at 4°C overnight. After primary antibody incubation, slides were washed three times in PBS for 5 min each and were incubated with secondary antibody, the Alexa Fluor 594 goat anti-rat IgG secondary antibody (1:500; Invitrogen, USA) was applied for 1 h at 24°C. The slides were washed in PBS and mounted with Permount (Fisher Scientific, Pittsburgh, PA, USA). Confocal images of stained muscle tissue were examined under an inverted fluorescence microscope.
All experiments were performed three times for confirmation of statistical significance. Mean ± standard deviation (SD) was calculated from data collected from individual mice. Significant differences were determined using one-way or two-way analysis of variance. Statistical analysis was performed with GraphPad Prism 5.0 software (GraphPad Software Inc., CA, USA).
The study was performed with approval from the Pusan National University Animal Care and Use Committee (IACUC protocol approval; PNU-2016-1175), in compliance with ‘‘The Act for the Care and Use of Laboratory Animals” of the Ministry of Food and Drug Safety, Korea. All animal procedures were conducted in a specific pathogen-free facility at the Institute for Laboratory Animals of Pusan National University.
In order to understand the collagen gene inducing effect of ES-P, transcription and protein expression levels of type I collagen and TGF-β1 signaling related proteins were compared in ear tissues of 6 and 14 week-old mice that had or had not received ES-P treatment (Fig 1A). The expression levels of the collagen I and TGF-β1 genes of the 14 week-old mice were significantly decreased compared to those of the 6 weeks mice. However, those of the ES-P treated 14 week-old mouse group were significantly increased compared to un-treated 14 week-old mice (Fig 1B).
To identify type I collagen inducing factors from the T. spiralis ES-P, the ES-P was fractionated to several fractions including three big fractions by gel filtration chromatography. The three major fractions obtained were named F1 (about 100 kDa– 140 kDa), F2 (about 80 kDa—120 kDa), and F3 (about 50 kDa -90 kDa) respectively (Fig 2A). In order to determine which major fraction had collagen gene expression inducing factors, each fraction was used to treat MEF cells, and expression levels of the type I collagen gene were measured. After F1, F2, and F3 treatment, only F3 treated MEF cells had significantly increased type I collagen gene expression (Fig 2B). Moreover, expression levels of collagen I in F3 treated MEFs was higher those of ES-P treated MEFs (Fig 2B).
In order to determine whether the collagen inducing ability of F3 is related with serine protease activity, we evaluated the collagen inducing ability of F3 following pre-treatment with a serine protease inhibitor, PMSF, on MEF cells. The collagen I gene expression levels were significantly decreased in MEF cells pre-treated with PMSF, the serine protease inhibitor. In addition, the gene expression levels were also not increased when treated with boiled F3 (Fig 2C). In order to confirm the existence and expression levels of F3 in ES-P, α-F3 polyclonal antibody was used against ES-P and F3 in a western blot analysis. The presence of a strong band was observed at 60–80 kDa in both the ES-P and F3 samples (Fig 2D).
In order to identify the collagen gene inducing factors from T. spiralis ES-P, immunoscreening was conducted against the T. spiralis muscle larvae Express cDNA library with the α-F3 antibody. Thirty-five positive plaques were detected in primary screening, among them, 10 plaques were confirmed by second screening (S1 Fig). These plaques were amplified and processed in an in vivo excision step. All the insert DNA from the 10 positive clones were sequenced and their amino acid sequences were determined. Two insert DNA fragments (TS 15–1 and TS 15–2) were similar to a putative trypsin of T. spiralis with 90% identity. Another clone (TS 15–3) was matched to a nuclear receptor-binding protein of T. spiralis with 35% of identity. Another clone (TS-16-1) was matched to a putative BTB/POZ domain protein of T. spiralis with 63% identity. The remaining 6 insert clones were not matched with any previously known genes.
Collagen inducing factors in ES-P and F3 had serine protease activity and were measured to be about 60–72 kDa in size. After evaluation of the size and serine protease activity of positive clone matched genes, the TS 15–1 gene was selected for downstream identification of the collagen inducing factor. The TS 15–1 fragment was 2,004 bp long and encoded a 667 amino acid protein, and the molecular weight and pI was calculated as 71.6 kDa and 8.83. The deduced TS 15–1 protein has two conserved catalytic motifs, an N-terminal Tryp_SPc domain (TS 15-1n) and a C-terminal Tryp_SPc domain (TS 15-1c) (Fig 3A). The TS 15-1n and TS 15-1c peptides were composed of 238 amino acids and 239 amino acids respectively, and the molecular weight was calculated to be 26.1 kDa and 26.2 kDa respectively. Unfortunately, collection of recombinant full length of TS-15 protein was very difficult because its expression level was very low. We conducted recombinant protein expression of the N and C terminal domains (about 26 kDa, Fig 3B) and evaluated their collagen gene inducing ability. collagen I expression levels in the recombinant TS 15-1c protein treated cells were significantly increased compared to a media control. However, recombinant TS 15-1n protein treated cells were not significantly changed compared to those of medium treated cells (Fig 3C). In order to know the protease activity of both recombinant proteins, zymogram analysis was conducted. A collagen digested clear zone was detected around ~26 kDa in the recombinant TS 15-1c protein lane, but the clear zone was not detected with the recombinant TS 15-1n protein (Fig 3D).
In this study, we determined a molecular model by homology modeling based on the structure of another serine protease (PDB ID: 1KYN, 1–235). In TS 15-1c (G342—T580), predictions of the active sites (H389, D444, and S533) and the substrate binding sites (G527, S553, and G555) are shown in blue and red letters, respectively (Fig 4A). Interestingly, these results indicated that these sites interact with inhibitors and ligands. Most residues in these regions had negative charges in a globular fold (Fig 4B). Surprisingly, both TS 15-1n and granzyme B structures were shown to have very similar folding patterns (S2 Fig). The TS 15-1n is approximately 35% homologous to granzyme B (21–247). We found TS-15-1c and TS 15-1n molecular 3D model was quite different each other.
In order to determine when the Ts 15–1 mRNA was the most highly expressed in the T. spiralis life cycle, real-time PCR was performed on new born larvae, adult worms, muscle stage larvae of T. spiralis, and during the T. spiralis infection period (at 0, 7, 14, 28 days after infection). As the results show, the Ts 15–1 gene was the most highly expressed in muscle stage larvae, and its expression is also highly elevated 28 days after infection (Fig 5A). In order to know whether TS 15–1 was secreted from parasites, an α-TS 15-1c antibody was produced and was reacted with T. spiralis ES-P and total extract. The TS 15-1c antibody strongly reacted with proteins around 72 kDa in ES-P and slightly reacted with a total extract at the same size (Fig 5B). Furthermore, to know whether TS 15-1c has antigenicity or not, T. spiralis infected mice sera (0, 1, 2, and 4 weeks after infection) were reacted with recombinant TS 15-1c protein (Fig 5C). rTS 15-1c most strongly reacted with mouse serum collected 4 weeks after infection. To know where TS 15–1 is secreted in the parasite, α-TS 15-1c antibody was reacted against serial sections of the T. spiralis infected muscle using immunohistochemical methods. α-TS 15-1c antibody strongly reacted with only the ladder shapes structure around the esophagus in muscle stage larvae that appear to be stichocytes (Fig 6).
In order to know whether TS 15-1c had type I collagen elevating ability, we applied TS 15-1c to the ear skin of 6 week- and 14 week-old mice and evaluated the expression levels of collagen I, Smad2/3, and TGF-β1 (Fig 7A). All of the tested genes’ expression levels, including the collagen I gene of the 14 week-old mice, were significantly lower than those in the 6 week-old mice. However, after 14 TS 15-1c treatments on 14 week-old mice, collagen I, Smad2/3, and TGF-β1 gene expression levels in these mice were significantly increased compared with non-treated mice of the same age (Fig 7B). We investigated protein levels of type I collagen, the phosphorylation form of Smad2/3, and levels of TGF-β1 in TS 15-1c treated 14 week-old mice and the protein levels were considerably recovered relative to those of the non-treated 14 week-old mice (Fig 7C).
In this study, we identified the host collagen inducing factor from T. spiralis, named it TS 15–1, and confirmed its serine protease activity and ability to elevate type I collagen, TGF-βI, and related signal proteins (Smad2/3) on a transcriptional and protein level. In addition, we found that it was secreted outside the parasite and elicited specific antibody production from the host immune system. In a previous study, we revealed that the ES-P of T. spiralis could induce collagen production of host muscle tissue during the infection period, and that it was closely related with serine protease activity [5].
We can carefully suggest that TS 15–1 is one of the key collagen inducing factors in ES-P revealed in our previous study. Although we could not demonstrate that TS 15–1 is one of the key molecules in the collagen capsules around the nurse cell formation step, it might be one of the central factors for collagen capsule formation. This is because TS 15–1 gene expression level was the highest during the T. spiralis muscle larva stage and its specific antibodies could be detected in mouse serum from 1 week up to 4 weeks after a T. spiralis infection (Fig 5). During the nurse cell formation period (1 week–4 weeks), T. spiralis might strongly secrete TS 15–1 to induce collagen capsule synthesis by the host muscle cell.
Parasite secretory proteases might have important functions in modulating the interactions between parasites and hosts because of their particular roles in the invasion of host tissues, parasite nutrition, and evasion of host immune responses [9–12]. A trypsin-like serine protease of parasites could be involved in host immune regulation [13–15]. Serine proteases in nematodes are known to be involved in invasion into host cells and tissues and are likely to be important in molting. TS 15–1 was revealed to be a serine protease, trypsin like protein, because its activity was inhibited by PMSF (Fig 2C) and it was composed two domains which were very similar but not identical to each other (Fig 3A). Several secreted serine proteases have been identified among T. spiralis ES proteins, including the 69 kDa putative serine protease TsSerP (two trypsin-like domains), the 45 kDa serine protease TspSP-1, and a 35.5 kDa serine protease [9, 11, 16–18]. Most of these have strong antigenicity, specific antibodies for them are easily detected experimentally in infected animal sera, and they have one or two trypsin like domains [9, 11, 16]. Most secreted proteases could elevate their specific antibodies during nematodiasis [16, 19]. Trap et al., reported the identification of the putative serine protease, TsSerP, isolated from the T. spiralis adult-newborn larvae stage. It has two trypsin-like serine protease domains flanking a hydrophilic domain, which is the same structure as TS 15–1. Immunohistochemistry analysis revealed that TsSerP was located on the inner layer of the cuticle and esophagus of the parasite, TS 15–1 was also detected on the inner layer of the cuticle and stichocytes in this study (Fig 6). These two serine proteases of T. spiralis might have similar functions, although the function of TsSerP was not clearly revealed [9].
In this study, it was revealed that TS 15–1 could elevate collagen expression via the TGF-β1 signaling pathway in host tissue of normal aged mice. Because, the recombinant TS 15-1c protein could elevate collagen I production and the TGF-βI signaling pathway related to Smad2/3 proteins (Fig 7). Type I collagen expression is closely related with the TGF-βI/Smad2/Smad3 signaling pathway [20–22]. This characteristic could be used for therapeutic effects including wound healing and cosmetic usefulness with wrinkle reduction. The various serine proteases may participate in physiological or pathological processes, like tissue repair, vascular remodeling, and wound healing, which depend on cell proliferation and migration [23, 24]. Type I collagen is the major structural protein in the skin. Collagen destruction is thought to underlie the appearance of aged skin and changes resulting from chronic sun exposure [25]. Ultraviolet irradiation from the sun has deleterious effects on human skin including cancer, photo-aging, and intrinsic aging [26]. TGF-β/Smad pathway is the major regulator of collagen homeostasis and plays a crucial role in dermal fibrosis [27, 28]. TGF-β is the most potent direct stimulator of collagen production. Moreover, TGF-β is central to the process of wound healing and fibrosis formation in skin [29, 30]. It is well understood that activation of TGF-β signaling pathways stimulus the Smad family downstream via phosphorylation. Wound healing is a well-orchestrated process, where numerous factors are activated or inhibited in a sequence of steps [31]. Numerous signaling pathways are involved, among of them, the TGF-β1/Smad pathway is representative and well known to participate in the wound healing process [31]. Hozzein et al., suggested that topical application of propolis would promote the wound healing process by promoting TGF-β/Smad signaling, leading to increased expression of collagen type I [32]. The gradual loss of collagen in skin with aging results in wrinkles and other signs of skin aging [33]. The content of type I collagen, the major collagen in the skin and a marker of collagen synthesis, is deceased by 68% in old skin versus young skin, and cultured young fibroblasts synthesize more type I collagen than old cells [33]. In addition, a possible influence of collagen membrane on extracellular matrix synthesis was addressed using analysis of TGF-β1 and Smad2/3 complex [34, 35].
In conclusion, we identified a host collagen inducing factor from ES-P using immune screening methods and demonstrated the molecular/genetic characteristics and function of TS 15-1c. Further study will be required to understand the detailed mechanisms for receptors in the host cells, and to identify the minimal structure that can induce collagen for cosmetic and medical purposes.
|
10.1371/journal.pgen.1004355 | A Lack of Parasitic Reduction in the Obligate Parasitic Green Alga Helicosporidium | The evolution of an obligate parasitic lifestyle is often associated with genomic reduction, in particular with the loss of functions associated with increasing host-dependence. This is evident in many parasites, but perhaps the most extreme transitions are from free-living autotrophic algae to obligate parasites. The best-known examples of this are the apicomplexans such as Plasmodium, which evolved from algae with red secondary plastids. However, an analogous transition also took place independently in the Helicosporidia, where an obligate parasite of animals with an intracellular infection mechanism evolved from algae with green primary plastids. We characterised the nuclear genome of Helicosporidium to compare its transition to parasitism with that of apicomplexans. The Helicosporidium genome is small and compact, even by comparison with the relatively small genomes of the closely related green algae Chlorella and Coccomyxa, but at the functional level we find almost no evidence for reduction. Nearly all ancestral metabolic functions are retained, with the single major exception of photosynthesis, and even here reduction is not complete. The great majority of genes for light-harvesting complexes, photosystems, and pigment biosynthesis have been lost, but those for other photosynthesis-related functions, such as Calvin cycle, are retained. Rather than loss of whole function categories, the predominant reductive force in the Helicosporidium genome is a contraction of gene family complexity, but even here most losses affect families associated with genome maintenance and expression, not functions associated with host-dependence. Other gene families appear to have expanded in response to parasitism, in particular chitinases, including those predicted to digest the chitinous barriers of the insect host or remodel the cell wall of Helicosporidium. Overall, the Helicosporidium genome presents a fascinating picture of the early stages of a transition from free-living autotroph to parasitic heterotroph where host-independence has been unexpectedly preserved.
| Helicosporidium is a highly-adapted obligate parasite of animals. Its evolutionary origins were unclear for almost a century, but molecular analysis ultimately and surprisingly showed that it is a green alga, which means it has undergone an evolutionary transition from autotrophy to parasitism comparable to that of the malaria parasite Plasmodium and its relatives. Such transitions are often associated with the loss of biological functions that are no longer necessary in their novel environment and with the development of molecular mechanisms, sometimes quite sophisticated, to invade and take advantage of their hosts. Yet, very little is actually known about the early stages of the transition of a free-living organism to an obligate intracellular parasite. Here we sequenced the genome and transcriptome of Helicosporidium, and use it to show that the outcome of this transition is quite different from that of Plasmodium.
| Helicosporidia are parasitic protists characterized by mature discoid cysts each containing a single filamentous and three ovoid cells [1], [2]. These parasites invade their invertebrate hosts per os and initiate their replicative stage within the digestive tract [3], [4]. The cysts, triggered by chemical changes in the gut, dehisce and release both the ovoid cells and filament cell. The ovoid cells remain in the gut lumen whereas the uncoiled and barbed filamentous cells penetrate the peritrophic membrane and become anchored to the host midgut cells. Over time the filamentous cells migrate through the midgut epithelium, breach the basement membrane, and invade the hemocoel. In the hemocoel the filament cells will transition to a vegetative stage that replicates by autosporulation; a select number at the four-cell stage will differentiate to the infectious cyst stage characterized by the three ovoid and a single filament cell [5], [6]. Unlike many parasites the vegetative cells of Helicosporidia can be cultured readily on defined media with limited nutrients, suggesting that despite being a parasitic species, they have retained a diverse slate of metabolic pathways allowing for saprobic growth.
The evolutionary origin of Helicosporidia remained uncertain for nearly 100 years since their initial description, although various characters were used to suggest some relationship with microsporidian, sporozoan, and myxosporidian parasites. Recently, however, ultrastructural observations surprisingly revealed that the vegetative state of Helicosporidium cells is similar to that of the achlorophylous trebouxiophyte green alga Prototheca [1], and subsequent phylogenetic inferences derived from actin/tubulin and plastid sequences strongly confirmed this affiliation [7], [8]. The discovery that Helicosporidia are trebouxiophycean green algae raises some interesting questions about the evolution of parasitism: within this single lineage are found free-living autotrophs like most other green algae, but also a variety of symbiotic species, opportunistic pathogens, and perhaps even obligate intracellular parasites, all of which diversified within a relatively narrow evolutionary timescale. The transformation from free-living to parasitic lifestyles often includes the shedding of metabolic functions that are no longer required as the parasite relies increasingly on its host for energy and nutrients [9]. The parasitic relationship may be opportunistic at first, but can switch to being obligate upon reaching a certain threshold of host-dependence, after which the formerly free-living organism can no longer revert to its previous lifestyle due to the ratchet-like nature of these losses. We do not often think of photosynthetic organisms with progenitors for parasitic ones, but a variety or parasitic lineages had at one time photosynthetic ancestors, including oomycetes, several dinoflagellates, and most famously the apicomplexan parasites such as the malaria parasite, Plasmodium (see [10], [11] and references therein). One of the first things to be lost in photosynthetic species is presumably their ability to harvest energy from light and fix carbon. Harnessing light from within large-bodied hosts is probably very difficult if not impossible, and the resulting metabolic deficit must lead to a significant shift in the balance between the host and parasite. Some of these lineages (e.g. oomycetes) probably evolved through a heterotrophic intermediate, but others possibly began their association with animals as phototrophs. How the transformation to parasites took place is of great interest, but unfortunately because it happened so long ago (around 1 bya for Plasmodium [12]) and is now so complete, the critical early stages have long been wiped away. Helicosporidia, in contrast, appear to have evolved from free-living autotrophs relatively recently [13], [14], and might therefore provide some interesting insights. Fossils records and molecular clock analyses suggest that Trebouxiophytes as a group arose in the early Neoproterozoic [13], from which the trebouxiophycean subgroup Chorellales later emerged around 100 million years ago (mya) [14]. Both Helicosporidium and the non-photosynthetic trebouxiophycean Prototheca arose from within the Chorellales [13], so the adaption to parasitism in Helicosporidia occurred less than 100 mya.
To specifically investigate how the metabolic and proteomic complexity of pathogenic Helicosporidia are distinguished from their free-living and symbiotic trebouxiophycean relatives, we sequenced the genome and transcriptome of Helicosporidium sp. ATCC50920, a parasite of the black fly Simulium jonesi [1], [15]. We show that the Helicosporidium genome is 2.5-fold smaller than genomes from the free-living and symbiotic trebouxiophytes, Coccomyxa subellipsoidea C-169 [16] and Chlorella variabilis NC64A [17], which are themselves extremely small for trebouxiophyte genomes. However, the reduction of the Helicosporidium genome is not tied to a massive reduction in metabolic functions: despite its small genome size and parasitic nature, it surprisingly still encodes all major metabolic pathways, with the exception of a small number specifically related to photosynthesis. Even here, the reduction is not complete: all genes relating to light harvesting and electron transport are missing, but the Helicosporidium carbon fixation pathway is nearly complete but for the lack of ribulose-1, 5-bisphosphate carboxylase/oxygenase (RuBisCO) and a pyruvate kinase. The smaller size of the Helicosporidium genome can be attributed to a greater degree of genome compaction (e.g. fewer and smaller introns, and smaller intergenic regions), and most significantly to a lower complexity of gene families, particularly those related to DNA packaging/replication pathways. We also show that the gene family complexity of other metabolic pathways has increased, in particular relating to chitin metabolism, which likely represented a key development in the ability of Helicosporidium to develop in the insect host. Overall, these results give our first view into the early stage in the transition from a free-living autotroph to an obligate pathogen.
Shotgun Illumina reads of total DNA were assembled into 11,717 contigs totalling 13,684,556 bp (62.2% GC). Contamination filters suggest a maximum of 1% overall contamination, located in the smaller-sized contigs. Removal of the mitochondrial and plastid genomes and filtering of the small contigs resulted in 5,666 contigs of at least 500 bp in size (12,373,820 bp total; N50 3,036 bp, 61.7% GC), with an average coverage of 62× (Table 1). Based on the current data, we estimate the Helicosporidium genome at a maximum size of 17±0.5 Mbp. This corresponds well to a genome size estimate of 13 Mbp, derived from karyotype visualisation by clamped homogeneous electrical field (CHEF) electrophoresis [18]. A total of 6,035 protein-encoding genes were predicted among the assembled 12.4 Mbp, with an average of 2.3 exons (366 bp/exon) and 1.3 introns (168 bp/intron) per gene. Coding density in the Helicosporidium genome is high (0.487 gene/kb) compared to the free-living and symbiotic trebouxiophytes Coccomyxa (0.197 gene/kb) and Chlorella (0.212 gene/kb), but lower than that of the 12 Mbp genomes of the picoplanktonic prasinophycean green algae in the genus Ostreococcus (0.626 and 0.580 gene/kb; Table 1, Figure 1). Identifiable transposable elements are rare in the assembled Helicosporidium contigs, although micro- and minisatellites and regions of generally low complexity were found (Data S1). Comparing the genomic assemblies with the transcriptome revealed a total of 95.4% of the genes attributed to known metabolic pathways (Table S1) were found in both datasets, and only 3.6% were found exclusively in the transcriptome. This correlated well with the overall percentages of transcriptomic contigs mapping on the genomic ones (≥1000 bp; 92.3%, ≥1500 bp; 95.9%), suggesting that the total coding potential of Helicosporidium is well-represented in the draft genome. The Helicosporidium genome shares little gene order conservation with the other green algal genomes: only 30% of the genes located within its ten largest contigs are arrayed in syntenic clusters with those of Chlorella (Figure 2), with no apparent metabolic relationship between the genes present in these clusters.
The Helicosporidium genome is small: it is approximately 2.5 times smaller than the two other complete trebouxiophyte genomes, Coccomyxa and Chlorella (Table 1, Figure 1), which are themselves at the extremely low end of the spectrum of estimated genome sizes in this lineage (Figure S1). But this small size is not a reflection of a severe reduction in metabolic potential. Indeed, the Helicosporidium genome encodes almost all of the major biological functions that are shared between the genomes of its trebouxiophycean relatives and that of the chlorophycean green alga Chlamydomonas reinhardtii (Table S1).
Gene loss in the Helicosporidium genome is significantly concentrated in photosynthesis-related pathways, and even here gene loss is surprisingly sparse given its non-photosynthetic, parasitic nature. The Helicosporidium genome encodes 56% of the plastid-targeted proteins predicted by the GreenCut2 database [19] (Figure 3), whereas both the photosynthetic Coccomyxa and Chlorella encode 96% of these proteins. The overall distribution of these losses in plastid metabolism is not random, but is concentrated on processes related to light-harvesting (Figure 4, Data S3, S4, S5, S6, S7, S8, S9, S10, S11). The heme synthesis branch of the tetrapyrrole pathway is complete in Helicosporidium, but the branch leading to the biogenesis of chlorophyll has been lost (Figures 4 and S2, Data S3, S4, S5, S6, S7, S8, S9, S10, S11). Similarly, Helicosporidium cannot synthesize carotenoids. It does not encode light-harvesting antenna proteins and photosystems I and II are completely absent, which parallels the loss of all photosynthesis-related genes in its plastid genome [20]. Surprisingly however, the Helicosporidium genome has retained an almost complete carbon fixation pathway despite lacking two major components rbcL/rbcS coding respectively for the large and small subunits of the ribulose-1,5-bisphosphate carboxylase oxygenase (RuBisCO) and ppdK, a pyruvate orthophosphate dikinase involved in pyruvate interconversions in the C4 pathway (Figure 4). Similarly, Helicosporidium has retained some proteins involved in electron transport and components of the F-type ATPase and cytochrome b6f (Figure 4). Starch and fatty acid metabolic pathways are more or less intact, as is the terpenoid biosynthesis pathway and its isoprenoid non-mevalonate MEP/DOXP synthesis branch. The SUF iron-sulfur cluster biosynthetic pathway is conserved as well, alongside its ISC/NIF mitochondrial counterpart [21], [22]. Not surprisingly the protein import and export systems are intact.
Outside the plastid, Helicosporidium metabolism shows little signs of significant reduction. The Helicosporidium genome encodes all proteins required for the biosynthesis of conventional aminoacyl-tRNAs, except selenocysteine. Despite its conservation across the green algae, we found no evidence for the presence of a selenocysteine synthase in the genomic and transcriptomic Helicosporidium datasets. The o-phosphoseryl-tRNA(sec) kinase also required for selenocystenyl-tRNA synthesis is missing too, however all other enzymes involved in the metabolism of selenocompounds are present. Helicosporidium appears incapable of endogenous RNA interference and, like the picoeukaryotes Ostreococcus tauri and Ostreococcus lucimarinus, lacks the genes coding for the Dicer and Argonaute proteins. These genes are found in single copies in the Chlorella and Coccomyxa genomes whereas three paralogous copies of DC1 and AGO1 are found in the Chlamydomonas genome [23]. In Chlamydomonas, this expanded set has been postulated to mediate the silencing of its numerous transposable elements [24]. The few losses observed in the remaining Helicosporidium pathways are either palliated by bypass enzymes or affect the synthesis or homeostasis of uncommon metabolites (Table S2).
The increased level of compaction and loss of photosynthetic genes in the Helicosporidium genome cannot explain its 2.5-fold reduction in genome size: other significant differences in gene content exist between the parasite and its free-living relatives. Given that Helicosporidium, Coccomyxa, and Chlorella were found to encode almost the same overall functional categories of genes in common with other green algae (Table S1), one possibility is that Helicosporidium possess fewer and/or smaller gene families. To investigate the complexity of gene families, we compared the Helicosporidium, Chlorella, and Coccomyxa predicted proteomes via an evolutionary gene network analysis [25]. A total of 100 connected components, excluding photosynthesis-related products, were found to exhibit a lower representation in Helicosporidium compared with its free-living relatives. These were manually curated into 9 functional categories based on annotation of the three gene sets (Tables S3 and S4, Figure 5). Interestingly, the functional categories where Helicosporidium has a reduced gene family complexity are for the most part not what are broadly defined as ‘operational’ genes where reduction that might be related to increased dependence on the host. Instead, the most drastic reductions in Helicosporidium are gene families in functional groups that are correlated with the size and complexity of the genome. The few exceptions are in amino acid and some other metabolic families, but most of the reduction relates to genes involved in chromosome packing, transcription, translation, post-translational modification, and protein turnover. Most surprisingly, Helicosporidium has not seen an increase in the complexity of transporter families, which might be expected of a parasite as host dependence grows; instead, this functional class is most reduced in Helicosporidium compared with Coccomyxa and Chlorella.
Taking an opposing view, to what gene families may have expanded due to the adaptation to parasitism, revealed a single obvious expansion of functional significance. A total of 14 genes with putative glycosyl hydrolase (GH) activity were identified throughout the Helicosporidium genome and were also found within its transcriptome (Table 2). All of these proteins appear to belong to the GH18 chitinase family, whereas plant chitinases normally come from the GH19 family. The Chlorella genome encodes two GH18 and one GH19 chitinase, and these are assumed to be involved in the remodelling of its cell wall, which has been experimentally demonstrated to contain chitin [17], [26]. The GH19 chitinase in Chlorella was acquired by horizontal gene transfer from a large DNA virus [17] and is not found in either the Helicosporidium or Coccomyxa genomes. Conversely, the Chlorella GH18 chitinases are found across the green algae. In Helicosporidium, the extra 12 copies appear to have been generated by recent duplication events. We found no evidence for the transfer of genetic material from insects in the Helicosporidium genome or transcriptome. A total of 13 of the 14 Helicosporidium chitinases contain the Dx2DxDxE/P motif essential for chitinolytic activity. Insect and bacterial chitinases with experimentally confirmed catalytic activity also contain one or more of three additional motifs: Kx6GG, MxYDx(x)G, and Gx3Wx2DxD [27]–[29]. Thirteen of the chitinases in Helicosporidium contain one or two of these additional motifs and demonstrate high conservation in their orientation (Table 2, Figure S3). However, in addition to H632_c1867p1 which appears to lack this domain, three show substitutions within the Dx2DxDxE motif and may be therefore be inactive.
Another class of proteins that might be expected to be relevant to the origin of parasitism in Helicosporidia are, unfortunately, the unidentified or ‘unique’ ORFs. To see whether these represented a high proportion of the genes in Helicosporidium, we identified predicted proteins of at least 100 amino acids that are not found in Coccomyxa and Chlorella, which resulted in 882 distinct proteins (Data S12). A small number of these proteins have clear or putative homologs in the Volvox and/or Chlamydomonas genomes but the vast majority are unique and have no known homologs. The few cases with homologues in Volvox and Chlamydomonas are sulfotransferases, glycosyltransferases or hydrolases with chitinase activity (as mentioned above), a 2-oxoglutarate Fe(II)-dependent oxygenase, and a cyclin. In contrast, the predicted proteins without known green algal homologs could not be assigned to any putative function in PFAM homology searches at an E-value cut-off of 1e-10. Five of these unknown proteins (H632_c233p3, H632_c338p0, H632_c531p0, H632_c1976p1, H632_c4072p0) display mid to low similarity with bacterial sequences of unknown function, but they are unambiguously encoded on contigs encoding clearly eukaryotic genes, and are therefore not bacterial contaminants. From the transcriptome, we can also conclude that the majority of these proteins are expressed, with 585 of the 882 found in transcriptome data at an E-value threshold of 1e-15.
We identified two transcripts sharing a high identity with viral sequences (E-value threshold of 1e-40), the closest relatives being Paramecium bursaria Chlorella viruses (PBCV) and Acanthocystis turfacea Chlorella viruses (ATCV). These two transcripts were also identified in the Helicosporidium genomic contigs, and both are assembled with Helicosporidium nuclear genes and are therefore not the result of viral contamination. The first transcript (a374428r16) codes for a dUDP-D-glucose 4,6 dehydratase that is also found in the genomes of Chlorella, Coccomyxa and various other green algae, and has been reported as an example of host to virus horizontal gene transfer (HGT) [30], [31]. The second transcript (a28443r121) encodes a D-lactate-dehydrogenase and is also found across the green lineage. Phylogenetic analyses including the closest D-lactate-dehydrogenase sequences clusters the green algal sequences with homologues from nucleocytoplasmic large DNA viruses that infect them, with strong bootstrap support (Figure 6). Overall, this suggests D-lactate-dehydrogenase represents another case of host-virus HGT in the green algal lineage.
A recurring theme in the evolution of parasite genomes and parasitism in general is reduction. In parallel with the more constructive process of developing sometimes sophisticated mechanisms to invade and take advantage of their hosts, parasite evolution generally involves the selective pruning of biological functions that are no-longer mandatory to survival in the host. This reduction has occurred independently many times during the evolution of parasites, and is generally reflected in their genomes, streamlined sometimes solely by a loss of genes, but other times also by an overall shortening of coding and intergenic regions as well as a loss of introns, resulting in much smaller and compact genomes than those of their free-living relatives [9]. In very few cases have photosynthetic algae made this transition, but the famous exception is the apicomplexans, including the malaria parasite Plasmodium. Here the same process has taken place and although the plastid has been retained, it has been reduced to a cryptic form that lacks most of its ancestral metabolic pathways and has retained nothing whatsoever related to carbon metabolism.
Helicosporidium breaks from these trends in significant ways. With an almost perfect conservation of the core green algal metabolic pathways, its genome is small, but can hardly be considered reduced, which may reflect its relatively recent adaptation to parasitism. Particularly surprising is the retention of an almost complete pathway for carbon fixation. Helicosporidium has lost nearly all genes associated with light harvesting, photosystems, and chlorophyll biogenesis, so how it uses carbon fixation pathways is an interesting question. Carbohydrate storage, and in particular the use of starch could be the main driver behind the retention of carbon fixation genes. Successful parasites often sequester resources from their host [32] and converting simple sugar molecules to large starchy polymers polarizes the directionality of carbohydrate exchanges. Exactly how efficient are the Helicosporidium permeases at sugar uptake and other energy-related metabolites from the immediate environment is unclear; considering that this is one of the gene families of reduced complexity, but such sequestration may be a prerequisite for survival.
The coding density of the Helicosporidium genome is only marginally higher than its closest relatives, and almost on par with those of a number of free-living prasinophytes (Table 1; Figure 1). Green algal genome sizes range widely across lineages (Figure S1), and expansions as well as contractions are likely to have occurred several times independently. Accordingly, we cannot currently distinguish between a reduction of DNA packaging/replication/translation pathways in Helicosporidium, an expansion of these pathways in Chlorella/Coccomyxa, or a combination of both. A better phylogenetic framework as well a greater sampling depth of green algal nuclear genomes will be required to polarize the directionality of this event, but the reduced gene family complexity observed in picoprasinophytes [33]–[35] does argue in favour of an expansion from a lean ancestral state.
Despite its small size, the Helicosporidium genome does feature one small expansion. Chitinases are uniformly rare in green algae where genomic data are available, suggesting this gene family is ancestrally limited in the green algae (which fits with its likely function in remodelling a minor component of the cell wall in some species). The expansion of chitinases in Helicosporidium over its sister taxa Chlorella and Coccomyxa therefore likely represents a unique adaptation that directly resulted from or even contributed to its parasitic lifestyle in insect hosts. The Helicosporidium infection mechanism consists of the oral uptake of cysts, dehiscence in the midgut lumen, ingression through the midgut, and entry into the hemocoel where the organism multiplies. In a number of insect parasites, including the malarial parasite Plasmodium, the presence of exogenous chitinases from the GH18 family within the arthropod midgut tract is either mandatory for infection or associated with increased pathogenicity by allowing pathogens to pass through the peritrophic membrane [36], [37]. Disruption of chitinase activity with blocking agents interferes with the sporogonic development of malarial parasites, which is restored upon addition of exogenous chitinases [38]. One may speculate that the Helicosporidium chitinases serve multifunctional roles and operate at various stages in vivo. Potentially, chitinases sequestered in the cysts are activated by insect digestive proteases, and then digest the cyst wall to initiate the release of the invasive filament cell in the gut lumen. Alternatively, the chitinases released by the ovoid cells, may loosen the chitin matrix of the peritrophic membrane, allowing the ingress of the filamentous cell into the ectoperitrophic space. It is important to note that members of the GH18, in addition to binding to and digesting the chitin can also target various GlcNAc-containing glycans that comprise various exocellular matrices including insect basement membranes. Binding to such substrates may aid in the establishment of infection on the hemocoel associated tissues. Finally the production of these enzymes could soften the exoskeleton, which could play a role in the egress of the infectious cyst stage from diseased insects.
Vegetative cells of the Helicosporidium sp. (ATCC50920) a parasite of the black fly Simulium jonesi [1], [15] was propagated in stationary cultures of sabouraud dextrose broth for five days at 27°C. Cells were harvested by centrifugation (5,000 rcf for 10 min) and pellets resuspended in a minimal volume sterile H2O and used for nucleic acid extraction. For genomic DNA preparation, a total of 2×109 cells were suspended in the yeast lysis buffer (Epicentre Biotechnologies, Madison WI) and homogenized with Bead Beater technology. The extracted nucleic acid phase was treated with DNase free RNase and subjected to chloroform phenol extraction, precipitated with ethanol, and suspended in TE buffer. A total of 14.1 µg high molecular weight DNA were recovered and submitted for sequencing. For total RNA extraction the resuspended harvested cells were immediately added to liquid N2 and ground with mortar and pestle to break the outer pellicles. The resulting frozen cell powders were processed initially with TRizol Reagent then processed with Purelink RNA Mini kit (Ambion). Column eluants were treated with RNase-free DNase and analysed with the 2100 Bioanalyzer (Agilent Technologies, Inc). Samples (10 µg) producing RIN values of 8.9 to 9.2 were selected for subsequent sequence analysis (see below).
Total DNA and RNA from Helicosporidium sp. ATCC 50920 (mitochondrial, plastid and nuclear) were sequenced by Fasteris SA (Plan-les-Ouates, Switzerland) using the Illumina platform. Two independent DNA sequencing runs were performed. In the first, 18,618,066 reads (54-bp paired ends; 323-bp inserts; average standard deviation, 19) totaling 1,005,375,564 bases were sequenced with the Illumina GA-IIx platform and the Chrysalis 36 cycles v 4.0 sequencing kit. In the second, 17,110,904 reads (51-bp paired ends; 241-bp inserts; average standard deviation, 64) totaling 872,656,104 bases were sequenced with the Illumina HiSeq 2000 platform and the TruSeq chemistry. Total RNA was sequenced using the Illumina directional mRNA-SEQ protocol with the Illumina HiSeq 2000 platform and the TruSeq chemistry. A total of 83,075,963 reads (100-bp single ends) were generated (8,307,596,300 bases total). Read quality for each Illumina data set was assessed with FastQC (version 0.10.1; Babraham Bioinformatics, Babraham Institute [http://www.bioinformatics.babraham.ac.uk]).
Paired-end reads were assembled de novo with Ray [39] 2.0.0 rc8 using iterative k-mer values of 21 to 31 on 8 processing cores (2 Intel Xeon E5506 CPUs at 2.13 GHz) with a maximum RAM allowance of 96 Gb. The resulting contigs were filtered by size with sort_contigs.pl (Advanced Center for Genome Technology, University of Oklahoma [www.genome.ou.edu/informatics.html]), and contigs shorter than 500 bp were discarded. The contigs of at least 500 bp were conserved for downstream analyses. The 500+-bp contigs were used as canvas to generate a BLAST [40] database with MAKEBLASTDB from the NCBI BLAST 2.2.26 package, the mitochondrial and plastid contigs were identified by BLAST homology searches using the mitochondrial (GenBank accession number NC_017841, [41]) and plastid (GenBank accession number NC_008100, [20]) genomes as queries, and separated from the nuclear contigs. Putative contaminants were assessed by homology searches against the NCBI non-redundant database.
RNA-Seq reads were filtered using a sliding-window quality approach with Sickle (Bioinformatics Core, University of California, Davis [https://github.com/najoshi/sickle]) under the default parameters, and the overall read quality reassessed after filtering with FastQC. Illumina adapter sequences were then removed from the filtered sequences using custom Perl scripts, and PolyA-tails were removed from the reads with TrimEST from the EMBOSS [42] 6.4.0 package. The filtered transcriptome reads were assembled with Trinity's Inchworm module with a maximum RAM allowance of 90 Gb (–JM 90G) on 8 processing cores (2 Intel Xeon E5506 CPUs at 2.13 GHz). Contigs were filtered by size with sort_contigs.pl and contigs of at least 250 bp were selected for downstream analyses. Transcriptomic contigs were mapped on the genomic ones with GMAP version 2014-01-21 [43] using the default parameters.
The nuclear contigs of at least 500-bp in length were sorted by size and renumbered incrementally using customs Perl scripts. Contigs were then processed with the Maker 2.11 annotation gauntlet [44], [45] using the Chlorella gene model as implemented in Augustus 2.5.5 [46]. The resulting GFF annotations files were processed, curated, and converted to GenBank annotations files using custom Perl scripts. Putative functions were assigned using homology searches against the PFAM database (E-value threshold of 1E-30; Table S5). Transposable elements were searched for with RepeatMasker [http://repeatmasker.org] using Repbase version 20130422 [47].
Illumina reads from the mitochondrial and plastid genome were first filtered out from the total dataset with bowtie 0.12.9 [48] using –un and –al the flags against indexes built from the organelle sequences. Filtered nuclear reads were then mapped with bowtie against the 5,666 contigs (≥500 bp) with the –S flag, and the coverage estimated from the SAM file with Tablet 1.12.12.05 [49] and the coveragestat.py python script. The genome size was then estimated using the following formula: [# of reads X read length]/coverage.
KEGG metabolic pathway maps for the green algae Chlamydomonas reinhardtii, Volvox carteri, Ostreococcus tauri and Ostreococcus lucimarinus were retrieved from the KEGG pathway databases [50], [51], the proteins sorted accordingly, and then used as queries for homology searches against the Helicosporidium, Chlorella and Coccomyxa proteomic, genomic and transcriptomic datasets (the Chlorella and Coccomyxa data was retrieved from the JGI website). BLASTP and TBLASTN searches were performed using E-value thresholds of 1E-10 and 1E-05, respectively. Genes not found in searches against any of the three datasets were considered absent from the corresponding organism. Network analyses were performed according to [25]. Specifically, all possible edges were drawn between pairs of genes if their reciprocal BLASTP comparisons to one another met all of the following conditions: E-value<1E-10, minimal hit identity >20, at least 20% of the shortest gene's length had identical residues in the match, and the hit length >20 amino acids. The network was then filtered to include underrepresented Helicosporidium genes compared to Coccomyxa and Chlorella. Functional annotations for the genes comprising each connected component (GenBank, KOG, KEGG, Interpro [52], and Pfam) were used to characterize each connected component by its inferred biological function. Plastid-targeted proteins from GreenCut2's Table S2 [19] were extracted from the corresponding Chlamydomonas reinhardtii (version 3.1) and Arabidopsis thaliana (http://www.arabidopsis.org/) protein catalogs and converted to custom BLAST databases with MAKEBLASTDB from the NCBI BLAST package. Helicosporidium, Chlorella and Coccomyxa were searched independently against both GreenCut2 databases with BLASTP (proteins) and TBLASTN (genome and transcriptome) using E-value thresholds of 1E-10 and 1E-05, respectively.
Putative glycosyl hydrolases identified in the Helicosporidium genome were annotated for catalytic and chitin binding domains using SMART 7 [53] and endo-proteolytic sites often located within developmental insect chitinases were identified with ePESTfind [54]. The glycosyl hydrolase catalytic domains were annotated manually for the presence and orientation of key amino acid motifs. Secretory signal motifs were searched for with TargetP 1.1 [55] and PredAlgo [56].
Amino acid sequences retrieved from GenBank were aligned with the L-INS-I algorithm from MAFFT 7.029b [57]. Phylogenetic models were selected with ProtTest 3.2 [58]. Maximum Likelihood phylogenetic reconstructions were performed with PHYML 3.0 [59] under the LG+Γ4+I model of amino acid substitution [60].
The Helicosporidium data was deposited at DDBJ/EMBL/GenBank under NCBI BioProject ID PRJNA188927 and accession AYPS00000000. The version described in this paper is version AYPS01000000. The predicted proteins and RNAs are also available in Data S13 and S14, respectively. All custom Perl scripts are available on GitHub (https://github.com/JFP-Laboratory).
|
10.1371/journal.pntd.0003643 | Gut Instincts: Knowledge, Attitudes, and Practices regarding Soil-Transmitted Helminths in Rural China | Soil-transmitted helminth (STH) infections affect more than two out of every five schoolchildren in the poorest regions of rural China, an alarmingly high prevalence rate given the low cost and wide availability of safe and effective deworming treatment. Understanding of local knowledge, attitudes, and practices regarding STH infection in rural China has until now, been sparse, although such information is critical for prevention and control initiatives.
This study aims to elucidate the structural and sociocultural factors that underlie high STH infection rates as well as explain why deworming treatment is rarely sought for children. In-depth, qualitative interviews were conducted in six rural villages in Guizhou Province; participants included schoolchildren, children’s parents and grandparents, and village doctors. Data analysis exposed three predominant reasons for high STH prevalence: (1) lack of awareness and skepticism about the high prevalence of STH infection, (2) local myths about STH infection and deworming treatment, and (3) poor quality of village health care.
The findings from this study reveal reasons for why deworming treatment is not sought, and inform specific recommendations for a deworming intervention that can more effectively address underlying barriers to deworming in areas of persistently high STH infection rates. The main barrier to seeking STH treatment is not availability or cost of the drugs, but rather the lack of impetus to seek the drugs. A comprehensive nationwide deworming program in China should involve annual provision of free deworming treatment in village clinics or schools, distribution of culturally appropriate educational materials to inform children and families about STH infection, and improvement of the quality of health care delivered by village clinicians.
| Soil-transmitted helminths (STHs) are parasitic intestinal worms that infect more than two out of every five schoolchildren in rural China, an alarmingly high prevalence given the low cost and wide availability of safe and effective deworming treatment. Understanding of local knowledge, attitudes, and practices regarding STHs in rural China has until now, been sparse, although such information is critical for prevention and control initiatives. This study elucidates the structural and sociocultural factors that explain why deworming treatment is rarely sought for schoolchildren in poor villages of rural China with persistently high intestinal worm infection rates. In-depth, qualitative interviews were conducted in six rural villages in Guizhou Province; participants included schoolchildren, children’s parents and grandparents, and village doctors. We found evidence of three predominant reasons for high STH prevalence: lack of awareness and skepticism about STHs, local myths about STHs and deworming treatment, and poor quality of village health care. The findings have significant relevance for the development of an effective deworming program in China as well as improvement of the quality of health care at the village level.
| Soil-transmitted helminths (STH) are a group of parasitic intestinal worms that can infect humans through ingestion of parasite eggs or skin contact with motile larvae. Four STH species are of particular significance in public health: roundworm (Ascaris lumbricoides), whipworm (Trichuris trichiura), and two species of hookworm (Necator americanus and Ancylostoma duodenale) [1]. As of June 2013, it was estimated that more than one billion people around the world are infected with at least one of these four species [2].
Infections with Ascaris lumbricoides, hookworm and whipworm are often asymptomatic, but can cause consequences such as anemia and other nutritional deficiencies, stunted growth, and cognitive impairment in the long run [3]. Infections with A. lumbricoides can cause abdominal pain, lactose intolerance, and decreased absorption of vitamin A and other nutrients. Severe infection with whipworm can cause inflammation at the site of attachment in the intestines and result in colitis and rectal prolapse. Infection with hookworm may lead to intestinal blood loss that results in iron-deficiency anemia [4]. Chronic and intense STH infections contribute to malnutrition and other significant consequences for growth and cognitive development, primarily in children [4, 5]. The diverse and non-specific nature of the clinical manifestations of STH infection hinders patients and even clinicians from making a definitive diagnosis without a stool sample.
Anti-helminthic drugs can effectively treat STH infections in afflicted individuals as well as be utilized in mass drug administration among populations of children living in STH-endemic areas [4]. Albendazole, the most common pharmaceutical treatment used to treat STH infection, is a broad spectrum antihelminthic with high cure rates and fecal egg count, although efficacy varies among the different STH species. A review of seven trials testing the efficacy of albendazole among infected schoolchildren in different endemic areas found cure rates of 98.2%, 87.8%, and 46.6% for A. lumbricoides, hookworm, and T. trichiura respectively, and fecal egg count (FEC) reductions of 99.5%, 94.8%, and 50.8% for A. lumbricoides, hookworm, and T. trichiura respectively [6]. Side effects from albendazole are minor and relatively rare, and it is safe for use in mass drug administration programs [7]. The low cost of albendazole facilitates its wide availability in many developing countries, including large developing countries such as China and India. This in turn increases the cost-effectiveness of large-scale deworming initiatives.
Historically, STH infections have been a longstanding public health challenge in China. In the 1950s and 60s, the Chinese government recognized the problem and took steps to control high infection rates, integrating STH control measures into the rural health care system [8]. Unfortunately, such health measures have since been discontinued, and the problem of STH infection has re-emerged with evidence of increased prevalence over the past several decades, especially in impoverished and remote communities [9].
Recent studies conducted in China have highlighted both high STH prevalence as well as disparities in infection prevalence between rural and urban areas [10]. In 2010, researchers at Stanford University, the Chinese Academy of Sciences, and the Chinese Center for Disease Control and Prevention found that over 40 percent of school-aged children in poor regions of rural Guizhou Province were infected with at least one of the four most common STH species [9]. These high rates of infection were confirmed by a follow-up survey in 2013 conducted by the same research team [5].
Given the evidence of high STH infection rates in China—where safe, effective, and affordable treatment is available—the goal of this qualitative research study is to understand why there is such a high prevalence of these infections. In this paper, we investigate knowledge, attitudes, and practices regarding STHs in rural China to reveal the sociocultural and structural factors underlying the persistently high infection prevalence. We also seek to understand why so few individuals in rural communities seek deworming treatment. We anticipate that the findings will be valuable for informing the design and implementation of effective deworming campaigns in China as well as broader public health efforts.
This research was approved by the Human Subjects Committee at Stanford University and by the appropriate authorities at the Chinese Centers for Disease Control and Prevention. All relevant research procedures adhered to the guidelines of both institutions. Prior to conducting the interviews, we ensured that the participants understood the information given in the written consent process, which was reviewed and approved by the ethics committee of the Institutional Review Board at Stanford University in Stanford, California (Protocol ID 25027), as well as the Institutional Review Board at Sichuan University in Chengdu, China (Protocol ID 2013005–02). All study participants received treatment for STH infection at the conclusion of the study.
The study was conducted in Guizhou Province, located on the eastern part of the Yunnan-Guizhou Plateau in southwest China. Guizhou's population, estimated to be about 35 million in 2011, is demographically diverse with ethnic minority groups making up more than 37 percent of the population [11]. Guizhou Province is the second poorest of China’s 31 provinces, with most people living in extremely remote mountainous areas that lack adequate road infrastructure. Agriculture is the main occupation of most Guizhou inhabitants, and farmers in Guizhou often live in poverty with poor access to basic health services [11].
The study area included six villages in four different townships in rural Guizhou Province (Fig. 1. Map of the study area and location of the six villages in Danzhai, Guizhou, China). Villages within Guizhou Province were chosen for this study based on inclusion in the research team's ongoing randomized controlled trial and accessibility over several days to conduct the fieldwork [5].
In total, 49 interviews were conducted: 23 elders, 23 children, and three village doctors. The elders were either parents or grandparents of the children who participated in our study. Children in each village were chosen randomly, with their ages ranging from 9 to 12 years old. Among the children, 12 were boys and 11 were girls. In the six villages that we visited, twenty households were of the Miao minority ethnic group while three were of the Shui minority ethnic group.
STH infection status (positive or negative) was determined at the laboratory of the county CDC using the Kato-Katz smear method on stool samples collected from schoolchildren. The study team collected one stool sample per day from each child for two consecutive days; two smears were taken from each stool sample for testing. Children were considered positive for STH infection if either one of their stool samples tested positive for one or more types of STH. Village level infection rates ranged from 13 to 57 percent (Table 1. STH infection prevalence by village in Danzhai County, Guizhou).
In our study sample of 23 children, 8 tested positive for infection with at least one of the four STH species (roundworm Ascaris lumbricoides, whipworm Trichuris trichiura, and hookworm Necator americanus and Ancylostoma duodenale) and 15 tested negative for infection. The researchers were blind to the children's infection status at the time of the interview.
Qualitative methodology for this study was comprised of in-person interviews based on semi-structured questionnaires. The interview questions consisted of an extensive list of open-ended questions that prompted individuals on various themes related to STH infection and treatment. The interview guide began with obtaining initial demographic information about the family members. The questions then progressed into inquiries about knowledge, attitudes, and practices regarding STHs and deworming treatment.
The interview protocol questions varied depending on whether the interviewee was a child, a parent/grandparent of a child, or a village doctor. The most extensive interviews were conducted with the parents and grandparents of the children, with several open-ended questions surrounding four topics: children's overall health and hygiene; opinions about the village doctor; knowledge about STH infection, prevalence, and health consequences of infection with STHs; and attitudes and practices regarding seeking of deworming treatment. Questions for the children focused on their knowledge, or lack of knowledge, about STHs. Questions for the village doctors aimed to assess the extent to which they were knowledgeable about STHs, whether deworming treatment was available in the clinic, and how often they prescribed deworming treatment to patients.
The interviews were conducted in both Mandarin Chinese and the Guizhou dialect of Chinese. One member of the research team in the field was a native of Guizhou Province, and clarified translations to the rest of the team. The interview protocol served as the primary guide, and it was ensured that all sections were covered. Some flexibility was utilized in following up on interviewees’ statements with additional probes and questions. Participants in the study were not paid monetarily, but each household surveyed was given a small gift of a hand towel (valued at around 3 USD) in gratitude for their participation.
Extensive field notes and transcriptions of audio-recorded interviews were closely analyzed using the constant comparative method described by Strauss and Corbin, in which codes from the data were constantly being compared to other identified codes to elucidate similarities, differences, and patterns in our collected data [12]. The coding process consisted of several readings, after which an initial process of open coding was performed on the detailed field-notes and transcripts, followed by focused coding, in which we extracted subcodes that emerged from prominent or recurring themes, trends, and ideas in the data.
Interview data from parents, children, and village doctors were also supplemented with memos and field observations in the households and villages. Thus, analyses from multiple data sources were synthesized to formulate the most accurate and complete representation possible of the knowledge, attitudes, and practices regarding STH infection and deworming treatment in rural Guizhou.
Three predominant reasons for high STH infection prevalence emerged from analysis of the data: lack of awareness about STHs; local myths about worms and deworming, and poor quality of village health care.
Parents and grandparents are both unaware and highly skeptical of the fact that STH infection is common in children today. In response to inquiries about how children become infected with STH, the most common response was “I don't know.” (Table 2. Frequency of Household Interview Responses)
A few parents seemed to have a slightly better recognition of possible transmission, responding that perhaps children become infected when they eat unclean, raw foods, or when they play outside in the fields and get their hands and feet soiled.
It should be noted that these responses were still quite vague, and seemed more like guesses than informed statements of known fact. Moreover, from the interview questions regarding health and hygiene practices in the household, all children admitted to drinking water straight from the tap and going barefoot while playing both indoors and outdoors. While some of the elders recognized that these behaviors could potentially be linked to risk factors for STH infection, they were not proactive about discouraging these behaviors among their children.
In addition, some parents and grandparents noted their perceived symptoms of STH infection, which included loss of appetite, yellow face, and teeth-grinding.
Similar to the elders' statements about STH transmission, these responses likewise seemed to be vague guesses about physical manifestations linked to general "sickness" rather than informed statements or observations corresponding to STH infection.
When asked whether they thought that their children were currently infected with STH, most parents responded with confident denial.
When asked to approximate the prevalence of STH in the village by estimating how many children out of a randomly chosen group of five village children would be infected, the majority of the parents and grandparents responded that it would be unlikely that any of the children would have STH infection.
However, when asked to estimate the frequency of STH infection in the villages when they were young children, the parents responded that most, if not all, of their peers back then were likely to have been infected with STH. In fact, many of them recalled having STH infections themselves. They believe that STH infections are a disease of the past because the quality of living conditions in the village has improved since their childhood years.
When asked to consider their reactions to the hypothetical situation that their child was in fact, infected with STH, the parents responded without hesitation that they would be very concerned. They stated that their response would be to take their child to the doctor and that they would want their child to be treated.
The responses from the parents about how they would respond if they knew that their child contrasted with the reality of the situation. The problem is that the parents and grandparents have little to no knowledge about STH infection and are highly skeptical that their children could possibly be infected. The knowledge gap serves as the primary barrier towards the seeking of deworming treatment.
Field interviews with parents, grandparents, children, and village doctors revealed insight into the knowledge, attitudes, and practices surrounding STH in rural China. The three major reasons identified in the Results section provide a comprehensive understanding of the sociocultural and structural factors that appear to contribute to high STH prevalence among schoolchildren in rural Guizhou.
First, a lack of knowledge among parents and grandparents about intestinal STH infection, prevalence, and prevention precludes them from seeking deworming treatment for their children. Elders are unlikely to suspect that their children are infected, primarily due to widespread skepticism about the high prevalence of STH infection in villages today, a paradigm that hinders them from seeking deworming treatment for their children.
Many elders stated that STH infections were much more common when they were children; their belief that STH infections are a problem of the past contributes to their skepticism that their child could possibly be infected. One possible reason for this belief might have been that the intensity of worm infection was higher among the population when the parents were children—this might have led to more obvious symptoms. Due to the very sparse data that is available regarding STH infection from past decades, however, such speculations remain unproven. Parents and grandparents seemed fairly candid about discussing the commonality of having worms in their villages when they were young, and even about having had worms themselves. Therefore, it seems that stigma did not create significant bias, and that elders were truly unaware of the possibility that their child had STH infection. Interestingly, knowledge that STH are common in piglets is widespread, and deworming of piglets was performed on a regular basis among all of the households in our study.
Second, myths about STH infections and deworming treatment, such as the false beliefs that STH are essential to people's digestion and that deworming medicine harms children's future fertility, serve as barriers to the idea that children will benefit from improved health upon receiving deworming medicine. Such myths were more common among older generations; this impacts children directly because grandparents have become the primary caretakers of the majority of the children in rural China today due to the phenomenon of left-behind children.
Third, the absence and overall unreliability of local doctors in the villages—who themselves have faulty knowledge about STH infection, prevalence, and treatment—serve as the most direct barrier to the seeking of STH treatment. The lack of reliable access to knowledgeable medical professionals who could provide deworming medicine was an especially significant obstacle in remote villages that were located hours away from the township health center.
The results from this qualitative study supplement our understanding of the myriad factors that contribute to high STH prevalence in rural China. This information serves to inform health policymakers about how to approach and design more effective and comprehensive deworming interventions.
First, the finding that parents are skeptical of the high prevalence of STH infection among children in the villages points to the need for greater awareness and health education. Parents, grandparents, and village doctors were uninformed about STH prevention, manifestations, and treatment. The rural Chinese health care system recognized STH as a public health problem and prioritized regular deworming of children from the 1960s to the 1980s. The subsequent disappearance of STH from the national health agenda likely facilitated the notion among adult generations that STH infection has diminished or disappeared as a problem in children today. Redirecting attention towards the problem of STH infections in rural China may provide the necessary stimulus for individuals to realize and internalize the continued significance and seriousness of the problem.
The practice of regularly deworming piglets, which was common among all households interviewed, portends significant implications for a public health intervention. The majority of households in rural China raise at least one or two pigs each year as a critical source of income [13]. From the interviews, it seemed that parents practiced regular deworming of their piglets for two simple reasons: first, because they receive free deworming pills every time they purchase pig feed, and second, because they were told that the pills would produce faster-growing, healthier pigs. If village doctors were to consistently provide parents with free deworming pills twice a year, they could prompt an analogous paradigm shift in which parents make two realizations to encourage deworming as a pervasive practice: first, STH infection is common in children (as much or even more so than in piglets), and second, deworming leads to improved child health and growth.
The problem of distant parent-child relations preventing children from opening up to their caregivers about their health symptoms is rooted deeply in sociology and therefore, more challenging to address through health policy. However, the implication of this finding is that regularly administering deworming treatment to children in school or at home (by village doctors or trained local health personnel, like barefoot doctors) should be preferred to an alternative approach that relies on caregivers themselves to take the initiative to seek deworming treatment.
The discovery of myths pertaining to STH infection and deworming medicine (i.e. that helminths are necessary for digestion and that deworming medication harms fertility) presents an extra dimension to the lack of accurate knowledge about STH infection and treatment. The educational component of a comprehensive deworming intervention should actively debunk these myths in a culturally sensitive way while also presenting factual information about STH infections and the benefits of treatment. Parents and grandparents should be cautioned that their beliefs in myths about STHs and deworming treatment are unsupported by scientific and medical evidence, and may be preventing them from properly attending to their child's health.
In summary, an understanding of parents’ and grandparents’ attitudes towards STH and deworming treatment indicates that the main barrier to seeking treatment is not availability or cost of the drugs, but rather the lack of impetus to seek the drugs. A successful deworming intervention can be designed to work around this barrier, firstly by providing accurate information, but also by tasking village doctors or community health workers with administering albendazole in the households, rather than relying on compliance by the elders or children themselves.
Furthermore, witnessing the absence and poor reliability of village doctors in rural Guizhou highlights the need for improvement in the local health care system. The New Cooperative Medical System (NCMS) requires all villages to have a designated village doctor, hence the "assignment" of individuals in the community to be the village doctor in the cases where none is present [14]. Despite the NCMS requirement, we were unable to locate village doctors in three out of the six villages that we visited. Lack of access to an adequately trained village doctor and, by extension, to deworming treatment, serves as the final barrier to accessing treatment for children. Even when village doctors were present, they themselves were uninformed about STH infections and some did not carry albendazole in their clinic.
Greater attention should be directed towards structurally improving the state of the rural health care system in China as well as increasing the competence of local doctors. In addition, stricter oversight—by township and county officials—over village doctors and the medications that are stocked in their clinics would improve the reliability of doctors and encourage more individuals to seek health care at the local level.
The study sample was comprised of twenty-three households and three village clinicians in six villages across four townships in Guizhou Province. While we attempted to include villages that had variable characteristics in terms of population size, geographical location, density of households in the village, and proximity to the nearest township center, the results of this study may not be representative of all households across all of the different villages in rural Guizhou Province. However, the diverse sample of households provided a collection of perspectives to inform conclusions that can be reasonably applied to poor rural households in Guizhou Province.
While we do have information on prevalence of STH infection in the sample areas, we do not have information on the intensity of infection in these areas, which could have enlightened us as to whether infected individuals would be likely to have experienced acute symptoms.
Despite adherence to a carefully designed and meticulously reviewed interview protocol, qualitative interview data has the potential to be influenced by inherent limitations: recall bias, social desirability bias, and possibly even the intentional misinformation or withholding of information on behalf of the participants. We attempted to minimize social desirability bias and intentional misinformation as much as possible by phrasing our questions carefully. For example, we asked adults what they perceived the deworming practices of their neighbors to be, in addition to their own practices, in case families wished to protect their own practices (no significant distinction was noted). Moreover, when appropriate, all interviews were all conducted in the local dialect in an attempt to build trust between the research team and the families.
The high prevalence of STH infections among millions of schoolchildren is a serious public health problem in rural China today. The findings of this study fill a critical gap in the current literature regarding the complex combination of factors that contribute to high STH burden in rural China despite the availability of effective and affordable treatment.
First and foremost, there is a lack of both knowledge about STH and awareness of high infection rates in the villages, which prevents parents and grandparents from suspecting the problem of STH infections in their children. In the rare case that caregivers do realize that children in the household have STH infections, myths and false local beliefs deter and discourage them from seeking deworming treatment. The final barrier lies in the inadequate village health care system, in which village doctors are absent from their clinics or are themselves misinformed about STH. This makes deworming medication difficult to obtain even when caregivers realize that their children are infected with STHs and are willing to seek treatment.
The results of this study suggest that a comprehensive deworming program to reduce STH infection rates in rural China should include the following components: free deworming treatment that is provided annually either through schools or village clinics, educational materials that provide accurate and necessary knowledge about STH, an emphasis on the impacts of deworming on children's educational attainment and future financial prospects, education about STH infections through community engagement, cultural sensitivity when overturning local myths about STH infections and deworming treatment, and lastly, greater efforts towards improving the quality of the village health care system.
|
10.1371/journal.pcbi.1003967 | Simulating the Complex Cell Design of Trypanosoma brucei and Its Motility | The flagellate Trypanosoma brucei, which causes the sleeping sickness when infecting a mammalian host, goes through an intricate life cycle. It has a rather complex propulsion mechanism and swims in diverse microenvironments. These continuously exert selective pressure, to which the trypanosome adjusts with its architecture and behavior. As a result, the trypanosome assumes a diversity of complex morphotypes during its life cycle. However, although cell biology has detailed form and function of most of them, experimental data on the dynamic behavior and development of most morphotypes is lacking. Here we show that simulation science can predict intermediate cell designs by conducting specific and controlled modifications of an accurate, nature-inspired cell model, which we developed using information from live cell analyses. The cell models account for several important characteristics of the real trypanosomal morphotypes, such as the geometry and elastic properties of the cell body, and their swimming mechanism using an eukaryotic flagellum. We introduce an elastic network model for the cell body, including bending rigidity and simulate swimming in a fluid environment, using the mesoscale simulation technique called multi-particle collision dynamics. The in silico trypanosome of the bloodstream form displays the characteristic in vivo rotational and translational motility pattern that is crucial for survival and virulence in the vertebrate host. Moreover, our model accurately simulates the trypanosome's tumbling and backward motion. We show that the distinctive course of the attached flagellum around the cell body is one important aspect to produce the observed swimming behavior in a viscous fluid, and also required to reach the maximal swimming velocity. Changing details of the flagellar attachment generates less efficient swimmers. We also simulate different morphotypes that occur during the parasite's development in the tsetse fly, and predict a flagellar course we have not been able to measure in experiments so far.
| Typanosoma brucei is a uni-cellular parasite that causes the sleeping sickness, a deadly disease for humans that also occurs in livestock. Injected into the mammalian host by the tsetse fly, the trypanosome travels through the blood stream, where it proliferates, and ultimately can be taken up again by a fly during a bloodmeal. In the tsetse fly, it continues its development with several morphological changes to the cell body plan. During its life cycle, the trypanosome meets different microenvironments, such as the mammalian's bloodstream and the tsetse fly's midgut, proventriculus, foregut, and salivary gland. The cell body of the trypanosome has the shape of a spindle along which an eukaryotic flagellum is attached. We have developed an accurate, in silico model trypanosome using information from live cell analyses. Performing computer simulations, we are able to reproduce all motility patterns of the blood-stream form in typical cell culture medium. Modifying the cell design, we show that the helical course of the flagellar attachment optimizes the trypanosome's swimming speed. We also design trypanosomal morphotypes that occur in the tsetse fly. Simulation science thereby provides an investigative tool to systematically explore the morphologcial diversity during the trypanosome's life cycle even beyond experimental capabilities.
| The African trypanosome, Trypanosoma brucei, is the causative agent of the deadly nagana and sleeping sickness in livestock and humans, respectively, which still persist as a main public health and economic problem for inhabitants in sub-Saharan Africa [1]–[3]. Trypanosomes are protozoan parasites with an elongated cell body shaped like a spindle. Their swimming mechanism is rather complex and subject of current research [4]–[6]. Trypanosomes are propelled by a beating eukaryotic flagellum attached along the cell body [7]–[9], which causes the whole cell body to deform and thereby creates a complex motility pattern [4], [5], [10]. Cell propulsion is crucial for parasite survival, morphogenesis, cell division, and infection in the mammalian host [11]–[15] and also for the life cycle in the tsetse fly [16]. Trypanosomes replicate in the tsetse fly before being transmitted into the bloodstream of the mammalian host during the fly's bloodmeal [17]–[19]. Moving through the mammalian blood vessels, they infect the skin, spleen, liver, heart, eyes, and ultimately the central nervous system. This causes irritability, speech problems, sleep disruption, and ultimately leads to death within weeks to months [20]–[23].
The different physical microenvironments, which trypanosomes encounter in the tsetse fly and the mammalian host, continuously exert selective pressure, to which the cells adjust with a variability in their architecture and behavior. For example, trypanosomes swim faster in the crowded environment of blood compared to the purely viscous culture medium [4] and remove surface-bound antibodies using hydrodynamic drag forces [24], or they reverse their swimming direction under different realizations of confinement such as pillar arrays or collagen networks in order to avoid becoming trapped [4]. Changes in cell architecture are most prominent during the development in the tsetse fly. However, the adaptions the cells experience with changing environment on their way through the tsetse fly are not well characterized.
Here, the cell goes through periods of proliferation, shape transformations, and migration through the midgut, foregut, proboscis, and salivary glands [17]–[19], [25], [26]. In more detail, once ingested by the tsetse fly with an infected blood meal from the mammalian host, the bloodstream form (BSF) of the parasite transforms into the procyclic form (PC) within the midgut lumen [19], [26]. Some of these PC parasites cross the peritrophic membrane of the gut, which separates the blood meal from the midgut epithelium, and migrate to the proventriculus in the anterior midgut. During this phase, they increase their body length, go through the long mesocyclic form of trypomastigotes [19], [27], and ultimately assume the long, slender form of the epimastigote cell [19], [26]. The parasites again cross the peritrophic membrane and continue their journey towards the salivary glands while undergoing an asymmetric cell division [17], [19], [26]. In the salivary glands the resulting short epimastigote cells attach to the microvilli of epithelial cells using their flagella. They evolve into the final free metacyclic form and are injected with the fly's proboscis into the next mammalian host [19], [28]. Thus, the entire life cycle consists of several striking morphological modifications and takes around 20–30 days.
Although the development of the different cell forms in the tsetse fly has been analyzed [17]–[19], [26], [28], the specific morphological adaptions the cells undergo in response to changing environments on their way through the fly are not well characterized. For example, despite recent experimental advances, a detailed analysis of how the trypanosome interacts with the tsetse host during the different stages of its life cycle, remains challenging due to technical constraints and time consuming procedures of in vivo experiments [19]. Furthermore, reliable cell cultures of the trypanosomal morphotypes in the tsetse fly have not been established yet. Therefore, relatively little is known about underlying physical mechanisms governing the life cycle of the trypanosome.
Here the predictive power of our computational approach to complex cell design comes in. Based on earlier work [6], in this article we present accurate, nature-inspired cell models which we have developed using information from live cell analyses but also by interpolating between known forms. The cell models account for the detailed geometry, elastic properties, and motility of the real trypanosomal morphotypes including the attached flagellum. Our best established model accurately simulates all motility modes of the blood stream form including forward and backward swimming as well as tumbling. Specific and subtle modifications in details of the flagellar attachment reveal how the blood stream form is optimized for swimming. Modifications of the general body plan allow to simulate different morphotypes of the parasite in the tsetse host. The results demonstrate the true predictive power of our approach for the quantitative analysis of complex cell morphology and motility in a fluid environment. By constructing in silico morphotypes and investigating them in computer simulations, we are able to guide the interpretation of experiments or access complex cell designs, which cannot be studied systematically by experiments alone.
To simulate and analyze the trypanosome's swimming mechanism, we constructed a refined elastic network model of the trypanosome based on experimental data from advanced video microscopy. A full description of the model is presented in the Section Materials and Methods. We simulated the fluid environment with a mesoscale simulation technique for solving the Navier-Stokes equation called multi-particle collision dynamics (MPCD) [29]–[32]. The trypanosome has a spindle-shaped elongated cell body with tapered ends. The cell body is about long, has a diameter of ca. at the thickest part in the posterior section, and becomes thinner towards both ends, in particular, towards the long anterior end [4], [5]. Correspondingly, our model trypanosome has circular cross sections of varying diameter and the cell surface is defined by a mesh of points which are connected by harmonic springs spanning also in the cross-sectional planes [see Fig. 1(a)]. Together with additional bending rigidity, we obtain a cell body with precisely controllable flexibility. The actuating flagellum is attached to the cell body, which distorts in response to the bending wave running along the flagellum [see Fig. 1(b),(c)]. To adjust the course of the flagellum on the cell surface, we applied results from a detailed morphometric analysis using fluorescence microscopy [4]. An example for a cell body with fluorescently labeled surface is shown in Fig. 1(d) and in S1 Video. The rendered cell surface in Fig. 1(e) highlights the course of the attached flagellum and in Fig. 1(f) the whole cell is rotated by about the horizontal. From a careful inspection of such images the following flagellar course for the cell model evolved [see Fig. 1(a)]: The flagellum originates from the flagellar pocket at the posterior end of the cell, follows a small straight segment and then wraps around the cell body with a left-handed half-turn. Altogether this needs a length of ca. [4]. The flagellum then follows a straight path along the thinning anterior part of the cell body and protrudes freely at the anterior end. In the experimental images of Fig. 1(f) and S1 Video, the helical segment of the attached flagellum is marked red.
We generate a sinusoidal bending wave along the flagellum, which runs from the thinner anterior tip of the cell body to the posterior end (tip-to-base beat). The bending amplitude decreases towards the posterior end to better match the shape changes of the real trypanosome. The wavelength , where is the length of the cell body, is adjusted to the real system and the experimental wave frequency sets the relevant time scale [4]. The cell body distorts in response to the flagellar wave, which generates translational motion in the anterior direction opposite to the wave direction (see S2 Video). Due to the helical flagellar attachment, the cell body assumes an overall asymmetric chiral shape [see Fig. 1(b),(c)] typical of a real trypanosome [see Fig. 1(d),(e)]. To quantify the chiral shape, we determined the centerline of the model cell body, reaching from the posterior to the anterior end, and calculated the torsion averaged over the full length and over several time periods. As shown in the Section Flagellar attachment optimizes swimming, the non-zero mean torsion at the flagellar winding angle clearly indicates the overall chiral shape. Therefore, it rotates counterclockwise about the long axis of the cell, when viewed in the direction of motion (S2 Video). Typically, for a full turn periods of the bending wave are needed in medium with the viscosity of blood [4]. Note, when the flagellar attachment is completely straight [winding angle ], the mean torsion and hence the rotational speed are zero. In order to obtain a realistic motility pattern, the simulation model was empirically optimized to meet real life conditions. We adjusted the flexibility of the cell body accordingly by changing the elastic properties of the surface mesh and tuned the bending amplitude of the sinusoidal flagellar wave. Ultimately, this led to a realistic realization of the motility pattern of an African trypanosome as Fig. 2 and Videos S3 and S4 demonstrate.
The elongated model trypanosome can be regarded as a long slender body moving in a viscous fluid. For such an elastohydrodynamic system one expects a dimensionless parameter called the sperm number [33]–[35] to determine the complex propulsive dynamics of our model trypanosome [6]. The sperm number compares viscous to bending forces and is defined as , where is the elastohydrodynamic penetration length, the perpendicular friction coefficient per unit length, the bending rigidity of the cell body, and the angular frequency of the driving wave. In Fig. 3 we plot the rescaled swimming velocity as a function of for two sets of shear viscosity which approximately fall on the same master curve. Moreover, in the range from to , corresponding to an increase in frequency by a factor of 7, we approximately identify the scaling or as predicted in [36] and in agreement with Ref. [6]. For larger values of a second scaling regime occurs, which we attribute to the fact that the model trypanosome no longer swims in the quasi-static regime. The cell body rotates with an angular frequency . The number of flagellar beats per full rotation of the cell body, , roughly scales as (inset of Fig. 3) in agreement with Ref. [6]. Beyond the quasi-static regime we find . For , assumes the value , which matches the experimental value of measured in medium with the viscosity of blood [4]. For this the resulting dynamics of the cell shape and the swimming pattern of the model trypanosome closely resembles the real swimming trypanosome as demonstrated in S3 Video.
We have designed a model trypanosome that very realistically reproduces the forward motion of the African trypanosome. We now test whether the model trypanosome can also show other motility patterns observed in experiments. Trypanosomes have the ability to reverse the direction of their flagellar bending wave and thereby swim backwards with a wave frequency , which is smaller compared to forward swimming by a factor of 0.6 [4]. Even waves travelling simultaneously from tip to base (forward swimming) and base to tip (backward swimming) are observed, especially in low viscosity fluids such as the standard cell culture medium [4]. With no predominant direction of the flagellar waves, this results in a tumbling motility pattern where the trypanosome constantly changes its swimming direction and typically produces no net translational movement at all. Tumbling is an important mechanism for cells to identify and swim along field gradients. It occurs in E. coli when the synchronized rotation of several flagella is perturbed [37] or when the two flagella of the algae Chlamydomonas beat out of synchrony [38]. Tumbling by two counterpropagating waves travelling along the trypanosome flagellum is an alternative strategy that we simulated with our model trypanosome. However, note that it is unclear if tumbling actually belongs to the in vivo behavior of the parasites, as they exhibit exclusively persistent directional swimming, when brought into surroundings corresponding to the confining situations in the bloodstream or in tissue [4]. In addition, there is no description of chemotactic abilities of trypanosomes so far.
We implemented bending waves travelling along the flagellum in both directions with increasing amplitude towards the tip. For both waves we chose the same wavelength . We kept the frequency of the tip-to-base wave fixed and varied the frequency or velocity of the base-to-tip wave. Fig. 4 plots the reduced swimming velocity versus the ratio of both frequencies, . For , where we did not implement any base-to-tip wave, the trypanosome performs its standard motion in forward direction. At a ratio of , the swimming velocity is reduced to half the value and in the interval between and 1.66 persistent swimming stops completely. For larger wave frequencies the swimming direction is even reversed as dictated by the base-to-tip wave. S5 Video shows both a real tumbling trypanosome in cell culture medium and the tumbling model trypanosome at . Both videos demonstrate with striking similarity the irregular motion and directional changes of a trypanosome. To quantify the directional persistence in the swimming motion, we determined the vector connecting both ends of the trypanosome. In the inset of Fig. 4 we plot the time-autocorrelation function for the orientation of the trypanosome, , where and means average over reference time and several simulation runs [39]. Tumbling at is indicated by a complete loss of orientational correlations after ca. three wave periods, whereas directional swimming at only shows a small decay, mostly due to the fact that the trypanosome does not swim on a perfectly regular helical trajectory, whereas small scale oscillations originate from periodic cell deformations. To conclude, our results demonstrate that disturbing the forward flagellar wave by a base-to-tip wave strongly affects the trypanosome motility pattern.
Trypanosomes perform sustained backward swimming with base-to-tip bending waves under conditions of confinement, i.e., in narrow spaces [4]. One can also force them to swim backward by inhibiting their forward motion. This is done when cells are depleted of the axonemal dynein motor protein DNAI1 by RNA interference against this protein. Cells missing a dynein outer arm can only produce base-to-tip flagellar waves [40] and thus constantly swim backwards in fluids with sufficiently large viscosities such as a culture medium with added methylcellulose solution which increases the viscosity to the value of blood viscosity [24]. Otherwise, the backward swimming is more erratic. We applied a sinusoidal bending wave to the model flagellum running from the posterior to the free anterior end. The wavelength is the same as for the forward swimming mode but the wave frequency is reduced by a factor 0.6. We observe backward swimming and rotation about the long axis as in real cells. Remarkably, the swimming pattern was efficient only under conditions of confinement, just like the behavior of wild type trypanosomes. In S6 Video we compare the simulated cell moving backwards in a confining tube with a real trypanosome swimming persistently backwards after inhibition of forward motion by RNA interference.
In this article we concentrate on simulating the swimming trypanosome in a pure viscous fluid. In a viscoelastic environment such as blood or a collagen network and also in pillar arrays trypanosomes swim faster compared to the purely viscous culture medium since they use the suspended obstacles to drag themselves forward [4]. As already mentioned, in such confining environments trypanosomes do not tumble but swim persistently forward [4], although, they also reverse their swimming direction when they become trapped [4]. We currently investigate trypanosome swimming under the confinement of microchannels and in the presence of fixed and moving obstacles to mimic blood cells or the microstructure contained in viscoelastic fluids and clearly observe the enhanced swimming speed.
We now use our model trypanosome to demonstrate how the flagellar attachment determined from video microscopy optimizes the motility pattern of the real trypanosome. In Fig. 5(a) we continuously tune the winding angle by which the flagellum wraps around the cell body from to well above the half-turn observed in the real cell. As before, the helical attachment begins after a short straight segment near to the flagellar pocket at the posterior end and then runs straight again towards the anterior end. Interestingly, the swimming speed plotted in Fig. 5(a) shows a clear maximum exactly at the half turn of the flagellar attachment. So the helical attachment seems to be optimized for the swimming speed. The helical attachment results in an overall chiral body shape which leads to rotational motion initiated by the flagellar wave [Fig. 5(b)]. The rotational motion then couples back to translational motion and enhances the swimming speed. A recent theoretical study of chiral microswimmers, driven by a torque, shows that the swimming speed is optimal, when the microswimmer has a bowlike shape rather than the form of a full screw such as the flagellum of an E. coli bacterium [41]. To quantify the shape of the model cell body, we determined its centerline and calculated from the local torsion and curvature values a mean torsion and curvature by averaging over the full cell length and several beating cycles of the flagellum. Details are given in the Materials and methods Section a). The results are plotted versus the winding angle in Fig. 5(c), while Fig. 5(d) shows the cell's mean end-to-end distance together with illustrative snapshots. The decreasing indicates the formation of a bow. In particular, for the mean curvature value shows that the whole body is bent on an arc while the mean torsion, as a measure for the strength of chiral distortions, is close to its maximum value. Together with the results from Ref. [41], this gives some indication why the swimming speed in our case becomes maximal for a winding angle around . Fig. 5(b) shows how the rotational speed of the model trypanosome about the longitudinal axis continuously increases with the winding angle , when the trypanosome becomes more chiral. Microscopic imaging reveals that the distortion of the real trypanosome at the anterior end is larger than at the posterior end. In our modeling of the trypanosome we take this into account by an increased bending flexibility of the anterior end but also by increasing the amplitude of the imposed flagellar bending wave. The inset of Fig. 5(b) illustrates the wave of the imposed bending angle for different growth factors , which is the ratio of the wave amplitudes at the anterior and posterior end, and is explained in the Materials and methods Section b). By adjusting the growth factor to a sufficiently large value [two curves in Fig. 5(b)], we can match the rotational velocity with the experimental value indicated by the error bar. This corresponds well with the approximate ratio of two inferred from microscopy images [4].
In Fig. 5(e) we demonstrate how the swimming speed depends on the position of the flagellar half turn along the cell body, where is the distance from the flagellar pocket. Our simulations show a reduction of swimming speed with increasing displacement which clearly correlates with a reduction of the cell's end-to-end distance . We plot the range of the oscillating as a function of in Fig. 5(f). Cells with a larger end-to-end distance are more elongated. They experience less drag in the fluid and, therefore, move faster. We thus confirm an experimental observation that trypanosomes with a larger end-to-end distance swim faster [10]. When the helical turn of the flagellum is shifted towards the more flexible anterior end, the cell body bends more easily and swimming is no longer optimal. S7 Video and Fig. 5(g) impressively demonstrate the relevance of the optimized cell morphology for an effective cell motility. The cell with optimized parameters [Fig. 5(g), left and S7 Video, bottom] shows the typical rotational motion of a trypanosome about its longitudinal axis and efficient swimming along a helical trajectory. In contrast, the cell with shifted helical turn [Fig. 5(g), right and S7 Video, top] moves much slower and on a path with much smaller pitch.
Our trypanosome model allowed us to generate and investigate in silico mutants by varying the position and winding angle of the helical flagellar turn. We thereby revealed that for optimal swimming performance the flagellum has to be attached precisely as in real trypanosomes. All alternative designs produced less efficient microswimmers. Having such in silico mutants of the trypanosome available, has the advantage to study their motility and morphology during swimming in full detail. This is a significant advance compared to the difficulties inherent in experiments that analyze three-dimensional movements with two-dimensional video data [4] or the effort needed to record three-dimensional swimming trajectories by holographic microscopy [42].
We have demonstrated that we are able to reliably simulate all motility modes of the blood stream form of trypanosomes. We now proceed further to model other cell morphologies and simulate their swimming behavior. Whereas the blood stream form is well characterized, much less is known about the different morphotypes the trypanosome assumes in the tsetse fly [19], [26]. These morphotypes are difficult to analyze in in vitro experiments and, as yet, there are no established cell culture conditions that enable the correct development of the fly stages. Therefore, creating appropriate in silico morphotypes will be an important tool to analyze structure and motility of all possible forms of the trypanosome life cycle, in particular, in the tsetse fly.
When taking a blood meal on an infected mammalian host, the tsetse fly incorporates the stumpy form of the bloodstream trypanosome, which elongates and transforms into the procyclic form in the fly's midgut. The trypanosomes cell body and flagellum are continuously elongated further in the midgut to assume the mesocyclic form, which moves to the proventriculus and becomes the long slender epimastigote form, which divides asymmetrically to produce short epimastigotes. These finally transform further into the metacyclic form, which can infect the mammalian host again. During the development of the epimastigote form, the flagellar pocket moves to a more anterior position of the cell body [19].
To model different morphotypes of the trypanosome, we tuned three morphological parameters: the position where the flagellum starts close to the posterior cell end, the cell length, and the length of the flagellum , which grows with the elongating cell body. To avoid a bending instability of the thin anterior part of the cell body and to make the posterior end stiffer, we increased the bending stiffness by a factor of two. For the wavelength of the bending wave we chose , as before, and also kept the wave frequency constant. Fig. 6 shows snapshots of several in silico morphotypes, which we discuss in the following. Starting at the top, Fig. 6(a) illustrates the model of the bloodstream form used in the previous simulations. We then generate a possible intermediate morphotype in the tsetse fly [see Fig. 6(b)], where we increase the total cell length by to and displace the flagellum with its helical half-turn by towards the anterior end. In Fig. 6(c) we illustrate an adjusted model for a mesocyclic form with a total length of , where the flagellum starts at a distance of from the posterior end and the winding angle of the helical turn is tuned to [see Fig. 6(c)], as explained below. Finally, elongating the cell model further towards the anterior end to a total length of and keeping the same attachment of the flagellum [Fig. 6(d)], results in a model that resembles an epimastigote form.
To model the mesocyclic morphotype, we started with a helical half-turn of the flagellum and observed that the cell moved slower than the real mesocyclic form in experiments. We attributed this to the stronger bending of the simulated cell body compared to the real cell. Already in Figs. 5(c) and (d) we have demonstrated that stronger bending decreases the swimming velocity. We therefore decided to decrease the winding angle of the helical flagellar turn, which indeed lowered the bend of the cell body or increased the mean end-to-end distance , as the inset of Fig. 7 demonstrates. In parallel with the smaller bend, the cell becomes more straight and hence its hydrodynamic friction decreases. This, in turn, increases the swimming velocity (see Fig. 7). At angles around the flagellar bending wave produces the most realistic swimming pattern compared to the swimming mesocyclic trypanosome in experiments (see S8 Video), where speed and end-to-end distance of the model and the real trypanosome agree with each other. Also the rotational velocity of the cell body, which is lower than in the blood stream form due to the smaller helical turn, agrees well with experiments. In order to reduce the bend of the model trypanosome, other modifications of the cell body such as varying the stiffness of the cell or the amplitude of the flagellar wave were not successful. So we think that the reduced helical turn makes a solid prediction for the morphology of the mesocyclic cell. Last but not least, S9 Video presents our swimming in silico version that resembles an epimastigote form.
Experimental methods for analyzing in detail the morphology and swimming pattern of trypanosome forms inside the tsetse fly are currently being established in order to gather high-speed light microscopy and 3D morphometric date analogous to Ref. [4]. The technically demanding confirmation of our simulation results will demonstrate the predictive power of simulations based on accurate complex cell designs.
In conclusion, we have designed and constructed an in silico trypanosome using information from live cell analyses. We simulated and analyzed its swimming pattern with the help of the mesoscale simulation technique called multi-particle collision dynamics. The in silico bloodstream form accurately reproduces the characteristic forward swimming together with the rotational motion about the long axis, as well as the trypanosome's tumbling and backward motion. Specific modifications in the flagellar course around the cell body reveal that the flagellar attachment in the real cell maximizes the swimming performance. We then modified our cell model to simulate different morphotypes of the trypanosome in the tsetse fly. In particular, a comparison with a swimming mesocyclic trypanosome in experiments predicts a winding angle of for the flagellar attachment and thereby makes a structural prediction for the cell morphology.
Our accurate cell modeling not only helps to explore design principles of real trypanosomes by performing specific modifications in the cell morphology, it also provides structural information which is not accessible with current experimental techniques. This demonstrates the predictive power of a sufficiently accurate in silico cell model. Similar to cell biology, we are able to generate in silico mutants of the trypanosome and thereby contribute new insights to cell morphogenesis during its life cycle. In future, we plan to explicitely explore the role of the attached flagellum during cell division.
To simulate the swimming trypanosome, we construct an elastic network model for the cell body of the trypanosome and couple it to its fluid environment, which we treat with a mesoscale simulation technique called multi-particle collision dynamics (MPCD), an efficient particle-based solver of the Navier-Stokes equations [29]–[32] and by now well established in numerous studies (see, for example, [43]–[54]). A first implementation of our trypanosome model was reported in Ref. [6] together with a study of relevant system parameters. However, for being able to accurately simulate a swimming trypanosome and to reproduce many features of the real system, we had to extend and refine this model in close collaboration of theory and experiment using information provided by live cell analyses. In concrete terms, compared to Ref. [6] we corrected the flagellar attachment and extended the flagellum beyond the cell body at the anterior end. We differently quantified the decrease of the axial bending rigidity towards the anterior end and newly adjusted the involved elasticity constants of our model.
In the following, we review in detail the construction of the model trypanosome and the attached flagellum. Here, we shortly summarize how we used experimental information and empirical fitting to set up the model. Both the cell body shape and the path of the attached flagellum along the cell body are taken from experimental data. The shape of the bending wave running along the flagellum is not obvious due to the flagellar attachment to the cell body. We therefore decided to choose a sinusoidal variation of the prescribed curvature which is supported by the deformations of the very thin anterior end visible in experimental images (see, for example, lower row of Fig. 2). Also, Fig. 2 in Ref. [55], where the beating free flagellum of closely related Leishmania and Crithidia species are shown, nicely illustrates the approximate sinusoidal flagellar shape. To better match the deformation of the whole cell body [4], we introduce an amplitude of the flagellar bending wave that increases from the posterior to the anterior end and is described by an empirical fit parameter. Finally, parameters to describe the rigidity of the cell body against bending and twisting were assigned such that realistic cell deformations occur, which suffices to accurately describe the swimming behavior of a trypanosome. The parameters are not taken from experiments but in constructing the cell model we tried to mimic the cell cortex of the trypanosome.
In the real cell, semi-flexible filaments called microtubules are attached to the cell membrane and run along the long axis of the cell body. They are linked to each other by proteins and therefore form a cortex that gives the trypanosome some stiffness, in particular, against bending [56]–[58]. The number of microtubules at a specific cross section of the cell body depends on the cell body diameter. It gradually reduces with the diameter towards both ends [56]–[58]. At the anterior, thinner end of the cell body, microtubules converge into a tightly closed tip and just a few microtubules reach the end, whereas at the broader posterior end many of them end at the same perimeter of the cell body, which creates an opening in the cortex [56]–[58]. Consequently the cell body becomes more flexible at the thinner part, particularly at the anterior end. Similar to red blood cells [59], [60], there are no filaments spanning across the cell so that it can deform easily. To implemented these characteristics, we constructed a model cell body for the African trypanosome on the basis of morphological data acquired from microscope images (Fig. 1 and Table 1). The cell body of the blood stream form has a total length of about , a relatively thick posterior end with a diameter of about 3 , and a very thin anterior end [4], [5]. Accordingly, we constructed a model trypanosome with a spindle-like shape whose surface is represented by an elastic network of vertices. The size and shape of the model cell body is adaptable and hence we are able to simulate completely different morphotypes. This is demonstrated for the blood stream form (Fig. 1 and Videos S2, S3, and S4) and the mesocyclic and epimastigote forms in the tsetse fly (Fig. 6, Fig. 7, S8 Video, and S9 Video).
As shown in Fig. 1(a), the vertices are arranged in circles along the long axis of the cell body. The circles or cross sections of the cell body are defined by 10 equally spaced vertices and are indexed from to in the blood stream form starting at the posterior end. The diameters were determined from microscope images using the graphical software Plot Digitizer and are listed in Table 1. The vertices on the circle and from neighboring circles are connected by Hookean springs. Lines along the cell body also resist bending so that the complete potential energy of the elastic network becomes (1)where is the potential energy of the springs, is the bending energy of lines of vertices running along the cell axis, and corresponds to the bending energy of the wave running along the flagellum. The harmonic spring potential provides membrane elasticity similar to that of a trypanosome,(2)where is the spring constant, the actual distance of two vertices, and the equilibrium length of the springs. For springs connecting vertices of neighboring cross sectional circles, , and the equilibrium lengths of the springs on a circle are with the radii from Table 1. In order to stabilize the cylindrical shape of the cell body and to approximately ensure constant area of the cell surface and constant volume of the cell body, we introduce diagonal springs between opposing vertices on a circle. Furthermore, we model the bending rigidity of the cell body by applying the bending energy(3)to each line of vertices running from the posterior to the anterior end [see Figs. 1(a), (b), and (c)]. Here, is the bending stiffness, and and are the actual and the equilibrium angles between two bond vectors, respectively. The equilibrium values are adjusted to give the equilibrium shape of the trypanosome. Since the bending stiffness of the real cell body progressively reduces towards the thinner body part, which becomes very flexible at the anterior end, we choose the bending stiffness at a given point along the body long axis proportional to the local cross section ,
(4)Here, and are the respective mean cross-sectional area and bending stiffness. This choice of helps to mimic the microtubule system of the trypanosome. In the following we choose spring constant and bending rigidity , where is thermal energy and is the characteristic length of the MPCD method as explained below in part c). The parameters are chosen such that the cell is sufficiently stiff to guarantee a constant cell length () and thermal fluctuations of the cell body are negligible.
To quantify the cell body distortion, we determine the centerline of the cell body from the centers of its circular cross sections. For this centerline we determine the local values of curvature and torsion and average them over the whole centerline and several beating cycles of the flagellum. The curvature is a measure how strong a curve is bent in the osculating plane and torsion measures how strongly a curve moves out of the osculating plane. The mean torsion is therefore a measure for the chiral distortion of the cell body. Since the centerline is defined by discrete points , we use a discrete definition for curvature and torsion [61]. We define the normalized tangent vector at point by and the binormal by . Then the local curvature and torsion become (5)where . To average out undulations of the cell body induced by the flagellar wave, we assign to each local curvature a sign. For this, we define the normal vector in the osculating plane. We start with defining a sign for the point . We keep this sign for the following as long as . When is encountered, the sign is reversed and the new sign is maintained as before as long as is satisfied. The sign of the torsion is given by the sign of . We then assign a positive for a local left-handed screw. The mean curvature and torsion follow by averaging the local values and over the whole centerline and over several beating cycles of the flagellum.
The flagellum, which is composed of a classical 9 + 2 microtubule axoneme and a paraflagellar rod, emanates from the flagellar pocket close to the posterior end of the cell body and runs along the long axis towards the very thin anterior end [4], [5], [12]. It is connected to the cellular cortex by connecting proteins [7]–[9] and appears as a thicker rope attached to the cell body in electron microscopy pictures [5], [12], [62]. We model the flagellum as a line with an additional bending potential connecting already existing vertices on the cell surface as indicated in Fig. 1(a). High-resolution microscopy reveals that the flagellum even extends beyond the anterior part of the cell body [see Figs. 1(d)–(f)] [4], [5], [12]. Here we use 3–4 additional vertices to extend the flagellum beyond the tip [see Fig. 1(a)] with modified stretching and bending constants. We give the first additional vertex a bending rigidity of , where is the total bending constant of the cell body at the anterior end, and progressively reduce it by a factor of 0.8 for the following vertices. We choose the stretching constant equal to . Because of the small bending rigidity of the free part of the flagellum, it can deform easily. Note the propulsive force of the trypanosome is significantly produced by the thinner part of the cell body and the free anterior piece.
The flagellum attached to the cell body is a typical eukaryotic flagellum driven by the collective motion of internal motors, which initiate a beating pattern along the flagellum. As S3 and S4 Videos demonstrate, there is a wave passing along the flagellum which distorts the whole cell body. To model this situation, we let pass a bending wave along the flagellum with constant frequency and wavelength, which travels from the free part to the thick posterior end of the cell body. To generate the bending wave, we use the bending energy (6)where is the bending rigidity and is a bond vector with length that connects vertices and on the flagellum. We choose the bending rigidity from an empirical optimization. The rotation matrix rotates by an angle about the local normal of the cell body and thereby locally defines an equilibrium bending so that the groundstate of the flagellum is not straight. The local bending angle varies according to a sinusoidal travelling wave,(7)where is the wavelength in units of the total cell length , is the distance from the posterior end of the flagellum to its vertex , and is the speed of the wave. It depends on the angular frequency and the wave number . Microscopic imaging results show that the amplitude of the distortion wave along the cell body increases towards the anterior end [4]. Since we cannot model this just by the increased flexibility or reduced bending rigidity of the cell body towards the anterior end, we introduce a wave amplitude that increases from the broad posterior end of the cell body to the thin tip [see inset of Fig. 5(b)] according to
(8)Here is a measure for the increase and is the length of the flagellum.
To model the surrounding fluid and simulate the flow fields created by the swimming cell body, we use the simulation method of MPCD. The fluid is modeled by a finite number of pointlike particles of mass and with density , where is the linear dimension of the collision cell to be introduced below. The point particles are distributed in a simulation box, typically with dimensions: . With 10 particles per collision cell, we simulate around coarse-grained fluid particles. Their dynamics consists of alternating streaming and collision steps. In the streaming step, the particles move ballistically along their velocities during a given time interval , where is the thermal energy. In the following collision step the simulation volume is divided into cubic cells of linear dimension that contain fluid particles. They interact with each other according to a specific collision rule (MPC-AT+a) adopted from the Anderson thermostat, which conserves linear but also angular momentum [32]. This procedure generates a solution of the Navier-Stokes equations. To ensure Galilean invariance, the cells for each collision step are generated with a random shift [63]. Both, the streaming and collision step contribute to the viscosity of the fluid, which can be tuned by density and . For 10 particles per collision cell and , we obtain , which we typically use in our simulations. A second value with amounts to , which was also used for a few simulations. In the simulations all quantities are given in the respective MPCD units of length, time etc, introduced in the previous paragraphs.
The motion of the model trypanosome is coupled to the surronding fluid in serveral ways as explained in Ref. [6]. During the streaming step, the vertices of the cell model perform several molecular dynamics (MD) steps, where we update their positions and velocities using the velocity Verlet algorithm and forces, which result from the potential energy of the elastic network of the cell body [6]. To avoid numerical instablities, we choose a very small time interval for the MD step, . If a fluid particle penetrates into the cell body, it is reflected with a stochastic bounce back rule, which implements an approximate no-slip boundary condition on the cell surface. We distribute the momentum changes of the reflected fluid particles to the neighboring vertices of the cell body to conserve linear momentum. In addition, the vertices defining the cell body take part in the collision steps. This procedure, which combines both elastic and hydrodynamic forces acting on the cell surface, determines the deformation and dynamics of the cell body. We checked that the total force and torque acting on the trypanosome was zero, as it should be for a low-Reynolds number swimmer.
On a single CPU, simulating the swimming trypanosome on a path with length of one to two body lengths takes approximately three to six months. Therefore, to reduce the computation time to a reasonable value, we developed a scalable version of our computer code to be used for parallel computing. To distribute the global computational load to local processors (CPU cores), a domain decomposition method is introduced. The communication between neighboring processors uses a message passing interface (MPI) library including non-blocking communication. We developed an in-house code, which is written in C language. In a test for strong scaling, the resulting speed-up increased almost linearly with the number of processors which enables us to keep the simulation time below two weeks when 20 processors are used in parallel. To validate our parallel computer code, we simulated the diffusion of the passive cell body () in the surrounding fluid and compared diffusion coefficients from parallel and single processor simulations with analytic results (S1 Fig.). The results nicely agree with each other.
Wildtype bloodstream form (BSF) Trypanosoma brucei brucei, strain 427, Molteno Institute Trypanozoon antigen type 1.6, was cultivated as described in [4]. Backward swimming trypanosomes were produced by RNAi against the dynein heavy chain (DNAI1) as described in [24]. RNAi was induced for 10 h. The pleomorphic strain Trypanosoma brucei brucei AnTat1.1 was cultured and tsetse flies were infected, maintained, and dissected as described in [26]. Flies were starved for at least 48 hours before being dissected. Dissection was performed 10 to 20 days after ingestion of the infectious meal. Tissues were then directly observed under the microscope or rapidly opened and flushed to resuspend parasites in culture medium or phosphate-buffered saline for further experiments.
Live cells were surface-stained with 1 mM of AMCA-sulfo-NHS (Pierce, Rockford, IL) or Atto488-NHS (Atto-Tec, Siegen, Germany) for 10 min, immediately before each experiment. The incubation was carried out on ice and cells were kept in the dark. Unbound dye was removed by washing twice with ice-cold TDB at 2000xg for 90 s.
Images were acquired with a fully automated fluorescence microscope iMIC (FEI), equipped with 100× (NA 1.4) and 60× (1.45 NA) objectives (Olympus), or a fully automated Leica DMI6000. Images were recorded with the CCD cameras sensicam.qe (PCO AG, Kelheim, Germany) or Leica DFC325fx. For high-speed light microscopy, a Phantom v9.1 camera (Vision Research, Wayne, NJ) was used and -image series were acquired at 200–1000 frames per second (fps). For high-speed fluorescence microscopy, the sCMOS camera pco.edge (PCO) was used at frame rates of 200–400 fps. Cells were imaged in a two-dimensional setup of 10 mm height between a microscopic slide and a mm coverslip.
For 3D-modeling of fixed cells, stacks were acquired in 100 nm steps. The cells were fixed in a final concentration of 4% w/v formaldehyde and 0.25% v/v glutaraldehyde in 0.1 M HEPES buffer over night at 4°C. The stacks were deconvolved using Huygens Essential software (SVI, Hilversum, Netherlands). 3D maximum intensity projection volume models were generated from these stacks, an edge detection filter (Sobel) was applied, and the model was false-colored in Amira (Visage Imaging, Berlin, Germany). Animations of 3D models and annotated Videos were produced with Amira or Imaris (Bitplane, Zürich, Switzerland). Flagella were traced in Amira. High speed videos of tumbling cells were manually annotated after single frame analysis in Amira. Arrows were included to follow every single wave crest travelling either from the anterior tip of the flagellum along the cell body to the thick posterior end (blue) or in the reverse direction from the posterior to the anterior end (yellow).
|
10.1371/journal.pntd.0006346 | Emergence of Orientia tsutsugamushi as an important cause of Acute Encephalitis Syndrome in India | Acute Encephalitis Syndrome (AES) is a major seasonal public health problem in Bihar, India. Despite efforts of the Bihar health department and the Government of India, burden and mortality of AES cases have not decreased, and definitive etiologies for the illness have yet to be identified.
The present study was undertaken to study the specific etiology of AES in Bihar.
Cerebrospinal fluid and/or serum samples from AES patients were collected and tested for various pathogens, including viruses and bacteria by ELISA and/or Real Time PCR.
Of 540 enrolled patients, 33.3% (180) tested positive for at least one pathogen of which 23.3% were co-positive for more than one pathogen. Most samples were positive for scrub typhus IgM or PCR (25%), followed by IgM positivity for JEV (8.1%), WNV (6.8%), DV (6.1%), and ChikV (4.5%).M. tuberculosis and S. pneumoniae each was detected in ~ 1% cases. H. influenzae, adenovirus, Herpes Simplex Virus -1, enterovirus, and measles virus, each was detected occasionally. The presence of Scrub typhus was confirmed by PCR and sequencing. Bihar strains resembled Gilliam-like strains from Thailand, Combodia and Vietnam.
The highlights of this pilot AES study were detection of an infectious etiology in one third of the AES cases, multiple etiologies, and emergence of O. tsutsugamushi infection as an important causative agent of AES in India.
| Acute encephalitis syndrome (AES) is a dreaded disease in India including the state of Bihar. Every year several people specially children, succumb to this disease and often the survivors are left with permanent residual disorders. The present research throws light on specific etiological agents that may cause AES and have found scrub typhus to be an important etiology. Knowing the specific etiology would help in definitive management of the patients that may improve the outcome both in terms of morbidity and mortality, as well as help the policy makers to take specific action for prevention and control of the disease.
| Acute Encephalitis Syndrome (AES) is a major seasonal public health problem in many states of India including Bihar. Muzaffarpur and adjacent districts, including Sitamarhi, Sheohar and East Champaran districts of Bihar consistently experience a great burden of the disease [1].The mean numbers of reported AES cases and deaths per year from Bihar during 2011 to 2014 were 835 and 243 respectively. During these years, the state of Bihar contributed 9.5% and 18% of the mean number of AES cases and deaths respectively reported from all over India (data from NVBDCP website) [2]. Japanese Encephalitis virus (JEV) was hitherto considered as the most important cause of AES, however, it contributed to only 20 (1.5%) of AES cases in 2014[2]. Other etiologies, including enteroviruses and Nipah Virus have also been implicated [1]. The National Centre for Disease Control (NCDC), New Delhi and the Global Disease Detection, Regional Centre, India, Center for Disease Control and prevention (CDC), US, have jointly classified Muzaffarpur, Bihar mystery to a noninfectious toxic encephalopathy associated with consumption of litchi fruit after ruling out pesticides, heavy metal poisoning and infectious diseases [3]. However, despite the efforts of the Bihar health department and the Government of India, the burden and mortality of AES cases have not decreased, and definitive etiologies for these illnesses have yet to be identified.
Identification of a specific agent is important for patient management and for understanding the epidemiology. Therefore, the present study was undertaken to study the specific etiology of AES in Bihar, India.
The patient enrollment and sample collection center was Patna Medical College and Hospital (PMCH) Patna (Coordinates: 25.5941° N, 85.1376° E), a tertiary care referral center catering to the population of Patna and the surrounding districts. The testing centers were (1) the Virus Research and Diagnostic Laboratory (VRDL) at King George’s Medical University (KGMU), Lucknow, Uttar Pradesh, and (2) VRDL at PMCH, Patna.
Cases presenting with clinical diagnosis of AES as per WHO [4] and admitted at PMCH were enrolled in the study during June 2015 to September 2016. Depending on the feasibility, samples of serum, CSF or both were collected from each case after obtaining written informed consent from the patient. In case of unconscious patient or children written informed consent was obtained from guardian of the patient. The study was approved by the institutional ethics committee (Reference code: 83rd ECM IIA/P8).
At the VRDL of PMCH, Patna, the CSF (preferred)/ serum (in the absence of CSF) sample was tested the same day for anti- JEV IgM antibodies (MAC ELISA kits manufactured by the National Institute of Virology, Pune, India) and the remaining samples were transported to the VRDL at KGMU Lucknow in dry ice for further testing (Fig 1).
At KGMU, the serum samples were tested by ELISA for anti-Dengue Virus (DV) IgM and anti- Chikungunya Virus (Chik V) IgM, using kits by the National Institute of Virology, Pune, India. Anti-West Nile Virus (WNV) IgM, and anti-Scrub typhus IgM (ST) antibodies were tested using Inbios International, USA kits. For anti-Scrub typhus IgM, samples with an optical density (OD) >0.5 were considered positive. For scrub typhus IgM the baseline titres need to be established for each region; for India this value has been calculated as 0.5 [5]. For all the other ELISAs the cut off values were calculated based upon the manufacturer’s instructions. All ELISAs were done in serum samples except anti-JEV IgM ELISA, which was preferably done in CSF samples (as per the manufacturer’s recommendations). External Quality Assessment for PMCH, Patna anti-JEV IgM ELISAwas done atVRDL, KGMU. Total 40% and 15% of the anti- JEV IgM antibody positive and negative samples respectively, were retested by ELISA using the same kit and protocol. All results from both the laboratories were concordant.
All Real time PCRs were done in CSF samples, except bacterial PCRs which were done in serum samples in case CSF was not available (Fig 1).
RNA was extracted from 140 μl processed clinical samples using the QIAamp Viral RNA mini kit (Qiagen, Hilden, Germany) following the manufacturer’s protocol. For DNA extraction, QIAamp Viral DNA mini kit (Qiagen, Hilden, Germany) was used. All the PCRs were done using Taqman chemistry. The target gene used for selecting the primer and probes of various pathogens, their product size and their references are described in Table 1. For Enterovirus, JEV and VZV the primers and probe were self-designed (Table 1). The properties of the primers were analyzed by IDT oligoanalyzer software. Amplification was done with Real Time PCR machine (ABI 7500, Applied Biosystems, USA). The Ct value of 35 was taken as the cutoff.
For samples testing positive for scrub typhus DNA, sequencing was done for the 56 kDa TSA gene region with a nested PCR [6] using a high fidelity Taq polymerase (Thermo Fisher Scientific, Waltham, MA). The gene sequence thus obtained spanned three of the four major variable regions. Sequencing was performed by utilizing two sets of primers as described by Ruang-areerate T. The outer primers were JG-OtF584 (5’-CAA TGT CTG CGT TGT CGT TGC) and RTS9 (5’-ACAGAT GCA CTA TTA GGC AA), and the inner primers were F (5’-AGC GCTAGG TTT ATT AGC AT) and RTS8 (5’-AGG ATT AGA GTG TGG TCCTT) [6].The PCR product was sequenced bidirectionally using the Big Dye Terminator cycle sequencing kit (Applied Biosystems, Foster City, CA) and ABI Prism Genetic Analyzer 3130 (Applied Biosystems). The GenBank accession numbers obtained for the sequences from this study are MG940993 to MG940998.
Phylogenetic analysis was performed and a tree was constructed using the Maximum Likelihood Method (Tamura-Nei model) and the MEGA version 6 program. The number of bootstrap replications was set to default. Phylogenetic tree was constructed using the sequences obtained and the reference sequences retrieved from the GenBank database (Karp, AY956315.1; Kato, AY836148.1; Gilliam, HQ718429.1 (Cambodia), HQ718460.1 (Vietnam), EF213099.1 (Thailand) and DQ485289.1).
The study was approved by the institutional (King George’s Medical University) ethics committee. Samples were collected after obtaining written informed consent from the patient or guardian in unconscious patients/ children.
All statistical analyses were done using GraphPad Prism software version 5. Intergroup comparisons of categorical and continuous variables were done using Fischer’s exact test and Chi square tests respectively.
Total 540 patients were enrolled. Both serum and CSF samples were obtained from 280 cases and only CSF and only serum were obtained from 139 and 121 cases respectively. Due to limited availability and quantity of sample, all the tests could not be performed in all the cases. The testing details are given in Fig 1.
Total 521/540 (96.5%) cases were children (aged< 180 months old, Mean age: 84.4 months, Range: 2 months to 78 years) and 312 (57.8%) were males (Male to female ratio; 1.4:1). Total 33.3% (180 of 540) patients tested positive for at least one pathogen. The total positivity of all the etiological agents combined together was not significantly different between age or sex groups (Table 2).
Most samples were positive for scrub typhus IgM or PCR (25%), followed by IgM positivity for JEV (8.1%), WNV (6.8%), DV (6.1%), and ChikV (4.5%). (Table 3). Since many samples were co-positive for 2 or more antibodies, the exact proportion of each agent could not be known. M. tuberculosis and S. pneumoniae each was detected in approximately 1% cases. H. influenzae, adenovirus, HSV-1, enterovirus, and measles virus, each were detected in less than 1% cases. N. meningitidis, HSV-2 and VZV were not detected in any case (Table 3).
Of the cases testing positive, co-detection of more than one pathogen was seen in 23.3% (42/180) cases; the co-detection of antibodies against more than one arboviruses was more common. The frequency varied from 50% for anti JEV IgM to 83.3% for anti- DV IgM (p value = 0.04, Chi square = 8.14). Difference between co-detection among flaviviruses (JEV, DV, WNV) and alphavirus (ChikV) was not statistically significant (p value = 0.59, 95% CI: 0.79–1.09). Anti- scrub typhus IgM antibodies showed a significantly lower co-detection than the arboviruses antibodies (p value<0.0001, 95% CI: 1.93–3.32) (Table 3). The different combinations of co-positives are shown in Table 4. Simultaneous detection of nucleic acid of more than one pathogen was found in only one case (scrub typhus and HSV1 DNA).
The clinical features were available for 124 patients out of 180 patients positive for any pathogen. The most common clinical features were fever (100%, n = 124), altered sensorium (79.8%, n = 99), headache (71.8%, n = 89), nausea/ vomiting (n = 53.2%, n = 66), seizures (50.8%, n = 63), and neck rigidity (32.3%, n = 40). No significant difference in clinical features was seen in cases with different etiologies.
Since scrub typhus was the most common etiology detected and AES due to scrub typhus has not been reported from Bihar till date, scrub typhus real time PCR was done in cases where samples were available. CSF was preferred over the serum sample (Fig 2). The PCR detected total eight cases of scrub typhus of which five also had anti-scrub typhus IgM antibodies. Six of eight Real Time PCR positive samples could be sequenced, which on BLAST analysis showed a maximum similarity with the Thailand, Cambodia and Vietnam scrub typhus strains. On conducting a molecular phylogenetic analysis by Maximum Likelihood method based on the Tamura-Nei model in MEGA6 software, the scrub typhus sequences obtained clustered with Gilliam like strains (Fig 3).
Most of the patients were referred from Patna and its surrounding districts. Nepal and Jharkhand (shared boundaries) referred 13 and 6 cases respectively. Geographic location of 23 patients could not be traced (missing data). Analysis was done only for 15 districts referring more than 10 cases, of which, eleven showed overall high positivity (>30% positives), three districts showed moderate positivity (>20–30%) and one (Muzaffarpur) showed low positivity (10%) (p value = 0.0014, Chi square = 13.14) (Fig 4A). District wise total number of samples referred, total number of tests positive, the names of tests positive and the co-positives are mentioned in Fig 4B.
A month wise analysis was done on the total AES cases referred to the virology laboratory and of arbovirus positivity (Fig 5). AES cases were reported throughout the year with a dip in the number of cases during February and March. Similarly, anti-DV IgM and anti-WNV IgM were positive throughout the year, but with a small peak during August through October. Anti-JEV IgM and anti ChikV IgM showed a distinct seasonality with maximum number of cases being observed during August to October and during June through July respectively. Scrub typhus peaks were also seen during September and October.
For over 20 years, the state of Bihar has witnessed periodic outbreaks of Acute Encephalitis Syndrome. The victims are usually malnourished children, with the median age been reported from 4–5 years. In the present study most of the AES cases were children less than 15 years of age. The disease has no remarkable sex preference as was also observed over the years during the Bihar AES outbreaks from 2011 to 2014 [7].
We screened all the patients for DV, WNV and ChikV because all these flaviviruses are closely related to JEV and are known to cause AES. We screened for scrub typhus since the organism Orientia tsutsugamushi has increasingly been recognised as a cause of AES [8,9]. Enteroviruses, HSV, VZV, and Adenovirus are already established causes of AES. We also wanted to know the percentage of bacterial infections presenting as AES as these are easily treatable conditions, provided patient is diagnosed and treated in a timely manner.
An infectious etiology could be determined in about one-third of the AES cases, which was a mixed pot showing the simultaneous presence of several pathogens. This observation is similar to that obtained from AES cases from Uttar Pradesh [10]. Since few samples were referred from each district, we could not draw any conclusion from the geographical distribution of the cases. JEV, ChikV and scrub typhus showed a definite seasonality with an increase in the number of cases in the monsoon and the post monsoon season as per the previous studies [11,12]. Seasonality of DV cannot be commented upon as most of the anti DV IgM positives were co-positives with antibodies against other pathogens.
JEV was found only in about 8% cases. In the year 2012–13 the Government of India initiated JE vaccination and other AES/JE control activities in following districts of Bihar i.e. Araria, East Champaran, West Champaran, Darbhangha, Gaya, Muzaffarpur, Gopalganj, Jehanabad, Nawada, Nalanda, Patna, Samastipur, Vaishali, and Saran [7]. We could not know the vaccination coverage in these districts. However, in the neighbouring state of Uttar Pradesh the JE vaccination coverage in 7 districts of the Lucknow region viz. Raebareli, Hardoi, Sitapur, Unnao, Lakhimpur Khiri, Lucknow ranged from 66.80% in the year 2014–15 to 76.54% in the year 2016–17 (personal communication with UP Vector Borne Disease Control Program). In north India, the protective efficacy of a single dose of SA-14-14-2 JE vaccine has been reported to be varying from 94.5% [13] to 84% [14]. JE vaccination program might have brought down the incidence of JE in Bihar.
Surprisingly, most of the data available from Bihar are only from Muzaffarpur district with limited data from other parts of the state. Several theories have been put forward by different researchers to explain the etiology of AES cases in Muzaffarpur district. These hypotheses include non-infectious, toxic encephalopathy due to the toxin methylenecyclopropyl-glycine present in lychee fruit, which causes hypoglycemia and encephalopathy on the background of malnourishment [3, 15]. Among infectious etiologies besides JEV, the usual cause of AES in India, Nipah virus was also thought as a possible etiology [1] because of a large number of bats being usually present in lychee orchards feeding on the lychee fruit, which were later consumed by the children. In 2014, the National Institute of Virology, Pune and the National Communicable Disease Center, New Delhi found that samples from AES patients like CSF, serum, urine, nasal swabs, throat swabs, rectal swabs, postmortem brain and liver biopsy were negative for JEV, Nipah virus, WNV and Chandipura virus [3]. The present study focuses on cases from Patna, which drains cases from all over Bihar. An infectious etiology could be determined in one third of the total cases studied, which comprised of scrub typhus, JEV, DV, WNV, ChikV in a good number of cases and M. tuberculosis, S. pneumoniae, H. influenzae, adenovirus, HSV-1, enterovirus, and measles virus occassionally.
For the first time, we detected scrub typhus in AES cases from Bihar, though O. tsutsugamushi is known to exist in this region [5]. O. tsutsugamushi has already been established to invade the central nervous system [8,9,16]. In fact in a recent prospective study from Laos, O. tsutsugamushi was detected in 12% patients with CNS infection and having evidence of bacterial or fungal infection [17]. Scrub typhus is easily treatable when diagnosed correctly, though untreated cases have a case fatality rate of 30–35% [5]. The differential diagnosis of scrub typhus is a long list, because of its nonspecific clinical and laboratory features, combined with limited diagnostic facilities in developing countries like India. Therefore, the clinicians need a high index of suspicion for detecting this neglected and treatable disease in cases with AES at least in endemic areas [8, 9, 12]. The clinicians may start specific treatment with doxycycline or azithromycin when scrub typhus is considered likely [5,16]. In the present study, we established the diagnosis of O. tsutsugamushi infection based upon an ELISA technique since IgM capture assays are the most sensitive tests for diagnosing recent rickettsial infections, as significant titers of IgM antibody appear by the end of first week [5,18]. Real time PCR could detect three extra cases, which we would have missed if we relied only on the serological test; samples from these cases were collected within the first week of illness thereby increasing the rickettsemia [5] detection. Studies recommend using whole blood/ buffy coat [19] for scrub typhus PCR and not serum/ CSF, which may account for its low positivity in the present study. We confirmed O. tsutsugamushi Gilliam-like strains presence, similar to those isolated from Vietnam and Thailand, by sequence analysis. In 2015, studies reported equal proportions of Karp-like and Kato-like strains from Northern India [20] and Gilliam-like strains from Vellore and Shillong but not from Northern India. Though, in 2007 studies reported Gilliam- like strains from Northern India [21]. This highlights the need for a comprehensive genotype study from this region, which will help in vaccine development as well as in understanding implications of strain variations and pathogenesis [22].
We tested the cases for M. tuberculosis since India is a country with a high burden of the disease and about 1% tuberculosis cases develop CNS complications. Moreover, the AES case definition given by the World Health Organization is very broad and includes viral encephalitis, bacterial meningitis, tubercular meningitis, cerebral malaria and acute disseminated encephalomyelitis [23]. Since CNS tuberculosis usually presents as acute to subacute meningitis with symptoms of less than two weeks duration, we can label these cases as having AES as per the clinical case definition. Identifying M. tuberculosis is important for initiating specific treatment in these patients, who would otherwise have higher chances of mortality and poor outcome.
Some cases showed co-positivity between the arboviruses. In a hyperendemic region, if a case is positive for more than one arbovirus antibodies, three possibilities exist a. cross-reactivity, the arboviruses share antigenic epitopes in the major envelope (E) protein due to which cross-reacting antibodies are produced [24]; b. pre-existing immunity due to previous flavivirus infection or vaccination [25]; c. co-infection, already reported in areas with high transmission rates varying from 2% in Gabon to 34% in Nigeria[26,27]. The limitation of the present study is that we could not accurately determine the etiological agent/ agents in these cases as we could not perform the gold standard plaque reduction and neutralization test (PRNT) due to logistic reasons. Similarly, cross-reactivity or co-infection in hyperendemic regions [28] may explain the co-positivity of scrub typhus and viruses, as was also indicated by the PCR results.
Obtaining both CSF and serum from an ailing child is not always possible. At times CSF tap cannot be performed owing to the low general condition of the patient or the amount of CSF tapped is low and just enough to do the cell counts and biochemistry for immediate patient management. At times venepuncture becomes difficult or in a few cases, the amount of blood obtained becomes a limiting factor. In the present study in about fifty percent cases, the samples obtained were either CSF or serum, hence all the tests could not be performed in each case.
The highlights of this pilot AES study were the detection of an infectious etiology in one-third of the AES cases, multiple etiologies, and the emergence of O. tsutsugamushi infection as an important causative agent of AES in Bihar, India. We need more comprehensive studies to confirm the findings of this study.
|
10.1371/journal.ppat.1001229 | Glacial Refugia in Pathogens: European Genetic Structure of Anther Smut Pathogens on Silene latifolia and Silene dioica | Climate warming is predicted to increase the frequency of invasions by pathogens and to cause the large-scale redistribution of native host species, with dramatic consequences on the health of domesticated and wild populations of plants and animals. The study of historic range shifts in response to climate change, such as during interglacial cycles, can help in the prediction of the routes and dynamics of infectious diseases during the impending ecosystem changes. Here we studied the population structure in Europe of two Microbotryum species causing anther smut disease on the plants Silene latifolia and Silene dioica. Clustering analyses revealed the existence of genetically distinct groups for the pathogen on S. latifolia, providing a clear-cut example of European phylogeography reflecting recolonization from southern refugia after glaciation. The pathogen genetic structure was congruent with the genetic structure of its host species S. latifolia, suggesting dependence of the migration pathway of the anther smut fungus on its host. The fungus, however, appeared to have persisted in more numerous and smaller refugia than its host and to have experienced fewer events of large-scale dispersal. The anther smut pathogen on S. dioica also showed a strong phylogeographic structure that might be related to more northern glacial refugia. Differences in host ecology probably played a role in these differences in the pathogen population structure. Very high selfing rates were inferred in both fungal species, explaining the low levels of admixture between the genetic clusters. The systems studied here indicate that migration patterns caused by climate change can be expected to include pathogen invasions that follow the redistribution of their host species at continental scales, but also that the recolonization by pathogens is not simply a mirror of their hosts, even for obligate biotrophs, and that the ecology of hosts and pathogen mating systems likely affects recolonization patterns.
| Global change is expected to cause the large-scale redistribution of species, including pathogens that threaten the health of domestic populations and natural ecosystems. Predicting the dynamics of invasive pathogens is therefore a major challenge for the 21st century ecologists and epidemiologists. Because past climatic events have shaped the current distribution of species, the study of migration of pathogens during glaciation cycles provides insights into the constraints upon pathogen spread. We studied the structure of European populations of two pathogenic fungal species infecting two species of wild plants. The pathogen with the host's broadest ecological tolerance has followed the postglacial colonisation pathway of its host from the major refugia in southern Europe, although the pathogen likely persisted in more fragmented refugia. The other pathogen showed less clear-cut genetic patterns and some evidence of possible northern refugia, in agreement with the host's more fragmented distribution and ecological preference. These results indicate that pathogen invasions are likely to follow large-scale migration events of their host species in response to climate change, but also that the recolonization by pathogens is not simply a mirror of their hosts, and that the correlation between the colonization patterns of hosts and pathogens depends on host ecology.
| Understanding the dynamics of emerging infectious diseases and their routes to colonize new geographic regions is a major challenge for ecologists in an effort to prevent negative impacts upon human, domestic, and natural populations. Because pathogens causing emerging diseases have not coevolved with the host or the ecosystem in which they emerged, they may be more likely to pose a threat to biodiversity through biomass loss and extinction of host species than those responsible for endemic diseases [1]. In recent years, concerns about emerging diseases have been increasing in light of the first evidence of a current period of global climate change. Indeed, the increase of average temperatures in many areas of the world is thought to promote the expansion of exotic pathogens [2]. In particular, invasion by fungal pathogens is a major concern in agricultural and biodiversity management, as they infect many crops and wild plants [3], [4]. About 30% of emerging infectious diseases of plants are caused by fungi, and the change of environmental conditions is thought to be the major driver of fungal invasions [1] and disease outbreaks [5]. Fungal disease outbreaks in humans have also been suggested to be linked to climate change [6], [7]. Warmer and wetter conditions favour the growth and transmission of fungal pathogens, and host shifts often occur in conjunction with episodes of global climate change [8]. Based on current trends, emerging infectious diseases caused by fungal pathogens are likely to increase in the near future, with significant severe ecological, economic and social consequences.
One way to predict the invasion routes and dynamics of emerging infectious diseases in response to current climate warming is to study the past migrations of pathogens and their hosts during historic periods of climate changes. During the last glacial maximum, the Arctic ice sheet extended into a large part of Europe and limited the survival of most organisms into Southern Mediterranean refugia (i.e., Iberia, Italy and the Balkans [9], [10]). As the climate warmed and the ice sheet retreated, many species that had persisted inside the glacial refugia experienced massive migrations into the newly available temperate territories [11]. Such processes are expected to lead to a strong, large-scale geographic structure of genetic variation, consistent with what is observed in many widespread European plants (e.g. [12]–[17]). Genetic differences between spreading populations are likely to result from both natural selection and stochastic processes in small populations. Successive founder events during the process of range expansion are expected to lead to a loss of variation and further divergence between lineages derived from different refugia [9], [18]–[21].
Few studies have investigated whether the population structures of pathogens have also been impacted by the glaciations in Europe (but see e.g. [22]–[25]). In the case of host-pathogen systems, comparative phylogeography can also provide insights into host and pathogen co-evolutionary histories and identify causal factors determining their combined distributions [26]–[28]. In fact, pathogen populations are often more differentiated than their hosts, and the study of pathogens can complement or improve our knowledge on the host population genetic structure [29]–[37]. The extent to which the phylogeographic structure of pathogen populations mirrors that of the host depends on the degree of specificity and the obligate nature of pathogenic interaction [38]. A significant co-structure between the populations of the host and the pathogen suggests that the distribution and migration of the host impose a major constraint on the distribution of the pathogen [8]. On the other hand, the absence of congruence in population structure is consistent with independent host and pathogen colonization routes. In addition to pathogen specialization, the hosts' niche breadth and demographic characteristics may affect the persistence of disease and opportunities for host range expansion during large-scale migrations that follow climate change [39]–[41]. Thus, an approach that integrates knowledge of host and pathogen biology is essential to many theoretical and applied issues related to disease emergence in response to climate change.
Microbotryum violaceum sensu lato is a species complex of basidiomycete fungi responsible for anther smut disease in many plants in the Caryophyllaceae. These fungi are obligate pathogens that sterilize their hosts. Infected plants contain fungal teliospores in place of the pollen and female structures do not mature; female plants in dioecious species also develop spore-bearing anthers. Teliospores are transmitted from diseased to healthy plants mostly by insects that normally serve as pollinators. Therefore, the dispersal routes of the host's pollen and of the pathogen's spores are constrained by the same vectors. Plants also disperse by seed, while the fungus is not vertically transmitted, resulting in higher genetic differentiation in the pathogen than in the host [42]. The sibling species encompassed in Microbotryum violaceum sensu lato [43]–[49] show strong host specificity [45], [47], [50]. The most widely studied species are Microbotryum lychnis-dioicae [51] (called MvSl in [46] and hereafter) and M. silenes-dioicae [51] (called MvSd in [46] and hereafter), which infect respectively Silene latifolia (white campion) and S. dioica (red campion). These two closely related host-pathogen systems are interesting models for studying the combined demographic histories of pathogens and their hosts because (i) populations of this pathogen are more differentiated than those of its hosts [42], (ii) the fungus is completely dependent on its hosts and the same vectors disperse the fungus and the host pollen, (iii) these Microbotryum species are highly host specific in the field ([52]), (iv) Silene and Microbotryum species have similar generation times (one per year [53], [54]), (v) there has been no attempt to control the disease because it affects plants without economic interest, and (vi) Silene and Microbotryum are model organisms for a variety of topics in ecology and evolution, with therefore numerous studies available on their life-history and ecology [55].
Recent studies on the phylogeographic history of the two host plants showed genetic evidence of post-glacial recolonization from Mediterranean refugia. In S. latifolia, analyses of chloroplast DNA (cpDNA) polymorphism showed clearly structured haplotype variation in Europe, with haplotypes from Eastern and Western Europe forming divergent groups descended from haplotypes currently distributed in southern Europe, and in particular from the Iberian and Balkan Peninsulas [56]. The phylogeography of S. dioica in Europe has been less well studied, although the pattern of cpDNA polymorphism was also suggestive of post-glacial recolonisation from multiple refugia [57], [58]. A goal of the current study was therefore to determine the extent to which population structure of Microbotryum species parasitizing S. latifolia and S. dioica showed similar patterns of post-glacial history. We also investigated whether life history differences between the two host species constrained the distributions of the pathogens within the host migration pathways. S. latifolia and S. dioica differ with respect to their ecologies, which might strongly impact the genetic structure and diversity of their specialized pathogens. S. latifolia has an extensive range and occurs in most of Europe, as well as in Middle Asia and the Steppe area of south Siberia [59]. This plant is found mainly in open areas, such as hedgerows and in arable fields, therefore often experiencing extinction-recolonisation events in frequently disturbed habitats. In contrast, the distribution of S. dioica covers mainly Central, Northern, and Western Europe [59], but not the Mediterranean regions. This plant is found in meadows, cliffs, moist forest, and mown pastures at higher elevations, preferring colder and more humid habitats than S. latifolia, and experiencing more stable population dynamics [59]–[62]. It has been suggested that these differences in host life history have affected the distribution of genetic diversity at a small geographical scale in the pathogen species, with lower microsatellite variation and higher differentiation among populations in the anther smut fungus parasitizing S. latifolia than in the fungus parasitizing S. dioica [63]. It is likely that the phylogeographic structure of the European populations of the two pathogen species will also be influenced by differences in the dynamics of the host-pathogen systems.
We therefore determined the population structures of the Microbotryum species infecting S. latifolia and S. dioica using microsatellite markers in order to address the following specific questions: (i) Do the phylogeographical structures of the Microbotryum species show signatures of post-glacial recolonisation of Europe, and in particular from Southern refugia? (ii) do the population structures of the two pathogens differ from each other, and if so, are these differences consistent with expectations based upon known ecological differences of their plant hosts? (iii) are the phylogeographic patterns of Microbotryum species comparable at a continental scale to those of their respective hosts?
Among Microbotryum samples collected on S. latifolia and on S. doica across Europe (Figure S1), analysis of variation at 11 microsatellite markers revealed that both pathogen species displayed much lower levels of heterozygosity than expected under Hardy-Weinberg Equilibrium (HWE). The dataset included 701 MvSl individuals from 187 localities and 342 MvSd individuals from 68 localities, where hybrids and cross-species disease transmission between MvSl and MvSd identified in a previous study [52] were removed from the dataset. Descriptive statistics on the polymorphism of MvSd and MvSl and on deviations from HWE are shown in Table 1 and additional details are given in Text S1 and Tables S1 and S2. MvSl for instance exhibited only 3% of heterozygous genotypes while 73% were expected under HWE, which is consistent with the high selfing rates previously reported in Microbotryum. One marker, SL19, showed extreme FIS values in MvSl, between −1 and 1, and was almost fixed in the heterozygous state in MvSd (Table 1). Analyses were therefore performed with and without this marker for subsequent analyses, but the results were highly similar.
For MvSl, multiple estimates of the genetic structure at the European scale showed the existence of at least three to five strongly supported clusters, i.e. populations genetically differentiated from each other. We used the model-based Bayesian clustering algorithms implemented in STRUCTURE, InStruct and TESS. The program STRUCTURE assumes a model with K clusters, each of which being characterized by a set of allelic frequencies. Assuming HWE and linkage equilibrium among loci within clusters, the program estimates allelic frequencies in each cluster and the proportion of ancestry from the different clusters in each individual. The program InStruct is an extension of the approach implemented in STRUCTURE, relaxing the assumption of HWE within clusters. InStruct instead jointly estimates selfing rates and individual membership on the basis of selfing rates, and is therefore well suited to selfing organisms such as Microbotryum. TESS is another extension of STRUCTURE, incorporating a spatial component into the clustering algorithm, so that geographically closer individuals are a priori more likely to belong to the same cluster. This may help revealing subtle geographical structure [64]. We attempted to identify the number of clusters (K) that best described the population structure using (1) the probability of the data under the considered value of K, i.e. Ln(Pr(X|K)), and its rate of change when increasing K; and (2) the Deviation Index Criterion (DIC), i.e. a model-complexity penalized measure of how well the model fits the data.
For MvSl, the programs STRUCTURE and InStruct showed that values of DIC decreased and LnP(X|K) increased from K = 1 to 10 (Figures S2a–c), indicating that increasing the number of clusters continuously improved the fit of the model to the data. However, the variation in LnP(X|K) showed a marked break at K = 5 in STRUCTURE analyses, with a much weaker increase of probability with increasing K afterwards (Figures S2a and b). The inclusion of space in the clustering modelling, as implemented in TESS 2.3, resulted in minimal DIC values at K = 5 and K = 6 (Figure S2d). Increasing K above 5 may therefore add little information for understanding large-scale population structure in Europe, although it would likely reveal a genuine population structure, relevant at smaller scale.
The admixture proportion (α) between clusters was low, as shown by the mean α = 0.033±0.000 over all the runs between K = 2 to 15 (10 replicates for each K) in the STRUCTURE analysis. This indicates that most of the genotypes are drawn from a single cluster, with little admixture among clusters (see Figure S3 for K = 5). There is therefore almost complete lack of gene flow among clusters.
Replicates conducted for each of the three algorithms showed dominant and minor modal solutions for membership probabilities (Figure S4). However, the dominant clustering solutions recovered from the three analyses (InStruct, STRUCTURE and TESS) were highly similar (see Figure S3 for K = 5). The three methods were therefore congruent in their inference of the population structure of MvSl in Europe. The differences between the dominant and minor modal solutions most often corresponded to a genetic structure appearing at higher K values (Figure S4). For instance, the Italian cluster was assigned to the Eastern group at K = 2 in the dominant solution, and to the Western group in other simulations.
Figure 1 shows the maps of mean membership probabilities per locality for MvSl genotypes from the InStruct analysis for K = 2 to 5. At K = 2, the analyses revealed a clear West-East partitioning. Simulation of a third cluster separated the Italian genotypes from the Eastern group. At K = 4, the Western cluster was subdivided into two clusters, one with a more northern distribution (blue, called hereafter Northwestern and abbreviated as Nwestern) and the other more to the south (yellow, called hereafter Southwestern and abbreviated as Swestern). At K = 5, the Eastern group splitted into two clusters, one bordering the Balkan peninsula (red, called hereafter the Balkan cluster) and one spreading toward Eastern Europe and Russia (purple, called hereafter the Eastern cluster). When increasing K, further clusters were identified, without evidence of admixture, and corresponding to more local geographical regions: for instance the UK became isolated, and then the most eastern part of Europe (Figure S4).
We also applied a Principal Component Analysis (PCA) on the microsatellite allele frequencies, which is a multivariate approach that does not rely on any model assumptions. It instead transforms a number of possibly correlated variables into a smaller number of uncorrelated components, the first principal components accounting for as much variability in the data as possible. The PCA fully recovered the population structure inferred by the three Bayesian clustering methods, as shown by the first four PCs, which explained 35% of the total variance in allelic frequencies (Figures S5 and S6).
The clusters displayed large and significant differences in allelic frequencies (global FST = 0.38, 95% CI: [0.29–0.46], P<0.001 for all pairs of clusters). The Nwestern and Swestern clusters showed the lowest differentiation (FST = 0.24) while the Balkan and Swestern clusters were the most different (FST = 0.47). The clusters also differed significantly with respect to their genetic diversities (Table 2), with the Italian cluster displaying significantly higher gene diversity (He) than the Balkan, Eastern and Nwestern clusters (Wilcoxon Signed Rank (WSR) tests, P = 0.008, P = 0.033, and P = 0.026, respectively, Table 2). The Balkan cluster exhibited a significantly lower allelic richness (number of alleles controlling for differences in sample size) than the Eastern and Italian clusters (P = 0.016 and P = 0.003, respectively), while the others had intermediate values.
Within clusters, significant isolation by distance (IBD) was detected in the Balkan, Nwestern, and Italian clusters (Table 2), indicating that genetic differentiation increased with geographic distance in these clusters. No significant IBD was detected within the Swestern and Eastern clusters (Table 2). The level of spatial structure was quantified by the Sp statistic, which accounts for variation in sampling intensities; high values of Sp are indicative of low population density and/or limited dispersal [65]. Sp values were close to 0 within the Italian, Swestern, and Eastern clusters, but were much higher within the Balkan and Nwestern clusters (Table 2).
Within-cluster selfing rates estimated from InStruct analyses were extremely high (s = 0.91±0.03 on average), in agreement with previous studies and with the high FIS values within clusters (Table 2).
A European spatial map of genetic diversity was generated by aggregating geographically close samples together on a grid, considering only grid cells where the sample size was higher than 4. The interpolated values of allelic richness showed that genetic diversity increased in the southward direction, with the highest value observed in the Italian and Iberian peninsulas and the lowest values in northern Europe (Figure 2). Such a latitudinal trend was confirmed by the highly significant negative correlation observed between latitude and allelic richness (r = −0.57, P<0.0001); no significant correlation was found between longitude and allelic richness (r = 0.036, P = 0.892). High genetic diversities were also observed in the northern half of France and along a longitudinal line separating the Eastern and Western parts of Europe (Figure 2).
We analyzed the relationships among clusters using neighbour-joining population trees, respectively based on Nei's DA distance, shared allele distance DSA, Chord's distance and Goldstein's (δµ)2 distance. As the different trees provided similar topologies, only the tree based on Nei's DA distance is presented (Figure 3). The trees suggested that the Eastern groups would have diverged first, followed by the Italian cluster and then by the Western groups. The Eastern and Western group would then have further split into two clusters each. Rough estimate of separation time between clusters can be deduced from distances between clusters assuming that the divergence between the two species occurred 400,000 yr BP [52] and assuming clocklike evolution of microsatellite markers [66]. The separation of the 5 clusters can thus be roughly estimated to have occurred between 200,000 and 350,000 yr BP (Figure 3).
In MvSd, multiple estimates of the genetic structure at the European scale provided confidence in existence of several distinct clusters. As for MvSl, the three Bayesian clustering analyses (InStruct, STRUCTURE and TESS) all indicated that DIC decreased and LnP(X|K) increased with increasing K (Figure S7). Again, genotype assignment probabilities were always very high, with very little admixture among clusters (mean α = 0.030±0.002 over the 140 runs from K = 2 to 15 simulated clusters; Figure S8), and were similar for the three algorithms used (data not shown). The spatial distributions of the two clusters identified at K = 2 appeared highly intermingled, with however a slight West-East trend of separation. Further clusters differentiated as K increased, but without any obvious large-scale geographical pattern (Figure 4). Similar genetic partitioning was recovered using PCA (Figure S9). The first PC accounted for 20% of the variance in the allelic frequencies and clearly separated genotypes into the same two groups as those identified using Bayesian clustering approaches at K = 2 (Figure S9). The differences in allelic frequencies between them were high, with a FST value of 0.34 (95% CI: 0.17–0.49). The successive PCs accounted for less than 11% of the total variance in allelic frequencies each and revealed the same clusters of genotypes as those observed in Bayesian clustering analyses (Figure S9).
The two clusters identified at K = 2 represented the only structure with a large-scale geographical pattern. The FST values between sites where the sampling was higher or equal to 10 samples showed that a very high level of genetic differentiation between clusters was observed, even in the regions where populations from different clusters were intermingled (Figure S10). Within clusters, there was no significant IBD, i.e. no significant increase in FST with geographic distance (Mantel test, P = 0.418). The two clusters showed a spatial genetic structure of a similar level to that observed in MvSl, with Sp values of 0.06 and 0.15 for the clusters 1 and 2, respectively (Table 2).
Selfing rates within each of the two clusters, inferred from Instruct, were high and similar to those in MvSl (mean selfing rates at K = 2: 0.93±0.01), consistent with the high FIS values (Table 2). The two clusters of MvSd showed genetic diversities comparable to those observed in MvSl (Table 2), and did not differ significantly from each other with respect to gene diversity (WSR test, P = 0.424) and allelic richness (WSR test, P = 0.594). In contrast to MvSl, spatially interpolated values of allelic richness increased northwards (Figure 2), although the correlation with latitude was significant only at a marginal level (r = 0.43, P = 0.073). The correlation with longitude was not significant (r = −0.008, P = 0.975).
Very high selfing rates were inferred in both Microbotryum species (s = 0.91 for MvSl and s = 0.93 for MvSd), in agreement with the high deficits in heterozygotes (Text S1, Table 1). Microbotryum species are in fact known to have a selfing mating system [67], [68], but the estimations of selfing rates in natural populations inferred here are more precise and even higher than previously thought [67]. These high selfing rates appear to result both from an intrinsic preference for intra-tetrad mating [69] and from lack of outcrossing opportunities when the spores are deposited on a new host plant. The lack of outcrossing opportunity is supported by the observation that selfing rates under choice experiments (when given the opportunity to self or outcross on plants) are lower (ca. 0.70 [70]) than those inferred here in natural populations.
The marker SL19 showed extreme FIS values in MvSl and was almost fixed in the heterozygous state in MvSd. This was not particularly surprising given that Microbotryum species undergo mostly intra-tetrad mating, which can lead to an excess in heterozygosity in regions of the genome near the centromeres and on the sex chromosomes carrying the mating type locus [69], [71]. Because the mating type segregates at the first meiosis division, intra-tetrad mating automatically restores heterozygosity in all regions linked to centromeres and linked to the mating type locus, as they also segregate at the first meiotic division [69], [71].
In addition to selfing, the study of local genetic structure and diversity for both Microbotryum species across Europe revealed patterns consistent with the dynamics of a metapopulation [72]. In particular, we observed very low genetic diversity within demes, and strong differences in allele frequencies (high FST values) between demes (see text S1). Metapopulation dynamics involve frequent extinctions and recolonizations, thus creating strong genetic drift in local populations. In addition, selfing reduces the local effective population size and the frequency of gene exchange between individuals and populations, which reinforces the effects of genetic drift upon allelic frequencies [72], [73]. These results are consistent with previous population genetics and demographic studies conducted at more local scales on Microbotryum species infecting S. latifolia and S. dioica, also showing patterns consistent with metapopulation dynamics [42], [74]–[77].
From a biogeographic point of view, Europe is a large peninsula with an East-West orientation, delimited in the south by a strong barrier, the Mediterranean Sea. During glaciation epochs, many species likely went through alternating contractions and expansions of range, involving extinctions of northern populations when the ice-sheet extended southward, and spread of the southern populations from different refugial areas as the glaciation receded. Such colonization processes were likely characterized by recurrent bottlenecks that would have led to lower diversity in the northern populations compared with the southern refugia [21]. The idea that refugia were localized in three areas (Iberia, Italy, Balkans) in Europe is now well-established [10], [11], [15], [17], although increasing evidence suggest that northern and eastern refugia also existed [78]–[84].
In MvSl, the strong phylogeographic structure observed at the European scale was composed of at least three genetic clusters with distributions strikingly similar to the major glacial refugia commonly recognized to have existed in Europe for many plant and animal temperate species (e.g., [14]). This pattern suggests that the pathogen likely colonized Northern Europe from at least the three main Mediterranean refugia (Iberian, Italian and Balkan).
The scenario may have been more complex, however, as the Eastern and Western clusters each further split into two groups, with divergence times of the five clusters roughly estimated between 200,000 and 350,000 yr BP. One of the eastern clusters was located north of the Balkans (mainly in Hungary and Czech Republic) and the other from Germany eastward. This pattern is consistent with colonization from distinct refugia located in the Balkans and further East in Eurasia, following a similar scenario as those reported in some animals, such as the bear Ursus actor [10], the vole Myodes glareolus [79], [80], some plant species [78], [85], [86], and also in some pathogens (e.g., [22]–[25]).
The two clusters identified in Western Europe had more diffuse geographic distributions, with a slight longitudinal partitioning. One of the clusters was distributed more towards the West of France while the second was more present in east-central France and in Eastern UK. Such a pattern may be due to the pathogen having survived in distinct regional refugia in Western Europe, from which they would have expanded their range over France and UK. Such a hypothesis is consistent with recent findings that the main glacial refugia in Europe were probably not composed of a single population, but instead could have been structured into several local refugia more or less isolated from one another (see the concept of “refugia within refugia” [87]–[91]).
While the north-south gradient in genetic diversity can be taken as a sign of range expansion from southern glacial refugia, a band of high genetic diversity was observed north of Italy and extending into Germany, as well as a hotspot of diversity in the centre of France. These areas of high of genetic diversity likely come from the colonization history of Europe by the different genetic clusters, establishing suture zones where genetic clusters meet and become intermingled. Such a pattern has been observed previously in a comparative approach of the history of colonization of 22 widespread and co-distributed European trees and shrubs [92], where hotspots of genetic diversity in the colonised ranges were found to be the result of mixed colonization from genetically isolated eastern and western European refugia [92]. The high genetic diversity found in MvSl in the Iberian peninsula also likely results from the co-occurrence of genotypes from four clusters.
Previous studies indicated that the phylogeographic pattern of the host plant S. latifolia similarly showed genetic evidence of post-glacial recolonization from Mediterranean refugia [56]. Analyses of cpDNA haplotypes revealed clear biogeographic structure in Europe, with haplotypes from Eastern and Western Europe forming divergent groups descending from haplotypes currently distributed in Iberian and Balkan Peninsulas [56]. The phylogeographic patterns in the plant S. latifolia and in its anther smut pathogen therefore seem to be congruent. In particular, the Eastern and Western clades identified in the host could correspond to the Eastern and Western genetic clusters in the pathogen MvSl. The pathogen however seems to display a finer genetic structure than its host, with particularly clear genetic evidence of an Italian glacial refugium for the pathogen, but not for the host plant (see figure 4 in [56]). More pronounced geographic structure is in fact expected in anther smut pathogens compared to their hosts, as has been observed at smaller scales [42]. This can be explained by the following observations: 1) the distribution of the pathogen is necessarily embedded within the range of its host, 2) anther smut pathogens are dispersed by the same vectors as the pollen of the plants, without being dispersed by seeds, so that their dispersal ability is lower than that of their host plants [42]. It is therefore likely that MvSl persisted in more fragmented refugia compared with its host. This highlights the potential use of pathogens as proxies for understanding host past migrations and distributions: the finding of distinct clusters in Italy and the Balkan in MvSl reveals that S. latifolia persisted in both these refugia during last glaciations, which was not obvious based solely on our current knowledge of the plant's phylogeograpy. However, statistically explicit comparative analyses linking the host and pathogen genetic polymorphisms, using comparable genetic markers, would be required to draw firm conclusions regarding correlations between the biogeographic structure of S. latifolia and MvSl (e.g. [93]).
The anther smut pathogen MvSd also exhibited a strong genetic structure, albeit with biogeographic patterns more difficult to interpret. The two main clusters had largely intermingled distributions, with an estimated time of divergence of the same order of magnitude as observed for MvSl. The distinct clusters in MvSd could correspond to genetic groups having diverged in distinct southern refugia during the glaciations, similar to MvSl, although the locations of the putative refugia are more difficult to identify. This may be due to the restricted distribution of MvSd, constrained by ecological specificities of the host and disease: the plant S. dioica is very rare in Mediterranean regions, and even more so the disease (we did not find anther smut symptoms on any S. dioica plant in the Pyrenees despite several years of searching). On the other hand, given the more northern current distribution of the plant S. dioica compared to S. latifolia, one can alternatively speculate that its tolerance to cold temperatures [62] might have allowed the host and the disease to remain in more northern refugia, as suggested for other species adapted to cold environments [84]. This could provide an explanation of the marginally significant increase in allelic richness with latitude in MvSd, although we cannot rule out that this pattern resulted from the co-occurrence of a greater number of different clusters in the north.
The phylogeography of the host plant S. dioica based on cpDNA RFLP similarly indicated the existence of genetically distinct groups, more or less longitudinally separated, albeit with large overlap in their ranges [58]. The distribution of the common cpDNA haplotypes was suggested to result from a post-glacial expansion of S. dioica across Europe from multiple southern refugia [57], [58]. However the absence of sampling from Mediterranean peninsulas in prior studies prevents any definitive conclusion regarding the number and location of these refugia. In addition, the geographic distribution of the shared haplotypes in S. dioica and S. latifolia was consistent with a history of hybridization and introgression events, making it difficult to assess whether the present distribution of these haplotypes resulted from the recolonization history of S. dioica or S. latifolia [58].
A striking pattern observed in both Microbotryum species was the low level of admixture among genetic clusters (≤3%), suggesting almost complete lack of gene flow, despite the existence of contact zones. Such low levels of gene flow among clusters are likely influenced by the very high selfing rates in Microbotryum. High selfing rates have been invoked to explain reproductive isolation between sympatric Microbotryum species [67], [94], and there could be a similar effect in keeping the genetic clusters distinct within species. The high selfing rates also explain why increasing the number of clusters in Bayesian analyses always increased the explanatory value in describing the population genetic structure, without the appearance of admixed clusters, even for very high K values: this is because each diploid individual mostly reproduces with itself and therefore the smallest ‘panmictic unit’ may indeed be the individual. In selfing species, the genetic structure extends to a much finer scale than in outcrossing species [32], [95]. The lack of gene flow among clusters may result in addition to metapopulation dynamics and rapid expansion during post-glacial recolonization. A theoretical study [96] indeed showed that rapid growth in population size after founding events resulted in gene frequency divergence that is resistant to decay by gene exchange.
Large-scale congruence between the pathogens' phylogeographic patterns and those of their respective hosts indicates that their glacial refugia and migration pathways during recolonization have been similar. While this may be expected for obligate pathogens like Microbotryum species, highly dependent on their hosts for survival and using the same dispersal vectors, we interestingly found that the pathogens likely subsisted during glaciations in a more fragmented distribution, with their genetic diversity divided among a higher number of smaller refugia. Moreover, the extent of large-scale dispersal across Europe after recolonization was less for the pathogen than for its host: in particular, the clusters were much more clumped in MvSl than in S. latifolia, and footprints of refugia appeared in MvSl that were absent in S. latifolia, such as the Italian peninsula. Our findings thus indicate that vector-borne, obligate pathogens may colonize new areas following climate warming with some delay compared to their hosts, and to a lesser extent. The invasive potential of pathogens following climate change is therefore likely to depend on the obligate nature of the interactions with their host and on the dispersal modes, as could be expected.
However, once the original host and its fungal pathogen invade a geographic region, the pathogen poses a risk of emerging as an infectious disease on new host species found in that area. For instance, MvSl was introduced in the United States some time after its host plant S. latifolia, and has remained in a much more restricted geographic area [97]. Cross-species disease transmission was nevertheless documented in the United States to another non-native species, S. vulgaris, that is otherwise free of anther smut disease in the continent [98]. In Europe, we have previously detected rare events of cross-species disease transmission between S. dioica and S. latifolia and of hybridization between MvSl and MvSd that occurred after secondary contact [52]. Host shifts are frequent in fungal pathogens [4], [99], [100] in particular in Microbotryum, where co-phylogenetic analyses showed that speciation events were most often associated with host shifts [47]. This suggests that climate warming may cause emerging infectious diseases, by resulting in contacts between different potential hosts that were allopatric, even when the intrinsic dispersal capacity of the pathogens is limited and their migration pathways are constrained by those of their hosts. Climate warming can also bring into contact differentiated populations from the same species, promoting introgression between previously geographically isolated populations, which can have important and unpredictable evolutionary consequences. We showed in the present study that secondary contact between genetically differentiated clusters happened after the glaciations in MvSl, and that the highly selfing mating system was here important in preventing introgression.
The substantive contrast in phylogeography for the anther smut fungi on S. latifolia and S. dioica, which may be attributed to differences in the hosts' ecology, is also relevant for predicting the fate of infectious diseases following global warming. The redistribution of pathogens under warmer climatic conditions should indeed be highly dependent on the hosts' ecological preferences and adaptive potentials, in particular regarding the temperature and competition in new ecological communities [39]–[41].
In this study, we showed that high selfing rates and metapopulation dynamics in two plant pathogenic fungi had strong impact on their genetic diversity and structure. At the scale of the species' distribution ranges, the population structures in the two fungal species were quite different, likely due to differences in the ecological preferences of the two host-pathogen systems. The broadly distributed S. latifolia and its anther smut pathogen have kept clear genetic footprints of postglacial colonization from the major southern European refugia. The pathogens showed striking evidence for more numerous and localized refugia than their hosts. On the other hand, the ecological preference of the plant S. dioica for wetter and colder habitats [62] probably led to a more restricted and more northern distribution of the plant and its anther smut pathogen, and may have induced a drastic contraction of their distribution ranges with the post-glacial warming and the fragmentation of suitable habitat conditions. The European genetic structures of the anther smut fungi seem to match those of their respective hosts, with even a finer genetic structure, so that the geographic distribution of genetic variation in the pathogens may be useful to draw inferences about host phylogeography.
Beyond the interest of our study for understanding the dynamics of diseases under climate warming and the impact of host life histories on the genetic structure of pathogens, our study illustrates several important points to take into account when performing clustering genetic analyses, which are still often poorly recognized. First, several K values are often interesting to consider in clustering analyses, and it may be non-heuristic to search for a “single optimal” number of clusters. As long as increasing K does not lead to admixed clusters, the new clusters revealed by increasing K probably reveal a genuine genetic structure that may be interesting to investigate. This appears especially true in selfing species, for which the smallest panmictic cluster may be the individual.
The individuals of Microbotryum analyzed in this study were collected as diploid teliospores from 187 localities on S. latifolia (n = 701) and 68 localities on S. dioica (n = 342) across Europe (Figure S10) and stored in silica gel (see Figure S1 for a detailed description of the sampling). DNA from teliospores of one flower per diseased plant was extracted for genetic analyses. Multiple infections by different genotypes are frequent in the Silene-Microbotryum system, but teliospores within a single flower originate from a single diploid individual [101].
DNA was extracted as described in [77]. Teliospores were genotyped using 11 microsatellite markers following the protocol of [77] (Table 1). Among the 11 microsatellite loci used, E14, E17, E18 were described in [102], SL8, SL9, SL12, SL19, SVG5, SVG8, SVG14 described in [103], and SL5 was described in [104].
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.