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10.1371/journal.pntd.0005033 | Estimating the Burden of Chagas Disease in the United States | In recent years, there has been growing awareness of the significant burden of Chagas disease in the United States (US). However, epidemiological data on both prevalence and access to care for this disease are limited. The objective of this study is to provide an updated national estimate of Chagas disease prevalence, the first state-level estimates of cases of T. cruzi infection in the US and to analyze these estimates in the context of data on confirmed cases of infection in the US blood supply.
In this study, we calculated estimates of the state and national prevalence of Chagas disease. The number of residents originally from Chagas disease endemic countries were computed using data on Foreign-Born Hispanic populations from the American Community Survey, along with recent prevalence estimates for Chagas disease in Latin America from the World Health Organization that were published in 2006 and updated in 2015. We then describe the distribution of estimated cases in each state in relation to the number of infections identified in the donated blood supply per data from the AABB (formerly American Association of Blood Banks).
The results of this analysis offer an updated national estimate of 238,091 cases of T. cruzi infection in the United States as of 2012, using the same method as was used by Bern and Montgomery to estimate cases in 2005. This estimate indicates that there are 62,070 cases less than the most recent prior estimate, though it does not include undocumented immigrants who may account for as many as 109,000 additional cases. The state level results show that four states (California, Texas, Florida and New York) have over 10,000 cases and an additional seven states have over 5,000 cases. Moreover, since 2007, the AABB has reported 1,908 confirmed cases of T. cruzi infection identified through screening of blood donations.
This study demonstrates a substantial burden of Chagas disease in the US, with state variation that reflects the distribution of at risk Latin American immigrant populations. The study lends important new insight into the distribution of this disease in the US and highlights the need for further research quantifying prevalence and incidence to guide interventions for control of Chagas disease across the US.
| Chagas disease is a parasitic infection that primarily affects poor populations in Latin America. However, awareness of this disease in the United States has increased in recent years. In this study, we utilize data from the American Community Survey and the World Health Organization to estimate the number of cases of Chagas disease in the United States. We find that there are an estimated 238,000 cases across the United States, along with four states that each has over 10,000 cases (California, Texas, New York and Florida). We also analyze data from the United States blood donation which shows that about 1,900 cases have been identified through blood donation. We conclude that there is still a substantial burden of Chagas disease in the US, though the burden is focused in certain geographic regions. We also highlight the need for further research to better quantify prevalence and incidence in order to guide interventions to diagnose and treat patients with Chagas disease across the US.
| Chagas disease, caused by infection with the parasite Trypanosoma cruzi, affects an estimated 8–10 million people globally. Among those infected with the parasite, approximately 30% will develop chronic Chagas cardiac disease, including serious arrhythmias and heart failure. This infection can also be transmitted congenitally, from infected mother to child. Though Chagas disease has historically been considered a condition of the rural poor in Latin America, in recent years there has been growing recognition of the burden of Chagas disease in the United States (US) and Europe due to human migration from Latin America.[1–3] Moreover, both autochthonous transmission in the southern US and congenital transmission have been documented, although the numbers of cases are small and the risk of transmission is undefined.[4, 5] In the United States, there is no national level surveillance for Chagas disease; the most recent study to estimate the prevalence of this disease in the US, published in 2009, concluded that approximately 300,167 individuals with Chagas disease were living in the United States in 2005.[1]
Since these estimates were published, a more recent American Community Survey (ACS) was released, allowing for an updated estimation of the numbers of T. cruzi infections at both the state and national level. In addition, screening of the blood supply was initiated in 2007 and surveillance data on cases identified in the donated blood supply since that time offer additional insight into the geographic distribution of Chagas disease in the US. The objective of this study is to provide an updated national estimate and the first state-level estimates of cases of T. cruzi infection in the US as of 2012–2013 and to analyze these estimates in the context of data on confirmed cases of infection in the US blood supply during the period from 2007–2013.
In this study, we calculated estimates of the state and national prevalence of Chagas disease. We did not estimate possible US-acquired infections, either congenital or vector-borne. State-level estimates of the number of residents originally from Chagas disease endemic countries were computed using data on Foreign-Born Hispanic populations by state from the ACS; state totals were summed to provide a national estimate. This was compared to the national level estimate generated using data on Foreign-Born Hispanic populations for the entire nation from the ACS. To estimate the number of cases of Chagas disease we used recent prevalence estimates for the Latin American countries of origin from the World Health Organization (WHO).[6–8] The WHO has provided country-based estimates of prevalence of T. cruzi infection in 2006 and updated these estimates in 2015 to reflect the impact of national vector control programs in the intervening period. State-Level Foreign-Born Hispanic Population Estimates by single Country of Origin group were computed using the 2012 ACS data for a five-year period (2008–2012).
The prevalence by state was estimated using Hispanic Country of Origin groups for each state as estimated by the ACS. For each single Country of Origin group, an expected number of cases were computed by multiplying the estimated state population by the proportion who had migrated prior to 2010 by the respective prevalence in 2006 in the Country of Origin and adding to that the estimated population that had migrated after 2010 multiplied by the respective prevalence reported by WHO in 2015. This method is represented in the following equation:
Estimated Cases of Chagas disease in State B from Country A Origin Group = (Population that migrated from Country A to State B prior to 2010 X National prevalence of T. cruzi infection in Country B per WHO 2006) + (Population that migrated from Country A to State B after 2010 X National prevalence of T. cruzi infection in Country B per WHO 2015)
The estimated number of cases for each state was calculated as the sum of the expected number of cases for each of the single Hispanic Country of Origin groups in that state. The state totals were then added to provide a national prevalence estimate. For comparability, we also calculated the estimated number of cases at the national level in 2012 by taking the sum of the estimated number of infections for each single Country of Origin group at the national level. This method is the same as that which was used in Bern and Montgomery (see Table 1).[1]
Finally, we describe the distribution of estimated cases in each state in relation to the number of infections identified in the donated blood supply per de-identified data from the AABB (formerly American Association of Blood Banks). AABB aggregates voluntary reports of donor Chagas disease testing from most but not all U.S. blood collection agencies. These data include all confirmed cases reported to them from January 2007 to September 2013. These data do not include cases diagnosed via mechanisms other than blood donation, such as community-based screening efforts or other clinical settings. [9] This study was approved by the Boston University Medical Center Institutional Review Board (IRB) (Protocol H-32356).
The results of this analysis offer an updated national estimate of 238,091 cases of T. cruzi infection in the United States as of 2012 using the same method as was used by Bern and Montgomery to estimate the number of cases in 2005 (Table 1). [1] This represents a decrease of 62,070 cases from this most recent prior estimate. By comparison, summing the number of cases estimated by state offers a slightly lower national estimate of 238,072 cases. The state level results show four states with over 10,000 cases and an additional seven states with over 5,000 cases (Fig 1). Moreover, since 2007, the AABB has reported 1,908 confirmed cases of T. cruzi infection identified through screening of blood donations.
This study offers the first state-level analysis of the burden of Chagas disease in the US and provides objective data to inform future policies on screening and clinical care provision for this disease. This updated national estimate of 238,091 cases of T. cruzi infection represents an approximately 20% decrease in the number of infections estimated for the US in 2005, most likely due to a decrease in the estimated population of foreign-born Latin Americans based on US Census population surveys conducted after 2005. The state level estimates show a geographically focal burden with highest estimated numbers of cases in California, Texas, Florida and New York. Data on cases identified through blood donor screening is largely but not completely congruent with the state level estimates. This difference may reflect variation in community blood donation preferences related to country of origin, since blood donations are voluntary, or possibly other cultural or social differences. [10]
There are several limitations to this study. First, we focused our estimation of the cases of Chagas disease to documented foreign born immigrants from endemic countries in Latin America only. Estimates of undocumented immigrants in the United States are no longer available from U.S. Department of Homeland Security, the source of population totals by country of origin used in the previously published estimate. Recent data from the Pew Research Center suggest that in 2012 there were approximately 11.2 million undocumented immigrants in the United States. Moreover, among the leading countries of origin for the undocumented population were several countries with substantial prevalence of Chagas disease, Mexico, Guatemala and El Salvador.[11] [1]. Based on these 2012 Pew Research Center estimates of undocumented Latin American immigrants in the United States and WHO country Chagas disease prevalence, an additional 88,000–109,000 people with Chagas disease may be living in the United States, bringing the estimated total number of cases to between 326,000 and 347,000. Because of gaps in data available, we cannot provide state level estimates of Chagas disease among undocumented Latin American immigrants. A second limitation of this study was the inability to obtain reliable data to estimate both autochthonous of T. cruzi transmission and congenital transmission of T. cruzi infection among populations of Latin American origin. A third limitation of this study was that we assumed the prevalence of Chagas disease in the foreign born US immigrant population was equal to that of the country of origin. Due to a lack of data, we were unable to account for age, sex or year of immigration. Finally, for states whose population in any of the Hispanic origin groups was too small to be estimated, they were assumed to have zero members in that group and consequently no expected infections. If anything, this would result in an underestimation of the true number of cases.
These findings have important implications for health policy regarding Chagas disease in the US. The estimate from this study is similar to the previous report and suggests a continuing burden of Chagas disease in the US. This research is a critical first step in addressing Chagas disease in the United States. Other important research gaps in our understanding of the epidemiology of this disease in the US include: (1) representative studies to generate an evidence-based prevalence of disease nationwide; (2) studies to estimate the risk for acquiring T. cruzi infection via congenital or autochthonous transmission in the US; and (3) a national level definition of the contribution of T. cruzi infections to cardiac disease. Defining the Chagas disease burden in the US is necessary to inform efforts to ensure access to appropriate care and treatment among the populations at risk.
In conclusion, this study demonstrates a sustained substantial burden of Chagas disease in the US and offers the first state level prevalence estimates for Chagas disease. We also show state variation in burden, reflecting the distribution of at risk Latin American immigrant populations. The study lends important new insight into the distribution of this disease in the US and highlights the need for further research quantifying prevalence and incidence to guide interventions for control of Chagas disease across the US.
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10.1371/journal.pgen.1005460 | Evolution and Design Governing Signal Precision and Amplification in a Bacterial Chemosensory Pathway | Understanding the principles underlying the plasticity of signal transduction networks is fundamental to decipher the functioning of living cells. In Myxococcus xanthus, a particular chemosensory system (Frz) coordinates the activity of two separate motility systems (the A- and S-motility systems), promoting multicellular development. This unusual structure asks how signal is transduced in a branched signal transduction pathway. Using combined evolution-guided and single cell approaches, we successfully uncoupled the regulations and showed that the A-motility regulation system branched-off an existing signaling system that initially only controlled S-motility. Pathway branching emerged in part following a gene duplication event and changes in the circuit structure increasing the signaling efficiency. In the evolved pathway, the Frz histidine kinase generates a steep biphasic response to increasing external stimulations, which is essential for signal partitioning to the motility systems. We further show that this behavior results from the action of two accessory response regulator proteins that act independently to filter and amplify signals from the upstream kinase. Thus, signal amplification loops may underlie the emergence of new connectivity in signal transduction pathways.
| Deciphering the circuit design of signal transduction networks is a fundamental question in cell biology. This task is challenging because many pathways are branched and control multiple cellular processes in response to one or several environmental signals. Studying pathway diversification in bacteria could be a powerful approach because these organisms contain so-called chemosensory systems, modular signaling units that have been adapted multiple times independently to regulate a large number of physiological processes. Here, we studied one such system, the Myxococcus xanthus chemosensory pathway (Frz) that controls the directionality of two distinct motility systems (A- and S-motility). By experimentally uncoupling the regulations, we found that the Frz pathway evolved from a simpler ancestral system that only controlled S-motility originally. Two major pathway remodeling events allowed the recruitment of A-motility to the regulation, (i) the duplication of a connector protein which created the branch point and (ii), the acquisition of a signal amplification mechanism to allow signal partitioning at the branch point. These results reveal the core structure of a complex chemosensory system and generally suggest that gene duplication and signal amplification underlie the diversification of signal transduction pathways.
| In living cells, adaptation to rapid changes in environmental conditions requires coordinated rearrangements of basic cellular processes to adjust the cellular homeostasis to the new conditions. In general, receptor molecules sense environmental changes and translate them into a cellular response by phosphorylation of a downstream regulator. Because the cellular response must be integrated to various cellular processes, the phosphorylation cascade frequently involves a number of intermediates, allowing multiple regulation layers and branch points (nodes) in the regulatory circuit [1,2]. Thus, for a given pathway, identifying nodes and understanding how they participate in the regulation is of fundamental importance to elucidate how signals are integrated toward a cellular response. One possible approach to elucidate the underlying structure of a multi-component signaling pathway is to study its evolution because the diversification of signaling pathways is under strong selection pressure and signaling intermediates may have been selected in some organisms [3]. Consequently, some proteins that appear central to the regulation in a given genetic context may in fact be dispensable in a different context where their function is not required. For example, a protein that insulates a pathway from another will become dispensable if the secondary pathway is removed. Thus, tracking back the evolutionary history of signaling pathways can reveal core regulatory motifs and principles underlying the acquisition of additional regulations [4,5]. Bacteria are exceptional model systems for such studies because they are highly tractable experimentally and thousands of genome have been sequenced.
In bacteria, signal transduction networks are frequently formed by so-called two-component systems. The core motif of a two-component system generally consists of a receptor, generally a membrane localized sensor histidine kinase (HK) and its cognate response regulator (RR). Following activation by environmental signals, the HK uses ATP to autophosphorylate on a conserved histidine residue and the phosphoryl group is transferred to a conserved Asp residue of the RR protein, regulating a number of downstream processes, gene expression, secondary messenger synthesis or protein-protein interaction [6]. HK/RR pairs often form autonomous signal transduction systems [7], but they are also modular and can be incorporated in more complex circuits, multiple phosphorelay systems and chemosensory-type systems [1,8]. In the enteric chemotaxis (Che) system, the HK (CheA) does not act directly as a sensor but resides in the cytosol where it is activated by a transmembrane Methyl-accepting protein (Mcp) via a coupling protein (CheW). Following activation, CheA transfers a phosphoryl group to two RR domains; one of them, the CheY protein constitutes the system output and interacts with a protein of the flagellum (FliM) to switch the direction of its rotation. The second RR domain is carried by the CheB methyl esterase, and its phosphorylation activates the de-methylation of the Mcp to reset the system to a pre-signaling state (adaptation, for a review, see [9]). In many bacteria, the core signaling apparatus of the enteric Che system has been coopted to the regulation of processes other than chemotaxis, such as surface motility, gene regulation and even cellular differentiation [8,10,11]. The genetic structure of these non-canonical chemosensory systems is quite diverse and their circuit architecture is generally poorly understood [8,10,11]. In this study, we investigate the evolution and genetic structure of a chemosensory-type system that controls two distinct motility machineries in Myxococcus xanthus.
Myxococcus xanthus, a gram negative deltaproteobacterium, uses surface motility to form multicellular spore-filled fruiting bodies when nutrient sources become scarce. During this process, the Myxococcus cells can move as single cells or within large coordinated cell groups and reverse their direction of movement in a process where the cell poles rapidly exchange roles [12]. In the cell groups, a Type-IV pilus (Tfp) assembled at the cell pole (the leading pole) acts as grappling hooks and pulls the cell forward by retraction (Fig 1A, [13]). Tfps retract when they are in contact with a cell surface-exposed exopolysaccharide (fibrils), giving rise to a cooperative form of group movements called S (Social)-motility [14]. At the colony edges, the single cells are propelled by the recently characterized Agl-Glt apparatus, otherwise called the A (Adventurous)-motility system [15,16]. The Agl-Glt complex is assembled at the leading cell pole and moves directionally towards the lagging cell pole, promoting movement when it contacts the underlying surface (Fig 1A, [12,16,17]). Thus, both motility systems are activated at the leading cell pole and their activation is switched coordinately to the opposite cell pole when cells reverse (Fig 1A, [18–20]). The frequency of the reversal events is under the genetic control of a chemosensory-like system called Frz [21]. This control is essential for Myxococcus multicellular behaviors because frz mutants that reverse at low frequencies do not form fruiting bodies and form characteristic “frizzy” filament structures [22].
The molecular link between Frz and the motility systems requires three intermediate polarity proteins, MglA, MglB and RomR (Fig 1A). MglA, a bacterial Ras-like G-protein, activates the motility systems at the leading cell pole (Fig 1A, [23,24]). As all members of this family of molecular switches, MglA is active in association with GTP, a form that interacts with two motility system-specific proteins, AglZ (A-motility) and FrzS (S-motility) [19,20,25]. The polar localization of MglA results from combined regulations: (i), by RomR, which recruits MglA-GTP to the cell poles [26,27]; and (ii), by MglB, a MglA GTPase Activating Protein (GAP), which prevents MglA access to the lagging cell pole (Fig 1A, [23,24,28]). The polarity axis formed by MglA, MglB and RomR is stable unless it is contacted by upstream Frz signals, which provokes its dynamic switching and a reversal (Fig 1A, [29]).
In vivo, unknown signals are sensed by the Mcp homologue (FrzCD) and lead to the autophosphorylation of the kinase of the system (FrzE) from ATP via FrzA, the major CheW-like coupling protein (Fig 1B, [30]. FrzB, another CheW-like homolog may also participate in the activation of the pathway, although contrarily to FrzA, it is not required for all Frz-dependent responses (Fig 1B, [31]). Following activation, FrzE may then activate a handful of RR domains, including the C-terminal RR domain of FrzE (FrzERR), the FrzZ protein and the N-terminal RR domain of RomR itself, to contact the polarity proteins and activate the polarity switch (Fig 1B, [30,32–34]). Thus, the presence of multiple RR domains and a branch point at a circuit node formed by MglA asks how signaling from the FrzE kinase is channeled to the downstream motility systems (Fig 1B).
Overall, the motility regulation circuit is assembled from four interconnected modules with genetically separable functions: the control of the reversal frequency (switch control module), the coordination of the A- and S-motility systems (polarity control module) and the function of A- and S-motility (A- and S-motility modules, Fig 1B). By investigating the evolutionary origin of each module, we identified the core structure of the Frz pathway, genetically uncoupled A- and S-motility regulations and thus identified key regulations that led to the emergence of the evolved pathway. Remarkably, we find that adaptation of the ancestral circuit to A- and S-motility regulation required amplification of the signaling efficiency, suggesting that the evolution of signal reinforcement mechanisms may be linked to signal transduction pathway diversification in cells.
From an evolutionary perspective, the co-regulation of the A- and S-motility complexes by the Frz system implied the connection of two machineries of different origins. While Tfp systems are found in all deltaproteobacterial genomes [26,35], the A-motility Agl-Glt machinery is only present in Cystobacterineae, a Deltaproteobacteria family [15,16,36]. Thus, S-motility may be more ancient than A-motility and the emergence of A-motility in Cystobacterineae could have expanded the phenotypic repertoire and adaptive capabilities of this family of bacteria. Accordingly in Myxococcus, S-motility is required for fruiting body formation on soft and hard surfaces (0.5% vs 1.5% agar) but A-motility is only required on hard surfaces (Fig 2A and 2B). Importantly however, Frz regulation is required on both types of surfaces (Fig 2A and 2B). To further understand how co-regulation of A- and S-motility emerged in Cystobacterineae, we performed in-depth phylogenetic analyses of the components of the switch, polarity and motility control modules.
Measuring reversals of the S-motility system is difficult because they occur in large cell groups where single cells cannot be easily tracked. As mentioned in the introduction, S-motility results from the action of an extracellular EPS that provokes Tfp retraction [14]. In fact, the requirement for EPS can be bypassed, in single cell assays where the Myxococcus cells are allowed to move in a carboxymethylcellulose-coated microfluidic chamber (See Experimental procedures). In this system, cells move by Tfp-dependent motility only with an average speed of 1.7 ± 0.8 μm.min-1 (measured for 63 cells) and Frz-dependent cellular reversals are observed and coincide with pole-to-pole oscillations of MglA-YFP and FrzS-YFP, similar to agar (Figs 4A, 4B, and S8A) [19,24]. A-motility is not active on the cellulose surface because the cell velocity and the reversal frequency of an A-motility A− (aglQ) mutant are unchanged compared to WT cells (1.6 ± 0.9 μm.min-1, for 61 cells, S1 Movie and Fig 4B). Therefore, we used A+ strains for the rest of this study.
We used the cellulose assay and high-throughput automated tracking (S8B Fig and S2 Movie) to test whether the core pathway defined by the phylogenetic analysis is sufficient to regulate Tfp-dependent reversals. Remarkably, Tfp-dependent reversals were still observed in the absence of each of the predicted accessory components, FrzZ, FrzB and AglZ (Fig 4B). The frequency of Tfp-dependent reversals was equivalent to WT levels in the frzB and aglZ mutants, showing that these proteins are dispensable for the control of Tfp-dependent reversals (Fig 4B). In the case of the frzZ mutant, the situation was intermediate, the reversal frequency was affected compared to WT cells but it was still significantly higher than in the frzE mutant (Fig 4B).
Because the reversal frequency of the frzZ mutant was intermediate, we decided to use a more precise reversal-scoring test to determine unambiguously if frzZ mutant cells can still reverse in a Frz-dependent way. Our interpretation could be biased by so-called Tfp-dependent “stick-slip” motions, a Frz-independent Tfp-driven movement that could be mistakenly counted as reversals (S9A and S9B Fig, stick-slip motions are short range and generally appear distinct from bona fide reversals, [42]). Therefore, to score reversals with high accuracy, we monitored FrzS-YFP oscillations as a proxy for Frz-dependent reversals (Figs 4A, S9A and S9B). As expected from the reversal measurements, pole-to-pole switching of FrzS-YFP coincident with directional changes was reduced on average in the frzZ mutant, but they were still observed and sometimes up to WT levels (Fig 4C). Confirming this, pole-to-pole switching of FrzS-YFP was completely abolished in the frzE mutant and a frzE frzZ double mutant behaved like the frzE mutant (Fig 4C). Therefore, although the reduction in the reversal frequency of the frzZ mutant is significant (Fig 4B and 4C), Frz-dependent Tfp-reversals can occur in the absence of FrzZ (but not in the absence of FrzE) suggesting that FrzZ acts positively on Tfp-dependent reversals but is not strictly required for their activation.
To test the properties of the predicted core Frz pathway, we further constructed a frzB, frzZ, aglZ triple mutant (Δ3). The Δ3 mutant still showed Frz-dependent reversals and its reversal frequency was again lower, similar to that of the single frzZ mutant (Fig 4B). However, this lower reversal frequency did not translate into obvious S-motility developmental phenotypes because the Δ3 mutant formed fruiting bodies on soft agar, which is strictly S-motility dependent (Fig 4D). This multicellular development was Frz dependent because a Δ3 frzE quadruple mutant did not form aggregates in similar conditions (Fig 4D). On the contrary and as expected, the Δ3 mutant did not form aggregates on hard agar, a condition where A-motility is required (Fig 4D). We conclude that although the predicted core Frz pathway has a lower activity than the evolved pathway containing FrzZ, this activity could be sufficient to allow strictly S-motility-dependent behaviors, ie the ability to make fruiting bodies on soft 0.5% agar surfaces. Thus, the acquisition of FrzB, FrzZ and AglZ could have further adapted this primary circuit to the regulation of two motility machineries, at least in part by boosting the signaling activity (see below).
The results above show that FrzE signaling is still efficient in the frzZ mutant (albeit at lower efficiency), suggesting that another response regulator delivers FrzE signals to the polarity complex. RomR is a possible candidate because it contains a phosphorylatable Nt-RR domain and because it interacts directly with MglA. Thus, phosphorylation of RomR could link Frz signaling to the polarity switch [34]. The regulatory function of the RomR protein could not be tested in the past because RomR is essential for A-motility (likely because MglA is delocalized in a romR mutant, [26,27,34]), and reversal frequencies are traditionally measured on A-motile cells. In the cellulose system, a romR deletion mutant showed WT Tfp-dependent motility (1.4 ± 0.8 μm.min-1, for 65 cells, S3 Movie) but it was dramatically affected in its ability to reverse in the FrzS-YFP oscillation assay (Fig 4C). A frzZ romR double mutant also had abolished reversals showing that RomR acts downstream from FrzZ in the regulation (Fig 4C). Importantly, while occasional reversals could be observed in frzE mutants, reversals were very rarely observed in romR and frzE romR mutants (Fig 4C). We conclude that RomR acts downstream from FrzE and FrzZ in the reversal pathway and could thus be a central output protein of the Frz pathway.
Both the frzZ and the frzB frzZ aglZ Δ3 mutants have similar and lower reversal frequencies than WT cells (Fig 4B), suggesting that the presence of FrzZ increases the steady-state signaling activity. Because the Δ3 mutant still forms fruiting bodies on 0.5% agar (Fig 4D), lower Frz activity may only translate in a developmental defect when A-motility is required. If so, differential Frz signaling activities may be required to regulate S-motility-dependent behaviors (ie development on soft agar) and A- and S-motility dependent behaviors (ie development on hard agar). This hypothesis can be tested in a strain expressing the frz operon under the control of an IPTG-inducible promoter (Fig 5A) where Frz activity should be related to the level of Frz protein expression. In the absence of IPTG, such strain formed fruiting bodies on 0.5% agar but not on 1.5% agar, indicating that promoter leakage is even sufficient to restore S-motility-dependent aggregation (Fig 5A). As expected, the addition of IPTG also restored fruiting body formation on 1.5% agar (Fig 5A). Thus, developmental processes that require the S-motility system require lower Frz activity levels than developmental processes that require A- and S-motility.
The FrzZ protein was thought to be central to Frz regulation because a frzZ mutant displays a typical frz phenotype on development hard agar [30]. However, if this phenotype is linked to lower Frz activity, it could be bypassed if Frz signaling is artificially increased in a frzZ mutant. To test this possibility, we took advantage of a chemical, Isoamyl alcohol (IAA) known to activate frz-dependent reversals. Although the exact target of IAA is not known, it appears to act on and de-methylate the FrzCD receptor (directly or indirectly, [43]) and its action is strictly Frz-dependent [31]. When added to hard developmental agar, IAA did not affect development of the WT strain up to concentrations of 0.075%, after which IAA blocked fruiting body formation (Fig 5B). A frzE mutant showed the typical frz phenotype and as expected, this phenotype was neither rescued nor modified by IAA addition (Fig 5B). Consistent with previous observations, the frzZ phenotype was indistinguishable from the frzE phenotype in absence of IAA (Fig 5B). However, and contrarily to the frzE mutant, IAA rescued aggregation of the frzZ mutant up to 0.15% IAA, a high dose that disrupts aggregation in WT cells (Fig 5B). Therefore, artificial activation of Frz signaling rescues the signaling defect of the frzZ mutant, suggesting that FrzZ acts to elevate Frz activity, allowing regulation of the two motility systems.
To further investigate the function of FrzZ in Frz-signaling activity, we tested the contribution of FrzZ in a strain where the Frz receptor is hyper active. So-called frzon mutations map to the C-terminal domain of FrzCD and result in the expression of a truncated receptor protein (FrzCDc) [31]. Because the expression of FrzCDc is trans-dominant to the expression of FrzCD, frzon mutations have been suggested to hyper activate Frz signaling [44]. To first test this assumption, we purified FrzCD, FrzCDc, FrzA and the kinase domain of FrzE (FrzEkinase, autophosphorylation of FrzE in vitro can only be detected if FrzERR is removed due to its phosphate sink activity, [32]) and compared the capacity of FrzCD and FrzCDc to activate FrzEkinase autophosphorylation from ATP in vitro. While both FrzCD and FrzCDc were able to activate the autokinase activity of FrzEkinase in a dose-dependent manner, FrzCDc was a more potent activator (Fig 6A and 6B). Thus, frzon mutations induce a hyper signaling state of the FrzE kinase.
We then proceeded to test the contribution of FrzZ to Frz-signaling in a frzon background. In the cellulose chamber assay, frzon mutants reversed at high frequency, as expected (Fig 6C, compare with the WT reversal frequency in Fig 4B). Remarkably, a frzon frzZ mutant still reversed but at a reversal frequency similar to the reversal frequency of the frzZ mutant (Fig 6C, compare with Fig 4B). A frzon romR mutant did not reverse (Fig 6C), confirming that Frz signaling is disrupted in absence of RomR. Thus, FrzZ acts downstream from the FrzCD receptor and exerts a positive effect on the transduction of Frz signals to the polarity switch.
To investigate how FrzZ exerts its positive effect on Frz signaling activity, we took advantage of the cellulose system to develop a high-resolution single cell assay in which FrzS-YFP oscillations are measured directly as a function of stimulation levels; here, the addition of increasing doses of IAA. In this assay, we first established that reversals were induced by IAA in dose- and FrzE-dependent manners. In WT cells, IAA induced a sharp dose-dependent reversal response until a plateau was reached at an IAA concentration of 0.15% (Fig 7A and 7B). As expected, a frzE mutant only reversed occasionally whatever the IAA concentration, showing that the observed IAA effects are strictly Frz-dependent (Fig 7A and 7B). Consistent with previous results, a frzZ mutant still showed an IAA-dependent response but it was more gradual than the WT and showed lower amplitude at the higher IAA doses (Fig 7A and 7B).
We also used the IAA single cell assay to test the function of FrzERR, the FrzE receiver domain. The FrzERR domain is not absolutely essential for Frz signaling and has been suggested to inhibit signaling because FrzERR inhibits FrzE autophosphorylation in vitro (Fig 1B, [32]). However, how such inhibition participates in Frz signaling is unclear. In the IAA assay, a frzERR mutant showed a behavior opposite of that of the frzZ mutant: this mutant reversed more than WT cells which was apparent IAA doses ranging between 0–0.075% (Fig 7A and 7B). Thus, FrzERR prevents Frz-signaling at low stimulation levels. Remarkably, at concentrations higher than 0.075% the frzERR mutant stopped reversing and no longer responded to IAA (Fig 7A and 7B). We hypothesize that this collapse is the result of an over-signaling state that disrupts Frz signaling function because (i), a frzERR frzZ double mutant showed a composite phenotype: frzERR-type reversal frequencies at IAA doses ≤ 0.03% and frzZ-type reversal frequencies at the higher IAA concentrations (Fig 7A and 7B); and, (ii), a frzon frzERR double mutant has a strongly reduced reversal frequency (Fig 6C). In the enteric Che pathway, high chemoreceptor stimulation also inhibits signaling suggesting that similar mechanisms are at work in the Frz system [45,46]
All together, the IAA experiments and the frzon mutant reversal frequencies suggest that FrzERR and FrzZ act independently in the pathway, FrzERR blocking activation at low signal levels and FrzZ amplifying signal transmission to allow a rapid switch-like response to stimulation (which is required for the regulation of A- and S-motility).
The Myxococcus motility apparatuses (A- and S-motility) allow this bacterium to perform an array of multicellular behaviors, which likely increases the competitiveness of this bacterium in the environment. Our results are consistent with an evolutionary scenario whereby these behaviors emerged following the stepwise assembly of four distinct functional modules, the Frz chemosensory apparatus, the polarity proteins MglAB, and the A- and S-motility systems, in a regulation pathway. Given that the studied genes likely form a minimal regulatory set and that not all players and interactions have been identified, a complete evolutionary scenario cannot be proposed. Nevertheless, we identify two major steps in the evolution of the pathway:
In bacteria, the cooption of signaling modules formed by Mcp (FrzCD), CheW (FrzA), CheA (FrzE), CheR (FrzF) and CheB (FrzG) homologues underlies the emergence of a large number of chemosensory-type pathways [11]. Therefore, it is likely that the primary deltaproteobacterial S-motility regulation apparatus first evolved by recruitment of MglAB to one such chemosensory system. RomR is a possible candidate to link Frz signaling to polarity regulation because this protein shares a similar evolutionary history (Fig 3A and 3B), it interacts directly with MglA [26,27] and it is essential for reversals (this work). However, we have not demonstrated that FrzE is the RomR kinase and thus formally, other intermediate proteins may relay FrzE signals to RomR. Nevertheless, our genetic analysis suggests that RomR functions as a core protein downstream from FrzE in the regulation pathway. Downstream from MglA, it is also possible that FrzS does not constitute the only link to the S-motility apparatus [12,18,47], but because the interaction between MglA and FrzS is essential for S-motility [25], the acquisition of FrzS was probably a key step for the emergence of the primary pathway.
Diversification of the primary pathway to the regulation of A- and S-motility occurred in the Cystobacterinaea family of bacteria and coincided with profound modifications of the regulation system. Additions of AglZ and FrzZ were probably key to adapt the primary circuit to the emergence of a branch point downstream from the FrzE kinase. First, the duplication of a frzS ancestor gene and connection of AglZ to the A-motility apparatus might have created the branch point itself, connecting A-motility to MglA regulation. Aside from numerous amino acid substitutions, the main difference between AglZ and FrzS resides in the lengths of their coiled-coil domains. Thus, AglZ might have evolved a new interaction with the Agl-Glt machinery (ie via the coiled-coil domain) while retaining its ability to interact with MglA. Consistent with this, AglZ also interacts with MglA and it co-localizes with the Agl-Glt machinery [16,25,39,48]. Second, FrzZ was incorporated into the upstream regulatory circuit, allowing signal partitioning to the two motility systems (see below). Other changes in the pathway not studied here may also participate in this regulation, including domain changes occurring in FrzCD and FrzG and the acquisition of FrzB (S4 Table and Figs 3A, 3B, S3A and S3E). The function of FrzB does not appear redundant to that of FrzA, the major Frz CheW protein, because Bustamante et al. [31] showed that a frzA mutant is indistinguishable from a frzCD or a frzE mutant, while a frzB mutant still responds to IAA stimulation in a bulk agar motility assay [31], which is consistent with our single cell experiments (Fig 4B). This lack of redundancy may not be surprising because FrzB is not phylogenetically related to FrzA (S3B and S3F Fig). It will be interesting to determine the exact function of FrzB and its potential connection to the branching of A-motility in the future.
Using a high-resolution microfluidic single cell assay we were able to elucidate the individual contribution of the RR domain proteins of the pathway. The IAA stimulation Frz-dependent response curve showed a biphasic-type response with an overall sigmoidal shape (Fig 8). Because this response is entirely abolished in a frzERR frzZ mutant but not in the respective individual mutants (Fig 7A and 7B), we conclude that FrzERR and FrzZ independently set distinct regimes of the signaling apparatus (Fig 8).
More precisely, FrzERR inhibits signaling at low stimulation levels, blocking noisy activation of the polarity switch (Fig 8). The inhibition mechanism is likely that of a phosphate sink because hybrid kinases phosphotransfer to their covalently-attached receiver domain at very high efficiency and the half-life of the Aspartate-phosphate bond on FrzERR is very short lived, a property of response regulators with phosphate sink functions [32,49,50]. Signal inhibition by the receiver domain of hybrid kinases is also emerging in other systems [51] and could be a widespread regulation of hybrid kinases.
At higher stimulation levels, the FrzERR inhibitory capacity becomes saturated and allows FrzE to phosphorylate the other receiver proteins of the pathway, FrzZ [30], possibly RomR or any other unidentified receiver domain of the pathway. The combined action of these phosphorylation events results in a steep response (Fig 8). Phosphorylation events downstream from FrzE and independent of FrzZ would transduce the signal to the MglAB proteins, this could occur through RomR or other uncharacterized regulators. In parallel, the phosphorylation of FrzZ amplifies the signal, impacting both the slope and amplitude of the response (Fig 8). Remarkably, processes that require S-motility alone can accommodate a graded response of moderate amplitude, while processes that require A- and S-motility require FrzZ amplification (Fig 8). It will be essential to determine how Frz signals are processed by MglA and each motility system to understand these signaling intensity requirements. At the molecular level, FrzZ must act between the FrzE kinase and RomR because (i), a hyper active FrzE kinase (frzon mutation) still requires FrzZ for maximal signal transmission (Fig 6C), suggesting that FrzZ does not exert its action by feedback stimulation of the autokinase activity of FrzE; and ii), a frzZ romR mutant behaves like a romR mutant (Fig 4C), showing clear epistatic relationships. FrzZ is a dual response regulator protein and it will be important to determine how the phosphorylation of each receiver domain contributes to signal amplification. The FrzZ phosphorylation sites appear partially redundant but only the phosphorylation of D52 and not D220 is important for the polar localization of FrzZ [33]. At the cell pole, the phosphorylated form of FrzZ could interact directly with RomR to facilitate the reversal switch.
Two-component cascades frequently employ accessory response regulator domains to achieve a variety of functions in phosphorelays, signal inhibition or negative feedback loops [52,53]. To our knowledge, this is the first time that a signal amplifier function is identified for a RR protein and because FrzZ-like CheY-CheY fusion proteins are predicted in other complex two component systems (Survey of the Microbial Signal Transduction database,[54]), characterizing the amplification mechanism may generally impact our understanding of bacterial signal transduction.
In summary, this work reveals the modular structure of the Frz signal transduction pathway and suggests that the new pathway branch emerged at least in part by (i), gene duplication followed by new functional specialization (ie the emergence of AglZ and its connection to the A-motility system) and (ii), by the re-wiring of the signal flow, in this case an amplification system to partition signals at the branch point. These modifications are linked to the molecular structure of the Myxococcus regulation circuit where MglA acts as a regulation checkpoint, integrating upstream Frz signals into the coordinate regulation of the A- and S-motility machineries. In principle, amplification systems could operate in any signal transduction pathways that converge to the regulation of a checkpoint protein (also called a master regulator). For example and conceptually similar to the Myxococcus system, eukaryotic Ras-like G-proteins must also partition their activity to several output proteins, ie during chemotaxis [55]. While different circuit designs could have evolved to achieve this function, the Myxococcus system could provide valuable lens to study the evolutionary and mechanistic processes that allowed one such diversification.
Strains, plasmids and primers used for this study are listed in S1, S2, and S3 Tables. In general, M. xanthus strains were grown at 32°C in CYE rich media as previously described [31]. Plasmids were introduced in M. xanthus by electroporation. Mutants and transformants were obtained by homologous recombination based on a previously reported method [31]. E. coli cells were grown under standard laboratory conditions in Luria-Bertani broth supplemented with antibiotics, if necessary.
Unless otherwise specified, soft and hard agar motility and development assays were performed as previously described [31]. In general, cell were grown up to an OD = 0.5 and concentrated ten times before they were spotted (10μL) on CYE or CF [31] 0.5% (soft) agar or 1.5% (hard) agar plates for motility or for developmental assays. Colonies were photographed after 48 H or 72 H for motility or development, respectively. Developmental assays in the presence of Isoamyl alcohol (IAA, Sigma Aldrich) were performed similarly except that plates also contained IAA at appropriate concentrations.
Single cell Tfp-dependent motility assays were initially developed by Sun et al. [13] in a system where Myxococcus cells are overlaid in methylcellulose. However, in this assay, reversals as observed on agar are not observed. In this media, the cells are loosely attached to the glass surface and they systematically detach and become tethered by one cell pole before a directional change is observed [13]. Many of these events may well result from actual reversal events, but other Tfp-dependent motions have been observed including stick-slip motions, sling-shot motions and walking up-right [42,56,57]. To avoid confusion linked to complex Tfp-dependent movements, we sought to optimize the methylcellulose assay. For this, homemade PDMS glass microfluidic chambers [58] were treated with 0.015% carboxymethylcellulose after extensive washing of the glass slide with water. For each experiment, 1mL of a CYE grown culture of OD = 0.5–1 was injected directly into the chamber and the cells were allowed to settle for 5 min. Motility was assayed after the chamber was washed with TPM 1mM CaCl2 buffer [58]. For IAA injections, IAA solutions made in TPM 1mM CaCl2 buffer at appropriate concentrations were injected directly into the channels and motility was assayed directly under the microscope. In TPM 1mM CaCl2, we found that most WT motile cells left the field of view before reversing, making statistically reliable measurements of reversal frequencies difficult. This low reversal frequency is due to the absence of stimulating signals in these conditions. To increase the reversals counts and unless otherwise stated, Frz signaling was stimulated by adding 0.1–0.15% IAA to the TPM mix. Time-lapse experiments were performed as previously described (Ducret et al., 2013) using a Nikon TE2000-E-PFS inverted epifluorescence microscope.
Image analysis was performed with a specific library of functions written in Python and adapted from available plugins in FIJI/ImageJ [59]. Cells were detected by thresholding the phase contrast images after stabilization. Cell tracking was obtained by calculating all objects distances between two consecutive frames, thus selecting the nearest objects. The computed trajectories were systematically verified manually and when errors were encountered, the trajectories were removed. The analysis of the trajectories is done automatically by a Python script that calculates the angle formed by the segments between the center of the cell at time t, the center of the cell at time t-1 and the center at time t+1. Directional changes were scored as reversals when cells switched their direction of movement and the angle between segments was less than 90°. For non-reversing strains, the number of reversals for each cells was plotted against time using R software (http://www.R-project.org/). For strains that frequently reversed, the mean time between two reversals for each cells was plotted against time using R software.
To further discriminate bona fide reversal events from stick-slip motions, the fluorescence intensity of FrzS-YFP was measured at cell poles over time. For each cell that was tracked, the fluorescence intensity and reversal profiles were correlated to distinguish bona fide reversals from stick-slip events with the R software. When a directional change was not correlated to a switch in fluorescence intensity, this change was discarded as a stick-slip event. The number of reversals was plotted against time using R software.
Statistics were done using R software: Wilcox test was used when the number of cells was less than 40 in at least one of the two populations compared, and student test (t-test) was used for a number of cells higher than 40.
The genes encoding FrzEkinase, FrzA, FrzCD and FrzCDc were amplified by PCR using M. xanthus DZ2 chromosomal DNA as template and the forward and reverse primers listed in S3 Table. The amplified product was digested with the appropriate restriction enzymes, and ligated either into the pETPhos or pGEX plasmids generating pETPhos_frzEkinase, pETPhos_frzCD, pETPhos_frzCDc and pGEX_frzA which were used to transform E. coli BL21(DE3)Star cells in order to overexpress His-tagged or GST-tagged proteins. All constructs were verified by DNA sequencing. Recombinant strains harboring the different constructs were used to inoculate 400 ml of LB medium supplemented with glucose (1mg/mL) and ampicillin (100μg/ml), and the resulting cultures were incubated at 25°C with shaking until the optical density of the culture reached an OD = 0.6. IPTG (0.5 mM final) was added to induce the overexpression, and growth was continued for 3 extra hours at 25°C. Purification of the His-tagged/GST-tagged recombinant proteins was performed as described by the manufacturer (Clontech/GE Healthcare).
In vitro phosphorylation assay was performed with E. coli purified recombinant proteins. 4 μg of FrzEkinase were incubated with 1μg of FrzA and increasing concentrations (0.5 to 7μg) of either FrzCD or FrzCDc in 25 μl of buffer P (50 mM Tris-HCl, pH 7.5; 1 mM DTT; 5 mM MgCl2; 50mM KCl; 5 mM EDTA; 50μM ATP, 10% glycerol) supplemented with 200 μCi ml-1 (65 nM) of [γ-33P]ATP (PerkinElmer, 3000 Ci mmol-1) for 10 minutes at room temperature in order to obtain the optimal FrzEkinase autophosphorylation activity. Each reaction mixture was stopped by addition of 5 × Laemmli and quickly loaded onto SDS-PAGE gel. After electrophoresis, proteins were revealed using Coomassie Brilliant Blue before gel drying. Radioactive proteins were visualized by autoradiography using direct exposure to film (Carestream).
669 bp upstream from frzCD were amplified with primers CDind1 (gaattcATGTCCCTGGACACCCCCAACGA) and CDind2 (actagtCATGGCCTGGATGAACTCGCCAAT) and cloned into pGEM T-easy (Promega) to obtain plasmid pEM140. pEM140 was digested with SpeI and EcoRI and the excised DNA fragment was cloned into pLacI (a derivative of pAK20 [60]) previously digested with the same enzymes. The resulting plasmid, pEM143, is a derivative of pBBR1MCS carrying the lacI gene under its promoter and followed by the first 669 of frzCD and thus its integration by homologous recombination places the entire frz operon under IPTG control.
Developmental plate assays were conducted in the presence (0.5 mM) or absence of IPTG. For western blotting, strains were grown overnight with or without appropriate concentrations of IPTG. The cultures were concentrated to OD = 4 and western blotting was performed as previously described with 1/10,000 dilutions of anti-FrzCD (Bustamante et al., 2004).
A local protein database containing the 2,316 complete prokaryotic proteomes available in the NCBI (http://www.ncbi.nlm.nih.gov/) as of May 23, 2013 was built. This database was queried with the BlastP program (default parameters, [61]) using the full length sequences of the signaling proteins (FrzF (MXAN_4138), FrzG (MXAN_4139), FrzE (MXAN_4140), FrzCD (MXAN_4141), FrzB (MXAN_4142), FrzA (MXAN_4143) and FrzZ (MXAN_4144)), the polarity control proteins (MglA (MXAN_1925), MglB (MXAN_1926) and RomR (MXAN_4461)) and the downstream proteins (FrzS (MXAN_4149) and AglZ (MXAN_2991)) of M. xanthus as a seed. The homology was assessed by visual inspection of each BlastP output (no arbitrary cut-offs on the E-value or score). The retrieved sequences were aligned using MAFFT version 7 (default parameters, [62]). Regions where the homology between amino acid positions was doubtful were removed using the BMGE software (BLOSUM30 option; [63]).
For each protein, preliminary phylogenetic analyses were performed using FastTree v.2 using a gamma distribution with four categories [64]. Most of the studied proteins belong to very large protein families. Based on the resulting trees, the subfamilies containing the sequences from M. xanthus were identified and selected for further phylogenetic investigations. The corresponding sequences were realigned using MAFFT version 7 with the linsi option, which ensures accurate alignments. The resulting alignments were trimmed with BMGE as previously described.
Maximum likelihood (ML) trees were computed using PHYML version 3.1 [65] with the Le and Gascuel (LG) model (amino acid frequencies estimated from the dataset) and a gamma distribution (4 discrete categories of sites and an estimated alpha parameter) to take into account evolutionary rate variations across sites. Branch robustness was estimated by the non-parametric bootstrap procedure implemented in PhyML (100 replicates of the original dataset with the same parameters). Bayesian inferences (BI) were performed using Mrbayes 3.2.2 [66] with a mixed model of amino acid substitution including a gamma distribution (4 discrete categories). MrBayes was run with four chains for 1 million generations and trees were sampled every 100 generations. To construct the consensus tree, the first 2000 trees were discarded as “burn in”.
The phylogenetic signal can be substantially increased by combining multiple sequence alignments of proteins involved in the same cellular function/biological process and sharing a common evolutionary history in a single large alignment (also called supermatrix), [16,67–71]. Among the 12 studied genes, we showed that FrzF, FrzG, FrzCD and FrzE are always clustered together in genomes and share a similar evolutionary history. These genes were thus combined to build a supermatrix (S4 Fig). For similar reasons, a second supermatrix was built by combining MglA and MglB (S2 Fig). The ML and BI phylogenetic trees corresponding to these two supermatrices were inferred as previously described [16].
The 79 complete proteomes of Delta/Epsilonproteobacteria available at the NCBI in May 17, 2013 were retrieved and assembled in a local database (S5 Table). We used SILIX to build the protein families of homologous sequences present in these genomes (default parameters; [72]).The homologous sequences corresponding to protein families present exactly in a single copy per genome (17 proteins) were aligned using MAFFT Version 7 (linsi option), trimmed with BMGE and combined to build a large supermatrix (6958 positions). The ML phylogenetic tree corresponding to this large supermatrix was inferred with PhyML, as described above.
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10.1371/journal.pgen.1007915 | Functional lability of RNA-dependent RNA polymerases in animals | RNA interference (RNAi) requires RNA-dependent RNA polymerases (RdRPs) in many eukaryotes, and RNAi amplification constitutes the only known function for eukaryotic RdRPs. Yet in animals, classical model organisms can elicit RNAi without possessing RdRPs, and only nematode RNAi was shown to require RdRPs. Here we show that RdRP genes are much more common in animals than previously thought, even in insects, where they had been assumed not to exist. RdRP genes were present in the ancestors of numerous clades, and they were subsequently lost at a high frequency. In order to probe the function of RdRPs in a deuterostome (the cephalochordate Branchiostoma lanceolatum), we performed high-throughput analyses of small RNAs from various Branchiostoma developmental stages. Our results show that Branchiostoma RdRPs do not appear to participate in RNAi: we did not detect any candidate small RNA population exhibiting classical siRNA length or sequence features. Our results show that RdRPs have been independently lost in dozens of animal clades, and even in a clade where they have been conserved (cephalochordates) their function in RNAi amplification is not preserved. Such a dramatic functional variability reveals an unexpected plasticity in RNA silencing pathways.
| RNA interference (RNAi) is a conserved gene regulation system in eukaryotes. In non-animal eukaryotes, it necessitates RNA-dependent RNA polymerases (“RdRPs”). Among animals, only nematodes appear to require RdRPs for RNAi. Yet additional animal clades have RdRPs and it is assumed that they participate in RNAi. Here, we find that RdRPs are much more common in animals than previously thought, but their genes were independently lost in many lineages. Focusing on a species with RdRP genes (a cephalochordate), we found that it does not use them for RNAi. While RNAi is the only known function for eukaryotic RdRPs, our results suggest additional roles. Eukaryotic RdRPs thus have a complex evolutionary history in animals, with frequent independent losses and apparent functional diversification.
| Small interfering RNAs (siRNAs) play a central role in the RNA interference (RNAi) response. Usually loaded on a protein of the AGO subfamily of the Argonaute family, they recognize specific target RNAs by sequence complementarity and typically trigger their degradation by the AGO protein [1]. In many eukaryotic species, normal siRNA accumulation requires an RNA-dependent RNA polymerase (RdRP). For example in plants, RdRPs are recruited to specific template RNAs and they generate long complementary RNAs [2–4]. The template RNA and the RdRP product are believed to hybridize, forming a long double-stranded RNA which is subsequently cleaved by Dicer nucleases into double-stranded siRNAs (reviewed in [5]). In fungi, RdRPs have also been implicated in RNAi and in RNA-directed heterochromatinization [6–9], but the exact nature of their products remains elusive: fungal RdRPs are frequently proposed to polymerize long RNAs which can form Dicer substrates after annealing to the RdRP template [10–12]. But the purified Neurospora crassa, Thielavia terrestris and Myceliophthora thermophila QDE-1 RdRPs tend to polymerize essentially short (9–21 nt) RNAs in vitro, suggesting that they may generate Dicer-independent small RNAs [13, 14]. In various unicellular eukaryotes, RdRPs have also been implicated in RNAi and related mechanisms (e.g., see [15, 16]). It is usually believed that their products are long RNAs that anneal with the template to generate a Dicer substrate, and that model has gained experimental support in one organism, Tetrahymena [17].
Among eukaryotes, animals are thought to constitute an exception: most classical animal model organisms (Drosophila and mammals) can elicit RNAi without the involvement of an RdRP [1]. Only one animal model organism was shown to require RdRPs for RNAi: the nematode Cænorhabditis elegans [18, 19]. In nematodes, siRNAs made by Dicer only constitute a minor fraction of the total siRNA pool: such “primary” siRNAs recruit an RdRP on target RNAs, triggering the production of short antisense RNAs named “secondary siRNAs” [20–22]. Secondary siRNAs outnumber primary siRNAs by ≈ 100-fold [20] and the major class of secondary siRNAs (the so-called “22G RNAs”) is loaded on proteins of the WAGO subfamily of the Argonaute family [22, 23]. WAGO proteins appear to be unable to cleave RNA targets [23]. Yet WAGO/secondary siRNA/cofactor complexes appear to be much more efficient at repressing mRNA targets than AGO/primary siRNA/cofactor complexes [24], possibly by recruiting another, unknown, nuclease. In contrast to Dicer products (which bear a 5′ monophosphate), direct RdRP products bear a 5′ triphosphate. 22G RNAs are thus triphosphorylated on their 5′ ends [20]. Another class of nematode RdRP products, the “26G RNAs”, appears to bear a 5′ monophosphate, and it is not clear whether they are matured from triphosphorylated precursors, or whether they are directly produced as monophosphorylated RNAs [25–27].
The enzymatic activity of RNA-dependent RNA polymerization can be mediated by several unrelated protein families [28]. Most of these families are specific to viruses (e.g., PFAM ID #PF00680, PF04196 and PF00978). Viral RdRPs are involved in genome replication and transcription in RNA viruses, and they share common structural motifs [29]. On the other hand, RdRPs involved in RNAi in plants, fungi and nematodes belong to a family named “eukaryotic RdRPs” (PFAM ID #PF05183). While viral RdRPs are conceivably frequently acquired by virus-mediated horizontal transfer, members of the eukaryotic RdRP family are thought to be inherited vertically only [30]. The eukaryotic RdRP family can be further divided into three subfamilies, named α, β and γ based on sequence similarity. Phylogenetic analyses suggest these three subfamilies derive from three ancestral RdRPs that could have coexisted in the most recent common ancestor of animals, fungi and plants [31].
Besides eukaryotic RdRPs, other types of RdRP enzymes have been proposed to exist in various animals. It has been suggested that human cells express an atypical RdRP, composed of the catalytic subunit of telomerase and a non-coding RNA [32]. While that complex exhibits RdRP activity in vitro, functional relevance of that activity is unclear, and other mammalian cells were shown to perform RNAi without RdRP activity [33]. More recently, bat species of the Eptesicus clade were shown to possess an RdRP of viral origin, probably acquired upon endogenization of a viral gene at least 11.8 million years ago [34].
Here we took advantage of the availability of hundreds of metazoan genomes to draw a detailed map of predicted RdRP genes in animals. We found RdRP genes in a large diversity of animal clades, even in insects, where they had escaped detection so far. Even though RdRP genes are found in diverse animal clades, they are lacking in many species, indicating that they were frequently and independently lost in many lineages. Furthermore, the presence of RdRP genes in non-nematode genomes raises the possibility that additional metazoan lineages possess an RdRP-based siRNA amplification mechanism. We sequenced small RNAs from various developmental stages in one such species with 6 candidate RdRP genes, the cephalochordate Branchiostoma lanceolatum, using experimental procedures that were designed to detect both 5′ mono- and tri-phosphorylated RNAs. Our analyses did not reveal any evidence of the existence of secondary siRNAs in that organism. While RNAi is the only known function for eukaryotic RdRPs, we thus propose that Branchiostoma RdRPs do not participate in RNAi.
Predicted animal proteome sequences were downloaded from the following databases: NCBI (ftp://ftp.ncbi.nlm.nih.gov/genomes/), VectorBase (https://www.vectorbase.org/download/), FlyBase (ftp://ftp.flybase.net/releases/FB2015_03/), JGI (ftp://ftp.jgi-psf.org/pub/JGI_data/), Ensembl (ftp://ftp.ensembl.org/pub/release-81/fasta/), WormBase (ftp://ftp.wormbase.org/pub/wormbase/species/) and Uniprot (http://www.uniprot.org/). The predicted Branchiostoma lanceolatum proteome was obtained from the B. lanceolatum genome consortium. RdRP HMMer profiles were downloaded from PFAM v. 31.0 (http://pfam.xfam.org/): 19 viral RdRP family profiles (PF00602, PF00603, PF00604, PF00680, PF00946, PF00972, PF00978, PF00998, PF02123, PF03035, PF03431, PF04196, PF04197, PF05788, PF05919, PF07925, PF08467, PF12426, PF17501) and 1 eukaryotic RdRP family profile (PF05183). Candidate RdRPs were selected by hmmsearch with an E-value cutoff of 10−2. Only those candidates with a complete RdRP domain according to NCBI’s Conserved domain search tool (https://www.ncbi.nlm.nih.gov/Structure/bwrpsb/bwrpsb.cgi) were considered (tolerating up to 20% truncation on either end of the domain). One identified candidate, in the bat Rhinolophus sinicus, appears to be a plant contaminant (it is most similar to plant RdRPs, and its genomic scaffold [ACC# LVEH01002863.1] only contains that gene): it was not included in Fig 1 and in Supplementary S1 Fig.
The Branchiostoma Hen1 candidate was identified using HMMer on the predicted B. lanceolatum proteome, with an HMMer profile built on an alignment of Drosophila melanogaster, Mus musculus, Danio rerio, Nematostella vectensis and Arabidopsis thaliana Hen1 sequences.
Amino acid sequences of the eukaryotic RdRP domain (Pfam #PF05183) were retrieved from PFAM [35], and supplemented with the RdRP domains of the proteins identified in the 538 animal proteomes (cf above). Sequences were aligned using hmmalign [36] using the HMM profile of the PF05183 RdRP domain. Sequences for which the domain was incomplete were deteled from the alignment. Sites used to reconstruct the phylogenetic tree were selected using trimAl [37] on the Phylemon 2.0 webserver [38]. Bayesian inference (BI) tree was inferred using MrBayes 3.2.6 [39], with the model recommended by ProtTest 1.4 [40] under the Akaike information criterion (LG+Γ), at the CIPRES Science Gateway portal [41]. Two independent runs were performed, each with 4 chains and one million generations. A burn-in of 25% was used and a fifty majority-rule consensus tree was calculated for the remaining trees. The obtained tree was customized using FigTree v.1.4.0.
Mediterranean amphioxus (Branchiostoma lanceolatum) males and females were collected at le Racou (Argelès-sur-mer, France) and were induced to spawn as previously described [42]. Embryos were obtained after fertilization in Petri dishes filled with filtered sea water and cultivated at 19°C. Total RNA was extracted from 8, 15, 36 and 60 hours post fertilization (hpf) embryos (three independent batches for each stage, pooled before small RNA gel purification) as well as from males (6 pooled individuals) and females (4 pooled individuals) using the RNeasy mini kit (for embryonic samples) and the RNeasy midi kit (for adult samples) (Qiagen).
The BL09945 locus was PCR-amplified from adult female DNA, cloned in the pGEM-T easy vector (cat. #A1360; Promega, Madison, WI, USA) and sequenced by MWG Eurofins Genomics (Ebersberg, Germany).
For Small RNA-Seq, 18–30 nt RNAs were gel-purified from total RNA (using between 92 and 228 μg total RNA per sample). One quarter of the small RNA preparation was kept untreated before library preparation (for “Libraries #1”). One quarter was incubated for 10 min at room temperature in 100 μL of freshly-prepared 60 mM sodium borate (pH = 8.6), 25 mM sodium periodate, then the reaction was quenched with 10 μL glycerol (for “Libraries #2”). One quarter was treated with 1.25 U Terminator exonuclease (Epicentre, Madison, WI, USA) in 25 μL 1X Terminator reaction buffer A for 1h at 30°C, then the reaction was quenched with 1.25 μL 500 mM EDTA (pH = 8.0) and ethanol-precipitated. RNA was then treated with 5 U Antarctic phosphatase (New England Biolabs, Ipswich, MA, USA) in 20 μL 1X Antarctic phosphatase buffer for 30 min at 37°C, the enzyme was heat-inactivated, then RNA was precipitated, then phosphorylated by 15 U T4 PNK (New England Biolabs) with 50 nmol ATP in 50 μL 1X T4 PNK buffer for 30 min at 37°C, then the enzyme was heat-inactivated (for “Libraries #3”). One quarter was treated successively with Terminator exonuclease, Antarctic phosphatase, T4 PNK then boric acid and sodium periodate, with the same protocols (for “Libraries #4”). Small RNA-Seq libraries were then generated using the TruSeq Small RNA library preparation kit (Illumina, San Diego, CA, USA), following the manufacturer’s instructions.
Libraries were sequenced by the MGX sequencing facility (CNRS, Montpellier, France). Read sequences were aligned on the B. lanceolatum genome assembly [43] using bowtie2. A database of abundant non-coding RNAs was assembled by a search for orthologs for human and murine rRNAs, tRNAs, snRNAs, snoRNAs and scaRNAs; deep-sequencing libraries were also mapped on that database using bowtie2, and matching reads were flagged as “abundant ncRNA fragments”. For pre-miRNA annotation, every B. lanceolatum locus with a Blast E-value ≤10−6 to any of the annotated B. floridae or B. belcheri pre-miRNA hairpins in miRBase v.22 was selected. Reads matching these loci were identified using bowtie2. For the measurement of miRNA abundance during development, hairpins were further screened for their RNAfold-predicted secondary structure and their read coverage: Supplementary S1 Table only lists unbranched hairpins with at least 25 bp in their stem, with a predicted ΔGfolding ≤ −15 kcal.mol−1, generating mostly 21- to 23-mer RNAs, and with at least 20 ppm read coverage on any nucleotide of the hairpin.
RNA-Seq data was taken in [43] for embryonic and juvenile samples. Adult sample libraries were prepared and sequenced by “Grand plateau technique régional de génotypage” (SupAgro-INRA, Montpellier). mRNA abundance data was extracted using vast-tools [44].
Small RNA reads that fail to map on the B. lanceolatum genome or transcriptome according to bowtie2 were collected and assembled using velvet [45], with k values ranging from 9 to 19 for better sensitivity [46].
Contigs at least 50 bp in length were then compared to the NCBI non-redundant nucleotide collection (as of October 31, 2018) by megablast on the NCBI server with default parameters. Contigs with a detected similarity to known sequences in the collection were annotated with phylogenetic information using the NCBI “Taxonomy” database.
Source code, detailed instructions, and intermediary data files are accessible on GitHub (https://github.com/HKeyHKey/Pinzon_et_al_2019) as well as on https://www.igh.cnrs.fr/en/research/departments/genetics-development/systemic-impact-of-small-regulatory-rnas/165-computer-programs.
Previous analyses showed that a few animal genomes contain candidate RdRP genes [28, 31, 34, 47]. Rapid development of sequencing methods recently made many animal genomes available, allowing a more complete coverage of the phylogenetic tree. A systematic search for RdRP candidates (including every known viral or eukaryotic RdRP family) in 538 predicted metazoan proteomes confirms that animal species possessing RdRPs are unevenly scattered in the phylogenetic tree, but they are much more abundant than previously thought: we identified 98 metazoan species with convincing eukaryotic RdRP genes (see Fig 1A). Most RdRPs identified in animal predicted proteomes belong to the eukaryotic RdRP family, but 3 species (the Enoplea Trichinella murrelli, the Crustacea Daphnia magna and the Mesozoa Intoshia linei) possess RdRP genes belonging to various viral RdRP families (in green, dark blue and light blue on Fig 1A), which were probably acquired by horizontal transfer from viruses. Most sequenced nematode species appear to possess RdRP genes. But in addition, many other animal species are equipped with eukaryotic RdRP genes, even among insects (the Diptera Clunio marinus and Rhagoletis zephyria), where RdRPs were believed to be absent [47, 48].
Our observation of eukaryotic family RdRPs in numerous animal clades therefore prompted us to revisit the evolutionary history of animal RdRPs: eukaryotic RdRPs were probably present in the last ancestors for many animal clades (including insects, mollusks, deuterostomes) and they were subsequently lost independently in most insects, mollusks and deuterostomes. It has been recently shown that the last ancestor of arthropods possessed an RdRP, which was subsequently lost in some lineages [47]: that result appears to be generalizable to a large diversity of animal clades. The apparent absence of RdRPs in some species may be due to genome incompleteness, or to defective proteome prediction. Excluding species with low numbers of long predicted proteins (≥ 500 or 1,000 amino acids) indeed eliminates a few dubious proteomes, but the resulting distribution of RdRPs in the phylogenetic tree is only marginally affected, and still suggests multiple recent RdRP losses in diverse lineages (see Supplementary S1 Fig).
Alternatively to multiple gene losses, such a sporadic phylogenetic distribution could be due to frequent horizontal transfer of RdRP genes in animals. In order to assess these two possibilities, it is important to better understand the evolution of metazoan RdRPs in the context of the whole eukaryotic RdRP family. We therefore used sequences found in all eukaryotic groups for phylogenetic tree reconstruction. The supports for deep branching are low and do not allow us to propose a complete evolutionary history scenario of the whole eukaryotic RdRP family (see Fig 2A). However, metazoan sequences are forming three different groups, which were named RdRP α, β and γ according to the pre-existing nomenclature [31], and their position in relation to non-metazoan eukaryotic sequences does not support an origin through horizontal gene transfer. The only data that would support horizontal gene transfer pertains to the metazoan sequences of the RdRP β group (see Fig 2C). Indeed, sequences of stramenopiles and a fungus belonging to parasitic species are embedded in this clade. For the RdRP α and γ groups, the phylogeny strongly suggests that they derive from at least two genes already present in the common ancestor of cnidarians and bilaterians and that the scarcity of RdRP presence in metazoans would be the result of many secondary gene losses. Even the Strigamia maritima RdRP was probably not acquired by a recent horizontal transfer from a fungus, as has been proposed [47]: when assessed against a large number of eukaryotic RdRPs, the S. maritima sequence clearly clusters within metazoan γ RdRP sequences. In summary, we conclude that RdRPs were present in the last ancestors of many animal clades, and they were recently lost independently in diverse lineages.
In an attempt to probe the functional conservation of RdRP-mediated RNAi amplification among metazoans, we decided to search for secondary siRNAs in an organism where RdRP candidates could be found, while being distantly related to C. elegans. We reasoned that endogenous RNAi may act as a gene regulator during development or as an anti-pathogen response. Thus siRNAs are more likely to be detected if several developmental stages are probed, and if the analyzed specimens are gathered in a natural ecosystem, where they are naturally challenged by pathogens. From these considerations it appears that the most appropriate organism is a cephalochordate species, Branchiostoma lanceolatum [49]. In good agreement with the known scarcity of gene loss in that lineage [50], cephalochordates also constitute the only bilaterian clade for which both RdRP α and γ sequences can be found, thus increasing the chances of observing RNAi amplification despite the diversification of eukaryotic RdRPs into three groups. According to our HMMer-based search, the B. lanceolatum genome encodes 6 candidate RdRPs, three of which containing an intact active site DbDGD (with b representing a bulky amino acid; [51]) (see Fig 1B). The current B. lanceolatum genome assembly contains a direct 1,657 bp repeat in one of the 6 RdRP genes, named BL09945. This long duplication appears to be an assembly artifact: we cloned and re-sequenced that locus and identified two alleles (with a synonymous mutation on the 505th codon; deposited at GenBank under accession numbers MH261373 and MH261374), and none of them contained the repeat. In subsequent analyses, we thus used a corrected version of that locus, where the 1,657 bp duplication is removed.
In most metazoan species, siRNAs (as well as miRNAs) bear a 5′ monophosphate and a 3′ hydroxyl [52, 53]. The only known exceptions are “22G” secondary siRNAs in nematodes (they bear a 5′ triphosphate; [20]), which may be primary polymerization products by an RdRP; Ago2-loaded siRNAs and miRNA in Drosophila, which are 3′-methylated on their 2′ oxygen after loading on Ago2 and unwinding [54, 55]; and a subset of “26G” secondary siRNAs in nematodes (those which are loaded on the ERGO-1 Argonaute protein), which also bear a 2′-O-methyl on their 3′ end [56–58].
In order to detect small RNAs with any number of 5′ phosphates, bearing either an unmodified or a methylated 3′ end, we prepared multiple Small RNA-Seq libraries (see Fig 3A). Total RNA was extracted from various embryonic stages: gastrula (8 hours post-fertilization, hpf), early neurula (15 hpf), premouth neurula (36 hpf) and larvae (60 hpf), as well as from adult male and female specimens collected from their natural ecosystem. Small (18 to 30 nt long) RNAs were gel-purified, then Small RNA-Seq libraries were prepared using either the standard Small RNA-Seq protocol (which detects 5′ monophosphorylated small RNAs, whether they bear a 3′ methylation or not; “Library #1”); or by oxidizing small RNAs with NaIO4 in the presence of H3BO3 prior to library preparation (such treatment renders unmodified 3′ RNAs non-ligatable, hence undetectable by deep-sequencing; [59]; “Library #2”); or by treating small RNAs with the Terminator exonuclease (which degrades 5′ monophosphorylated RNAs) then with phosphatase then T4 PNK (to convert 5′ polyphosphorylated RNAs and 5′ hydroxyl RNAs into monophosphorylated RNAs, suitable for Small RNA-Seq library preparation; “Library #3”); or by a combination of both treatments (to detect only small RNAs bearing a 5′ polyphosphate or a 5′ hydroxyl, and a 3′ modification; “Library #4”). If the same experiments were performed in classical animal model organisms, such as Drosophila, nematodes and vertebrates (where miRNAs are essentially 5′ monophosphorylated and 3′-unmodified, and piRNAs are 5′ monophosphorylated and 3′-methylated), miRNAs would be expected to be detected in Libraries #1 and piRNAs, in Libraries #1 and 2. Nematode “22G” siRNAs would be detected in Libraries #3.
In the course of library preparation, it appeared that Libraries #4 contained very little ligated material, suggesting that small RNAs with a 3′ modification as well as n ≥ 0 (with n ≠ 1) phosphates on their 5′ end, are very rare in Branchiostoma regardless of developmental stage. This observation was confirmed by the annotation of the sequenced reads: most reads in Libraries #4 did not map on the B. lanceolatum genome, probably resulting from contaminating nucleic acids (see Supplementary S2 Fig).
In Libraries #1 in each developmental stage, most Branchiostoma small RNA reads fall in the 18–30 nt range as expected. Other libraries tend to be heavily contaminated with shorter or longer reads, and 18–30 nt reads only constitute a small fraction of the sequenced RNAs (see Fig 3B for adult male libraries; see Supplementary S1 File. section 1 for other developmental stages). miRNA loci have been annotated in two other cephalochordate species, B. floridae and B. belcheri (156 pre-miRNA hairpins for B. floridae and 118 for B. belcheri in miRBase v. 22). We identified the B. lanceolatum orthologous loci for annotated pre-miRNA hairpins from B. floridae or B. belcheri. Mapping our libraries on that database allowed us to identify candidate B. lanceolatum miRNAs. These RNAs are essentially detected in our Libraries #1, implying that, like in most other metazoans, B. lanceolatum miRNAs are mostly 22 nt long, they bear a 5′ monophosphate and no 3′ methylation (see Fig 3C for adult male libraries; see Supplementary S1 File. section 2 for other developmental stages). Among the B. lanceolatum loci homologous to known B. floridae or B. belcheri pre-miRNA loci, 56 exhibit the classical secondary structure and small RNA coverage pattern of pre-miRNAs (i.e., a stable unbranched hairpin generating mostly 21–23 nt long RNAs from its arms). These 56 loci, the sequences of the miRNAs they produce, and their expression profile during development, are shown in Supplementary S1 Table.
In an attempt to detect siRNAs, we excluded every sense pre-miRNA-matching read and searched for distinctive siRNA features in the remaining small RNA populations. Whether RdRPs generate long antisense RNAs which anneal to sense RNAs to form a substrate for Dicer, or whether they polymerize directly short single-stranded RNAs which are loaded on an Argonaute protein, the involvement of RdRPs in RNAi should result in the accumulation of antisense small RNAs for specific target genes. These small RNAs should exhibit characteristic features:
The analysis of transcriptome-matching, non-pre-miRNA-matching small RNAs does not indicate that such small RNAs exist in Branchiostoma (see Figs 4 and 5 for adult males, and Supplementary S1 File, section 3, for the complete data set). In early embryos, 5′ monophosphorylated small RNAs exhibit the typical size distribution and sequence biases of piRNA-rich samples: a heterogeneous class of 23 to 30 nt long RNAs. Most of them tend to bear a 5′ uridine, but 23 to 26 nt long RNAs in the sense orientation to annotated transcripts tend to have an adenosine at position 10 (especially when the matched transcript exhibits a long ORF; see Supplementary S1 File, section 4). Vertebrate and Drosophila piRNAs display very similar size profiles and sequence biases [79–85]. These 23–30 nt long RNAs may thus constitute the Branchiostoma piRNAs, but surprisingly, they do not appear to bear a 2′-O-methylation on their 3′ end (see Discussion). Note that piRNAs appear to be mostly restricted to the germ line and gonadal somatic cells in other model organisms. But they are so abundant in piRNA-expressing cells, and so abundantly maternally deposited in fertilized eggs, that they can still be readily detected in embryonic or adult whole-body small RNA samples [25, 86–90]. It is thus not surprising to observe piRNA candidates in our Branchiostoma whole-body Small RNA-Seq libraries.
In summary, transcriptome-matching small RNAs in our Branchiostoma libraries contain miRNA and piRNA candidates, but they do not contain any obvious class of presumptive secondary siRNAs that would exhibit a precise size distribution, and possibly a 5′ nucleotide bias. If Branchiostoma RdRPs generated secondary siRNAs by polymerizing mature short antisense RNAs (similarly to nematode 22G RNAs according to the prevalent model), then such hypothetical siRNAs should be detected in libraries #3. If Branchiostoma RdRPs generated long antisense RNAs, that would anneal to sense RNAs to produce a Dicer substrate (similarly to fungus and plant RdRP-derived siRNAs according to the prevalent model), then secondary siRNAs should be detected in libraries #1. As we did not observe candidate siRNA populations in either libraries #1 or 3, our data seem to rule out the existence of secondary siRNAs in Branchiostoma, regardless of the mechanistical involvement of RdRPs in their production.
One could imagine that transcriptome-matching siRNAs were missed in our analysis, because of issues with the Branchiostoma transcriptome assembly. It is also conceivable that siRNAs exist in Branchiostoma, but they do not match its genome or transcriptome (they could match pathogen genomes, for example if they contribute to an anti-viral immunity). We therefore analyzed other potential siRNA types: (i) genome-matching reads that do not match abundant non-coding RNAs (rRNAs, tRNAs, snRNAs, snoRNAs or scaRNAs); (ii) reads that match transcripts exhibiting long (≥ 100 codons, initiating on one of the three 5′-most AUG codons) open reading frames; (iii) reads that do not match the Branchiostoma genome, nor its transcriptome (potential siRNAs derived from pathogens). Once again, none of these analyses revealed any siRNA population in Branchiostoma (see detailed results in Supplementary S1 File, sections 1, 4 and 5). This is in striking contrast to Cænorhabditis elegans, where antisense transcriptome-matching siRNAs (mostly 22 nt long, starting with a G) are easily detectable (see Supplementary S1 File, section 6, for our analysis of publicly available C. elegans data; [22]).
Our failure to detect siRNA candidates may simply be due to the fact that they are poorly abundant in the analyzed developmental stages. In order to enrich for small RNA populations derived from RdRP activity, and exclude all the other types of small RNAs, we considered small RNAs mapping on exon-exon junctions in the antisense orientation. The antisense sequence of the splicing donor (GU) and acceptor (AG) sites does not constitute a donor/acceptor pair itself, implying that any RNA antisense to a spliced RNA must have originated from the action of an RdRP on the spliced RNA—it cannot derive from the splicing of an RNA transcribed in the antisense orientation.
We therefore selected all the 18–30 nt RNA reads that map on exon-exon junctions in the annotated transcriptome, and fail to map on the genome. Such reads map almost exclusively in the sense orientation (see Table 1). When focusing on the developmental stage where some transcripts exhibit the highest observed numbers of antisense exon-exon junction reads (15 hpf embryos, for the transcripts of genes BL05604 and BL00515), it appears that these antisense junction reads are highly homogeneous in sequence (sharing the same 5′ and 3′ ends), they do not map perfectly on the spliced transcript (with 1 mismatch in each), and their total abundance remains very small (less than 10 raw reads per transcript in a given developmental stage) (see Supplementary S3 Fig). RdRP genes themselves appear to be developmentally regulated, with candidate RdRPs harboring intact active sites showing expression peaks at 8 and 18 hpf (see Supplementary S4 Fig).
It is formally possible that the few antisense exon-exon junction reads that we detected derive from an RNA polymerized by an RdRP. But their scarcity, as well as their extreme sequence homogeneity, suggests that they rather come from other sources (e.g., DNA-dependent RNA polymerization, either from a Branchiostoma genomic locus or from a non-Branchiostoma contaminant) and map fortuitously on the BL05604 or BL00515 spliced transcript sequences. We note that C. elegans secondary siRNAs are highly diverse in sequence, and even low-throughput sequencing identifies antisense reads mapping on distinct exon-exon junctions [20]. We thus tend to attribute our observation of rare antisense exon-exon junction small RNAs to rare contaminants or sequencing errors, rather than to genuine RNA-dependent RNA polymerization in Branchiostoma.
In various other organisms, RNAi participates in the defence against pathogens (reviewed in [91]). Pathogen-specific siRNAs may exist in Branchiostoma, and they may have been too poorly abundant to be detected in our analyses of extragenomic, extratranscriptomic reads (see Supplementary S1 File, section 5). We thus decided to interrogate specifically the populations of small RNAs mapping on Branchiostoma pathogen genomes. Several pathogenic bacteria (Staphylococcus aureus, Vibrio alginolyticus and Vibrio anguillarum; [92, 93]) have been described in various Branchiostoma species. We asked whether RNAi could target those pathogens in vivo. Focusing on the small RNA reads that do not map on the Branchiostoma genome or transcriptome, we observed large numbers of small RNAs deriving from these three bacterial genomes, indicating that the analyzed Branchiostoma specimens were in contact with those pathogens (after excluding reads that map simultaneously on 2 or 3 of these bacterial genomes, we detected 1,457,122 S. aureus-specific reads, 113,398 V. alginolyticus-specific reads and 103,153 V. anguillarum-specific reads in the pooled 24 Small RNA-Seq libraries; for reference: there are 125,550,314 Branchiostoma genome-matching reads in the pooled libraries). Small RNAs mapping on these pathogenic bacterial genomes do not display any obvious size distribution or sequence bias, thus suggesting that they constitute degradation products from longer bacterial RNAs rather than siRNAs (see Supplementary S1 File, sections 7–9).
Our analyzed Branchiostoma specimens may also have been challenged by yet-unknown pathogens. Pooling every read that does not map on the Branchiostoma genome or transcriptome, across all 24 Small RNA-Seq libraries, offers the opportunity to reconstruct genomic contigs for the most abundant non-Branchiostoma sequences. In total, we collected 23,557,012 such extragenomic, extratranscriptomic reads. 42,946 contigs at least 50 bp long could be assembled from these reads using velvet [45]. Of these, 4,804 contigs could be annotated by homology search (see Table 2): 291 appear to match the Branchiostoma genome, and the reads supporting these contigs had probably failed to map properly on the genome because of sequencing errors or sequence polymorphism.
We screened these contigs for potential Branchiostoma pathogens, which could be targeted by RNAi. Detected prokaryotic, fungal or non-Branchiostoma metazoan sequences may derive from symbiotic or commensal species rather than actual pathogens. Our analyzed adult specimens were collected from the natural environment, where unrelated organisms are expected to contaminate the samples; and our analyzed embryos were produced from gametes collected in non-sterile sea water. Following spawning, these gametes transit through the “atrium” (an open body cavity that putatively hosts various micro-organisms): so in vitro-fertilized embryos are also likely to be contaminated with non-pathogenic non-Branchiostoma species.
But we also observed several viral contigs, including 4 contigs from eukaryotic viruses. Three of them are matched by low numbers of small RNA reads, but the last one (a contig matching the Acanthocystis turfacea Chlorella virus 1 genome) is covered with high read counts in various developmental stages (see Supplementary S5 Fig). That virus is known to infect endosymbiotic algae of the protist Acanthocystis turfacea, and some reports suggest that it may also infect mammalian hosts [94], suggesting a broad tropism. Though still disputed [95, 96], this observation could suggest that Branchiostoma may also be sensitive to that virus. Yet, for this potential pathogen too, detected small RNA reads fail to display any size or sequence bias: they do not appear to be siRNAs (see Supplementary S1 File, section 10).
Finally, we considered the possibility that some of the 38,142 un-annotated extragenomic contigs (see Table 2) may originate from unknown pathogens. We selected the 5 contigs displaying the highest read coverage (more than 200 ppm after pooling all 24 Small RNA-Seq libraries): small RNAs mapping on these hypothetical unknown pathogens also do not exhibit particular size or sequence biases, arguing against their involvement in RNAi (see Supplementary S1 File, sections 11–15).
Because unambiguous RdRP-derived small RNAs could not be detected with certainty despite our efforts, and because we did not observe any small RNA population with classical siRNA size or sequence bias, we conclude that Branchiostoma RdRP genes are not involved in RNAi.
In cellular organisms, the only known function for RdRPs is the generation of siRNAs or siRNA precursors. It is thus frequently assumed [32, 47] or hypothesized [34] that animal RdRPs participate in RNAi. In particular, it has recently been proposed that arthropod RdRPs are required for RNAi amplification, and arthropod species devoid of RdRPs may rather generate siRNA precursors through bidirectional transcription [47]. While this hypothesis would provide an elegant explanation to the sporadicity of RdRP gene distribution in the phylogenetic tree, the provided evidence remains disputable: it has been proposed that a high ratio of antisense over sense RNA is diagnostic of bidirectional transcription, yet it remains to be explained why RNA-dependent RNA polymerization would produce less steady-state antisense RNA than DNA-dependent polymerization.
Branchiostoma 5′ monophosphorylated small RNAs do not appear to bear a 2′-O-methyl on their 3′ end: Libraries #2 contain few genome-matching sequences, and their size distribution suggests they are mostly constituted of contaminating RNA fragments rather than miRNAs, piRNAs or siRNAs. In every animal model studied so far, piRNAs were shown to bear a methylated 3′ end [25, 56–58, 85, 87, 97–99]. The enzyme responsible for piRNA methylation, Hen1 (also known as Pimet in Drosophila, HENN-1 in nematodes), has been identified in Drosophila, mouse, zebrafish and nematodes [55–58, 100–102]. In order to determine whether the absence of piRNA methylation in Branchiostoma could be due to an absence of the Hen1 enzyme, we searched for Hen1 orthologs in the predicted Branchiostoma proteome. Our HMMer search identified a candidate, BL03504. Its putative methyl-transferase domain contains every known important amino acid for Hen1 activity according to [103] (see Supplementary S6 Fig), suggesting that it is functional. Further studies will be required to investigate the biological activity of that putative enzyme, and to understand why it does not methylate Branchiostoma piRNAs.
Focusing on small RNA reads mapping on exon-exon junctions in the antisense orientation, we did not observe convincing evidence of RdRP activity in Branchiostoma. Even if RdRPs do not participate in RNAi, it could have been anticipated that Small RNA-Seq libraries could capture short degradation products of RdRP-polymerized long RNAs. This observation raises the possibility that the Branchiostoma RdRP genes do not express any active RdRP. At least these genes are transcribed: analysis of gene expression in long RNA-Seq data [43] shows a dynamic regulation, especially for the three genes with an intact predicted active site (see Supplementary S4 Fig).
One could hypothesize that these RdRPs do not play any biological function. Yet at least two of them, BL02069 and BL23385, possess a full-length RdRP domain with a preserved catalytic site. The conservation of these two intact genes suggests that they are functionally important. It can therefore be speculated that Branchiostoma RdRPs play a biological role, which is unrelated to RNAi. Such a function may involve the generation of double-stranded RNA (formed by the hybridization of template RNA with the RdRP product), but it could also involve single-stranded RdRP products. Future work will be needed to identify the biological functionality of these enzymes. We also note that the fungus Aspergillus nidulans, whose genome encodes two RdRPs with a conserved active site, does not require any of those for RNAi [104].
Animal RdRPs thus constitute an evolutionary enigma: not only have they been frequently lost independently in numerous animal lineages, but even in the clades where they have been conserved, their biological function seems to be variable. While RNAi is an ancient gene regulation pathway [1], involving the deeply conserved Argonaute and Dicer protein families, the role of RdRPs in RNAi appears to be accessory. Even though RdRPs are strictly required for RNAi in very diverse extant clades (ranging from nematodes to plants), it would be misleading to assume that RNAi constitutes their only biological function.
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10.1371/journal.pgen.1007120 | A conserved maternal-specific repressive domain in Zelda revealed by Cas9-mediated mutagenesis in Drosophila melanogaster | In nearly all metazoans, the earliest stages of development are controlled by maternally deposited mRNAs and proteins. The zygotic genome becomes transcriptionally active hours after fertilization. Transcriptional activation during this maternal-to-zygotic transition (MZT) is tightly coordinated with the degradation of maternally provided mRNAs. In Drosophila melanogaster, the transcription factor Zelda plays an essential role in widespread activation of the zygotic genome. While Zelda expression is required both maternally and zygotically, the mechanisms by which it functions to remodel the embryonic genome and prepare the embryo for development remain unclear. Using Cas9-mediated genome editing to generate targeted mutations in the endogenous zelda locus, we determined the functional relevance of protein domains conserved amongst Zelda orthologs. We showed that neither a conserved N-terminal zinc finger nor an acidic patch were required for activity. Similarly, a previously identified splice isoform of zelda is dispensable for viability. By contrast, we identified a highly conserved zinc-finger domain that is essential for the maternal, but not zygotic functions of Zelda. Animals homozygous for mutations in this domain survived to adulthood, but embryos inheriting these loss-of-function alleles from their mothers died late in embryogenesis. These mutations did not interfere with the capacity of Zelda to activate transcription in cell culture. Unexpectedly, these mutations generated a hyperactive form of the protein and enhanced Zelda-dependent gene expression. These data have defined a protein domain critical for controlling Zelda activity during the MZT, but dispensable for its roles later in development, for the first time separating the maternal and zygotic requirements for Zelda. This demonstrates that highly regulated levels of Zelda activity are required for establishing the developmental program during the MZT. We propose that tightly regulated gene expression is essential to navigate the MZT and that failure to precisely execute this developmental program leads to embryonic lethality.
| Following fertilization, the one-celled zygote must be rapidly reprogrammed to enable the development of a new, unique organism. During these initial stages of development there is little or no transcription of the zygotic genome, and maternally deposited products control this process. Among the essential maternal products are mRNAs that encode transcription factors required for preparing the zygotic genome for transcriptional activation. This ensures that there is a precisely coordinated hand-off from maternal to zygotic control. In Drosophila melanogaster, the transcription factor Zelda is essential for activating the zygotic genome and coupling this activation to the degradation of the maternally deposited products. Nonetheless, the mechanism by which Zelda functions remains unclear. Here we used Cas9-mediated genome engineering to determine the functional requirements for highly conserved domains within Zelda. We identified a domain required specifically for Zelda’s role in reprogramming the early embryonic genome, but not essential for its functions later in development. Surprisingly, this domain restricts the ability of Zelda to activate transcription. These data demonstrate that Zelda activity is tightly regulated, and we propose that precise regulation of both the timing and levels of genome activation is required for the embryo to successfully transition from maternal to zygotic control.
| During the first hours following fertilization, the zygotic genome is transcriptionally silent, and maternally deposited products control early development. These maternal products establish regulatory networks that enable the rapid and efficient transition from two specified germ cells to a population of totipotent cells, which give rise to a new organism. This dramatic change in cell fate is coordinated with the transition from maternal to zygotic control of development, resulting in a complete reorganization of the transcriptome of the embryo. The maternal-to-zygotic transition (MZT) is comprised of two essential and coordinated events, (I) transcriptional activation of the zygotic genome, and (II) destabilization and degradation of maternally supplied RNAs [1–4]. The concerted action of two RNA clearance pathways ensures the timely elimination of maternally deposited transcripts [5–11]. The first is a maternally encoded pathway that initiates the degradation of maternal RNAs in the absence of fertilization and zygotic transcription. The second pathway is zygotically triggered and contributes to maternal RNA clearance near the end of the MZT. Thus, transcriptional activation of the zygotic genome is precisely coordinated with degradation of the maternally provided products [5,10,12]. Regulation of these events is required for development, as failure to undergo this transition is lethal to the embryo. Nonetheless, the mechanisms that precisely control the timing and levels of gene expression necessary to successfully navigate this dramatic developmental transition remain to be elucidated.
In Drosophila melanogaster, the MZT occurs over the first few hours of development. The transcription factor Zelda (ZLD; Zinc-finger early Drosophila activator) is a critical regulator of the MZT, and its absence is lethal to the embryo [13–17]. zld transcripts are maternally deposited and robustly translated following fertilization leading to ubiquitous protein expression in the pre-blastoderm embryo [14,17,18]. ZLD binds to thousands of cis-regulatory modules and is required for transcriptional activation of the zygotic genome [13–15]. ZLD is necessary for gene expression both early and late during the MZT; ZLD drives expression of a small number of genes as early as the eighth mitotic division and is required for the later activation of hundreds of genes during the major wave of zygotic genome activation at mitotic cycle 14 [13]. Among the genes that require ZLD for expression are components of the RNA degradation pathways that destabilize maternal RNAs [14,16]. These ZLD-target genes include several zygotically expressed miRNAs and lncRNAs, including the miR-309 cluster of miRNAs that mediates degradation of over one hundred maternally loaded RNAs [16,19]. Thus, maternally supplied zld is essential for zygotic genome activation and maternal mRNA decay, driving the coordinated transition from maternal to zygotic control. ZLD is also required zygotically, such that embryos homozygous for a deletion in zld die late in embryogenesis [14,17].
Maternally deposited zld encodes a protein of 1596 amino acids, including six C2H2 (Cys-Cys-His-His motif) zinc fingers, but no known catalytic activity (Fig 1) [14,17,20]. In tissue culture, ZLD is a robust transcriptional activator, and this function requires the C-terminal cluster of four zinc fingers that comprise the DNA-binding domain and a low-complexity region proximal to this domain [20]. Functional data combined with phylogenetic analysis supports a shared role for ZLD in genome activation among insects and crustaceans [20–25]. Thus, we were surprised to discover that while transcriptional activation is a conserved function of ZLD, in cell culture this activity does not require highly conserved regions in the N-terminus, including two of the C2H2 zinc fingers and an acidic patch [20,25]. A truncated splice isoform of zld is also conserved throughout the Drosophila genus. This variant is expressed in late embryos and in larvae, but lacks coding sequence for three of the four C-terminal zinc fingers in the DNA-binding domain and is therefore unable to bind DNA (Fig 1A) [20,26–28]. Conservation of these additional domains and splice isoforms suggests a potential function that has been retained through evolution, but which may not have been evident in cell culture.
Our recent development of techniques for Cas9-mediated genome engineering in Drosophila enabled us to directly test the roles of these conserved features of ZLD in vivo [29]. Previous approaches to investigate the in vivo function of specific protein domains relied largely upon the use of transgenes, which do not always adequately reflect the endogenous expression patterns, levels, or alternative splice isoforms. We therefore developed a rapid and efficient means to screen for Cas9-mediated point mutations. Generation of specific point mutations allowed us to interrogate the function of conserved features of zld in vivo. Using a combination of epitope tags and targeted deletions, we demonstrated that a truncated zld isoform was unlikely to be translated and was not required for viability in D. melanogaster, despite being conserved amongst Drosophilidae. We generated targeted loss-of-function alleles for conserved domains in the N-terminus, including the two zinc fingers and the acidic patch. Mutations in either the first C2H2 zinc finger (ZnF1) or the acidic patch (EDD) did not affect viability. To our surprise, the second zinc finger (ZnF2) was required for maternal, but not zygotic function of ZLD. Embryos laid by mothers homozygous for mutations in the second zinc finger died late in embryogenesis. Contrary to our expectations, mutations in ZnF2 resulted in a hyperactive version of ZLD that caused precocious activation of the zygotic genome and increased degradation of maternal transcripts. Together these data demonstrate, for the first time, a separable function for maternally and zygotically expressed ZLD and suggest that the early embryo is exquisitely sensitive to ZLD activity such that too little or too much activity results in embryonic lethality.
zld transcripts are present throughout the Drosophila life cycle. They are strongly expressed during oogenesis, resulting in ubiquitous protein expression in the pre-blastoderm embryo. Subsequently, zld is zygotically expressed in the developing embryonic germ layers, nervous system, imaginal disc primordia and in larval wing and eye discs [17,27,28,30]. In the pre-blastoderm embryo zld contains a single, unspliced open reading frame and a single five-prime intron (Fig 1A). This open reading frame translates into the 1596 amino acid protein product ZLD-PB. In addition to this maternally deposited isoform, a truncated isoform, zld-RD, which contains a second unique downstream exon and alternative splice junction, is expressed during zygotic development [26–28]. This alternatively spliced isoform codes for a 1373 amino acid protein lacking three of the four C-terminal zinc finger motifs required for DNA binding (Fig 1A) [20,31]. The truncated product resulting from translation of the zld-RD isoform acts as a dominant negative when co-expressed with the 1596 amino acid isoform in cell culture [20]. Nonetheless, it was unknown whether this shorter isoform was translated to form a protein product in vivo and if so, whether it was expressed in the same cell as the longer 1596 amino acid isoform, which would be required for any dominant negative effect on ZLD activity.
To determine the expression pattern of a protein product from the zld-RD isoform, we used Cas9-mediated genome engineering to tag each of the two protein isoforms with mCherry. Because we had previously shown that the N-terminal 900 amino acids of ZLD are dispensable for activating transcription in cell culture [20], we tagged the N-terminus to avoid interfering with protein function. Flies carrying this mCherry tag are homozygous viable and fertile demonstrating that the tag does not interfere with any of the essential functions of ZLD. Since all zld splice isoforms encode proteins with a shared N-terminus, expression of the N-terminal mCherry-tagged protein is indicative of the expression pattern of all known ZLD isoforms. To specifically determine the expression pattern of a protein product of the shorter zld-RD isoform, we engineered an mCherry tag upstream of the stop codon in the downstream exon that is specific to zld-RD (Fig 1A). This addition did not affect the zld-PB isoform. Like flies carrying the N-terminal fluorescent tag, these flies were also homozygous viable and vertile.
We imaged stage 5, 12–13, and 14–16 embryos homozygous for either the N-terminal mCherry tag or the zld-RD specific mCherry tag to determine the expression patterns of ZLD protein products (Fig 1B). zld-RB is ubiquitously expressed in the pre-blastoderm embryo, while post-blastoderm expression is limited to the tracheal primordium, central nervous system (CNS), and midline neurons [14,27]. Similar to the expression pattern for the mRNA, the N-terminally tagged protein was expressed throughout embryogenesis (Fig 1B). We did not detect fluorophore expression from either of the two strains containing the mCherry-tagged zld-RD (Fig 1B). Thus, despite high levels of zld-RD in the CNS of stage 12–16 embryos [27], the ZLD-PD isoform does not appear to be expressed. We detected gut auto fluorescence in all genotypes, including control w1118 embryos.
To investigate additional tissues that might express ZLD-PD at later stages of development, we imaged imaginal wing discs from third instar larvae (L3). Previous reports had suggested that ZLD-PD was expressed in larval tissues [28]. Using our engineered fly lines, we could detect ZLD expression in several L3 tissues, including ubiquitous, nuclear expression in imaginal wing discs (Fig 1C). By contrast, we could not detect mCherry expression in the lines specifically tagging ZLD-PD (Fig 1C). In addition to zld-RD, a second splice isoform of zld, zld-RF, has been identified and is predicted to produce a protein product very similar to the predicted product of zld-RD. The evidence for zld-RF is weaker than for zld-RD, and it has been speculated to be the result of a cloning artifact [26,27,32]. Nonetheless, both truncated isoforms have been reported to be expressed in the wing imaginal disc [28]. To determine if any truncated protein product is translated from either the zld-RD or zld-RF isoforms, we immunoblotted protein extract from wing imaginal discs using our antibody that recognizes all isoforms of ZLD [20]. We identified only a single isoform, corresponding to the 1596 amino acid protein (Fig 1D). This evidence suggests that neither zld-RD nor zld-RF isoforms are translated to a stable protein in the wing imaginal disc.
The zld-RD splice isoform is conserved throughout the Drosophila genus [26,27], suggesting a retained function. Thus, it remained possible that the truncated RNA or the splicing reaction was instrumental to zld function and could explain the conservation, even if the protein isn’t stably expressed. To test this possibility, we determined the in vivo effects of eliminating the zld-RD isoform by using Cas9-mediated mutagenesis to delete the splice acceptor and downstream coding region of zld-RD (Fig 1E). We obtained two strains carrying the deleted sequence, both of which were viable and fertile (Fig 1E). Because the exons encoding zld-RF are within the required longer isoform, we were unable to make a deletion targeting only this isoform as we did for zld-RD. Therefore, we cannot rule out the possibility that this isoform is important in vivo. While zld-RD is expressed in multiple post-blastoderm tissues as an RNA [26–28], our data demonstrated that this truncated splice-isoform is not required for development and is not abundantly translated.
Because the 1596 amino acid ZLD-PB isoform is the predominantly expressed form of ZLD, we investigated the functional requirements of domains within this large transcription factor. ZLD-PB is comprised of six C2H2 zinc fingers and many low-complexity regions, but no identifiable enzymatic domains. Alignment of ZLD orthologs showed sequence conservation within insects of all six zinc fingers as well as an N-terminal acidic patch (Fig 2A–2C) [20,25]. We previously demonstrated that the cluster of four C-terminal zinc fingers constituted the DNA-binding domain, and the low-complexity domain just N-terminal to the DNA-binding domain mediated transcriptional activation (Fig 1A) [20]. Therefore, the regions required for both DNA binding and transcriptional activation in cell culture were encompassed within the 600 C-terminal amino acids of ZLD [20], while the functional significance of the conserved N-terminal zinc-fingers and acidic domain was unknown. Because domains under high evolutionary constraint possess important structural or functional roles, we hypothesized that these highly conserved domains might have an essential developmental function that was missed in our previous cell-culture assays.
The recent development of Cas9-mediated genome editing has allowed us to facilely create point mutants in vivo. This strategy enabled efficient creation of endogenous mutant alleles to probe the functional importance of individual protein domains of interest. A single-stranded donor oligonucleotide (ssODN) and a single guideRNA (gRNA) construct were injected into flies expressing Cas9 to create loss-of-function mutations in the highly conserved N-terminal domains. We developed a streamlined protocol to molecularly screen for the desired mutations; ssODNs contained both the desired coding mutations and silent mutations that generated a restriction digest site not found in the endogenous locus, allowing for screening by PCR and restriction enzyme digest (Fig 2D and 2E). Instead of creating deletions, we introduced point mutations in the conserved N-terminal domains with the purpose of maintaining overall protein stability. To disrupt the zinc-finger domains, we mutated a subset of the zinc-chelating residues in each of the N-terminal zinc fingers. Within the acidic domain, we mutated the conserved glutamate and aspartate residues to alanine to abrogate the negative charges in the domain. Using this streamlined strategy, we generated three distinct mutant alleles, individually disrupting each of these conserved protein domains and allowing us to interrogate protein structure and function in vivo (Fig 2E).
We assessed the viability and fertility of each of the mutants we generated by counting the number of homozygous flies carrying the mutations as compared to heterozygous siblings (Fig 3). Flies homozygous for mutation of either the first zinc finger (ZnF1) or the acidic domain (EDD) were viable to near wild-type levels (Fig 3A). Both homozygous males and females were fertile. Similarly, hemizygous males carrying mutations in zinc finger 2 (ZnF2) were viable, albeit to a reduced degree, and were fertile. Homozygous ZnF2 mutant females were viable at reduced levels, but, in contrast to their male counterparts, were sterile (Fig 3A). Mutations in ZnF2 resulted in a maternal-effect lethal phenotype in which homozygous zldZnF2 females lay fertilized embryos that arrest late in embryogenesis during stage 17 after tracheal branches have clearly formed. A subset (18%) of male and female adults homozygous for zldZnF2 had small, malformed eyes, suggesting additional developmental processes were affected by the mutation. Thus, contrary to our expectations loss-of-function mutations in all three regions were dispensable for development to adulthood even though their conservation suggested they were required for ZLD function. Our mutational analysis delineated discrete maternal and zygotic functions for ZLD; maternal deposition of zld mutant for ZnF2 was lethal to the embryo, causing arrest late in embryogenesis, whereas zygotic expression of zld with a disruption in ZnF2 supported development to adulthood.
To determine if protein stability was disrupted in any of the mutants, we compared expression of each mutant allele to a GFP-tagged endogenous, wild-type allele in heterozygous embryos. Embryos were laid by heterozygous females such that they received maternal deposition of RNA encoding both ZLD protein variants. Equivalent amounts of protein were expressed from alleles carrying mutations in ZnF1, ZnF2, or the acidic domain as compared to the GFP-tagged control (Fig 3B). Furthermore, the observed phenotype for the ZnF2 mutant was retained in trans-heterozygotes carrying a deletion in zld (zld294) (S1 Table). Thus, the maternal-effect lethality associated with maternal inheritance of zld mutant for ZnF2 was not a result of protein instability or a background mutation, but instead was the result of changes in ZLD function.
To test the functional importance of these conserved domains on ZLD-mediated transcriptional activation, we used our previously established cell-culture system to transiently express ZLD mutants and assay their ability to activate luciferase reporters [20]. ZLD with mutations in either ZnF1 or the acidic domain was able to activate transcription to a level similar to the wild-type protein (Fig 4), consistent with these mutations producing viable and fertile flies (Fig 3A, S1 Table). The single amino acid change (C554S) in the ZnF2 mutant significantly hyperactivated the scute reporter, resulting in luciferase activity at least 3-fold greater than wild type. None of the ZLD proteins activated gene expression from a mutant promoter, confirming the specificity of the assay. Immunoblots confirmed the expression of all mutated proteins was at approximately equivalent levels (Fig 4). These data suggest that ZnF2 may serve as an inhibitory domain that regulates the level of ZLD-mediated transcriptional activation. This conclusion is further supported by our previous data demonstrating that truncations to the N-terminus of ZLD that remove ZnF2 elevated transcriptional output [20].
Prior studies demonstrated that either overexpression of maternal zld or the loss of maternally deposited zld resulted in defects in nuclear division in the blastoderm embryo [17]. The similarity of the loss-of-function and overexpression phenotypes suggests that the early embryo is sensitive to the precise levels of ZLD activity and that both too little and too much activity is detrimental to embryonic development. To confirm the impact of ZLD overexpression, we used mat-α-GAL4 to drive overexpression of a UASp-zld transgene. Overexpression of maternally deposited zld caused a late embryonic lethal phenotype similar to that of animals inheriting maternal zldZnF2, albeit at a lower frequency (S1 Fig). Based on our tissue culture data and the fact that zld overexpression phenocopies the ZnF2 mutation, we propose that disruption of zinc finger 2 hyperactivated ZLD protein and that this increased activity was lethal to the embryo.
The second zinc finger in ZLD is the most highly conserved domain in the entire 1596 amino acid protein [25]. To determine whether conserved residues outside of the zinc-chelating amino acids of zinc finger 2 were required for function, we mutated four conserved residues (F561, S563, Y571 and N578) to alanine, generating the zldJAZ allele (Fig 5A–5C). These residues were chosen because they are shared between the second zinc finger in ZLD and the consensus sequence for JAZ (Just Another Zinc finger)-domains (pfam: zf-C2H2_JAZ), a domain initially identified in the mammalian double-stranded RNA-binding zinc finger protein JAZ (Fig 5A) [33]. Alanine substitutions in the JAZ domain did not affect protein stability (Fig 5D). Animals homozygous for zldJAZ were viable at reduced frequencies, and males were fertile (Fig 5E). Females homozygous for this allele were sterile, laying embryos that later died (Fig 5E), phenocopying the zldZnF2 mutants (Fig 3A, S1 Table). Cell-culture assays further demonstrated that mutating the JAZ-like domain hyperactivated transcription, similar to the serine substitution in C554 (Fig 5F). Thus, both zinc-chelation and residues conserved within the JAZ zinc finger domain are critical for negatively regulating the ability of ZLD to activate transcription.
The cell-culture assays demonstrated that the JAZ-like ZnF2 negatively regulated ZLD activity. This raised the possibility that the lethality in embryos inheriting zld with mutations in this domain might result from hyperactivation of ZLD targets. To determine the functional consequences of the zldZnF2 allele on early gene expression, we performed mRNA-sequencing on hand-sorted stage 5 embryos with wild-type maternal zld (w1118) or zldZnF2 (C554S). The high degree of reproducibility amongst the three replicates (S2 Fig) allowed us to identify genes misexpressed in embryos inheriting the mutated version of zld (Fig 6). We identified 287 genes that were up-regulated in the zldZnF2 mutant and 270 genes that were down-regulated (Fig 6A and S2 Table). Stage 5 embryos possess both mRNAs that have been deposited by the mother along with newly transcribed zygotic mRNAs. To distinguish between these two classes of mRNAs, we used previously published data to determine whether the mis-regulated genes were maternally, zygotically or both maternally deposited and zygotically expressed (mat-zyg) [34]. 74% (n = 212) of up-regulated genes were zygotically expressed, including those expressed exclusively in the zygote and those maternally deposited and zygotically expressed (Fig 6B). By contrast, only 11.5% (n = 31) of the genes that were down-regulated were zygotically expressed. 73% (n = 197) of the down-regulated genes were maternally deposited (Fig 6B). Thus, the majority of the up-regulated genes were zygotically expressed while the majority of the down-regulated genes were maternally deposited.
ZLD is required for transcriptional activation of hundreds of zygotic genes during early embryogenesis [13–15,35]. Thus, we tested whether the hyperactive zldZnF2 allele up-regulated expression of direct ZLD-target genes. We used our previous ZLD ChIP-seq data to identify ZLD-bound regions in the stage 5 embryo and associated them with the nearest gene to identify 3836 potential direct ZLD targets [13]. More than half of the genes up-regulated in embryos inheriting the zldZnF2 allele overlapped with likely direct ZLD targets (Fisher’s exact test, p < 0.0001 (S2 Table)), suggesting that these genes were directly hyperactivated by the mutant ZLD protein (Fig 6C). To determine the regulatory networks influenced by ZLDZnF2 hyperactivity, we identified enriched Gene Ontology (GO) terms for the 108 likely direct targets. The most enriched GO terms were related to transcription-factor activity, DNA binding, and RNA Pol II activity (Fig 6D). Misexpression of these genes may therefore affect multiple downstream processes required for embryonic development, ultimately leading to the late-stage lethality of these embryos.
Zygotic gene activation is coordinated with the degradation of maternally deposited RNAs during the MZT. Two sets of machinery remove maternally deposited transcripts from the early embryo with one functioning just after fertilization and one functioning later during genome activation [2,5]. The early decay pathway is encoded by maternal factors and is triggered by egg activation. The late-decay pathway is encoded by zygotic factors expressed at the onset of zygotic genome activation [10,12]. Because we found an enrichment for maternally deposited mRNAs amongst the down-regulated transcripts in the embryos inheriting maternal zldZnF2, we hypothesized that mRNAs in these mutant embryos might be precociously degraded due to hyperactivation of the zygotic genome. To test this, we determined whether the down-regulated mRNAs corresponded to genes subject to either the early or late decay pathways [10,12]. 58% (n = 114) of the down-regulated maternal mRNAs overlap with mRNAs subject to degradation late during the MZT, while just 1.5% (n = 3) were degraded early in the MZT (Fig 6E). These data support a model whereby ZLDZnF2 hyperactivates a set of zygotic genes and that this leads to precocious decay of a set of maternally deposited mRNAs (Fig 6F).
The dramatic changes in cell fate that occur during the MZT require precise coordination of activation of the zygotic genome and degradation of the maternally deposited products that drive the initial stages of embryogenesis. For development to proceed, this transition must be smoothly executed. In Drosophila, ZLD is required for this transition and facilitates activation of hundreds of zygotic genes. Using a combination of evolutionary analysis, Cas9-mediated genome editing and high-throughput sequencing, we identified an essential regulatory domain within ZLD. Our data demonstrate a maternal-specific function for the highly conserved second zinc finger and suggest that the early embryo is exquisitely sensitive to precise regulation of ZLD activity.
D. melanogaster have been a premier organism for studies of gene regulation and development for over a century, but studies have been limited by the inability to precisely engineer mutations in the genome using homologous recombination. Our establishment of Cas9-mediated genome engineering in D. melanogaster overcame this limitation [29]. Here we have used this facile method of gene editing to identify the functional domains of the essential transcription factor ZLD. We developed a molecular screening strategy that enabled us to generate four distinct mutations to directly query the necessity of conserved protein domains. Editing the endogenous locus provided confidence that any phenotypes we observed were not due to differences in levels or localization of gene expression that might result from the use of a transgene. This was supported by confirmation that all the mutations we generated were expressed at endogenous levels. Thus, we are confident that the absence or presence of a clear mutant phenotype represented the endogenous requirement for specific protein sequences. Our use of genome editing to determine the requirements for specific protein domains within ZLD highlights more generally the power of Cas9-mediated editing to characterize protein structure and function. The easy PCR-based screening approach described here allows for the generation and identification of novel alleles in as little as one months time, providing an additional powerful tool to study gene function in Drosophila.
Cas9-mediated genome editing also enabled us to specifically determine the protein expression pattern from a conserved splice isoform of zld that is predicted to produce a truncated protein product. Using a combination of an isoform-specific mCherry tag, a targeted deletion, and immunoblot, we clearly demonstrated that while zld-RD may be expressed as an RNA it is not translated at detectable levels in either the embryo or the larval wing disc and is not required for viability (Fig 1). Visualization of an N-terminal mCherry tag that marks all possible ZLD isoforms demonstrated that ZLD is expressed in embryos well past the MZT (Fig 1B–1D). Thus, the 1596 ZLD-PB isoform that binds DNA and drives transcriptional activation is likely the predominant protein product at all stages of development and in all cell types.
A single zld ortholog with a set of highly conserved domains is found within the genomes of insects and some crustaceans. These ZLD orthologs are required for embryonic development and transcriptional activation within multiple insect species [20,25,36]. We had previously shown that ZLD-mediated transcriptional activation in Drosophila cell culture did not require either of the conserved N-terminal C2H2 zinc fingers or a recently identified conserved acidic patch [20,25]. Here, we used Cas9-mediated genome editing to test the functional significance of these conserved domains in vivo by generating point mutations that were likely to result in loss of function. We individually mutated both conserved N-terminal zinc fingers as well as the acidic patch (Fig 2). Because coordination of zinc ions plays an essential structural role in zinc finger domains [37], the cysteine-to-serine mutations are likely to lead to structural changes that abrogate function. Similarly, removing acidic residues from the acidic patch is likely to disrupt any interactions that rely on the negative charge of these residues. For example, it has been suggested that this negatively charged domain might contact positively charged histones [25], and the alanine substitutions would be expected to block this interaction. Mutation of either the first zinc finger or the acidic patch did not disrupt the ability of ZLD to activate transcription in culture (Fig 4), consistent with our previous cell-culture studies [20], and mutant flies homozygous for these mutations were viable and fertile (Fig 3). These domains are therefore not necessary for ZLD-mediated transcriptional activation in D. melanogaster.
In contrast to the high degree of sequence conservation of the six C2H2 zinc fingers present in D. melanogaster ZLD, sequence alignments have identified an additional zinc finger (ZF-Novel) in the N-terminus of ZLD orthologs in multiple insect species that has been eroded in Drosophila [25]. Thus, this novel zinc finger domain may have functions specific to other species. It will be interesting to investigate whether there is a phenotypic consequence of introducing such a zinc finger into D. melanogaster ZLD. Nonetheless, the high-degree of conservation amongst the six zinc fingers maintained in D. melanogaster suggests they have a function that has led to their retention. Our data demonstrated that specific conserved residues within these domains are not required for viability or fertility. It remains possible that additional residues within these conserved domains are sufficient for functionality, that these domains have redundant functions, that they are instrumental in other as of yet unidentified functions for ZLD or that they serve as a buffer against environmental perturbations during development.
We demonstrated that the second zinc finger of ZLD is required for female fertility. This zinc finger is the most highly conserved domain of the entire protein and has similarity to the double-stranded RNA-binding, JAZ-like zinc finger family [17,20,25,33]. We generated two distinct loss-of-function alleles by mutating either a required zinc-chelating cysteine or four residues that are shared with the JAZ zinc finger domains. These mutations resulted in maternal-effect lethality due to an increase in ZLD-mediated transcriptional activation (Figs 4–6). Thus, we propose that this domain suppresses the ability of ZLD to activate transcription. While ZnF2 has the canonical architecture of the JAZ-like C2H2 zinc finger, it lacks positively charged lysine residues that are conserved in double-strand RNA-binding zinc fingers and are thought to be required for RNA binding [38]. Therefore, it is unlikely that this domain functions through interaction with double-stranded RNA. Co-immunoprecipitations failed to identify homotypic interactions between differently tagged ZLD molecules, suggesting that ZLD does not multimerize [20]. Nonetheless, it remains possible that this domain could inhibit ZLD activity by preventing multimerization. It is likely that this domain interacts with a protein partner, a nucleic acid, or intramolecularly within ZLD to effect its suppression of transcriptional activation. To date, no such interactions within ZLD or between ZLD and a protein or RNA partner have been identified.
zld is required as a maternally deposited mRNA that is translated following fertilization. Embryos lacking maternally deposited zld die early in embryogenesis due to a failure to undergo the MZT [14]. Embryos homozygous mutant for zygotic zld die late in embryogenesis [14,17], but the cause of this lethality is currently unknown. While ZLD is required throughout embryogenesis for viability, it was previously unclear if there were functions distinctly required at either stage of development. The maternal-effect lethality we demonstrate for mutations in the second zinc finger provides the first evidence of separable functional requirements for maternal and zygotic ZLD. Using both tissue culture assays and RNA-sequencing, we showed that this highly conserved zinc finger suppresses the ability of ZLD to activate transcription. Thus, early embryonic development is particularly affected by these mutations. Based on these data, we propose that the early embryo is exquisitely sensitive to ZLD activity such that too little or too much is lethal to the embryo.
One possible explanation for the maternal-specific nature of mutations in ZnF2 is that a cofactor that binds this domain and suppresses activity is only expressed in the early embryo. In this case, loss-of-function alleles would lead to loss of cofactor binding specifically in the early embryo. The fact that we identified increased ZLD-mediated transcriptional activation by ZnF2 mutants in S2 cells demonstrates that any such cofactor must also be expressed in these cells. Because S2 cells are derived from late-stage embryos and zld is not normally expressed in these cells [39], we propose that any factor that mediates the ZnF2-mediated inhibition is likely broadly expressed during development. Rather than stage-specific expression of a cofactor, we suggest that the maternal-specific nature of the allele is the result of distinct features of the early embryo that makes this time in development particularly sensitive to ZLD activity. Supporting this hypothesis, we have previously demonstrated that ZLD protein levels are controlled in the early embryo and that ZLD levels increase when its activity is first required [13]. Furthermore, in all insects studied only a single zld ortholog has been identified despite extensive expansion of other transcription factor families [25]. This suggests that even a single extra copy of the zld locus may be detrimental to development.
Both the mutations we generated in zinc finger 2 and the overexpression of maternally deposited zld result in late embryonic lethality rather than lethality during the MZT when the resulting protein is expressed. Our RNA-sequencing data suggest that mutation in ZnF2 results in precocious activation of the zygotic genome (Fig 6). This is supported by our demonstration that not only are a subset of ZLD-target genes up-regulated in these mutants, but that there is a coordinated decrease in levels of maternally deposited mRNAs. These maternal mRNAs are enriched for those that depend on zygotic transcription for degradation. Increased activation of ZLD-target genes and precocious degradation of maternal mRNAs leads to misregulation of genes essential for subsequent patterning of the embryo, including transcription factors as evidenced by our GO term analysis. These changes in gene expression levels are likely to have cascading effects that result in the subsequent lethality later in embryonic development.
The first few hours of embryonic development require the rapid transition from a specified germ cell to a population of totipotent cells. A maternally provided program controls the transition from maternal to zygotic control to drive these dramatic changes in cell fate. The coordination of this event is facilitated by the requirement for zygotically encoded proteins to degrade a population of maternally provided transcripts. As a master regulator of transcriptional activation in the zygote, ZLD is essential for executing this transition with precision. Here we have shown that ZLD activity is strictly controlled through a conserved, maternal-specific inhibitory domain–the lack of ZLD, its overexpression, or its hyperactivity via mutations in the inhibitory domain are all lethal to the embryo. We propose that rapid transitions in cell fate, such as those that occur during the MZT, must be precisely executed and that this requires tight control of both the levels and activities of the master regulators of these cell fate changes.
Antibody used for immunoblots were rabbit anti-ZLD antibodies at 1:750 [40]. Firefly luciferase reporters containing the scute promoter were previously described in Hamm et al. 2015 [20]. Actin:renilla was used to control for transfection efficiency [41]. Protein coding regions were cloned into pAc5.1 (Invitrogen) for protein expression in S2 tissue culture cells. PCR was used to amplify the open reading frames of zld containing point mutations from genomic DNA obtained from engineered CRISPR mutants. Amplified products were cloned into pAc5.1 for expression in Drosophila S2 cells.
Drosophila S2 cells were cultured at 25°C in Schneider’s Media (Life Technologies) supplemented with 10% Fetal Bovine Serum (Omega Scientific) and antibiotic/antimycotic (Life Technologies). Transfections were performed in triplicate in 24 well dishes with a total of 300 ng of DNA using Effectene Transfection Reagent (Qiagen). Luciferase assays were performed using the Dual Luciferase assay system (Promega). Fold activation was determined by comparison with transfections using a plasmid containing the actin promoter but no expression sequence. Representative data sets are shown with error bars indicating the standard deviation.
Fly strains used in this study include: w1118, vasa-Cas9 (BDSC #51324); w1118; Cyo, P{Tub-PBac\T}2/wgSp-1 (BDSC #8285), mat-α-GAL4-VP16 (BDSC #7062), wzld294/FM7 [14]. UASp-zld flies were made by PhiC31 integrase-mediated transgenesis into the M{3xP3-RFP.attP}ZH-86Fb docking site (BDSC #24749). The following zld mutant alleles were generated using Cas9-mediated genome engineering (outlined in detail below): sfGFP-zld, mCherry-zld-RB, zld-RD-mCherry, zld-RD deletion, zldZnF1 zldEDD, zldZnF2, zldJAZ.
Complementation with previously identified recessive mutation zld294 [14] was performed to verify phenotypes were a result of Cas9-mediated mutagenesis within zld. Trans-heterozygous females were crossed to w1118 males to determine fertility.
mat-α-GAL4-VP16 males were crossed with UASp-zld transgenic females. w:mat-α-GAL4-VP16/+:UASp-zld/+ were recovered from previous crosses and mated to siblings. The percentage of embryos hatched was determined by lining up approximately two hundred and fifty 0–3 hour old embryos and counting the hatched eggs after at least 24 hours. All genetic experiments were carried out at 25°C.
Embryos were dechorionated and analyzed under halocarbon oil to determine stage. Third instar larval (L3) wing discs were dissected and mounted in PBS. Confocal images were acquired on an A1R-S Confocal Microscope (Nikon) with 20x objective. Images were analyzed using NIS-Elements AR software. Z-stacks were flattened using the Maximum Intensity Z-projection function.
RNA-seq experiments were done on hand-sorted stage 5 embryos laid by zldZnF2 (C554S mutation) homozygous females and w1118 embryos as a wild-type control. Embryos were dechorionated, analyzed under halocarbon oil to determine stage, collected and lysed in TRIzol (ThermoFisher) supplemented with 150 μg/ml glycogen. RNA was extracted, and cDNA libraries were prepared using Truseq RNA sample prep kit (Illumina). Three replicates of each were sequenced. The cDNA 100 bp single-end reads were sequenced at the UW Biotechnology Center DNA Sequencing Facility using an Illumina HiSeq 2000. Using the Galaxy platform [44], reads were examined for quality, trimmed, and filtered. The reads were then mapped to the BDGP D. melanogaster (dm6) genome using RNA-STAR (Galaxy Version 2.5.2b-0). Cufflinks (Galaxy Tool version 2.2.1) was used with default settings for transcript assembly. The resulting assembled transcripts were compared using Cuffdiff [45](R version 3.1.2) to identify genes that change significantly (p-value<0.05, >two-fold change) in expression. Only genes that were significantly mis-expressed in all replicates were used for further analysis.
Prior to comparisons, all gene IDs were converted to current FlyBase identifiers (FBgn#) using the ‘Upload/Convert IDs’ tool available on FlyBase [32]. Single-embryo expression data from Lott et al. 2010 [34] were used to classify up- or down-regulated genes in mutant embryos as (1) zygotic, (2) zygotic-maternal, and (3) maternal only. Translation and stability datasets of maternal mRNAs from Thomsen et al. 2010 [10] were used to classify up-regulated maternal genes as targets of maternal degradation or zygotic degradation. mRNAs degraded by the maternal pathway were considered as Classes II and III (‘exclusively maternally degraded’ and ‘maternally degraded and transcribed’); and mRNAs degraded by the zygotic pathway, were considered as Class IV (‘exclusively zygotically degraded’). ZLD ChIP-seq data from Harrison et al. 2011 [13] were used to identify the number of down-regulated zygotic genes bound by ZLD. Enrichments and depletions for comparisons to data from Lott et al. 2010 [34], Thomsen et al. 2010 [10], and Harrison et al. 2011 [13] were determined using a Fisher’s exact test.
Gene ontology (GO) annotation was performed using the online GO Consortium tool (http://geneontology.org/), which uses the PANTHER classification system [46]. Lists of gene names were entered searching for enrichment in molecular function using a Bonferroni correction. The data were collected using the PANTHER over-representation test release 20170413 with the 2017-06-29 GO ontology database release.
The genomic data in this work was deposited in the Gene Expression Omnibus: accession number GSE103914. Strains and plasmids are available upon request.
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10.1371/journal.ppat.1000278 | A Legionella pneumophila Effector Protein Encoded in a Region of Genomic Plasticity Binds to Dot/Icm-Modified Vacuoles | Legionella pneumophila is an opportunistic pathogen that can cause a severe pneumonia called Legionnaires' disease. In the environment, L. pneumophila is found in fresh water reservoirs in a large spectrum of environmental conditions, where the bacteria are able to replicate within a variety of protozoan hosts. To survive within eukaryotic cells, L. pneumophila require a type IV secretion system, designated Dot/Icm, that delivers bacterial effector proteins into the host cell cytoplasm. In recent years, a number of Dot/Icm substrate proteins have been identified; however, the function of most of these proteins remains unknown, and it is unclear why the bacterium maintains such a large repertoire of effectors to promote its survival. Here we investigate a region of the L. pneumophila chromosome that displays a high degree of plasticity among four sequenced L. pneumophila strains. Analysis of GC content suggests that several genes encoded in this region were acquired through horizontal gene transfer. Protein translocation studies establish that this region of genomic plasticity encodes for multiple Dot/Icm effectors. Ectopic expression studies in mammalian cells indicate that one of these substrates, a protein called PieA, has unique effector activities. PieA is an effector that can alter lysosome morphology and associates specifically with vacuoles that support L. pneumophila replication. It was determined that the association of PieA with vacuoles containing L. pneumophila requires modifications to the vacuole mediated by other Dot/Icm effectors. Thus, the localization properties of PieA reveal that the Dot/Icm system has the ability to spatially and temporally control the association of an effector with vacuoles containing L. pneumophila through activities mediated by other effector proteins.
| The survival of intracellular pathogens often involves the modification of the host vacuole in which the pathogen resides. This can be achieved through the function of effector proteins that are delivered into the host cell cytoplasm using specialized transport machinery. In the case of Legionella pneumophila, the bacterium that causes a severe pneumonia known as Legionnaires' disease, a type IV secretion system, termed Dot/Icm, delivers a number of proteins into host cells, resulting in altered trafficking of the L. pneumophila–containing vacuole. The mechanisms by which effector proteins are spatially and temporally regulated in the host cell to facilitate the survival of the pathogen are not well understood. In this work, we report the identification of several L. pneumophila effectors encoded in a genomic region of high plasticity, among them the protein PieA. We demonstrate the Dot/Icm dependent recruitment of PieA to the L. pneumophila vacuole and show that the protein binds to the cytoplasmic face of the vacuole as a result of L. pneumophila–induced modifications to this vacuole. Our findings demonstrate that the association of an effector with host vacuoles can be spatially controlled through activities mediated by other effector proteins.
| L. pneumophila is the causative agent of a severe pneumonia called Legionnaires' disease [1],[2]. In the environment it can be found in fresh water reservoirs [3], in a very large spectrum of environmental conditions [4]. In these environments L. pneumophila resides within protozoan hosts, where it is able to survive and replicate [3]. A large number of protozoa species can provide a habitat for L. pneumophila, among them Acanthamoeba castellanii, Hartmanella sp. and Naeglaria sp. [5]. When humans come in contact with aerosolized contaminated water sources, L. pneumophila can access human alveolar macrophages. The bacterium is engulfed by these cells, where it is able to proliferate, and may cause severe disease [6]. L. pneumophila is not transmitted between individuals [3] and is therefore thought to have evolved to survive within its Protozoan environmental hosts, and only infect humans as an accidental pathogen.
To survive within eukaryotic cells, L. pneumophila requires a type IV secretion system designated the Dot/Icm system [7],[8] that delivers bacterial effector proteins into the host cell cytoplasm [9],[10]. The Dot/Icm system is crucial for the ability of the bacterium to remodel the vacuole in which it resides by preventing delivery of the vacuole to lysosomes [11], and promoting recruitment of endoplasmic reticulum (ER)-derived vesicles to this vacuole to create a unique organelle in which the bacterium survives and replicates [12]–[16]. To date, four L. pneumophila serogroup1 isolates have been fully sequenced. These are the Philadelphia1 strain [17], which was derived from the original isolate obtained from the eponymous outbreak at an American Legion convention in 1976 [1], the Lens and Paris strains [18], an epidemic and endemic strain, respectively, isolated in France, and the recently completed Corby strain (GeneBank number CP000675). Sequence comparison revealed a high degree of genomic plasticity, with a large number of strain-specific genes found in each genome [18].
Using genomic data in conjugation with genetic and biochemical methods, many Dot/Icm substrate proteins have been identified [18]–[29]. The function of most of these substrates remains unknown, however, for some effectors biochemical and genetic studies demonstrate activities important for the biogenesis of an organelle that is permissive for L. pneumophila replication (reviewed in [30]). The number of substrate proteins identified to date is higher than was initially predicted, and it is not yet clear why so many effectors are required for the survival of the bacteria.
Genomic plasticity and effector abundance could be related to the versatile lifestyle of L. pneumophila. These bacteria can survive within a variety of protozoan hosts found in different environments. Because natural environments probably support a defined subset of protozoan hosts, it can be predicted that L. pneumophila strains that have evolved in different environments would possess slightly different sets of effector proteins that best facilitate the survival within their environmental hosts. As a first step in addressing this hypothesis, we have focused our investigation on a chromosomal region that displays a high degree of plasticity among the sequenced L. pneumophila genomes. We show that effectors of the Dot/Icm system are abundant in this region and demonstrate that one of the effectors encoded in this region is recruited to vacuoles containing L. pneumophila by a process requiring Dot/Icm-dependent modifications to the vacuole surface.
In previous work aimed at identifying novel L. pneumophila effectors, a screen was conducted using the Dot/Icm component IcmW as bait in a yeast-two-hybrid system. The screen was successful at identifying several effectors [26]. Further analysis of data generated in that screen has led to the identification of an additional protein fragment capable of interacting with the IcmW protein. This fragment consists of amino acids 715 to 988 of the protein encoded by open reading frame (ORF) lpg1965. A calmodulin-dependent adenylate cyclase (Cya) gene fusion approach was used to test whether the lpg1965 gene encodes a Dot/Icm-translocated substrate protein [31]–[33]. Because Cya enzymatic activity is very low in the absence of calmodulin, this enzyme is inactive in bacterial cells, and is activated when delivered into eukaryotic cells. Fusion of Cya with a translocated effector results in delivery of the hybrid protein into host cells, resulting in a dramatic elevation in cAMP levels. Infection of CHO cells with wild-type L. pneumophila expressing the Cya-lpg1965 fusion protein resulted in an increase in cAMP that was three logs above the background levels found in uninfected control cells. When a dotA mutant devoid of a functional Dot/Icm system was used, cAMP levels were similar to background levels, indicating that translocation of lpg1965 is mediated by the Dot/Icm system. Because lpg1965 was identified as an IcmW-interacting protein, dependency of the IcmS-IcmW complex for efficient translocation of lpg1965 was tested. As demonstrated for other IcmW-interacting substrates of the Dot/Icm system [26], translocation of lpg1965 was highly dependent on the IcmS-IcmW protein complex (Figure S1).
Analysis of other sequenced L. pneumophila strains revealed that lpg1965 is absent in the Lens and Paris genomes. Genomic plasticity in the chromosomal region encoding lpg1965 was apparent upon local sequence alignment between the four available genome sequences (Figure 1). We decided to investigate the genomic region delineated by the housekeeping genes encoded by ORFs lpg1962 (peptidyl-prolyl cis-trans isomerase, (ismr)) and lpg1977 (ThiJ protease, (thiJ)) to determine whether other effector proteins are present. Several genes that reside within this chromosomal region are found in all four strains, where they share extremely high sequence identity, and then there are multiple genes that are absent from one or more of the genomes. One mechanism that could account for genomic plasticity within this region is the acquisition of genetic material by horizontal gene transfer, followed by incorporation of the foreign DNA into the genome [34]. Genetic material incorporated by horizontal gene transfer typically has a different GC content compared with the average GC content of the receiving genome [35]. When compared to the average genomic GC content of 38.3%, lpg1965 and its neighboring genes that are not present in all four strains have a significantly lower GC content of 30.4% (lpg1963), 27.3% (lpg1964), 33.3% (lpg1965) and 33.2% (lpg1966). Although this analysis supports the hypothesis that these genes were acquired through a process of horizontal gene transfer, validation of this hypothesis requires further analysis. Regardless of the mechanism, these data indicate that lpg1965 is located in a region where genomic rearrangements have occurred.
The observation that the Dot/Icm substrate encoded by lpg1965 was located in a region of genomic plasticity suggested a location where other potential substrates of the Dot/Icm system might reside. To directly test whether additional proteins in the lpg1965 genomic region are Dot/Icm translocated substrates we fused Cya to the amino terminus of nine predicted proteins encoded in this region that were either novel or contained eukaryotic-like domains, and to three proteins encoded elsewhere on the chromosome that were predicted paralogues of proteins encoded in the plasticity region. This analysis revealed ten additional substrates of the Dot/Icm system (Figure 2A). Thus, these genes encode Pie (Plasticity Island of Effectors) proteins that are translocated substrates of the Dot/Icm system. Proteins within the region of genomic plasticity were designated PieA to PieG. Proteins outside of the Pie region were designated PpeA and PpeB, for the two translocated PieE paralogues, and PpgA for the translocated PieG paralogue. Similar to lpg1965 (PieC), translocation of the other Pie proteins was reduced greatly in a mutant strain of L. pneumophila deficient in the IcmSW protein complex (Figure S3). Even with the observation that these pie genes encode proteins with a functional C-terminal secretion signal recognized by the Dot/Icm system, expression of the pie genes was analyzed by reverse-transcription-PCR (RT-PCR) to ensure that these were not pseudogenes. These data show all the pie genes are expressed by L. pneumophila (Figure S2).
In Table 1 the Pie proteins and paralogues are organized into families based on amino acid identity. The degree of homology between the different family members was calculated using the multiple sequence alignment software ClustalW [36]. Proteins PieC and PieD share 14.7% sequence identity and 22.6% similarity. PieE shares 17.3% and 20.7% identity with PpeA and PpeB respectively, and 29.4% and 33.1% similarity with these proteins, respectively. PieG shares 15.7% and 16% identity with lpg1975 and PpgA respectively, and 23.2% and 25.4% similarity, respectively.
L. pneumophila strain SN178 is derived from parental strain Lp01 and is deficient in nine of the translocated Pie proteins and related paralogues. SN178 has in-frame chromosomal deletions removing the genes pieA, pieB pieC, pieD, pieE, pieG, ppeA, and ppeB. Insertional inactivation of the ppgA gene in SN178 resulted in the strain SN179, which is a mutant deficient in ten of the Pie proteins and related paralogues. A. castellanii was infected with Lp01, SN178 and SN179, and intracellular growth of these strains was compared to an isogenic Dot/Icm-deficient strain having a mutation in dotA. These data indicate that both SN178 and SN179 replicate as well as the parental strain Lp01 in A. castellanii (Figure 2D). The fold increase in colony-forming units (cfu) recovered from cells infected with Pie-deficient L. pneumophila was similar to the number recovered from cells infected with the parental strain Lp01. As expected, the dotA mutant did not replicate in these cells. Similar results were obtained when replication was measured in bone marrow-derived macrophages from an A/J mouse (Figure 2C). Thus, a strain deficient in the repertoire of Pie proteins and related paralogues has no measurable intracellular growth defect in macrophages or protozoan host cells, indicating that these proteins do not play an essential role in establishment and maintenance of a vacuole that supports replication of L. pneumophila in cell culture conditions.
Several of the Pie proteins contain predicted eukaryotic homology domains (Table 1). Putative coiled coil regions are found in PieA, PieC, PieD, PieF, and in the PieE family. This domain is predominantly found in eukaryotic proteins where it participates in the establishment of protein-protein interactions involved in a wide range of cellular processes including membrane tethering and vesicle transport [37]. Another eukaryotic homology domain identified is the RCC1 motif found in PieG and the related protein PpgA. RCC1 is a guanine nucleotide exchange factor for the Ran-GTPase, which is involved in cell cycle control and other cellular processes [38]. The presence of these putative domains in the Pie proteins suggests that once within the host cells, Pie proteins might function to mimic and manipulate cellular processes to facilitate the intracellular survival of L. pneumophila. Because subcellular localization of effectors can provide important insight into their biochemical functions, CHO cells were transfected with plasmids encoding GFP fusions of different Pie proteins to examine the distribution of these protein in mammalian cells. As shown in Figure 2B, GFP-Pie fusion proteins had different subcellular localization properties. There were several Pie proteins that appeared to localize to intracellular membranes. GFP-PieA was concentrated on vesicular structures in the perinuclear region of the cell. GFP-PieE displayed an ER-like reticulate pattern, and GFP-PieG localized to small vesicular-like structures throughout the cell (Figure 2B). None of the Pie proteins disrupted the structure of the Golgi apparatus when overproduced (data not shown), which is a phenotype observed for a number of other Dot/Icm effectors [24],[25],[39]. Thus, Pie proteins have unique subcellular distribution phenotypes that could relate to their ability to target different host proteins and possibly vesicular transport pathways.
The localization of PieA during infection was investigated further to independently address whether Pie proteins are translocated into host cells during infection. A polyclonal antibody specific for the PieA protein was used to determine whether PieA is found on vacuoles containing L. pneumophila. Vacuoles were isolated from U937 macrophage-like cells two hours after infection with L. pneumophila. PieA staining was evident on vacuoles containing wild-type bacteria (Figure 3A). No staining was observed on vacuoles containing a pieA mutant (Figure 3A). PieA staining was conducted in the absence of permeabilization, and under conditions where the majority of the vacuoles remain intact. Thus, the PieA associated with the vacuoles corresponded to protein on the cytoplasmic face of the vacuole.
In cells ectopically producing GFP-PieA there was a clustering of LAMP-1-positive late-endosomal/lysosomal vesicles (Figure 3B). The GFP-PieA protein was found in association with these LAMP-1-positive vesicles. These data suggest that PieA overproduction leads to an alteration in the morphology of host endocytic compartments. Cells producing GFP-PieA were infected with L. pneumophila to see if PieA overproduction interfered with any cellular processes important for L. pneumophila trafficking and growth. Surprisingly, there was a redistribution of GFP-PieA observed in cells infected with L. pneumophila. The GFP-PieA protein was found circumferentially localized to vacuoles containing L. pneumophila (Figure 3C). The observed redistribution of the protein upon infection is unique to PieA, and was not observed for any of the other GFP-Pie fusion protein (data not shown). The GFP-PieA staining on vacuoles containing replicating L. pneumophila delineated the membrane surrounding the bacteria. Anti-KDEL staining was used to visualize ER proteins with this retention motif, and showed that vacuoles containing L. pneumophila that stained positive for GFP-PieA also stained positive with anti-KDEL (Figure 4A). Thus, the GFP-PieA-positive organelles containing L. pneumophila have the expected properties of the specialized ER-derived vacuoles that support L. pneumophila replication.
GFP-PieA co-localization with ER markers was observed only with the L. pneumophila-containing vacuoles in infected cells, suggesting that protein recruitment occurs in response to a pathogen-mediated alteration in the vacuole. To investigate whether pathogen subversion of the ER to create a vacuole that permits replication was sufficient to induce relocalization of PieA to an ER-derived vacuole, GFP-PieA producing cells were infected with Brucella abortus, which similarly to L. pneumophila requires a type IV secretion system to create an ER-derived vacuole that supports intracellular replication [40]. GFP-PieA showed partial co-localization with LAMP-1-positive compartments in B. abortus-infected cells, but no co-localization of GFP-PieA with the ER marker calreticulin was detected in these cells, and no co-localization of GFP-PieA was observed with the B. abortus-containing vacuole (Figure 4B). These data suggest that intracellular L. pneumophila induce a specific modification to the vacuole in which they reside, and that this change mediates GFP-PieA recruitment to the vacuole.
Deletion derivatives were constructed to identify amino acid regions within PieA that are important for interaction of the protein with vacuoles containing L. pneumophila. All of the eukaryotic expression plasmids encoding the GFP-PieA deletion constructs described in Figure 5 produced similar levels of protein after transfection (data not shown). The recruitment of each PieA deletion derivative to vacuoles containing L. pneumophila was measured by fluorescence microscopy after infection (Figure 5). These data show that a GFP fusion protein containing C-terminal residues 513–699 of PieA was recruited to vacuoles containing L. pneumophila as efficiently as the full-length GFP-PieA protein. This region of PieA was designated the Vacuole Recruitment Domain (VRD). The GFP-PieA(1–512) protein, having the C-terminal VRD deleted, did not co-localize with vacuoles containing L. pneumophila, which indicates that the VRD is both sufficient and important for vacuole recruitment of PieA. A central region of PieA was found to have homology to the C-terminal region containing the VRD (Figure 5, grey bars). Although the internal region with similarity to the VRD region could not mediate recruitment of GFP-PieA(1–512) and GFP-PieA(1–614) to the vacuole, production of GFP-PieA(1–320) resulted in localization of the protein to the vacuole at low efficiency. Thus, there are discrete regions in PieA that can target this effector protein to vacuoles containing L. pneumophila.
To better characterize the binding of PieA to vacuoles, a cell-free system was established. Vacuoles containing an L. pneumophila pieA mutant were isolated from infected U937 macrophage-like cells and immobilized on glass coverslips. Purified PieA(513–699) protein was incubated with vacuoles. PieA association was determined by immunofluorescence microscopy following staining of vacuoles with an αPieA antibody (Figure 6). Fluorescence microscopy clearly revealed PieA(513–699) protein surrounding vacuoles containing L. pneumophila. These data show that the C-terminal VRD region in PieA defined in vivo mediates protein binding to isolated vacuoles in vitro. Vacuoles containing ΔdotA mutant bacteria were used to determine whether in vitro binding of PieA to vacuoles containing L. pneumophila was dependent on Dot/Icm-mediated alterations to the organelle (Figure 6A and 6B). There was no detectable binding of PieA(513–699) to vacuoles containing ΔdotA bacteria. Thus, Dot/Icm-dependent modifications to vacuoles containing L. pneumophila are required for PieA binding both in vivo and in vitro. The efficiency of PieA binding to vacuoles varied depending on the time the vacuoles were isolated after infection. Vacuoles isolated later in infection achieved a higher level of PieA binding. Optimal PieA binding was obtained for vacuoles isolated from cells that were infected for at least two hours (Figure 6D). PieA binding to vacuoles containing the icmS icmW double mutant was also tested because the translocation of multiple effector proteins is abrogated in this mutant [26],[41]. PieA binding to vacuoles containing the icmS, icmW mutant was impaired, with only 50% binding activity relative to wild-type-containing vacuoles (Figure S4). Taken together, these data indicate that Dot/Icm-dependent maturation events mediated by effectors, requiring IcmSW function for translocation, enable the efficient binding of PieA to vacuoles containing L. pneumophila.
The in vitro assay was used to determine whether there is a protein determinant on the vacuole containing L. pneumophila that is important for PieA binding. Proteins on the surface of vacuole containing L. pneumophila were digested with Proteinase-K (PK) prior to incubation with purified PieA(513–699). Treatment of vacuoles with PK greatly reduced PieA(513–699) binding (Figure 7A, left panel). PieA(513–699) was associated with 79±1 percent of vacuoles in the untreated control reactions compared to less than 3 percent of the vacuoles that were digested with PK (Figure 7B). Because PK treatment might disrupt the vacuole membrane surrounding L. pneumophila, membrane integrity was assessed after PK digestion by staining isolated vacuoles with an antibody that binds to LPS on the bacterial surface (αLP). The percentage of untreated vacuoles that stained positive using the αLP antibody (24±0.6) did not increase after PK treatment (16±3). All of the vacuoles stained positive with the αLP antibody when the surrounding membrane was permeabilized with methanol before antibody incubation (Figure 7A and 7B). Thus, PK treatment did not affect the integrity of the membrane surrounding isolated vacuoles containing L. pneumophila, indicating that the inability of PieA to bind to these vacuoles is caused by digestion of a protein exposed on the cytoplasmic surface of the organelle.
Genomic plasticity has been demonstrated for the L. pneumophila genome in work comparing the genomes of two serogroup1 isolates Lens and Paris [18]. More than ten percent of the genes in each genome were found to be strain specific [18]. How genomic plasticity might impact the repertoire of translocated effectors and bacterial phenotypes has not been investigated. It is possible that L. pneumophila strains have evolved in association with different protozoan hosts and that the predominant host species encountered by an individual strain in nature will impact the acquisition and maintenance of genes encoding translocated effectors. In support of this hypothesis, we demonstrate in this study a region of genomic plasticity that encodes multiple translocated effectors of the Dot/Icm system, which we called the Pie region.
The Pie region within the strain Philadelphia1 was found to contain genes encoding seven different Dot/Icm substrates. Analysis of the Pie region in other sequenced strains of L. pneumophila revealed that the pieB gene is found only in the Philadelphia1 genome, pieE, pieF and pieG are present in all four genomes, and pieA, pieC and pieD are present in two or more of the four genomes (Figure 1). pieA, pieB, pieC and pieD have a significantly lower GC content than the genomic average. This finding suggests that these pie genes were acquired through horizontal gene transfer, from foreign DNA. Because some of the pie genes are not present in all L. pneumophila genomes it is possible that they were acquired after the strain sub-speciation took place. Another possibility is that the genes were lost from the Lens and Paris genomes due to lower selection pressure for their existence in these strains.
This work clearly illustrates that regions of genomic plasticity in L. pneumophila can contain genes encoding Dot/Icm substrates. L. pneumophila effectors have been identified using several genetic and biochemical methods [21]–[29], including a systematic search for eukaryotic-like L. pneumophila proteins [18]–[20]. Our data suggests that further examination of other regions of genomic plasticity will likely reveal additional L. pneumophila effectors that have no obvious homology to eukaryotic proteins, which would include Dot/Icm substrates that arose by convergent evolution of proteins unrelated to the eukaryotic factors they mimic or perturb.
PieA was further investigated because of the unique ability of this protein to bind to vacuoles containing L. pneumophila. Data obtained from in vivo recruitment and in vitro binding of PieA to vacuoles containing L. pneumophila indicate that a protein on the cytoplasmic surface of vacuoles is required for PieA interaction. Other requirements for PieA binding were that the bacteria within the vacuoles must have a functional Dot/Icm system and vacuoles must have matured in the host cell for at least one to two hours. These data suggest that accumulation of an effector or effector complex on the vacuole is necessary for PieA recruitment and that it takes roughly two hours to achieve the required concentration of the PieA recruitment-determinant on the vacuole. Alternatively, it is possible that the Dot/Icm system mediates vacuole recruitment of a host protein that is not found on other ER-derived vesicles in the cell, and it is this host determinant that is critical for PieA binding. Importantly, PieA was not recruited to the ER-derived vacuole containing B. abortus. These data suggest that PieA recruitment to vacuoles is not a general phenomenon that occurs after invasion of the ER by pathogens with a functional type IV secretion system. Thus, it is likely that PieA recruitment is either mediated directly by a L. pneumophila effector or indirectly by an activity mediated by a L. pneumophila effector that is not present in B. abortus.
The survival of L. pneumophila within host cells involves an ordered series of events that are controlled by the Dot/Icm system. Within minutes of infection, the Dot/Icm system stimulates efficient uptake of L. pneumophila by the host phagocyte [42], rapidly prevents fusion of endocytic vesicles with the vacuole [43]–[45], and stimulates transport and binding of ER-derived vesicles to the limiting membrane of the vacuole [14],[15]. During replication there is evidence the Dot/Icm system stimulates ubiquitination of protein on the vacuole surface [46], modulates NF-kB activation [47], and interferes with protein translation [48]. At late stages of infection the Dot/Icm system assists in controlling bacterial egress from the spent host cell [32].
There is evidence that L. pneumophila effectors involved in controlling distinct cellular processes are spatially and temporally regulated. Transcriptional control of effector protein expression is one mechanism that could account for temporal regulation [28],[29]. Spatial regulation of DrrA on the vacuole has been demonstrated [25], and is mediated by host determinants on the plasma membrane that are presumably lost or modified as vacuoles mature and acquire new membrane from early secretory vesicles. Proteasome-mediated degradation of ubiquitinated effectors [46], and phosphoinositide metabolism on the vacuole membrane [49], are other mechanisms that have been proposed to spatially control L. pneumophila effectors. Studies presented here on PieA indicate that specific modifications to vacuoles controlled by other L. pneumophila effectors provide spatial and temporal information that is recognized by other effectors. Based on the observation that PieA binding requires a protein determinant on the cytoplasmic face of the vacuole membrane, we speculated that some effectors act as scaffolding proteins that function at specific stages of infection to recruit and retain a subset of effectors that have biochemical functions important for stage specific maturation events. Future studies will focus on determining the proteins on the vacuole membrane that interact with PieA, functioning as determinants important for spatial regulation of PieA. This may lead to the identification of L. pneumophila effectors involved in regulating different stages of vacuole maturation.
All bacterial strains, plasmids and oligonucleotide primers used in this study are listed in Table S1. Unless otherwise noted, chemicals were purchased from Sigma. Bacto-agar, tryptone, and yeast extract were purchased from Difco. L. pneumophila strains used in this study were grown on charcoal-yeast extract (CYE) plates as described previously [50],[51]. When needed, chloramphenicol was added to the media at a concentration of 10 µg ml−1.
Primary cells and cell lines were cultured at 37°C in 5% CO2. CHO cells were grown in minimal essential alpha medium (Gibco) containing 10% heat-inactivated fetal bovine serum (FBS). U937 cells were grown in RPMI-1640+10% FBS. The cells were activated with PMA for 48 h and replated in 6-well tissue culture dishes at a concentration of 3×106 per well before infection with L. pneumophila. Bone-marrow derived macrophages were cultured from female A/J mice as described previously [52]. HeLa cells (ATCC clone CCL-2) were cultured in Dulbecco's-Modified Eagle Medium (DMEM) supplemented with 10% fetal calf serum (FCS) and 2 mM L-glutamine.
All L. pneumophila mutants used in this study were derived from the wild-type strain Lp01. Gene deletions were introduced onto the chromosome of L. pneumophila by allelic exchange as described previously [53]. Deletion alleles of the pie genes were constructed using polymerase chain reaction (PCR) to generate DNA fragments encoding regions of flanking homology that were immediately 5′ to the start codon and 3′ to the termination codon of each gene, or set of consecutive genes. The primers used were SN76–SN77 and SN78–SN79 for the ppeA–ppeB deletion allele, SN152–SN153 and SN154–SN155 for the pieE deletion allele, SN129–SN149 and SN150–SN151 for the pieA–pieD deletion allele, SN133–SN134 and SN135–SN136 for deletion allele lpg1975–pieG. For each deletion allele, the 5′ and 3′ PCR products were joined by recombinant PCR and the final product was digested with the enzymes indicated in Table S1, and ligated into the gene replacement vector pSR47s digested similarly, creating the plasmids listed in Table S1. Deletion mutant strain SN178 was created by progressive gene deletions using the above replacement vectors. For the insertional-inactivation of pie genes, primers SN72–SN73 were used for the ppgA allele, and primers SN74–SN75 for the pieA allele. The resulting PCR products were ligated into suicide vector pSR47 creating plasmids pSN44 (ppgA) and pSN45 (pieA). Mutant strain SN179 was constructed by integrating plasmid pSN44 into the genome of strain SN178. Mutant strain SN122 was constructed by integrating plasmid pSN45 into the genome of strain Lp01.
The following sets of primers were used for cloning potential effector proteins: SN34 and SN35 for PieC, SN44 and SN45 for PieD, SN46 and SN47 for PieG, SN48 and SN49 for PieE, SN50 and SN51 for PieA, SN141 and SN142 for PieB, SN145 and SN146 for lpg1975, SN166 and SN167 for PpeA, SN168 and SN169 for PpeB, SN170 and SN171 for PpgA, SN175 and SN176 for PieF, SN143 and SN144 for lpg1968, SN177 and SN178 for lpg1973. The resulting PCR products were digested with the appropriate enzymes (see Table S1), and ligated to both vector pCya digested BamHI/PstI, or vector pEGFP-C2 digested BglII/PstI. The resulting pCya derived vectors encode for fusion proteins consisting of an amino-terminal M45 epitope tag, followed by amino acid residues 2–399 of the B. pertussis CyaA enzyme followed by the indicated L. pneumophila protein. Expression of the Cya fusion proteins is driven by the icmR promoter located upstream. The resulting pEGFP derived vectors encode for fusion proteins consisting of EGFP followed by the indicated L. pneumophila protein. The names and content of the generated plasmids are listed in Table S1. For the construction of 6His-tagged PieA primers SN91 and SN99 (PieA) or SN147 and SN148 (PieA513–699) were used. The resulting PCR products were digested with NdeI and BamHI, and ligated with the pET15b vector digested similarly, resulting in plasmids pSN63 and pSN73 respectively. For the construction of a DsRed-Express L. pneumophila expression vector, primers DsRed T1 Fwd and DsRed T1 Rev were used to amplify the DsRed-Express coding sequence from plasmid pDsRed-Express. The PCR product was digested with BamHI and HindIII, and ligated with plasmid pMMB207 digested similarly, resulting in plasmid pEMC22 encoding for the DsRed-Express protein under the control of the tac and icmR promoters located upstream. For the construction of PieA C-terminal deletion constructs fused to GFP, plasmid pSN39 was digested with NheI and HindIII, NheI and EcoRI, or NheI and SacI to obtain DNA fragments encoding for GFP-PieA(1–323), GFP-PieA(1–512) and GFP-PieA(1–614) respectively. The resulting fragments were ligated with plasmid pEGFP-C2 digested similarly, creating plasmids pSN56, pSN55 and pSN54 respectively. For the construction of PieA N-terminal deletion constructs fused to GFP, plasmid pSN39 was digested with HindIII and DraIII, EcoRI and DraIII, or SacI and DraIII to obtain DNA fragments encoding PieA(324–699), PieA(513–699) and PieA(615–699), respectively. The resulting fragments were ligated with plasmid pEGFP-C1 (for PieA(324–699) and PieA(513–699)), or with plasmid pEGFP-C3 (for PieA(615–699)) digested similarly, creating plasmids pSN59, pSN60 and pSN61 respectively.
Translocation of potential substrates into host cells was assayed using the Cya fusion approach described previously [26],[33]. Briefly, a stable CHO cell line producing FcγRII [54] was used, cells were plated at 1×105 cells per well in a 24-well tissue-culture-treated dish, and infected on the next day with the desired L. pneumophila strain carrying plasmids pSN24 (PieA), pSN65 (PieB), pSN20 (PieC), pSN27 (PieD), pSN25 (PieE), pSN87 (PieF), pSN28 (PieG), pSN81 (PpeA), pSN82 (PpeB), pSN83 (PpgA), pSN66 (lpg1968), pSN88 (lpg1973) or pSN67 (lpg1975) expressing the Cya fused to the gene of interest. The cells were infected at a multiplicity of infection (MOI) of 30, and then spun five minutes at 1000 rpm to initiate contact and synchronize the infection. Infected cells were incubated for one hour at 37°C with 5% CO2. Cells were washed three times in ice-cold phosphate-buffered saline (PBS) and lysed in cold buffer containing 50 mM HCl and 0.1% triton x-100 for 30 minutes at 4°C. The lysates were boiled for five minutes, and neutralized with 30 mM NaOH. Levels of cAMP were determined using the cAMP Biotrak enzymeimmunoassay (EIA) system (Amersham Biosciences).
Intracellular growth assays were conducted in A. castellanii (ATCC strain 30234) or in bone marrow derived murine macrophages, as described previously [26],[55].
A derivative of B. abortus strain 2308 constitutively expressing monomeric DsRed (DsRedm) was generated as described [56]. HeLa cells seeded on 12 mm glass coverslips in 24-well plates were transfected using the FuGene 6™ transfection reagent (Roche) to express GFP-PieA 24 hours before infections. For infections, bacteria grown to late log phase in Tryptic Soy broth were diluted in complete medium and added to chilled cells at a theoretical multiplicity of infection (MOI) of 500. Bacteria were centrifuged onto cells at 400× g for 10 minutes at 4°C, and infected cells were incubated for 30 minutes at 37°C under 7% CO2 atmosphere following a rapid warm up in a 37°C water bath to synchronize bacterial entry. Infected cells were then washed five times with DMEM to remove extracellular bacteria, incubated for an additional 60 minutes in complete medium before medium containing 100 µg/ml gentamicin was added for 90 min to kill extracellular bacteria. Thereafter, infected cells were maintained in gentamicin-free medium. At 24 hours post infection infected cells were washed three times with PBS, fixed with 3% PFA.
For localization of GFP-tagged effectors, CHO FcγRII cells were plated on 12-mm glass coverslips in 24-well tissue culture plates at a density of 104 cells per well. FuGene 6™ (Roche) was used to transfect the cells with plasmids pSN39 (GFP-PieA), pSN69 (GFP-PieB), pSN30 (GFP-PieC), pSN35 (GFP-PieD), pSN37 (GFP-PieE) or pSN33 (GFP-PieG). After 18 hours of expression cells were either directly fixed using 2% paraformaldehyde (PFA), or first infected with wild-type L. pneumophila strain Lp01 at an MOI of 30, and fixed seven hours post infection. For PieA deletion analysis, cells were seeded similarly, and transfected with plasmids pSN39 (GFP-PieA), pSN54 (GFP-PieA(1–614)), pSN55 (GFP-PieA(1–512)), pSN56 (GFP-PieA(1–323)), pSN59 (GFP-PieA(324–699)), pSN60 (GFP-PieA(513–699)) or pSN61 (GFP-PieA(615–699)). After 18 hours of expression cells were infected with wild-type L. pneumophila strain Lp01 at an MOI of 20. Seven hours post infection cells were washed with PBS supplemented with 0.9 mM CaCl2 and 1 mM MgCl2 and pre-permeabilized using 0.1% saponin in pipes buffer (80 mM pipes, 5 mM EGTA, 1 mM MgCl2, pH 6.8) for five minutes before fixing with 2% PFA. For detection of host and bacterial DNA, cells were stained with 4,6-diamidino-2-phenylindole (DAPI) for ten minutes at 25°C. For Lamp1 detection cells were permeabilized with 0.1% saponin (Sigma), and stained with UH1 mouse anti-hamster Lamp1 monoclonal antibody (Developmental Studies Hybridoma Bank), followed by secondary TexasRed anti–mouse IgG (Invitrogen). For KDEL detection, cells were first permeabilized using cold methanol, and stained with a mouse anti-KDEL monoclonal antibody (Stressgene) followed by secondary TexasRed anti–mouse IgG (Invitrogen). Polyclonal antibodies against PieA were produced at Pocono Rabbit Farm and Laboratory (Canadensis, Pennsylvania) using affinity purified histidine-tagged protein as antigen to immunize rabbits. Digital images were acquired with a Nikon TE300 microscope using a 100× 1.4 N.A objective lens and a Hamamatsu ORCA-ER camera controlled by IP Lab software. Images were exported as TIFF files and labeled in Adobe Photoshop.
For B. abortus infection experiments samples were blocked and permeabilized in 10% horse serum, 0.1% saponin in PBS for 30 min at room temperature. Cells were labeled using mouse anti-human LAMP-1 clone H4A3 antibody (developed by J. T. August and obtained from the Developmental Studies Hybridoma Bank developed under the auspices of the NICHD and maintained by The University of Iowa, Department of Biological Sciences, Iowa City, Iowa) or rabbit polyclonal anti-calreticulin antibodies (Affinity BioReagents) for 45 min at room temperature. Bound antibodies were detected using Cyanin 5-conjugated donkey anti-mouse antibodies (Jackson ImmunoResearch Laboratories). Samples were observed and imaged on a Carl Zeiss LSM 510 confocal laser-scanning microscope. Confocal images of 1024×1024 pixels were acquired as projections of 3 consecutive slices with a 0.38 µm step and assembled using Adobe Photoshop CS. For endogenous PieA detection vacuoles were isolated from U937 macrophage-like cells as described below and stained using rabbit polyclonal αPieA antiserum at 1∶100 dilution, followed by secondary FITC anti–mouse IgG (Invitrogen).
U937 macrophage-like cells were seeded into 6-well tissue culture plates at a density of 3×106 macrophages per well. The next day, the cells were infected with the indicated L. pneumophila strain harboring plasmid pEMC22, that were plate grown for two days in the presence of 1 mM Isopropyl β-D-1-thiogalactopyranoside (IPTG) to induce the expression of the fluorescent protein DsRed-Express. The bacteria were added to the cells at an MOI of 5 in the presence of 1 mM IPTG, and spun five minutes at 1000 rpm to initiate contact and synchronize the infection. One hour post-infection extracellular bacteria were removed by washing each well three times with warm PBS. Wells were refreshed with tissue culture medium containing 1 mM IPTG and incubated at 37°C for an additional hour. Next, the cells were placed on ice, and the wells were washed with cold PBS, before cells were lifted using a cell scraper into 1 ml cold homogenization buffer (H.B) containing 250 mM sucrose, complete protease inhibitor cocktail (Roche), and 20 mM Hepes pH 7.2. The cells were homogenized using a ball-bearing homogenizer, and the cell homogenate was spun for three minutes at 1500 rpm to sediment cell nuclei and unbroken cells. The post-nuclear supernatant (PNS) was diluted 1∶5 in cold H.B. and spun onto poly-L-lysine coated 12-mm glass coverslips in 24-well tissue culture plates. The PNS was fixed by the addition of PFA to 2% for 20 minutes at 25°C. To test for PieA binding, coverslips were incubated for one hour at 4°C with a blocking solution containing 50 mM ammonium sulfate and 2% goat serum, supplemented with 2 µg/ml of affinity purified 6His-PieA(513–699) protein. To remove unbound protein, the coverslips were washed three times in PBS. For detection of bound protein coverslips were stained with αPieA polyclonal antiserum at 1∶500 dilution, followed by secondary FITC anti–rabbit IgG (Invitrogen). Where indicated, vacuoles were permeabilized for 10 s with ice-cold methanol. The integrity of the vacuolar membranes was tested using polyclonal antiserum specific for L. pneumophila serogroup1 (αLP), and FITC-conjugated anti-rabbit IgG (Invitrogen). Where indicated, coverslips were first treated with PK by incubating them for two hours at 37°C in PBS to which PK was added to a final concentration of 10 µg/ml. The reaction was stopped by washing the coverslips three times in PBS, and then incubating them for 10 minutes in 1 mM PMSF in PBS, and finally washing three times with PBS. Control samples were treated similarly, but without the addition of PK.
RNA was isolated from broth-grown L. pneumophila using the TRIzol Max bacterial RNA isolation kit (Invitrogen). The RNA was digested with DNase Using an On-Column DNase digestion kit (Qiagen). RT-PCR was performed in two steps. First strand synthesis was performed using superscript II reverse transcriptase kit (Invitrogen) using 5 µg of total RNA and random primers (Invitrogen), and included a negative control reaction without reverse transcriptase. The PCR was preformed using taq-polymerase (Invitrogen) with gene-specific primers, and a 1∶50 dilution of the first strand mix as template.
The NCBI accession numbers for the proteins discussed in this paper are L. pneumophila PieA (YP_095979), PieB (YP_095980), PieC (YP_095981), PieD (YP_095982), PieE (YP_095985), PpeA (YP_095728), PpeB (YP_095729), PieF (YP_095988), PiG (YP_095992) and PpgA (YP_096236).
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10.1371/journal.ppat.1004931 | A Critical Role for CLSP2 in the Modulation of Antifungal Immune Response in Mosquitoes | Entomopathogenic fungi represent a promising class of bio-insecticides for mosquito control. Thus, detailed knowledge of the molecular mechanisms governing anti-fungal immune response in mosquitoes is essential. In this study, we show that CLSP2 is a modulator of immune responses during anti-fungal infection in the mosquito Aedes aegypti. With a fungal infection, the expression of the CLSP2 gene is elevated. CLSP2 is cleaved upon challenge with Beauveria bassiana conidia, and the liberated CLSP2 CTL-type domain binds to fungal cell components and B. bassiana conidia. Furthermore, CLPS2 RNA interference silencing significantly increases the resistance to the fungal challenge. RNA-sequencing transcriptome analysis showed that the majority of immune genes were highly upregulated in the CLSP2-depleted mosquitoes infected with the fungus. The up-regulated immune gene cohorts belong to melanization and Toll pathways, but not to the IMD or JAK-STAT. A thioester-containing protein (TEP22), a member of α2-macroglobulin family, has been implicated in the CLSP2-modulated mosquito antifungal defense. Our study has contributed to a greater understanding of immune-modulating mechanisms in mosquitoes.
| Entomopathogenic fungi represent a promising class of bio-insecticides for mosquito control. Detailed knowledge of molecular mechanisms governing anti-fungal immune response in mosquitoes is essential. CLSP2 composed of serine protease and lectin domains functions as a modulator of the mosquito immune system during the anti-fungal response. Transcriptome analysis indicated that the Toll pathway and melanization genes are highly up-regulated in CLSP2 RNA interference depleted mosquitoes infected with the fungus Beauveria bassiana. A thioester-containing protein TEP22, a member of α2-macroglobulin family, is involved in the CLSP2-modulated mosquito antifungal defense. Our study has contributed to the understanding of immune-modulating mechanisms in mosquitoes.
| Female mosquitoes require repeated blood feedings during their life cycle to satisfy their reproductive nutritional needs and, as a consequence, they serve as vectors of numerous human diseases [1]. Malaria, transmitted by the Anopheles genus, is the most devastating vector-borne human disease and causes about one million deaths per year. The annual number of cases of Dengue fever, a viral disease transmitted by Aedes aegypti, has reached over a hundred million. Major reasons for this serious situation include the lack of effective vaccines against major mosquito-borne diseases, rapidly developing drug and insecticide resistance, and socio-economic problems in endemic countries.
It is imperative to design novel specific biological pesticides, since mosquitoes have developed resistance to most currently used chemical insecticides [2]. While entomopathogenic fungi Beauveria bassiana and Metarhizium anisophliae infect insects by direct penetration of the cuticle, bacteria and viruses often need to be ingested, which makes fungi more promising as pesticides. However, fungal pathogens still require improvements due to the relatively low virulence when compared with chemical pesticides [3]. Detailed studies of antifungal immunity in mosquitoes are essential for future improvements of fungal biocontrol agents.
Multicellular organisms have evolved complex and powerful systems of immune responses to counteract continuous attacks of various pathogens. An essential feature of the immune system in any organism is its capacity to sustain equilibrium between reactivity and quiescence [4]. A loss of such a balance leads to severe consequences, such as autoimmune and inflammatory diseases in humans. Inhibitory receptor systems modulating immune responses have been identified in vertebrates [4,5]. However, the detail mechanism of the analogous system in insect is still not very clear. Our studies have revealed that CLSP2 functions as a key modulator of the mosquito immune system and contributed to a better understanding of immune modulating mechanisms in insects.
In insects, Toll is the principal innate immune pathway responsible for the anti-fungal response [6,7,8]. The Toll pathway is induced by fungal β1,3-glucan and also by Gram-positive bacteria harboring Lys-type peptidoglycan [7]. This pathway is crucial in activating immune responses especially in production of antimicrobial and anti-fungal peptides (AMPs) [6,9,10]. Gram-negative binding protein 3 (GNBP3), a member of the β-1, 3-glucan recognition protein (βGRP) family, binds to fungal cell components and initiates the Toll pathway [11]. Two Clip domain serine proteases (CLIPs)—Persephone and Späetzle-processing enzyme (SPE)—are components of an extracellular serine protease (SP) cascade and cause the cleavage of a cysteine knot cytokine, Späetzle (Spz) [12,13]. The cleaved Spz then functions as a ligand of the Toll receptor, which in turn passes the signals into the intracellular signal cascade consisting of MyD88, Tube, Pelle and TRAF6. Mosquitoes have a single orthologue of Dorsal, Rel1 [6]. Activation of the intracellular signal cascade by Toll results in the phosphorylation and degradation of Cactus, which is an inhibitor of the NF-кB transcription factors Dorsal and Dif [14]. Removal of Cactus releases and causes nuclear translocation of Dorsal and Dif, which eventually leads to the expression of AMPs, including Drosomycin, an antifungal peptide. Previously, Ae. aegypti orthologues of Drosophila genes of the Toll pathway—Spz1C, Toll5A, CLIPB5, and CLIPB29—have been identified and shown to mediate the Toll pathway in response to fungal infection [6,15]. In mosquitoes, Cecropins and Defensins are the major AMPs involved in the systemic antifungal immune responses [16].
Melanization represents a second immune pathway that is essential in the systemic antifungal immune responses [17]. It is the arthropod-specific defense mechanism that plays an essential role in wound healing and innate immunity [17]. The key enzymes for this reaction are prophenoloxidases (PPOs), which, once activated, catalyze the formation of toxic melanin. Melanin is then deposited around the wound or invading pathogens, including fungi. CLIPs constitute a cascade for amplification of a signal triggered by pathogen infection that results in PPO cleavage into an active PO by a melanization protease (MP). The melanization cascade is tightly regulated by serine protease inhibitors (SRPNs), which prevent spontaneous initiation of the reaction. The analysis of the mosquito genomes has shown that genes encoding immune signaling and effector molecules, and the number of melanization pathway genes have undergone major expansion [16]. For example, there are 10 PPO genes in the Ae. aegypti genome [13]. However, the precise roles of each PPO in melanization process are poorly understood. Our previous study revealed a novel level of complexity in the melanization cascade of the mosquito Ae. aegypti. Namely, we identified that there are several independent pathways leading to melanization, each requiring a different protease/SRPN regulatory module [15]. Of particular interest is a clear separation of tissue melanization, represented by melanin tumors often associated with the damage of host tissues, and immune melanization involved in the recognition and killing of pathogens, including fungi [13]. The melanization response has also been shown to significantly retard the growth and dissemination of B. bassiana in the An. gambiae mosquito [18].
Previously, we have identified an immune factor in Ae. aegypti, CLSP2 (AAEL011616), that is composed of an elastase-like serine protease (ESP) and CTL-type domains [19]. In this study, we show that CLSP2 is the key negative modulator of immune responses during anti-fungal infection. The expression of the CLSP2 gene is elevated upon B. bassiana infection. CLSP2 is cleaved upon challenge with B. bassiana and the liberated CLSP2 CTL-type domain binds to fungal cell components. Moreover, RNAi depletion of CLPS2 (iCLSP2) significantly increases the resistance to the fungal challenge. RNA-sequencing (RNA-seq)-based transcriptome analysis indicated that the Toll pathway and melanization genes are highly up-regulated in CLSP2 RNAi-silenced mosquitoes infected with B. bassiana (iCLSP2Bb). TEP22, a member of α2-macroglobulin family, was identified to be regulated by CLSP2 and to participate in the antifungal immune response in the Ae. aegypti mosquitoes.
We investigated the CLSP2 responses to fungal infection at the gene and protein levels. Real-time RT-PCR (qPCR) analysis showed that the CLSP2 mRNA level in mosquitoes was significantly up-regulated after septic injections of conidia of the fungus B. bassiana (S1A Fig). This result was consistent with our previously reported Northern results [19], indicating a CLSP2 response to infections at the gene level.
Aedes CLSP2 consists of two domains: the N-terminal elastase-like serine protein (ESP) and the C-terminal galactose-type C-type lectin (CTL), which includes a signature QPD sequence. CLSP2 includes a signal peptide and no transmembrane domain, suggesting that it is a secreted peptide. To investigate the infection effect on the CLSP2 protein composition, hemolymph samples were resolved on SDS-PAGE and subjected to immunoblot analysis utilizing anti-CLSP2 polyclonal antibodies. In the hemolymph of control mosquitoes injected with sterile phosphate buffered saline (PBS), CLSP2 remained as a single band of about 47 kDa, which disappeared in RNA-interference (RNAi) CLSP2 silenced mosquitoes (iCLSP2) (Fig 1A). However, two bands, corresponding to the 33-kDa ESP and 14-kDa lectin domains, were detected in the mosquito hemolymph after infection with B. bassiana conidia (Fig 1A). This suggested that CLSP2 was cleaved upon immune challenge. When we used the hemolymph from mosquitoes with silenced CLSP2, no bands were evident in the immunoblot, indicating that the 33-kDa and 14-kDa bands observed in the mosquito hemolymph after infection with B. bassiana conidia belong to CLSP2. Additional controls for the specificity of anti-CLSP2 antibodies are presented in S1B and S1C Fig.
In an attempt to understand the biochemical properties of CLSP2, we cloned and produced its lectin domain, designated as rLectin, using an Escherichia coli expression system. Myc tag rLectin fused with hexahistidine-tagged SUMO was purified using an affinity Ni-NTA agarose column. The isolated product was cleaved by SUMO protease, and then reloaded onto the Ni-NTA column, so that rLectin was in the flow-through fraction and the His-tagged protease was retained on the column. The purified rLectin migrated as a single band with the expected molecular weight (MW) of 14 kDa on SDS-PAGE (Fig 1B) that was not recognized by the anti-Histidine monoclonal antibody (Fig 1B).
To address the CLSP2 role in immune responses, we investigated the susceptibility of iCLSP2 mosquitoes to fungal infections. Three days after CLSP2 dsRNA injection, mosquitoes were infected with B. bassiana and their survival rate was evaluated. The survival rate of iCLSP2 mosquitoes challenged with B. bassiana (iCLSP2Bb) was significantly higher than that of mosquitoes infected with this fungus alone in the iLuc background (iLucBb). Mosquitoes with Luciferase (Luc) gene silencing served as a control (iLuc) (Fig 1C). qPCR and immunoblotting tests confirmed efficiency of CLSP2 RNAi (S2A–S2C) Fig. Thus, silencing of CLSP2 in mosquitoes led to an increased resistance to fungal infection, suggesting a role of CLSP2 in modulating immune activation.
In order to decipher the interaction between CLSP2 and fungi, we examined binding properties of rLectin by means of the agglutination assay. As fungal representatives, we tested zymosan, which is a component of the Saccharomyces cerevisiae cell wall composed of β-glucans and mannan, and GFP-conjugated B. bassiana in the rLectin agglutination assay (Fig 1D). Neither zymosan nor GFP-conjugated B. bassiana aggregates were observed in the presence of bovine serum albumin (BSA) used as a control. Only minor aggregates were found in the presence of EDTA. However, large aggregates were observed in the presence of Ca2+, indicating that it was required for the agglutination reaction by rLectin of either zymosan or GFP-conjugated B. bassiana (Fig 1D).
Next, we performed enzyme-linked immunosorbent assay (ELISA) to test whether rLectin directly bound to the fungal cell component curdlan. Different amounts of rLectin (20, 30, 50, 70 and 80 μg/ml; 50 μl each) were added to microtiter plate wells coated with curdlan, and the bound rLectin was detected using Myc antibodies. ELISA has demonstrated that rLectin effectively binds to curdlan (Fig 1E). Taken together, these results suggest that the CLSP2 lectin domain is capable of recognizing and binding to fungal carbohydrate surface molecules in a saturable and Ca2+ dependent manner.
Next, CLSP2 effect on expression of immune genes was elucidated by means of the RNA-sequence-based transcriptome analysis (RNA-seq) linked with RNAi screens. For this analysis, we used mosquitoes after three different treatments: control iLuc infected with B. bassiana (iLucBb), silenced with CLSP2 RNAi (iCLSP2), silenced with CLSP2 RNAi and infected B. bassiana (iCLSP2Bb). iLuc mosquitoes served as a control. Regulated immune gene repertoire (fold change ≥ 1.5) in different groups is shown in S1–S3 Tables. The hierarchical clustering analysis has revealed a stunning up-regulation of major immune gene transcripts in iCLSP2Bb mosquitoes (Fig 2A and S2D Fig).
Serine proteases (SPs) play important roles in a wide range of biological processes, including innate immunity. They constitute an integral part of immune reactions, such as the Toll and melanization cascades in arthropods [7,14]. There were 40 CLIPs (almost half of Ae. aegypti genome CLIPs) in the iCLSP2Bb upregulated transcriptome (S3 Table). A high elevation of expression levels of several immune genes in iCLSP2Bb mosquitoes was confirmed by means of the qPCR analysis (Fig 2B and S2E Fig). Our previous study has shown that CLIPB5 and CLIPB29 are involved in the activation of Toll pathway by fungal infection or by infection-independent manner, respectively [15]. Indeed, we found that both CLIPB5 and CLIPB29 were moderately up-regulated in iCLSP2Bb (S3 Table). Two Toll pathway regulators—Spz2 and Spz3A—were also dramatically up-regulated in the iCLSP2Bb mosquitoes (Fig 2A and 2B and S2E Fig). Moreover, the gene encoding the pattern recognition receptor GNBP1 was also significantly activated in iCLSP2Bb mosquitoes. Thus, our results have shown that CLSP2 modulates the transcriptional expression of the Toll pathway upstream genes (Fig 2C). However, the expression of genes encoding intracellular components of the intracellular Toll pathway signaling, including Rel1, was not significantly affected in these mosquitoes (Fig 2B and 2C). Interestingly, Cactus was elevated as a result of B. bassiana infection, however its transcript was reduced in iCLSP2Bb (Fig 2B).
Genes encoding pattern recognition receptors from the fibrinogen-related protein family (FREP) represented another highly elevated group of genes in the iCLSP2Bb mosquitoes (Fig 2B and S2E Fig, S3 and S4 Tables). FREP3, FREP5 and FREP10 were particularly up-regulated. The FREPs are an evolutionarily conserved immune gene family found in mammals and invertebrates [20]. It is the largest pattern recognition receptor gene family in mosquitoes, with 59 putative members in An. gambiae [20] and 35 in Ae. aegypti [16]. Genes encoding thioester-containing proteins, TEP2, TEP3 and TEP22 were also up-regulated. These data suggest that CLSP2 is an immune factor working upstream of the pattern-recognition receptor system, modulating their responses.
We then studied the effect of CLSP2 on mRNA levels of anti-microbial effector peptides (AMPs). Defensin A (DefA) and Cecropin A (CecA) represent the major mosquito AMPs [16], which also convey anti-Plasmodium activity [21]. As shown using qPCR, the mRNA levels of AMP genes, DefA, CecA, CecE, and CecF were induced in B. bassiana infected mosquitoes at 20- to 40-fold levels. Impressively, much higher induction levels of DefA, CecA, CecE, and CecF were observed in the iCLSP2Bb mosquitoes (Fig 3A). Thus, the CLSP2 played essential role in systemic immunity in mosquitoes by preventing the spontaneous transcription activation of downstream AMP immune genes. Next, we investigated interrelationship of CLSP2 and Rel1, which is a factor directly controlling the expression of AMP genes. As expected, the high expression of DefA and CecA brought by iCLSP2 was significantly decreased in mosquitoes with double CLSP2 and Rel1 RNAi silencing (Fig 3B). Moreover, the extremely high level of AMP expression in iCLSP2Bb mosquitoes was almost completely eliminated in mosquitoes with double knockdown of CLPS2Bb and Rel1 (Fig 3C). These experiments indicate that the action of CLSP2 due to modulation of upstream regulatory factors of the Toll immune cascade. According to the result from our RNA-seq analysis and qPCR, the induced immune genes belong mainly to the Toll pathway (Fig 2C), whereas the genes involved in the IMD and JAK/STAT pathways were not affected by the depletion of CLSP2 (S3A Fig). The only exception is PGRP-LC, which was influenced by CLSP2 and surprisingly by fungal challenge (S3B Fig). However, unlike GNBP1, it was not up-regulated the in iCLSP2Bb mosquitoes, thus pointing to the lack of modulation of this gene encoding the IMD pattern-recognition receptor by CLSP2. Moreover, CLSP2 had no effect on expression of the Rel2 gene, the principal regulator of the IMD pathway (S3A and S3B Fig). Toll and IMD share regulation of AMPs [22]. However, our experiments strongly suggest that CLSP2 effect on expression of the AMP genes is likely solely due to its influence on the Toll pathway.
We next selected up-regulated gene cohorts from mosquitoes after three different treatments—iCLSP2 (72 genes), iLucBb (93 genes) and iCLSP2Bb (108 genes) for further analysis. Forty immune genes were induced under all three experimental conditions (Fig 4A and S4 Table). The ontology analysis demonstrated that, except for several effector genes, the majority of co-upregulated genes belonged to regulatory categories located upstream of immune cascades (Fig 4B and S4 Table). The hierarchical clustering indicated that transcript levels of most of these genes are considerably higher in iCLSP2Bb than in the other two groups (Fig 4B). These results further suggest that CLSP2 is the modulator of the immune response involved in the anti-fungal infection.
Our analysis suggests that the modulating factor CLSP2 acts upstream of immune cascades, possibly interacting with other factors. To explore this possibility, we analyzed eight genes selected from those co-up-regulated in iLucBb, iCLSP2 and iCLSP2Bb mosquitoes (S5 Table), and their functions were studied by means of RNAi depletions in a combination with B. bassiana infection. After treatment with TEP22 dsRNA, a member of α2-macroglobulin family (S4 Fig), mosquitoes became extremely sensitive to the B. bassiana infection, and the survival rate dramatically decreased. However, the survival of affected mosquitoes could be partially rescued after the knockdown of CLSP2 was performed simultaneously with that of TEP22 (Fig 4C). Additionally, TEP22 was significantly regulated in iCLSP2Bb mosquitoes (Fig 4D). The results indicate that TEP22 is required for the anti-fungal response in a mosquito. Moreover, these results suggested that CLSP2 is likely mediated the response to fungal infection via interaction with TEP22 as a recognition molecule. Seven other tested genes from the iCLSP2Bb did not yield a similar phenotype indicating that they were not involved in the CLSP2 immune modulation directly (S5 Fig).
CLSP2 has been shown to be a negative modulator of hemolymph melanization [19]. To examine whether CLSP2 was involved in regulation of PPO gene expression, we utilized CLSP2 RNAi silencing in combination with B. bassiana infection (Fig 5A). The RNA abundance of 10 Aedes PPOs was investigated by means of qPCR analysis. Whereas transcript abundance of PPO genes did not change significantly after infection with B. bassiana alone, a highly pronounced activation of several PPO genes was observed in the iCLSP2Bb mosquitoes. PPO1 transcript increased dramatically by 9-fold, while levels of PPO2, PPO3, PPO4, PPO5 and PPO8 were elevated to about 4- to 6- fold. These results suggest that CLSP2 is an essential modulator of PPO gene expression. Moreover, this modulation is highly specific to just a few PPO genes that are most likely involved in immune responses during fungal infection. In addition to PPO genes, according to the results of RNA-seq, we also found that transcripts of several genes involved in melanization were up-regulated in iCLSP2Bb mosquitoes, including CLIPB9 and SRPN2 (S3 Table). The elevation of these gene transcripts in iCLSP2Bb was confirmed by qPCR (S2E Fig).
We selected PPO3 for further protein analysis, because its gene transcript was elevated in response to the fungal infection in iLucBb and was also highly upregulated in the iCLSP2Bb mosquitoes. Proteolytic cleavage of hemolymph PPO3 was detected by immunoblotting using polyclonal antibodies against Aedes PPO3. There was only a precursor PPO band in the iLuc control mosquitoes (Fig 5B). However, it was cleaved in the hemolymph of B. bassiana-infected and CLSP2-silenced mosquitoes as marked by the appearance of around 20-kDa-protein band (Fig 5B). The PPO3-derived cleavage proteins were also observed in iCLSP2Bb mosquitoes. At present we cannot assume that CLSP2 is responsible for cleavage of only PPO3 as we lack specific antibodies to other PPOs to investigate this question. The cleavage of PPO is likely not a direct effect of CPLS2 and occurs as a consequence of activation of melanization pathway factors by iCPLS2 and/or infection with B. bassiana as shown above. However, this experiment demonstrates importance of CLSP2 as a modulating factor working upstream in the PPO cascade.
Innate immune responses are initiated by the interaction between pathogen surface molecules and pathogen-related receptors (PRRs). C-type lectin recognition receptors (CTL or CTR) comprise a large family of PRRs that are engaged in the recognition of a broad spectrum of pathogens. They are also defined as Ca2+—dependent carbohydrate (lectin) binding proteins identified in a wide range of animal groups [23]. CTLs interact with glycans on cell surfaces, in the extracellular matrix, or on soluble secreted glycoproteins, mediating processes such as cell adhesion, cell-cell interactions and pathogen recognition [23].
CTLs have been implicated in pathogen evasion of a host immune system. In mammals, a large family of C-type lectin receptors modulating immune responses has been characterized[24]. Two CTLs (CTL4 and CTLMA2) have been shown to act as protective agonists during the development of Plasmodium ookinetes to oocysts in the mosquito An. gambiae [25]. Similarly, another C-type lectin, mosGCTL-1, an equivalent to CTLMA15, was found to facilitate the infection of West Nile virus in the mosquito Ae. aegypti [26]. Although CTLs have been identified as negative regulators of the immune response against malaria parasites and virus [25,26], details of mosquito CTL-based pathways are still unknown. Mosquitoes with the CLSP2 RNAi depletion displayed an elevated resistance to B. bassiana infection as compared to those with pathogens alone. Previously, we have also demonstrated that CLSP2 also functions during Plasmodium infection [17]. Thus, our study has uncovered an important role of CLSP2 as a factor modulating immune responses in the mosquito Ae. aegypti.
Ae. aegypti CLSP2 is a composite protein consisting of an elastase-like SP (ESP) domain located at the N-terminal and a CTL-type domain at the C-terminal. Composite immune proteases, such as Manduca sexta HP14 and the factor C of the horseshoe crab Tachypleus tridentatus, undergo cleavage after immune challenge [27,28]. Our experiments have shown that CLSP2 is also cleaved upon challenge with B. bassiana. Two bands were detected corresponding to the 14-kDa lectin and 33-kDa ESP domains in the hemolymph samples of mosquitoes with fungal infection by means of immunoblot analysis utilizing anti-CLSP2 antibodies. We have provided two lines of evidence clearly showing that the CLSP2 CTL-type domain binds to fungal sugar cell components. The purified recombinant CTL-type domain (rLectin) agglutinates zymosan and B. bassiana conidia in a calcium-dependent manner. Moreover, ELISA has shown that rLectin directly binds to the polysaccharide fungal cell component, curdlan, and this binding is saturable. However, whether the CTL domain binding to fungal surface molecules occurred before or after the CLSP2 cleavage in the hemolymph could not be determined. It also remains to be clarified whether upon infection-induced cleavage the CLSP2 domains undergo conformational changes still remaining as a single molecule in its native state or yielding completely separate molecules corresponding to the C-Type Lectin and SP domains.
Our study of the CLSP2-mediated immune activation using RNAseq-based transcriptome analysis further supports a hypothesis that CLSP2 is a modulator of the transcription responses involved in innate immunity and suggests that CLSP2 acts upstream of extracellular pathogen-recognition factors. CLSP2 depletion affected genes encoding FREP pattern recognition receptors and TEPs indicating that CLSP2 is an immune factor working upstream of the pattern-recognition receptor system, modulating their responses.
We identified TEP22 as an important player of the anti-fungal response in Aedes mosquitoes. TEPs are immune effectors genes that are conserved from insects to mammals. TEP molecules contain a motif harboring an intra-chain β-cysteinyl-γ-glutamyl thioester bond, which binds to target surfaces and prompts a series of complement cascades against microbes and parasites [29]. Knockdown of AgTEP1 in the resistant strain of An. gambiae led to a massive increase in the number of Plasmodium oocysts [30]. AgTEP1 is essential for blocking oocyst development in the midgut of An. gambiae by forming complex with two proteins from leucine-rich repeat family, LRIM1 and APL1C [31,32,33]. Nine TEP genes have been identified in the Ae. aegypti genome [16]. Our phylogenetic analysis has shown that Aedes TEP20, 22, 23, and 25 form an independent clade supported by high bootstrap value (S4 Fig). Transcript levels of TEP20, 22, and 23 were elevated in the fat body Rel1 and Rel2 gain-of-function transgenic mosquitoes and also in response to the Plasmodium infection [22]. We have shown in the present study that the TEP22 expression is dramatically elevated in iCLSP2Bb mosquitoes. Furthermore, TEP22-depeleted mosquitoes are extremely sensitive to B. bassiana infection, while CLSP2 knockdown in these mosquitoes rescues their survival. Thus, our findings suggest that TEP22 is involved in the antifungal immune pathway and it could interact with CLSP2 in this immune response. This interaction would be reminiscent of the mannose-binding lectin (MBL) triggered complement activation in mammals [34] or TEP1/LRIM1/APL1C complex in An. gambiae [30–33]. However, the detailed mechanism of complement-like factor action in the anti-fungal immunity and TEP22 association with the immune modulating factor CLSP2 requires further mechanistic study.
Our study has demonstrated that the intracellular signal transduction components of the Toll pathway are not regulated by CLSP2 at the transcriptional level. In vertebrates, inhibitory receptor systems modulating immune responses depend on the intracellular phosphorylation pathway and not regulation at the transcription level [4,5]. Similar mechanism is also identified in the negative regulation of Toll-like receptor mediated pathways [35,36]. Interestingly, we observed that the activation of Cactus, the Rel1 inhibitor in Toll-mediated infection [37], brought by fungal infection was abolished by the RNAi depletion of CLSP2. This iCLSP2 effect on Cactus is completely opposite from those on other immune genes. Although Cactus target Rel1 is not affected by CLSP2, the downstream gene cohorts highly activated in the iCLSP2Bb mosquitoes include those encoded effector molecules such as AMPs. The unique interaction of CLSP2 with Cactus suggests that it contributes in the control of AMP gene activation. Moreover, the abolishment of activation of AMPs, brought by iCLSP2 by the double knockdown of CLSP2 and Rel1, indicates that Rel1 mediates the action of CLSP2 on these immune genes.
We also have uncovered the CLSP2 role in modulating the melanization pathway in Ae. aegypti. represents a second immune pathway that is essential in the systemic antifungal immune responses [17]. CLSP2 not only modulates the hemolymph activation of PPO, but also negatively regulates the expression of PPO genes. The melanization cascade is tightly regulated by serine protease inhibitors (SRPNs), which prevent spontaneous initiation of the reaction. The analysis of the mosquito genomes has shown that genes encoding immune signaling and effector molecules, and the number of melanization pathway genes have undergone major expansion [16]. For example, there are 10 PPO genes in the Ae. aegypti genome [13]. However, the precise roles of each PPO in melanization process are poorly understood. Our previous study revealed a novel level of complexity in the melanization cascade of the mosquito Ae. aegypti. Namely, we identified that there are several independent pathways leading to melanization, each requiring a different protease/SRPN regulatory module [15]. Of particular interest is a clear separation of tissue melanization, represented by melanin tumors often associated with the damage of host tissues, and immune melanization involved in the recognition and killing of pathogens, including fungi [13]. The melanization response has also been shown to significantly retard the growth and dissemination of B. bassiana in the An. gambiae mosquito [18].
Multicellular organisms have evolved complex and powerful systems of immune responses to counteract continuous attacks of various pathogens. An essential feature of the immune system in any organism is its capacity to sustain equilibrium between reactivity and quiescence [4]. A loss of such a balance leads to severe consequences, such as autoimmune and inflammatory diseases in humans. Inhibitory receptor systems balancing immune responses have been identified in vertebrates [4,5]. Our study has revealed that CLSP2 functions as a key modulator of the mosquito immune system and contributes to a better understanding of immune mechanisms in insects.
The UGAL strain of Ae. aegypti mosquitoes was maintained in the laboratory as described previously [38]. Adults were fed continuously on water and 10% sucrose solution. To initiate egg development, mosquitoes were blood fed on chickens. All procedures for using vertebrate animals were approved by the Institute of Zoology Animal Care and Use Committee.
B. bassiana strain ARSEF 2680 and B. bassiana strain expressed GFP were cultured on potato dextrose agar plates at 25°C and 80% humidity [39]. B. bassiana strain ARSEF 2680 was used in immune challenge and the strain 252-GFP was used in the agglutination assay. Conidia (fungal spores), used for mosquito challenge were harvested from 3- to 4-week-old cultures and diluted to 5×107 conidia/ml in PBS. Septic injures were carried out by pricking the rear part of the mosquito abdomen with an acupuncture needle dipped into fungal conidia suspension [6].
For the immune response of CLSP2 to fungal infection, 3 days old adult mosquitoes were divided into two groups (30 adults / group): the control group (control) was challenged with PBS; the experiment group (Bb 24h) with B. bassiana spores. Tissue samples were collected 24 h later.
For the RNA-seq, immune genes expression and survival rate analysis, new emergence mosquitoes were divided into four groups (30 adults / group): two groups (luciferase groups) were injected with luciferase dsRNA; another two groups (CLSP2 groups) were injected with CLSP2 dsRNA. 3 days later, one of the luciferase groups were challenged with PBS (iLuc), and the other one were challenged with B. bassiana spores (iLucBb). One of the CLSP2 groups was challenged with PBS (iCLSP2), and the other one with B. bassiana spores (iCLSP2Bb). The same treatments were also used in the survival rate analysis of TEP22 and other immune genes.
cDNA templates of target genes were generated by means of RT-PCR using both sense and antisense primers fused with T7-phage promoter sequences. RT-PCR was performed using the cDNA samples as templates to generate 400-bp to 1-kb gene-specific cDNA fragments. Synthesis of dsRNA was accomplished by simultaneous transcription of both strands of template DNA using T7 RNA polymerase from the T7 RiboMAX Express RNAi kit (Promega). The luciferase gene was used to generate control iLuc dsRNA. A Nanoliter 2000 injector (World Precision Instrument) was used to introduce corresponding dsRNA into the thorax of CO2-anesthetized mosquito females within 1 day post-eclosion. Primers used for generating dsRNA are listed in S6 Table. The transcripts of specific genes decreased to 50–70% 1 week after dsRNA injection, confirmed by real-time RT-PCR.
At 3 days after eclosion, 30 female mosquitoes were challenged with B. bassiana conidia [6]. The mosquitoes were maintained in individual containers and fed continuously on water and 10% sucrose solution. The survival curves were compared using Kaplan-Meier, and the threshold of p value was calculated with a Log-rank or Mantel Cox test, and p < 0.01 were considered to be statistically significant. Graphpad 6.0 software was used in all statistical analyses.
Hemolymph from 20 decapitated mosquitoes was collected into 20 μl of 1×protease inhibitor cocktail (Roche) by centrifugation at 5,000 rpm for 5 min with Qiashredder column (QIAGEN) [15]. Aliquots of hemolymph samples were resolved on 4–15% gradients SDS-polyacrylamide gels (Bio-Rad) and electrotransferred to PVDF membranes (Invitrogen). After blocking, the membranes were incubated with the primary antibody against CLSP2 or PPO3 overnight at 4°C. We used polyclonal antibodies against Ae. aegypti Lipophorin II [15,40] and β-actin (Sigma) as the loading controls. Immune complexes were visualized by means of SuperSignal West Pico Substrate (Pierce).
rLectin (Lectin domain of CLSP2) was amplified by RT-PCR from cDNA with specific primers (S6 Table). The PCR product was subcloned into PSFM (a kind gift from Dr. Haobo Jiang, Oklahoma State University), a vector with a Sumo at the N-terminal, which increases the solubility of the fusion protein and can be removed by SUMO protease afterwards. The N-terminal FLAG and the C-terminal Myc are short sequences for detection of the expression of fusion protein and its cleavage products using commercially available monoclonal antibodies against these two tags, respectively. SUMO-rLectin was first purified on a Ni-NTA (nickel-nitrilotriacetic acid, Qiagen) agarose column. Then, SUMO-rLectin was cleaved using SUMO protease, as per the manufacturer’s protocol (GeneCopoeia), and re-purified on the Ni-NTA agarose column. Monoclonal antibodies were prepared against KLH-peptide from CLSP2 (Beijing Protein Innovation). Polyclonal antibodies were prepared against recombinant CLSP2 and recombinant PPO3 (Beijing Protein Innovation). Specificity tests of these antibodies are presented in S1 Fig.
FITC-conjugated zymosan (Molecular Probes) or GFP-conjugated B. bassiana conidia suspended in Tris-buffered saline (TBS) (25 mM Tris-HCl, 137 mM NaCl and 3 mM KCl, pH 7.0) were incubated with purified rLectin (80 μg/ml) for the agglutination assay, as described by Yu et al. [41]. After incubation for 45 min at RT, samples were examined using fluorescence confocal microscopy (Zeiss 710).
For binding assay, wells of a flat-bottom, 96-well plate (Nunc, Fisher Scientific) were coated with 2 mg (50 μl of 40 mg/ml per well) of curdlan (Sigma) as described [41]. The plate was then blocked with BSA (100 μl/well of 1 mg/ml) for 2 h at 37°C and rinsed with binding buffer (50 mM Tris-HCl, 50 mM NaCl, pH 8.0) (200 μl/well). rLectin diluted with binding buffer containing 5 mM CaCl2 and 0.1 mg/ml BSA was adjusted to 50 μl/well binding at RT for 4 h. The plates were rinsed as before, and bound rLectin was measured using mouse anti-C-myc antibody (1:1000), and horseradish peroxidase (HRP) conjugated antibodies against mouse. The pre-immune antiserum was used as control. Soluble TMB Substrate Solution (100 μl/well, Tiangen) was added to react for 20 min, and then stopped with 8.5 M acetic acid. Absorbance at 450 nm of the samples in each well was determined using a microplate reader (Molecular Devices).
To investigate the immune response to B. bassiana infection in the mosquito Ae. aegypti, we used a high-throughput sequencing (HTS) platform (HiSeq 2000) to analyze gene expression in carcasses of fungal infected mosquitoes. Four fat body libraries were built from iLucBb, iCLSP2, iCLSP2Bb, and iLuc mosquitoes. Three replicates of each sample (25 mosquitoes/sample) were pooled for analysis and 100 ng of total mRNA from each sample was used to construct libraries with an Illumina kit v2.
Raw reads generated from the sequencing were preprocessed using in-house perl scripts, including adaptor removing and low quality reads filtering. Those with average quality lower than 20 and read length shorter than 35 bp were discarded automatically. To minimize the sequencing noise from other species, we mapped the filtered reads against both the bacteria and virus databases in NCBI, and made sure the remainder was highly reliable. The genome of Ae. aegypti was downloaded from VectorBase (https://classic.vectorbase.org/genomes), as was the annotation file. The clean reads were mapped to the genome using GSNAP to estimate the expression level of all of the transcripts [42]: three mismatches were tolerated during processing, and the parameter of new transcript finding was shut down to guarantee the precise matching. We used flux-capacitor to calculate the FPKM of the transcripts, and DEGseq package in R Scripts to determine the DEGs [43]. P values less than 0.05 indicated genes were differentially expressed. All immune genes were then assigned according to immunodb [16]. Hierarchical clustering of gene expression intensity was performed using Pearson distance as the distance measure between genes and libraries [44]. Cross comparison performing within each treated sample was normalized by their reads count (FPKM), and iLuc sample was considered as background value while the ratio of fold change was calculated. Phylogenetic trees were constructed using MEGA6 by the neighbor-joining method [45].
Total RNA samples were prepared from dissected abdominal carcasses of 10–15 individual mosquitoes. Malpighian tubules, midguts, and ovaries were removed, then abdominal carcasses with adhered fat body tissue and sessile hemocytes were rinsed in PBS, transferred into TRI reagent (Sigma), and homogenized using a motor-driven pellet pestle mixer (Kontes, Vineland, NJ). A 2-μg sample of total RNA was treated with DNase I (Invitrogen) to remove contaminating genomic DNA, and then used for cDNA synthesis (M-MLV reverse transcriptase kit, Promega). Actin was used as an internal standard to normalize the templates in a preliminary PCR experiment. After template adjustment, PCRs were performed to detect relative levels using specific primers. Primers were designed by software Primer5. Real-time RT-PCR (qPCR) reaction was performed on the MX3000P system (Stratagene, CA), and we used a SYBR green PCR Master Mix (Tiangen, Beijing) for these reactions. Thermal cycling conditions were: 94°C, 20 s; 59°C, 20 s; 68°C 20 s. Quantitative measurements were performed in triplicate and normalized to the internal control of S6 ribosomal protein mRNA for each sample. Primers and gene accession numbers are listed in S6 and S7 Tables, online. Real-time RT-PCR data were collected and exported to EXCEL for analysis. Values were represented as the mean ± SEM, and the statistically significant difference between samples was calculated using the Student-t test (Graphpad 6.0).
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10.1371/journal.pgen.1002553 | Mitochondrial Oxidative Stress Alters a Pathway in Caenorhabditis elegans Strongly Resembling That of Bile Acid Biosynthesis and Secretion in Vertebrates | Mammalian bile acids (BAs) are oxidized metabolites of cholesterol whose amphiphilic properties serve in lipid and cholesterol uptake. BAs also act as hormone-like substances that regulate metabolism. The Caenorhabditis elegans clk-1 mutants sustain elevated mitochondrial oxidative stress and display a slow defecation phenotype that is sensitive to the level of dietary cholesterol. We found that: 1) The defecation phenotype of clk-1 mutants is suppressed by mutations in tat-2 identified in a previous unbiased screen for suppressors of clk-1. TAT-2 is homologous to ATP8B1, a flippase required for normal BA secretion in mammals. 2) The phenotype is suppressed by cholestyramine, a resin that binds BAs. 3) The phenotype is suppressed by the knock-down of C. elegans homologues of BA–biosynthetic enzymes. 4) The phenotype is enhanced by treatment with BAs. 5) Lipid extracts from C. elegans contain an activity that mimics the effect of BAs on clk-1, and the activity is more abundant in clk-1 extracts. 6) clk-1 and clk-1;tat-2 double mutants show altered cholesterol content. 7) The clk-1 phenotype is enhanced by high dietary cholesterol and this requires TAT-2. 8) Suppression of clk-1 by tat-2 is rescued by BAs, and this requires dietary cholesterol. 9) The clk-1 phenotype, including the level of activity in lipid extracts, is suppressed by antioxidants and enhanced by depletion of mitochondrial superoxide dismutases. These observations suggest that C. elegans synthesizes and secretes molecules with properties and functions resembling those of BAs. These molecules act in cholesterol uptake, and their level of synthesis is up-regulated by mitochondrial oxidative stress. Future investigations should reveal whether these molecules are in fact BAs, which would suggest the unexplored possibility that the elevated oxidative stress that characterizes the metabolic syndrome might participate in disease processes by affecting the regulation of metabolism by BAs.
| Cholesterol metabolism, in particular the transport of cholesterol in the blood by lipoproteins, is an important determinant of human cardiovascular health. Bile acids are breakdown products of cholesterol that have detergent properties and are secreted into the gut by the liver. Bile acids carry out three distinct roles in cholesterol metabolism: 1) Their synthesis from cholesterol participates in cholesterol elimination. 2) They act as detergents in the uptake of dietary cholesterol from the gut. 3) They regulate many aspects of metabolism, including cholesterol metabolism, by molecular mechanisms similar to that of steroid hormones. We have found that cholesterol uptake and lipoprotein metabolism in the nematode Caenorhabditis elegans are regulated by molecules whose activities, biosynthesis, and secretion strongly resemble that of bile acids and which might be bile acids. Most importantly we have found that oxidative stress upsets the regulation of the synthesis of these molecules. The metabolic syndrome is a set of cardiovascular risk factors that include obesity, high blood cholesterol, hypertension, and insulin resistance. Given the function of bile acids as metabolic regulators, our findings with C. elegans suggest the unexplored possibility that the elevated oxidative stress that characterizes the metabolic syndrome may participate in mammalian disease processes by affecting the regulation of bile acid synthesis.
| In mammals, cholesterol is necessary for the structure and function of membranes, and is the substrate for the biosynthesis of signalling molecules such as sexual steroids, bioactive compounds such as vitamin D, and bile acids (BAs) [1]. Cholesterol is converted into BAs through a series of oxidation reactions, as well as a shortening of the side chain in mammals (Figure S1). The enzymes that catalyze the individual biosynthetic steps of BA synthesis are localized in different cellular compartments, including the endoplasmic reticulum, cytosol, mitochondria, and peroxisomes. For example, the oxidation of the side-chain takes place in the mitochondria, but side-chain shortening takes place in the peroxisomes. In vertebrates, these reactions occur predominantly in hepatocytes.
BAs regulate cholesterol and lipid metabolism in a variety of ways. They participate in cholesterol, lipid and hydrophobic vitamin uptake through their properties as detergents. They also participate in cholesterol elimination as they are secreted into the gut from where a fraction is lost every day in the feces. However, most of the secreted BAs are taken up again through the gut epithelium and can be re-circulated to the liver and re-secreted into bile, a process that is called the entero-hepatic circulation of BAs. In addition, BAs are signalling molecules that integrate several aspects of metabolism, including fat, glucose, and energy metabolism by regulating gene expression through nuclear hormone receptors such as the farnesoid X receptor (FXR), the pregnane X receptor (PXR), and the vitamin D receptor (VDR) (BA biology is reviewed in detail in [2], [3]).
In mammals, BA excretion and recirculation depend on a number of membrane transporters such as ATP8B1 and ABCB11. ATP8B1, a type 4 P-type ATPase is a predicted phospholipid flippase [4]. Flippases transfer lipids from one leaflet of the membrane to the other thus changing the composition of both leaflets and the properties of the membranes. Several studies in mice suggest that ATP8B1 deficiency causes loss of canalicular membrane phospholipid asymmetry and as a result the resistance of the canalicular membrane to hydrophobic BAs is decreased, which impairs the activity of ABCB11, the BA export pump, and causes cholestasis, a pathological retention of bile [5]. Mutation of ATP8B1 in humans leads to progressive familial intrahepatic cholestasis type 1 (PFIC1) [6].
ATP8B1 shares 56% sequence identity with C. elegans TAT-2 (for Transbilayer Amphipath Transporters) [4], [7], [8]. A tat-2 mutant was found to exhibit hypersensitivity to low dietary cholesterol with decreased reproductive growth [8]. tat-2 mutation also suppresses the conditional growth arrest phenotypes resulting from mutation of elo-5, a gene encoding a very long chain fatty acid (VLCFA) elongase, which is required for the production of two monomethyl branched-chain fatty acids (mmBCFAs) in C. elegans [7]. As tat-2 also partially suppresses developmental defects caused by reduction of the expression of sptl-1, which disrupts sphingolipid biosynthesis, the authors proposed that TAT-2 acts by affecting the localization of mmBCFA-containing sphingolipids.
Like vertebrates, C. elegans need sterols (reviewed in [9]). However, as C. elegans is capable only of modifying sterols and not of synthesising them de novo, worms are auxotrophic for sterols, which have to be added to the culture media (generally at 5 µg/ml cholesterol). A reduction in sterol supplementation leads to a complex phenotype that includes abnormal moulting, and inappropriate dauer formation. A complete lack of sterol supplementation leads to lethality. As sterols appear to be required only in very small amounts for normal physiology in worms, the deficit resulting from the absence of dietary cholesterol might result from deficits in the synthesis of signalling molecules derived from cholesterol. Indeed BA-like molecules derived from cholesterol have been identified in C. elegans and shown to have roles in signalling [10]. Dafachronic acid, which is required for bypassing dauer formation, has some characteristics of BAs, with oxidation of the steroid ring and of the side-chain, but its oxidation is not extensive and the side-chain is not shortened [10]. Yet, like vertebrate steroids and BAs, it acts via a nuclear hormone receptor, encoded by daf-12 [10].
In mammals, after BA-mediated absorption, ingested lipids, cholesterol, and lipid-soluble vitamins, are transported from the gut to the tissues that need them via circulating lipoproteins such as chylomicrons. Other lipoproteins such as low density lipoproteins (LDL) distribute lipids and cholesterol from the liver to peripheral tissues, and high density lipoproteins (HDL) transport cholesterol from peripheral tissues back to the liver in a process termed reverse cholesterol transport. The best known lipoproteins in C. elegans are the yolk particles. The protein moieties of yolk particles are vitellogenins, distant homologues of ApoB, which is the apolipoprotein in chylomicrons and LDL [11]. In C. elegans, cholesterol, fatty acids, and possibly other nutrients are transported from the gut to developing oocytes through the pseudocoelomic cavity by means of yolk particles [12], [13]. However, several observations suggest that there is another lipid transport system in worms [14]. For example, hermaphrodites are capable of transporting cholesterol before the vitellogenins are expressed and males do not express vitellogenins yet accumulate cholesterol in developing sperm [13]. Furthermore, a mutation in dsc-4, which encodes the worm homologue of the microsomal triglyceride transfer protein (MTP) [15], which is required in mammals for the synthesis of LDL in the ER, produces multiple phenotypic effects without affecting yolk production.
CLK-1 is a conserved mitochondrial enzyme that is necessary for the biosynthesis of the antioxidant and redox cofactor ubiquinone (co-enzyme Q; CoQ). Mutations in C. elegans clk-1 or its mouse orthologue affect mitochondrial function [16], [17], in particular they increase mitochondrial oxidative stress in both organisms [18], [19]. In worms, this results in a number of phenotypes, in particular slow development, slow aging, and slow rhythmic behaviours such as defecation [20].
The defecation cycle of C. elegans generates rhythmic body muscle contractions. This is a well-studied, highly regulated behaviour that is readily quantifiable [21]. dsc-4/mtp was originally identified as a mutation that suppresses the slow defecation of clk-1 mutants [22]. Given the known function of MTP it was concluded that a type of MTP-dependent, LDL-like lipoprotein, distinct from yolk, affects the rate of defecation [14]. Reducing the level of dietary cholesterol mimics the effects of dsc-4 on the defecation cycle length of clk-1 mutants [15], [23]. These observations suggest that clk-1 mutants have slow defecation because they have high levels of LDL-like lipoproteins biosynthesis and secretion. Furthermore, the MTP-dependent lipid transport system appears to be so well conserved between mammals and C. elegans that drugs that have been developed to lower lipid levels in humans can act as suppressors of the slow defecation rate of clk-1 [23]. In particular, the slow defecation is suppressed by drugs that antagonize high LDL levels by increasing HDL levels (e.g. an inhibitor of the HDL receptor SR-BI [24]), or that reverse cholesterol transport by stimulating gene expression through nuclear hormone receptors (e.g. gemfibrozil [25]). Thus, although it is not yet known how elevated lipoprotein biosynthesis slows down the defecation cycle, the clk-1 mutants provide a tractable genetic model for characterizing the mechanisms of lipids and sterol uptake and the biosynthesis and secretion of LDL-like lipoproteins.
Here, using genetic and pharmacological approaches, we show that sterol uptake in C. elegans depends on molecules that are functionally similar to BAs and might be structurally similar as well. These molecules are distinct from dafachronic acids and are synthesized and secreted through a pathway that appears to be molecularly very similar to that of BA synthesis and secretion in mammals. We also show that this pathway is altered by the high mitochondrial oxidative stress of clk-1 mutants. A link of oxidative stress and aging with dyslipidemia and with the other cardiovascular risk factors that constitute the metabolic syndrome has repeatedly been evidenced in mammals, but its mechanistic basis has not yet been elucidated. Our findings suggest that the link could be a perturbation of BA biosynthesis, a possibility that has not yet been explored in mammals.
We previously carried out a genetic screen to find suppressors of the slow defecation phenotype of clk-1 mutants [22]. In this screen we identified the dsc-4/mtp mutation (described in the Introduction) as well as another mutation, dsc-3(qm179), which produced a very similar phenotype [22]. As the effects of dsc-4/mtp and dsc-3(qm179) are not additive (Table S1), they may act in a common pathway or affect a common process. We mapped dsc-3(qm179) between dpy-13 and unc-5 on LG IV [22]. Using the hypotheses that dsc-3 is involved in lipoprotein metabolism (based on the identity of dsc-4/mtp) we identified tat-2 as a candidate gene in that chromosomal region. We determined that qm179 is allelic to tat-2(tm1634) based on the following experiments, whose results are shown in full in Table S1 (Table S1 lists all numerical values, samples sizes and statistical analyses for all defecation data shown in figures or mentioned in the text). Firstly, both RNAi against tat-2 and the deletion mutation of tat-2(tm1634) were phenotypically similar to qm179 in both the wild type and clk-1 backgrounds. Secondly, the tat-2(tm1634) deletion mutation fails to complement qm179 (Figure 1A). Thirdly, transgenic expression of tat-2 rescues the suppression of clk-1 by qm179 (Figure 1A). Finally, a G-to-A point mutation that results in an amino acid change from Alanine to Threonine at residue 665 of the protein was found by sequencing the coding region of tat-2 in qm179 mutants (Figure 1B). We name the gene tat-2 from this point on. The allele analyzed is always qm179 except when otherwise specified.
The high sequence conservation between TAT-2 and ATP8B1 suggests that their functions could be conserved as well. To test this directly we introduced a cDNA coding for mouse ATP8B1 in clk-1;tat-2 mutants under the C. elegans tat-2 promoter (Figure 1C). This could partially rescue the suppression of the defecation phenotype, and was abolished by RNAi against the mouse gene sequence (Figure 1C). Moreover, rescue by the mouse Atp8b1 gene was also prevented by introduction of mutations corresponding to either the tat-2(qm179) mutation or the human G308V mutation (Table S1), strongly indicating a functional conservation.
In order to determine the focus of action of tat-2, we constructed a reporter gene in which the tat-2 gene with 3.4 kb of upstream promoter sequence was fused in frame to gfp. This construct was capable of rescuing the defecation phenotype of tat-2(qm179) in the clk-1 background (Table S1). The fusion protein was expressed in the gut, spermatheca, proximal gonad, vulva, excretory cell, excretory gland cell, pharyngeal procorpus, the pharyngeal-intestinal valve and the rectal gland cell (Figure S2), which is consistent with what was previously found by others [7], [8]. We also constructed three other reporters in which 3.4 kb of the tat-2 promoter were replaced by the promoters from the intestinal specific ges-1, spermatheca-specific sth-1, or excretory canal-specific pgp-12, genes. Only the Pges-1::tat-2::gfp construct could rescue the phenotype (Table S1).
Given the known function of ATP8B1 in bile acid secretion in mammals (see Introduction), and the fact that eliminating the function of tat-2 suppresses clk-1, we wondered whether a pharmacological agent that targets BAs could also suppress clk-1. Cholestyramine is a BA-binding resin that is taken orally by people to lower the availability and thus the re-absorption of BA in the gut, which ultimately results in lowering in the level of circulating LDL [26]. We found that addition of 0.025% cholestyramine to worm plates partially suppresses the slow defecation cycle of clk-1 mutants (Figure 2A). Cholestyramine had no effect on the wild type or on isp-1 mutants, which, like clk-1 mutants, have mitochondrial defects and a slow defecation cycle [27]. There was also no effect on tat-2 or dsc-4/mtp mutants (Table S1). Cholestyramine can bind organic molecules of intermediate to low polarity that bear an acidic group. This supports the hypothesis that C. elegans secretes molecules that have chemical properties resembling those of BAs and that the altered defecation cycle of clk-1 mutants is due to enhanced secretion of such molecules.
We reasoned that if there are mammalian-like BAs in worms they might be synthesized by enzymes that are similar to those in mammals. Reducing BA synthesis by depleting such enzymes by RNAi knock-down should suppress clk-1, similar to the effect of tat-2 mutations and cholestyramine treatment. The biosynthesis of BAs in mammals is complex and involves a variety of enzymatic steps carried out in diverse cellular compartments [1]. In order to determine if BA-like molecules are synthesised in a similar manner in worms, we examined 17 of the most common of these steps by identifying the best C. elegans homologues of the mammalian enzymes, and testing their impact on the defecation cycle of clk-1 mutants by RNAi (Table 1). Six of these enzymes are part of the same class of proteins, the P450 oxidases. As all the C. elegans proteins of this class are more or less equally similar to each of the vertebrate proteins, we tested all those we found by homology searching (78 genes). For other classes we tested several of the most homologous proteins (Table 1). Some classes of homologues did not have any effect on the defecation phenotype of clk-1 mutants, e.g. 3β-hydroxy-Δ5-C27 steroid oxidoreductase, 2-methylacyl-CoA racemase, and bile acid CoA: amino acid N-acyltransferase. However, thirteen P450 enzymes as well as worm genes encoding proteins that are highly similar to mammalian branched-chain acyl-CoA oxidase and 3α-hydroxysteroid dehydrogenase, cholesterol 25-hydroxylase, bile acid CoA ligase, the D-bifunctional protein, and the two genes (daf-22 and nlt-1) that separately encode the two activities of mammalian peroxisomal thiolase, were effective in affecting the defecation cycle of clk-1 mutants (Table 1), suggesting that they may participate in the biosynthesis of BA-like molecules. Note that RNAi against daf-9/cyp-22A1 and hsd-1, which encode activities that are known to participate in the synthesis of dafachronic acids, did not affect clk-1 defecation (Table 1). daf-12, the nuclear receptor target of dafachronic acids, was also knocked down by RNAi under the same conditions as the enzymes: it produced only a very small, not significant, suppression (−2.8±3.9 seconds (P = 0.4795); n = 27 for the control, n = 38 for daf-12(RNAi)). Interestingly, in addition to suppressors of the phenotype, we also obtained a few enhancers, mostly among the P450s (Table 1). P450s in mammals have numerous functions besides BA synthesis, and thus have the potential to affect the rate of defecation in ways unrelated to the synthesis of BA-like molecules. This is consistent with the observation that most genetic changes that affect defecation tend to slow it down [21].
DAF-36 is a Rieske oxygenase that acts as a cholesterol 7-desaturase that converts cholesterol to 7-dehydrocholesterol [28], [29]. DAF-36 is necessary for dafachronic acid biosynthesis, which is why mutation of daf-36 leads to a dauer constitutive phenotype. We found that daf-36(k114) also suppresses the slow defecation cycle of clk-1 (by 19.4 seconds), with only a very small effect (1.5 seconds) on the wild type (Table S1). This is consistent with the hypothesis that the active molecules that are transported by TAT-2 and are bound by cholestyramine could be oxidized cholesterol derivatives, although they are clearly distinct from dafachronic acids (see above).
Lowering the level of the hypothetical BA-like molecules by reducing their secretion via mutation of tat-2, reducing their biosynthesis by RNAi or mutations against potential biosynthetic enzymes, or by sequestration through cholestyramine suppresses the clk-1 phenotype. We reasoned that the phenotype might therefore be enhanced by BA supplementation. We treated clk-1 mutants with mixed mammalian BAs and found that their phenotype was indeed enhanced while the wild type was completely insensitive (Figure 2B). These findings show that externally applied BAs can act on C. elegans. It also suggests that in the wild type the processes that are affected by BAs and that ultimately determine the defecation cycle, such as cholesterol handling (see below) and lipoprotein metabolism (see Introduction), are better regulated than in clk-1 mutants.
In mammals, BAs of different structures have been found to interact differently with nuclear hormone receptors thus affecting differently the regulation of BA synthesis and secretion, and also to be more or less efficient in cholesterol uptake [2]. In particular, more hydrophobic BAs appear to result in greater cholesterol uptake [30]. To test whether the structures of the BAs are important for their effects on clk-1 mutants we treated the wild type and clk-1 mutants with three concentrations of cholic acid (CA), one of the main relatively hydrophilic mammalian BA, and chenodeoxycholic acid (CDCA), one of the main relatively hydrophobic mammalian BA. No treatment had any effect on the wild type (Table S1). However, at two concentrations (0.15 mM and 0.6 mM), CA suppressed the defecation cycle of clk-1 mutants, although it enhanced the phenotype at 2.5 mM, while CDCA enhanced the phenotype in a dose-dependent manner at all concentrations tested (Figure 2C). One possibility to explain the ability of CA to suppress clk-1 suggests that it might be more hydrophilic than the average BA-like molecules secreted by worms, thus effectively diluting their strength in taking up cholesterol. This notion is also supported by the observation that CA was a better suppressor at lower (0.15 mM) than at the higher (0.6 mM) concentration, and enhanced the phenotype at the highest concentration (2.5 mM). This suggests that at the higher concentrations the greater amount of BA (here CA) provided by the treatment in part compensates for the fact that CA is a more hydrophilic BA. CDCA had no effect at the lowest concentration but enhanced the phenotype at higher concentrations (Figure 2C).
The results presented above suggest that C. elegans produces and secretes molecules with BA-like properties, and possibly structures, and that this process is deregulated in clk-1 mutants. We reasoned that the hypothetical endogenous BA-like molecules should have the same effect on the wild type and clk-1 mutants as exogenous BAs. To test this we made lipid extracts [31] from both the wild type and clk-1 mutants and assayed them on the defecation cycle of both genotypes. The lipid extracts were applied to plates in the same way as BAs in previous experiments. Extracts from both genotypes had no effect on the defecation of the wild type. However, extract from clk-1 mutants at 0.02 and 0.1 mg significantly enhanced the phenotype of clk-1 mutants (Figure 2D). At these concentrations wild type extracts had no significant effect on the mutants. Thus to establish that the wild type also contains the activity, and to measure how much higher the activity was in clk-1 mutants, we produced a large quantity of extract from the wild type, which allowed to test 0.4 mg of activity on the wild type and clk-1. The high concentration of wild type extract was again ineffective on wild type animals but enhanced the phenotype of clk-1 as much as 0.1 mg of extract from clk-1 (Figure 2D). We conclude that both the wild type and clk-1 mutants contain the activity but that clk-1 mutants contain approximately 4× time higher steady-state levels of the activity.
One of the functions of BAs is to regulate cholesterol uptake and handling. We therefore measured the level of cholesterol in the wild type and in clk-1 mutants grown under low (2 µg/ml), standard (5 µg/ml) and high (50 µg/ml) levels of cholesterol supplementation. Both the wild type and clk-1 mutants grown on high cholesterol contained significantly more cholesterol than when grown under standard conditions (Figure 3A). However the increase was significantly greater in clk-1 mutants. There was no significant difference between 2 µg and 5 µg/ml of supplementation for either genotype. We also assayed the cholesterol content of tat-2 and clk-1;tat-2 mutants. The cholesterol content of tat-2 was similar to that of the wild type at all levels of cholesterol supplementation. However the increase of cholesterol content observed in clk-1 mutants under high cholesterol supplementation was fully abolished in clk-1;tat-2 double mutants (Figure 3A and Table S2). Furthermore, cholesterol content in the double mutants was elevated at low and standard level of supplementation and thus similar at all levels of cholesterol supplementation, indicating that clk-1 and tat-2 interact in determining the level of cholesterol uptake and content.
We have previously shown that the defecation cycle of clk-1 mutants, but not that of the wild type, is suppressed by lowering the levels of dietary cholesterol from 5 µg/ml to 2 µg/ml [23]. We have now extended this observation to the effect of high cholesterol (50 µg/ml), which has no effect on the wild type but further slows down the defecation of clk-1(qm30) mutants (Figure 3B). We had observed (Figure 3A) that clk-1 and tat-2 interact in determining the level of cholesterol uptake. We therefore wondered if the metabolism of the BA-like molecules was involved in these effects of the level of dietary cholesterol on the defecation cycle. We found that low cholesterol shortened the defecation cycle of clk-1;tat-2, but that the effect of high cholesterol on clk-1 mutants was fully suppressed in clk-1;tat-2 mutants (Figure 3B). The observation that altering the level of media cholesterol can affect the defecation phenotype of clk-1 mutants in both directions suggests that uptake or subsequent handling of cholesterol can change the phenotype caused by the deregulated metabolism of the BA-like molecules in clk-1 mutants.
The results described above suggest that the suppression produced by the tat-2 mutation might be due to lower secretion of the BA-like molecules. To test this directly we treated tat-2 and clk-1;tat-2 mutants with a small amount (0.015%) of mixed mammalian BAs (Figure 2E and Table S1). These exogenous BAs had no effect on the wild type or dsc-4 mutants but rescued the tat-2 phenotype in both the wild-type and clk-1 backgrounds. Furthermore, these effects of the exogenous BAs were abolished in the absence of cholesterol supplementation (Figure 2E). We also found that the effects of BAs we have previously observed, such as suppression and enhancement of clk-1 by pure CA or CDCA at various concentrations require cholesterol supplementation (Figure 2C). These results indicate: 1) that the effect of tat-2 on clk-1 mutants is mediated by a reduction in the secretion of BA-like molecules; and 2) that the effects of BAs and tat-2 on clk-1 mutants implicate changes in cholesterol uptake.
We have shown above that the phenotypes of clk-1 mutants include deregulated metabolism of BA-like molecules, which results in altered cholesterol content and abnormal sensitivity to the level of cholesterol supplementation. Previous studies of clk-1 indicated that the principal cellular defect of these mutants is an elevated level of mitochondrial oxidative stress, characterized by elevated mitochondrial ROS production [18], elevated oxidative damage [32], [33], and increased sensitivity to pro-oxidant drugs [34]. In addition, several of the clk-1 phenotypes are strongly enhanced when the expression of the main mitochondrial superoxide dismutase (SOD-2) is reduced by RNAi [32] or mutation [33]. In fact defecation was among the phenotypes that were found to be enhanced in the clk-1;sod-2 double mutants [33].
To further explore the link between ROS and the clk-1 defecation phenotype we first determined whether RNAi against the other C. elegans sod genes had any effect. We found that in addition to sod-2, RNAi knockdown of sod-3, the gene encoding the other mitochondrial superoxide dismutase, enhanced the defecation phenotype of clk-1 (Figure 4A). However, RNAi against the three non-mitochondrial sod genes (sod-1, sod-4, and sod-5) did not affect the phenotype (Figure 4A), indicating that the enhancement of the phenotype is specific to alterations in mitochondrial ROS levels. Consistent with previous findings, this suggests that the slow defecation phenotype of clk-1 mutants might be due to their elevated mitochondrial ROS production. In order to test this further we treated clk-1 mutants with the antioxidant N-acetyl-cysteine (NAC) a commonly used hydrophilic antioxidant, which can reduce mitochondrial ROS production [18]. We found that NAC treatment could partially suppress the slow defecation cycle in a dose-dependent manner (Figure 4B). Complete suppression could not be obtained because higher levels of the compound was toxic, possibly because of inhibition of normal ROS levels in other compartments. Finally, to test whether the increased mitochondrial oxidative stress is the cause of the deregulated metabolism of the BA-like molecules we tested whether the tat-2(qm179) mutation could suppress the effect of antioxidant treatment. We found that treatment with 10 mM NAC was without effect on tat-2; clk-1 (Figure 4C and Table S1), indicating that tat-2(qm179) is epistatic to antioxidant treatment. This is consistent with the elevated mitochondrial oxidative stress being the primary cause of the deregulation of the metabolism of the BA-like molecules observed in clk-1 mutants.
The hypothesis suggested by the results described so far is that the clk-1 defecation phenotype is the result of increased mitochondrial oxidative stress in these mutants, which increases the level of activity of BA-like molecules. We tested this hypothesis directly by producing and testing lipid extracts from clk-1 mutants treated with NAC and from clk-1(qm30);sod-2(ok1030) double mutants (Figure 5). NAC treatment reduced the level of the activity found in the extract, and the extract from clk-1;sod-2 double mutants contained substantially higher level of activity than the clk-1 extract. For an unknown reason the clk-1;sod-2 extract was the most variable in terms of its activity on individual worms (Figure 5 and Table S1).
Here we have shown that: 1) clk-1 mutants are suppressed by mutations of TAT-2, the worm orthologue of an ATPase that is necessary for BA secretion in mammals, 2) the suppression by tat-2 can be rescued by exogenous BAs, 3) RNAi knockdown of several C. elegans enzymes homologous to those that are implicated in BA synthesis in mammals suppress the clk-1 phenotype, but not the knockdown of some of the enzymes known to be necessary for dafachronic acid synthesis, 4) clk-1 mutants display a cholesterol-dependent sensitivity to exogenous BAs, as well as a sensitivity to cholestyramine, a drug that sequesters BAs, 5) clk-1 mutants but not the wild type are sensitive to an activity contained in lipid extracts from worms, 6) the clk-1 defecation phenotype is suppressed by a mutation in daf-36, which encodes a cholesterol 7-desaturase, suggesting that the activity is a cholesterol derivative, 7) clk-1 mutants contain more of this activity, 8) the level of the activity is altered by mitochondrial oxidative stress, 9) clk-1 mutants have a deregulated cholesterol metabolism, as indicated by the fact that their phenotype can be affected by reducing or increasing the level of dietary cholesterol and that they accumulate more cholesterol than the wild type when supplied with high levels of dietary cholesterol, 10) clk-1 and tat-2 interact in determining cholesterol content as, in contrast to what is observed in the wild type, the cholesterol content of clk-1;tat-2 is similar at all levels of dietary cholesterol supplementation. This last observation suggests that the abnormal cholesterol metabolism is caused by the deregulated metabolism of the BA-like molecules that are affected by clk-1 and tat-2. Together all these observations imply that there are BA-like molecules involved in cholesterol uptake in C. elegans, but also that these molecules are likely to be structurally similar to BAs, as their biosynthesis and secretion are affected by activities that are known to affect BAs in mammals.
The results summarized in the previous paragraph lead to a model of regulatory relationships between cholesterol availability, cholesterol uptake, the synthesis and secretion of BA-like molecules, and LDL-like lipoprotein synthesis and secretion in C. elegans (Figure 6). All our findings appear to be remarkably consistent with what is known about the synthesis and regulation of BAs and LDL in vertebrates. Thus we propose that secreted BA-like molecules participate in cholesterol uptake and that the function of TAT-2 is required for their secretion. Cholesterol is used in the synthesis of the BA-like molecules and, as in mammals, the BA-like molecules act directly on cholesterol uptake but also as signalling molecules that positively regulate the synthesis of LDL-like lipoproteins. The core of our model is that CLK-1, via its effect on limiting mitochondrial ROS generation, is required for a negative feedback mechanism that down-regulates the synthesis of the BA-like molecules as a function of cholesterol uptake. In the absence of CLK-1 more BA-like molecules are synthesized (Figure 2D) and more cholesterol can be taken up (Figure 3A). The increased synthesis of the BA-like molecules up-regulates the level of LDL-like lipoprotein synthesis and secretion, which in turn determines the length of the defecation cycle.
Our data show that availability of BA-like molecules and the rate of defecation are tightly linked as shown by the sensitivity of the mutant defecation cycle to BA supplementation, sequestration of the BA-like molecules, and the inhibition of the synthesis of the BA-like molecules. The hypothesis that CLK-1 is necessary for a feed-back from cholesterol uptake to the synthesis of the BA-like molecules provides the link between the level of cholesterol supplementation and the level of the BA-like molecules (and thus between the level of cholesterol supplementation and defecation) (Figure 6). However, the model cannot accurately predict the effect of mutations on the level of whole-animal cholesterol. Indeed, the level of cholesterol likely depends on cholesterol flux through the entire organism. This is determined by a number of factors that we cannot precisely quantify at this stage, including the exact quantitative relationship between the level of cholesterol uptake and the level of synthesis of the BA-like molecules via the CLK-1-dependent mechanism, the level of cholesterol loss through the synthesis of BA-like molecules if these are cholesterol-derived, and the loss of the BA-like molecules through secretion, the level of cholesterol loss through LDL-like lipoprotein secretion (whose target in the organism is unknown), the level of cholesterol loss through yolk synthesis and egg-laying, and in fact any other form of cholesterol elimination or storage, whether or not regulated by the BA-like molecules.
Suppression of the clk-1 defecation phenotype can be obtained by knocking down the enzymes necessary for peroxisomal β-oxidation that in mammals are necessary for shortening the side-chain of cholesterol (Table 1). This suggests that if the C. elegans BA-like molecules are cholesterol derived they might have a shortened side-chain. This is in contrast to dafachronic acid (Figure S1), which is a steroid that acts as a hormone that regulates development in C. elegans [35]. We have not yet tested if the BA-like molecules can affect other clk-1 phenotypes in addition to defecation, such as slow aging. More detailed structural information on the C. elegans BA-like molecules, and possibly the availability of synthetic molecules, might be necessary to test rigorously their effect on phenotypes that are harder to quantify than defecation.
The suppressive effect of cholic acid (CA) at very low concentrations is difficult to explain unless the BA-like molecules are indeed structurally similar to BAs. However, if this is the case the observed effect might result from the dilution by CA of the native and potentially more hydrophobic BA secreted by worms. However, as CA serves as negative feedback for BA synthesis and secretion in mice [36], it is possibly that it could carry out a similar role in C. elegans, which would provide an alternative explanation for its paradoxical action at low concentration. If this is the case, further study of this phenomenon might help in identifying the nuclear hormone receptors (NHRs) through which the C. elegans BAs might regulate metabolism and their own synthesis. We have already identified a number of nuclear hormone receptor loci whose down-regulation suppresses clk-1 mutants (not shown). One or several of these could be the receptors for the BA-like molecules.
The metabolic syndrome is a collection of age-associated disease risk factors that includes obesity, insulin resistance, hypertension and dyslipidemia. Oxidative stress, which is well known to increase with age and in obese individuals [37], has been implicated in most of the components of the metabolic syndrome and might be the common link between them [38], [39], [40]. Our findings with C. elegans, where there appears to be BA-like molecules whose synthesis, secretion and activity shares strong similarities with BAs in mammals, suggest that mitochondrial oxidative stress can lead to deregulation of BA synthesis. Abnormal BA levels in turn could lead to metabolic disease processes via the action of BAs on sterol, lipid and glucose metabolism by signalling through BA receptors. Interestingly, the possibility of an involvement of oxidative stress on the regulation of BA synthesis and thus on the consequences of a deregulation of this process has not yet been explored in mammals.
Fourth larval stage (L4) animals were transferred to the test plates and grown at 20°C. The effects of the different cholesterol concentrations or compounds were scored after raising the worms on the test plates for one generation. Defecation cycle rates were measured as previously described [22], at 20°C for all experiments except for the RNAi and antioxidant treatments for which 25°C was used. Compounds (cholestyramine, mixed bile acids, cholic acid, and chenodeoxycholic acid were tested by spreading them on plates, except that N-acetyl-L-cysteine was added to the nematode growth media (NGM) prior to pouring it into plates. See also Text S1.
dsc-3 had previously been mapped to LG IV, between unc-33 and dpy-4 [22]. By using 2-point and 3-point mapping strategies, the genetic position of qm179 was refined to a position between the two cloned gene dpy-13 and unc-5. Due to the incomplete cosmid coverage of the tat-2 gene, no cosmid that spans this region can rescue the qm179 mutants. Therefore qm179 mutants were rescued by injecting two partially overlapping PCR fragments of tat-2 genomic DNA (from −3123 to +7277 and from +7252 to +13567, which includes the UTRs) for in vivo recombination. Two other mutations allelic to qm179 had been originally identified, qm180 and qm184 [22]. The lesion in qm184 was identical to the qm179 lesion, and the lesion in qm180 was not found in the tat-2 exonic sequences. The tat-2(tm1634) allele was obtained from the National Bioresource Project and outcrossed three times. See also Text S1.
The tat-2 transcriptional reporter, Ptat-2::gfp (pCDB898) was used as backbone to build the Ptat-2::mAtp8b1, Ptat-2::mAtp8b1 A705T, Ptat-2::mAtp8b1 G308V clones. The PCR product of 3.4 kb upstream of the initiating ATG of tat-2 was cloned into the PstI and SmaI sites of the pPD95_77 vector. The full length of mouse Atp8b1 cDNA was amplified from the RIKEN clone F830210O18.
To construct Ptat-2::tat-2::gfp (pCDB902) a 3945 bp long wild type tat-2 cDNA containing 22 exons was inserted into the SmaI site of pCDB898. To construct Pges-1::tat-2::gfp, Psth-1::tat-2::gfp, and Ppgp-12::tat-2::gfp, (pCDB906, pCDB905 and pCDB904, respectively) the tat-2 promoter of pCDB902 was replaced by PCR products of 2 kb upstream of the ges-1 initiation codon, 1.6 kb upstream of the sth-1 initiation codon or 2.7 kb upstream of the pgp-12 initiation codon. These constructs were injected into clk-1; tat-2(qm179) mutants at a concentration of 0.1 ng/µl along with the transformation marker ttx-3::gfp at a concentration of 200 ng/µl. See also Text S1.
Lipids were extracted following [41], and the cholesterol content was determined with a kit (10007640) from Cayman Chemical. The final concentration of Triton X-100 in each sample was 0.5%. We also measured the volumes of young adults for all genotypes as previously described [42], and no difference from the wild type was found (data not shown). See also Text S1.
The lipid extracts were prepared as previously described [31] and re-suspended in DMSO. To assay the activity of extracts from the wild type, clk-1(qm30), clk-1(qm30); sod-2(ok1030) or clk-1 mutants treated with NAC, 36 µl of DMSO-dissolved extract (or 36 µl of DMSO as control) was spread onto 5 cm plates. Phenotypes of adult progeny were measured after raising L4 animals on the test plates for one generation. Due to the sensitivity of clk-1 mutants to dietary cholesterol level, we measured and calculated that the final concentrations of extracts applied to the plates contained less than 0.1 µg/ml of cholesterol, which cannot therefore be responsible for any of the effects observed (Figure 2D). See also Text S1.
5–10 clk-1(qm30) hermaphrodites L4 larvae were picked to RNAi plates. For the following 3 days, worms were transferred to new RNAi plates to rid of contaminating OP50 bacteria. Progeny worms were grown to the L4 stage and were then picked to new RNAi plates for scoring. 18 hours later, they were transferred to 25°C. After two hours of acclimation, their defecation phenotype was scored. We used 25°C for all RNAi experiments, except those shown in Figure 1C, because the responses tend to be more robust [22]. For each RNAi clone, five worms were scored for one defecation cycle. Clones that had a significant effect on defecation rate were re-screened 2–3 times.
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10.1371/journal.pgen.1003859 | Inhibition of the Mitotic Exit Network in Response to Damaged Telomeres | When chromosomal DNA is damaged, progression through the cell cycle is halted to provide the cells with time to repair the genetic material before it is distributed between the mother and daughter cells. In Saccharomyces cerevisiae, this cell cycle arrest occurs at the G2/M transition. However, it is also necessary to restrain exit from mitosis by maintaining Bfa1-Bub2, the inhibitor of the Mitotic Exit Network (MEN), in an active state. While the role of Bfa1 and Bub2 in the inhibition of mitotic exit when the spindle is not properly aligned and the spindle position checkpoint is activated has been extensively studied, the mechanism by which these proteins prevent MEN function after DNA damage is still unclear. Here, we propose that the inhibition of the MEN is specifically required when telomeres are damaged but it is not necessary to face all types of chromosomal DNA damage, which is in agreement with previous data in mammals suggesting the existence of a putative telomere-specific DNA damage response that inhibits mitotic exit. Furthermore, we demonstrate that the mechanism of MEN inhibition when telomeres are damaged relies on the Rad53-dependent inhibition of Bfa1 phosphorylation by the Polo-like kinase Cdc5, establishing a new key role of this kinase in regulating cell cycle progression.
| A key aspect of the division of cells is that the genomic material must be carefully duplicated, protected from damage, and correctly distributed during this process. When the cells detect problems that affect the integrity or the proper distribution of the genome, they trigger different surveillance mechanisms to stop the division process until the problem is fixed. The functionality of some of these surveillance mechanisms depends on the inhibition of the final stages of cell division, a process known as mitotic exit. This is the case for the DNA damage checkpoint (DDC), which is triggered by chromosomal DNA damage. Here, we propose that the inhibition of mitotic exit is specifically required by the DDC only when the telomeres (the chromosomal ends) are damaged. Additionally, we have demonstrated that the DDC blocks mitotic exit by inhibiting the key cell cycle regulator Cdc5, which triggers the inactivation of Bfa1 and Bub2, two negative regulators of the mitotic exit process.
| During mitosis, different surveillance mechanisms ensure that the replicated genomic material is protected from damage and correctly distributed between the daughter and the mother cells. In this way, chromosomal DNA damage triggers a stress response pathway known as the DNA damage checkpoint (DDC) [1], which arrests the cell cycle to provide the cells with time to repair the genomic material before further progressing into mitosis. The cells must also ensure that all the chromosomes are attached to the spindle, a bipolar array of microtubules that allows for the segregation and distribution of the chromosomes between the daughter and mother cells. The proper attachment of all kinetochores to the spindle is monitored by the spindle assembly checkpoint (SAC), which otherwise delays the onset of anaphase [2]. Finally, in cells that display some type of asymmetry during mitosis, as it is the case of the budding yeast Saccharomyces cerevisiae, it is also essential that the spindle be correctly positioned with respect to the division site. In this organism, this is ensured by the spindle position checkpoint (SPOC), which restrains mitotic exit when the spindle is not properly aligned along the mother-bud axis and perpendicular to the bud neck [3]. A similar mechanism, the centrosome orientation checkpoint (COC), has also been described in Drosophila male germline stem cells [3]. The COC delays the commitment to mitosis in case of centrosome misorientation.
Despite the diverse signals that trigger the DDC, the SAC and the SPOC, as well as the different cell cycle stages where these surveillance mechanisms are triggered, the three checkpoints have been shown to inhibit mitotic exit in S. cerevisiae by activating the two-component GTPase-activating protein (GAP) Bfa1-Bub2 [4]. This GAP inhibits Tem1, a GTPase that initiates signaling by the Mitotic Exit Network (MEN) [5]. Activation of the MEN drives a sustained release of the Cdc14 phosphatase out of the nucleolus in anaphase, where it is sequestered from G1 to metaphase by its inhibitor Cfi1/Net1 [6], [7]. Once released into the nucleus and the cytoplasm, Cdc14 reverts the phosphorylation events triggered by the mitotic cyclin-CDK complexes, leading to their inactivation and the completion of mitosis [5]. During a normal cell cycle, the activity of Bfa1-Bub2 is regulated through phosphorylation. At the onset of anaphase, Bfa1 is phosphorylated by the Polo-like kinase Cdc5, which inactivates the GAP and promotes MEN signaling [4], [8], [9]. Cdc5 also plays an essential role during mitotic exit by recruiting the MEN kinase Cdc15 to the spindle pole bodies (SPBs, the yeast equivalent of the centrosomes) [10]. When the SAC or the SPOC are triggered, however, Bfa1 is maintained in a hypo-phosphorylated and therefore active state, which restrains mitotic exit [4]. The protein kinase Kin4 plays a key role in the SPOC by promoting the inhibitory action of Bfa1 on MEN signaling. When the spindle is not properly positioned, Kin4 phosphorylates Bfa1 impeding its inactivation by Cdc5 [11], [12]. In addition, Kin4 actively excludes Bfa1 from the SPBs after SPOC activation [13]. Since Tem1 localization to the SPBs depends on Bfa1 [14] and it is essential for MEN signaling [15], this also contributes to the inactivation of mitotic exit under these circumstances.
While the role of Bfa1 and Bub2 in the inhibition of mitotic exit after the activation of the SAC and the SPOC has been extensively studied, the mechanism by which these proteins prevent MEN function after the generation of DNA damage is still unclear. In S. cerevisiae, a central regulator of the DDC is the Mec1 kinase, the yeast homolog to mammalian ATR, which activates Chk1 and Rad53, two kinases that form parallel branches of the Mec1-dependent DDC pathway [1]. After DNA damage, Chk1 inhibits the metaphase to anaphase transition by stabilizing securin (Pds1) and therefore maintaining separase (Esp1) inactive [16]. Rad53 also inhibits the metaphase to anaphase transition by regulating Pds1 stability, but it has been additionally shown to prevent mitotic exit by regulating Bfa1-Bub2 [4], [17]. The mechanism by which Rad53 inhibits Bfa1-Bub2 is, at present, not clear. The kinase activity of Cdc5 was initially found to be high in DNA-damaged cells [18], while Bfa1 was described to be phosphorylated but active in a Rad53-dependent manner after DDC activation [4]. Thus, and since phosphorylation of Bfa1 by Cdc5 inhibits its activity, it was formally proposed that, in contrast to the SAC and the SPOC, the inhibition of mitotic exit after DNA damage is independent of the phosphorylation of Bfa1 by Cdc5 [4], and that, under these circumstances, Rad53 would be then controlling the activity of Bfa1 by a yet-unknown mechanism [4]. More recently, however, it has been demonstrated that Cdc5 is in fact partially inhibited by Rad53 after the generation of DNA damage [19]. This inhibition restrains the elongation of the spindle by preventing the inactivation of the anaphase-promoting complex cofactor Cdh1, thus limiting the accumulation of the bimC kinesin family proteins Cin8 and Kip1 [19]. Therefore, and based on these new observations, it is necessary to reevaluate whether Bfa1 phosphorylation by Cdc5 needs to be actively prevented also after DDC activation in order to maintain the GAP in an active state and prevent MEN signaling.
Here, we have analyzed the regulation of mitotic exit in response to DNA damage. We have determined the consequences of the lack of Bfa1 activity on the functionality of the DDC in response to a wide variety of chromosomal DNA damage. Based on our results, we propose that the inhibition of MEN signaling is essential when telomeres are damaged, but it is not a general requirement for the functionality of the DDC. Additionally, we have analyzed the mechanism by which the DDC impedes MEN signaling, and the role of Cdc5 in this process. We have demonstrated that the Rad53-dependent inhibition of Cdc5 after DNA damage is essential to maintain Bfa1 in a hypo-phosphorylated form that inactivates Tem1 and thus inhibits mitotic exit.
The role of Bfa1-Bub2 in the response to DNA damage was originally determined by using the cdc13-1 allele [4], [20]. Cdc13 is an essential protein that protects the telomere from degradation and regulates telomerase activity [21]–[24]. Cells carrying the cdc13-1 mutation cannot “cap” the telomeres and accumulate single-stranded DNA at the restrictive temperature, which triggers a DDC-dependent cell cycle arrest in G2/M [21], [25]. Accordingly, cdc13-1 cells synchronized in G1 using pheromone accumulated as large budded cells with an undivided nucleus after their release into pheromone-free medium at 34°C, while DDC-deficient cdc13-1 rad53Δ sml1Δ cells could not hold this arrest (Figure 1A, [26]; deletion of SML1 is necessary to bypass the lethality associated to the inhibition of the ribonucleotide reductase and the subsequent reduced dNTPs levels in rad53Δ cells [27]). Approximately 30% of cdc13-1 rad53Δ sml1Δ cells still remained arrested, but this percentage was reduced to only 15% when CHK1 was also deleted and the other branch of the DDC was thus additionally inactivated (Figure S1A). The metaphase arrest observed for cdc13-1 cells at the restrictive temperature was also dependent on both Bfa1 and Bub2 (Figures 1A and B, [20]), which indicates that inhibition of MEN by the two-component GAP must also be ensured after the telomeres are damaged.
In order to analyze whether inactivation of the MEN is a general requirement for the functionality of the DDC, we tested the capacity of bfa1Δ cells to maintain a DDC-dependent cell cycle arrest in the presence of other types of chromosomal damage. Wild type, rad53Δ sml1Δ, and bfa1Δ cells were synchronized in G1 using pheromone. After pheromone washout, cells were released into medium containing zeocin, a chemical that generates DNA double strand breaks (DSBs) [28]. The DNA damage generated by zeocin led to an accumulation of large budded cells in the wild type strain (Figures 1C and S1C), as previously observed for the cdc13-1 mutant at the restrictive temperature. The cell cycle arrest induced by zeocin treatment was dependent on the functionality of the DDC, since it could not be held in rad53Δ sml1Δ cells (Figures 1C and S1C). However, and in contrast to what was observed for the cdc13-1 bfa1Δ mutant at 34°C, zeocin-treated bfa1Δ cells accumulated as large budded (Figures 1C and S1C). The specific requirement for Bfa1 after telomere damage cannot be attributed to different levels of checkpoint activation, since it was still observed in cdc13-1 cells growing at 37°C and wild type cells treated with zeocin at the same temperature, for which the extent of DDC activation, as measured by the effect of RAD53 deletion on the percentage of large budded cells, was similar (Figure S1D and E). Inhibition of MEN signaling was also not necessary to restrain cell cycle progression in response to the generation of a single unrepaired DSB induced by expression of the HO-endonuclease in cells in which an HO recognition site was introduced in chromosome II and that also carried a MATa allele that cannot be cleaved by HO (MATa-inc). The induction of the DSB in these cells led to a Rad53-dependent cell cycle arrest (Figures 1D and S1F). However, this DSB-induced arrest could still be maintained in bfa1Δ cells (Figures 1D and S1F). Therefore, our results indicate that, while necessary to block the cell cycle in response to uncapped telomeres, inhibition of mitotic exit is not essential for the DDC to maintain a cell cycle arrest after generation of DSBs in the DNA.
To further evaluate the role of Bfa1 in the response to DNA damage, we also assessed the survival of wild type, rad53Δ sml1Δ, and bfa1Δ cells after irradiation with gamma rays and UV, as well as their capacity to grow in the presence of different compounds that induce DNA damage (camptothecin, methyl methanesulfonate (MMS) and hydroxyurea (HU)). While, as expected, rad53Δ sml1Δ cells showed poor survival after irradiation with both gamma rays and UV (Figure 1E, [26]) and reduced viability in the presence of DNA-damaging chemicals due to an impairment of the DDC (Figures 1F and G, [29]), cells lacking Bfa1 behaved as wild type cells (Figures 1E, F and G). Therefore, our results suggest that rather than a general role in the protection against DNA damage, Bfa1 plays a more specific role in the response of the cell to the damage induced by a failure in telomere capping.
During a normal cell cycle, the activity of Bfa1-Bub2 is regulated through phosphorylation. At the onset of anaphase, Bfa1 is phosphorylated by Cdc5, which inactivates the GAP and allows for MEN signaling [4], [8]. When chromosomes are not correctly attached to the spindle and the SAC is activated, Bfa1 is maintained in a hypo-phosphorylated and therefore active state, which restrains mitotic exit [4]. The same happens when the spindle is not properly aligned and the spindle position checkpoint (SPOC) is activated [4]. In order to analyze the molecular mechanism by which damaged telomeres trigger a Bfa1-dependent inhibition of mitotic exit, we determined the phosphorylation status of a N-terminal 3HA-tagged version of Bfa1 in cells carrying the cdc13-1 allele. We also analyzed the phosphorylation of Bfa1 in cdc15-2 cells, which cannot exit mitosis at the restrictive temperature due to a block in MEN signaling downstream of Bfa1 [9], [30]. The cells were synchronized in G1 at the permissive temperature using pheromone, and then released into pheromone-free medium at the restrictive temperature. As expected, the cdc15-2 mutant arrested in anaphase, while cdc13-1 cdc15-2 cells were blocked already in metaphase due to the activation of the DDC (Figures 2A and B). This also indicates that the N-terminal 3HA-tag of Bfa1 does not affect its functionality. Phosphorylation of Bfa1 was analyzed in these cells after optimization of the experimental conditions to detect as many modified forms of the protein as possible. Bfa1 was heavily modified in the cdc15-2 arrest (Figures 2C and D). These modifications were caused by phosphorylation of the protein, since they were no longer detected after phosphatase treatment (Figure S2A). Interestingly, activation of the DDC in cdc13-1 cells at the restrictive temperature prevented hyper-phosphorylation of Bfa1 (Figures 2C and D). Although Bfa1 was still phosphorylated to some extent, the two highest phosphorylated forms of the protein disappeared in the cdc13-1 arrest when compared to the cdc15-2 mutant.
We next analyzed the role of Rad53 in the regulation of Bfa1 activity by determining the phosphorylation status of Bfa1 in cdc13-1 rad53-21 cells. The checkpoint-deficient rad53-21 allele does not need the additional deletion of SML1 [31], which simplifies strain construction. As observed for cdc13-1 rad53Δ sml1Δ cells, the cdc13-1 rad53-21 mutant could not hold the metaphase arrest at the restrictive temperature (Figure 2B). Accordingly, rad53-21 cells also displayed low survival after irradiation with gamma rays and UV (Figure S2D) and reduced viability in media containing camptothecin, zeocin, MMS or HU (Figures S2E and F). In order to block anaphase progression, the cdc13-1 rad53-21 mutant also carried the cdc15-2 allele. Analysis of the spindle and nuclear morphologies indicated that the cdc13-1 rad53-21 cdc15-2 cells indeed elongated the spindle and arrested at this cell cycle stage (Figures 2A and B). Bfa1 was found to be hyper-phosphorylated in cdc13-1 rad53-21 cdc15-2 cells at the restrictive temperature to the same extent than in a cdc15-2 mutant (Figures 2C and D). The results were similar when the cdc14-3 allele was used instead of cdc15-2 in order to promote the final anaphase arrest (unpublished observations). The inhibition of the hyper-phosphorylated forms of Bfa1 after telomere damage is dependent on Rad53 activity, and it is not exclusively associated to the rad53-21 allele, since the phosphorylation pattern of the protein was the same in cdc13-1 rad53Δ sml1Δ cdc15-2 cells at the restrictive temperature (Figures S2B and C). Interestingly, and even though Bfa1 was not necessary to hold cell cycle progression after zeocin treatment, the Rad53-dependent inhibition of Bfa1 phosphorylation was also observed when cells were treated with this compound (Figure S3A).
We also analyzed the role of other components of the DDC in the phosphorylation of Bfa1 when telomeres are damaged. Mec1 is the main sensor of the DNA damage response in cdc13-1 cells, and triggers both the Rad53 and Chk1 branches of the DDC [1]. Accordingly, cdc13-1 mec1Δ sml1Δ cells did not arrest and Bfa1 was found to be hyper-phosphorylated at the non-permissive temperature (Figures S3B and C). On the contrary, Tel1 was not required to maintain the cell cycle block when cdc13-1 cells were shifted at the restrictive temperature (Figure S3C), and the hyper-phosphorylation of Bfa1 was effectively inhibited in the cdc13-1 tel1Δ mutant (Figure S3B). This is in agreement with the fact that Rad53 is still phosphorylated and active in this mutant [32]. Finally, we checked whether the Chk1-dependent branch of the DDC plays any role in the regulation of Bfa1 phosphorylation. Although the cdc13-1 chk1Δ cdc15-2 mutant was also unable to hold the metaphase arrest induced by damaged telomeres at the restrictive temperature (Figure 2E), Bfa1 was still hypo-phosphorylated in these cells in a Rad53-dependent manner (Figure 2F). Therefore, our results demonstrate that Rad53, but not Chk1, inhibits the hyper-phosphorylation of Bfa1 after DDC activation.
The Polo-like kinase Cdc5 phosphorylates Bfa1 in anaphase, which inhibits the GAP and activates MEN signaling [4]. It has been recently demonstrated that Rad53 partially inactivates Cdc5 after induction of DNA damage to protect Cdh1 from inhibition and therefore restrain spindle elongation [19]. Therefore, the partial inactivation of Cdc5 by Rad53 could also be determining the DDC-dependent hypo-phosphorylation of Bfa1. To test this hypothesis, the cdc5-2 allele was introduced in cdc13-1 rad53-21 cells. At the restrictive temperature, cdc5-2 cells cannot exit mitosis. This phenotype can be recovered by deleting BFA1, which indicates that Cdc5-2 is specifically impaired in its ability to inactivate Bfa1 [4]. After their synchronization in G1 using pheromone, cdc13-1 rad53-21 cdc5-2 cells were released at 34°C. Even though Cdc5-inactivation delayed cell cycle progression [4], [17], the cells finally reached anaphase (Figure 3A), as previously shown [17], [26]. However, and according to our hypothesis, the hyper-phosphorylation of Bfa1 observed in the cdc13-1 rad53-21 cdc15-2 mutant was prevented in cdc13-1 rad53-21 cdc5-2 cells (Figures 3B and C). We obtained the same results using the cdc5-as1 allele to inactivate Polo-like kinase activity. Cdc5-as1 can be conditionally inhibited by adding the CMK-C1 ATP analog to the medium [33]. G1-synchronized cdc13-1 rad53-21 cdc5-as1 cdc15-2 cells were allowed to enter the cell cycle at 34°C in pheromone-free medium containing or not the CMK-C1 inhibitor. As previously observed with the cdc5-2 allele, inactivation of Cdc5-as1 prevented the hyper-phosphorylation of Bfa1 in the cells that escaped the DDC-dependent metaphase arrest (Figures S4A and B). The hyper-phosphorylated forms of Bfa1 were also absent in anaphase-arrested cdc5-2 cells at the restrictive temperature (our unpublished observations, [11]). This suggests that the two most heavily phosphorylated forms of Bfa1 are indicative of the Cdc5-dependent inhibition of the GAP in anaphase.
Bfa1 localizes to the cytoplasmic side of the SPBs during mitosis [14], and its localization was not significantly affected in cdc13-1 cells at the restrictive temperature (Figure S5G) or after treatment with zeocin (our unpublished observations). Cdc5 also localizes to the SPBs during mitosis, where it phosphorylates Bfa1 (Figure 3D, [5]). Interestingly, 3HA-Cdc5 strongly accumulated in the nucleus in cdc13-1 cells at the non-permissive temperature (Figure 3E). This accumulation was also observed in cdc13-1 rad53-21 cells while in metaphase. However, the cells that managed to escape the DDC-induced arrest released 3HA-Cdc5 from the nucleus and the protein could be observed on the SPBs during anaphase, as in wild type cells (Figure 3F). Although the strong accumulation of Cdc5 in the nucleus did not allow us to assess whether Polo kinase is loaded on the SPBs during the DDC-dependent arrest, our results suggest that Rad53 may additionally impair localization of Cdc5 to the SPBs, which would contribute to the inhibition of Bfa1 phosphorylation by Polo kinase.
Interestingly, Bfa1 was still phosphorylated to some extent after Cdc5 inactivation in cdc13-1 rad53-21 cells, which indicates that this residual phosphorylation of the GAP is independent of the Polo-like kinase. Phosphorylation of a protein by Cdc5 is sometimes preceded by a priming phosphorylation of a Polo-binding site in the protein by cyclin-dependent kinases (CDKs) [34], [35]. However, Bfa1 phosphorylation in cdc13-1 cells at the restrictive temperature is not dependent on CDK, since it was preserved after addition of the ATP analogue 1-NM-PP1 to cdc13-1 cells carrying the analogue-sensitive cdc28-as1 allele [36] (Figure 4A).
The protein kinase Kin4 plays a key role in promoting the inhibitory action of Bfa1 on MEN signaling after SPOC activation [11], [12]. Kin4 impedes the inhibition of Bfa1 by Cdc5 [12] and actively excludes the GAP from the SPBs after the SPOC is triggered [13]. Since Tem1 localization to this structure depends on Bfa1 and it is essential for MEN signaling [15], this exclusion also contributes to the inactivation of mitotic exit once the SPOC is activated. However, Kin4 does not determine Bfa1 phosphorylation in the cdc13-1-dependent arrest, and it is not necessary to maintain the functionality of the DDC (Figures 4B and C). Therefore, the kinase that phosphorylates Bfa1 under these conditions remains to be identified, as it is also yet unclear whether this phosphorylation plays a role in the functionality of the DDC. Interestingly, deletion of BUB2 completely impairs the phosphorylation of Bfa1, including the Cdc5-independent phosphorylation observed in cdc13-1 cells at the restrictive temperature (Figures 4D and 4E). This result suggests that the Cdc5-independent phosphorylation could take place at the SPBs, since Bub2 is necessary for Bfa1 to localize on this structure. In any case, and independently of the nature of this remnant phosphorylation, our results demonstrate that the Rad53-inhibition of Polo kinase activity not only restrains spindle elongation [19], but also promotes a Bfa1-dependent block of mitotic exit.
According to our results, and even though mutants affected in either Rad53 or Bfa1 are deficient for the DDC in the presence of uncapped telomeres, they should display different phenotypes after induction of DNA damage. Rad53 should act upstream of Bfa1, and in its absence neither spindle elongation (Figure 2A) nor mitotic exit (Figure 1A) could be halted by the DDC. On the contrary, in a bfa1Δ mutant mitotic exit should occur without spindle elongation taking place. To test this, we analyzed cell cycle progression and budding in cdc13-1 rad53-21 and cdc13-1 bfa1Δ cells. As previously indicated, and while the cdc13-1 mutant at the restrictive temperature arrested as large budded cells, cdc13-1 rad53-21 and cdc13-1 bfa1Δ cells could not hold the DDC-dependent arrest and entered a new cell cycle (Figure 5A). However, and as previously shown for cdc13-1 rad53Δ sml1Δ cells (Figure 1A), cdc13-1 rad53-21 cells exited mitosis faster than cdc13-1 bfa1Δ, as evidenced by the faster decrease in large budded cells (Figure 5A). Furthermore, and as predicted, while most cdc13-1 rad53-21 cells that escaped the arrest elongated their spindles, carried out cytokinesis, and accumulated as unbudded cells, cdc13-1 bfa1Δ cells did not elongate their spindles and most of them entered a new cell cycle without cytokinesis, which led to an accumulation of rebudded cells with a single nucleus (Figures 5A and 5B). A small percentage of cdc13-1 rad53-21 cells also entered a new cell cycle without cytokinesis, but in this case, and in contrast to the cdc13-1 bfa1Δ mutant, some rebudded cells had elongated their spindles before exiting mitosis and therefore showed two separated nuclei (Figure 5B). The cdc13-1 rad53-21 bfa1Δ mutant behaved as the cdc13-1 rad53-21 (Figure 5A), which further indicates that Bfa1 acts in the same pathway as Rad53. According to this, and also in agreement with our analysis of Bfa1 phosphorylation (Figure 2F), deletion of BFA1 accelerated the mitotic exit phenotype of cdc13-1 chk1Δ cells (Figure S1B), which indicates that they are acting in different branches of the DDC.
The previous genetic analysis was consistent with the viability observed for cdc13-1, cdc13-1 rad53-21, and cdc13-1 bfa1Δ cells when compared to wild type cells in a drop test. None of the strains carrying the cdc13-1 allele could grow already at 30°C (Figure 5C). At 27°C, cdc13-1 showed very limited growth due to the cell cycle block induced by the presence of uncapped telomeres (Figure 5C). Interestingly, and even though their viability was reduced when compared to wild type cells, inactivation of Rad53 by the introduction of the checkpoint-deficient allele allowed cdc13-1 rad53-21 cells to grow at this temperature to a higher extent than the cdc13-1 mutant (Figure 5C). This indicates that the telomere damage induced by the cdc13-1 allele at 27°C, despite leading to a strong DDC-dependent cell cycle arrest, does not severely affect viability of the cells when the checkpoint is not functional. It is also worth noting that the cdc13-1 rad53-21 mutant could grow at 27°C because most of the cells that escaped the arrest elongated their spindles and carried out cytokinesis as they exited mitosis (Figure 5A). In contrast, we have demonstrated that cdc13-1 bfa1Δ cells mainly exited mitosis as mono-nucleated and rebudded cells, which are not viable (Figures 5A and B). Accordingly, the cdc13-1 bfa1Δ mutant at 27°C showed extremely limited viability at 27°C (Figure 5C). Together, our results are consistent with a dual role of the Rad53-dependent branch of the DDC, which not only prevents spindle elongation [19], but also impedes MEN signaling by maintaining Bfa1 in a hypo-phosphorylated and active form that inhibits Tem1.
To further demonstrate that inhibition of Bfa1 phosphorylation by Cdc5 is essential for the Rad53-dependent cell cycle arrest originated after DNA damage, we analyzed the effect of mutations that affect Bfa1 phosphorylation on the mitotic exit phenotype of rad53-21 cells. We first made use of the cdc5-2 mutant, which can elongate the spindle and reach anaphase, but it is specifically impaired in its ability to phosphorylate and inactivate Bfa1 (Figures 3B and C, [4]). A cdc13-1 rad53-21 cdc5-2 mutant at the restrictive temperature accumulated large budded cells due to the anaphase block (Figures 3A and 6A), although a small percentage of cells finally managed to escape the arrest (Figure 6A). According to our results, and if Cdc5-dependent phosphorylation of Bfa1 plays a key role in restraining mitotic exit after DDC activation, the arrest observed for cdc13-1 rad53-21 cdc5-2 cells could be explained by a Bfa1-dependent inhibition of mitotic exit due to the inability of Cdc5 to phosphorylate the GAP, even though Rad53 is inactive. If so, we reasoned that deletion of BFA1 should accelerate the mitotic exit phenotype of the cdc13-1 rad53-21 cdc5-2 cells. Indeed, this was the case. Both the decrease in the percentage of large budded cells and the accumulation of rebudded cells were accelerated in the cdc13-1 rad53-21 cdc5-2 mutant in the absence of BFA1 (Figure 6A). Additionally, the percentage of rebudded cells was increased and matched the level observed in cdc13-1 rad53-21 (Figure 6A). Exit from mitosis was still delayed in cdc13-1 rad53-21 cdc5-2 bfa1Δ cells as compared to the cdc13-1 rad53-21 mutant probably due to problems in the progression through the cell cycle associated to the defect in Cdc5 [4], [17].
To strengthen our conclusions, we followed a parallel approach. The Bfa1-4A mutant cannot be efficiently phosphorylated by Cdc5 (Figures S5A and B, [37]). This mutant has been shown to symmetrically localize in anaphase [37], and its localization was not significantly affected after telomere damage (Figure S5G). Additionally, this mutant delayed mitotic exit in otherwise wild type cells [37] and it could hold the DDC-dependent cell cycle arrest induced by telomere damage (Figure 6B), zeocin treatment (Figure S5C) or the treatment with other DNA damaging agents (Figures S5D, E, and F), as expected by the fact that Cdc5-phosphorylation leads to inactivation of Bfa1 and promotes mitotic exit [37]. However, and according to our hypothesis, the Bfa1-4A mutant should delay the mitotic exit phenotype of cdc13-1 rad53-21 cells, since even though Cdc5 would be active, it could not promote MEN signaling by the inhibition of Tem1's GAP. Indeed, cdc13-1 rad53-21 BFA1-4A cells exited mitosis at the restrictive temperature later than a cdc13-1 rad53-21 mutant, as demonstrated by the decrease in the percentage of large-budded cells (Figure 6B). Additionally, the rebudding percentage was lower than in the case of cdc13-1 rad53-21 cells (Figure 6B). MEN signaling was only delayed and not completely avoided probably due to the fact that an active Cdc5 kinase promotes mitotic exit not only through Bfa1 inactivation but also downstream of the GAP 9,10,38–40. The striking similarity between the cdc13-1 rad53-21 BFA1-4A and the cdc13-1 rad53-21 cdc5-2 mutants demonstrates that the inhibition of Bfa1 phosphorylation by Polo-like kinase plays an essential role in the Rad53-dependent arrest generated by telomere damage.
The inhibition of MEN signaling by the SPOC is essential for anaphase cells with mispositioned spindles in order to provide them with time to reposition their spindles before exiting mitosis [3]. Interestingly, the functionality of mitotic checkpoints that are triggered earlier in the cell cycle is also dependent on the active inhibition of mitotic exit. In all the previous cases, this inhibition of MEN signaling is achieved by means of the activation of Bfa1/Bub2, a two-component GAP that negatively regulates Tem1 [4], [41]. While the SAC and the SPOC maintain Bfa1 in an active state by preventing its phosphorylation by the Polo-like kinase Cdc5, a different mechanism was originally proposed for the inhibition of MEN signaling by the DDC that would rely on a Rad53-dependent yet Cdc5-independent activation of Bfa1 [4], [17].
Here, we have analyzed the inhibition of mitotic exit after generation of chromosomal DNA damage and the resulting activation of the DDC. This inhibition of MEN signaling seems to be specifically required to face certain types of damage. In this way, and while Bfa1-Bub2 is necessary to maintain a DDC-dependent cell cycle arrest due to the generation of uncapped telomeres, the viability of a bfa1Δ mutant is not affected as compared with that of wild type cells after the treatment with a wide variety of DNA damaging agents, including UV or gamma-rays and compounds that generate DSBs or replicative stress. In agreement with this, bub2Δ cells do not show increased sensitivity to MMS or UV irradiation [42]. Even though uncapped telomeres resemble one half of a DSB, the checkpoint response partially differs for both structures [43]. Our results are extremely interesting, and provide a new piece of evidence that demonstrates that the cells respond differently to DNA DSBs than to the presence of uncapped telomeres. A possible explanation for these observations is that telomere damage could be triggering a weaker G2/M arrest that would rely on the additional inhibition of mitotic exit by Rad53. Our data, however, exclude this possibility, since Bfa1 is essential for the functionality of the DDC even when the DNA damage caused by telomere damage is increased to the same extent than in cells treated with zeocin. Instead, we favor a distinct alternative scenario, in which Bfa1-Bub2 could collaborate with the DDC to specifically protect the cells when telomere integrity is compromised. Interestingly, it has been recently shown that in cells from the marsupial Potorous tridactylis, laser-induced damage at the telomeres during anaphase causes cell cycle delay and cytokinesis failure [44]. Therefore, our results are in agreement with the idea of a specific requirement for mitotic exit inhibition when telomeres are damaged [44].
Our study also sheds light into the mechanism by which mitotic exit is inhibited in the presence of uncapped telomeres. To this end, we have analyzed the pattern of phosphorylation of Bfa1 both during a normal cell cycle and after checkpoint activation. Bfa1 is gradually phosphorylated throughout the cell cycle, reaching its highest phosphorylated status once the cells are in anaphase. The hyper-phosphorylation of Bfa1 during anaphase is dependent on the Polo-like kinase Cdc5, and it is therefore likely to represent the phosphorylation events that inactivate the Bfa1-Bub2 GAP at this cell cycle stage [8]. We have demonstrated that in response to DNA damage, Bfa1 is maintained in a hypo-phosphorylated and active state that inhibits Tem1 activity and therefore impedes mitotic exit. Additionally, we have also shown that the maintenance of the active form of Bfa1 after DDC activation relies on the Rad53-dependent inhibition of the Polo-like kinase Cdc5, but not on the Chk1-dependent branch of the checkpoint. Our results contrast with previous observations suggesting that Bfa1 was hyper-phosphorylated in a Rad53-dependent manner after DDC activation [4]. We do not know the basis for this discrepancy, which might be due to the use of a different genetic background or to the possibility that a 3HA-tag in the C-terminus [4] instead of the N-terminus of Bfa1 could be affecting its functionality after DDC activation. In any case, our results are fully consistent with our genetic analysis of the Rad53-Cdc5-Bfa1 pathway and are in agreement with a more recent report that demonstrates that Cdc5 activity is inhibited in a Rad53-dependent manner after DNA damage [19]. It is worth noting that the inhibition of the Cdc5-dependent hyper-phosphorylation of Bfa1 also takes place when cells are treated with zeocin, a compound that generates DSBs in the DNA. However, and as previously stated, our data clearly demonstrates that inhibition of Bfa1 is not required to maintain a DDC-dependent cell cycle arrest when DNA DSBs are generated. The specific requirement for mitotic exit inhibition after telomere damage suggests that additional mechanisms to inhibit cell cycle progression must be triggered when cells are exposed to other types of DNA damage that are not induced in the presence of uncapped telomeres, therefore relieving the requirement for the inactivation of the MEN by Bfa1-Bub2.
Ours and previous results demonstrate that Rad53 fulfills a dual role in preventing cell cycle progression in response to telomere damage: it restrains the metaphase-to-anaphase transition avoiding spindle elongation [19], but it also prevents mitotic exit by maintaining Bfa1 in an active state that blocks MEN signaling (Figure 6C). This dual action of Rad53 is in agreement with the different behavior in terms of mitotic exit observed at the restrictive temperature for cdc13-1 rad53-21 cells, which can promote both spindle elongation and mitotic exit, and mutants in which exclusively either spindle elongation (e.g., cdc13-1 bfa1Δ) or mitotic exit (e.g., cdc13-1 rad53-21 BFA1-4A) is blocked. The inactivation of only one of the two Rad53-dependent functions in a cdc13-1 background reduces the ability of the cells to escape from the G2/M arrest induced at the restrictive temperature as compared to cdc13-1 rad53-21 cells. Therefore, the Cdc5-dependent inhibitory phosphorylation of Bfa1 by Rad53 plays a key role in the maintenance of the DDC-dependent cell cycle arrest determined by telomere damage. Interestingly, and besides regulating the activity of Cdc5, our results suggest that Rad53 may also regulate the localization of the Polo-like kinase to the outer plaque of the SPBs, where it phosphorylates and inhibits Bfa1 [4]. This could represent an additional mechanism by which Rad53 blocks mitotic exit in the presence of damaged telomeres.
Even though Rad53 inhibits the hyper-phosphorylation of Bfa1 by Cdc5, Bfa1 still displays a considerable degree of phosphorylation in the G2/M cell cycle arrest induced by the DDC in response to telomere damage. This phosphorylation of Bfa1 is Cdc5-independent, which is in agreement with previous observations [11], [41]. We have demonstrated that this remnant phosphorylation is also not dependent on Clb-CDK activity or the Kin4 kinase. Therefore, our results suggest the existence of an additional kinase that phosphorylates Bfa1 during metaphase. Since deletion of BUB2 completely abrogates Bfa1 phosphorylation and the localization of Bfa1 and Bub2 on the spindle poles is interdependent [14], it is likely that this Cdc5-independent phosphorylation of Bfa1 could also take place at the SPBs. At present, however, the identity of this kinase remains to be established, as it is also not known what is the actual role of this phosphorylation in the regulation of the activity of Bfa1 during a normal cell cycle or in the functionality of the different cell cycle checkpoints. One possibility is that this phosphorylation could be involved in the regulation of Bfa1 loading onto the SPBs, since Bfa1 and Nud1 (its anchor on the SPB) preferentially co-immunoprecipitate in their phosphorylated forms [45].
Based on our results, the inhibition of Bfa1 phosphorylation by Cdc5 is a common theme for all the mitotic checkpoints that rely on the inhibition of mitotic exit. In agreement with this observation, overexpression of Cdc5 not only inhibits Bfa1-Bub2 activity in anaphase, but it is also able to bypass the cell cycle arrest induced by the activation of the DDC and the SAC [4], [40], [46]. These surveillance mechanisms therefore mainly diverge in the strategies by which the inhibition of Polo-kinase is achieved in each case. In this way, while Kin4 plays a critical role in the functionality of the SPOC [11], [12], this kinase is dispensable for the DDC and the SAC. Even though DDC activation in mammalian cells mainly blocks mitotic entry, exit from mitosis and cytokinesis are also restrained in these cells in response to DNA damage [44], [47], [48]. Furthermore, Polo-like Kinase I (Plk1) is inhibited after DNA damage in an ATM and ATR-dependent manner [49], [50], and it has been demonstrated to interact and co-localize in centrosomes and the midbody during mitosis with Chk2, the mammalian homolog of Rad53 [51]. Therefore, our data could provide new insights into common mechanisms by which exit from mitosis is prevented when the DNA is damaged in higher eukaryotes.
All strains are derivatives of W303 and are described in Table S1. Unless otherwise indicated, all the strains are RAD5. Strain F1333, which expresses Bfa1-GFP, was constructed by first linearizing the pRS304-BFA1-GFP with the NruI endonuclease and then integrating it within the BFA1 promoter in F533, a strain in which the endogenous BFA1 gene is deleted. Strain F1367, which expresses Bfa1-4A-GFP, was constructed as F1333, but integrating the pRS304-BFA1-4A plasmid [37].
Immunofluorescence was performed as described in [15]. In brief, cells were fixed overnight at 4°C in 3.7% formaldehyde, washed twice with 0.1 M potassium phosphate buffer (pH 6.4), and resuspended in 1.2 M sorbitol/0.12 M KH2HPO4/0.033 M citric acid (pH 5.9). Fixed cells were digested for 15 min at 30°C with 0.1 mg/ml zymolyase-100T (US Biological) and 1/10 volume of glusulase (Perkin Elmer). Anti-tubulin (Abcam) and anti–rat FITC (Jackson ImmunoResearch) antibodies were used at 1∶200. 3HA-Cdc5 was detected using anti-HA antibody (HA.11; Covance) at 1∶500 and anti–mouse Cy3 antibody (Jackson ImmunoResearch Laboratories, Inc.) at 1∶1000. Samples for GFP and DAPI imaging were prepared as described in [15]. Microscope preparations were analyzed and imaged at 25°C using a DM6000 microscope (Leica) equipped with a 100×/1.40 NA oil immersion objective lens, A4, L5, and TX2 filters, and a DF350 digital charge-coupled device camera (Leica). Pictures were processed with LAS AF (Leica) and ImageJ (http://rsbweb.nih.gov/ij/) software.
Cells were fixed in 70% ethanol, incubated for 12 h in phosphate-buffered saline with 1 mg/ml of RNase A, and stained for 1 h with 5 µg/ml propidium iodide. After sonication of the sample to separate single cells, DNA content was analyzed in a FACSCalibur flow cytometer (Becton Dickinson).
Protein extracts were prepared using the TCA precipitation method described in [11] and were loaded on 6% polyacrylamide gels. Electrophoresis was carried out using a SE600 Hoefer electrophoresis system. Samples subjected to phosphatase treatment were incubated with 150 U of bovine phosphatase alkaline (Sigma) for 12 h at 37°C in 50 mM Tris-HCl, 1 mM MgCl2 buffer (pH = 9). 3HA-Bfa1 was examined with monoclonal HA.11 (Covance) at 1∶5000 and anti–mouse HRP-linked antibodies (GE Healthcare) at 1∶10000. GFP-tagged proteins were analyzed using JL-8 Living colors® monoclonal antibody (Clontech) at 1∶1000 and anti-mouse HRP-linked antibody (GE Healthcare) at 1∶2000. Pgk1 levels were measured using anti-Pgk1 antibody (Invitrogen) at 1∶10000 and anti-mouse HRP-linked antibody (GE Healthcare) at 1∶20000. In all cases, the protein signal was detected using the Western Bright ECL system (Advansta).
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10.1371/journal.pgen.1007648 | Direct-to-consumer DNA testing of 6,000 dogs reveals 98.6-kb duplication associated with blue eyes and heterochromia in Siberian Huskies | Consumer genomics enables genetic discovery on an unprecedented scale by linking very large databases of personal genomic data with phenotype information voluntarily submitted via web-based surveys. These databases are having a transformative effect on human genomics research, yielding insights on increasingly complex traits, behaviors, and disease by including many thousands of individuals in genome-wide association studies (GWAS). The promise of consumer genomic data is not limited to human research, however. Genomic tools for dogs are readily available, with hundreds of causal Mendelian variants already characterized, because selection and breeding have led to dramatic phenotypic diversity underlain by a simple genetic structure. Here, we report the results of the first consumer genomics study ever conducted in a non-human model: a GWAS of blue eyes based on more than 3,000 customer dogs with validation panels including nearly 3,000 more, the largest canine GWAS to date. We discovered a novel association with blue eyes on chromosome 18 (P = 1.3x10-68) and used both sequence coverage and microarray probe intensity data to identify the putative causal variant: a 98.6-kb duplication directly upstream of the Homeobox gene ALX4, which plays an important role in mammalian eye development. This duplication is largely restricted to Siberian Huskies, is strongly associated with the blue-eyed phenotype (chi-square P = 5.2x10-290), and is highly, but not completely, penetrant. These results underscore the power of consumer-data-driven discovery in non-human species, especially dogs, where there is intense owner interest in the personal genomic information of their pets, a high level of engagement with web-based surveys, and an underlying genetic architecture ideal for mapping studies.
| The genetic underpinnings of many phenotypic traits in domestic dogs remain undiscovered. Although two genetic loci are known to underlie blue eye color in dogs, these do not explain all cases of blue eyes. By examining > 3,000 dogs from the Embark Veterinary, Inc. customer database, representing the first genome-wide association study (GWAS) driven by consumer genomics in dogs and the largest dog GWAS cohort to-date, we have shown that a region of canine chromosome 18 carrying a tandem duplication near the ALX4 gene is strongly associated with blue eye color variation, primarily in Siberian Huskies. We also provide evidence that this duplication is associated with blue eye color in non-merle Australian Shepherds. While beyond the scope of this work, future studies of the functional mechanism underlying this association may lead to discovery of a novel pathway by which blue-eyes develop in mammals. These results highlight the power and promise of consumer-data-driven discovery in non-human species.
| Humans have been exerting multifarious selection on dogs since their domestication from wolves, including strong natural selection during adaptation to a domesticated lifestyle followed by intense artificial selection during modern breed formation [1–3]. One unintended consequence of this selection is that the canine genome now encodes dramatic phenotypic diversity highly amenable for genetic mapping, with moderate genome-wide divergence between breeds except near loci under selection [4–6] and long tracts of linkage disequilibrium that can be effectively scanned with microarrays [7]. Genetic discoveries in dogs benefit breeding efforts and animal welfare, and they are valuable for translational studies in humans because dogs and humans exhibit many analogous physical traits, behaviors, and diseases in a shared environment [5, 8].
In humans, blue eyes first arose in Europeans [9] and may have been favored by sexual selection due to an aesthetic preference for rare phenotypic variants [10], as an informative recessive marker of paternity [11], and/or as a by-product of selection for skin de-pigmentation to increase UVB absorption [12]. Whatever the cause, this selection has acted on the regulatory machinery of OCA2 (Oculocutaneous Albinism II Melanosomal Transmembrane Protein), which controls transport of the melanin precursor tyrosine within the iris [13, 14]. Because blue eyes result from reduced melanin synthesis, other mutations affecting melanocyte and melanosome function in the retinal pigment epithelium (RPE) can also recapitulate the phenotype [15].
In dogs, blue eyes are iconic of the Siberian Husky, a breed of northern latitudes. Prized among breeders, it is not known whether blue eyes confer adaptive benefits for high latitude dogs as has been hypothesized for humans, and the genetic basis has not yet been discovered. According to breeders, blue eyes in Siberian Huskies are a common and dominant trait, including solid blue and complete heterochromatism (one blue and one brown eye), whereas blue eyes appear to be a rare and recessive trait in breeds like the Border Collie, Pembroke Welsh Corgi, and Old English Sheepdog. The only genetic factors known to produce blue eyes are two cases associated with coat coloration: “Merle” and “piebald” dogs have patchy coat colors due to mutations in Premelanosome Protein (PMEL17) and Melanogenesis Associated Transcription Factor (MITF) that can lead to one or two blue eyes, or slices of sectoral heterochromia, when de-pigmented regions extend across the face [16, 17]. PMEL is regulated by MITF, the master regulator of melanocyte development [18]. Rarely, non-merle Australian Shepherds have unexplained cases of solid blue eyes or complete heterochromia, as in huskies, and the genetic basis of this trait is similarly unknown [19].
We employed a novel genomic resource—a panel of 6,070 dogs genetically tested on a high-density 214,661-marker platform, with owners that had contributed phenotype data via web-based surveys and photo uploads—to examine the genetics of blue eyes in a diverse panel of purebred and mixed-breed dogs.
Using a discovery panel of 3,180 dogs, we performed a genome-wide association study and detected two significant associations with blue eyes, one on chromosome 10 at PMEL17 (“merle”; canFam3.1 position 292,851; P = 7.5x10-49) and a novel locus on chromosome 18 (CFA18) that had not been previously characterized (position 44,924,848; P = 1.3x10-68; Fig 1A, S1 Fig). Markers near MITF were not significantly associated with blue eyes (P = 0.02–0.90 from positions 21,834,567–21,848,176 on CFA20), likely because piebald coat color causes blue eyes in only a small subset of cases.
The novel association on CFA18, located in the first intron of ALX4, was robust to whether heterochromia (complete or sectoral) was considered (solid blue only P = 3x10-71, heterochromia only P = 1x10-12; S2 Fig), and remained strong when we restricted our analysis to only purebred or mixed-breed dogs (purebred P = 3x10-9, mixed-breed P = 3x10-63; S3 Fig). Although the minor allele (A) at the CFA18 locus was carried (in one or two copies) by only 10% of dogs in this dataset (both blue- and brown-eyed), it was carried by 78% of non-merle blue-eyed dogs (32% homozygous, 68% heterozygous) and 100% of blue-eyed purebred Siberian Huskies (N = 22).
Supplemental Illumina microarray data, specifically log-transformed probe intensity data (log R), were available for 87% of the discovery panel dogs (N = 2,769 total, N = 108 blue-eyed dogs) and from these we defined a fine-mapping panel using 314 dogs that did not carry merle and carried at least one copy of the CFA18 allele associated with blue eyes. Of these, 87 (26%) had at least one blue eye. All blue-eyed dogs homozygous for the CFA18 marker (N = 26) shared a long haplotype in the region containing that SNP (S4 Fig; positions 44,633,453–45,170,144), and 92% were homozygous for that haplotype (N = 18) or a core subset of that haplotype from positions 44,737,897–45,170,144 (N = 6). Within this core haplotype, however, we observed four SNPs (positions 44,800,358, 44,822,014, 44,825,760 and 44,849,276) that were frequently heterozygous, suggesting a non-balanced structural variant overlapping those markers in dogs carrying the blue-eyed haplotype (S4 Fig).
To examine this putative structural variant, we used 17 canine whole genome sequences available on the NCBI Sequence Read Archive (SRA). These sequences included five Siberian Huskies and five representatives of other breeds that were heterozygous or homozygous for the CFA18 allele associated with blue eyes (S1 Table). Genome-wide read depth for the Siberian Huskies carrying one or two copies of the allele abruptly increased across an intergenic region from 44.79–44.89-Mb that encompasses the four frequently heterozygous SNPs in our microarray data (Fig 1B; S1 Table). Furthermore, 30% of the paired-end reads spanning 44,791,417–44,791,584 had a mate that mapped in an opposite orientation to positions 44,890,024–44,890,166 (S5 Fig), consistent with a 98.6-kb tandem duplication for which the midpoint span was less than the insert size of the paired end reads (< 350-bp) [20, 21]. Increased read depth and evidence of a duplication from paired-end mapping was not observed in a sixth Siberian Husky that did not carry the CFA18 allele associated with blue eyes, nor was it observed in other breeds related to Siberian Huskies for which whole genome sequences were available through SRA (e.g. East Siberian Laika, Alaskan Malamute, Samoyed, German Shepherd Dog; Fig 1B).
In the resequenced data, the haplotype bearing the associated CFA18 allele, and the 98.6-kb duplication identified from read depth and paired-end read orientation, contained 48 other variants within a 1Mb window that were not seen on other haplotypes (28 SNPs, and 19 indels that ranged from 1-bp to 30-bp in length; S2 Table). Only two of these candidate SNPs occurred in coding regions (at 45,253,714 and 45,253,740 in the first exon of CHID1), and both were synonymous changes. Two small insertions occurred at 44,963,936 in ALX4 and at 45,140,589 in an ACS homolog, but the variants fell in the 3’ untranslated regions (UTRs) of both genes. We therefore prioritized the duplication for further investigation as it was most likely to be the causal variant underlying the phenotype, or very closely associated with the causal variant.
To characterize the duplication, we designed forward and reverse PCR primers to amplify the midpoint span of the duplication (mapping to CanFam3.1 chr18: 44,890,025–44,890,047 and chr18: 44,791,538–44,791,564 respectively), as well as the 5’ and 3’ ends of the duplicated region as controls (Fig 2; S3A Table). Midpoint products amplified from three blue-eyed purebred huskies, and three mixed-breed dogs with Siberian Husky ancestry: one blue-eyed, predicted to carry the duplication based on log R data, and two brown-eyed, also predicted to carry the duplication based on log R data but with German Shepherd Dog ancestry. Sequencing for all midpoint products in all dogs were identical (S3B Table) and were approximately 300-bp in size. This was consistent with a tandem or near-tandem duplication, which we inferred based on 118-bp of sequence between our forward primer and midpoint, 123-bp of sequence between the reverse primer and midpoint break, and 50-bp of the forward and reverse primers themselves, leading to an 289-bp product in the event of a clean tandem duplication. The sequence aligned with greater than 98% homology to CanFam3.1 chr18: 44,791,409–44791566 and chr18: 44,890,025–44,890,185, as predicted.
We tested for the presence of the core haplotype associated with the blue-eyed phenotype (N = 43 markers, excluding those located within the duplication; S4 Fig), and compared log R for SNPs located inside vs. outside the duplicated region (Δ log R) for dogs that did not carry the haplotype, or were heterozygous or homozygous (Fig 3).
The presence of the duplication-associated haplotype (in one or two copies) explained 75% of blue-eyed cases (N = 81 / 108) and was rare in brown-eyed dogs (N = 46 / 2,661). Indeed, the haplotype bearing the duplication predicts the blue-eyed phenotype considerably better than the most associated SNP in our GWAS analysis (chi-square duplication P = 5.2x10-290; GWAS SNP P = 4.9x10-120; S4 Table). Atypical coat pigmentation or facial markings explained the remaining 25% of cases (Supplementary Information), with the exception of three blue-eyed mixed-breed dogs that possessed recombinant versions of the core haplotype (S7 Fig).
Heterozygotes and homozygotes exhibited distinct distributions of Δ log R (P = 2.0x10-13) consistent with the haplotype also carrying the duplication in these breeds (Fig 3; S8 Fig), with the exception of one blue-eyed mixed-breed dog with low Δ log R that exhibited log R values at individual SNPs suggestive of a partial duplication (Supplemental Information). Dogs that did not carry the associated haplotype had similar log R intensity at SNPs within the duplicated region compared to flanking regions, with a lower Δ log R distribution that overlapped with zero (P = 2.2x10-16 comparing dogs without the haplotype to those with it), indicating that they did not possess the duplication (with the exception of the three recombinant haplotypes discussed above).
We compiled a dataset of 2,890 diverse dogs distinct from those included in our GWAS panel to perform a validation test of the association between the duplication and blue eyes. The haplotype existed at low frequency in this panel (41 homozygotes, 26 heterozygotes) and all but two carriers had Δ log R values above the minimum bounds observed for heterozygotes in the discovery panel (N = 67 / 2,890 with Δ log R > 0.15), indicating that the duplication was almost always present on this haplotype. Most dogs that possessed the haplotype and the duplication were Siberian Huskies (N = 59 / 67; 41 homozygotes, 17 heterozygotes; S9 Fig), and the remainder were Klee Kai (a breed derived from Siberian Husky; N = 2), Australian Shepherd (N = 5), and one Australian Cattle Dog (S10 Fig; S5 Table). Profile photos were available for 67% of dogs with the haplotype and duplication (N = 46 / 68), and all but one had blue or heterochromic eyes instead of solid brown. The exception was a Siberian Husky with brown eyes despite having one copy of the haplotype (S9 Fig) and Δ log R values consistent with being heterozygous for the duplication on that haplotype (0.31). The owner/breeder of this dog was able to provide additional information that confirmed it was a likely carrier of the duplication: It had blue-eyed parents and had sired all blue-eyed or heterochromic litters. The two haplotype carriers with low log R (suggesting that the duplication was not present on the haplotype in their case) were both Australian Shepherds, one brown-eyed and one with unknown eye color (no profile photo available).
In this study, we discovered a haplotype containing a 98.6-kb duplication that is strongly predictive of blue eyes and heterochromia in dogs. While we cannot definitively rule out a different typed or untyped variant on this haplotype causing the trait, we feel that the duplication is a plausible causal candidate worthy of further functional investigation. We were able to validate the presence of this duplication with three independent methods: log R intensity from our microarray data, PCR and Sanger sequencing, and read-depth analysis of 17 whole-genome sequences. Further, we showed strong concordance between log R intensity and a consistent haplotype identified from blue-eyed cases in phased haplotype data. Using those phased haplotypes, we found that the duplication-carrying haplotype was more strongly associated with the blue-eyed phenotype than any single marker on our genotyping array (chi-square duplication P = 5.2x10-290; GWAS SNP P = 4.9x10-120; S4 Table). Further investigation of variant calls in resequencing data, a comparison of sequences between carriers and non-carriers of the duplication, interestingly revealed no convincing functional targets (S2 Table). While direct functional validation of the duplication is outside of the scope of this research, we suggest that the proximity of this duplication to ALX4 makes it a prime candidate for functional investigation.
To date, the most familiar examples of duplications affecting phenotype are those related to dosage, cases where one or more duplication events increased gene copy number and, therefore, the amount of translated protein product available for cellular function [22, 23]. However, this duplication sits in an intergenic region between the tetraspanin CD82 and Homeobox gene ALX4 (NCBI; UCSC Genome Browser [24]). Two non-coding RNAs (ncRNAs) are annotated on the complementary strand, including an uncharacterized long noncoding RNA (lncRNA) that overlaps the 3’ breakpoint of the duplication (Fig 1; S4 and S6 Figs). We could find no evidence that CD82 is functionally associated with eye color in humans or any other animal, but ALX4 and its paralogs play an important role in both mammalian eye development [25, 26] and pigmentation [27].
Research on the genetics of striping patterns in African striped mouse (Rhabdomys pumilio) and Eastern chipmunk (Tamias striatus) demonstrated that a close paralog of canine ALX4, ALX3, is a repressor of MITF with dorsally striped expression, leading to reduced melanin content and lighter coat color where ALX3 is upregulated [27]. Gene expression studies in humans have additionally demonstrated that ALX4 itself is expressed in the RPE [28], and in zebrafish (Danio rerio), expression of ALX4 orthologs, alx4a and alx4b, are enriched in iridophores, which originate in common with melanocytes from the neural crest [29]. Given the importance of cis-regulatory elements in local gene regulation [30, 31] and the location of the duplication upstream of ALX4, we propose that this large duplication in a candidate regulatory region could cause blue eyes by increasing expression of ALX4 in the RPE, leading to repression of MITF and a reduction in melanin in the iris.
The high proportion of blue-eyed heterozygotes in our analyses (53% of blue-eyed dogs) suggests that the duplication, if causal, is dominant in its phenotypic effect. However, the existence of 46 brown-eyed heterozygotes with similarly elevated Δ log R (P = 0.35 comparing blue and brown heterozygote distributions) suggests that one or more additional genetic factors may modify or mask the duplication’s effect on eye color (Fig 3; S8 Fig). This effect is not completely explained by an individual’s genotype at four previously characterized pigmentation genes (A, E, B, and K loci; S6 Table), although carriers of the duplication that also carry at least one copy of the dominant melanistic mask (Em) allele are significantly more likely to have brown eyes than duplication carriers without melanistic mask (P = 0.0018).
Functional follow-up studies are needed to explicitly assay regulatory changes in ALX4 caused by this duplication; however, we have shown that this mutation is highly (but not completely) penetrant and most common in Siberian Huskies. The presence of the duplication explains at least 75% of blue-eyed cases in our discovery panel of customer dogs (81 / 108 blue-eyed dogs carried the associated haplotype and have elevated Δ log R values consistent with duplicated markers).
In summary, by using consumer genomic data to drive this research, we were able to build the largest canine GWAS dataset to date, determine the prevalence of a putatively causal variant, a duplication upstream of ALX4 highly associated with blue eyes, across a diverse population, and utilize our relationship with owners of specific dogs to learn more about the inheritance of the trait. As more canine genetic testing is done on high-density array platforms, these databases hold particular promise for unlocking the genetic basis of complex phenotypes for which dogs are a particularly useful model, including cancer, behavior, and aging.
We solicited phenotype data from customers whose dogs have been genetically tested by Embark Veterinary, and who have agreed to participate in research, by implementing an online survey about their dog’s morphological traits at http://embarkvet.com and encouraging participation via email. We initiated the survey on February 7, 2017, and, as of November 23, 2017, owners of 3,248 adolescent and adult dogs whose eye color can be assumed to be developmentally complete (6 months or older) had submitted a response to the section of that survey that asks about eye color (S11 Fig). Most were owners of mixed-breed dogs, and 21% were owners of purebred dogs (N = 668 / 3,180). A subset of owners (N = 68) selected "other", indicating that their dog had an eye color not represented by any of the seven options. In total, 156 dogs in this dataset were reported to have either solid blue eyes (N = 73) or heterochromic eyes (partially blue; N = 83), compared to 3,024 with some shade of solid brown. We encoded this trait as a binary phenotype in case-control format (0: brown, 1: blue) and considered both solid blue and heterochromic dogs as cases. Ancestry from 185 different dog breeds, landraces (village dogs) and gray wolves was represented in this dataset.
Customer dogs were genotyped on Embark’s custom high-density 214,661-marker platform (213,245 filtered to autosomal, chromosome X, and pseudoautosomal region markers). Total genotyping rate was 99.5%, and all dogs (N = 3,180) had less than 2.5% missing data and passed standard filtering in PLINK [32]. After filtering, 90% of variant sites (192,108 / 213,245) were genotyped across at least 95% of individuals and were included in subsequent analyses.
We constructed a relatedness matrix from centered genotypes and ran a genome-wide association test based on a univariate linear mixed-model in GEMMA [33], using the eigenvalues and eigenvectors of the relatedness matrix to control for confounding effects of shared ancestry, particularly among dogs of the same breed or breed group (groups of closely related or recently derived breeds). We identified significant associations by applying a threshold of P < 5.0 x 10−8 to the Wald test statistic.
We downloaded whole genome sequence data for 17 dogs from the NCBI Sequence Read Archive [34], conducted an end-to-end alignment using Bowtie2 in—very-sensitive alignment mode [35] calculated read depth coverage across sites using SAMtools [36], and investigated mapped paired-end reads in regions of interest using the Integrative Genomics Viewer (IGV) [37, 38]. For each dog, we calculated the change in read depth between the putative duplicated region, demarcated by discordantly mapping paired-end reads (44791417 to 44890166-bp), and 5Mb of flanking sequence immediately surrounding it (chr18:42999825-44791417 and chr18:44890166-48000173). We called variants across a 1-Mb region surrounding the most associated GWAS SNP using HaplotypeCaller from the Genome Analysis Tool Kit (GATK) [39] for 9 SRA samples (ERR911199, ERR911200, ERR1014362, ERR1990016, SRR1122359, SRR1124049, SRR1124304, SRR1784129, SRR2095539) with robust read depth and compatible alignment formatting (including four huskies with the duplication, and one husky and four other breeds without the duplication). Following batch variant-calling for SNPs and indels independently, we refined the call set by filtering variants that did not meet QC requirements for minimum read depth (SNPs and indels < 2), the phred-scaled p-value from a Fisher’s exact test for strand bias (SNPs > 60, indels > 200), the variant position relative to the end of the read (Mann-Whitney rank sum test, SNPs < -8, indels < -20) and, for SNPs, mapping quality (root mean square < 40, Mann-Whitney rank sum test < -12.5). We then identified 47 SNPs or indels present in the dogs with the duplication, and absent from those without the duplication (S2 Table). Since eye color phenotypes were not available for datasets archived to SRA, this approach was able to identify additional variants that co-segregate with the duplication (and are therefore at least as explanatory for the phenotype), but it was not possible to scan for variants that perform better (i.e. those that might additionally explain brown-eyed duplication carriers).
We designed primers to amplify the midpoint span of the duplication, as well as the 5’ and 3’ flanking regions of the duplicated region as positive controls. Genomic DNA (gDNA) remaining from microarray analysis (2 uL) was used for PCR reactions using the following primer combinations: ALX4_5Fl_1F + ALX4_5Fl_1R; ALX4_Dup_2F + ALX4_Dup_2R; ALX4_3Fl_1R + ALX4_3Fl_1R. All PCR reactions were performed using Q5 High Fidelity Taq Polymerase (NEB Cat No M0491) in a total volume of 20 uL following the manufacturer’s protocol. The following cycling parameters were used: 98°C 30s, 40X (98°C 10s, 60°C 30s, 72°C 30s), 72°C 5m, 16°C hold. PCR product was visualized on a 1% agarose gel with 1X GelRed (Biotium Cat No 41003); product from 9 dogs were submitted for purification and Sanger sequencing at Genewiz (Genewiz.com).
Genotype data for all dogs was phased against a proprietary reference panel, with missing data imputed using Eagle2 [40]. We examined phased data around the putative duplication breakpoints in 26 blue-eyed dogs in the GWAS data set that were homozygous for the CFA18 A allele. This revealed a single haplotype bearing this allele that spanned 81 markers between positions 44,336,453 and 45,170,144 that was present in all 26 dogs in at least one copy (S4 Fig). We further defined a 43 marker core haplotype between positions 44,737,897 and 45,170,144 that was present in two copies in 24 of the 26 dogs, and in one copy in the remaining two dogs. We compared the change in intensity between duplication markers and flanking regions (Δ log R) according to both the eye color phenotype and haplotype state of each dog. For all remaining dogs in the discovery panel and all dogs in the validation panel, we calculated the number of copies of the core haplotype present in their phased data (excluding the markers inside the duplication: 44,825,760, 44,838,433, 44,849,276, 44,855,038, 44,858,831, 44,876,627).
The validation panel included 2,890 dogs of various breeds, none of whom carried the most associated CFA10 marker for merle. Eye color could be scored for these individuals when customers had uploaded high resolution profile photos. Photos were available for 70% of dogs bearing both the associated haplotype and a log R signature of the duplication on that haplotype.
Owners of participating dogs were part of the Embark Veterinary, Inc. customer base. Owners provided informed consent to use their dogs’ data in scientific research. Owners provided photographs of their dogs and filled out online survey questions concerning their dog’s eye color; no invasive methods for genotype or phenotype collection were used, nor were dogs ever handled by researchers. Owners were given the opportunity to opt out of the study at any time during data collection. The discovery cohort was selected from data available before August 2017; the fine mapping cohort was selected from data available before Dec 2017. All published data have been deidentified of all Personal Information as detailed in Embark’s privacy policy (embarkvet.com/privacy-policy/).
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10.1371/journal.pcbi.1004880 | Hippocampal CA1 Ripples as Inhibitory Transients | Memories are stored and consolidated as a result of a dialogue between the hippocampus and cortex during sleep. Neurons active during behavior reactivate in both structures during sleep, in conjunction with characteristic brain oscillations that may form the neural substrate of memory consolidation. In the hippocampus, replay occurs within sharp wave-ripples: short bouts of high-frequency activity in area CA1 caused by excitatory activation from area CA3. In this work, we develop a computational model of ripple generation, motivated by in vivo rat data showing that ripples have a broad frequency distribution, exponential inter-arrival times and yet highly non-variable durations. Our study predicts that ripples are not persistent oscillations but result from a transient network behavior, induced by input from CA3, in which the high frequency synchronous firing of perisomatic interneurons does not depend on the time scale of synaptic inhibition. We found that noise-induced loss of synchrony among CA1 interneurons dynamically constrains individual ripple duration. Our study proposes a novel mechanism of hippocampal ripple generation consistent with a broad range of experimental data, and highlights the role of noise in regulating the duration of input-driven oscillatory spiking in an inhibitory network.
| Our memories are consolidated while we sleep through a bidirectional exchange of information between two brain areas called cortex and hippocampus. Neurons that were active in behavioral tasks reactivate again in both structures during sleep in a process of linking and modifying memories from the short term storage of the hippocampus to permanent storage in the neocortex. This process occurs mainly during short oscillatory hippocampal electrical events called sharp wave-ripples. We propose a novel mechanism of ripple generation consistent with a wide range of experimental data, to explain how hippocampal network properties shape ripple frequency and duration. Understanding the neuronal mechanism underlying ripples is crucial to explaining how the interaction between hippocampus and cortex during sleep enables memory consolidation.
| Sleep, which consumes about a third of our lives, is thought to play a critical role in memory consolidation. Specifically, sleep influences unconscious post-encoding processes that result in long term memory consolidation and reconsolidation. Behavioral studies show that performance in various memory tasks improves after sleep compared to a similar period of wake [1, 2], and such improvement was observed in declarative, procedural and emotional memory tasks [3–7].
Cortical and hippocampal circuits show characteristic oscillatory activities at different sleep stages [1]. During slow-wave sleep (SWS), cortex is synchronized by low-frequency slow oscillations (0.2–1 Hz) between Down states–in which most cells are hyperpolarized–and Up states, in which firing activity is intense and cells are depolarized [8]. The hippocampus generates sharp wave-ripple complexes (SWR), in which a strong excitatory input from CA3 pyramidal cells leads to broadly distributed postsynaptic potentials (the sharp waves) in CA1 stratum radiatum, while the pyramidal layer shows a quick bout of high frequency LFP activity (the ripple) [9–11]. Ripples exist both in a quiet awake state and during slow-wave sleep, and disruption of ripple activity is known to impair memory [12, 13].
In the rat, one of the mechanisms thought to underlie memory consolidation is place cell replay: a phenomenon in which the pattern of relative firing of hippocampal pyramidal cells that code for position (place cells) re-occurs during post-task sleep [14, 15]. Importantly, hippocampal replay has been shown to take place during CA1 ripples, on very short time scales during which synaptic plasticity is likely to arise. Interestingly, SWRs are more likely to occur during cortical Up states [16] and may potentially influence the spatio-temporal pattern of Up state generation. Thus, understanding the process of ripple generation is a crucial step towards identifying the mechanism of brain-wide sleep-dependent memory consolidation.
In this work, we propose a novel mechanism of CA1 ripple generation during sleep. Our in vivo data show that ripples have a broad frequency distribution, exponential inter-arrival times and a highly non-variable duration. In our model, high-frequency firing in perisomatic interneurons is caused by input from area CA3, and mediates high-frequency local field potential (LFP) oscillations in CA1 pyramidal neurons. The main novelty of this model, compared to ones already proposed in the literature [17–19] (see [20] for a review), is the prediction of the ability of CA1 to self-time ripple durations, and hence limit the extent of replay in a dynamic fashion, from ripple to ripple. We propose that phase-dispersion (loss of synchrony) induced by noise on the oscillatory dynamics constrains the duration of a ripple event. This minimal model is not only able to explain experimental data regarding basic ripple properties, but is also consistent with recent data on ripples and ripple-like activity triggered by optogenetic stimulations in vivo [21].
This paper is organized as follows: we first introduce the experimental results, then describe our computational model and show that it can produce ripple-like oscillations. We then use those observations to inform predictions that can be made by the full model. Next we study the role of interneurons in setting inhibitory transient mechanisms underlying ripple oscillations in the model, and the role of pyramidal cells in the overall ripple structure. We conclude showing that selective input from CA3 can induce sequential reactivation of CA1 pyramidal cells during ripples in our model.
Data shows that ripples are local events [22], and that a given CA1 pyramidal cell rarely spikes more than once per ripple oscillation [10, 23, 24]. Many pyramidal cells are not recruited by ripples recorded across different sessions, while some are recruited by almost all events. On average 10% of pyramidal cells are active in any given ripple, and their spikes are locked to the trough of each the oscillations within a ripple event [25, 26]. On the other hand, inhibitory interneurons in the pyramidal layer spike across the ripple, with a firing rate consistent with the ripple frequency [23, 24]. This suggests a predominant role for pyramidal layer interneurons in organizing CA1 ripple firing. Moreover, spiking in CA3 is not locked to CA1 ripples [27, 28], which further advances the idea that ripples are an intrinsic CA1 rhythm, which can be initiated by sufficiently strong incoming inputs.
We started investigating the nature of ripple oscillations by studying LFP recordings in hippocampal CA1 in rats. Representative examples of the wide-band and band-passed recordings are shown in Fig 1A. We focused on a few salient characteristics of ripple waves, such as frequency, duration and inter-arrival time (defined as the time between a ripple event and the next). Fig 1 shows that ripple frequency, defined as the inverse of the average inter-peak interval during a ripple event, was normally distributed around 163.5 (± 20.6) Hz (Fig 1B), their inter-arrival times were approximately exponential, with fitted rate 1.7748 Hz (Fig 1C), and their duration (Fig 1C) was centered about 51 (± 9.4) ms showing a high-kurtosis distribution (K = 20.1952, where for a normal distribution K = 3) [28]. Note that ripple frequency is representative of the peak-to-peak time within a given ripple (see S1 Fig for a representation), while the count of ripple events in a given time interval would be called ripple density, and can be found as the inverse of the inter-arrival times in such interval.
Ripples are events specific to the pyramidal layer of CA1, and ripples simultaneously recorded across different tetrodes appear to have amplitudes that vary independently [22]. This suggests that ripples are local events within the CA1 pyramidal layer [22]. Furthermore, data show that ripple events turn to epileptic activity when GABAA is blocked in CA1 slices [29] suggesting that interneurons limit the extent and sculpt the frequency content of these events. The population of pyramidal cells is most active close to the peak of a ripple event, which is defined as the time when the filtered LFP reaches its maximum amplitude (often the biggest trough). Moreover, only a few pyramidal cells are recruited by ripples in CA1. On the other hand, most basket cell interneurons spike in ripples, and across the event duration [23, 24].
Our data revealed that the length of time between successive ripples is not dependent on any feature of the current ripple (frequency or duration). In fact, scatter plots of ripple frequency vs time-to-next ripple, and ripple duration vs time-to-next ripple, did not show any particular correlation (see S2 Fig in supporting information). Poincaré return maps show that both ripple frequency and duration are not dependent on the frequency or duration of the preceding ripples (S2 Fig). This implies that we can model each ripple in CA1 as an event directly triggered by an incoming CA3 input volley. Hence, we looked for a ripple mechanism that can generate Gaussian-distributed frequencies for a fairly constrained time interval.
We reasoned that our model needed to represent a small patch of CA1, reached by strong incoming excitation from CA3, so that the activity of all cells we modeled would be picked up by a single electrode. Given that ripples are measured in the pyramidal layer, we chose to model only pyramidal cells and parvalbumin positive basket cells. In fact, other interneuron types that might be active during sharp waves impinge on pyramidal cells at different layers [30, 31], which do not show ripple frequency oscillations. Hence, they might have a modulatory influence on the overall ripple appearance but are not in a position to set the pace of ripple frequency. The network consisted of 800 pyramidal cells and 160 interneurons, a ratio in agreement with CA1 anatomy [32], and we used all-to-all connectivity, with the exception of the synapses between pyramidal cells, which were few and much weaker than all others [33], consistent with CA1 anatomy [34]. Fig 2 shows a network representation (Fig 2A), example traces from a pyramidal cell and an interneuron (Fig 2B), and the distributions of synaptic weights (Fig 2C) in the model.
To model each neuron, we used the Adaptive Exponential Integrate-and-fire model [35], as it is simple (only two variables) and has been shown to reproduce many different spiking behaviors [36], because of the expressed essential non-linearities [37]. To account for heterogeneity, each neuron received a different independent Ornstein-Uhlenbeck (OU) noise and a mean DC current to set baseline excitability (see Materials and Methods for details). The noise term represents the in vivo state of the voltage in each cell, which is likely receiving a much higher barrage of synaptic inputs than the one provided by the network spiking activity in our model. The OU process, which can be thought of as a filtered white noise process, is used in dynamic-clamp experiments to mimic in vivo state in hippocampal slice recordings [38]. We assigned fast time scales to the synapses, in agreement with recent in vitro estimates [39]. The average synaptic strengths values were chosen to induce post-synaptic potentials of less than 1mV. To represent the integrated input from CA3 localized in time, we delivered input current to all cells, with different magnitudes for pyramidal cells and interneurons, due to the lower input resistance of pyramidal cells [40, 41] (see the first panel of Fig 2D; details in Materials and Methods). In the text below we will refer to this current as CA3 input to the CA1 network. All details of model implementation and justifications for specific parameter choices are reported in Materials and Methods.
The rastergram and spike probability curves in Fig 2D show that when inputs from CA3 reached a patch of CA1, high-frequency firing was triggered in the interneuron population, which self-organized in oscillations. Firing in pyramidal cells increased as well, but the probability of firing for the pyramidal cell population was much smaller than for interneurons (0.2 vs 0.005%). To compare our model to experimentally recorded ripples, we approximated the LFP in the pyramidal layer using the average net synaptic input (from both excitatory and inhibitory cells) received by all pyramidal cells, and derived a wideband LFP signal (Fig 2D). The model generated ripple-like oscillations that could be detected by band-passing our LFP estimate, shown in the bottom panel of Fig 2D.
Ripples produced by our computational model have properties consistent with the ones recorded in vivo. Fig 3 shows that the mean frequency for a given input intensity is 162.4 ± 12.5 Hz (Fig 3A), and ripple duration is 57.2 ± 3.1 ms (Fig 3B). Also, most pyramidal cells do not spike during a ripple, in fact on average a ripple shows spikes from 14.76% of the pyramidal cell population (Fig 3D), and those that do will not spike across the ripple duration, but only once (Fig 3C) in agreement with previous experimental observations [42]. Furthermore, the spiking activity of pyramidal cells is known to precede the spiking of interneurons within each ripple wave [43]. We tested this property in our model by computing the cross-correlation between the filtered LFP and the firing probability of each cell population, averaged across 40 ripples. Fig 3E shows that peaks in the correlation between pyramidal cell spiking and LFP preceded those for interneuron activity across ripple waves. For completeness, we also verified that there was no inherent rhythmic activity in the network background state that could be inducing this relationship within ripples beyond the mechanistic phenomena we report (S3 Fig). This model is consistent with CA1 activity during non-REM sleep in vivo, when ongoing theta-gamma activity is not present in the background.
We next studied the main properties of the dynamics of ripple oscillations in our model, looking for intrinsic CA1 properties that played a role in shaping ripple spiking. We found that CA1 properties determine ripple duration, while ripple frequency is not controlled by the time scale of inhibitory synapses. Also, the amount of pyramidal cells spiking during ripples is determined by competing forces: the excitatory drive they receive from CA3 and the amount of local inhibition they receive from the CA1 inhibitory population.
Since the input current (representing the sum of synchronized spiking in CA3) caused ripples to initiate, we asked if ripples would continue oscillating for as long as the input was present. Fig 4A shows that the ripple LFP duration stayed un-varied independently of the different CA3 input durations we tested. The band-passed LFPs for 40 ripples across different input durations are shown in gray, while the black line is their average. The graph shows that even if spiking was still enhanced for the duration of CA3 inputs, the organized oscillatory activity was lost after about 60 ms for all cases considered. This emphasizes that CA1 can control ripple duration, even if it cannot control their initiation.
Pyramidal cell spiking within a ripple is responsible for carrying information to downstream areas. Hence, it is important to understand what factors regulate the overall recruitment of CA1 pyramidal cells to a given ripple in our model. The ratio of CA1 pyramidal cells recruited in a given ripple in our model is modulated by both excitatory drive from CA3 on this population and inhibitory currents within CA1 [44]. In fact, increasing CA3 input to this population raised the percentage of pyramidal cells spiking on every ripple (Fig 4B, doubling the peak value takes the recruitment percentage from 14% to above 90%), and increasing the time scale of inhibition onto pyramidal cells reduced their recruitment (Fig 4C, setting τ = 6ms results in an average 4.4% of pyramidal cells spiking in any given ripple). Fig 4C also shows that increasing inhibition onto interneurons resulted in higher pyramidal cell excitability, because the inhibitory population activity was reduced overall. Thus, a net change in the GABAA time scale has competing effects on pyramidal cells recruitment to ripple activity, leaving the fine regulatory function to highly selective CA3 input. The predominance of CA3 input over local inhibition in choosing which pyramidal cells are recruited to a specific ripple is consistent with the known synaptic plasticity at Shaffer collateral synapses between CA3 and CA1 pyramidal cells [45]. Furthermore, we found that changing the time scale of inhibition did not drastically slow ripple frequency (Fig 4D).
To better understand mechanism underlying the dynamics of ripple oscillations, we next moved to studying a simplified system. Since pyramidal cells typically spike less and at much lower frequencies than inhibitory cells during ripples, we started from studying the role of interneurons in the ripple dynamics we observed in the full model. To do that, we considered a network of only inhibitory neurons receiving a step of DC current, and studied the resulting input-driven high-frequency population firing.
The step of DC current delivered to all inhibitory interneurons amounted to the same value as the peak input current from CA3 to interneurons in the full model (700 pA). To construct the profile of spiking probability in response to the current step, we run 100 simulations for each parameter set, and built the cumulative histogram of probability of spiking as a function of time. Fig 5A shows a schematic of the reduced network, while Fig 5B shows that the common input step (at time = 1s) initially organized the network as indicated by the rhythmic oscillations of the population activity. The amplitude of these oscillations progressively decreased, indicating a transient nature of the high-frequency activity, which was de-synchronized by the intrinsic noise. Eventually, the population firing rate stopped oscillating and settled to a mean constant value, which depended on the size of the current step. This behavior of the isolated interneuron population is consistent with data [46]: in vitro optogenetic experiments (albeit in area CA3) show that activating only parvalbumin positive interneurons with a step of light, of duration up to 50ms, results in an average oscillatory behavior in which the peaks are progressively attenuated in time.
Once we found that synchronous inputs organize transient oscillatory patterns in our purely inhibitory network, we asked what role synaptic inhibition could play in setting the properties of this transient oscillation. We reasoned that the role of inhibitory synapses in network behavior could be twofold: they promote synchrony while enough synaptic inputs are aligned (early after the initiation of the step of current), but synaptic currents switch to propagating de-synchronization if enough neurons are asynchronous. We numerically studied the role of inhibition in transient high-frequency synchronization by changing two main parameters: the decay time scale of the inhibitory synaptic conductance (τ) and the strength of synaptic inhibition. To address synaptic strengths while respecting the choice of a normal distribution of synaptic conductances around a mean, we introduced a non-dimensional scaling factor α, which we systematically varied. When α = 0, no synapse was active, and when α = 2 all synapses were twice as strong as their baseline values. Fig 5B is provided to show the effect of changing the inhibitory signal: the overall impact of inhibition on the oscillation pattern depended on the decay time scale τ, or synaptic strength α, or both. As one can see in Fig 5, changing strength of synaptic inhibition in the inhibitory network affects both the duration of transient synchronization (Fig 5C) (defined as a time window when amplitude of oscillations is still high, see Materials and Methods, Analysis of inhibitory network model) and its frequency (Fig 5D) (defined as the inverse of the average peak-to-peak time delay, within transient duration). Because of that, the number of oscillatory peaks within a transient changed as well (Fig 5E).
Fig 5 shows that frequency remained within ripple range for a time scale of inhibition within a broad range of 2–6 ms as long as synaptic weights were limited to within about 200% of the baseline value. Also, in the condition of α = 0, when the network is disconnected, one can still see the noise-induced de-synchronization; when the network has active and fast (small τ) synaptic connections the de-synchronization is delayed (Fig 5C). The increase of inhibition (through longer decay times or stronger conductances) resulted in the reduction of both the transient oscillation frequency (Fig 5D) and its peak count (Fig 5E). This is an important observation and it highlights the point that oscillations in this system are not a rhythm in the traditional sense of the word: they are not an oscillation that would persist in time as long as there are no changes to the CA1 model or the input, but rather a transient arising from a strong initial input capable of synchronizing CA1 neurons. The last point is critical: given that the stationary state of the network receiving a step current input is non-oscillatory, if the initial common step fails to synchronize enough neurons, then the transient oscillation would only last about 1 or 2 cycles, if at all.
To further explore this point, we studied how the transients organize when the initial step of the input current is halved (Fig 6A). In that case, much fewer interneurons were recruited to the initial synchronous population (note the scale of firing probability on the y-axes of Fig 6A). Inhibitory currents still affected oscillations, but the transient lasted very few cycles (Fig 6B) and peaks were smaller. We concluded that if the initial current step failed to synchronize a large enough population of neurons, the resulting slower oscillation faded in only 2 to 3 cycles. Hence, this network shows an all-or-none property: a smaller step of input that could in principle recruit lower frequency oscillations cannot recruit a transient at all. In fact, it takes an input of sufficient size to generate a transient that lasts enough cycles and recruits enough neurons for a fast oscillation to be visible in the LFP.
We next investigated the role of noise in setting the duration and frequency of oscillatory transients in the inhibitory network. We progressively reduced the noise coefficient using a scaling factor σ. Fig 6C shows the network behavior in the case of reduced noise amplitude, where spikes were far more synchronized around the network peaks compared to the 100% noise case shown in Fig 5B. Fig 6D shows that the duration of transient oscillations was controlled by the noise level (larger noise resulted in shorter transients), while frequency was not. In summary, the network oscillations we observed in our purely inhibitory network do not emerge out of interaction among the neurons, but rather from receiving a common, synchronizing, input. If the neurons were identical, received identical inputs, had no noise, and had no CA1 synapses then the oscillations would be trivially persistent, since all of the interneurons would remain in the phase locked state. The transient nature of this oscillation was due to network heterogeneity, introduced in our model via a different direct current to each neuron, and independent noise (where larger noise introduced more heterogeneity). Input strength, noise size, synaptic inhibition strength and duration are all factors that can control and tune the transient behavior reported in our model.
Our findings in the purely inhibitory network have direct implications for the full hippocampal model: due to the underlying transient mechanism that is highlighted by the dynamics of the reduced inhibitory network, local CA1 properties such as the level of noise and the strength of inhibitory synapses will control ripple duration, while the degree of synchrony of CA3 inputs (which is represented by the amplitude of the input current in our model) will determine whether a ripple occurs. In fact, if CA3 spikes were not synchronous enough, the net sum of the post-synaptic currents impinging on CA1 cells would be small, and hence the size of the CA3 current input we deliver to the model would be small as well. Therefore, the size of our current input from CA3 is effectively a model for synchrony in CA3 pyramidal cells spiking. Ripple frequency depends on balancing the number of interneurons recruited by the ongoing activity with the lateral inhibition known to exist between interneurons. Since, in principle, we could find oscillations in reduced model in the case of α = 0, where no synapses between the interneurons were present, we checked if removing I-to-I synapses in the full model would affect the properties of CA3-input driven ripples (S5 Fig). We found that ripple frequency transients can indeed be elicited in the network, however duration of ripples was generally reduced and the likelihood for occasional very short transients increased.
The inhibitory network also showed that an input step of current too small would result in very few (1 to 3) oscillatory peaks, effectively failing to induce the transient. In the full model, we verified that reduction of the input size (to both pyramidal cells and inhibitory neurons) resulted in decrease of the ripple amplitude and disruption of ripple events (S4 Fig). This was particularly evident when the input was scaled to 30% of its magnitude. As a result, the ripple, and consequently a bout of memory consolidation, only occurs if activity in CA3 is sufficiently synchronous, and results in a strong enough input to CA1. This ‘synchrony threshold’ ensures that uncorrelated CA3 inputs are essentially ignored by CA1. Furthermore, this means that a transient oscillation is triggered only at high frequencies above the gamma (30–90 Hz) range. As it is known that the interaction between pyramidal cells and basket cells in hippocampal CA1 region underlies gamma (30–90 Hz) oscillations [47, 48], the above findings suggests the mechanism recruiting ripple oscillations can co-exist with, and be independent from, other slower rhythms that arise in the same region.
It is also important to note that ripple characteristics (frequency, duration) in the full model receiving input from CA3 that was scaled in size saturated at around 80% of the baseline input and remained stable above this value (S4 Fig). Thus, ripple properties predicted by our model to match experimental data are observed in the broad range of CA3 mediated input amplitudes, i.e., structurally stable. Our results on the effects of reducing CA3 input on CA1 ripples are consistent with experimental observations made in simultaneous CA3-CA1 recordings [27], in which sizeable (hence synchronous enough) CA3 activity was related to CA1 ripples, while smaller (less synchronous) CA3 activity did not induce ripples in CA1.
After investigating the role of inhibitory spikes in ripples, we moved to study the role of pyramidal cells activity in our model. In fact, recent optogenetics work [21] has raised the interesting idea that the minimal circuit to obtain ripples in CA1 may include local excitatory synapses on inhibitory interneurons. Specifically, CA1 in vivo recordings showed that when activating both parvalbumin positive interneurons and pyramidal cells in the pyramidal layer, high-frequency oscillations (HFO) in the LFP emerged. When only pyramidal cells were driven optogenetically with a step of light, HFO were also measured, although their duration was shorter than HFOs obtained driving both neuron populations. In contrast, if only fast-spiking parvalbumin positive inhibitory interneurons in the pyramidal layer were driven to fire, LFP oscillations were not found. We set out to see if our model could show results consistent with these experiments.
We started by delivering input current only to interneurons (Fig 7A): since simulations of a purely inhibitory network showed that current size controls synchrony among the interneurons (Fig 6A), the size of the current step was first kept the same as for the full model. In Fig 7B we show examples of spiking probabilities for two stimulation conditions (50% or 100% of all interneurons were stimulated) in the model when only interneurons received inputs. The inhibitory activity was still oscillatory, however all pyramidal cells were hyperpolarized, resulting in a field potential consisting of shunted currents. As a consequence, the LFP was much smaller than the one in the full model (compare Fig 7B with Fig 2D); LFP amplitude increased with the percent of interneurons recruited (Fig 7C). Specifically, in the case where 50% of interneurons were stimulated (which is likely much larger than any optogenetic stimulation in vivo), we found that the LFP amplitude was only about 10% of the LFP observed when both excitatory and inhibitory neurons received an input drive from CA3. Increasing the amplitude of current stimulation in the model led to even stronger shunting of pyramidal neurons and even smaller LFP amplitude. In comparing with experimental results, we emphasize that the reduction of the LFP amplitude in our model depends on shunting effect of inhibition on pyramidal neurons, which in actual in vivo experiments would depend on the cell geometry, location of excitatory and inhibitory synapses, and other factors that our minimal cell model cannot explicitly capture. Even in this simplified setting, we are able to show a qualitative match with the strong reduction of the amplitude of oscillations in the LFP. Thus, our model reveals that if only interneurons are driven by a light source, LFP oscillations will be very small in amplitude, regardless of the stimulation amplitude, to the point that they will be experimentally negligible, in agreement with in vivo results [21].
We next tested whether the input delivered only to pyramidal cells (Fig 7D) could give rise to short bursts of oscillations. Note that to achieve an initial spiking rate capable of triggering fast network oscillations, pyramidal cells had to receive a current step size bigger than the one used for the full model. Since light-driven cells in optogenetics experiments are not receiving a fixed step of current that is identical across populations, we chose to adjust the current size to the measured behavior. In vivo, the LFP initially showed high frequency oscillations (about 150 Hz), but only for about 25 ms [21]. Immediately after the initial high frequency response, the LFP amplitude in the high-frequency band decreased and the activity slowed down to about 80 Hz. In agreement with these data, in our model, we first achieved a high frequency firing for about 25 ms, but the pyramidal cell population was not able to sustain firing much longer (Fig 7E).
A variable controlling the ability of pyramidal cells to sustain firing is the strength of spike-frequency adaptation, which in the model equations is controlled by the size of the jump (b) imposed on the slow refractory variable (w) after each spike (see Materials and Methods). Since change in adaptation can be biologically achieved by a variety of neuromodulatory phenomena, and the less adaptive the cell is, the longer it could sustain firing, we tested whether the current input to pyramidal cells could generate ripple-like oscillations in the networks with progressively less spike-frequency adaptation in the pyramidal cell population. To reduce adaptation in our neurons, we multiplied the b value by a scaling factor, in a range 10–100%. We found that even when pyramidal cell firing can be sustained (Fig 7E), oscillations still showed the same profile of initial high frequency response that quickly slows down to a frequency below the ripple range as observed in vivo. Specifically, the amplitude of the LFP filtered in the high-frequency band (to detect the HFO) initially peaked but then dropped very quickly, which is consistent with what was shown in vivo (Fig 7F). We concluded that our model, in which CA3 input to both pyramidal cells and interneurons is necessary to trigger a ripple in CA1, is consistent with optogenetics data [21].
So far we have focused on a global mechanism of ripple generation, in which CA1 pyramidal cell timing within ripples is regulated by the ongoing rhythmic inhibition and incoming CA3 input. During sleep, CA1 pyramidal cells that spike in ripples are known to reactivate in firing order consistent with the one recorded during behavioral tasks [15, 26], and this property is thought to be a hallmark of a memory trace in the hippocampus. Memory trace reactivation has been measured in CA1, but it is not clear whether the mechanism inducing reactivation within ripples is intrinsic to CA1 or due to input. We set out to see which biologically relevant properties could control ordered pyramidal cell reactivation across ripples in our model.
It is known that neurons recruited by behavior to form a memory trace tend to have higher excitability [49–51]. We reasoned that cells in CA1 that reactivate during a ripple might have higher intrinsic excitability. Since, in our model, pyramidal cells are picking windows of opportunity to spike during ripples, cells with substantially higher DC levels will have an easier time finding a window of opportunity in which to spike (because overcoming the incoming inhibition would be easier for them, compared to all other cells). In our model, we tested whether all it takes to be replayed in sequence for a set of pyramidal cells activated during the training phase (and therefore known to have higher DC, and to spike in more ripples because of that) is the fact that they are more depolarized. If that was the case, than we would know that the sequential reactivation in our model results from competition between intrinsic cell depolarization and ongoing inhibitory oscillations.
It is also known that the Schaffer Collaterals (the projections from CA3 pyramidal cells to CA1 neurons) are plastic [52], hence potentially target-selective. In other words, neurons in CA1 could receive selective inputs from CA3: higher and more intense inputs from some CA3 pyramidal cells (presumably the ones correlated with the same behavior) and lower and less intense from other CA3 pyramidal cells. The selectivity of CA3 input could be further modulated by hippocampal inhibitory neuron types that are not modeled in our network, which are hypothesized to gate Entorhinal cortex input and CA3 input on CA1 pyramidal neurons [18, 53].
Hence we have two possible mechanisms with the potential of inducing reactivation: intrinsic CA1 excitability is a parameter of CA1 properties that could induce sequence reactivation, while CA3 selective input is a potential input-dependent mechanism for sequence replay in CA1. To test these two properties, we randomly selected 10 pyramidal cells (“sequence” cells) to represent neurons that reactivate sequentially during ripples. First, we increased the constant current input that the 10 selected neurons received (Fig 8A), which resulted in the appearance of a small peak at high values in the distribution of excitability of all pyramidal cells of the CA1 network (Fig 8A ii). This was introduced as a mechanism to increase the likelihood of selected cells to spike during ripples. In a second case, we changed the time course of incoming current input, only for the selected sequence cells (Fig 8B). As shown in Fig 8B–8I, each sequence cell received an input that had a peak in a narrow time window within the duration of a ripple. This peak represented the spiking of the sub-set of CA3 pyramidal neurons that were preferentially connected to the target CA1 cell; thus we assumed that there are CA3 neurons that would spike during a sequence-specific time window of the sharp wave event. In other words, in this case our model assumes that during a sharp wave-ripple there is an organized reactivation in CA3, inducing selective inputs to CA1, which results in sequential spike reactivation in CA1. Finally, in a third case, we combined both manipulations on our selected cells: increased excitability and selective CA3 inputs. For each case, we show whether the inputs from CA3 are selective in panel i, and whether sequence cells received extra excitability in panel ii.
In our model, sequence replay needed to lead to two properties: (a) specific spike sequences repeated more often than chance and (b) the temporal order of the spikes of CA1 cells within those sequences was consistent across ripples. For the replay properties to be satisfied, we checked if our sequence cells spiked in a greater fraction of ripples compared to all other cells (panel iv), and spiked in a consistent order across ripples (panel iii). To verify that, we computed the spike time difference between sequence cells, and averaged across all ripples in a 10 s simulation (41 ripples). If the order of cells was maintained across ripples, the average spike time differences will look like shifted versions of the same line.
Fig 8 shows the effect of these manipulations. In Fig 8A, selected cells only received enhanced intrinsic excitability (see a ii) without selective temporal ordering in CA3 input (see a i). Note that, in this case, there was no sequential spiking behavior during ripples (see a iii), and sequence cells did not spike in more ripples than all other cells (see a iv). In Fig 8B selected cells only received selective CA3 input (see b i), without enhanced intrinsic excitability provided by constant direct current (see b ii). Note that, in this case, the orderliness of spiking across ripples was overall preserved (see b iii). Also note that the fraction of ripples visited (b iv) was higher for sequence cells compared to all other cells. In Fig 8C we show that CA1 neurons that received selective input from CA3 (c i) and higher intrinsic excitability (c ii) showed spiking in a greater fraction of ripples compared to all other cells (c iv), and spiked in a consistent order across ripples (c iii). Note that compared to condition b, in which the additional intrinsic excitability is not present, the consistency of ordered firing across ripples is improved.
In summary, we found that being more depolarized served the selected cells well in terms of the number of ripples during which they spike, but not for spiking in the orderly fashion that is found experimentally. On the other hand, selective CA3 input seemed to be effective at inducing sequence reactivation. The necessary elements of the model to allow for mapping of the structure in CA3 input onto spiking in CA1 pyramidal cells were 1) generalized input lasting throughout the sharp wave event to most cells in CA1; 2) time-selective, ordered input from some CA3 cells to some pyramidal cells in CA1; 3) the selective input, when present, results in current delivered to its target (sequence) cells at higher magnitude than generalized input delivered to the non-sequence cells (because of plasticity in CA3-to-CA1 synapses). Our model therefore predicts that replay in the hippocampus is generated in CA3 (and possibly in the dentate gyrus) and ordered reactivation in CA3 is required for reliable sequential spiking in CA1, where it is packaged in a fast-rhythm at rates that are conducive to synaptic plasticity, to be transmitted to target regions, such as the cortex. Intrinsic CA1 properties, such as heightened excitability of selected cells, can enhance but not cause the ordered replay.
Synchronized neuronal activity manifested by different types of EEG and LFP rhythms takes over cortex and hippocampus across sleep stages. During non-REM sleep, cortical slow oscillations alternate between highly active phases (Up states) and Down states, in which most cells are hyperpolarized, at very low frequencies (0.2–1 Hz) [8]. In the hippocampus, sharp wave-ripples (SWR) are brief high-frequency (>150 Hz) events that have a temporal relationship with transitions to Up states during slow oscillations [16]; and which might be crucial to sleep-dependent memory consolidation [12, 13, 54]. Moreover, hippocampal reactivation, a process in which cells active during a task fire during subsequent sleep in a task-consistent order, is known to take place within SWR [15]. Replay has also been found in cortex [55], and it has been shown to co-occur in visual cortex and hippocampus during sleep [56]. Despite the important role of SWR in the interplay of hippocampus and neocortex during memory consolidation, neuronal mechanisms underlying the process of ripple generation are still largely unknown [20, 57].
In this study, we propose a novel mechanism of hippocampal CA1 ripple generation consistent with a broad set of experimental data. We design a model of CA1 where synchronization of interneurons is due to common input from CA3, rather than reciprocal inhibition, and where inhibition regulates the windows of opportunity for pyramidal cells to spike. Our in vivo data show that ripples have a broad frequency distribution but very small variability in duration, which cannot be explained by existing models of high frequency oscillations. Our study predicts that ripple duration is constrained by the CA1 architecture; specifically that it depends on the transient nature of the synchronization of inhibitory neurons driven to fire at high frequencies by incoming excitation from CA3 in the presence of noise. Our model makes testable predictions on the effect of manipulating of GABAA on ripple frequency and percent of pyramidal cells recruited.
In general, there is an agreement in the field that parvalbumin positive interneurons are involved in LFP ripple oscillations, since they spike at ripple frequency across the event duration [31] and removing GABAA cancels ripple activity [29]. Differences arise in the interpretation of the specific mechanism underlying the oscillations. Two main hypotheses have been proposed. According to one idea, a ripple in CA1 is exactly the same phenomenon as its triggering CA3 excitatory event, simply propagating from one hippocampal sub-region to the next. In this setting, a ripple can be thought of as fundamentally one whole excitatory event reverberating across the hippocampus, much like throwing a small stone in still water [17, 58]. Models that account for such behavior have to include non-standard excitatory mechanisms among CA1 pyramidal cells, such as more-than-linear dendritic summation [58] or electrotonic connections (known as gap junctions) [17, 59, 60]. However, recent in vivo recordings from a strain of gap-junction deficient mice still showed sharp wave-ripple events at frequencies similar to that of the wild-type [61, 62]. This led to the idea [19, 53, 57] that it is appropriate to introduce models in which CA1 synapses can generate ripples. According to this approach ripples are seen as a local phenomenon in CA1 triggered by incoming CA3 excitation [18, 19, 57], in which inhibitory synaptic connections are responsible for the oscillations in CA1. Our model is consistent with the latter hypothesis. We propose that the minimal model capable of ripple generation is an inhibitory network receiving a brief wave of excitation. The crucial role of parvalbumin positive basket cells in organizing ripple oscillations has been previously shown by Schlingloff et al [46], who used a network of only parvalbumin positive interneurons to study ripple frequency when a step of current was applied to the population. While in this earlier model the loss of reciprocate inhibitory synapses induced a loss of rhythmicity, we now show that oscillatory firing in such inhibitory networks is controlled by a synchronous and strong common input, which is characteristic of a transient oscillation. The strength of recurrent connectivity between inhibitory interneurons plays a critical role in determining the type of oscillatory activity the inhibitory network can produce. Indeed, if synaptic connections were strong, a purely inhibitory network with enough reciprocate connections would give rise to gamma oscillations (30–90 Hz), paced by the duration of inhibitory currents: a mechanism known as Interneuron-Network Gamma (ING) [63, 64]. ING oscillations persist as long as cells are driven to fire. In contrast, the stationary behavior of our model is a disorganized firing state. Oscillatory persistent firing in a network of irregularly spiking inhibitory neurons depends on synaptic strengths and the size of noise [65]: our model belongs to the asynchronous stable state part of the bifurcation diagram (Fig 5A in [65]), where the size of noise overcomes the ability of mutual inhibitory synapses to organize the firing rate in oscillations that would be below ripple frequency. Crucially, we assume synapses to be weak, meaning that inhibitory currents do not overcome the effect of intrinsic noise even when a lot of interneurons are spiking. Hence, interneurons are driven to fire by CA3 inputs and not slowed down by inhibition. As a consequence, the ING mechanism is not arising in the network, and inhibitory currents are not setting the firing frequency, but merely modulating it. Instead, upon input arrival, inhibitory interneurons become transiently synchronized, leading to high frequency LFP oscillations, with properties matching in vivo data; the synchronization then disappears after a characteristic duration in the presence of noise. While the minimal mechanism is identified in a purely inhibitory network, we emphasize that transient oscillations in that reduced model did not last longer than 40ms. This underscores the role that pyramidal cells and their interaction with interneurons still play in shaping a realistic ripple oscillation in the full model we present.
Our model predicts that synchrony of interneuron firing should decrease over the ripple duration. Spontaneously occurring ripple-like activity in slices suggest that during a ripple excitatory and inhibitory currents onto pyramidal cells oscillate at ripple frequency and increase in size during the progression of a ripple [66], which likely reflects the increasing number of cells involved in the slice spontaneous event, rather than a higher synchronicity in cell firing. On the other hand, optogenetically induced ripples in vivo and in vitro seem to have a number of similar properties, and in vivo light-triggered events are likely to account for spontaneous, physiological ripple oscillations. Hippocampal slices in which ripples are triggered with optogenetic drive show that synchrony among nearby inhibitory neurons decreases across a ripple event [46].
Beyond parvalbumin positive basket cells, different hippocampal interneuron types show different spiking behaviors during ripples [30, 31, 43, 67], and they might be involved in the fine timing of specific pyramidal cell spiking or recruitment, and in gating entorhinal and thalamic inputs on CA1 pyramidal cells. Furthermore, post-inhibitory rebound in pyramidal cells has been proposed as a mechanism for ripple initiation in CA3 [57]. Previous models have been trying to dissect potential separate roles for different interneuron types in gating entorhinal and CA3 input on CA1 and potentially contribute to fine selectivity of the pyramidal cells recruited by a given ripple [53]. The goal of our study was to find a minimal model capable to explain ripple oscillations frequency and duration in CA1, therefore we did not model the multitude of interneurons beyond parvalbumin positive basket cells. We focused on the pyramidal layer and its main spiking actors during the events our model represents. We emphasize that in previous modeling approaches [17, 19, 58] ripple duration is established by the duration of excitatory propagation within CA3. We show here that this does not have to be the case. In fact, we predict that the CA1 network is responsible for determining the duration of the organized firing during ripple oscillations.
The origin of the excitatory CA3 event which initiates a ripple in CA1 is not yet clear [46], and goes beyond the scope of this work. We predict that such CA3 events could show a broad range of durations, but they will still induce ripples of a fixed length, lasting about 50–80 ms in CA1. This implies that the CA1 region can produce a standardized “package” of information with every ripple, which is projected to downstream regions. Within this package, pyramidal cells spiking is organized by selectivity of CA3 input, and possibly input directly from other brain areas. We also predict that pyramidal cell replay in CA1 is organized by pyramidal cell replay in CA3, consistent with the fact that CA3 to CA1 connectivity is crucial for memory consolidation [54].
A common hypothesis is that synaptic connections between CA1 pyramidal cells and intrinsic cell properties regulate the order of spiking in a CA1 sequence [53]. In our study we tested this hypothesis first. Since in CA1 very few pyramid-to-pyramid synapses are found, we only had to check the effect of DC input. We found that preferential DC levels, even much stronger than average, were not enough to guarantee a robust repetition of the correct order of firing in our target cells. We then tested the potential role of the incoming CA3 input. Given the projection onto CA1 pyramidal cells are plastic, and that CA3 is the most typical set in which Hebbian learning is found in the brain, we formulated the hypothesis that CA3 is replaying its own cells, and projecting selectively to CA1 the activity that is relevant to behavior, because cells in CA3 and CA1 were active together during behavior (and hence learning). We then found that a combination of the two factors was best at inducing hippocampal replay in CA1 in our model.
Our study further predicts that when activating only a fraction of basket cells, the mechanism for ripple oscillations is present but not measurable in the LFP at the stimulation location. In fact, pyramidal cells would be shunted by an incoming barrage of inhibition and would not be sustained by excitation. This results in a shunted LFP in the CA1 pyramidal layer. While our minimal model does not show an exact quantitative match with experiments, our representation of the effect of optogenetically driving only interneurons results in a strong reduction of the magnitude of the LFP, which we believe is consistent with what has been found in published data [21]. Our model is also consistent with experimental results on optogenetic stimulation of only CA1 pyramidal cells. Both data and our model show the initial high frequency events lasting about 25 ms were quickly followed by oscillations at a slower frequency. This means that ripple generation in vivo has to rely on additional mechanisms other than excitation of pyramidal cells to sustain firing beyond 25–50 ms. We suggest that the CA3 input driving both the excitatory and the inhibitory populations in CA1 pyramidal layer raises the firing rate of basket cells to within ripple frequency range, inducing transient oscillations, while maintaining the excitatory population above shunting level, so that selected pyramidal cells can fire during ripples, and so that their spike timing stays modulated by ripple phase.
In our model, input from CA3 activates both CA1 interneurons (hence triggering ripple activity) and CA1 pyramidal cells. The selection of which pyramidal cell is recruited to spike within a given ripple, and their potential sequential activation, is the result of a balance between CA3 drive, (potentially filtered by different types of interneuron populations omitted in the model [18, 53]) and local feedback inhibition in CA1. Hence, CA3 is seen as the place in which the initial reactivation of a memory can take place, while CA1 optimizes CA3 output for downstream transmission. In other words, ripples in CA1 prepare a time-bounded package of carefully selected pyramidal cell spikes, encoding information that can then be routed across neocortex. It is also possible that incoming excitatory input from entorhinal cortex could be responsible for initiating a CA1 ripple phenomena, and/or regulate the recruitment of CA1 pyramidal cells within a ripple. In fact, this computational model represents CA1 as a network which is ready to burst, just waiting for an incoming input to select a local group of interneurons to oscillate, so that they can organize the timing of pyramidal cells, recruited by the input combined with their initial excitability.
Data were recorded using extracellular tetrodes targeted to region CA1 of the hippocampus in 6–7 months old Brown Norway rats during natural sleep. All experiments were approved by the University of Arizona IACUC and followed NIH guidelines. The recorded LFP where band-passed between 0.1 and 500 Hz, and SWR complexes were found when the filtered LFP (100–300 Hz) crossed a threshold of 2 standard deviations of the baseline. The center of the SWR was positioned at the peak values of the LFP envelope and SWR start and end were found as the first points around the peak where the envelope passed below a threshold of half the distance between the peak and the baseline, see S1 Fig for a schematic. Ripple duration was then computed as the time between SWR start and end. Note that discrepancies between the numbers reported for ripple duration in our work compared to other reports could be induced by different methods of finding ripple durations, beyond potential differences between rats and mice. In our method of detecting ripple starts and ends, we use as a threshold the half distance between baseline and peak. That is, every ripple has its own threshold, which depends on the size of the ripple amplitude. If one were to use one absolute threshold for all ripple envelopes to find their start and end time points, ripples that have larger peak amplitudes would stay longer above threshold, and the average value of ripple duration would be larger as a result. This difference in methods can contribute to the apparent difference in the data across the many ripple papers in the field. Ripple frequency was calculated as the average inter-peak interval of the filtered LFP within the SPW duration, see S1 Fig for a schematic. The Gaussian fit for the frequency distribution was found using MATLAB (www.themathworks.com) function fitdist. Ripple inter-arrival times are simply the time difference between a ripple onset and the next. The exponential fit for the distribution was obtained using function expfit in MATLAB. Data is presented as standard deviation from the mean.
The CA1 computational model includes 160 interneurons and 800 pyramidal cells. For each neuron, the equations are
Cv˙=−gL(v−EL)+gLΔexp((v−Vt)Δ)−w+I(t)
τww˙=a(v−EL)−w
v(t)=Vthr⇒v(t+dt)=Vr,w(t+dt)=w(t)+b
Parameter values are as follows. For all pyramidal cells: C = 200 pF; gL = 10 nS; EL = -58 mV; a = 2; b = 100 pA; Δ = 2 mV; τw = 120 ms; Vt = -50 mV; Vr = -46 mV, Vthr = 0 mV. For fast spiking inhibitory interneurons: C = 200 pF; gL = 10 nS; EL = -70 mV; a = 2; b = 10 pA; Δ = 2 mV; τw = 30 ms; Vt = -50 mV; Vr = -58 mV.
Every cell is receiving a different input, all with the same structure:
I(t)=IDC+βηt+Isyn(t)+Iinp(t)
τdηt=−ηtdt+dWt
Iinp(t)=Imax(1+exp(−t−tonk))−1(1+exp(t−toffk))−1
Where IDC is a constant, different for each cell, selected from a normal distribution (mean 40 pA for pyramidal cells and 180 pA for interneurons; standard deviation 10% of the mean for both populations). ηt is a stochastic process known as Ornstein-Uhlenbeck (OU) process, with a filter time scale imposed by the τ value, in our case 100Hz. The coefficients were β = 80 for pyramidal cells, β = 90 for interneurons. To computationally introduce a stochastic process that solves the equation for ηt above, we first generated its representation in the frequency space taking advantage of its known power spectral density [68], and then computed its inverse Fourier transform (ifft in MATLAB, www.themathworks.com).
Synaptic currents are modeled with double exponential functions, for every cell n we have Isyn(t)=∑j=1160gj→nsj→n(t)(vn−Ei)+∑j=1800gj→nsj→n(t)(vn−Ee)
sj→n(t)=∑spikesofcelljF(eH(−t−tkτD)−eH(−t−tkτR))
with F a normalization factor that ensures at every spike the double exponential peaks at one, H(·) is the Heaviside function. Every gj -> n is selected from a Gaussian distribution with a given mean and standard deviation 10% of the mean (see Fig 2); values below 0 are rectified to 0. Mean synaptic values are g¯Int→Int = 0.0234 nS, g¯Pyr→Int = 0.0083 nS, g¯Int→Pyr = 0.0521 nS, g¯Pyr→Pyr = 0.001 nS. The reversal potential for synaptic currents are Ei = -80 mV and Ee = 0 mV. The time scales of synaptic rise and decay are as follows: for excitatory synapses on pyramidal cells [34, 39] we have τR = 0.5 ms and τD = 3.5 ms; while on interneurons we have τR = 0.9 ms and τD = 3 ms. For inhibitory synapses on interneurons: τR = 0.3 ms and τD = 2 ms, on pyramidal cells τR = 0.3 ms and τD = 3.5 ms.
Iinp(t) represents the net effect of CA3 synaptic excitation on CA1 cells. ton and toff, in most simulations are kept 50ms apart. Imax = 210 pA for pyramidal cells and Imax = 700 pA. Network simulations were solved with a 1-step Euler algorithm (Δt = 0.001 ms) using MATLAB. (www.mathworks.com)
In this section we introduce the rationale behind the choices of equations and parameters used to model CA1 ripple activity and CA3 input to CA1.
We model only the pyramidal layer of CA1, because that is where ripples are usually measured. To reveal the basic mechanisms of ripple generation, we do not model the rich number of interneuron cell types known to exist in CA1, but only the parvalbumin positive basket cells, which are active during ripples [32] and are a predominant interneuron type in the pyramidal layer. Ripple oscillations are only seen in the pyramidal layer [24], so we had to include at least the populations known to be dominant in such layer: pyramidal cells and fast-spiking interneurons. While a rich number of interneuron cell types are known to exist in CA1, we thought that they were going to have limited impact on ripples, for the following reasons. Bi-stratified cells are known to spike during ripples, hence they could potentially also contribute, but they project on the proximal dendrites of pyramidal cells, while basket cells project at the soma. This means that synaptic events due to basket cells spiking will have bigger representations and effect on pyramidal cells voltage, and ultimately spikes. Another cell type known to be important to hippocampal theta rhythm, the OLM cells [69], might be relevant outside (that is, before and after) ripples. OLM cells spike only in about half the ripple episodes in naturally sleeping animals [23], and transmit inhibition to pyramidal cells via slower IPSCs (longer than 10ms time scale [70]). This inhibitory time scale, together with their intrinsic oscillatory properties, makes OLM cells amazing candidates in the participation to hippocampal theta rhythms [69, 71–73]; however, ripples are fast oscillatory events caused by incoming CA3 signals: the role that OLM cells could reasonably play in such events (given they do not spike during half of them) is potentially modulate the amount of excitation it would take to a CA3 input to start a ripple. In other words, they could be contributing to switching from ongoing theta activity to ripple activity in awake state [74]. Since our is a model of what happens when the sharp wave activity from CA3 hits CA1 and therefore fast oscillations arise, we are not modeling the switch between theta and other rhythms. Therefore, we consider it acceptable to omit the modeling of OLM cells activity during ripples in our model. We emphasize that if this was a model of awake state, in which ongoing theta-gamma oscillations are interspersed with sharp wave-ripples, modeling of the activity of these interneuron types would be necessary.
In our model, we only have parvalbumin positive basket cells to represent the overall disorganized background inhibition that matches and balances the ongoing excitation. Hence, when we set up the ratio of pyramidal cells to interneuron, we choose a ratio that encompasses the overall excitatory to inhibitory ratio in CA1, rather than the fine count of basket cells. We also model pyramidal cells as one uniform population even if it is known that they are quite selective in their specific connectivity, because we are interested in representing the overall oscillatory phenomena more than the specific cell-to-cell variability. The proportion of interneurons and pyramidal cells in the model is in agreement with CA1 anatomy [32]. We choose to model both neuron types with Exponential Integrate and Fire equations, because of its simplicity (only two variables) combined with a formalism that can represent explicitly spike-triggered adaptation and intrinsic cell resonances (essential to give CA1 pyramidal cells their characteristic bursting spiking profile [34]). In looking for appropriate parameter values for the model cells types, we build on work by Gerstner [36], who has classified parameter sets for the Adaptive Exponential IF model that match known spiking behaviors such as bursting (for pyramidal cells) and regular fast spiking (for basket cells). We chose parameters that guaranteed a time scale (see C/gL ratio) for membrane voltage of about 20ms for both cell populations, the ability of pyramidal cells to burst when driven to fire (the value of Vr is crucial for that), a theta resonance in pyramidal cells given by τw, and the regular spiking behavior of basket cells. Given that all cells of a population are modeled by the same equation, we introduce heterogeneity in the network using input currents and variability in synaptic strengths.
Special attention was made to design an input term I(t) that is composed of different parts. The IDC term variability produces variability in the excitability level of each cell, therefore introducing a first level of heterogeneity in the network. The noise term βηt represents the in vivo state of the voltage in each cell, which is likely receiving a much higher barrage of synaptic inputs than the one provided by the network spiking activity. ηt is an Ornstein Uhlenbeck (OU) process, which can be thought of as a filtered white noise process. This kind of noise does not introduce slow frequency forcing, which could alter the network behavior, or too high frequency voltage fluctuations, which are known to not be present [75]. This kind of noise is also used in dynamic-clamp experiments to mimic in vivo state in hippocampal slice recordings [38]. The β coefficients establishing noise size were chosen so that hyperpolarized cells show a voltage fluctuation of about 2mV in size [38, 75].
Isyn is the term representing synaptic current in the input. With the chosen formalism for synaptic equations, the strength of synaptic connections gj→n scales the maximum peak size of a post-synaptic potential. Every gj→n is selected from a Gaussian distribution to introduce heterogeneity. The reversal potential for synaptic currents are chosen in agreement with many published hippocampal models [34], and the time scales of synaptic rise and decay are estimated from literature [34, 39], in particular taking advantage of the fact that EPSP on interneurons are faster than on principal cells [76] and IPSPs from basket cells are slower on pyramidal cells than on other basket cells [39]. Average synaptic strength values in the model induced post-synaptic potentials of less than 1mV and have been tuned to induce a balanced average of excitatory and inhibitory currents in pyramidal cells. Thus we applied a common approach of normalizing the average synaptic weight by total number of cells in each population [77], hence taking into account the fact that the total incoming excitatory connections in a given cell are more than the inhibitory ones (note the magnitude of g¯Int→Int compared to g¯Pyr→Int). The synaptic values we chose resulted in small post-synaptic potentials, where a single or few synchronous incoming spikes are not enough to cause a spike or suppress one in the post-synaptic cell. This condition implies the network is weakly coupled [78] as a dynamical system.
The incoming current input Iinp(t) is bell-shaped, gradually rising and falling, between ton and toff. Incoming input from CA3 reaches a maximum value Imax different across populations, chosen to obtain a fraction of pyramidal cell spiking during ripples consistent with experimental data [24, 43] and a firing frequency for interneurons about 120 Hz [23]. When a net current is used to represent the sum of a barrage of incoming post-synaptic currents, such current has to take into account the scaling that cell properties will operate on the post-synaptic currents. Since cell membranes have capacitive and resistive properties, there will be a time scaling by a time constant which is the cell time constant. Since we add the current input to the right-hand side of the dv/dt equation, this filtering is operated by solving for voltage in time. The other filtering that cells can operate is due to their size. Input resistance is defined as the ratio between difference in voltage induced by a step of current and the size of the current step. As such, it interacts with cell size: more current is required to change the voltage of a larger cell. Our model cells are point cell, that is, they do not have a radius. We have not defined the overall cell parameters based on a per-area scaling, but rather on their dominant time-scale properties. As a result, the intuitive choice of giving to both pyramidal cells and inhibitory interneurons the same size of current step (because the same CA3 cells are connected to them) would be overlooking the well-known fact that pyramidal cells are much bigger cells that parvalbumin positive interneurons. Rather than re-scaling the model to introduce size properties, we re-scale the incoming current.
While the exact nature of the LFP components likely includes both synaptic activity and spikes [79], we here focus on the CA1 pyramidal layer, where perisomatic interneurons are known to synapse, and are interested in a phenomena in which pyramidal cells spikes are known to not contribute significantly. To obtain an LFP estimate, the average synaptic current input across all pyramidal cells was calculated, and then rescaled by 1 mS to represent a potential, such that 100 pA of synaptic current produce a 100 μV LFP change. In the model, SWR were found when the filtered LFP (50–350 Hz) exceeded a threshold of 5 standard deviations of the mean computed in one SWR-free second of activity.
To estimate the duration of synchrony in a purely inhibitory network receiving a current step input of size k at time t = 1s (Fig 5), we considered histograms of spike probability constructed averaging 200 trials. The asymptotic value (lim) of the histogram was the average firing probability between 1.5 s and 2 s. The heights of peaks in the histograms were decreasing in time. The size of the first peak (p1) defined a threshold 0.2*(p1-lim). The first peak that was far from lim less than threshold marked the end of the transient.
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10.1371/journal.ppat.1005570 | ToxR Antagonizes H-NS Regulation of Horizontally Acquired Genes to Drive Host Colonization | The virulence regulator ToxR initiates and coordinates gene expression needed by Vibrio cholerae to colonize the small intestine and cause disease. Despite its prominence in V. cholerae virulence, our understanding of the direct ToxR regulon is limited to four genes: toxT, ompT, ompU and ctxA. Here, we determine ToxR’s genome-wide DNA-binding profile and demonstrate that ToxR is a global regulator of both progenitor genome-encoded genes and horizontally acquired islands that encode V. cholerae’s major virulence factors and define pandemic lineages. We show that ToxR shares more than a third of its regulon with the histone-like nucleoid structuring protein H-NS, and antagonizes H-NS binding at shared binding locations. Importantly, we demonstrate that this regulatory interaction is the critical function of ToxR in V. cholerae colonization and biofilm formation. In the absence of H-NS, ToxR is no longer required for V. cholerae to colonize the infant mouse intestine or for robust biofilm formation. We further illustrate a dramatic difference in regulatory scope between ToxR and other prominent virulence regulators, despite similar predicted requirements for DNA binding. Our results suggest that factors in addition to primary DNA structure influence the ability of ToxR to recognize its target promoters.
| The transcription factor ToxR initiates a virulence regulatory cascade required for V. cholerae to express essential host colonization factors and cause disease. Genome-wide expression studies suggest that ToxR regulates many genes important for V. cholerae pathogenesis, yet our knowledge of the direct regulon controlled by ToxR is limited to just four genes. Here, we determine ToxR’s genome-wide DNA-binding profile and show that ToxR is a global regulator of both progenitor genome-encoded genes and horizontally acquired islands that encode V. cholerae’s major virulence factors. Our results suggest that ToxR has gained regulatory control over important acquired elements that not only drive V. cholerae pathogenesis, but also define the major transitions of V. cholerae pandemic lineages. We demonstrate that ToxR shares more than a third of its regulon with the histone-like nucleoid structuring protein H-NS, and antagonizes H-NS for control of critical colonization functions. This regulatory interaction is the major role of ToxR in V. cholerae colonization, since deletion of hns abrogates the need for ToxR in V. cholerae host colonization. By comparing the genome-wide binding profiles of ToxR and other critical virulence regulators, we show that, despite similar predicted DNA binding requirements, ToxR is unique in its global control of progenitor-encoded and acquired genes. Our results suggest that factors in addition to primary DNA structure determine selection of ToxR binding sites.
| Bacteria emerge as pathogens by horizontally acquiring new genetic functions from their environment and neighboring organisms [1,2]. Vibrio cholerae, the etiological agent of cholera, is a paradigm of this process. Benign environmental V. cholerae isolates emerge as pandemic pathogens through the horizontal acquisition and incorporation of genetic elements encoding virulence factors into their progenitor genomes [3–5]. The factors gained by the benign progenitor genome include cholera toxin, encoded on the CTX prophage, and the colonization pilus TCP, along with regulators TcpP and ToxT, encoded on the Vibrio Pathogenicity Island 1 (VPI-1) [6–9]. Moreover, current 7th pandemic V. cholerae strains are genetically distinguished from the previous 6th pandemic strains by the acquisition of two new horizontally acquired elements, Vibrio Seventh Pandemic islands 1 and 2 (VSP-1, 2) [5,10]. The acquisition of VSP-1 and 2 are thought to have promoted the emergence and dominance of 7th pandemic strains.
The progenitor genome-encoded transcription factor ToxR plays a critical role in V. cholerae virulence and stress response. ToxR is a membrane-bound transcriptional regulator with a partner protein, ToxS, that enhances ToxR activity [4,11,12]. The major role of ToxR in pathogenesis is to act with TcpP and induce expression of toxT. ToxT then triggers expression of genes encoding colonization factors and cholera toxin, resulting in disease [13–17]. When overexpressed, or in the presence of bile, ToxR can also directly activate the genes encoding cholera toxin, ctxAB [18,19]. On the progenitor genome, ToxR directly regulates expression of V. cholerae’s major outer membrane proteins: OmpU and OmpT [20,21]. Expression of OmpU and OmpT is important for V. cholerae to survive host-relevant stresses including bile, antimicrobial peptides, and pH changes [22–25]. ToxR’s ability to regulate both progenitor-encoded and recently acquired DNA allows for new and existing gene functions to be coordinated, which has supported V. cholerae’s emergence as a successful pathogen.
ToxR expression and activity are responsive to stimuli, including pH, oxygen, temperature, and metabolites [24,26–28]. Other transcription factors likely compete with ToxR for binding sites to control gene expression under different conditions [29–31]. The complexity of ToxR regulation may be necessitated by the many processes ToxR impacts [32,33]. Despite its critical role in virulence, ToxR has only been shown to directly regulate four target genes [15,20,34,35]. Here, we integrate chromatin-immunoprecipitation sequencing (ChIP-seq) data with gene expression data and phenotype studies to map the regulon directly controlled by ToxR. We identify ToxR regulation in several new roles affecting V. cholerae virulence and biofilm formation, which correlate with the emergence of 7th pandemic strains. Analysis of our ChIP data was unable to identify a motif that could explain how ToxR identified its target binding location in vivo. However, it did describe an affinity of ToxR for low GC-content locations that were frequently shared with the histone-like nucleoid structuring protein H-NS (VC1130;VicH). Our results show ToxR antagonizes H-NS transcriptional regulation, and that this interplay controls V. cholerae host colonization and impacts biofilm formation. A comparison between ToxR and additional prominent virulence regulators TcpP and ToxT shows a unique global role for ToxR gene regulation.
ToxR is a major virulence regulator in V. cholerae, yet we only know of four genes that it can directly regulate: toxT, ompU, ompT and ctxA [13,15,19–21,36]. Microarray experiments performed under conditions that induce virulence factor expression have implicated ToxR in the regulation of more than 100 genes [33], suggesting a much larger regulon. However, it is unclear how much of this regulation is direct. To determine the direct regulon of ToxR, we used chromatin-immunoprecipitation-sequencing (ChIP-seq) to identify ToxR binding sites across the genome. We ectopically expressed ToxR with a C-terminal V5 tag under control of an arabinose inducible promoter in 7th pandemic V. cholerae strain C6706. This approach allows reproducible induction and immunoprecipitation of ToxR without prior knowledge of all the environmental factors that may control its expression. Expression levels of ToxR are shown S1A Fig. This method has proven effective for ChIP-seq in V. cholerae and other bacteria [37–40].
To confirm the DNA binding activity of the tagged ToxR, we induced its expression and performed ChIP as previously described [37,38]. Quantitative PCR (qPCR) analysis of ToxR ChIP DNA samples demonstrates that V5-tagged ToxR strongly binds known target sites in the toxT, ompT and ompU promoters, but not to a negative control site at the icd promoter (S2 Fig). We performed ChIP-seq and identified genome-wide ToxR binding locations as previously described [37,38]. Alignment of sequencing reads from each sample gave average genome coverage of 41-fold. This depth of coverage allowed us to use a stringent false-discovery rate (FDR) cutoff of 0.001% to identify ToxR ChIP-enriched genomic regions, which are referred to as peaks. ChIP peaks are identified when the sequence coverage of a given genomic region in the experimental sample exceeds the non-immunoprecipitated input control sample at a rate specified by the FDR. ChIP peak enrichment ranged from 5—to 19-fold over the input. qPCR analysis of ChIP DNA generally showed a much higher fold enrichment (S2 Fig). This is likely because computational ChIP-seq enrichment is a measurement of the average enrichment across the whole peak, while our qPCR analysis generally measures enrichment at specific locations within the peak.
We compared the ToxR ChIP peak lists generated from two biological replicates and set a limit that a peak must be identified in both replicates to be included as a potential ToxR binding location for our analysis. Peaks meeting this standard were then manually curated for accuracy [41]. We associated a ToxR peak with a gene based on its proximity to promoters and translation start sites. With these criteria, a ToxR peak can associate with more than one gene if 1) the translational start sites of two or more genes are close together, or 2) if ToxR binds multiple sites that are too close together to be accurately separated by peak-calling algorithms [42]. In these cases we used published gene expression data and data generated in this study to interpret which gene(s) ToxR is likely to directly regulate. For example, there is a ToxR peak overlapping the 172 bases between divergently transcribed genes VC0844 and VC0845. Previous studies have described ToxR affecting regulation of both genes [33,43].
Our analysis identified 35 ToxR peaks associated with 39 genes by our criteria (Table 1). Three ToxR peaks remained associated with more than one gene. The coordinates encompassing the raw ToxR ChIP-seq peak locations and their associated genes are given in S1 Table. Schematics of ToxR ChIP enrichment at select loci are shown in S3 Fig. One peak was identified covering each of the promoters for toxT, ompU, and ompT, validating our procedure for identifying ToxR binding locations. Table 1 shows several genes in horizontally acquired elements and genes that have previously been connected with ToxR regulation through microarray and additional studies. Analysis of the locations and functions of genes associated with ToxR peaks identified two overrepresented groups: 18% of the genes identified in this study are known or predicted to function in biofilm formation, and 40% are located on horizontally acquired elements.
We identified ToxR peaks in the promoter regions of six genes and one small RNA (sRNA) all known or suspected to play a role in biofilm formation: ryhB, vpsL (VC0934), VC1145, VC1330, VC1599, leuO (VC2485), and VC2697 [44–49]. These genes are all encoded on the progenitor genome [50]. ToxR was previously shown to induce leuO expression [51]. Our ChIP-seq analysis identified ToxR binding covering the leuO promoter region (Table 1), which shows that the observed positive regulation is likely direct. To further understand how ToxR regulates expression of genes involved in biofilm formation, we determined the impact of ToxR on the expression of ryhB, vpsL, and VC1599. These genes were chosen because they have not been previously associated with ToxR regulation and encode diverse biological functions. RyhB is a small regulatory RNA involved in regulation of iron metabolism [46,52]. VC1599 is a diguanylate cyclase that produces the signaling molecule cyclic-di-GMP (cdiGMP) [45,53]. vpsL encodes a glycosyltransferase for Vibrio polysaccharide production and is the first gene of the Vibrio polysaccharide vps-II operon [44,54,55].
qPCR analysis of ToxR ChIP DNA confirmed our sequencing data and showed ToxR enrichment of ryhB, vpsL, and VC1599 promoter regions, but not of a negative control site (Fig 1A). We used northern blots and quantitative reverse-transcription PCR (qRT-PCR) to determine ToxR regulation of ryhB, vpsL and VC1599. Northern blot analysis showed that deletion of toxRS led to an increase in ryhB abundance, consistent with direct ToxR repression of ryhB expression (Fig 1B). Deletion of toxRS alone did not affect vpsL or VC1599 expression (S4 Fig). The free-living planktonic cells used for our gene expression assays might not recapitulate the environmental signals needed for ToxR regulation of vpsL and VC1599 utilized for biofilm formation [56]. In an attempt to bypass this potential signaling hurdle, we compared expression of vpsL and VC1599 in a toxRS deletion strain carrying either an empty vector or a vector with an arabinose inducible toxRS operon to specifically increase ToxRS levels. In this comparison, induction of toxRS led to an increase in vpsL expression and a decrease in VC1599 expression, supporting direct ToxR regulation of these genes (Fig 1C). Our results establish both positive and negative control of biofilm-associated genes by ToxR. It also ties ToxR regulation to small regulatory RNAs and cdiGMP, both of which influence a wide spectrum of genes and biological processes [46,57] that may be responsible for indirect effects associated with ToxR regulation.
We assessed the ability of wild type, toxRS, ryhB, VC1599, and vpsL mutant strains to form biofilm in a static microtiter assay in rich broth at 30°C (Fig 1D and 1E). The toxRS deletion strain showed reduced biofilm formation, supporting its regulatory role in this process. This phenotype was complemented by ectopic expression of toxRS (S5 Fig). The requirement of ryhB, vpsL, and genes downstream of vpsL in the vps-II operon for biofilm formation was previously established [44,46,55,58]. Supporting those results, a vpsL in-frame deletion mutant and a ΔryhB::kanR mutant both showed a defect in biofilm formation (Fig 1D and 1E). These phenotypes were complemented by ectopic expression of the respective gene (S5 Fig). Overexpression of VC1599 had been shown to increase biofilm formation [45]. Supporting this observation our VC1599 deletion strain showed decreased biofilm production (Fig 1D and 1E). This phenotype was complemented by ectopic expression of VC1599 from a plasmid, which led to biofilm overproduction (S5 Fig).
Loss of toxRS or vpsL decreased biofilm formation under the experimental conditions used for our assay. Positive regulation of vpsL by ToxR could explain the biofilm defect of our ΔtoxRS mutant. We tested the ability of a ΔtoxRSΔvpsL double mutant to form biofilm, as well as ΔtoxRSΔryhB::kanR and ΔtoxRSΔVC1599 double mutants. We did not observe a significant difference in biofilm formation for any double mutant relative to the ΔtoxRS mutant (S6 Fig). The resolution of our assay may not be sufficient to identify synergies or additive effects of these mutants.
Our ChIP-seq results showed that ToxR binds locations on all four of V. cholerae’s major acquired pathogenicity islands: VPI-1, VPI-2, VSP-1, and VSP-2 (Table 1). In addition to the toxT promoter, our analysis shows ToxR binds the promoter regions of VPI-1 genes VC0824 (tagD), VC0825 (tcpI), VC0844 (acfA), and VC0845 (acfD) (Table 1). qPCR analysis of ToxR ChIP DNA validated our sequencing results that ToxR binds the promoter regions of VC0824 (tagD), VC0825 (tcpI), and the promoter region shared by VC0844 (acfA) and VC0845 (acfD) (Fig 2A). Combined with gene expression studies describing positive regulation of tagD, acfA, and acfD genes by ToxR, independent of ToxT [33,43,59], our results support a direct role for ToxR in the positive regulation of these genes, expanding ToxR’s known targets on VPI-1. While the function of these genes is under investigation, tcpI, acfA, and acfD are known to be required for V. cholerae colonization of a model host [16,60].
When overexpressed or activated by specific compounds, ToxR can activate ctxA expression [18–20]. However, the physiological relevance of this interaction is unclear. Our ChIP-seq analysis did not identify a ToxR binding site in the ctxA promoter, suggesting the event was either below our level of detection or does not occur to an appreciable extent in V. cholerae under our experimental conditions.
Seventh pandemic V. cholerae is genetically distinguished from previous 6th pandemic strains by the presence of acquired islands VSP-1 and 2. Little is known about the origin, content, and regulation of these islands, though VSP-1 carries at least one gene that influences the ability of V. cholerae to colonize the infant mouse model [38,61]. Our results show ToxR binding across the promoter regions of genes located on both VSP-1 and VSP-2 (Table 1). qPCR analysis of ToxR ChIP DNA validated that ToxR binds the promoter regions of VC0176, VC0178, VC0182, and VC0183 on VSP-1, and VC0490 and VC0493 on VSP-2 (Fig 2B and 2C). Microarray analysis suggested that ToxR can repress of VC0176, VC0490, and VC0493 expression under virulence-gene inducing conditions [33], supporting a direct role for ToxR in their regulation. To corroborate and expand ToxR regulation of VSP-1 and 2, we used qRT-PCR to determine if ToxR regulated expression of selected VSP-1 and 2 genes. Deletion of toxRS alone did not affect expression of VSP-1 or 2 genes when V. cholerae was grown exponentially in rich broth (S4 Fig). We again considered that conditions for ToxR regulation of VSP-1 and 2 genes were not recapitulated by exponentially growing cells in rich broth. We compared expression of VSP-1 and 2 genes in a toxRS deletion strain carrying an empty vector or a vector with an arabinose inducible toxRS operon. In this comparison, induction of toxRS led to repression of VC0176, VC0178, and VC0493, supporting a direct role for ToxR regulation of VSP-1 and VSP-2 genes [33] (Fig 3A).
Considering the central role of ToxR in virulence regulation, we questioned whether the ToxR-regulated genes on VSP-1 also affected V. cholerae colonization. VC0178 was previously shown not to influence V. cholerae host colonization; however, VC0176 was not tested [38]. We constructed an unmarked VC0176 deletion mutant and tested its ability to colonize the infant mouse. We found that the ΔVC0176 mutant showed approximately a 5-fold defect in colonizing the infant mouse intestine in competition with the parental strain (Fig 3B). No defect was observed when the strains were competed in liquid culture (Fig 3B). The phenotype was complemented by ectopic expression of VC0176 (S7 Fig).
These results expand the regulatory role of ToxR on virulence islands VPI-1 and 2, which are found in all pandemic V. cholerae strains. They further show that ToxR has gained control over expression of recently acquired genetic elements that define the current 7th pandemic strains, including a new VSP-1 colonization factor VC0176. These results implicate ToxR as a regulatory hub for integrating expression of progenitor genome-encoded functions with newly acquired genes to promote V. cholerae fitness.
Our results demonstrate that ToxR binds all four Vibrio pathogenicity islands, and implicates ToxR as a global regulator of horizontally acquired genetic elements. Horizontally acquired DNA generally has a lower GC-content than the progenitor genome [62]. For example, the average GC-content of the N16961 V. cholerae genome is 47%, while the average GC-content of VPI-2 and VSP-1 is 42% and 40% respectively [63,64]. Analysis of the DNA sequences comprising the ToxR ChIP-seq peak locations showed they contain an average GC-content of just 40%. This suggests that ToxR preferentially binds DNA with base composition more similar to acquired elements than to that of the progenitor genome average. This result agrees with the low GC-content of the predicted ToxR consensus binding motif (TNAAA-N5-TNAAA), which was based on ToxR binding and/or activation of toxT, ompT, ompU, and ctxA promoters [15,20]. The preference for binding low GC-content DNA is shared with the histone-like nucleoid structuring protein (H-NS) that binds and silences horizontally acquired DNA [65]. V. cholerae H-NS binds and silences genes identified in our ToxR regulon study, including toxT and vpsL [31,66–68]. These observations prompted us to question if ToxR and H-NS may share additional genomic binding locations. We added a V5-tag to the C-terminus of the chromosomally encoded H-NS in V. cholerae C6706 to facilitate immunoprecipitation. We performed ChIP-seq for H-NS-V5 and determined its genome-wide binding profile under the same conditions as we used for ToxR ChIP-seq (S2 Table). We compared the genome binding profiles and found that 39% of regions bound by ToxR were also identified in our H-NS ChIP-seq analysis (Table 1).
Previous studies have shown genetic interactions between toxR, tcpP, and hns influence expression of the toxT promoter [31], and that H-NS can directly regulate vpsL [54,66]. Our results suggest that ToxR might antagonize H-NS regulation at multiple locations to gain access to gene targets. Rather than a defined consensus motif, topology has been implicated as a critical factor controlling H-NS binding to DNA. Low GC-content DNA forms structures that are preferentially bound by H-NS [69–71]. Since DNA topology and H-NS binding changes with environmental conditions [65,69,72,73] we wanted to test if ToxR could antagonize H-NS binding in vivo, in the context of the bacterial cell. To do this, we introduced an empty or arabinose-inducible, toxRS-encoding plasmid into our V. cholerae strain containing V5-tagged H-NS. We induced toxRS expression with arabinose and performed ChIP against H-NS-V5. We next used qPCR to determine H-NS enrichment at shared ToxR binding locations. We chose to examine the vpsL promoter on the progenitor genome, and toxT and VC0844-5 promoter regions on VPI-1. At each location we found that H-NS occupancy decreased following induction of toxRS, indicating that ToxR can antagonize H-NS binding at these locations (Fig 4A). These experiments were performed in the presence of the chromosomally-encoded toxRS. Thus, the impact of ToxR on H-NS binding may be even greater than observed here. As H-NS is a global silencer of horizontally acquired genetic material, our results indicate that ToxR has the ability to antagonize H-NS binding and bring the regulation of new genetic material under virulence gene control.
ToxR is essential for V. cholerae virulence through its regulation of many genes important for host colonization and pathogenesis [13–17]. Supporting this role, our ΔtoxRS deletion strain was strongly outcompeted by the wild type strain in infant mouse intestinal colonization assays (Fig 4B), which agreed with previous reports [74]. This defect was complemented by ectopic expression of toxRS (S8 Fig). H-NS represses many virulence genes, and deletion of hns results in their induction [31], suggesting deletion of hns should not impair V. cholerae intestinal colonization. Supporting our hypothesis the Δhns mutant did not show a significant defect in colonizing the infant mouse intestine in competition with the wild type strain (Fig 4B). Our data showed that ToxR and H-NS both bind the promoter regions of many of the same genes that are important for V. cholerae virulence (Table 1). It also showed that ToxR could antagonize H-NS binding at shared binding locations (Fig 4A). If ToxR antagonizes H-NS repression of important colonization factors, then deletion of H-NS should alleviate the need for ToxR regulation in intestinal colonization. To genetically test our hypothesis, we constructed a double ΔtoxRSΔhns mutant and assayed its ability to colonize infant mice (Fig 4B). Agreeing with our hypothesis for the importance of ToxR’s genetic interaction with H-NS for colonization, the double ΔtoxRSΔhns mutant showed no competitive defect compared to wild type or the Δhns mutant alone. Ectopic expression of hns in the ΔtoxRSΔhns mutant produces a competition defect similar to that of ΔtoxRS mutant alone (S8 Fig). Removing H-NS activity genetically eliminates the need for ToxR regulation in V. cholerae host colonization.
We observed a similar genetic effect for biofilm formation, where deletion of hns compensated for the biofilm defect of the toxRS mutant (Fig 4C). This effect was complemented by ectopic expression of hns, though not to wild type levels (S9 Fig). This may be because expression of hns from a plasmid does not recapitulate H-NS levels necessary for normal biofilm regulation in our strain. Our results indicate that for both host colonization and biofilm formation, the major purpose of the ToxR regulation is to antagonize H-NS activity.
ToxR co-operates with transcription factor TcpP to activate toxT gene expression [13–17]. Like ToxR, TcpP is a membrane-bound transcription factor with an enhancer partner protein, TcpH, and is responsive to environmental conditions and upstream regulation [7,26,31,75,76]. TcpP is only known to regulate toxT. The region of the toxT promoter that affects TcpP binding also shows low GC-content and low sequence complexity (TGTAA-N6-TGTAA) [77]. Given the similarity of TcpP’s and ToxR’s binding motifs, we hypothesized that TcpP may also directly regulate more genes, alone or in association with ToxR. Previous microarray studies found that deletion of tcpP changed the expression of 58 genes under conditions that activate colonization factor expression [33], supporting a possible broader role for TcpP regulation.
To define the regulon directly controlled by TcpP, we performed ChIP-seq in a similar manner as for ToxR. tcpP expression levels are shown in S1B Fig. qPCR analysis of TcpP ChIP DNA showed that the V5-tagged TcpP bound the toxT promoter, but not to a negative control locus (Fig 5A). In stark contrast to ToxR (and despite its relatively weak predicted binding motif constraints), our ChIP-seq analysis identified only three TcpP peaks in the entire V. cholerae genome (Table 2). We identified a strong TcpP peak upstream of toxT, agreeing with our initial validation of our TcpP construct (Fig 5A). A schematic of ChIP-seq DNA enrichment at this site is shown in S3 Fig. In addition, we identified TcpP peaks upstream of VC1854 (ompT) and hypothetical gene VCA0536. qPCR of TcpP ChIP DNA validated our sequencing data, showing TcpP binding of ompT and VCA0536 promoter regions, but not a negative control locus (Fig 5A). Enrichment of TcpP at ompT and VCA0536 promoter regions was similar to enrichment at the toxT promoter.
Microarray analysis previously suggested TcpP can repress ompT expression [33]. Supporting this observation, we found that ectopic expression of tcpPH in a ΔtcpPH mutant repressed ompT expression compared with the empty plasmid control (Fig 5B). Along with toxT, ompT is now the second gene recognized as co-regulated by ToxR and TcpP. Moreover, TcpP repression of ompT shows that like ToxR, TcpP can act as either a transcriptional activator or repressor. VCA0536 has not previously been associated with TcpP regulation. VCA0536 encodes a putative cyclic di-GMP phospodiesterase that was found to be expressed in vivo by IVIAT [78], and is affected by the biofilm regulator VpsT [57]. Induction of tcpPH activated VCA0536 expression compared to the empty plasmid control (Fig 5B), supporting direct positive regulation by TcpP. Our results show that TcpP does regulate genes in addition to toxT, but does not share global regulation with ToxR despite similar predicted binding requirements.
We computationally scanned seven V. cholerae genomes, including both El Tor and Classical strains, for previously determined ToxR (TNAAA-N5-TNAAA) and TcpP (TGTAA-N6-TGTAA) binding motifs [15,20,77] using FIMO motif search software [79]. We used a cut-off p-value of < 0.0001 to identify significant sequence matches. For each motif, we identified many more matching sites in the genomes than were identified in their respective ChIP-seq analysis (S3 Table). This suggests that while primary DNA structure is undoubtedly important for ToxR and TcpP binding, the motif sequences alone are not sufficient to explain the selectivity of ToxR and TcpP binding in vivo
These motifs were constructed based on a small set of binding locations; four for ToxR and only 1 for TcpP. To attempt to improve the specificity of these motifs, we analyzed our ChIP-seq data sets for ToxR and TcpP binding site motif sequences using GLAM2 motif predication software [80,81]. We screened motifs generated through our analysis by determining if they overlapped with experimentally proven binding sites for TcpP in the toxT promoter, and for ToxR in the toxT, ompU, and ompT promoters. For ToxR and TcpP, we analyzed their respective ChIP-seq data sets as a whole and as peaks found on genomic islands compared to peaks found on the progenitor genome.
The V. cholerae N16961 genome has an average GC-content of 47% [50]. ToxR ChIP peak sequences found in genomic islands and on the progenitor genome had lower average GC-contents of 38% and 42% respectively. Using all ToxR ChIP peak sequences, we were able to generate a motif that overlapped the previously published sequence important for ToxR binding and regulation of the toxT, ompU, and ompT promoters (Fig 6). This motif resembles the previously published motif and, like it, showed low sequence complexity and low GC-content. We computationally scanned seven V. cholerae genomes for this new motif using FIMO and again found it present more times throughout the genome than were identified by our ToxR ChIP-seq analysis (S3 Table). Use of this new motif alone also appears insufficient to predict locations bound by ToxR in vivo. We were unable to identify a TcpP binding motif from our ChIP peak dataset that also overlapped TcpP’s known binding site in the toxT promoter.
Our results indicate that ToxR directly controls a much larger gene set than previously recognized. This expands our understanding of virulence control and biofilm formation, and implicates ToxR as a broad regulator of acquired genetic information (Fig 7).
ToxR expression level and activity are regulated by many environmental signals [26–28,82,83]. ToxR also competes and interacts with other proteins to control transcription of target genes [29–31]. These factors likely allow V. cholerae to differentially control subsets of the ToxR regulon depending on the environmental conditions. The exact protein levels and activity of ToxR during each stage of infection or in biofilm development are unclear. In an attempt to overcome unknown environmental signals and broadly identify genomic sites for ToxR binding, we chose to use ectopic ToxR expression. This approach allows reproducible induction and immunoprecipitation of ToxR without prior knowledge of all the factors that may control its expression, and has proven effective for elucidating transcription factor regulons in V. cholerae and other bacteria [37–39].
A concern of this approach is that ectopic expression of ToxR or TcpP may cause aberrant binding or transcriptional regulation. While this remains a possibility, theoretical [84] and experimental studies [37,38,40] indicate that transcription factor overexpression does not lead to significant off target binding in vivo. Supporting our approach, the 35 ChIP loci we identified for ToxR is relatively small compared to many other prokaryotic ChIP-seq studies, which identified anywhere from several dozen to several hundred binding sites for other transcription factors [40,85–87]. Also, the ctxA promoter has been shown to bind ToxR in vitro, but the in vivo relevance of this is uncertain [18–20]. We did not identify this interaction with ChIP-seq, supporting that the expression level of ToxR used in our study did not promote ToxR binding to all available sites in vivo.
Our results indicate that ToxR regulation extends to all four V. cholerae pathogenicity islands, including VSP-1 and VSP-2, which genetically define seventh pandemic strains. The ability of ToxR to regulate new VSP-1 and VSP-2 functions along with existing cellular processes may have helped promote the emergence of 7th pandemic strains. We identified a potential role for ToxR-regulated VSP-1 gene VC0176 in host colonization. VC0176 expression was found to be upregulated during intestinal colonization of the infant mouse model [88]. However, ToxR represses VC0176 expression and deletion of VC0176 results in a colonization defect. This suggests that ToxR may act on VC0176 to limit V. cholerae colonization at some point during the infection cycle, possibly in preparation for exiting the host. This is similar to the recent observation that ToxR can downregulate virulence gene expression through its regulation of leuO [51]. The ability of ToxR to gain direct control over VSP-1 and integrate it with existing virulence networks may have potentiated exploitation of VSP-1 gene functions and promoted the emergence of 7th pandemic strains.
Our analysis identified additional ToxR regulated genes encoded on the progenitor genome, including those that function in biofilm formation. The positive regulation of vpsL by ToxR most adequately explains the defect in biofilm formation of the ΔtoxRS mutant under our conditions. vpsL is the first gene in the vps-II operon [44,55,58]. Thus, ToxR activity likely influences additional genes downstream of vpsL that are also important for biofilm formation. This model would also help explain how the deletion of hns elevates the ΔtoxRS biofilm defect. The regulatory relationship between toxR, ryhB, and VC1599 is less straightforward, but may be relevant for biofilm formation under different environmental conditions. ToxR regulation of ryhB and VC1599 could also be important for other aspects of V. cholerae biology, such as iron regulation, in which ryhB figures prominently. Deletion of toxR was recently shown to enhance biofilm formation of V. cholerae strain A1552 through an unknown mechanism in a standing culture in a silica tube [89]. The differences between those results and ours may be due to differences in assay conditions or, more likely, strain differences. Our studies used strain C6706, while Valeru et al. used strain A1552. Phenotypic differences between these strains have previously been observed with competency and Vibrio polysaccharide regulation, and may be attributed to strain variation in cAMP-CRP or quorum-sensing regulation [90,91]. It is worth noting that biofilms can enhance gene transfer [92–97] and ToxR is involved in both biofilm formation and broad regulation of acquired genes. It will be interesting to test if ToxR also enhances gene transfer or stability of acquired elements.
Our results provide genetic evidence that the master regulator ToxR antagonizes H-NS activity at sites across the genome to affect important phenotypes. This result is consistent with previous studies describing interactions between H-NS, ToxT, and ToxR in regulating expression of toxT, tcpA and ctx [31,67]. Importantly, we demonstrate that deleting hns eliminates the requirement of ToxR for host colonization in modern 7th pandemic V. cholerae. This result suggests that the major role of ToxR in virulence is to antagonize H-NS repression of colonization factors.
The mechanism of ToxR antagonism is unclear. Rather than one mechanism, the way in which ToxR and H-NS interact may vary with genomic location. Moreover, since H-NS gene silencing is regulated by environmental factors [69,72], the interaction between H-NS and ToxR may change as V. cholerae cycles between host and environmental reservoirs. Our ChIP-seq analysis shows that ToxR and H-NS share certain binding locations across genome (such as the toxT promoter), and induction of toxRS results in decreased DNA binding of H-NS. ToxR may directly compete with and displace H-NS at shared binding sites, as has been suggested for other H-NS/transcription factor interactions [67,98]. Rather than sequence alone, H-NS has an affinity for DNA structure, favoring the binding of curved DNA [99–101], and is known to form nucleoprotein filaments that promote DNA silencing [102]. ToxR may bind and alter DNA topology near H-NS, which could destabilize its interactions with DNA. Alternatively, ToxR may directly interact with H-NS and destabilize its DNA association, as has been shown for phage protein Arn [103].
Understanding how ToxR recognizes its target DNA sequences will be important in deciphering its antagonism of H-NS. Our analysis of ChIP-seq peaks identified an expanded ToxR consensus DNA motif that may facilitate its DNA binding. However, the large number of locations of this motif in the genome compared to the number of ToxR binding sites we identified suggests that our motif is still inadequate to predict ToxR binding specificity in vivo alone. It is possible that a primary structure of A, T, G, and C that does dictate ToxR binding was left undiscovered by our analysis. Differences between predicted and actual in vivo binding sites were also observed for ToxT, which also has a low GC-content and low-complexity consensus motif. Computationally, the ToxT consensus motif (toxbox) maps to a large number of locations across the V. cholerae genome [104]. However, in vitro biochemical interactions between purified ToxT and fragmented V. cholerae genome identified just 199 ToxT binding sites [105]. Subsequently, in vivo ChIP-seq identified and validated only seven of these ToxT binding sites, which is in line with transcriptome studies of ToxT regulated genes [33,38].
In eukaryotic gene regulation, factors in addition to linear DNA sequence, including topology, partner proteins, and DNA localization, all contribute to in vivo selectivity of transcription factor DNA binding [106,107]. Analysis of 119 transcription factors from the ENCODE project database has shown up to 99% of motif locations in a genome are not bound by their respective transcription factor [108]. Like H-NS, ToxR has a propensity to bind low GC-content DNA. Thus, ToxR binding may also use DNA topology in addition to sequence. ToxR is also unique in that it is a membrane-bound transcription factor. ToxR’s localization may limit its access to genome locations in a packed nucleoid. Super-resolution microscopy has suggested that H-NS sequesters bound DNA into two compact clusters per chromosome in E. coli [109]. Similar nucleoid structuring in V. cholerae could also act to limit ToxR access to all genomic locations. Future localization and chromosome conformation capture studies may yield important information on factors in addition to primary DNA structure that dictate how ToxR reaches its target sequences. Continued research to understand how ToxR finds its regulatory targets may provide insight into the evolutionary trajectory of V. cholerae and its potential for future acquisition of foreign genes.
The animal experiments were performed with protocols approved by the University of Texas at Austin, Institutional Animal Care and Use Committee. Protocol number AUP-2013-00052. The University of Texas at Austin animal management program is accredited by the Association for the Assessment and Accreditation of Laboratory Animal Care, International (AAALAC), and meets National Institutes of Health standards as set forth in the Guide for the Care and Use of Laboratory Animals (DHHS Publication No. (NIH) 85–23 Revised 1996).
Strains and plasmids are listed in S4 Table. Strains were grown in Luria Broth (LB; rich medium). The following antibiotic concentrations were used: carbenicillin 75 μg/mL, kanamycin 25 μg/mL, streptomycin 100 μg/mL and chloramphenicol 2.5 μg/mL for V. cholerae and 10 μg/mL for E. coli. Arabinose was used at 0.2% for induction. X-gal was used at 40 μg/mL.
All cloning products were sequence-verified, and the nucleotide sequences of all primers used for cloning are listed in S5 Table. For in-frame gene deletions of toxRS, tcpPH, VC1599, VCA0536, vpsL and H-NS, genomic DNA surrounding the respective gene was amplified by crossover PCR and cloned into pWM91 or pSSK10 for subsequent sacB mediated allelic exchange as described [110,111]. For complementation constructs, the respective gene was amplified from chromosomal DNA and cloned into plasmid pBAD18 or pWKS30 [112,113]. For genes cloned into pWKS30, the respective native promoter was also included. Full length ToxR and TcpP were cloned into pBAD18 with C-terminal 3XV5 tags as previously described [37–39]. Genes cloned into pBAD18 were induced by adding arabinose to the growth medium.
Biofilm assays were performed essentially as described [114]. V. cholerae C6706 wild-type and mutants strains where grown overnight on LB agar plates. Each strain was back-diluted in a 5 mL culture of LB and grown to mid-log phase. The culture was then diluted 1:100 in fresh LB, and 100μL of the diluted culture was added to a round-bottom PVC microtiter plate in replicates of three. Strains were allowed to grow for 22 hours at 30°C. Planktonic cells were removed, and bound cells were washed twice with 200 μL sterile water and then stained with 0.1% crystal violet for 15 mins. Stain was removed and cells were washed three times with 200 μL PBS and allowed to air dry for 15–30 mins. Stain was then solubilized with 200 μL 95% ethanol for 15 mins. Finally, 125 μL of solubilized stain was transferred to a new 96-well, flat bottom polystyrene plate. The optical density was measured at 595 nm using a SpectraMax Plus384 absorbance microplate reader with SOFTmax Pro v6.2.2 software.
ChIP was performed as previously described [37,38]. 50 mL of exponentially growing culture in LB was induced with 0.1% arabinose for 30 min at 37°C. No induction was required for H-NS ChIP. Formaldehyde was added to 1% final concentration and incubated at 25°C for 20 min with occasional swirling. Crosslinking was quenched by adding glycine to 0.5 M. Cell pellets were washed in 1X TBS and resuspended in lysis buffer (10 mM Tris pH 8.0, 100 mM NaCl, 1 mM EDTA, 0.5 mM EGTA, 0.1% DOC, 0.5% N-lauroylsarcosine) + protease inhibitor cocktail (Sigma) and 1 mg/mL lysozyme and incubated at 37°C for 30 min. The cells were sonicated 1X 30sec with a needle sonicator, and unlysed debris was pelleted by centrifugation. The lysate was sonicated for 20 min with a 10 s on/ 10 s off cycle (QSonica; www.sonicator.com). Sheared samples had an average DNA fragment size of ~300bp with a spread of 50-800bp. A sample was taken as a non-immunoprecipitated input control for sequencing. Following clarification by centrifugation, 1/10 volume of 10% Triton X-100 in lysis buffer was added to each sample followed by 100 μl of Dynal-Protein G beads coated with anti-V5 monoclonal antibody (Sigma) and incubated overnight with rotation. The beads were washed 5X with ChIP RIPA buffer [50 mM HEPES pH 7.5, 500 mM LiCl, 1 mM EDTA, 1% NP40, 0.7% DOC], then 1X in TE + 50 mM NaCl and resuspended in 100 μL elution buffer [50 mM Tris-HCl, pH 7.5, 10 mM EDTA, 1% SDS]. Samples were incubated at 65°C for 30 min and the beads pelleted by centrifugation. Supernatants were incubated at 65°C overnight to reverse crosslinks. Samples were incubated with 8 μL of 10 mg/mL RNase A for 2 hr at 37°C, then 4 μL of 20 mg/ml proteinase K at 55°C for 2 hr, then purified. Experiments were repeated in at least biological duplicate. Sequencing sample preparation was performed as previously described [37].
Samples were sequenced using Illumina HiSeq. Data processing for ChIP-seq was performed as previously described [37–39]. Sequence reads were aligned to the V. cholerae N16961 genome using CLC genomic workbench software. CLC genomic workbench ChIP-seq software was used to compare control input and experiment alignments to identify peak enrichment. Our DNA sonication method results in an average DNA fragment size of ~300bp with a spread of 50-800bp. A transcription factor can occupy the extreme ends of up to an 800bp fragment allowing a raw peak to be called that spans up to ~1600bp. We have reported these maximum raw coordinates in S1 Table (ToxR) and S2 Table (H-NS), without computational refinement. Peaks that were identified in both replicates were scored as real peaks.
All motif studies were performed using the MEME Suite of motif-based sequence analysis tools [79–81]. Genome scanning for motifs was performed with FIMO version 82 with a stringent p-value cut-off of <0.0001. FIMO returns sequences that match the input motif with a probability specified by the p-value. Identification of ToxR and TcpP binding motifs from ChIP-seq data was performed with both MEME and GLAM2. We analyzed the respective ChIP-seq data as a whole, and separated into peaks found on genomic islands compared to peaks found on the progenitor genome. We screened motifs generated through our analysis to determining if they overlapped with the biochemically proven binding sites for TcpP in the toxT promoter, and for ToxR in the toxT, ompU, and ompT promoters. We focused on identification of ungapped motifs. We did not identify a TcpP motif that meets our criteria. We identified a ToxR motif using GLAM2 present in all ToxR ChIP-seq peak sequences that met our criteria.
For ChIP-seq peak validation, relative abundance quantitative PCR (qPCR) was performed with Kapa Biosystems Sybr Fast One-Step qRT-PCR kit using 16S rDNA as the internal reference. Relative target levels were calculated using the ΔΔCt method, with normalization of ChIP targets to 16S rDNA signal [37]. For gene expression analysis, relative expression reverse-transcription quantitative PCR was performed with Applied Systems RNA-Ct one-step system. Relative expression levels were calculated using the ΔΔCt method, with normalization of gene targets to16S rRNA signals [37].
RNA was prepared from logarithmic cultures in triplicate under the same growth conditions used for ChIP-seq. Equal amounts of total RNA were separated on a 6% TBE-urea gel and transferred to Hybond N membrane. After crosslinking and prehybridization, membranes were incubated with 100 pmol of 32P labeled probe. Washed membranes were exposed to film overnight. Bands were quantified by densitometry. RyhB and 5S probes are listed in S5 Table.
A modified version of the protocol of Baselski and Parker [115] was performed for infection and recovery of all strains. Strains were grown on selective medium overnight at 37°C. Wild-type and mutant strains were mixed together in LB. 50 μL of this competition mixture (∼50,000 bacteria) was inoculated into a 5-day-old CD1 mouse pup (Charles River Company). One strain carried an active lacZ allele. Serial dilutions of the competition mixture were plated on selective medium and enumerated to determine the input ratio of wild type and mutant strain. After incubation at 30°C for 18 hr the mouse pups were sacrificed and small intestines were removed and homogenized in 10 mL of LB. Serial dilutions were plated in LB + Sm100 + Xgal and enumerated to determine the output ratio of wild-type and mutant strain. The competitive index for each mutant is defined as the output ratio of mutant/wild-type strain divided by the input ratio of mutant/wild-type strain. Statistical significance was determined by comparing the resulting ratio to the ratio of WT versus WT lacZ−. At least five mice were tested for each mutant.
Data were analyzed using GraphPad Prism 5 Software. Statistical significance between two groups was assessed using an unpaired two-tailed Student’s t test. Statistical significance when comparing more than two groups was assessed using a One-Way ANOVA analysis followed by a Tukey’s multiple comparison post-test. Standard error of the mean (SEM) is shown.
The sequence data have been deposited with the NCBI’s Gene Expression Omnibus under Accession Number GSE72474.
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10.1371/journal.pntd.0004404 | Borrelia persica Infection in Immunocompetent Mice - A New Tool to Study the Infection Kinetics In Vivo | Borrelia persica, a bacterium transmitted by the soft tick Ornithodoros tholozani, causes tick-borne relapsing fever in humans in the Middle East, Central Asia and the Indian peninsula. Immunocompetent C3H/HeOuJ mice were infected intradermally with B. persica at varying doses: 1 x 106, 1 x 104, 1 x 102 and 4 x 100 spirochetes/mouse. Subsequently, blood samples were collected and screened for the presence of B. persica DNA. Spirochetes were detected in all mice infected with 1 x 106, 1 x 104 and 1 x 102 borrelia by real-time PCR targeting the flaB gene of the bacterium. Spirochetemia developed with a one- to two-day delay when 1 x 104 and 1 x 102 borrelia were inoculated. Mice injected with only four organisms were negative in all tests. No clinical signs were observed when infected mice were compared to negative control animals. Organs (heart, spleen, urinary bladder, tarsal joint, skin and brain) were tested for B. persica-specific DNA and cultured for the detection of viable spirochetes. Compiled data show that the target organs of B. persica infections are the brain and the skin. A newly developed serological two-tiered test system (ELISA and western blot) for the detection of murine IgM, IgG and IgA antibody titers against B. persica showed a vigorous antibody response of the mice during infection. In conclusion, the infection model described here for B. persica is a platform for in vivo studies to decipher the so far unexplored survival strategies of this Borrelia species.
| The spirochete Borrelia persica is a tick-borne bacterium that is transmitted by the vector Ornithodoros tholozani to its vertebrate host in the Middle East, Central Asia and the Indian peninsula. Current migration of vast numbers of individuals from this area increases the likelihood that B. persica infections will be introduced into new geographic regions. After infection and distribution by the bloodstream, relapsing fever episodes occur in humans. Since no reliable in vivo tools have existed so far to study this organism, a murine model was established in this study to characterize the infection kinetics in immunocompetent mice. Aspects of the potential infectivity of the laboratory strain and of potential clinical signs, spirochetemia and antibody response as well as organ tropism and histopathological reactions were studied. With the successful infection model presented here, further studies are conceivable in order to gain advanced insights into the pathogenesis of B. persica infection and to characterize in detail the host immune response mounted against the bacterium. We propose that this model could also be used for the development of new rapid diagnostic approaches to initiate or monitor treatment regimes in order to clear or prevent the infection with B. persica.
| Spirochetes of the genus Borrelia (B.) are vector-borne, spiral-shaped bacteria that can be divided into two functional groups [1]. One large group of spirochetes belongs to the B. burgdorferi sensu lato complex (e.g. B. burgdorferi sensu stricto, B. afzelii, B. garinii, B. bavariensis). Lyme disease borreliae are transmitted by hard-shelled Ixodes ticks [2]. The second group includes the relapsing fever (RF) borreliae which spread primarily via soft ticks with an exception of B. recurrentis that is transmitted by the body louse (Pediculus humanus). Among others, B. hermsii, B. duttonii and also B. persica are tick-borne RF borreliae which induce tick-borne relapsing fever (TBRF) (reviewed in [3]). B. persica is transmitted by the soft tick Ornithodoros tholozani during blood meals [4]. These ticks are prevalent in areas such as the Middle East, Central Asia and the Indian peninsula and feed on humans as well as on animals (reviewed in [5]). Moreover, TBRF can occur in non-endemic countries due to travel of infected people [6, 7]. Clinically, the disease manifests with fever attacks in human patients related to high numbers of spirochetes in the blood circulation during fever episodes [8–10] and non-specific clinical signs such as chills, headache, nausea, vomiting, sweating, abdominal pain, arthralgia, cough and photophobia which may occur [9]. Rodhain reviewed as early as 1976 [11] that a high level of experimental pathogenicity of B. persica can be perceived in guinea pigs, hedgehogs and rabbits whereas lower levels seem to occur in monkeys, adult white mice and rats. So far primarily guinea pigs have been used to multiply B. persica [12–14] and just recently it was possible to maintain B. persica in vitro [14, 15]. However, for other RF borrelia species the mouse is usually considered to be the appropriate animal model [16–19] and Babudieri investigated relapsing fever in Jordan by injecting blood of diseased patients into mice in order to confirm TBRF spirochetosis [20]. The mice used in the experiment tested positive two to five days after injection. Furthermore, Babudieri studied the infection rate of captured Ornithodoros tholozani ticks. Squashed ticks were inoculated into mice, but infection was not initiated, while a very low infection rate was obtained when the ticks were allowed to feed directly on these animals. Spirochetes were not present constantly and uniformly in the mice’s blood. In addition, the author mentioned that the spirochetes survived in the mice’s brains. In 2006, Assous et al. inoculated intraperitoneally blood of TBRF patients from Israel into ICR mice and detected spirochetes in the mice’s blood samples on day four as well as on day six of the experiment [21]. Since comprehensive data for B. persica in mice were not available, we aimed to establish and characterize in detail an infection model for B. persica in immunocompetent mice. Therefore, we infected intradermally 44 C3H/HeOuJ mice with B. persica strain LMU-C01. In order to gain insight into the infection with these TBRF spirochetes, we investigated (a) whether this laboratory strain of B. persica is able to establish an infection in immunocompetent mice; (b) whether the mice develop clinical signs; (c) when and in which quantity the spirochetes appear in the blood circulation; (d) confirm that B. persica disseminates into organs and investigate the histopathological changes; (e) characterize the mice’s immune response during infection; and (f) define the minimal dose necessary to infect animals. After compilation of all data, we came to the conclusion that the infection model described here is a reliable tool that can be used for further research studies.
For this study, 100 μl of thawed B. persica passages (strain LMU-C01, isolated from a cat in Israel; passage 2, 3.9 x 106 organisms/ml) were cultivated in Pettenkofer/LMU Bp medium as described previously [15]. Cultures were incubated for five days and viable bacteria were counted with a Petroff-Hausser counting chamber (Hausser Scientific, Horsham, Pennsylvania, USA). Bacteria suspensions were adjusted to the required cell concentration by dilution of cultures with plain medium.
In total, 54 six- to seven-week-old female C3H/HeOuJ mice (Charles River Wiga Deutschland GmbH, Sulzfeld, Germany) were kept in individually ventilated cages (ISOcage N System; Tecniplast Deutschland GmbH, Hohenpeißenberg, Germany) at the animal facility of the Institute for Infectious Diseases and Zoonoses, Ludwig-Maximilians-Universität (Munich, Germany). Animals were manipulated in laminar flow systems in order to sustain specific-pathogen-free conditions. The health status of all mice and the body temperature, which was measured with a subcutaneous transponder (IPTT-300 Temperature Transponders; Plexx B. V., PW Elst, Netherlands), were recorded twice a day.
Initially, 20 mice were exposed to 1 x 106 B. persica spirochetes in 100 μl medium by intradermal injection into the shaven back. In addition, four animals were injected with 100 μl medium alone and served as negative controls. The injection volume was divided into small portions (10 x 10 μl), placed close to each other into the skin (~ 4 cm2 area). For the dose finding study, eight mice per group were injected with B. persica suspensions with varying concentrations. Group #1: 1 x 104 spirochetes per mouse; group #2: 1 x 102 spirochetes per mouse; group #3: 4 x 100 spirochetes per mouse. Two additional animals in each group served as negative and infection/transmission controls.
To study the kinetics of bacteremia and the development of specific antibodies post infection, blood samples were collected at preassigned intervals. Two drops of blood were collected in a Microvette 100 K3E (preparation K3EDTA; Sarstedt AG & Co., Nümbrecht, Germany) by facial bleeding after cutting the skin with a 4-mm Goldenrod Animal Lancet (Braintree Scientific, BioMedical Instruments, Zöllnitz, Germany). The bleeding scheme was as follows: during the first two weeks each mouse was bled every second day. However, in order to collect data for each single day of the first 14-day interval, the group was divided into two equal subgroups and these subgroups were bled according to alternating schedules. After day 14, all animals were bled together once a week until the final days 49/50. The bleeding scheme for the dose finding study was: during the first 20 days, each animal was bled every second day. Subgroups were formed and bled according to alternating schedules to obtain blood samples for each experimental day. After day 20, blood samples were collected every second day up to the final days 30/31/32. Alternating schedules were applied to the subgroups (each mouse was bled every fourth day).
For DNA-extraction, 5 μl from each blood sample were transferred into a 1.5-ml safe-lock tube (Eppendorf Vertrieb Deutschland GmbH, Wesseling-Berzdorf, Germany) and frozen at -30°C until used. Surplus blood samples of the regular blood collection from animals that had received 1 x 106 B. persica organisms were pooled subgroup-specific in another 1.5-ml safe-lock tube for plasma production. After euthanasia, a final blood sample of each mouse was collected in a micro tube (1.1ml Z-Gel; Sarstedt AG & Co.) for serum production. Plasma and serum preparation were done by a two-time centrifugation (Centrifuge 5430 R V 1.1, rotor FA-45-30-11; Eppendorf Vertrieb Deutschland GmbH) at 350 x g for 10 min at 24°C. The supernatants were collected in a 1.5-ml safe-lock tube and were frozen at -30°C until serological analyses were performed.
AS3000 Maxwell 16 MDx Instrument and the Maxwell 16 LEV Blood DNA Kit (Promega GmbH, Mannheim, Germany) were used for DNA extraction from blood and tissue samples. In the case of blood: 5 μl thawed blood, 300 μl sterile phosphate-buffered saline (PBS), 300 μl lysis buffer and 30 μl Proteinase K were mixed. The following steps were done according to the manufacturer’s technical manual # TM333 (Maxwell 16 LEV Blood DNA Kit and Maxwell 16 Buccal Swab LEV DNA Purification Kit Technical Manual; Promega GmbH). DNA was eluted in 60 μl elution buffer and frozen at -30°C. In the case of tissue: 200 μl of incubation buffer (Promega GmbH) were filled in a 1.5-ml safe-lock tube containing thawed tissue (weight less than 50 mg). Then, 200 μl lysis buffer and 30 μl Proteinase K were added and the tissue sample was squeezed and disrupted with a micro pestle (Faust Lab Science GmbH, Klettgau, Germany). Samples were incubated in a ThermoMixer comfort 5355 V 2.0 (Eppendorf Vertrieb Deutschland GmbH) at 56°C and 500 rpm overnight. Additional 200 μl of lysis buffer were added and DNA was extracted with the Maxwell 16 MDx Instrument. DNA was eluted and frozen as written above.
B. persica DNA was detected with a real-time quantitative PCR (qPCR) assay in a Mx3005P qPCR System (Agilent Technologies Sales & Services GmbH & Co.KG, Böblingen, Germany). The primers and the probe were designed according to the flaB target gene of B. persica using the software Primer3Plus (Free Software Foundation, Inc., Boston, Massachusetts, USA; http://primer3plus.com; [22]). Synthesis of following sequences was carried out by Eurofins Genomics (Ebersberg, Germany): Bp_flaB_fw 5’-GAG GGT GCT CAA CAA GCA A-3’, Bp_flaB_probe 5’-FAM-AAA TCA GGA AGG AGT ACA ACC AGC AGC A-3’-TAM and Bp_flaB_re 5’-CAA CAG CAG TTG TAA CAT TAA CTG G-3’. The expected amplicon size was 106 base pairs. Real-time PCR was carried out in 96 Multiply PCR plate natural (Sarstedt AG & Co.) containing 1.2 μl of each primer (final concentration 600 nM), 0.8 μl of the probe (final concentration 200 nM), 10 μl GoTaq Probe qPCR Master Mix (2 x; Promega GmbH; final concentration 1 x, added CXR reference dye following the manufacturer’s protocol) and 2.5 μl target DNA solution. The reaction volume was 20 μl in total and was pipetted in duplicate for each DNA sample. The amplification program was as follows: initial activation at 95°C for 5 min, 40 cycles of 95°C for 15 s and 60°C for 60 s and a final step at 25°C for 15 s. In each qPCR run a positive control (B. persica strain LMU-C01, P3), no template control (NTC, 2.5 μl nuclease-free water; Promega GmbH) and samples for calibration of the standard curve (serial dilution of B. persica DNA, P4) were included. According to the standard curve (considering slope, efficiency and R-squared value), the absolute spirochete number per ml mouse blood was calculated using the MxPro QPCR Software version 4.10 (Agilent Technologies Sales & Services GmbH & Co.KG) based on threshold cycles (Ct). Graphs were constructed with the OriginPro 9.1 Software (Additive GmbH, Friedrichsdorf, Germany).
As regards tissue, additional to DNA of B. persica flaB gene mouse-specific glyceraldehyde-3-phosphate dehydrogenase (GAPDH; TaqMan Gene Expression Assay, Mm99999915_g1, VIC dye-labeled MGB probe, 20 x; Applied Biosystems, Thermo Fisher Scientific GmbH, Ulm, Germany) was detected to control the DNA content in the tissue sample. TaqMan Gene Expression Assay was used according to the manufacturer’s recommendations. The reactions mix (total volume 20 μl) contained 10 μl GoTaq Probe qPCR Master Mix (Promega GmbH; final concentration 1 x, added CXR reference dye), 1 μl of the TaqMan Gene Expression Assay (final concentration 1 x) and 2.5 μl DNA solution. PCR conditions were as described above, with the exception of the initial activation step that was separated into two steps: 50°C for 2 min followed by 95°C for 10 min.
At the end of the infection study with 1 x 106 borrelia per mouse, animals were euthanized at days 49/50 post infection. Tissue processing was carried out as described previously [23]. Heart, spleen, urinary bladder, left tarsal joint, skin from infection areal and brain were collected from each mouse and divided into two parts (brain into three parts). One part was put in a 1.5-ml safe-lock tube and frozen at -30°C for DNA-extraction. The other part was transferred into a second 1.5-ml safe-lock tube filled with 200 μl of Pettenkofer/LMU Bp medium. Subsequently, the tissue was squeezed and disrupted with a micro pestle and the suspension was transferred into a 12-ml tube (Centrifuge Tube 12; TPP, Faust Lab Science GmbH) filled with 10 ml Pettenkofer/LMU Bp medium. The organ cultures were incubated at 37°C in humidified air for three weeks. Observation for viable mobile spirochetes was performed weekly using a dark-field microscope. The third part of the brain, right kidney and right tarsal joint were transferred into a 50-ml centrifuge tube (114x28mm, PP; Sarstedt AG & Co.) filled with 20 ml of 4% formalin and were stored at room temperature until histopathology analyses were carried out. For the dose finding study the skin, brain, right kidney and right tarsal joint were collected on final days 30/31/32 and prepared as described above.
Brain parts, right kidneys and right tarsal joints (in 4% formalin) from five infected mice (infection dose 1 x 106 B. persica/mouse) and one control animal were used for the histopathological evaluations. Parts of the brains and kidneys were embedded in paraffin and were cut into 2–3 μl thin slices. The other parts of brains and kidneys as well as tarsal joints were embedded in plastic and sectioned into 1 μl thin slices. After staining with hematoxylin and eosin (HE) as well as Giemsa, observations for histopathological changes were carried out under a bright-field microscope.
A low-passaged culture of B. persica (strain LMU-C01) was used for antigen production. The purified bacterial lysate was utilized to detect mouse antibodies in the serological two-tiered test system (ELISA and western blot). Spirochetes were first cultured as described elsewhere [15]. When bacteria reached the late exponential phase (after five days), 150 μl were transferred into each of two 12-ml tubes (Centrifuge Tube 12; TPP, Faust Lab Science GmbH) containing 6 ml medium and were further incubated for three days. These 6-ml bacteria suspensions were transferred to a sterile glass bottle containing 1 l medium and incubated until late exponential phase of growth (five days of incubation). Antigen preparation was done via ultrasound disruption according to Töpfer et al. [24] and the centrifuged supernatant of the whole cell lysate was stored at -80°C until used. Determination of protein concentration and quality control of the antigen solution was carried out as described previously [24].
The microdilution plates (Nunc-Immuno Microwell Maxisorp C96; Thermo Scientific, VWR International GmbH, Ismaning, Germany) were coated with whole cell antigen lysate of B. persica at a concentration of 0.2 μg per well as described by Barth et al. [25]. Detection of specific antibodies against B. persica was done with a computer-assisted, kinetic-based ELISA after Shin et al. [26]. Serum and pooled plasma samples were diluted 1:100 in sample buffer containing PBS, 0.05% Tween 20 (neoLab Migge Laborbedarf-Vertriebs GmbH, Heidelberg, Germany) and 2% non-fat dry milk (Merck KGaA, Darmstadt, Germany). Four control serum samples were added in each run. Peroxidase-conjugated goat IgG fraction to mouse immunoglobulins (IgG, IgA, IgM; MP Biomedicals, LLC, Heidelberg, Germany) were diluted 1:4,000 in sample buffer and used as the secondary antibody. As a final step, substrate (TMB Microwell Peroxidase Substrate Kit; KPL, medac GmbH, Wedel, Germany) was added and after 1 min 45 s the extinction of each well was read five times in 35-s intervals at 650 nm in a SpectraMax Plus 384 Microplate Reader (Molecular Devices (UK) Ltd, Wokingham, United Kingdom). Results were calculated with the SoftMax Pro software 5.3 (Molecular Devices (UK) Ltd). To standardize the sample evaluation and for the comparability of the plates of each run, the results of the samples were adjusted to the evaluated values of the control samples. Graphs were constructed with the OriginPro 9.1 Software (Additive GmbH).
For antigen preparation, three parts of antigen were mixed with one part of reducing sample buffer (Roti-Load 1; Carl Roth GmbH & Co. KG, Karlsruhe, Germany) and heated for 10 min at 90°C in a ThermoMixer comfort 5355 V 2.0 (Eppendorf Vertrieb Deutschland GmbH). The diluted antigen was loaded into a precast gel (4–15% Mini-PROTEAN TGX Stain-Free Precast Gels, IPG well comb, 86 x 67 mm (W x L); Bio-Rad Laboratories GmbH, Munich, Germany) and a protein weight ladder (Precision Plus Protein WesternC Standards; Bio-Rad Laboratories GmbH) was included separated from each other with a 5-mm wide polytetrafluoroethylene (PTFE) stick. Gel electrophoresis was performed with 1:10 diluted running buffer (10x Tris/Glycine/SDS Buffer; Bio-Rad Laboratories GmbH) in a Mini-PROTEAN Tetra cell (Bio-Rad Laboratories GmbH) at 250 V for 22 min. Western blot and immunodetection were done according to the Protein Blotting Guide (Bulletin #2895; Bio-Rad Laboratories GmbH) and a house-intern protocol as outlined below. Buffers and solutions were produced following recipes of the Protein Blotting Guide. Blotting of proteins onto a nitrocellulose membrane (MemBlot CN—Rolle, 0.45 μm, 10 x 7.5 cm; membraPure, Bodenheim, Germany) was carried out at 30 V for 960 min using a Mini Trans-Blot module (Bio-Rad Laboratories GmbH) in Towbin Buffer. The membrane was washed with tris-buffered saline (TBS, pH = 7.5) for 7 min and blocked for 1 h at room temperature in 5% non-fat milk-TBS. Subsequently, the membrane was washed with TTBS twice for 7 min (0.05% Tween 20 in TBS) and subsequently cut into strips (3–4 mm wide). Serum and plasma samples were diluted 1:100 in 5% non-fat milk-TTBS and incubated with the membrane strips for 1 h at room temperature hhhhhhh. Protein standard strips were incubated with plain 5% non-fat milk-TTBS. After washing (four times, 7 min, in TTBS), the strips were incubated with 1:1,000 diluted detection antibody in TTBS (peroxidase-conjugated goat IgG fraction to mouse immunoglobulins IgG, IgA, IgM; MP Biomedicals, LLC), and the strips with the protein standard were incubated with Streptactin solution (Precision Protein StrepTactin-HRP Conjugate; Bio-Rad Laboratories GmbH) for 1 h at room temperature, respectively. Strips were washed four times with TTBS for 7 min. After a final wash step with TBS for 1 min, color development was achieved by adding substrate (Opti-4CN Substrate Kit; Bio-Rad Laboratories GmbH) and stopped after 4 min by washing in distilled water. Images were taken with the CemiDoc MP System and Image Lab Software Version 5.0 (Bio-Rad Laboratories GmbH).
Mouse experiments were carried out according to the guidelines approved by the Animal Welfare Committee of the Sachgebiet 54, Regierung von Oberbayern (Munich, Germany). The animal care and use protocols adhere to the German Tierschutzgesetz, the Tierschutz-Versuchstierverordnung and the recommendations of GV-SOLAS.
For direct pathogen detection, DNA was extracted from murine blood samples and the flaB gene of B. persica was detected with a real-time PCR. Data are shown as box plots using a log10-scale of the absolute spirochete numbers per ml blood (Y-axis) and plotted against the blood sampling days (X-axis; Fig 1A–1C).
When mice were inoculated intradermally with 1 x 106 B. persica organisms (Fig 1A), spirochetes were detectable in their blood starting one day after injection. Median spirochete concentration ranged from 4.80 to 6.59 (log10 x organisms/ml) during the first 12 days. A substantial decline in detectable spirochete numbers was observed from day 12 to 14. The median spirochete numbers dropped from 4.80 to 0 (log10 x organisms/ml). After day 14, the majority of the mice tested negative for spirochetes in the blood, while during the same period three mice showed reduced numbers of borrelia and only one of them produced a positive signal on the final day of the experiment. The highest spirochete burden observed in a blood sample of an individual mouse was 1.9 x 107 B. persica/ml. When less spirochetes were used for intradermal inoculation (1 x 104 and 1 x 102 B. persica/mouse), spirochetes appeared in the blood circulation of the mice with a delay compared to the experiment performed with 1 x 106 B. persica/mouse. When 1 x 104 B. persica organisms were injected (Fig 1B), the earliest spirochetes were detectable two days after inoculation. Three peaks in spirochete concentration were recorded until day 15 (median spirochete concentration ranged from 3.90 to 6.43; log10 x organisms/ml) and then the spirochete number decreased from day 15 onwards (6.31 to 0; log10 x organisms/ml). After day 16, the median spirochete concentration varied at a low level (1.60 to 4.21; log10 x organisms/ml). The majority of the mice tested negative from day 26 onwards. The highest spirochete load observed in a blood sample of an individual mouse was 2.3 x 107 organisms/ml. When 1 x 102 B. persica organisms were injected (Fig 1C), spirochetes were detectable beginning on day 3. Varying spirochete numbers in blood were observed during the first 16 days of the experiment and from day 17 onwards the median number of spirochetes decreased substantially (from 5.28 to 2.01; log10 x organisms/ml). Mice showed spirochetemia at a low level until day 20. Then, the majority of the animals tested negative. No signals for flaB DNA were recorded for mice that were exposed to only four B. persica organisms per mouse and from mice which served as negative controls.
In summary, intradermal injection of decreasing numbers of B. persica resulted in delayed appearance of the spirochetes in the blood of infected mice. After an initial fluctuation of the median spirochete concentration at a high level, a sudden decrease in spirochete numbers was observed in each group from day 13 to 18. The infection rate was 100% when mice were injected intradermally with doses of 1 x 106 (20/20), 1 x 104 (8/8) and 1 x 102 (8/8) B. persica/mouse. The lowest dose tested (four B. persica/mouse) did not result in infection of any mouse (0/8).
When analyzed at the individual level, most animals showed two to three peaks of spirochetemia and only a few (3/36) produced one peak. Fig 2A–2C show the absolute spirochete numbers per ml blood of three selected mice. These individual animals, which received 1 x 106 B. persica/mouse, revealed three different relapse patterns. In terms of mouse #2 (Fig 2A), peak spirochetemia intensities declined over time. First, spirochete numbers increased up to 2.2 x 106 organisms/ml on day 3. Subsequently, spirochetemia intensity decreased until day 5, rose up to 1.3 x 106 organisms/ml on day 7, declined again on day 9 and showed a last small peak of 6.0 x 105 organisms/ml on day 11. After that, the animal was negative until the end of the experiment. The lowest detectable number of spirochetes between the peaks was recorded on day 9 (1.2 x 105 organisms/ml). In mouse #12 (Fig 2B), two peaks (5.3 x 106 organisms/ml on day 3 and 4.7 x 106 organisms/ml on day 7) were noted. Between the peaks the detectable spirochete concentration was 1.2 x 106 organisms/ml. This mouse was negative after day 13. Fig 2C depicts the kinetics of spirochetemia in mouse #18: the first high peak with 7.1 x 106 organisms/ml on day 2 was followed by a second low peak with 3.8 x 106 organisms/ml on day 6, which again was followed by a last high peak with 7.4 x 106 organisms/ml on day 10. Between the first and the second peak the lowest spirochete count was 1.1 x 106 organisms/ml on day 4, while only 1.7 x 105 organisms/ml were observed between the second and the third peak on day 8. This mouse also remained negative from day 12 onwards.
Interestingly, none of the infected mice showed any clinical signs or elevated temperatures during spirochetemia and the following periods when compared to negative control animals.
Tissue samples were collected from the skin around the infection area, heart, spleen, urinary bladder, left tarsal joint, and brain at the end of the infection experiment with 1 x 106 borrelia/mouse on days 49/50. Cultures with liquid medium were started to attempt the cultivation of the borrelia. The organ cultures were investigated for the presence of viable spirochetes under a dark-field microscope once a week over a period of three weeks. One week after initiating the cultures, at least some single borrelia organisms were observed in most brain cultures. After three weeks, 13 out of 20 observed brain samples were found positive with some cultures showing massive numbers of rapidly-moving spirochetes (Table 1). Three skin cultures were also observed as positive after three weeks. In total, 70% (14/20) of the infected mice tested positive by culture. By real-time PCR, 90% (18/20) of the infected mice tested positive for the B. persica flaB gene in tissue samples. In addition to 18 positive brain and three skin samples from 18 mice, one heart and one splenic sample of the same mouse were positive by real-time PCR (this mouse was also positive by blood real-time PCR performed on its final day of experiment). Control animals tested negative in both methods. According to the results, only brain and skin samples were investigated on the final days of the dose finding study. Spirochetes were seen in 87.5% (7/8; infection dose: 1 x 104 B. persica/mouse) and 100% (8/8; infection dose: 1 x 102 B. persica/mouse) of brain cultures. All tissue cultures of animals that had received four B. persica/mouse and all negative controls tested negative. Real-time PCR was 100% (8/8) positive for brain samples from mice infected with doses of 1 x 104 and 1 x 102 B. persica/mouse. None of the skin samples tested positive in both methods.
Five mice (inoculated with 1 x 106 B. persica/mouse) which were positive according to all other test methods were selected for histopathologic evaluation. A negative mouse served as a control. The paraffin-embedded slices of brains and kidneys as well as the plastic-embedded slices of brains and joints revealed no histopathological changes indicative for inflammatory responses. Two specimens of in plastic-embedded kidneys (one infected and the uninfected mouse) contained small scattered interstitial infiltrations of lymphocytes (mild interstitial non-suppurative focal nephritis).
The specific antibody response against B. persica was measured with a kinetic ELISA and characterized by western blotting. Plasma samples of mice injected with 1 x 106 B. persica/mouse were collected and pooled according to subgroups from day 1 to 50 according to an alternating sample collection schedule. Plasma samples as well as individual final serum samples of all animals were tested with an ELISA for the detection of murine IgM, IgG and IgA antibodies. Antibody levels developed immediately after spirochete injection and rose to 381.6 KELA units until day 21. Antibody levels plateaued (416.4 to 479.5 KELA units) until day 50 (Fig 3A). Antibody levels of individual final serum samples are shown in Fig 3B. The highest antibody levels were obtained in animals injected with 1 x 106 B. persica/mouse on days 49/50 of the experiment. Mice exposed to 1 x 104 or 1 x 102 B. persica/mouse showed medium to high levels of specific antibodies, however their antibody levels were lower when compared to the high-dose exposed group on day 28 (pooled plasma samples). Sera of animals receiving only four B. persica/mouse and negative mice showed non-specific antibody responses.
Western blots of individual final sera from each infection dose group showed bands between 15 kDa and 100 kDa. The patterns of the infected mice were similar. Nevertheless, the lower antibody levels induced by the smaller infection doses were reflected in the intensities of the immunoblot bands. Negative controls and animals that had received four B. persica/mouse showed only non-specific bands at 25 kDa and 37 kDa (Fig 3C).
A murine infection model for B. persica strain LMU-C01 was established in this study. Investigated parameters such as clinical signs, spirochete burden, target organs of infection, histopathology and antibody response should provide further insights into the development of TBRF. Intradermal infection was 100% successful for the infection doses of 1 x 106, 1 x 104 and 1 x 102 spirochetes per mouse. The dose of four B. persica organisms per mouse did not initiate infection in any mouse. This seems contradictory to other studies in other RF species, in which infection succeeded with single spirochetes of B. recurrentis var. turicatae [27] and B. hermsii [28]. It seems as though the pathogenicity of different RF species or even isolated single strains is decisive for the minimal infection dose and for a successful infection in animals. Furthermore, the route of infection might be a crucial factor for the initiation of an infection. Whereas in other studies animals were infected intraperitoneally with different RF species [27, 28], mice were infected intradermally with B. persica in our study. It is possible that innate defense mechanisms involving the skin immune cells might kill off a certain number of injected spirochetes. This could be a reasonable explanation for the unsuccessful intradermal infection with the infection dose of only four B. persica per mouse. To our knowledge, the number of transmitted B. persica spirochetes by its vector Ornithodoros tholozani during the feeding process is unknown. However, one can speculate that the number of transmitted spirochetes is low, because of the short (minutes) blood meal of soft ticks [29] compared to hard ticks (several days of attachment on the host; [30]). Up to 1 x 104 B. burgdorferi organisms have been found in infected Ixodes scapularis (formerly Ixodes dammini) nymphal ticks after feeding on experimentally infected mice [31]. And yet it is unknown how many B. burgdorferi spirochetes are transmitted exactly during the feeding process from Ixodes scapularis to the host animal.
Spirochete burden in the blood of the infected animals was dependent on time and infection dose, and varied within the study groups displaying individual kinetics and extent of spirochetemia. High doses of inoculated B. persica induced a prompt appearance and an early high load of B. persica in the circulation, but had just a small influence on the maximal spirochete number. At the same time, these early large numbers of bacteria seem to have effectively stimulated the mice’s immune response. Most of the mice in the group that had received 1 x 106 spirochetes per mouse had cleared spirochetemia by day 14, while the other two groups (1 x 104 and 1 x 102) controlled their spirochetemia days later or incompletely (compare Fig 1A, 1B and 1C). During days of investigation mice showed individual relapse patterns with varying numbers of bacteria (Fig 2A–2C). Such fluctuations in spirochete presence of individual mice were also seen in a study in Jordan [20]. In our study, every infected mouse showed one to three peaks of spirochetemia during the time course of blood sampling. Spirochetal numbers were low between the peaks, but still detectable by real-time PCR. To our knowledge, previous publications did not investigate the genetic mechanisms of the cyclic nature of B. persica at the level of variable major proteins (VMPs) as described for other RF borreliae [32]. However, VMP sequences have been identified for B. persica (e.g. variable large protein 18 under NCBI accession number: WP_024653159). Changes in VMPs expression are likely to be associated with recurring spirochetemia presented in this study. Further investigations are required to explore the VMPs and the underlying gene sequences for B. persica in more detail. The infection of our mice resulted in similar maximal bacteria numbers per ml when compared to an earlier study in guinea pigs (~6.8 x 106 B. persica/ml [13]) and so far, these animals have routinely been used to study and maintain B. persica [12–14]. Interestingly, the mouse strain used in this study (C3H/HeOuJ) did not show any clinical signs of disease. Other relapsing fever spirochetes were reported to have varying influences on temperature profiles in mice. For example, B. microtti induced fever in white mice (mouse strain unknown; [14]). Nevertheless, our infection model could be useful for research, because it allows comparative studies with other RF spirochetes in mice [16–19].
An interesting and crucial aspect of RF infections is the target tissues/organs, since these locations of spirochete persistence determine the final outcome of infection. Data obtained in this study clearly show that B. persica disseminates into the brains of mice (Table 1). Babudieri reported already in 1957 [20] that uncharacterized spirochete isolates from Jordan survived in mice’s brains after experimental infection. Similarly, B. crocidurae and B. duttoni are known to infiltrate the brains of mice [18]. In humans, neurological symptoms due to B. persica infection are rarely reported (reviewed in [33]). It is, therefore, not surprising that the mice in this study did not display clinical signs of brain infection. Results of our histopathological investigations also support the assumption that short-term infections with B. persica not necessarily lead to apparent clinical signs. Whether the latent brain infection in immunocompetent mice remains without any inflammatory response needs to be evaluated. Further studies aiming at long-term infections or at reactivation of the spirochetes as a result of immunosuppression could shed some light on this issue. Spirochetes were also detected in skin samples from five mice which received an inoculum of 1 x 106 organisms (two mice positive in culture, two mice positive for specific DNA, one mouse positive for both). Furthermore, one heart tissue sample and one splenic sample taken from the same mouse (1 x 106 B. persica/mouse) tested positive for B. persica flaB gene. Interestingly, the blood sample of this mouse collected on the final day of experiment (day 50) was also positive for B. persica DNA. Since the heart and the spleen are blood-rich organs, it is likely that these positive results indicate the presence of B. persica in the circulation rather than dissemination to these organs. The facts that the blood-rich organs of the other animals (infection dose 1 x 106 B. persica/mouse) were negative and that no borrelial DNA was detectable in the blood samples during the final days of the experiments suggest that brain and skin infections in these animals were real rather than due to contamination with spirochetemic blood.
Our histopathological investigations revealed mild interstitial non-suppurative focal nephritis in two kidneys from an infected and an uninfected mouse. It seems that the findings in our mice are an incidental event considering the changes in the kidney of the negative control animal. It is necessary to focus further investigations on this B. persica strain to clarify the non-conclusive histopathological results we had seen using a larger number of tissue samples or examining organs earlier post infection.
Comparing all tests used in this study, the highest detection rate for infection was achieved by antibody detection followed by real-time PCR performed on repeated blood samples collected during the first week of infection. Results of real-time PCRs performed on tissue samples (brains) also produced high detection rates. Yet, tissue cultivation is the most difficult and error prone method due to contamination with other fast-growing bacteria.
With the infection model presented here, further investigations are possible in order to gain advanced insights into the pathogenesis of B. persica infection and to characterize the host immune response mounted against in detail. We propose that this murine model could also be useful for the development of further diagnostic methods for treatment studies in order to detect, clear or prevent the infection with B. persica.
The results of this study show that B. persica strain LMU-C01 can be used to establish infection in immunocompetent C3H/HeOuJ mice. The minimal infectious dose was between four and 1 x 102 B. persica organisms by intradermal inoculation in this study. Spirochetes were detected in the blood, brain and skin tissue samples thereby defining the brain and the skin as target organs of B. persica dissemination. The infection model presented in this study can serve as a platform for further ensuing in vivo investigations to gain new insights into the pathogenesis of B. persica.
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10.1371/journal.pbio.1002112 | Natural Selection Constrains Neutral Diversity across A Wide Range of Species | The neutral theory of molecular evolution predicts that the amount of neutral polymorphisms within a species will increase proportionally with the census population size (Nc). However, this prediction has not been borne out in practice: while the range of Nc spans many orders of magnitude, levels of genetic diversity within species fall in a comparatively narrow range. Although theoretical arguments have invoked the increased efficacy of natural selection in larger populations to explain this discrepancy, few direct empirical tests of this hypothesis have been conducted. In this work, we provide a direct test of this hypothesis using population genomic data from a wide range of taxonomically diverse species. To do this, we relied on the fact that the impact of natural selection on linked neutral diversity depends on the local recombinational environment. In regions of relatively low recombination, selected variants affect more neutral sites through linkage, and the resulting correlation between recombination and polymorphism allows a quantitative assessment of the magnitude of the impact of selection on linked neutral diversity. By comparing whole genome polymorphism data and genetic maps using a coalescent modeling framework, we estimate the degree to which natural selection reduces linked neutral diversity for 40 species of obligately sexual eukaryotes. We then show that the magnitude of the impact of natural selection is positively correlated with Nc, based on body size and species range as proxies for census population size. These results demonstrate that natural selection removes more variation at linked neutral sites in species with large Nc than those with small Nc and provides direct empirical evidence that natural selection constrains levels of neutral genetic diversity across many species. This implies that natural selection may provide an explanation for this longstanding paradox of population genetics.
| A fundamental goal of population genetics is to understand why levels of genetic diversity vary among species and populations. Under the assumptions of the neutral model of molecular evolution, the amount of variation present in a population should be directly proportional to the size of the population. However, this prediction does not tally with real-life observations: levels of genetic diversity are found to be substantially more uniform, even among species with widely differing population sizes, than expected. Because natural selection—which removes genetically linked neutral variation—is more efficient in larger populations, selection on novel mutations offers a potential reconciliation of this paradox. In this work, we align and jointly analyze whole genome genetic variation data from a wide variety of species. Using this dataset and population genetic models of the impact of selection on neutral variation, we test the prediction that selection will disproportionally remove neutral variation in species with large population sizes. We show that genomic signature of natural selection is pervasive across most species, and that the amount of linked neutral variation removed by selection correlates with proxies for population size. We propose that pervasive natural selection constrains neutral diversity and provides an explanation for why neutral diversity does not scale as expected with population size.
| The level of neutral genetic diversity within populations is a central parameter for understanding the demographic histories of populations [1], selective constraints [2], the molecular basis of adaptive evolution [3], genome-wide associations with disease [4], and conservation genetics [5]. Consequentially, numerous empirical surveys have sought to quantify the levels of neutral nucleotide diversity within species, and considerable theory has focused on understanding and predicting the distribution of genetic variation among species. All else being equal, under simple neutral models of evolution, levels of neutral genetic diversity within species are expected to increase proportionally with the number of breeding individuals (the census population size, Nc). Although this prediction is firmly established, surveys of levels of genetic variation across species have revealed little or no correlation between levels of genetic diversity and population size [6–9]. This discrepancy—first pointed out by Richard Lewontin in 1974 [6]—remains among the longest standing paradoxes of population genetics.
One possible explanation for this disagreement is an inverse correlation between mutation rate and population size. This is expected if there is relatively weak selection against alleles that cause higher mutation rates [8,10]. Alternatively, this paradox could result from greater impact in large populations of nonequilibrium demographic perturbations such as higher variance in reproductive success [11] or population size fluctuations [12]. Indeed, one recent empirical study suggests that demographic factors play an important role in shaping levels of genetic diversity within animal populations [13]. However, none of these potential explanations is sufficient to fully account for the observed patterns of neutral diversity across species [8].
Another potential cause of this paradox is the operation of natural selection on the genome [7,14,15]. Natural selection can impact levels of neutral diversity via the adaptive fixation of beneficial mutations (hitchhiking; HH) [7,15,16] and/or selection against deleterious mutations (background selection; BGS) [17,18]. Both processes purge neutral variants that are linked to selected mutations, implying that if natural selection is sufficiently common in the genome, it can reduce observed levels of neutral polymorphism. Furthermore, theoretical arguments [7,14,19] suggest that, when the impact of natural selection is substantial, the dependence of neutral diversity on population size is weak or even nonexistent. Although many authors have demonstrated that natural selection could, in principle, be sufficiently common to explain Lewontin’s paradox [7,8,14–16,20], few direct empirical tests of this explanation exist.
One unique prediction of the hypothesis that natural selection is a primary contributor to disparity between Nc and levels of neutral genetic variation within species is that natural selection will play a greater role in shaping the distribution of neutral genetic variation in species with large Nc. To test this prediction, we relied on the fact that the impact of natural selection on linked neutral diversity depends on the local recombinational environment. In regions of relatively low recombination, selected variants affect more neutral sites through linkage, and vice versa, in regions of relatively high recombination. The resulting correlation between recombination and polymorphism [21–26] (reviewed in depth in [27]) allows a quantitative assessment of the magnitude of the impact of selection on linked neutral diversity (e.g., [22,23,26,28]). Specifically, if the effects of linked selection can explain the lack of correlation between neutral diversity and population size, we expect that species with larger population sizes will display stronger correlations between recombination and polymorphism than those with smaller population sizes and show a concurrently larger impact of natural selection on levels of neutral diversity across the genome.
Although empirical studies that explore the relationship between neutral diversity and population size are relatively infrequent compared to theoretical studies on this topic, there are two interesting patterns that merit consideration here. First, the proportion of nonsynonymous substitutions that have been driven to fixation by positive selection varies widely across taxa. In humans [29], yeast [30], and many plant species [31], estimates of this proportion are close to zero. In contrast, in Drosophila [32,33], mice [34], and Capsella grandiflora [35], as well as other taxa (reviewed in [8]), a large fraction of nonsynonymous substitutions are inferred to have been driven to fixation by positive selection, implying that natural selection is common in the genomes of these organisms (which generally have large Nc). Second, the strength of the correlation between polymorphism and recombination varies widely among the limited number of taxa [8,27] that have been studied in depth. Here again, Drosophila [21,25,36] is among the taxa that shows the strongest correlation and thus the clearest evidence for natural selection, and the correlation in Drosophila is substantially larger than, for example, in humans [28].
In a related study to the work presented here, Bazin et al. [37] showed that there is no correlation between nucleotide diversity in nonrecombining mtDNA and nucleotide diversity in the nuclear genome. While this is consistent with some predictions of theoretical work on this subject, the mitochondrion has unusual patterns of replication and inheritance, and it is therefore challenging to disentangle the processes that generate diversity from those that shape its distribution across the genome. Although suggestive, the evidence accrued thus far is fragmentary, has not been analyzed in aggregate, and varies widely in quality of samples, data collection, and analyses performed [8,27]. It is therefore difficult to draw firm conclusions about the relative importance and prevalence of natural selection in shaping patterns of genetic variation in the genome based on existing studies.
Due to rapid advances in genome sequencing technologies, whole genome polymorphism data are now available for a wide variety of species (e.g., [36,38]), and these data enable us to conduct a quantitative test of the natural selection hypothesis as an explanation for Lewontin's paradox. Towards this, we identified 40 species with sufficiently high quality reference genomes, linkage maps, and polymorphism data to enable a broad-scale, robust comparison of the relative strength of correlation between polymorphism and recombination rate within a single unified alignment, assembly, and analysis framework. Using these data, and reasonable proxies for Nc, we show that the effect of selection on linked nucleotide diversity is indeed strongly correlated with population size. In other words, natural selection plays a disproportionately large role in shaping patterns of genetic variation in species with large Nc, confirming the idea that natural selection is an important contributor to Lewontin’s paradox.
We identified 40 species (15 plants, 6 insects, 2 nematodes, 3 birds, 5 fishes, and 9 mammals) for which a high-quality reference genome, a high-density, pedigree-based linkage map, and genome-wide resequencing data from at least two unrelated chromosomes within a population were available (Table 1, S1 Table, S2 Table). Because our model (below) requires that recombination has been sufficiently frequent to uncouple genealogies across large tracts of DNA on chromosomes, we required that each species have an obligatory sexual portion of its life cycle. This requirement necessarily excludes clades such as bacteria, which are predominantly clonally propagated. Nonetheless, extending this framework to bacterial taxa will be an important step towards understanding the mechanisms by which natural selection shapes patterns of variation across the tree of life. Additionally, our sampling is biased towards more commonly studied clades (e.g., mammals), but this is unavoidable in this type of analysis, and there is no reason in principle why this taxonomic bias would affect the basic conclusions we describe here, as the sampled taxa likely span a large range of census population sizes.
After acquiring sequence data, we developed and implemented a bioinformatic pipeline to align, curate, and call genotype data for each species (see S1 Fig and methods for a full description of the bioinformatics pipeline). We further used the available genetic maps to estimate recombination rates across the genomes. Across all species, we analyzed recombination across nearly 385,000 markers and aligned more than 63,000,000,000 short reads. This is therefore one of the largest comparative population genomics dataset that has been assembled to date.
We used both simple nonparametric correlations and explicit coalescent models to test for a relationship between the impact of selection on linked neutral diversity and census size. Although correlations between recombination rate and neutral diversity are informative, the extensive literature in theoretical population genetics provides an opportunity to develop a robust modeling approach. Two primary types of selection can introduce a correlation between recombination rate and levels of nucleotide diversity: background selection (BGS) and hitchhiking (HH). Here, we are not primarily concerned with distinguishing between the two models, and so focus on their joint effects. In addition to combining BGS and HH, we would also like to relax the assumption that these processes act uniformly across the genome. All else being equal, regions of the genome with a higher density of potential targets of selection should experience a greater reduction in neutral diversity.
Starting from considerable prior theoretical work [14,17,18,32,39–41], we develop an explicit model relating polymorphism, recombination rate, and density of functional elements in the genome. We fit both a joint model that allows for both HH and BGS, as well as models of BGS only, HH only, and a purely neutral model (in which there is no predicted correlation between recombination or functional density and neutral diversity). Using these models, we estimate the proportion of neutral diversity removed by linked selection for beneficial alleles and/or against deleterious alleles (Fig. 1) for each species, as well as the relative likelihood of each model.
In practice, it is not feasible to determine Nc for the majority of species we studied. Instead, we used the species’ geographic range and individual body size as proxies for Nc. Size has been previously validated as a proxy for individual density in a wide variety of taxa and ecosystems (e.g., [42–44]). Under some simplifying assumptions, the product of geographic range and local density should be sufficient to roughly estimate a species census population size, and each factor is expected to independently capture some information related to species’ Nc. Specifically, we expect that range will be positively correlated with Nc, size will be negatively correlated with Nc, and Nc will be positively correlated with the impact of selection.
For many of the species that we studied, it is clear that selection plays a central, even dominant, role in shaping patterns of neutral genetic diversity. Specifically, both our correlation analysis and our explicit modeling support the hypothesis that natural selection on linked sites eliminates disproportionately more neutral polymorphism in species with large Nc, and in this way, natural selection truncates the distribution of neutral genetic diversity.
At a coarse scale, there is a stronger correlation between polymorphism and recombination in invertebrates (mean partial τ after correcting for gene density = 0.247), which likely have a large Nc on average, than in vertebrates (median partial τ = 0.118), which likely have a smaller Nc on average (two-tailed permutation p = 0.021). We observe similar patterns for herbaceous plants (mean partial τ = 0.106) versus woody plants (mean partial τ = −0.020; two-tailed permutation p = 0.058) and for medians as opposed to means (Fig. 2). When we repeat the analysis with alternate window sizes, we observe consistent effect sizes, albeit occasionally with reduced statistical support (S3 Table).
More generally, we tested the hypothesis that Nc is positively correlated with the impact of selection by fitting a linear model that includes body size, geographic range, kingdom, and the significant interactions among them as predictors, and uses the impact of selection estimated from our coalescent model as the response variable (Table 2; Fig. 3). Both size and range are significant predictors of the impact of selection in the expected directions (Table 2; log10(size): coefficient = −0.092, p = 0.0005; log10(range): coefficient = 0.112, p = 0.0002), and model as a whole explains 63.88% of the variation in impact of selection across species (Table 2; overall p = 3.518 x 10−8). This is clear evidence that more variation is removed by linked selection from the genomes of species with smaller body size and larger ranges than from the genomes of species with larger body size and smaller ranges.
A number of confounding factors could potentially influence our conclusions, including variation in map or assembly quality across species, differences in overall recombination rate, and differences in genome size. To test whether these factors can explain our results, we fit a confounder-only model including two measures of genetic map quality (density of useable markers and proportion of total markers scored as useable); two measures of assembly quality (proportion of assembly that is not gaps and proportion of total assembly assembled into chromosomes); overall recombination rate; and genome size. We then compare this confounder-only model to a model that includes all confounding parameters and, in addition, includes our population size proxies (kingdom, size, and range). The model with proxies for Nc both explains substantially more total variation in impact of selection (adjusted R2 of 0.6359 compared to 0.3388 for the confounder-only model) and is a significantly better fit to the data (F = 7.7322, df = 4, p = 0.0002).
In order to ensure that variable sampling of chromosomes is not a source of bias (given that the number of chromosomes sampled ranges from a minimum of 2 to a maximum of 517; S1 Table), we tested whether sampling depth is correlated with either size or range. In neither case do we find a correlation (size versus sampling depth: Kendall’s τ = 0.022, p = 0.84; range versus sampling depth: Kendall’s τ = 0.044, p = 0.699). We also find no evidence that species with only two chromosomes sampled are atypical with respect to range (Wilcoxon Rank Sum Test, p = 0.944) or size (Wilcoxon Rank Sum Test, p = 0.423). Finally, we find no evidence that mean depth per individual is correlated with either size (Kendall's τ = −0.044, p = 0.683) or range (Kendall's τ = −0.02, p = 0.862). Taken together, these results strongly suggest that the variable sampling across species, both in terms of sequencing depth and in terms of number of chromosomes sequenced, does not bias our conclusions.
To get a lower bound on the proportion of variation in impact of selection explained by our parameters of interest (range, size, kingdom, and the kingdom–size interaction), we fit a linear model with these parameters as predictors and the residuals of the confounder-only model as the response variable (S4 Table, S5 Table). This is a conservative test, as genome size is strongly correlated with body size (Kendall's τ = 0.296, p = 0.007 in our dataset). Nonetheless, our proxies for Nc explain 34.05% of the remaining variation in impact of selection after accounting for all confounding parameters (overall model p = 0.0008, S4 Table), and 47.36% of the variation after accounting for all confounding parameters except genome size (overall model p = 2.042 x 10−5, S5 Table).
For five species, our polymorphism data included individuals from domesticated populations, which could potentially affect our conclusions if selection has a different signature during domestication events than it leaves in natural populations. However, removing these five species has virtually no impact on our model fit (overall adjusted R2 = 0.6281, overall p = 6.094 x 10−7, S6 Table), suggesting that their inclusion has not biased our results. Additionally, we obtain similar results if we fit our model (excluding the kingdom term and its interaction with size) to animals and plants independently (S7 Table, S8 Table). Finally, varying the filtering criteria, window size, assumed deleterious mutation rate (U), or population genetic modeling approach produces nearly identical results (Fig. 3C), implying our primary conclusion is robust to a wide range of analysis choices. Taken together, our analysis demonstrates that the central pattern—natural selection reduces neutral diversity more strongly in species with large Nc than in species with small Nc—is consistently observed with both nonparametric model free approaches (Fig. 2; S3 Table) and with explicit population genetic models (Fig. 3A,B, Table 2) across a wide range of possible analysis and filtering choices (Fig. 3C, S4–S8 Tables).
If the process of recombination is itself mutagenic, neutral processes could produce a correlation between recombination and polymorphism [21,25,27]. However, no or very weak correlations between divergence and recombination have been found in most species that have been closely studied [21,25] (reviewed in [27]). Moreover, for those species in which a positive correlation between divergence and polymorphism has been found (e.g., [45,46]), it is likely at least partially the result of linked selection acting on polymorphisms present in the ancestral population [27,32]. Furthermore, the two species that showed the strongest correlation between polymorphism and recombination (partial τ = 0.5196 for D. melanogaster, partial τ = 0.4637 for Drosophila pseudoobscura) have no such correlation between recombination rate and divergence either on broad scales [21] or fine scales [25]. Finally, many authors have found strong evidence that recombination is not mutagenic in a number of other animal species (e.g., [28,47,48]), and it therefore appears a general consensus has emerged that recombination-associated mutagenesis is unlikely to influence the overall patterns we report in this work [27].
As an alternative approach to estimating the impact of natural selection on linked neutral diversity, we considered whether our proxies for Nc correlate with the strength of evidence that selection shapes patterns of neutral diversity, derived from our population genetic modeling approach. To do this, we focus on the relative likelihoods (Akaike weights) of four models: the BGS+HH model, the BGS-only model, the HH-only model, and the neutral model. These relative likelihoods can be interpreted as the probability that a particular model is the best model according to Akaike Information Criteria (AIC), given the set of models tested and the underlying data.
We initially focus on the relative likelihood of the support for a purely neutral model. Species with weak or no support for neutrality (relative likelihood of the neutral model < 0.05) have significantly larger ranges (p = 0.006, Wilcoxon Rank Sum Test, Fig. 4A) and significantly smaller sizes (p = 0.0001, Wilcoxon Rank Sum Test, Fig. 4B) than species with moderate (relative likelihood of neutral model ≥ 0.05 and < 0.90) or strong (relative likelihood of neutral model ≥ 0.90) support. This pattern also holds if we compare the species with strong support for neutrality or species with moderate support for neutrality individually to species with weak or no support (moderate versus weak: p = 0.0005 for size and 0.02 for range; strong versus weak: p = 0.02 for size and 0.02 for range, all p-values from Wilcoxcon Rank Sum Tests). This suggests that the evidence for non-neutral processes (BGS and/or HH) is significantly stronger in species with larger ranges and/or smaller sizes, consistent with our results above and with the hypothesis that natural selection explains Lewontin's paradox.
Given the extensive debate on the relative importance of HH versus BGS in shaping patterns of diversity across the genome [17,21], we also attempt to disentangle the relative roles of these two processes in reducing neutral diversity. This is potentially relevant to the resolution of Lewontin's paradox, as models of frequent, recurrent HH (i.e., genetic draft [7]) demonstrate that recurrent HH can remove the dependence of neutral diversity on population size entirely. Thus, evidence that HH specifically is more likely to occur in species with large census sizes would be compelling evidence for a role of selection in resolving the discrepancy between population sizes and neutral diversity. However, it is crucial to note that our test does not take into account features, such as patterns of polymorphism around amino acid fixations [23,49], that are particularly powerful for distinguishing HH and BGS, and thus suffers from many of the limitations of previous work relying purely on patterns of neutral diversity across the genome (e.g., [26,28,40,41]).
With that caveat, we begin by noting that, consistent with recent work in Drosophila [49,50] and other organisms [26,28,48], background selection is ubiquitous. Either the BGS-only model or the BGS+HH model has at least some support (relative likelihood ≥ 0.05) for 95% (38 of 40) of the species we analyzed, and for 90% (36 of 40) of species one of the BGS-containing models was the best fit, as measured by AIC. Thus, it seems clear that, in most cases, BGS is a more appropriate null model for tests of natural selection than strict neutrality.
To test whether species with moderate (relative likelihood of HH or BGS+HH ≥ 0.05 and < 0.9) or strong (relative likelihood of HH or BGS+HH ≥ 0.9) evidence for HH differ from species with little or no evidence for HH (relative likelihood of HH or BGS+HH < 0.05), we examined our proxies for Nc among these evidence classes. Species with moderate or strong evidence for HH have significantly larger ranges than species with weak or no evidence for HH (p = 0.03, Wilcoxon Rank Sum Test, median range (weak) = 2,681,693 sq km, median range (moderate/strong) = 5,592,037 sq km), and these species tend to have smaller sizes as well (p = 0.15, Wilcoxon Rank Sum Test, median size (weak) = 0.91 m, median size (moderate/strong) = 0.54 m).
As a second test of this pattern, we compared whether the relative likelihood of HH was greater for species estimated to have particularly high Nc compared to species estimated to have particularly low Nc. We define the high-Nc class as those species with ranges greater than the median range, and sizes below the median size, and we define the low-Nc class as those species with ranges below the median range and sizes above the median size. The relative likelihood of HH models is greater for species in the high-Nc class than the low-Nc class (p = 0.023, Wilcoxon Rank Sum Test), and the proportion of species with moderate or strong evidence for HH (either alone or in combination with BGS) is higher in the high-Nc class than the low-Nc class (4/10 in high-Nc class, 0/10 in low-Nc class, p = 0.086, Fisher's Exact Test).
Despite the fact that our test is unlikely to have substantial power to distinguish BGS and HH models, we suggest that these results imply that HH in particular is a stronger force shaping genomic diversity in species with large Nc, while BGS appears to be much more pervasive. The observation that pervasive HH may predominantly occur in species with large Nc suggests that genetic draft may play a substantial role in limiting neutral diversity among the species with the largest population sizes. More data on species with very large Nc, and the application of tests specifically designed to detect HH to a wider taxonomic range, will be necessary to fully disentangle the relative roles of HH and BGS in shaping levels of neutral diversity.
On the strength of early allozyme polymorphism data, Lewontin [6] observed that in contrast with theoretical predictions of the neutral theory [51–53], the range of neutral genetic variation among species is substantially smaller than the range of Nc among species. Because both positive selection via HH and negative selection via BGS purge linked neutral mutations, the operation of natural selection affects patterns of neutral genetic variation at linked sites across the genome. Although many authors have suggested that natural selection may play a role in truncating the distribution of genetic variation and may play a greater role than neutral genetic drift in shaping patterns of neutral nucleotide polymorphism [7,8,14,15], few empirical tests of this hypothesis have been proposed or conducted. Here, we show that species with larger Nc display a stronger correlation between neutral polymorphism and recombination rate, and that natural selection removes disproportionately more linked neutral variation from species with larger populations. This indicates that natural selection plays a disproportionately large role in shaping patterns of polymorphism in the genome of species with large Nc.
One important consideration when interpreting our results is that cryptic population structure can influence patterns of variation across the genome in a way that obscures the effects of selection. In the extreme case, where populations do not exchange any migrants for an extended period of time, genetic divergence is expected to accumulate at equivalent rates across the genome and would obscure the effects of linked selection. Elucidating the complex relationship between population structure and patterns of natural selection is an important and longstanding question in population genetics (for recent work see [54,55]). Nonetheless, especially given the scope of our analysis, it is not feasible to simultaneously estimate the effects of linked selection and population structure, and there are many reasons to believe that the results presented here will be robust to potential cryptic population structure.
So long as the population subdivision is not especially ancient (in the timescale of coalescence, on the order of Ne generations), a correlation between recombination and polymorphism is expected to remain due to the effects of selection on linked sites in the ancestral population [27,32]. Additionally, if migration is sufficiently common, it is reasonable to treat data derived from samples from separate localities as a single population [56]. One straightforward assumption is that species with larger geographic ranges will have greater opportunity on average to accumulate cryptic population structure than species with small ranges, which would imply we should preferentially underestimate the effects of linked selection in species with larger ranges. If population structure is a primary determinant of patterns of nucleotide diversity in taxa that we studied, we could reasonably expect a negative correlation between species range and the effects of selection on linked sites. Given that we instead obtain the opposite effect—one consistent with the effect of selection on linked neutral sites—it is reasonable to conclude that cryptic population structure has not drastically influenced the basic results presented herein.
Understanding the proximate and ultimate factors that affect the distribution of genetic variation in the genome is a central and enduring goal of population genetics and it carries important implications for a number of evolutionary processes. One implication of this work is that in species with large Nc, such as D. melanogaster, selection plays a dominant role in shaping the distribution of molecular variation in the genome. Among other things, this can affect the interpretation of demographic inferences because it indicates that even putatively neutral variants are affected by natural selection at linked sites. Furthermore, to whatever degree standing functional variation is also affected by selection on linked sites (e.g., [40]), local recombination rate in organisms with large Nc may also predict what regions of the genome will contribute the greatest adaptive responses when a population is subjected to novel selective pressures.
More broadly, this work provides direct empirical evidence that the standard neutral theory may be violated across a wide range of species. Indeed, it is clear from this work that in many taxa, natural selection plays a dominant role in shaping patterns of neutral molecular variation in the genome. It is therefore essential to consider selective processes when studying the distribution of genetic diversity within and between species. Incorporating selection into standard population genetic models of evolution will be a central and important challenge for evolutionary geneticists going forward.
Reference genome versions, annotation versions, map references, and other basic information about the genetic and genomic data for species we included in our analysis is summarized in S1 Table and S2 Table, and described in more detail below.
Our approach to estimating recombination rates is to first obtain sequence information and genetic map positions for markers from the literature, map markers to the genome sequence where necessary, filter duplicate and incongruent markers, and finally estimate recombination rates from the relationship between physical position and genetic position. Specific details of map construction for each species are described in S1 Text.
We begin with the very general selective sweep model derived by Coop and Ralph [41], which captures a broad variety of HH dynamics. To include the effects of BGS, we rely on the fact that to a first approximation, BGS can be thought of as reducing the effective population size and therefore increasing the rate of coalescence. This effect can be incorporated by a relatively simple modification to equation 16 of [41]. Specifically, we scale N by a BGS parameter, exp(-G), in equation 16, which then leads to a new expectation of average pairwise genetic diversity (π):
E[π]=θ1/exp(−G)+α/rbp
(1)
where α = 2N * Vbp * J2,2 (per [41]) and rbp is the recombination rate per base pair. This is very similar to previously published models of the joint effects of background selection and HH (e.g., [39]). To account for variation in the density of targets of selection, we build upon the approach of Rockman et al. [40] and Flowers et al. [26], which derives from the work Hudson, Kaplan, Charlesworth, and others that originally described models of background selection in recombining genomes [17,18]. Specifically, we fit the following model to estimate G for each window i:
Gi=ΣkU*fdi*sh2*(sh+P|Mi−Mk|)*(sh+P|Mi−Mk+1|)
(2)
where U is the total genomic deleterious mutation rate, fdi is the functional density of window i, sh is a compound parameter capturing both dominance and the strength of selection against deleterious mutations, Mk and Mi are the genetic positions in Morgans of window k and window i, respectively, and P is the index of panmixis, which allows us to account for the effects of selfing. We estimate functional density as the fraction of exonic coding sites in the genome that fall within the window in question. We focus on exonic coding sites as a proxy for targets of selection as they are the only functional measure that is uniformly available for all the species in our study.
Because P, U, and sh are not known, we fit this BGS model with a variety of parameter combinations. As U is generally unknown, and estimating U is difficult in most cases (e.g., [67,68]), we fit our models with three different values: Umin, where we assume U is equal to the mutation rate times the number of exonic protein-coding bases in the genome; Uconst, where we assume that U is equal to one for all species; and Umax where we assume that U is equal the lesser of the mutation rate times fives times the number of exonic protein-coding bases in the genome or the mutation rate times the genome size. Umin and Umax are multiplied by two to convert to diploid estimates. We believe that these estimates of U should roughly span the reasonable range for most species. Umin is likely to underestimate the true deleterious mutation rate as the number of exonic protein-coding bases will typically underestimate the number of evolutionarily conserved bases in a genome. Umax assumes that 20% of conserved bases are exonic coding bases and 80% are noncoding, which we admit is a relatively arbitrary assumption, but likely close to the maximum plausible U.
For P, we assume one for all vertebrates, insects, and obligate outcrossers among plants; 0.04 for highly selfing species, and 0.68 for partial selfers. These estimates correspond to selfing rates of 0%, ∼98%, and ∼50%, respectively. Estimates of selfing are available in S14 Table. For a few species of plants, we were unable to obtain reliable estimates of selfing rate (indicated by NA in S14 Table), and in this case we include all estimates of P in our model selection approach below. For sh, we fit a range of values evenly spaced (on a log scale) between 1e-5 and 0.1. Code to estimate Gi was implemented in C++ and is available from the GitHub repository associated with this manuscript.
To incorporate functional density into the HH component of the model, we make the simplifying assumption that sweeps targeting selected sites outside a window will have little effect on neutral diversity within a window, and that sweeps occur uniformly within a window. Under this assumption, we can consider functional density as a scaling factor on the rate of sweeps, Vbp. Specifically, we reparameterize the rate of sweeps, Vbp, as V, the total sweeps per genome, and then consider the fraction of sweeps that occur in a particular window i as V*fdi. This results in a simple scaling of α in Equation 1. While we note that this assumption is likely to be violated in practice, it allows us to use the homogeneous sweep model of [41] with different rates of sweeps for each window across the genome. Ultimately, of course, it would be preferable to derive a nonhomogenous sweep model that directly incorporates variation in functional density, but doing so is beyond the scope of this work. However, we believe that our simplifying is likely adequate, as the largest reduction in diversity associated with a sweep is localized to the window containing the swept site (e.g., [41]).
Incorporating the effects of functional density in both BGS and HH, our final model for the expectation of neutral diversity in window i is:
E[πi]=θneutral1/exp(−Gi)+α*fdi/rbpi
(3)
To obtain an estimate of the effect of selection for each species, we fit this model for estimates of Gi derived from different parameter combinations (see above), using the nlsLM() function from the minpack.lm package in R. In addition, we fit three simpler models: a BGS-only model (in which α is 0 and thus the second part of the denominator is 0), an HH-only model (in which G is 0 for all i, and thus the first part of the denominator is 1), and a neutral model in which both G and α are 0, and thus the model predicts that neutral genetic diversity is equal to mean genetic diversity across the genome. Together, we refer to these four models as model set 1. Finally, we fit a second set of models (model set 2) in which we use the same approach to model background selection, but use the homogenous HH model of [41] without modification to allow for variation in functional density across the genome, and thus remove the fdi term from Equation 3.
From each model fit we estimate θneutral for all four models (full, BGS-only, HH-only, and neutral) and also extract the likelihood of the fit. We then compute the AIC for each parameter combination, extract the fit with the best AIC for each model, and use that AIC to estimate the Akaike weight (relative likelihood) of each model j as
RELj=e(AICmin−AICj)/2
(4)
which we then normalize so that the weights for all four models for a species sum to one. We focus on AIC as it provides a straightforward way to compare non-nested models.
We estimate expected neutral genetic diversity in the absence of selection (θneutral) for each species as the parameter value obtained by the model with the best AIC. We then compute average observed genetic diversity for each species, and report the magnitude of the impact of selection on linked neutral diversity as 1 – (observed / neutral). Values below zero are replaced by zero. This value can be interpreted as the proportion of neutral variation removed by selection acting on linked sites, averaged across the genome.
This modeling approach has some important limitations: in particular, our approach calculates the effects of BGS and HH in windows across the genome instead of per base and we use the parameter sh instead of integrating across the distribution of fitness effects (as is done in e.g. [48,50]). Additionally, we do not use information such as locations of amino acid fixations, as is used by [49]. We fully acknowledge that these simplifying assumptions will, to a certain extent, degrade the accuracy of our modeling approach compared to other possible approaches. We argue, however, that these assumptions are necessary for this work: more sophisticated models typically require additional data (e.g., the distribution of fitness effects of new mutations or the location of recent amino acid fixations), or significantly increased computational time (i.e., by computing the effects of background selection at each base instead of in windows). For most of the species we studied, the necessary additional data are not clearly available to fit more complex models, and the increased computational time to fit per-base models would rapidly make our analysis computationally intractable. Thus, we believe that we have made reasonable tradeoffs between modeling complexity, data availability, and taxonomic breadth.
Our goal is to test whether Nc predicts the degree to which selection shapes patterns of neutral diversity, using log-transformed measures of body size and geographic range as proxies for Nc. However, many other factors could potentially influence our measure of strength of selection, including biological factors such as genome size and average recombination rate; and experimental factors such as map quality and assembly quality. In particular, we might expect to underestimate the strength of selection in species with low-quality assemblies or maps, and we might expect that on average, larger genomes and higher recombination rates would reduce the impact of selection.
In order to account for these parameters that are not directly of interest, we use two approaches. First, we compare a model that includes both our parameters of interest and our parameters not directly of interest to a model that includes only the parameters not directly of interest, in order to test whether our proxies for Nc result in a significantly better fit. Second, we fit our proxies for Nc to the residuals of a linear model including only parameters not directly of interest, in order to determine how much variation proxies for Nc explain after accounting for all the variation that can be explained by genome size, average recombination rate, and quality parameters.
We obtain assembly quality from NCBI, Phytozome, the original genome publication, or compute it directly from fasta files. C-values for plants come from http://data.kew.org/cvalues/, and C-values for animals come from [69]. In all cases, most recent estimates, "prime" estimates, or flow cytometry estimates are preferred; where several seemingly equally good estimates are available, the average is used. In some rare cases, a related species is used instead of the sequenced species if the C-value for the sequenced species is not available. We focus on C-values instead of assembly size as using assembly size as a measure of genome size confounds genome size and assembly quality (lower quality assemblies will be on average less complete and therefore smaller). Assembly parameters and sources are listed in S15 Table. We estimate average recombination rate as the overall map size divided by the size of the genome covered by the map.
In order to determine which interactions among proxies for Nc (size, range, and kingdom) to include, we start with the full model including all interactions and remove all non-significant interactions. After doing so, our model is
selection strength ~ log10(size) + log10(range) + kingdom + log10(size):kingdom
(5)
The data we analyze in this manuscript, and the scripts we used to produce our results, are available as follows. All genomes, polymorphism datasets, and GFF annotation files are publicly available from NCBI or other sources. Genome references and versions are listed in S1 Table, and URLs pointing to the location of genome sequence and GFF annotations are available in S2 Table. Sequence Read Archive (SRA) accessions for polymorphism datasets are listed in S10 Table, and references for polymorphism datasets, where available, are listed in S1 Table. Genetic maps for each species are available from the references listed in S1 Table, or as an R data file available at the GitHub page associated with this manuscript (https://github.com/tsackton/linked-selection). All Perl scripts, R scripts, and C++ code associated with this manuscript are available from GitHub (https://github.com/tsackton/linked-selection), and the function of each piece of code is documented both in comments in the code itself and in the Github README. Programs used for read mapping and genotyping, along with command line parameters, are described in the methods. The GitHub page also provides several intermediate data files, including range and size data for each species, neutral diversity and recombination rate for 100 kb, 500 kb, and 1,000 kb windows across each species, and the final dataset analyzed with the linear models described above.
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10.1371/journal.ppat.1002126 | A Genome-Wide Approach to Discovery of Small RNAs Involved in Regulation of Virulence in Vibrio cholerae | Small RNAs (sRNAs) are becoming increasingly recognized as important regulators in bacteria. To investigate the contribution of sRNA mediated regulation to virulence in Vibrio cholerae, we performed high throughput sequencing of cDNA generated from sRNA transcripts isolated from a strain ectopically expressing ToxT, the major transcriptional regulator within the virulence gene regulon. We compared this data set with ToxT binding sites determined by pulldown and deep sequencing to identify sRNA promoters directly controlled by ToxT. Analysis of the resulting transcripts with ToxT binding sites in cis revealed two sRNAs within the Vibrio Pathogenicity Island. When deletions of these sRNAs were made and the resulting strains were competed against the parental strain in the infant mouse model of V. cholerae colonization, one, TarB, displayed a variable colonization phenotype dependent on its physiological state at the time of inoculation. We identified a target of TarB as the mRNA for the secreted colonization factor, TcpF. We verified negative regulation of TcpF expression by TarB and, using point mutations that disrupted interaction between TarB and tpcF mRNA, showed that loss of this negative regulation was primarily responsible for the colonization phenotype observed in the TarB deletion mutant.
| Vibrio cholerae is the causative agent of the diarrheal disease cholera, which remains a significant public health issue in Africa, South Asia and recently Haiti. To better understand virulence gene regulation in V. cholerae we sought to investigate the contribution of small non-coding regulatory RNAs (sRNAs) to regulation of virulence in V. cholerae. We undertook a genome wide approach to sRNA discovery combining direct sequencing of sRNA transcript cDNA and genome wide binding studies of the master protein regulator of virulence, ToxT. This approach yielded one previously known and 17 new potential sRNAs under the control of ToxT. We investigated one of these new sRNAs and showed that it negatively regulates expression of the secreted colonization factor TcpF, adding a new facet to the complex gene regulatory network necessary for virulence in V. cholerae.
| Vibrio cholerae is the causative agent of cholera [1], a disease characterized by voluminous secretory diarrhea that is frequently fatal in the absence of treatment [2]. Cholera is endemic in parts of South Asia and Africa and is capable of causing massive epidemics whenever clean drinking water is lacking. While the precise in vivo signals that lead to expression of the pathogenesis program in V. cholerae have not yet been determined, the regulatory events leading to expression of the primary virulence factors, cholera toxin (CTX) and the toxin co-regulated pilus (TCP), have been well studied and the major protein factors in the cascade have been identified [3], [4]. Central to transcription of the major virulence factors is production of the AraC family transcriptional activator ToxT [5], [6]. ToxT activates production of CTX and TCP by binding to sequences known as toxboxes upstream of the −10 and −35 promoter elements in those operons and stimulating transcription [7], [8]. ToxT has also been shown to inhibit expression of the mannose-sensitive hemagglutinin (MSH) pilus, which is an anti-colonization factor, both by stimulating its degradation and inhibiting its transcription [9]. Expression of these and other factors during infection is dynamic [10]–[12] presumably due to rapidly changing conditions within the small intestine as the infection proceeds. We hypothesized that some steps in this dynamic expression may be controlled by ToxT-regulated small non-coding RNAs (sRNAs). Such regulators would have the advantage of being fast acting since an sRNA need only be transcribed in order to function.
sRNAs influence a variety of processes in bacteria, mostly at the post-transcriptional level through sRNA-mRNA interactions [13], [14]. Processes impacted by sRNA regulators include the DNA damage (SOS) response [15], [16], sugar uptake [16], quorum sensing [17], [18], expression of outer membrane proteins [19], [20] and many others. Recent investigation into the sRNA transcriptome of bacteria has indicated much greater complexity than was previously appreciated [16], [21]–[23]. Given that sRNAs are such ubiquitous regulators of gene expression, we were interested in investigating whether they contributed to virulence factor regulation in V. cholerae.
There are several pieces of evidence that suggest the existence of sRNA regulators of virulence in V. cholerae. The major sRNA chaperone Hfq, a protein which many sRNAs act in conjunction with, is required for V. cholerae pathogenesis [24]. In addition, two sRNAs that contribute to virulence were recently discovered. The first regulates the porin OmpA and outer membrane vesicle formation [20] but is not under the control of the virulence regulon, while the second regulates glucose uptake and is a member of the ToxR regulon as it is transcriptionally activated by ToxT downstream of ToxR [25]. To conduct a thorough survey of the possible ToxT-regulated sRNAs, we took a genome-wide approach to discover sRNAs involved in virulence gene regulation by direct cloning and sequencing of sRNA transcripts and by identifying genomic sites bound by purified ToxT.
We used direct cloning and deep sequencing of RNA transcripts 50–250 nucleotides in length [16] to compare a culture in which ToxT or an inactive version missing the helix-loop-helix DNA binding domain (ΔHLH) [11] was expressed from an arabinose inducible promoter on a plasmid (pToxT or pToxTΔHLH). The highly abundant 5S rRNA and tRNAs present in this size range were depleted prior to sequencing as described [15]. After sequencing we removed residual tRNA and rRNA reads and aligned the remaining reads to the V. cholerae genome. The number of reads of each unique transcript in each library was normalized to the number of reads of MtlS, an abundant sRNA [16] that does not vary between the conditions tested here (data not shown). A total of 14,578 unique sequences were identified between the two libraries, of which 13,309 were present in only one library or the other. Many sequences not shared between the libraries were very low in abundance and may represent products of random RNA degradation either in vivo or during preparation of the libraries. The positions of all reads aligned to the N16916 genome and their relative abundances in the two libraries is shown in (Table S5). The short sequencing reads were organized into clusters to provide an approximation of each putative sRNA sequence. Many of the 1,269 clusters shared between the libraries had large variations in abundance between the libraries. While this may reflect the true difference in the sRNA transcriptome between these two strains, to help us narrow the list of potential sRNAs we sought a method to determine which sRNAs were directly regulated by ToxT.
Because sRNA promoters share many characteristics with open reading frame promoters, it seemed reasonable that any sRNA directly controlled by ToxT would have a ToxT binding site in cis. To investigate this we undertook a genome-wide ToxT pulldown of genomic DNA fragments 200–500 bp in length that were modified to allow for subsequent deep sequencing (figure 1A and 1B), similar to an approach taken with the transcription factor CodY from Staphylococcus aureus [26]. Using a cut off of 3-fold enrichment in pulldown libraries over input libraries, we identified 199 putative binding sites of which 67 overlapped between technical replicates and likely represented the most specific sites (table s6). A DNA binding motif generated from the 67 enriched sites was a close, though not identical, match to the canonical toxbox [27] (figure 1 panel C). Of the overall 199 putative binding sites, 64 mapped to the Vibrio Pathogenicity Island (VPI), which is consistent with the fact that this locus contains the majority of ToxT-regulated genes. Most, but not all previously described ToxT binding sites were present in the pulldown library, notably absent are sites within the tcpA promoter [27] and sites within the MSH pilus operon [9].
Cross-referencing the putative ToxT binding sites with ToxT-regulated sRNA sequencing data yielded a collection of 18 potential sRNAs transcribed from intergenic regions with cis ToxT binding sites. The locations of these pulldown sites, sRNA transcripts and relative abundance between ToxT and ToxTΔHLH expressing strain libraries are shown in table 1. This analysis revealed two putative sRNAs within intergenic regions in the VPI. To investigate whether these two sRNAs represented genuine transcripts, we probed for each by northern blot using total RNA from cultures expressing ToxT or ToxTΔHLH. Both of these sRNAs are dramatically upregulated upon expression of ToxT and both are present at the expected size predicted by the sRNA deep sequencing experiment (figure 2). One of these sRNAs was discovered independently by another group and was named TarA [25] (for ToxT activated RNA A). The other, to the best of our knowledge, remains uncharacterized. Since it also showed dramatic up regulation upon expression of ToxT, and given its role in virulence (described below), we named it TarB. Having now determined that at least two ToxT-regulated sRNAs were present in the VPI, we set out to determine whether they played detectable roles in the virulence of V. cholerae.
Deletion of each sRNA was constructed in the genome and the mutants were competed against the fully virulent parental strain carrying a ΔlacZ marker. No significant difference in virulence was observed for the ΔtarA strain either when competed against the parental strain or a strain harboring tarA (promoter and toxboxes included) on a high-copy vector (figure 3A). It was previously reported that a ΔtarA mutant had a decreased fitness relative to its parental strain [25], however, those experiments were performed with a classical biotype strain of V. cholerae, and hence regulation by TarA may be less critical or perhaps is masked in the current pandemic El Tor biotype tested here.
In contrast the ΔtarB strain outcompetes the parental strain by a small but statistically significant factor of 1.6 (figure 3A) suggesting TarB is a negative regulator of virulence. The ΔtarB and complemented strains show no change in growth rate or cell yield in Luria-Bertani (LB) broth or in a minimal medium, nor a change in survival in pond water (figure S1).
To see if the negative effect on virulence could be complemented in trans, we competed a ΔtarB strain containing the sRNA with its own promoter cloned onto a low copy plasmid (ptarB) against a ΔtarB strain carrying empty vector (pMMB). The ΔtarB strain out-competed the complemented strain to an extent that exceeds out competition of the parental strain (figure 3A), which may be due to overexpression of TarB from ptarB. If expression of TarB is detrimental to colonization, as these data indicate, the plasmid carrying TarB may be selected against during the infection. To investigate this, small intestine homogenates were plated on LB agar and colonies were replica plated onto medium containing ampicillin, which selects for colonies containing the plasmid. Consistent with our hypothesis, the plasmid carrying TarB was lost more frequently than the empty plasmid (figure 3B). This was not the case during growth in LB in the absence of antibiotic selection (data not shown).
For further confirmation of the hypercolonization phenotype of the ΔtarB mutant, we performed single strain infections with the ΔtarB and wildtype strains (Figure 3C). Total colonization in these two strains indicated that, as seen in competition experiments, the ΔtarB mutant showed significant hypercolonization reflected by increased CFUs in the output.
The out-competition phenotype of the ΔtarB strain in infant mice and more drastic attenuated phenotype of the complemented ΔtarB strain suggest that TarB is deleterious to colonization of the small intestine. The model that TarB is positively regulated by the master virulence gene activator ToxT, yet functions as a negative regulator of virulence, is counterintuitive. To investigate this model further we performed competitions after incubation of the competing strains for varying times in filter sterilized pond water in an attempt to test the strains in a scenario more similar to a natural infection. After 4 hours (h) of incubation in pond water, the ΔtarB mutant retained its ability to outcompete the parental strain, but this phenotype was lost after 6 h of incubation in the pond (figure 3D). After 24 h of pond incubation, the parental now had a statistically significant advantage over the ΔtarB mutant when competed in vivo, but not when competed for in vitro growth in LB.
To further investigate the ability of ToxT to control TarB expression, we measured expression of TarB under an in vitro virulence factor inducing condition, which is growth for 4 h static in AKI broth containing sodium bicarbonate followed by 4 h with aeration [28]. Expression of TarB is induced during the initial static phase of growth, but returns to background level after 4 h of growth with aeration (figure 4A, top panel). The initial induction is dependent on toxT as well as toxR and tcpP/H (figure 4A, bottom panel), which are genes upstream in the ToxR regulon that induce ToxT expression [3], [4], [29]. We also noted that TarB is overexpressed between 7–10 fold in the complemented strain, which is consistent with its in vivo phenotype being more dramatic then the parental strain in competitions with the ΔtarB mutant.
We also investigated the role of the RNA chaperone Hfq in TarB stability and action as many sRNAs that act in conjunction with Hfq are destabilized in its absence [30], [31]. To investigate expression from the TarB promoter we constructed a transcriptional fusion of a reduced half-life allele of GFP (GFP-ASV) [10], [32] to the TarB promoter. The fusion was used to measure activity of the TarB promoter during induction of ToxT from the pToxT plasmid in both Hfq+ and Hfq− strains. In these same strains, steady state levels of TarB from a native copy of the gene were measured by northern blot. The results of these experiments are summarized in Figure S2 and indicate that Hfq likely does not play a role in stabilizing TarB or in its interaction with its target (described later).
Some basal expression of TarB is seen during culture in LB, which is greatly enhanced at the transition to stationary phase, however this increase is independent of ToxT (figure 4B). Enhanced expression of TarB during late exponential and stationary phase growth in LB broth and in the static phase of AKI growth (see above) may be related to oxygen tension in solution. To investigate the contribution of oxygen tension during AKI static growth to TarB expression we measured expression of ToxT, TcpF and CadC by qRT-PCR and TarB via the TarB-GFP fusion over the static growth period of AKI. The transcription factor CadC is activated by the LysR homologue AphB under low oxygen and low pH conditions [33], and its measurement is used here as a method of determining when the culture is undergoing those conditions. Additionally, AphB has been shown to be critical for activation of TcpP/H [34], [35], which in turn activates ToxT expression. The results of this experiment are summarized in figure S3. As measured against expression after two hours of static growth, expression of ToxT and TcpF have more or less reached maximum by 3 hours of static culture (Figure S3 A), though expression of TarB-GFP and CadC continue to rise, suggesting additional activation of the TarB and CadC promoters. Western blot for TcpF in the TcpF-FLAG fusion strain grown under the same conditions independently confirms this finding for TcpF (Figure S3 A), though OmpU could not be used as a loading control for this blot [36], so we did not carry out quantification of this blot. To investigate the contribution of anaerobiosis to expression of TarB, we prepared cultures of wildtype and ΔtoxT strains in phosphate buffered LB media to prevent large alterations in pH and with glucose to support anaerobic growth [37]. These cultures were prepared in an anaerobic chamber and then grown either aerated in 2 mL or in sealed 10 mL cultures to approximately the same optical density, RNA extracted from these cultures was used in northern blots for TarB (Figure S3 B). The results show that anaerobic conditions do stimulate TarB expression independent of ToxT, though increases in expression of TarB in the wildtype culture were approximately twice as great when adjusting for loading, indicating that under anaerobic conditions, ToxT does drive some expression of TarB. Taken together these results suggest that anaerobic conditions activate TarB expression possibly through the action of AphB.
The sequence upstream of the predicted TarB start site was investigated and revealed putative −10 and −35 sequences, as well as a direct repeat of putative toxboxes (figure 5A). The 3′ end of TarB determined by deep sequencing corresponded to the poly-U tract of a Rho independent terminator. The toxboxes upstream of tarB are arranged in similar fashion to those upstream of the virulence gene tcpA [7]. To confirm binding of ToxT to this site, a DNA probe consisting of basepairs −100 to +1 relative to the predicted transcription start site was assayed for ToxT binding by gel shift assay. ToxT bound to this region with an affinity within the range of other reported toxboxes [38], but not to a non-specific probe of similar length consisting of a PCR product of the 4.5S RNA sequence (figure 5B).
We next wanted to determine the target(s) of TarB that were responsible for the observed negative role of TarB in virulence. Nineteen putative mRNA targets were identified using the program targetRNA [39], which searches for complementarity between the query sRNA and the 5′ untranslated region (UTR) of mRNAs of annotated ORFs within a given genome. To validate putative targets, we looked for changes in the steady-state level of the candidate mRNAs using quantitative reverse transcription PCR (qRT-PCR) on total RNA from TarB+ and ΔtarB strains both over-expressing ToxT. Of the six putative targets we selected for further analysis only two, tcpF and VC2506, had any detectable expression under the conditions tested. When levels of the potential target transcripts were normalized to toxT transcript levels, a significant difference between the TarB+ and ΔtarB strains was revealed for the tcpF mRNA but not for VC2506 (figure 6A). The observed increase in tcpF mRNA in the ΔtarB background suggests that TarB negatively regulates tcpF, which would be consistent with the negative role of TarB in virulence.
To determine if TarB similarly affects TcpF protein expression level, we generated a C-terminal FLAG tag fusion to TcpF in the genome to measure expression by western blot after AKI induction. We also generated two sets of three point mutations each within the predicted region of complementarity between TarB and the 5′ UTR of tcpF, yielding tcpF* and tarB* alleles. These mutations are underlined in figure 6B. Because the tcpF and tcpE ORFs are very close together, there is some overlap between the coding sequence of tcpE and the 5′ UTR of tcpF, however the substitutions made do not affect the amino acid coding sequence of the upstream gene tcpE nor do they alter the Shine-Dalgarno sequence of tcpF. Moreover, the mutations were designed to preserve GC content of the region altered. Either set of mutations present alone (tarB* or tcpF*) would be predicted to disrupt the interaction between TarB and the tcpF 5′ UTR while the presence of both is compensatory and would be predicted to restore the interaction.
A strain deleted for tarB was then used as the parent strain to construct derivatives having either the tcpF-FLAG or tcpF*-FLAG allele. These two derivatives were then complemented with either ptarB, ptarB* or empty vector (pMMB). These six strains along with the wild type strain carrying the TcpF-FLAG fusion were grown through the static culture phase of an AKI induction and were western blotted to measure TcpF-FLAG expression. The blots were then stripped and probed for OmpU, which is not regulated by ToxT [40], to serve as a loading control. Compared to the wild type strain (figure 6C, first column) the ΔtarB and ΔtarB tcpF* strains carrying the empty vector showed elevated TcpF levels. When the ΔtarB and ΔtarB tcpF* strains were complemented with ptarB* and ptarB, respectively, levels of TcpF remain largely unchanged, indicating that when either the tcpF mRNA or tarB sRNA are mutated, no interaction can take place and these strains show expression of TcpF similar to the ΔtarB mutant. However, when the ΔtarB and ΔtarB tcpF* strains were complemented with ptarB and ptarB*, respectively, to observe affects of the wild type or compensatory interaction when the sRNA is overexpressed, the levels of TcpF drop substantially. Six replicates of this experiment were performed and reveal that statistically significant drops in expression of TcpF occur only in strains containing either the wildtype TcpF target sequence complemented with wildtype TarB or strains in which the target sequence and sRNA have compensatory mutations (Figure S4). When these strains were blotted after the aeration growth phase of AKI induction, no differences in TcpF expression were visible (data not shown), which would be expected given the up regulation of tarB during the static phase but return to basal level of expression during the aeration phase of AKI induction.
To determine if the interaction of TarB with the 5′ UTR of tcpF was responsible for the phenotype in mice, competitions were carried out using tcpF* strain derivatives. Competition of the ΔtarB tcpF*(ptarB*) strain against the same strain carrying empty vector yielded the expected result of out-competition by the latter strain, which lacks tarB* (figure 6D). Competition of the ΔtarB tcpF*(ptarB) strain against the same strain with vector alone yielded a competitive index that was significantly closer to one, which is expected since neither strain should have an interaction between sRNA and target. The difference between the two competitive indices was highly significant (p<0.003).
To determine if the pond water-incubation phenotype of the ΔtarB mutant was related to expression of TcpF or TarB in this environment we carried out experiments to measure TarB and TcpF levels over the course of pond water incubation, the results of these experiments are summarized in Figure S5. TcpF expression was followed through the course of pond water incubation via the C-terminal FLAG fusion in both the wildtype and ΔtarB backgrounds by anti-FLAG western blot. The results indicate that the wildtype and ΔtarB mutant show similar levels of TcpF expression initially, however, over the course of pond incubation, TcpF levels drop in the wildtype strain, but not the ΔtarB strain. Transcription of TarB, as measured by production of GFP from the TarB promoter-GFP fusion indicates that levels of TarB expression do not change dramatically over the course of pond water incubation. Northern blots for TarB expression over the course of pond water incubation suggest that TarB steady state levels drop (data not shown), but this may be due to the observed wholesale degradation of RNAafter increasing time of incubation in pond water, such that accurate measurements of TarB expression via northern blot are not possible. These results indicate that while TarB expression levels do not vary dramatically over the course of pond water incubation, TcpF protein levels do drop, and this drop was absent in the ΔtarB mutant. This enhanced TcpF expression in the ΔtarB mutant may contribute to the phenotype of the ΔtarB mutant in vivo after pond water incubation, as over expression of TcpF in pond water would contribute to metabolic drain prior to infection.
Deep sequencing has allowed the interrogation of processes in bacteria with unprecedented detail. Here we used two complementary approaches, deep sequencing of cloned sRNAs and ToxT-bound DNA fragments, to identify ToxT-regulated sRNAs. The number of previously estimated ToxT binding sites in the V. cholerae genome was between 17 and 20 [9], [27]. We have now uncovered what may be a greatly expanded set of targets for ToxT to coordinate expression of protein coding genes as well as sRNAs. The results of the pulldown experiment returned regions of a few hundred basepairs in length that were enriched and many predicted sites are overlapping, which is due to the size range of the fragments used in the pulldown and the automated analysis of the pulldown data. Although many of these sites remain to be validated we are confident in proposing that the ToxR regulon encompasses many more transcripts, both protein coding and otherwise, than was previously thought.
The results of the sRNA deep sequencing reveal the method to be exquisitely sensitive. Because of our exclusion of larger RNA transcripts and depletion of tRNA and 5S RNA in the sRNA size range and the use of Illumina massively parallel sequencing technology we have achieved tremendous depth of coverage of potential sRNA genes in V. cholerae [16]. Transcripts represented by ∼40 or more reads could be detected by northern blot (this study and data not shown). However, transcripts represented by fewer than ∼40 reads, which may represent low abundance sRNAs, are difficult or impossible to detect by northern blot and other methods such as qRT-PCR are needed for independent validation. Of the 18 candidate ToxT-regulated sRNAs we report here, 11 (including tarB) were not identified as putative sRNAs in previous sequencing experiments or bioinformatics-based approaches to sRNA discovery [16], [41], displaying the depth of information that can be gained with high throughput sequencing technologies and the conditional expression of sRNAs. In comparison to other methods of sRNA discovery, our approach has the advantage of being targeted in its search for ToxT-regulated sRNAs but unbiased in its identification of sRNAs. Approaches utilizing RNA binding proteins such as Hfq [42], [43], are not exhaustive as the sRNA we report here likely does not interact with Hfq, though those methods do have the potential to identify mRNA targets as well as sRNAs. Additionally, this approach benefits from the vast strides made in high throughput sequencing recently which generates far more depth of data then microarray based methods [44], including exact 3′ and 5′ ends and unbiased coverage of positive and negative strand sRNAs. Keeping the latter in mind, this approach can also identify many potential sense and anti-sense sRNAs [16] overlapping with protein coding genes although these potential sRNAs are not discussed here.
In this study we identified a new sRNA member of the ToxR regulon that fine-tunes expression of a virulence factor also within the ToxR regulon, thus adding a new facet to the elaborate virulence gene regulation program in V. cholerae. However, when placed in the larger context of V. cholerae pathogenesis, it is not entirely clear why a repressor of an essential virulence factor would be produced at the same time as the virulence factor it negatively regulates. The answer may lie in the biphasic nature of V. cholerae gene expression during intestinal colonization [10], [11]. The initial induction of virulence factors requires ToxR/S- and TcpP/H-dependent ToxT expression in the intestinal lumen. This is followed by a more robust activation of the TCP and CTX operons closer to the epithelial surface of the small intestine, driven by a positive feedback loop in ToxT expression that is thought to activated in part by the presence of bicarbonate [28], [45].
During AKI induction in vitro in the absence of bicarbonate, ToxT production is stimulated during static growth but the transition to aerated growth is required for CTX production [46]. All experiments reported here included bicarbonate in the medium over the course of the experiment, which is sufficient to cause CTX production even during static growth [28], [47]. Research done on the contribution of anaerobiosis to virulence gene expression in V. cholerae El Tor isolates has shown stimulation of VPI gene products [48], and that the AphB protein, which functions upstream of tcpP/H, is active primarily at low oxygen tension and low pH [33]. Since TarB expression is greatest during the static phase of AKI induction, but repressed during aerated growth even though bicarbonate had been added to induce CTX and TCP expression prior to aeration, it is tempting to speculate that TarB expression is enhanced in microaerobic conditions. The experiments we performed under anaerobic conditions also suggest that oxygen plays a role in TarB expression, though it may be only one of a host of signals, which act on TarB in vivo. TarB's function under low oxygen tension could be to repress TcpF expression prior to penetration of the mucous barrier of the small intestine. Upon reaching the epithelial surface, the higher oxygen tension would contribute to reduced TarB expression, allowing TcpF to be fully expressed. This would fit with the proposed role of TcpF in colonization of the epithelium [49]. The intestinal brush border is a highly vascular structure, commensurate with its role in absorbing nutrients, and it would not be unreasonable to speculate that the lumenal space adjacent to it would have greater oxygen tension then the luminal fluid. The actual oxygen tension of the small intestine may be quite low as oxygen requiring luciferase reporter systems in bacteria do not function in the small intestine [50], [51]. However, to the best of our knowledge, oxygen measurements at the brush border have not been reported.
Other possible factors responsible for controlling TarB expression could be entry into stationary phase, as increased TarB expression is observed in V. cholerae grown in LB broth to late exponential and stationary phase. Also, during AKI induction, 4 hours of growth in static culture corresponds with entry into stationary phase [46]. Stationary phase regulation of TarB may also occur via an alternative sigma factor as was observed for the sRNA VrrA [20], or possibly via CRP-cAMP mediated repression as carbon sources become depleted [35].
Coordination of TcpF expression by TarB appears to have a positive effect on colonization if the bacteria are coming from a resource poor environment, such as contaminated pond water, and even then, the differences in colonization efficiency of the ΔtarB mutant are quite small. In contrast, if the bacteria are grown in a rich medium prior to infection, overexpression of TcpF in the ΔtarB mutant appears to be beneficial. The reasons for this may relate to the details of the experimental system used here, wherein immunologically naïve infant mice are used as a host. In contrast, in nature many hosts in endemic areas will have some level of pre-existing immunity, and may harbor anti-TcpF antibodies as TcpF is a known antigenic protein [49]. It is possible that tight repression of TcpF provides a more pronounced fitness advantage in nature under different conditions then those used here, which would explain TarB's presence among all sequenced isolates of toxigenic V. cholerae (data not shown). Further insight into the functional role of TcpF in colonization may shed more light on the necessity of the TarB-mediated post-transcriptional regulation observed here.
All animal experiments were done in accordance with NIH guidelines, the Animal Welfare Act and US federal law. The experimental protocol using animals was approved by Tuft University School of Medicine's Institutional Animal Care and Use Committee. All animals were housed in a centralized and AAALAC-accredited research animal facility that is fully staffed with trained husbandry, technical, and veterinary personnel.
V. cholerae O1 serogroup El Tor biotype isolate E7946 and derivatives were grown at 37°C in LB broth with aeration. For AKI induction, strains were grown in AKI broth (1.5% peptone, 0.4% yeast extract, 0.5% NaCl, 0.3% NaCHO3) statically for 4 h at 37°C followed by aeration for 4 h 37°C. To induce expression of cloned genes on plasmids, arabinose was added to 0.04% upon reaching mid-exponential phase (optical density at 600 nm [OD] = 0.3). All DNA manipulations were done in E. coli DH5α or derivatives with plasmids maintained with the appropriate antibiotics.
All PCR reactions were carried out with EasyA polymerase according to the manufacturer's specifications using the indicated primers, the sequences of which can be found in table S1. The descriptions of all plasmids used in this study are included in table S2.
Plasmids pToxT and pToxT ΔHLH plasmids we constructed by PCR amplification of the toxT ORF including native RBS from gDNA from either wildtype V. cholera E6749 or an E6749 strain carrying an internal deletion of the helix-loop-helix DNA binding domain [11] using primers NcoI_ToxT_F and XbaI_ToxT_R. This PCR product was then cloned into the NcoI and XbaI sites of the pBAD24 plasmid [52] to allow expression of ToxT upon addition of L-arabinose. Unmarked deletions of chromosomal genes were constructed by SOE PCR introduced using a derivative of the pCVD442 allelic exchange vector, pCVD442-lac which contains the pUC19 LacZ gene and MCS, as described [53].
Point mutations in the tarB gene were generated by SOE PCR using primers xbaI_TarB comp_F, TarB_mut_R1 and TarB_mut_F2 and SacI_TarB_comp_R, using E6749 genomic DNA as template. PCR products were mixed in a one to one ratio, and added to a PCR reaction run for 25 cycles at an annealing temperature of 50°C without primers and the mutated sRNA sequence plus promoter were amplified with XbaI_TarB_comp_F and SacI_TarB_comp_R which contain SacI and XbaI restriction sites which were subsequently used for cloning into pMMB67EH to generate ptarB*. The wildtype complementation vector ptarB was generated by cloning a PCR product generated using XbaI_TarB_comp_F and SacI_TarB_comp_R primers and genomic DNA as a template.
Point mutations in the tcpF 5′ UTR were also generated by SOE PCR using primers XbaI_TcpF_mut_F1, TcpF_mut_R1, TcpF_mut_R2 and XbaI_TcpF_mut_R2 using an identical procedure as above. The final ∼2 kb product containing the mutated tcpF 5′ UTR sequence which was subsequently cloned into the XbaI site of the pCVD442-lac vector which was then mated into strains of interest. Double crossovers were selected on 10% sucrose plates. Individual double crossovers were screened for the mutated sequences by sequencing with the TcpF seq primer and the XbaI_TarB_comp_F primer and confirming double crossover by streaking on 10% sucrose as well as ampicillin containing plates to ensure sucrose resistance and ampicillin sensitivity.
C-terminal FLAG fusions to TcpF were generated by amplification of the C-terminal 346 bp using the TcpF_qt_F primer and the TcpF-FLAG_R primer to add the FLAG amino acid sequence [54], this product was subcloned into Topo pCR2.1 (Invitrogen). The resulting plasmid was cut using KpnI and EcoRV and the insert containing the C terminus of TcpF with the FLAG fusion was cloned into a modified pGP704 suicide vector [55] which contains a chloramphenicol resistance drug marker in place of an ampicillin marker (pGP704cat). This construct was then mated into strains of interest and single crossovers were selected for on chloramphenicol plates at 2 µg/mL. Proper insertions were confirmed by PCR using the TcpF-FLAG reverse primer and TcpF seq forward primer. A merodiploid strain was constructed by plasmid integration resulting in the placement of GFP(ASV) under the control of one copy of the TarB promoter followed by the native TarB locus downstream of the integrated plasmid sequence. The plasmid borne fusion was generated by amplifying the +3 to −376 positions in the TarB promoter from E6749 genomic DNA using primers TarB_F and TarB_-300_R and subcloning the product into pCR2.1 yielding ptarB-300. GFP was amplified from pGfpmut3.1 plasmid (Clonetech) using primers Fgfp2 and Rgfp2 which adds a ribosomal binding site and SacI site at the 5′ end and the destabilizing (ASV) [32]C terminal amino acids and a SmaI site at the 3′ end. The GFP(ASV) PCR product was cloned in a triple ligation with the SacI/EcoRV fragment from ptarB-300 into pGP704cat digested with SmaI to generate the transcriptional fusion. The resulting plasmid (pTarB-GFP) was mated into E6749 strains and single crossovers were selected on chloramphenicol and confirmed by PCR using primers Rgfp2 and XbaI_ΔTarB_R2.
Single colonies of strain AC3763 (ΔtoxT) transformed with either pToxT or pToxTΔHLH plasmids were picked and grown in LB broth containing streptomycin and ampicillin both at 100 µg/mL overnight. Strains were back diluted from overnight cultures to an OD of 0.03 in 200 mL LB supplemented with streptomycin and ampicillin both at 100 µg/mL and were grown with aeration at 37°C until the strains reached mid-exponential phase (OD = 0.3). Arabinose was then added to 0.04% to induce expression of toxT alleles from pToxT plasmids, and induction was allowed to proceed for 20 minutes prior to RNA extraction. Total RNA was purified from the bulk culture by phenol/chloroform extraction and isopropanol precipitation. Cloning and sequencing of sRNA was carried out as previously described [16], sequences of the micro RNA cloning linkers (IDT) used are included in table S4. In order to further decrease tRNA and 5S rRNA in the final sequenced products, the depletion step described in the previously published procedure was carried out twice with the addition of an oligo targeting the serGCC tRNA (5′-GCGGTGAGTGAGAGATTCGAACTCTC-3′). The final cDNA products were prepared for Illumina Genome Analyzer II sequencing using Illumina primers 1a, 1b and 1c (table S1) for the first 10 cycles of PCR, followed by gel purification and Illumina primers 2a and 2b (table S1) for the final 4 cycles of PCR followed by PCR clean up (Stratagene). Final products were run on a Bioanalyzer High-Sensitivity DNA chip (Agilent) prior to Illumina sequencing to normalize loading of the two samples and ensure quality of the libraries. The libraries were pooled and placed on one lane of an Illumina Genome Analyzer IIx paired-end sequencing run at Tufts University Core Facility. Briefly, a paired-end sequencing run sequences both the 5′ and 3′ end of every DNA molecule attached to the flowcell. The first read is downstream of linker 1 and the second read is downstream of linker 2 (ToxT library) or linker 3 (ΔHLH library) so that for every pair, the directionality of the original RNA molecule could be determined. Sequence reads were trimmed to remove linker sequences and filtered so that 100% of the sequenced bases in each read had a minimum quality score of 5 (base call accuracy at least 68%). Reads were aligned to the O1 biovar N16961 genome (NCBI Accession Nos. NC_002505, NC_002506) using Bowtie (http://bowtie-bio.sourceforge.net). Reads matching rRNA or tRNA regions were removed from the alignment, leaving 1,062,048 reads in the ToxTΔHLH library and 2,212,216 reads in the ToxT library. Unique transcripts totaled 6,815 for ToxTΔHLH and 27,787 for ToxT. The alignments were then processed to generate a library of clustered transcripts using the method previously described [16]. This resulted in 3,309 clusters for the ToxTΔHLH library and 12,534 clusters for ToxT library. Clustered reads were output into “gff” format and viewed using GenomeView (http://genomeview.org). The number of reads in sRNA clusters were normalized by dividing the number of reads in that cluster by the ratio of MtlS reads in that library to total MtlS reads. For example normalized readsToxT = cluster readsToxT/(MtlSToxT/(MtlSToxT+MtlSToxTΔHLH)).
E. coli strain BL21(DE3) was transformed with the plasmid pMAL-TEV-His-thr-ToxT (table s3). The resulting strain was grown on LB agar plates containing ampicillin and a single colony was picked for growth of a 4 mL overnight culture. The overnight culture was used to inoculate 1 L LB broth containing ampicillin at 100 µg/mL and was grown with aeration at 37°C. Transcription was induced once the culture had reached exponential phase (OD = 0.5–1) by addition of IPTG to 1 mM. Induction was allowed to proceed shaking at 20°C for 16 h, after which, cell pellets were collected by centrifugation and resuspended in 20 mL lysis buffer (20 mM Tris-HCl pH 8, 2 mM DTT, 1 mM EDTA, 250 mM NaCl) plus Complete protease inhibitors (Roche). Cell pellets were lysed and the lysate was cleared by centrifugation at 18,000 rpm in a SS34 rotor.
The cleared lysate was then applied to a 5 mL dextrin MBPtrap column (GE Life sciences). The column was washed with lysis buffer followed by elution with MBP elution buffer (as lysis buffer, +1 mM maltose). The elution fractions were subsequently diluted 10-fold with buffer QB1A (20 mM Tris-HCl pH 8.0, 1 mM DTT) and applied to an 8 mL Source15Q anion exchange column equilibrated in QB1A. The protein was eluted using a 0 to 20% gradient of QB1B (20 mM Tris-HCl pH 8.0, 1 M NaCl, 1 mM DTT) developed over 25 column volumes. The peak fractions were diluted 5-fold in SB1A buffer (25 mM phosphate buffer pH 6.0, 1 mM DTT) and applied to a 8 mL Source15S cation exchange column equilibrated in SB1A. The protein was eluted using a 15 to 35% gradient QB1B (25 mM phosphate buffer pH 6.0, 1 mM DTT, 1M NaCl), which resulted in two peaks, the second peak was known to be a soluble aggregate and was discarded. The initial peak was split into two aliquots, one of which was applied to a Superose 12 gel filtration column in EMSA buffer (10 mM Tris-HCl pH 7.5, 200 mM KCl, 10 mM βME) for use in mobility shift assays, the other aliquot was cleaved with TEV protease overnight at 4°C and subsequently diluted 5-fold in SB1A and applied to a 2 mL Source15S cation exchange column to separate His-ToxT from the cleaved MBP fusion protein. His-ToxT was eluted from this column with a 35 to 100% gradient of SB1B developed over 12 column volumes. Finally, His-ToxT peak fractions were applied to a Superdex 75 gel filtration column in EMSA buffer. These final steps did leave a small amount of TEV protease in the final purified product.
Genomic libraries were prepared by centrifuging 10 mL of overnight growth of wild type (AC53) V. cholerae, washing 2× with TBS and resuspending in 5 mL TBS. To generate gDNA fragment sizes of 300 to 1,000 bp, the cell pellet was subjected to four 30 second sonication cycles on ice using a sonicator micro tip (Branson); each sonication cycle was separated by a 30 second incubation on ice. After sonication, RNAase A was added to a concentration of 2 µg/mL, the samples were incubated at 37°C for 20 min to allow for degradation of RNA. DNA was purified with 2 rounds of extraction with citrate buffered phenol∶chloroform (Ambion) followed by a final extraction with chloroform only and then concentrated by ethanol precipitation. Fragmented DNA was used to prepare three different bar-coded libraries using adapters BC1a/BC1b, BC2a/BC2b and BC3a/BC3b (table S4) as described [26]. For the final amplification and purification of bar-coded libraries, ten PCR reactions were done using linkered and size selected gDNA as template using primers Olj 139 and 140 and EasyA polymerase (Stratagene). PCR conditions were as follows, denaturation for 5 minutes at 95°C, annealing for 30 seconds at 65°C, elongation for 30 seconds at 72°C, cycling back to denaturation at 95°C for 30 seconds for 15 cycles after which reactions were pooled and incubated with 50 µL ExoSAP-IT (USB) at 37°C for 1 h. Final purification of libraries was carried out by phenol∶chloroform extraction and ethanol precipitation and resuspension of libraries in 100 µL deionized water.
Binding reactions contained 15 µg bar-coded DNA library in a total volume of 250 µL with 200 nM purified His6-tagged ToxT purified as above or with His6-tagged TEV protease in EMSA buffer with 10 µg/mL sheared salmon sperm DNA, 0.3 mg/mL BSA and 10% glycerol. Reactions were allowed to incubate with gentle mixing at 37°C for 1 h, after which the reaction was added to a microcentrifuge spin column (Pierce) packed with a 50 µL bead volume column of HisPur cobalt resin (Pierce) that had been equilibrated in the above buffer. The reaction was allowed to bind to the column by mixing gently at 37°C for 1 h. Flow through was then collected by spinning the column in a microcentrifuge at 3,000× g for 1 minute. The column was washed 3× by gentle resuspension of the bead volume in 250 µL of EMSA buffer with the above additions, followed by centrifugation. The column was washed an additional 3× as above, but in EMSA buffer only. After the final wash, the bead volume was resuspended in 10 mM Tris-HCl pH 8 and boiled for 5 minutes and allowed to cool to room temperature, then incubated with proteinase K (5 µg/ml) for 30 minutes at 65°C, followed by boiling for 5 minutes. After centrifugation for 1 min at 3,000× g, the resulting 100 µl of the supernatant fluid was purified by using a PCR purification kit (Qiagen) and then subjected to 10 cycles of PCR amplification with primers Olj139 and Olj140, repurified, quantified on the Bioanalyzer high sensitivity DNA chip (Agilent), and subjected to deep sequencing, along with aliquots of the input libraries prior to pulldown, using the Illumina Genome Analyzer II on the paired end setting.
Reads from the Illumina libraries were aligned to the N16961 genome. Sequence alignment and assembly were performed as described above. After alignment, reads that did not match the genome were discarded and the sets were normalized so that each set contained the same number of reads. Alignment positions were shifted by half their insert length as determined by each mapped pair, giving the center position of each sequenced DNA molecule. These positions were then tabulated and used to generate a coverage map of the genome using a rolling average with window size of 35 bases. Coverage maps were generated for every sample. For each genomic DNA and corresponding pulldown sample, an enrichment map was created, which represented the ratio of the values from the pulldown sample over that of the genomic DNA sample. Enrichment maps were then scanned to identify regions that had more than 3× the average coverage for more than 100 consecutive positions. The false discovery rate (FDR) was then calculated by performing the same analysis with the control and pulldown samples switched. At 3× coverage, the FDR was 0.03 and 199 enriched sites were identified totaled between the libraries, of which 67 were observed in both replicates. Significance of each enriched region was assessed using two methods [56]. First, the number of reads in that region in the control sample was used to generate a Poisson distribution. This was then used to assess the probability of the same number of reads occurring in the pulldown sample. Using this method, all regions identified had a p-value of <1×10−98. Second, a Z-score was found by comparing the proportion of tags in the control sample to that in the pulldown. All of the regions identified had a significant difference in the proportion of tags counted between the control and pulldown samples, with z-scores >7.7. The nucleotide sequences from the overlapping set were used as a training set for finding motifs using MEME 4.1.0. We allowed MEME to find motifs that occurred at least one time in each fragment. The motif reported in figure 1 panel C is the lowest E-value motif for the 67 sites combined in both libraries.
Primers TarB promoter R and TarB promoter F were used to amplify the upstream 100 bp of TarB, predicted to contain promoter elements and ToxT binding sites to serve as a probe in the mobility shift assay. The PCR product was purified (Stratagene) and 3.3 pmoles was end-labeled using T4 Polynucleotide Kinase (NEB) and 32P γ-ATP according to the manufactures instructions, and then purified using a Performa DTR spin cartridge (Edge Biosciences). A negative control probe of similar size consisting of 4.5S RNA sequence was prepared in parallel. The binding reaction occurred in 20 µL with 3 nM labeled probe and varying concentrations of purified MBP-his-thr-ToxT in EMSA with 10 mM 10 µg/mL sheared salmon sperm DNA, 6 µg/mL BSA, 10% glycerol and 0.002% Orange G dye added. Binding was allowed to occur for 30 minutes at 30°C followed by loading of the entire reaction onto a 5% TBE-Polyacrylamide gel, which was then run at 100 V for 60 minutes. The gel was then used to directly expose a phosphor screen and the image was read on a FLA-9000IR using the IP setting.
For AKI induction experiments, strains were grown overnight with aeration at 37°C in LB broth containing streptomycin at 100 µg/mL, ampicillin at 50 µg/mL (excluded in the case of the TcpF C-FLAG integration in the wild type background and TarB-GFP strains without plasmid) and chloramphenicol at 2 µg/mL. Overnight cultures were then diluted into prewarmed AKI media [47] containing 0.3% NaHCO3 and ampicillin at 50 µg/mL (again excluded for the wild type background strain and TarB-GFP fusions) to an OD of 0.01. Strains were grown statically in an incubator at 37C for the indicated times at which culture aliquots were removed for analysis. After 4 hours of static growth, cultures were split into 1 mL aliquots and grown shaking at 37C for 4 hours.
For anaerobic growth experiments, overnight cultures were prepared by inoculation of strains into phosphate buffered LB media containing 60 mM K2HPO4, 33 mM KH2PO4, 0.5% glucose and 100 µg/mL streptomycin. These cultures were grown overnight in an anaerobic chamber and used to subsequently inoculate either 2 mL aerated cultures or 10 mL cultures in sealed tubes prepared in the anaerobic chamber to an OD of approximately 0.01. Aerobic and anaerobic cultures were then grown in parallel in a shaking 37C incubator to approximately the same OD and snap frozen on liquid nitrogen and subsequently used for RNA extraction and northern blots. For each culture the pH of the media was measured after growth was recorded and ranged between 6.3 and 6.5 for anaerobic cultures and 6.7 to 6.8 for aerobically grown cultures.
For pond water incubation experiments, strains were grown overnight on M9 minimal media+glucose plates containing the proper antibiotics. Overnight growth was resuspended in saline and washed twice. After the final wash, strains were resuspended in filter-sterilized pond water and inoculated into 2 mL culture tubes of filter sterilized pond water to an OD of 0.1 and incubated shaking at 37°C for the indicated times. At those times, culture aliquots were prepared either for western blot by centrifugation followed by resuspension in sample buffer and boiling or diluted to a density of 1×103/µL as measured by OD for mouse infections.
Experiments involving induction of ToxT from the arabinose inducible plasmid were carried out similarly to those used in sRNA sequencing experiments. Overnight cultures of the indicated strains were grown at 37°C overnight in LB containing the appropriate antibiotics. Overnight cultures were then diluted to an OD of 0.03 in 25 mL of the same media and allowed to grow shaking at 37°C. Once cultures reached mid-exponential phase (OD = 0.3), arabinose was added to a final concentration of 0.04% and induction was allowed to proceed for 1 h with 2 mL aliquots of culture taken at the indicated times and either spun down for western blot analysis or snap frozen in liquid nitrogen for RNA extraction later.
Between 2.5–10 µg of total RNA purified using the Ambion mirVana kit from the indicated cultures was run on 10% TBE-urea polyacrylamide gels. Prior to transfer, gels were stained with GelStar (Invitrogen) and scanned on the FLA-9000IR (Fuji) to assess total RNA loading in each well and to use for normalization during quantification. RNA was transferred to Hybond N+ membranes (Amersham) in 1× TBE using the Mini Trans-Blot Cell apparatus (Bio-Rad) according to the manufacturer's instructions. Blots were prehybridized in Ultrahyb (Ambion) prior to addition of probe. RNA probes were transcribed from PCR-derived templates with T7 promoters using 32P-UTP and T7 polymerase (Promega) according to the manufacturer's instructions. Ambion Decade ladder labeled with 32P-ATP was run alongside RNA samples to provide estimations for the sizes of RNA bands. Hybridzation was carried out at 65°C overnight followed by washing 3× with low stringency buffer (2× SSC+0.05% SDS) wash at room temp, followed by washing 3× with high stringency buffer (0.2×SSC+0.05% SDS) at 65°C. Blots were then exposed to phosphor storage screens (Fuji) overnight. The image was subsequently read on a FLA-9000IR scanner. When reporting quantification, measurements taken from the phosphor screen after exposure were divided by fluorescent measurements of the 5S rRNA taken prior to transfer to normalize signal for loading using the MultiGage software (Fuji).
Total RNA was purified from cultures grown under the indicated conditions using the mirVana RNA purification kit. Total RNA was treated with DNAase with the TURBO-DNAfree kit (Ambion) prior to reverse transcription. cDNA used as template was generated using iScript complete kit (BioRad) from 2 µg of total RNA using random hexamers. Quantitative PCR was run using Strategene Mv3005P equipment and MxPro qPCR software. Each sample was measured in technical triplicate. In all cases, controls lacking reverse transcriptase were included to assess DNA contamination, all results were either below the baseline of detection, or were subtracted from values obtained with those templates.
For western blot analysis of TcpF and GFP expression, strains carrying the TcpF C-terminal FLAG allele or the TarB-GFP fusion were grown under the indicated conditions at which times 2 mL culture aliquots were removed. Culture aliquots were immediately centrifuged at 10,000× g for 5 minutes to pellet cells, and supernatants were removed. Cell pellets were boiled in 50 µL (static timepoints and plasmid induction experiments) or 100 µL (4 h aeration timepoint) of SDS loading buffer (50 mM Tris-HCl, pH 6.8, 2% SDS, 0.5% bromophenol blue, 10% glycerol, 100 mM βME). Samples were cooled and a volume adjusted for differences in OD was loaded on an SDS-polyacrylamide gel electrophoresis (PAGE) gel and run 90 minutes at 125 V. Proteins were transferred to a nitrocellulose membrane at 25 V for 1 h. Membranes were loaded onto the SNAP-ID Western blotting system (Millipore) and blocked with 1× NAP blocking agent (G Biosciences) diluted in PBS+0.01% Tween-20. Primary antibody to the FLAG peptide (Invitrogen) or against GFP (Abcam) was added to the membrane 1∶600 or 1∶1200 respectively, diluted in 3 mL 1× NAP block for 10 minutes and the membrane was washed with 90 mL PBS+0.01% Tween-20. Secondary antibody (Invitrogen) (Cy5 conjugated goat anti mouse for anti-FLAG blots or Cy5 conjugated goat anti rabbit for GFP blots,) was added to the membrane at 1∶600 and diluted in 3 mL 1× NAP block for 10 minutes and the membrane was washed with 90 mL PBS+0.01% Tween-20. Bands were visualized using the Cy5 setting the FLA-9000IR. After visualization of TcpF-FLAG, blots were stripped by incubating in 20 mL acid stripping buffer (25 mM glycine pH 2, 1% SDS) shaking for 30 minutes followed by washing 2× with 20 mL PBS+0.01% Tween-20. After stripping, blots were reprobed as above with primary anti-OmpU at 1∶600 in 1× NAP block and secondary Cy5 conjugated goat anti-rabbit (Invitrogen) again in 1× NAP block and scanned on Cy5 setting on the FLA-9000IR.
Fluorescence measurements were quantified using MultiGage software (Fuji). Measurements TcpF-FLAG bands, adjusted for area and background, were divided by fluorescence measurements of corresponding OmpU bands adjusted for area and background. Loading-adjusted fluorescence values were then standardized to wild type expression and reported as fold expression of TcpF relative to wild type expression. The experiment shown is representative of six biological replicates.
Single strain infections and competition assays in infant mice, LB broth and filter sterilized pond water were performed with the TarB unmarked deletion strain (AC3744) (LacZ+) and wild type with a lacZ deletion (AC3745) for 24 h as described [57]. Inputs for competition assays and single strain infections were prepared by growth overnight on LB plates containing the appropriate antibiotics followed by resuspension in LB to an approximate density of 1×103/µL as measured by OD, mixing of equal volumes of either culture (for competition experiments) then inoculation of infant mice by oral gavage. Samples from pond water incubations were prepared as described above, mixed in equal volumes and then used for innocualtion of infant mice. Immediately after inoculation, input ratios and total CFU were determined by plating on LB plates containing 5-bromo-4-chloro-3-indolyl-D-galactopyranoside (X-gal). The target input dose for all experiments was 105 bacteria/mouse, although over the course of the experiments doses ranged between 104 and 106. Results are shown by the competition index (CI), which is the ratio of mutant CFU to wild type CFU normalized for the input ratio. To show complementation in trans in all assays in this study, ΔtarB derivatives (LacZ+) were complemented with either ptarB or ptarB* and were competed against the respective isogenic strain (LacZ−) carrying the pMMB67EH plasmid alone. CIs for these experiments are expressed as the ratio of mutant to complemented CFU corrected for input. To assess plasmid loss frequency, output plates were replica plated onto LB agar plates containing streptomycin and ampicillin at 100 µg/mL and X-Gal at 40 µg/mL to determine plasmid containing CFUs, and LB agar plates containing streptomycin and X-Gal to determine total CFUs.
Growth of strains was determined by measured OD using a Bio-Tek microplate reader. Cultures grown overnight in LB plus streptomycin and (ampicillin at 50 µg/mL for complemented strains) or M9 glucose plus streptomycin and (ampicillin at 50 µg/mL for complemented strains) were resuspended to an OD of 0.01 in the respective media and pipetted into a 96-well plate in triplicate. Each growth curve was performed in biological triplicate. Bacteria were grown with aeration for 17 h at 37°C in the microplate reader with the OD being read every 17 minutes.
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10.1371/journal.ppat.1004117 | Papillomavirus Genomes Associate with BRD4 to Replicate at Fragile Sites in the Host Genome | It has long been recognized that oncogenic viruses often integrate close to common fragile sites. The papillomavirus E2 protein, in complex with BRD4, tethers the viral genome to host chromatin to ensure persistent replication. Here, we map these targets to a number of large regions of the human genome and name them Persistent E2 and BRD4-Broad Localized Enrichments of Chromatin or PEB-BLOCs. PEB-BLOCs frequently contain deletions, have increased rates of asynchronous DNA replication, and are associated with many known common fragile sites. Cell specific fragile sites were mapped in human C-33 cervical cells by FANCD2 ChIP-chip, confirming the association with PEB-BLOCs. HPV-infected cells amplify viral DNA in nuclear replication foci and we show that these form adjacent to PEB-BLOCs. We propose that HPV replication, which hijacks host DNA damage responses, occurs adjacent to highly susceptible fragile sites, greatly increasing the chances of integration here, as is found in HPV-associated cancers.
| Papillomavirus cause persistent, but mostly self-limiting, infections of the host epithelium. However, a subset of oncogenic papillomaviruses is the causative agent of certain human cancers. In persistent infection the viral genomes are tethered to host chromosomes to maintain and partition the extrachromosomal viral genomes to daughter cells. However, in cancers viral DNA is often found integrated close to common fragile sites, regions prone to breakage, amplification and deletion. We show that the viral E2 and cellular BRD4 proteins are associated with fragile regions of the human genome and nucleate viral replication foci at these sites. This is a resourceful strategy for a virus that uses the host DNA damage response to amplify viral DNA. However, the outcome may be increased accidental integration of viral DNA, which in the case of the oncogenic viruses can promote carcinogenesis.
| Papillomaviruses are an ancient group of viruses that establish a persistent infection in the host epithelium. To maintain such a long-term infection, the E2 protein from a subset of papillomaviruses binds to the viral genome and tethers it to the host chromosomes [1]–[3]. The bromodomain protein, BRD4, binds to mitotic chromosomes with E2 [4], [5], is essential for regulation of viral transcription [6]–[9] and is recruited to early viral replication foci [10], [11]. BRD4 is a mitotic chromosome-associated protein [12] that interacts with acetylated histone tails [13] and is a key regulator of the pTEF-b elongation factor [14]. There has been a recent explosion of data as BRD4 has been implicated in regulation of cell cycle, mitotic memory, transcription of MYC and regulation of viral gene expression [15]–[19]. BRD4 is highly enriched at super-enhancers that maintain expression of oncogenes in tumors [20] and is a promising therapeutic target for a number of cancers [21].
Most HPV infections result in benign lesions, but several are oncogenic and the causative agents of human cancer [22]. Almost all cervical cancer is associated with HPV infection, and oncogenic HPVs are responsible for many anal, penile, vaginal and oropharyngeal cancers [23]. The HPV genome is found integrated into the host genome in over 80% cancers and this promotes malignant progression. The integration event is accidental, but the resulting deregulation of expression of the E6 and E7 oncogenes gives cells a selective growth advantage [24]. There is a predilection for integration within the vicinity of fragile sites [25], [26].
Papillomaviruses are adept at hijacking host functions and induce a host DNA damage response (DDR) in nuclear foci, resulting in an influx of repair factors that the virus exploits to amplify its own DNA [11], [27]–[31]. We show that the HPV E2 protein binds with BRD4 to regions that are highly susceptible to replication stress and overlap many common fragile sites. Common fragile sites are hypersensitive to DNA damage and their replication is often incomplete in the G2 phase of the cell cycle [32]. Thus, they represent a vulnerable and very clever target for papillomavirus replication. Furthermore, replication adjacent to fragile sites may explain the high incidence of integration of oncogenic HPV genomes at these loci.
Many papillomavirus E2 proteins bind readily to host mitotic chromosomes with the BRD4 protein [9]. To identify the targets of these E2 proteins we analyzed chromatin binding sites of HPV1 E2, a protein that binds BRD4 and host chromosomes with high affinity. In a natural infection E2 levels range from almost undetectable in basal cells to fairly high levels in differentiated cells [33]; thus we were careful to titrate E2 to low, but detectable, levels for the experiments presented (Figure S1A and S1B). Chromatin was prepared from mitotic C-33 cells expressing HPV1 E2 (C-33-1E2), and analyzed by ChIP-chip analysis for binding to a portion of the human genome (chromosomes 3, 4, 5, 18, 19, 20, 21, 22 and X). We have previously shown by ChIP-chip analysis of 5 kb promoter regions that E2 and BRD4 bind to active promoters in interphase C-33 cells [34]. In the present study we used whole genome tiling arrays to study E2 and BRD4 binding. As shown in Figure 1A, in mitosis E2 was observed to bind to a few extremely broad peaks on several chromosomes. These peaks ranged in size from several hundred Kb to >1 Mb and, for the most part, overlapped coding regions. Two detailed examples of the genomic regions covered by the peaks are shown in Figure S1C.
The mitotic E2 binding peaks were further validated by conventional ChIP assays (Figure 1B) with primers selected from eight of the peaks indicated in Figure 1A. E2 binding to these regions was strong in both asynchronous and mitotic cells, showing that it persisted throughout the cell cycle, consistent with the concept that E2 partitions the viral genome by linking it to mitotic chromosomes [1]. The levels of E2 bound to the broad mitotic regions were several-fold higher than those bound to active promoter regions. Furthermore, the levels of E2 bound to promoters dropped to almost background levels in mitotic cells (Figure 1B), consistent with cessation of transcription and displacement of most transcription factors from promoters in mitosis [35].
Since E2 binds to mitotic chromosomes in complex with BRD4 [4], [5], [7] we carried out ChIP assays to determine whether BRD4 bound the same regions of mitotic chromatin. As shown in Figure S1D, S1E, and S1F (and summarized in Figure 1C) BRD4 bound to five sites selected from an E2 positive region from chromosome 5, even in the absence of E2. However, expression of E2 increased BRD4 binding at least two fold, consistent with the stabilization of BRD4 binding by E2 [7]. In contrast HPV31 E2, which does not stabilize binding of BRD4 to chromatin [9], had little effect on the binding of BRD4 to mitotic chromatin (data not shown). Figure S1G shows a comparison of the size of these broad regions compared to promoter binding of BRD4 and E2 that we had detected previously using promoter microarrays. As we show in more detail below, E2 and BRD4 bind together to these exceptionally large regions of mitotic chromatin that likely correspond to the mitotic chromatin tethering target used by papillomaviruses for genome partitioning. Thus, we have named these regions Persistent E2 and BRD4-Broad Local Enrichments of Chromatin, or PEB-BLOCs.
In C-33-1E2 cells, E2 colocalizes with BRD4 in approximately 50 punctate speckles on mitotic chromosomes (data not shown) and so we extended the pilot experiment described above to analyze BRD4 binding in the entire human genome. BRD4 binding was analyzed by ChIP-chip using whole genome arrays and the BRD4 binding profile is shown in Figure 2A (for chromosome 4) and S2 (for the entire genome). Almost all chromosomes showed large peaks similar in size to, and overlapping with, the E2 peaks identified in the subset of chromosomes shown in Figure 1A. A visual inspection showed that approximately 50 broad BRD4 binding regions were detectable on C-33 mitotic chromatin in the entire genome and about 100 regions were detected in the presence of E2 (Figure S2). Therefore, BRD4 binds to some PEB-BLOCs in the absence of E2, but E2 enhances the BRD4 binding signal. In contrast, BRD4 is only detected on mitotic chromosomes by immunofluorescence in the presence of E2 [7]. This likely reflects differences in sensitivity between the techniques. We have shown previously that the dimerization property of E2 increases the ability of E2-BRD4 complexes to bind mitotic chromosomes, most likely by promoting the formation of higher order complexes [36]. The genomic localization and characteristics of 53 of the strongest PEB-BLOCs identified by visual inspection are listed in Table S1.
We computationally defined and identified the enriched binding regions for E2 and BRD4 (shown in red in Figure 2A and S2). The best algorithm was able to identify all of the visually identified binding peaks, with the exception of one on chromosome 20 (Chr20-P3 in Table S1). Using this algorithm, the overlap between E2 and BRD4 binding regions was calculated, as defined in Methods. Figure 2B shows the overlap among the three binding profiles for chromosomes 3, 4, 5, 20, 21, 22 and X (only a subset of chromosomes was analyzed for binding in these experiments). There was a complete overlap between the BRD4 binding regions in control and E2 expressing cells and >50% overlap with the E2 binding enriched regions and BRD4 binding regions. The overlap with the E2 binding enriched regions is underestimated because of the different resolution of the microarray chips used for the E2 and BRD4 binding studies. However, as can be seen visually in Figure 2A, there is a substantial overlap in the major binding peaks. Figure 2C shows the overlap of computationally defined BRD4 enriched regions, in the presence and absence of E2 expression, for all human chromosomes. Therefore, many PEB-BLOCs exist even without E2 expression and E2 stabilizes and increases Brd4 binding to a subset of PEB-BLOCs. Presumably, different stages of the viral life cycle the levels of E2 would determine which PEB-BLOCs were highly occupied by E2 and BRD4.
Two residues in the transactivation domain of E2 (R37 and I73) are essential for interaction with BRD4 [5], [37]. Therefore, we analyzed binding of an E2 R37A/I73A mutated protein to PEB-BLOCs by ChIP. Wild-type E2 and R37A/I73A E2 were expressed at equivalent levels and had no effect on the levels of BRD4 (data not shown). Both wild-type E2 and BRD4 bound strongly to PEB-BLOC regions in asynchronous cells (Figure 3A) and while BRD4 bound to most PEB-BLOCs in the absence of E2, binding was about two fold higher in the presence of E2. However, E2 R37A/I73A was minimally recruited onto and did not augment BRD4 binding to PEB-BLOCs. While BRD4 and E2 colocalize as distinct speckles on mitotic chromosomes, in cells expressing the R37A/I73A protein, neither E2 nor BRD4 was detected on chromosomes (Figure 3B). Therefore the interaction with BRD4 is essential for E2 binding to PEB-BLOCs, but in turn E2 stabilizes the binding of BRD4 to these regions.
To confirm the requirement for BRD4 in E2 binding to mitotic PEB-BLOCs, BRD4 gene expression was downregulated with siRNA. In the absence of BRD4, E2 no longer bound to mitotic chromosomes (Figure 3C) or colocalized in speckles with BRD4 in the nucleus of interphase cells (data not shown). Small molecule inhibitors such as GSK525762A+ interfere with binding of the specific bromodomains of the family of BET proteins (bromodomain plus extraterminal domain) to their acetylated target [38]. In cells treated with GSK525762A+, neither E2, nor BRD4, could be detected bound to PEB-BLOC regions by ChIP (Figure 3D). Likewise, E2 and BRD4 speckles were no longer observed in the nuclei of interphase cells (Figure 3E) or on mitotic chromosomes (data not shown) after GSK525762A+ treatment. Therefore, E2 binding to PEB-BLOC regions is dependent on BRD4 and its interaction with acetylated histones.
To further investigate the nature of PEB-BLOCs, histone modifications were analyzed by ChIP (Figures 4A and S3) using the primers listed in Table S9. PEB-BLOCs were highly acetylated at positions K9, K14, K18, K23, K27, K56, K9/14, and K9/18 in histone H3 and K5, K8, K12, and K5/8/12/16 in histone H4. E2-BRD4 bound promoter regions showed higher acetylation levels than E2-negative regions, but the acetylation status of PEB-BLOCs was consistently several-fold higher than in active promoter regions, which are already acetylation-rich. Therefore, PEB-BLOCs are highly acetylated at many positions, consistent with the ability of BET inhibitors to abolish E2 and BRD4 binding. Histone methylation, especially of H3K4, is also associated with active chromatin [39]. PEB-BLOCs have consistently high H3K4me1 and H3K4me2, but low H3K4me3. Conversely, promoter regions had high H3K4me2 and H3K4me3, but low H3K4me1 (Figure 4A).
To validate these findings, we performed ChIP-chip analysis for binding of E2, BRD4, H4K8ac, and H3K4me1 on a subset of the genome (chromosome 4 and part of chromosome 3). Each PEB-BLOC overlapped with prominent peaks of H4K8ac and H3K4me1 modification (Figure 4B and 4C). Therefore, PEB-BLOCs contain highly acetylated histones and high levels of H3K4me1, a pattern similar to that described for enhancers [39]. Notably, as shown in Figure 4C, between 65% and 71% of H4K8ac and BRD4 broad enriched regions overlapped and the E2 bound regions were contained completely within this overlap. All E2 bound peaks also completely overlapped with enriched regions of H3K4me1.
To confirm these findings, mitotic and interphase C-33-1E2 cells were analyzed for global histone modification patterns by immunofluorescence (Figure S4 and data not shown). E2-BRD4 speckles colocalized with acH4K8 and acH3K56 on mitotic chromosomes, and were also highly enriched in H3K4me1 and H3K4me2, but not H3K4me3. The E2-BRD4 speckles observed in interphase nuclei also showed an enrichment of acH4K8, acH3K56, H3K4me1 and H3K4me2.
To ascertain the histone acetyl transferase (HAT) responsible for acetylation of PEB-BLOCs, we downregulated expression of EP300, CREBBP and KAT5 by siRNA treatment. In control cells, BRD4 speckles colocalized with H4K8ac, CREBBP and EP300. However, siRNA downregulation of CREBBP or EP300 resulted in a great reduction in the appearance of BRD4 speckles as well as the focal regions of histone acetylation in the nucleus (Figure 4D). In contrast, KAT5 only partially colocalized with BRD4 speckles and downregulation of KAT5 had no effect on the acetylation or localization of BRD4 to PEB-BLOCs. Therefore, CREBBP and EP300 are both recruited to PEB-BLOCs where they acetylate histones, thus providing binding sites for E2 and BRD4. Notably, E2 proteins interact with CREBBP/EP300 [40] and this could enhance the formation and development of PEB-BLOCs in a natural infection. However, in C-33 cells these regions are already genetically unstable and highly acetylated, and acetylation is not obviously increased by E2. Regions of chromatin that are methylated on H3K4 show highly dynamic acetylation mediated by CREBBP/EP300, while H3K4 methylation remains more stable [41]. This is consistent with the histone modifications of PEB-BLOCs and the requirement for CREBBP/EP300.
To verify that BRD4 nuclear speckles correspond to the regions identified by ChIP-chip, we performed combined IF-FISH with a BRD4 antibody and FISH probes for PEB-BLOCs. In many cases, the BRD4 speckles colocalized with only one of the two PEB-BLOC FISH signals (Figure S5). BRD4 speckles were often observed as doublets on one chromosome, which in mitotic cells colocalized with a similar doublet of FISH signal (Figure 5A and 5B). In contrast, the second PEB-BLOC allele was detected as a condensed FISH signal that didn't colocalize with BRD4, indicating that BRD4 binds to PEB-BLOCs on one allele on mitotic chromosome.
Analysis of the PEB-BLOC FISH signals in interphase cells revealed that the two alleles often replicated at different times. When this occurred, the early replicating allele was observed as a doublet FISH signal, while the late replicating allele was a single FISH signal. When these exist in the same nuclei due to asynchronous replication they are termed SD (singlet-doublet) FISH signals (Figure 5C). We calculated the rate of asynchronous DNA replication for loci corresponding to PEB-BLOCs and non-PEB-BLOCs by counting the number of SD FISH signals in individual nuclei (Figure 5C). Non-PEB-BLOC regions, displayed an SD pattern in ∼12% S-phase cells, as previously reported [42]. In contrast, the SD pattern was present in ∼30% PEB-BLOCS. There was no difference in the percentage of SD signals in control C-33 or C-33-1E2 cells, showing that this is an inherent property of the cells and not due to E2 expression. Notably, asynchronous replication is also a property of common fragile sites [43].
PEB-BLOCs span large chromosomal regions, which mostly contain annotated genes (Table S1). To determine whether these genes were transcriptionally active, RNA was prepared from control C-33 and C-33-1E2 cells and analyzed by microarray gene expression analysis (data not shown). This showed that most genes located in the PEB-BLOCs were transcribed at low to moderate levels. To determine whether there was additional transcription (perhaps non-mRNA) from apparently non-coding regions, we conducted RNA seq analysis (available at GEO: GSE52367). This confirmed that most PEB-BLOC genes were transcriptionally active, and also identified ten long (>0.2 Mb), previously un-annotated, genes in these regions. These novel genes are shown, along with RNA seq signals for 53 of the strongest PEB-BLOCs in Table S1.
About 35 cellular genes were differentially regulated by E2 expression in both microarray and RNA seq analysis. However, these genes were not associated with PEB-BLOCs and have no obvious connection to E2 function.
There has been reported to be a strong correlation between transcription of very long genes and the expression of fragile sites resulting from a conflict in transcriptional and replication machineries [32]. To date, there are 56 annotated human genes that are >1 Mb and another 219 that are between 0.5–1 Mb long. In the 53 strong PEB-BLOC loci listed in Table S1, there are ten known genes >1 Mb and 19 genes >500 kb. Therefore, there is a vast enrichment of long genes in the PEB-BLOC regions. This calculation does not include the transcriptionally active long segments in PEB-BLOCs that contain unknown genes. Figure 5D shows the size range of genes that overlap PEB-BLOCs.
Many of the properties described above for PEB-BLOCs are also attributes of common fragile sites. These sites are genetically unstable (reviewed in [44]) and are common sites of viral genome integration [25]. Like PEB-BLOCs, common fragile sites often replicate asynchronously, have monoallelic expression and contain large genes [32]. Fragility can arise because of a conflict between transcription and replication of very long genes as a paucity of replication initiation sites can result in failure to complete replication before mitosis [45]. We compared the location of PEB-BLOCs with mapped common fragile sites in the human genome (retrieved from HUGO, www.genenames.org) and found that a subset of visually identified strong PEB-BLOCs (22 out of 53) contain 25 known fragile sites in the same chromosomal band (Table S1).
However, most common fragile sites have been mapped cytogenetically and span large portions of the human genome, making it difficult to statistically correlate with the enriched binding regions. Furthermore, common fragile sites are cell type specific [46] and the majority have been mapped in lymphocytes. To further examine the association of PEB-BLOCs with common fragile sites we mapped aphidicolin-inducible fragile sites in C-33 cells. C-33 cells were treated with aphidicolin to cause mild replication stress and the resulting fragile sites were identified using a novel ChIP-chip method with an antibody to FANCD2, which is involved in replisome surveillance and binds fragile sites [47]–[49]. Approximately 100 strong FANCD2 binding regions were visually identified and are shown aligned with the BRD4 binding profile in Figure S7. Large, enriched FANCD2 binding regions were further defined by computational analysis and are shown as red blocks under the signal map (Figure S7). Figure 6A shows the alignment of PEB-BLOCs, FANCD2 binding sites and known common fragile sites for chromosome 4 and detailed alignments can be found for all strong PEB-BLOCs in Table S1. It is clear that many PEB-BLOCs and FANCD2 binding peaks overlap precisely, others are slightly offset, and some prominent peaks do not overlap. As shown in Figure 6B, a significant subset of FANCD2 enriched binding regions (∼30%) and PEB-BLOCs (∼36%) overlap (P<0.002). Therefore, there is a strong association between PEB-BLOCs and sites of genomic instability. An absolute distance analysis showed that ∼27% PEB-BLOCs and ∼30% FANCD2 enriched binding sites are within 2 Mb of a known common fragile site (Figure S8).
The association between PEB-BLOCs, FANCD2 binding sites and common fragile sites was further examined in three subsets of fragile sites that are most closely related to our biological system. As shown in Table 1 (with details in Table S6) these consisted of: aphidicolin induced fragile sites recently mapped in epithelial cells [50]; fragile sites that have been cloned and therefore are of much higher resolution [51]; and fragile sites that have been mapped in cervical cancer cells [25]. This showed that there was a significant association between these fragile sites and the FANCD2 regions and a near-significant association between these fragile sites and the enriched PEB-BLOC regions.
We noted evidence of deletion in several PEB-BLOCs as there was an abrupt loss of BRD4 signal in certain regions of the BRD4 ChIP-chip binding profiles. We found eight loci showing obvious loss of ChIP signals in PEB-BLOCs and/or FANCD2 binding regions (Figures 6C and S6). Five of these regions are located in the same chromosome bands as known fragile sites, four are in PEB-BLOCs and the others are in non-PEB-BLOC FANCD2 binding regions. To verify these deletions, we performed FISH using two adjacent FISH probes. One probe (245M5) was targeted to the putatively deleted region and the other (451M10) was derived from an adjacent, undeleted BRD4 binding region of the PEB-BLOC. As predicted, the 245M5 probe gave rise to only one FISH signal per cell due to the deletion of this locus on one chromosome (Figure 6C). In contrast, the 451M10 probe showed two clear FISH signals, demonstrating that both chromosomal loci were intact. Because there was an abrupt and complete loss of BRD4 signal in these deleted regions (despite the intact locus on the other chromosome) we can conclude that only the BRD4 bound allele sustained the deletion. Thus, PEB-BLOCs sustain frequent deletions.
This finding is supported by our previous observation that BRD4 is often bound to only one allele of PEB-BLOCs (Figure 5A and 5B). Analysis of the RNAseq signal in these regions confirms that there are no detectable transcripts from the missing exons, reinforcing the hypothesis that the deletion is present in the transcribed allele (Figure 6D). Four of the eight regions shown in Figure S6 also show transcription spanning a deleted allele, supporting this conclusion. Therefore, PEB-BLOCs frequently contain deletions in the transcriptionally active allele.
The experiments described above used the E2 protein from HPV1, a virus that causes benign papillomas. The HPV1 E2 protein binds BRD4 with high affinity, but E2 proteins from the Alpha genus have a relatively low affinity for BRD4 and host mitotic chromosomes [9]. Nevertheless, when expressed together with the E1 replication protein both alpha-PV E1 and E2 proteins colocalize in nuclear foci that recruit markers of a DNA damage response (DDR) and recruit BRD4 [11], [29]. Because of the links among E2, BRD4, DDR, replication stress and fragile sites, we questioned whether these nuclear viral replication foci formed at PEB-BLOCs/fragile sites. HPV16 E1 and E2 proteins were transiently expressed in C-33 cells and chromatin was extracted for ChIP-chip analysis. Regions of E1–E2 binding were isolated with an antibody directed against an epitope tag on E1. The resulting E1 binding profile was very similar to that of BRD4 (in the presence of HPV1 E2) and thus to PEB-BLOCS (Figure 7A, 7B and S7). Computation of the E1 enriched regions showed a significant overlap (p<0.002) among the PEB-BLOCs, HPV16 E1 (in the presence of HPV16 E2) and FANCD2 (aphidicolin treated cells) (Figure 7C and Table S5). Therefore, PEB-BLOCs are also targets for alpha-HPV E1/E2 protein complexes and therefore there is a strong link among PEB-BLOCs, fragile sites and viral DNA replication proteins. Highly notable is the fact that HPV genomes are very often integrated close to fragile sites in HPV-associated cancers [25].
It has been noted for many years that HPVs (and other oncogenic viruses) are often found integrated close to common fragile sites [25], [26], [52]. However, most of the HPV integration sites have been mapped at low resolution, similar to the cytogenetically mapped common fragile sites. To allow for a more detailed analysis, we collated the precise HPV integration sites from several studies [53]–[55] as well as those listed in the DrVIS database [56] (Table S7). The overlap between these sites and the BRD4 and FANCD2 enriched regions is highly significant, as shown in Figure 7D and Table 2. The human genome contains a number of hotspots for HPV integration. For example, chromosomes regions 8q24.21 (the MYC locus) and 13q22.1 contain many HPV integration sites [57]; notably these two regions overlap PEB-BLOCs. A recent high resolution study of HPV integration sites in cervical and head and neck cancers demonstrated focal genomic instability; cellular DNA flanking the viral integration site contained amplifications, rearrangements and translocations and concatameric viral DNA was often interspersed with host sequences [58]. Thus, genomic instability continues after the initial integration event.
Since HPV16 E1 and E2 replication proteins associate with PEB-BLOCs, these are likely sites of viral replication. To verify this we studied the association of HPV genomes with PEB-BLOCs: HPV1, HPV16 and HPV18 genomes were transfected into C-33 cells and the association of viral DNA with specific regions of host chromatin was analyzed by FISH. Transfected viral DNA often gave rise to a single nuclear signal that was closely associated with different PEB-BLOC regions more frequently than control regions (Figure S9).
To further explore this association, we isolated C-33 cells containing replicating HPV16 genomes and analyzed the association between the resulting replication foci, PEB-BLOCs and control regions (Figure 8A). Five of the six PEB-BLOCs tested associated with HPV16 replication centers in ∼9% cells while control regions were associated with replication foci in ∼2% cells (Figure 8B). Of importance, the PEB-BLOC allele associated with the replication factory in Figure 8A appears to be late replicating (two, still tightly linked, FISH signals) compared to the “double-dot” pattern observed in the other allele. However, it was difficult to quantitate this observation as the PEB-BLOC signal adjacent to the replication factory was often disrupted and sometimes dispersed throughout the viral DNA. This made it difficult to determine whether it was a singlet or doublet.
When one considers that we are only measuring the interaction of replication factories with one PEB-BLOC at a time (and only a few PEB-BLOCs are likely to be associated with replication foci in any single cell), the observed association is noteworthy. Also of note, some PEB-BLOCs are only associated with HPV replication foci in certain cell lines; for example, Chr3-P4 does not show increased association in C-33 cells, but does in 9E cells (Figure 8). It is possible that the virus replication foci form only at the PEB-BLOC regions with highest affinity for E2 and BRD4.
We carried out a similar analysis in CIN-612 9E cells, which contain large numbers of HPV31 genomes. Large and small viral replication foci can be generated in these cells by differentiation with calcium [27]. Four out of six PEB-BLOCs tested were closely associated with HPV31 replication foci in ∼12% cells, compared to a ∼4% association with control regions (Figure 8C and 8D). In a parallel study, we find that these large foci are frequently ringed with small BRD4 foci [11] that presumably represent additional PEB-BLOCs. In conclusion, replicating HPV genomes are commonly associated with PEB-BLOCs. Figure 8E shows an example of a small replication focus in CIN-612 9E cells stained by immunofluorescence for BRD4, and by FISH for HPV31 DNA and a single PEB-BLOC. As shown, it appears that the replication foci “grow” from the PEB-BLOC foci and BRD4 is localized at the interface between viral and host DNA.
We show that HPV1 E2, and the HPV16 E1/E2 protein complex, bind with BRD4 to common fragile sites in the human genome. Like other persistent viruses that form long-term associations with their host, HPVs are masters at hijacking cellular processes. The E2 proteins interact with BRD4 to regulate viral transcription, and associate with host chromatin to partition the viral genome in dividing cells. We demonstrate that this association is not random, and that the virus has taken advantage of very susceptible regions of the host genome that are prone to replication stress. Both oncogenic and non-oncogenic papillomaviruses induce a DDR and both probably replicate adjacent to these susceptible regions. Most likely, both oncogenic and non-oncogenic HPV types have the propensity to become integrated into unstable regions of the genome on rare occasions. However, only oncogenic HPVs could give the cells a selective growth advantage, in turn further increasing genetic instability, and eventually leading to carcinogenic progression.
The precise role of the BRD4 protein in the HPV lifecycle remains somewhat elusive [19]. BRD4 binds to all PV E2 proteins and regulates viral transcription in an E2-dependent manner. The E2 proteins of viruses such as BPV1 (and HPV1) bind BRD4 with high affinity and link the viral genome to mitotic chromosomes in complex with BRD4, most likely to mediate genome partitioning [4], [59]. However, alpha-PVs (such as HPV16 and HPV31 studied here) bind to BRD4 and chromatin with lower affinity and the role of the E2-BRD4 interaction in replication is enigmatic [10], [11]. In fact, HPV31 genomes encoding an E2 protein that is unable to bind BRD4 in vitro, can replicate persistently, and induce late viral functions, in keratinocytes [60], [61]. One explanation for these findings is that the interaction of E2 and BRD4 is important to establish an efficient infection when limiting amounts of genome are delivered to the nucleus by a viral particle rather than by DNA transfection. Also, the nucleation of viral replication factories at regions of the nucleus highly susceptible to replication stress could be important, but not absolutely required, for efficient viral replication in a natural infection.
While the HPV replication proteins are sufficient to induce a DDR, the viral oncogenes can contribute. E7 induces a DDR by associating with ATM [27] and induces oncogenic replication stress by pushing cells into continual, unscheduled division [62]. This could increase replication stress at fragile sites and potentiate the association with the E2-BRD4-genome complex. Our experiments were carried out in C-33 cancer derived cells and we believe that PEB-BLOCs are very prominent in these cells because they are already very genetically unstable. Thus, under these circumstances E2 and BRD4 bind with high affinity to pre-existing fragile sites without the need for other viral factors to promote replication stress. Normal cells do not show much FANCD2 binding and fragile sites must be induced by replication stress such as that induced by low levels of aphidicolin. In a natural HPV infection, this replication stress could be induced by the viral E7 protein [62].
Common fragile sites are often caused by a paucity of replication origins and/or collisions of transcription and replication machinery in very long genes [32], [63]. Often, fragile sites remain incompletely replicated as cells progress into mitosis. Papillomaviruses amplify their DNA in differentiated cells that are in G2 [64], [65]; hijacking the DDR at this time allows the virus to replicate outside S-phase and without competition from host DNA synthesis. By associating with fragile sites that undergo replication stress at this stage, the virus has to do little but be “in the right place, at the right time”, simply amplifying the DDR response to generate a replication factory. Notably, almost 25 years ago when the correlation between viral integration and fragile sites was first recognized Popescu and DiPaolo predicted that “It is conceivable that because of their replication pattern at a certain point in the cell cycle fragile sites may be the only replicating regions available for the integration of viral DNA” [26].
The role of BRD4 in binding to fragile sites has not been completely defined. Previously, chromatin in fragile sites was reported to be hypoacetylated [66], however, we find that the PEB-BLOC regions are highly acetylated and have an “enhancer-like” chromatin signature. It has recently been shown that BRD4 is enriched at super-enhancers that regulate key cell identity genes and tumor drivers [20], [67]. However, despite a common chromatin signature (high H3K4me1 and H3K27ac), PEB-BLOCs are much larger in size than super-enhancers and we do not detect a significant overlap in these elements. Since fragile sites are approaching mitosis with unreplicated regions of DNA, there needs to be a mechanism to keep the chromatin open and accessible to finish replication or repair and to resist the chromosome condensation required for mitosis. BRD4 might maintain an accessible chromatin environment conducive to the processes of DNA damage sensing and repair.
Notably, while BRD4 can preserve chromatin acetylation, decompact chromatin and modulate higher-order chromatin structure [68], a short isoform of BRD4 actually limits the DDR by compacting chromatin to insulate it from ATM signaling [69]. The image shown in Figure 8E is very compatible with the idea that BRD4 is protecting host chromatin from a full-blown viral-mediated DDR. If BRD4 assists in the repair of fragile sites in genetically unstable cells, inactivation of this function could result in a rapid accumulation of catastrophic DNA damage. Normal, genetically stable cells would not depend on this function, and this could help explain the sensitivity of cancer cells to BET inhibitors.
In conclusion, show that the viral E2 and cellular BRD4 proteins associate with fragile regions of the human genome and nucleate replication foci at these sites. This is a resourceful strategy for a virus that uses the host DNA damage response to amplify viral DNA. However, the consequence could be increased accidental integration of viral DNA, which in the case of oncogenic viruses can promote carcinogenesis.
The pMEP4 expression vectors for FLAG-tagged E2 have previously been described [70]. E2 proteins containing alanine substitutions in residues R37 and I73 were described previously [9]. Standard mutagenesis procedures were used to substitute HPV1 E2 residues R37 and I73 with alanines in pTZ18U-FLAG HPV1 E2. FLAG-HPV1 E2 (R37A/I73A) was subcloned into the Asp718 and blunted NheI sites of pMEP4. The FLAG-HA tag was introduced into the HindIII and blunted NotI sites of pMEP4 to generate the control plasmid, pMEP-fh. Plasmids expressing HPV16 E1 and E2 proteins have been described previously [29]. RPCI-11 BAC clones were purchased from Empire genomics (Table S10). HPV1, HPV16, HPV18 and HPV31 genomes have been described previously [24], [71]–[73] and sequences can be found at http://pave.niaid.nih.gov [74].
All antibodies are described in Table S8.
GSK525762+ or the inactive enantiomer GSK525762− were synthesized as described previously [11], following the methods described [38].
C-33 cells [75] were cultured in DMEM, 10% FBS, 100 U/ml penicillin, and 100 µg/ml streptomycin. The HPV31 positive cell line, CIN-612 9E cells [76] were obtained from Lou Laimins (Northwestern University, Chicago, Illinois, USA) and was grown on irradiated 3T3-J2 feeder cells in F medium (3∶1 [v/v] F-12 [Ham]-DMEM, 5% FBS, 0.4 µg/ml hydrocortisone, 5 µg/ml insulin, 8.4 ng/ml cholera toxin, 10 ng/ml EGF, 24 µg/ml adenine, 100 U/ml penicillin, and 100 µg/ml streptomycin).
Inducible E2 expressing cell lines were generated in an HPV-negative cervical carcinoma derived cell line, C-33 by transfecting with the pMEP4-E2 expression plasmids, using Fugene (Roche). Cells containing the pMEP episomal plasmids were selected with 80 µg/ml of hygromycin B (Roche). Drug-resistant colonies were pooled after 2 weeks. E2 protein expression was induced with CdSO4 for 4 h before harvest and the levels of E2 proteins were titrated and adjusted by differential CdSO4 concentration to ensure that binding to the identified chromatin regions increased in an E2-dependent and specific fashion. For transient HPV16 E1/E2 expression, C-33 cells were cotransfected with pMEP9/EE-HPV16 E1 and pMEP4/FLAG-HPV16 E2. E2 expression was induced with 3 µM CdSO4 induction for 4 h before harvest at 24 h post-transfection.
CIN-612 9E cells were differentiated with calcium, essentially as described previously [27]. Feeders and CIN-612 9E cells were seeded as described above. When 90% confluent, the medium was changed to Lonza Growth medium (KBM plus supplement media). Twenty four h later, the medium was changed to Differentiation medium (Lonza KBM/1.5 mM CaCl2/no supplements). Cells were cultured for the times indicted before harvest or fixation.
C-33 cells were treated with 0.2 µM aphidicolin for 24 h before harvesting for ChIP-chip experiments described in other sections.
ChIP experiments were performed as previously described [34]. For mitotic cells, C-33 cells were blocked by 2 mM thymidine overnight and released into medium without thymidine for 9 h. Four hours before harvesting, E2 expression was induced with 3 µM CdSO4 and mitotic cells were collected by mitotic shake off at which point cells were fixed in formaldehyde. For conventional ChIP assay, 0.5 mg of chromatin prepared from asynchronous or mitotic cells was incubated overnight with a specific antibody and collected with Dynabeads conjugated to Protein G (Invitrogen). For ChIP-chip analysis, 2 mg of chromatin was incubated overnight with a specific antibody prebound to Dynabeads conjugated to Protein G. Further processing for ChIP-chip was as described by Jang et al. [34]. DNA isolated from immunoprecipitated chromatin was amplified using the whole genome amplification system (WGA, Sigma). Two HG18 build whole genome arrays were used. C-33-1E2 amplified DNA was labeled and hybridized to the 385K Whole-Genome Tiling Array or the 2.1M Whole-Genome Tiling Array by NimbleGen. E2 binding signals on the arrays for ChIP DNA were normalized to the input signals for total DNA. The ratios were plotted against genomic position to identify regions where increased signal is observed relative to the control sample. All datasets are available at GEO: GSE52312.
Real-time Q-PCR was performed using the ABI Prism 7900HT Sequence Detector (Applied Biosystems) and SYBR Green PCR master mix (Applied Biosystems). An aliquot of ChIP DNA was analyzed with 12.5 µl of SYBR Green PCR master mix and 0.3 µM each oligonucleotide primer in total volume of 25 µl. In each run, a four-fold dilution series of pooled input chromatin DNA was used to generate a standard curve of threshold cycle (Ct) versus log of quantity. PCR was performed at 95°C for 15 min, followed by 40 cycles of denaturation at 95°C for 10 sec and annealing and extension at 60°C for 60 sec. The specificity of each primer pair was determined by dissociation curve analysis. The data were analyzed with SDS 2.1 software (Applied Biosystems). The primers used are listed in Table S9.
Cells were arrested in G1/S phase by culture in 2 mM thymidine overnight, washed to release, and grown for 9 h in the absence of thymidine to select for cells in mitosis. The metallothioneine promoter was induced by the addition of 3 µM CdSO4 for 4 h. Cells were fixed at room temperature in 4% paraformaldehyde (PFA) in PBS for 20 minutes, blocked and stained with mouse monoclonal anti-FLAG M2 antibody and FITC or Alexa 488 anti-mouse antibody; various primary rabbit antibodies and Texas Red or Alexa 596 anti-rabbit antibody. Cellular DNA was stained with DAPI. Images were collected using a Leica TCS-SP5 laser scanning confocal imaging system.
Cells were seeded at a density of 1×106 cells per 10 cm dish, incubated for 24 h, and transfected with 750 ng of siRNA (Table S11) using 40 µl of HiPerFect (Qiagen). Cells were incubated for three days at which point E2 expression was induced by 3 µM CdSO4 for 4 h. The efficiency of BRD4 downregulation was verified by immunoblot analysis using specific antibodies for the target proteins. siRNA treated cells were fixed for immunofluorescence using specific antibodies, as described above.
Cells were cultured on coverslips or glass slides. 9E cells were differentiated for 5 days in the KBM media with 1.5 mM CaCl2. The cells were fixed three times with cold methanol∶acetic acid (3∶1) for 15 mins. For chromosome spreads, C-33 cells were prepared as described for indirect immunofluorescence, treated with 0.1 mg/ml of Colcemid Karyomax (Invitrogen) for 90 mins, and collected by mitotic shake-off. Cells were resuspended in 10 ml of hypotonic buffer (0.075 M KCl) and incubated at 37°C for 20 mins. After pelleting, the cells were resuspended and fixed three times in 10 ml of cold methanol∶acetic acid (3∶1) for 15 mins. The fixed cells were resuspended in 0.5 ml methanol∶acetic acid, applied onto glass slide by dropping, and dried for O/N. The cells were treated with RNace-it cocktail for 1 h, dehydrated with 70%, 85%, and 100% ethanol, and dried for several hours. For combined immunofluorescence-FISH analysis, mitotic cells were collected as described above and treated with H1 buffer (10 mM Tris, pH 7.4, 10 mM NaCl, 5 mM MgCl2) for 15 mins and H2 buffer (0.25× PBS) for 15 mins. Cells were centrifuged at spun at 1500 rpm for 10 mins in a Cytocentrifuge 7620 (Wescor) and fixed at room temperature in 4% PFA/PBS for 20 minutes. After immunofluorescent detection as described above, cells were treated with methanol∶acetic acid (3∶1) for 10 min, 2% paraformaldehyde for 1 min, before dehydration through a series of 70%, 90%, and 100% ethanol.
FISH probes were prepared using ULysis nucleic acid labeling kit (Molecular Probes), purified through Illustra ProbeQuant G-50 micro column (GE Healthcare), and resuspended in TE containing 0.3 µg/µl of Cot-1 DNA. For hybridization, 2 µl 5-fluorescein-labeled BAC probe (Empire Genomics) or 50 ng ULysis FISH probe was mixed with 8 µl FISH hybridization buffer (Empire Genomics), applied to the slide, covered with coverslip, and sealed with rubber cement. The cells and probes were denatured at 75°C for 5 minutes and incubated overnight at 42°C. Cells were washed in 1× phosphate-buffered detergent (Qbiogene) for 5 min at room temperature, 1× wash buffer (0.5× SSC, 0.1% SDS) for 5 min at 65°C, and 1× phosphate-buffered detergent (Qbiogene) for 5 min at room temperature. Cellular DNA was stained with DAPI. Images were collected using a Leica TCS-SP5 laser scanning confocal imaging system. Images were processed using Leica AS Lite software, or Bitplane Imaris software (Zurich, Switzerland) or deconvolved with Huygens Essential software (Scientific Volume Imaging B.V., VB Hilversum, Netherlands), where indicated.
C-33 cells were seeded at a density of 1×106 cells per 10 cm dishes and grown for 2 days. E2 expression was induced by the addition of 3 µM CdSO4 for 4 h. Total RNA was purified using RNeasy (Qiagen), and analyzed for integrity using the Agilent RNA 6000 nano kit on 2100 Bioanalyzer (Agilent). Both polyadenylated and non-polyadenylated (after rDNA subtraction) RNA was sequenced. Two different libraries were constructed for each sample. For one library, total RNA was purified by poly A selection following manufacturer's instructions. For the second library, 1.5 µg total RNA was rRNA depleted using Ribo-Zero (Epicentre, Madison, WI), followed by library generation using the Illumina TruSeq RNA protocol, beginning at the fragmentation step. Libraries were sequenced on an Illumina GAIIx. The adapters were trimmed from raw sequences and low quality reads were filtered out. Processed reads were mapped to human genome assembly hg19 using Tophat and differentially expressed gene analysis was performed using Cufflinks [77]. Data was visualized using the Integrative Genomics Viewer (Broad Institute). The dataset can be accessed at GEO: GSE52367.
HPV genomes were removed from the plasmid vector by restriction digestion and religated as described [78]. C-33 cells expressing either HPV1, HPV16, or HPV18 E2 proteins were transfected using FuGene 6 with the corresponding recircularized HPV genome and incubated for 24 h. E2 expression was induced with 3 µM CdSO4 for 4 h and the cells were prepared for FISH experiments as described above. PEB-BLOCs were detected using 5-fluorescein or Alexa 488 labeled probes, produced from BAC clones by Empire Genomics (Table S10), and the HPV genomes were detected using HPV DNA, purified from vector sequences by PCR amplification and labeled by an Alexa 594 ULysis labeling kit (Molecular Probes).
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10.1371/journal.ppat.1005895 | The Breadth of Synthetic Long Peptide Vaccine-Induced CD8+ T Cell Responses Determines the Efficacy against Mouse Cytomegalovirus Infection | There is an ultimate need for efficacious vaccines against human cytomegalovirus (HCMV), which causes severe morbidity and mortality among neonates and immunocompromised individuals. In this study we explored synthetic long peptide (SLP) vaccination as a platform modality to protect against mouse CMV (MCMV) infection in preclinical mouse models. In both C57BL/6 and BALB/c mouse strains, prime-booster vaccination with SLPs containing MHC class I restricted epitopes of MCMV resulted in the induction of strong and polyfunctional (i.e., IFN-γ+, TNF+, IL-2+) CD8+ T cell responses, equivalent in magnitude to those induced by the virus itself. SLP vaccination initially led to the formation of effector CD8+ T cells (KLRG1hi, CD44hi, CD127lo, CD62Llo), which eventually converted to a mixed central and effector-memory T cell phenotype. Markedly, the magnitude of the SLP vaccine-induced CD8+ T cell response was unrelated to the T cell functional avidity but correlated to the naive CD8+ T cell precursor frequency of each epitope. Vaccination with single SLPs displayed various levels of long-term protection against acute MCMV infection, but superior protection occurred after vaccination with a combination of SLPs. This finding underlines the importance of the breadth of the vaccine-induced CD8+ T cell response. Thus, SLP-based vaccines could be a potential strategy to prevent CMV-associated disease.
| The majority of infections with the betaherpesvirus human cytomegalovirus (HCMV) are clinically unnoticed, but in immunocompromised hosts HCMV infections can be severe and even fatal. Here we investigated in preclinical mouse models the efficacy and mechanisms of synthetic long peptide (SLP)-based vaccines eliciting mouse CMV (MCMV)-specific CD8+ T cells as a platform modality to protect against CMV infection. The percentages of MCMV-specific T cells in the circulation elicited by prime-booster SLP vaccination were equivalent or higher compared to those induced by the virus itself. We further show that the naive T cell precursor frequency rather than the functional avidity of T cells predicts the magnitude of SLP-induced CD8+ T cell responses. Superior protection against MCMV infection depends strongly on the combined use of distinct SLP vaccines leading to broader viral-specific responses. This finding highlights the importance of the breadth of vaccine-induced CD8+ T cell responses.
| Human cytomegalovirus (HCMV) contributes substantially to morbidity in immunocompromised individuals. Organ or hematopoietic stem cell transplant recipients, people infected with HIV and patients with lymphocytic leukaemia are particularly vulnerable to HCMV-associated disease [1]. Moreover, congenital HCMV infection of unborn and new born children can lead to severe and permanent neurological symptoms [2]. Although currently available antivirals for HCMV are able to decelerate viral progression, thereby reducing the odds for major side effects, they require prolonged treatment periods and are accompanied with significant toxicity. Adoptive transfer of HCMV-specific T cells is an alternative treatment modality but is costly and laborious. The apparent burden of HCMV-associated disease and the paucity of cost-effective measures without side-effects have led to major efforts to develop effective HCMV vaccines but unfortunately no licensed vaccines are currently available [3, 4].
There is accumulating evidence that effective control of persistent viral infections requires the induction of a balanced composition of polyfunctional T cell responses [5]. T cell immunity against CMV plays a critical role in controlling the primary viral infection and latency [6]. Whereas CMV-specific CD4+ T cells are important during the primary infection phase, CD8+ T cells are associated with greater benefits at the persistent infection phase and confer superior protection during reactivation and re-exposure [7–9]. Upon CMV infection, extra-ordinary large CD8+ T cell responses of diverge phenotype arise. CD8+ T cell response kinetics specific to most antigens follow the traditional course comprised by expansion after antigen encounter, rapid contraction, long-term maintenance at low levels and acquisition of a central-memory phenotype. Interestingly, CD8+ T cell responses to certain CMV antigens do not dwindle post-infection but inflate and exhibit a polyfunctional effector-memory phenotype [10–13]. In immunocompromised hosts, the balance between CMV and cellular immunity is apparently underdeveloped or lost, and therefore instigating the development and/or restoration of the T cell compartment specific for CMV would be particularly informative.
The overarching aim of this study was to test a potential prophylactic vaccine platform against CMV based on synthetic long peptides (SLPs) containing immunodominant T cell epitopes. Previously, we reported that in therapeutic settings SLP-based vaccines can be successfully designed to stimulate effector and memory T cells against human papilloma virus-associated disease in mice and human [14–16]. As the efficacy of SLP-based vaccines is directly linked to the phenotypical and functional characteristics of the vaccine-induced CD8+ T cell response, we rigorously evaluated SLP-induced T cell responses. MCMV-specific SLP vaccines, assessed in two different mouse strains (C57BL/6 and BALB/c mice), lead to strong polyfunctional T cell responses, and combined SLP vaccines targeting different antigens provide a successful vaccine modality to control MCMV infection.
To assess the potential of SLP-based vaccines in eliciting protecting CD8+ T cell responses against MCMV infection, we designed SLPs containing immunodominant MHC class I T cell epitopes from MCMV encoded proteins, and evaluated this vaccine platform in two different immunocompetent mouse strains with different susceptibility to MCMV; the C57BL/6 strain (MHC haplotype H-2b) and the more MCMV-susceptible mouse strain BALB/c (MHC haplotype H-2d) (S1 Table). C57BL/6 mice are less susceptible to MCMV infection compared to BALB/c mice because C57BL/6 mice express the NK cell-activating receptor Ly49H, which recognizes the MCMV protein m157 at the surface of infected cells [17–20].
Mice were vaccinated subcutaneously with SLPs along with the TLR9 ligand CpG as adjuvant. The SLP vaccine administration was well tolerated without adverse events. At day 7 after SLP immunization, epitope-specific CD8+ T cell responses were detected in the blood but a booster vaccination was required for induction of vigorous CD8+ T cell responses (Fig 1A and 1B). Prime-boosting with SLP vaccines induced very high frequencies of circulating CD8+ T cells against the noninflationary epitopes M45985-993 and M57816-824 in C57BL/6 mice, and were even higher than the percentages of the circulating MCMV-induced CD8+ T cells at the peak of infection (day 7). Also the response against m139419-426, known to be non-inflationary during the early phase after MCMV and at later time points as inflationary, is strong. The response against the non-inflationary M45507-515 epitope in BALB/c mice was even much higher in the SLP-vaccinated group as compared to the MCMV infected mice. The frequencies of the circulating CD8+ T cells against the inflationary M38316-323 and IE3416-423 epitopes in C57BL/6 mice and the inflationary m164257-265 and IE1168-176 epitopes in BALB/c mice were comparable (Fig 1A and 1B).
SLP vaccines containing MHC class I epitopes may comprise unidentified class II epitopes and linear B cell epitopes leading to CD4+ T cell and antibody responses. To exclude this possibility, we performed polychromatic intracellular cytokine staining with the SLPs and performed SLP-specific antibody ELISAs, respectively (S1 and S2 Figs). Neither MCMV-specific CD4+ T cells nor peptide specific Abs were detected in these assays, indicating that the designed SLPs lead exclusively to antigen-specific CD8+ T cell responses and that epitope-specific responses induced by SLP or MCMV can only be compared for CD8+ T cells.
Longitudinal analysis of the antigen-specific CD8+ T cell responses revealed that all SLP-induced T cell responses in both mice strains contracted gradually over time after the booster immunization (Fig 1B). Two months after the booster vaccination, the SLP-induced responses to most epitopes were still clearly detectable in blood. During MCMV infection, the epitope-specific CD8+ T cell responses followed a different course, consistent with previous reports [10, 11]. T cell responses to the non-inflationary epitopes M45985-993, M57816-824, and M45507-515 rapidly contracted after the peak response and were stably maintained in time while T cell responses to the epitopes M38316-323, m139419-426, m164257-265, IE1168-176 and IE3416-423 inflated (Fig 1B). These data indicate that the context of epitope expression determines the kinetics of the T cell responses, which is uniform for diverse epitopes after SLP vaccination but in the case of MCMV infection this results in a dichotomy of responses related to the chronic nature of this infection.
At the peak after the booster SLP immunization (day 7–8), high frequencies of epitope-specific CD8+ T cells, analogous to the responses elicited by MCMV virus were observed in the spleen (Fig 1C). However, in absolute numbers, MCMV infection led to a higher T cell magnitude compared to SLP vaccination, which can be attributed to virus-associated inflammation leading to splenomegaly. At the memory phase (day 60), MCMV-specific T cell responses to the non-inflationary epitopes were significantly lower than the equivalent SLP vaccine-induced responses (Fig 1C). The MCMV-induced CD8+ T cell responses to the inflationary epitopes were of higher magnitude compared to those induced by SLP vaccination.
Taken together, these results show that prime-boost vaccination with SLP vaccines containing MHC class I MCMV epitopes elicit in mouse strains with different susceptibility to MCMV high percentages of effector and memory CD8+ T cells that contract gradually in time.
Next, we aimed to dissect the underlying mechanisms of the relatively low responses to some of the SLPs (i.e. M38 and IE3 in C57BL/6; M45 in BALB/c) compared to the other. First, we endeavoured to alter the SLP sequences by altering the C-terminal cleavage, which may improve the immunogenicity (S3 Fig). However, the altered M38316-323 SLP did not exhibit a significant improvement in the SLP-induced T cell response whilst the altered SLP containing the IE3416-423 epitope elicited responses that were actually reduced.
Then we questioned if the differences in the magnitude of the T cell responses triggered by the various single SLP vaccines might be related to the functional avidity of the T cells, which is determined by the affinity of the peptide for MHC and the TCR affinity for the peptide-MHC complex (Fig 2A and 2B). The SLPs elicited T cells with different levels of functional avidity but no correlation was found with the strength of the CD8+ T cell response. Moreover, in both C57BL/6 and BALB/c mice the functional avidity of the T cells, elicited either by SLP vaccines or MCMV infection, were remarkably similar and remained stable in time as they were similar during the acute and memory phase of response. Thus, differences in TCR affinity are not involved in the observed difference in the magnitude of the T cell responses.
The data presented above illustrated that factors other than peptide-MHC/TCR affinity are implicated in shaping the strength of SLP-induced T cell responses. Recently, it was shown that the precursor frequency of naive T cell populations can predict the immunodominance hierarchy of viral epitope specific CD8+ T cell responses [21]. To test whether the precursor frequency is predictive for the magnitude of SLP-induced T cell responses we determined the precursor frequency of all the epitopes included in this study in naive C57BL/6 and BALB/c mice (Fig 2C). In C57BL/6 mice, the precursor frequencies for the M45985-993 and M57816-824 epitopes were among the highest followed by the precursor frequencies to the m139419-426 epitope. The lowest precursor frequencies were detected to the M38316-323 and IE3416-423 epitopes, confirming a previous report [22]. In BALB/c mice, the highest precursor frequencies were observed for the m164257-265 and IE1168-176 epitopes whereas the frequency of M45507-515 specific T cells was lower (Fig 2C). Markedly, the average level of the precursor frequency of each epitope-specific CD8+ T cell population was proportional to the expansion of the antigen-specific populations found in mice following either SLP immunization or MCMV infection. Together, these results indicate that naive precursor frequencies rather than TCR avidity determine the magnitude of SLP vaccine-mediated CD8+ T cell responses.
To assess the phenotypical and functional quality of MCMV-specific CD8+ T cells induced by either the SLPs or the virus, we determined the formation of the diverse T cell subsets that develop after antigenic challenge. Early after the booster, SLP vaccination resulted in the induction of a highly activated CD8+ T cell subset exhibiting an effector-like phenotype (CD62Llo, CD44hi, CD127lo, KLRG1hi), which completely resembled the MCMV-specific T cell phenotype during the acute phase of the infection (Fig 3A and 3B). In the memory phase, both SLP- and MCMV-induced T cell phenotypic traits diverged (Fig 3C and 3D). All SLP-induced CD8+ T cells exhibited a fairly mixed phenotype sharing features of both central-memory T cells (CD62Lhi, CD44lo, CD127hi, KLRG1lo), effector-memory T cells (KLRG1hi, CD44hi, CD127lo, CD62Llo) but also an intermediate phenotype (i.e. KLRG1hi, CD127hi). As expected, during MCMV infection, the non-inflationary M45985-993, M45507-515 and M57816-824-specific CD8+ T cells gained a predominant central memory-like phenotype while the inflationary M38316-323, m139419-426, IE3416-423, m164257-265 and IE1168-176-specific T cells appeared mostly effector-memory like.
To assess the cytokine profiles of the SLP-induced CD8+ T cells, we performed intracellular cytokine staining for IFN-γ, TNF and IL-2 and compared these to MCMV-induced T cells. At the peak response after booster vaccination, SLP-induced T cells consisted mainly of single IFN-γ and double IFN-γ/TNF producing populations (Figs 4A and S4). The cytokine producing traits of the MCMV-induced effector CD8+ T cells matched in general with the SLP-elicited T cells. Except relatively more single IFN-γ producing CD8+ T cells after MCMV infection compared to SLP vaccination were found in the T cell populations reactive to the epitopes IE3416-423, IE1168-176, M45507-515 and m164257-265.
At the memory phase, the SLP-specific CD8+ T cells gained the ability to co-produce the three cytokines, at the expense of single cytokine producing cells (Figs 4B and S4). This gain in triple cytokine production (IFN-γ/TNF/IL-2) during MCMV infection is mainly observed in the non-inflationary CD8+ T cells. Both during the acute and memory phase, the percentage of the total CD8+ T cell population producing IFN-γ, either in case of SLP vaccination or MCMV infection, corresponded to the percentage of MHC class I tetramers, indicating full differentiation of the elicited T cells.
A hallmark of memory T cells is the ability to undergo secondary expansion upon antigenic challenge [23]. To assess this property of vaccine-induced memory T cells, we performed adoptive transfer experiments in which congenically marked (CD45.1+) memory M45985-993 and m139419-426-specific CD8+ T cells from SLP vaccinated and MCMV infected mice were isolated and transferred into naive recipient mice, which were subsequently challenged with MCMV (Fig 5). SLP-induced M45985-993 and m139419-426-specific T cells expanded; albeit to a lesser extend as compared to the MCMV-induced (Fig 5). The MCMV-elicited M45985-993-specific T cells exhibited, corresponding to their central-memory phenotype, a superior capacity in expansion as compared to the MCMV-elicited m139419-426-specific T cells with an effector-memory phenotype. Of note, the expansion of the SLP-induced M45-specific T cells was comparable to the m139-specific T cells induced by MCMV, although the phenotype of SLP-induced cells were more central-memory like. This indicates that the instruction that T cells receive in different settings can result in cells with a different expansion potential despite a seemingly similar phenotype based on markers for central/effector memory cells. All together we conclude that SLP-based vaccines induce a heterogeneous pool of memory T cells with a secondary expansion potential that is somewhat lower as compared to memory T cells elicited by virulent virus.
The various SLP vaccine formulations were evaluated for their capacity to confer protection against MCMV challenge (at day 60 after booster vaccination). In C57BL/6 mice, the viral load of unvaccinated (naive) mice challenged with MCMV was found to be significantly higher in spleen, liver and lungs, when compared to the viral load of MCMV re-challenged mice that successfully controlled a previous MCMV infection, indicating that pre-existing immunity to MCMV can clearly reduce the viral load upon re-infection (Fig 6A). All the different SLP vaccines resulted in a reduction in viral load in the spleen compared to unvaccinated mice, albeit less effective when compared to MCMV infected mice. Mice vaccinated with the SLPs containing the M38316-323 and m139419-426 epitopes display a significant reduction in viral titres in the liver and lungs. Also, the M57816-824 and IE3416-423 epitope containing SLPs were capable in reducing the viral replication in the liver after MCMV challenge, albeit to a lesser extent (Fig 6A).
Re-challenge of MCMV infected BALB/c mice resulted in substantial protection of the m164257-265 epitope containing SLP vaccine in spleen and liver (Fig 6B). The M45507-515 and IE1168-176 epitope containing SLPs however did not induce protective immunity in vaccinated mice. These results indicate that certain SLPs but not all have the potency to elicit protective immunity against virus challenge, and that this protection is not necessarily correlating to the size of the SLP-induced CD8+ T cell response.
Since vaccination with the m139419-426 and M38316-323 epitope containing SLPs was accompanied with some reduction of the viral load, we examined in C57BL/6 mice whether vaccination with these two, or even more, SLPs combined is able to exceed the protection efficacy of single SLP immunization. Strong and long-lived peptide-specific CD8+ T cell responses were measured in mice vaccinated with the mixture of the m139 SLP plus the M38 SLP and with a mixture of all 5 SLP vaccines (Fig 7A). Notably, the T cell response against each peptide epitope with the combined SLP vaccines was lower as compared to single SLP vaccination (except for the m139-specific response), indicating that competition among antigen-specific CD8+ T cell populations can occur in multivalent vaccines. Especially, altered were the responses to the epitopes in M57 and IE3 because these were not boosted (Fig 7A). Such competition among T cells during boosting has also been observed after viral infection [24]. The kinetics of the combined SLP vaccine-induced T cell responses was found to be similar to single SLP vaccines, and the phenotype (Fig 7B) and cytokine polyfunctionality of the T cells as well (S5 Fig).
At day 60 post booster vaccination, mice were challenged with MCMV and 5 days later viral titres were measured in different organ tissues. The efficacy of the combined SLPs to protect upon acute MCMV challenge was remarkably improved compared to the single SLP vaccines, as all mice that received a mixed SLP vaccine exhibited significant reduction in the viral load, especially in the liver (Fig 7C), suggesting that the breadth of the response or the magnitude of the total anti-viral response is important. Remarkably, the combination of the m139 SLP with the M38 SLP was as efficacious as the combination with all 5 SLPs. To assess if superior viral control was related to the breadth of the response, we adoptively transferred 1 × 104 m139 SLP-induced CD8+ T cells, 1 × 104 M38 SLP-induced CD8+ T cells, or an equal total number of a pool of both m139 (0.5 × 104) and M38 (0.5 × 104) SLP-induced CD8+ T cells in naive recipient mice (Fig 7D). The transfer of SLP-induced CD8+ T cell populations with a dual specificity resulted in a significant reduction in viral titres, while the transfers of equal amounts of T cells with single specificity did not. Thus, combinations of at least two distinct SLP vaccines have an increased potency to protect compared to single SLP vaccines, indicating that the breadth of the vaccine-induced CD8+ T cell responses plays a crucial role in anti-viral immunity. We conclude that vaccination with single SLPs can be applied as a prophylactic vaccine strategy against CMV infection, but vaccination with combinations of different SLPs serve as a superior vaccine technology platform against viral challenge.
In this study we report that SLP-based vaccines are an effective modality against CMV infection. In a prime-boost vaccine regimen, SLPs containing single MCMV epitopes are highly immunogenic in both C57BL/6 and BALB/c mice, and generate long-lasting polyfunctional CD8+ T cell responses. Our study revealed three key findings. First, the magnitude and phenotype of the SLP-induced T cell responses initially resemble those evoked by a real viral infection. Second, the magnitude of the SLP-induced T cell response strongly correlated to the naive T cell precursor frequency, and third the protection against viral infection by SLP-induced memory CD8+ T cells was most pronounced when vaccination was performed with combinations of distinct SLPs leading to an increased breadth of the antigen-specific T cell response.
In the last decades many vaccine strategies such as attenuated virus, DNA constructs, protein, and virally vectored vaccines targeting HCMV have been developed [3, 4]. The focus of most of these vaccines was to generate protective antibodies. Our finding that SLP-based vaccines that solely provoke CD8+ T cell responses are efficacious suggests that the design of more efficient vaccines against CMV should incorporate the induction of CD8+ T cell immunity. Although we observed some epitope competition among SLP vaccine-induced CD8+ T cell responses, we anticipate that inclusion of CD4+ T cell and B cell epitopes will further improve the vaccine efficacy given that CD4+ T cells and antibodies have also antiviral actions against CMV. Moreover, SLP-based vaccines allow further refinement by different prime-booster regimens and by combinations with adjuvants, immunomodulatory antibodies or other vaccine platforms [25]. Conceivably, this will positively impact the phenotype and effectivity of the vaccine-induced T cells.
As to date, the high CD8+ T cells responses elicited with the SLP vaccines encoding MCMV epitopes have not been observed before with other SLPs including those containing epitopes of human papilloma virus (HPV) [14], lymphocytic choriomeningitis virus (LCMV) [26], influenza [27] or model antigens [28]. This may be explained by the relatively high precursor frequency of T cells responding to some of the MCMV epitopes. Our study indicates that it is of interest for T cell-based vaccines to determine the antigen-specific T cell precursor frequencies as these correlate to the magnitude of the vaccine-induced antigen-specific response, allowing the selection of epitopes generating the most robust responses. This knowledge can be very useful for development of vaccines that are based on selection of epitopes. Nevertheless, the magnitude of the vaccine-induced T cell response appears not necessarily to correlate to protective immunity but seems to depend also on the specificity. For example, in C57BL/6 mice, the large vaccine-elicited responses to the M45985–993 and M57816-824 epitopes do not provide as good protection as the seemingly lower response to the M38316–323 epitope. Similarly, in BALB/c mice the m164 SLP confers immunity in liver and spleen whereas the IE1168-176 epitope containing SLP, which is analogous in magnitude, does not show protective effects. Previous studies using short peptide or DNA vaccination also reported that the strength of the vaccine-induced IE1-specific CD8+ T cell response does not necessarily correlate to protection [29–31], suggesting that the quality of the vaccine-induced T cell is more decisive.
Dissimilarities in transcription of viral genes [32], which may even vary in different tissues, as well as the efficiency of peptide processing and presentation at the cell surface, may also be implicated in the differential efficacy of the T cell response to each particular epitope to confer resistance to MCMV. In this respect it is of interest to note that SLP vaccines containing “inflationary” epitopes (i.e., M38 and m139) elicit better protection as compared to the non-inflationary epitopes. This may relate to differences in the presentation of the inflationary epitopes as compared to the non-inflationary epitopes by infected cells and/or by (cross-presenting) APCs.
An important requirement for memory inflation is chronic antigenic exposure [12]. The fact that SLPs do not elicit inflation suggests that SLPs are broken down in such a manner that epitopes are not presented over a long period of time as occurs during persistent CMV infection. Other factors important for memory inflation during CMV infection, such as dependence on certain T cell costimulatory interactions (e.g., CD27-CD70 [33]), are likely also not in place at late time points post SLP vaccination. In addition, a characteristic feature of inflationary T cells is their predominant effector-memory like phenotype. The SLP vaccine-induced T cells are not mostly effector-memory like, as may be expected because of the apparent absence of memory inflation. Although the expansion of the SLP-induced CD8+ T cells seems to be somewhat negatively influenced as compared to virus-induced T cells, it remains to be determined whether protection on a per-cell basis is influenced as well. Nevertheless, the SLP-induced T cells were well capable to reduce the viral load upon viral challenge, especially when a mixture of distinct SLPs was used for vaccination. The somewhat lesser expansion potential of the SLP-induced T cells might relate to some of the differences in the phenotype of the SLP and MCMV-elicited T cells. Although the effector T cells induced by either SLP boost vaccination or MCMV infection had an analogous phenotype (KLRG1hi, CD44hi, CD127low, CD62Llow, IL2+/-) and cytokine profile, the memory T cells elicited by SLPs displayed a mixed profile of effector-memory (KLRG1hi, CD127lo), central-memory (KLRG1lo, CD127hi) and double-positive T cells (KLRG1hi, CD127hi). In contrast, MCMV infection induces a more polarized phenotype: either a central-memory phenotype (mainly non-inflationary responses) or an effector-memory phenotype (mainly inflationary responses). Whether a lack of CD4+ T cell helper signals [34] or a lack of virus-associated inflammatory signals [26] is responsible for the observed SLP vaccine-associated phenotype and secondary expansion potential remains to be examined in future studies.
We showed that the efficacy of SLP vaccines to protect against MCMV is primarily driven by the breadth of the CD8+ T cell responses rather than the magnitude of the individual SLP vaccine-induced T cell responses. A possible explanation is that viral infected cells are to a certain degree resistant to CD8+ T cell mediated killing due to sophisticated immune evasion mechanisms including downmodulation of MHC class I molecules and prevention of apoptosis [35–37]. Accordingly, it has been estimated that one effector CD8+ T cell kills only 2–16 MCMV-infected cells per day and the probability of death of infected cells increases for those contacted by more than two CTLs, which is indicative of CTL cooperation [38]. Our study suggests that multiple encounters with cytotoxic CD8+ T cells with different specificity result in more effective killing of infected cells.
Overall, this study provided evidence that SLP-based vaccines eliciting memory CD8+ T cell responses have protective effects against acute MCMV infection with respect to lowering the viral load in tissues. These promising results highlight the need for additional studies to elucidate the role of vaccine-induced T cells against CMV and other persistent viral infections.
C57BL/6 mice and BALB/c mice were purchased from Charles River Laboratories (L'Arbresle, France). CD45.1 (Ly5.1) congenic mice on a C57BL/6 background were obtained from The Jackson Laboratory. Mice were maintained under specific-pathogen-free conditions at the Central Animal Facility of Leiden University Medical Center (LUMC), and were aged 8–10 weeks at the beginning of each experiment. The mice did not undergo any immunosuppressive treatments and were fully immunocompetent.
All animal experimental protocols were approved by the LUMC Animal Experiments Ethical Committee in accordance with the Dutch Experiments on Animals Act and the Council of Europe (numbers 13156 and 14187).
MCMV virus stocks were prepared from salivary glands of BALB/c mice infected with MCMV-Smith (American Type Culture Collection (ATCC)). The viral titres of the produced virus stocks were determined by viral plaque assays with 3T3 mouse embryonic fibroblasts (MEFs) (ATCC). Age- and gender-matched C57BL/6 mice were infected with 5 × 104 PFU MCMV, and age- and gender-matched BALB/c mice with 5 × 103 PFU MCMV. Viruses were administered intraperitoneally (i.p) in a total volume of 400 μl in PBS. At 65 days post-booster vaccination or infection, SLP vaccinated or MCMV infected mice were (re)-challenged with 5 × 104 PFU MCMV. Determination of viral load was performed by real-time PCR as described previously [39].
Short (9–10 aa) and long (20–21 aa) peptides containing MHC class I-restricted T cell epitopes from MCMV encoded proteins in C57BL/6 and BALB/c mice were produced at the peptide facility of the LUMC (peptide sequences are described in S1 Table). The purity of the synthesized peptides (75–90%) was determined by HPLC and the molecular weight by mass spectrometry. Synthetic long peptide (SLP) vaccinations were administered subcutaneously (s.c.) at the tail base by delivery of 50 μg SLP and 20 μg CpG (ODN 1826, InvivoGen) dissolved in PBS in a total volume of 50 μl. Booster SLP vaccinations were provided after 2 weeks. Vaccination with a mixture of SLPs was done with 50 μg of each SLP and 20 μg CpG.
Cell surface and intracellular cytokine stainings of splenocytes and blood lymphocytes were performed as described [40]. For examination of intracellular cytokine production, single cell suspensions were stimulated with short peptides for 5 h in the presence of brefeldin A or with long peptides for 8 h of which the last 6 h in presence of brefeldin A (Golgiplug; BD Pharmingen). MHC class I tetramers specific for the following MCMV epitopes: M45985–993, M57816–824, m139419-426, M38316–323, and IE3416–423 in C57BL/6 mice and M45507–515, m164257-265 and IE1168-176 in BALB/c mice were produced as reported [41]. Fluorochrome-conjugated mAbs were purchased from BD Biosciences, Biolegend or eBioscience. Flow cytometry gating strategies are shown in S6 Fig. Samples were acquired with the LSRFortessa cytometer (BD Biosciences) and analysed with FlowJo-V10 software (Tree star).
A peptide dose-response titration was performed to determine and compare the TCR avidity of the CD8+ T cells induced after SLP vaccination and MCMV infection at the acute and memory phase. In brief, splenocytes were stimulated with various concentrations of short peptide in presence of 2 μg/ml brefeldin A for 5 h at 37°C. Subsequently, cell surface staining and an intracellular IFN-γ staining were performed. Responses were analysed using the same approach as described above.
Blood was collected from the retro-orbital plexus and after brief centrifugation, sera were obtained and stored at −20°C. Specific immunoglobulin levels in serum were measured by ELISA as described [39]. Briefly, Nunc-Immuno Maxisorp plates (Fisher Scientific) were coated either with 2 μg/ml SLPs or with MCMV-Smith in bicarbonate buffer, and after blocking with skim milk powder (Fluka BioChemika) diluted sera were added. Next, plates were incubated with HRP-conjugated antibodies (SouthernBiotech) to detect different Ab isotypes. Plates were developed with TMB substrate (Sigma Aldrich) and the colour reaction was stopped by addition of 1M H2SO4. To serve as a positive control, a peptide from the M2 protein (eM2) of influenza A virus with identified ability to induce antibodies and corresponding serum was used. Optical density was read at 450 nm (OD450) using a Microplate reader (Model 680, Bio-Rad).
To determine the endogenous naive precursor frequency of MCMV-specific CD8+ T cell populations in C57BL/6 and BALB/c mice, enrichment assays of antigen-specific CD8+ T cells were performed as described [42]. In short, single cell suspensions were generated from pooled spleen and lymph nodes (mesenteric, inguinal, cervical, axillary, and brachial) of individual mice. Cells were stained with PE and APC-labelled MHC class I tetramers for 0.5 h at RT, then washed, labelled with anti-PE and anti-APC microbeads (Miltenyi Biotec), and passed over a magnetized LS column (Miltenyi Biotec). The tetramer-enriched fractions were stained with fluorochrome labelled Abs against CD3 (clone 500A2), CD4 (clone L3T4), CD8 (clone 53–6.7) for 30 min at 4°C, and subsequently analysed. Samples were acquired with the LSRFortessa cytometer (BD Biosciences).
The expansion capacity and vaccine efficacy of SLP vaccine and/or MCMV-induced antigen-specific CD8+ T cells was determined by adoptive transfers. Splenic CD8+ T cells from chronically (day 60) infected and SLP vaccinated CD45.1+ mice were enriched with magnetic sorting using the CD8+ T cell isolation kit in accordance with the manufacturer’s protocol (Miltenyi Biotec). Next, cells were stained with MHC class I tetramers and with fluorochrome labelled antibodies against CD3 and CD8. Tetramer positive CD8+ T cells were sorted using a FACSAria II Cell Sorter (BD Biosciences) and 1 × 104 tetramer+ CD8+ T cells were transferred (retro-orbital in a total volume of 200μl in PBS) into naive CD45.2+ C57BL/6 recipients. Recipients were subsequently (2 h later) infected with 5 × 104 PFU MCMV. At day 5 post viral challenge the viral titres were determined by qPCR and the number of donor-specific CD8+ T cells by flow cytometry.
Statistical significance was assessed with Student’s t-test or ANOVA using GraphPad Prism software (GraphPad Software Inc., USA). The level of statistical significance was set at P<0.05.
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10.1371/journal.pgen.1003552 | NTRK3 Is a Potential Tumor Suppressor Gene Commonly Inactivated by Epigenetic Mechanisms in Colorectal Cancer | NTRK3 is a member of the neurotrophin receptor family and regulates cell survival. It appears to be a dependence receptor, and thus has the potential to act as an oncogene or as a tumor suppressor gene. NTRK3 is a receptor for NT-3 and when bound to NT-3 it induces cell survival, but when NT-3 free, it induces apoptosis. We identified aberrantly methylated NTRK3 in colorectal cancers through a genome-wide screen for hypermethylated genes. This discovery led us to assess whether NTRK3 could be a tumor suppressor gene in the colon. NTRK3 is methylated in 60% of colon adenomas and 67% of colon adenocarcinomas. NTRK3 methylation suppresses NTRK3 expression. Reconstitution of NTRK3 induces apoptosis in colorectal cancers, if NT-3 is absent. Furthermore, the loss of NTRK3 expression associates with neoplastic transformation in vitro and in vivo. We also found that a naturally occurring mutant NTRK3 found in human colorectal cancer inhibits the tumor suppressor activity of NTRK3. In summary, our findings suggest NTRK3 is a conditional tumor suppressor gene that is commonly inactivated in colorectal cancer by both epigenetic and genetic mechanisms whose function in the pathogenesis of colorectal cancer depends on the expression status of its ligand, NT-3.
| NTRK3 is a neurotrophin receptor and appears to be a dependence receptor in certain tissues. NTRK3 has been previously shown to be an oncogene in breast cancer and possibly hepatocellular carcinoma. Through a genome-wide methylation screen, we unexpectedly found that NTRK3 is commonly methylated in colorectal cancers but not in normal colon samples, which led us to assess whether NTRK3 could be a tumor suppressor gene in the colon. We now demonstrate that NTRK3 is frequently methylated in colorectal adenomas and cancers. Induced NTRK3 expression in the absence of its ligand, NT-3, causes apoptosis and suppresses in vitro anchorage-independent colony formation and in vivo tumor growth. Reintroduction of NT-3 releases colon cancer cells from NTRK3-mediated apoptosis, which is consistent with NTRK3 being a dependence receptor in the colon. Finally, somatic mutations of NTRK3 have been observed in primary human colorectal cancer. We provide evidence that a subset of these mutations inactivate tumor suppressor activities of NTRK3. These findings suggest that NTRK3 is a conditional tumor suppressor gene in the colon that is inactivated by both genetic and epigenetic mechanisms and whose function in the pathogenesis of colorectal cancer depends on the expression status of its ligand, NT-3.
| Colorectal cancer (CRC) arises through the accumulation of gene mutations and epigenetic alterations that result in the transformation of normal colon epithelial cells into adenocarcinomas [1]. One of the most common epigenetic changes observed in CRC is the aberrant methylation of CpG islands in the promoter region of genes. Aberrant CpG island methylation is associated with gene silencing and can inactivate tumor suppressor genes in the colon, which promotes tumor formation through the deregulation of various cellular processes including proliferation and apoptosis, among others [1], [2].
In order to identify methylated tumor suppressor genes that play a role in the formation of CRC, we conducted a genome-wide screen for methylated genes in colorectal cancers and matched normal colon epithelium tissue samples. Through this screen, we found a set of novel methylated genes, which included neurotrophin tyrosine kinase receptor 3 (NTRK3), a gene that has been found to be hypermethylated in esophageal adenocarcinoma [3]. The identification of methylated NTRK3 in CRC was unexpected given that NTRK3 has been shown to be an oncogene in breast cancer and possibly hepatocellular carcinoma [4], [5]. However, NTRK3 has also been shown to be a tumor suppressor gene in neuroblastomas [6]. Thus, our findings raised the question of whether NTRK3 acts as an oncogene or tumor suppressor gene in the pathogenesis of CRC.
NTRK3 is a member of the NTRK neurotrophin receptor family, which includes NTRK1 (TRKA), NTRK2 (TRKB) and NTRK3 (TRKC). NTRK family members and their ligands, nerve growth factor (NGF), brain-derived neurotrophic factor (BDNF), neurotrophin-3 (NT-3) and NT4/5, are crucial to the development of the nervous system and have poorly defined roles in other tissues [7]. NTRK1 is the receptor for NGF, and NTRK2 preferentially binds BDNF and NT4/5. NT-3 is the only known physiologically relevant ligand for NTRK3. The NTRKs have been shown to play oncogenic roles in certain cancers, such as breast cancer and liver cancer [8]. For example, a fusion of ETV6 (ETS translocation variant 6) to TRKC leads to the constitutive activation of TRKC tyrosine kinase, which promotes tumor formation and progression in human breast carcinoma [9].
However, rather than being classic tyrosine kinase receptors, recent data suggests that NTRK1 and NTRK3 may be dependence receptors [6], [10], [11]. Dependence receptors are characterized by their ability to induce opposing biological effects depending on the availability of their ligands. In the presence of the receptor's ligand, a positive cellular differentiation or survival signal is transduced, whereas lack of the ligand results in cleavage of a death-domain peptide and induction of apoptosis [12]. Indirect support for the role of NTRK3 as a dependence receptor and conditional tumor suppressor gene is provided by the observation that NTRK3 is a favorable-prognostic factor in a variety of cancers, such as melanoma [13] and medulloblastomas [14]. These findings suggested that NTRK3 might likewise serve as a conditional tumor suppressor gene in colorectal cancer.
With regards to the possibility that NTRK3 could act as a tumor suppressor gene in the colon rather than as an oncogene, somatic inactivating mutations of NTRK3 have been identified in CRC [15], as well as in other cancers including breast, lung, and pancreatic [16]. These mutations are missense mutations that are predicted to inhibit the function of NTRK3 [16] (See Table S1). Thus, the discovery of mutant, as well as methylated, NTRK3 in CRC suggested the possibility of NTRK3 being a CRC tumor suppressor gene. Consequently, we carried out a series of studies to determine the effect of aberrant DNA methylation on the expression of NTRK3 and to determine if NTRK3 had oncogene or tumor suppressor activities in colorectal cancer cell lines.
DNA from CRCs and normal colon mucosa samples was subjected to analysis using Infinium HumanMethylation450 BeadChip arrays (Illumina). After filtering the data as described previously [3], we identified a number of genes that were aberrantly methylated in the CRCs. One of these methylated genes was NTRK3, which was methylated in all the CRCs and in none of the normal colon epithelium samples. In light of the preferential methylation of NTRK3 in CRCs and because of its role as a neurotrophin receptor, which suggested it could have a functional role in the formation of colorectal cancer, we carried out a series of studies to further assess the effect of NTRK3 methylation on CRCs.
The promoter region of NTRK3 (NM_002530) contains a dense CpG island located from nucleotides −96 to +179 relative to the transcription start site (TSS; Figure 1A). After observing methylated NTRK3 in the colorectal cancers run on the HumanMethylation450 arrays, we assessed the methylation status of NTRK3 in a second independent set of normal colon mucosa, colon adenomas, and CRCs using a quantitative methylation-specific PCR assay (qMSP; MethyLight) designed to assess the promoter region of NTRK3. We first established that a Percent of Methylated Reference (PMR) threshold of 13.7% had a specificity of ∼90% for cancer vs. normal tissue. Using this PMR threshold, we detected NTRK3 promoter methylation in 67% of colorectal cancers (N = 76) (See Text S1 for methods used to determine optimal PMR. Figure S11 and Table S4). Using this same PMR threshold, NTRK3 promoter methylation was found in 60% of adenomas (N = 55) and 10% of the normal colon samples (N = 98; normal versus cancer: p<0.0001; normal versus adenoma: p<0.0001). The frequency of normal colon mucosa cases adjacent to CRC with NTRK3 promoter methylation did not differ significantly from that observed in the normal mucosa of cancer-free individuals (Table 1). We also assessed the status of NTRK3 in a panel of colon cancer cell lines (N = 9) and found that all the cell lines had methylated NTRK3 using the 13.7% PMR threshold.
In addition, we performed bisulfite sequencing of the promoter region of NTRK3 in representative cases of normal colon epithelium, adenomas, and adenocarcinomas (5 samples/group) and correlated these results with those of the NTRK3 qMSP assay. The bisulfite sequencing results correlated well with the qMSP results (Figure 1B–E).
Colorectal cancer can be classified into molecular classes, which include the Microsatellite Unstable (MSI), Chromosome Unstable (CIN, also known as Microsatellite Stable, MSS), and CpG Island Methylator Phenotype (CIMP) [1]. These classes of CRC appear to have unique pathogenic mechanisms that give rise to the CRCs. We assessed the association of methylated NTRK3 with these classes of CRC and with mutations that are commonly found in CRC. As shown in Table 2, methylated NTRK3 is more frequent in tumors in women, and is independent of CIMP and MSI status, as well as KRAS, BRAFV600E, TP53, PIK3CA or APC mutations. In addition, as shown in Figure S1, methylated NTRK3 appears to be independent of other genes that are frequently methylated in CRC, such as MLH1, CDKN2A/p16 or RASSF1A.
As mentioned above, we noticed that all nine of the colon cancer cell lines analyzed carried methylated NTRK3. Consistent with methylation silencing NTRK3 expression, we did not detect NTRK3 mRNA expression in any of these cell lines. Next, the CRC cell lines RKO and HCT116, which carry methylated NTRK3, were treated with 5-aza-2′-deoxycytidine (5-AZA), which inhibits DNA methyltransferase1 (DNMT1), to determine if demethylation of the NTRK3 promoter would induce NTRK3 expression. Following 5-AZA treatment, NTRK3 mRNA expression was induced in both HCT116 and RKO cells (Figure 2A). We next assessed NTRK3 mRNA expression in normal colon mucosa samples, colorectal adenomas, and primary colon adenocarcinomas. NTRK3 mRNA expression was significantly lower in colorectal adenocarcinomas and adenomas as compared to the matched normal colon mucosa, which carried unmethylated NTRK3 (Figure 2B). Moreover, the expression of NTRK3 was significantly higher in the primary colon tumors that carry unmethylated NTRK3 compared to the tumors that carry methylated NTRK3 (Figure 2C).
We also assessed NTRK3 protein expression in normal colon mucosa and in adenomas and colorectal cancer by immunostaining. The normal colon mucosa showed heterogeneous membrane and cytoplasmic staining using an anti-NTRK3 monoclonal antibody, whereas almost no expression was detected in most adenoma and cancer cases (N = 30). Among the 20 adenoma and cancer samples, only 6 samples showed weak or moderate NTRK3 expression, while 9 out of 10 normal samples showed strong or moderate NTRK3 expression (Figure 2D). Taken together, these data provide support for the aberrant methylation of the NTRK3 promoter silencing NTRK3 expression in colon neoplasms.
Because NTRK3 has been shown to function as a dependence receptor in certain tissues, we also assessed the expression of NTRK3's ligand, NT-3, in CRC cell lines and primary CRCs. NTRK3's preferred ligand is NT-3, and NT-3 has been shown to inhibit NTRK3 mediated apoptosis and to induce NTRK3-mediated activation of signaling pathways involved in cell proliferation, apoptosis and motility [6], [17]. Therefore, we assessed the expression levels of NT-3 in the panel of colon cancer cell lines previously assessed for methylated NTRK3. No NT-3 expression was detected in RKO, HCT116, FET, Vaco400 and HT-29, whereas NT-3 was expressed at a low (although relatively high level in relation to the other CRC cell lines) in SW480. NT-3 expression was present at low levels in Lovo, LS174T, and AAC1/SB10 (Figure S2A). We next assessed the expression of NT-3 in primary CRC tissues and in matched normal colon mucosa specimens. NT-3 expression was significantly lower in the CRC's when compared to the normal colon (Figure 3A). Interestingly, we found a direct correlation between NT-3 expression and NTRK3 expression in the normal colon and in the CRC's (r2 = 0.81, Pearson's correlation P<0.0001), suggesting that the presence of NT-3 relieved the selective pressure to silence NTRK3 (Figure 3B).
In order to determine the mechanism responsible for loss of NT-3 expression, we assessed the methylation status of the promoter region of NT3 using an NT3 MSP assay and correlated these results with the NT3 mRNA expression levels. We found that the colon cancer cell lines lacking NT3 expression have aberrantly methylated NT3, whereas those that express NT3 mRNA carry unmethylated NT3 (Figure S2C). We also found that 5-AZA treatment of two cell lines that carry methylated NT3, HCT116 and RKO, induces the expression of NT3 (Figure S2B). Therefore, we conclude that the methylation of NT3 can repress the expression of NT3.
NTRK3 has been shown to be a dependence receptor in certain tumors and can trigger caspase-based apoptosis when not bound by NT-3 [6], [10]. In the presence of NT-3, NTRK3 induces differentiation, guidance or survival in neurons; however, NTRK3 can alternatively induce apoptotic cell death in the absence of NT-3 in neuroblastoma cells and presumably other cell types [6]. The dependence receptor aspect of the biological effects of NTRK3 suggests it has the potential to be either an oncogene or a tumor suppressor gene, depending on the presence of NT-3. In order to assess the effect of NTRK3 on colorectal cancer, NTRK3 was transfected into the HCT116 (MSI), RKO (CIMP) and HT29 (CIMP/MSS) cell lines, which lack NTRK3 and NT3 mRNA expression (Figure S3A and B). In these cell lines, NTRK3 reconstitution increased caspase activity by 2–3 fold compared to the control vector transfected cells. Furthermore, the addition of NT-3 (100 ng/mL) suppressed apoptosis induced by NTRK3 reconstitution (Figure 4A, B and C). These results were confirmed using an independent assay that assesses apoptosis by detecting apoptosis specific DNA:histone complexes (Cell Death Detection Assay (Roche); Figure S4 A, B and C).
Somatic mutations of NTRK3 have been identified in primary colorectal cancers [15]. In order to determine the effect of the mutant NTRK3 genes on the behavior of colorectal cancers, we constructed plasmids that express the following NTRK3 mutants: NTRK3-G608S, NTRK3-I695V and NTRK3-L760I [15]. The mutant NTRK3 constructs were then transfected into the CRC cell line RKO. Transfection of NTRK3-L760I into the RKO cells did not induce apoptosis (Figure 5B), but the wild-type NTRK3, NTRK3-G608S or NTRK3-I695V alleles did induce apoptosis. Moreover, inhibition of colony formation by NTRK3 was not induced by NTRK3-L760I, but was induced by NTRK3-G608S and NTRK3-I695V (Figure 5C). These findings demonstrate that NTRK3-L760I inactivates NTRK3 with regards to its apoptosis and colony formation ability. However, the other mutant alleles do not affect the function of NTRK3 and are presumably passenger mutations. (Of note, the mutation status of all the constructs was confirmed by direct sequencing, see Figure S5). Also, we did not observe any change in NT-3 expression after transfection with the wild-type or mutant NTRK3 constructs. These findings suggest NTRK3 is a tumor suppressor gene in the colon that can be inactivated by both epigenetic and genetic mechanisms. The identification of both methylated NTRK3 and inactivating NTRK3 mutations in colorectal cancers provides evidence that inactivation of NTRK3 promotes tumor formation in the colon.
As shown in the experiments above, NTRK3 can act as a tumor suppressor gene in colon cancer cell lines and can induce apoptosis in CRC cell lines through the activation of caspase 3 or caspase 7. We next assessed the signaling pathways that are affected by NTRK3, which have been shown to include the MAPK/Erk, NF-κB and PI3K/Akt pathways, to determine if they may be mediating NTRK3 induced apoptosis in the colon cancer cell lines [18]. We initially assessed the activation status of the MAPK/Erk pathway in the HCT116 and RKO cell lines. HCT116 and RKO cells reconstituted with NTRK3 show increased activation of the MAPK/Erk pathway as determined by increased phospho-Erk1/2 (p-Erk1/2) expression. This increase in p-Erk1/2 was accompanied by increased caspase3/7 activity. The NTRK3-induced apoptosis was inhibited by the MAPK inhibitor U0126 (Figure 6A and B). In order to confirm that increased p-Erk1/2-induced apoptosis was specific to NTRK3, we used 16% FBS as an extracellular stimulus to induce increased p-Erk1/2. Not surprisingly, cells treated with 16% FBS showed significant increased p-Erk1/2, which was not accompanied by increased apoptosis (Figure S6). Since the addition of NT-3 inhibited apoptosis induced by NTRK3 expression, we assessed whether the introduction of NT-3 affected the activation status of the MAPK/Erk pathway. Interestingly, the addition of NT-3 decreased NTRK3 protein expression and decreased p-Erk1/2 levels (Figure S7). These findings suggest that at least part of NTRK3's pro-apoptotic effects occur through the MAPK signaling pathway in colon cancer cell lines.
In light of the prior reports implicating a fusion gene involving NTRK3 affecting TGF-β signaling and TGF-β mediated cell behavior, we also assessed the TGF-β, BMP signaling and EMT markers in colon cancer cells after transfection with NTRK3 [4], [5]. However, we did not observe a significant change in the expression of any of these proteins (Figure S8).
Since NTRK3 is frequently methylated in colorectal adenomas, we carried out a series of studies to determine if loss of NTRK3 could induce transformed behavior in normal colon epithelial cells. We knocked down the expression of Ntrk3 in an immortalized murine colon epithelial cell line (YAMC) and then assessed the cells for transformed behavior using a soft agar colony formation assay. As shown in Figure 7, the knockdown of Ntrk3 was ∼80% as measured by RT-PCR, and this level of knockdown promoted anchorage independent growth in the YAMC cells. Of note, the parental YAMC cells grow slowly in soft agar. These findings suggest that loss of NTRK3 could be an early event in CRC formation.
We also performed studies on anchorage independent growth and tumor xenograft formation in established CRC cell lines to assess the putative tumor suppressor role of NTRK3 in CRC. First, we assessed the effect of NTRK3 expression on soft agar colony formation in HCT116 (MSI), RKO (CIMP) and HT29 (CIMP/MSS) cells. Transfection of full-length NTRK3 induced a nearly 5- to 10-fold reduction in colony number of HCT116 (Figure 8A), RKO and HT29 cells (Figure S9A and B). We also assessed the tumor-suppressor activity of NTRK3 in xenografts in immunodeficient nu/nu nude mice. NTRK3 reconstitution significantly suppressed tumor growth compared to xenografts containing a control vector (Figure 8B). Twenty-one days after subcutaneous injection of the cells, the mice were sacrificed and the tumors were excised and measured. We found that both the size and weight of the NTRK3-expressing tumors were significantly reduced compared to the control xenografts (Figure 8C and Figure S10). NTRK3 expression in the xenografts from the cells transfected with NTRK3 was confirmed by IHC (Figure 8D). Taken together, these results provide support for a tumor suppressor role for NTRK3 in CRC.
The aberrant methylation of CpG islands in the promoter regions of genes is a common event in many cancers [19]. The average colon cancer genome contains 1,000–3,000 abnormally methylated genes [20]. In many cases, the aberrant methylation of these genes can silence the expression of tumor suppressor genes and consequently promote tumor formation. However, it is also apparent that the hypermethylation of many genes in cancer has no effect on the expression of the methylated gene and does not influence tumor formation. This later class of methylated genes is felt to represent passenger events in tumorigenesis [20]. Through a genome-wide screen for methylated genes in colon cancers, we identified methylated NTRK3 in colon adenomas and adenocarcinomas. Methylated NTRK3 was found in 67% of colorectal adenocarcinomas and 60% of adenomas. With regards to the functional significance of this epigenetic alteration, we found that the aberrant methylation of NTRK3 suppressed NTRK3 expression, which suggested NTRK3 might act as a tumor suppressor gene in colon cancer. Our findings are in contrast to other studies in breast cancer that have demonstrated that NTRK3 is oncogenic [4]. These opposing results appear to be a consequence of NTRK3 being a dependence receptor, which means that it can induce proliferation when it binds its ligand, NT-3, but induces apoptosis when NT-3 is not available [6]. Because NT-3 is expressed in the colon epithelium but not in colon neoplasms, our findings suggest that silencing of NTRK3 releases colon cancer cells from NTRK3-mediated apoptosis. These findings suggest that NTRK3 might function as a novel conditional tumor suppressor gene in CRC.
Although somatic mutations of NTRK3 that are predicted to inactivate function have been observed in CRC, NTRK3's role as a tumor suppressor gene in CRC has not been clearly demonstrated to date [15]. In the present study, we have provided evidence that NTRK3 can have conditional tumor suppressor activities in CRC. A similar role for NTRK3 in neuroblastomas has recently been shown [6]. Reconstitution of NTRK3 in the absence of NT-3, the ligand for NTRK3, induced caspase-related apoptosis and cell death in the colon cancer cell lines RKO, HT29 and HCT116. We found that the effects on apoptosis could be suppressed by the treatment of the NTRK3 expressing cell lines with NT-3. Perhaps most importantly, NTRK3 inhibited colony formation in soft agar colony formation assays and suppressed the growth of tumor xenografts, which are hallmark in vitro effects of tumor suppressor genes. In addition, we have shown that the naturally occurring NTRK3-L760I mutation impairs NTRK3's ability to induce apoptosis and suppress anchorage independent growth. These findings suggest that NTRK3 is a CRC tumor suppressor gene that is inactivated by both genetic and epigenetic mechanisms.
The demonstration of NTRK3 as a potential conditional tumor suppressor gene in the colon suggests NTRK3 may be the latest member of a class of dependence receptors that suppress colon cancer formation. Other conditional tumor suppressor genes identified in CRC and other cancers, include DCC, UNC5C, p75NTR and MET [12], [21]. The dependence receptor model purports that some receptors induce different biological effects on cells depending on whether they are in a ligand-bound or ligand-free state. These receptors can induce caspase-mediated apoptosis in the absence of ligand, but induce proliferation when bound by their ligands. Therefore, one of the critical aspects of this study is the assessment of the expression of the NTRK3 ligand NT-3 in the colon. NTRK3's preferred ligand, NT-3, was found to be substantially suppressed in both colorectal adenomas and adenocarcinomas, presumably secondary to hypermethylation of the NT3 promoter region. It is plausible that the loss of NT-3 expression precedes the loss of NTRK3, which would create a clonal survival advantage for those CRC cells that silence NTRK3. Our studies suggest that inactivation of NTRK3 occurs early in the polyp→cancer sequence and that it contributes to the transformation of colon epithelial cells.
With regards to the results of our studies, it is also important to consider the effects of loss of NT-3 and NTRK3 in the context of the entire neurotrophin receptor and ligand families because cross-talk between the ligand and receptor family members can occur. It has been shown that a precursor of NT-3, proNT-3, can activate p75NTR and that NT-3 can activate NTRK1 or NTRK2, although this happens with low efficiency [18]. However, despite the potential for cross-talk, we did not observe any effects on colon cancer cells that lacked NTRK3 after being treated with NT-3. Therefore, our findings suggest that NTRK3 is the primary and perhaps only receptor for NT-3 in the colon and in colon neoplasms.
When bound to NT-3, NTRK3 functions as a typical receptor tyrosine kinase. Its activation is stimulated by neurotrophin-mediated dimerization and transphosphorylation of an activation loop tyrosine [22]. The major pathways activated by the NTRKs are MAPK, PI3K and PLC-γ1, among others [18], [22], [23]. Activation of the NTRKs and p75NTR promote activation of NF-κB, and p75NTR can activate the JNK pathway [18], [24], [25]. Previous studies have demonstrated that the activation of the MAPK and PI3K pathways by NTRK3 promotes cell differentiation, which in turn affects tumor progression [4], [23]. In this study, we also found that NTRK3 expression can activate the MAPK pathway. However, in this context the activation of ERK1/2 appears to be involved in the apoptotic response in colon cancer cells. There is a possibility that the MAPK activation we observed in this setting is an indirect effect of NTRK3 and a consequence of unopposed activation of p75NTR [18]. Our studies do not allow us to exclude this possibility, although even if such a mechanism was present, it would not change the interpretation of NTRK3 as being a colorectal cancer tumor suppressor gene.
In summary, we have identified NTRK3 as a novel conditional tumor suppressor gene in the colon that is inactivated by epigenetic and genetic mechanisms. We have provided evidence that NTRK3 can trigger apoptosis and inhibit tumor growth in the absence of its ligand NT-3 and that these effects are reversed by the addition of NT-3. We also showed that suppression of NTRK3 can induce transformed behavior in immortalized colon epithelial cells. Our studies provide further insight into the complex relationship between NTRK3 and NT-3 in cancers as well as into dependence receptor biology in the colon. This class of tumor suppressor genes may offer new therapeutic strategies in the colon.
All studies in this manuscript have been approved by the FHCRC IRB committee and the IACUC committee. The studies of human tissues were all done on anonymous samples. The IRB protocol covering this study is IRB 1989 and is available upon request.
Nine human colorectal cancer cell lines (SW480, Vaco400, LS174T, HT29, Vaco576, RKO, Vaco503, HCT116 and Lovo) representing the spectrum of CRC molecular subtypes [MSI, CIN (aka MSS) and CIMP] were used. The cell lines were either purchased from ATCC or were kindly provided by Sanford Markowitz (Case Western Reserve University School of Medicine and Case Medical Center, Cleveland, OH). All cell lines had their identity confirmed by DNA genotyping. Some of the cell lines were treated with the DNMT1 inhibitor (5 µM) 5-aza-2′-deoxycytidine (5-AZA; Sigma) in the experiments in this study.
Primary tissue samples used in the methylation array studies (N = 8 CRC's) were obtained from the ColoCare CRC cohort study (Fred Hutchinson Cancer Research Center, Seattle, WA) and from healthy individuals undergoing screening colonoscopy at the University of Washington Medical Center (Seattle, WA) (N = 6). Detailed information on these samples is shown in Table S2.
Formalin-fixed, paraffin-embedded (FFPE) and fresh-frozen colon neoplasms and normal colon tissue samples were obtained from the pathology archives at Vanderbilt University Medical Center (Nashville, TN), the Department of Veterans Affairs Tennessee Valley Health Care System, Meharry Medical Center (Nashville, TN), and the University Hospital of Cleveland (Cleveland, OH) following IRB approved protocols at each institution. Colorectal cancers, colon adenomas, and adjacent normal tissue samples were also provided by the Cooperative Human Tissue Network. In total, these samples included 52 cases of histologically normal colonic mucosa from individuals without cancer or inflammatory bowel disease (IBD) and 25 samples of histological normal colonic mucosa from individuals who had undergone colon resection for CRC or colon adenomas.
DNA and RNA were extracted from these samples as previously described [26].
These studies were conducted using Infinium HumanMethylation450 BeadChip arrays (Illumina) with DNA from CRCs (N = 8) and normal colon epithelium samples from cancer-free individuals (N = 6). Specific details regarding the platform, sample preparation and data filtering strategies have been described in our previous studies [3].
Bisulfite conversion of DNA was performed as described previously [27], [28].
For sequencing, bisulfite-converted DNA was PCR amplified, and the amplicons were then subjected to direct sequencing. The primers are described in detail in Table S3. The sequencing was conducted as described previously [27].
Quantitative methylation-specific PCR (qMSP; MethyLight) was performed using an ABI Prism 7700 detection system (Applied Biosystems). Detailed methods are provided in the Text S1 as well as in previous publications [27]. The primers and probes targeting NTRK3 are described in detail in Table S3.
The CpG Island Methylator Phenotype (CIMP) status and Microsatellite instability (MSI) status of a subset of the colorectal neoplasms were determined as described previously [27]. The gene mutation status of KRAS, BRAF, APC, TP53 and PIK3CA was assessed by using the qBiomarker Somatic Mutation PCR System Arrays/Human Colon Cancer (Qiagen) following the manufacturer's protocol.
Human CRC cell lines (SW480, Vaco400, LS174T, HT29, Vaco576, RKO, Vaco503, HCT116, and Lovo) were grown in Dulbecco's Modified Eagle media (DMEM; Invitrogen) supplemented with 10% fetal bovine serum (FBS; Invitrogen). The YAMC (Young Adult Mouse Colon) cell line was a kind gift from Dr. Robert H. Whitehead (Vanderbilt University School of Medicine, Nashville, TN) and was cultured as described previously [29]. To investigate the effects of re-expression of NTRK3, HCT116 and RKO cells were incubated for 72 hours with 5 µM 5′-AZA (Sigma). The media was replaced every 24 hours with fresh 5′-AZA. After 72 hours of treatment, the cells were washed twice with PBS and then grown in drug-free media for another 72 hours before harvesting.
The full-length NTRK3 cDNA (IOH54159, Invitrogen) was subcloned into the pDEST27 Vector (Invitrogen) to create pDEST27-NTRK3. The correct orientation of the insert was confirmed by restriction digest.
The pDEST27-based plasmids containing NTRK3-G608S, NTRK3-I695V and NTRK3-L760I were constructed based on the wild-type NTRK3 expression vector by using the GENEART site-direct mutagenesis system (Invitrogen) following the manufacturer's protocol. Successful mutagenesis was confirmed by direct sequencing (Figure S5). The sequencing primers are described in Table S3.
pGIPz-based short hairpin RNA (shRNA) constructs specifically targeting mouse Ntrk3 mRNA were purchased from Thermo Scientific/Open Biosystems Mouse shRNAmir Libraries maintained by the Genomics Shared Resource Core at the Fred Hutchinson Research Center (FHCRC, Seattle, WA). The details regarding the target regions of each shRNA are shown in Table S3. All 3 shRNA constructs were confirmed by direct sequencing (Table S3). Lentivirus containing the shRNA constructs were generated by co-transfecting pGIPz-shRNA with the packaging plasmid psPAX2 and the envelope plasmid pMD2.G (kindly provided by Michael Davis, FHCRC, Seattle, WA) into 293T packaging cells. Virus supernatant was filtered through a 0.2-µm filter and stored at −80°C until use. shRNA-mediated knock-down studies were performed by infecting YAMC cells with shRNA lentivirus for 48 hours, followed by an additional 72 hours of puromycin selection (4 µg/mL; Invitrogen). The stable YAMC cells were maintained in media containing 2 µg/mL puromycin. pGIPz-shNSC (Thermo Scientific/Open Biosystems), which expresses a scrambled shRNA with no known target sequence in the mouse genome, was used as a control for the transduction procedure.
HCT116 (MSI), RKO (CIMP) and HT29 (CIMP/MSS) cells were transiently transfected with pDEST27-based constructs (Invitrogen) using the XtremeGENE 9 DNA transfection reagent (Roche) following the manufacturer's protocol. Transfected cells were grown for 10–14 days in media containing 2400 µg/mL G418 (RKO cells) (Invitrogen) or 1200 µg/mL G418 (HCT116 and HT29 cells). For the experiments using NT-3, recombinant human neurotrophin-3 (NT-3) (R&D System) was added into the culture media 24 hours after transfection and incubated for another 24 hours.
Total RNA was isolated from CRC cell lines and primary tissues using TRIzol (Ambion) and was purified with the RNeasy Mini kit (Qiagen) according to the manufacturers' protocols. For human samples, TaqMan On-Demand primers and probes (Applied Biosystems) were used to determine the relative expression levels of NTRK3 (Hs00176797_m1) and NT-3 (Hs01548350_m1). GUSB (Hs99999908_m1) was used as a RNA input loading control. For YAMC cells (mouse) related RT-PCR, Ntrk3 (Mm00456222_m1) and Gusb (Mm01197698_m1) were used. All reactions were run in triplicate on an ABI Prism 7700 detection system.
FFPE sections of CRCs, adenomas and matched normal colonic mucosa were subjected to immunostaining with a rabbit anti-human TrkC monoclonal antibody (C44h5, Cell Signaling Technology). Briefly, 4 µm tissue sections were deparaffinized, rehydrated, and subjected to antigen retrieval by boiling in sodium citrate buffer (10 mmol/L, pH 6.0). The sections were incubated for 60 minutes with TrkC primary antibody (1∶50), stained with 3,3-diaminobenzidine, counterstained with hematoxylin, and mounted as described previously [30].
Programmed cell death was analyzed using the Cell Death Detection ELISA (Roche Molecular Biochemicals) following the manufacturer's instructions. Briefly, 48 hours after transfection or 24 hours after treatment with NT-3 or a selective ERK inhibitor, the cells were lysed, and the lysates analyzed using the ELISA kit.
Caspase-3 and caspase-7 activity was measured using the Caspase-Glo3/7 Assay (Promega) following the manufacturer's protocol. Relative caspase activation was calculated as the ratio of the caspase activity of the NTRK3- transfected cells and the negative control GUS-transfected cells.
The soft agar colony formation assays were performed as previously described [26], [27]. Briefly, 6,000 stably transfected HCT116, RKO or YAMC cells were mixed with 0.4% Sea Plaque agarose containing tissue culture media (top layer) and pipetted onto a solidified layer of 0.8% Sea Plaque agarose containing tissue culture media (bottom layer) in 35 mm petri dishes in triplicate. The tissue culture media was exchanged every 2∼3 days. After 14 days, colonies larger than 50 µm in diameter were counted using a phase-contrast microscope equipped with a reticule in 4 randomly selected fields in the three replicate dishes. The experiment was performed in triplicate.
Western blotting experiments were conducted as described previously [30]. Briefly, cells were lysed using RIPA buffer containing Phosphatase Inhibitor Cocktail (P5726, Sigma) at 0°C. The protein extracts (50 µg/sample) were subjected to electrophoresis through 12% Bis-Trispolyacrylamide gels (BioRad) and then transferred to PVDF membranes. Antibodies used were purchased from Cell Signaling Technology: anti-phospho-Akt (Ser473, #9271), anti-phospho-NF-κB (Ser536, #3033), anti-phospho-Erk1/2 (Thr202/Tyr204, #4370), anti-phospho-Smad1 (Ser463/465)/Smad5 (Ser463/465)/Smad8 (Ser426/428) (#9511), anti-phospho-Smad2 (Ser465/467, #3101), anti-phospho-Smad3 (C25A9, Ser423/425, #9520), anti-Smad2/3 (#3102), anti-TrkC (C44h5, #3376), anti-BMPR2 (#6979), and Epithelial-Mesenchymal Transition (EMT) antibodies, anti-N-Cadherin (#4061), anti-Vimentin (D21H3, #5741), anti-E-Cadherin (24E10, #3195), anti-ZO-1 (D7D12, #8193), anti-Snail (C15D3, #3879). Immunoreactive proteins were then visualized by incubating the PVDF membranes with ECL plus detection reagents, followed by imaging of chemiluminescence on an imager (X-ray film-based).
The care and use of the mice was conducted following protocols approved by the Fred Hutchinson Cancer Research Center IACUC. The IACUC protocol covering this work is IACUC 1624 and is available upon request. All IACUC protocols at the FHCRC require that animal suffering is eliminated unless required for the studies, in which case extensive justification is required.
Three to four week-old female athymic nu/nu mice were obtained from Harlan Laboratories. The mice were housed for one week in a pathogen-free animal facility prior to tumor cell injection. 1×107 GUS or NTRK3 stably transfected HCT116 cells in 200 µL DMEM and Matrigel (1∶1 mix) (BD Biosciences) were injected subcutaneously into the right flank of each mouse. The tumor sizes were measured using a caliper, and the tumor volume was calculated as follows: 0.5(length×width2) [6]. The mice were assessed every three days and sacrificed at three weeks after injection.
Receiver operating characteristic (ROC) curves and area under the curve (AUC) for NTRK3 methylation frequency of primary tissues were constructed on the basis of methylation levels. The Chi-squared test was used to compare the frequency of methylated NTRK3 between cancer and normal samples. The Fisher's exact test was used to test the association between the NTRK3 methylation status and clinical/molecular characteristics of CRC patients. Student t test or analysis of variance (ANOVA) was used to analyze the RT-PCR data. The Mann-Whitney rank sum test was used to analyze the data obtained from the cell death and apoptosis assays. Statistical analysis was performed using SPSS 13.0 software. All p values are two-sided, and a p value<0.05 was considered statistically significant.
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10.1371/journal.pgen.1004358 | Regulatory Mechanisms That Prevent Re-initiation of DNA Replication Can Be Locally Modulated at Origins by Nearby Sequence Elements | Eukaryotic cells must inhibit re-initiation of DNA replication at each of the thousands of origins in their genome because re-initiation can generate genomic alterations with extraordinary frequency. To minimize the probability of re-initiation from so many origins, cells use a battery of regulatory mechanisms that reduce the activity of replication initiation proteins. Given the global nature of these mechanisms, it has been presumed that all origins are inhibited identically. However, origins re-initiate with diverse efficiencies when these mechanisms are disabled, and this diversity cannot be explained by differences in the efficiency or timing of origin initiation during normal S phase replication. This observation raises the possibility of an additional layer of replication control that can differentially regulate re-initiation at distinct origins. We have identified novel genetic elements that are necessary for preferential re-initiation of two origins and sufficient to confer preferential re-initiation on heterologous origins when the control of re-initiation is partially deregulated. The elements do not enhance the S phase timing or efficiency of adjacent origins and thus are specifically acting as re-initiation promoters (RIPs). We have mapped the two RIPs to ∼60 bp AT rich sequences that act in a distance- and sequence-dependent manner. During the induction of re-replication, Mcm2-7 reassociates both with origins that preferentially re-initiate and origins that do not, suggesting that the RIP elements can overcome a block to re-initiation imposed after Mcm2-7 associates with origins. Our findings identify a local level of control in the block to re-initiation. This local control creates a complex genomic landscape of re-replication potential that is revealed when global mechanisms preventing re-replication are compromised. Hence, if re-replication does contribute to genomic alterations, as has been speculated for cancer cells, some regions of the genome may be more susceptible to these alterations than others.
| Eukaryotic organisms have hundreds to thousands of DNA replication origins distributed throughout their genomes. Faithful duplication of these genomes requires a multitude of global controls that ensure that every replication origin initiates at most once per cell cycle. Disruptions in these controls can result in re-initiation of origins and localized re-replication of the surrounding genome. Such re-replicated genomic segments are converted to stable chromosomal alterations with extraordinarily efficiency and could provide a potential source of genomic alterations associated with cancer cells. This publication establishes the existence of a local layer of replication control by identifying new genetic elements, termed re-initiation promoters (RIPs) that can locally override some of the global mechanisms preventing re-initiation. Origins adjacent to RIP elements are not as tightly controlled and thus more susceptible to re-initiation, especially when these global controls are compromised. We speculate that RIP elements contribute to genomic variability in origin control and make some regions of the genome more susceptible to re-replication induced genomic instability.
| The initiation of eukaryotic DNA replication is tightly regulated so that it occurs at most once per cell cycle [1]. This regulation is critical because re-replication of a chromosomal segment makes that segment highly susceptible to genomic alterations [2]. Preventing re-replication throughout the genome is particularly challenging for eukaryotic cells because their genomes contain hundreds to thousands of replication origins. Hence, each individual origin must be tightly controlled if a genome is to avoid any re-initiation events [3].
The basic strategy eukaryotic cells use to prevent re-initiation is to prevent the reassembly of replication initiation complexes at origins that have fired. The critical assembly step that is regulated is the loading of the core replicative helicase Mcm2-7, which forms a toroidal complex that encircles the origin DNA [4]. This loading is carried out by four factors: the origin recognition complex (ORC), Cdc6, Cdt1, and Mcm2-7 [5], [6]. In the budding yeast, Saccharomyces cerevisiae, cyclin dependent kinases (CDKs) use multiple mechanisms targeting each of these proteins to prevent the reloading of Mcm2-7 once cells enter S phase [7]. In other organisms, additional CDK-independent mechanisms have been identified that inhibit Cdt1. The precise mechanisms used differ among species, but the reliance on multiple mechanisms targeting each of the initiation proteins involved in Mcm2-7 loading is highly conserved [3], [5].
The paradigm that has thus developed for the cell cycle control of replication initiation is that a multitude of overlapping mechanisms collaborate to globally inhibit initiation proteins throughout the cell, thereby minimizing the odds of re-initiating at any origin [8]. Consistent with this paradigm, disruption of individual mechanisms often does not lead to measurable re-replication even though the suspected consequences of re-replication, e.g. DNA damage or genomic alterations, have been observed [9]–[11]. Therefore, any investigation into the role of individual regulatory mechanisms in the block to re-initiation must be conducted in a sensitized system where a number of other overlapping mechanisms have been disrupted and re-replication can be readily detected.
Development of such sensitized systems revealed that origins re-initiate with diverse efficiencies and challenged the implicit assumption that all replication origins are uniformly regulated by global inhibition mechanisms [12], [13]. For example, when ORC, Cdc6, and Mcm2-7 are deregulated, many (∼100) origins detectably re-initiate, but many more (∼200) do not. Moreover, the amount of re-initiation from each origin varies widely. This diversity of re-initiation efficiency does not correlate with the diversity of S-phase origin timing and efficiency and thus cannot be explained by the chromosome context effects that are responsible for the latter [14]. Instead, the diversity in re-initiation efficiencies suggests that origins are not solely and uniformly regulated by global controls. Thus, we believe the paradigm for re-initiation control needs to be modified by the addition of a local layer of control that can modulate how tightly the global regulatory mechanisms inhibit re-initiation at specific origins.
Here, we explore the workings of this local control by asking why some budding yeast origins re-initiate more readily than others when global restrictions on re-initiation are partially inactivated. We show that local sequence elements adjacent to these origins specifically promote their re-initiation without enhancing their initiation activity. Furthermore, these elements act independently of the chromosomal context and silencing effects that regulate S-phase origin timing and efficiency. These elements, which we term re-initiation promoters (RIPs), map to ∼60 bp segments that work in a distance- and sequence- dependent manner. Analysis of the re-association of Mcm2-7 with origins suggests that these RIP elements antagonize an inhibitory mechanism that operates after Mcm2-7 association with origins. These findings provide our first insight into how diversity can be introduced in the regulation of eukaryotic replication origins.
To investigate the mechanisms underlying the diversity of origin regulation in the block to re-initiation, we examined S. cerevisiae origins whose ability to escape this regulation stood out the most from other origins. We previously reported that re-initiation occurs predominantly from ARS317 in a strain where a subset of global replication controls was disrupted [12]. This “MC2Ao” strain was deregulated in three ways: (1) (M) - the CDK driven export of Mcm2-7 from the nucleus [15]–[17] was blocked by fusing a constitutive nuclear localization signal onto the endogenously expressed Mcm7; (2) (C2A) – the CDK inhibition of Cdc6, which occurs through transcriptional regulation [18], phosphorylation-directed degradation [8], [19], [20], and direct CDK binding [21], was completely disrupted by expressing an extra copy of Cdc6 lacking CDK phosphorylation and binding sites under a galactose-inducible promoter; and (3) (o) - the CDK inhibition of ORC by phosphorylation of Orc2 and Orc6 was minimally perturbed by eliminating one of four CDK consensus phosphorylation sites on Orc6 [2]. We note that this ORC deregulation was not necessary for the preferential re-initiation of ARS317, but enhanced it approximately 3-fold (Figure S1). Importantly, of the known mechanisms preventing re-initiation in budding yeast, two are retained in this strain: (1) CDK phosphorylation of Orc2 and Orc6 (9 out of 10 CDK consensus phosphorylation sites remain unmutated) [7]; and (2) Clb5-Cdc28 binding to an RXL docking site on Orc6 [22].
Re-initiation was not detectable in the MC2Ao strain until the deregulated Cdc6 was induced. We could thus arrest cells at metaphase with a normal 2C DNA content across the genome, induce the deregulated Cdc6, and detect re-initiation and re-replication as a >2C DNA copy number using array comparative genomic hybridization (aCGH). Although the primary re-initiation event after a 3 hr induction of re-replication was at ARS317 [12], the re-replication profiles showed hints of additional re-replication peaks at other genomic loci. At least two of these peaks were readily confirmed with a longer 6 hr induction of re-replication, one on the right arm of Chr 5 near position 575 kb, and one on the right arm of chromosome 12 near position 890 kb (Figure 1A). The latter was dependent on ARS1238, establishing that this origin also preferentially re-initiated in the MC2Ao strain (Figure 1B). Because ARS317 and ARS1238 were among the two most efficient re-initiating origins, we focused on them to investigate why some origins are more susceptible to re-initiation than others.
We first sought to determine whether the preferential re-initiation of ARS317 and ARS1238 was conferred by the origin and immediate surrounding sequences or required a broader chromosomal context that spans kilobases of DNA. An example of the latter is the poorly understood chromosome position effect that has been implicated in the diversity of yeast origin timing and efficiency during normal S phase initiation (discussed in [14], [23]). We and others had previously shown that there was no correlation between this diversity of origin activity in S phase and the diversity of re-initiation efficiency displayed in strains where many origins re-initiate due to complete deregulation of ORC, Mcm2-7, and Cdc6 [12], [13]. Nonetheless, a different chromosomal context could be conferring preferential re-initiation on ARS317 or ARS1238 in the MC2Ao strain.
To distinguish between local sequence determinants and a broader chromosomal context, we investigated whether small fragments containing the ARS317 or ARS1238 origins could preferentially re-initiate when transplanted to ectopic genomic loci. We focused initially on fragments that we hoped would be small enough to dissect at the nucleotide level but large enough to encompass the origin and any possible additional sequences that might be needed for preferential re-initiation. A 537 bp fragment previously shown to contain ARS317 [24] preferentially re-initiated when transplanted from its endogenous location to sites on other chromosomes (ChrIV_567 kb, ChrIV_1089 kb) [2], [12]. In all cases, the amount of re-initiation induced after 3 hr (2.7–3.0 C) at the ectopic locus was comparable to the amount of re-initiation at the endogenous locus (2.8–3.2 C) [2], [12]. Hence, neither the chromatin context nor the replication timing (early or late in S-phase) of the transplant location were key determinants of the re-replication activity on these origins. Consistent with this notion, Figure 2A shows that an even smaller 406 bp fragment containing ARS317 preferentially re-initiates when transplanted to position ChrIV_567 kb. At this same location, a 233 bp ARS1238 fragment that contains the ORC binding site (OBS) and 100 bp of flanking sequence on either side [25] also re-initiates (Figure 2A). Thus, the preferential re-initiation of ARS317 and ARS1238 is conferred by local sequence determinants and is independent of a broader chromosomal context.
ARS317 is a core element of a 138 bp transcriptional silencer HMR-E, one of several silencers that recruit the silencing proteins Sir1-4 to organize the surrounding DNA into a heterochromatin-like structure (reviewed in [26]). The entire HMR-E silencer is included within the transplanted ARS317-containing fragments described above, so the preferential re-initiation of this fragment could be associated with its organization into heterochromatin [27], [28]. Such a connection is reminiscent of reports that heterochromatin preferentially re-replicates in budding yeast and Drosophila [13], [29]. To test this possibility, we individually deleted each of the four SIR genes and analyzed the re-replication profiles around ARS317 for each sir mutant. These profiles resembled those from the wild-type SIR control strains (Figure 2B and Figure S2A), indicating that none of the Sir proteins are required for the preferential re-initiation of ARS317. We also observed re-replication in a truncated ARS317 clone lacking the RapI and AbfI binding sites that are critical for HMR-E silencer function [28] (Figure 2C). We conclude that a silent chromatin state is not necessary for the preferential re-initiation of ARS317. ARS1238 is not assembled into heterochromatin, so one would expect its preferential re-initiation to be independent of Sir proteins. Our data are consistent with this expectation (Figure S2C), although the profiles are not as clear-cut.
Other factors known to influence nearby origin function are the forkhead transcription factors Fkh1 and Fkh2. Association of these proteins with origins and ORC has been implicated in the spatial organization of origins in the nucleus. This organization is thought to alter the S phase replication timing of some origins, including ARS1238 [30]. Although Fkh proteins do not influence ARS317 replication timing, searches for their proposed binding motifs have identified predicted binding sites within a few kilobases of both ARS317 and ARS1238 [31], [32]. To test whether Fkh1 or Fkh2 are critical for re-initiation of either origin, we examined the re-replication profiles in fkh1Δ, fkh2Δ, and fkh1Δfkh2Δ strains. At both ARS317 (Figure 2D and Figure S2B) and ARS1238 (Figure S2D), fkhΔ strains re-replicated significantly more than negative control strains that lack re-replicating origins at these loci. These results confirm that the forkhead proteins are not essential for the preferential re-initiation of either origin. We did observe a partial reduction of re-replication in the fkh1Δfkh2Δ background, so we cannot rule out a role for these proteins in supporting re-initiation. However, we suspect that this reduced re-replication may be an indirect consequence of the severe growth defect and cell clumping exhibited by the double mutant during growth in liquid media [30].
The preferential re-initiation activity seen in transplanted fragments containing ARS317 and ARS1238 could be intrinsic to the origin sequences themselves, or be conferred on these origins by neighboring sequences that are dispensable for initiation activity. The former possibility is particularly relevant for ARS317, whose especially tight interaction with ORC appears to govern the activity of this origin in S phase [33]. If this possibility is correct, any minimal segment containing origin activity should also exhibit preferential re-initiation. In contrast, if the latter possibility is correct, the fragments should be separable into an origin segment that can initiate but not preferentially re-initiate, and an adjacent segment that can neither initiate nor preferentially re-initiate on its own but confers preferential re-initiation on the origin segment. To test this separability, of functions for both ARS317 and ARS1238 we generated subclones of the transplanted fragments described in Figure 2A and assayed them for both initiation and re-initiation activity.
Initiation activity requires a 33 bp consensus ORC binding site (OBS) and less well-defined flanking sequences [34], [35]. The OBS is comprised of a 17 bp extended ARS consensus sequence (eACS), formerly known as the A domain, and a WTW sequence [36] formerly known as the B1 subdomain. The required flanking sequences usually lie 3′ of the T-rich strand of the OBS, where they comprise the rest of the B domain (B2 and B3), but occasionally can lie 5′ of the OBS, where they are referred to as C domain sequences [37]. We numbered nucleotides in our subclones relative to the OBS [35], assigning +1 and +33 to the first and last nucleotide, respectively, of the T-rich strand of the OBS. In this scheme, B domain sequences outside the OBS are numbered +34 and higher, and C domain sequences have negative coordinates (Figure 3A). The 406 bp preferentially re-initiating fragment containing ARS317 is thus designated 317(+300..-106), and the equivalent 233 bp fragment for ARS1238 is designated 1238(+133..-100).
The initiation activity of an origin can be assayed by the ability of a plasmid containing the origin to be maintained in cells. One measure of this ability is the mitotic stability assay, which measures the steady state percentage of cells containing the plasmid in a culture grown under selection for the plasmid [38], [39]. The mitotic stability of several subfragments containing ARS317 showed that full origin activity was retained by 317(+76..−106) (Figure 3B). This origin segment failed to re-initiate when inserted at ChrIV_567 kb (Figure 3C), demonstrating that ARS317 does not have an intrinsic ability to re-initiate. The adjacent segment 317(+300..+77) was also not able to re-initiate when examined in the context of a slightly larger fragment 317(+300..+34) at ChrIV_567 kb (Figure 3D). This adjacent segment does contain sequences that are essential for a weak cryptic origin (Figure 3A) [36], but a mutation that disrupts this cryptic origin did not reduce the ability of these adjacent sequences to induce re-initiation (Figure S3; mutant A). In contrast, a mutation of the ARS consensus sequence in the ARS317 OBS did eliminate re-initiation, confirming that the re-initiation is dependent on ARS317 ([12], also Figure S3 mutant E). These data show that the preferentially re-initiating fragment 317(+300..−106) can be separated into an ARS317 origin segment 317(+76..−106) and an adjacent segment 317(+300..+77) that confers preferential re-initiation on ARS317 in the MC2Ao strain. We call the sequence element that confers this activity a re-initiation promoter (RIP) and will refer to it as RIP317.
We used a similar approach to identify a subsegment of 1238(+69..−100) that retains full ARS1238 origin activity (Figure 3B) but is not sufficient to preferentially re-initiate. This inability to re-initiate was demonstrated in the context of a slightly larger segment 1238(+83..−100) at ChrIV_567 kb (Figure 3E). Further evidence that neither origin segment nor adjacent segment have re-initiation activity on their own comes from insertion mutations (discussed later) that separate the two segments by 153 bp, and abolish re-initiation. In addition, the adjacent segment 1238(+133..+70) does not contain the origin activity needed to support maintenance of an autonomous plasmid. Thus, like ARS317, ARS1238 acquires its ability to preferentially re-initiate from an adjacent re-initiation promoter, which we will refer to as RIP1238.
In order to map RIP317 with finer resolution, we first analyzed the re-initiation efficiency of a nested series of deletions extending into the left border (plus side) of the 406 bp 317(+300..−106) fragment. These deletion constructs were introduced into ChrIV_567 kb, and their re-initiation efficiency measured by normalizing the amount of re-initiation for each deletion (i.e. the copy number increase above 2C) against the amount of re-initiation for the full-length fragment. Deletions up to nucleotide +153 had limited effect on re-initiation efficiency, but further deletion into the fragment caused a precipitous drop (Figure 4A). Thus, nucleotide +153 in the 259 bp deletion fragment 317(+153..−106) defines a left-hand boundary for RIP317.
To further map RIP317 we used 317(+153..−106) as the parent sequence for a linker scan analysis of RIP317 structure (Figure 4A; bold line). Most of the linker mutations that showed a noticeable reduction in ARS317 re-initiation efficiency were from L4 to L15, which covers the 51 bp from nucleotide +137 to +87 (Figure 4B). On the left end of this 51 bp region were linker mutations (L4–L7), which drastically reduced or eliminated ARS317 re-initiation and identified sequences that are critical for RIP function. Other linker mutations (L8–L15) showed less striking reductions in re-initiation individually (Figure 4B), but eliminated re-initiation when combined together (Figure S4A). Thus, the sequences mutated by linkers L8–L15 are also important for RIP function but may contain partially redundant sequence elements.
In contrast to linker mutations L4–L15, the remaining linker mutations from L16–L32 each had limited effects on ARS317 re-initiation (Figure 4B). We note that ARS317 differs from most yeast origins in that the WTW sequence of its OBS is dispensable for initiation activity [36], [40]. Linker L29, which mutates the WTW sequence, and linkers L30 and L31, which intrude further into the OBS, still leave intact the 17 bp extended ARS consensus sequence (eACS), which forms the core of the ORC binding site [41]. Thus, although these linkers mutate parts of the OBS, they presumably do not disrupt ARS317 re-initiation efficiency because they leave ARS317 origin activity intact. Linker L33, on other hand, does mutate part of the eACS, so its partial disruption of ARS317 re-initiation is likely due to impairment of origin function. Replacement of the entire sequence covered by L17–L31 (nucleotides +86 to +23) with sequence of similar AT content did not have much effect on ARS317 re-initiation (Figure S4B). Additional replacement of sequences covered by L1–L3 decreased re-initiation efficiency by a third, indicating that these sequences contribute to optimal RIP317 activity (Figure S4B). These results suggest that RIP317 resides in the 67 bp from nucleotides +153 to +87 and contains a core region of approximately 19 bp (+137 to +119) that is crucial for its function.
As discussed above, we had narrowed down RIP1238 to a 64 bp segment from nucleotide +133 to +70. Linker scan analysis revealed that linker mutations spanning 40 bp (+117 to +78) of this segment abolished ARS1238 re-initiation, while the remaining mutations showed a more modest reduction (Figure S4C). Thus, like RIP317, RIP1238 has a core segment that is crucial for RIP function and surrounding sequences that enhance this function.
The most obvious common feature of RIP317 and RIP1238 is the high AT-content of these sequences (92% and 84% AT respectively). Regions of high AT-content have been postulated to exclude nucleosomes (Reviewed in [42]) or to provide regions of reduced helical stability that facilitate DNA unwinding during replication initiation [43]. Therefore, we wondered if RIP elements were stimulating re-initiation through such a positioning or thermodynamic mechanism.
To test this possibility we generated various mutants that preserved the AT content of RIP317 while altering its sequence identity. Neither predicted nucleosome exclusion [44] nor predicted DNA helical stability [45] of RIP317-ARS317 is changed by these mutations. These mutations profoundly compromised re-initiation activity, with many of the mutants showing no re-initiation even after 6 hours of induction (Figure S5). These findings suggest that RIP elements do not simply act as a DNA unwinding element or a nucleosome exclusion site. We do note that many of the mutations disrupted a palindrome in RIP317 (5′-TTTATAAA-3′) that is also present in shorter form in RIP1238 (5′-TTATAA-3′). However, the palindrome in RIP1238 is not necessary for RIP function (Figure S4C, mutant B), and the palindrome in RIP317 is not sufficient (Figure S5, mutant D2). Thus, although our mutational data does not rule out a role for the palindrome that is specific for RIP317, the sequence dependence we observed is consistent with the RIP acting as a recruitment site for factors that promote re-initiation.
The origin proximal boundary of RIP317 is 53 bp away from the B-side boundary of the ARS317 OBS. To determine whether the size of this spacing is important for RIP317 function, RIP-OBS spacing was increased by inserting randomly generated DNA of 38% AT-content (the average AT-content of genomic DNA in S. cerevisiae) between RIP317 and ARS317 and decreased by deleting portions of ARS317 in this 53 bp spacing (See Materials and Methods and Table S1). The resulting clones were analyzed for re-initiation efficiency (Figure 5A). Re-initiation declined with increased spacing and was abolished by 153 bp, suggesting that RIP317 must be relatively close to the origins to confer preferential re-initiation. Re-initiation could tolerate a decline in spacing to 37 bp but was significantly reduced by a spacing of 21 bp. The latter reduction, however, could simply be a secondary consequence of excessive removal of the B domain, which lies in the 53 bp spacing. Nonetheless, the overall finding is that re-initiation requires close but not precise spacing (within ∼35 to ∼75 bp) between the RIP and the OBS.
A spacing of only 36 bp separates RIP1238 from the OBS of ARS1238. This short spacing suggested that ARS1238 might re-initiate less efficiently than ARS317 because the spacing is suboptimal. We thus performed a similar analysis of the spacing requirements between RIP1238 and the OBS of ARS1238 (Figure 5B). Like ARS317, re-initiation of ARS1238 also required relatively close spacing of the RIP and OBS (∼25 to ∼55 bp). Moreover, re-initiation levels were relatively constant across this range of spacings, indicating that the lower levels of re-initiation for ARS1238 versus ARS317 cannot be attributed to suboptimal RIP-OBS spacing for the former. This requirement for close proximity between RIP and origin raise the possibility that proteins bound to both sites must closely interact in some manner to facilitate re-initiation.
If the RIP elements promote preferential re-initiation by influencing common regulatory pathways controlling origins, they should be able to promote re-initiation from heterologous origins. To test this possibility, we fused RIP317 and RIP1238 to other replication origins, keeping the spacing between RIP and origin OBS between 46–53 bp, within the optimal range of spacing determined for both ARS317 and ARS1238. These RIP-origin chimeras were then assayed at ChrIV_567 kb for re-initiation in an MC2Ao strain.
RIP317 promoted preferential re-initiation from ARS1021 and ARS301 (Figure 6A) at levels comparable to the re-initiation it promoted from ARS317 (Figure 2D) following a 3 hr induction of re-replication (2.8–3C), while fusions to a non-functional rip317 (equivalent to Figure 4B linker 6) failed to re-initiate. RIP317 also stimulated re-replication from ARS305, ARS209, and ARS1238, but a longer 6 hr induction of re-replication was needed to show an unequivocal stimulation (Figure S6A and S6B). RIP1238 was similarly able to promote preferential re-initiation from ARS1021 and ARS301 (Figure 6B). In these cases the re-initiation levels (4-4.5C) were comparable to the re-initiation RIP317 promoted at ARS317 following a 6 hr induction of re-replication (compare to Figure 1). Thus, both RIPs can promote preferential re-initiation on heterologous origins.
We did observe some origins (ARS306, ARS702) that exhibited no detectable preferential re-initiation when fused to RIP317 (Figure S6B). One possible reason is that the optimal spacing between the origin OBS and the RIP element places constraints on the size of the B domain that can fit between these two elements. Origins requiring larger B domains would be expected to have their initiation, and thus any re-initiation, compromised in their corresponding RIP fusion constructs. Consistent with this explanation, the truncated ARS306 and ARS702 fragments fused to RIP317 displayed defective origin function when assayed by plasmid mitotic stability (Figure S6C).
Just as compromising origin function can reduce re-initiation efficiency, one can imagine that RIP elements might promote re-initiation by simply enhancing the intrinsic initiation efficiency of an origin. Such an effect was difficult to detect by plasmid mitotic stability because origins that re-initiate when fused to RIP317 (ARS317, ARS1021, and ARS301) appear to have maximal mitotic stability in this assay (Figure 7A). However, when integrated in the chromosome, ARS317, ARS1021, and ARS301 exhibited much lower initiation activity, allowing us to look for stimulation of this activity by RIP317.
We used array CGH analysis of S phase replication to assay the activity of these origins with, and without, a functional RIP317 element. In the resulting replication profiles, the heights of the peaks represent a combination of the efficiency and timing of origin initiation in S phase. Low but measurable peak heights for the origins are ideal, because they leave open the maximal dynamic range for detecting a stimulation of origin activity by RIP317.
We observed no measurable difference in replication peak heights for ARS317, ARS1021, and ARS301 with or without a functional RIP317 (Figure 7B). At its endogenous location ARS317 initiates in approximately 10–15% of cells each S phase based on 2-dimensional gel analysis of initiation bubble intermediates [46], [47]. Such origin activity at ChrIV_567 kb would be at the limit of detection for our aCGH replication assay, and any significant RIP317 stimulation of ARS317 activity should have been detectable as a larger peak. More striking is the detection of clear origin activity from ARS1021 and the absence of any stimulation of this activity from RIP317. These results argue that RIP317 does not advance the timing or enhance the initiation efficiency of adjacent origins. We thus favor a model in which RIP elements specifically promote re-initiation by antagonizing a mechanism(s) that prevents re-initiation.
In vitro studies have shown that the loading of Mcm2-7 at origins can be subdivided into a sequence of discrete steps: (1) binding of ORC to origins; (2) recruitment of Cdc6 to ORC; (3) recruitment of Cdt1-Mcm2-7 to ORC-Cdc6; and (4) loading of a double hexamer of Mcm2-7 as a ring around the duplex origin DNA [48]. The numerous global mechanisms used by CDKs to prevent Mcm2-7 loading are thought to inhibit one or more of these steps, because once Mcm2-7 loading is complete, origins are primed to be activated by CDKs [49]–[51]. The partial deregulation of these mechanisms in the MC2Ao strain presumably allows some but not all of these steps to proceed, accounting for why the majority of origins do not re-initiate. RIP elements could therefore function by locally releasing an origin from the remaining block(s), allowing the origin to complete a re-initiation cycle. Thus, to gain insight into the mechanism of RIP action, we investigated which step in the loading process was blocked for the majority of origins that do not re-initiate in MC2Ao strains.
We examined Mcm2-7 ChIP association at three origins that do not re-initiate in MC2Ao strains: ARS305, ARS418, and ARS1420. As expected, Mcm2-7 associated more with these origins relative to nonspecific DNA in G1 phase (Figure 8B) but not in M phase (Figure 8C). After a 90 minute induction of re-replication, Mcm2-7 became enriched 2–4× at these origins but not at a non-origin locus ACT1 (Figure 8D). ChIP also detected a similar degree of re-replication-induced association of Mcm2-7 with the two re-initiating origins, ARS317 and ARS1238 (Figure 8D). As expected, given the association of Mcm2-7 with origins that cannot re-initiate, preventing re-initiation of ARS317 by disrupting its adjacent RIP317 did not prevent the association of Mcm2-7 with ARS317 (Figure 8D). On the other hand, disrupting the ORC binding site in ARS317, did lead to loss of Mcm2-7 association, specifically with this origin (Figures 8C, 8D). This result is consistent with the in vitro dependence of Mcm2-7 origin association on ORC binding [52].
Taken together, our data indicate that the global deregulation of re-initiation in the MC2Ao strain allows Mcm2-7 to associate with most origins. Thus, in this strain the RIP elements must promote re-initiation at adjacent origins by facilitating or deregulating a step that is blocked after this association. As discussed below, determining more precisely which step is involved will require better in vivo tools to distinguish between the two types of association (Mcm2-7 recruitment versus loading) that have been identified in vitro.
Preventing re-initiation at the hundreds to thousands of replication origins in a eukaryotic genome is critical for preserving genome stability [2]. Models for how such tight regulation can be achieved emphasize the importance of using numerous overlapping inhibitory mechanisms to reduce the probability that any origin will re-initiate [3], [8]. These mechanisms all inhibit the loading of the Mcm2-7 core replicative helicase onto origins, and each does so by reducing the total cellular activity of one of the four proteins required for this step: ORC, Cdc6, Cdt1, or Mcm2-7 [5], [6]. Given their global nature, these regulatory mechanisms are presumed to act equally at all origins throughout the genome. Thus, current models cannot account for the broad range of efficiencies with which origins re-initiate when global mechanisms are compromised. This diversity suggests that the models may be missing the contribution of local factors that can modulate the regulation of individual origins.
Our work here demonstrates that such a local layer of regulation does indeed exist by identifying a local control that makes ARS317 and ARS1238 more susceptible to re-initiation when global regulation of Cdc6 and Mcm2-7 is removed. Our analysis of this control establishes some of its key mechanistic properties and constraints. First, this control specifically enhances the propensity of an origin to re-initiate and not its efficiency or timing during normal S phase initiation. Second, this preferential re-initiation is not imposed by a diffuse chromosomal context but is conferred by discrete sequence elements that are adjacent to but distinct from the origin. Third, these elements, which we call re-initiation promoters (RIPs), have specific sequence requirements and function best within a narrow range of distances close to the origin. Finally, these RIPs appear to overcome inhibitory mechanisms that block a step in initiation that follows the association of Mcm2-7 with origins. These results provide a paradigm for the local control of origin re-initiation and lay the groundwork for a more detailed molecular analysis of this control.
Our results do not address the question of whether the presence and activity of these RIPs is incidental to some other genomic function of these elements or whether they arose for the purpose of modulating replication control in cells with intact replication controls. Nonetheless, as discussed below, the existence of RIP elements has potential biological ramifications in both mutant and wild-type settings.
One of the questions raised by our results is whether RIP function is mediated by proteins that specifically recognize these sequences or is mediated by some other property of these elements. The two RIP sequences we identified, RIP317 and RIP1238, are both AT-rich, especially in their core regions. They do not share an obvious consensus sequence, and in fact, their AT-rich character makes it difficult to find meaningful conservation of these elements throughout the genome. Importantly, this AT-rich character raises the possibility that these elements just act thermodynamically to facilitate the DNA unwinding needed to re-initiate DNA replication. Another possibility is that they simply influence nucleosome positioning around origins, as AT-rich DNA tends to be excluded from nucleosomes [53]. These hypotheses, however, are not sufficient to account for RIP function, because we were able to abrogate RIP317 function using mutations that preserved AT content without significantly perturbing their calculated unwinding potential or predicted propensity to exclude nucleosomes [44], [45].
These considerations suggest to us that RIP elements may act through proteins that bind to them. Such a possibility is compatible with the poor nucleosome occupancy over RIP317 that has been observed at its endogenous chromosomal location [35], [54], [55]. A quick attempt to uncover such proteins by screening through yeast transcription factors with potential binding motifs [56], [57] in both RIP317 and RIP1238 did not yield any promising candidates (See Materials and Methods); deletions in NHP6A NHP6B, YAP1, SUM1, YNR063W, GAT4, SMP1, or YOX1 failed to disrupt the function of either RIP. Hence, we are pursuing more systematic studies to identify proteins that bind RIP elements in vivo and are essential for RIP function. If RIPs do indeed work by recruiting proteins near an origin, the distance dependence of RIP function suggests that these proteins may have to interact in close proximity with specific initiation or regulatory protein that assemble at origins.
Our work also demonstrates that origins that do not re-initiate in the MC2Ao strain associate with Mcm2-7 by ChIP analysis and thus can at least recruit Mcm2-7 to origins. Apparently, these origins are blocked at an initiation step subsequent to Mcm2-7 recruitment, and the RIP elements confer preferential re-initiation on neighboring origins by deregulating this step.
Exactly which step is deregulated by RIP elements is not resolved by our experiments, but there are two major possibilities. The elements could be deregulating the transition between Mcm2-7 recruitment and Mcm2-7 loading, which has been defined in vitro [58] but not yet demonstrated in vivo. Alternatively, they could be deregulating a step following Mcm2-7 loading. We favor the former possibility because the latter requires us to violate a fundamental principle of the current paradigm for re-initiation control [3], [5], namely that this control only targets steps preceding Mcm2-7 loading. Nonetheless, resolution of this question must await the development of more sophisticated in vivo protein-DNA binding assays that are capable of distinguishing recruited from loaded Mcm2-7 at individual origins.
Importantly, this role in enabling a step of initiation subsequent to Mcm2-7 origin association distinguishes RIP elements from B2 elements, one of the core elements of budding yeast origins. Both elements are AT rich, positioned 3′ of the T-rich strand of the OBS, and have relaxed positioning requirements relative to the OBS. However, the B2 elements are needed for Mcm association with origins [59], and RIP elements are not. This distinction provides further support for a model in which RIP elements antagonize an inhibitory mechanism, rather than simply promote a normal initiation function.
How might RIP317 and RIP1238 locally override a block to Mcm2-7 loading that prevents origins from re-firing in the MC2Ao background? The simplest model is that the block is imposed by one or more of the regulatory mechanisms that remain intact in MC2Ao strains, e.g. CDK phosphorylation of Orc2 and Orc6 [7] or CDK binding to Orc6 [22]. According to this model, RIP elements locally antagonize some or all of these mechanisms, relieving enough of the block to allow detectable re-initiation at RIP-associated origins. This model is consistent with in vitro studies that indicate these inhibitory mechanisms still permit ORC binding and some Mcm2-7 recruitment to origins, but completely block Mcm2-7 loading onto origins [58]. The model is also consistent with our observation that globally antagonizing CDK phosphorylation of ORC in the MC2Ao background by mutating all CDK consensus phosphorylation sites on Orc2 and Orc6 allows many origins to join ARS317 and ARS1238 in re-initiating at detectable levels [12]. However, direct support for this model will require analysis of ORC phosphorylation and CDK binding at origins to determine if they are indeed reduced at RIP-associated origins as might be predicted by the model.
We note that the induction of re-initiation in the MC2Ao strain is limited and slow compared to the usual efficiency of origin initiation in a normal S phase. After 3 hr of induction, over one and a half cell cycles for this strain, only 50% and 25% of ARS317 and ARS1238, respectively, have re-initiated. This inefficient re-initiation suggests that RIP317 and RIP1238 only partially antagonize the inhibitory mechanisms blocking Mcm2-7 loading. Such incomplete relief of inhibition may explain why completely antagonizing inhibitory phosphorylation of Orc6 on one CDK consensus site (S116A) can further enhance ARS317 and ARS1238 re-initiation in the MC2Ao strain relative to the MC2A strain (Figure S1).
The preferential re-initiation of ARS317 and ARS1238 is reminiscent of the localized re-initiation that occurs in several cases of developmentally programmed gene amplification [60]. One of the best characterized is the amplification of the chorion gene locus in Drosophila ovarian follicle cells during oogenesis. Like the RIP elements identified in this work, an Amplification Control Element (ACE3) of ∼320 bp has been identified that has little origin function on its own and confers preferential re-initiation on a nearby origin (ori-beta). However, the mechanism by which ACE3 and other potential ACE elements promote re-initiation at a select group of origins remains a mystery [60].
Our work in budding yeast offers a conceptual framework for exploring the mechanism of developmentally regulated gene amplification, even if the details prove to be different. For example, characterizing how far the initiation reaction can proceed on the majority of origins that don't re-initiate may give insight into the key step that allows amplification origins to re-initiate. Similarly, it may be informative to investigate the status of inhibitory modifications on initiation proteins associated with re-initiating origins to see if these modifications are reduced relative to the bulk protein population.
In addition to its established role in developmentally programmed gene amplification, there are several hints that DNA re-replication may also contribute to the amplifications and abundant duplications observed in cancer cells. First, we have shown in budding yeast that re-replication arising from deregulated replication initiation proteins can be an extremely efficient source of segmental amplification [2]. Second, overexpression of initiation proteins in murine models has been shown to promote oncogenesis [61]–[63]. Third, overexpression of replication initiation proteins has been observed in some human cancer cells [64]–[67]. And finally, the tandem direct repeat structure of some oncogene amplifications and many of the duplications detected in cancer cells is consistent with the structures that could arise from re-replication [68].
Should re-replication prove to be a new source of copy number variation (and possibly other genomic alterations) in cancer cells, local modulation of origin control, such as that described in this work, could make some regions of the genome more susceptible to re-replication induced genetic alterations than others. One can therefore imagine that an irregular genomic landscape of re-initiation susceptibility could give rise to an irregular genomic landscape of genetic instability in cancer cells. Preliminary indication for such position dependent variability in genetic instability has been obtained by experiments showing that the frequency and structure of DHFR amplification in a cancer cell line was different for different genomic positions of DHFR [69].
Copy number variation may also play an important role in normal cells. For example, gene duplications are thought to provide the functional redundancy that enables the functional diversification of genes during molecular evolution [70]. In addition, copy number increases, which occur with high prevalence in normal human genomes [71], may directly provide phenotypic variation that can be selected for during evolution. In both examples, the mechanism of copy number change is not clear. We speculate that extremely rare re-initiation events may occur despite the presence of normal re-initiation controls and contribute to copy number increases. Should re-initiation drive some of these copy number increases, variable susceptibility of origin re-initiation throughout the genome would be expected to make some regions of the genome more subject to evolutionary change than others. Thus, the presence of a local layer of re-initiation control provided by RIP-like elements may have far reaching ramifications on oncogenesis and evolution.
Integrative plasmids were used to test RIP-origin re-replication or replication activity in a chromosomal context. These plasmids were all derived from pBJ2889 [2]. This plasmid contains a portable re-replication integration cassette made up of the following elements: Homology Left (sequences centromere proximal to ARS419, which is located at 567 kb on Chromosome IV), the kanMX6 reporter gene [72], the ade3-2p color reporter gene [73], a polylinker, which includes the XbaI restriction site, and Homology Right (sequences centromere distal to ARS419). SpeI – XbaI fragments containing RIP-origin inserts and additional restriction sites were integrated into the XbaI site of the pBJL2889 polylinker, creating a SpeI/XbaI fusion site (TCTAGT) on the ade3-2p side of the insert and re-creating an XbaI (TCTAGA) site on side adjacent to Homology Right. We report the sequence of these clones in Table S1 from the SpeI/XbaI fusion site to the intact XbaI site. The re-replication integration cassette was excised from the plasmid using SacI-NotI or SacI-SalI and introduced into yeast using standard techniques. Integration of these cassettes at ARS419 destroyed its origin activity.
The ARS activity of RIP-origin constructs was measured by mitotic stability assays utilizing centromere-containing plasmids. These CEN-ARS plasmids were derived from pFJ11 [36], a plasmid containing ARS317 and CEN4. As a preliminary step, the BamHI site adjacent to CEN4 was destroyed by BamHI digestion, klenow fill-in of the cut overhangs, and blunt-end ligation. The ARS317 in this modified pFJ11 was then replaced with our origin or RIP-origin constructs by cloning these constructs into the HinDIII and EcoR1 sites of the plasmid (exact sequences listed in Table S1). These plasmids were transformed into YJL310 [74] using standard techniques.
The full sequence of all insertion and deletion mutants used to alter RIP-OBS spacings are listed in Table S1. They were generated as follows:
Genotypes and derivations for all strains used in this manuscript can be found in Table S2. Almost all the MC2Ao yeast strains in this paper were generated from the previously published strain YJL3758 [2] by one or more of the following genetic alterations: (1) integration of a re-replication cassette (described in Plasmids above and detailed in Table S1); (2) deletion of ARS317, ARS418, or ARS1238 (Table S3); (3) deletion of SIR or FKH genes (Table S3) [72], [75], [76]. MC2A strains YJL8923 and YJL8924 are congenic to YJL3758 but have wildtype ORC6 instead of orc6(S116A).
Oligonucleotides used to PCR marked deletion fragments for deleting origins or genes encoding transcription factors are listed in Table S3. Oligonucleotides used in quantitative PCR are listed in Table S4.
Synthetic complete medium containing 2% wt/vol dextrose (SDC) was made up as described [77] except that we used twice the concentration of amino acids and purines for all but leucine, which was added to a final concentration of 120 µg/mL, and serine, which was added to a final concentration of 200 µg/mL. Drop out media like SDC-URA, simply lacked the indicated component. For nonselective rich media cells were grown in YEPD (YEP +2% wt/vol dextrose) or YEPRaf (YEP +3% wt/vol raffinose +0.05% wt/vol dextrose). All cell growth was performed at 30°C.
To induce re-replication, freshly thawed log phase cultures in YEPD were extensively diluted into YEPRaff and grown for 12–15 hr until they reached an OD600 of 0.2–0.8. At this cell density (approximately 1×107 cells/ml), nocodazole (US Biological N3000) was added to a final concentration of 15 µg/mL for 120–135 min to arrest cells in metaphase. GAL1 promoter driven pGAL-Δntcdc6,2A was then expressed by the addition of 2–3% galactose for 3 hr or 6 hr where indicated.
Strains were grown overnight in YEPD at 30°C to an OD600 of 0.2–0.4. At this cell density, 50 ng/mL alpha factor was added to arrest cells in G1 phase. Arrested cells were released into fresh YEPD media containing 0.1 M hydroxyurea (US Biological H9120), 100 µg/mL pronase (EMD 53702), and 15 µg/mL nocodazole (US Biological N3000) to permit a single, slowed S phase to occur. Cultures were harvested after 135 minutes when 30–60% of the genome was replicated as verified by FACS analysis [78]. To increase the sensitivity of detecting initiation activity from the integrated re-replication cassettes, we deleted the closest early origin ARS418 so that its forks would not run through the origins in the cassettes and preclude their initiation.
Full details of array CGH data analysis are described in [12]. Briefly: arrays were scanned on a GenePix 4000B scanner and quantified using GenePix 6.0 (Axon Instruments). The Cy5/Cy3 ratios were normalized such that the average ratio was equivalent to DNA content for that specific point in the cell cycle (e.g. 2C for M arrested or induced samples, and 1.5C for S phase samples). Medians for these raw normalized data were then calculated across a 10 kb moving window. Smoothed curves were calculated from this moving median using Fourier Convolution Smoothing (FCS). The degree of smoothing is determined by a parameter called the convolution kernel [81], and for the chromosomes we display we used the following values optimized for re-replication profiles: Chromosome III, 9; Chromosome IV, 11.25; Chromosome V, 9; Chromosome XII, 10.75. For S phase replication profiles, the convolution kernel for Chromosome IV was set to 6.25. For presentation purposes, smoothed lines for each individual re-replication or S-phase profile were averaged into one composite profile. Most figures in the manuscript show these composite profiles as black lines surrounded by a gray zone representing ±1 standard deviation. The raw data and the smoothed lines for each individual experiment performed for this work can be seen in Document S1.
We note that, because of cross hybridization among the various repetitive sequence elements, these elements (tRNA genes, subtelomeric repeats, Ty elements and long terminal repeats) were removed from the analysis. In the Saccharomyces Genome Database, the two rDNA genes representing the large rDNA repeat arrays are adjacent to a Ty element and additional repeated sequences, so the entire ∼44 kb region between YLR153C and YLR163C was omitted from the analysis.
Also, because each chromosome was effectively circularized during the calculation of the moving window median and the FCS, deviations of the smoothed curve from baseline values at one chromosome end can artifactually cause the curve to deviate from baseline at the other end [82]. Thus, when ARS317 preferentially re-initiated at its endogenous location near the right end of Chromosome III, it caused the smoothed re-replication curves to rise at the left end. We have masked the left 20 kb of the smoothed re-replication curves for Chromosome III in Figures 1A, 2B, and S1A, but left the curves unmasked in the individual experimental profiles shown in Document S1.
Bar graphs were generated to compare the amount of re-initiation seen in experimental vs control strains. aCGH re-initiation peak heights were measured relative to the expected G2/M copy number (2C) for both experimental and control strains. Replicates of each array were then averaged (xexp and xcont) and a standard deviation calculated (sexp and scont). The ratio xratio formed by xexp divided by xcont was converted to a percentage and plotted as shown. The error for this ratio was calculated by solving the equation:
Re-replication of each experimental strain (n = 2) was measured at one of the following re-replicating loci: ChrIII_292 kb (endogenous ARS317), ChrIV_567 kb (transplanted locus), or ChrXII_889 kb (endogenous ARS1238). Relevant control strains lacking (negative control) a re-replicating origin at each location were measured to provide a background (i.e. non re-replicating) baseline. Sample size for these negative control strains ranged from n = 5 to n = 10 as indicated in figure legends. Mean profile heights of the experimental and negative control strains were compared using Welch's t-test. Significant (p<0.05) results reject the null hypothesis and confirm that re-replication of sirΔ and fkhΔ strains is significantly different from re-replication of the relevant negative control strain.
CEN-ARS plasmids containing RIP-origin, RIP, or origin constructs were transformed into YJL310 [77], a strain with intact re-replication controls. Three independent transformants were inoculated into media selective for the plasmids (SDC-URA) and grown overnight to saturation. Cultures were subsequently diluted back into fresh selective media and grown overnight to an optical density of 0.1–0.6. Each log phase culture was plated to five selective (SDC-URA) and five non-selective plates (SDC) at a density of 200–400 cfu/plate. Plates were grown for 3-4 days and the fraction of cells harboring a plasmid was determined by dividing the number of colonies on the selective plates over the number on non-selective plates. Values reported are averaged from the three independent plasmid transformants.
ChIP experiments were performed with approximately 20 OD units of cells in a media volume of 50 mL. Cultures were handled as described above for re-replication cultures except induction was restricted to 90 minutes. We reasoned that anti-Mcm ChIP would work best immediately after Mcms were re-loaded onto origin DNA but before most of these origins had re-fired and distributed Mcms throughout the genome. Thus, we selected the 90-minute induction time point as this was the latest induction time before re-replication became visible by array CGH. This rationale is similar to that used in earlier ChIP-chip analysis of re-replicating strains [13].
Terminal cultures were fixed by addition of formaldehyde (37% w/v) to a final concentration of 1%. Fixation proceeded for 15 minutes at room temperature and was quenched by the addition of glycine to a final concentration of 0.125 M. Fixed cells were harvested by centrifugation, washed once in 1× TE pH 7.5, and frozen at −80C.
Cell pellets were resuspended in 500 µL lysis buffer (50 mM HEPES/KOH pH 7.5, 140 mM NaCl, 1 mM EDTA, 1% Triton, 0.1% Na-Deoxycholate) with protease inhibitors (Roche mini complete #04693159001+2 mM PMSF) and transferred into 2 mL screw-cap tubes (Sarstedt #72.694.006). 0.5 mm glass beads (Biospec Products 11079–105) were added to the level of the meniscus and cells were disrupted using a FastPrep 24 for two cycles of 45 sec at 6.0 m/s with 2 min incubation on ice in between. All subsequent steps were performed in low adhesion DNAse/RNAse free 1.5 mL microfuge tubes at 4°C unless otherwise indicated. Lysates were cleared by centrifugation at 20,000 rcf for 10 min and pellets (containing chromatin) were resuspended in 500 µL of fresh lysis buffer + protease inhibitors. Each pellet was sonicated using a 1/8” tapered microtip attached to a Branson 450 sonicator for 4 cycles of 30 sec at setting 1.5 with >2 min on ice in-between. The resulting slurry was cleared again by centrifugation at 20,000 rcf for 10 min and the supernatant was retained as whole cell extract (WCE).
Immunoprecipitation, washes, and elution were performed on 80–90% of the WCE volume using methods described in [83]. These extracts were exposed to UM174 antibodies (rabbit polyclonal anti-Mcm2-7, 1∶500 dilution) [58] (generous gifts from Steve Bell) in the presence of 30 uL slurry of Protein G Dynabeads (Life Technologies, 10004D). Immunoprecipitations were performed for 20 hr at 4°C. Beads were washed 3× with 1 mL of Wash Buffer (10 mM Tris-Cl pH 8, 250 mM LiCl, 0.5% NP-40, 0.5% Na-Deoxycholate, 1 mM EDTA) and 1× with 1 mL of TE (10 mM TrisCl pH 8, 1 mM EDTA) with 50 mM NaCl. DNA was eluted from the beads by incubating them in 100 µL of 65°C Elution Buffer (50 mM Tris-Cl pH 8, 10 mM EDTA, 1% SDS) for ten minutes.
Crosslink reversal and DNA purification was performed essentially as described in [84]. Briefly, IP samples were digested in proteinase K (final concentration 1 mg/mL) for 2 hr at 37°C and incubated at 65°C for 6 hr to reverse crosslinks. WCE samples omitted the proteinase K but were otherwise subjected to the same incubation conditions. DNA from both IP and WCE were purified using PCR purification columns (Qiagen Inc 28106) and eluted into 300 µL of 1× TE pH 8.
For each genotype, three independent cultures were analyzed and the average fold enrichments of origin DNA by ChIP were reported. The IP and WCE DNA samples from each individual culture were analyzed in triplicate on a Stratagene MX3000P qPCR machine using primer pairs listed in Table S4. Each reaction was performed using Power SYBR Green PCR Master Mix (Applied Biosystems) in a total volume of 20 µL with primers at a final concentration of 300 nM. Because of the AT-rich nature of template origin DNA, we used an annealing temperature of 57°C and an extension temperature of 65°C. Fold enrichment of the assayed DNA segments over the average of two non-origin DNA segments (ADH1 and SLH1) was calculated using the 2-ΔΔCt method essentially as described [85].
The UNIPROBE database of in vitro DNA binding specificities [57] was searched using RIP317 and RIP1238 sequences. The search was restricted to S. cerevisiae datasets and the stringency filter was set to the lowest setting. Nonessential candidate RIP-binding proteins found in both sequences were NHP6A NHP6B, YAP1, SUM1, YNR063W, GAT4, SMP1, and YOX1. These factors were knocked out genetically and the resulting strains were tested for re-replication activity at ARS317 and ARS1238.
All array CGH data from this study have been deposited in the Gene Expression Omnibus (GEO) (http://www.ncbi.nlm.nih.gov/geo) database (Series Accession #GSE55420).
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10.1371/journal.pgen.1003815 | Whole-Exome Sequencing Reveals a Rapid Change in the Frequency of Rare Functional Variants in a Founding Population of Humans | Whole-exome or gene targeted resequencing in hundreds to thousands of individuals has shown that the majority of genetic variants are at low frequency in human populations. Rare variants are enriched for functional mutations and are expected to explain an important fraction of the genetic etiology of human disease, therefore having a potential medical interest. In this work, we analyze the whole-exome sequences of French-Canadian individuals, a founder population with a unique demographic history that includes an original population bottleneck less than 20 generations ago, followed by a demographic explosion, and the whole exomes of French individuals sampled from France. We show that in less than 20 generations of genetic isolation from the French population, the genetic pool of French-Canadians shows reduced levels of diversity, higher homozygosity, and an excess of rare variants with low variant sharing with Europeans. Furthermore, the French-Canadian population contains a larger proportion of putatively damaging functional variants, which could partially explain the increased incidence of genetic disease in the province. Our results highlight the impact of population demography on genetic fitness and the contribution of rare variants to the human genetic variation landscape, emphasizing the need for deep cataloguing of genetic variants by resequencing worldwide human populations in order to truly assess disease risk.
| Recent resequencing of the whole genome or the coding part of the genome (the exome) in thousands of individuals has described a large excess of low frequency variants in humans, probably arising as a consequence of recent rapid growth in human population sizes. Most rare variants are private to specific populations and are enriched for functional mutations, thus potentially having some medical relevance. In this study, we analyze whole-exome sequences from over a hundred individuals from the French-Canadian population, which was founded less than 400 years ago by about 8,500 French settlers who colonized the province between the 17th and 18th centuries. We show that in a remarkably short period of time this population has accumulated substantial differences, including an excess of rare, functional and potentially damaging variants, when compared to the original European population. Our results show the effects of population history on genetic variation that may have an impact on genetic fitness and disease, and have implications in the design of genetic studies, highlighting the importance of extending deep resequencing to worldwide human populations.
| Genetic variation in humans is a result of stochastic processes, selection and demographic history [1]. Modern humans show a reduced level of differentiation due to recent population dispersion less than 100,000 years ago, and differences between populations are thought to account for little more than 15% of all genetic variation across individuals [2]. However, this picture is based on the allele frequency differences of common and shared variants between populations, representing only a small fraction of the total number of variants. Recently, much effort has been put into the description of the total variation landscape in human populations by resequencing hundreds to thousands of individuals from the same population at particular loci or for complete exomes [3]–[8]. Additionally, the 1000 Genomes Project has characterized the complete genomic sequences of more than one thousand humans covering worldwide diversity [9], [10].
Two important conclusions have arisen from studies deeply characterizing the allele frequency spectrum in human populations. First, the high number of low frequency variants is likely only explainable by models of recent demographic explosion [3]–[8]. Furthermore, low frequency variants are enriched for functional variants, particularly for nucleotide changes that affect protein function, and are therefore putatively more related to disease [3]–[8], [11]. Second, most rare variants are private or show very little sharing among continents [7], [8], [12], [13]. This may be particularly important in terms of genetic fitness, since rare variants are enriched for deleterious alleles. However, until now differences in the relative amount of detrimental variants have only been shown over relatively large timescales by comparing African and European populations [14], [15]. Furthermore, these findings predict a lack of replication in association studies using rare functional variants across populations, since rare variants can show higher levels of stratification [16], thus emphasizing the need of population-specific catalogues of genetic variation [12].
In this work, we analyze whole-exome sequence data from French-Canadian individuals, comparing various population level statistics to those for French and European populations, which allow us to make inferences about the fitness of a population with a unique demographic history. The current population of six million French-Canadians in Quebec are descendants of about 8,500 French settlers who colonized the province between 1608 and 1759, before the English conquest [17], [18]. Although colonization included emigrants from all of France, the migration event mostly originated from the Atlantic coast and Paris region. After 1760, French immigration virtually stopped, and the French-Canadian population experienced rapid growth due to a high birth rate, and became genetically isolated from France with limited exchange with other non-French communities in the same geographical area [19]. Overall, French-Canadians have experienced a growth from 8,500 to six million individuals, which represents a population expansion of more 700% in less than 20 generations. While other colonized territories in America or Oceania may have experienced a similar growth, the uniqueness of the French-Canadian population is due in part to the reduced contribution of new immigration after the first settlers [20] and the founding population is estimated to have contributed 90% of the current French-Canadian genetic pool [21]. In addition, during the 19th century new territories were colonized by a reduced number of settlers, contributing massively to the genetic pool in these regions, giving place to several regional founder effects. This particular component of the demographic history of the French-Canadian population has resulted in a geographic heterogeneity of genetic diseases in Quebec, with more than twenty Mendelian diseases occurring at unexpectedly high frequencies in some areas of the province [19], [22].
Here, we specifically test the theory that deleterious mutations accumulate and/or persist in a population that has undergone a demographic bottleneck and rapid expansion in a short period of time, potentially as a consequence of reduced selection, using the French-Canadian population of Quebec. It has been argued that colonists at the forefront of expansions have a fitness advantage [23]. Here we show that if this is the case, then this short-term fitness advantage may come at an overall long-term cost. We also aim to describe how this complex demographic scenario has shaped the genetic variation in a modern population; as of yet, no study has described how the original genetic bottleneck and subsequent population expansion have affected the full-spectrum of genetic variation among French-Canadians.
Through exome-sequencing, we set out to determine how the distribution of variants in a founder population differs both in overall frequency, and potential functional impact relative to the source or progenitor population. All major observations were replicated on two different sequencing platforms and with similar sample sizes (see Material and Methods and results below). In total, we detect 64,631 high-quality SNPs in 109 individuals from the French-Canadian population with low error rates (see Material and Methods). Using previously described data [24], we find a total of 46,662 high-quality SNPs from 30 individuals in the French population. The difference in the number of SNPs detected is largely driven by the different sample sizes. The numbers of SNPs falling into each functional category are shown in Table S1. Compared to French individuals, French-Canadians have lower levels of heterozygosity (on average 19.2% and 11.5% of the variants per individual are heterozygous in French and French-Canadians, respectively) and have lower average nucleotide pairwise diversity (Table 1). Reduced genetic diversity in the French-Canadian population is consistent with the historically documented population bottleneck.
The French-Canadian population also exhibits an excess of low frequency variants in comparison to the French population (Figure 1), and the proportion of variants with MAF<5% is significantly higher in the French-Canadian population (p<0.01). The excess is not a consequence of different sample sizes; if we resample the same number of individuals from each population and include only sites where all individuals pass identical quality filters, we observe a similar excess of rare variants in the French-Canadian population compared to the French population (Figure S1). The distribution of allele frequencies is likely indicative of the population expansion undergone by French-Canadians after the bottleneck out of Europe and is supported by lower per locus Tajima's D values (Table 1) when compared to the French population (t-test p value = 6.51e-15) (Figure S2). As seen in previous studies, low frequency classes are enriched for nonsense and missense variants in relation to synonymous variants (Figure S3). Strikingly, among the total number of SNPs, only a relatively small fraction (36.5%) are shared between the two populations (Figure S4) and this fraction decreases for functional SNPs (missense, nonsense, splice site), which are enriched for rare variants. When considering those variants shared between populations, we find a high level of agreement; of the 29,767 variants shared by both populations, the vast majority have extremely low FST scores (97.6% are less than 0.05), indicating little population differentiation for most common variants.
In order to compare the French-Canadian SNPs to a larger dataset, we extended the comparison to a list of variants discovered from high-coverage sequencing of exomes in 85 CEU individuals in the 1000 Genomes Project [9], as well as 1,007 individuals from other populations from the same resource, and find that the French-Canadian population shows a high percentage of private variants not found in any other population (Table 2). The distribution of these non-shared variants is asymmetric, and is enriched for rare and missense variants. The proportion of private variants is lower than those reported in comparisons across different continents, but higher than proportions observed across populations in the same continent [12], [13]. Roughly, populations in different continents share only about 10% of rare variants, while close populations in the same continent, such as individuals from the CEU and Tuscany (Italy) populations, share about 90% of rare variants [7], [8], [12], [13].
Given that we observe an excess of rare variants at functional sites in the French-Canadian population, we consider the effect of these variants on fitness and selection using a number of different approaches. First, we test for differences in the ratio of missense to synonymous changes within the SFS (Figure 2). Whilst the missense to synonymous ratio in the French population for SNPs with MAF<5% (1.31) is very similar to that observed in other populations [11], the French-Canadian ratio of 1.47, points to a major fraction of deleterious SNPs in the population, which carries a significantly larger proportion of rare mutations at missense sites (p<0.01, chi-squared test). For the most common variants (MAF>0.25), the French and French-Canadian populations have identical missense to synonymous ratios (0.77). Second, we consider the predicted effects of nonsynonymous variants using GERP scores [25] and find more evidence for an excess of potentially damaging mutations in the French-Canadian population. GERP is a measure of conservation that is calculated across 34 mammalian species [25] and since it inversely correlates with derived allele frequency (DAF) [26], [27], it can be used to classify genetic variants and is often used as part of a criteria to prioritize functional variants in disease studies [28]. Comparing missense and nonsense SNPs in the French and French-Canadian populations, we find that the average GERP score is significantly higher for mutations in the French-Canadian population (Wilcoxon signed-rank test, p = 0.004). The difference is particularly strong for SNPs at the lowest frequencies (Figure 2), which are enriched for mutations with a higher impact on protein function, but the average GERP score for variants with MAF>10% is also significantly higher in the French-Canadian population (p<0.01). Conversely, we do not observe significant differences between populations when synonymous changes are compared (Wilcoxon signed-rank test, p = 0.846). The same enrichment for higher GERP scores in the French-Canadian population is also seen when comparing the distribution of average GERP scores for alleles carried at missense sites within each individual (Figure 2), and overall French-Canadian individuals have significantly higher mean GERP scores than French Individuals (Wilcox-rank sum test, p<0.001). Third, a significantly higher proportion of missense variants are predicted to be damaging in the French-Canadians compared to the French population using Polyphen [29] (49.5% and 45.5% respectively, p<0.01), indicating that on average variants segregating in the French-Canadian population tend to be putatively more damaging.
The inference of a higher proportion of deleterious alleles in the French-Canadian population is not a consequence of different sample sizes; resampling thirty individuals from the French- Canadian population, we again find a significantly larger missense to synonymous ratio for rare alleles (1.44, p<0.01), a significantly higher average GERP score for rare alleles at missense sites (2.194 for resampled French-Canadians, 2.067 for French, p<0.01) and a significantly larger proportion of missense variants predicted to be damaging by Polyphen (49.6%, p<0.01) for French-Canadians when compared to the French population. Furthermore, it is unlikely that the excess of rare deleterious alleles in French-Canadians is driven by data quality since we estimate a false positive rate of ∼0.2% for singletons (see Material and Methods), which are most likely to be enriched for error.
To understand why there is an excess of putative damaging variants in the French-Canadian population, we analyzed the intensity of natural selection in both the French and French-Canadian populations. First, we estimated the demographic parameters and the population selection parameter (γ = Ne(s)) using the Poisson Random Fields method implemented in prfreq [14]. To estimate population demographic parameters, we used synonymous sites to test different demographic models and we find a significantly better fit for models that include a bottleneck and expansion compared to neutral stationary models for both the French and the French-Canadian populations (Tables S2 and S3, Kolmogorov-Smirnov (KS) tests, p>0.05 in both cases). Although these models are necessarily simplified to capture key demographic processes rather than a literal history of the populations, we used them as a correction factor when next attempting to infer selection parameters at nonsynonymous sites. To this end, models including both the three-parameter demographic history and negative selection have a significantly better fit to the observed site frequency spectrums for both the French and French-Canadian populations at nonsynonymous sites than models assuming neutrality or including demographic history alone (Tables S2 and S3 and Figure S5, p<0.001). As expected, including selection does not significantly improve the fit to the site frequency spectrum at synonymous sites, which provides a good check on the demographic model. The estimated γ parameter in French-Canadians is substantially less negative than that in the French population (γ = −115 in French population, γ = −82 in French-Canadian population, p<0.001), which could be at least partially a result of smaller Ne in the French-Canadian population.
Second, we estimated the distribution of fitness effects (DFE) of mutations segregating in French and French-Canadian populations using the DFE-alpha software (http://homepages.ed.ac.uk/eang33/), which predicts the effects of new deleterious mutations using the site frequency spectrum [30]. The DFE estimated for the French population is broadly similar to that predicted for the European population in a previous study [31] using the two epoch model (Table 3), and the mean selective effect (Ne(s)) is similar to the γ value predicted by prfreq. Interestingly, the DFE estimated for the French-Canadian population has a much lower mean selective effect for new deleterious mutations of 12.8 (compared to 104.9 for the French population). Furthermore, the proportion of strongly selected deleterious mutations is much lower in the French-Canadian population compared to the French (Table 3), which could reflect a relaxation of selection in the French-Canadian population due either to a reduction in Ne or the new environment, that has subsequently led to an accumulation or the persistence of potentially harmful rare variants.
Finally, to test whether the differences we observe between the two populations are driven by different sequencing platforms, we analyzed data from an additional 50 French-Canadian individuals sequenced on Illumina's HiSeq platform and compared the results to the French dataset; we replicate all of the major findings. First, we observe a significant excess of rare variants in the French-Canadian Illumina dataset compared to the French (57.4% and 45.3% of variants with MAF≤5% respectively, p<0.01, Figure S6, Table S4). Similarly, comparing datasets sequenced on the SOLiD platform by considering a further European dataset (CEU population from the 1000 Genomes Project), we again find an excess of rare variants in the French-Canadian population (p<0.01, Figure S6). Second, we find a significantly larger missense to synonymous ratio for rare alleles (1.39, p<0.01, Table S4) and a significantly larger proportion of missense variants predicted to be damaging by Polyphen (48.2%, p<0.01, Table S4) for the French-Canadian Illumina dataset compared to the French. Finally, rare alleles at missense sites have a significantly larger GERP score on average in the French-Canadian Illumina data (2.194, p<0.01, Table S4) when compared to the French population and when considering the distribution of average GERP scores at missense sites within these individuals, French-Canadians have significantly higher mean GERP scores than French Individuals (p<0.01, Figure S7).
Recent deep resequencing of human populations has highlighted an accumulation of rare variants above that expected under Wright-Fisher models [3]–[8]. Using exome resequencing data from over a hundred French-Canadian individuals, we show that a human founding population that has undergone rapid expansion contains an excess of private and rare variants compared to the French population after a colonization event less than 20 generations ago. Genetic variants in French-Canadians tend to be putatively more deleterious than those in the French. On the population level, evidence for this comes from the fact that mutations in the French-Canadian population tend to occur at functional sites with higher conservation scores and/or sites predicted to be damaging, are located preferentially at missense sites, and have higher missense to synonymous ratios than in French and European populations. Furthermore, at the individual level, this potentially translates into an increased genetic burden, since although French-Canadians carry a similar number of derived alleles as the French, these alleles tend to occur at more putatively damaging sites, as indicated by alleles in French-Canadians occurring at sites that on average have higher GERP scores (Figure 1C). Furthermore, since the French-Canadian population shows lower levels of heterozygosity (and thus higher levels of homozygosity), this may have implications for disease susceptibility.
It is known that the incidence of around twenty Mendelian diseases is higher in Quebec [19], [22] and some hereditary diseases show a particular pattern in the French-Canadian population, with local enrichments within particular geographical areas originated by regional founder effects [19], [22], [32]. Although it is difficult to translate our results into specific population genetic risk estimates, it may be possible that the increase of rare deleterious variants and reduced heterozygosity in the French-Canadian population is leading to higher disease risk. Rare alleles that were present at damaging sites in the original population may subsequently have been removed in the French population, yet still persist in French-Canadian individuals due to sampling effects, smaller population sizes, less competition and a higher birth rate. Although this seems unlikely to impact upon diseases caused by recessive variants in homozygous form, damaging mutations that have arisen since the founder event may be dominant or serve as the second, and ultimately vital, mutation within an important gene under a compound heterozygous model of Mendelian disorders. Furthermore, we find some evidence that higher frequency variants (MAF>10%) are on average more damaging in the French-Canadian population when compared to the French, since they tend to have higher GERP scores (see Results), which may impact upon the incidence of Mendelian diseases under a homozygous recessive model.
It has previously been shown that there is proportionally more deleterious variation in European populations after the out of Africa expansion [14], [15]. However, this process occurred over a much longer timeframe and also relies on a long bottleneck to explain the increase in deleterious variants in Europeans [14], [15]. In French-Canadians we observe a similar increase of rare deleterious variants but over a markedly short time frame. Furthermore, since the French-Canadian population did not undergo a long population bottleneck, the excess of deleterious variants could be explained by a rapid expansion of the population as well as other demographic factors such as subsequent regional founder effects in Quebec. To test this we performed a number of forward simulations incorporating selection and the demographic history of Europe, as inferred in a recent study [14], followed by a simple population bottleneck and rapid expansion in the French-Canadian population, and a less extreme expansion in the French population (for details, see Materials and Methods). We modeled population bottlenecks of varying sizes, performing 100 replicates for each scenario, and then calculated the difference in the proportion of variants with MAF<5% between the French and French-Canadian populations (Table S6). The scenarios modeled likely represent a simplified version of the actual demographic history of the French and French-Canadian populations, however we use them here to test differences between populations undergoing different rates of expansion under selective constraint after sharing a large proportion of demographic history. Under these models, the largest increase in rare variants in French-Canadians occurs when the population did not undergo a bottleneck, showing differences as large as 5.23% across the 100 replicates, with an average shift of 1.09%. Furthermore, we also observe on average an additional 8.32 deleterious alleles per megabase (defined as having a negative selection coefficient) per replicate segregating in the French-Canadian population compared to the French population. For simulations including a bottleneck, the biggest increase of rare variants in the French-Canadian population occurs for a bottleneck of 75%, with differences as large as 5.74% across the 100 replicates, an average increase of 0.74% and an additional 5.86 deleterious alleles per megabase per replicate in the French-Canadian population.
Although these simple models lead to an increase in the proportion of rare variants in the French-Canadian population, the shift observed in the empirical data, which shows an increase of 9.8% of variants with MAF<5% in the French-Canadian population compared to the French population when using the same sample sizes (see above and Figure S1), is larger than that generated by simulations; there are several possible explanations for this. First, it may be that current tools are not able to accurately model recent events such as a rapid population expansion. Second, it is likely that a more complex demographic scenario is needed to explain the size of the increase in rare variants in the French-Canadian population, that may also include changes in selective forces as a consequence of the reduced competition occurring between a small number of founders. In fact, the French-Canadian population is genetically stratified into subpopulations with differentiated demographic histories [19], [21], [22]. Independent settlements and expansions with partially reduced genetic exchange across subpopulations, unequal contribution to the current genetic pool, as well as some admixture with other populations could have also contributed to the shift in the site frequency spectrum. Consistent with these notions, a recent study focusing on a specific sub-founding population within Quebec presented evidence that individuals on the wave front of colonization events have a heritable advantage and a higher contribution to the current genetic pool [23]. In this study, we have not focused on specific regions within the population and have not tested this observation. However, our results do demonstrate that the recent founding event and subsequent colonization events may have had a substantial deleterious impact across genomes. To a lesser extent, rare variants could also arise from the inclusion of founders from different regions in France or other European countries, which could be also related to the level of genetic diversity in Quebec, similar to that reported for European populations [21]. Similarly, the unequal sex ratio of the Quebec settlers of more than ten times more men than women [18], may also have contributed to a shift in the effective population size and loss of heterozygosity. Finally, although there is evidence of a population bottleneck in the French-Canadian population, such as reduced levels of heterozygosity, given the results of our simulations it seems unlikely that the bottleneck was particularly strong.
In this study, we show that even in the case of two very close populations that are separated by only 400 years approximately, the differences in the landscape of genetic variation can be substantial under particular demographic conditions. Rare variants are presumed to explain some of the missing heritability not accounted for by common variants in genome wide association analyses for complex disorders [33] as well as most of the rare diseases. Furthermore, there is mounting evidence that coding rare variants are contributing to complex traits [34]. The high number of population private rare functional variants described in this study constitutes a challenge for genetic association studies, affecting the replicability and correlation of genetic risk factors across human populations. Indeed, even from a relatively limited number of French-Canadian chromosomes, we discovered a substantial number of missense mutations that are not found on the widely used Illumina exome-arrays built from SNPs ascertained across a number of major sequencing studies. One third of the missense SNPs we discovered from sequencing over one hundred exomes are not found on these arrays, variation that likely influences complex traits and disease phenotypes, but is missing from analysis of disease risk. Although we understand from population genetics that most variants will be rare, this observation speaks to the need for continued sequencing of isolated or semi-isolated populations. Beyond the particular case of the French-Canadian population, this study highlights the importance of local demographic events in shaping genetic variation, and the need for creating population-based catalogues of human genetic variation [12].
This research has been approved by the CHU Sainte-Justine's ethical committee. Data was analyzed anonymously.
One hundred and fourteen French-Canadians were selected for sequencing. French-Canadian samples are the healthy parents of four disease cohorts (primary immunodeficiencies, acute lymphoblastic leukemia, schizophrenia and autistic spectrum disorder) recruited at the Sainte-Justine Hospital (Montreal). Additionally, sequences from 30 French samples previously analyzed were included in the study [24]. We used principal component analysis to identify and remove the genetic outliers (see below).
Exome capture was performed with the SureSelect Target Enrichment System from Agilent Technologies optimized for Applied Biosystems SOLiD sequencing, using the Agilent SureSelect All Exome Kit (38 Mb) and the Human All Exon 50 Mb kit covering exons annotated in the consensus CCDS [35]. Analyses were performed considering the coding regions targeted in the Agilent SureSelect All Exome Kit (38 Mb). Briefly, 3–5 µg of DNA were sheared by sonication, 5′ ends repaired, and the resulting fragments were ligated to adaptors, which were then run in size-select gels to select fragments of 150–250 bp in size. The extracted DNA was amplified by PCR and hybridized to the capture library containing the human exome. Hybridization was performed in a solution at 65°C for a minimum of 24 hours, followed by washing and capture of the hybridized DNA through magnetic bead selection, PCR and purification. Quantification of DNA libraries was performed using a Bioanalyzer and qPCR instrument. Exome sequencing was performed using SOLiD 3 Plus and SOLiD 4 Systems (Applied Biosystems), following the manufacturer's recommended protocols. Sequence reads were aligned to the human genome reference sequence (hg18, downloaded from http://genome.ucsc.edu) with BioScope, the available mapping tool for the SOLiD technology. GATK recalibration [36] was applied after mapping, PCR duplicates removed with Picard (http://picard.sourceforge.net) and SNP calling was performed using Samtools [37]. In total, 61 gigabases of sequencing reads mapped to the reference genome, with an average of 86% of the targeted regions being covered by at least one sequencing read. Each individual had an average coverage of 17-fold (see supplementary material, Table S5). SNP annotation was performed using the SeattleSeq Annotation tool (http://gvs.gs.washington.edu/SeattleSeqAnnotation/). Variants from the French population were generated from exome sequencing of the same targeted exons using Illumina sequencing [24].
Stringent variant calling criteria were applied to produce a high quality dataset of both the French and French-Canadian populations, including only variants that satisfy all of the following conditions: (i) fall within the regions targeted by the Agilent SureSelect exome capture kit, (ii) with SNP consensus or variant quality of 30 or higher, (iii) with sequence coverage of 10-fold depth or greater and (iv) in Hardy-Weinberg equilibrium (using a stringent p-value of 0.001). Furthermore, variants were included only if these criteria were met in at least 20 individuals in both the French and French-Canadian populations. The average transition/transversion ratio for all the French and French-Canadian samples in the coding variants was 3.32, as expected for exonic sequences [38] and we detected no significant difference between French and French-Canadian samples (3.38 and 3.30, respectively). Similarly, frequencies of the twelve possible nucleotide changes are similar between the two populations (Figure S8). For the resampling analyses, we randomly choose thirty individuals from the French-Canadian population and applied the same filters as above.
In order to use the most genetically homogeneous group of individuals in each population we performed principal component analysis (PCA) for each population sample using SmartPCA as implemented in the program eigenstrat [39]. First, PCA was performed within each population including variants called in at least 80% of the individuals in each population to avoid the effects of missing values; these variants totaled 13,035 positions for the French-Canadian population and 26,843 for the French population. Significant PCs were inferred using the TW-statistic (p-value<0.01) and outlier individuals were identified based on their individual loading exceeding two standard deviations from the mean of each significant axis. This analysis revealed five outlier individuals in the French-Canadian population and none for the French samples (Figure S9). Removing outlier individuals based on population structure analysis of each population separately resulted in the retention of 109 French-Canadian and 30 French individuals for subsequent analyses. Next, we performed PCA combining both populations, including only positions called in at least 80% of the combined samples, and only individuals with missing data less than 1%. This represented a total of 4,588 SNPs in 89 samples. We find no obvious differences between the two populations (Figure S9), although the French-Canadian population seems to show a slightly lower level of diversity and represents only a subset of the total genetic variation in the French population. The joint frequency spectrum of genetic variation was represented using the δaδi software [40].
We performed a number of validation procedures to check the quality of our data. First, we performed Sanger sequencing on a total of 113 heterozygous calls detected in the individuals included in this study (89% of the 97 variants have MAF<5% and 54% were singletons). In total we confirmed 109 calls, giving a false positive rate of 3.5%. This figure probably represents an upper bound, since the variants selected for validation are enriched for rare variants which are known to be more prone to sequencing errors [41]. Second, we sequenced the offspring of 16 individuals from the French-Canadian population, using the same protocols and filtering steps as in the parents, in order to confirm the presence of certain alleles in the population. Thus, to check the false positive rate for variants that are likely to contain the most errors (singletons), we isolated any positions in the parents that were singletons in our population and then checked to see if the variant is called in the child, only including the position if the same quality filters were met in the offspring (variant quality>30, coverage>10). Under normal patterns of Mendelian inheritance we expect 50% of singletons to be inherited by the child. Overall, we observe 4,666 singletons across the 16 individuals, 2,328 of which are present in the offspring (49.89%), representing a false positive rate for singletons of ∼0.2%.
Third, we also tested the quality of our data by comparing DNA and RNA sequences for three French-Canadian individuals using the same high quality filtering criteria in both datasets (consensus or variant quality greater than 30, coverage greater than 10). For RNA sequencing, RNA was enzymatically fragmented, and cDNA generated by reverse transcription from adaptors ligated to ends of the RNA molecule. Then, the cDNA was amplified using primers complementary to adaptors and purified. Sequencing was performed in a single SOLiD slide containing barcoded samples. Sequence reads were aligned to the human genome reference sequence (hg18, downloaded from http://genome.ucsc.edu) with SOLiD's BioScope mapping tool. Recalibration was performed with GATK [36], and PCR duplicates were removed with Picard (http://picard.sourceforge.net). SNP calling was performed using Samtools [37]. As differences may exist between DNA and RNA as a consequence of RNA editing [42]–[45] and allelic expression [46], for positions that are heterozygous in DNA, we considered a site as successfully validated if at least one read was present in RNA for both alleles; we confirm 474 of 506 sites. Since it is known that approximately 28% of genes show greater than a 4-fold difference in the expression of two alleles in RNA [46], it is likely that some differences between DNA and RNA are driven by allelic specific expression. Indeed, 5 out of the 32 sites that fail validation in one individual show evidence for being heterozygote (displaying at least one read from each allele) in the RNA of at least one of the other two individuals that were sequenced. Differences between DNA and RNA at heterozygous sites are not significantly enriched for rare variants; only 5 out of 32 sites that fail validation have MAF<5% (variants with MAF<5%, 5/66 not validated, p = 0.92). Furthermore, we also considered sites that contained homozygous non-reference alleles in DNA sequences and then checked the corresponding position in RNA. All 242 positions were validated, further confirming the quality of the data.
Finally, to consider the quality of common variants, we compared the genotype frequencies at polymorphic sites obtained from our exome sequencing that overlapped with data from 521 French-Canadian individuals that were genotyped on Illumina's Omni 2.5M arrays. In each case we compared the number of homozygous reference, homozygous alternative and heterozygous calls in our exome data with the same number of randomly sampled individuals from the chip data. In total, 23,231 sites were overlapping, 99.94% of which were not significantly different between exome sequencing and array data (p>0.05, after Bonferroni correction).
To estimate the strength of purifying selection in the French and French-Canadian populations we applied two methods. First, we used prfreq, a program that uses Poisson random fields [14] to estimate the maximum likelihood values for different scenarios given an observed site frequency spectrum (SFS). For the French and French-Canadian populations, we projected the SFS down to 60 alleles by randomly sampling individuals from the French-Canadian population and including only sites with 0% missing data. The ancestral allele was inferred from the homologous chimpanzee sequence obtained from Seattleseq annotation (http://gvs.gs.washington.edu/SeattleSeqAnnotation/) and since mutation rates vary across the genome as a function of neighbouring nucleotides [47], we corrected for the uncertainty of the ancestral sequence following the method of Hernandez et al [48]. Maximum likelihood values for each scenario were obtained with a multinomial calculation that estimates the probability of each SNP segregating at a given derived allele frequency. P-values associated with various demographic and selective models were estimated using likelihood-ratio tests. Demographic parameters were inferred from the site frequency spectrum of synonymous variants comparing three scenarios: a stationary population, contraction/expansion, and a population bottleneck and expansion (Tables S2 and S3). Finally, the selective parameters were obtained by comparing the likelihood of the missense SFS using the demographic model inferred from synonymous variants (see above) to the likelihood for the same demographic model incorporating a selection parameter (γ = 2Ne(s)). To compare the γ values estimated in the French and French-Canadian populations we compared the likelihoods estimated in each case with the likelihoods computed using the γ values from the other population.
Second, to calculate the distribution of fitness effects associated with mutations occurring in the French and French-Canadian populations we used the DFE-alpha software [31] (http://homepages.ed.ac.uk/eang33/). To construct the unfolded site frequency spectrums for the two populations we included variants and sites in the targeted region in which at least 30 and 90 individuals passed the high quality filters for the French and French-Canadian populations respectively. These numbers were chosen to reduce the amount of missing data at each site, whilst retaining the majority of polymorphic sites for analysis. We then counted the number of sites that had zero to 180 derived alleles in the French-Canadian population, where derived alleles represent sites that have diverged from chimpanzee. The same approach was applied for the French population using 60 chromosomes. For the French-Canadian population, ninety individuals were sampled randomly without replacement at sites where the number of alleles passing quality filters exceeded 180. Derived alleles were inferred from chimpanzee sequences and human and chimpanzee pairwise alignments were downloaded from the UCSC website (http://hgdownload.cse.ucsc.edu/downloads). As in the original DFE analysis [31], intronic sites served as the neutral standard, the distribution of fitness effects was fit to zero-fold degenerate sites and any sites that were part of a CpG dinucleotide were removed. Confidence intervals were generated by bootstrapping; sites were selected randomly across the site frequency spectrum with replacement to generate 100 new datasets for each population.
To replicate the major findings of this study we analyzed data from a cohort of fifty French-Canadian individuals sequenced on the Illumina platform representing the unaffected parents from different disease projects (developmental delay and fetal malformations). Exomes were captured from 3 µg of blood genomic DNA, using the Agilent SureSelect Human All Exon Capture kit (V3 and V4; Mississauga, ON), and sequenced paired end using the Illumina Hi2000seq technology. Raw sequencing data was processed using the same pipeline and filtering process as described above, including only those sites that are sequenced in all datasets. PCA was performed as before, taking SNPs with MAF>5% and missing data<5% - zero outliers were removed (Figure S10). For the CEU population, we obtained BAM files for 35 individuals from the 1000 Genomes Project ftp site (ftp://ftp-trace.ncbi.nih.gov/1000genomes/ftp/) sequenced on the SOLiD platform and applied the same pipeline and filters as detailed above.
To test for an increase in rare variants in the French-Canadian population we simulated a number of demographic scenarios under selection using the forward simulator SFS_code [49]. First, we implemented timing and population size scaling for the European demographic history, as detailed in the SFS_code documentation (http://sfscode.sourceforge.net/SFS_CODE_doc.pdf, figure 2, model taken from [14]). This model includes an initial burn-in period with a population size of 7,895, followed by a bottleneck at time zero to a population size of 5,699. Following this, the population remains at constant size for 7,703 generations before an instantaneous growth to 30,030, which remains for a further 874 generations. We scaled this model using an ancestral population size of 1,000. Then, we simulated a population split and a bottleneck of 50%, 75% and 100% (no bottleneck) for one of the populations to represent the founding of Quebec, scaled using the initial population size to occur twenty generations ago. This was then followed by exponential growth over twenty generations in the European and Quebec populations to increase their size by 3 and 600 respectively (as documented in historical records), using 100 replicates for each scenario. In total, we simulated 360 unlinked genes per replicate, each consisting of five 400 bp exons separated by introns of size 2 kb (similar to the average exon and intron sizes documented in [50]). We used a mutation rate per site of 1.5×10−8 and an average recombination rate of half this value. We ignored positive selection since it is likely to be rare and used an average selection coefficient of −0.03, as inferred in [14], sampled from a gamma distribution. In each replicate and for each population, we selected 100 individuals and then compared the proportion of variants with MAF<5%. The results of these simulations are shown in Table S6.
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10.1371/journal.pgen.1004239 | Cancer Evolution Is Associated with Pervasive Positive Selection on Globally Expressed Genes | Cancer is an evolutionary process in which cells acquire new transformative, proliferative and metastatic capabilities. A full understanding of cancer requires learning the dynamics of the cancer evolutionary process. We present here a large-scale analysis of the dynamics of this evolutionary process within tumors, with a focus on breast cancer. We show that the cancer evolutionary process differs greatly from organismal (germline) evolution. Organismal evolution is dominated by purifying selection (that removes mutations that are harmful to fitness). In contrast, in the cancer evolutionary process the dominance of purifying selection is much reduced, allowing for a much easier detection of the signals of positive selection (adaptation). We further show that, as a group, genes that are globally expressed across human tissues show a very strong signal of positive selection within tumors. Indeed, known cancer genes are enriched for global expression patterns. Yet, positive selection is prevalent even on globally expressed genes that have not yet been associated with cancer, suggesting that globally expressed genes are enriched for yet undiscovered cancer related functions. We find that the increased positive selection on globally expressed genes within tumors is not due to their expression in the tissue relevant to the cancer. Rather, such increased adaptation is likely due to globally expressed genes being enriched in important housekeeping and essential functions. Thus, our results suggest that tumor adaptation is most often mediated through somatic changes to those genes that are important for the most basic cellular functions. Together, our analysis reveals the uniqueness of the cancer evolutionary process and the particular importance of globally expressed genes in driving cancer initiation and progression.
| Cancer is a short-term evolutionary process that occurs within our bodies. Here, we demonstrate that the cancer evolutionary process differs greatly from other evolutionary processes. Most evolutionary processes are dominated by purifying selection (that removes harmful mutations). In contrast, in cancer evolution the dominance of purifying selection is much reduced, allowing for an easier detection of the signals of positive selection (that increases the likelihood beneficial mutations will persist). Mutations affected by positive selection within tumors are particularly interesting, as these are the mutations that allow cancer cells to acquire new capabilities important for transformation, tumor maintenance, drug resistance and metastasis. We demonstrate that, within tumors, positive selection strongly affects somatic mutations occurring within genes that are expressed globally, across all human tissues. Fitting with this, we show that genes that are already known to be involved in cancer tend to more often be globally expressed across tissues. However, even when such known cancer genes are removed from consideration, there is significantly more positive selection on the remaining globally expressed genes, suggesting that they are enriched for yet undiscovered cancer related functions. The results we present are important both for understanding cancer as an evolutionary process and to the continuing quest to identify new genes and pathways contributing to cancer.
| Cancer initiation and progression are short-term evolutionary processes that occur within our bodies (reviewed in [1]–[5]). A full understanding of cancer requires learning the dynamics of this evolutionary process. All evolutionary processes depend on the existence of genetic variation. In cancer this variation is generated by somatic mutation. The ultimate fate of somatic mutations is affected by natural selection, which acts in two ways: First, it reduces the likelihood that deleterious mutations will persist (purifying selection). Second, it increases the likelihood that functionally advantageous mutations will persist (positive selection). The subset of mutations that persist to the point that we can observe them through DNA sequencing are referred to as substitutions.
Those somatic mutations that are subject to positive selection within tumors are of particular interest, as these are the mutations that contribute positively to transformation, tumor maintenance, expansion, drug resistance, and metastasis. Thus, by inferring what groups of genes are most affected by positive selection within tumors we can gain insight into genes that contribute most positively to the cancer phenotype.
Natural selection affecting somatic mutations acts at the cellular level, in contrast to selection affecting germline (hereditary) mutations which acts at the organismal level. Germline mutations that have a fitness effect are more likely to be deleterious than advantageous because of the complexity of organisms, and because organisms are generally well adapted [6]–[9]. Indeed, it has been shown for many organisms that in germline evolution purifying selection is much more pronounced than positive selection (e.g. [10]–[13]). Much less is understood about how natural selection affects the dynamics of somatic substitution accumulation during cancer initiation and progression.
It is possible to quantify selection by examining patterns of substitution. The ratio of the rates of non-synonymous (change the amino acid sequence) and synonymous (do not change the amino acid sequence) protein-coding substitutions (dN/dS) [14], [15] is the most commonly used metric of selection operating on a system (e.g. [12], [13], [16]–[20]). Since non-synonymous substitutions tend to have a stronger effect on gene function, selection will affect non-synonymous substitutions more often than it affects synonymous substitutions. In the absence of selection, non-synonymous mutations and synonymous mutations will be equally likely to persist (dN/dS ∼1). Purifying selection will more often remove non-synonymous mutations from the population (reducing dN/dS), while positive selection will more often increase their frequency within the population (increasing dN/dS). It is also possible to further classify non-synonymous substitutions as more or less likely to be functional based on considerations of protein sequence conservation (e.g. the SIFT algorithm [21]), or based on protein sequence and structure considerations (e.g. the Polyphen-2 algorithm [22]). Selection is expected to affect more strongly the rates of substitution at more functional (MF) sites than at less functional (LF) sites. Thus, we expect purifying selection to reduce the ratio of the rates of MF and LF substitutions, dMF/dLF, and positive selection to increase dMF/dLF.
Each of these three measures of selection has associated biases and/or limitations. dN/dS may be affected by selection acting on synonymous sites [13], . The SIFT algorithm has a bias by which it is more likely to assign high functionality to residues within proteins that are conserved only over a short evolutionary distance, but are highly similar within this short time-frame [21]. The Polyphen algorithm has a bias by which its likelihood of assigning functionality to a mutation is higher, if the mutated allele happens to be the allele represented in the human reference genome [25]. Each of these biases may affect the results obtained by any one measure. By combining and contrasting results obtained using the three measures we can examine whether patterns we observe are more likely to be truly significant.
Many past studies have used dN/dS to search for genes under strong positive selection in both organismal evolution and within tumors (e.g. [26]–[29]). Such studies have attempted to identify genes for which dN/dS is significantly higher than 1. It is important to note however that this is an extremely conservative test for positive selection [14], [30]. After all, in order for dN/dS to reach values significantly higher than 1, positive selection would have to be strong enough to overcome the contradictory action of purifying selection, which acts to reduce dN/dS. It is quite likely that many genes that are subject to positive selection will have dN/dS values equal or lower than 1 due to the fact that they are also subject to strong purifying selection.
Here we suggest an alternative approach for identifying whether a group of genes is subject to positive selection within tumors. In this approach we identify a group of genes, which we can demonstrate is enriched for important functionality, and therefore subject to stronger purifying selection in the germline. If we can then show that when examining cancer somatic substitutions these genes have higher dN/dS and dMF/dLF values than other genes, such increased values are very unlikely to be explained by more relaxed purifying selection acting on such important genes. Rather, higher cancer somatic functional variation within more important genes is likely to represent increased positive selection. This method allows us to detect positive selection on important genes even if such positive selection does not lead to dN/dS and dMF/dLF values that are higher than 1.
We used extensive data of cancer somatic substitutions to examine the directionality and intensity of selection acting within tumors. We find that natural selection affects somatic mutations within tumors in a much different manner compared to the way it affects germline mutations. More specifically, somatic mutations within tumors are subjected to much more relaxed purifying selection, and to much more pronounced positive selection relative to germline mutations. Positive selection is particularly strong within tumors on genes that are expressed globally across human tissues. Indeed, we show that known cancer genes (which we know to be positively selected in tumors) are highly enriched for global expression patterns, and substantially depleted for tissue-specific expression patterns, compared to non-cancer associated genes. Yet, even if known cancer genes are removed from consideration, we can still detect stronger positive selection on the remaining globally expressed genes, suggesting that globally expressed genes are enriched for yet undiscovered cancer related functions.
We calculated dN/dS, dMF/dLF(SIFT) and dMF/dLF(Polyphen-2) for germline and breast cancer (BrCa) somatic substitutions. Germline data were extracted from the 1000 human genome project [11], and data of BrCa somatic substitutions were extracted from The Cancer Genome Atlas (TCGA) project [31]. We found that dN/dS and dMF/dLF of BrCa somatic substitutions are much higher than observed for germline substitutions (Figure 1, Table S1). These results are consistent with the results of a previous study that examined dN/dS in four other tumor types and found elevated values [17]. That study attributed these elevated dN/dS values to a sharp relaxation in purifying selection. Indeed, it makes intuitive sense that purifying selection on somatic substitutions should be relaxed, compared to its effect on germline mutations, because somatic mutations affect only the cells in which they occur and their progeny, while germline mutations affect the entire organism. Thus, many deleterious mutations that would be affected by purifying selection in the germline may not be subject to such selection when they occur as somatic mutations in a tissue in which the gene they affect is not active. Additionally, the efficiency of selection on moderately deleterious mutations in tumors may be reduced due to hitchhiking and the effects of Muller's ratchet [32]. Small effective population sizes of stem cell pools may increase the power of genetic drift, relative that of selection, which would further reduce the efficacy of selection. Interestingly, both dN/dS and dMF/dLF(Polyphen) are significantly lower than 1 for BrCa somatic substitutions (P<<0.0001), indicating that somatic substitutions in breast tumors remain significantly affected by purifying selection, albeit weakly. At the same time dMF/dLF(SIFT) is significantly higher than 1 (P<<0.0001), indicating a strong effect of positive selection on breast cancer somatic substitutions. It is important to note that each of the three measures of selection may have intrinsic biases (see Introduction). We are therefore hesitant to draw any conclusions that are not supported by at least a majority of the measures used.
Since cancer is a process of cellular adaptation in which cells acquire the capability to proliferate more efficiently and gain additional functions, it is reasonable to expect that a significant number of BrCa somatic mutations are under positive selection. Increased dN/dS and dMF/dLF values can stem not only from reductions in purifying selection but also from increases in positive selection. As discussed in the Introduction, the most commonly used methodology to detect positive selection is to examine whether dN/dS values for a certain gene or group of genes are significantly greater than 1. To estimate how well this method would be expected to work in detecting positive selection within tumors, we calculated dN/dS for each gene within the human genome, based on data of somatic cancer substitutions extracted from the TCGA. To maximize our ability to detect positive selection, gene-by-gene, by maximizing the number of substitutions available for each gene, we combined data of all 16 tumor-types available within the TCGA, for which no publication restriction applied as of the end of December 2013 (Table S2). This allowed us to calculate dN/dS for 18,299 human genes, in which at least one synonymous substitution was observed in the entire dataset. Of these genes 456 have been causatively implicated in cancer according to the ‘cancer gene census’ database [33]. Such known cancer genes should be subject to positive selection in cancer evolution, as they carry mutations that allow cells to gain the proliferation and invasion capabilities needed to develop and maintain a tumor, and for its metastasis. Of the 18,299 genes for which we could calculate dN/dS only 104 had values significantly greater than 1 (P<0.05 according to a χ2 test, Table S3). Seventeen of these (16.3%) were known cancer genes (a significant enrichment, P<<0.0001 according to a χ2 test). This is consistent with previous results that have shown that the known cancer genes, contained within the cancer gene census, are more likely to show a significant signal of positive selection [32]. At the same time, 439 of the 456 cancer genes (96.3%) did not have dN/dS values significantly greater than 1. It is therefore apparent that attempting to identify genes under positive selection within tumors by requiring dN/dS to be higher than 1 would fail to identify the vast majority of genes that are subject to positive selection within tumors.
A similar conclusion can be reached when considering only those cancer genes that have been implicated specifically in breast cancer, and considering only BrCa somatic substitutions. Of the 15 genes contained in the cancer gene census for which somatic mutations were implicated in BrCa, only two (TP53, and PIK3CA) present dN/dS values significantly higher than 1. It therefore becomes apparent that in order to identify many genes that are important for cancer and thus evolving under positive selection within tumors one must devise more sensitive means. This becomes even more apparent when one considers that of the 772 BrCa tumors for which there is available sequence data, 360 (46.6%) have no somatic mutations in the 15 genes already implicated in BrCa. This strongly suggests that there are multiple BrCa drivers yet undiscovered.
It is important to note that our results do not imply that looking for genes with dN/dS significantly greater than 1 is not a useful approach. Indeed this approach has allowed us to identify some good candidates for involvement in cancer that have not been previously implicated in cancer, according to the cancer gene census (Tables S3 and S4). When looking for genes in which dN/dS of BrCa somatic substitutions is significantly higher than 1 (Table S4), we find two genes that are not contained in the cancer gene census database. These are the Titin gene, TTN, and MLL3, which has been associated with other types of cancer, but not with BrCa. These two genes may be good candidates for involvement in BrCa. Additionally, 87 of the 104 genes we identified as having dN/dS significantly greater than 1, based on combined data of somatic cancer substitutions from 16 types of tumors (Table S3) have not been implicated in any type of cancer, according to the cancer gene census. These genes may also provide good novel candidates for involvement in cancer. Combined, these results show that screening for genes for which dN/dS is significantly greater than 1 may indeed identify some novel genes under positive selection within tumors. However, this method lacks sensitivity and misses much of the positive selection occurring within tumors.
The results presented above demonstrate that a signal of positive selection of dN/dS significantly higher than 1 can only be obtained for a very small minority of cancer genes. This likely stems from the fact that both positive and negative selection affect dN/dS of genes in consort. While positive selection increases dN/dS, purifying selection pushes it down. In order to allow us to detect positive selection acting within tumors with higher sensitivity, we devised a different approach. The idea behind this approach is to identify instances in which higher levels of somatic functional variation are observed within genes that are more important and more constrained at the germline level, compared to less important, less constrained genes. Since more important, more constrained genes are expected to be subject to stronger purifying selection, higher levels of functional variation within such genes will likely reflect the action of positive, rather than purifying selection.
We considered separately two groups of genes: those globally expressed across 16 examined tissues (Materials and Methods), and those whose expression is restricted to only a few or to none of these tissues. We have four lines of evidence that globally expressed genes are more important than non-globally expressed genes, and are likely evolving under increased constraint. First, globally expressed genes are enriched for important housekeeping functions [34]. Second, we found that globally expressed genes are significantly more likely to be predicted as essential, compared to non-globally expressed genes (P<<0.0001, according to a to a χ2 test, based on data extracted from [35] (Materials and Methods)). Third, it has been previously demonstrated, using data of sequence divergence between humans and rodents, that rates of non-synonymous substitution are almost threefold lower for globally expressed genes, compared to genes with tissue-specific expression patterns [36]. Fourth, we found that for human polymorphism data, dN/dS of globally expressed genes is significantly lower than that of non-globally expressed genes (0.2 vs. 0.28 for germline substitutions appearing at a frequency of >0.1, P<<0.0001), directly demonstrating stronger purifying selection on globally expressed genes. dMF/dLF(Polyphen) is also significantly (albeit only marginally so, P = 0.05) lower for globally expressed genes, when data of human polymorphism is considered (0.13 for globally expressed genes vs. 0.15 for non-globally expressed genes). This again indicates that there is stronger constraint acting on germline mutations occurring within globally expressed genes. No significant difference in human polymorphism dMF/dLF(SIFT) is observed between globally and non-globally expressed genes (P = 0.16).
In sharp contrast to the increased constraint acting on globally expressed genes in the germline, dN/dS of globally expressed genes in BrCa tumors is significantly higher than that of non-globally expressed genes (Table 1, P<<0.0001). This difference remains consistent and even intensifies when the BrCa dN/dS of globally expressed genes is compared to that of non-globally expressed genes that are not expressed in breast (Table 1, P<<0.0001). As demonstrated above, globally expressed genes are expected to be subject to more, rather than less, selection than the “average” gene, and BrCa somatic mutations occurring in genes not expressed in breast tissue should be under the weakest selection of all. Therefore, higher somatic dN/dS values in globally expressed genes are unlikely to reflect weaker purifying selection on these genes. Rather, such higher dN/dS values likely reflect stronger positive selection acting on globally expressed genes than on non-globally expressed genes in BrCa. Similarly, we find that dMF/dLF (SIFT) and dMF/dLF(Polyphen-2) are significantly higher for somatic BrCa substitutions in globally expressed genes than in non-globally expressed genes (Table 1, P<<0.0001, for all comparisons). We observe this result even with a less stringent threshold for globally expressed genes of expression in 14–16 tissues (Table S5, P<<0.0001 for all comparisons). It is important to note that these results indicate that positive selection on globally expressed genes is very strong. After all, we are able to observe its signal even in the face of an opposite force (purifying selection) that almost certainly acts to remove non-synonymous and more functional somatic substitutions more efficiently from globally expressed genes.
Interestingly, dN/dS and dMF/dLF of globally expressed genes are also significantly higher when compared only to non-globally expressed genes that are expressed in breast tissue (Table 1, P<<0.0001 for dN/dS and dMF/dLF(SIFT), P = 0.01 for dMF/dLF(Polyphen-2)). Thus, the increased positive selection on globally expressed genes in breast tumors cannot be due solely to their expression in breast.
We examined whether our findings of increased functional variation of cancer somatic compared to germline substitutions, and of increased functional variation in globally expressed compared to non-globally expressed genes extend to additional tumor types. We analyzed data of somatic substitutions in 13 additional types of tumors, for which at least 5000 somatic non-synonymous and synonymous substitutions were available in the TCGA dataset, and for which there were no publication restrictions as of the end of December 2013 (Table 2 and Table S2). Similarly to what we found for BrCa, dN/dS and dMF/dLF values tend to be much higher for somatic compared to germline substitutions across all tumor types (Table 2). This indicates that purifying selection is relaxed on all types of tumors. Also consistent with what we find in BrCa, dN/dS and dMF/dLF are statistically significantly higher in globally expressed compared to non-globally expressed genes (P<0.05) in 8, 13, and 9 of the 13 additional tumor types, for dN/dS, dMF/dLF(SIFT), and dMF/dLF(polyphen) respectively. Strikingly, even when this difference is not strong enough to be statistically significant, dN/dS and dMF/dLF are always higher for globally compared to non-globally expressed genes, except for two cases in which they are equal (Table 2). Thus, our finding of higher somatic functional variation within globally expressed genes extends to many, if not all cancer types.
To further support our conclusion that increased functional variation within globally expressed genes stems from increased positive selection acting on these genes within tumors, we examined the expression patterns of a group of known cancer genes, contained in the ‘cancer gene census’ database [33]. As discussed above these known cancer genes are expected to be subject to positive selection in cancer evolution, as they carry mutations that allow cells to gain functions important for cancer. We found that known cancer genes tend to be significantly more globally expressed than other genes (P<<0.0001 according to a χ2 test, Figure 2). More than half (∼54%) of known cancer-associated genes are expressed across all 16 examined tissues (∼1.5 fold higher than for genes with no known cancer function); a minority (∼10%) of known cancer associated genes are expressed in three or less tissues (∼2.5-fold lower than for non-cancer associated genes, a significant depletion (P<<0.0001), Figure 2). This result provides further support for positive selection affecting globally expressed genes more strongly within tumors, as it demonstrates that genes that are known to be under positive selection within tumors tend more often to be globally expressed.
We also examined the expression patterns of genes for which we could observe a clear signal of positive selection (dN/dS significantly higher than 1, P<0.05 see above). We identified 104 such genes, when combining data of cancer somatic substitutions from all tumors types covered by the TCGA, for which no publication restrictions apply (Table S2). For 98 of these genes there was expression data available, and of these genes 50 (51%) are globally expressed across all 16 tissues examined (Table S3). This is a weakly significant enrichment (P = 0.03) compared to what is observed for genes that do not have dN/dS values significantly higher than 1 (40.2% globally expressed). The enrichment in global expression patterns becomes stronger when a higher significance is required for dN/dS being higher than 1. Of the 15 genes for which dN/dS is higher than 1 with a significance lower than 0.001, 12 are globally expressed (80%, significant enrichment compared to genes with dN/dS not significantly higher than 1, P = 0.004). Furthermore, when considering the four genes we identified as having dN/dS higher than 1 in the BrCa somatic dataset, all four of these genes are globally expressed (Table S4). Thus, genes with a clear signal of positive selection of dN/dS significantly higher than 1 are significantly enriched for global expression patterns. This again supports our conclusion that positive selection more often affects globally expressed genes.
To investigate whether the signal of stronger positive selection on globally expressed genes stems only from the fact that known cancer genes are more often globally expressed, we repeated our analyses after removing all known cancer genes from consideration. Interestingly, even when we remove known cancer genes from our analyses, we find that dN/dS and dMF/dLF are significantly higher on globally compared to non-globally expressed genes (Table 1, P ranges between 0.015 and <<0.0001 for all three dN/dS and dMF/dLF comparisons). This suggests that significant positive selection acts on globally expressed genes that have not yet been implicated in cancer, suggesting that there are a substantial number of yet undiscovered globally expressed genes carrying mutations that can confer a growth advantage on tumor cells.
Our results demonstrate that the proportion of functional variation is much higher within somatic cancer substitutions compared to germline substitutions. These results indicate that natural selection affects somatic mutations within tumors in a different manner than it affects germline mutations. Specifically, patterns of somatic substitutions within tumors are affected much less by purifying selection, compared to patterns of germline variation between different humans. The strong effects of purifying selection on patterns of germline substitution make it difficult to observe positive selection when examining patterns of variation within and between species. Observing positive selection on somatic substitutions within tumors is much easier, likely both because purifying selection affects a much smaller proportion of mutations, and because positive selection affects a much higher proportion of mutations.
As discussed above, purifying selection on cancer somatic mutations is likely relaxed due to a combination of factors, including the effects of hitchhiking [32], the fact that somatic mutations affect only a small subset of cells while germline substitutions affect the entire organism, and small effective population sizes of stem cell pools. At the same time, it seems reasonable that positive selection would affect a larger proportion of somatic cancer mutations compared to germline mutations. Organisms tend to be well adapted and thus close to their optimum fitness. Most mutations that affect fitness will reduce rather than increase the fitness of a well-adapted organism [9]. In contrast cancer cells may be far from their fitness optimum when it comes to their ability to replicate independently, avoid organismal defenses, maintain themselves, protect themselves against chemotherapies and eventually invade other tissues. Therefore, it is likely that a much higher proportion of functional mutations would have an advantageous effect on a cancer cell compared to an organism. Furthermore, the microenvironment of cancer cells is thought to be highly dynamic [37], [38], both due to the effects of the cancer on its environment (e.g. acidification [39]), and due to host attempts to combat the cancer [40]. Populations of microbes exposed to novel environments have been shown to experience increased positive selection [41], and it is reasonable that tumor cells would experience a similar effect.
In our analyses we assumed that dMF/dLF would increase under positive selection. Such an assumption may be violated if mutations within the LF category of sites are more likely to be moderately functional than mutations at the MF category of sites, and if fitness is near an optimum. Under a scenario of nearly optimal fitness, adaptation may advance via small steps (i.e. through mutations with moderate effects). If such mutations are enriched within the LF category of sites positive selection may then increase dLF relative to dMF and reduce dMF/dLF. In light of this possibility and in order to examine whether it was reasonable for us to expect dMF/dLF to increase under positive selection within tumors we compared dMF/dLF of BrCa somatic substitutions within known cancer genes (which we know are under positive selection) to dMF/dLF within all remaining genes. We find that fitting with positive selection increasing dMF/dLF within tumors, known cancer genes have significantly higher values of dMF/dLF (SIFT) and dMF/dLF (Polyphen) than the remainder of genes (P<<0.0001 for both comparisons, Table 1). These results support our assumption that dMF/dLF increases under positive selection within tumors, and also supports the idea that adaptation within tumors occurs far from a fitness optimum.
Globally expressed genes are enriched for important housekeeping functions and more likely to be predicted as essential, compared to genes that are not globally expressed. In the germline, globally expressed genes are significantly more strongly affected by purifying selection than non-globally expressed genes, leading to lower levels of non-synonymous variation within globally expressed genes. In sharp contrast to this, functional somatic variation is increased in globally expressed genes, compared to non-globally expressed genes within tumors. This is very unlikely to be the result of less constraint acting on globally expressed genes within tumors. After all, there is no reason to believe that genes enriched for housekeeping and essential functions will suddenly be significantly less important within tumors than other genes. Therefore, higher levels of cancer somatic functional variation within globally expressed genes compared to genes not expressed in breast very likely reflects increased positive selection on globally expressed genes, rather than relaxed purifying selection. Supporting this conclusion we can further demonstrate that known cancer genes as well as genes showing a clear signal of positive selection of dN/dS of cancer somatic substitutions significantly greater than 1 are enriched for global expression patterns.
We compared levels of BrCa functional variation between globally expressed genes and two groups of non-globally expressed genes: those that are not expressed in breast and those that are. We found a higher enrichment in BrCa somatic functional variation when globally expressed genes were compared to genes that are not expressed in breast. It is extremely unlikely that globally expressed genes would be evolving under less constraint within breast, than genes that are not at all expressed in breast. This provides further support for our conclusion that increased functional variation in globally expressed genes stems from increased positive selection rather than from relaxed purifying selection. At the same time, we also observed a significant enrichment in BrCa somatic functional variation of globally expressed genes, compared to genes that are not globally expressed but are nevertheless expressed in breast. This indicates the increased positive selection on globally expressed genes within breast tumors does not stem solely from the fact that such globally expressed genes are expressed in breast. Rather, it is likely that at least part of the reason for the increased positive selection on globally expressed genes is their enrichment for important housekeeping and essential functions. Our results therefore indicate that changes required for cancer development, maintenance and progression tend to occur in genes that carry out the most basic and important cellular functions. These results fit well with previous results that have demonstrated that cancer genes tend to evolve under more constraint in the germline, compared to other genes [42].
Even when we remove from consideration those genes that we already know are involved in cancer (and which tend to more frequently have global expression patterns), we still detect a strong enrichment in BrCa somatic functional variation within globally expressed, compared to non-globally expressed genes. This strongly suggests that globally expressed genes are enriched for yet undiscovered cancer related functions, and that it would be wise to pay special attention to globally expressed genes when searching for novel cancer genes.
The approach we used to demonstrate that globally expressed genes are evolving under more pervasive positive selection than non-globally expressed genes can be applied to other groups of genes. Our approach works by classifying groups of genes based on their predicted importance, and then finding instances in which genes that are expected to be more important and therefore evolving under more constraint are nevertheless enriched for functional substitutions. We expect that very soon data of somatic substitutions within tumors will be abundant enough to allow us to modify our approach to identify individual genes that are evolving under positive selection within tumors. Identifying such cancer related genes is a major goal of cancer genomics.
Many of the positively selected mutations within tumors may confer a relatively small fitness advantage during cancer development, acting as “mini-drivers” of cancer. Classical methods that rely on a strong phenotypic effect of cancer mutations (e.g. gene transfer) may not be able to identify such mini-drivers. Yet, such moderately advantageous mutations may still play an important role in cancer development and progression. The approach described here could provide a framework for detecting mini-drivers and for detecting drivers with larger fitness effects, both of which will be essential for understanding the evolution of cancers and designing therapies to exploit their weaknesses.
Data of breast cancer somatic substitutions from tumors of 772 patients were extracted from the Cancer Genome Atlas project (TCGA) [31] on March 8th 2013. Data of somatic substitutions from all additional 15 cancer types for which no publication restrictions apply as of the end of December 2013 (Table S2) were downloaded from the TCGA on July 22nd 2013. The data was then parsed to count only once any duplicated substitution that appears more than once within a single tumor. Somatic substitutions were identified by TCGA through sequencing of tumors and healthy tissues of the same individuals. Since variable sites appearing within each tumor would have to be present at relatively high frequency within the tumor in order to be detected, such variable sites had time to be affected by natural selection. It is therefore possible to characterize the intensity with which purifying and positive selection act on somatic mutations within tumors by examining dN/dS and dMF/dLF of the substitutions found in these tumors.
Data of germline substitutions were extracted from the 1000 human genome project [11] (Phase 1, V3, latest version as of May 2013). To determine whether each of these germline substitutions were coding or not, and if coding whether they were non-synonymous or synonymous, we used the SnpEff program [43]. This resulted in 512,903 coding non-synonymous or synonymous substitutions, 36,167 of them appearing at a frequency of higher than 0.1.
Gene expression data were extracted as described in TissueNet [44]. Data of gene expression across tissues were extracted from Su et al.[45] and Illumina Body Map 2.0 [46]. Genes with intensity value above 100 [47] or at least 1RPKM were considered as expressed. Matching tissues were consolidated manually.
In order to parse the different datasets, gene name conversion tables were extracted from ENSEMBL [48], the HUGO Gene Nomenclature Committee (HGNC) [49], and the Genecards database [50].
A list of the genes currently known to be associated with cancer was downloaded from the Catalogue of Somatic Mutations in Cancer (COSMIC) [33]
Data of predicted gene essentiality was extracted from [35]. In this study essentiality of human genes was predicted according to whether their orthologs in mice were essential.
Somatic substitutions or germline substitutions within protein coding genes were classified as non-synonymous or synonymous. For each gene we considered only the longest possible transcript (so as not to count single substitutions within a single patient twice). It is expected that following the inactivation of a gene through a nonsense or frameshift mutation, subsequent mutations within that gene, within that same tumor, may not be under selection. For this reason, we removed from consideration somatic substitutions from a certain gene in a certain patient if a nonsense or frameshift substitution was found in the same gene in the same patient. This left us with 41,657 non-synonymous or synonymous coding somatic substitutions. (Note that results reported in this paper remained entirely stable even when we did not perform this clean up step).
Unlike in germline evolution, somatic substitutions at the same site, and with the same nucleotide change, have to occur repeatedly in order to be seen in more than one individual. Therefore we counted substitutions as many times as they appeared within the TCGA data.
A script was written to calculate the number of synonymous and non-synonymous sites within each human protein-coding gene. These calculations were carried out as in [15]. Briefly, we calculated for each protein-coding DNA site the proportion of changes that would be non-synonymous (alter the amino-acid sequence of the encoded protein), and the proportion of changes that would be synonymous. We then added up these proportions across the gene to obtain the proportion of non-synonymous and synonymous sites. The sum of these two proportions is the length of the considered gene. Data regarding the numbers of non-synonymous and synonymous sites are summarized in Table S6.
Once we know the number of non-synonymous substitutions (n), and synonymous substitutions (s) that have occurred within a group of genes in a group of breast tumors, and we also know how many non-synonymous sites (N), and synonymous sites (S) there are within these genes, we can calculate the ratio of the rates of non-synonymous and synonymous substitutions (dN/dS) for that group of genes, in the considered tumors, as:
The exact same approach was used to calculate dN/dS for germline substitutions.
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10.1371/journal.pbio.2006040 | QTL mapping of natural variation reveals that the developmental regulator bruno reduces tolerance to P-element transposition in the Drosophila female germline | Transposable elements (TEs) are obligate genetic parasites that propagate in host genomes by replicating in germline nuclei, thereby ensuring transmission to offspring. This selfish replication not only produces deleterious mutations—in extreme cases, TE mobilization induces genotoxic stress that prohibits the production of viable gametes. Host genomes could reduce these fitness effects in two ways: resistance and tolerance. Resistance to TE propagation is enacted by germline-specific small-RNA-mediated silencing pathways, such as the Piwi-interacting RNA (piRNA) pathway, and is studied extensively. However, it remains entirely unknown whether host genomes may also evolve tolerance by desensitizing gametogenesis to the harmful effects of TEs. In part, the absence of research on tolerance reflects a lack of opportunity, as small-RNA-mediated silencing evolves rapidly after a new TE invades, thereby masking existing variation in tolerance. We have exploited the recent historical invasion of the Drosophila melanogaster genome by P-element DNA transposons in order to study tolerance of TE activity. In the absence of piRNA-mediated silencing, the genotoxic stress imposed by P-elements disrupts oogenesis and, in extreme cases, leads to atrophied ovaries that completely lack germline cells. By performing quantitative trait locus (QTL) mapping on a panel of recombinant inbred lines (RILs) that lack piRNA-mediated silencing of P-elements, we uncovered multiple QTL that are associated with differences in tolerance of oogenesis to P-element transposition. We localized the most significant QTL to a small 230-kb euchromatic region, with the logarithm of the odds (LOD) peak occurring in the bruno locus, which codes for a critical and well-studied developmental regulator of oogenesis. Genetic, cytological, and expression analyses suggest that bruno dosage modulates germline stem cell (GSC) loss in the presence of P-element activity. Our observations reveal segregating variation in TE tolerance for the first time, and implicate gametogenic regulators as a source of tolerant variants in natural populations.
| Transposable elements (TEs), or “jumping genes,” are mobile fragments of selfish DNA that leave deleterious mutations and DNA damage in their wake as they spread through host genomes. Their harmful effects are known to select for resistance by the host, in which the propagation of TEs is regulated and reduced. Here, we study for the first time whether host cells might also exhibit tolerance to TEs, by reducing their harmful effects without directly controlling their movement. By taking advantage of a panel of wild-type Drosophila melanogaster that lack resistance to P-element DNA transposons, we identified a small region of the genome that influences tolerance of P-element activity. We further demonstrate that a gene within that region, bruno, strongly influences the negative effects of P-element mobilization on the fly. When bruno dosage is reduced, the fertility of females carrying mobile P-elements is enhanced. The bruno locus encodes a protein with no known role in TE regulation but multiple well-characterized functions in oogenesis. We propose that bruno function reduces tolerance of the developing oocyte to DNA damage that is caused by P-elements.
| Transposable elements (TEs) are omnipresent and abundant constituents of eukaryotic genomes, comprising up to 80% of genomic DNA in some lineages (reviewed in [1]). The evolutionary footprint of TEs is extensive, including dramatic genome size expansions [2,3], acquisition of new regulatory networks [4,5], structural mutations [6], novel genes [7–9], and adaptive insertions [10–12]. However, the charismatic and occasionally beneficial impact of TEs over evolutionary time masks their fundamental identity as intragenomic parasites and mutagens. In addition to causing deleterious mutations [13,14], TEs can exert lethal, genotoxic effects on host cells by producing abundant double-stranded breaks (DSBs) during insertion and excision [15,16]. TEs are therefore intragenomic parasites.
Host developmental and evolutionary responses to parasites, pathogens, and herbivores are broadly delineated into two categories: resistance and tolerance (reviewed in [17,18]). Mechanisms of resistance prevent—or limit the spread of—infection or herbivory. By contrast, mechanisms of tolerance do not affect propagation but rather limit the fitness costs to the host. With respect to TEs, resistance by eukaryotic genomes is enacted by small-RNA-mediated silencing pathways [19] and Kruppel-associated box zinc-finger proteins (KRAB-ZFPs)[20], which regulate the transcription and subsequent transposition of endogenous TEs. However, it remains unknown whether genomes can also evolve tolerance of TEs by altering how host cells are affected by TE activity. Tolerance therefore represents a wholly unexplored arena of the evolutionary dynamics between TEs and their hosts. Germline tolerance of TEs is predicted to be of particular importance because of the significance of this cell lineage in ensuring the vertical transmission of the parasite and the reproductive fitness of the host.
The absence of research on tolerance is at least partially due to the primacy of resistance: endogenous TEs are overwhelmingly repressed by host factors in both germline and somatic tissues [19,21,22]. However, the invasion of the host genome by a novel TE family, which happens recurrently over evolutionary timescales (reviewed in [23]), provides a window of opportunity through which tolerance could be viewed, both empirically and by natural selection. The absence of evolved resistance in the host against a new invader could reveal differential responses of germline cells to unrestricted transposition. A classic example of genome invasion by a novel TE is provided by P-elements, DNA transposons that have recently colonized two Drosophila species. P-elements first appeared in genomes of D. melanogaster around 1950 (reviewed in [24]) and later colonized its sister species D. simulans around 2006 [25,26]. Particularly for D. melanogaster, a large number of naïve strains collected prior to P-element invasion are preserved in stock centers and laboratories, providing a potential record of ancestral genetic variation in tolerance [27,28]. Furthermore, 15 of these naïve strains were recently used to develop the Drosophila Synthetic Population Resource (DSPR), a panel of highly recombinant inbred lines (RILs) that serve as a powerful tool kit for discovering the natural genetic variants influencing quantitative traits [29–31].
Here, we harness the mapping power of the DSPR to screen for phenotypic and genetic variation in the tolerance of the D. melanogaster female germline to unrestricted P-element activity. We developed a novel screen for phenotypic variation in host tolerance by taking advantage of the classic genetic phenomenon of hybrid dysgenesis, in which TE families that are inherited exclusively paternally can induce a sterility syndrome in offspring because of an absence of complementary maternally transmitted regulatory small RNAs (Piwi-interacting RNA [piRNAs], reviewed in [24,32,33]). The dysgenesis syndrome induced by P-elements in the female germline is particularly severe and can be associated with a complete loss of germline cells [15,34,35]. P-element hybrid dysgenesis is directly related to genotoxic stress, as apoptosis is observed in early oogenesis in dysgenic females [15], and the DNA damage response factors checkpoint kinase 2 and tumor protein 53 (p53) act as genetic modifiers of germline loss [35]. Variation in the sensitivity of the DNA damage response to DSBs therefore represents one potential cellular mechanism for tolerance.
By phenotyping the germline development of >32,000 dysgenic female offspring of RIL mothers, we uncovered substantial heritable variation in female germline tolerance of P-element activity. We furthermore mapped this variation to a small 230-kb quantitative trait locus (QTL) on the second chromosome and associated it with the differential expression of bruno, a well-studied developmental regulator of oogenesis with no known function in TE repression or DNA damage response [36–39]. We further demonstrate that bruno loss-of-function alleles act as dominant suppressors of germline loss, and relate these effects to the retention of germline stem cells (GSCs) in dysgenic females. Our findings represent the first demonstration of natural variation in TE tolerance in any organism. They further implicate regulators of gametogenesis, such as bruno, as a source of tolerant variants that could be beneficial when new TEs invade the host.
We first sought to uncover phenotypic variation in germline tolerance of P-element activity among a panel of highly recombinant RILs derived from eight founder genomes [29]. To quantify tolerance, we performed dysgenic crosses between RIL females and males from the P-element containing strain Harwich (Fig 1A). Harwich is a strong paternal inducer of hybrid dysgenesis, producing filial 1 (F1) females with 100% atrophied ovaries in crosses with naïve females at the restrictive temperature of 29 °C [28]. We therefore performed our crosses at 25 °C, a partially permissive temperature at which intermediate levels of ovarian atrophy are observed [40,41]. Because P-element dysgenic females may recover their fertility as they age, through zygotic production of P-element–derived piRNAs [42], we assayed both 3-day-old and 21-day-old F1 female offspring from each RIL. In total, we documented the incidence of atrophied ovaries among 17,150 3-day-old and 15,039 21-day-old F1 female offspring, and estimated the proportion of F1 atrophy within broods of ≥20 3-day-old and 21-day-old offspring from 592 and 492 RILs, respectively (Fig 1B and 1C). Notably, because we phenotyped F1 offspring, our phenotypic variation will be determined only by variants in which one of the RIL alleles is at least partially dominant to the Harwich allele, or maternal effects that reflect only the RIL genotype.
We observed continuous variation in the proportion of F1 atrophy among broods of both 3-day-old and 21-day-old offspring of different RIL genotypes, capturing the full range of proportional values from 0 to 1 (Fig 1B and 1C). After accounting for the effects of experimenter and experimental block, the incidence of ovarian atrophy is strongly correlated among 427 RILs for which we sampled broods of both 3-day-old and 21-day-old F1 females (Pearson’s R = 0.56, p > 10−15, Fig 1D). Because broods of different age classes were sampled from separate crosses and experimental blocks, this correlation strongly implies that phenotypic differences are explained by the maternal genotype. Indeed, based on the F1 atrophy proportions measured in broods of different ages, we estimate that the broad sense heritability of F1 ovarian atrophy among the RIL offspring was 40.35% (see Materials and methods). Furthermore, we saw greater reproducibility across a small sample of 14 RILs, for which we phenotyped two independent 21-day broods (Pearson’s R = 0.97, p > 10−8), suggesting even higher heritability among offspring of the same age class.
Despite the previous observation of developmental recovery from hybrid dysgenesis [42], the relationship between age and the proportion of F1 atrophy is only marginally significant in our data (F1,1037 = 3.57, p = 0.058). Furthermore, 21-day-old dysgenic females exhibited only a 0.63% decrease in the proportion of F1 atrophy when compared to 3-day-old females, indicating that the overall effect of age in our crosses was very modest. Finally, we did not observe a group of RILs in which ovarian atrophy is much more common among 3-day-old as compared with 21-day-old F1 females (Fig 1D), as would be predicted if there were genetic variation for developmental recovery across the RIL panel. The absence of developmental recovery in our experiments could reflect differences in developmental temperature between our two studies (22 °C in [42] and 25 °C here). Alternatively, the causative variant that allows for developmental recovery could be absent from the founder RILs.
To identify the genomic regions that harbor causative genetic variation in tolerance, we performed a QTL analysis using the published RIL genotypes [29]. In these data, the founder allele (A1–A8) carried by each RIL is inferred probabilistically for 10-kb windows along the euchromatic regions of the major autosomes 2 and 3, and the X chromosome [29]. The fourth chromosome is ignored because the absence of recombination makes it uninformative for QTL mapping (reviewed in [43]).
Consistent with the strongly correlated phenotypes of 3-day-old and 21-day-old offspring (Fig 1D), we identified a single major effect QTL associated with phenotypic variation at both developmental time points (Fig 2A). Additionally, the Δ2-LOD drop confidence intervals (Δ2-LOD CIs) of the logarithm of the odds (LOD) peak from each analysis are both narrow (<300 kb) and highly overlapping (Table 1). The peak explains 14.2% and 14.8% of variation in ovarian atrophy among 3-day-old and 21-day-old F1 females, indicating it is a major determinant of heritable variation. Multiple minor peaks close to the centromere on chromosome 3 left (3L) may represent another source of heritable variation in 3-day-old females.
To further narrow the location of genetic variation in tolerance, we took advantage of the striking concordance in QTL mapping for the 3-day-old and 21-day-old data sets and performed a combined analysis, including all 660 RILs whose F1 offspring were sampled at either developmental time point. F1 female age was included as a covariate (see Materials and methods). From this analysis we obtained a final Δ2-LOD CI for the major QTL peak, which corresponds to a 230-kb genomic region containing 18 transcribed genes—15 protein-coding and 3 noncoding (Fig 2B). Simulation testing of the statistical properties of the DSPR indicates that a causative variant explaining 10% of phenotypic variation lies within the Δ2-LOD drop CI 96% of time for sample sizes of 600 RILs [30]. Furthermore, overestimation of variance explained by a QTL (i.e., the Beavis effect) is rare in DSPR studies sampling greater than 500 RILs, particularly for variants explaining ≥10% of variation [48]. Therefore, given our sample (660 RILs) and effect (about 14%) sizes, the Δ2-LOD CI we infer for our major peak should be conservative. Indeed, Bayesian credible intervals, an alternate approach for identifying QTL windows [44], are even narrower than those estimated by Δ2-LOD CI (Table 1).
Of the 18 genes within the QTL peak, only bruno and crooked are highly expressed in the D. melanogster ovary [47]. While bruno is a translational repressor whose major essential role is in oogenesis [37,49,50], crooked is a more broadly expressed component of septate junctions, which is essential for viability [51]. The LOD peak resides within the 138-kb bruno locus, which, in addition to its function, makes it the strongest candidate for the source of causative variation.
We next sought to partition founder alleles at the QTL peak into phenotypic classes, in order to better understand the complexity of causative genetic variation. First, we identified all sampled RILs whose genotype was probabilistically assigned (p > 0.95) to a single founder (A1–A8) at the LOD peak, and estimated the phenotypic effect associated with each founder allele (Fig 3A and 3B). We then used stepwise regression (see Materials and methods) to identify the minimum number of allelic classes required to explain phenotypic variation associated with the founder alleles. For both age classes, we found strong evidence for two allelic classes, one sensitive and one tolerant, which were sufficient to explain phenotypic variation in tolerance. This implies that the major QTL peak could correspond to a single segregating genetic variant. Furthermore, with the exception of founder A8, founder alleles were assigned to the same allelic class for both age cohorts, revealing that allelic behavior is highly biologically reproducible.
To further study phenotypic differences between tolerant and sensitive alleles, we identified three pairs of background-matched RILs, which exhibited a tolerant (founder A4) or a sensitive (founder A5) haplotype across the QTL window but otherwise shared a maximal number of alleles from the same founder across the remainder of the genome. Consistent with our QTL mapping, these RIL pairs differed dramatically in the incidence of ovarian atrophy they displayed in crosses with Harwich males (Fig 4A). While we did not detect a significant effect of genetic background (drop in deviance = 3.57, df = 2, p = 0.17), the QTL haplotype was strongly associated with the incidence of ovarian atrophy (drop in deviance = 52.01, df = 1, p = 5.36 × 10−13). RILs carrying the tolerant haplotype exhibited 39% less F1 ovarian atrophy than those carrying the sensitive haplotype.
To determine whether reduced ovarian atrophy conferred by tolerant alleles increases female reproductive fitness, we examined the presence and number of filial 2 (F2) adults produced by young (0–5-day-old) F1 female offspring of tolerant and sensitive dysgenic crosses (Fig 4B). The proportion of F1 sterility was somewhat lower than the proportion of F1 atrophy for the same dysgenic cross (Fig 4A versus 4B), consistent with loss of germline cells in early adult stages. Equivalent to ovarian atrophy, there was no significant effect of genetic background (drop in deviance = 5.26, df = 2, p = 0.07), but the QTL haplotype was strongly associated with F1 sterility (drop in deviance = 65.787, df = 1, p = 5.55 × 10−16). F1 females carrying the tolerant haplotype exhibited a 54% reduction in sterility as compared with those carrying the sensitive haplotype. Interestingly, when we examine the number of F2 offspring produced by fertile F1 females from resistant and tolerant crosses (Fig 4C), we detect dramatic effects of genetic background (F2,110 = 29.05, p = 7.48 × 10−11) but no significant effect of the tolerant allele (F1,110 = 1.01, p = 0.31). Therefore, while tolerant alleles enhance female reproductive fitness by increasing the odds of fertility, other genetic factors likely determine the number of offspring produced by those fertile females.
In light of the simple biallelic behavior of our phenotype, we sought to identify polymorphisms within the founder strains whose genotypic differences matched their phenotypic classifications (i.e., “in-phase” polymorphisms [29,31], Fig 3C). We excluded A8 from these analyses because of the ambiguity of its allelic class. In total, we identified 36 in-phase single nucleotide polymorphisms (SNPs), which potentially affect the function of only seven transcribed genes in the QTL interval ([46] S1 Table, Figs 2B and 3C). We did not identify any in-phase, segregating TE insertions, although a recent reassembly of the founder A4 genome based on long single-molecule real-time sequencing reads suggests many TE insertions remain unannotated [52]. Focusing on bruno and crooked, the two genes in the QTL window that are highly expressed in the ovary [47], 22 of the in-phase SNPs are within bruno introns, while none are found in the gene body or upstream of crooked. Furthermore, none of the 36 in-phase SNPs are nonsynonymous, implying a regulatory difference between the tolerant and sensitive alleles.
To determine if tolerance is associated with bruno regulation, we compared ovarian expression of bruno in young (3-day-old) females from our background-matched RIL pairs carrying a tolerant or sensitive haplotype across the QTL locus. While we observed only modest effects of genetic background on bruno expression (likelihood ratio test = 6.57, df = 2, p = 0.04), we observed dramatic effects of founder haplotype at the QTL window (likelihood ratio test = 29.47, df = 1, p = 5.67 × 10−8). Across genetic backgrounds, tolerant alleles were associated with a 20% reduction in bruno expression (95% CI: 14%–26%), suggesting that bruno function reduces germline tolerance of P-element activity.
Given the differences in bruno expression between sensitive and tolerant alleles, we wondered whether bruno loss-of-function alleles affect the atrophy phenotype. For comparison, we also considered available alleles from three other ovary-expressed genes that are located within the Δ2-LOD CI of the 21-day-old or 3-day-old female analyses, but not in the combined analysis: ced-12, Rab6, and Threonyl-tRNA synthetase (ThrRS). We reasoned that, because the causative variant is almost certainly not recessive, being found only in the maternal RIL genotype, mutant alleles might also exhibit non-recessive effects. We therefore used balanced heterozygous females as mothers in dysgenic crosses with Harwich males and compared the incidence of F1 ovarian atrophy among their 3–7-day-old F1 females (mutant/+ versus balancer/+). Strikingly, while ced-12, Rab6, and ThrRS alleles had no effect on the incidence of ovarian atrophy (Fig 5A), two different bruno alleles (brunoRM and brunoQB) acted as dominant suppressors (Fig 5B). In contrast to their balancer control siblings, who exhibited 64%–75% ovarian atrophy, brunoRM/+ and brunoQB/+ offspring exhibited only 13% and 20% atrophy, respectively—a dramatic reduction. We further observed that an independently derived bruno deficiency [53,54] suppresses ovarian atrophy to a similar degree (Fig 5B), indicating these effects cannot be attributed to a shared linked variant on the brunoRM and brunoQB chromosomes [49].
Notably, the non-recessive, fertility-enhancing effects of bruno loss-of-function alleles in dysgenic females contrasts with their effects on the fertility of non-dysgenic females, in which they act as recessive female steriles [49]. Our observations therefore suggest that a novel phenotype of bruno alleles is revealed by the dysgenic female germline. Furthermore, our observation that reduced bruno dosage suppresses ovarian atrophy is fully consistent with our observation that tolerant alleles are associated with reduced bruno expression (Fig 4D).
bruno is a translational regulator with three known functions in D. melanogaster oogenesis. At the start of oogenesis in the ovarian substructure called the germaria, bruno is required to promote the differentiation of cystoblasts (CBs), the immediate daughters of GSCs [37,55]. In mid-oogenesis, Bruno protein blocks vitellogenesis if not properly sequestered by its mRNA target oskar [38,39]. Finally, in late oogenesis, Bruno repression of oskar translation is required to establish dorsoventral (DV) patterning in the subsequent embryo [36]. This final role of bruno affects only the morphology of egg chambers, but not their production, suggesting that it cannot account for bruno’s effects in dysgenic germlines. We therefore focused on bruno’s earlier roles in GSC differentiation and vitellogenesis, which are distinguishable by their dependency on oskar mRNA. While bruno’s functions in GSC differentiation are independent of oskar mRNA [36,37,56], bruno and oskar’s impact on vitellogenesis are interdependent, because of the requirement for oskar mRNA to sequester Bruno protein [39].
To determine if the effect of bruno alleles on hybrid dysgenesis are independent of oskar mRNA, we examined whether two oskar mRNA null alleles, osk° and oskA87, as well as an oskar deficiency, affected the incidence of ovarian atrophy when compared with a balancer control (Fig 5B). We observed that osk° and deficiency on chromosome 3R over oskar (Df(3R)osk) exhibited no effect on the atrophy phenotype, while oskA87 was associated with only a marginal increase in ovarian atrophy (p = 0.07). If bruno suppression of ovarian atrophy reflects reduced sequestration by oskar mRNA in the dysgenic female germline, atrophy should be enhanced by oskar mRNA mutants [57].
Comparison of single and double heterozygotes of bruno and oskar also do not strongly suggest that bruno suppression is dependent on oskar mRNA dosage (Fig 5B). In comparisons involving two separate balancer third chromosomes, Df(2L)bru/+; oskA87/+; double heterozygotes did not differ from their single Df(2L)bru/+;balancer/+ heterozygous siblings with respect to ovarian atrophy. A second double heterozygote Df(2L)bru/+;Df(3R)osk/+; was associated with significantly increased atrophy when compared with the single heterozygote (Df(2L)bru/+; balancer/+). However, because this behavior was unique to the deficiency chromosome and was not also exhibited by the oskA87 mRNA null mutant that specifically eliminates oskar function [38], we suspect it is a synthetic consequence of hemizygosity in both deficiency regions, rather than evidence for a genetic interaction between oskar and bruno, with respect to hybrid dysgenesis. Consistent with this, we also did not detect any Bruno mislocalization in the developing egg chambers of wild-type dysgenic females (S1 Fig), nor did we see any evidence of an arrest in mid-stage oogenesis in wild-type dysgenic ovaries, as occurs when Bruno is not sequestered by oskar mRNA [38,39].
To evaluate whether bruno suppression of ovarian atrophy could be explained by its oskar-independent role in GSC differentiation [55,58], we directly examined and compared GSCs between brunoQB/+ and CyO/+ dysgenic ovaries (Fig 5C and 5D). While we did not observe any direct evidence of delayed GSC differentiation in brunoQB/+ germaria, we did observe that ovarioles containing developing egg chambers overwhelmingly retained all oogenic stages, including GSCs (Fig 5D). In contrast, atrophied ovaries lacked any developing egg chambers including GSCs (Fig 5C). These observations are consistent with recent evidence that GSC retention is a key determinant of P-element–induced ovarian atrophy [35]. They further suggest that reduced bruno signaling for differentiation may stabilize GSCs in their niche, allowing them to be retained despite the genotoxic effect of P-elements.
While the evolution of TE resistance through small-RNA-mediated silencing is a topic of immense research interest, the existence and evolution of tolerance factors that may reduce the fitness costs of TEs on the host remain undocumented. By opportunistically employing a panel of genotyped RILs, which uniformly lack small-RNA-mediated silencing of the recently invaded P-element, we have here uncovered the first example of segregating variation in host-TE tolerance in any organism. The natural variation in tolerance that we uncovered is unlikely to be exceptional. Population A RILs were generated from only eight genotypes, sampling but a small subset of alleles that were present in the ancestral population. Furthermore, major QTL peak that we identified here in Population A explains only about 35% of the heritable variation in tolerance. Therefore, other segregating variants in the Population A RILs must also affect female germline response to P-element activity. Our inability to map these variants likely reflects the fact that they are rare, exhibit small effects, or both [30]. Finally, because our phenotyping scheme involved crossing genetically variable RILs to the same paternal strain, zygotic recessive alleles that are not found in the paternal Harwich genotype would remain undetected.
While differences in tolerance may be masked by resistance after small-RNA-mediated silencing evolves, our study reveals that in the absence of silencing, tolerance can be a major determinant of fitness. While the major peak we identified is modest in its functional effects, explaining about 14% of variation in ovarian atrophy, variation in fertility of this scale would be dramatic in the eyes of natural selection. Segregating tolerance alleles, such as the one we have detected here, could therefore be subject to strong positive selection during genome invasion by a novel TE. Tolerance may therefore be an important feature of the host evolutionary response to invading TEs. Indeed, the correlation between P-element dosage and the severity of hybrid dysgenesis is poor at best, causing many to suggest that other genetic factors, such as tolerance alleles, may also be important determinants of the dysgenic phenotype [59–61]. Furthermore, the hybrid dysgenesis induced by recent collections of D. melanogster tends to be mild when compared to collections from the 1970s and 1980s, providing circumstantial evidence that tolerance factors may have increased in frequency over time [61]. Once the causative variant responsible for the tolerance phenotype we uncovered here is identified, we will be poised to ask whether its increase in frequency has enhanced tolerance in extant populations.
Based on its high expression in the Drosophila female ovary [47], the presence of 22 SNPs that are in phase with founder QTL alleles, its differential expression between tolerant and sensitive alleles, and the dominant suppressive effect of classical loss-of-function alleles on dysgenic ovarian atrophy, bruno is a very strong candidate for the source of causative variation in P-element tolerance that we mapped on chromosome 2L. Identifying the causative variant(s) within the very large (138 kb) bruno locus and understanding how its altered function relates to hybrid dysgenesis present exciting challenges for future work. On the surface, it is not obvious how bruno function could be related to P-element activity. Because Bruno physically interacts with the piRNA pathway component Vasa [62] and localizes to nuage [63], the multifunctional germline organelle in which piRNA biogenesis occurs (reviewed in [64,65]), a straightforward explanation is that bruno function is unrelated to tolerance but rather suppresses piRNA-mediated resistance of P-elements. However, resistance suppression is inconsistent with several important aspects of piRNA biology and bruno function. First, piRNA-mediated silencing of P-elements is short-circuited in the absence of complementary maternally deposited piRNAs (absent from the RILs), and P-element–derived piRNAs are exceptionally rare in the ovaries of young dysgenic females [42,66]. Thus, the dramatic suppression of ovarian atrophy exhibited by bruno alleles in young dysgenic females (Fig 5B) is developmentally inconsistent with piRNA-mediated silencing, which can occur only in older female offspring of dysgenic crosses [42]. Additionally, germline knock-down of bruno does not significantly affect TE expression and, if anything, is associated with increased expression of some TE families [67]. If bruno suppressed piRNA silencing, reduced TE expression would be predicted upon knock-down.
We propose that our results are best explained by bruno’s function in promoting GSC differentiation [37,55], which could determine the tolerance of GSCs to DNA damage resulting from P-activity. GSC maintenance is dependent on a balance between self-renewal and differentiation (reviewed in [68]), and is disrupted by the presence of DNA damage, leading to GSC loss [69]. We recently have discovered that the DNA damage response factor p53 is ectopically activated in the GSCs and CBs of dysgenic germlines [35], which explains why GSCs are frequently absent from dysgenic ovaries (Fig 5C, [15,34,35]). bruno loss-of-function alleles could therefore stabilize damaged GSCs in their niche in dysgenic germaria by reducing signals for differentiation. Indeed, loss-of-function mutations in two other GSC differentiation factors, bag of marbles and benign gonial cell neoplasm, have been associated with enhanced retention of GSCs in the niche of non-dysgenic germaria [70]. This model is fully consistent with our observation that bruno suppression of ovarian atrophy is accompanied by a rescue of oogenesis at all stages, including enhanced maintenance of GSCs (Fig 5C).
Our observations with bruno suggest an unexpected and novel role for developmental regulators of gametogenesis as determinants of germline tolerance of transposition. Interestingly, multiple regulators of female GSC maintenance and differentiation in D. melanogaster exhibit recent or ongoing signatures of positive selection [71–73]. Tolerance to the selfish bacterial endosymbiont Wolbachia has already been implicated in driving some of this adaptive evolution [74]. The fact that bruno alleles act as strong repressors of P-element hybrid dysgenesis suggests that another class of parasites, TEs, may also contribute to the adaptive evolution of stem cell determinants.
RILs from Population A were generously provided by Stuart Macdonald. Harwich (#4264), Ced-12c06760/CyO (#17781), Rab6GE13031/CyO (#26898), Rab6D23D/CyO (#5821), and ThrRSK04203/CyO (#10539) were obtained from the Bloomington Drosophila stock center. Harwich was sibmated for one generation to increase homozygosity. Bruno and oskar mutants and deficiencies, in single and double heterozygous combinations, were generously provided by Paul MacDonald. Canton-S was obtained from Richard Meisel. All flies were maintained in standard cornmeal media. All experimental flies were maintained at 25 °C.
Virgin RIL females were crossed to Harwich males and flipped onto fresh food every 3–5 days. Resulting F1 offspring were maintained for 3 days or 21 days, at which point their ovaries were examined using a squash prep [60]. Twenty-one-day-old females were transferred onto new food every 5 days as they aged to avoid bacterial growth. For the squash prep, individual females were squashed in a food-dye solution allowed to incubate for ≥5 minutes. After incubation, the slide was examined for the presence of stage 14, chorionated egg chambers, which preferentially absorb the dye. In the interest of throughput, we assayed F1 females for the presence or absence of mature egg chambers: females who produced ≥1 egg chambers were scored as having non-atrophied ovaries, and females producing 0 egg chambers were scored as having atrophied ovaries. A phenotyping schematic is provided in Fig 1A.
Crosses and phenotyping were performed for 656 RILs across 24 experimental blocks for 3-day-old F1 females and 606 RILs across 21 experimental blocks for 21-day-old F1 females. If fewer than 20 F1 offspring were phenotyped for a given cross, it was discarded and repeated, if possible. In total, we phenotyped ≥20 3-day-old and 21-day-old F1 female offspring for 592 RILs and 492 RILs, respectively, and 660 RILs were assayed for at least one of the age groups.
For age-class-specific QTL mapping (3-day- and 21-day-old), the arcsine transformed proportion of F1 females (S2 Fig) with atrophied ovaries produced by each RIL (S1 and S2 Data) was used as the response variable in a random effects multiple regression model that included experimental block and undergraduate experimenter.
For the combined analysis of both age classes, we used the full set of arcsine transformed proportions, and accounted for female age as an additional fixed effect in the regression model.
All models were fit using the lmer function from the lme4 package [75] and are described in S2 Table.
The raw residuals of the regression models above were used as the phenotypic response for QTL analysis (S3–S5 Data), implemented with the DSPRqtl package [29] in R 3.02 [76]. The output yields a LOD score for the observed association between phenotype and genotype at 11,768 10-kb intervals along the euchromatic arms of the X, second, and third chromosomes. The LOD significance threshold was determined from 1,000 permutations of the observed data, and the confidence interval around each LOD peak was identified by a difference of −2 from the LOD peak position (Δ2-LOD), as well as a Bayesian credible interval [44].
Maternal genotype was added as a random effect to the models above, in order to determine the genetic variance in the phenotype (VG). VG was obtained by extracting the variance component for maternal genotype using the VarCorr() function from the nlme package [77]. Broad sense heritability (H2) was then the estimated proportion of overall variance (VG/VP).
To estimate the phenotypic effect of each founder at the QTL peak, we considered the residual phenotype for each used in QTL mapping and then determined the founder allele carried by the RIL at the LOD peak position [29]. RILs whose genotype at the LOD peak could not be assigned to a single founder with >0.95 probability were discarded.
Founder alleles were phased into phenotypic classes by identifying the minimal number of groups required to describe phenotypic variation associated with the QTL peak [29]. Briefly, founder alleles were sorted based on their average estimated phenotypic effect, which was provided by the sampled RILs. Linear models containing all possible sequential partitions of founder alleles were then fit and compared to a null model in which all founder alleles are in a single partition, using an extra-sum-of-squares F-test. The two-partition model with the highest F-statistic was retained and fixed only if it provided a significantly better fit (p < 10−4) than the null model. The two partitions of founder haplotypes were then fixed, and all possible three-partition models were explored. This process was continued until the model fit could not be improved.
Founders were assigned a “hard” genotype for all annotated TEs [78] and SNPs [29] in the QTL window if their genotype probability for a given allele was greater than 0.95 [29]. We then looked for alternate alleles (SNPs and TEs) that were in phase with our inferred allelic classes [29,31]: the sensitive class (A3 and A5) and the tolerant class (A1, A2, A4, A6, and A7). A8 was excluded because its assignment to the sensitive or tolerant class differed between the data sets from 3-day-old and 21-day-old females.
To identify RILs containing either the A4 (“tolerant”) or A5 (“sensitive”) haplotypes for the QTL window, we took advantage of the published, hidden Markov model–inferred genotypes for the Population A RIL panel [29]. We first identified RILs that carried a contiguous A4 or A5 haplotype for the Δ2-LOD confidence interval for the combined analysis with a genotype probability of greater than 0.95 (Table 1). Then, for all possible RIL pairs (A4 and A5), we calculated the number of 10-kb genomic windows for which they carried the same RIL haplotype, also with a genotype probability of greater than 0.95. We selected three pairs of background-matched RILs, which carry the same founder haplotype for 67% (11374 and 11120), 64% (11131 and 11200), and 60% (11435 and 11343) of genomic windows but alternate haplotypes for the QTL window.
Virgin female offspring of dysgenic crosses between tolerant (11120, 11200, 11343) and sensitive (11374, 11131, 11435) RILs and Harwich males were collected daily and placed individually in a vial with two ywF10 males. Females were allowed to mate and oviposit for 5 days, and adults were discarded when the females reached 6 days of age. The presence and number of F2 offspring were quantified for each dysgenic female. The effects of genetic background and QTL haplotype on the presence and number of F1 offspring were assessed by logistic and linear regression models, respectively. Models were fit using the glm (logistic) and lm (linear) functions in R 3.02 [76].
Ovaries were dissected from young, 3-day-old females from tolerant (11120, 11200, 11343) and sensitive (11374, 11131, 11435) RILs and homogenized in TRI-reagent (Sigma-Aldrich). RNA was extracted according to manufacturer instructions. Purified RNA was treated with DNAse, reverse transcribed using oligo(dT)15 primers and M-MLV RNAseH−, and then treated with RNAse H, according to manufacturer instructions (Promega). Synthesized cDNA was diluted 1:125 for qRT-PCR.
Abundance of bruno and rpl32 transcript was estimated using SYBR green PCR mastermix (Applied Biosystems) according to manufacturer instructions. Three biological replicates were evaluated for each genotype, with three technical measurements for each replicate, for a total of nine measurements of each genotype. Bruno expression was estimated relative to rpl32 for each replicate, according to a five-point standard curve. Primers were as follows: bruno-F: 5′-CCCAGGATGCTTTGCATAAT-3′, bruno-R: 5′- ACGTCGTTCTCGTTCAGCTT-3′, rpl32-F: CCGCTTCAAGGGACAGTATC, and rpl32-F: GACAATCTCCTTGCGCTTCT. The relationship between genetic background and QTL haplotype with bruno expression was evaluated with mixed-effects linear regression, accounting for the biological replicate as a random effect. The regression model was fit with the lme4 package [75] in R 3.02 [76].
Single and double heterozygote mutant virgin females (mutant/balancer) were crossed to Harwich males at 25 °C. Because the vast majority of Drosophila lab stocks are P-element–free, with the exception of any P-element–derived transgenes, these crosses are dysgenic. Resulting F1 dysgenic female offspring were collected and aged at 25 °C for 3–7 days, when their ovaries were assayed using the squash prep described above [60]. The incidence of ovarian atrophy was then compared between mutant/+ and balancer/+ siblings from the same cross.
Ovaries from 3–7-day-old female offspring of dysgenic crosses were dissected and immediately fixed with 4% EM-grade methanol-free paraformaldehyde (Thermo Scientific). Ovaries were washed with 0.1% Triton X-100 in PBS and blocked with 5% goat serum albumin (Sigma-Aldrich). Primary antibody concentrations were as follows: anti-Hts 1B1 1:4 (DSHB [79]), anti-Vasa 1:40 (DSHB), anti-Bruno 1:1,000 (provided by Paul MacDonald [50]), and anti-Orb 4H8 and 6H4 1:20 (DSHB [50]). Secondary antibody concentrations were 1:500.
Ovaries were visualized with an SP8 Upright Confocal DM6000 CFS (Leica) Microscope, outfitted with a 60× oil immersion lens. Images were collected using an EM-CCD camera (Hamamatsu) and LAS-AF software (Leica).
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10.1371/journal.pcbi.1003745 | Hydrophobin Film Structure for HFBI and HFBII and Mechanism for Accelerated Film Formation | Hydrophobins represent an important group of proteins from both a biological and nanotechnological standpoint. They are the means through which filamentous fungi affect their environment to promote growth, and their properties at interfaces have resulted in numerous applications. In our study we have combined protein docking, molecular dynamics simulation, and electron cryo-microscopy to gain atomistic level insight into the surface structure of films composed of two class II hydrophobins: HFBI and HFBII produced by Trichoderma reesei. Together our results suggest a unit cell composed of six proteins; however, our computational results suggest P6 symmetry, while our experimental results show P3 symmetry with a unit cell size of 56 Å. Our computational results indicate the possibility of an alternate ordering with a three protein unit cell with P3 symmetry and a smaller unit cell size, and we have used a Monte Carlo simulation of a spin model representing the hydrophobin film to show how this alternate metastable structure may play a role in increasing the rate of surface coverage by hydrophobin films, possibly indicating a mechanism of more general significance to both biology and nanotechnology.
| Filamentous fungi release a specific type of protein, belonging to a protein family known as “hydrophobins” into their environment to control interfaces in a fashion that promotes growth. Such protein coatings are the mechanism that allows for the mycelia to grow out of the water and into the air. When these hydrophobins form films at the air-water interface and on the surface of solid objects immersed in water, they impart properties to those surfaces that has led to their use in a wide range of industrial applications. Of particular interest is the properties they impart to air liquid interfaces, and as a mechanism to bring protective materials to coat nanoparticles in nanotechnology applications. A more detailed knowledge of the structure of these surfaces will allow for augmentation of their function that is possible through genetic engineering of the hydrophobins themselves. In this study we have combined computational and experimental methods to develop atomistic level insight into the structure of this surface for two important hydrophobins: HFBI and HFBII of Trichoderma reesei. In addition to insight into the surface structure, we have uncovered an intriguing possible new mechanism for film formation, which may explain some of the striking properties of hydrophobin films, and could be extended to a more general mechanism.
| Hydrophobins are a group of proteins produced by filamentous fungi [1]–[4]. They assemble at surfaces, and perform their function through the alteration of these surfaces. Functions performed by hydrophobins through this mechanism include the lowering of the surface tension of water, and adding a hydrophobic coating to the mycelia to allow for aerial growth [5], adhesion to surfaces [6], and coating of a variety of fungal structures [7], [8]. Hydrophobins can be seen as a mechanism through which the fungi fine tune the properties of interfaces in their environment, resulting in their invasive and adaptive behaviour. When hydrophobins locate to surfaces they are known to form assemblies with long range order [9]–[11]. In addition, the presence of hydrophobin coatings on interfaces is known to affect the properties of the interfaces in a highly specific fashion, beyond merely decreasing surface tension. In particular it has been observed that hydrophobin films at the air-water interface have an elasticity orders of magnitude higher than that observed for other surfactants [12]. Surface-adhered hydrophobin films also display additional unique characteristics [13].
Hydrophobins are divided into two classes, class I and class II hydrophobins. For class I, highly characteristic structures, named rodlets [14], are formed. An example of a class I hydrophobin is the hydrophobin EAS [15], [16]. Class II hydrophobins do not show rodlet structures, instead, they are amphiphilic and form 2D crystalline films on the air-water interface, as confirmed through grazing incidence small angle X-ray scattering (GISAXS) [9]–[11]. In both cases, the unique macroscopic properties observed in hydrophobin films will arise from cooperative effects in the stabilisation of the film that result from the interactions between the interlocking proteins in the structured surface network that they form.
In addition to their role as an adaptive strategy of the fungi that produce them, the interfacial assemblies of hydrophobins have led to numerous industrial applications [17]. Examples include foams [18], protein immobilisation [19], emulsification [20], and dispersion of insoluble compounds [21], [22]. Most of these applications rely on the unique properties of the interfacial films that hydrophobins form. It is particularly noteworthy that the above mentioned exceptional properties can be found on a wide variety of different interfaces, including liquid-liquid and gas-liquid interfaces.
Two hydrophobin proteins, that are of particular interest in relation to their possible industrial applications, are produced by Trichoderma reesei, known as HFBI and HFBII, that belong to the class II family of hydrophobins. Their property of forming amphiphilic 2D crystalline structures at the air-water interface, rather than rodlet structures, allows for these structures to be transferred to hydrophobic surfaces where they play a role in bringing macromolecules, to which the proteins are attached, to these surfaces, for example hydrophobic nanoparticles [22]. Some progress has been made in the determination of the structure and mechanisms involved in the formation of HFBI and HFBII films. The first high resolution structure of a hydrophobin, determined through x-ray crystallography, was obtained for HFBII of T. reesei [23]. It was found that the protein structure is crosslinked by disulphide bridges and has a diameter of approximately 2 nm. A clearly distinguishable patch on one side of the protein consists of only aliphatic amino acid side chains. This results in an amphiphilic structure with an exposed and flat hydrophobic face. A high resolution crystal structure has now also been determined for HFBI [24], showing a similar structure. Further insight into the structure of films of HFBI and HFBII has been obtained from GISAXS [9]–[11] and Langmuir-Blodget films [9], indicating a triangular lattice symmetry [9] with unit cell sizes of 55 and 56 Å respectively.
We have combined cryo-EM measurement, protein-protein docking and molecular dynamics simulation, to obtain a detailed atomic resolution picture of the assembled structure of hydrophobin films at the air-water interface. We have studied the structures of HFBI and HFBII of T. reesei for which high resolution structures of both are available [23]–[27]. Our protein-protein docking results indicate a unit cell composed of six proteins with a structure very close to a P6 2D point group symmetry class [28]. Electron cryomicroscopy results, however, indicate a structure with only P3 symmetry - possibly a structure connecting two air-water interfaces in a thin film. In addition, through the protein-protein docking results we have found a possible metastable structure with P3 symmetry and a smaller lattice size, and have used a Monte Carlo simulation of a simplified model of the surface to demonstrate the role this alternate possible ordering could have in the formation of the surface structure.
As shown in Fig. 1a the experimentally observed unit cell size for the 2-D crystal structure of the hydrophobins matches one of the lattice vectors of a close packed (hexagonal) arrangement of the proteins, i.e. . This resulting unit cell may in principle host seven protein molecules, however, this surface would be seen by AFM as a uniform sheet, and published results show this not to be the case [29], [30]. By removing one or more proteins per unit cell we obtain four protein “tetramer” (Fig. 1b), five protein “pentamer” (Fig. 1c) and six protein “hexamer” (Fig. 1d) structures. These represent the only possible structures that are interconnected throughout the 2-D surface. In all cases, they are composed of trimer units, by which we mean a set of three mutually interacting proteins. The tetramer structure requires that the proteins have three fold symmetry, which our hydrophobin proteins clearly do not, thus we shall consider only the pentamer and hexamer structures for protein docking.
Our first step was to perform protein-protein docking calculations of three proteins, “trimers”, for both HFBI and HFBII. The orientation of the proteins is further constrained so that the main hydrophobic surface orients to the air-water interface. We used protein-protein docking software following the protocol described in the methods section. Selecting all structures with scores in the top 1%, we found four trimer structures for HFBI and five structures for HFBII. These structures are all shown in Fig. 2. Structures C and D for HFBII (see Fig. 2) are extremely similar, with a very small RMSD between them, thus it can be assumed that these are the same structure.
For both HFBI and HFBII, it is possible to construct the pentamer structure by combining the trimers A and B (see Fig. 2) and the resulting structures are shown in Fig. 3. In order for this to be the unit cell of the surface layer, the resulting pentamers must be capable of “docking” to each other, in the arrangement shown in Fig. 1 (c). We attempted to perform protein-protein docking of this structure with itself, but this was unsuccessful. Thus we are able to rule out this structure.
We are thus left with the six protein unit cell structure shown in Fig. 1 (d). A unit cell of this structure would be composed of two “docked” trimers, either different or identical. To determine possible combinations, docking was performed for all possible pairs of trimers. In no case did any trimer dock to a trimer different from itself. For HFBI we found two solutions: both trimer A and D were able to successfully dock with themselves, for two structures, that we will name structure α and structure β. For HFBII only trimer A was found to dock with itself. These results are also shown in Fig. 3. Once again, in order for this structure to be a possible unit cell, it must be able to dock with itself, and in this case our docking of three of these structures was successful, as shown in Fig. 3. It can be seen that the ring structure, found in Fig. 3, is an element of the surface structure composed of the six protein unit cells, seen in Fig. 1 (d), and shown in the six protein structure in Fig. 4.
Now that we have determined that the structure is composed of two identical trimers in the unit cell we may refine the results by symmetry arguments. First, all the docked trimers are within the accuracy range of structures with trigonal symmetry, i.e. a 3-fold symmetry axis at the point where the three proteins contact. Further, the two docked trimers are at the same height and are seen to be oriented so that there is a two-fold rotation axis at the midpoint between the trimer axes. Docking the resulting hexamers to themselves was successful (Fig. 3), resulting in an arrangement with P6 symmetry [28] in the unit cell (see six protein structure in Fig. 4).
While the raw result of the protein-protein docking produced a structure that did not have exact P6 symmetry, we were, however, able to demonstrate that with minor adjustments to this structure, well within the accuracy of the docking calculation, a structure with exact P6 symmetry could be obtained. We iterated a genetic algorithm with energy minimisation to impose P6 symmetry. Disregarding the internal structure of the proteins, the protein arrangement within P6 symmetry is described by 6 parameters; the three Euler angles of the proteins in the trimer relative to the radial axis from the trimer center, the distance of proteins from their trimer center, the distance between the two trimers, and the rotational angle of the two trimers. The value of these six parameters closest to the docked structure was determined through direct geometric calculation, and the positions of the proteins were adjusted to the position conforming to the P6 symmetry. As a result of variances in the docked internal structures of the proteins, the resulting structure contained some clashes. These were resolved through alternately selecting new values for the six system parameters through a genetic algorithm and performing standard energy minimisation on the result. This process was repeated until the result converged. The resulting (.pdb) structures are found in the file “Dataset_S1.zip” in the Supporting Information (Dataset S1). Both the electrostatic potential, and amino acid distribution of the three hexamers are shown in Fig. 5. We see, as expected, that the air interface surface is both electrostatically neutral and non-polar.
It must be pointed out that we have so far only imposed the symmetry of the lattice and only taken into account the result from Langmuir film experiments [11], to the extent of the symmetry of the structure. Our results for the lattice parameter are in rough agreement with previously published experimental results for HFBI and slightly larger for HFBII: our hexagonal lattice has a = b = 54.7 Å for structure α and 57.1 Å for structure β, and for HFBII, a = b = 64.5 Å, in comparison to experimental results of Kisko et al. [11] of 55 and 56 Å respectively at zero surface pressure and 54 and 55 Å respectively under pressure. Maintaining the symmetry of the unit cell, we compressed the distance between the two trimers that make up the six protein unit cell to match the experimental lattice under pressure and were able to re-minimize the structure with only a small enthalpy gain and no significant unresolved clashes. Since our docking calculation involved no restructuring of the protein, it is to be expected that our results for the lattice parameter will err on the side of being too large; some minor reorientation of the loops in contact is to be expected. We can see that this is the cause of the result for HFBII showing more discrepancy with the experimental result than the case of HFBI: for HFBII there is a loop protruding into the contact region which can be easily restructured. The fact that our structures re-minimize perfectly when compressed to the experimentally determined structure supports this.
We conducted electron cryo-microscopy studies of HFBI and HFBII films forming ordered two-dimensional crystals in water in parallel with the protein-protein docking. A surface film was seen to form on 3µl drops of both aqueous HFBI and HFBII solutions sitting on holey carbon coated copper grids. The HFBII film was formed using a protein concentration that was a hundredfold greater than the case for HFBI. The film formed in seconds for HFBII, for HFBI the film required up to 10 minutes to form at room temperature. The drops were carefully blotted with filter paper and vitrified for imaging at zero-tilt by electron cryo-microscopy. In the resulting micrographs, we found arrays of HFBI and HFBII. The water layer contained multiple crystals, in a mosaic array, and some of these were sufficiently large and ordered for Fourier analysis. When these were analysed by Fourier methods, it was obvious that some of the arrays were actually two-dimensional crystals which diffracted. Although the micrographs of both the HFBI and HFBII films contained more than one ordered areas, representing regions of single 2D crystals (Fig. 6). Several hundred images of both HFBI and HFBII were scanned for detectable crystals, and 16 micrographs of HFBI and 12 micrographs of HFBII containing crystals were found. For the HFBII preparations we were able to find several processable images with good statistics giving the same solution, however for HFBI, while the images gave similar diffraction patterns, and lattice parameters, the data were not to high resolution, and thus although we could show the same film formation and mosaicity, we were unable to obtain any processable images. Three images of HFBII were processed (Fig. 6 and Dataset S1 and S2 in Supporting Information). In agreement with x-ray scattering experiments the lattices were hexagonal with α = β = 56 Å, γ = 120° (see supplementary tables S1 and S2). Analysis of the phases of the Fourier patterns suggests a structure composed of a hexamer of proteins with density and lattice parameters in agreement with the docking results, but with P3 rather than P6 symmetry (Fig. 6). Possible reasons why the result shows P3 rather than P6 symmetry are discussed below.
Returning to the pentamer protein structure shown in Fig. 3, while this structure cannot be expanded into a structure with a 5 protein unit cell (Fig. 1 c) the structure can, however, be expanded into a surface covering lattice in a different manner, as a hexagonal lattice with a unit cell comprised of three proteins with the two vertexes of the structure being trimers A and B, as shown in Fig. 4. In order to verify this we performed protein-protein docking of three trimer structures, and were able to successfully duplicate this result, as shown in Fig. 7. This structure cannot be the experimentally observed surface layer structure; neither the lattice size nor surface density match the experimental value. The lattice size is of the lattice size of both our computational and experimental results; the result is well outside the error bars for both results. The fact that this structure can be made is unlikely to be a coincidence, and a discussion of the relevance of this structure follows. The same symmetrization operation as performed for the structure with the six protein unit cell could be performed for this structure, and a continuous sheet of this structure could also be completed, as shown in Fig. 4 comparing the two structures. This structure belongs to the point symmetry group P3 [28].
The six protein structure found for HFBII, and the six protein structure α for HFBI are able to restructure from the P6 symmetric structure to the P3 symmetric structure through a simple rotation with very minor structural readjustment as shown in an animation, “Movie_S1.mpg”, included in the Supporting Information. There is no experimental evidence of this particular P3 structure existing as a stable state; the structure has a different, lower, lattice parameter than the P3 structure we have experimentally observed, and unlike the experimentally observed structure, has a three protein rather than six protein unit cell, however, this does not preclude links with this structure forming temporarily during the formation of the hydrophobic film. This P3 symmetry structure can be seen as a metastable structure. Proteins capable of forming 2D crystal structures with both P3 and P6 symmetries are not without precedent: the Annexin A5 protein forms both P6 and P3 structures on lipid bilayers [31], [32]. For this system, however, the P3 structure represents a more compact rather than expanded structure, that the system collapses to with increased lateral pressure.
In order to explore the role of this possible ordering in the long range properties of the hydrophobin film, we have constructed a spin model that allows for both P6 and P3 local ordering, or only P6 ordering. The model involves spins in six possible orientations, each orientation a 60° clockwise rotation from the previous, on a hexagonal (triangular) lattice. A specific lattice site can either contain a spin or be vacant, representing the presence of absence of a hydrophobin at the surface. Each spin has six neighbour interactions, dictated by the angle between the spin and the spin at the given neighboring site. In order to incorporate both possible symmetries a minimum unit cell of the triangular lattice was 14×14 sites. The two possible symmetries are selected through the allowed neighbour interactions, as described in Fig. 8a.
The model involves three different interactions: between proteins (spins) in the same trimer (−J0), between proteins in neighboring trimers in the P6 structure (−J1), and between proteins in neighboring trimers in the low density P3 structure (−J2). The relative values of J0, J1, and J2, that match the real hydrophobin film, are possible to obtain through force biased simulation of the all atom model. We, however, have not performed this here, the relevance of the specific values of of J0, J1, and J2 is to be discussed in a future publication. All near neighbour spin orientations apart from the specific symmetries allowed (either P6 + P3 or only P6) are forbidden (infinite energy). In this study we have performed Monte Carlo simulation on this model with J0 = 5/kb, J1 = 2/kb and J2 = 1/kb for both P6 and P3 symmetry structures permitted, and J0 = 5/kb and J1 = 2/kb for only the P6 symmetry structure permitted. The specific values of J0, J1 and J2 were decided based on the following reasoning: the trimer interaction is seen to be far more stable than the other interactions and the P6 interaction has significantly greater protein contact area, in comparison to the P3 interaction. In addition to the spin – spin interactions we included an energetic benefit to the presence of a protein at the surface, to simulate the effect of the reduction in surface energy due to the presence of an amphiphilic hydrophobin. This energy was chosen to be Jsurf = −10/kb, to be significantly greater than any spin-spin interaction as indicative of the dominance of the amphiphilic nature of the hydrophobins. Similar spin models have been used in the past to model biological self assembly [32], [33].
Since the specific question we intend to answer is the role of the P3 metastable symmetry in the formation of the hydrophobin film, we designed the Monte Carlo algorithm that we performed on this specific interaction set (Hamiltonian) with the Monte Carlo steps designed in a fashion that mimics the possible motions of the individual hydrophobin proteins in the formation of the film. We thus allowed the following trial moves: 1) a spin appearing or disappearing on the lattice - corresponding to a hydrophobin rising to the surface or disappearing down off the surface 2) a spin hopping to a neighboring empty lattice site and/or rotating 60° and 3) a spin trimer moving one lattice site and/or rotating 60° together. Trial move type 2) corresponds to a single hydrophobin protein diffusing across the surface, and trial move 3) corresponds to tightly bound trimers diffusing in the same fashion as individual monomers.
Since the length scale of the objects/interactions being simulated is too large for temperature effects to have real physical meaning, the temperature was chosen to realise an ideal balance between system fluidity and the degree of ordering. The important property we measured was the size of the largest cluster of interacting spins as a function of Monte Carlo time. The rate of the growth of this is directly indicative of the rate at which the hydrophobin film forms an elastic network; the elastic properties of the hydrophobin film depend on the network of proteins connected by attractive interactions percolating the surface. In order to probe the role of the aforementioned metastable P3 structure, we monitored the rate of growth of the largest cluster in two separate models: 1) a model where both P6 and P3 symmetries are permitted, but the P6 structure is in the ground state and 2) a model where only the P6 structure is permitted.
Our Monte Carlo simulation result was striking: The rate at which contiguous regions of connected proteins grow increases dramatically when the P3 metastable interaction is allowed. We show this result in the plot in Fig. 8b, and a visualisation of this result is shown in Fig. 8c. In both cases there is an initial phase of increasing growth rate as the surface is being filled in, followed by a steady state region, where the growth is both independent of the initial conditions and finite size effects, thus our effective window on the real infinite system. When the cluster size reaches the scale of the system size then the rate levels off, and this can be seen as a finite size effect not relevant to gaining insight into the real system, however, this can be seen as analogous to growth up to a saturation level that occurs on a much larger length scale in an experimental system. We see that when the P6 symmetry alone is permitted the growth of the surface area of the largest cluster is linear in Monte Carlo time. When the P3 symmetry interaction is allowed the rate at which the largest cluster size grows not only increases faster in the steady state region, but the increase is exponential rather than linear. We then simulated the effect of unsaturated hydrophobin density by adding a certain probability, that when a space at the surface is to be filled by a hydrophobin, there is no hydrophobin present to fill it. With this probability set to 50%, we found the exponential cluster growth did not occur (data not shown). An interpretation of the reason for this, and thus the role of the P3 interaction in the formation of the hydrophobin film, is described in the discussion section.
We performed molecular dynamics simulations for 200 ns using the three hexamer structures determined from the docking calculation as starting configurations. The simulations were performed with the hydrophobins at the air-water interface and constant volume conditions with the unit cell set to the experimentally determined hexagonal lattice unit cell of length 56 Å. From the RMSD and hydrogen bond network formation we found the structure to equilibrate after 100 ns. The hexamer structure with approximate P6 symmetry maintained its integrity throughout the simulation. H-bond and salt bridge analysis has been performed (Dataset S3 in Supporting Information). Of particular note is the dominant salt bridge in the HFBI β structure between LYS 32 and ASP 30. This suggests that this is the more stable structure in comparison to the HFBI α structure, however a set of H-bonds and salt bridges is found for both structures so the results are inconclusive. These results could be used in a future mutagenesis study to test which of these structures is correct.
Through both protein-protein docking and electron microscopy analysis of vitrified film experiments we have determined that the structure of both HFBI and HFBII are composed of a six protein unit cell. The docking results, though not precisely P6, indicate a structure with P6 symmetry. The electron cryo-microscopy results, however show a structure with a six protein unit cell, but with P3 symmetry. Our result from the two-dimensional crystals gave lattice parameters of 56 Å for both systems. The lattice parameters found for HFBII crystals in cryoEM were similar to those reported in previous experiments [9]–[11]. The thin self-assembling films of HFBI and HFBII formed spontaneously in pure water, producing fragile crystals which are likely to be the ground state of the protein layer at the air-water interface in a thin film. However, the mosaicity within the films, and potentially, the lower order P3 symmetry observed compared to the P6 symmetry from simulations, may have been induced by the blotting procedure prior to vitrification, or by beam induced movement [34]. Another explanation could be the differences in entropy experienced within a thin water film, compared to bulk water. Recent findings from experiments [16] and simulations [15] with the class I hydrophobin EAS, that forms rodlets, has shown that the structures and conformational entropy of the class I hydrophobin EAS are substantially different when the protein is assembling in the air-water interface or is in bulk water. Finally, as we only have projection data for the crystals, we can not rule out the possibility that the crystals actually contain two layers of protein, one at each air-water interface, and thus an inherently different structure to that in the simulation where a single protein layer was assumed.
The process of the formation of the surface film, while an interesting question, is beyond the scope of this work, and the study of this is a possible future project. We can, however, state that there is no direct link between the tetramer found in the crystal structure and the film structure, since the contact points in the crystal structure are the hydrophobic surfaces at the air-water interface in the film.
From our protein-protein docking results we were able to independently obtain docked structures with P6 symmetry. For the HFBII system these showed a lattice parameter slightly larger than both the existing experimental result [9]–[11] and the new result we found. The system could, however, easily be compressed and re-minimized to the experimental lattice parameter. In addition to the near P6 structure our protein-protein docking results also yielded a lower surface density structure with near P3 symmetry but a lattice size that is not in agreement with the experimental results, thus a structure not experimentally observed. For the sake of clarity, it must be stressed that there is no relationship between this low density P3 structure, and the P3 structure found in the cryo-EM results, that has approximately the same lattice parameter and density of the P6 structure found in the docking results. We then explored the possible relevance of this low density P3 structure in the formation of the hydrophobin film using a Monte Carlo model.
Our docking results yielded two structures with near P6 symmetry for the hydrophobin HFBI, that we labelled α and β, and one structure for HFBII. Molecular dynamics (MD) simulations performed for 200 ns on all three structures showed equilibration to a stable structure at 100 ns. From our MD simulation we were able to identify a key set of H-bonds and salt bridges that could form between the proteins in each structure. Targeted mutagenesis of these key residues, coupled with studies of film formation could be used to distinguish whether the α or β structure for HFBI is correct. For both the HFBI α structure and the HFBII structure the transformation between the P6 structure and the metastable low density P3 structure, that we found for both proteins, could be achieved through a simple rotation, shown in the animation provided in Supporting Information. In the Monte Carlo analysis, to be discussed next, we assumed the P6 and low density P3 structures to be linked in this fashion.
We have performed a Monte Carlo simulation of a spin model constructed to investigate the effect of allowing for the P3 lattice on the formation of the hydrophobin film. When the P6 symmetry alone is permitted, the growth of the surface area of the largest connected cluster is linear in Monte Carlo time. When the P3 symmetry interaction is allowed, the rate of increase is exponential rather than linear. When the P6 lattice alone is permitted the system has 7 possible ordered structures, corresponding to the unit cell shown in Fig. 8a centered around each of the seven sub-lattices. The model thus roughly maps to a spin model known as a “two dimensional seven state Potts model”, belonging to the class of “two dimensional Q-state Potts models”. It has been shown that for this class of models the domain size scales roughly as [35], where r is the radius of the region assumed to be circular and t is Monte Carlo time. This corresponds to linear expansion of the surface area of the domains in Monte Carlo time, exactly as we observe in our simulation. When the P3 symmetry interaction is allowed, P3 symmetry links can form between neighboring domains along the domain boundaries. Each time such a link is formed the area of the connected cluster doubles. Since the probability of such a link forming along the domain boundaries is constant, the rate at which this event occurs, thus doubling domain size, is also constant. The result is the domain size doubling at a constant rate: exponential growth. We additionally found that when we limit the availability of hydrophobin proteins to fill new holes at the surface, thus simulating below saturation concentration of hydrophobin proteins, the exponential growth in the domain size no longer occurs. This could explain the observed difference in the time for film formation between the two experiments for HFBI and HFBII, 10 minutes and seconds respectively, since the HFBII films were formed at a hundred fold higher concentration than the HFBI films.
We thus see evidence that the protein is specifically evolved to have both the P6 and P3 interactions with the P6 interaction dominant, but with the P3 interaction playing an important role; greatly accelerating the rate at which a new percolating network is formed within the hydrophobin film when it is subject to perturbation. This may contribute to the enhanced elasticity of this layer, and act as an additional mechanism to the mechanism of folding resulting in multilayers on the surface [36].
We have thus found an entirely new mechanism in the self organisation of biological structure which could play a role in a wide range of biological phenomena where effective 2D crystals of proteins are laid down on surfaces, including, for example, complement activation [37] in the human bloodstream, where we see a similar extremely fast growth of ordered protein surfaces. Taking an even broader perspective, this mechanism can be applied in developing biomimetics to construct amphiphilic nanoparticles with tuned interactions able to order in both the extended P3 and compressed P6 symmetry group structures at the water surface, possibly imparting novel properties to the surface as a result of this.
Along with this text there are a set of atomic model files included in PDB format. The .zip file “Dataset_S1” contains eight .pdb files. For each of the three hexamer structures (α (A) and β (B) for HFBI and HFBII) there is one copy of the raw docking result with suffix “_raw” and one copy of the six protein structure squeezed to the experimental lattice parameters under pressure of 54 and 55 Å respectively (Kisko et al. [11]) with suffix “_exfit”. For all cases the lattice vectors are along the x axis and rotated 120° counterclockwise from the x axis. Thus for HFBI with the lattice distance constrained to match the experimental system the lattice vectors are a = 54.00,0.0,0.0 b = −27,46.765,0.0 and for HFBI α structure with raw docking fit a = 54.723,0.0,0.0 b = −27.362,47.392,0.0, HFBI β is a = 57.113,0.0,0.0 b = −28.557,49.461,0.0. For HFBII the lattice vectors are a = 64.537,0.0,0.0 b = −32.269,55.891,0.0 and when constrained to fit the experimental lattice size a = 55.0,0.0,0.0 b = −27.5,47.631,0.0. The two structures of the metastable low density P3 structure with the three protein unit cell are also included. Both of these structures are aligned in the xy plane with lattice axis along x axis, like the six protein structures, and include a triangle of three unit cells. The Supplementary tables “Table_S1.pdf”, “Table_S2.pdf” and “Table_S3.pdf”, are also included, with S1 and S2 concerning details of the electron cryo-microscopy results, and S3 concerning results from the molecular dynamics simulation. The animation “Movie_S1.mpg” demonstrates the simple transition between the P6 and extended P3 2D crystal structures, described in the text.
All protein docking studies were carried out using the MZ-dock package, developed by Pierce et al. [38]. MZ-dock uses a grid based approach for determination of the multimeric structure of the protein. In all cases resulting configurations that scored in the top 1% were then considered for further study, and a subset of these was selected based on criteria described below. Regarding the protein structures used in the docking, for HFBI we used chain b of structure 2FZ6 from the PDB database (resolution 1.67 Å). For HFBII, chain a of structure 1R2M was used (resolution 1.0 Å). In both cases the specific chain was chosen to be the chain with the longest defined protein structure.
As described above, the basic structural unit was deduced to be a trimer of three proteins, and docking of three proteins in this fashion was attempted, with the constraint on the docking algorithm that the major hydrophobic surface be blocked from docking, and all results where this surface is not oriented in the same direction for all three proteins as a flat surface were manually discarded.
Once protein trimers were determined, the larger scale structures, composed of many trimers (described in the results section) were determined by attempting docking of trimers to each other. For these docking attempts the trimer structures, discovered previously, were held fixed. The highest scoring structures were screened manually to remove all results where the major hydrophobic surface of the proteins did not form a flat structure.
In order to further investigate the structure of the three determined hexamer structures (HFBI α, HFBI β, and HFBII) we performed 200 ns simulations of the structures determined through docking with the lattice size set to the experimentally determined value of 56 Å. Through periodic boundary conditions we were able to simulate a fully coated air-water interface through the simulation of the single hexamer with the hydrophobic surface exposed at the air-water interface. the three simulations were performed with a 1 fs time step and the simulation was carried out for 200 ns. We have followed the same methodology as used by Abigail et al. [39] for the simulation of the proteins at the air/water interface. We have used the Amber99 force field [40] with TIP3P water model within the Gromacs [41] software package to perform the molecular dynamics simulations at constant volume. The covalent bond lengths were preserved using the linear constraint solver (LINCS) algorithm [42]. All systems were simulated at constant volume and number of particles with the temperature controlled using the Nosé-Hoover thermostat [43], [44], with solvent and solute controlled independently. Lennard-Jones interactions were cut off at 1.0 nm, and for the electrostatic interactions the particle mesh Ewald method (PME) [45] was used. All simulations were carried out at ambient temperature (298 K).
The HFBI film was prepared by reconstituting dry powder in fresh milliQ water (pH 7) to a concentration of 10 mg/ml, sonicating for 30 seconds, and then diluting, to reach a concentration of 100 µg/ml, followed by a second sonication. A 30µl drop at that dilution was incubated in a closed petri dish for one hour at room temperature and atmospheric pressure. A visible film formed on top of the drop. The film was picked up with a freshly glow-discharged Quantifoil R2/2 (Quantifoil MicroTools GmbH, Germany) grid, then blotted and vitrified as described previously. The HFBII film was prepared by reconstituting dry powder in milliQ water to a concentration of 10 mg/ml and sonicating for 30 seconds. The HFBII system was not further diluted, thus the film was prepared in conditions of hundredfold greater protein concentration than the case for the HFBI film. After aliquoting 3µl onto freshly glow-discharged Quantifoil R2/2 (Quantifoil MicroTools GmbH, Germany) grids at room temperature and atmospheric pressure, the drop was blotted from the front of the grid within a few seconds and then vitrified as described previously [46]. The grids were held in a GATAN 626 cryoholder maintained at −180°C in an FEI Tecnai F20 microscope (EM Unit, Institute of Biotechnology, University of Helsinki) operated at 200 kV. Images were recorded on Kodak SO163 film at a magnification of ×50,000 and the negatives were digitised using a Zeiss Photoscan TD scanner with a 7µm step size. Micrographs were processed using the 2dx software package [47].
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10.1371/journal.pntd.0004798 | Low Prevalence of Ocular Chlamydia trachomatis Infection and Active Trachoma in the Western Division of Fiji | Trachoma is the leading infectious cause of blindness and is caused by ocular infection with the bacterium Chlamydia trachomatis (Ct). While the majority of the global disease burden is found in sub-Saharan Africa, the Western Pacific Region has been identified as trachoma endemic. Population surveys carried out throughout Fiji have shown an abundance of both clinically active trachoma and trachomatous trichiasis in all divisions. This finding is at odds with the clinical experience of local healthcare workers who do not consider trachoma to be highly prevalent. We aimed to determine whether conjunctival infection with Ct could be detected in one administrative division of Fiji.
A population-based survey of 2306 individuals was conducted using the Global Trachoma Mapping Project methodology. Population prevalence of active trachoma in children and trichiasis in adults was estimated using the World Health Organization simplified grading system. Conjunctival swabs were collected from 1009 children aged 1–9 years. DNA from swabs was tested for the presence of the Ct plasmid and human endogenous control.
The prevalence of active trachoma in 1–9 year olds was 3.4%. The age-adjusted prevalence was 2.8% (95% CI: 1.4–4.3%). The unadjusted prevalence of ocular Ct infection in 1–9 year-olds was 1.9% (19/1009), and the age-adjusted infection prevalence was 2.3% (95% CI: 0.4–2.5%). The median DNA load was 41 Ct plasmid copies per swab (min 20, first quartile 32, mean 6665, third quartile 161, max 86354). There was no association between current infection and follicular trachoma. No cases of trachomatous trichiasis were identified.
The Western Division of Fiji has a low prevalence of clinical trachoma. Ocular Ct infections were observed, but they were predominantly low load infections and were not correlated with clinical signs. Our study data suggest that trachoma does not meet the WHO definition of a public health problem in this Division of Fiji, but the inconsistency with previous studies warrants further investigation.
| Trachoma, caused by ocular strains of Chlamydia trachomatis, represents a major global public health issue, and is the subject of an international elimination campaign. Until recently, data on trachoma in the Pacific Island states have been sparse. The most recent studies have conflicted in their estimates of trachomatous disease burden in Fiji, therefore, surveys using alternative markers (infection testing plus grading) to those already used (grading alone) are warranted to try to shed further light on trachoma epidemiology in this setting. We used an externally validated clinical assessment protocol to show that evidence of active trachoma is present at a low prevalence, and we did not find any cases of trichiasis, the sight-threatening stage of trachoma. From testing of conjunctival swabs with a validated, next-generation PCR, we also found that C. trachomatis was present at a low prevalence. Our clinical data suggest that trachoma does not meet the WHO definition of a public health problem in this Division of Fiji, but the inconsistency with previous studies warrants further investigation.
| Trachoma is the leading infectious cause of blindness, and is caused by ocular infection with the bacterium Chlamydia trachomatis (Ct). Trachoma is thought to be a public health problem in 51 countries, with 232 million people at risk of blinding disease [1]. Infection may present as an acute and self-limiting keratoconjunctivitis, but numerous re-infections can lead to potentially blinding sequelae.
Globally, the highest prevalence of active trachoma is found in sub-Saharan Africa [1]. Transmission of infection is thought to be through direct contact with hands or cloths which transfer ocular or nasal discharge between individuals [2], although the bacteria can also be spread by passive contact with eye-seeking Musca sorbens flies [3].
Trachoma is diagnosed by clinical examination of the eye. Active trachoma is characterised by the presence of 5 or more >0.5mm lymphoid follicles in the central part of the upper tarsal conjunctiva (trachomatous inflammation–follicular, TF) and/or pronounced inflammatory thickening of the upper tarsal conjunctiva obscuring more than half the normal deep tarsal vessels (trachomatous inflammation-intense, TI) [4]. Scar tissue deposited during resolution of inflammatory disease episodes leads, in some individuals to the misdirection of eyelashes so that they touch the eyeball; this state is known as trachomatous trichiasis (TT) [4,5].
The World Health Organization (WHO) advocates the use of the SAFE strategy–Surgery for trichiasis, Antibiotics, Facial cleanliness, and Environmental improvement, for elimination. Annual mass drug administration (MDA) of the antibiotic azithromycin is recommended for at least 3 years in any district where the prevalence of TF in 1–9 year olds is estimated to be 10% or greater. The decision to undertake MDA is informed by data from a population-based prevalence survey (PBPS) [6] in any district that has been identified as being of concern. The Global Trachoma Mapping Project (GTMP) [7] is currently undertaking PBPSs in all secure probably-endemic districts worldwide, in an effort to complete the baseline trachoma map by the end of 2015.
Cases of trachoma have historically been reported in Fiji [8–11]. A 2007 rapid assessment found a high prevalence of active trachoma in targeted Fijian villages, but no cases of TT [12]. In 2012, a PBPS was undertaken in each of Fiji’s four divisions, which estimated division-level prevalences of TF in 1–9 year olds ranging from 10.4–20.9% (19.6% in Western Division). Individuals aged over 15 years were examined only in Western and Northern Divisions, with prevalences of TT in that age group estimated at 8.7% and 6.2%, respectively [13]. The 2012 PBPS results suggested that trachoma was highly endemic in Fiji, and that the prevalence of TT was among the highest in the world. This was in stark contrast to the experience of Fijian ophthalmologists who see cases of TT quite infrequently [12–14].
We conducted a PBPS for TF and TT in the Western Division of Fiji (total rural population 184,039; Fig 1) [15]. In addition we collected conjunctival swabs from children, which were then processed and subjected to PCR with the aim of estimating the prevalence of ocular Ct infection.
The study was conducted in accordance with the Declaration of Helsinki. Consent was obtained from the leader of each community prior to entry into the community. Where possible, village chiefs, local headmen or local leaders were contacted in advance of the survey to gain consent to enter the respective villages. In indigenous Fijian villages, sevu-sevu, a traditional welcome ceremony involving sharing a Kava root-infused water with village leaders, was performed in accordance with the local custom. The study was designed to be paper-free which enabled real-time data upload and review, and streamlined field logistics. In this rural Fijian context, it was considered culturally appropriate for those over the age of 15 to consent for themselves. Verbal informed consent to be examined was obtained from each participant over the age of 15 years. For participants under the age of 15 years, consent for examination and to have specimens collected was given on their behalf by a legally responsible parent or guardian. The Fiji National Research Ethics Review Committee and the London School of Hygiene & Tropical Medicine ethics committee approved this consent protocol. All consent was recorded electronically via the Android phone-based data-capture system [7].
A cross-sectional, cluster random sample survey methodology was conducted in November and December 2013. Villages identified from local census lists were considered as potential clusters in the sampling. A total 31 villages were selected for inclusion, with 30 households sampled per cluster. The total number of villages was calculated based on the anticipated number of children per household from the latest available census data [15]. In the first stage, after the exclusion of urban centres, villages were sampled with probability proportional to size. At the second stage of sampling, 30 households within a village were selected. Households were selected at random on the day of survey from a list of village households compiled by the village health worker or the village leader. Any person aged one year or more living in a sampled households was invited to participate. Effort was made to ensure participation by absent household members by returning later in the day where possible.
The study was powered to estimate a 10% prevalence of ocular Ct infection in 1–9 year olds with absolute precision of ±3% and 95% confidence. A design effect correction of 2.65 was used, based on previous trachoma surveys [7]. We included 10% oversampling in order to account for non-response, the required sample size was 1120 children in this age group. Based on 2007 census data, we expected to find 1.2 children aged 1–9 years per household, therefore we estimated 30 households from each of 31 clusters would be sufficient to recruit 1120 children. The overall sampling methodology was in accordance with that used in the GTMP [7].
Data were collected on an Android smartphone using a slightly modified version of the GTMP LINKS app, which is an implementation of the Open Data Kit toolbox for mobile data collection efforts (https://opendatakit.org/) and has been described elsewhere [7]. GPS coordinates were recorded for each participating household. The age and sex of each household member was then recorded, along with a record of consent to examination, refusal or absence at the time of the survey.
A single GTMP-certified [7] trachoma grader examined both eyes of each participant using a 2.5× binocular loupe and sunlight. Each eye was assessed for the presence or absence of TT, TF and TI, according to the WHO simplified grading system [4,7]. An individual trained in the use of the Android phone application recorded results. Disposable gloves were used during swab collection, and alcohol hand gel was used between individuals to prevent carry-over contamination from one subject to the next. Participants found to have active trachoma were provided with a course of 1% tetracycline ointment and directions in its method of application. Participants found to have any significant ocular pathology were referred to the nearest eye care centre for management.
For each participating child aged 1–9 years, a specimen was taken from the right upper tarsal conjunctiva with a polyester swab (Puritan Medical Products, ME, USA) and using a standardised collection procedure [16]. The specimen was taken immediately after clinical grading and the swab was immediately returned to its packet, secured and labelled with an anonymised five-digit numeric code. Swabs were kept in the field in a cool, dry container and were then air-dried overnight, before being transferred to 5°C storage the following morning; they were then maintained at this temperature until processing, between 1 and 5 months later.
Fifteen negative field control swabs were collected by passing a swab within 15 cm of the eyes of a seated subject, chosen by random selection from the list of all specimen labels prior to commencement of the survey; specimen labels were used sequentially. Positive control swabs were prepared by briefly submerging swab heads in an homogenized solution of Ct strain A2497[17] elementary bodies and cultured hep2C cells at a dilution factor of 1 in 500, suspended in a phosphate-buffered saline. 15 such positive control swabs were prepared in London and stored in 2 mL Eppendorf tubes, then frozen and retained at LSHTM, UK. 15 further positive controls were prepared in the field, and were stored at 5°C until analysis. Field control swabs and swabs from study subjects were indistinguishable, and laboratory staff were masked to swab status.
Genomic DNA was extracted in to 50 μL nuclease free water using the Norgen Genomic DNA Purification kit (Norgen Biotek, Canada) according to manufacturer's protocol. For quality control, a sample with DNA extracted from a clean swab was included in each extraction batch.
A Ct-specific droplet digital PCR (ddPCR) assay was used according to a published protocol[18], and with the minor modification that an 8 μL aliquot of DNA was used in each reaction. Briefly, each well contained 1X ddPCR supermix, 0.2 μM fluorescent probes and 0.9 μM forward and reverse primers for Homo sapiens RPP30 and Ct plasmid ORF 2. Thermal cycling conditions were 95°C for 10 minutes, followed by 40 cycles of 95°C for 15 seconds and 60°C for 1 minute; then a final hold for 10 minutes at 98°C. A modified omcB probe was used to improve quenching efficiency and limit background fluorescence (Table 1).
Specimens from persons with TF and/or TI were tested for the presence of Ct omcB, a well-conserved genomic target, to ensure that cases of infection were not missed due to insertion/deletion or recombination events disrupting the site of the diagnostic primers for plasmid DNA [20].The number of plasmids per chromosome was also assessed using the method described by Last et al [19]. A single well was run for each sample.
Data analysis was carried out using R [21]. Observed cluster-level frequencies of TF were adjusted for age in one-year age-bands using data from the 2007 Fiji census [7,15]. Confidence intervals were calculated by bootstrapping adjusted cluster-level proportions [22]. A binomial confidence interval was used for the upper bound of the TT prevalence estimate [23]. ddPCR data were analysed using QuantaSoft software (BioRad, Hemel Hempstead, UK). A positive ddPCR result was defined as one having a greater than 95% confidence in a non-zero load under a Poisson approximation, as described elsewhere [18].
A total of 413 households were visited over 31 clusters. No data were collected on non-participation of households. We enumerated 2306 individuals for inclusion in the study, of whom 1038 were aged 1–9 years, 335 were aged 10–14 years and 933 were aged 15 years and over. Ten (0.4%) individuals declined consent to participate; 2296 were examined. The median age of those examined was 11 years (mean: 20; min: 1; 1st quartile: 5; 3rd quartile: 32; max: 91), and 1289 (56.1%) were female. Following data cleaning, 1009 children were included in the study, which is very close to the targeted sample size of 1018.
Data records without paired clinical and swab data (n = 29/1038) were discarded from the analysis. TF was observed in 34/1009 (3.4%) 1–9 year-olds. The age-adjusted prevalence of TF was 2.8% (95% CI 1.4–4.3%;). TI was observed in 2/1009 (0.2%) 1–9 year-olds. The age-adjusted prevalence of TI was 0.1% (95% CI: 0.0–0.3). No cases of TT were observed in 928 examined participants aged 15 and over. The age-adjusted prevalence of TT in those aged 15 years and above was 0% (95% CI 0–0.2%).
In addition to the 34 cases of TF found in those aged 1–9 years, 6.4% (21/330) of those aged 10–14 years and 1.1% (10/928) of those aged 15 years and over were found to have clinical signs of TF and/or TI. The median age of those with TF was 8 years (mean: 11; min: 1; first quartile: 5; third quartile: 11; max: 83).
A total of 1038 children aged 1–9 years had ocular swabs taken and analysed. 16 (1.5%) swabs were unusable due to labelling errors. Of the 1022 remaining, 13 (1.3%) failed quality control because there was no detectable endogenous human target. 1009 (97.1%) specimens passed quality control (>95% confidence in non-zero human RPP30 load). The mean droplet number per well was 14062 (first quartile 12634, median 13837, third quartile: 15247). The median endogenous control load was 9576 RPP30 copies/swab. 19/1009 (1.9%) tested positive for the presence of Ct plasmid DNA. 1/34 (3.1%) children with active disease tested positive for Ct DNA and 18/977 (1.8%) children without active disease tested positive (Table 2). There was no association between cases of TF and cases of infection (p = 0.644 Mantel-Haenszel Chi-square). No new cases of infection were detected when active disease cases were retested with a multiplex plasmid/omcB ddPCR assay.
The median load of infection in positive specimens was 41 Ct plasmid copies/swab (min 20, first quartile 32, mean 6665, third quartile 161, max 86354). Among the 19 swabs that tested positive for the plasmid, seven additionally tested positive for presence of omcB in the multiplexed plasmid/omcB test. The mean plasmid:chromosome ratio was 4.4 plasmids per chromosome (range 1–12), consistent with the findings of Last et al [19]. The age-adjusted prevalence of ocular Ct infection in 1–9 year-olds was 2.3% (95% CI: 0.4–2.5%).
All 30 positive control swabs (15 field and 15 lab) tested positive for Ct. The field control swabs had a 58.7% reduction in mean Ct plasmid load as compared to those stored frozen. Mean Ct omcB load for the field storage group was also reduced by 58.7%. The reductions in mean load of both plasmid and omcB were statistically significant (Pearson’s Chi-squared test p = 0.0002 and 0.000016 for difference in plasmid and omcB, respectively)). The mean load of human DNA from hep2C cells was 45% lower in the frozen swabs than in the swabs that were stored in the field, but this difference was not statistically significant (80 vs 148 copies/swab; p = 0.18) (Fig 2). Of the 15 negative control swabs collected in the field, 1 swab was lost in transit. The other 14 negative control swabs tested negative for both human and Ct DNA.
We estimate there to be a low prevalence of active trachoma and a correspondingly low prevalence of ocular Ct infection in the Western Division of Fiji. These data represent the first data on ocular Ct infection in the Pacific Island small states and represent a significant step towards improving knowledge of trachoma in the region.
We found no association between infection and active trachoma. This result is unsurprising, given the generally low correlation between signs of disease and ocular Ct infection in low prevalence settings [24–27]. It has been suggested that this may be because where Ct prevalence is low, other pathogens may be associated with the active trachoma phenotype [27,28]. It is possible that nucleic acid amplification testing may miss some low-load Ct positive samples due to the relatively high sampling variation when an analyte is at very low concentrations. Some commentators have suggested the sensitivity of this ddPCR assay may be too low for trachoma programs [29] due to the sensitivity observed in a ‘face value’ diagnostic evaluation by Roberts and colleagues. However, it was demonstrated that the discrepant results occurred in a mathematically predictable manner related to the analyte concentration and that most PCR-based technology will share an absolute limit to the number of analyte copies per test that will be reproducibly detected. It was highlighted that in a traditional discrepant analysis the sensitivity of this ddPCR assay could have been as high as 98% [30] and we therefore believe the test was appropriate in this setting.
For logistical reasons, swabs that were collected during this survey were not frozen during storage. A number of studies have illustrated that host [31] and chlamydial DNA [32,33] are stable in the short term when stored dry. Evidence from our positive control swabs indicates that a substantial proportion of chlamydial and human DNA is lost during storage over a few months at 5°C. However, this will only result in loss of qualitative diagnostic accuracy at very low loads of infection, consistent with the findings of Dize and colleagues [34]. In our positive control swabs, the ratio of plasmid to chromosome targets was the same regardless of storage conditions, indicating a similar rate of degradation between both genome components. We described above the difficulties caused by sampling error when diagnosing very low load infections, and degradation during storage may have caused previously detectable samples to become not reproducibly detectable. Improving specimen transport and storage conditions may have resulted in a closer association between clinical signs of disease and Ct infection. However, as low-load infections may be poorly associated with TF [16] and of limited importance in driving transmission at community level [35], we consider their detection not to be critical. The loss of sensitivity is however a limitation when considering the findings of this cross-sectional prevalence study.
We did not find any cases of TT in this population. This is in contrast to the previous (2012) PBPS, which found a high prevalence of TF (19.6% in 1–9 year-olds) and TT (8.7% in ≥15 year-olds) in this Division [13,36]. A 2009 rapid assessment found communities in which a high proportion of examined children had TF, but–like the present work—no cases of TT [12]. The source of these discrepancies is the subject of on-going research but we have observed social practices of eyelash epilation in Fiji that may have been misdiagnosed as trachomatous trichiasis.
The number of children per household was higher in this survey than in the 2007 national census, and we therefore reached our sample size in a lower number of households than expected. Data were not collected on households not enrolled in the study, nor on household demographics that may have explained why our sample size was reached with fewer houses than originally thought. However, the target sample size was very nearly achieved, and our individual participation rate was over 99%, therefore the risk of attrition bias is considered to be low. The difference in TF prevalence between the present data and the 2012 PBPS could be due to poor consensus between graders, seasonal variation in trachoma prevalence or an artefact of the cross-sectional study designs. The 2012 survey followed a PBPS protocol with random selection at village and household level with a comparable number of children sampled overall. The survey presented here sampled lower numbers of villages and households, but this is unlikely to sufficiently explain the large difference between the resultant TF prevalences found. Specific information on which villages were surveyed in the earlier study was not available, therefore it is not clear whether there was overlap between clusters visited; our randomisation process may have missed trachoma hotspots in Western Division by chance. The low estimate of Ct infection prevalence in this study does not support the estimated prevalence of TF observed in the previous survey, and could be a result of the relative non-specificity of phenotypic markers in trachoma.
The reported prevalence of TF and TT in Fiji varies significantly between studies. Tests for infection confirm that ocular Ct infections do still occur in Fiji, albeit infrequently and at relatively low load. Our clinical data suggest that trachoma does not meet the WHO definition of a public health problem in this Division of Fiji, but the inconsistency with previous studies warrants further investigation. It is also not clear whether the results from this division will be generalizable to the rest of the country. Recommendations on how best to incorporate this information into trachoma management plans are sparse. Estimating the age-specific prevalence of serological markers for exposure to chlamydial infection [37,38], photographic evidence of phenotype, and exploration of other associations of TF in Fiji could be beneficial in developing those recommendations.
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10.1371/journal.ppat.1007888 | Bacteriophages benefit from generalized transduction | Temperate phages are bacterial viruses that as part of their life cycle reside in the bacterial genome as prophages. They are found in many species including most clinical strains of the human pathogens, Staphylococcus aureus and Salmonella enterica serovar Typhimurium. Previously, temperate phages were considered as only bacterial predators, but mounting evidence point to both antagonistic and mutualistic interactions with for example some temperate phages contributing to virulence by encoding virulence factors. Here we show that generalized transduction, one type of bacterial DNA transfer by phages, can create conditions where not only the recipient host but also the transducing phage benefit. With antibiotic resistance as a model trait we used individual-based models and experimental approaches to show that antibiotic susceptible cells become resistant to both antibiotics and phage by i) integrating the generalized transducing temperate phages and ii) acquiring transducing phage particles carrying antibiotic resistance genes obtained from resistant cells in the environment. This is not observed for non-generalized transducing temperate phages, which are unable to package bacterial DNA, nor for generalized transducing virulent phages that do not form lysogens. Once established, the lysogenic host and the prophage benefit from the existence of transducing particles that can shuffle bacterial genes between lysogens and for example disseminate resistance to antibiotics, a trait not encoded by the phage. This facilitates bacterial survival and leads to phage population growth. We propose that generalized transduction can function as a mutualistic trait where temperate phages cooperate with their hosts to survive in rapidly-changing environments. This implies that generalized transduction is not just an error in DNA packaging but is selected for by phages to ensure their survival.
| Viruses (phages) that only attack bacteria are highly common. Some of these phages naturally reside within the bacterial chromosome for extended periods of time. Upon release and propagation on phage susceptible cells, new phage particles are made but occasionally bacterial rather than phage DNA is packaged into phage heads. Transfer of random pieces of bacterial DNA between cells by such transducing particles has been termed generalized transduction and has long been used as a tool for genetic engineering. We show here that bacteria and generalized transducing phages cooperate to survive adverse conditions such as the presence of antibiotics by phage integration and transduction of antibiotic resistance genes from neighboring cells. The resulting cells are resistant to phage attack due to immunity provided by the integrated prophage and they are able to exchange bacterial DNA with other cells containing a similar prophage via the transducing particles. Previously, transduction has been considered an accident in phage biology. Here we propose that rather than being an accident, generalized transduction is an evolved trait selected by some temperate phages to persist in rapidly-changing environments.
| Temperate bacteriophages (phages) have a dual life cycle. They reside in the bacterial chromosome as prophages until induction initiates lytic replication, where phage structural proteins are produced, phage DNA is packaged into virions, and the cell ultimately lyses releasing the phage progeny. Prophages are common in bacteria and almost half of the bacterial genomes carry prophages with pathogens more likely being lysogens than non-pathogens [1]. For the Gram-positive, human pathogen, Staphylococcus aureus, essentially all clinical strains carry between 1 and 4 prophages [2] and for the Gram-negative pathogen, Salmonella enterica serovar Typhimurium, prophages are present in the majority of strains [3,4]. While prophage induction obviously can reduce host viability through lytic replication [5,6] and negatively impact host fitness either by providing a metabolic cost [3] or by disrupting host genes upon integration [4,7], there are also examples of mutualistic interactions between temperate phages and their hosts [8]. Foremost, prophages provide immunity to attack from related phages by expressing the phage repressor protein that controls the transition between temperate and lytic replication [5,9]. They can also express a variety of adaptive accessory genes [8,10], and for bacterial pathogens, prophage encoded virulence factors contribute to colonization and pathogenesis [4,8,11]. Despite these mutalistic interactions, phages are largely considered parasites in a continous arms race with the host [12] where the constant attack from phages have led to the evolution of an impressive arsenal of anti-phage systems [13].
One example where bacteria have been thought to benefit from phages is transduction. In this process bacterial DNA is packaged into phage particles and is transferred between bacterial cells. In organisms such as S. aureus, transduction is considered to be the major contributor to the spread of antibiotic resistance genes and the success of the pathogen [2,14]. Transduction can take place in various forms. In specialized transduction, DNA flanking the prophage attachment site (attB) is transferred as a consequence of an aberrant prophage excision. In the recently discovered lateral transduction, late excision and in situ replication of an integrated prophage leads to highly efficient packaging of bacterial DNA several hundred kilobases downstream of the integration site [15]. Finally, in generalized transduction phages randomly package bacterial DNA instead of their own and thus, can essentially transfer any piece of the bacterial genome [16]. Generalized transduction is mediated by temperate phages that employ the pac site–headful mechanism for DNA packaging [16] as opposed to the cos-phages which package DNA of exactly one genome delimited by cos sites [17]. After being discovered, generalized transduction soon became a powerful genetic tool, which at the time revolutionized microbial genetics [18] [19]. Early on it was also observed that the transductants, namely the cells receiving bacterial DNA, often carry a copy of the phage in their genome thus becoming lysogens [20–22]. This made it unclear whether the transduced bacterial DNA was transferred independently or in functional phage particles [22,23]. Subsequent studies demonstrated that viral particles mediating generalized transduction contain bacterial DNA and are non-functional from a phage perspective [24]. Based on these observations, transduction has for many years been considered to be a consequence of errors in the phage DNA packaging machinery allowing bacterial rather than phage DNA to be packaged [4,16,25,26].
Recently we found that lysogens can acquire genes from non-lysogens by infecting them with phages that transfer back these genes to the original population by transduction [9]. We have continued to study the role of transduction in phage-host interactions and propose here that infection of a bacterial cell by a temperate phage and a transducing particle can increase fitness of both by allowing the host cell to acquire adaptive genes, whose benefits are shared with the prophage. This creates a direct association between transduction and phage fitness that could explain the existence of high rates of transduction by certain phages. Using S. aureus and S. enterica Typhimurium as experimental models and in silico modelling, we show that under a certain range of conditions, generalized transduction provides the phage with bet-hedging opportunities that increase its own and the hosting cells chances of surviving in changing environments. These findings indicate that transduction is an intrinsic part of phage biology and that transducing phages benefit from the process by providing adaptive power to the hosting bacterium.
In a previous study we observed that lysogens of S. aureus carrying the generalized transducing phage ϕ11 were able to acquire DNA from non-lysogenic cells following spontaneous phage release in a process we termed autotransduction [9]. To address more generally what happens when phages, propagated on antibiotic resistant cells, meet bacteria that are neither resistant to antibiotics nor carry prophages, we examined a variety of phages infecting either S. aureus, S. enterica serovar Typhimurium or E. coli. In these experiments we monitored generalized transduction as the phage population used for infection was prepared by propagation on antibiotic resistant cells rather than by induction of a lysogen. At a multiplicity of infection (MOI) of 1 and an initial cell density of 0.01 (OD600), the number of colony forming units, CFU, transductants and lysogens were determined following overnight incubation. For the generalized transducing and temperate S. aureus phages ϕ11, ϕ52A, ϕ53 and 80α and Salmonella enterica serovar Typhimurium phage P22, transductants formed with a frequency of 103 to 104 CFU/ml and essentially all (95–100%) were lysogens (Table 1). In contrast, no transductants were observed with the non-generalized transducing E. coli cos-type temperate phage λ, the lytic S. aureus phage Sa012 or the S. aureus 80α-vir, a virulent derivative of the transducing 80α phage. Despite the lack of transductants for both S. aureus phage Sa012 and S. aureus 80α-vir, the phage lysates contained transducing particles packed with bacterial DNA as determined by PCR (S1 Fig). When monitoring over time the infection with a derivative of ϕ11 encoding resistance to erythromycin (ϕ11-ERM) propagated on chloramphenicol resistant cells, the bacterial culture after 3 hours was already dominated by lysogens (105 cells) and of these, 102 were transductants (Fig 1). These results show that the survival of transductants depends on lysogeny providing resistance to infection by similar phages, at least under the examined conditions where the phage infection process is not limited.
We used an individual-based model to understand how generalized transduction and lysogeny affects population outcome when there is selection for a transduced marker. The framework, eVIVALDI [27], simulates populations composed of discrete individual organisms reproducing and dying in a lattice. Each individual has a number of attributes and behaviours, including explicit genomes. These types of models are suited for making qualitative analyses of a complex system where many different variables can potentially affect its outcome. Population-level dynamics emerge from the interactions among these individuals and with their environment (for a recent review see [28]). Here, we defined a community with bacteria and phages, and explored various scenarios with different types of phages (Fig 2A). Bacteria reproduce and compete for space in the lattice whereas phages reproduce by infecting bacteria. The complete model, including parameters and their explanation, is specified in the ODD (Overview, Design concepts, and Details) protocol of the model provided as S1 Text. In an initial phase (first phase, first 10 iterations), the phages infected bacteria carrying the antibiotic resistance marker. A sample of the virions including generalized transducing particles was then introduced to a population of antibiotic sensitive bacteria (second phase), and a dose of antibiotics was added at the indicated time point. Initially, when the phage was temperate and a transducer (Fig 2B, first column, scenario 1), the bacterial population decreased because of phage predation. When antibiotics were added to the environment, only populations of bacteria that acquired the antibiotic resistance gene were predicted to have survived. First the phage population increased as a result of replication of virulent and temperate forms of the phage, and subsequently decreased matching the dwindling bacterial populations. Individuals in our model have explicit genomes, and transduction results in genomic changes. As expected, the analysis of the genomes of the surviving bacteria predicted them to carry a prophage (i.e. they become lysogens) and an antibiotic resistance gene (S2 Fig). At the end of the simulations all bacteria were lysogens (they are protected from phages) and carried the antibiotic resistance gene (were infected by a transducing particle). By this time, almost all phages were in the state of prophages, and the number of free phages is dictated only by the rate of spontaneous prophage induction.
We also simulated the use of temperate but non-generalized transducing phages (Fig 2C, second column, scenario 2). In this case, the dynamics were the same up to the antibiotic spike, after which the bacterial population was predicted to become extinct by the combined effect of phage predation and antibiotics. The lack of host cells led to the extinction of the phage population. When the phages were virulent, and capable of generalized transduction (Fig 2B, scenario 3), the bacterial population decreased following propagation of the phage. The spike of antibiotics led to the extinction of the population in all simulations. This fits the expectation that if bacteria cannot become lysogens, and thus are not protected from lytic phage infections, transduction cannot help them survive. Similar results were obtained using virulent non-generalized transducing phages (Fig 2B, scenario 4). Overall, in 100 replicate simulations for each scenario, both bacteria and phage survived in nearly all (98%) of the replicates in scenario 1, and always went extinct in scenarios 2, 3 and 4. In conclusion, phages only survive if they are lysogens and transducers. This shows that transduction favours phage survival.
The model is parameter-rich and these parameters are not always easy to define. This raises questions on their biological relevance and on how their variation can affect the final outcome. The parameters for these simulations (see Section 2 of S1 Text) were empirically set to understand whether the joint role of transduction and lysogeny could lead to long-term (i.e., after 100 iterations) phage survival in these conditions. Since other parameters could also play a role in these dynamics, we performed an unbiased combinatorial Random Forest Analysis (RFA) of 5000 different combinations of parameters that contribute to phage survival. Each combination of parameters was used in 20 different simulations, for a total of 100,000 simulations (see Methods). The ranges of parameters explored are indicated in S1 Text (Section 3). In general, we aimed at having broad ranges of parameters that allow a diversity of outcomes. The analysis indicates that the probability of generalized transduction is the most influential parameter for phage survival (either as lysogens or active particles), followed by the probability of lysogenization (Fig 3A). The results are robust to variations in the other parameters (S3 Fig). We then explored in further detail the likelihood of bacterial survival, which in this system is equivalent to phage survival as bacterial survivors are lysogens and essentially all phages are prophages. Here the survival was assessed as a function of the probability of generalized transduction and two of the variables explored in the RFA (phage burst size and the number of sampled phage between the first and second phase), as well as an additional one, namely the size of the bacterial genome on which the phage had been propergated. As seen in Fig 3B, first panel, the frequency of antibiotic resistant transductants is predicted to decrease with increasing genome size because the probability that the virion carries the bacterial antibiotic resistance gene decreases with increasing bacterial genome size. In contrast, survival increases with phage burst size because larger bursts are predicted to result in more transducing particles, thus increasing the probability of transfer of the resistance gene (Fig 3B, second panel). Finally, survival is calculated to increase when the number of phage particles sampled between the experimental steps is initially increased and then makes a plateau, because a very low sample size may lack a transducing particle with the resistance gene (Fig 3B, third panel). All these analyses show that transduction of the antibiotic trait occurs within a wide, but defined, region of parameters. Overall, the model fits the notion that transduction and lysogeny jointly facilitate the acquisition of the adaptive trait. In particular, our results suggest that infection by temperate phages capable of forming lysogens, and their transducing particles, enable survival of the phages (and their hosts) in a dynamic environment exposed to antibiotics, when the transfer of resistance genes is possible.
To experimentally examine the role of transduction in phage survival under changing environments we compared the fate of two S. aureus temperate phages namely the generalized transducing ϕ11 and the non-generalized transducing (cos-type) ϕ12 (not to be confused with the lytic ϕSa012) before and after antibiotic exposure. Both phages were propagated on a chloramphenicol resistant strain and the resulting phage lysates were used to infect phage-susceptible cells. After 8 generations of growth in non-selective media (Fig 4A, before selection) chloramphenicol was added and growth was followed (Fig 4A, after selection). In the culture infected with the generalized transducing ϕ11 phage, the CFU was approximately 1x109 ml-1 before addition of chloramphenicol and of these cells, 1x104 ml-1 were transductants (being resistant to chloramphenicol) and lysogens (100%) (Fig 4A). Following growth in the presence of chloramphenicol, the transductants propagated and reached 5x108 CFU ml-1 with 100% of the cells being lysogens. In contrast, and as predicted, for the culture infected with the non-generalized transducing ϕ12 phage, no chloramphenicol-resistant transductants were observed neither before nor after selection (Fig 4B). Before antibiotic exposure, ninety-five percent of the cell population infected with ϕ12 were lysogens demonstrating that ϕ12 was able to lysogenize its host. However, after selection, the antibiotic had eliminated bacteria and thus the prophage. This experiment confirms that the generalized transducing ability of ϕ11 enables the phage and the hosting lysogen to survive in the presence of antibiotic exposure when transducing particles are available with the observed trait.
We further explored the adaptive potential of transduction by investigating the transfer of two unlinked genetic markers. We engineered strains of S. aureus and S. enterica Typhimurium to contain chromosomal and plasmid-encoded antibiotic resistance markers. Phage lysates (ϕ11-ERM and P22, respectively) obtained by propagation on these strains were used to infect phage-susceptible cells and after overnight incubation the number of transductants was determined. The result shows that both plasmid and chromosomal markers were transferred by transduction in S. aureus (Fig 5A) and S. enterica Typhimurium (Fig 5B). Notably, some recipient cells (approximately 103) were resistant to both the plasmid and the chromosomally-encoded markers, showing that within the time frame of the experiment, two unrelated markers were transduced into a single cell. Again, the majority (98–100%) of the transductants were lysogenized by the transducing phage (Fig 5A and 5B) demonstrating that bacteria and phages alike benefit from the transduction process.
To address the capacity of transducing particles to carry bacterial DNA we used quantitative real time (qRT-PCR) to detect the chloramphenicol resistance gene in a ϕ11 phage lysate propagated on S. aureus carrying pRMC2 expressing chloramphenicol resistance and found that the transducing particles constituted 1 in every 700 infective phage particles in the lysate as determined as described by Varga et al. [29] (S1 Table) corroborating other studies reporting approximately 1 out of 1000 S. aureus phages to be a generalized transducing particle [30]. Our S. aureus phage lysates contain 1x109 PFU ml-1 and thus approximately 1.5x106 transducing particles ml-1. As each of these particles can harbour up to 43 kbp of bacterial DNA [31] each millilitre of a phage lysate can carry up to 61 billion bp bacterial DNA, the equivalent of more than 20,000 full S. aureus genomes. Importantly this number is an average estimate based on the information on the number of generalized transducing particles. If the phage lysates was generated by induction of a lysogen the frequency of genes downstream of the integration site in the viral particles is likely to be much higher due to lateral transduction [15]. These numbers underline the potential for transduction to move large amounts of bacterial DNA between cells. In summary, our results show that transduction linked to lysogenization allows the phage to efficiently generate genetic variation in a population of host bacteria that helps the phage and its bacterial host to acquire novel genes required for survival in changing environments.
In the previous experiments, high titer phage lysates obtained on strains carrying transducible antibiotic resistance markers were used to infect phage-susceptible recipient cells. Under natural conditions the generalized transducing phages are released from the lysogens following induction, for example by DNA damaging agents, or in smaller numbers by spontaneous release [9] [32] [33]. This suggests that during co-cultivation, genetic material can be exchanged between different lysogens (carrying the same or related prophages) through the induction of their prophages, without significant lysis being observed.
We first addressed this issue using modelling. We simulated a mixed bacteria culture, with two bacterial strains, where both are lysogens but each carry a different antibiotic resistance gene (Fig 6A). In this model we assumed that transfer occurs by generalized transduction with the transferred marker being present either on a plasmid or distantly located from the prophage integration site. Following an initial growth period, the two antibiotics were introduced in the environment. We observed that the lysogens were predicted to survive the dual antibiotic exposure (Fig 6B). A control simulation shows as expected that, in the absence of prophages, the entire population became extinct, since each of the strains resisted one but not both of the drugs (S4A Fig). Analysis of the genomes of the individuals from the simulations in Fig 6B showed that they were predicted to have acquired the missing antibiotic resistance gene through transducing particles originating from the other strain (S4D Fig), effectively exchanging the resistance traits. Importantly, these double resistant variants emerged even in the absence of antibiotics (i.e., in the absence of selection and antibiotic-associated induction), when spontaneous prophage induction was sufficiently high (S4B and S4C Fig).
To experimentally confirm the theoretical observations we grew, in a 1:1 ratio, two strains of S. aureus both lysogens for ϕ11. S. aureus AA001 is a derivative of 8325–4 carrying pRMC2 expressing chloramphenicol resistance and AA002 is a USA300-LAC containing the LAC-p03 plasmid encoding erythromycin resistance (Fig 6C). Again, as both resistance markers are located on plasmids we monitor generalized transduction. After overnight growth the abundance of cells resistant to either chloramphenicol, erythromycin or both antibiotics were determined on selective agar plates. Because of differences in pigmentation and hemolysis-phenotype between the 8325–4 and USA300 LAC backgrounds (S5 Fig) we were able to assign the resistance profile to the strain background. As seen in Fig 6C each of the two initial strains reached approximately 1x1010 CFU ml-1 but importantly, approximately 1x103 CFU ml-1 of each of the strains had become resistant to both antibiotics indicating that both strains, in addition to their own plasmid, had received the resistance gene from the co-cultured strain. The co-culture experiments with strains lacking ϕ11 did not lead to double resistant colonies excluding the possibility that the plasmids were transferred by conjugation or natural transformation. Furthermore, in the presence of a sublethal concentration of the DNA damaging agent, mitomycin C, we observed both increased release of phage from the co-cultured strains (S6 Fig) and a 10-30-fold increase in transduction frequency (Fig 6C) supporting that DNA transfer occurs by generalized transduction.
In sum, both the theoretical and the experimental results support the conclusion that generalized transduction is a powerful mechanism of DNA transfer between strains, allowing the emergence of single and even double resistant variants. Hence, generalized transduction allows the phage to gain access to the pan-genome of the infected bacteria and generate novel genetic strain variants, which can increase the chances of survival for both the phage and its host in changing environments.
Generalized transduction has long been used as a tool for genetic engineering [22] and, at the same time, is considered a major driver of bacterial evolution [9,14,16,34–36]. Yet, we know relatively little of the process under natural conditions. Here we have examined the impact of generalized transduction on the interactions between temperate, transducing phages and two important bacterial pathogens, namely the Gram-positive S. aureus and the Gram-negative S. enterica Typhimurium. We find that with the examined pathogens and conditions, transduction and lysogeny contribute synergistically to the survival of transducing phages and their hosts. This is because transductants that are lysogens receive the genetic information without enduring mortality by the surrounding phage virions, leading to rapid spread of a trait when there is selection for it. In the rare cases where transductants are not becoming lysogens we anticipate that the cells have become resistant to phage attack by another mechanism. Once lysogens have established, they can exchange genetic material via transducing particles without the risk of being killed by the phage. In our experiments we monitored transfer of plasmid-encoded antibiotic resistance markers suggesting that transfer occurs by generalized rather than lateral transduction, a process which just recently was demonstrated to enable lysogens to transfer chromosomal markers positioned downstream of the phage integration site at very high frequencies [15]. We predict that if we were to monitor such markers even greater transfer frequencies would be observed.
The benefits of generalized transduction to the phage questions the idea that it is just an error in phage packaging. The potential for natural selection on phage to set the rate of transduction is further supported by the existence of genetic variability in these rates. In Salmonella phage P22 [37] and E. coli phage P1 [38], mutants have been found with either increased or decreased transducing ability. In P22, this was shown to depend on point mutations in the small terminase, the protein that recognizes the pseudo-pac-site and initiates packaging during transduction [39]. Moreover, a wide range of transduction efficiencies is observed among naturally occurring phages of both Gram-positive and Gram-negative bacteria with many being unable to transduce [40–42]. On the host side, bacteria benefit from the immunity, prophages confer towards related phages [43] and from prophage encoded products such as virulence factors [16] [44]. Once established, the transducing prophages may acquire genes from other cells through autotransduction [9]. Combined with the results provided here where phage survival is enabled through lysogeny and acquisition of traits such as antibiotic resistance, transduction appears as a cooperative strategy undertaken by some phages to ensure survival of the host. The computational model used here allowed a mechanistic exploration of the different parameters involved in these phage-bacteria interactions, and a qualitative prediction of experimental conditions that are not easily captured by traditional modelling approaches [45]. The model allowed an exploration of conditions that are not experimentally feasible, such as a fine modification of transduction rates. Furthermore, the analysis of the simulations performed with eVIVALDI has identified generalized transduction and lysogeny as the key promotors of both bacterial and phage survival, since variation in other parameters have, by themselves, a limited effect on the outcome of the simulations.
The apparent benefits of generalized transduction, and the absence of prophages from around half of the bacterial genomes [1], prompts the question of why not all bacteria are lysogenized with transducing phages and why not all temperate phages are tranducing. First, transduction may be considered costly as it is associated with fewer infectious particles due to packaging of bacterial rather than phage DNA and with fewer viable bacteria due to cell lysis. This cost could explain why non-generalized transducing phages exist in nature. Secondly, bacteria protect themselves against phages by various resistance mechanisms such as abortive infection, restriction-modification systems and CRISPR [46,47]. Defence systems based on the prevention of phage absorption will prevent the entry of DNA from transducing particles and restriction-modification systems will block transfer if the DNA originates from a strain lacking the system. In those cases, acquisition of new traits may take place by other types of mobile genetic elements such as conjugative plasmids or by natural transformation. CRISPR-Cas systems can recognize specific sequences of phage DNA and restrict phage entry into bacterial cells [48]. In this case, generalized transduction may still occur and may even be promoted by the CRISPR-Cas activity as it helps transductants to survive by blocking phage infection [49]. For the bacteria investigated in the present study, lysogeny is highly common with essentially all clinical strains being lysogens [4,50]. CRISPR-Cas systems are only found in a subset of strains and exchange of phages and DNA frequently occurs within lineages formed by the barriers of restriction-modification systems [50]. In these bacteria, cooperation with temperate and potentially transducing phages may be a survival strategy that not only allows exchange of mobile genetic elements and resistance genes but potentially also shapes the evolution of the bacterial genome within lineages.
When we examined the bacterial content of a phage lysate from the staphylococcal phage ϕ11, we found that the amount of bacterial DNA carried in phage lysates supports the transduction of un-linked chromosomal and plasmid markers and that each millilitre of phage lysate will contain approximately 20,000 copies of the bacterial genome. We have previously seen that there is substantial spontaneous release of ϕ11 from a lysogenic strain and have proposed that this may be important for the acquisition of traits by auto-transduction whereby released phage propagate on susceptible bacteria in the surroundings and returning transducing particles provide resistance to for example antibiotics [9]. Phage particles released spontaneously from or by induction of lysogens are likely to carry even greater percentages of transducing particles as they may be released as part of lateral transduction [15]. These transducing particles will be able to exchange genetic information between cells carrying the same temperate phage as we show here indicating that lysogeny with a transducing phage enables genetic exchange either by general or lateral transduction. In conclusion, our data show that generalised transduction is a mutualistic trait that promotes the survival of phage and lysogen alike and importantly that temperate phages benefit from transduction. While the potential for evolutionary conflict between phage and host clearly still exists, the strongly overlapping interests of temperate phages and their hosts are expected to help stabilise their relationship, and may explain why lysogens are so common in species like S. aureus and S. Typhimurium.
Bacteria and phages used are listed in S2 Table. S. aureus strains were grown in Trypsic Soy Broth (Oxoid) with the addition of 10mM CaCl2 when phage propagation was desired. Salmonella Typhimurium and E. coli strains were grown in Luria-Bertani Broth (Sigma). Incubation was done in 15 ml disposable centrifuge tubes at 37 °C with shake (200rpm) unless otherwise noted. Erythromycin (10 μg ml-1), Kanamycin (30 μg ml-1), Chloramphenicol (10 μg ml-1) or Tetracycline (5 μg ml-1 or 20 μg ml-1), all purchased from Sigma, was added when appropriate.
Phages were induced from lysogenic strains by mitomycin C (2 μg ml-1, Sigma) at OD600 of 0.3 at which time the cultures were incubated at 32°C with 80 rpm shake overnight. The resulting lysate was sterile filtered and enumerated using the soft-agar overlay method. Plate lysates were prepared by harvesting soft agar plates with phages in SM buffer followed by sterile filtration. Phage resistance was examined by first spotting 10 μl of the given strain (overnight culture) as well as a susceptible indicator strain (for positive control) on an agar plate containing soft agar. After brief desiccation 5μl of an appropriate phage lysate allowing detection of 25–50 plaques on the indicator strain was spotted on top of the test strain and the indicator strain. Resistance was determined by the absence of plaques after overnight incubation on the test strain but not on the indicator strain.
Plasmid and chromosomal markers were transferred between strains using standard phage transduction protocols [51]. To produce the virulent ϕ80α, allelic exchange was performed using derivatives of plasmid pMAD [52] carrying ΔcI, as described previously [53]. Plasmid pJP1686 (ΔcI) was constructed by cloning PCR products obtained with the following primers into vector pMAD: orf6phi80alpha-3mB (5’-CGCGGATCCTTTGCTTTGTTTAGAAGCATCG-3’), orf6phi80alpha-4c (5’-CTTCCGTTCAGACATAATTTG-3’), orf6phi80alpha-5m (5’-AAATTATGTCTGAACGGAAGAAGTATGATGATATCAAAGTCGC-3’) and orf6phi80alpha-6cE (5’-CCGGAATTCTTTCTCTTCCATCCCTCATCC-3’).
Lysate from phage propagated on strains carrying a transferable marker was used to infect a recipient strain with a multiplicity of infection (MOI) of 1 at a cell density of OD600 = 0.01. Following overnight incubation, the number of cells (colony forming units, CFU) and transductants were determined by plating on non-selective and selective plates, respectively. The number of lysogens was determined for ϕ11-ERM by enumeration of erythromycin resistant colonies or, in the cases where no antibiotic marked phage was used, by inducing 96 transductants with mitomycin C (2 μg ml-1) and determine the presence of free phages on a suitable indicator strain. In time course infection experiments, ϕ11-ERM was propagated on a donor strain containing the non-conjugative plasmid, pRMC2. Using multiplicity of infection of 1, the cultures were infected at OD600 = 0.01 and grown for 16h in batch cultures and at regular intervals, 30 transductants selected on chloramphenicol were tested for erythromycin resistance. When antibiotic selection was applied in liquid cultures infected cultures were diluted 1000-fold in TSB containing 30μg ml-1 chloramphenicol and allowed to grow for 7h before another 1000-fold dilution in TSB containing chloramphenicol was applied. After overnight incubation the number of chloramphenicol-resistant transductants and CFU was determined in the cultures.
qRT-PCR was used to determine the ratio of transducing particles to phage particles as previously described [29] including treatment of phage lysate with RNaseA and DNaseI at 1 and 5 ug ml-1, respectively. Briefly, control plasmid DNA was purified from a pUC18 vector containing the 4867-bp HindIII DNA fragment of bacteriophage ϕ53 (GenBank accession number, AF513856) designated p53D and a 139 bp product specific for all serogroup B phages [2] was amplified by the primers 53D -F (CGACAAAAGGCATTCAACAA) and 53D-R (ACGTTCAAAAATCGCTTGCT). The same primers were used for qRT-PCR reactions in reactions that contained 10ul of 2xSYBR green master, 500nM of each primer and 5ul of template DNA being either plasmid or phage DNA. For the standard curve, the numbers of control plasmid DNA molecules in reactions ranged from 3x107 to 3x102 in 10-fold fashion. The program of qRT-PCR is 10min of preincubation at 95°C followed by 45 cycles of amplification (95°C for 10s, 58°C for 10s and 72°C for 10s) and the final melting (95°C for 15s, 65°C for 60s and 97°C for 1s). According to the standard curve, which was made with the data of control plasmid, the copy number of infective phage particles was calculated. On the other hand, plasmid pRMC2 was used as standard to calculate the absolute copies of transduced plasmid DNA in the same lysate. The primers used in the second qRT-PCR was CAT-F (5´-TGGTTACAATAGCGACGGAGA-3´) and CAT-R (5’-TACAGGAGTCCAAATACCAGAGA-3´). The reactions were almost the same as the first ones except for primers and template. For the standard, the numbers of pRMC2 plasmid DNA molecules in reactions ranged from 3x106 to 30 in a 10-fold fashion. The program was 10min of preincubation at 95°C followed by 45 cycles of amplification (95°C for 10s, 62°C for 10s and 72°C for 10s) and the final melting (95°C for 15s, 65°C for 60s and 97°C for 1s). According to the standard curve of pRMC2 plasmid, the copy number of transducing particles was calculated. Ratio of transducing particles to infective phage particles was calculated with the numbers of two types of particles.
Phage lysate was treated with RNaseA (Thermo Fisher) and DNaseI (Thermo Fisher) at 1 and 5 ug ml-1, respectively to remove bacterial DNA and RNA as previously described [29]. DNA was prepared from 1 ml of the lysates used in the infection experiments (Table 1) using the MAGattract HMW DNA kit (Qiagen) according to the manufacturers’ instructions. PCR was performed on the isolated phage-lysate DNA with pRMC2 plasmid DNA as positive control and untreated phage lysate as negative control for free pRMC2 DNA in the lysate. Positive PCR reactions were confirmed to be originating from pRMC2 by sequencing. Primers pRMC2_fw (5’-GCGACGGAGAGTTAGGTTATTGGG-3’) and pRMC2_rev (5’-ACCTTCTTCAACTAACGGGGCAGGT-3’) were used for PCR. Primer pRMC2_fw was also used for sequencing.
Simulations were performed based on the model described in https://doi.org/10.1101/291328. Both bacterial cells and phage particles are independent individuals on an environment represented as a two-dimensional grid with Moore neighbourhood (the 8 connected grid spaces of each location, for a Moore distance of one). The environment is simulated as well-mixed, meaning that positions of bacteria and phage are randomized at each iteration. Bacteria are simulated either with or without a gene conferring the ability to survive antibiotic exposure, and this trait cannot be evolved due to genomic mutation, it has to be acquired by transduction. Both bacterial strains have a similar growth rate, but the gene that provides resistance to antibiotics carries a cost. Each location in the environment can hold a single bacterial cell and several phage cells. Free space is the bacterial resource to be consumed, and it is freed whenever bacteria die. Bacterial death can be intrinsic (e.g., of old age) or explicit (e.g., exposure to antibiotics or lysed by phage). When a free space is available, the neighbouring bacteria compete for reproduction. The outcome of the competition is chosen through a roulette wheel method that accounts for the fitness of each bacterium. The successful bacterium generates an offspring into the free space. Phages have different lifestyles (temperate or virulent) and can become defective phage particles due to generalized transduction. The decision between a lytic or lysogenic cycle varies across the types of phages, and depends on the concentration of free phage particles in their proximity. The host range of phage is similar for all phages and is the same for both bacterial species. The burst size is also similar for all phage types. The superinfection exclusion rules amongst phages is parameterized according to the experimental setup simulated. The parameters used in the simulations are described in detail in S1 Text.
The Random Forest Analysis (RFA) is based on simulations performed with the model, covering 5000 random combinations of parameters, with 20 simulated repeats per combination. The output of this cohort of simulations is grouped and resumed in response variables. This results in a large table with input parameters and response variables to which we add a column with 5000 rows of a random parameter (i.e., a choice of a number between 1 and 3). This parameter allows to assess the impact of random variables in the RFA. This table is used as input of the randomForest package in R (version 4.6.12), where the randomForest function is run with the parameters ntrees set to 10000. The relative importance of each parameter (the percentage increase in minimum squared error, %IncMSE) is assessed using the importance function from the same package. This function evaluates the effect of excluding each parameter on the ability of the RFA to predict the response variable (the percentage of simulations where phage survive, for our study). In the context of RFA, exclusion is performed by randomly assigning values to a parameter, rather than those used for the actual simulations. The predictive ability is compared between the original data and the permuted data (with the “excluded” parameter) and parameters that lead to high increases in prediction error (increased MSE) are deemed of high importance. A purposefully random parameter is also included in our analysis, to assess the impact of random noise in the quantification of the relative importance of the parameters. Non-important parameters have a small or null increase in MSE, similar to that of random noise. The parameters used in the simulations are described in detail in S1 Text and S3 Table.
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10.1371/journal.pgen.1001245 | A Single Enhancer Regulating the Differential Expression of Duplicated Red-Sensitive Opsin Genes in Zebrafish | A fundamental step in the evolution of the visual system is the gene duplication of visual opsins and differentiation between the duplicates in absorption spectra and expression pattern in the retina. However, our understanding of the mechanism of expression differentiation is far behind that of spectral tuning of opsins. Zebrafish (Danio rerio) have two red-sensitive cone opsin genes, LWS-1 and LWS-2. These genes are arrayed in a tail-to-head manner, in this order, and are both expressed in the long member of double cones (LDCs) in the retina. Expression of the longer-wave sensitive LWS-1 occurs later in development and is thus confined to the peripheral, especially ventral-nasal region of the adult retina, whereas expression of LWS-2 occurs earlier and is confined to the central region of the adult retina, shifted slightly to the dorsal-temporal region. In this study, we employed a transgenic reporter assay using fluorescent proteins and P1-artificial chromosome (PAC) clones encompassing the two genes and identified a 0.6-kb “LWS-activating region” (LAR) upstream of LWS-1, which regulates expression of both genes. Under the 2.6-kb flanking upstream region containing the LAR, the expression pattern of LWS-1 was recapitulated by the fluorescent reporter. On the other hand, when LAR was directly conjugated to the LWS-2 upstream region, the reporter was expressed in the LDCs but also across the entire outer nuclear layer. Deletion of LAR from the PAC clones drastically lowered the reporter expression of the two genes. These results suggest that LAR regulates both LWS-1 and LWS-2 by enhancing their expression and that interaction of LAR with the promoters is competitive between the two genes in a developmentally restricted manner. Sharing a regulatory region between duplicated genes could be a general way to facilitate the expression differentiation in duplicated visual opsins.
| Among vertebrates, fish may have the most advanced color vision. They have greatly varied repertoires of color sensors called visual opsins, possibly reflecting evolutionary adaptation to their diverse photic environments in water, and are an excellent model to study the evolution of vertebrate color vision. This is achieved by multiplying opsin genes and differentiating their absorption light spectra and expression patterns. However, little is understood regarding how the opsin genes are regulated to achieve the differential expression pattern. In this study, we focused on the duplicated red-sensitive opsin genes of zebrafish to tackle this problem. We discovered an “enhancer” region near the two red opsin genes that plays a crucial role in their differential expression pattern. Our results suggest that the two red opsin genes interact with the enhancer competitively in a developmentally restricted manner. Sharing a regulatory region could be a general way to facilitate the expression differentiation in duplicated visual opsin genes.
| Gene duplication is a fundamental step in evolution [1]. Most often, one of the resulting daughter genes simply becomes a pseudogene and may be eventually lost from the genome due to functional redundancy between the duplicates and reduction of selective constraint to maintain its function. However, observation of another fate for duplicated genes, such as acquisition of a new function (neofunctionalization) or subdivision of parental gene function between daughter genes (subfunctionalization), implies an evolutionary advantage by the process [2]. Subfunctionalization often involves differentiation of expression pattern between daughter genes and has been a subject of intense scrutiny to understand the regulatory mechanism to achieve the differentiation [3]–[5].
In vertebrates, color vision is enabled by the presence of multiple classes of cone visual cells in the retina, each of which has a different absorption spectrum. The absorption spectrum of a visual cell is mainly determined by the visual pigment it contains. A visual pigment consists of a protein moiety, visual opsin, and a photo-sensing chromophore, either 11-cis retinal or 11-cis 3,4-dehydroretinal [6]. The five types of visual opsins found among extant vertebrates are RH1 (rod opsin or rhodopsin) and four types of cone opsins: RH2 (RH1-like, or green), SWS1 (short wavelength-sensitive type 1, or ultraviolet-blue), SWS2 (short wavelength-sensitive type 2, or blue) and M/LWS (middle to long wavelength-sensitive, or red-green) [7]. The SWS2 and M/LWS type genes are closely located on the same chromosome [8]–[10] and could represent the most ancient gene duplication in vertebrate visual opsin genes, from which other types could have arisen through whole-genome duplications and subsequent gene losses in early vertebrate evolution [7], [11]–[13]. Thus, visual opsin genes represent an excellent case of gene duplication to study the mechanism of neofunctionalization (in absorption spectrum) and subfunctionalization (in expression pattern). While the spectral tuning mechanism of visual opsins has been intensively studied [14]–[18], the regulatory mechanism of their expression differentiation, especially that of cone opsins, has been less explored.
Among vertebrates, fish are known to possess a rich and varied repertoire of visual opsins, including two or more opsin subtypes within the five types by further gene duplications [19]–[21], presumably reflecting their evolutionary adaptation to diverse aquatic light environments [22]. In fish, the eyes continue to grow throughout their lifetime by adding new cells to the peripheral zones, such that the peripheral cells are developmentally younger than central cells [23], [24]. Thus, in the fish retina the timing of gene expression is partly reflected in the region of expression in the retina. All visual opsin genes have been isolated and characterized for zebrafish (Danio rerio) [25], medaka (Oryzias latipes) [26] and cichlids (Family Cichlidae) [27]–[30]. Among them, the expression pattern of visual opsin genes has been best documented for zebrafish.
Zebrafish have nine visual opsin genes consisting of two M/LWS (red), four RH2 (green), and single-copy SWS1 (UV), SWS2 (blue) and RH1 (rod) opsin genes [25]. The red, green, UV and blue opsin genes are expressed in the long-member of double cones (LDCs), the short-member of double cones (SDCs), the short single cones (SSCs) and the long single cones (LSCs), respectively, which are arranged in a regular mosaic pattern in the retina [31], [32]. The two red opsin genes, LWS-1 and LWS-2, are arrayed in a tail to head manner, in this order, and encode photopigments with wavelengths of maximal absorption (λmax) at 558 and 548 nm, respectively [25]. The four green opsin genes, RH2-1, RH2-2, RH2-3 and RH2-4, are also arrayed in a tail to head manner, in this order, and encode photopigments with λmax at 467, 476, 488, and 505 nm, respectively [25]. In both red and green opsins, expression of longer-wave subtypes occurs later in development and is confined to the peripheral, especially ventral-nasal region of the adult retina, whereas expression of shorter-wave subtypes occurs earlier and is confined to the central region of the adult retina, shifted slightly to the dorsal-temporal region [33]. It remains largely unknown how subtypes of an opsin class are directed to express in different regions of the retina while keeping the cell type identical between them. Thus, the zebrafish visual opsins are an excellent model to study the regulatory mechanism of not only cell-type specific expression of opsin types, but also developmental-stage (and thus retinal-region) specific expression of opsin subtypes.
With the feasibility to employ transgenic technology, cis-regulatory regions relevant to the cell-type specific expression of opsin types have been elucidated using a living color reporter such as the green fluorescent protein (GFP) for zebrafish single-copy opsin genes (i.e., rod opsin [34]–[37], UV opsin [38], [39] and blue opsin genes [40]). A regulatory region relevant to not only cell-type specific but also retinal-region specific expression of opsin subtypes has also been reported for the zebrafish green opsin genes [41]. In the present study, we focus on the zebrafish red opsin genes, LWS-1 and LWS-2, and report a cis-regulatory region, “LWS-activating region” (LAR), which is relevant to their expression differentiation.
In the two PAC clones we obtained [LWS-PAC(E) and LWS-PAC(H)], the first exons of LWS-1 and LWS-2 were replaced after their initiation codons with DNA segments encoding green and red fluorescent proteins (GFP and RFP), respectively (Figure 1A). The modified clones were designated LWS1/GFP-LWS2/RFP-PAC(E) and LWS1/GFP-LWS2/RFP-PAC(H), respectively. One transgenic zebrafish line was established using each construct: Tg(LWS1/GFP-LWS2/RFP-PAC(E))#1229 and Tg(LWS1/GFP-LWS2/RFP-PAC(H))#430.
Adult fish of both lines expressed RFP in the central-dorsal-temporal region of the retina and GFP in the peripheral-ventral-nasal region of the retina circumscribing the RFP region (Figure 1B). It was also confirmed that the expression was specific to the LDCs, which were immunostained by an antibody against the zebrafish red opsin (Figure 1C). Thus, in both transgenic lines, the expression of GFP and RFP reporter genes recapitulated the expression of LWS-1 and LWS-2, respectively, demonstrating that both the LWS-PAC(E) and LWS-PAC(H) clones contain sufficient regulatory region(s) for the proper expression of the two red opsin genes.
Next, we used only the intergenic region between the stop codon of SWS2 and the initiation codon of LWS-1, designated LWS1up2.6kb, and the region between the stop codon of LWS-1 and the initiation codon of LWS-2, designated LWS2up1.8kb. We created a double-reporter construct consisting of the LWS1up2.6kb, GFP reporter, LWS2up1.8kb and RFP reporter, in this order (LWS1up2.6kb:GFP-LWS2up1.8kb:RFP, Figure 2A), and obtained three transgenic lines: Tg(LWS1up2.6kb:GFP-LWS2up1.8kb:RFP)#1464, 1631, and 1640. In two of the three lines, #1631 (Figure 2B, 2C) and #1640, the GFP and the RFP recapitulated the expression patterns of LWS-1 and LWS-2, respectively. In the third line, #1464, the expression of RFP was weaker and sparser but still confined to the central region of the retina and the expression of GFP appeared to be identical to the other two lines (Figure S1). The expression pattern of LWS-1 was also recapitulated when only the LWS1up2.6kb was used with GFP (LWS1up2.6kb:GFP) in all three transgenic lines obtained: Tg(LWS1up2.6kb:GFP)#1508, 1509 (Figure 2A, 2D–2F), and 1515. On the other hand, when only the LWS2up1.8kb was used with GFP (LWS2up1.8kb:GFP), no GFP signal was observed in the transgenic line obtained: Tg(LWS2up1.8kb:GFP)#1433. These results suggest that the LWS1up2.6kb contains a regulatory region not only for LWS-1, but also for LWS-2.
In order to search for the regulatory region from the LWS1up2.6kb, we employed the transient transgenic assay in which the regulatory activity of a GFP-reporter construct was evaluated in the fish injected with the construct. This was done by examining the incidence of fish bearing GFP-expressing eyes at a larval stage. As in previous studies [39]–[41], the expression level of GFP was graded into four categories, +++, ++, +, −, at 7 days post-fertilization (dpf) (Figure 3). First, we used a whole PAC clone, LWS-PAC(E), and modified it to LWS1/GFP-PAC(E) and LWS2/GFP-PAC(E), in which the first exon of LWS-1 and LWS-2, respectively, was replaced after its initiation codon with GFP-encoding DNA (Figure 3A left). We confirmed that the GFP expression pattern from the two PAC constructs was consistent with the expression patterns of LWS-1 and LWS-2, respectively, at the larval stage (Figure 3A right) (i.e., LWS-2 is expressed predominantly and LWS-1 is expressed only faintly in the retina [33]).
Next, as shown in Figure 3B left, we isolated from LWS2/GFP-PAC(E) a series of DNA regions consisting basically of the LWS2up1.8kb-GFP-LWS-2 region and varying ranges of its upstream region. The GFP signal was apparent when the upstream region contained 1.3-kb or more upstream of LWS-1, but was almost undetectable when it contained 0.6-kb or less upstream of LWS-1 or when only the LWS2up1.8kb-GFP-LWS-2 region was used (Figure 3B right). This implies that the LWS2up1.8kb region does not contain a sufficient regulatory region for the expression of LWS-2, consistent with the absence of a GFP signal in the transgenic line Tg(LWS2up1.8kb:GFP) described above. This also suggests that the regulatory region is located in the 0.6-kb region between 1.3-kb and 0.6-kb upstream of LWS-1.
To test if the 0.6-kb region plays a regulatory role by itself for the expression of LWS-2, a coinjection protocol was employed using mixed concatamers of separate DNA fragments formed upon integration into the genome [42]. The LWS2up1.8kb-GFP-LWS-2 region was injected together with a variety of DNA segments from the LWS1up2.6kb region (Figure 3C left). GFP expression was apparent in the retina only when the segment contained the 0.6-kb region (Figure 3C right). We thus designated the 0.6-kb region as an “LWS-activating region” (LAR).
Using a DNA construct consisting of LAR and LWS2up1.8kb:GFP, designated LAR:LWS2up1.8kb:GFP (Figure 4A), we obtained five transgenic lines: Tg(LAR:LWS2up1.8kb:GFP)#1481, 1491, 1496, 1499, and 1501. In one line, #1499, a GFP signal was observed specifically in the LDCs but across the entire outer nuclear layer, not confined to the central-temporal-dorsal region (Figure 4B–4D). The absence of retinal region specificity is in sharp contrast to the case in which the double reporter construct, LWS1up2.6kb:GFP-LWS2up1.8kb:RFP, was used (Figure 2B, 2C). This suggests that the relative position of the LAR to the gene is relevant to the regional specificity of the retina.
In another line of Tg(LAR:LWS2up1.8kb:GFP)#1501, the GFP signal also appeared throughout the retina, but was sparser (Figure S2A). At a finer level, the signal appeared not only in LDCs but also weakly in some bipolar cells (Figure S2B). In the other three Tg(LAR:LWS2up1.8kb:GFP) lines, the GFP signal was not detectable. This instability of the reporter signal among the transgenic lines could be attributed not only to the general effect of their insertion sites in the genome, but also to the dependency of LAR to work cooperatively with its adjacent regions in the LWS1up2.6kb and LWS2up1.8kb. Consistently, as in the transient transgenic assay shown in Figure 3B, GFP expression level was much higher when the entire LWS1up2.6kb region was used than when only the proximal 1.3 kb region was used.
To examine if the LDC-specificity of the GFP expression was attributed to LAR itself, we tested the 564-bp adjacent upstream region of a non-retinal keratin 8 gene [43], designated krt8up564bp [41]. The krt8up564bp induces gene expression specifically in the epithelial tissues, but not in the retina, and has been used for enhancer trapping as a basal promoter [44]. When krt8up564bp was conjugated to the LAR and GFP reporter (LAR:krt8up564bp:GFP), no GFP expression was observed in the retina of the two transgenic lines obtained: Tg(LAR:krt8up564bp:GFP)#1469 and 1477. This is in sharp contrast to the case in which krt8up564bp was conjugated to the RH2-LCR and GFP expression was observed in the SDCs throughout the zebrafish retina in our previous study [41]. This suggests that LAR itself is not capable of determining the cell-type specificity of gene expression, unlike RH2-LCR, but works as an enhancer which interacts with cell-type determining regions that should reside in both LWS1up2.6kb and LWS2up1.8kb.
Next, we removed the LAR from LWS1/GFP-LWS2/RFP-PAC(E) and LWS1/GFP-LWS2/RFP-PAC(H) (designated ΔLAR-LWS1/GFP-LWS2/RFP-PAC(E) and ΔLAR-LWS1/GFP-LWS2/RFP-PAC(H), respectively) (Figure 5A). Two transgenic lines were found for each of the two constructs: Tg(ΔLAR-LWS1/GFP-LWS2/RFP-PAC(E))#1143 (Figure 5C) and 1166 and Tg(ΔLAR-LWS1/GFP-LWS2/RFP-PAC(H))#1107 (Figure 5D, 5E) and 1100. All four of these transgenic lines showed a similar expression pattern of the reporters in the retina with the LAR-bearing Tg(LWS1/GFP-LWS2/RFP-PAC(E)) or Tg(LWS1/GFP-LWS2/RFP-PAC(H)) (Figure 5B) line. The reporter-expressing cells were confined to LDSs (Figure 5E). The GFP and RFP signals were observed in the ventral and dorsal regions of the retina, respectively (Figure 5B–5D). However, the fluorescent signal in each cell was lowered. The number of the reporter-expressing cells decreased and their spatial distribution was restricted to a narrow range in both of these regions (Figure 5B–5E). These results support the deduced role of LAR as an enhancer but not as the cell-type determining factor from the experiments thus far. This experiment also provided the first direct evidence that LWS-1 expression is affected by LAR.
The present study identified a 0.6-kb regulatory region, named LAR, for the expression of the duplicated red opsin genes of zebrafish, LWS-1 and LWS-2, in the upstream of the gene array. The LAR functions to enhance the LDC-specific expression of both genes but does not determine the cell-type specificity of the gene expression. The regulatory region for the cell-type specificity of the gene expression appears to reside in the 2.6-kb and 1.8-kb upstream regions of the two genes. The relative position of LAR to a gene is relevant to the retinal region specificity of the expression of the gene.
In the primate L/M opsin genes, the locus control region (LCR) is located at ∼3.5-kb upstream of the gene array and is necessary for the expression of both L and M opsin genes [45], [46]. Although there is no clear overall similarity between the zebrafish LAR and primate L/M opsin LCR, LAR contains two OTX (A/GGATTA) and one OTX-like (TGATTA) sequences (Figure S3) which are also present in the primate L/M opsin LCR [47], [48]. These sequences, or their reverse complement sequences, are the binding sites of the cone-rod homeobox (Crx) protein, a member of the Otx family of the paired-like homeodomain proteins and a key trans-acting regulatory factor responsible for the gene expression in the retina and pineal organ [47], [48]. The mammalian Crx is produced predominantly in both the retinal photoreceptors and pineal cells and regulates expression of retinal photoreceptor-specific genes and of pineal-specific genes [47]–[50]. In zebrafish, Otx5, a paralog of Crx, is produced in the retina and pineal organ and regulates genes that show circadian expression in the pineal organ [51]. The OTX or OTX-like sequences have also been found in the upstream region of the zebrafish SWS2 [40] and in the RH2-LCR [41]. Thus, the LAR could be orthologous to the primate L/M opsin LCR and also be paralogous to the SWS2 regulatory region and the RH2-LCR (see ref. [12] for a similar discussion).
The primate L/M opsin LCR interacts with only the most proximal or the second proximal gene of the array, often L and M opsin genes respectively, through their proximal promoters [46], [52], [53]. The choice of the promoters by the LCR is largely a stochastic process [54], [55]. These characteristics enable the mutually exclusive expression of the L and M opsin genes and nearly a random distribution of the L and M cone photoreceptor cells in the primate retina. In zebrafish, the expression of LWS-1 and LWS-2 is also nearly mutually exclusive in the retina [33]. Unlike the primate L/M opsin system, however, the expression of the two zebrafish red opsin genes is temporally and spatially organized and not random in the retina [33]. Whereas expression of LWS-2 is first observed at 40 hours post-fertilization (hpf) and spread throughout the retina by 72 hpf, initial expression of LWS-1 is observed at 3.5–5.5 days post-fertilization (dpf) in the marginal side of the ventral retina [33]. In sexually mature adults, LWS-2 is expressed in the central-dorsal-temporal region of the retina. Expression of LWS-1 is complementary to the LWS-2 observed in the peripheral-ventral-nasal region of the rest of the retina, although cells at the boundary of the two fields appear to express both gene subtypes and LWS-1 is sparsely expressed in the LWS-2 zone [33].
In this study, the spatially restricted patterns of gene expression were recapitulated by fluorescent reporters for both LWS-1 and LWS-2 in the adult retina of Tg(LWS1up2.6kb:GFP-LWS2up1.8kb:RFP) (Figure 2B, 2C). The expression pattern of LWS-1 was also recapitulated in Tg(LWS1up2.6kb:GFP) (Figure 2D, 2E), whereas that of LWS-2 was not, and GFP was expressed throughout the adult retina in Tg(LAR:LWS2up1.8kb:GFP) (Figure 4B, 4C). This suggests that the LWS1up2.6kb contains a region susceptive to a developmental control that represses gene expression in the early stage or activates it in the later stage in LDCs, while the LWS2up1.8kb allows LDC-specific expression throughout development with the aid of LAR. This also suggests that LAR, which is shared by LWS-1 and LWS-2, interacts with the LWS-2 promoter during the time LWS-1 expression is repressed (or not activated) in the early stage and then interacts with the LWS-1 promoter once the LWS-1 expression is enabled. This preference in interaction of LAR for LWS-1 over LWS-2 could be attributed to the closer distance of LAR to LWS-1, as in the case of the primate L/M opsin LCR [46], [52], [53] and the zebrafish RH2-LCR [41].
Sharing a regulatory region among duplicated genes is a common feature among the zebrafish M/LWS (red) and RH2 (green) and the primate M/LWS (L and M) opsin genes. This system should be advantageous in facilitating differential (i.e., mutually exclusive) expression of duplicated opsin genes by using the regulatory region in a competitive manner between the duplicated genes. If the competition is largely stochastic, an intermingled pattern of photoreceptor cells expressing different daughter genes can be expected in the retina, as in the case of primate L/M opsin genes. The trichromatic color vision is enabled by this stochastic-type system in primates. If the competition is developmentally controlled, for example, so that the regulatory region interacts with a proximal gene in an early stage and shifts the interaction target to a distal gene, the proximal gene would be expressed in the central region and the distal gene in the peripheral region of the retina as in the case of the zebrafish green opsin genes. In the case of the zebrafish red opsin genes, the interaction would start with the distal gene and switch to the proximal gene. In fish, such a control is feasible because the retina continues to grow throughout their lifetime by adding new cells to the peripheral zone [24]. Expression of different opsin genes among different retinal regions results in sights with varying wavelength sensitivity as a function of visual angles, which could be advantageous in the aquatic light environment where wavelength composition differs depending on directions [56]. This could explain why many examples of gene duplication have been found in fish visual opsin genes. Further studies of the regulatory mechanism of differential expression of fish visual opsin genes should contribute to our understanding of the adaptive significance of gene duplications in general.
All animal protocols were approved by the University of Tokyo animal care and use committee.
Through the screening service of the Resource Center Primary Database (RZPD, Germany; https://www.rzpd.de) of a zebrafish PAC library (no. 706, originally created by C. Amemiya), two clone DNAs (BUSMP706E19271Q9 and BUSMP706H1397Q9), designated LWS-PAC(E) and LWS-PAC(H), were obtained using the LWS-2 cDNA as a probe. Both clones encompass SWS2, LWS-1 and LWS-2 in their ∼80-kb and ∼110-kb inserts, respectively (Figure 1A). Sequencing both ends of the inserts revealed that the nucleotide sequences of LWS-PAC(E) and LWS-PAC(H) correspond to the nucleotide positions 25222648–25311454 and 25174505–25295119 of chromosome 11 in the Ensembl zebrafish assembly version 8, respectively (http://www.ensembl.org/Danio_rerio/Info/Index).
The I-SceI meganuclease system [57] was used for efficient transgenesis of the PAC-derived constructs. Two I-SceI recognition sites (5′-TAGGGATAACAGGGTAAT-3′) were introduced into the vector backbone of the LWS-PAC clones as follows. The ampicillin-resistance (Ampr) gene was PCR-amplified from the pUC18 plasmid using primers harboring the I-SceI recognition site at their 5′ ends to create the I-SceI-Ampr-I-SceI segment (see “I-SceI-Ampr-I-SceI” in Table S1 for primers). The I-SceI-Ampr-I-SceI segment was inserted into the EcoRV site of pBluescript II (SK-) plasmid vector (Stratagene, Tokyo). The I-SceI-Ampr-I-SceI segment was isolated from the pBluescript clone using primers harboring the flanking sequences of the kanamycin-resistance (Kmr) gene site of the LWS-PAC clones to create the I-SceI-Ampr-I-SceI cassette (see “Kmr<>I-SceI-Ampr-I-SceI” in Table S1 for primers). The Kmr of the LWS-PAC clones was replaced with the I-SceI-Ampr-I-SceI cassette by the site-specific homologous recombination system coupled with drug selection using the E. coli strain EL250 [58] as in our previous study [41].
The first exon after the initiation codon of LWS-1 and LWS-2 in the LWS-PAC clones was replaced with the GFP or the RFP gene as follows. The chloramphenicol acetyl transferase (CAT) and the Kmr gene fragments were PCR-amplified from pBR328 and pCYPAC6 plasmids, respectively (see “CAT” and “Kmr” in Table S1 for primers). The CAT gene was inserted into the pEGFP-1 plasmid vector (BD Biosciences Clontech, Tokyo) at the AflII site, which is located immediately downstream of the SV40 polyadenylation signal (polyA), linked downstream of the GFP coding sequence to create the GFP-polyA-CAT segment. Similarly, the Kmr gene was inserted into the pDsRed-1 or pDsRed-Express-1 plasmid vector (BD Biosciences Clontech, Tokyo) at the AflII site to create the RFP-polyA-Kmr segment. For the LWS1/GFP-LWS2/RFP-PAC(H), the pDsRed-1 was used. For the other RFP-containing constructs (LWS1/GFP-LWS2/RFP-PAC(E), ΔLAR-LWS1/GFP-LWS2/RFP-PAC(E) and ΔLAR-LWS1/GFP-LWS2/RFP-PAC(H)), the pDsRed-Express-1 was used. The GFP-polyA-CAT segment was isolated from the pEGFP-1 construct by PCR using primers harboring the flanking sequences of the exon 1 of LWS-1 or LWS-2 to create the GFP-polyA-CAT cassette (see “LWS-1<>GFP-polyA-CAT” and “LWS-2<>GFP-polyA-CAT” in Table S1 for primers). The RFP-polyA-Kmr segment was isolated from the pDsRed-1 or the pDsRed-Express-1 construct by PCR using primers harboring the flanking sequences of the exon 1 of LWS-2 to create the RFP-polyA-Kmr cassette (see “LWS-2<>RFP-polyA-Kmr” in Table S1 for primers). These cassettes were replaced with the exon 1 of LWS-1 or LWS-2 in the LWS-PAC clones by the site-specific homologous recombination system in EL250.
The LAR was removed from the LWS-PAC clones by the site-specific homologous recombination system and by the flpe-FRT recombination system for excision of a DNA region sandwiched by FRT sequences in EL250 [41], [58] as follows. The CAT gene was PCR-amplified from the pBR328 using primers harboring the FRT sequences to create the FRT-CAT-FRT segment (see “FRT-CAT-FRT” in Table S1 for primers). The FRT-CAT-FRT segment was inserted into the EcoRV site of pBluescript II (SK-) plasmid. Then, the FRT-CAT-FRT segment was isolated by PCR using primers harboring the flanking sequences of the LAR to create the FRT-CAT-FRT cassette (see “LAR<>FRT-CAT-FRT” in Table S1 for primers). The LAR was replaced with the FRT-CAT-FRT cassette in the LWS-PAC clones by the site-specific homologous recombination system in EL250. The FRT-CAT-FRT cassette was then excised from the modified LWS-PAC clones in EL250 by the flpe-FRT recombination system for excision of a DNA region sandwiched by FRT sequences, leaving one FRT sequence in this region of the clones [41], [58].
A plasmid construct, pT2AL200R150G [59], was modified as follows. The pT2AL200R150G contains a GFP-expression cassette between XhoI and BglII sites surrounded respectively by the L200 and R150 minimum recognition sequences of the Tol2 transposase. The Tol2 transposase excises the DNA region between the recognition sequences from the plasmid and integrates it into the host genome as a single copy with the recognition sequences attached as in the plasmid [60]. The GFP-expression cassette contains a promoter sequence of a ubiquitously expressed gene (the Xenopus elongation factor (EF) 1α), the rabbit β-globin intron, GFP gene and the SV40 polyA signal. The construct contains two Not I sites, one in the junction between the GFP gene and the SV40 polyA and another upstream of the L200 in the vector backbone. We first removed the Not I site in the vector backbone by eliminating a DNA segment between a Sac I site and L200 encompassing the NotI site. Next, we removed the promoter from the construct by replacing the region from the EF1α promoter to the GFP gene (from XhoI to NotI sites) with a DNA segment in the pEGFP-1 vector consisting of a part of the multiple cloning site (MCS) and the GFP gene (from XhoI to NotI sites of pEGFP-1). Finally, we replaced the SV40 polyA signal in the construct (from NotI to BglII sites) with the polyA signal sequence derived from the herpes simplex virus thymidine kinase (HSV-TK), which was PCR-isolated from the pEGFP-1 vector using a forward primer harboring NotI site and a reverse primer harboring BglII site (“HSV-TK-polyA” in Table S2). This modified construct was designated pT2GFP-TKPA. The replacement of the polyA signal from SV40 to HSV-TK was done to facilitate, in a later stage, the insertion of a DNA fragment containing the SV40 polyA in an appropriate orientation into the pT2GFP-TKPA by avoiding a possible interaction between the two SV40 polyA sequences.
Using the pT2GFP-TKPA as a transfer vector, we constructed the LWS1up2.6kb:GFP and the LWS2up1.8kb:GFP in Figure 2A as the followings. The region from LWS1up2.6kb to GFP in the LWS1/GFP-PAC(E) clone and the region from LWS2up1.8kb to GFP in the LWS2/GFP-PAC(E) clone were isolated by PCR using forward primers harboring a SalI site and reverse primers harboring a NotI site (“LWS1up2.6kb:GFP” and “LWS2up1.8kb:GFP” in Table S2). A DNA segment in the pT2GFP-TKPA from SalI in MCS to NotI in the junction between GFP and HSV-TK polyA was replaced with those segments isolated from the PAC constructs through restriction digestion and ligation at the SalI and NotI sites. In the resulting constructs, the region from LWS1up2.6kb to GFP and that from LWS2up1.8kb to GFP were connected to the HSV-TK polyA (LWS1up2.6kb:GFP and the LWS2up1.8kb:GFP, respectively) at the NotI site.
The LWS1up2.6kb:GFP-LWS2up1.8kb:RFP (Figure 2A) was constructed as follows. The SV40 polyA in the pEGFP-1 vector was isolated together with a NotI site located at its 5′ side by PCR using a forward primer harboring a KpnI site and a reverse primer harboring a SalI site (“SV40-polyA” in Table S2). The isolated fragment was cloned into the pBluescript II (SK-) vector at KpnI and SalI sites. The region from the LWS2up1.8kb to the RFP gene including a NotI site located just downstream of the RFP gene in the LWS1/GFP-LWS2/RFP-PAC(E) clone was isolated with a SalI site attached to the 5′ end of the LWS2up1.8kb. The region was connected to the 3′ side of the SV40 polyA cloned in the pBluescript II (SK-) at SalI site. Then, from the pBluescript construct, the region consisting of the SV40 polyA, LWS2up1.8kb, and RFP gene (from the Not I site at 5′ side of the SV40 polyA to the NotI site at 3′ side of the RFP gene) was inserted into the LWS1up2.6kb:GFP construct in the pT2GFP-TKPA at the NotI site located between the GFP gene and the HSV-TK polyA. This results in the LWS1up2.6kb-GFP segment connected to 5′ side of the SV40 polyA and the LWS2up1.8kb-RFP segment connected to 5′ side of the HSV-TK polyA in the pT2GFP-TKPA (LWS1up2.6kb:GFP-LWS2up1.8kb:RFP).
The LAR:LWS2up1.8kb:GFP and the LAR:krt8up564bp:GFP (see Figure 4A and Results section) were constructed as follows. The LAR was isolated from the LWS-PAC(E) clone by PCR using a forward primer harboring a HindIII site and a reverse primer harboring an EcoRI site (“LAR” in Table S2) and was inserted into the HindIII/EcoRI sites in the MCS of pT2GFP-TKPA. For making the LAR:LWS2up1.8kb:GFP, the GFP gene region in the LAR-inserted pT2GFP-TKPA construct (from the SalI site in MCS to the NotI site at the 3′ side of the GFP gene) was replaced with the region from the LWS2up1.8kb to the GFP gene in LWS2up1.8kb:GFP construct in the pT2GFP-TKPA (from the SalI site at the 5′ side of LWS2up1.8kb to the NotI site at the 3′ side of the GFP) by restriction digestion and ligation at the SalI and NotI sites. Similarly, for making the LAR:krt8up564bp:GFP, the GFP gene region in the LAR-inserted pT2GFP-TKPA was replaced with the region from the krt8up564bp to the GFP gene in LCR:krt8 construct reported in ref. [41] by restriction digestion and ligation at the SalI and NotI sites.
A series of the GFP-reporter constructs and DNA fragments for the transient transgenic assay (Figure 3B, 3C) were obtained by PCR from LWS-2/GFP-PAC(E) using primers listed in Table S3. These DNA fragments were purified through gel extraction before the microinjection.
Zebrafish were maintained at 28.5°C in a 14-h light/10-h dark cycle as described by ref. [61]. The LWS-PAC derived constructs bearing the I-SceI recognition sequence were injected into the cytoplasm of embryos at the one-cell stage at 20 ng/µl with I-SceI meganuclease (0.5 units/µl) (New England Biolabs, Beverly, MA) in a solution of 0.5× commercial meganuclease buffer with tetramethyl-rhodamin dextran tracer [57].
The reporter constructs in the pT2GFP-TKPA vector were resuspended at a final concentration of 25 ng/µl in 0.1 M KCl and tetramethyl-rhodamin dextran tracer. They were co-injected with mRNA of Tol2 transpsase of 27 ng/µl that was prepared through in vitro transcription from pCS-TP using the mMESSAGE mMACHINE kit (Ambion, Austin, TX) [59], [60].
For generation of transgenic lines, the injected embryos were grown to sexual maturity and crossed with non-injected fish in a pair-wise fashion. Founders and fish of subsequent generations transmitting a reporter transgene were screened by PCR-based genotyping as described in ref. [41]. All the transgenic lines analyzed in this study are listed in Table S4.
The GFP-reporter constructs for the transient transgenic assay (Figure 3B) were microinjected with 0.1 M KCl and tetramethyl-rhodamin dextran at a final concentration of 25–50 ng/µl. The LWS2up1.8kb-GFP-LWS-2 region was injected together with a variety of DNA segments from the LWS1up2.6kb region (Figure 3C) at a final concentration of approximately 25–50 ng/µl each in 0.1 M KCl and tetramethyl-rhodamin dextran tracer.
For transient transgenic assay of GFP expression, embryos injected with the GFP-expression constructs were grown in 0.003% 1-phenyl–2-thiourea after 12–24 hpf to disrupt pigment formation. One eye per injected embryo was examined at 7 dpf for GFP fluorescence under a dissecting fluorescent microscope. The eyes were scored as “+++”, “++”, “+”, and “−” when GFP was expressed in more than 50 cells, in 11–50 cells, in 1–10 cells, and in no cell per eye, respectively [39]–[41].
Immunostaining was carried out against adult retinal sections following the procedure of ref. [39]. An antibody against the zebrafish red opsin raised in rabbits [32] was used to stain LDCs. The Cy3-conjugated anti-rabbit IgG was used as a secondary antibody. Images of GFP, RFP and Cy3 fluorescence of the sections were captured using a Zeiss 510 laser-scanning confocal microscope (Zeiss, Thornwood, NY).
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10.1371/journal.pntd.0005937 | Estimating the prevalence and intensity of Schistosoma mansoni infection among rural communities in Western Tanzania: The influence of sampling strategy and statistical approach | Schistosoma mansoni is a parasite of major public health importance in developing countries, where it causes a neglected tropical disease known as intestinal schistosomiasis. However, the distribution of the parasite within many endemic regions is currently unknown, which hinders effective control. The purpose of this study was to characterize the prevalence and intensity of infection of S. mansoni in a remote area of western Tanzania.
Stool samples were collected from 192 children and 147 adults residing in Gombe National Park and four nearby villages. Children were actively sampled in local schools, and adults were sampled passively by voluntary presentation at the local health clinics. The two datasets were therefore analysed separately. Faecal worm egg count (FWEC) data were analysed using negative binomial and zero-inflated negative binomial (ZINB) models with explanatory variables of site, sex, and age. The ZINB models indicated that a substantial proportion of the observed zero FWEC reflected a failure to detect eggs in truly infected individuals, meaning that the estimated true prevalence was much higher than the apparent prevalence as calculated based on the simple proportion of non-zero FWEC. For the passively sampled data from adults, the data were consistent with close to 100% true prevalence of infection. Both the prevalence and intensity of infection differed significantly between sites, but there were no significant associations with sex or age.
Overall, our data suggest a more widespread distribution of S. mansoni in this part of Tanzania than was previously thought. The apparent prevalence estimates substantially under-estimated the true prevalence as determined by the ZINB models, and the two types of sampling strategies also resulted in differing conclusions regarding prevalence of infection. We therefore recommend that future surveillance programmes designed to assess risk factors should use active sampling whenever possible, in order to avoid the self-selection bias associated with passive sampling.
| Schistosomiasis constitutes a major public health problem in Tanzania, with up to 80% prevalence of infection in some areas. Infection with the disease causes abdominal pain, diarrhoea, stunted growth and impaired cognitive abilities in children. Accurate information on the distribution of schistosomiasis within Tanzania is not available for most rural areas. To establish the prevalence of the intestinal form of the disease among communities residing along the shores of Lake Tanganyika, we quantified the presence of the causal agent (Schistosoma mansoni) by searching for their eggs in faecal samples. Children were actively sampled from schools but adult sampling relied on volunteers, so the data from the two age groups were analysed separately. The number of positive samples was high in both but significant differences in prevalence and intensity were found between the sampled villages only for the children. Statistical models accounting for false negative stool samples indicated that the apparent prevalence was a gross under-estimate of the true prevalence of infection. Our results emphasise the importance of considering the type of sampling when assessing risk factors associated with parasitic disease. The information obtained from this study will help to guide the optimal distribution of schistosomiasis control resources in the region, such as through targeted allocation of drugs and personnel to those areas with higher estimated prevalence.
| Schistosomiasis, which is caused by trematode parasites in the genus Schistosoma, is a typically chronic disease that can result in debilitation and severe pathology in infected patients [1]. Infection with the parasite can also be asymptomatic and can remain undetected for a long period of time [2, 3], particularly when presenting intestinal schistosomiasis. This can lead to complacency and tolerance of the disease by both patients and the community. Thus, the disease does not receive as much treatment or financial support as malaria, tuberculosis and HIV/AIDS [4]. This has led the World Health Organisation to categorise schistosomiasis as a Neglected Tropical Disease [5].
Both S. mansoni (which typically causes intestinal schistosomiasis) and S. haematobium (now termed urogenital schistosomiasis) are endemic throughout Tanzania, with a prevalence of up to 80% in some areas [6–8]. The disease constitutes a major public health problem [9], but the control efforts have been limited by a lack of reliable data on the distribution and prevalence of the parasite across different parts of the country. Recent estimates indicate that S. haematobium infection is distributed mainly along the coast of the Indian Ocean and in inland villages around Lake Victoria [10], while S. mansoni infections have been reported in most parts of the country except the eastern coastal areas and Zanzibar and Pemba islands [3, 10, 11]. However, reliable data on schistosomiasis infection in Tanzania are mostly limited to the northeast and Lake Victoria areas, which have been more extensively studied due to their accessibility and more advanced infrastructure compared to other parts of the country [10]. Similar information is currently lacking for the southern and western areas, including the Kigoma District. In these areas, information on schistosomiasis comes mostly from hospital records, but as a consequence of poor recording and non-random sampling [12], this information gives biased estimates of population health attributes such as prevalence and infection intensity [13].
Furthermore, a general limitation of assessing the impact of parasitic infections is the typically highly aggregated nature of the data, and the statistical models that these characteristics demand. Schistosomes tend to show over-dispersion in abundance, with some host individuals having very high observed faecal egg or adult worm counts and others having few or zero counts, which is typical of many parasite species [14]. For some such data, a zero-inflated negative binomial (ZINB) distribution has also been used [15, 16], with a proportion of the ‘zero’ count observations described by a latent class of individuals that are not infected with the parasite, and the remainder of the ‘zero’ count observations obtained from the negative binomial distribution describing the infected individuals. Standard analytical approaches have often assumed a Poisson or negative binomial distribution for count data but have tended to separately estimate the prevalence based on the proportion of observed counts above zero. This under-estimates the true prevalence because of the imperfect sensitivity of egg detection in infected individuals [17, 18]. It may therefore be preferable to use a ZINB distribution to allow simultaneous consideration of both the skewed underlying distribution, including zero counts from infected individuals, and the proportion of truly uninfected individuals [19]. This allows a less biased estimation of prevalence because it does not assume that all ‘zero’ count samples represent uninfected individuals; rather, it allows for some parasite infections to be present but not detected. This is reflected in the zeros expected under a negative binomial distribution: in a theoretical population where all individuals are infected with equal numbers of parasites and a standard Kato-Katz test is applied, a distribution of counts would arise due to the non-random distribution of parasite eggs in faeces. This distribution may well contain zeros arising from the imperfect sensitivity of the diagnostic method, but these would not reflect uninfected individuals: they are in fact ‘false negative’ counts. Conversely, a ZINB model reflects two underlying processes: an infected/uninfected status for each individual whereby each uninfected individual must have a count of zero (the ‘extra’ zeros as estimated by the zero-inflation part of the model), and a distribution of observed counts from the infected individuals, which may take any positive discrete value including zero (the negative binomial part of the model). Therefore, each of the zero observations may actually be derived from either uninfected (zero-inflated) or infected (negative binomial) individuals. Extending this principle, such models can use the model’s zero-inflation and negative binomial terms to separate the factors affecting the presence/absence of infection in the host (or more correctly, infection with adult female parasites) from the factors affecting the distribution of egg shedding intensity between infected hosts. This is done by estimating the effects of a set of potential risk factors for the degree of zero-inflation using a logistic regression model (binomial response with logit link), and separately estimating the effects of a set of potential risk factors describing the intensity of observed counts from infected individuals using a negative binomial regression (typically with a log link). Either the same set of risk factors can be used for these two parts of the model, or different sets of risk factors can be used where there is an a priori justification for excluding a risk factor used in either the zero-inflation or negative binomial term from the other term.
Another factor to consider in parasite control measures is whether there are other reservoirs of infection that might maintain the disease even if all humans were treated [20]. For schistosomiasis, there is some evidence that non-human primates (mostly baboons and vervet monkeys) can harbour the same species of schistosomes as humans [21–24]. The prevalence of schistosomes in the wild animals is not clearly known, but neither is the prevalence in humans in major areas of contact such as at Gombe National Park in western Tanzania [22, 24]. Given the economic importance of primate ecotourism in Tanzania [25], an important knowledge gap to address is the potential schistosomiasis risk humans might pose to animals and the potential risk posed to tourists. The park was made famous by Jane Goodall for its chimpanzees and is now a popular tourist destination [26]. Although very little is known about schistosome prevalence or intensity in humans in this area [10, 22, 27], in an accompanying study we confirmed the presence of S. mansoni in the main vector of disease in the region, Biomphalaria pfeifferi [24]. The purpose of this study was to establish the distribution and prevalence of S. mansoni in humans residing within Gombe National Park and its surrounding villages, using appropriate sampling and analytical approaches.
Ethical Clearance for this study (No. NIMR/HQ/R.8a/Vol.IX/892) was issued by the Tanzania’s National Institute for Medical Research (NIMR). The permission to survey schistosomiasis in villages and schools in the study area was obtained from the Tanzania Commission for Science and Technology (COSTECH) through the Executive Director of Kigoma District (Ref. No. KDC/G1/6/70). Before collecting stool samples full consent was obtained from each participant. A meeting was held with prospective participants who were informed in Kiswahili (the official language used in the study area) about the goals of the study, their voluntary participation and its implications. Those willing to participate in the study were asked to give consent through writing (signature) and oral for those who could not read or write. Parents, guardians and teachers gave consent on behalf of the children involved in the study. The WHO recommended dose of anthelmintics was given to all consenting individuals infected with schistosomes and other helminths.
This study was conducted in Gombe National Park and the neighbouring villages to the north (Kiziba, Bugamba, Mwamgongo) and south (Mtanga), along the eastern shores of Lake Tanganyika in the Kigoma District (Fig 1). Gombe National Park (4◦53′ S, 29◦38′ E) is a narrow strip of rugged terrain and hills along the shores of Lake Tanganyika [24, 28]. The top of the hills forms the park’s eastern boundary while the lakeshore forms its western boundary. The park is directly bordered by Mtanga and Mwamgongo villages, each of which are inhabited by approximately 5000 people, with most of them engaged in fishing activities in Lake Tanganyika [29]. The more northern villages (Bugamba and Kiziba) each harbour about 10,000 residents, the majority of whom are farmers [30]. Each of the studied villages has at least one stream that runs through it, where B. pfeifferi snails known to transmit schistosomes in the area have been identified [24, 30].
Two separate sampling strategies were employed for this study. The first dataset was obtained by active sampling of a target of 60 school children selected from "standard three" classes at a single school per village. This class was chosen because the pupils represent the median age of primary school children in Tanzania (9–12 years), and also so that the results obtained would be comparable with other studies that have been used to inform control programmes [31]. In schools where standard three class had fewer than 60 children, additional pupils were recruited from standard four class. There was no primary school in Gombe National Park at the time of study, so this site was not included in the statistical modelling for the active sampling dataset.
The second dataset was obtained by passive sampling of adult individuals presenting voluntarily at the village clinics, which is therefore potentially subject to self-selection bias. Village sub-divisions were used as selection units for adults, with a cut-off point of at most ten individuals from each sub-division to ensure equal village representation. At Gombe, sampling was conducted in the main residence areas of Kalande near the park’s southern boundary, Kasekela in the centre of the park and Mitumba near its northern boundary. At each site, any dependent children accompanying adults to the clinic (“accompanying children”) were also sampled. These were not included in the statistical modelling due to small sample sizes and potentially different self-selection bias, so egg counts are reported for qualitative comparison only. This included all nine children above 12 months of age who were resident in Gombe at the time of sampling, because there were no schools to be sampled.
Sampling was conducted in 2010 during the wet season (January to April). For adults, consenting participants were registered, weighed and their age and sex recorded. For school children, age was obtained from the school register while for accompanying children, their medical clinic cards were used to estimate their age.
Sampling kits and instructions on the collection protocol were distributed in the morning of the first sampling day and the samples collected back in the morning of the following day. Each sampling kit was comprised of a wooden spatula for picking up a stool sample, a pre-labeled plastic vial (120 ml) for depositing the stools and a locally made polythene plastic bag for keeping the samples. Infection status was based on a single sample from each individual and a single Kato-Katz slide per sample. The WHO recommends that samples are obtained over three days and multiple slides counted per sample [31] to avoid underestimating prevalence and over-estimating intensity of infections [32–34], so our approach is conservative as a first assessment of whether schistosomiasis is present at a substantial level in the region. This approach has been used by previous epidemiological studies [35] and we followed recommendations to scan entire Kato-Katz slides rather than extrapolating from sampling a subset of slides, to increase accuracy [36]. We have also employed appropriate distributions in our statistical analyses to fully account for the imperfect sensitivity of egg detection in faecal samples.
Stools were examined for S. mansoni and other helminths using the Kato-Katz kit, following the manufacturer’s guidelines (Bio-Manguinhos, Rio de Janeiro, Brazil) and descriptions in the literature [31, 36, 37]. The faecal material was first pressed through a mesh screen filter to remove large particles. The filtrate was then transferred onto a microscope slide through a template hole that holds 41.7 mg of faecal material, which is a recommended standard [37, 38]. The template was then removed and a hydrophilic cellophane paper previously soaked in glycerol-malachite green solution placed on the faecal material. A second slide was placed onto the cellophane strip and pressed to spread out the sample for easy observation of parasite eggs. The bottom slide was left to clear for 30–60 minutes. The slide was then placed under a compound microscope and the entire preparation examined using a 10x objective and the parasite identified using 40 x objectives. All schistosomes and other helminth eggs observed on each slide were identified and counted based on standard guidelines [24, 37, 39]. The eggs of S. mansoni were easily identified based on their distinguishing lateral spines, while other helminth eggs were identified based on their morphology, size and appearance of eggs/larvae. The observed number of schistosome eggs observed per Kato Katz slide (holding 41.7 mg of faeces) was recorded as raw faecal worm egg counts (FWEC), and also converted to the standardised eggs per gram (EPG), as typically used in the literature by simply multiplying the FWEC by 24. EPG is used as a proxy for estimating the intensity of parasitic infections since it is related to total worm count but can be readily estimated from live patients [31, 40–44]. We refer to the proportion of samples with EPG greater than zero as the “apparent prevalence” to distinguish it from the prevalence as estimated by the statistical models, which take into account uncertainty about which zero counts reflect true absence of eggs and which represent lack of detection of eggs. The observed FWEC were used for the statistical models instead of the EPG in order to fulfil the requirement that the response for the statistical model is distributed according to a count [45], and where appropriate, model estimates were transformed to the equivalent scale as EPG by multiplying by the same constant of 24.
Because of the fundamental difference in the sampling procedures between the actively sampled (school children) and passively sampled (self-selected adults) data, the two datasets were analysed separately, but using the same procedure. A negative binomial model was first fit to the data, using a stepwise algorithm to select the best-fitting model from the four possible explanatory variables of site, sex, linear effect of age, and quadratic effect of age. Sex and site were fitted as categorical variables with 'Male' as the reference category for sex, and the site with the highest number of observations for each dataset chosen as the reference category (Kiziba for the actively sampled dataset and Mwamgongo for the passively sampled dataset). Once the best fitting negative binomial (NB) model had been found, two zero-inflated generalisations of this selected negative binomial model were tested: first using only an intercept term in the zero-inflation part of the model (ZINB1), and secondly using the same predictors in the zero-inflation part of the model as were used in the negative binomial part of the model (ZINB2). Both ZINB models allow the underlying distribution of FWEC to be conceptually split into two groups: those individuals belonging to the egg shedding group, and those individuals belonging to the ‘zero’ group. This assumes that all samples containing one or more observed eggs originated from an individual in the infected group, whereas samples containing no eggs could have originated from either an infected individual, from whom a positive sample may have been obtained on another day, or from an individual in the ‘zero’ group, from whom it is not theoretically possible to obtain a sample containing eggs. Using this distribution effectively allows separate generalized linear models to be fit simultaneously to the intensity of egg shedding in infected individuals, using a negative binomial distribution with log link, and to the prevalence of egg shedding between individuals, using a binomial distribution with logit link. The ZINB1 model allows for a set of extra zeros that do not belong to the distribution of 'infected' individuals, with an equal probability of each individual being in this extra zero set independent of the predictors in the model. The ZINB2 model allows the probability of each individual being in the extra zero set to depend on the explanatory variables also used for the NB part of the model. Both ZINB1 and ZINB2 models collapse to the NB model in the special case that the extra-zero component is estimated to be negligible (i.e. prevalence is estimated to be close to 100%).
All statistical analyses were performed in R Version 3.2.2 [46]. The NB models were fit using the glm.nb function of the MASS package [47], and the step-wise selection algorithm was based on the Akaike Information Criterion (AIC) [48]. Models including zero-inflation terms were fit using the zeroinfl function in the pscl package [49]. Assessment of model fit for zero-inflated models is not valid using AIC, so two alternative approaches were used based on: (a) the Vuong statistic [49]; and (b) a distribution of 1000 likelihood ratio test statistics obtained from data generated under the NB model [50]. The ZINB models were only considered preferable to the NB model in the case that both fit statistics indicated that this was the case.
A total of 198 and 149 FWEC observations were made for the actively and passively sampled datasets respectively, of which 105 and 54 were counts of greater than zero, corresponding to apparent prevalence of 53% and 36% (Table 1). For accompanying children, 35 individuals were sampled, of which 15 had egg counts greater than zero, corresponding to an apparent prevalence of 43%. The number of participants and ratio of adults to children varied substantially between sites, with Mwamgongo showing the highest number of people who participated in the study and the highest apparent prevalence compared to the other villages (Table 1 and S1 Table).
For accompanying children, although sample sizes were small, Mwamgongo also showed higher apparent prevalence than the other sites (Table 1).
Qualitatively, there was substantial variation in observed FWEC between sites, and variation between age groups for some sites (S1 Table, Figs 2 and 3). There were also differences between male and female adults at some sites. For example, at Bugamba, adult females showed lower apparent prevalence and lower FWEC than adult males or children. This was not the case at Mwamgongo, where females with non-zero counts showed higher loads than males. Overall, the qualitative data suggest high variance among individuals and sites.
This study represents the first large-scale attempt to quantify the prevalence and intensity of schistosome infection in the Gombe ecosystem in western Tanzania. Overall, the results suggest that schistosomiasis could pose a substantial threat to human health in this under-sampled region. Despite the previous impression that the disease is rare in this region [10], Mwamgongo village, for instance, showed an apparent prevalence of more than 89% in children and an estimated true prevalence of approximately 100% for both adults and children, which is far higher than the estimated national average of 51.5% [51]. Although observed FWEC suggested extensive variation among villages, estimation of true prevalence of infections using zero-inflated models suggested that all of the sampled villages had a high proportion of individuals with schistosome infections, although with a low mean egg shedding rate in passively sampled adults relative to actively sampled children.
Encouragingly, the data suggest that prevalence of schistosomiasis is lower for the residents of Gombe National Park, although the estimated true prevalence still indicates that at least two thirds of adults are infected. This is despite the finding that the closest villages to the north (Mwamgongo and Bugamba) showed high prevalence and intensity of infection, both in terms of observed FWEC and parameters estimated from the models (Table 1 and S1 Table). Within the park, low infection levels could be due to the transient nature of residents (who might originate from other regions where schistosomiasis prevalence is lower or where treatment is more readily available) but there does not seem to be spill-over from the neighbouring villages. This also suggests that interactions between humans and potential primate reservoirs (the baboons) are not posing a major risk factor either to humans or to the wild animals in these regions. However, active sampling of school children was not possible in Gombe so these conclusions are based only on the passively sampled dataset. So, a difference in the magnitude of self-selection bias within Gombe relative to the other sites cannot be ruled out.
The apparent discordance in prevalence and intensity between the actively and passively sampled datasets within the same sites is also illustrative of the potential disadvantages of over-reliance on passively sampled data. Although school surveys using stool samples collected over three consecutive days is the WHO recommended protocol for prevalence and intensity of infection mapping [31], prevalence estimated for these remote areas tend to rely on passively sampled data from hospital records. The actively sampled data can be assumed to be a representative and random sample of the target population of school children within each site, which therefore gives an unbiased estimate of the prevalence and intensity of infection within this population. In contrast, the passively sampled data is subject to self-selection bias of unknown and inestimable magnitude, which is a problem that is well known within the field of surveillance [52]. Despite this, passively sampled data is frequently used because it is typically cheaper and easier to collect, particularly when the dataset has already been collected for other purposes [53]. Although the magnitude of the bias is variable, passive surveillance systems typically under-estimate prevalence of infections [54, 55]. In contrast, we found higher estimates of prevalence in the passively sampled data relative to the actively sampled child data, which could be explained by individuals that suspect themselves to be infected being more likely to present for sampling and treatment. It is also possible that the actively sampled data may itself be subject to some bias; for example, due to school absenteeism or clinically sick children not attending school. Since the target populations for active and passively sampled data are not identical (school children vs. the adult population), we cannot directly assess the consequences of relying on passive sampling alone for robust assessment of prevalence. It is also not possible to directly compare the relative prevalence in the two age groups.
Application of models that can separately estimate variables explaining variation in prevalence and intensity of parasite loads emphasised two important limitations of traditional approaches: 1) assessing prevalence based on the proportion of non-zero FWEC; and 2) confounding differences due to age with sampling strategies (active sampling of all individuals vs self-selected volunteers). The estimated prevalence based on the ZINB2 model for the child data was higher than the apparent prevalence, because a proportion of the zero counts were due to a low counting sensitivity. Note that the converse is not possible (observed eggs are never assumed to be false positives), so the apparent prevalence is an estimate of the true prevalence that is necessarily biased downwards. The ZINB2 model provided a significantly better fit to the data than the NB model for the actively sampled data, and there is evidence to suggest that the true infection prevalence amongst children is less than 100% in some villages. There was no evidence that the zero-inflated models produced a better fit to the passively sampled data than the simpler NB model, which means that all of the observed zero egg counts in this data were consistent with a failure to detect eggs in truly infected individuals due to imperfect sensitivity of egg detection. This could be interpreted as the data being consistent with a true infection prevalence of 100%. However, it is impossible to exclude the possibility that a zero-inflated model would provide a better fit to a larger dataset, and when the ZINB2 model is used, there is some evidence that the prevalence is less than 90% in Gombe.
Since we did not have a large enough sample size of accompanying children they were excluded from the statistical analyses, but for a future study it would be worth exploring the consequences of including different sampling schemes when age is not a confounding factor. Age has often been suggested as a risk factor for parasite infections, in relation to development of immunity and behaviours that increase risk of infection [56–60]. If we had considered only the raw FWEC counts or combined the two datasets, our conclusions might also have been that children tended to show higher infection levels than adults. However, the ZINB modeling suggested that true prevalence in adults was actually higher than in children at some sites, which is likely due to self-selection bias in the passively sampled data.
The significantly better fit of the ZINB2 model compared to the NB model for the actively sampled individuals also suggests that a single over-dispersed Poisson distribution is not able to adequately explain all of the zero count observations. Zero-inflated distributions have been used in the fields of parasitology [16, 61], bovine mastitis [62], forest science [63], and medical epidemiology [64], although their use has also been criticized in modeling road traffic accident analysis data [65]. They are conceptually useful when zero observations can arise from either count data (such as a Poisson, negative binomial or other distribution) or from a truly zero individual [19], and may be of value in other similar applications. Correct identification of the degree of zero-inflation depends on the correct choice of distribution for the infected group, so that the number of ‘expected’ zeros is accurate. The negative binomial distribution is widely used, but other distributions such as the lognormal-Poisson may be more correct for some datasets [66] and may influence the estimates for zero-inflation [67]. Considerable care should therefore always be put into selection of the most biologically sensible distribution model for analysis of count data, and if appropriate some consideration towards the possibility of using a zero-inflated model should be made. In this case, there is a biologically plausible explanation for modeling a process where some individuals are uninfected, with a distribution of FWEC between the infected individuals, so the choice of ZINB model is justified.
A single Kato Katz slide was used to estimate the infection intensity, combined with rigorous statistical analysis to overcome the differences in sensitivity for prevalence and infection intensity of S. mansoni in epidemiological studies. A larger number of Kato Katz slides (as recommended by WHO guidelines [31]) would have decreased the relative size of the 95% confidence intervals by adding more information to the data. However, the coefficient estimates obtained should not be biased by the reduced sample size, except for the estimate of the over-dispersion parameter k, which partly reflects the variability between samples and would therefore be affected by the increased precision associated with more slides. In contrast, the bias in the apparent prevalence estimates would be expected to decrease as the number of slides was increased due to the increased probability of detecting eggs, and therefore decreased false negative rate. This reflects the difficulties in interpreting apparent prevalence, and emphasises the value of an unbiased estimate of the prevalence such as that given by the ZINB model.
Based on the ZINB and negative binomial models respectively, there was no significant effect of sex on FWEC within either the actively or passively sampled datasets. Previous studies have also not found any effect of sex on parasitic infections and predicted that other factors may be determining the infection levels [68–70]. However, since schistosomiasis transmission is highly related to occupational activities and water contact, in areas where fishing or farming are mostly done by women, such as among the Mende people in the Sierra-Leone, higher prevalence of schistosomiasis has been reported among females than males [71]. It is therefore possible that both men and women in the Gombe ecosystem, their social and cultural duties notwithstanding, are equally exposed to schistosome transmission in the local streams. As there is an insufficient supply of running tap water in the studied villages, both women and men have to use the streams or lake, albeit for different purposes. While men often come in contact with the stream water for bathing and ablution before prayers for Muslims, women use the stream water mostly for performing domestic chores as well as bathing. Nevertheless, interpretation of results could be confounded by self-selection and a gender bias in the willingness of individuals to participate in voluntary studies for the passively sampled data. In our study, for example, there were 82 male and 67 female volunteers. However, such forms of bias should not be present in the actively sampled data.
We also found that, while there was a trend for decreasing raw FWEC in both children and adults (Figs 2 and 3), statistical models suggested a weak (although non-significant) effect of age in adults. Age-related differences in infection distributions have been found in other studies [56–60], where the prevalence and intensity of S. mansoni were found to rise slowly in children and then slowly decline in older individuals. It has been suggested that young people tend to be more susceptible to infection due to being in contact with water more often than adults [58, 72] and that the decrease in schistosome infection with age may be due to acquired immunity after repeated exposure [58, 72, 73]. Sampling of a broader range of age classes would be required to test these effects but with consistent sampling strategies across age groups. While difficult to implement, active sampling of adults would be required to fully test for age-specific differences in susceptibility.
The most pronounced differences in FWEC in our study appeared to be due to site. Although statistical modeling suggested that there were no dramatic differences among the villages, both raw egg counts and prevalence and intensity estimated from the ZINB models suggested differences between sites in children, with Bugamba and Mwamgongo standing out as the highest risk areas. In other communities living along the shores of Lake Victoria in northwest Tanzania, local variation in schistosome infections has been attributed to patchy distribution of snails [7, 74]. It is possible, therefore, that differences among the study villages in the present study could be due to differences in the resident snail populations. In a recent survey, Bakuza [24] did not find any infected snails in the streams sampled within the Gombe National Park boundaries and no snails at all were found in the stream running through Mtanga. This could help to explain the lower prevalence and intensity of infections at these sites. Further work is required to quantify whether differences in infection levels in humans are related to differences in snail densities in the various villages. Variation in schistosomiasis infection among villages could arise due to possible ecological risk factors, such as contact rates with infested water sources, human population density, socio-economic levels and differences in local snail ecology.
Our findings offer some guidance on how to optimally distribute the limited resources for schistosomiasis control in areas along the shores of Lake Tanganyika, Tanzania and similar resource-poor settings in other endemic countries. Some differences between sites were found, which could be of relevance to designing future studies to improve understanding of the social and occupational risk factors for transmission of the disease. However, consistent with WHO guidelines, we recommend that surveillance should be conducted using active sampling wherever possible, to enable accurate estimation of prevalence and intensity. Moreover, we recommend that statistical models are applied based on the most appropriate distributions explaining the data. Compared to other studies, we found little influence of age or sex, which could reflect either differences between sampling procedures or real cultural or biological differences in the geographic region sampled for this study. Finally, qualitatively different prevalence estimates were obtained using the ZINB model compared to the observed prevalence based on the simple proportion of observed egg counts above zero, which demonstrates the potential for erroneous inference when ignoring the biasing effect of imperfect egg detection methods when estimating prevalence.
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10.1371/journal.pntd.0001048 | Ziehl-Neelsen Staining Technique Can Diagnose Paragonimiasis | We evaluated the Ziehl-Neelsen staining (ZNS) technique for the diagnosis of paragonimiasis in Laos and compared different modifications of the ZNS techniques.
We applied the following approach: We (1) examined a paragonimiasis index case's sputum with wet film direct examination (WF) and ZNS; (2) re-examined stored ZNS slides from two provinces; (3) compared prospectively WF, ZNS, and formalin-ether concentration technique (FECT) for sputum examination of patients with chronic cough; and (4) compared different ZNS procedures. Finally, we assessed excess direct costs associated with the use of different diagnostic techniques.
Paragonimus eggs were clearly visible in WF and ZNS sputum samples of the index case. They appeared brownish-reddish in ZNS and were detected in 6 of 263 archived ZNS slides corresponding to 5 patients. One hundred sputum samples from 43 patients were examined with three techniques, which revealed that 6 patients had paragonimiasis (13 positive samples). Sensitivity per slide of the FECT, ZNS and the WF technique was 84.6 (p = 0.48), 76.9 (p = 0.25) and 61.5% (p = 0.07), respectively. Percentage of fragmented eggs was below 19% and did not differ between techniques (p = 0.13). Additional operational costs per slide were 0 (ZNS), 0.10 US$ (WF), and 0.79 US$ (FECT). ZNS heated for five minutes contained less eggs than briefly heated slides (29 eggs per slide [eps] vs. 42 eps, p = 0.01). Bloodstained sputum portions contained more eggs than unstained parts (3.3 eps vs. 0.7 eps, p = 0.016).
Paragonimus eggs can easily be detected in today's widely used ZNS of sputum slides. The ZNS technique appears superior to the standard WF sputum examination for paragonimiasis and eliminates the risk of tuberculosis transmission. Our findings suggest that ZNS sputum slides should also be examined routinely for Paragonimus eggs. ZNS technique has potential in epidemiological research on paragonimiasis.
| Lung fluke (Paragonimus) infection causes similar symptoms to pulmonary TB and is an important differential diagnosis in endemic areas. Standard diagnosis is wet film (WF) microscopic examination of sputum samples. For the last fifty years, Ziehl-Neelsen stain (ZNS) has been believed to destroy Paragonimus eggs. However, our investigation of stored ZNS slides and our prospective comparison of wet film, ZNS, and formalin-ether concentration technique of sputum of chronic cough patients in Laos showed that (1) similarly to wet film and FECT, Paragonimus eggs were hardly fragmented by ZNS; and (2) ZNS had a higher nominal sensitivity for detection of Paragonimus eggs than WF at lowest costs. Examination of bloody sputum parts revealed more eggs; while on the other hand, ZNS with continuous heating of the slides reduced the quantity of eggs compared to the current heating technique. Further, ZNS should also be investigated with the 10× lens for Paragonimus eggs, in addition to the 100× lens for TB, to reduce misdiagnosis of sputum-negative TB. Finally, the ZNS methodology appears to diminish biosafety risks of the standard wet film procedure. ZNS could be a valuable technique in epidemiological research on paragonimiasis.
| Paragonimiasis is a primary pulmonary food-borne trematodiasis and zoonosis present in numerous countries, especially in tropical Asia where 293 million people are estimated at risk of infection [1]. Causing symptoms similar to pulmonary tuberculosis (TB), it is frequently misdiagnosed and treated as sputum-negative TB [2]–[5]. Standard diagnosis in endemic areas relies on sputum examination by direct microscopy of fresh sputum (wet film mount, WF) and concentration techniques such as formalin-ether concentration technique (FECT), as well as stool sample examinations [3], [6]–[9]. In 1960, Sadun and Buck reported from their studies in South Korea that only debris of Paragonimus eggs were found in Ziehl-Neelsen stained (ZNS) sputum slides [7]. Since then, Paragonimus eggs diagnosis based on ZNS sputum has been abandoned [4], [5], [9]. In the meantime, however, there have been numerous modifications of the ZNS technique [10], especially the use of different decolorizers such as sulphuric acid [11] and hydrochloric acid-alcohol [9], [12]. Furthermore, different durations of heat application during the carbol-fuchsin staining process have been introduced and investigated, ranging from a single period of a few seconds - as in current practice [9], [13] - to continuous heating of the slide for several minutes (e.g. 5 minutes as described in 1976) [12]. However, it is unknown which ZNS modification was used by Sadun and Buck [7]. Additionally, the WF technique has the potential for TB transmission, and thus poses an obvious biosafety hazard. Furthermore, a reliable later quality control of the WF cannot be performed after the slide has dried up. In practice it is often only considered after a negative TB examination at a time when the sputum sample usually is discarded.
Lao People's Democratic Republic (Laos, Lao PDR) is endemic for paragonimiasis and TB [3], [4], [14]–[17]. In May 2009, a local farmer was examined at the Luang Namtha (LN) provincial hospital, Northern Laos, with a four-year history of cough and haemoptysis. Microscopic analysis revealed an extraordinary high number of Paragonimus eggs in the direct sputum examination, also clearly detected in the ZNS slides. It was this surprising confirmation of the wet film analysis by the ZNS slides that prompted our interest in re-evaluating the current sensitivity of ZNS technique for the diagnosis of paragonimiasis.
The objective of our study was to evaluate the ZNS procedure as a diagnostic tool for paragonimiasis in sputum samples in comparison to different currently used diagnostic techniques, namely the WF and FECT, and to compare different historic and current modifications of the ZNS technique for the detection of Paragonimus eggs.
In August 2009, a paragonimiasis index case was diagnosed by WF sputum examination in Luang Namtha provincial hospital, Northern Laos. Two sputum samples were examined with four different diagnostic techniques: (i) the standard WF (2 WF slides, 1 slide per sputum) employing a magnification of 40× and 100× [9]; (ii) the ZNS [11], [13] (2 ZNS slides, 1 slide per sputum), where samples were examined using a magnification of 40×, 100×, and 1000×; (iii) the auramine staining (2 AS slides, 1 slide per sputum) using fluorescence microscopy with a magnification of 600× [18]; (iv) the examination of an additional sputum sample with and without the bleach concentration technique, a newer method which has lately been suggested to improve the TB detection rate in Laos [13].
We re-examined ZNS slides for the presence of Paragonimus eggs from suspected TB patients. These slides were stored in the laboratories of the provincial tuberculosis program of LN province, Northern Laos, and Attapeu province, Southern Laos. The analyses were carried out by one trained laboratory technician/doctor using a magnification of 100×. Positive slides were double-checked by a second laboratory technician and photo-documented.
We collected sputum samples taken on two consecutive days from patients with chronic cough (>two weeks) in LN province, from September 2009 until April 2010, according to the Lao TB guidelines [11]. Included were patients from the index case's village (Phonthong), and from other villages where previously paragonimiasis patients were detected or suspected. Furthermore, we enrolled chronic cough patients from LN provincial hospital, and Vieng Phoukha and Muang Sing district hospitals.
One slide from each sputum sample was examined using WF, ZNS and FECT. Two independent laboratory technicians at the LN provincial hospital examined each slide in a blinded way. The technicians were not aware of the identity of the patient and the results of previous examinations (coded slides). In addition, they were working in separate rooms without the possibility to communicate. The slides were given random numbers and kept in a closed box with no further indications while being provided one by one to the technicians. The number of Paragonimus eggs detected per slide was recorded in separate booklets. One of us (GS) ensured that blinding procedures were respected. After unblinding discordant slides were rechecked, results confirmed by a third laboratory technician, and detected eggs photo-documented.
From blood stained sputum samples with clearly defined non-bloody parts two sets of WF and ZNS slides were established; one from the bloody and one from the non-bloody sputum portion (Figure 1A).
More sputum samples were asked from Paragonimus eggs-positive patients and as many sets as possible performed of: 1 wet film, 1 ZNS using sulphuric acid as decolorizer [11], [13], and 1 ZNS using hydrochloric acid-ethanol as decolorizer [9]. In addition, a subsample of sputum was processed using the historical ZNS procedures with continuous heating during the carbol-fuchsin staining process [12] (Figure 1B).
The study was approved by the National Ethics Committee for Health Research, Ministry of Health, Vientiane, Laos (No. 272/NECHR). All patients were counseled and provided written informed consent prior to enrollment. In case of detection of AFB or Paragonimus eggs, the patient was explained the findings and treated according to the Lao TB guidelines [9], [11]. Sputum negative patients were referred to the provincial hospital for further diagnosis and treatment. All paragonimiasis patients were treated with praziquantel (75 mg/kg/day for 3 days) according to international standards [4], [14], [16]. TB patients were treated according to the guideline of the National TB Control Program [11].
Laboratory procedures were performed according to standards. For direct examination, sputum was transferred on microscopic slides, covered with a cover slide, and examined with a magnification of 100× (10× objective) [9]. The standard ZNS “hot staining” was performed according to the Lao TB guidelines [11] with sulphuric acid as decolorizer and only briefly heated (until it started to steam) at the beginning of the carbol-fuchsin staining [13]. Other ZNS slides were continuously heated and kept steaming during the total five minutes of the carbol-fuchsin staining process [12]. Another ZNS technique used (instead of sulphuric acid) acid alcohol to decolorize slides [9]. All ZNS slides were examined with a magnification of 100× (10× objective) for Paragonimus eggs and with a magnification of 1000× (100× objective with oil) to identify acid-fast bacilli (AFB). For the FECT, sputum was homogenized with 0.9% NaCl, 10% formalin added, mixed and centrifuged. Supernatant was discarded, 0.9% NaCl and ether added, mixed, centrifuged, and the sediment examined as for direct examination [9]. Auramine staining and the bleach method were performed as described by Trusov et al. [18] and Ongkhammy et al. [13], respectively. The number of normal and fragmented Paragonimus eggs were identified and recorded per slide.
Calculation of average costs per slide included operating costs (working time, chemicals, disposable materials, electricity) but not capital costs (laboratory equipment such as centrifuge, vortex mixer for the FECT) presuming its availability in a laboratory offering ZNS. Working time was estimated based on the used standard operating procedures; time for microscopy was assumed as equal for all techniques and depending on the examiner's experience and therefore not included. Yearly costs were calculated for the number of about 1000 sputum samples examined for TB at LN provincial hospital taking into account time savings for grouped sample testing.
Data were entered in EpiData (version 3.1, the EpiData Association, Odense, Denmark). All records were cross-checked against original data sheets. Statistical analysis was performed with GraphPad Instat and QuickCalcs (GraphPad Software, California, USA). Agreements between the two readings were assessed with Cohen's Kappa (κ) coefficient. Paired categorical variables were compared using McNemar's test. Wilcoxon ranksum test and Friedman test were performed for comparison of two and three continuous variables, respectively. 95% confidence intervals (95% CI) were calculated for continuous and categorical data. The diagnostic “gold standard” for a Paragonimus spp. infection was defined as detection of at least one Paragonimus spp. egg in any of the examinations (three techniques) per sputum sample. Sensitivity and negative predictive value (NPV), and inter-observer's agreement of one slide's examination for the detection of Paragonimus eggs was calculated for each diagnostic technique (WF, ZNS, and FECT).
In the ZNS sputum slides, Paragonimus eggs appeared in a brownish to reddish color with often one or two convex or concave inner lines resembling a deflated American football. Specific characteristics of the Paragonimus spp. eggs were clearly visible such as the operculum and shoulders, the thick walls and the three dimensional shape (Figure 2A–C). The auramine stained slide showed much fewer but similar eggs (7 and 6 versus ZNS 47 and 67, and WF 146 and 162 eggs, Figure 2D). Paragonimus specific features were clearly visible. The bleach concentration technique mostly altered Paragonimus eggs in the direct microscopy (Figure 2E). However, all eggs remained clearly identifiable and 25 of 117 observed eggs (21.4%) were unchanged. The remaining eggs were either empty, fragmented or had open opercula.
When the bleach concentration technique was combined with the ZNS or the auramine stain the slide that was further stained by the ZNS revealed only 4 eggs. When further stained by auramine stain not a single egg was detected.
In June and July 2009, we examined 211 ZNS slides produced between January and March 2009 for the presence of Paragonimus eggs. The patients all originated from five districts of the Attapeu province. We identified Paragonimus eggs in four of 211 slides (1.9%). The slides belonged to four different patients in whom the diagnosis of paragonimiasis had not been done before.
In February 2010, we examined, 52 ZNS slides produced between October and December 2009 at the Muang Sing district hospital, LN province. In two of these slides (3.8%) we found Paragonimus eggs. Both slides belonged to an already diagnosed paragonimiasis patient.
We identified 43 patients with chronic cough which we enrolled in the study (Figure 1A). In total, one hundred sputum samples were obtained (mean 2.3 samples per patient; range: 1–6). Fifteen sputum samples contained macroscopic blood. In ten of these samples bloody parts were clearly distinguishable from non-bloody parts of which extra sets of WF and ZNS slides were performed. Thirteen of one hundred samples of six patients had Paragonimus eggs in at least one of the slides. One additional patient with paragonimiasis was only diagnosed in a sample examined outside of the study but not in the 2 included samples.
Patients' age, sex, and symptoms did not differ except previous consumption of raw or insufficiently cooked crabs (3 of 7 Paragonimus positive patients, 42.9%, vs. 3 of 36 Paragonimus negative patients, 8.3%, p = 0.045, Table 1).
The results on the validity of the different diagnostic techniques to identify Paragonimus eggs are shown in Table 2. Sensitivity was lowest in the WF and highest in the FECT.
The mean number of Paragonimus eggs per slides (eps) in WF (2.23 eps), ZNS (1.95 eps) and FECT (4.95 eps) were not statistically different (p = 0.34). The mean rate of at least partly fragmented eggs varied considerably from 18.3% (range 0–50%) in WF, 10.7% (0–100%) in FECT, and 12.5% (0–100%) in ZNS but showed no significant difference (p = 0.13).
Average operational costs per slide were calculated for consumables at 0.09 US$ and 0.65 US$ for WF and FECT, respectively. Additional costs for working time was 0.01 US$ (2 minutes), and 0.14 US$ (21 minutes) for WF and FECT respectively. No additional costs occur in ZNS staining procedures. Overall, yearly additional costs of 100 US$ (25 hours) and 692 US$ (200 hours) occur for WF and FECT, respectively.
Fifteen sputum samples contained macroscopic blood, of which 10 had both bloody and non-bloody parts (Figure 1A). Eight of these ten samples belonged to patients diagnosed with paragonimiasis. Slides performed from bloody parts of paragonimiasis patients' samples (8 WF, 8 ZNS) showed a higher mean number of eggs than from areas without blood (3.3 eps, range 0–10 eps, 95%CI 1.3–5.4 versus 0.7 eps, range 0–5 eps, 95%CI 0–1.4, p = 0.016).
Comparison of the different ZNS techniques by additional sputum samples (Figure 1B, n = 27) revealed more eggs per slides in standard ZNS (41.9 eps, 95% CI 5.5–78.3) compared to the technique using continuous heating during the carbol-fuchsin staining process (29.3 eps, 95% CI 4.1–54.4, each n = 23, p = 0.01). The number of eggs per slide detected with the two different decolorizers was not statistically different (sulphuric-acid: 24.7 eps, 95% CI 4.0–45.4 versus acid-alcohol: 29.7 eps, 95% CI 2.1–57.3, each n = 41, p = 0.51). The rate of fragmented eggs did not differ between the different ZNS techniques (standard ZNS 1.3% (13 of 964 eggs) versus ZNS with continuous heating 1.9% (13 of 673 eggs, n = 23, p = 0.44); standard ZNS 1.5% (15 of 1011 eggs) versus ZNS with acid-alcohol decolorizer 1.9% (23 of 1217 eggs, n = 41, p = 0.52)).
Our study showed that the currently widely used ZNS technique for AFB diagnosis is able to detect Paragonimus eggs. Furthermore, we provide evidence that its sensitivity might even be higher than the WF technique which is today's parasitological reference technique for paragonimiasis and we found that FECT appears superior to WF for paragonimiasis diagnosis. However, the costs related to latter technique highlights the disadvantage of FECT as special technical material and additional time are required. In addition to validity and costs, safety concerns must be considered. WF working procedures exposes laboratory staff to potentially infectious agents, i.e. AFB. FECT includes the utilization of ether which is an additional, non-neglectable hazard in a laboratory that uses open fire for ZNS technique. FECT is therefore not available as a routine diagnostic test in health services in Laos and we would only recommend it as a test for paragonimiasis in specialized settings where this technique already has been well established, e.g. central referral laboratories.
Sadun and colleagues 1960 [7] described that in 20% of the microscopically diagnosed cases with pulmonary paragonimiasis eggs were found only after numerous direct examinations; in one case only after the 27th examined sample. Repetition of direct parasitological tests has successfully been used for diagnosis of other trematode infections; an examination of a second and third slide had increased Schistosoma mansoni egg positivity from 64.8 to 74.3 and 83.8% [19]. This indicates that examination of further sputum samples with the ZNS technique, which, according to the national TB guidelines, would anyways need to be done, might be much more cost-effective and more appropriate than to invest in a more sophisticated method like the FECT with a possibly slightly higher detection rate.
Currently, the simple and cheap WF microscopy is still the standard examination for paragonimiasis in most developing countries including Laos [3], [4], [9], [14]–[16]. However, it has several disadvantages compared to the ZNS technique: first, processing potentially infective material can further increase the already existing higher risk of TB transmission among laboratory workers in low-income countries lacking appropriate control measures [20]. Second, quality control by another laboratory technician is difficult because slides quickly dry up and cannot be stored and re-read. Finally, in the routine work at health services paragonimiasis is only considered when TB examination is negative. At this time point the sputum sample is already discarded, and further slides for diagnosis can not be established any more.
In contrast, ZNS slides are recorded and stored for external quality control according to the TB policy and can be reviewed later, without safety concerns. The successful diagnosis of several patients by reexamination of archived ZNS slides demonstrates that Paragonimus eggs are preserved on the ZNS slides. At this stage we do not know for which time period these eggs remain visible. However, each egg identified and paragonimiasis case detected provides information on an existing focus of transmission and a community based follow-up can be launched. This method has the potential to be applied in epidemiological research on paragonimiasis, e.g. estimations of infection prevalence, identification of endemic areas and more. Serological examinations show a higher sensitivity but are usually not available in developing countries. Furthermore, they are prone to overestimate infection rates due to possible persistent antibodies and cross-reactions with other helminthic infections [21]. A definitive diagnosis of paragonimiasis is still carried out by the demonstration of lung-fluke eggs in sputum, feces, or thoracic tissue [21], [22].
The mucus in the ZNS and the auramine staining of sputum helps to keep the eggs attached to the slide. During the bleach concentration technique, mucus and fibers are resolved [13]. This may explain why slides processed with ZNS after bleaching yielded only very few Paragonimus eggs.
We recovered higher numbers of eggs from bloody compared to non-bloody parts of the sputum specimen. This proves to be a simple way of improving the pretest likelihood as it is suggested in general text books [23]. Paragonimus infected lungs contain nodular areas with necrosis and numerous eggs. Adjacent richly perfused granulation tissue is the basis for hemorrhagic pneumonia [6], [24]. In potentially paragonimiasis endemic areas direct examination should preferably be done from bloody portions of sputum.
One patient had a co-infection of TB and Paragonimus which highlights the importance of correct diagnostic procedures for both diseases. An integration of routine ZNS examinations for Paragonimus eggs could help to avoid misdiagnosis of sputum-negative TB due to Paragonimiasis [2]–[5] in endemic areas and contribute to correct diagnosis of co-infections. As for TB, paragonimiasis diagnosis requires repeated sputum examinations [7]. As such, the ZNS technique represents the ideal common diagnostic procedure.
However, why did Sadun and Buck [7] find only debris of Paragonimus eggs in the ZNS? The ZNS techniques have evolved over the last decades [9], [10], [12] which might be one of the reasons that nowadays Paragonimus eggs can indeed be found. We detected a significantly lower number of eggs in those slides that were continuously heated during the carbol-fuchsin staining process. Evidently, extensive heat can degenerate the egg wall proteins. The type of decolorizer did not influence the detection of eggs. There might be other factors during specimen transport such as sun exposure, heat, and shaking that could have altered the eggs in the case of Sadun and Buck [7], while further possible reasons might be attributed to species differences of Paragonimus. Korea is endemic mainly for P. westermani [25] whereas in Laos P. heterotremus is the main species [3], [17], [26], [27].
Our study is limited by its rather low sample size and thus differences between the diagnostic techniques might be underestimated. There might be paragonimiasis patients misclassified due to low or varying numbers of expelled eggs requiring examination of multiple samples or ectopic paragonimiasis [4], [7], [8] which might have diluted our results. We did not include feces samples in our investigation nor did we further investigate fluorescence microscopy, bleach concentration, and cold ZNS techniques. We did not investigate how time might affect Paragonimus eggs fixed in stored ZNS slides. We did not record the individual working time used per slide and therefore cannot give a variance. Since microscopy depends on examiner's experience it might initially take slightly longer when low-magnification ZNS microscopy is introduced.
Our cost effectiveness analysis did not account for capita costs and possible differences in quality-adjusted life years (QALYs) due to increased risk of TB transmission among laboratory technicians which both would further increase the cost-minimization by the ZNS. Another important differential diagnosis for TB is lung cancer which however remains challenging for resource-limited settings [28] and was not included since in Laos pathohistological diagnosis is limited to few central hospitals and unfortunately specific treatment is not yet available.
In conclusion, the current study, in contrast to previous reports, documents the usefulness and validity of the ZNS technique for detection of Paragonimus eggs. It appears to have superior sensitivity to the standard WF microscopic examination and has the best cost-effectiveness. Furthermore, ZNS examination for paragonimiasis does not carry biosafety risks and allows better post-test quality control. In addition to its use for the diagnosis of TB, we also recommend routine examination for Paragonimus eggs of each slide with the 10× lens (100× magnification) in geographic areas where paragonimiasis may be endemic. Its integration into the standard TB diagnostic procedure could help to reduce the misdiagnosis of sputum-negative TB due to paragonimiasis and could contribute to delineate endemic areas for this neglected parasitic infection.
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10.1371/journal.pntd.0000483 | Assessment of Yellow Fever Epidemic Risk: An Original Multi-criteria Modeling Approach | Yellow fever (YF) virtually disappeared in francophone West African countries as a result of YF mass vaccination campaigns carried out between 1940 and 1953. However, because of the failure to continue mass vaccination campaigns, a resurgence of the deadly disease in many African countries began in the early 1980s. We developed an original modeling approach to assess YF epidemic risk (vulnerability) and to prioritize the populations to be vaccinated.
We chose a two-step assessment of vulnerability at district level consisting of a quantitative and qualitative assessment per country. Quantitative assessment starts with data collection on six risk factors: five risk factors associated with “exposure” to virus/vector and one with “susceptibility” of a district to YF epidemics. The multiple correspondence analysis (MCA) modeling method was specifically adapted to reduce the five exposure variables to one aggregated exposure indicator. Health districts were then projected onto a two-dimensional graph to define different levels of vulnerability. Districts are presented on risk maps for qualitative analysis in consensus groups, allowing the addition of factors, such as population migrations or vector density, that could not be included in MCA. The example of rural districts in Burkina Faso show five distinct clusters of risk profiles. Based on this assessment, 32 of 55 districts comprising over 7 million people were prioritized for preventive vaccination campaigns.
This assessment of yellow fever epidemic risk at the district level includes MCA modeling and consensus group modification. MCA provides a standardized way to reduce complexity. It supports an informed public health decision-making process that empowers local stakeholders through the consensus group. This original approach can be applied to any disease with documented risk factors.
| This article describes the use of an original modeling approach to assess the risk of yellow fever (YF) epidemics. YF is a viral hemorrhagic fever responsible in past centuries for devastating outbreaks. Since the 1930s, a vaccine has been available that protects the individual for at least 10 years, if not for life. However, immunization of populations in African countries was gradually discontinued after the 1960s. With the decrease in immunity against YF in African populations the disease reemerged in the 1980s. In 2005, WHO, UNICEF, and the GAVI Alliance decided to support preventive vaccination of at-risk populations in West African endemic countries in order to tackle the reemergence of YF and reduce the risk of urban YF outbreaks. Financial resources were made available to scale up a global YF vaccine stockpile and to support countries with limited resources in the management of preventive vaccination campaigns. This article describes the process we used to determine the most at-risk populations using a mathematical model to prioritize targeted immunization campaigns. We believe that this approach could be useful for other diseases for which decision making process is difficult because of limited data availability, complex risk variables, and a need for rapid decisions and implementation.
| After several decades of relative calm, yellow fever (YF) outbreaks have had a resurgence in Africa, posing an immediate risk to the affected populations across the continent. Increasing migration, accelerating urbanization, and improved travel infrastructure are global trends that increase the risk of YF spreading to parts of the world where the disease has disappeared, such as Europe or North America, or never seen before, such as Asia [1]–[14]. Because of the risk of international spread, YF is one of the diseases officially reported under the International Health Regulations. The continued use of the YF vaccination certificate is a tangible sign of the constant threat posed by the disease at a global level [15]–[20].
The most effective measure for preventing and controlling YF outbreaks is vaccination. The development of a YF vaccine in the 1930s was a turning point in the history of the disease, because a single dose of the vaccine that is considered safe and effective is sufficient to protect an individual for at least 10 years and probably up to 35 years [21]–[24]. Between 1933 and 1961, mass vaccination campaigns were carried out in several francophone West African countries, resulting in the rapid disappearance of the disease over the subsequent 40 years [25]–[27]. The mass vaccination campaigns stopped in the 1960s when the French neurotropic vaccine (FNV) stopped being recommended for children under 10 years old because of the noted association with a high incidence of encephalitis reaction in this age group [28],[29]. The production of FNV stopped in 1980. Today the 17D vaccine is the only type of YF vaccine produced and used for vaccination.
While anglophone countries such as Nigeria experienced devastating YF outbreaks in the 1980s, francophone countries reported limited YF outbreaks. These outbreaks mainly appeared in nomadic communities or among seasonal workers (e.g., in Senegal 1965 and Burkina Faso 1983) who did not benefit from previous mass vaccination campaigns [30]–[34].
Since 2000, a number of YF outbreaks have been reported in West African countries, especially in capitals and large cities [35]–[40] such as Abidjan, Ivory Coast (2001); Dakar, Senegal (2002); Touba, Senegal (2002); Conakry, Guinea (2002); and Bobo Dioulasso, Burkina Faso (2004). The outbreaks were rapidly controlled by emergency reactive vaccination campaigns, and the number of YF cases has remained low.
The resurgence of this disease is related to a high proportion of non-protected individuals in exposed communities. The YF vaccine has been introduced into routine infant immunization programs in 19 of the 23 (83%) high-risk African countries endemic for YF [41]. However, with routine immunization of children alone, it takes several decades to reduce significantly the proportion of non-immune people in the population and thus the risk of outbreaks. Four strategies proposed by WHO–UNICEF have the potential to bring YF under control in Africa: (i) Rapid response to outbreaks, (ii) routine childhood immunization, (iii) mass preventive campaigns, and (iv) improved surveillance. Unfortunately, in most of the YF endemic countries, coverage for routine YF immunization is low (below 60%) and preventive campaigns have not been carried out programmatically. The limited implementation of recommended control strategies is due to many factors, including competing public health priorities such as meningitis or cholera outbreaks, the cost of the vaccination campaigns, and limited availability of affordable YF vaccine on the global market. The resurgence of YF is also linked to the interaction of various environmental, economic, social, and political factors. All these factors and their interactions make YF epidemic risk analysis a complex and difficult process: it requires an assessment of multiple criteria [42].
In the face of the resurgence of YF, the Global Alliance for Vaccine and Immunization (GAVI) has funded a joint WHO–UNICEF proposal in December 2005 to reduce YF epidemic risk in the following 12 high-risk countries in Africa: Benin, Burkina Faso, Cameroon, Côte d'Ivoire, Ghana, Guinea, Liberia, Mali, Nigeria, Senegal, Sierra Leone, and Togo. This initiative of US$62 million is aimed at providing sufficient funds by 2010 for the immunization of 48 million people, which represents approximately 17% of the population of the 12 targeted countries. In order to best use this limited international funding it is important to define levels of risk for YF outbreaks and to target high-risk communities for priority vaccination.
This article describes the process, methodology, and tools used to identify high-risk populations for priority vaccination, and the multiple correspondence analysis (MCA) and consensus assessment at the country level used as decision-making tools.
The frame of reference chosen to identify communities at highest risk is derived from the model of Sutherts to assess the vulnerability of the population to vector-borne diseases. The vulnerability may be defined as the economic, social, or political predisposition of a community to destabilization by an external, natural, or man-made phenomenon [43]. YF vulnerability depends on three parameters: susceptibility of the community to infection, exposure to the YF virus, and resilience of the population at risk [26],[44].
The susceptibility to or likelihood of a community being affected by a YF outbreak depends on population immunity, which is mainly related to the proportion of vaccinated people in a community. Susceptibility usually varies among countries and districts. Since the reemergence of YF in Africa, affected countries have embarked on preventive immunization campaigns or epidemic response campaigns, and have incorporated YF vaccine into the routine infant immunization schedule. Evidence suggests that epidemic risk in a community diminishes considerably once 60%–80% of the population in that community has been immunized [45]–[47]. Since mass vaccination campaigns may not reach 100% of the population, sporadic cases in a vaccinated population can still occur, but transmissions rate will remain low and will not amplify into epidemic transmission. Sporadic cases are also seen in non-immunized migrants settling in areas with infected mosquitoes.
The exposure is defined by the likelihood for a community to be in contact with the YF virus through infected Aedes mosquitoes. Resilience is the ability to control and recover quickly from an outbreak, and it depends on the capacity to quickly detect outbreaks and rapidly launch mass vaccination campaigns.
The final product was a district-level assessment of vulnerability within countries. The assessment was derived from two processes, one quantitative and one qualitative. The quantitative assessment was based on the selection of key variables followed by a formal MCA [48]–[54]. This process led to a graphic representation of districts' vulnerability profile, allowing the definition of vaccination priorities according to the profile of vulnerability. The qualitative process consisted of consensus expert groups meeting at the country level that reviewed the results from the quantitative assessment and adjusted the districts' classifications based on additional information available at the country level. These groups consisted of various experts in the fields of epidemiology, virology, entomology, and public health from Ministries of Health, WHO, UNICEF, Institut Pasteur, and international nongovernmental organizations (NGOs).
The vulnerability graph obtained for rural districts in Burkina Faso illustrates the results of the quantitative analysis (Figure 5).
The rural districts were clustered into five distinct groups. The two clusters of districts on the right of the graph were judged to be very vulnerable (Profile 1, see Methods for definition). Therefore, the threshold for exposure was defined by consensus to be placed at point 0.4 of the exposure axis.
The qualitative process identified additional clusters for inclusion in Profile 1. The district of Yako, although in quadrant III (Profile 3 in Figure 4), was considered to be very vulnerable because of its market-gardening industry and the significant cross-border migration occasioned by this commercial activity. Both characteristics, which were not considered in the exposure variable, were included in the MCA model.
The same analysis was performed separately for urban districts. A map of the vulnerability of rural and urban districts in Burkina Faso is presented (Figure 6). The capital city of Ouagadougou as well as Tenkodogo, Koudougou, and Fada were classified as Profile 1 and will be prioritized for vaccination in the next preventive campaign, whereas the cities of Bobodioulasso, Dedougou, and Banfora were in Profile 2. They have already been vaccinated and they do not need to be revaccinated in the coming years unless the migration rate is known to be high enough to renew the population in a few years time. The other cities, Kaya, Ouhigouya, and Dori, were not priorities for preventive campaigns in the coming years.
On the basis of this analysis a vaccine prioritization schedule was developed including 32 districts out of 55 (see Figure 6) representing 7.8 million people at highest priority for an immediate preventive campaign.
In this article we present the process, which included MCA and consensus group modification, of defining priority communities that would benefit from a YF preventive immunization campaign.
This process starts from a small set of quantitative elements that identify most districts at risk but also engages local decision makers to complement and interpret the results. The MCA allowed the representation of a complex multidimensional situation into a two-dimensional graph that visualizes the communities at highest risk in a concise and reader-friendly way. MCA provides a standardized way to reduce complexity to support an informed decision-making process and allocate effectively the limited resources that are available for this preventive intervention.
The risk assessment is based on the combination of the standardized analysis of defined factors through MCA and information gathered during the consensus meeting that draws on a range of data sources such as epidemic investigation reports, routine surveillance data, or interviews with key personnel in the risk management system. Countries are involved at different steps in the process. The data matrix used for the modeling was verified by the Ministry of Health of Burkina Faso. During the consensus meeting local stakeholders provided information on important risk factors that cannot be handled by the model, for example migratory flows, nomadic populations, and resilience in a particular region. The joint interpretation of the results including the modelers and local stakeholders also provided a better assimilation of the results by the nationals. The full endorsement by the country of the result of the modeling is critical, as the final result of the RA will be translated into the practical implementation of vaccination campaigns, which requires national funding in addition to international financial support. This approach for risk assessment not only supports evidence-based decision making but also empowers decision makers in countries receiving international support for YF control. A criterion for validity of such a process could be the catalytic authenticity which is the extent to which action is stimulated and facilitated by the risk assessment process [59].
One limitation of the described process is that factor analysis such as MCA requires a good dataset with no missing data. The nature of the dataset influences the selection of variables, as mentioned before, and requires control of the quality of the data input into the matrix. The limited amount of information over a long period of time for some indicators was a constraint for integrating more variables into the model. Moreover, the lack of data for some variables, initially considered important during the expert panel meeting, did not allow their final integration into the model. However, the addition of the qualitative consensus review allowed us to overcome this limitation in an efficient manner. The exposure analysis we obtained is similar to the results from other risk mapping studies [14]. The result of the pilot study in Burkina Faso showed that the risk assessment reflects what was instinctively assumed by local health experts. This evidence-based confirmation of subjective knowledge was an important step in the buy-in of political decision makers and the planning of the vaccination campaigns. To date, the risk assessment approach has been used in another seven countries, and results have confirmed the pragmatic approach of this decision-making tool.
A second limitation of the risk assessment tool is the relative (not absolute) characteristics of the aggregated exposure indicator. This limitation means that this value must be compared and interpreted in the frame of one given country and cannot be compared with values calculated from other analyses. If the aggregated exposure indicator equals 1.2 for a district in Burkina Faso, it does not mean that a Togolese district with the same value has the same exposure. This is not a major constraint, as the objective of the YF risk assessment is to rank districts primarily for facilitation of national-level decision-making processes.
The main advantage of multiple correspondence analysis is that the model analyses data without altering the parameters beforehand through scoring procedures and weighting systems. Variables are not weighted before being introduced into the model, but MCA itself, through the comparison of the data of each district, defines the reciprocity of variables for each model outcome. Allowing MCA to define which variables best describe the risk situation for each analysis ensures the highest attainable degree of objectivity. Scoring procedures have often been used for risk assessment using the additive function, with or without weights. Unfortunately, the scoring technique (additive or multiplicative) is unable to discriminate various exposure profiles. For instance, for the YF risk assessment, different values for the five exposure indicators could lead to a similar exposure score. Furthermore, additive scoring procedures often imply managing qualitative variables with subjective assumptions.
This experience shows the robustness of MCA when used with a limited number of variables. It also highlights the potential use of such a methodology for supporting an evidence-based public-health decision-making process in countries where surveillance data of good quality are scarce. A similar methodology based on an original, robust, and reproducible technique—able to give a simple representation of a complex reality—could be used for other infectious diseases such as avian influenza when multiple risk factors at the animal–human interface are interconnected.
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10.1371/journal.pcbi.1005388 | graph-GPA: A graphical model for prioritizing GWAS results and investigating pleiotropic architecture | Genome-wide association studies (GWAS) have identified tens of thousands of genetic variants associated with hundreds of phenotypes and diseases, which have provided clinical and medical benefits to patients with novel biomarkers and therapeutic targets. However, identification of risk variants associated with complex diseases remains challenging as they are often affected by many genetic variants with small or moderate effects. There has been accumulating evidence suggesting that different complex traits share common risk basis, namely pleiotropy. Recently, several statistical methods have been developed to improve statistical power to identify risk variants for complex traits through a joint analysis of multiple GWAS datasets by leveraging pleiotropy. While these methods were shown to improve statistical power for association mapping compared to separate analyses, they are still limited in the number of phenotypes that can be integrated. In order to address this challenge, in this paper, we propose a novel statistical framework, graph-GPA, to integrate a large number of GWAS datasets for multiple phenotypes using a hidden Markov random field approach. Application of graph-GPA to a joint analysis of GWAS datasets for 12 phenotypes shows that graph-GPA improves statistical power to identify risk variants compared to statistical methods based on smaller number of GWAS datasets. In addition, graph-GPA also promotes better understanding of genetic mechanisms shared among phenotypes, which can potentially be useful for the development of improved diagnosis and therapeutics. The R implementation of graph-GPA is currently available at https://dongjunchung.github.io/GGPA/.
| Recently, there has been accumulating evidence suggesting pleiotropy, i.e., genetic components shared across multiple phenotypes. Incorporation of pleiotropy in genetic analysis might improve statistical power to identify risk associated genetic variants. Several statistical approaches have been proposed to utilize pleiotropy for association mapping but they are currently still limited to a relatively small number of phenotypes, e.g., a pair of phenotypes. This restricts potential gain in statistical power in association mapping and investigation of pleiotropic structure among a large number of phenotypes. In order to address this challenge, in this paper, we propose graph-GPA, a novel statistical framework to integrate a large number of phenotypes using a hidden Markov random field architecture. Application of the proposed statistical method to GWAS datasets for 12 phenotypes showed that graph-GPA does not only provide a parsimonious representation of genetic relationship among these phenotypes, but also identify significantly larger number of novel genetic variants that are potentially functional. We believe that this novel approach might help investigation of common etiology and improvement of diagnosis and therapeutics.
| Genome-wide association studies (GWAS) have identified many single-nucleotide polymorphisms (SNPs) associated with various phenotypes, including cancer, diabetes, and autoimmune diseases, among others. As of July 2016, more than 20,000 SNPs have been reported to be significantly associated with at least one complex trait in the NHGRI-EBI catalog of published GWAS [1] (http://www.ebi.ac.uk/gwas/). Despite these great achievements, researchers have had concerns about the fact that these significantly associated SNPs could explain only a small portion of genetic contributions to complex traits/diseases, which is referred to as “missing heritability” [2–4]. For example, while human height is known to be highly heritable and its heritability is estimated to be up to 80% [5], genome-wide significant SNPs (p-value < 5 × 10−8 after Bonferroni correction) together can only explain 16% of variation in height [6]. Researchers have tried to identify sources of this missing heritability phenomenon and progress has been made towards explaining some sources of the phenomenon. Among these efforts, Yang et al. [7] reported that, in their cohort genotyped on HumanCNV370-Quad v3.0 BeadChips (∼351K SNPs) or Human610-Quad v1.0 BeadChips (∼582K SNPs) platforms, all genotyped common SNPs together can explain 45% of variation in human height using a random effects model. This result suggests that a large proportion of the heritability is not actually missing: given the limited sample size, many individual effects of genetic markers are too weak to pass the genome-wide significance, and thus those variants remain undiscovered, which is usually referred to as “polygenicity”.
Although polygenicity provides attractive explanation for the missing heritability phenomenon, the polygenic architecture imposes a great practical challenge in GWAS. While identification of genetic variants with small effect sizes requires a larger sample, it is often not a practical option to recruit a larger sample because recruitment may be expensive and time-consuming. Hence, it would be desirable if we could increase statistical power to detect risk associated genetic variants with smaller effect sizes, without extensive additional subject recruitment. Integrative analysis of genetic and genomic data has recently been considered as a promising direction, including combining GWAS data of multiple genetically related phenotypes. In the last few years, researchers have provided convincing evidence of “pleiotropy”, i.e. the sharing of genetic factors, especially between human complex traits. For example, a systematic analysis of the NHGRI GWAS Catalog demonstrated that 16.9% of the reported genes and 4.6% of the reported SNPs are associated with multiple traits [8]. As a specific example, recent genetic studies for five psychiatric disorders reported high genetic correlation between schizophrenia and bipolar disorders [9, 10]. Furthermore, a series of studies have shown that integration of genetic data for multiple phenotypes can boost statistical power to identify risk associated genetic variants [11–15]. For example, Andreassen et al. [12] showed that exploiting the pleiotropy between schizophrenia and cardiovascular disease greatly improved the statistical power to detect schizophrenia-associated genetic variants.
In order to address these challenges and opportunities, Chung and others proposed a statistical framework, namely GPA (Genetic analysis incorporating Pleiotropy and Annotation), that integrates genetic data for multiple phenotypes [11]. Using extensive simulation studies, the authors showed that GPA outperforms other methods utilizing pleiotropy for association mapping, such as conditional FDR approach [13]. The GPA algorithm was successfully applied to a joint analysis of five psychiatric disorders, including attention deficit hyperactivity disorder (ADHD), autism spectrum disorder (ASD), bipolar disorder (BPD), major depressive disorder (MDD), and schizophrenia (SCZ). For example, GPA detected strong genetic relationship between BPD and SCZ and the joint analysis of GWAS data for these two diseases significantly improved statistical power to identify genetic variants associated with risks of BPD and SCZ. Specifically, at the local false discovery rate (FDR) of 0.05, separate analyses identified only 14 and 409 genetic variants associated with BPD and SCZ, respectively, while joint analysis using GPA uncovered 383 and 821 associated genetic variants. Furthermore, some of these additionally identified SNPs were confirmed by the findings of Cross-Disorder Group of the Psychiatric Genomics Consortium [9, 10].
This paper is motivated by the joint analysis of GWAS data for 12 phenotypes, including psychiatric disorders, autoimmune diseases, lipid-related phenotypes, and cardiovascular phenotypes. The extensive integrative analysis is of interest because it is expected that genetic basis is not shared only within a phenotype group (e.g., psychiatric disorders) but also between phenotype groups (e.g., between psychiatric disorders and autoimmune diseases), as suggested in recent literature [16]. However, the state-of-the-art approaches to integrating GWAS datasets are still limited to a relatively small number of phenotypes, e.g., a pair of phenotypes [11–15, 17]. For example, GPA considers all possible combinations of phenotype associations of which the number increases exponentially with the number of phenotypes. Specifically, the joint analysis of 12 phenotypes requires consideration of 4,096 combinations of association status, which is computationally prohibitive and can generate various estimation issues. As a result, information sharing is permissible only among a small number of phenotypes with existing methods, limiting potential improvement in statistical power to identify risk associated genetic variants. This limitation highlights the urgent need for a statistical framework that enables integration across a large number of GWAS datasets.
In order to address the challenge, in this paper, we propose a novel statistical framework, namely graph-GPA. Specifically, we utilize the Markov random field (MRF) architecture to model hidden binary indicators for association of a SNP with each phenotype. The MRF architecture does not only provide a parsimonious representation of genetic relationship among phenotypes, but also improves statistical power to identify risk associated genetic variants by sharing information across GWAS data for these phenotypes. This paper is structured as follows. We first propose our novel Bayesian hierarchical model for the joint analysis of GWAS data for multiple phenotypes. Then, we evaluate the proposed method with simulation studies and conduct the joint analysis of GWAS datasets for 12 phenotypes, including psychiatric disorders, autoimmune diseases, lipid-related phenotypes, and cardiovascular phenotypes. Our real data analysis reveals genetic mechanisms shared among the phenotypes. Furthermore, graph-GPA improves statistical power to identify both risk variants associated with each phenotype (bipolar disorders) and those with pleiotropic effects (psychiatric disorders and inflammatory bowel diseases).
In this paper, we integrate GWAS data for multiple phenotypes at the level of summary statistics rather than full genotype and phenotype datasets. For example, they might be p-values obtained from a logistic regression of phenotype on genotype or a contingency table for genotype-phenotype association. This approach allows broader application of the proposed method because summary statistics are more readily available. Let pit denote the p-value of association testing of SNP t = 1, 2, ⋯, T with phenotype i = 1, 2, ⋯, n. To facilitate data visualization and modeling, we transformed pit as yit = Φ−1(1 − pit), where Φ(⋅) is the cumulative distribution of the standard normal variable [18]. Let eit denote the latent (unobservable) association indicator for t-th SNP and i-th phenotype, where eit = 1 if SNP t is associated with phenotype i and eit = 0 otherwise. We model the density of yit given the latent association status eit by a normal mixture
p ( y i t | e i t , μ i , σ i 2 ) = e i t LN ( y i t ; μ i , σ i 2 ) + ( 1 - e i t ) N ( y i t ; 0 , 1 ) , (1)
where LN(y; μ, σ2) and N(y; 0, 1) denote the log-normal density with mean e μ + σ 2 / 2 and the standard normal density, respectively. Here, the standard normal distribution assumption for background SNPs (eit = 0) is equivalent to the theoretical null distribution assumption (uniformity of p-values) [18]. We assume the log-normal distribution for yit corresponding to associated SNPs (eit = 1) because it is likely that associated SNPs will only account for a negligible proportion of the SNPs with p-values larger than 0.5, i.e., yit smaller than zero. We evaluated the appropriateness of these assumptions using real GWAS datasets and confirmed that no significant violation of these assumptions is detected (Sections 6 and 7 in S1 Text). However, we note that other distributions can also be considered, especially for associated SNPs, because we chose the log-normal distribution mainly due to convenience and interpretability. In order to check this issue and further confirm the robustness of our results, we also considered Gamma distribution as an alternative distribution for associated SNPs instead of log-normal distribution. The results indicate that our results are still robust to this alternative choice of emission distribution for associated SNPs (Section 8 in S1 Text).
For effective integration of multiple GWAS datasets for genetically related phenotypes, we suggest a graphical model based on an MRF [19] that represents a conditional independent structure for genetic relationship among phenotypes. Let G = (V,E) denote an MRF graph, where V = (v1, …, vn) are the vertices and E represents the edges such that E(i, j) = 1 if there is an edge between vi and vj and E(i, j) = 0 otherwise. Here, vi corresponds to phenotype i and E(i, j) = 1 implies that phenotypes i and j are genetically correlated in the sense of conditional dependence. Assuming an auto-logistic spatial scheme, the conditional distribution of et = (e1t, …, ent) is written by
p ( e t | α , β , G ) = C ( α , β , G ) · exp ∑ i = 1 n α i e i t + ∑ i ∼ j β i j e i t e j t , (2)
where
C ( α , β , G ) - 1 = ∑ e * ∈ E exp ∑ i = 1 n α i e i * + ∑ i ∼ j β i j e i * e j * , (3) E is the set of all possible values of e * = ( e 1 * , … , e n * ), βij is the MRF coefficient for the pair of phenotypes i and j, and i ∼ j denotes that vi is adjacent to vj, i.e., the sum of the second term is over all pairs of phenotypes i and j such that E(i, j) = 1.
For Bayesian inference, we introduce conjugate prior distributions for Model (1),
μ i ∼ N ( θ μ , τ μ 2 ) , B B σ i 2 ∼ IG ( a σ , b σ ) , (4)
where IG denotes the inverse-gamma distribution. For coefficients in the MRF Model (2), prior distributions are assumed as
α i ∼ N ( θ α , τ α 2 ) , B B β i j ∼ E ( i , j ) Γ ( β i j ; a β , b β ) + { 1 - E ( i , j ) } δ 0 ( β i j ) , (5)
where Γ(a, b) denotes the gamma distribution with mean a/b and δ0 denotes the Dirac delta function at zero. This prior setting for βij reflects the graph structure G by putting βij = 0 if there is no edge between phenotypes i and j in the graph, i.e., E(i, j) = 0. We put p(G) = 1/|E|, where |E| denotes the number of edges in the current graph G, which leads the model to favor simple graphical representation with a small number of edges, i.e., the small number of βij having non-zero values. As informed by a referee, our estimation of the graphical structure G is conceptually similar to “learning the structure” of MRF in the machine learning literature. Specifically, in the “learning the structure” approach, the L1 (Lasso) regularization is directly imposed instead of using the prior distribution of G in our current model. This approach tends to lead to sparse models where many βij have zero values while not much sacrificing the model fit (e.g., [20–22]). In our simulation and application studies, weakly informative priors are used by setting the following hyperparameters: θμ = 0, τ μ 2 = 10000, θα = 0, and τ α 2 = 10000. We set aσ = bσ = 0.5 to put substantial prior mass on modest-sized variances. We put aβ = 4 and bβ = 2 to let most of βij with E(i, j) = 1 a priori distinct from zero, having mean 2 and variance 1, while allowing a handful of values be close to zero. Note that we defer the discussion about model robustness to the hyperparameters to the Results Section.
Given the p-values from GWAS datasets, we estimate the parameters of the graph-GPA model and other related interesting quantities based on posterior samples from a Markov Chain Monte Carlo (MCMC) sampler. Specifically, we implement a Metropolis-Hastings within Gibbs algorithm whose full details are provided in Section 1 in S1 Text. Here, we address a few key points about the MCMC implementation. As adopting some conjugate forms of prior distributions, most MCMC steps consist of Gibbs updating which is computationally efficient. To deal with the dimensional changing structure of βij given G, we adopt the reversible jump MCMC [23] to add or remove edges in the graph G during the MCMC run.
The graph structure of G representing genetic relationship among phenotypes can be summarized by p(E(i, j)|Y), interpreted as the posterior probability that two phenotypes i and j are genetically correlated with each other. In addition, the posterior summary of βij can be interpreted as a relative metric to gauge the degree of correlation between phenotypes i and j. Using these posterior summaries, we can come up with several strategies to make an inference about the pairwise relationship between phenotypes. A convenient and widely used approach is to set an arbitrary threshold for p(E(i, j)|Y) to declare phenotypes i and j to be correlated, for example, p(E(i, j)|Y) > 0.5 as in [24, 25]. However, we found that this approach gives suboptimal results in estimation of a graph structure because it does not exclude the case that two phenotypes are connected in the graph (E(i, j) = 1) but their corresponding MRF coefficient (βi, j) is close to zero. Hence, here we adopt an alternative approach to improving specificity in identifying correlated pairs of phenotypes using both p(E(i, j)|Y) and the posterior summary of βi, j. Specifically, we declare phenotypes i and j to be correlated when p(E(i, j)|Y) > 0.5 and βij is significantly different from zero, e.g., p(βij > 0|Y) > 0.95.
Association mapping of a single SNP on a specific phenotype is inferred from p(eit = 1|Y), i.e., the posterior probability that SNP t is associated with phenotype i. SNPs associated with both i-th and j-th phenotypes can be inferred based on p(eit = 1, ejt = 1|Y) and SNPs associated with more than two phenotypes can also be identified in similar ways. We use the direct posterior probability approach [26] to control global false discovery rates to determine associated SNPs. Specifically, given the graph-GPA model fitting, we first sort SNPs by their local false discovery rates from the smallest to the largest. For example, we can sort SNPs by their local false discovery rates for phenotype i, denoted as fit ≡ 1 − p(eit = 1|Y). Then, we increase the threshold for local false discovery rates, κi, from zero to one until
Fdr i ≡ ∑ t = 1 T f i t 1 f i t ≤ κ i ∑ t = 1 T 1 f i t ≤ κ i ≤ τ ,
where τ is the pre-determined bound of global false discovery rates, and 1{⋅} is the indicator function with value one if the statement is true and zero otherwise. Finally, we declare SNPs with corresponding fit < κi to be associated with phenotype i. Similarly, SNPs shared between phenotypes i and j can be identified by using
Fdr i j ≡ ∑ t = 1 T f i j t 1 f i j t ≤ κ i j ∑ t = 1 T 1 f i j t ≤ κ i j ≤ τ ,
where fijt ≡ 1 − p(eit = 1, ejt = 1|Y).
We conducted simulation studies to evaluate the performance of the proposed graph-GPA model. In the simulation studies, we mainly investigate the proposed model from the perspectives of 1) parameter estimation and false discovery rate control; 2) estimation of genetic relationship among diseases (graph structure); and 3) statistical power to identify risk-associated SNPs. We generated our simulation data as follows. First, we assumed the true phenotype graph (G) as depicted in Fig 1a. Specifically, we considered a group of tightly linked phenotypes (P1, P2, and P3) and a group of weakly linked phenotypes (P3, P4, and P5). In addition, we also assumed two isolated phenotypes (P6 and P7) as negative controls. Given this graph, we set α1 = −4.7, α2 = −3.0, α3 = −5.5, α4 = −4.8, α5 = −3.6, α6 = −2.5, and α7 = −3.5, while setting β12 = 4.0, β13 = 1.8, β23 = 2.3, β34 = 2.5, and β45 = 5.0 (all the remaining βij were set to zeros). These MRF coefficients were determined based on the estimated coefficients obtained using real GWAS datasets. Then, given the MRF coefficients, we generated association status of 20,000 common SNPs (eit) from the model in the Eq (2) by running the Gibbs sampler for 1,000 iterations [25]. Finally, given the association status of SNPs, we generated yit from N ( μ i , σ i 2 ) if eit = 1, and N(0, 1) if eit = 0, where μ = (1.1, 1.0, 1.2, 1.2, 1.3, 1.1, 1.3) and σ = (0.4, 0.3, 0.35, 0.3, 0.45, 0.4, 0.3). Note that we set the signal strengths relatively weak in order to mimic the distribution of p-values in real GWAS datasets. We applied the graph-GPA model with 10,000 burn-in and 40,000 main MCMC iterations to the simulation dataset. We also analyzed the simulation dataset with GPA as a representative method that can utilize a smaller number of phenotypes because GPA outperformed other currently available methods such as conditional FDR [12, 13] in previous studies [11].
We first evaluated the estimation accuracy of the proposed method. Figure A in S1 Text indicates that all the parameters are accurately estimated with high confidence. Specifically, for all parameters, the point estimates are close to true values and their 95% credible intervals always include true values. In addition, Figure B in S1 Text indicates that the global FDR is well controlled at the nominal FDR level for a wide range of FDR levels, regardless of whether it is for genetically related phenotypes or independent phenotypes. We then evaluated the estimation of genetic relationship among phenotypes. Fig 1a and 1b show the phenotype graph estimated using graph-GPA and GPA, respectively. We can see that graph-GPA accurately recovers true phenotype graph, while GPA adds an additional edge between P3 and P5. This is essentially because P3 and P5 are still correlated (which GPA infers) although they are conditionally independent given P4 (which graph-GPA infers). The result implies that graph-GPA has potential to better prioritize important pairs of genetically correlated phenotypes while it also provides more parsimonious representation of genetical correlation.
Finally, we evaluated the SNP prioritization performance of graph-GPA. As a baseline, we ran a null model without interaction term (βij) as follows:
P ( e t | α , G ) ∝ exp ∑ i α i e i t . (6)
The no interaction model is interpreted as a special case of our suggested model in Eq (2) by putting the point mass at zero for prior distribution of βij, i.e., the extremely strong prior belief that the phenotypes are independent. Note that these models correspond to separate analyses of GWAS datasets because they do not share information between phenotypes. Fig 1c shows the receiver operating characteristic (ROC) curves for graph-GPA, GPA, and separate analysis for the phenotype P1, which is genetically highly correlated with other phenotypes. Compared to the separate analysis, both graph-GPA and GPA improve area under the curve (AUC) values. More importantly, graph-GPA significantly outperforms GPA in the sense of AUC, due to more extensive information sharing across phenotypes. On the other hand, for independent phenotypes such as P7 illustrated in Fig 1d, there is no gain in AUC by employing graph-GPA or GPA compared to the separate analysis. The ROC curves for all the phenotypes are also provided in Section 5 of S1 Text and our conclusion remains valid across seven phenotypes. Tables A and C in S1 Text show numbers of SNPs identified by graph-GPA and in separate analyses at nominal FDR level of 10%. As expected, graph-GPA identified significantly larger number of associated SNPs (Table A in S1 Text) than separate analyses (Table C in S1 Text). In summary, graph-GPA significantly improves statistical power to identify associated SNPs compared to GPA and separate analyses by sharing information among larger number of phenotypes. Additionally, graph-GPA promotes understanding of genetical relationship among phenotypes by providing a parsimonious representation of pleiotropic architecture among these phenotypes.
To evaluate the potential of the proposed model for genetic studies, we applied the proposed graph-GPA model and the GPA model to the GWAS data of European ancestry for 12 phenotypes, using summary statistics that are publicly available from consortium websites. Specifically, we considered 1) five psychiatric disorders, including attention deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), bipolar disorder (BPD), major depressive disorder (MDD), and schizophrenia (SCZ) from the Psychiatric Genomics Consortium [9, 10] (http://www.med.unc.edu/pgc); 2) three autoimmune diseases, including Crohn’s disease (CD) and ulcerative colitis (UC) from the International Inflammatory Bowel Disease Genetics Consortium [27, 28] (http://www.ibdgenetics.org/) and rheumatoid arthritis (RA) [29] (http://www.broadinstitute.org/ftp/pub/rheumatoid_arthritis/Stahl_etal_2010NG/); 3) two lipid-related phenotypes, including high-density lipoprotein (HDL) from the Global Lipids Consortium [30] (http://csg.sph.umich.edu//abecasis/public/lipids2010/) and type 2 diabetes (T2D) from the DIAbetes Genetics Replication And Meta-analysis Consortium [31] (http://diagram-consortium.org); and 4) two cardiovascular phenotypes, including coronary artery disease (CAD) from the CARDIoGRAM Consortium [32] (http://www.cardiogramplusc4d.org/data-downloads/) and systolic blood pressure (SBP) from the International Consortium for Blood Pressure [33] (http://www.georgehretlab.org/icbp_088023401234-9812599.html). We used the intersection of SNPs among these datasets, which consists of 228,944 SNPs. For the graph-GPA model, we collected the posterior sample of 40,000 MCMC iterations, removing the first 10,000 iterations as burn-in. The MCMC converged quickly to a stationary distribution (Figure K in S1 Text) and our sensitivity analysis results indicate that the model is not sensitive to misspecification of hyperparameters (Tables E and F in S1 Text).
Fig 2a shows the phenotype graph estimated using the graph-GPA approach, where edges are connected if p(E(i, j)|Y) > 0.5 and the 95% credible interval of βij does not include zero. Tables G and H in S1 Text show estimates of P(Gij|Y) and βij, which demonstrates the effectiveness of our approach in constructing a phenotype graph using both βij and Gij, discussed in the Materials and Methods Section. Specifically, by additionally considering credible interval of βij, we could exclude the case to have nonzero E(i, j) with βij close to zero. In Fig 2a, as expected, clinically related phenotypes make a cluster, e.g., a psychiatric disorder cluster of ADHD-BPD-SCZ-MDD and an autoimmune disease cluster of CD-UC-RA. In addition, this phenotype graph indicates pleiotropy between psychiatric disorders and autoimmune diseases, also recently reported [34, 35]. On the other hand, the pleiotropy between T2D and CAD, and between cardiovascular complications and diabetes in general, has also been reported in multiple literature [36–38]. Fig 2b shows the phenotype graph estimated using the GPA algorithm. Although some key edges (such as RA-ASD) are still observed here, the phenotype graph estimated using GPA is denser than that estimated using graph-GPA and this makes it challenging to interpret genetic relationship among phenotypes. For example, all of T2D, CAD, UC, CD, and RA are tightly linked in the phenotype graph estimated using the GPA model. Moreover, in the graph, we also lost some edges between phenotypes, for example, ADHD is totally isolated from all the other phenotypes.
In order to further demonstrate stability of our findings, we introduced slight perturbation to the dataset. Specifically, we partitioned the cohorts for RA into two groups (WTCCC/EIRA/CANADA vs. NARAC-I/NARAC-III/BRASS, namely cohort groups A and B, respectively; see [29] for more details about these cohorts) and fitted graph-GPA models to each of two GWAS datasets (i.e., based on each RA GWAS data while GWAS data for all the remaining 11 phenotypes remain fixed). In general, the estimated phenotype graph for each RA cohort group (Figure L in S1 Text) is similar to the phenotype graph estimated using the full RA cohorts (Fig 2a). Not surprising, the estimated phenotype graph for each RA cohort group is slightly sparser than that using the full RA cohorts because we significantly reduce the sample size for RA by partitioning its cohorts into two groups. Although the weaker signals for RA affected the estimated graph, its effects are essentially local and limited to the first neighbors of RA in the estimated phenotype graphs. These results indicate that the proposed graph-GPA model can achieve a good balance between sensitivity and specificity in construction of a phenotype graph, which can be beneficial for prioritizing important pairs of phenotypes for further investigation of common etiology.
We next evaluated the association mapping performance of graph-GPA model. Table 1 shows the number of identified SNPs when we control the global FDR for each phenotype or each pair of phenotypes at the nominal level of 10%. These results confirm our finding in the previous section in the sense of identified SNPs. Specifically, we observe strong pleiotropy between BPD and SCZ, among autoimmune diseases (RA, CD, and UC), and between T2D and CAD, among others. It might look contradictory to observe that ADHD does not share SNPs with any other phenotypes in Table 1 while ADHD is linked to BPD, CD, and HDL in Fig 2a. However, this is likely due to less confidence in identification of SNPs associated with ADHD with weak effect sizes and, as a result, no SNP could pass the cut-off at the nominal FDR level. We confirmed this by observing that SNPs associated with ADHD were identified when we used looser FDR level (Tables M and N in S1 Text). In addition, as a baseline, Table 2 shows the number of SNPs identified using the null model (i.e., without interaction terms, corresponding to separate analysis) at the same global FDR level. As expected, in this case, a much lower number of SNPs are shared between phenotypes, which means that separate analysis has reduced statistical power in identifying pleiotropic SNPs. More importantly, the numbers of SNPs associated with each phenotype (diagonal terms in Table 2) were also smaller than those in Table 1. This implies that information sharing via the graph-GPA model significantly boosts statistical power to identify SNPs associated with each phenotype as well. Finally, as in the previous section, we further demonstrated stability of the results in Table 1 by partitioning the cohorts for RA into two groups, fitting graph-GPA models for each of two GWAS datasets, and evaluating their association mapping results (Tables I–L in S1 Text). As before, the association mapping results for each RA cohort group are similar to those obtained using the full RA cohorts. Also, such perturbation in dataset had local effects; for the pairs involving RA, less associated SNPs were identified as the sample size for RA decreased in this case. In summary, graph-GPA robustly improved statistical power to identify both SNPs with pleiotropic effects and SNPs associated with each phenotype by information sharing across wider range of phenotypes.
We next considered novel SNPs identified by graph-GPA by evaluating and comparing functional importance between the SNPs identified in separate analyses and by graph-GPA. Specifically, we used the GenoCanyon score [39] that measures functional importance of each genomic locus by integrating functional annotations for conservation, open chromatin, histone modification, and transcription factor binding. For this purpose, we first implemented a lift-over of genomic coordinates to HG19 and annotated with the GenoCanyon annotation file downloaded from its website (http://genocanyon.med.yale.edu/). Fig 2c indicates that the GenoCanyon scores for graph-GPA are larger than 0.5 on average for most phenotypes considered above. Moreover, the GenoCanyon scores for the SNPs identified by graph-GPA are comparable to or higher than those for the SNPs identified in separate analyses. The GenoCanyon scores for GPA are significantly lower than those for graph-GPA, and sometimes even lower than those for separate analysis. We found that the phenotypes with low GenoCanyon scores in GPA analysis correspond to those with weak signals (Tables 1 and 2). This might imply that when signals are weak for certain phenotype, some “bad” combinations of phenotypes might result in false positives for GPA (Table O in S1 Text) because information sharing is allowed between a much smaller number of phenotypes. However, it is often not a trivial task to determine optimal pairs of phenotypes to integrate a priori. In contrast, graph-GPA integrates a much larger number of GWAS datasets and this might result in more robust improvement in statistical power in the sense of both sensitivity and specificity, regardless of its signal strength. These results indicate that graph-GPA improves statistical power to identify associated SNPs in a more robust way and the novel genetic variants identified by graph-GPA might also be potentially important ones associated with disease risks.
Fig 3a and 3b show Manhattan plots for separate analysis and joint analysis using graph-GPA for bipolar disorders (BPD). In the separate analysis under the global FDR level of 10%, we already identified some interesting SNPs, including those located in GNL3, SYNE1, and ANK3 genes, among others. These SNPs were previously identified to be associated with BPD in the literature [10, 40]. For example, GNL3 encodes nucleostemin, which is thought to be a critical regulator of the cell cycle. Aberrant regulation of nucleostemin would be consistent with the neurotrophic hypothesis of mood disorders, which posits that stem-cell proliferative potential in the brain modulates BPD risk [40]. At the same global FDR level, graph-GPA could identify a significantly larger number of novel risk SNPs, especially in chromosomes 3 and 6. Specifically, graph-GPA did not only identify more SNPs located in these genes such as GNL3, but also identified additional biologically interesting SNPs, such as SNPs located in ITIH1 gene in chromosome 3 and multiple SNPs located in the MHC region. These SNPs were also previously identified to be associated with BPD in the literature [10, 40]. For example, ITIH1 encodes a serine protease inhibitor, which is thought to play an anti-inflammatory role [40]. In-depth cross-phenotype investigation indicates that some of these SNPs associated with BPD risk are also related to risk of other diseases, as already suggested by the phenotype graph (Fig 2a). For example, a joint analysis using graph-GPA determined that the GNL3 and ITIH1 genes were also associated with SCZ risk.
We next evaluated overall and tissue-specific functional importance of novel SNPs identified by graph-GPA. Here, in addition to the GenoCanyon scores, we also utilized GenoSkyline scores [41] that measure tissue-specific functional importance of each genomic region, using epigenetic data (H3K4me1, H3K4me3, H3K36me3, H3K27me3, H3K9me3, H3K27ac, H3K9ac, and DNase I hypersensitivity) selected from the Epigenomics Roadmap Project’s 111 consolidated reference epigenomes database. Again, we first implemented a lift-over of genomic coordinates to HG19 and annotated with the GenoSkyline annotation files downloaded from its website (http://genocanyon.med.yale.edu/GenoSkyline/). Fig 3c and 3d indicate that overall functional importance of associated SNPs increased in graph-GPA compared to separate analyses (GenoCanyon). Moreover, the associated SNPs identified by graph-GPA were enriched for more diverse group of tissues. Specifically, while the SNPs identified in a separate analysis were mainly enriched for brain and epithelium, we observed enrichment for blood, brain, epithelium, and gastrointestinal tissues in the associated SNPs identified in graph-GPA analysis (GenoSkyline). Note that although the GenoSkyline score for brain tissue might look lower in Fig 3d compared to Fig 3c, it is actually not the case because graph-GPA identified a significant number of additional SNPs with both high and low scores in brain but simply more SNPs with lower scores in brain were identified by graph-GPA (Figure U in S1 Text). We observed similar enrichment patterns for other psychiatric disorders as well (ASD, MDD, and SCZ; Figures M–O in S1 Text). We note that the SNPs associated with autoimmune diseases are specifically enriched for blood, epithelium, and gastrointestinal tissues (Fig 4 and Figure P in S1 Text), as previously reported [41]. This might imply that graph-GPA identified more pleiotropic genetic variants that are potentially associated with both bipolar disorder and autoimmune diseases. This is again consistent with our observation of genetic correlation between psychiatric disorders and autoimmune diseases (Fig 2a).
We first investigated the genes that were previously reported to be associated with both CD and UC, which are collectively called as inflammatory bowel disease (IBD) [42]. Separate analysis at the global FDR level of 10% could identify some of these genes, including KIF21B, BRE, IL18RAP, DAP, NKX2-3, IL23R, SMAD3, JAK2, CREM, STAT3, TYK2, REL, CCR6, and TYK2. Joint analysis using graph-GPA at the same FDR level identified comparable number of additional IBD-risk associated genes, including IL10, FOSL2, FCGR2A, STAT4, NDFIP1, ORMDL3, IL2RA, MAP3K8, and CD226. These results indicate that the proposed graph-GPA model can potentially improve statistical power to identify risk associated genetic variants by leveraging pleiotropy structure. In order to further validate novel genetic variants identified by graph-GPA, we compared KEGG pathways enriched for the SNPs identified in separate analyses and those identified only by graph-GPA, using the DAVID functional annotation tool (https://david.ncifcrf.gov/). As expected, for each of CD, UC, and RA, multiple pathways related to autoimmune diseases (antigen processing and presentation, allograft rejection, type I diabetes mellitus, and autoimmune thyroid disease, among others) are enriched for both the SNPs identified in separate analyses and those identified by graph-GPA. In addition, these pathways are also enriched for the SNPs associated with both RA and CD, and for the SNP associated with both RA and UC (regardless of separate or joint analyses). Moreover, cardiomyopathy pathways are enriched for both the SNPs identified in separate analyses and the SNPs identified only by graph-GPA for IBD (CD and UC). Various signaling pathways (GnRH signaling pathway and calcium signaling pathway, among others) are also enriched for the SNPs identified in separate analyses for CD and the SNPs identified only by graph-GPA for UC. Finally, we evaluated overall and tissue-specific functional importance of novel SNPs using the GenoCanyon and GenoSkyline scores for these SNPs. Fig 4 and Figure P in S1 Text show the results for UC, CD, and RA, and they indicate that overall functional importance of the SNPs identified by graph-GPA is comparable to the SNPs identified in separate analyses (GenoCanyon). Moreover, the SNPs associated with each of UC, CD, and RA are enriched for blood, epithelium, and gastrointestinal tissues in both separate analyses and joint analyses using graph-GPA (GenoSkyline), which is consistent with the previous findings [41]. These results indicate that the novel SNPs identified by graph-GPA share similar functions with those identified in separate analyses for autoimmune diseases, and this implies that these novel genetic variants identified by graph-GPA are potentially associated with risk of these autoimmune diseases.
Here we first briefly discuss some key assumptions made in the graph-GPA framework. First, in the current paper, we assumed the standard normal distribution to model probit transformed p-values for background SNPs, which is equivalent to the uniformity assumption of p-values for background SNPs (i.e., theoretical null distribution assumption). While this assumption is mathematically justified [18] and also works well in practice as shown in its application to GWAS data for 12 phenotypes, it is still important to confirm that the p-values used as an input for graph-GPA reasonably satisfy this assumption. For example, the distribution assumption for background SNPs can be violated and type I errors of graph-GPA can be inflated if population stratification and cryptic relatedness are not properly taken into account. Hence, these confounding effects should be checked carefully and addressed before applying graph-GPA to the p-values. Second, in the proposed model, genetic variants were assumed to be independent. This independence assumption greatly simplifies our model structure and results in efficient computation, which allows practical use of the graph-GPA framework. However, in real GWAS data, genetic variants are often correlated due to linkage disequilibrium (LD) [43]. In practice, this issue can be addressed by promoting independence among genetic variants by LD pruning approaches, for example. Third, in the proposed model, it is assumed that the edge in the phenotype graph is purely due to genetic correlation between these phenotypes. However, when some subjects are shared between genetic studies, this can potentially generate artificial correlation among phenotypes. We investigated the impact of overlapping subjects on the estimation of phenotype graph using simulation data (Section 17 of S1 Text). The results indicate that the phenotype graph estimation of graph-GPA is robust to substantial sharing of subjects among genetic studies. However, we still recommend to check this issue carefully when using the proposed graph-GPA approach.
In spite of great successes of the proposed graph-GPA framework, it can be further improved as follows. First, in this paper, we assumed a uniform distribution over all possible subgraphs as a prior distribution for the phenotype graph. While this approach already provided a parsimonious graph that is well supported by the literature, it can be further improved by utilizing other related information useful to construct a phenotype graph. Second, in our real data analysis with the GWAS data for 12 phenotypes, we conservatively generated 50,000 MCMC iterations to guarantee the chain’s mixing and convergence. In this setting, our MCMC algorithm ran in a reasonable time, i.e., about three computation days on a standard single-core desktop machine. However, in practice, a significantly smaller number of iterations can be used after checking the chain’s convergence. For example, we re-analyzed our real data by generating 10,000 MCMC iterations, which took about 16 computation hours, and obtained inference results that were very similar to those generated from the five-time longer run of the MCMC chain. However, we note that as the number of phenotypes increases, the computation time will also increase dramatically. In order to improve the scalability of the proposed method, we plan to implement parallel computing algorithms for cluster and GPU computing. Specifically, in our MCMC steps for posterior inference (Section 1 of S1 Text), the update for et (Step S1), which is the most computationally expensive part, can be easily updated in parallel across SNPs because the conditional distribution of et given (α, β, G) is independent of that of e t ′ when t ≠ t′ in our model. In addition, more computationally efficient algorithms beyond standard MCMC can also be considered, including advanced Monte Carlo techniques such as Hamiltonian MCMC [44] and simulated annealing [45, 46], and approximated calculation approaches such as pseudo likelihood [47] and variational Bayes [48].
In summary, we proposed graph-GPA, a novel statistical framework to integrate GWAS datasets for multiple phenotypes using a hidden MRF approach. By effectively sharing information across multiple GWAS datasets for genetically related phenotypes, the proposed statistical model improves statistical power to identify risk-associated genetic variants. In addition, the proposed method provides a parsimonious graph representing genetic relationship among a large number of phenotypes. Our simulation studies indicate that graph-GPA has potential to prioritize genetically more related pairs of phenotypes and to improve statistical power to identify risk-associated genetic variants, compared to both separate analysis and the statistical method integrating smaller number of GWAS datasets. In our application of graph-GPA to GWAS datasets for twelve phenotypes, we observed that clinically related phenotypes are tightly linked in the estimated phenotype graph, while edges across different groups of phenotypes could also be supported by multiple literature. At the same nominal false discovery rate, joint analyses of graph-GPA could identify significantly larger number of additional genetic variants associated with each phenotype and those with pleiotropic effects. The pathway analysis and functional analysis of these novel genetic variants indicate that these novel variants might potentially have functional importance. We expect that graph-GPA would provide a powerful approach for prioritizing risk-associated genetic variants and elucidating the pleiotropic architecture of complex traits, which can contribute to a better understanding of shared genetic mechanisms and the development of improved diagnosis and therapeutics.
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10.1371/journal.ppat.1007611 | Gut microbiota from high-risk men who have sex with men drive immune activation in gnotobiotic mice and in vitro HIV infection | Men who have sex with men (MSM) have differences in immune activation and gut microbiome composition compared with men who have sex with women (MSW), even in the absence of HIV infection. Gut microbiome differences associated with HIV itself when controlling for MSM, as assessed by 16S rRNA sequencing, are relatively subtle. Understanding whether gut microbiome composition impacts immune activation in HIV-negative and HIV-positive MSM has important implications since immune activation has been associated with HIV acquisition risk and disease progression. To investigate the effects of MSM and HIV-associated gut microbiota on immune activation, we transplanted feces from HIV-negative MSW, HIV-negative MSM, and HIV-positive untreated MSM to gnotobiotic mice. Following transplant, 16S rRNA gene sequencing determined that the microbiomes of MSM and MSW maintained distinct compositions in mice and that specific microbial differences between MSM and MSW were replicated. Immunologically, HIV-negative MSM donors had higher frequencies of blood CD38+ HLADR+ and CD103+ T cells and their fecal recipients had higher frequencies of gut CD69+ and CD103+ T cells, compared with HIV-negative MSW donors and recipients, respectively. Significant microbiome differences were not detected between HIV-negative and HIV-positive MSM in this small donor cohort, and immune differences between their recipients were trending but not statistically significant. A larger donor cohort may therefore be needed to detect immune-modulating microbes associated with HIV. To investigate whether our findings in mice could have implications for HIV replication, we infected primary human lamina propria cells stimulated with isolated fecal microbiota, and found that microbiota from MSM stimulated higher frequencies of HIV-infected cells than microbiota from MSW. Finally, we identified several microbes that correlated with immune readouts in both fecal recipients and donors, and with in vitro HIV infection, which suggests a role for gut microbiota in immune activation and potentially HIV acquisition in MSM.
| The communities of commensal microbes that colonize the human gut comprise the gut microbiome, which has been shown to play a significant role in shaping the immune system. Recent studies have reported a distinct gut microbiome composition in men who have sex with men (MSM) exhibiting HIV-risk behaviors when compared with low-risk men who have sex with women (MSW), regardless of their HIV infection status. Whether these gut microbiome differences in high-risk MSM directly impact immune activation is important to understand since increased T cell activation is associated with increased HIV transmission risk and more severe disease. To test the immunological effect of the gut microbiome in MSM, we transplanted stool from HIV-negative MSW, HIV-negative high-risk MSM, and HIV-positive MSM to germ-free mice. DNA sequencing showed that specific microbiome differences associated with MSM were successfully engrafted in mice, and that these differences were associated with increased CD4+ and CD8+ T cell activation in the mice. These results provide evidence for a direct link between microbiome composition and immune activation in HIV-negative and HIV-positive MSM, and rationale for investigating the gut microbiome as a risk factor for HIV transmission.
| Men who have sex with men (MSM) comprise over half of all people living with HIV in the United States and accounted for 67% of new U.S. infections in 2016 [1]. Prevention and treatment in the MSM population is of high priority in the effort to eradicate HIV/AIDS in the U.S. Identifying novel biological factors that potentially impact transmission and/or disease in MSM could open opportunities for unique prevention and treatment strategies.
Recent studies have found that a distinct gut microbiome composition is found in MSM when compared with heterosexual men (men who have sex with women, MSW) even in the absence of HIV infection [2–4]. Interestingly, several studies have shown that high-risk HIV-negative MSM also exhibit immune differences, such as higher blood T cell activation [5], increased endotoxemia [6], and increased T cell pro-inflammatory cytokine production in colon mucosa [3], when compared with HIV-negative MSW. Given that the gut microbiome plays a significant role in shaping the immune system [7], it is possible that the gut microbiome differences observed in HIV-negative MSM may be a driving factor of increased immune activation in this population. Subtler microbiome differences have also been linked with chronic HIV infection itself when controlling for MSM [2, 4, 8], and previous work from our group has shown that these subtle differences are associated with stronger stimulation of immune cells in vitro [8]. How HIV-associated microbiome differences impact immune activation in vivo remains unknown. Establishing a direct impact of the gut microbiome on immune activation in HIV-negative and HIV-positive MSM may have important implications for transmission and disease, since vaginal immune activation in women has been associated with HIV acquisition risk [9–12] and T cell activation is well known to be a correlate of disease progression in HIV-positive individuals [13]. Whether the gut microbiome may be a risk factor for HIV transmission in MSM has not been investigated.
Direct causation of immune activation by the microbiome in HIV-negative and HIV-positive MSM is difficult to demonstrate with human studies, which can be limited to correlational analyses to establish microbial-immune relationships. Human studies can also be confounded by factors such as diet, age, and lifestyle behaviors, all of which can be variable across individuals and populations and may influence both the microbiome and the immune system [14–17]. Deciphering microbiota-associated immune effects in HIV-positive MSM is further complicated by HIV itself, which can cause depletion of critical microbiome-interacting T cells [18, 19] and stimulate immune activation through TLRs [20]. These challenges raise the need for an in vivo model to elucidate the immunological impacts of gut microbiota from HIV-negative and HIV-positive MSM. Mice have been crucial for describing the roles of gut microbiota in health and disease [21], and offer a controlled system to isolate effects of live, active microbes on the immune system while excluding effects of lifestyle and HIV itself. In this study, we leveraged gnotobiotic (germ-free) mice to investigate the immunological impacts of whole fecal transplantation from HIV-negative MSW, HIV-negative MSM, and HIV-positive antiretroviral therapy (ART)-naïve MSM. We report that mouse fecal recipients recapitulated some of the microbiome differences associated with MSM, and that mouse recipients of feces from HIV-negative and HIV-positive MSM exhibited higher gut T cell activation than recipients of HIV-negative MSW. We extended our findings from the mouse model to an in vitro HIV infection model, and found that stimulation of primary human lamina propria cells (LPCs) with isolated fecal microbiota from either HIV-negative or HIV-positive MSM promoted HIV infection in cell culture. These results demonstrate the connection between gut microbiota and immune phenotypes observed in MSM, which may have implications for HIV acquisition and disease.
Previous studies have reported higher blood CD4+ and CD8+ T cell activation and higher levels of inflammatory cytokine producing colonic CD8+ T cells in HIV-negative MSM compared with HIV-negative MSW [3, 5, 6]. To confirm these findings in an independent cohort, we analyzed blood T cell phenotypes in 18 HIV-negative MSW and 19 HIV-negative MSM, and included 13 HIV-positive untreated MSM as controls since immune activation is well known to be increased with HIV infection [13, 22, 23] (S1A Table). Increased frequencies of CD38 HLADR expressing CD8+ T cells, but not CD4+ T cells, were found in the blood of HIV-negative MSM compared with HIV-negative MSW (Fig 1A–1C). As expected, HIV-positive MSM had the highest levels of blood T cell activation. Expression of CD69 on peripheral blood T cells was also measured (S1A and S1B Fig), and frequencies of CD69+ CD4+ T cells trended higher for HIV-negative MSM and HIV-positive MSM than HIV-negative MSW, though this did not reach statistical significance. Stimulated T cells were analyzed for intracellular TNF-α and IFN-γ expression, and significantly higher frequencies of inflammatory cytokine producing T cells were observed in HIV-positive, but not HIV-negative, MSM when compared with HIV-negative MSW (S1C and S1D Fig). Finally, a lower CD4/CD8 T cell ratio in the blood has been previously reported for high-risk HIV-negative MSM [6], but was not reproduced in our cohort (S1F Fig). The CD4/CD8 T cell ratio was expectedly decreased in HIV-positive MSM who were not receiving antiretroviral treatment.
Gut mucosal T cell activation plays an important role in HIV replication and disease [24]. We therefore evaluated expression of the mucosal homing marker CD103 (also known as integrin αEβ7) [25] on blood T cells. Frequencies of CD103+ CD4+ T cells were significantly higher and frequencies of CD103+ CD8+ T cells trended higher in HIV-negative MSM compared with HIV-negative MSW (Fig 1D and 1E). CD103+ CD8+ T cell, but not CD4+ T cell frequencies were higher in HIV-positive MSM than in HIV-negative MSW. No significant differences were observed in frequencies of CD103+ T cells between HIV-negative and HIV-positive MSM. Frequencies of CD103+ CD8+ T cells significantly correlated with frequencies of CD38+ HLADR+ CD8+ T cells (Fig 1F), while expression of HIV coreceptor CCR5 [26]–which was not significantly different across groups (S1E Fig)–significantly correlated with frequencies of CD103+ CD4+ T cells (Fig 1G). Taken together, previous findings of higher immune activation in HIV-negative MSM compared with HIV-negative MSW are confirmed here in our cohort, and our results additionally show an association between blood T cell activation and mucosal homing in MSW and MSM.
Microbiome differences have been previously associated with MSM and HIV [2–4, 8], but the extent to which these differences drive immune activation in vivo is unclear. To directly assess the in vivo immunological effect of the gut microbiome from HIV-negative and HIV-positive MSM, we transplanted feces from human donors to gnotobiotic mice. Each mouse received a single gavage of feces from a unique human donor. Stool donors were 16 HIV-negative MSW, 19 HIV-negative MSM, and 12 HIV-positive ART-naïve MSM randomly selected from a previously described cohort [4]. Of these donors, 13 HIV-negative MSW, 17 HIV-negative MSM, and 9 HIV-positive MSM are represented in the above immune data (Fig 1, S1 Table). HIV-positive MSM donors had a median viral load of 101400 copies/ml with a median CD4 T cell count of 574 cells/μl (S1 Table). Each donor was tested in a single mouse recipient, with the exception of 4 HIV-negative MSW, 8 HIV-negative MSM, and 4 HIV-positive MSM which were randomly chosen to be tested in 2–3 replicate mice. A subset of donors was also tested for fecal bacterial load (8 HIV-negative MSW, 12 HIV-negative MSM, 11 HIV-positive MSM), which was not found to be significantly different across donor groups (Fig 2B), indicating that each recipient group did not receive a significantly higher or lower amount of bacteria in the fecal transplant.
We performed 16S rRNA gene sequencing on donor fecal samples and fecal pellets from mouse recipients at 7, 14, and 21 days post-gavage. Since there was little variation in composition of the mice over time (S2 Fig), we focused our analysis on the terminal timepoint. Consistent with previous reports [2, 4, 8], unweighted UniFrac analysis showed that microbiome composition in the human donors (large circles) clustered distinctly by MSM and not HIV status (Fig 2C). Although the overall composition shifted along PC1 following fecal transfer, the distinct clustering of MSW and MSM along the PC2 axis was replicated in the mouse recipients (small circles), which are connected to their respective donors by lines (Fig 2C). Six 16S rRNA sequence variants were identified to be significantly different (with an FDR corrected p-value<0.1) between HIV-negative MSW and HIV-negative MSM recipients, 3 of which increased (Desulfovibrio sp., Holdemanella biformis, Howardella ureilytica) and 3 of which decreased (Clostridium sp., Bacteroides uniformis, Flavonifractor sp.) with MSM (S2 Table). Five of six of these variants were also found to be significantly different between HIV-negative MSW and HIV-negative MSM donors (S2 Table). Holdemanella biformis (formerly Eubacterium biforme) and Bacteroides uniformis have also previously been described to be different with MSM [4, 8]. In contrast, no statistically significant differences (FDR p<0.1) in variant abundance were detected between HIV-negative and HIV-positive MSM donors or recipients, and previously reported bacteria differing with HIV in analyses of larger cohorts [2, 4] were not observed in this smaller donor sample size (S3 Table). These data suggest that MSM-associated microbiome differences are readily transferred to mice through fecal transplant, while transfer of subtler HIV-associated differences may require an increased sample of donors.
The natural gut microbiome in mice is compositionally different from that in humans [27], therefore changes in relative abundance of microbes less or better adapted to the mouse gut were expected to occur following fecal transfer. To characterize microbiome shifts following colonization of mice, we identified bacterial families that significantly increased or decreased in relative abundance from donors to recipients. When analyzing all groups together (HIV-negative MSW, HIV-negative MSM, HIV-positive MSM), 25 families significantly changed (FDR p<0.1) as determined by nonparametric t-test comparing donors to recipients (S4 Table). These consisted of 19 families that decreased and 7 families that increased in relative abundance in recipients compared with donors. When stratifying the analyses by group, differences between donors and recipients for 20/25 families maintained consistent trends within each group, and differences for 11/25 families maintained statistical significance within each group (S4 Table). A striking donor-to-recipient change was the relative abundance of Prevotellaceae, which was significantly higher in MSM compared with MSW (Fig 2C) [2–4], and decreased in abundance for all groups following transfer (S4 Table). Taxa bar charts showing donors and recipients grouped separately (Fig 2D), or donors and their respective recipients side-by-side (S3 Fig), demonstrate the overall loss of Prevotellaceae in mice. Though Prevotellaceae was undetectable in almost all mouse recipients of HIV-negative and HIV-positive MSM donors, it maintained a strong presence in the two mouse recipients of Prevotellaceae-rich HIV-negative MSW donors (Fig 2D, S3 Fig). One reason that there may be colonization differences in MSW versus MSM recipients is the presence of different species or strains of Prevotella. We thus sought to explore whether different sequence variants within the Prevotella genus showed different colonization patterns. In the two mice that Prevotella successfully colonized, we identified only three Prevotella sequence variants to be present and all were classified as Prevotella copri (S4A Fig). Two out of three P. copri variants were of the highest abundance in MSW donors (S4B Fig), and increased in relative abundance from donors to mice. The third variant was the dominant and most abundant Prevotella sequence in both MSW and MSM donors, but only colonized recipients of MSW. Six other P. copri sequence variants that were present in at least 20% of MSM donors were identified, and only three of these could be found in Prevotella-rich MSW. None of these other six sequence variants were detectable in mouse recipients (S4 Fig). To investigate the ability of Prevotella to colonize mice in the absence of competing microbes, we gavaged gnotobiotic mice (n = 3) with a monoculture of P. copri (DSMZ 18205) or with a monoculture B. uniformis (ATCC 8492) for comparison (Fig 2D). By 16S gene sequence, the P. copri monoculture was determined to be the same variant as P. copri 3 found in fecal donors and their recipients. After 21 days of colonization, sequencing showed that P. copri was still present in feces of 2/3 mice, while B. uniformis was present in 3/3 mice (Fig 2D). Thus, three P. copri variants were able to colonize mice, with two of these variants being at a higher relative abundance in Prevotella-rich MSW than in MSM. The third variant, which was the dominant variant in both MSW and MSM, could only successfully colonize mice in pure culture or in the context of an MSW microbiome.
Since MSM have more Prevotella than MSW, we next investigated whether the inability of Prevotella to colonize mouse recipients of MSM resulted in a larger magnitude donor-to-recipient compositional shift for MSM. Unweighted Unifrac distance, a measurement of relative compositional difference, between each donor and their recipient was calculated. For donors with replicate mouse recipients, a representative mouse was randomly selected. Donor-recipient distances were not found to be significantly different across groups, indicating the overall composition of each donor group was altered on average by the same magnitude after transfer (S5A Fig). The percentages of unique sequence variants identified in each donor that were present in their mouse recipient were also not significantly different across groups, indicating equal colonization fidelity (S5B Fig). Despite the loss of taxa that differed with MSM following transfer, differences in specific microbes were recapitulated in mouse recipients to maintain distinct MSW and MSM compositions. Any immunological differences between recipient groups would therefore be associated with these compositional differences.
To investigate the immunological effect of the MSM and HIV-associated microbiome on mouse recipients, mice were sacrificed 21 days post gavage, and ileum, colon, and mesenteric lymph nodes were evaluated for markers of T cell activation. Due to lower cell recovery, only colons from a subset of mice (8 HIV-negative MSW, 10 HIV-negative MSM, 9 HIV-positive MSM) yielded enough cells for analysis by flow cytometry. Ileum tissues from all mice were analyzed and are highly relevant to HIV infection due to the small intestine’s abundance of Th17 cells [28], which are targets of HIV infection and play an important role in disease [18]. CD69 and CD103 were used to measure gut T cell activation, and though lowly expressed in the blood [29], these are relevant markers in the gut, and are used to define tissue-resident memory T cells retained in gut tissue [30, 31]. For stools that were tested in replicate mice, immune data from one replicate was used for each stool. In the ileum, frequencies of CD69+ CD8+ T cells were found to be significantly higher in mouse recipients of HIV-negative MSM compared with mouse recipients of HIV-negative MSW (Fig 3B). Frequencies of CD103+ T cells were significantly elevated for both CD4+ and CD8+ T cells in the ileum of recipients of HIV-negative MSM (Fig 3C and 3D). Interestingly, ileum frequencies of CD69+ or CD103+ T cells were not significantly different between recipients of HIV-negative and HIV-positive MSM, and only CD103+ CD4+ T cells were significantly higher in recipients of HIV-positive MSM compared with recipients of HIV-negative MSW (Fig 3D). Finally, stool recipients that were successfully colonized with Prevotella did not display noticeably different levels of immune activation compared to the MSW group median (S7B Fig).
In colon tissue, frequencies of CD69+ CD4+ T cells were significantly higher for recipients of HIV-negative MSM than recipients of HIV-negative MSW (Fig 3E). Recipients of HIV-positive MSM did not statistically differ from recipients of HIV-negative MSM or HIV-negative MSW in colonic T cell activation, but did trend higher for frequencies of CD69+ CD8+ and CD4+ T cells, and CD103+ CD8+ T cells, compared with either HIV-negative group (Fig 3E, S6 Fig). Statistical power in detecting differences in the colon may have been limited by a small sample size and a high variance of immune measures. We therefore examined mesenteric lymph nodes (mLN), which were analyzed for all mouse recipients, as a proxy of gut immune phenotypes since the mLN drain ileum and colon tissues [32]. In the mLN, mouse recipients of HIV-positive MSM displayed significantly higher frequencies of effector memory (Tem) CD4+ T cells (CD62L-, CD44+) than recipients of HIV-negative MSW (Fig 4A). Furthermore, frequencies of CD4+ Tem in the mLN significantly correlated with frequencies of CD69+ CD4+ T cells in the colon (Fig 4B), indicating mLN phenotypes were representative of immune readouts in colon tissue. In mLN of monocolonized mice, which were also examined at 21 days post gavage, CD4+ Tem frequencies were within the range detected in stool recipients, and did not differ between mice gavaged with B. uniformis and mice gavaged with P. copri (S7A Fig).
Overall, mouse recipients displayed a significant MSM-associated effect in immune activation in the gut. Recipients of MSM also tended to have higher variation in T cell measures, with CD103+ CD4+ T cell frequencies in the ileum (Fig 3D) and CD69+ CD4+ T cell frequencies in the colon (Fig 3E) being significantly different in variance as determined by F test between HIV-negative MSM and HIV-negative MSW. Despite the lack of a significant HIV-associated effect, immunological differences between MSW and MSM recipients were enough to drive significant immune correlations between donors and recipients, including a correlation between frequencies of recipient ileum CD69+ CD8+ T cells and frequencies of donor blood CD38+ HLADR+ CD8+ T cells (S8A Fig), and a correlation between frequencies of recipient colon CD69+ CD4+ T cells and frequencies of HIV-negative donor blood CD103+ CD4+ T cells (S8B Fig). These correlations highlight immunological similarities between donors and their recipients.
Elevated markers of gut mucosal damage have been reported for MSM and correlated with MSM-associated microbiota [3, 6], and are well known to be associated with HIV infection [33]. To determine if bacteria from the fecal transfer directly caused intestinal damage, ileum and colon tissue samples from 5 HIV-negative MSW, 7 HIV-negative MSM, and 6 HIV-positive MSM mouse recipients were analyzed for histopathology. Tissue health and epithelial integrity was determined to be within normal limits for all mice examined (Fig 5A). Each tissue analyzed was graded with an overall inflammation score on a scale of 1–4 [34], and all tissues were assigned the same minimum inflammation rating of 1. In support of this, myeloperoxidase concentrations (measured in 12 HIV-negative MSW, 11 HIV-negative MSM, and 11 HIV-positive MSM recipients) were not different across groups in the colon (Fig 5B). Levels of soluble CD14 (sCD14) in the blood, a marker of monocyte activation in response to LPS [35] and bacterial translocation across the gut barrier [33], was measured in 11 HIV-negative MSW, 15 HIV-negative MSM, and 9 HIV-positive MSM recipients, and was also not found to be significantly different across groups (Fig 5C). Lack of signs of barrier damage suggest that transfer of microbiota from HIV-negative or HIV-positive men were not sufficient to cause gut tissue injury in mice (at least within 3 weeks of colonization), and that barrier breakdown was not necessary for microbiota from MSM to promote gut T cell activation.
Recapitulation of microbial and immune differences in mouse recipients supports a link between microbiome composition and immune activation in MSM. Microbial-immune correlations consistent across donors and recipients would further strengthen this link. We therefore correlated the relative abundance of gut microbes in donors and recipients with their respective immune data (S5 and S6 Tables). Only immune data significantly different with MSM in either donors or recipients were selected for analyses, since only MSM-associated microbiome differences were detectable in this cohort. Frequencies of blood CD103+ CD8+ T cells in donors, though not significantly different with MSM, were additionally selected for analysis due to their relevance to the gut. Correlations were computed with both the full donor group, as well as with HIV-negative donors only, since HIV may independently drive immune activation in HIV-positive MSM and confound microbial correlations. All correlations that were and were not significant in donors and in mice are shown in tables S5 (donors) and S6 (recipients). After comparing significant correlations across these datasets, we identified 8 microbes that correlated with at least one donor and one recipient immune measurement (Table 1), with 3 of these being negative and 5 of these being positive correlates. Out of these 8 microbes, 5 significantly correlated using the full donor cohort, while 3 significantly correlated using only HIV-negative donors. Two microbes, Bacteroides uniformis and Howardella ureilytica, significantly differed in relative abundance with MSM in both donors and recipients (S2 Table), and also significantly correlated with immune measurements in both donors and recipients. Though few of the shared microbe-immune correlates were statistically significant with FDR correction, consistency of correlations across humans and mice suggest biological significance, and provide further evidence that immune activation in MSM is influenced by the gut microbiome.
T cell activation has been correlated with HIV viral load [13], and associated with HIV transmission in women [36]. We therefore investigated whether microbiota from MSM could promote HIV infection in vitro. Fecal bacterial communities (FBCs) were isolated from stools of 12 HIV-negative MSW, 15 HIV-negative MSM, and 9 HIV-positive MSM by separating whole intact bacteria in stool from debris using Histodenz columns as previously described [8]. All donors of stool used for FBC isolation are represented in the PBMC immune phenotyping data (Fig 1), while FBCs from 7 HIV-negative MSW, 15 HIV-negative MSM, and 5 HIV-positive MSM were also used as donors in the fecal transplant experiments. HIV-positive individuals used for FBC isolation had a median viral load of 47650 copies/ml and a median CD4 T cell count of 577 cells/μl (S1 Table). Primary human lamina propria cells (LPCs) were stimulated by isolated FBCs, infected with HIVbal, and intracellular HIVgag antigen expression was measured after five days. We found that LPCs stimulated with FBCs from HIV-negative and HIV-positive MSM had higher frequencies of HIV-infected cells compared with LPCs stimulated with microbiota from HIV-negative MSW (Fig 6A and 6B). LPCs stimulated with FBCs from HIV-negative and HIV-positive MSM displayed significantly elevated CD8+ T cell activation at the end of infection (Fig 6C), and frequencies of HIV-infected cells significantly correlated with both CD4+ and CD8+ T cell activation of stimulated LPCs (Fig 6D and 6E). FBCs from 7 HIV-negative MSW, 11 HIV-negative MSM, and 8 HIV-positive MSM used in this infection assay were also previously used to stimulate PBMC in vitro [8], and frequencies of HIV-infected cells induced by these FBCs significantly correlated with activation levels of peripheral blood CD4+ and CD8+ T cells stimulated by these same FBCs found previously (S8B and S8C Fig). 16S rRNA gene sequencing of FBCs showed that the compositional distinction between MSW and MSM was maintained following bacterial isolation (S9A and S9C Fig). Unweighted UniFrac distances between donor samples and their isolated FBCs were not significantly different across groups (S9B Fig), indicating the average magnitude of stool-FBC compositional shifts were consistent between groups. Specific bacteria that differed with MSM and with HIV in FBCs, and bacteria that significantly changed from stool to FBCs, were previously described [8], and are not presented here. Infection and T cell activation levels trended higher for cells stimulated with HIV-positive MSM FBCs than cells stimulated with HIV-negative MSM FBCs, though these differences were not statistically significant. Finally, six microbes that significantly correlated with in vitro HIV infection also correlated with fecal transplant donor and/or recipient immune measurements (Table 1). Interestingly, two sequence variants of H. biformis correlated with in vitro infection, with one significantly correlating with donor blood T cell activation and the other significantly correlating with recipient gut T cell activation. Significant and non-significant correlations between all 16S sequence variants in FBCs with in vitro HIV infection are shown in S7 Table. Consistencies were therefore observed between the in vivo transplant system and the in vitro assays. These data support the findings from the mouse model, and suggest that the gut microbiome in HIV-negative and HIV-positive MSM could impact HIV infection.
Immune differences previously associated with MSM were reproduced in our cohort of fecal donors. We showed elevated blood frequencies of activated CD38+ HLADR+ CD8+ T cells in HIV-negative MSM compared with HIV-negative MSW as previously described [5], as well as increased frequencies of CD103+ CD4+ T cells in HIV-negative MSM, which to our knowledge, is a novel finding. Frequencies of CD103+ CD4+ T cells also correlated with frequencies of CCR5+ CD4+ T cells, suggesting an association between gut homing and CCR5 expression in MSM. Other previously reported immune phenotypes in MSM, such as increased frequencies of gut mucosal Th17 cells and IFN-γ+ TNF-α+ CD8+ T cells [3], increased endotoxemia, and lower blood CD4/CD8 ratios [6] were either not observed or not measured in our cohort. A limitation of our study was the unavailability of immune data from gut tissues in our donors, which would have indeed strengthened associations between our donors and mice. However, our observations in mice are well supported by previous findings of gut T cell activation in MSM [3]. Overall, immune differences associated with MSM in our study were subtle and observed in a small sample size, though the magnitude of these differences reflects what others have found. The subtlety of these differences is also unsurprising, since our cohort of high-risk MSM were healthy individuals without disease. Taken together, the blood T cell profiling presented here has reproduced and expanded on previous knowledge of immune phenotypes associated with MSM, and further emphasize the importance of controlling for MSM in immunological analyses of HIV-positive populations.
Microbiome differences previously associated with MSM were also replicated in our donor cohort (Fig 2C), including increases in Prevotellaceae, decreases in Bacteroidaceae [2–4], and changes in specific bacterial species such as Holdemanella biformis and Bacteroides uniformis (S3 Table) [4, 8, 37]. However, previously reported microbiome differences between HIV-negative and HIV-positive MSM, such as increased abundance of Turicibacter sanguinis with HIV [4, 8], were not observed in our cohort. Given these results, it was unsurprising that recipient mice reproduced microbiome differences associated with MSM, but showed no significant microbiome differences between recipients of HIV-negative and HIV-positive MSM. Immune activation differences in mouse recipients largely reflected the microbiome composition, with an apparent significant increase in T cell activation associated with recipients of MSM. Immunological differences between HIV-negative and HIV-positive MSM recipients were less clear. Though CD4+ T cell activation in the colon trended higher for recipients of feces from HIV-positive MSM compared with HIV-negative MSM (Fig 3E), this did not reach statistical significance. These trends reflect our previously published results showing FBCs from HIV-positive MSM could promote higher in vitro T cell activation than FBCs from HIV-negative MSM [8], and suggest that a larger sample size of donors is needed to investigate immunological differences between mouse recipients of feces from HIV-negative and HIV-positive MSM. However, a small sample size may only partially explain why a significant effect of HIV-associated microbiota was not observed in this study, either in mice or in the in vitro infection experiments. In mice, it is possible that HIV-associated microbiota of immunological importance may not have colonized. Furthermore, loss of epithelial integrity is a hallmark of HIV infection and was not evident in these mice. Modeling barrier breakdown in mouse recipients may therefore reveal effects of HIV-associated microbiome differences in the context of HIV-induced disease. In the in vitro infection experiments, one finding from our previous work was that differences between HIV-negative MSW and HIV-negative MSM in in vitro activation of CD4+ and CD8+ T cells were detected when stimulations were performed with non-autoclaved FBCs but not autoclaved FBCs, whereas the differences in HIV-positive versus HIV-negative MSM were strongly evident with the autoclaved FBCs. Autoclaving was done to ensure that bacteria were killed and that no sporadic growth of antibiotic resistant, aerotolerant bacteria occurred. However, autoclaving could have potentially denatured immune-modulatory compounds. The in vitro infection data from this study used non-autoclaved FBCs. Taken together these results suggest that there are differences in the molecular mechanisms of immune activation in HIV-positive and HIV-negative MSM, with those in HIV-positive MSM being more driven by molecular factors that are heat-tolerant.
T cell activation in stool donors and recipients were detected with different markers (CD38 and HLADR in donors, and CD69 and CD103 in recipients) in different compartments (blood and gut), but these markers may have identified related cell populations commonly influenced by gut microbes. In support of this, gut T cell activation in mice significantly correlated with blood T cell activation in donors, and the same bacteria consistently correlated with both donor and mouse immune measurements. Several of these microbial correlates have been previously associated with disease and/or known to have immune-modulating potential. This includes Holdemanella biformis (formerly known as Eubacterium biforme), which is increased with MSM in donors and recipients, and has been previously associated with stimulation of inflammatory cytokine production and correlated with T cell activation in vitro [8, 38]. Bacteroides uniformis, a Treg and IL10 inducer [39] that has been found to be reduced in patients with Crohn’s disease [40], is also reduced with MSM in our donors and recipients. Akkermansia muciniphila, a mucolytic bacteria associated with health and disease depending on context, has been found to be reduced in patients with ulcerative colitis and Crohn’s disease [41], and was negatively correlated with CD4+ T cell activation in recipient colons and mucosal homing CD8+ T cells in donor blood. Finally, Desulfovibrio piger, a sulfate-reducing bacteria associated with inflammatory bowel disease [42], was positively correlated with gut homing T cells in recipients and donors. Thus, bacteria correlated with immune function in our dataset is consistent with previous findings. Organisms identified in these correlations are ideal candidates for further investigation of their immune-modulating properties in HIV-negative and HIV-positive MSM.
Transfer of the microbiome from human donors to mice was imperfect, as significant differences between MSW and MSM were not reproduced in the mice. Chief among these was Prevotella, which was lost from recipients of MSM but not recipients of MSW. Though two of the P. copri sequence variants identified may be unique features of Prevotella-rich MSW that are better adapted for mice, the inability of the third variant to colonize mice in the context of an MSM microbiome suggests that the broader community structure, which is different between MSM and Prevotella-rich non-MSM [4], influences Prevotella colonization. Since Prevotella has been associated with inflammation in other studies, including being positively correlated with gut immune activation in HIV-positive individuals [43], and being associated with rheumatoid arthritis [44], successful colonization in recipients of MSM stool may have driven an even larger effect of immune activation. Alternatively, Prevotella may not be a major influencer of immune activation, since it did not induce noticeably higher activation in either monocolonized mice or Prevotella-rich MSW fecal recipients. Additionally, Prevotella variants were also not identified as significant correlates with immune measurements in donor blood. Another intriguing hypothesis is that Prevotella may only promote inflammation in a diseased state. Indeed, in mice with DSS-induced colitis, the presence of P. copri (identical to P. copri variant 3 in this study) led to more severe inflammation [45]. Inducing colitis to mimic gut disease during HIV infection in mouse recipients of Prevotella-rich MSW may reveal similar effects. Importantly, these results demonstrate that despite prominent microbiome differences like Prevotella abundance not being reproduced in mouse recipients, compositional distinction between MSW and MSM was maintained in mice by successful transfer of critical immune-modulating bacteria.
The ability of microbiota from HIV-negative and HIV-positive MSM to promote HIV infection in vitro suggests that the gut microbiome composition could contribute to HIV infection and disease progression in MSM. There is evidence that the vaginal microbiome is a risk factor for HIV transmission in women: vaginal bacteria modulate immune activation in vaginal tissue [9], bacterial vaginosis has been linked to vaginal HIV transmission [11, 12], and specific microbes in the vaginal microbiome have been associated with inflammation and increased HIV acquisition [36]. Thus, it is possible that the gut microbiome may also contribute to risk in MSM by promoting immune activation in gut tissues. Increased frequencies of CD69+ and CD103+ T cells in mouse recipients of MSM suggest there are more tissue-resident memory CD4+ T cells in the gut [31], and memory CD4+ T cells are known to be the primary targets of HIV infection [46]. Though these cells were assessed in the ileum and colon rather than the rectum, it is clear they are driven by the gut microbiome–which was linked to both rectal T cell activation and rectal SHIV transmission in macaques [47]. Furthermore, gut microbes that correlated with T cell measures in the ileum and colon of mouse recipients also correlated with in vitro HIV infection. Therefore, microbes that influence T cell activation along the length of the gut likely also impact HIV infection at the specific site of transmission. These findings are strong rationale for conducting longitudinal studies examining the association between gut microbiome composition and HIV transmission in populations of high-risk MSM.
In conclusion, this study provides evidence of a direct link between the gut microbiome and immune activation observed in high-risk MSM, by demonstrating that both microbiome and immune phenotypes in MSM donors are transferrable to mouse recipients through fecal transplantation. Mice were receptive of colonization by key immune-influencing microbes that induced immunological differences associated with MSM donors, suggesting the gut microbiome influences immune activation in MSM.
Stool samples were obtained from HIV-negative MSW, high-risk HIV-negative MSM, and HIV-positive MSM not on antiretroviral therapy. Male HIV-positive individuals enrolled in the study were determined to be MSM using a behavioral questionnaire. High-risk MSM were recruited from a high-risk cohort assembled for a study of a candidate HIV-1 preventative vaccine [48]. Designation of high-risk was according to a number of different behaviors including 1) a history of unprotected anal intercourse with one or more male or male-to-female transgender partners 2) anal intercourse with two or more male or male-to-female transgender partners and 3) being in a sexual relationship with a person who has been diagnosed with HIV. All enrolled individuals live in metropolitan Denver. Other inclusion criteria used were: 18–70 years old, body mass index (BMI) between 21–29 mg/kg2 and weight stable for at least 3 months; for HIV-positive individuals, <10 days of ART treatment at any time, or previously on ART but off treatment for the previous 6 months, prior to stool and blood collection; liver and kidney function tests within normal range. Exclusion criteria used were: weight <110 pounds; received antibiotics within the prior 90 days; active gastrointestinal disease; history of bowel resection. All subjects used in this study were previously characterized for diet and sexual behavior as part of a larger cohort; diet or any particular sexual behavior that we measured were not identified as driving factors of the most prominent microbiome differences between MSM and MSW [4].
Written informed consent was obtained from healthy HIV-negative and HIV-positive individuals for use of stool. The study protocol was approved by the Colorado Multiple Institution Review Board (COMIRB No: 14–1595). All subjects were adults. Mice were handled in accordance with the recommendations in the National Institutes of Health Guide for the Care and Use of Laboratory Animals and protocols were approved by the University of Colorado Institutional Animal Care and Use Committee Permit Number 00097. Primary human lamina propria cells were collected from otherwise discarded tissues from gut resection surgery, and was determined to not be human subject research under protocol number 14–1184 approved by the University of Colorado Multiple Institutional Review Board. Tissues were obtained from anonymous adult patients who were not asked for consent.
PBMC were isolated from patient blood by Ficoll-Paque (GE), and cryopreserved and stored in liquid nitrogen before analysis. PBMC were thawed, and 5 x 105 cells were stained for 30 min at 4° C with the following antibodies: CD3-APC-Cy7 (OKT3, Biolegend), CD4-PerCP-Cy5.5 (OKT4, Biolegend), CD8-APC (SK1, Biolegend), CD38-BV421 (HIT2, Biolegend), CD69-BV510 (FN50, Biolegend), HLADR-FITC (L243, Biolegend), CD103-PE (BerACT8, Biolegend), CCR5-PE-Cy7 (J418F2, Biolegend). Cells were then washed in FACS buffer and fixed in 1% paraformaldehyde before being acquired on a BD FACS Canto II. 1 x 106 PBMC were stimulated with a PMA/ionomycin cell stimulation cocktail (eBioscience), and treated with a protein transport inhibitor cocktail (eBioscience) for 4 h at 37° C, 5% CO2. Cells were then stained for 30 min at 4° C with the following antibodies: CD3-APC-Cy7 (OKT3, Biolegend), CD4-PerCP-Cy5.5 (OKT4, Biolegend), CD8-APC (SK1, Biolegend). Cells were washed and treated with fix/perm buffer (eBioscience) for 30 min at 4° C, before being stained for intracellular cytokines for 30 min at 4° C with the following antibodies: IFN-γ-PE (4SB3, Biolegend), TNFα-APC-Cy7 (MAb11, Biolegend). Cells were then washed in FACS buffer and fixed in 1% paraformaldehyde before being acquired on a BD FACS Canto II.
1.5 grams of frozen feces stored at -80° C for up to one year from each donor was thawed in an anaerobic chamber and homogenized in 3 ml of anaerobic PBS, using a syringe handle. Fecal solutions were then filtered through a 100 μm nylon filter into a 50 ml conical tube. Tubes were sealed before removing from the anaerobic chamber and transferred to the mouse facility within 1 hour of thawing. 200 μl of each fecal solution was used to gavage each mouse.
2 mg of stool from donors were homogenized in PBS, and bacteria were then isolated using two sequential rounds of sucrose density centrifugation. Isolated bacterial cells were stained with thiazol orange and propidium iodide, and acquired by a FACS Canto II (BD) flow cytometer in the presence of counting beads. Data were processed using Flowjo software, and the concentration of live bacterial cells per mg of stool was calculated.
Germ-free C57/BL6 mice were purchased from Taconic and bred and maintained in flexible film isolator bubbles. Male mice between 6–8 weeks of age were gavaged with fecal solutions prepared from donor fecal samples and housed individually (to ensure mice would not contaminate each other) following gavage for 3 weeks in a Tecniplast iso-positive caging system, with each cage having HEPA filters and positive pressurization for bioexclusion. Feces were collected from mice at day 0 for 16S rRNA gene sequencing to confirm germ-free status. 16S sequencing of feces from day 7 and day 14 showed relatively little variation in composition from day 21 (S2 Fig). Mice were euthanized at 21 days post gavage using isoflurane overdose and all efforts were made to minimize suffering. Blood from euthanized animals was collected using cardiac puncture and cells were pelleted in K2-EDTA tubes; plasma was then aliquoted and stored at -80° C. Data was combined from 10 batches of mice analyzed independently, with at least 4 mice per batch, and at least two donor groups represented in each batch.
Ileum and colon tissues from mice at 21 days post gavage were collected for immune phenotyping and histology. Tissues were dissected length-wise and feces were washed away using PBS. 1–5 mm slices of ileum and colon closest to the cecum were taken from a subset of mice and fixed in 10% formalin and reserved for histology or ELISA. The remaining tissue was then washed in a PBS solution containing 1% EDTA for two 5 min intervals on a vortexer. Tissues were washed with PBS and strained through a 100 μm nylon filter in between intervals. Tissues were then washed for a third time in plain 1X PBS. Tissues were then minced using an octoMACs tissue dissociator (Miltenyi) and digested in 2.5 ml of a solution of complete RPMI (10% FBS, 1% PSG) containing 1 mg/ml collagenase D type I (Worthington Biotech) at 37° C, 5% CO2. Released cells were quenched with cold complete media, filtered through a 70 μm nylon filter and washed with complete media before staining for flow cytometry.
0.5–1 x 106 ileum and colon cells from each mouse were stained in FACS buffer for 30 min at 4° C with the following antibodies: CD3-PerCP-Cy5.5 (17A2, Biolegend), CD4-FITC (RM4-4, eBioscience), CD8-BV510 (53–6.7, Biolegend), CD69-PE-Cy7 (h1.2F3, eBioscience), CD103-PE-Dazzle-594 (2E7, Biolegend). Mesenteric lymph node cells were additionally stained with CD44-AlexaFluor-700 (IM7, eBioscience), and CD62L-APC-eFluor780 (MEL-14, eBioscience). Cells were then washed with FACS buffer and fixed in 1% paraformaldehyde before being acquired on a BD LSRii flow cytometer. Colon samples that had less than 1,000 T cells were excluded. Plasma samples were thawed and diluted 1:10 before being assayed for sCD14 by ELISA (R&D Systems) according to the manufacturer’s instructions. Ileum and colon tissues cultured for 24 h at 37° C, 5% CO2 in complete RPMI were digested with CellLytic MT solution with proteinase inhibitor (SIGMA), and assayed for myeloperoxidase concentrations by ELISA (R&D Systems). ELISA plates were read using a Vmax Kinetic plate reader (Molecular Devices). Tissue sections from the distal end of the ileum and proximal end of the colon were processed for histology and H&E stained. Scoring [34] was performed by a pathologist blinded to experimental conditions.
Stool samples were collected by the patient, both on a sterile swab and with a sterile scoop within 48 hours of a clinic visit. Samples were stored immediately in a cooler with -20°C freezer packs. After delivery to the clinic, the swab was subsequently stored at -80° C to await DNA extraction. Isolation of fecal bacterial communites (FBCs) from stool was described previously [8]. Briefly, two grams of feces were homogenized in sterile PBS and filtered through a 100uM filter. Homogenate was then subjected to two density gradient centrifugations with 80% Histodenz (Sigma). Bacterial layers were collected after each spin, visualized with a light microscope, and quantified by flow cytometry using a bead counting kit (BD Biosciences). FBCs were stored at -80° C before use in in vitro assays.
16S rRNA targeted sequencing was conducted according to earth microbiome project standard protocols (http://www.earthmicrobiome.org). DNA was extracted from donor stool swabs, mouse fecal pellets, and from a 250 μL aliquot of FBC isolates using the standard PowerSoil protocol (QIAGEN). PCR amplification of the extracted DNA, along with water controls, was conducted with barcoded primers targeting the V4 region of the 16S rDNA gene (515F-806R; FWD:GTGCCAGCMGCCGCGGTAA; REV:GGACTACHVGGGTWTCTAAT). Amplified DNA was quantified using a PicoGreen assay (Invitrogen) so equal amounts of DNA from each sample could be pooled and cleaned using the UltraClean PCR Clean-up protocol (Qiagen). The final DNA pool was sequenced using the Illumina MiSeq platform (San Diego, CA) using the V2 2x250 kit.
Raw sequences were assigned to samples based on their barcodes using QIIME 2.6 [49]. The libraries were denoised and grouped by sequence variants using dada2 1.2.2. Samples contained from 21,357 to 159,296 sequences; analyses that contained donor and mouse recipient feces were conducted on the standardized sequence number of 21,357, analyses that contained FBC isolates and their original stool were conducted on the standardized sequence number of 21,812, and analyses that contained mice monocolonized with P. copri and B. uniformis were conducted on the standardized sequence number of 31,812 –these numbers were determined as the minimum read number acquired for a single sample in each independent analysis. Sequence variants were classified taxonomically using the RDP classifier [50] trained on the greengenes 3_8 taxonomic database [51]. Principal Coordinates Analysis of unweighted UniFrac distance matrices, and rendering of taxa bar charts, were conducted using QIIME 2.6. For immune correlations, comparisons of relative abundance between groups, and P. copri variant analysis, sequence variants not observed in at least 20% of the samples in each analysis were removed.
Macroscopically healthy jejunum tissues from patients undergoing elective gut resection surgery were digested to release lamina propria cells (LPCs). Briefly, tissues were trimmed of fat and the muscularis layer was removed using scissors. Trimmed tissues were incubated on a rocker with a 1.6 mM dithiolthreitol (DTT) PBS solution for 45 min at 37° C, and then with a 1 mM EDTA PBS solution for 60 min at 37° C. Tissues were then washed with PBS on a vortexer, minced using scissors, and incubated for three 45 min intervals with a digestion solution of complete RPMI (10% HS, 1% PSG) containing 1 mg/ml collagenase D (Sigma-Aldrich) and 10 μg/ml DNase I (Sigma-Aldrich). Released cells were collected and filtered through a 70 μm nylon filter after each digestion interval. Cells from each interval were pooled together, RBC lysed, and cryopreserved.
LPCs were thawed in complete RPMI containing 10 μg/ml DNase I (Sigma-Aldrich), and washed again with complete RPMI before plating. 5 x 105 LPCs were plated per well in a 96-well round bottom plate. Bacterial cells were added to specified wells at 5:1 bacteria:LPC ratio. HIVbal passaged in Molt4 T cells and quantified by qPCR were then added to each well at 107 HIV RNA copies/well. Cells were incubated at 37° C for 24 h to allow for infection, and then washed twice with complete RPMI to remove cell-free virus. Bacteria were added back to each well at a 5:1 ratio after washing, and cells were incubated for another 4 days at 37° C. LPCs were then stained with anti-CD3-APC-Cy7, CD4-PerCP-Cy5.5, CD8-APC, HLADR-FITC, CD38-BV421, CD69-BV510, and CCR5-PE-Cy7. Cells were then washed and treated with fix/perm buffer (eBioscience) for 30 min at 4° C, and stained with anti-HIVgag-PE (KC57, Fisher) for 1 h at 4° C. Cells were then fixed in 1% paraformaldehyde and acquired using a FACS Canto II flow cytometer (BD Biosciences). Unstimulated, uninfected cells were used as controls to gate on HIVgag+ cells.
Immune data were compared between groups using t-tests if data from both groups passed the D’Agostino and Pearson normality test, and Mann-Whitney tests if data from at least one group did not pass the normality test. All immune data were analyzed using Prism 7 (Graphpad). Comparisons of bacterial relative abundances between groups were performed using non-parametric t-tests on non-transformed data. Spearman correlations were used for all correlation analyses. Replicate recipient mice were treated as independent observations for all microbiome comparisons and correlations. Corrected p-values were determined with the False Discovery Rate (FDR) technique of Benjamini and Hochberg. All statistical analyses involving 16S sequencing data were performed using QIIME 1.9.
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10.1371/journal.pntd.0003548 | Factors Associated with Severe Human Rift Valley Fever in Sangailu, Garissa County, Kenya | Mosquito-borne Rift Valley fever virus (RVFV) causes acute, often severe, disease in livestock and humans. To determine the exposure factors and range of symptoms associated with human RVF, we performed a population-based cross-sectional survey in six villages across a 40 km transect in northeastern Kenya.
A systematic survey of the total populations of six Northeastern Kenyan villages was performed. Among 1082 residents tested via anti-RVFV IgG ELISA, seroprevalence was 15% (CI95%, 13–17%). Prevalence did not vary significantly among villages. Subject age was a significant factor, with 31% (154/498) of adults seropositive vs. only 2% of children ≤15 years (12/583). Seroprevalence was higher among men (18%) than women (13%). Factors associated with seropositivity included a history of animal exposure, non-focal fever symptoms, symptoms related to meningoencephalitis, and eye symptoms. Using cluster analysis in RVFV positive participants, a more severe symptom phenotype was empirically defined as having somatic symptoms of acute fever plus eye symptoms, and possibly one or more meningoencephalitic or hemorrhagic symptoms. Associated with this more severe disease phenotype were older age, village, recent illness, and loss of a family member during the last outbreak. In multivariate analysis, sheltering livestock (aOR = 3.5 CI95% 0.93–13.61, P = 0.065), disposing of livestock abortus (aOR = 4.11, CI95% 0.63–26.79, P = 0.14), and village location (P = 0.009) were independently associated with the severe disease phenotype.
Our results demonstrate that a significant proportion of the population in northeastern Kenya has been infected with RVFV. Village and certain animal husbandry activities were associated with more severe disease. Older age, male gender, herder occupation, killing and butchering livestock, and poor visual acuity were useful markers for increased RVFV infection. Formal vision testing may therefore prove to be a helpful, low-technology tool for RVF screening during epidemics in high-risk rural settings.
| Rift Valley fever virus (RVFV) causes serious disease in both animals and humans. Large-scale outbreaks result in devastating economic losses and create many urgent public health concerns. Among humans, the symptoms of RVF are variable, having a broad spectrum of disease that ranges from mild to severe fever symptoms, and can include ocular complications, encephalitis, and sometimes hemorrhagic disease. In this study, 1082 at-risk Kenyan subjects were serum antibody-tested for evidence of prior RVFV infection and their demographic, health, and exposure data were collated. Seroprevalence was moderately high across the study area (15%) but did not differ significantly among villages across the study region. Age, gender, and herding occupation were all significantly associated with being RVFV seropositive. Older age, village and certain animal husbandry activities were associated with more severe disease. Poor visual acuity was more likely in the seropositive group. This better definition of risk factors and associated symptom complexes should prove helpful for RVF screening during future outbreaks in high-risk rural settings.
| Rift Valley fever virus (RVFV) is a mosquito-borne zoonotic disease that poses a significant risk to human health in endemic regions of Africa and the Middle East [1]. Epizootics usually precede epidemics and can result in large-scale abortion storms in local livestock populations [2]. These RVFV outbreaks in human and animal populations result in significant economic damage from trade embargos and significant livestock losses in affected areas [3]. Recent data also demonstrate that RVFV can be transmitted to humans during interepidemic periods [4–6]. RVFV infection is categorized as a neglected tropical disease due to the fact that RVFV disproportionately affects resource-limited semi-nomadic herding communities, is poverty promoting, and has long-lasting sequelae [5]. Additionally, RVF is expanding its range, threatening other areas of the world as an emerging infectious disease; notably, both Europe and the United States have the necessary vectors and livestock reservoirs to sustain autochthonous RVFV transmission [7,8]. The severity of RVFV manifestation, its devastating economic and public health effects, and its potential to be sustained in new regions make the study of RVFV transmission and disease a high priority.
Clinically, most often RVFV causes no symptoms or a mild illness manifesting with fever and liver abnormalities [4]. More rarely, RVFV is known to cause cases of retinitis, encephalitis, or hemorrhagic diathesis with hepatitis during epidemics [9], but these manifestations are variable and currently unpredictable. Most primary infections are thought to cause only self-limited febrile illness followed by complete recovery. It is not yet clear why severe cases occur- these consist of patients with neurologic dysfunction (up to 8%), and hemorrhagic cases (up to 1%, which is then associated with mortality of up to 50%) [2,4]. Furthermore, RVFV causes visual disturbances including reversible anterior uveitis (up to 30% of cases), and permanent retinitis (up to 20%) [10]. This broad spectrum of human RVF disease has been most recently confirmed in investigations of the 2006–2007 epidemics in East Africa [9]. Outbreaks in NE Kenya (Garissa County) were reported during the last epidemic [2], but RVFV activity in nearby Ijara constituency (Masalani and Sangailu), was not specifically monitored. Other reports have shown evidence of interepidemic human RVFV transmission in Ijara constituency (Masalani)[5–6]. It has been suggested that clinical phenotype of disease may be in part determined by the route of RVFV transmission, with animal-related transmission likely to be more severe than mosquito borne disease [1]. To expand this knowledge, the goal of the present study was to identify the exposures and other risk factors associated with human RVFV transmission and disease severity in a typical East African endemic setting, the Ijara constituency, Sangailu location, Kenya.
All participants provided written consent under a protocol approved by the Human Investigations Review Board of University Hospitals Case Medical Center (No. 11–09–01) and the Ethical Review Committee of the Kenya Medical Research Institute, Nairobi, Kenya (Non-SSC Protocol No. 195). Before participation, written informed consent was obtained from adult study subjects, and parents provided written informed consent for their participating children. Children over 7 years of age also provided individual assent.
This study was performed in the semi-arid Sangailu Location of Ijara constituency, Kenya. Six villages (Golabele, Sabenale, Gedilun, Matarba, Korahindi, and Tumtish) were sampled for demographic, epidemiological, and health information during area-wide household surveys performed from August through November of 2011, five years after the last known RVF epidemic in the area (2006–2007). The villages are located off a main road across a span of approximately 40 Km, in a transect running southwest to northeast (Fig. 1) centered around coordinates 1 deg. 19 min S, 40 deg. 44 min E. The northern-most village, Tumtish, is located 39 Km from the border with Somalia. The participating populations studied were comprised predominantly of herders and semi-nomadic pastoralists of Somali ethnicity. A typical household landscape is shown in Fig. 2. For unique identification and subsequent analysis of the spatial distribution of RVFV serostatus, participating household locations were geo-referenced by Global Positioning System with the use of a Garmin eTrex handheld device (Garmin, Schaffhausen, Switzerland).
Study recruitment began after consultation and approval by local leaders and administrators. After an initial demographic census was performed to determine the current local population and its distribution, a systematic survey of the total populations of six Northeastern Kenyan villages was performed. The villages were systematically surveyed in sequence to reach the desired sample size of >1000 enrolled individuals. All residents were eligible for inclusion, except that those residing in the area for <2 years, and children less than 1 year of age were excluded. The study sample was representative of the local ethnic mix of 99% Kenyan Somali and < 1% Bantu, Indian, or other Asian.
Participants had formal interviews to detail their demographics, occupation, mosquito exposure, and animal exposure within the last 6 months, and any previous nonspecific symptoms related to RVF in the last 30 days, or severe symptoms at any time (see Supporting S1 Text). Visual acuity testing by use of the Snellen chart and physical exams were performed, with an emphasis on signs of recent or remote RVF. Children under 5 yrs. old (N = 358) were excluded from visual acuity testing. Poor visual acuity was defined as a score less than or equal to 6/9 meters in either eye by standard Snellen eye chart testing. A stratified subset (118) of visually symptomatic and asymptomatic subjects also underwent dilated fundoscopic eye exam and imaging using a retinal camera. Peripheral blood was then collected for serological testing for anti-RVFV IgG.
Sera were tested for IgG against RVFV by standardized ELISA protocol [11,12] and confirmed by plaque reduction neutralization testing (PRNT), as previously described [5,6,12–14]. Specimens having an ELISA OD of >0.25 and a PRNT titer of ≥1:20 were considered positive. The confirmatory plaque reduction neutralization testing (PRNT) was performed at the University of Texas, Medical Branch at Galveston.
Statistical analysis examined the association of subject demographic and exposure factors with two primary outcomes: i) odds of seropositivity and ii) odds of having had the more severe symptoms of RVF. Initial chi-square tests were performed to identify the association of categorical factors with RVFV seropositivity and t-tests were used for continuous variables. A series of nested multivariable logistic regression models were next developed that initially included predictors significant in bivariate comparisons, as well as those considered of biological relevance prior to conduct of the study. Bivariate results and stepwise regression models were used to aid in the selection of variables to be included in the final models. Non-significant variables (p > = 0.10) were removed in stepwise fashion to help identify the variables with the greatest multiply adjusted links to RVFV seropositivity or symptom score. For this analysis, statistical significance was set at the 0.05 level. Following analysis of individual symptoms, a relative RVF severity score was developed using the two-step cluster algorithm in SPSS v. 21 (IBM, Armonk NY, USA) to empirically define significant constellations of milder, moderate, and more severe symptom states among RVFV seropositive subjects [15]. The severe disease phenotype was defined as having somatic symptoms of acute fever plus eye symptoms and possibly one or more meningoencephalitic or hemorrhagic symptoms (S1 Fig). Mild disease phenotype was defined as RVFV seropositivity with few to no symptoms. Multivariate logistic regression models were run using severe vs. mild disease categories and significant variables from bivariate analysis, excluding those variables used to define disease severity. These statistical models were performed using SAS software (SAS Institute Inc. version 9.3, Cary, NC, USA).
Statistical analysis of spatial patterns of seropositivity among the participating households was performed with the use of Point Pattern Analysis software [16] and Clusterseer 2.0 software (Biomedware, Ann Arbor, MI)
Of the 1134 participants enrolled in the study, 1082 completed all phases of the examination and were tested for RVFV infection. Of these, 164 were RVFV seropositive (15%; CI95% 13–17%). Males were more likely to be RVFV infected: 18% (79/433) were seropositive compared to 13% (85/646) of females (P = 0.023; Table 1). Adults (≥16 years old) were also more likely than children to be RVFV infected: Thirty-one percent (152/487) of adults were seropositive compared to 2% (12/595) of children (P < 0.001). The average age of seropositive people was 42 ± 19.5 years (range 6–85 years) vs. 17 ± 17 for seronegatives (range 1–84 years). No significant differences in seropositivity were seen among the six villages studied in the Sangailu region: Golabele (17.6%; 15/85), Korahindi (17.0%; 49/288); Sabenale (15.6%; 10/64); Matarba (14.3%; 33/231); Gedilun (13.9%; 32/230), and Tumtish (13.6%; 25/184). This was not surprising given the uniformity of landscape and environmental features, and the socioeconomic homogeneity within pastoralist communities of this region.
From initial bivariate analysis, RVFV seropositivity was significantly associated with multiple environmental exposures, as well as certain physical signs and reported symptoms (see Table 1). After multivariable adjustment, our most parsimonious logistic model of seropositive status found that older age (4% increase per year CI95% 2–9%, p<0.0001), male gender (adjusted Odds Ratio (aOR) = 1.8, CI95% 1.2–2.7, P < 0.01), poor measured visual acuity (aOR = 1.7, CI95% 1.01–3.0, P < 0.05), a history of malaise (aOR = 1.6, CI95% 1.06–2.5, P < 0.03), and a history of killing livestock (aOR = 2.0, CI95% 1.4–3.3, P < 0.001) were each independently associated with seropositivity (Table 2).
Ten percent (112/1081) of people surveyed self-reported poor vision. Those who were RVFV-infected were more likely to report poor vision: 26% (43/164), as compared to 7.5% (69/917) among uninfected (P < 0.0001). Sixteen percent (111/710) of tested subjects had poor measured visual acuity. Those who were anti-RVFV positive were more likely to have poor measured visual acuity, 36% (57/159) compared to 9.8% (54/551) of seronegatives (P < 0.0001). Fundoscopic exams were performed on 118 study participants. Here, objective eye disease was defined as having uveitis, retinitis, retinal scar or retinal hemorrhage. Overall, 26% (30/118) were found to have eye disease defined as retinitis or retinal hemorrhage. Of those serologically tested for RVFV seropositivity, 21% (6/28) were RVFV seropositive.
Across the study landscape, we did not find any significant global pattern of clustering for anti-RVFV serostatus beyond the underlying distribution of households in each village (using Ripley’s weighted K-function testing [17] over a range from 50 to 850 meters). However, within Korahindi, Matarba, Tumtish, and Sabenale there was evidence (using the Getis G-statistic [18]) of significant local clustering, at the 25–100 m scale, of greater per-household density of cases within the certain sections of these communities. Most remarkably, it was noted that all of the confirmed RVFV-positive subjects in Sabenale came from just 3 of 20 houses sampled (Fig. 3, left panel). One of these three seropositive houses in Sabenale had 7 seropositive people out of 11 total household residents. Fig. 3‘s right panel indicates the clustering pattern within the Sangailu area’s central village of Matarba.
One hundred sixty-four people were RVFV exposed and included in this analysis. Ninety-three percent (152/164) were adults and 52% (85/164) were female. Thirty percent (49/164) were from Korahindi, 20% (33/164) from Matarba, 20% (32/164) from Gedilun, 15% (25/164) from Tumtish, 9% (15/164) from Golalbele, and 6% (10/164) from Sabenale. Those in the moderate/severe group (N = 111) were more likely to be older (p = 0.007; mean age 44.9 years vs. 36.13 years) than those in the mild group (N = 53). In bivariate analysis, the most severe of the RVF symptom-cluster phenotypes was associated with older age, village, recent illness, and death of a family member (Table 3). In a multivariable logistic model controlling for age and village, it was found that those who sheltered livestock or disposed of livestock fetuses were at significantly greater risk for having this more severe illness complex (Table 4). Sheltering livestock put one at three and a half times the risk for more severe disease (aOR = 3.5 CI95% 0.93–13.61, P = 0.064). Disposing of livestock abortus put one at four times risk of having a severe disease phenotype (aOR = 4.11, CI95% 0.63–26.79, P = 0.140).
A significant proportion of the population in the semi-arid areas of northeastern Kenya have been infected with RVFV. Other than older age, most of the factors significantly associated with anti-RVFV seropositivity and RVF disease severity were related to pastoralist lifestyles and animal exposures. These included the common practices of livestock shelter at home and livestock fetus disposal, as typically observed in this region. During epizootics, RVFV-infected herds will experience abortion storms and affected virus-contaminated abortus is often handled by herders, significantly increasing their risk for RVFV infection by aerosol and direct contact, and possibly, their risk for more severe RVF disease [1]. Similarly, infected animals brought to slaughter provide potential avenues for transmission via direct blood contact or aerosolization. Animal husbandry exposures were similar in each of the studied villages, which may explain the lack of difference in seropositivity among villages. No empiric global clustering effect was observed for household anti-RVFV seroprevalence across the study landscape, but within some villages, significant local clustering of seropositive households was documented and severe disease manifestations were more common in certain villages than others. It seems that, within certain communities, a few high-risk households carry the burden of RVFV infection, perhaps defined by combined eco-social landscape factors. Between the identified high-risk households, differences in animal husbandry practices could not be determined, but their (unmeasured) animal herd seropositivity could have differed, leading to greater individual household exposures. Other factors such as socioeconomic status and local landscape (vegetation and soil) may have also played a role in exposure risk variation as seen in the last RVFV Kenyan outbreak [19], but these were not measured in our study.
As noted in other studies, RVFV seropositivity rates were much lower among children [5,6,12]. Although there is a built-in age/time bias, in that older people have had a longer time to be exposed to RVFV infected mosquitoes and livestock, the present study and past studies suggest a significant step in RVFV infection risk over the age of 15 years [5,6]. Cultural practices may have limited children’s exposure, as they may be less likely to directly handle infectious materials before the age of 16 years.
Of the subject symptoms we elicited, backache was the most strongly associated with RVFV seropositivity. Among confirmed, hospitalized RVF patients, an initial syndrome consisting of severe headache, fever, arthralgias, and general malaise has been described that occurs prior to the onset of delirium and mental confusion and/or hemorrhagic manifestations [9]. Among encephalitis-related symptoms, photophobia, mental confusion, and meningismus were all associated with evidence of past RVFV infection (Table 1). This is significant because longitudinal case-series in West Africa have noted that RVF may result in long-term schizophrenic or dementia-like manifestations [20]. Our observed association between mental confusion symptoms and RVFV seropositivity is consistent with this previously documented RVFV-related finding.
Self-reported visual impairment and reduced measured visual acuity were both correlated with RVFV seropositivity. Uveitis and/or retinitis are two of the most common sequelae of human RVF [6]. In our study, a history of eye pain, red eyes, or photophobia (eyes sensitive to light) were significantly associated with RVFV seropositivity, which may have been due to RVF uveitis at the time of acute infection. Of the subset of subjects who had retinitis on fundoscopic examination, only one quarter were seropositive, demonstrating a larger burden of unrelated retinal disease in this community.
The analysis of RVF disease severity (Tables 3 and 4) suggests that exposure factors have only a minimal impact on the risk for disease severity, even though they increase risk for infection (as seen in Table 1). This agrees with a recent paper from Anyangu et al [1], in which four exposure factors were associated with severe disease versus nonsevere RVF disease during bivariate analyses (animal contact/herding animals, caring for animals during birthing, touching an aborted animal fetus, and being a herdsperson); however, only one factor (touching an aborted animal fetus) was associated with disease severity in the multivariable model. Our study found that sheltering livestock and disposal of a livestock fetus were associated with severity of disease, but these were not statistically significant in our model. The lack of statistical significance may be due to the small study number (164) in this analysis and could be affected by the remote timing of our study in relation to the last outbreak. It is likely that individual level factors (genes, co-morbidities) also determine risk of disease severity, and only not mode of infection.
This study was limited by the self-reported nature of the exposures and symptom data, which are subject to recall bias. Other prevalent infections, such as malaria, may have accounted for the reported fever-related symptoms. In particular, other arbovirus infections, such as West Nile virus, chikungunya virus, and dengue, might have accounted for reported ocular symptoms, as they, too, are known to cause uveitis and retinitis [21–23]. Because those local residents who had experienced severe RVFV disease in 2006–2007 could have had up to a 50% chance of dying, a survival bias is inherent in our study, and the factors associated with the most severe RVFV disease phenotype (hemorrhagic fever and death) may not have been identified. Also we performed our analysis of severe disease with a small sample size of only 164 participants. This limited sample size may have prevented the elucidation of some factors associated with severe RVF disease.
In conclusion, RVFV infection in northeastern Kenya is significantly associated with older age, male gender, livestock harvesting, and poor vision. Spatial analysis suggests that very high-risk households exist within at-risk communities, which appear to harbor most of the RVFV infection burden. Animal exposure factors were linked to severity of human RVF disease symptoms, as suggested in previous studies [1]. Finally, the prominence of vision-related symptoms and ocular findings suggests that these may prove to be useful indicators of active or recent RVF disease in at-risk settings where serological or PCR RVFV testing is not available.
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10.1371/journal.pgen.1007399 | Germline mutations and somatic inactivation of TRIM28 in Wilms tumour | Wilms tumour is a childhood tumour that arises as a consequence of somatic and rare germline mutations, the characterisation of which has refined our understanding of nephrogenesis and carcinogenesis. Here we report that germline loss of function mutations in TRIM28 predispose children to Wilms tumour. Loss of function of this transcriptional co-repressor, which has a role in nephrogenesis, has not previously been associated with cancer. Inactivation of TRIM28, either germline or somatic, occurred through inactivating mutations, loss of heterozygosity or epigenetic silencing. TRIM28-mutated tumours had a monomorphic epithelial histology that is uncommon for Wilms tumour. Critically, these tumours were negative for TRIM28 immunohistochemical staining whereas the epithelial component in normal tissue and other Wilms tumours stained positively. These data, together with a characteristic gene expression profile, suggest that inactivation of TRIM28 provides the molecular basis for defining a previously described subtype of Wilms tumour, that has early age of onset and excellent prognosis.
| The germline and somatic molecular events associated with Wilms tumour, a childhood kidney cancer, have been progressively defined over the past three decades. Among the uncharacterised tumours are a group of tumours that have monomorphic epithelial histology, familial association, distinctively clustered gene-expression patterns, early age of diagnosis, and excellent prognosis. Here, we describe germline mutations and loss of function of TRIM28 in familial Wilms tumours, along with somatic loss of function in a non-familial Wilms tumour. All TRIM28-mutant tumours showed the rare monomorphic epithelial histology, suggesting that loss of TRIM28 expression could be a useful marker to define a group of tumours with excellent prognosis. Future studies could lead to identification and reassurance of families that carry TRIM28 mutations, and to the use of reduced intensity of treatment for children who develop TRIM28-null tumours.
| The study of Wilms tumour, a rare childhood kidney tumour [1], has facilitated the discovery of mechanisms of organogenesis and the neoplastic transformation of embryonic tissue. First, the discovery of inactivating mutations and deletions of WT1 in Wilms tumours [2] led to the revelation of its key roles in development of numerous embryonic tissues [3, 4]. Similarly, activating mutations of CTNNB1 in Wilms tumours highlighted the importance of WNT pathway activation in renal development and in multiple tumour types [5]. In addition, altered expression of the imprinted IGF2 locus demonstrated the occurrence of genomic imprinting in humans, as well as the consequences of its disruption during embryogenesis [6, 7]. Mutations in microRNA processors DGCR8, DROSHA, and DICER1 have underscored the importance of this pathway in developmental tumours [8–11], whereas mutations in SIX1 and SIX2 reflect their critical role in renal development [9, 10, 12]. Characterisation of other recently reported recurrent somatic mutations [9, 10, 13] will further clarify the mechanisms of nephrogenesis and neoplasia.
Familial and syndromic Wilms tumours have demonstrated the susceptibility of the developing kidney to germline variants of WT1 in children with genitourinary abnormalities [14], BRCA2 and PALB2 in Fanconi anaemia patients [15, 16], GPC3 in Simpson-Golabi-Behmel syndrome patients [17], DIS3L2 in Perlman syndrome [18], DICER1 in DICER1-related disease [19], BUB1B and TRIP13 in mosaic variegated aneuploidy (MVA) syndrome [20, 21], and CTR9 [22], REST [23], PALB2, and CHEK2 [13] in non-syndromic Wilms tumour families. In addition, linkage of familial Wilms tumours to 17q12-q21 [24] and 19q13.4 [25] implicate further causative gene variants, although the evidence supporting the 19q13.4 locus was not conclusive [26].
Molecular characterisation of Wilms tumours has assisted in the stratification of tumours into clinically relevant subgroups [27]. For example, children with tumours with diffuse anaplasia, associated with TP53 mutations [28], are recommended to receive more intense therapy [27]. In addition, losses of chromosomal arms 1p or 16q are associated with poorer outcomes [29] and augmented therapy has been recommended [27]. Conversely, small stage 1 tumours with favourable histology in young children can be treated with less intense regimens [27]. Over-represented in this last group are a cluster of tumours (S1) described by Gadd and colleagues that do not harbour mutations in WT1, CTNNB1 or AMER1. These tumours usually show retention of imprinting at IGF2, have a distinct gene expression pattern and have highly differentiated monomorphic epithelial histology [30, 31].
Additional characterisation of Wilms tumour subtypes by molecular events and gene expression should enable the refinement of clinically significant risk categories and enhance therapeutic outcomes. Here we report the presence of truncating germline variants, somatic mutation, and epigenetic silencing of TRIM28, in familial and non-familial cases of Wilms tumour. Tumours with these alterations have the characteristic histology, gene expression and outcome typical of the previously described S1 subtype [30].
We performed whole-exome sequencing on Wilms tumours and matched adjacent kidney from 18 unrelated patients. Following processing of the sequence reads to variant calls, we first assessed the non-neoplastic kidney sequences for rare germline variants using a candidate gene approach. Genes containing variants previously associated with Wilms tumour and genes within regions of familial linkage, 17q12-q21 and 19q13.4, were included in this analysis.
One case (case 37, diagnosed at 39 months) showed a constitutional frameshift variant of TRIM28 (NM_005762.2; c.525_526del) in the non-neoplastic kidney sample (Table 1, Fig 1A, S1 Text). TRIM28, which encodes a transcriptional co-repressor, is located at 19q13.4 in the proximity of a putative familial Wilms tumour locus [25]. Analysis of the sequence of the associated tumour (37T) revealed loss of heterozygosity with retention of the variant allele (Fig 1A, S1 Fig). Peripheral blood DNA from the patient’s sister, who was diagnosed with bilateral Wilms tumours at 8 months of age (case 39), showed heterozygosity for the same 2-bp deletion. DNA was then extracted from one of the paraffin-embedded tumours of case 39 revealing loss of heterozygosity, with retention of the variant allele (Fig 1A). The same 2-bp deletion was also present in peripheral blood from their asymptomatic mother thereby confirming maternal transmission of the TRIM28 variant. Notably, the mother had no history of cancer.
Loss of function (LoF) variants of TRIM28 are exceedingly rare. To determine the prevalence of these events in the population, we interrogated the gnomAD database (http://gnomad.broadinstitute.org/) which contains sequence data for more than 140,000 individuals. In total four LoF variants were detected, two of which are described as low confidence variants. In addition, the probability of LoF intolerance (pLI) for TRIM28 was 1.0 [ExAC database (http://exac.broadinstitute.org/)], where pLI ≥ 0.9 indicates extreme LoF intolerance [32]. Furthermore, TRIM28 is constrained with respect to missense variation, having a constraint z score of 3.16 (ExAC database) indicating high intolerance to variation [32].
Tumour-kidney pairs were then analysed for acquired somatic pathogenic mutations (S1 Table). Among these 18 pairs, a heterozygous frameshift mutation in exon 13 of TRIM28 (c.1935delinsGA) was detected in a sporadic tumour (W117) (Table 1, Fig 1C), though a second inactivating mutation or deletion could not be detected from the exome data. On inspection of exon read-depth it was noted that exon 1 was not represented in the aligned exome sequences despite being included in the capture platform. In addition, exon 1 was intractable to standard PCR approaches, presumably because of its high GC content (greater than 80%).
To overcome this issue, W117 tumour DNA was bisulfite-treated to reduce the GC content of the template, and Sanger sequence was produced for both treated DNA strands to determine mutational status. No variants were detected, but extensive methylation across a 480-bp portion of the CpG island that flanks exon 1 (S2 Fig) was discovered. Massively-parallel sequencing of bisulfite-converted DNA was then used to quantify methylation, revealing dense methylation of all CpGs throughout the amplified 220-bp region in 39% of 1043 sequence reads (Fig 1D; S3 Fig). In histologically normal adjacent kidney tissue, exon 1 methylation was also detected in 1.2% of sequence reads whereas the exon 13 mutation was not detected, suggesting low level mosaicism for TRIM28 hypermethylation (S4 Fig). In contrast, seven other Wilms tumours, including five with similar histology, showed absence of methylation in this region (S2 Fig). In addition, three normal kidney samples showed absence of methylated TRIM28 alleles.
The observations of a heterozygous frameshift truncating mutation and a heterozygous region of dense exon 1 CpG island methylation suggest that both alleles of TRIM28 have been inactivated, although it cannot be formally excluded that the mutation and CpG island methylation affect the same allele.
Remarkably, the tumours from case 37, case 39 (sister of case 37) and case W117 shared the same rare monomorphic epithelial histological pattern that occurs in approximately 5% of Wilms tumours [30]. We, therefore, sought other tumours to determine whether loss of function of TRIM28 was a shared feature of monomorphic epithelial tumours.
A literature search to find other similar tumours identified a family involving an affected mother and two children with monomorphic epithelial Wilms tumours (Table 1, cases 249 and 399) [33]. Targeted Sanger sequencing of all TRIM28 exons was achieved using tumour DNA from both children and the blood DNA of case 399. A frameshift variant in exon 13 (c.1746_1747delinsC) was detected in the blood DNA of case 399 and in the tumours from the children. Both tumours showed loss of the non-variant allele (Fig 1B). DNA from the mother’s tumour or normal tissue was not available for sequencing.
We then identified a sporadic tumour with monomorphic epithelial histology among 92 Wilms tumours in the Sydney Children’s Tumour Bank Network (SCTBN). Targeted Sanger sequencing of all TRIM28 exons of this tumour (SCTBN 88) did not identify any mutations and exon 1 was unmethylated.
Immunohistochemistry for TRIM28 protein was done for tumours 37T, 39T and W117 to determine whether mutations in TRIM28 led to loss of protein expression. All three tumours had a complete absence of TRIM28 protein in neoplastic cells (Fig 2A & 2B), although non-tumour-derived endothelial cells and residual non-neoplastic kidney epithelial structures (K) showed positivity. Nine other Wilms tumours were examined and all showed immunohistochemical expression of TRIM28 in epithelial elements, examples of which are shown in Fig 2C. Tumour SCTBN 88, which also showed monomorphic epithelial histology but no TRIM28 mutations, exhibited a normal pattern of TRIM28 expression by immunohistochemistry.
Whole-exome sequencing of 37T and W117 revealed no somatic mutations of other genes known to be mutated in Wilms tumour, including WT1, AMER1, CTNNB1, DROSHA, DGCR8, SIX1, SIX2 and REST. Indeed, no additional missense or non-functional mutations that passed standard filtering criteria were detectable in any other gene in these tumours. By comparison, the 16 sequenced tumours without TRIM28 variants had a mean and median of 4 (range, 0–12) detected somatic variants (Fig 3).
Exome sequencing data were then used to detect copy number change and loss of heterozygosity in the Wilms tumours (ADTEx, http://adtex.sourceforge.net). Tumour 37T showed copy-neutral loss of heterozygosity at fourteen contiguous SNPs from chr19:59023166 (hg19) to chr19:qter (chr19:59,118,983) without copy number variation, consistent with homozygosity of the inherited variant (S5 Fig). The most distal heterozygosity on 19q was detected at chr19:59,010,819 (rs2278497); therefore, the homozygous region includes the genes SLC27A5, ZBTB45, TRIM28, MIR6807, CHMP2A, UBE2M, MZF1, and MZF1-AS1.
Apart from loss of heterozygosity within 19q13.43, tumour 37T showed no other chromosomal regions with copy number changes or loss of heterozygosity, above the baseline noise level. The fractional length of aberrant copy number segments was quantified using segmentation data obtained with ADTEx, based on exome sequencing read depth. Tumour W117 also showed no evidence of regional gains or losses or loss of heterozygosity throughout the sequenced genome. In comparison, 12 of 16 other tumours without TRIM28 variants showed extensive copy number change (Fig 3, S6 Fig). The fractional length of aberrant copy number segments across the genome was 0.0003 in both 37T and W117 compared to a median of 0.06 for all tumours (Fig 3). The remarkable genomic simplicity of tumours 37T and W117 provides strong evidence that loss of TRIM28 is the sole driver of tumorigenesis in these cases.
The gene expression of 17 of these 18 Wilms tumours had previously been obtained using Affymetrix Human Genome U133 Plus 2.0 Arrays [34]. Unsupervised hierarchical clustering of tumour samples using 25,387 probes showed that the two TRIM28-mutated tumours for which RNA was available (37T and W117) clustered together (Fig 4). In agreement with the lack of TRIM28 protein, the expression of TRIM28 mRNA (probe 200990_at) in 37T and W117 was substantially lower than in the other Wilms tumours, consistent with complete or marked loss of expression (Fig 5).
We then compared the gene expression of 37T and W117 to that previously described for the “S1” subgroup of Wilms tumours that have a distinctive monomorphic epithelial histology [30]. First, we examined the gene expression data (GSE31403, Affymetrix Human Genome U133A Array) from the publication of Gadd and colleagues that described 224 cases of favourable histology Wilms tumour [30]. To facilitate comparison with our tumour cohort, we identified and ranked the probes that showed the greatest difference in gene expression between S1 tumours (n = 11) and the S2-S5 tumours (n = 213). Using the data from Gadd and colleagues, we identified 2476 and 2085 probes that showed higher and lower expression respectively in the S1 tumours compared to the other tumours (analysed using limma software with an adjusted p value cut-off of 0.05, Benjamini and Hochberg adjustment) [30]. Similarly, in the TRIM28-mutant tumours (with less statistical power) 80 probes showed significantly higher expression than non-mutant tumours, whereas 19 probes showed lower expression.
Of the probes that showed higher and lower expression in the TRIM28-mutant tumours, 51 (64%) and 15 (79%) were included in the differentially expressed probes from the data of Gadd et al. [30]. The expression levels of the five most down-regulated and five most up-regulated genes in the S1 compared to S2-S5 subgroups were examined in the TRIM28-mutant and non-mutant tumours (Fig 5). The gene expression pattern is remarkably similar, suggesting that TRIM28-mutant tumours 37T and W117 have the gene expression characteristics of the S1 subgroup. Indeed, the probe showing the most significant down regulation in S1 tumours compared to the other tumours was probe 200990_at that targets TRIM28. Furthermore, in eight of the 11 S1-subtype tumours the expression level of TRIM28 was distinctly lower than that in all the 213 non-S1 tumours (Fig 5).
We then determined whether the differentially expressed genes (S1 vs S2-S5) elucidated the processes of tumorigenesis or the tissue composition of the S1 tumours. We selected the 302 genes that showed at least two-fold higher expression in S1 compared to S2-S5 tumours and an adjusted p < 0.01. We similarly selected 126 genes that had lower expression in S1 tumours. Pathway and process enrichment analysis (http://metascape.org/) of the 302 over-expressed and 126 under-expressed genes revealed enrichment of several unrelated biological processes (S2 Table) from which convincing conclusions could not be drawn about the mechanisms of tumorigenesis. Instead, we adopted a targeted approach by examining the expression of genes shown to be involved in the different stages of nephrogenesis as documented by the GenitoUrinary Development Molecular Anatomy Project [35]. Marker genes that are highly expressed at each of several specific stages of kidney development were used to create “metagenes” that represent the expression pattern of a matrix of genes [36]. S1 tumours showed significantly higher metagene scores for marker genes expressed during stage I / stage II nephron development (including renal vesicle, comma-shaped body and s-shaped body development) (S7 Fig). This association was largely driven by LHX1, CDH4, BMP2, POU3F3, CCND1, and JAG1. In contrast, marker genes for stage III and IV nephron development, including renal corpuscle and proximal tubule development were not associated with S1 tumours. Therefore, the monomorphic epithelial elements of the S1 tumours are developmentally equivalent to renal-vesicle-derived structures and not to mature epithelial elements.
TRIM28 has numerous roles as a transcriptional co-repressor, including involvement in the establishment of imprinting [37, 38]. Therefore, we examined the allelic expression of H19 and IGF2, genes known to be aberrantly imprinted in some Wilms tumours. IGF2 had normal monoallelic expression in both tumours (37T and W117). In addition, H19 was monoallelically expressed indicating retention of normal imprinting in tumour 37T. Together with the observations of retention of normal epigenetic status at IGF2/H19 in the previously published S1 subgroup [30], our observations suggest that the tumorigenic effects of TRIM28 variants are not mediated through defects in the establishment or maintenance of imprinting at the IGF2/H19 locus.
Since it has been reported that TRIM28 interacts with AMER1 (WTX) [39] we postulated that tumours with mutations in AMER1 might share common features with TRIM28–mutated tumours. Neither of the TRIM28-mutated tumours (37T and W117) had AMER1 mutations. Following unsupervised clustering analysis of genome-wide gene expression (Fig 4) the TRIM28 and AMER1-mutated tumours did not cluster together. Furthermore AMER1-mutated tumours did not show the characteristic histological features of TRIM28-variant tumours in our cohort, nor in that of Gadd and colleagues [30]. Therefore, there is no evidence to suggest that TRIM28 and AMER1 variants are functionally equivalent in Wilms tumour, or affect related pathways of tumorigenesis.
Here we report that mutations of TRIM28, a gene located in proximity of the candidate familial Wilms tumour locus on 19q13.4, are present in the germline in families with Wilms tumours. Remarkably, all four familial and one sporadic TRIM28-inactivated tumours had monomorphic epithelial morphology. Their morphology and gene expression pattern accord with those reported for the “S1” subgroup of tumours that have an early age of onset and for which causative mutations have not yet been identified [13, 30]. Previous genome-wide sequencing studies of Wilms tumours, which have targeted high risk blastemal tumours [10] and relapsed or anaplastic tumours [9, 13], did not reveal any germline TRIM28 variants in Wilms tumours although a single somatic TRIM28 splice-site mutation has been detected in a TP53-mutated tumour with diffuse anaplastic histology [13].
High levels of TRIM28 expression occurs in many tumour types [40], but loss of TRIM28 function has not previously been implicated in human cancer. The combination of frameshift mutations and loss of heterozygosity or promoter methylation of the non-variant allele indicates complete loss of TRIM28 function in the tumours, that was confirmed by immunohistochemistry. Critically, TRIM28 appears to be essential for normal nephrogenesis, in that silencing of Trim28 in cultured rat kidney rudiments resulted in branching arrest of the ureteric bud structures [41]. It is plausible that loss of ureteric bud development leads to a failure to inhibit the growth of early epithelial structures from the undifferentiated metanephric mesenchyme. Current models of kidney development suggest, however, that differentiation and growth of the earliest nephron-associated structures rely on inductive signals from the ureteric bud tips, the absence of which is associated with failure of nephrogenesis [42, 43]. TRIM28 is known to contribute to the regulation of a wide range of cellular processes including suppression of retrotransposons, regulation of gene expression through heterochromatisation, mediation of DNA damage response, stimulation of epithelial mesenchymal transition and maintenance of stem cell pluripotency [40], highlighting multiple paths by which inactivation of TRIM28 might induce Wilms tumorigenesis.
Wilms tumours are reported to have a low mutation burden. For example, Wegert and colleagues detected an average of 6 (0–15) non-synonymous somatic mutations, including missense, stop loss, stop gain, and splicing mutations in 58 blastemal type tumours by exome sequencing [10]. Similarly, Walz and colleagues [9] reported an average of 11 high-quality non-synonymous somatic mutations in favourable histology tumours (range 2–42). Here we report a mean of four (range 0–12) high quality somatic variants per tumour, but unusually the two TRIM28-mutant tumours analysed by exome sequencing revealed no additional mutations. Using an exome-sequencing-based analysis, there were no meaningful structural changes in these tumours except, in one case, copy-neutral loss of heterozygosity at 19q13.43 which encompasses TRIM28. The absence of other identifiable genomic changes in two TRIM28-inactivated tumours suggests that loss of TRIM28 might be the sole driver of tumorigenesis. As such these Wilms tumours could represent rare examples of the “two-hit” model of Wilms tumorigenesis predicted by Knudson [44].
Interactions of TRIM28 with other known Wilms tumour-associated proteins raise the possibility of functional links to tumorigenesis. For example, TRIM28 has been identified as a binding partner of REST [45], which is known to have germline or somatic mutations in approximately 2% of Wilms tumours [23]; however, reported tumours with REST mutations had more varied histology and older ages at diagnosis than our group of TRIM28-mutant tumours. TRIM28 has also been reported to co-immunoprecipitate with AMER1, which is mutated or deleted in 20–30% of Wilms tumours [46]. The expression patterns of AMER1 and TRIM28 mutant tumours did not, however, cluster together, nor did they show similar histological features, suggesting that these two proteins contribute to different tumorigenic pathways.
The clinical behaviour of all five TRIM28-variant tumours supports previous observations that the monomorphic epithelial subtype of Wilms tumour is usually associated with excellent prognosis and presentation with early stage disease [30].
However, not all monomorphic epithelial tumours have these features; those that do not, tend to have presentation at later stages of diseases and at an older age [30]. In our study, one monomorphic epithelial tumour had neither TRIM28 mutations nor loss of TRIM28 expression. We hypothesise that loss of TRIM28 expression or the presence of TRIM28 mutation, in combination with monomorphic epithelial histology, can be used to identify the good prognosis S1 subtype of tumours. If this hypothesis is supported by future analysis of S1 tumours, it is likely to provide a molecular basis for down-staging treatment in affected children, thereby minimizing adverse effects of chemotherapy.
Wilms tumours and normal samples were collected and analysed with approval from the Health and Disability Ethics Committees, Ministry of Health, New Zealand (approval number CTY/01/10/141). Informed verbal consent was given to the treating surgeon or oncologist prior to tumour resection.
Exome libraries were constructed and sequenced by the Kinghorn Centre for Clinical Genomics (Garvan Institute of Medical Research, Sydney) using an Illumina HiSeq 2500 machine, with prior enrichment using the SeqCap EZ Exome v3 (Roche) capture platform. Sequence reads were paired end, with read lengths of 125 bases.
Processing for alignment and standard variant calling was based on GATK Best Practice Guidelines (https://software.broadinstitute.org/gatk/best-practices/). GATK version 3.5 was used.
Paired-end reads in fastq format, derived from a single individual, were aligned to the reference sequence (GRCh37 assembly) using the Burrows-Wheeler Aligner v0.7.13 [47] with the mem algorithm. Duplicate reads were identified using Picard MarkDuplicates. The data were locally realigned around indels followed by Base Quality Score Recalibration to produce the aligned files in bam format.
A variant call of single nucleotide variants (SNVs) and short insertions/deletions (indels) were generated for each sample using GATK HaplotypeCaller. Joint genotyping was done using GATK GenotypeGVCFs to produce a standard variant calling dataset containing variant information for all samples. This was followed by GATK LeftAlignAndTrimVariants and then Variant Quality Score Recalibration was undertaken independently for SNPs and indels. To facilitate the filtering of germline variants in the non-tumour samples, SnpEff version 4.2 [48] was used to annotate with gene context information [49]. Annotation for population allele frequencies was added using GATK VariantAnnotator, with data from the 1000 Genomes Project [50], and the Exome Aggregation Consortium [32].
Somatic SNVs and indels in tumour samples were called using the MuTect2 workflow (https://software.broadinstitute.org/gatk/best-practices/mutect2.php). Non-tumour samples from 28 individuals, whose exome sequences were obtained using the same capture platform, was used to create a panel of normals to exclude recurrent variants. dbSNP v137 was used as a “red” list, and the COSMIC database v54 as a “white” list in the recommended workflow.
Copy number variants and loss of heterozygosity (LOH) were assessed in tumours using the ADTEx (Aberration Detection in Tumour Exome) package v2.0 [51]. Initially, for each tumour-normal pair, all biallelic variants that were heterozygous in the normal sample, with a Genotype Quality greater than or equal to 14 and read depth between 11 and 1001 in both the normal and the tumour sample, were extracted from the multi-sample standard variant call file described above.
B-allele fractions were calculated and used in conjunction with the aligned bam files for the tumour-normal pair and a bed file for the SeqCap EZ Exome v3 capture regions, as input for the ADTEx package to identify regions of copy number variation (S6 Fig) and loss of heterozygosity in each tumour. To provide a simple quantitative measure of the genomic regions affected by copy number change, the segmentation data produced by ADTEx was used to estimate the fraction of the genome affected. The total length of segments with copy gain or loss, relative to the total length of segments reported for that tumour, was calculated as the fractional copy number aberration score. The R package ‘ggplot2’[52] was used for visualisation of regions of copy number variation and loss of heterozygosity (S5 and S6 Figs).
Genomic DNA was bisulfite converted using EZ DNA Methylation kit (Zymo #D5002) and PCR amplified using KAPA HiFi HotStart Uracil + polymerase (KAPA Biosystems KK2802) and primers designed to a 253 bp region of TRIM28 exon 1 (GRCh37/hg19 chr19:59056298–59056550) followed by a second round of PCR (10 cycles) to add indexed Illumina sequencing adaptors (S3 Table). Products were then sequenced on an Illumina MiSeq sequencer (Reagent kit V2, Nano). The methylation patterns of reads were visualised using BiQ Analyzer.
Tumour mRNA expression data, generated using an Affymetrix HG-U133 Plus 2.0 GeneChip Array, were available for 17 of the tumours in this study [34]. Expression data generated by Gadd and colleagues using an Affymetrix HG-U133A GeneChip Array were downloaded from GEO [53] (accession number GSE31403 [30]). Data were normalised using Robust Multi-array Average algorithm implemented in the ‘affy’ R package [54]. Probe sets from both datasets were filtered independently on inter-sample variance, and the 50% most variable probes were retained. Further, probes with known cross-hybridisation issues were excluded [55]. After filtering, 25387 probe sets were retained from this study’s data, while 9863 probe sets remained from Gadd and colleague’s data.
Hierarchical clustering of tumours was performed using Euclidean distance and average linkage. Differential expression between S1 tumours and non-S1 tumours was detected using the R package 'limma', accounting for multiple comparisons through the BH method [56]. To facilitate comparison between the two datasets, only probe sets present in both datasets that mapped to known genes, were used.
For comparison of gene expression of S1-S5 tumours with kidney development marker genes annotated in the GUDMAP database [35], the expression of each marker gene was scaled to a mean of 0 and standard deviation of 1, a metagene value was determined (based on first eigenvector from Singular Value Decomposition of the marker genes for that developmental stage [36]) and the tumour subtypes were compared. The p values shown in S7 Fig are not corrected for multiple comparisons.
TRIM28 immunohistochemistry was performed using an anti-KAP1 rabbit polyclonal antibody (Abcam ab10484) at a 1:2000 dilution, following antigen retrieval at pH 9.
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10.1371/journal.pbio.2004974 | Timescales of influenza A/H3N2 antibody dynamics | Human immunity influences the evolution and impact of influenza strains. Because individuals are infected with multiple influenza strains during their lifetime, and each virus can generate a cross-reactive antibody response, it is challenging to quantify the processes that shape observed immune responses or to reliably detect recent infection from serological samples. Using a Bayesian model of antibody dynamics at multiple timescales, we explain complex cross-reactive antibody landscapes by inferring participants’ histories of infection with serological data from cross-sectional and longitudinal studies of influenza A/H3N2 in southern China and Vietnam. We find that individual-level influenza antibody profiles can be explained by a short-lived, broadly cross-reactive response that decays within a year to leave a smaller long-term response acting against a narrower range of strains. We also demonstrate that accounting for dynamic immune responses alongside infection history can provide a more accurate alternative to traditional definitions of seroconversion for the estimation of infection attack rates. Our work provides a general model for quantifying aspects of influenza immunity acting at multiple timescales based on contemporary serological data and suggests a two-armed immune response to influenza infection consistent with competitive dynamics between B cell populations. This approach to analysing multiple timescales for antigenic responses could also be applied to other multistrain pathogens such as dengue and related flaviviruses.
| It is challenging to determine the true extent of influenza infection and immunity within a population, because a person’s immune response to a specific influenza strain depends both on past infections with that strain as well as immunity generated by related influenza strains. To untangle these processes, we developed a mathematical model that considered individual histories of influenza infection and immune dynamics acting at multiple timescales. We combined this model with surveys of antibody levels in different individuals, showing how antibody dynamics are influenced by a short-lived, broadly cross-reactive response against a wide range of strains that wanes over time to leave a long-term response against a narrower collection of strains. By accounting for such short- and long-term responses, we also found that it was possible to obtain better estimates of the frequency of influenza infection. These methods could help to guide the design of studies to estimate key aspects of influenza immune dynamics or to estimate historical infection rates and would also be applicable to other pathogens with multiple strains.
| Immunity against influenza A can influence the severity of disease [1, 2], the effectiveness of vaccination strategies [3], and the emergence of novel strains [4, 5]. Understanding the accumulation of immunity and infection has proven challenging because observed human antibody responses—typically measured by haemagglutination inhibition (HI) assays or microneutralisation titres [6]—reflect a combination of past infections to specific strains and the potentially cross-reactive responses generated by these infections [7].
Several aspects of influenza antibody dynamics have been well described through measurement of individual antibody repertoires. In particular, there is evidence of long-lived, strain-specific antibody responses directed against epitopes in the haemagglutinin (HA) glycoprotein head domain [8, 9], as well as weaker, cross-reactive responses directed at conserved epitopes in the HA stalk [7]. There is also evidence that influenza infection leads to ‘back-boosting', generating a transient, broadly cross-reactive response against historical strains [10–12]. In addition, it has been suggested that influenza responses are influenced by antigenic seniority, with strains seen earlier in life shaping subsequent antibody responses [13]. This is a refinement on the earlier concept of ‘original antigenic sin', whereby the largest antibody response is maintained against the first infection of a lifetime [14].
Although there are established techniques for the analysis of single-strain immunising pathogens such as measles [15], potential cross-reactivity between different influenza A strains means serological analysis must account for the dynamics of antibody responses across multiple infections [16]. The concept of an antibody landscape has been put forward as one way to represent the immune response developed as a result of a sequence of processes such as infection, antibody boosting, antibody waning, and cross-reactivity [10]. Previous work has also used cross-sectional data to explore the life course of immunity by explicitly modelling both the processes of infection and immunity [17]. However, such analysis could not quantitatively estimate the contribution of different antibody mechanisms operating at multiple timescales. These cross-reactive dynamics, combined with measurement error in available assays, have made it challenging to uncover an individual’s exposure history from serological responses. It has been shown that measurement error in HI assays can lead to uncertainty in the estimation of serological status [18], and cross-reactive antibody dynamics can make it difficult to estimate the true extent of influenza infection during an epidemic [2]. Accurate estimation of attack rates is crucial for estimating influenza burden and hence the design and evaluation of vaccination campaigns [19].
To quantify antibody kinetics over time and estimate historical infections with influenza A/H3N2, we used a dynamic model of immune responses that generated expected titres against specific strains [17] by combining infection history—which was specific for each individual—with an antibody response process that was universal across individuals. We assumed that the response included both a short-term and long-term component (S1 Fig). The short-term component consisted of a boost in log titre following infection, which decayed over time, as well as a rise in log titre as a result of cross-reaction with antigenically variable strains. The long-term response featured a boost in log titre, which did not decay, and a separate cross-reaction process that led to increased titres against other strains. Titres were also influenced by antigenic seniority, with later infections generating lower levels of homologous boosting than that generated against strains encountered earlier in life (see Materials and methods). Historical strains were assumed to follow a smooth path through a two-dimensional antigenic space over time [20] (S2 Fig). We fitted this model to 2 publicly available serological datasets in which participants were tested against a panel of A/H3N2 strains. The first contained cross-sectional HI and microneutralisation data for individuals living in the Guangdong province in southern China, collected in 2009 [13, 21, 22]; the second included longitudinal HI data from Ha Nam in Vietnam [23], with sera collected between 2007 and 2012 [10, 24].
Using our serological model, we jointly estimated influenza infection history for each study participant, as well as subsequent antibody response processes and assay measurement variability. Although the contributions of short- and long-term processes to antibody responses cannot be robustly estimated from cross-sectional data [17], simulation studies showed that both timescales were identifiable using a simulated dataset similar to that of the Vietnam samples (S3 and S4 Figs). We therefore included the short-term dynamic antibody processes in the model when fitting longitudinal data but not when fitting to cross-sectional data. The fitted model could reproduce both cross-sectional and longitudinal observed titres for each participant (Fig 1, Table 1), and it was possible to identify specific years with a high probability of infection and the corresponding antibody profile this infection history had generated (S5 and S6 Figs). Using the longitudinal Vietnam data, we could identify specific years in which individuals had a high probability of infection, particularly during the period of testing (Fig 1A–1I). There was more variability in estimates from the cross-sectional HI China data, although time periods with a high probability of infection could still be identified (Fig 1J–1L).
The model fits to longitudinal data described an antibody response to influenza that is initially dominated by a broadly cross-reactive response, which rapidly decays, leaving a long-term response that cross-reacts only with antigenically similar viruses (Table 2, S7–S9 Figs). We estimated that primary infection generated a short-lived boost of an average of 2.69 (95% credibility interval [CrI]: 2.50–2.88) units of log titre against the infecting virus (a 4-fold rise would be equivalent to a 2-unit rise in log titre) and a long-term boost of 2.02 log-titre units (95% CrI: 1.96–2.08). The short-term response decayed quickly: we estimated that the response had reached its final equilibrium level after 1.27 years (95% CrI: 1.19–1.35). The timescale of this short-term response is consistent with previous qualitative estimates based on laboratory-confirmed infections, which suggested there was a negligible change in titre more than 1 year post infection [10, 11, 25].
For the long-term response inferred from longitudinal data, we estimated that cross-reactivity between infecting strain and tested strain dropped off at a rate of 0.26 units of log titre (95% CrI: 0.25–0.27) per unit of antigenic distance between them. The estimated drop was larger than that inferred with the cross-sectional China HI data: log titres decreased by 0.096 (0.07–0.12) with each antigenic unit. This suggests the cross-sectional model may be capturing some of the broadly cross-reactive response, which was explicitly included in the model fitted to longitudinal data. As a sensitivity analysis, we also fitted the cross-sectional model independently to microneutralisation assay titres for the same individuals and test strains in the China study (Table 2). A previous study of these data found high correspondence between HI and microneutralisation titres [22]. Applying our model to these microneutralisation data, we estimated that cross-reactive log titres decreased by 0.18 (0.12–0.23) with each antigenic unit. The specific parameter estimates for boosting and cross-reactivity were different between the HI and microneutralisation assays, but the distribution of estimated number of infections in the population was similar, with a median of 15 infections (95% CrI: 4–30) using the HI data and 14 (2–29) using the microneutralisation data (S10 Fig). Moreover, the estimates for antigenic seniority and observation error were not significantly different between the two assays (Table 2). The estimated error structure of the two assays suggests that for a log titre mid-way between two integer cutoffs (e.g., 1.5), there was a 0.26 (0.25–0.28) probability that the microneutralisation test would return the correct log-titre measurement (i.e., 1) and a 0.23 (0.22–0.25) probability of a correct observation in the HI assay. For the broader short-term response, the model fitted to longitudinal HI data suggested cross-reactive titres decreased by 0.082 (95% CrI: 0.067–0.098) with each unit of antigenic distance. This result suggests that short-term titres are influenced by antigenic distance, albeit weakly, and hence provides quantitative support for previous suggestions that the observed broad short-lived boost is part of a memory B cell response [10].
To illustrate the inferred short- and long-term antibody dynamics against A/H3N2, we used our infection history model to simulate antibody responses following 2 sequential infections, the first in 1968 and the second in 1988 (Fig 2). Following primary infection, individuals would be expected to have raised titres to strains in nearby regions of antigenic space, but these titres would quickly decay to leave a more localised long-term response (S11 Fig). Upon secondary infection, a similar boost in titres would be observed, which would not be present in tests conducted in subsequent years. This highlights the importance of accounting for multiple timescales when analysing immune assay data: in simulations, serology taken in 1988 indicated a rise in titre to the first infecting strain compared to serology between 1969 and 1987 and showed detectable titres against all strains in the region of antigenic space between the two infecting strains (Fig 2F). However, serology taken 1 year later only displayed localised responses against the infecting strains (Fig 2H). Depending on time of sampling, our results suggest it would be possible to observe either longitudinal increases or decreases in log titres against previously seen strains or stable log titres [7].
As well as examining antibody dynamics, we reconstructed historical annual attack rates. In simulation studies, the model could accurately recover attack rates from Vietnam-like serological data, particularly for recent years (Fig 3A). The reduced accuracy of estimation in earlier years reflected the limited coverage of test strains during this period (Fig 3A, inset). Estimates of attack rates based on the traditional gold standard of a 4-fold rise in titre underestimated the actual simulated values (Fig 3B), and an overestimate was obtained if a 2-fold rise in titre was considered instead [18]. This suggests that commonly used metrics could substantially bias estimates of population-level attack rates and hence conclusions about the potential extent of herd immunity and required vaccination coverage. In contrast, estimates from our joint inference framework consistently recovered the true simulated infection dynamics during the period of sampling (Fig 3B, inset). Applying our inference framework to real data from Vietnam to estimate annual attack rates (Fig 3C), we found that estimates were consistent with observed epidemiological dynamics in Vietnam between 2008 and 2012, as measured by the number of influenza A/H3N2 isolates during the testing period (Fig 3D). The correlation between model estimates and observed values was ρ = 0.996 (p < 0.001), with a weaker association when a 2-fold rise (ρ = 0.862, p = 0.14) or 4-fold rise (ρ = 0.799, p = 0.20) was used to estimate attack rates. The model-estimated attack rate for 2002 was significantly larger than surrounding years (Fig 3C). However, this is consistent with the larger clinical attack rates for H3N2 that coincided with the emergence of A/Fujian/411/2002(H3N2)-like influenza strains [26], and an attack rate of 70% would suggest a reproduction number of around 1.8, using a simple epidemic model [27]. Most of the uncertainty in attack rate estimates resulted from individuals with multiple estimated infections; there was little variation in estimated number of infections when individuals had fewer than around 8 median infections (S12 Fig). Based on the median numbers of estimated infections and years at risk, we estimated a median annual risk of infection of around 20% during the period 1968–2012.
Our analysis shows that detailed mechanistic insights can be gained from longitudinal data by jointly considering individual infection histories and antibody dynamics acting at multiple timescales. Building on previous analyses [13,17,28,29], we estimated that nonprimary influenza exposures generate a large, short-lived broad humoural response and a smaller persistent narrow response, with each accumulating and degrading to different degrees over the course of a human lifetime. As well as quantifying processes that shape the antibody response against different influenza strains, our results suggest that accounting for such dynamics leads to improved estimation of population attack rates.
The short-lived broad response, which we estimated makes the largest contribution to titres following infection, is likely to influence selection pressure imposed on the virus as a result of population immunity; it has been suggested that such short-term nonspecific immunity could explain the constrained genetic diversity of circulating influenza viruses [4]. Measuring this ‘dynamic herd immunity’ would have implications for use of serology to investigate the evolutionary dynamics of influenza and hence identify potential vaccine strain candidates [28, 30, 31]. Because we could infer broadly cross-reactive memory responses from HI titres, such responses likely target the influenza HA head domain [32] rather than conserved epitopes in the stalk [7, 33]. This is consistent with studies that have identified such conserved epitopes in the HA head [34, 35]. If a large proportion of a population had recently experienced infection, they may exhibit a short-term antibody response against such epitopes. If these responses provided protective immunity, it would reduce the transmission potential of strains occupying a large region of antigenic space. However, these strains may become more transmissible as the short-term response wanes to leave a more specific long-term response.
Additional insight into the temporal antibody dynamics described in Fig 2 could be generated directly using modern methods of sorting and sequencing individual B cells [36]. During nonprimary infections, existing memory B cells generated during prior infections, which are genetically diverged from germline B cells, are rapidly stimulated (S13 Fig). These B cells may reach high peripheral frequencies rapidly but, on average, have lower avidity against the current strain than they would have had against that host’s previous infections [37]. If a serum sample were tested at this point, it may therefore exhibit a large, broadly cross-reactive response similar to that observed in Fig 2E. However, there will be competing demands on these cell lines to produce antibodies and possibly to differentiate further to increase their avidity. In addition to these memory cells, there is also the potential for the stimulation of germline B cells, which may take longer to achieve functional peripheral frequencies but have higher avidity [38]. When observed early in the infection, these new lineages would be much more similar to germline B cells and would form fewer phylogenetic clades per sorted cell than the rapid response. Later during infection, cells making up the persistent response would be at higher frequencies and be more differentiated but still form only few clades. Antigenic seniority [13] may arise because novel lineages during later life infections have to compete with existing lineages for antigenic stimulation [39, 40]. After infection, the memory frequency of the B cells making up the broad response likely returns to preinfection levels, and the new B cells establish new subordinate memory populations that are less broadly cross-reactive. The aggregate effect of these mechanisms that would be observed in serological samples is consistent conceptually with the results we have presented (S13 Fig).
There are some limitations to our analysis. First, we assumed that the antigenic evolution of influenza results in a sequence of strains that follow a smooth path in a two-dimensional antigenic space (S1 Fig). However, it has been suggested that the multiple epitopes of the influenza HA mean that the antigenic relationship between strains may not be necessarily explained by a gradual accumulation of antigenic distance over time [34, 35, 41]. As we are analysing serological samples taken from individuals without a fully known exposure history, it would be challenging to infer antigenic relationships between historical strains without any a priori assumptions about antigenic space [28]. If we were to try and estimate the antigenic locations of circulating A/H3N2 strains from 1968 to 2008 in a two-dimensional space—rather than assume their locations as we did—it would add 40 × 2 = 80 parameters to the model, which would not be identifiable from the data we used in this study. If we imposed no constrains on the dimension of antigenic space, we would have to estimate up to 40 × 40 = 1,600 pairwise distances between strains. One aim for future research would be to design a cohort study with sufficient information on prior influenza exposures to infer antigenic distances between specific historical strains; the model presented here could then be used to compare the relative explanatory power of different assumptions about the path of antigenic evolution.
Second, we analysed data from 2 types of assay. HI and microneutralisation tests capture 2 different aspects of the antibody response—namely, anti-haemagglutination activity and neutralising effects—and hence, measured titres are not directly comparable [6]. Although there was a high correlation between HI and microneutralisation titres observed in the China data we analysed [22], we fitted models independently to each dataset, meaning that any differences in assay characteristics would be reflected as differences in our parameter estimates (Table 2). With a larger number of repeat serological samples tested using both assays, it would be possible to compare specific aspects of the assay dynamics in more detail.
The modelling approaches we have described could also be employed in evaluating the effectiveness of influenza vaccination strategies, which depends on an ability to reliably infer population attack rates. For example, metrics that systematically overestimate influenza attack rates could result in underpowered studies. Moreover, the broader concept of multiple timescales of antibody response would have potential implications for the design of innovative vaccines, such as highly valent vaccines [10]. If broad responses have shorter durations than narrow responses, then the trade-off between current vaccines and other proposed candidates may be time dependent. Participants in trials of novel influenza vaccines should therefore be followed up over multiple seasons so that the dynamics of their immune response to both vaccination and natural infection can be assessed. At best, such vaccination against influenza A/H3N2 may stimulate a similar response to natural infection. However, there is evidence that vaccine-mediated immunity wanes quickly [42], that vaccine effectiveness declines after multiple immunisations [43], and that broad response against a novel subtype fades after repeated vaccination [44]. With appropriate data on serology and vaccination history, the differences in dynamics between the two processes could be elucidated using the model structure we have presented.
As well as examining differences in vaccination-mediated immunity and antibody response following natural infection, future empirical studies could refine our estimate of the short-term response by collecting serological samples at intervals of less than 1 year. Alternatively, or additionally, having information on timing of confirmed influenza infection between sample collections would make it possible to constrain possible infection events and hence improve estimates of short-term dynamics. In our model, we also accounted for individual-level heterogeneity in titres by including normally distributed error in our observation model. Our results suggest that this error parameter is well identified (S1 Table), but it would be challenging to examine other potential heterogeneity in antibody responses—such as age-specific biases—in more detail with the data available without making strong assumptions about the nature of such heterogeneity.
Our inference approach could be used in the future to guide the design of studies to infer key aspects of antibody dynamics or to estimate historical attack rates. Joint analysis of infection history and antibody dynamics could provide more accurate information about infection rates, particularly in the years preceding sample collection, and inform studies that rely on robust attack rate estimates. As a result, such methods could help ensure that serological studies to examine influenza immunity profiles have adequate statistical power to test hypotheses and identify key mechanistic processes. Our approach is also likely to be applicable to other cross-reactive pathogens, such as dengue fever and Zika viruses [45].
We used 2 publicly available datasets in our analysis. In the southern China data, cross-sectional serology was taken in 2009 from 151 participants in the Guangdong province in southern China and tested using HI and microneutralisation assays against a panel of 9 strains: 6 vaccine strains (A/Hong Kong/1/1968, A/Victoria/3/1975, A/Bangkok/1/1979, A/ Beijing/353/1989, A/Wuhan/359/1995, and A/Fujian/411/2002) and 3 strains that circulated in southern China in recent years preceding the study (A/Shantou/90/2003, A/Shantou/806/2005, and A/Shantou/904/2008) [13, 21]. The Vietnam data included longitudinal serology collected between 2007 and 2012 from 69 participants in Ha Nam [23], with sera tested using HI assays against a panel of up to 57 A/H3N2 strains isolated between 1968 and 2008 [10]. All of the Vietnam participants were unvaccinated against influenza, and 19% of the southern China participants reported prior influenza vaccination. In analysis of both datasets, we represented antibody responses by log titre. For a titre dilution of 10 ≤ D ≤ 1,280, log titre was defined as log2(D10)+1. The minimum detectable titre in both datasets was 10, so a dilution <10 was defined to have a log titre of 0. The maximum observable titre in both datasets was 1,280, which corresponded to a log titre of 8. There were 9 possible observable log titres in our analysis, ranging from 0 to 8. The antigenic summary path used to represent strains in our analysis was generated by fitting a two-dimensional smoothing spline through 81 points representing the published estimated locations of strains in ‘antigenic space’ [10] (S1 Fig). The positions of strains in such a space depends on the distance between influenza antigens and reference antisera as measured by titre in an HI assay [20]. In the model, we assumed that strains circulating between 1968 and 2012 were uniformly distributed along this summary path.
We expanded and refined a previous modelling framework designed for cross-sectional data [17] to include short- and long-term dynamics. For an individual who had previously been infected with strains in the set X, the expected log titre against strain j depended on 5 specific antibody processes:
To combine the 5 processes in the model, we assumed that the expected log titre individual i had against a strain j was a linear combination of the responses from each prior infection:
λij=∑m∈Xs(X,m)[ μ1d1(j,m)+μ2w(m)d2(j,m) ].
(1)
Depending on parameter values, our model could incorporate several specific mechanistic features, including long-term response only (μ2 = 0), waning response only (μ1 = 0), or long-term/short-term boosting independent of a cross-reactive memory response (σ1, σ2 = 0).
For an individual i who was infected with strains in the set X, we assumed their true titre against strain j titre followed a normal distribution with mean λij, standard deviation ε, and cumulative distribution function f(x). The observed distribution of titres was censored to account for integer-valued cutoffs. The likelihood of observing titre k ∈ {0,…,8} given history X and parameter set θ was therefore as follows:
L(k|θ,X)={f(x<1)ifk=0;f(k≤x<k+1)if1≤k<8.f(x≥8)ifk≥8;
(2)
Note that there are several key differences between the model framework presented here and the one we described previously [17]. These changes were designed to increase model flexibility and biological detail: infections occur annually, rather than within antigenic epochs; we assume waning and cross-reaction decays linearly with log titre, rather than exponentially; the observation model explicitly accounts for censoring, rather than using a discrete observation distribution; and strains are explicitly located in an antigenic space, rather than distance represented temporally. It is therefore not possible to directly compare parameter estimates from this model with the previous framework, because the values need to be interpreted in the context of the specific assumptions in the underlying models.
We fitted the model independently to each serological dataset using Markov chain Monte Carlo (MCMC). Using the likelihood function in Eq 2, we jointly estimated θ across all individuals and estimated X for each individual via a Metropolis-Hastings algorithm. We used uniform positive priors for all θ parameters, with ω constrained to be in the interval [0,1), as it would not be identifiable on timescales of less than a year, given annual sample collection. If individual sera were collected in more than 1 year, parameters were jointly estimated across all test years. We used a data augmentation approach to estimate individual infection histories. Every second iteration, we resampled model parameters, which were shared across all individuals, and performed a single Metropolis-Hastings acceptance step. On the other iterations, we resampled infection histories for a randomly selected 50% of individuals. These histories were independent across individuals, so we performed a Metropolis-Hastings acceptance step for each individual separately.
To ensure the Markov chain was irreducible, resampling at each step involved one of the following: addition of infection in some year, removal of infection in some year, or moving an infection from some year to another [46]. We also used adaptive MCMC to improve the efficiency of mixing: at each iteration, we adjusted the magnitude of the covariance matrix used to resample θ to obtain an acceptance rate of 0.234 [47]. As we had data on participants’ individual ages in the southern China data, we constrained potential infections in the model to years in which participants would have been alive. To estimate the median and 95% credible interval for attack rates, we sampled from the posterior distribution of infection histories and calculated the total participants who were estimated to have been infected in each year at each iteration. The resulting attack rates were therefore implicitly binomially distributed. The model was implemented in R version 3.3.1 and C and used the Rcpp and doMC packages. Source code and data are available at https://github.com/adamkucharski/flu-model/.
Correlation plots indicated that all parameters in the full model were identifiable (S14 Fig), with ESS above 200 (S1 Table). We included both short- and long-term dynamics when fitting to longitudinal data, because the model that included short-term antibody dynamics performed substantially better than the model with long-term dynamics only. As the model with long-term-only dynamics was a nested version of the model with both short-term and long-term dynamics (i.e., with μ2 = 0), we used the Savage-Dickey density ratio (SDDR) to approximate the Bayes factor [48]. The prior for μ2 was flat, and the posterior density for μ2 did not include 0 (S9 Fig), which meant the SDDR was 0. This indicated overwhelming support for the more complex model. The more complex model also required a lower variance in the observation model distribution to fit the data: the estimated error term, ε, was 1.29 (95% CrI: 1.27–1.31) when the model with short-term dynamics was fitted to longitudinal data and 1.39 (1.37–1.41) when short-term dynamics were omitted (S9 and S15 Figs). We also compared model performance using a training/test approach. We fitted the models with and without short-term dynamics to the same training dataset, constructed by randomly selecting 90% of the titre results available for each participant in the Vietnam dataset. We then used these fitted models to predict titres in a test dataset, which consisted of the remaining 10% of titres. The mean absolute error between observed and median predicted titres in the test dataset was 1.18 for the model with short-term response and 1.23 for the model without; the root-mean-square error (RMSE) was 1.54 for the short-term response model and 1.61 for the simpler model. However, it is worth noting that inclusion of a short-term response would not have been expected to improve predictions for all participants and strains: short-term dynamics only influence titres for participants who have evidence of infection shortly prior to sample collection, and titres against recently isolated strains will be more influenced by the short-term response than titres against older strains.
In our simulation study, we first generated simulated influenza attack rates between 1969 and 2012 using a lognormal distribution with mean 0.15 and standard deviation 0.5. For 1968, we used a lognormal distribution with mean 0.5 to reflect higher incidence in the pandemic year [49]. Using these simulated attack rates, we generated individual infection histories for 69 participants using a binomial distribution and then generated observed individual-level titres against the same strains as in the Vietnam dataset using our titre model. Simulated samples were tested each year between 2007 and 2012. Based on the parameters estimated using real data, we assumed μ1 = μ2 = 2, τ = 0.05, ω = 0.75, σ1 = 0.2, σ2 = 0.1, and ε = 1 in simulations. Finally, we used the model to reestimate infection history and attack rates. For Fig 3B inset, we simulated 12 independent sets of observed titres and then inferred the proportion of the population infected in the 4 years between 2008 and 2011 inclusive. The resulting distribution of model residuals (i.e., estimated minus actual simulated value) for these 48 data points was plotted as kernel density plots.
Reported influenza A/H3N2 activity in Vietnam was obtained from the WHO FluNet database [50] (S16 Fig). We aggregated reports into temporal windows based on dates of serological sample collection [10] and used the cumulative number of isolates in each period to compare observed activity with model estimates. To calculate attack rates from the model outputs, we scaled the posterior distribution of total number of infections across all participants for each year between 1968 and 2012 by the proportion of participants who were alive in that year, which we calculated based on the age distribution of participants. This produced the estimates in Fig 3C and 3D.
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10.1371/journal.ppat.1000998 | Extreme CD8 T Cell Requirements for Anti-Malarial Liver-Stage Immunity following Immunization with Radiation Attenuated Sporozoites | Radiation-attenuated Plasmodium sporozoites (RAS) are the only vaccine shown to induce sterilizing protection against malaria in both humans and rodents. Importantly, these “whole-parasite” vaccines are currently under evaluation in human clinical trials. Studies with inbred mice reveal that RAS-induced CD8 T cells targeting liver-stage parasites are critical for protection. However, the paucity of defined T cell epitopes for these parasites has precluded precise understanding of the specific characteristics of RAS-induced protective CD8 T cell responses. Thus, it is not known whether quantitative or qualitative differences in RAS-induced CD8 T cell responses underlie the relative resistance or susceptibility of immune inbred mice to sporozoite challenge. Moreover, whether extraordinarily large CD8 T cell responses are generated and required for protection following RAS immunization, as has been described for CD8 T cell responses following single-antigen subunit vaccination, remains unknown. Here, we used surrogate T cell activation markers to identify and track whole-parasite, RAS-vaccine-induced effector and memory CD8 T cell responses. Our data show that the differential susceptibility of RAS-immune inbred mouse strains to Plasmodium berghei or P. yoelii sporozoite challenge does not result from host- or parasite-specific decreases in the CD8 T cell response. Moreover, the surrogate activation marker approach allowed us for the first time to evaluate CD8 T cell responses and protective immunity following RAS-immunization in outbred hosts. Importantly, we show that compared to a protective subunit vaccine that elicits a CD8 T cell response to a single epitope, diversifying the targeted antigens through whole-parasite RAS immunization only minimally, if at all, reduced the numerical requirements for memory CD8 T cell-mediated protection. Thus, our studies reveal that extremely high frequencies of RAS-induced memory CD8 T cells are required, but may not suffice, for sterilizing anti-Plasmodial immunity. These data provide new insights into protective CD8 T cell responses elicited by RAS-immunization in genetically diverse hosts, information with relevance to developing attenuated whole-parasite vaccines.
| Plasmodium infections are a global health crisis resulting in ∼300 million cases of malaria each year and ∼1 million deaths. Radiation-attenuated Plasmodium sporozoites (RAS) are the only vaccines that induce sterilizing anti-malarial immunity in humans. Importantly, “whole parasite” anti-malarial RAS vaccines are currently under evaluation in clinical trials. In rodents, RAS-induced protection is largely mediated by CD8 T cells. However, the quantitative and qualitative characteristics of RAS-induced protective CD8 T cell responses are unknown. Here, we used surrogate markers of T cell activation to reveal the magnitude and kinetics of Plasmodium-specific CD8 T cell responses following RAS-immunization in both inbred and outbred mice. Our data show that, independent of host genetic background, extremely large memory CD8 T cell responses were required, but not always sufficient for sterilizing protection. These data have broad implications for evaluating total T cell responses to attenuated pathogen-vaccines and direct relevance for efforts to translate attenuated whole-Plasmodium vaccines to humans.
| Plasmodium infections are a global health crisis resulting in ∼300 million cases of malaria each year and ∼1 million deaths [1], [2], [3], [4], [5]. At present, there are no effective licensed anti-malarial vaccines. Most vaccines under clinical evaluation are only partially protective and, for unknown reasons, immunity rapidly wanes [6]. Thus, development of an effective malaria vaccine that provides long-term protection remains an important goal to improve global health.
Immunization with radiation-attenuated sporozoites (RAS) is the only documented means to induce sterilizing protection in both humans [7], [8] and rodents [9] and, importantly this approach is under evaluation in clinical trials [10]. Studies with inbred mouse strains reveal a prominent and often essential role for CD8 T cells in RAS-induced protection [11]. However, RAS-immune inbred mice also exhibit substantial differences in resistance to challenge with Plasmodium berghei (Pb) or P. yoelii (Py) sporozoites, two major models of experimental malaria that are thought to differ in virulence. Despite decades of research, the precise characteristics of protective memory CD8 T responses following RAS-vaccination remain poorly understood. One reason for this relates to the limited number of defined CD8 T cell epitopes derived from rodent species of Plasmodia.
BALB/c mice mount H-2Kd-restricted CD8 T cell responses against single defined circumsporozoite (CS) protein-derived epitopes from either Pb or Py and these epitopes can be targets of protective CD8 T cells [12], [13]. However, despite evidence that non-CS antigens can also be targets of protective immunity [14], [15], there are few additional Plasmodium-specific epitopes identified from antigens other than CS in BALB/c mice, and no identified protective epitopes in H-2b C57BL/6 (B6) mice. Thus, the paucity of epitope information for these parasites has contributed to our incomplete understanding of the specific quantitative and qualitative characteristics of RAS-induced CD8 T cell responses in inbred mice that are relatively easy (BALB/c) or difficult (B6) to protect against Plasmodium sporozoite challenge [11]. Moreover, we recently showed that the threshold of memory CD8 T cell responses to the Pb-CS epitope (monospecific responses) required for sterilizing immunity against sporozoite challenge was extremely large [16]. Importantly, it is unknown whether a more diverse memory CD8 T cell response generated by whole parasite based RAS vaccination will decrease the threshold number of memory cells required for protection. This issue is of great relevance to translation of the attenuated whole parasite vaccines to humans.
The identification and characterization of infection- or vaccination-induced, antigen-specific CD8 T cell populations has historically required defined antigenic peptide determinants with known MHC restriction. However, specific activation markers can be used to track effector, but not memory, CD8 T cell responses to viral vaccines in humans in the absence of defined antigenic determinants or known MHC-restriction [17]. We recently described an alternative surrogate actiation marker approach, relying on concurrent downregulation of surface CD8α and upregulation of CD11a (α-chain of LFA-1) on effector and memory antigen-specific CD8 T cells responding to bacterial and viral-infections in mice [18]. Herein, we apply this surrogate marker approach to identify and longitudinally track the total CD8 T cell response following RAS-immunization in rodents. This surrogate marker approach allowed us for the first time to evaluate CD8 T cell responses and protective immunity to RAS-immunization in both inbred and outbred hosts. Collectively, our data show that despite broadening the number of antigenic targets through whole-parasite vaccination, extraordinarily large numbers of memory CD8 T cells are required, but not always sufficient, to protect the host against liver-stage Plasmodium infection. These data provide fundamentally new insight into protective CD8 T cell responses elicited by RAS-immunization in genetically diverse hosts, information with relevance to developing attenuated whole-parasite vaccines to protect humans.
Relative resistance after RAS-vaccination of both rodents and humans is commonly studied by sporozoite challenge 1–2 weeks following the last immunization [8], [11], [19], [20] and thus evaluates immunity mediated by recently stimulated T cell populations. Herein, we wished to examine RAS-induced protection only after stable memory immune responses have been generated. Thus, we challenged RAS-vaccinated mice >60 days post-immunization, when numerically and phenotypically stable memory CD8 T cell populations are established following acute infections [21]. At this memory time point, a single Pb-RAS vaccination protected 100% of BALB/c mice, but failed to protect any B6 mice against homologous Pb sporozoite challenge, whereas one Py-RAS vaccination had minimal (BALB/c, 10%) or no (B6, 0%) protective efficacy against homologous Py sporozoite challenge (Figure 1A). These data demonstrate both mouse strain and Plasmodium species-dependent protection after single RAS-immunization of mice challenged at a bona fide memory time point.
To examine the protective CD8 T cell response elicited by RAS-vaccination, we applied our recently described surrogate activation marker approach, based on downregulation of CD8α and upregulation of CD11a (CD8αloCD11ahi) [18], to identify RAS-induced CD8 T cells. We chose to focus our initial analyses on peripheral blood (PBL) so that individual mice could be analyzed longitudinally. Importantly, long-term longitudinal analyses of naïve mice in our colony reveal that the circulating CD8αloCD11ahi T cell pool remains low (2–3% of all circulating CD8 T cells) and stable for >250 days (data not shown). For vaccinated mice, the fraction of CD8αloCD11ahi T cells in the PBL was determined prior to, and at various intervals after, immunization with 2×104 Pb-RAS in individual animals. We detected substantial increases in the frequency of CD8αloCD11ahi T cells in the blood of vaccinated mice at 7 and 61 days (effector and memory time points, respectively) post-immunization (Figure 1B, left column as an example). Interestingly, only 16±3% of Pb-RAS-induced effector (day 7) CD8 T cells in BALB/c mice are specific for the known H-2Kd-restricted CS252–260 epitope and, importantly, all of these defined antigen-specific CD8 T cells are found in the CD8αloCD11ahi population (Figure 1B, right columns). Moreover, the fraction of CS252–260-specific CD8 T cells among the CD8αloCD11ahi population consistently remains ∼16% throughout the memory phase of the response (day 61) (Figure 1B, right columns). Based on previous studies showing that T cell responses against diverse epitopes are coordinately regulated [22], these data further support that the surrogate activation marker approach identifies true RAS-induced, Plasmodium-specific CD8 T cells. Thus, ∼85% of Pb-RAS-induced CD8 T cells in BALB/c mice are reactive against epitopes from undefined antigens. Similar results were obtained for the CS280–288 epitope after single immunization of BALB/c mice with Py-RAS, although the fraction of CS280–288-specific memory CD8 T cells in the circulating CD8αloCD11ahi compartment was only ∼7% (data not shown).
To further demonstrate specificity of the surrogate activation marker approach, we determined that the increase over baseline (PBL analyzed before immunization) in the fraction of circulating CD8αloCD11ahi T cells 5–7 days after immunization depended on the immunizing dose of Pb-RAS in both BALB/c and B6 mice (Figure 1C and E) and was not observed in mice immunized with an equivalent suspension of irradiated salivary gland homogenates from non-infected mosquitoes (Figure 1D and F). Thus, the CD8αloCD11ahi T cell response is specific for Plasmodium-antigens and not mosquito salivary gland antigens. Moreover, CD8 T cell responses in the blood of RAS-immune B6 and BALB/c mice were representative of CD8αloCD11ahi responses in the spleen and liver, both in terms of frequency (Figure 2A) and total number (Figure 2B). Finally, we addressed specificity at the memory stage by transferring sort purified CD8αhiCD11alo (naïve) or CD8αloCD11ahi (memory) cells from day 78 RAS-immune B6 mice (CD45.2) into CD45.1 hosts. Only the population of transferred CD8αloCD11ahi T cells underwent secondary expansion after RAS-immunization of the recipient mice (Figure S1). Thus, the CD8αloCD11ahi phenotype cells present at memory time points after RAS-immunization are Plasmodium-specific (Figure S1). As a composite, these data demonstrate that the changes in frequency of circulating CD8αloCD11ahi T cells in individual RAS-immunized mice reflects the distribution of parasite-specific effector and memory CD8 T cells in peripheral tissues and can be used to evaluate the total CD8 T cell response to RAS-immunization prior to sporozoite challenge.
We next examined the magnitude and kinetics of total CD8 T cell responses in the PBL of BALB/c and B6 mice following Pb- or Py-RAS vaccination (Figure 3A and B, respectively), using an immunizing dose of RAS (2×104 sporozoites) that fell within the linear range of the CD8 T cell response in both inbred mouse strains (Figure 1C,E). We observed substantial increases in the frequency of CD8αloCD11ahi T cells in the PBL of all groups, which peaked 6 days after RAS-immunization, followed by contraction and the formation of numerically stable primary (1°) memory populations (Figure 3A,B). Importantly, although B6 mice are more susceptible to sporozoite challenge following a single Pb- or Py-RAS immunization compared to BALB/c mice [11] (Figure 1A), and CD8 T cells are necessary to mediate protection in Pb-RAS immune mice (Figure 3C), Pb- or Py-RAS vaccination of B6 mice induced 1° effector and memory CD8 T cell responses that were ∼2-fold higher (p<0.0001) than observed in BALB/c mice (Figure 3A,B). Thus, our surrogate activation marker approach revealed that the susceptibility of single RAS-immunized B6 mice to homologous Pb or Py challenge is not due to a diminished total anti-Plasmodial CD8 T cell response.
RAS remain infectious to host hepatocytes, but are unable to undergo differentiation into blood stage merozoites [23], [24]. Interestingly, persistence (up to 6 months) of radiation-attenuated parasites was reported in the livers of RAS-vaccinated rats [25] and persistence of attenuated parasites has been hypothesized to underlie the long-term protective capacity of RAS-induced memory CD8 T cells [25], [26]. To address this hypothesis, we treated BALB/c mice with 60 mg/kg primaquine on days 5 and 6 following Pb-RAS-vaccination to eliminate persisting parasites. In contrast to previous studies [25], [26], we found that primaquine treatment did not decrease protection against sporozoite challenge at a memory time point (Figure 3E). Consistent with this result, primaquine treatment at these time points did not reduce RAS-specific circulating CD8 T cell frequencies (Figure 3D,E). In parallel, we verified the efficacy of primaquine (route, dose, schedule) by treating naive BALB/c mice 24 and 48 hrs following challenge with 1000 infectious sporozoites. Primaquine treatment effectively stopped the development of blood stage infection in 100% (5/5) mice, whereas 5/5 vehicle-treated mice developed patent blood stage parasitemia. Thus, following the induction of CD8 T cell responses via Pb-RAS-vaccination of BALB/c mice, the persistence of attenuated parasites in the liver does not regulate the stability or protective capacity of the RAS-induced memory CD8 T cell populations.
Short-interval (every 2–3 weeks) booster RAS-immunizations improve protection against sporozoite challenge of mice [11], [27], [28] although the impact on the Plasmodium-specific CD8 T cell compartment is unknown. Additionally, the impact of long-interval boosting, as generally employed in human vaccines, on RAS-induced protection at a secondary memory time point is unknown. Thus, we examined the effect of homologous RAS-boosting on bona fide memory CD8 T cell populations. Booster immunization at memory time points (60–80 days after initial priming) with Pb-RAS in B6 mice or Py-RAS in BALB/c mice induced secondary expansion of CD8αloCD11ahi T cells (Figure 4A,C). Surprisingly, the peak secondary response did not exceed the magnitude of the peak primary response to initial priming. Still, booster immunization resulted in a doubling of the secondary (2°) memory CD8 T cell populations in both mouse strains (Figure 4B,D). Importantly, we observed 100% protection in Pb-RAS-vaccinated B6 mice and Py-RAS-vaccinated BALB/c mice following sporozoite challenge at 2° memory time points after boosting (days 168 and 154, respectively) (Figure 4B,D), which remained wholly CD8 T cell-dependent (Figure S2). Of note, homologous Pb- or Py-RAS-boosting enriched the fraction of CS252–260- or CS280–288-specific CD8 T cells (to ∼30% and 15%, respectively) within the CD8αloCD11ahi compartment compared to single immunized BALB/c mice (Figure 1B and Figure S3). Importantly, these secondary CS252–260- or CS280–288-specific memory CD8 T cells are also found exclusively in the CD8αloCD11ahi compartment (Figure S3). Thus, homologous Pb-RAS or Py-RAS boosting of B6 or BALB/c mice, respectively, doubles the frequency of circulating RAS-specific 2° memory CD8 T cells and affords CD8 T cell-dependent sterilizing immunity against a stringent sporozoite challenge. Moreover, enrichment of the CS-specific responses in RAS-boosted BALB/c mice suggests that although ∼85–95% of the total initial CD8 T cell response targets antigens of undefined specificity, the CS-specific response in BALB/c mice dominates the recall response. These data for the first time reveal the effect of homologous RAS boosting on bona fide memory CD8 T cell responses, and further demonstrate that antigen-specific 2° memory CD8 T cell populations are also accurately identified using the CD8αloCD11ahi surrogate activation marker approach.
Consistent with the results described above, homologous boosting of Py-RAS immune B6 mice also doubled (on average) the frequency of RAS-induced 2° memory CD8 T cells (Figure 4E,F). However, these mice exhibited only modest (40%) protection against sporozoite challenge at a 2° memory time point (day 154) (Figure 4F). Interestingly, a second booster immunization with Py-RAS resulted in a sustained increase in the frequency of CD8αloCD11ahi T cells, which now represented on average ∼40% of the CD8 T cell compartment of the PBL at day 215 (Figure 4E,F). However, even this extreme commitment of Py-RAS-induced tertiary (3°) memory CD8 T cells did not improve protection when these mice were challenged at a 3° memory time point (day 215) (Figure 4F). Thus, we could not achieve substantial levels (>70–80%) of protection against Py sporozoite challenge in B6 mice boosted every 60–70 days and challenged 60 days after the last boost. This contrasts sharply with reports that examine protective immunity following short interval boosting (every 2–3 weeks) followed by challenge ∼14 days after the last boost [11], [29]. One clear difference between these two immunization regimens is the substantial role for CD4 T cells in protection after short-interval boost and challenge approaches in B6 mice [11], [29], whereas we could detect no role for CD4 T cells in protection against Pb sporozoite challenge of B6 mice, or against Py challenge of BALB/c mice in our long-interval prime-boost approach (Figure S2). These disparate results strongly suggest that the timing of RAS-immunization and sporozoite challenge significantly influences both the composition and protective capacity of the RAS-induced cellular response. Indeed, we are currently evaluating quantitative and qualitative characteristics of the total CD8 T cell response and protection following short-interval, prime-boost RAS vaccination and challenge, as well as evaluating surrogate activation marker approaches to specifically identify antigen-experienced CD4 T cells.
We show that BALB/c and B6 mice fall on opposite ends of the spectrum regarding their ability to resist sporozoite challenges at memory time points following either Pb- or Py-RAS long-interval prime-boost vaccination. Indeed, many studies [14], [30], [31], [32], [33], [34], [35], [36], [37] employ BALB/c mice to evaluate whole-attenuated parasite vaccine-induced protective immunity, and it is unclear how these data model CD8 T cell responsiveness and protective immunity in outbred populations, such as humans, following RAS-vaccination. Thus, we next turned our attention toward analyses of the CD8 T cell response in outbred Swiss Webster mice. Due to the lack of information on MHC alleles and antigens in outbred populations, this analysis was only made possible through development of the CD8αloCD11ahi surrogate activation marker approach [18]. On the population level (N = 30 mice), the kinetics and magnitude of Py-RAS-induced CD8 T cell responses of outbred mice mirrored those observed in inbred mice (Figure 5A). However, and in striking contrast to the inbred mice, the initial CD8 T cell response following Py-RAS-vaccination in outbred mice was not uniform and varied widely, both in magnitude and day of the peak (Figure 5C–F). Consistent with this, outbred mice also exhibited more variability in the magnitude of the 1° memory (Figure 5G) and 2° memory (Figure 5H) CD8 T cell response, compared to inbred mice. Similar to what was observed in both BALB/c and B6 mice singly vaccinated with Py-RAS, Swiss Webster mice challenged with Py sporozoites at a 1° memory time point (day 79) were not efficiently protected (Figure 5B). However, boosting Swiss Webster mice with Py-RAS resulted in a doubling (on average) of sporozoite-specific 2° memory CD8 T cells, and 80% of these mice were protected against a sporozoite challenge on day 154 (Figure 5B). Thus, CD8αloCD11ahi surrogate markers can be used to identify and longitudinally track protective CD8 T cell responses in outbred mice following RAS prime-boost vaccination. In addition, these data show that despite the variability in magnitude of initial RAS-induced CD8 T cell response of outbred hosts, homologous boosting increases the secondary memory CD8 T cell population and protective immunity against sporozoite challenge.
While protection of RAS-vaccinated mice using the long-interval (>60 days), prime-boost scenario described above is CD8 T cell dependent (Figure S2), RAS-immunization also elicits a strong sporozoite-specific antibody response [29], [38]. To determine whether differences in the Py-RAS-induced sporozoite-specific antibody response correlated with relative resistance or susceptibility to Py sporozoite challenge, we analyzed serum from individual BALB/c, B6 and Swiss Webster mice for sporozoite-specific IgG titers at each memory time-point. Importantly, an examination of anti-sporozoite titers at the secondary memory time point (day 154), where significant protection was achieved in BALB/c and Swiss Webster but not B6 mice (Figures 4D and 5B, respectively), revealed no clear correlation between IgG titers and protection from sporozoite challenge (Figure S4A). Moreover, antibody titer was not significantly different between 2° memory BALB/c mice (100% protected) and 3° memory B6 mice (20% protected) (P = 0.0789, Figure S4B), or between individual protected and non-protected B6 mice (P = 0.4484, Figure S4C). Thus, reduced anti-sporozoite IgG antibody titers do not appear to explain the enhanced susceptibility of RAS-vaccinated B6 mice to sporozoite challenge.
We next examined potential qualitative differences in phenotype and specific functional attributes of protective and non-protective memory CD8 T cells. The nature of our longitudinal analyses precluded the collection of large quantities of blood from individual immunized mice. Thus, the small clinical sample limited our initial analyses to a key subset of markers that distinguish “central memory” (TCM; CD62Lhi, CD27hi) from “effector memory” (TEM; CD62Llo, CD27lo) CD8 T cell populations [39], [40], and a marker associated with memory CD8 T cell survival (IL7 receptor α chain, CD127) [41]. At 60 days post-immunization most RAS-induced memory CD8 T cells in each mouse strain, vaccinated with either Pb- or Py-RAS, expressed a TEM phenotype (CD62Llo, CD27lo, CD127lo, data not shown). However, to more directly address potential relationships between RAS-induced memory CD8 T cell phenotype and protection we evaluated expression of the same markers after Py-RAS boosting of BALB/c and Swiss Webster mice (both protected) and B6 mice (not protected). The most striking difference between non-protective 2° memory CD8 T cells in B6 mice and protective memory CD8 T cells in BALB/c and Swiss Webster mice was the differential expression of CD62L (Figure 6A). Forty percent (on average) of Py-RAS-induced 2° memory cells in B6 mice expressed the CD62Lhi TCM phenotype (Figure 6A). In contrast, representation of the CD62Lhi TCM phenotype among Py-RAS-induced 2° memory cells in BALB/c and Swiss Webster mice was reduced 3-fold or 4-fold, respectively (Figure 6A). A similar trend was observed for the CD27hi phenotype (Figure 6B), while no correlation between CD127 expression and protective capacity was observed (Figure 6C). Thus, non-protective 2° memory CD8 T cells in Py-RAS boosted B6 mice exhibit a more TCM phenotype, expressing significantly higher levels of CD62L and CD27 relative to protective 2° memory CD8 T cells in BALB/c or Swiss Webster mice (Figure 6A,B).
As a complimentary approach, we next performed a series of adoptive transfer studies in order to more clearly identify and directly compare RAS-induced 2° memory CD8 T cells in BALB/c and B6 mice. We transferred 8×104 Py-RAS-induced, CD8αloCD11ahi 1° memory (d78) CD8 T cells into allelically disparate BALB/c and B6 recipients, which were subsequently immunized with Py-RAS to generate populations of endogenous 1° memory and allelically marked 2° memory parasite-specific CD8 T cells. One month after the booster immunization, we performed extensive phenotypic and functional analyses of the donor-derived, 2° memory CD8 T cells in each B6 and BALB/c recipient mouse. Surface expression of many markers, such as CD25, CD69, CD43glyco (Figure 6D and Figure S5) were indistinguishable between these populations. In addition, we found no statistically significant differences in expression of integrins (β1, β2, β7, αM, αX, αE, α4, α5 or α6) or inhibitory receptors (PD-1, LAG-3, 2B4, CD160, KLRG-1 or CTLA-4) on RAS-induced, 2° memory CD8 T cells in B6 and BALB/c mice (data not shown). However, in line with our initial observations, RAS-induced, non-protective 2° memory CD8 T cells in B6 mice exhibit a more TCM-like phenotype, relative to BALB/c mice, characterized by relatively higher proportions of CD27 and CD62L expressing cells (Figure 6D and Figure S5). We also observed significantly higher CD122 and CD127 expression on RAS-induced, 2° memory CD8 T cells in B6 mice (Figure 6D and Figure S5). Collectively, our phenotypic analyses support the notion that a TEM phenotype among RAS-induced, parasite-specific CD8 T cells strongly correlates with protection against liver stage Plasmodium infection.
To examine specific functional attributes of RAS-induced memory CD8 T cells in BALB/c and B6 mice we relied on polyclonal TCR cross-linking to trigger the ex vivo induction of Granzyme B and inflammatory cytokine expression by allelically marked, 2° memory CD8 T cells. We found that similar fractions of BALB/c and B6 2° memory CD8 T cells expressed Granzyme B in response to dose-titrations of plate-bound anti-CD3ε (Figure 6E). Moreover, we observed equivalent IFN-γ production (% positive and MFI) by RAS-induced CD8αloCD11ahi 2° memory CD8 T cells in BALB/c and B6 mice (data not shown). Interestingly, however, a significantly higher fraction of B6 2° memory CD8 T cells co-expressed TNF-α and IFN-γ relative to BALB/c 2° memory CD8 T cells, the majority of which expressed IFN-γ alone (Figure 6G). Collectively, these data show that a TEM phenotype, but neither Granzyme B nor polyfunctional cytokine expression, correlates with protective anti-Plasmodial liver stage immunity mediated by RAS-induced memory CD8 T cells.
We previously reported that the numerical threshold for protection of BALB/c mice against Pb-sporozoite challenge mediated solely by memory CS252–260-specific CD8 T cells is exceedingly high (>1% of PBL (refs [16], [42]), or >8% of CD8 T cells, Figure 7). One explanation for the enormously high threshold for sterilizing protection in that scenario is that protective memory CD8 T cells recognize only a single antigenic determinant from P. berghei. On the other hand, RAS-vaccination has been shown to elicit protective CD8 T cells targeting non-CS antigens [14], [15] and our data are consistent with the majority of RAS-induced CD8 T cells targeting non-CS antigens (Figure 1B and Figure S3). Thus, broadening the number of antigens (i.e. additional parasite-derived proteins that may be more efficiently processed or presented compared to CS) through whole parasite RAS-vaccination may lower the numerical requirements for protective immunity mediated by memory CD8 T cells. However, when we tabulated memory CD8 T cell responses in groups of RAS-vaccinated inbred and outbred mice that resisted sporozoite challenge, we identified similarly extreme numerical relationships between circulating memory CD8 T cell responses and protective anti-sporozoite immunity (Table 1). This is most evident for RAS-induced protective immunity against P. yoelii, which replicates faster in the liver [43] and exhibits a lower ID50 [32] compared to P. berghei, and thus, may better mimic the virulence of P. falciparum in humans. For example, anti-Py memory CD8 T cell responses representing ∼9% of CD8 T cells in BALB/c mice and ∼19% of CD8 T cells in Swiss Webster mice confer protection against a Py-sporozoite challenge, and responses exceeding 40% of CD8 T cells failed to efficiently protect B6 mice. Thus, the numerical requirements for sterilizing immunity following Plasmodium RAS-vaccination are extraordinarily high, regardless of whether the protective pool of memory CD8 T cells react to a single antigenic determinant after subunit vaccination, or whether the CD8 T cell response is directed against a broader set of antigenic determinants after whole-parasite immunization.
Although a critical protective role for CD8 T cells in RAS-immune mice was established more than 25 years ago, the characteristics of the protective CD8 T cell response remained essentially undefined due to the lack of defined Plasmodium epitopes. Here, we used surrogate activation markers to identify and longitudinally track RAS-induced CD8 T cell populations in the blood of individual hosts. This approach enabled us to describe specific quantitative and qualitative characteristics of memory CD8 T cell populations that mediate protection against sporozoite challenge. Moreover, the surrogate activation marker approach allowed us to monitor RAS vaccine-induced CD8 T cell responses in individuals within outbred populations of mice, without a priori knowledge of MHC alleles or parasite-specific antigenic determinants. These latter analyses revealed that, despite variability to the initial immunization, prime-boost RAS-vaccination effectively enhances parasite-specific memory CD8 T cell responses and affords sterilizing protective immunity among individuals of an outbred population. Collectively our studies show that independent of genetic background, extremely high frequencies of RAS-induced memory CD8 T cells are required, but may not always suffice for sterilizing anti-Plasmodial immunity, information directly relevant to ongoing efforts to translate attenuated whole-malaria parasite vaccines to humans.
We previously reported that an extraordinarily large frequency of circulating CS-specific memory CD8 T cells generated by subunit vaccination is required to protect BALB/c mice against a stringent Pb sporozoite challenge [16]. In that study, memory CD8 T cell populations were generated such that they only targeted a single antigenic determinant derived from the parasite CS protein, CS252–260. Herein we report the striking observation that diversifying the targets of the CD8 T cell response through whole-attenuated-parasite vaccination only minimally (if at all) reduces the numerical requirements for memory CD8 T cell-mediated protective immunity. For example, we show that following Pb-RAS vaccination of BALB/c mice (the scenario in which protection is easiest to achieve) resistance to sporozoite challenge at a memory time point is associated with ≥4% of the CD8 T cell compartment exhibiting the antigen-experienced phenotype (CD8αloCD11ahi). Thus, the magnitude of the RAS-induced, poly-specific memory CD8 T cell response associated with protection against P. berghei challenge is only ∼2-fold lower than the mono- (CS252–260)-specific memory CD8 T cell response (∼8% of the CD8 T cell compartment). Additionally, protection against Pb is associated with even larger memory CD8 T cell responses in B6 and outbred Swiss Webster mice (11% and 12%, respectively). Further, resistance to P. yoelii has even more extreme requirements, with protection associated with RAS-induced memory CD8 T cell responses exceeding ∼9 or ∼19% of the CD8 T cell compartment in BALB/c and Swiss Webster mice, respectively. Thus, our data demonstrate that poly-specific memory CD8 T cell-mediated sterilizing immunity to sporozoite challenge, regardless of the relative virulence of the Plasmodium species, requires commitment of a substantial fraction of the entire CD8 T cell compartment.
This extreme numerical requirement is perhaps not surprising given the extraordinarily low ratio of Plasmodium-infected cells to total hepatocytes in the mammalian host following challenge with physiological numbers of sporozoites (∼1000). Indeed, the gold standard readout for protection against sporozoite challenge is sterilizing immunity, or the prevention of blood stage infection. From a conservative perspective, this level of protection requires that each of a maximum of 1000 infected hepatocytes (among >108 or >1011 total hepatocytes in the mouse or human liver, respectively) be targeted through direct CTL activity or indirectly via the release of cytokines by parasite-specific memory CD8 T cells in order to prevent the development of blood stage infection. Thus, each RAS-induced memory CD8 T cell must surveil an extremely large number of hepatocytes in order to identify all cells that harbor parasites. The exceedingly low number of infected cells among the whole liver (needle in the haystack [16]), coupled with the fact that every single infected cell must be successfully targeted to prevent blood stage infection, is the likely explanation for why the numerical requirements for memory CD8 T cell-mediated, anti-Plasmodial liver stage immunity are so high.
While it is unclear why commitment of nearly 40% of the CD8 T cell compartment to the anti-Plasmodial memory CD8 T cell response is insufficient to effectively protect B6 mice, our studies extend the literature [11], [20] by showing that host genetics play a significant role in determining the outcome of sporozoite challenge following RAS-vaccination at bona fide memory time points. We were unable to detect CS-specific CD8 T cell responses in RAS-vaccinated B6 mice (data not shown), which could account for reduced protection. However, experiments have consistently shown that RAS-immune B10.D2 mice are equally as difficult to protect as B6 and B10 mice [11], [20]. Importantly, B10.D2 mice express the same MHC genes as BALB/c mice and thus are able to mount CD8 T cell responses against the defined CS epitopes. Thus, vaccine-induced CD8 T cell responses against the defined immunodominant CS determinant are not sufficient for protection, underscoring the role of non-MHC-linked genes in regulating RAS-induced, anti-liver stage immunity.
Another hypothesis to explain the dramatic susceptibility of hyper-Py-RAS-immune B6 mice is that critical phenotypic or functional attributes of the memory CD8 T cell response, that differ in B6 and BALB/c, regulate protective liver stage immunity. Although we found no differences in granzyme B, IFN-γ, TNF-α or IL-2 production by BALB/c- compared to B6-derived, RAS-induced memory CD8 T cells we did observe differential expression of key molecules that differentiate TCM from TEM populations. RAS-induced memory CD8 T cell populations in B6 mice consistently exhibited elevated proportions of CD62LhiCD27hi populations (TCM phenotype) compared to the predominantly TEM populations found in BALB/c and Swiss Webster mice. These data demonstrate a clear correlation between the expression of the TEM (CD27loCD62Llo) phenotype of secondary memory CD8 T cells and the ability of RAS-immune BALB/c and Swiss Webster mice to resist sporozoite challenge. The reason(s) for the difference in memory phenotype between RAS-immune B6 and BALB/c or/Swiss Webster mice are unknown. Interestingly, a recent report using a sensitive in vivo assay suggests that CS antigen persists for long periods of time after RAS immunization [44]. These studies were carried out only in BALB/c mice due to reagent availability. Given the potential of prolonged antigen encounter to influence memory T cell phenotype, it will be of interest to determine if antigen fails to persist in B6 mice and accounts for the altered T cell phenotype and reduced protection after RAS-immunization. Finally, it should be noted that, due to the enormous numbers of memory CD8 T cells required for sterilizing immunity, adoptive transfer studies to compare the per cell protective capacity of CD8αloCD11ahi memory populations from RAS-immune BALB/c and B6 have not succeeded Although it may be possible to measure reductions in parasite liver burden in CD8 T cell-recipient mice using quantitative PCR, this generally requires challenging mice with supraphysiological doses of sporozoites, a scenario that we wished to avoid. In addition, our interests are focused on the properties of memory CD8 T cells that result in sterilizing immunity and it is currently unclear how reduction in parasite burden after high dose challenge models complete elimination of infected hepatocytes. Clearly many other characteristics of the RAS-induced memory CD8 T cell response may contribute to protective immunity and warrant further investigation. Importantly, our surrogate activation marker approach should allow for detailed, prospective characterization of RAS-induced memory CD8 T cell responses in individual hosts, so that potential links between specific memory CD8 T cell attributes and protection can be evaluated. The identification of additional factors that correlate with or determine CD8 T cell-mediated protective immunity following RAS-vaccination should provide key insight into the pathways of protective CD8 T cell-mediated immunity elicited through whole-parasite vaccination.
Our data highlight the utility of the surrogate activation marker approach for understanding Plasmodium-induced CD8 T cell responses in genetically diverse populations by providing a framework with which the field can begin to address several additional critical knowledge gaps. First, use of outbred rodents to evaluate vaccine-induced responsiveness and protective immunity is much more likely to mimic responses in genetically diverse humans or non-human primates. Identifying and characterizing individual-to-individual variability in response to vaccination should provide additional critical information that will complement data obtained through studying the highly overlapping responses in genetically identical, inbred rodent populations. Second, our studies provide a framework with which to optimize whole parasite immunization. Identifying ways to enhance potentially suboptimal delivery routes, vaccine doses or schedules, based on a quantitative and qualitative assessment of the total CD8 T cell response, will significantly improve efforts to optimize RAS-vaccination, or any other candidate vaccine delivery approaches. Lastly, the surrogate activation marker approach now permits direct comparisons between RAS and the genetically attenuated parasite (GAP) vaccines. Recent work has shown that such genetically attenuated Plasmodium parasites, harboring defined mutations in one or more key genes required for full liver stage differentiation, afford CD8 T cell-dependent protective immunity in rodents [33], [45]. Moreover, it has been shown that the targeted gene(s) precisely control the point during liver stage development that the GAP arrests [31], [45], [46], [47]. Whether early arresting or late arresting GAP-vaccine candidates differentially impact the protective characteristics of the CD8 T cell response is unknown. Given the potential safety advantage of GAP vaccination, these will be critically important questions that can now be directly addressed using surrogate activation markers to identify vaccination-induced effector and memory CD8 T cell responses.
All animal studies and procedures were approved by the University of Iowa Animal Care and Use Committee, under PHS assurance, Office of Laboratory Animal Welfare guidelines.
Specific pathogen-free BALB/c, C57BL/6, and Swiss Webster mice were purchased from the National Cancer Institute (NCI) and housed at the University of Iowa animal care unit at the appropriate biosafety level.
Female Anopheles stephensi mosquitoes infected with either P. berghei (NK65) or P. yoelii (17XNL) were purchased from the New York University insectary.
P. berghei and P. yoelii sporozoites were isolated from the salivary glands of infected A. stephensi mosquitoes. Sporozoites were attenuated by exposure to 200 Gy (20,000 rads). Mice were immunized with 200 to 100,000 RAS i.v. Boosted mice received 20,000 RAS no less than 60 days apart. In some experiments mice were injected with 60 mg/kg primaquine (Sigma-Aldrich, St. Louis, MO) i.p. on days 5 and 6 following RAS immunization. Subunit immunizations were performed as previously described [16]. Briefly, BALB/c mice were primed via tail vein injection of 1×106 splenic dendritic cells coated with peptides corresponding to CS252–260 of P. berghei (DC-CS252–260). Seven days later, mice were boosted with 2×107 CFU of recombinant Listeria monocytogenes expressing the CS252–260 determinant as a secreted minigene (LM-CS252–260).
P. berghei and P. yoelii sporozoites were isolated from the salivary glands of infected A. stephensi mosquitoes. Immunized and naïve age-matched mice were challenged with 1000 sporozoites i.v. Thin blood smears were performed 10 days after sporozoite challenge. Parasitized red blood cells were identified by Giemsa stain and oil-immersion (1000×) light microscopy. Protection is defined as the absence of blood stage parasites. At least 10 fields (∼10–15,000 red blood cells) were examined for each mouse designated as protected. Protected mice were subsequently rechallenged following T cell depletion to verify that protection was CD8 T cell-dependent.
RAS vaccine-induced CD8 T cell populations were identified by staining spleen or liver single cell suspensions, or peripheral blood following lysis of red blood cells, with anti-CD8α clone 53–6.7 (eBioscence, San Diego, CA) and anti-CD11a (LFA-1α) clone M17/4 (eBioscience) antibodies. Sporozoite-specific CD8 T cells were phenotyped by staining cells with anti-CD27 clone LG.7F9, anti-CD43 clone 1B11, anti CD62L clone MEL-14, anti-CD127 clone A7R34, anti-CD25 clone PC61, anti-CD69 clone H1.2F3, anti-CD44 clone IM7, anti-CD122 clone 5H4 antibodies, all from eBioscience. In some experiments, 8×104 Py-RAS-induced primary memory cells from CD90.2+ BALB/c or CD45.2+ B6 mice were adoptively transferred to naïve, congenic (CD90.1+ or CD45.1+) recipients. One day following transfer, recipients were boosted with 1×105 Py-RAS. Thirty-three days later, splenocytes were stimulated ex vivo in anti-CD3ε-coated wells. BALB/c and B6 donor cells were identified by CD11ahiCD8αloCD90.2+ or CD11ahiCD8αloCD45.2+ surface staining and further characterized by intracellular staining for IFN-γ, TNF-α and IL-2, or Granzyme B. CS252–260- and CS280–288-specific CD8 T cells were identified by incubating peripheral blood leukocytes with Kd/CS252–260-APC labeled tetramers or Kd/CS280–288-APC labeled tetramers, respectively. Cells were then stained with anti-CD8α, anti-CD90.2 and anti-CD11a. Following subunit immunization, the frequency of circulating CS252–260-specific CD8 T cells was determined by ex vivo intracellular cytokine staining for IFN-γ following a 5.5 hour incubation with brefeldin A in the presence or absence of CS252–260 peptide-coated P815 cells were used as antigen presenting cells. Cells were analyzed using a BD FACSCanto and data was analyzed using FLOWJO Software (Tree Star, Inc, Ashland, OR). All animals were pre-bled prior to RAS vaccination to establish individual background circulating CD8αloCD11ahi T cell frequencies.
Immunized mice were injected with 0.4 mg i.p. rat IgG, anti-CD4 (clone GK1.5), or anti-CD8 (clone 2.43) antibodies on day −3 and day −1 prior to challenge with sporozoites. Depletion was verified by analyzing CD4 (clone RM4-5) and CD8 (clone 53-6.7) T cell populations in the blood of individual mice prior to challenge. In each case, the relevant population represented <0.5% of the PBL.
The serum sporozoite-specific antibody titer from immunized mice was determined by the indirect fluorescent antibody test (IFAT). Sporozoites were air dried on a multiwell microscope slide (Cel-Line Thermo Scientific) and blocked with 1% BSA/PBS. Sporozoite-specific IgG antibodies were detected by incubating with Cy3-conjugated goat anti-mouse IgG (Jackson Immunoresearch Laboratories). Titers are expressed as the inverse of the lowest dilution of serum that retained immunoreactivity against air-dried sporozoites.
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10.1371/journal.pgen.1000640 | A Role for E2F Activities in Determining the Fate of Myc-Induced Lymphomagenesis | The phenotypic heterogeneity that characterizes human cancers reflects the enormous genetic complexity of the oncogenic process. This complexity can also be seen in mouse models where it is frequently observed that in addition to the initiating genetic alteration, the resulting tumor harbors additional, somatically acquired mutations that affect the tumor phenotype. To investigate the role of genetic interactions in the development of tumors, we have made use of the Eμ-myc model of pre-B and B cell lymphoma. Since various studies point to a functional interaction between Myc and the Rb/E2F pathway, we have investigated the role of E2F activities in the process of Myc-induced lymphomagenesis. Whereas the absence of E2F1 and E2F3 function has no impact on Myc-mediated tumor development, the absence of E2F2 substantially accelerates the time of tumor onset. Conversely, tumor development is delayed by the absence of E2F4. The enhanced early onset of tumors seen in the absence of E2F2 coincides with an expansion of immature B lineage cells that are likely to be the target for Myc oncogenesis. In contrast, the absence of E2F4 mutes the response of the lineage to Myc and there is no expansion of immature B lineage cells. We also find that distinct types of tumors emerge from the Eμ-myc mice, distinguished by different patterns of gene expression, and that the relative proportions of these tumor types are affected by the absence of either E2F2 or E2F4. From these results, we conclude that there are several populations of tumors that arise from the Eμ-myc model, reflecting distinct populations of cells that are susceptible to Myc-mediated oncogenesis and that the proportion of these cell populations is affected by the presence or absence of E2F activities.
| The diversity of human cancers reflects the variety of genetic changes that cause tumors to emerge and progress. Even for mice engineered with a specific cancer-causing mutation, the resulting tumors are often divergent, reflecting different additional mutations. We wanted to investigate how activities that work together can collaborate in tumorigenesis. Specifically, we are interested in Myc and the E2F family of proteins, intersecting activities that influence a cell's decision to replicate, rest, or die. We made use of an engineered mouse that develops pre-B and B cell lymphoma initiated by Myc and tested whether the loss of particular E2F family members influences these lymphomas. We found that tumor emergence was accelerated by E2F2 loss and delayed by E2F4 loss. We attributed these results to the finding that the mice lacking E2F2 have a greater proportion than usual of the most susceptible, early-stage B lineage cells and the mice lacking E2F4 have fewer of these cells. Distinct tumor types emerged with their relative proportions influenced by E2F2 and E2F4 status. We conclude that the variety of tumors probably reflect different stages of B lymphoid development that respond to Myc and that E2F proteins can influence the proportions of these different stages.
| A hallmark of human cancer is genetic complexity, reflecting the acquisition of multiple mutations and gene rearrangements that give rise to the tumor phenotype. Indeed, recent large-scale DNA sequencing efforts have provided direct evidence for this complexity, revealing large numbers of alterations that characterize various tumor types [1]–[4]. Undoubtedly, this genetic complexity of cancer underlies much of the challenge in developing effective therapeutic strategies. Not only is it likely that combinations of drugs will be necessary to match the complexity and effectively treat these tumors but equally important is the ability to identify subgroups of cancers that represent more homogeneous mechanisms of disease.
An ability to model the complexity that gives rise to the tumor heterogeneity seen in human cancers would clearly enhance the understanding of the oncogenic process but also would enable the development and testing of combination therapeutics that might match this complexity. Mouse models of cancer have generally employed the use of an activated oncogene or the disruption of a tumor suppressor gene to initiate the oncogenic process. Although this represents a defined genetic alteration, it is also true that in most instances this single event is not sufficient to allow for tumor development. This can be seen in the often protracted latency of tumor development as well as the identification of specific additional genetic alterations that appear in these tumors.
An example of a well-studied genetic model for the analysis of pre-B and B cell lymphoma is the Eμ-myc transgenic mouse. In the Eμ-myc transgenic mouse c-myc is constitutively expressed in the B lineage [5],[6]. The resulting polyclonal expansion of pre-B cells is initially limited by increased apoptosis [7]. Additional mutations, many of which inactivate the p53 tumor suppressor pathway [8], then arise. This leads to the emergence of a clonal pre-B or B cell lymphoma by six months of age in mice of a mixed C57Bl/6 and 129 strain background.
Myc has been shown to induce a large number of genes that contribute to cell proliferation. These include the direct transcriptional activation of D cyclin genes, the cdk4 gene encoding the kinase partner for cyclin D, and the Cdc25A gene encoding the phosphatase that removes negative regulatory phosphates from the Cdks. The induction of Cyclin D/cdk4 activity leads directly to the phosphorylation of Rb and thus activation of E2Fs. Numerous studies have demonstrated a central role for the Rb-E2F pathway in the regulation of cellular proliferation. The majority of genes encoding DNA replication and mitotic activities are under the control of E2F proteins. Indeed, recent experiments provide evidence for a role for E2Fs in coordinating transcriptional regulatory events at G1/S and G2/M [9]–[11]. Other work has shown that E2Fs also link this critical proliferative pathway with the p53 response through a capacity to induce the p19ARF/Mdm2 pathway leading to the accumulation of p53 protein [12],[13]. As such, E2Fs provide a mechanism to directly link the control of cell proliferation with the determination of cell fate. In addition to the connection between Myc and E2F in the control of cellular proliferation, Myc expression couples cellular proliferation with the induction of apoptosis under specific growth conditions where survival growth factors are limiting. Myc-induced apoptosis is largely dependent upon p53 signaling and, similar to E2F1, involves the induction of p19ARF, inhibition of Mdm2, and elevated p53 [14].
The shared functional properties of the Myc and E2F transcription factors, coupled with the finding that Myc can induce E2F gene expression [15],[16], raise the possibility that Myc function might be mediated, at least in part, through the action of the E2F transcription factors. Indeed, work by the Bernards laboratory revealed that in addition to targeting p27Kip1, the mitogenic activity of Myc likely involves regulation of E2Fs [17]. This possibility has been more directly assessed using mouse embryo fibroblasts (MEFs) from embryos deleted for specific E2F genes to evaluate the functional relationship between Myc and various E2F proteins [18]. Experiments using these E2F-deficient MEFs showed that the ability of Myc to induce S phase in the absence of other mitogens is severely impaired in MEFs deleted for E2f2 or E2f3, but not E2f1 or E2f4. In contrast, Myc induced apoptosis in primary serum-deprived MEFs was delayed in cells deleted for E2f1, but not affected by E2f2 or E2f3 deletion. Thus, at least in cell culture, the induction of specific E2F activities is an essential downstream event in the Myc pathway that controls cell proliferation versus apoptosis, and some of the functions of Myc, such as the induction of p19ARF and p53 could be explained, at least in part, with one pathway leading through E2F activation.
To address the significance of the Myc-E2F connection in a relevant, in vivo setting, we have made use of a series of E2F-deficient mouse strains, in combination with the Eμ-myc transgenic model of lymphomagenesis (MGI:2448238), to investigate whether deficiencies in E2F1, E2F2, E2F3 or E2F4 (MGI:1857424, 2179111, 2177428 and 1888951) can influence Myc's oncogenic potential. We find that there is a critical role for two E2F activities in affecting the potential for Myc-induced oncogenesis.
As a prelude to the investigation of a role for E2F activity in Myc-mediated oncogenesis, we analyzed the pattern of tissue-specific expression of the various E2F genes. RNA was prepared from selected tissues of wild type mice and then analyzed by quantitative RT-PCR. E2f1 and E2f3 genes were widely expressed whereas expression of the E2f2 gene was largely restricted to the hematopoietic tissues assayed including bone marrow, spleen, and thymus (Figure S1A). Please note that Text S1 (Supporting Materials and Methods) describes the procedures specific to Figures S1, S2, S3, S4, S5, S6, S7, S8, S9, and S10. This restricted E2f2 expression pattern at the RNA level was recapitulated at the protein level (Figure S1B). E2f4, while expressed widely at the RNA level, was also particularly strongly expressed in hematopoietic tissues at the protein level (Figure S1C) [19]. While we did not measure E2F5 in these assays, it has previously been shown that the expression of this E2F family member is restricted to the differentiating epithelial layers of the skin, intestine and brain [20],[21]. As such, we chose to focus our studies on the E2f1-4 genes and gene products.
Male Eμ-myc transgenic mice (backcrossed and maintained in the C57BL/6 strain) were bred into the four different E2F-deficient mouse lines. E2F2, E2F3 and E2F4 cohorts were maintained as C57BL/6×129 while the E2F1 cohort was predominantly C57BL/6 in background. In particular, the E2F3 and E2F4 cohorts required maintenance on a mixed, rather than inbred, background because the yield of the E2f3-null and E2f4-null mice was severely compromised upon inbreeding (data not shown). Sibling mice, wild type, heterozygous or null for a particular E2F gene, and bearing the Eμ-myc transgene, were examined weekly for any sign of lymphoma emergence. Each mouse was checked for enlarged lymph nodes, a swollen abdomen, a hunched posture, ruffled fur and/or tachypnea [22]. Upon the appearance of any of these symptoms, the mouse was sacrificed, dissected to identify any lymph node enlargement, and tumor tissue harvested for analysis. For studies assessing the pre-tumor phenotype, mice were sacrificed within three to five weeks after birth and bone marrow and spleen recovered. Such samples were characterized as pre-neoplastic only if lymph node and spleen enlargement was nil or modest at the time of dissection and/or Southern analysis of B lineage cell DNA revealed no specific heavy chain rearrangements indicative of the emergence of tumor clones.
Tumor emergence was evaluated in the four E2F cohorts (Figure 1), as well as for our Eμ-myc C57BL/6 congenic stock mice (Figure S2A). When the wild type mice in each cohort were compared to the stock transgenic mice, median onsets did diverge (Figure S2B), with earlier onsets associated with greater 129 strain contribution based on breeding history. In spite of this, the overall appearance of each of the wild type curves was similar, with some mice succumbing early and others succumbing late.
As shown in Figure 1A, the loss of E2F1 function did not alter the timing of lymphoma appearance; there was no statistical difference between tumor onset curves when comparing E2f1+/+ (n = 37), E2f1+/− (n = 91) and E2f1−/− (n = 40) mice. The failure of E2F1 status to influence lymphomagenesis conflicts with the earlier finding that E2F1 deficiency delays lymphoma development in Eμ-myc mice [23]. That study attributed the delay to a defect in p27Kip1 degradation and reduced Myc-induced proliferation when E2F1 is reduced or absent. In our assessments the level of p27Kip1 protein in splenic B lineage cells did not vary according E2F1 status but rather with progression to disease: p27Kip1 was highest in cells isolated from non-transgenic siblings, reduced in healthy Eμ-myc transgenics, and lower still in very sick mice and tumors (Figure S3A). In addition, the accelerated proliferation induced by expression of the Eμ-myc transgene [24] was unaffected by E2F1 deficiency: splenic B lineage cells isolated from E2f1 wild type, heterozygous and null Eμ-myc transgenic mice all exhibited the same dramatically higher proliferative index when compared to that of cells isolated from non-transgenic siblings (Figure S3B). We note that, analogous to our results, E2F1 deficiency did not alter Myc-induced T cell lymphomagenesis [25]. Given that the timing of Eμ-myc-driven tumor development can be influenced by strain background [26] and breeding strategy, we can only surmise that these potential differences or the specific E2f1-null allele [27],[28] used in our studies versus that of Baudino and colleagues are sufficient to account for the discrepant effects of E2F1 deficiency.
As shown in Figure 1B, lymphoma onset was also not appreciably influenced by E2F3 status. While the number of E2f3-null animals was low in this study, reflecting the low number of E2f3-null mice born, there is nevertheless no suggestion that E2F3 loss was protective as several E2f3-null mice died before the average age of onset for their wild type siblings.
In contrast to the results seen with the E2f1 and E2f3 knockout animals, a deficiency of E2F2 dramatically accelerated the appearance of lymphoma (Figure 1C). The E2f2−/− mice were prone to early tumor onset with tumors appearing on average 60 days earlier than in their wild-type siblings and there was a significant difference (p<0.0001) between the E2f2+/+ and E2f2−/− tumor curves. Notably, the E2f2+/− mice exhibited a median tumor-free span of 92 days and a significantly accelerated course of disease compared to wild-type siblings (p<0.0001). The intermediate phenotype of the E2f2 heterozygotes suggests a degree of haploinsufficiency. In addition, the E2f2+/− lymphomas showed no loss of heterozygosity demonstrating that E2F2 does not behave like a classic tumor suppressor in the Eμ-myc context (data not shown).
Finally, a deficiency in E2F4 also had a dramatic effect on Myc-induced lymphoma development (Figure 1D). A comparison of E2f4+/+, E2f4+/− and E2f4−/− mice revealed that E2f4−/− mice remained tumor-free for significantly longer than siblings (p<0.0001) with a median tumor-free span of 375 days past birth. Lymphoma onset may be modestly delayed in E2f4+/− mice (p = 0.0465).
Taken together, these data would suggest roles for two E2F proteins, both positive and negative acting, in affecting the onset of Myc-mediated lymphomas. A role for these two E2F family members also coincides with the prominent expression of these proteins in hematopoetic tissues (Figure S1).
Previous work has shown that in the B cell lineage Myc induces proliferation and apoptosis and retards differentiation [24],[29]. As such, the effects of E2F loss of function on Myc oncogenesis could result from alterations in one or more of these processes. To address the potential for differential effects on proliferation, weanling mice were injected with BrdU, three hours later bone marrow was isolated, and the cell cycle distribution of B lineage cells (B220+ CD19+) determined. As shown in Figure 2A, proliferation of B lineage cells was similar for non-transgenic E2f2 wild type and null cells. Importantly, the effect of Myc on cell cycle entry of B lineage cells, with increased S-phase cells and reduced G0/G1 cells, was independent of E2F2 status. As shown in Figure 2B, E2f4 wild type and null B lineage cells were similarly proliferative in non-transgenic mice. As well there was still the expected increase in proliferation associated with bearing the Eμ-myc transgene for E2f4-null mice (E2f4−/− compared to E2f4−/− Eμ-myc+: p = 0.0002) (Figure 2B). It appears that the acceleration of cell cycle progression driven by Myc in B lineage cells is not significantly affected by the loss of E2F2 or E2F4 activities.
An alteration in the apoptotic potential of Myc could account for the differences in Myc-initiated tumor onset among wild type, E2f2-null and E2f4-null animals. Possibly in E2f2−/− mice apoptosis is reduced whereas in E2f4−/− mice apoptosis is potentiated. Freshly isolated bone marrow B lineage cells (B220+) from E2f2−/− Eμ-myc transgenics and E2f4−/− Eμ-myc transgenics were found to have comparable percentages of activated caspase-3 positive cells as their wild type and heterozygous Eμ-myc siblings, around 0.6%, and all the non-transgenic siblings had less than half this percentage of apoptotic cells (data not shown). Since divergent viability may be masked by clearance in vivo, the survival of B lineage cells under culture conditions where deregulated Myc induces apoptosis was assessed [30],[31]. The bulk of B lineage cells, excepting progenitors upstream of small pre-B cells, was enriched by negative selection from the bone marrow, spleen and mesenteric lymph node. The resulting population of small pre-B cells, immature B cells and mature B cells from each mouse was cultured for eight hours in medium lacking cytokines. The cultured cells were sampled at two-hour intervals, and B220+ cells assessed for viability based on activated caspase 3 and 7-AAD staining (Figure S4). As expected, the decline in viability was faster for cells from Eμ-myc transgenics than from non-transgenics (Figure S4 and Figure 3A and 3B). Notably, when mice in the E2F2 cohort were compared, cells from E2f2−/− Eμ-myc mice lost viability to a similar extent over eight hours as cells from wild type and heterozygous siblings (Figure 3A). Likewise, in an experiment assessing E2F4 cohort mice, cells from E2f4−/− Eμ-myc mice declined in viability similarly to those from their wild type and heterozygous siblings (Figure 3B). Thus, the faster tumor onset for E2f2-deficient mice appears not attributable to a general apoptotic deficiency and the slower tumor onset for E2f4-null mice unlikely the result of increased apoptosis, at least in the small pre-B cells and more mature stages assessed here that constitute the large majority of B lineage cells.
Myc-mediated tumor emergence is almost invariably associated with a disabling of the ARF-p53 tumor suppressor pathway [8],[14]. Mutations that are indicative of pathway disruption include: p53 deletion, ARF deletion, or overexpression of ARF, Mdm2 or mutant p53. Examples of these disruptions in a sampling of tumors are shown in Figures S5, S6, and S7. Consistent with the earlier studies, tumors from E2F wild type Eμ-myc mice showed evidence of disruption of the ARF-Mdm2-p53 pathway (Figure 3C). The largely late-onset lymphomas from E2f4-null mice also demonstrated disruptions in this pathway. Importantly, the spectrum and overall incidence of defects in the E2f2+/− and E2f2−/− lymphomas were very similar to that shown by the E2f2+/+ lymphomas. In contrast, other modifiers of Myc-induced lymphomagenesis such as Bim and Bax relieve or modify the strong selective pressure for functional inactivation of this pathway [32],[33]. That loss of ARF-p53 function was still associated with development of tumors in the E2f2−/− mice further supports the evidence that E2F2 deficiency does not compromise Myc-induced apoptosis (Figure 3A).
Given that E2F2 deficiency does not alter the proliferation or apoptosis of pretumorous B lineage cells in response to Myc, we focused on the possibility that there may be a different underlying mechanism driving the accelerated tumor emergence, one involving development of the B lineage and the response to Myc. As noted in one of the earliest descriptions of the Eμ-myc model, it is possible that different onsets could reflect different extents of lineage expansion in response to Myc and therefore numbers of vulnerable cells [24]. Overall viable white blood cell number in the bone marrow did not change with E2F2 status while Eμ-myc positive mice had modestly higher counts (data not shown). As expected, the B cell lineage expanded as a proportion of the bone marrow in response to the Eμ-myc transgene in E2f2+/+ mice (Figure 4A) and the expansion favored less mature over more mature B cells across all three genotypes (Figure 4B). Importantly, there was a similar degree of expansion for the E2f2+/− and E2f2−/− mice.
Recent studies have suggested, however, that lymphomagenesis likely initiates in B lineage progenitor cells making the effects of various mutations on progenitor populations particularly relevant [34]. For instance, the Eμ-myc bcl2−/− mice develop tumors at the same rate as Eμ-myc bcl2+/+ mice despite decreased pre-B, immature B and mature B lymphocytes; significantly, they do have similar numbers of pro-B cells [34]. Similarly, Eμ-myc/max41 mice develop lymphoma almost as quickly at Eμ-myc mice despite a severe deficit in more mature, peripheral B cells [35]. Conversely, the increased progenitor population of early/large pre-B stage cells exhibited by Phospholipase Cγ2-deficient mice is associated with accelerated lymphomagenesis [36]. As shown in Figure 4C, CFU pre-B colony assays using bone marrow from non-transgenic E2F2 cohort mice indicate that there were more B lineage progenitors in E2f2−/− marrow (P<0.0001) and E2f2+/− marrow (P = 0.0406) than wild type marrow. The proportion of early B lineage cells (B220+ CD43+) identified by flow cytometry was also greater in non-transgenic E2f2−/− marrow than in non-transgenic E2f2+/+ marrow (P = 0.0008; Figure 4D). The increased proportions of progenitors in E2f2-deficient mice extended into pre-tumorous Eμ-myc positive mice: there was the trend, although not statistically significant, for more immature B lineage cells as a proportion of the total bone marrow in E2f2−/− (P = 0.0769) and E2f2+/− Eμ-myc transgenics (P = 0.1393) compared to E2f2+/+ Eμ-myc transgenic mice (Figure 4D).
Additional analysis revealed that a significant proportion of the E2f2−/− Eμ-myc lymphomas were not monoclonal. Assessment of Igh locus rearrangement patterns by Southern analysis indicated that almost 40% of the E2f2−/− tumors were biclonal or oligoclonal whereas tumors from other genotypes, whether in the E2F2 cohort or in other cohorts, were predominantly monoclonal, in agreement with past studies [5] (Figure 5A and Figure S8A and S8B). This degree of oligoclonality was, however, less extensive than that which occurs with the homozygous Eμ-myc/Eμ-myc mice [37] and when retrovirally-expressed myc was expressed in mice reconstituted with p53−/− hematopoietic stem cells [38]. In addition, several tumors were analyzed by flow cytometry for isotypic surface marker expression (Figure 5B). Seven out of ten tumors emerging from E2f2−/− mice displayed a complex pattern of isotypic surface markers. Such complexity, while not uncommon for Eμ-myc lymphomas in general [22], is consistent with multiple clones. Of note, two out of eight tumors from E2f2+/− mice were similarly complex. In contrast, the majority of tumors from other Eμ-myc mice displayed single, homogeneous patterns of surface markers and could be clearly classified as being either pro/pre-B or immature B lymphomas.
Taken together, these findings support the conclusion that in the E2f2−/− Eμ-myc mice there is an increased population of B lineage cells susceptible to lymphomagenesis resulting in the occasional emergence of more than one independent tumor. Using a different Myc transgenic system, Cory and colleagues noted the emergence of mixed T cell tumors in their study and concluded that such mixed tumors originated as separate clones and could be expected with a high rate of tumorigenesis [39]. Also consistent with the hypothesis that the enhancement of early onset tumors in E2f2−/− mice is the consequence a larger pool of susceptible cells, rather than of differently behaving cells, is the finding that the lymphomas that emerged were very similar to lymphomas that arose early in E2f2+/+ Eμ-myc mice. For instance, when mice showing signs of illness were sacrificed the degree of splenomegaly was comparable (Figure S9A) and the histopathology of the lymphomas was similar. The majority of early-onset tumors, either from E2f2+/+ or E2f2−/− mice, featured high mitotic indices and extensive apoptosis with tingible body macrophages and a starry sky appearance similar to that of human Burkitt lymphoma (data not shown). In addition, B lineage cells (B220+) isolated from E2f2+/+ and E2f2−/− lymphomas exhibited comparable rates of proliferation and apoptosis (Figure S9B and S9C).
It has recently been shown that E2f4-deficient mice have defects that extend from early hematopoietic progenitor cells, through common lymphoid precursors and into the B and T lineages [40]. Specifically in the B lineage, E2f4−/− mice exhibit a partial block early in B lineage development prior to immunoglobulin gene rearrangement that results in a deficiency in the most mature pro-B subpopulation and a reduction in more mature B lineage cells [19] (Glozak et al., manuscript in preparation).
Total viable white blood cell counts in the bone marrow were modestly higher for Eμ-myc E2f4+/+ and E2f4+/− mice than for non-transgenic siblings. In the case of E2f4−/− mice, there was no increase associated with the Eμ-myc transgene and Eμ-myc E2f4−/− mice had about half as many cells as Eμ-myc wild type siblings (p = 0.0269). As expected, within the bone marrow, non-transgenic E2f4−/− mice had a lower proportion of B lineage cells compared to non-transgenic E2f4+/+ mice (P = 0.0133, Figure 6A). Strikingly, the usual expansion of the B lineage in response to the myc transgene failed to occur in the E2f4−/− mice. As a consequence, the proportion of B lineage cells in the bone marrow was significantly less in E2f4−/− Eμ-myc mice than in E2f4+/+ Eμ-myc mice (P = 0.0019). Myc did, however, elicit the usual reduction in the relative proportion of mature to less mature B lineage cells in the E2f4-deficient mice as in E2f4 wild type mice (Figure 6B). Motivated by the hypothesis that Eμ-myc lymphomas originate in early stage B lineage cells [34], we assessed progenitor populations in the E2f4−/− mice compared to their siblings. In CFU pre-B colony assays using bone marrow from non-transgenic E2F4 cohort mice, there were significantly fewer progenitors in E2f4−/− marrow than wild type marrow (data not shown; Glozak et al., manuscript in preparation). Notably, the pre-tumorous E2f4−/− Eμ-myc mice exhibited no expansion of immature B lineage cells (B220+ CD43+) as a proportion of the total bone marrow compared to their non-transgenic E2f4−/− siblings (Figure 6C). We suggest that the Myc transgene fails to overcome the inefficient developmental progression of the B lineage in E2f4-deficient mice, there is a reduced number of susceptible progenitor cells, and consequently delayed tumor emergence.
As indicated by Figure 1D, E2f4−/− Eμ-myc mice displayed a much delayed tumor onset. Possibly because of this delay, of the twenty-six mice assessed, nine died before lymphoma emergence or were still healthy at analysis. The three mice that developed lymphoma early (within 150 days of birth) and one older mouse exhibited the standard Eμ-myc lymphoma phenotype, characterized by an enlarged spleen and multiple enlarged lymph nodes. Thirteen mice developed lymphoma very late in life. Three of these mice exhibited lymphoma with modest spleen enlargement and isolated lymph node enlargement, similar the uncommon late onset lymphomas that occasionally develop in Eμ-myc mice wild type for E2F. The ten remaining E2f4−/− Eμ-myc mice displayed an atypical tumor phenotype that was only rarely noted in E2F wild type Eμ-myc mice (10 of 17 E2f4−/− mice compared to only 3 of 79 E2f4+/+ mice). These atypical tumors featured a loose tumor mass of multiple small nodules in the mediastine with little or no associated spleen or peripheral lymph node enlargement. Despite their unusual appearance, the atypical tumors that were tested demonstrated Igh gene rearrangement confirming their B lymphoid origin. Overall, eleven E2f4−/− tumors, including examples of standard, late, and atypical types, were assessed for clonality and all proved to be monoclonal. In summary, along with the general delay in tumor onset there was also a difference in the predominant site of lymphomagenesis and gross morphological appearance of tumors in the E2f4−/− Eμ-myc mice.
As a further basis for exploring the effects of E2F loss of function on the development of Myc-induced tumors, we have made use of genome-scale gene expression profiles to characterize the tumors arising in the E2f2-null and E2f4-null Eμ-myc mice. Our recent work has identified expression profiles that distinguish different tumor types within the Eμ-myc mice including a cluster characterized by generally early onset and pre-B markers as well as three distinct clusters characterized by late onset and different sets of more differentiated B lineage markers [41]. Examples of wild type tumors exhibiting this early and late onset pattern are shown in Figure 7A. Analysis of the tumors from the E2f2−/− mice indicated that they were relatively homogeneous with respect to their expression profiles and reflected the characteristics of the “early” category of wild type tumors. In contrast, the tumor types from the E2f4−/− mice were heterogeneous with a distribution across both broad categories of the wild type tumors. The distribution of the E2f4−/− tumors corresponded with their dissection phenotypes - standard, late and atypical - described above. Three early-onset E2f4−/− tumors, all with the standard morphological phenotype, clustered with the “early” wild type and E2f2−/− tumors. Three more E2f4−/− tumors, all with the late morphological phenotype, clustered alongside a group of wild type tumors that overexpress genes characteristic of plasmacytomas. These particular E2f4−/− tumors shared marginally decreased myc mRNA and low Myc protein compared to other E2f4−/− tumors (data not shown). The final six E2f4−/− tumors were all of the atypical phenotype and segregated in the “early” category despite being late onset chronologically. These tumors shared qualities with the “early-standard” tumors in that they featured high levels of myc mRNA and Myc protein (data not shown). Notably, these tumors fell at the extreme end of the early cluster and beside a rare group of wild type tumors that had similarly modest spleen enlargement and late chronological onset. In fact, these tumors highlight a significant subgroup within the “early” category that we have designated “early-atypical”. This tumor subgroup was notable for a high incidence of p53 deletion or mutation (67% of tumors assessed versus 18% of other wild type tumors assessed; by Fisher's exact test P = 0.0061). Among the genes that characterized each tumor cluster, increased expression of number of genes distinguished this subgroup from both “early-standard” and “late” clusters (Figure S10). Cdkn2a, the locus that encodes the two tumor suppressors p16(INK4a) and p19(ARF), was preferentially expressed in these tumors. Given that most of these tumors were mutant for p53, the increased expression may be a consequence of a role for p53 in negatively regulating the expression of ARF [42]. Other genes that were particularly highly expressed in the “early-atypical” tumors included Dlk1, a member of the epidermal growth factor-like family that influences B lineage differentiation [43],[44] and Fzd6, a receptor for Wnt signaling and a frizzled family member [45].
To further characterize the distinctions in the Eμ-myc tumors, we have made use of signatures of various cell signaling pathways that have previously been shown to distinguish human Burkitt lymphoma (BL) from diffuse large cell B lymphoma (DLBCL) [46]. These include signatures for Myc pathway activity, the expression of a subgroup of germinal-center B-cell genes, the expression of MHC class I genes, and NFκB pathway activity. An analysis of tumors from the E2f2-null and E2f4-null mice using these pathway signatures is shown in Figure 7B. Consistent with the analysis of whole genome gene expression data in Figure 7A that revealed distinct types of B lymphoma, the analysis using pathway signatures also revealed that the tumors from the E2f2-null mice exhibited a pattern similar to the “early” tumors, characterized by high Myc and germinal center signatures, whereas the tumors from E2f4-null mice were heterogeneous with respect to these patterns. The atypical E2f4−/− tumors did not feature the usual “late” characteristics but instead had elevated Myc and germinal center signatures and low MHC Class I and NF-KB signatures. These tumors highlight the existence of certain tumors of late chronological onset, whether E2F wild type or E2f4−/−, that were unusual in their behavior.
A dominant characteristic of the oncogenic process is complexity – the realization that the development of a tumor results from a complex array of genetic alterations that ultimately combine to contribute to the oncogenic phenotype. While transgenic mouse models reduce this complexity by fixing one event, various studies have shown a clear heterogeneity in the tumors that develop reflecting the acquisition of additional alterations. Based on the studies we describe here, we suggest that there are at least two distinct types of B lineage lymphoma that develop in the Eμ-myc mice. In mice with wild type E2F function, the predominant tumor arising with an early onset likely reflects the relative abundance of the target cell population. We suggest that the emergence of these tumors reflects a stochastic acquisition of additional mutations. Since this is a probabilistic event, there is then an opportunity for tumors to emerge from other populations of cells with characteristics distinct from the “early-standard” tumors. We propose that the late-onset tumors develop more slowly because the target cell populations are less abundant. It is also possible that these late-onset tumors are genetically more complex, requiring more mutations than the predominant form of tumors.
The work we present here provides clear evidence for an interaction between Myc and two E2Fs as seen by the effect on timing of tumor onset and the different characteristics of the lymphomas that arise in the absence of E2F2 or E2F4. The results suggest distinct roles for the E2F2 and E2F4 proteins in the process of B lineage development that then impacts Myc-mediated oncogenesis. A role for E2F4 in cell differentiation is consistent with past work that documented abnormalities in hematopoietic lineage development as well as the development of the gut and nasal epithelia with loss of E2F4 activity [19], [40], [47]–[49]. This past work revealed a deficiency of various mature hematopoietic cell types and defects in the differentiation of immature cells in the absence of E2F4. Taken together, these observations suggest a critical role for E2F4 activity in controlling the maturation of hematopoietic lineage cells, including the B lymphoid compartment that generates the target cells for Eμ-myc-induced lymphomagenesis.
A role for E2F2 appears to be more complex. E2F2 has generally been characterized as one of the E2Fs involved in the activation of transcription of genes essential for cell proliferation. While the other two activating E2Fs (E2F1 and E2F3) appear to be broadly expressed, E2F2 expression is largely restricted to cells of the hematopoietic lineage. Indeed, previous work has pointed to a role for E2F2 in the function and development of various hematopoietic lineages. S-phase progression is impaired in B, erythroid, and myeloid lineages in the absence of E2F1 and E2F2, consistent with a role for these E2F products as positive regulators of cell proliferation [50]. Nevertheless, the possibility of a more complex role for E2F2 in hematopoietic cells emerged from studies demonstrating that the loss of E2F2 appears to enhance the proliferation of T cells following antigenic stimulation, suggesting a negative role for E2F2 in defining a threshold for Ag-stimulated proliferation [51],[52]. Although this latter observation is not consistent with the results we present here regarding proliferative capacity in E2f2-null B lineage cells, it certainly is true that with Myc overexpression, the absence of E2F2 function may accelerate tumor onset by increasing the proportion of progenitor B lineage populations.
Opavsky and colleagues have recently used a bitransgenic model of Myc-induced T cell lymphomagenesis to probe the importance of E2F1, E2F2 and E2F3 for Myc activities [25]. Analogous to our findings for B lineage lymphomagenesis, they found that E2f1 or E2f3 deficiencies have no effect on T cell lymphoma progression. Most importantly, E2f2 deficiency accelerates tumor onset in the T cell model as it does for the Eμ-myc model. In addition, other aspects of their findings are reminiscent of our results, namely that E2f2 heterozygotes have an intermediate phenotype, suggesting some haploinsufficiency, and that with E2f2 deficiency there is an increased incidence of multiple tumor clones. The salient difference is that in their study, E2f2 deficiency is associated with reduced apoptosis. Specifically, Opavsky and colleagues detected a reduced percentage of annexin-positive T cells in moribund mice that were E2f2−/− versus E2f2+/+ whereas in our experiments, E2f2 deficiency was not associated with reduced apoptosis, whether assessing the bulk of B lineage cells from pretumorous mice, or when looking at B220+ cells isolated from tumorous lymph nodes (Figure 3A and Figure S9C).
In marked contrast to the study of Baudino et al. [23], the work described here did not demonstrate that E2f1 deficiency influenced lymphoma development or p27Kip1 regulation. This discrepancy could be the consequence of the different knockout alleles utilized and possible differences in strain background and breeding strategies. But we also note that the wild type E2f1 Eμ-myc mice in the Baudino et al. study [23] exhibit a more precipitous and overall earlier tumor-onset curve than reported in most other studies with C57BL/6 congenic, wild type Eμ-myc mice.
Our results indicate that E2f2 deficiency enhances the emergence of the “early-standard” form of lymphoma likely because the absence of E2F2 activity expands the population of cells that is the usual target for the oncogenic process in the Eμ-myc model. As a result, the population of E2f2−/− tumors is also more homogeneous with respect to their phenotype, as reflected by the gene expression profiles. In contrast, we propose that the loss of E2f4 results in the decrease of this population of cells and thus the frequency of appearance of standard morphology early onset tumors. There is not a complete absence of these cells since a few tumors do arise in the absence of E2F4 that cluster with the “early-standard” wild type and E2f2−/− tumors. But the consequence of this depletion is enrichment for tumors with a late chronological onset, whether to the extreme of the “early” cluster or in the “late” cluster, likely due to an opportunity for these tumors to develop because of the reduced frequency of the “early-standard” variety. Taken together, these results point to a role for E2F activities in determining the population of B lineage cells that contribute to the development of tumors and highlights the interplay between two cell regulatory activities, E2F and Myc, in determining the outcome of the oncogenic process.
Mice were housed in a Duke University Medical Center Division of Laboratory Animal Resources facility and experiments approved by the Duke University Institutional Animal Care and Use Committee. The generation of the specific lines of E2F-deficient mice has been previously described [19],[28],[53]. The original 129 substrain background was 129/SvJae for the E2F1, E2F2 and E2F3 cohorts and 129/OlaHsd for the E2F4 cohort. Based on breeding history, the E2F1 cohort mice used in this study were predominantly C57BL/6 (backcrossed five generations into C57BL/6) while the E2F2, E2F3 and E2F4 cohorts were mixed C57BL/6×129. The four E2F cohorts were maintained separately and breeding involved crossing heterozygous mice to yield wild type, heterozygous and null mice in each generation.
The Eμ-myc transgenic mouse line 292-1 [5] extensively backcrossed into C57BL/6 and originally from Dr. Alan Harris (Walter and Eliza Hall Institute, Melbourne, Australia), was kindly provided by Dr. Scott Lowe (Cold Spring Harbor Laboratory). For each E2F cohort, stock Eμ-myc positive C57BL/6 congenic males were bred to E2Fn+/− females and of the progeny only the Eμ-myc positive E2fn+/− males, designated the F1 males, were kept. These F1 males, E2fn+/− myc+, were then bred to E2fn+/− myc− females. The Eμ-myc positive progeny of this cross, E2fn+/+, E2fn+/−, and E2fn−/− were then compared. Because maternal transmission is associated with reduced latency [34], transmission of the Eμ-myc transgene was exclusively paternal in this breeding scheme. Eμ-myc negative siblings were also kept as a source of related mice that lacked myc transgene effects.
Eμ-myc positive mice were monitored weekly to identify any mice with malignant disease. Mice were evaluated for any visible or palpable lumps, a hunched posture, tachypnea, a swollen belly, or ruffled fur and sacrificed promptly upon the appearance of any such symptoms. Lymphomas that emerged were dissected from sacrificed mice, washed in PBS, and frozen in liquid nitrogen or processed for flow cytometric analysis. The frozen tissue provided material for Southern and western analysis.
Tumor onset data refer to the time in days between birth and the first indication of illness. Using GraphPad's Prism program, the time values were plotted to generate Kaplan-Meier survival curves and the curves compared by a logrank test. For comparisons of means and standard deviations, the paired student t-test was performed and statistical significance was determined if the p<0.05.
To assess alterations in p19ARF, Mdm2 and p53 protein expression, lymphoma samples were dissected from morbid mice and immediately frozen. Samples were then weighed, ground to a powder in liquid nitrogen and resuspended in 60 mM Tris (pH 6.8)/1% SDS at 1 ml per 0.2 g tumor weight, boiled, sonicated, and any remaining debris removed by centrifugation. In parallel, whole cell extracts were made from mouse embryonic fibroblasts infected with the indicated adenoviruses for controls. Protein was quantitated using the BCA Protein Assay Reagent Kit (Pierce). Samples (150 µg) were boiled in sample buffer and subjected to SDS-PAGE on 8.5% polyacrylamide gels for p53 and Mdm2 assessment and 15% gels for ARF assessment. Western analysis was performed as previously described [54]. The blots were probed with antibodies specific for p53 (monoclonal antibody Ab-1 OP03 at 1∶1000, Calbiochem), p19ARF (polyclonal antibody Ab-1 PC435 at 1∶10,000, Calbiochem), and Mdm2 (polyclonal antibody C-18 sc812 at 1∶1000, Santa Cruz Biotechnology). Equal protein loading was verified by staining blots with Ponceau Red (0.2% ponceau red in 3% trichloroacetic acid).
Genomic DNA was isolated from lymphomas, normal spleen cells, tail samples and MEFs of specified genotypes. DNA (10 µg) was digested with BamH1 (for the p53 locus), AflII (for the p19ARF locus) or EcoRI (for the heavy chain locus). The restricted DNA was separated by agarose gel electrophoresis (0.8% gels), transferred to Hybond N+ membrane, and probed. The p53 probe was a human cDNA fragment (686 base pair DrdI-StuI fragment extending from exon 4 to exon 10). The ARF probe was the exon 1B portion of the ARF cDNA (kindly provided by Charles Sherr). The heavy chain locus probe was the heavy chain J3-J4 joining region genomic fragment [37]. On occasion, to verify that weanling mice were essentially tumor-free, genomic DNA isolated from B lineage cells was assessed by Southern analysis for any emergence of clonal heavy chain rearrangements.
Mononuclear cells were harvested from the bone marrow of 3-week-old littermates and from lymphomas that arose. Cells were stained with various combinations of antibodies to IgD (11-26c.2a), IgM (R6-60.2), CD19 (1D3), B220 (RA3-6B2), CD43 (S7), BrdU, and active caspase-3. All antibodies and staining reagents were from BD Biosciences. Cell staining procedures were performed either manually or using a Biomek 2000 robotic fluid handler (Beckman Instruments, Schaumburg, IL using a series of mini-programs developed with BioWorks software (Beckman Instruments). FACS analysis was performed on a FACSCalibur device equipped with a 488 nm argon laser and a ∼635 nm red dye laser (Becton Dickinson (BD), San Jose, CA). Data was analyzed using FlowJo Software (TreeStar, Palo Alto, Ca).
Three hours before analysis, mice were injected with 100 mg/kg BrdU. Bone marrow mononuclear cells were collected and stained with the B220 and CD19 antibodies to identify B lineage cells, with 7-AAD, and with anti-BrdU antibodies. BrdU Flow Kit reagents and directions were followed (BD/Pharmingen). The proportion of cells that had proceeded through S-phase, or resided in G0/G1 or in G2/M phases was determined.
Hematopoietic cells were harvested from the bone marrow, spleen and mesenteric lymph node, combined and enriched for B lineage cells using negative selection (SpinSep Mouse B Cell Enrichment Cocktail, Stem Cell Technologies). The antibodies used to label unwanted cell types for depletion were directed against CD4, CD8, CD11b, CD49b, Gr-1, TER119 and CD43. The approach yielded a subset of B lineage cells from the small pre-B stage through more mature stages. These cells were cultured at 37°C for eight hours in DMEM plus 10%FCS/100 µM L-aspargine/50 µM 2-mercaptoethanol at a concentration of 4×106 cells/ml. At selected time points cells were removed and stained with 7-AAD and B220 antibody, fixed and permeabilized, stained with activated caspase-3 antibody and analyzed by flow cytometry. Viable cells were negative for both 7-AAD and activated caspase-3.
Equivalent numbers of bone marrow cells from non-transgenic 4–6 week old mice were resuspended in Methocult M3630 (Stemcell Technologies) according to manufacturer's specifications to assay for pre-B cell colonies. This media, formulated for the detection and counting of mouse pre-B progenitors in bone marrow, is comprised of methylcellulose in Iscove's MDM supplemented with recombinant IL-7, 2-Mercaptoethanol, L-glutamine, and fetal bovine serum. All samples were assayed in duplicate. After seven days colonies were counted using an inverted microscope. The count was based on the manufacturer's description of expected colony appearance - namely that colonies are composed of at least 30 cells and that individual cells are tiny and irregular to oval in shape.
RNA was extracted from lymphoma samples using Qiagen RNeasy Kits (Qiagen). RNA sample integrity was verified by agarose gel electrophoresis or using an Agilent 2100 Bioanalyser. We prepared the targets for DNA microarray analysis and hybridized to Affymetrix Mouse 430 2.0 GeneChip arrays according to the manufacturer's instructions and as previously published. To allow merging of expression array results from samples arrayed independently, some duplicate samples were arrayed to provide reference samples and the expression values standardized using ComBat [55]. The method for cross-platform comparison across different versions of Affymetrix GeneChip arrays was described previously [56].
Hierarchical clustering and visualization were performed using Gene Cluster 3.0 (http://bonsai.ims.u-tokyo.ac.jp/~mdehoon/software/cluster/) and Java TreeView (http://jtreeview.sourceforge.net/). Genes and tumors were clustered by average linkage using uncentered correlation as the similarity metric. Analysis of expression data was described previously [56]. In summary, we collected training sets consisting of gene expression values of samples where the pathway activity was known. We created gene expression signatures by choosing the genes whose expression profiles across the training samples most highly correlated with the phenotype. Then, to predict the status of the phenotype on a tumor expression dataset, we fit a Bayesian probit regression model that assigned the probability that a tumor sample exhibited evidence of the phenotype, based on the concordance of its gene expression values with the signature.
The Supporting Materials and Methods are available in Text S1.
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10.1371/journal.ppat.1005551 | Prion Strain Differences in Accumulation of PrPSc on Neurons and Glia Are Associated with Similar Expression Profiles of Neuroinflammatory Genes: Comparison of Three Prion Strains | Misfolding and aggregation of host proteins are important features of the pathogenesis of neurodegenerative diseases including Alzheimer’s disease, Parkinson’s disease, frontotemporal dementia and prion diseases. In all these diseases, the misfolded protein increases in amount by a mechanism involving seeded polymerization. In prion diseases, host prion protein is misfolded to form a pathogenic protease-resistant form, PrPSc, which accumulates in neurons, astroglia and microglia in the CNS. Here using dual-staining immunohistochemistry, we compared the cell specificity of PrPSc accumulation at early preclinical times post-infection using three mouse scrapie strains that differ in brain regional pathology. PrPSc from each strain had a different pattern of cell specificity. Strain 22L was mainly associated with astroglia, whereas strain ME7 was mainly associated with neurons and neuropil. In thalamus and cortex, strain RML was similar to 22L, but in substantia nigra, RML was similar to ME7. Expression of 90 genes involved in neuroinflammation was studied quantitatively using mRNA from thalamus at preclinical times. Surprisingly, despite the cellular differences in PrPSc accumulation, the pattern of upregulated genes was similar for all three strains, and the small differences observed correlated with variations in the early disease tempo. Gene upregulation correlated with activation of both astroglia and microglia detected in early disease prior to vacuolar pathology or clinical signs. Interestingly, the profile of upregulated genes in scrapie differed markedly from that seen in two acute viral CNS diseases (LaCrosse virus and BE polytropic Friend retrovirus) that had reactive gliosis at levels similar to our prion-infected mice.
| Accumulation of aggregates of misfolded protein in brain is a common feature of the damage seen in several neurodegenerative diseases including prion disease, Alzheimer’s disease and Parkinson’s disease. In the present work three strains of prion disease differed in accumulation of the disease-associated prion protein (PrPSc) on neurons and astroglial cells. These patterns were first detectable in the thalamus at 40–60 days after inoculation. This coincided with initial detection of gliosis and PrPSc deposition, but was far in advance of clinical signs or spongiform pathology. In spite of the different patterns of cellular PrPSc deposition, these three strains had similar patterns of expression of a large number of genes known to be active during neuroinflammatory responses and gliosis. However, the gene upregulation in scrapie differed markedly from that seen in two neurovirulent viral diseases, which also had abundant glial responses similar to those observed with prion infection.
| Several neurodegenerative diseases including Alzheimer’s disease (AD), Parkinson’s disease (PD), frontotemporal dementia (FTD) and prion diseases are characterized by accumulation of aggregates of misfolded protein in brain [1]. The particular protein or proteins involved in each of these diseases are different, but in each disease the protein misfolding appears to be spread within the brain by a seeding process where one misfolded aggregate can seed the misfolding of other normally folded molecules of the same protein by a mechanism known as “seeded polymerization” [2, 3]. In the case of prion diseases, seeded amplification results in increased levels of the misfolded protein and spread to adjacent brain regions. In addition, extracts from these brains can transmit prion disease to new individuals by experimental, iatrogenic or natural routes [4]. The realization that seeded polymerization is a similar process, not only in infectious prion diseases but also in some other non-infectious neurological diseases, has led to a resurgence of interest in studies of prion-like effects in many neurodegenerative diseases [5]. One goal is to develop common strategies of therapeutic intervention against the seeded polymerization events.
Prion diseases are slowly progressive, usually fatal brain diseases characterized by the development of vacuoles in the gray matter, prominent gliosis involving astroglia and microglia, and deposition in brain of aggregated partially protease-resistant isoforms (PrPSc or PrPres) derived from host-encoded normal prion protein (PrPC or PrPsen) [6]. These diseases occur naturally in humans and ruminants and can be transmitted to rodents, nonhuman primates, felines, mustelids and other animals. Interestingly, within a given animal species multiple strains of prion infectivity have been identified based primarily on differing patterns of regional brain pathology at the clinical end-point. The molecular explanation for the maintenance of diverse strain phenotypes in a single mouse strain with only one type of PrP protein sequence is not clear. However, the secondary structure of the PrPres aggregates is known to differ among certain strains, and such structures appear to be maintained during templated replication of prions using a single primary PrP protein sequence [7].
Most studies of prion strains have focused on strain-specific differences in the regional patterns of prion-induced vacuolar neuropathology and/or PrPSc deposition [8, 9, 10], but a few papers have also described strain differences in association of PrPSc with particular brain cell types. For example, at late clinical times sheep infected naturally or experimentally with sheep or mouse scrapie were found to have strain-specific patterns of PrPSc accumulation with neurons or glia [11–13]. In hamster experiments, accumulation of PrPSc in neuronal soma at the clinical end-point varied among 8 scrapie strains and appeared to correlate with shorter incubation periods [14]. In other studies using mice, morphological patterns of PrPSc deposition at the clinical end-point were shown to differ among certain scrapie strains; for example, ME7 was primarily neuronal, and 79A was both neuronal and astroglial [15]. Although certain patterns of cell association were clear in these experiments, the extensive spread and deposition of PrPSc at the clinical end-point might obscure the initial specificity of PrPSc for certain brain structures or cells. However, few preclinical studies have followed scrapie strain-specific PrPSc cellular associations. In two studies comparing scrapie strains ME7, 79A and 22L at 77 and 91 dpi after intra-hippocampal microinjection, strain-specific differences were seen in gliosis and synaptophysin staining, but cell specificities of PrPSc accumulations were not examined [16, 17].
Recently, after intracerebral microinjection of 22L scrapie in mice, we observed generation of new PrPSc beginning at 3–7 dpi, which was often on blood vessels near the injection site in the striatum [18]. In other microinjection experiments, we detected new PrPSc accumulations at 40 dpi in ipsilateral thalamus and cortex at levels 2–3 mm caudal to the injection site [19] suggesting that spread from the needle track to these sites was likely following neuronal circuitry. This strategy of microinjection followed by serial sectioning to identify the regions of interest at early times post-injection provides a useful approach to studying early events in the pathogenesis of scrapie infection in vivo. Although the intracerebral route is not a “natural” route, intracerebral infection does occur in humans via iatrogenic infection by contaminated surgical instruments and dural grafts. Thus studies involving this route may have practical relevance.
In the present study, dual-staining for PrPSc and for cell specific antigens was used to compare the cellular associations of PrPSc from three mouse-adapted scrapie strains at a time of early PrPSc deposition after microinjection in the striatum. To avoid artifacts, most studies were done at sites away from the needle track, such as thalamus, cortex and midbrain. In this work, PrPSc generated early after infection by the three strains tested was found to vary in its association with astroglia and neurons.
Because of the well-known association of micro- and astrogliosis with prion disease pathogenesis, we also studied whether prion strain-specific PrPSc deposition was correlated with the neuroinflammatory response seen during scrapie infection [20–25]. In the present paper expression of multiple neuroinflammatory genes in the thalamus was analyzed at preclinical time-points from 40 to 100 dpi using a sensitive real-time polymerase chain reaction (RT-PCR) method. Surprisingly, despite the differences seen in cell-association of PrPSc among the strains, all three strains had similar patterns of upregulation of genes associated with neuroinflammation. Interestingly, neuroinflammation early after prion infection in thalamus differed considerably from the pattern of genes upregulated in brains of mice infected with two acute CNS viruses that had similar levels of gliosis to the prion-infected mice.
In the current experiments we compared spread of PrPSc from three well-known scrapie strains (22L, RML and ME7) following microinjection of 0.5 μl of 10% scrapie brain homogenate into the striatum. In previous experiments, PrPSc was generated near the needle track site starting at 3–7 dpi and continued to spread in this local region [18]. This intracerebral microinjection caused a small amount of trauma to the local nerves and capillaries. However, PrPSc was subsequently found at 40 dpi in ipsilateral thalamus at a distance of 2–3 mm caudal to the injection site, suggesting that intracerebral microinjection did not alter long distance spread by neuronal circuitry [19]. The tempo of infection of the thalamus by the three different strains was determined by immunohistochemistry (IHC) analysis of PrPSc. At 40 dpi 22L PrPSc was readily detected in thalamus (Fig 1A), whereas RML and ME7 PrPSc were at the lower limit of detection (Fig 1C and 1E). In contrast, at 60 dpi PrPSc from all three strains was easily seen in thalamus, although ME7 was somewhat less abundant (Fig 1B, 1D and 1F). The faster pace of 22L infection may be due to the 10-25-fold higher amount of infectivity inoculated compared to RML and ME7 (see methods for details). In contrast, the titer of ME7 was 2-fold higher than that of RML, but still the onset of detectable PrPSc in thalamus was slower for ME7. Clinical disease was also delayed in ME7 (174 dpi for ME7 versus 149 dpi for RML), suggesting that this difference was a property of these two strains. The slower course of ME7 infection has been reported previously by others [10].
To obtain a quantitative analysis of thalamic PrPSc levels in this experiment, thalamic brain tissue of replicate mice was analyzed for PrPSc by immunoblot and densitometry. In thalamic homogenates of mice infected with strains 22L or RML PrPSc bands were similar but weak at 40 dpi, and were much more intense at 60dpi (Fig 2). In contrast, in samples from ME7-infected mice PrPSc bands were undetectable at 40dpi, but weak bands were visible 60dpi. In fact, the band intensities observed in ME7 samples from 60dpi closely resembled that seen in 22L and RML samples at 40dpi (compare adjusted volumes for Fig 2B ME7 to 2A 22L and RML).
Thalamus was an area with reliable early PrPSc generation, which was also sufficiently far (2–3 mm) from the needle track in the striatum to avoid artifacts due to the needle track wound. Therefore, we studied thalamus in mice at various times after microinjection of scrapie agent from strains 22L, RML or ME7 by dual-staining IHC to detect the early cell associations of newly generated PrPSc. Sections were examined at several different times from 20 to 100dpi, and the time-points giving the optimal density of PrPSc for evaluation of the cell type associations are shown in Fig 3. At 20 dpi PrPSc from strain 22L was associated with stellate cells, which did not stain positive for GFAP (Fig 3A, inset). Based on their morphology and staining for S100, these cells were likely astroglia that were not yet fully reactive and thus did not express detectable levels of GFAP [26]. At 40 dpi we also observed cells with a star-like morphology expressing PrPSc, but these cells expressed GFAP and thus appeared to be astroglia (Fig 3A). Surprisingly, at 40 dpi using anti-NeuN neuronal staining, 22L PrPSc was not associated with NeuN-positive neurons, but instead was associated with NeuN-negative cells with smaller nuclei that were consistent with astroglia (Fig 3B). Less dense PrPSc was also seen in the neuropil between various cell bodies, but this material could not be assigned to any particular cell type since processes of many cell types occupy this space (Fig 3B, blue arrow). After dual staining with anti-Iba1 to detect microglia, 22L PrPSc was also occasionally associated with microglia (Fig 3C, blue arrow), but several PrPSc-positive cells negative for Iba1 staining are also seen (black arrows). Dual-staining with anti-Olig2, typically a marker for oligodendroglia, also showed co-association with 22L PrPSc (Fig 3D), and therefore these cells were likely to be Olig2-positive reactive astroglia, which have been described previously [27].
Similar examination of strain RML PrPSc in thalamus with these same four dual stains showed a pattern nearly identical to that seen with 22L PrPSc (Fig 3E–3H). RML was examined at 60 dpi in thalamus because the extent of PrPSc spread was optimal for cell-type analysis at that time and was very similar in density to 22L at 40 dpi. Thus, in thalamus 22L and RML PrPSc seemed to be mainly associated with astroglia, but were also associated to a lesser extent with microglia and oligodendroglia. These data indicated that astroglia were either a primary target of infection and PrPSc generation by these two strains or that astroglia were efficient at sequestering 22L and RML PrPSc made by other cell types.
In contrast to above data, results with strain ME7 in thalamus at 60 dpi were quite different. There was no obvious association of ME7 PrPSc with GFAP, Iba1 or Olig2, indicating this PrPSc was not associated with astroglia, microglia or oligodendroglia (Fig 3I, 3K, and 3L). In contrast, ME7 PrPSc was associated with NeuN-positive neurons (Fig 3J), and also appeared to be present in numerous aggregates of varying size located in the neuropil (Fig 3I–3L). Thus neurons and neuropil seemed to be the main sites of deposition of ME7 PrPSc in thalamus.
PrPSc from these three scrapie strains is known to be deposited in other brain regions in addition to thalamus. Therefore, we also examined the cell-type association of PrPSc in several other brain regions, including cerebral cortex, substantia nigra (sn) and hypothalamus (ht), which all showed evidence of detectable PrPSc at preclinical times just slightly later than in thalamus. In cortex, 22L PrPSc was associated with GFAP-positive stellate cells (Fig 4A) that appeared to be astroglia and were not NeuN-positive neurons (Fig 4B). In sn and ht, 22L PrPSc was also associated with similar NeuN-negative astroglia with a stellate morphology (Fig 4C and 4D).
In cortex RML PrPSc was also associated with astroglia (Fig 4E and 4F), but surprisingly, in sn and ht, RML PrPSc was associated with neurons and neuropil (Fig 4G and 4H). The reasons for this regional difference in cellular associations of RML PrPSc were not clear.
Similar to the situation in thalamus, ME7 PrPSc was associated with neurons and neuropil in cortex, sn and ht. ME7 PrPSc was not associated with GFAP-positive cells (Fig 4I), but there was abundant PrPSc deposition in neuropil, association with NeuN-positive neurons in cortex (Fig 4J) and in sn (Fig 4K). Similarly, in hypothalamus, ME7 PrPSc deposits were noted surrounding numerous neurons and were also in the neuropil (Fig 4L).
In summary, at relatively early stages of scrapie infection in several brain regions, strain 22L PrPSc showed a consistent association with astroglia and occasional association with microglia, whereas strain ME7 PrPSc was associated with neurons and neuropil. In contrast, strain RML PrPSc was associated mainly with astroglia in thalamus and cortex, but with neurons and neuropil in sn and ht.
We next carried out experiments to investigate the functional biochemical effects of scrapie infection in thalamus at early times post-infection using these same three scrapie strains. Because of the strong association of PrPSc with glia and the early appearance of gliosis prior to visible neuronal vacuolation or cell death, we focused on analysis of transcript levels of a group of genes previously thought to be involved in neuroinflammation and gliosis [20]. Initially we quantified the amount of detectable gliosis in the thalamus of preclinical scrapie-infected mice, using Gfap and Vimentin as markers of astroglia, and Gpr84 and Cx3cr1, as markers of microglia (Fig 5A and 5B) [28–30]. For scrapie strains 22L and RML, an increase in Gfap expression compared to mock-infected mice was observed at the earliest times of significant detection of PrPSc in thalamus by IHC (40 and 60 dpi, respectively), and further increases were also seen at two subsequent time-points 20 and 40 days later (Fig 5). For strain ME7, which was slightly slower than RML in PrPSc generation, Gfap upregulation was not seen until 80dpi (Fig 5). Interestingly, significant expression of the microglial activation marker, Gpr84, in the thalamus was delayed by 20 dpi relative to that of Gfap for all three strains; however, Gpr84 levels increased for all strains at later time-points (Fig 5). For all three prion strains Vimentin and Cx3cr did not increase at these same early time-points of infection, suggesting that these genes were not markers of glial activation in these prion models.
Having established that astroglial and microglial activation markers were detectable early in the infected thalamus, we used qRT-PCR to analyze expression levels of 86 genes known to be associated with neuroinflammation (S1 Table). Thalamic mRNA obtained at three preclinical times from mice infected with strains 22L, RML, or ME7 was studied. Eleven genes were found to be significantly upregulated after 22L or RML infection at the earliest times measured (40 dpi for 22L and 60 dpi for RML) (Fig 6). These genes included the astrogliosis marker, Gfap, as well as other genes likely to be expressed by astroglia, including Cxcl10 and Ccl2. However, other genes in this group, such as Tnf, are thought to be mainly expressed by microglia. The remaining genes could have been expressed by astroglia, microglia or other cell types such as neurons or oligodendroglia. At later time-points more genes were upregulated after infection with each of the scrapie strains. Table 1 shows fold change and statistical significance at 40, 60 and 80 dpi of a list of 32 genes found to be upregulated at least 2.0 fold after infection with 22L at 80 dpi, and genes higher than 3.0 fold change were highlighted in black. Tables 2 and 3 show the fold change for these same genes at 60, 80 and 100 dpi after RML or ME7 respectively. With each strain there was a progressive increase in the number of upregulated genes over time. RML appeared to be about 20 days slower than 22L, but using 3.0 fold change as a cutoff, there were five genes upregulated after RML, but not after 22L infection (Cx3cr1, Il4, Cxcl5, Ccr6, and Ccl11). ME7 was more than 20 days slower than RML, and at 100 dpi only 14 genes were elevated above the 3.0 fold change cutoff (Table 3)
Overall these data suggested that similar inflammatory processes occurred in the thalamus during the early stages of infection regardless of the strain of scrapie used or the cellular pattern of PrPSc deposition. The timing of the upregulation of neuroinflammatory genes differed among the strains, but appeared to correlate with the pace of the infection in thalamus for each of the scrapie strains studied.
The upregulation of neuroinflammatory genes in early scrapie appeared to follow the generation of PrPSc and the development of gliosis. The activated astroglia and microglia are likely to contribute to the profile of neuroinflammatory gene upregulation observed in our studies. Therefore, we next investigated whether the expression pattern of neuroinflammatory genes in scrapie was different from that seen in two fatal CNS viral diseases with prominent gliosis reactions: LaCrosse Virus (LACV), a bunyavirus causing encephalitis in children and mice [31–34] (reviewed in [35]), and BE virus, a chimeric neurovirulent retrovirus of mice [36, 37]. After ip inoculation in young mice, each of these viruses causes a fatal disease with prominent astrogliosis and microgliosis. In the case of BE virus neurons are not infected, and there is minimal degenerative pathology [37–39]. However, in LACV neurons are infected and killed, and in addition to gliosis there is a strong leukocyte infiltration from the periphery [31, 32, 34].
By analysis using qRT-PCR 26 genes noted to be elevated during scrapie were upregulated after infection with LACV (Table 4). Since many of these genes are known to be expressed by activated astroglia and/or microglia, the strong gliosis response seen with both scrapie and LACV might account for this overlap in their gene expression profiles. However, LACV infection also upregulated an additional 31 genes that were not elevated by scrapie (S2 Table). Ccr2 was one of these latter genes, and its upregulation by LACV correlated with leukocyte infiltration in the LACV infected brain. This infiltration in turn might give rise to the very high expression levels of cytokine genes seen in LACV infection as well as the elevation of many genes not upregulated by scrapie (S2 Table).
In contrast to LACV, BE retrovirus infection induced upregulation of only 17 genes in the array tested, and 15 of these were also upregulated in scrapie (Table 5). Also, of the 27 genes upregulated in scrapie, eleven were not increased in BE (Table 5). Cxcl11 and Cxcl1 were the only genes upregulated by BE and not by scrapie in these samples (S3 Table). Cxcl11 was in fact found previously to be elevated in the late stages of scrapie [20]. Thus, the expression profiles for BE and scrapie were similar, but there were also significant differences. In the case of BE, the reasons for these differences were not clear. However, the microglial response appeared to be weaker in BE infection than in scrapie infection, as measured by expression of Gpr84 (microglial activation marker) (S3 Table), and this might account for the lower number of elevated genes after BE infection.
In the present work we studied the spread of PrPSc from the injection site in the striatum to several areas distant from this site. Microinjection into a small area made it possible to localize progression of infection over time. Previous studies by others indicated that spread of scrapie infection from the periphery and within the CNS was primarily via nerves using neuroanatomical pathways [40–45]. Here we compared three scrapie strains (22L, ME7 and RML). Outside the striatum, PrPSc from all three strains appeared first on the ipsilateral side in dorsomedial thalamus and lateral cortex [19], and then sequentially in contralateral cortex and ipsilateral substantia nigra (Fig 4). These findings supported the interpretation that the long distance spread for all three strains was via neural circuitry, and not by the brain interstitial fluid (ISF) as was seen previously over short distances within the striatum [18].
In the current paper, we studied early events of PrPSc infection in vivo by following the cellular associations of PrPSc at preclinical times (mostly 40–60 dpi). These early times were also selected in order to allow a precise identification of the cell types where PrPSc accumulated prior to the onset of extensive neuropathology. In pilot experiments, at the clinical end-point, PrPSc deposition usually became too dense for accurate determination of PrPSc accumulation around, in or near individual cells using dual staining. However, at earlier preclinical time-points using dual staining IHC in various brain regions, cell specificity of PrPSc accumulation was found to differ markedly among three scrapie strains. For example, starting at 40 dpi in thalamus and lateral cortex, 22L PrPSc accumulated mainly around parenchymal astroglia in all areas distant from the needle track including lateral cortex, thalamus, hypothalamus and substantia nigra. Other works previously found a similar possible association of 22L PrPSc with hippocampal astroglia at 56 dpi, but data on other brain regions was not reported [46]. In contrast to strain 22L, strain ME7 PrPSc almost never localized with astroglia, microglia or oligodendroglia, but instead associated primarily with neurons and neuropil, similar to what was previously reported at clinical times by others [12, 15].
Interestingly, strain RML appeared to combine the properties of strains 22L and ME7. For example, in thalamus and cortex, RML PrPSc colocalized mostly with astroglia, which was similar to 22L. However, in substantia nigra and hypothalamus, RML colocalized with both astroglia and neurons as well as neuropil, and similar findings were also noted in pons and vestibular nuclei. Our findings with RML were similar to studies using the closely related scrapie strains 79A and 79V, where PrPSc was associated with neurons and astroglia in several brain regions at clinical time-points [15]. Because of the early times of observation of these strain-specific cellular PrPSc accumulations in our experiments, these cellular associations are unlikely to be the result of extensive neuropathology; instead they more likely represent patterns of host/pathogen cellular interactions with PrPSc from individual scrapie strains.
The impact of the astroglial association of PrPSc of strains 22L and RML is not known. Possibly PrPSc accumulation of astroglia results in higher local levels of PrPSc, which in turn might increase the tempo of the disease. Indeed, 22L and RML have been found to progress more rapidly than strain ME7 by others [10] and by us (see methods for data on microinjections). Furthermore, astrocytic PrPSc has been previously shown to mediate neuronal damage indirectly by interaction with adjacent neuronal processes, even in the absence of PrPC expression on neurons [47].
The mechanism of association of PrPSc from specific scrapie strains with particular cell types is also not clear. Perhaps cell-specific molecules capable of acting as cofactors for strain-specific PrPSc amplification might be an explanation for these findings [48, 49]. Such molecules located on the external surface of the plasma membrane of specific cell types could potentiate PrPSc localization and new generation around neurons or astroglia. Similarly there might be intracellular factors capable of favoring intracellular PrPSc formation in specific cell types [50]. Neuropil PrPSc accumulation might be favored by factors on axons or dendrites or on glial cell processes located in these areas. If such factors could be identified in the future, this might provide new targets for drug therapy of specific strains of prion diseases. This same principle might also apply to other more prevalent neurodegenerative diseases where protein aggregation within or near specific cell types is a common feature.
In previous control experiments, the inflammatory gene upregulation associated with microinjection into the striatum was cleared within 14 days and was never detected in the thalamus. In the current study, using mRNA derived from thalamus at various early times starting at 40 days after infection, we were able to obtain data on expression levels of numerous transcripts possibly involved in the host response to scrapie infection and brain injury. By using quantitative RT-PCR arrays, the sensitivity for detection of transcripts was markedly increased above that seen in standard hybridization microarrays [20, 51–53]. This resulted in detection of numerous transcripts possibly overlooked previously. The three scrapie strains studied showed a slightly different temporal pattern of gene upregulation, as was also noted by IHC and Western blotting detection of PrPSc. The upregulated inflammatory genes were similar in the three strains correlating with early deposition of PrPSc and onset of glial activation.
Surprisingly, the scrapie strains did not show differing patterns of inflammatory gene upregulation, as may have been expected, due to the differing cell-type localizations of PrPSc observed by dual-staining IHC. However, neuronal injuries are well-known to stimulate neuroinflammation, and astrocytes might be more resistant to such damage. However, in the case of strain 22L with astroglial PrPSc, there may be additional PrPSc present on neurons at a level not detected by IHC. Alternatively, astrocytes injured by PrPSc accumulation might not take up glutamate properly leading to glutamate neurotoxic effects, or PrPSc accumulation on astrocytes might stimulate release of toxic molecules capable of injuring neurons [26, 54]. If so, neuroinflammation in all three scrapie strains may be stimulated primarily by neuronal damage induced directly or indirectly by PrPSc, and this might explain the similarity of the patterns of gene upregulation we observed with the three strains studied.
The chemokine ligands upregulated at early times from 40–60 dpi were: Cxcl10, Ccl2, Ccl4, Ccl5, Ccl7, and Ccl8. The receptors for these ligands are Cxcr3, Ccr1, Ccr2, Ccr3, and Ccr5. These systems are complex as most receptors can bind multiple ligands and each ligand can often bind multiple receptors [55]. Activation of these Ccr receptors leads to activation of many signaling pathways culminating in activation of Erk, Jun, STATs, and NF-kB, which are known to be stimulated in the brain during scrapie infection [20, 56–60]. Several of these receptors have been individually assessed for their role in scrapie pathogenesis. Deletion of Ccr1 (ligands: Ccl5, Ccl7, and Ccl8) led to a compensatory increase in Ccr5 and Ccl3 resulting in shortened survival times [56]. In contrast, elimination of either Ccr2 (ligands: Ccl2, Ccl7, Ccl8, and Ccl12) or Ccr5 (ligands: Ccl3, Ccl5, and Ccl8) did not alter prion incubation times [61]. Interestingly, elimination of Cxcr3 (ligands: Cxcl9, Cxcl10, and Cxcl13) led to a 30 day increase in survival times but greater gliosis and PrPSc accumulation was noted [62]. The diversity of these effects makes definitive conclusions difficult, and considerable redundancy in these cytokine stimulatory systems may be confusing the outcome. Perhaps experiments using simultaneous deletion of multiple receptors will be required to better understand the roles of cytokines and their receptors in scrapie and other CNS diseases.
To understand whether neuroinflammatory gene expression differed in scrapie versus other brain diseases with similar levels of astrogliosis, we attempted to make cross-platform comparisons of neuroinflammatory gene upregulation between our scrapie data versus hybridization array data from traumatic brain injury and tauopathy models [63, 64]. However, these comparisons gave unsatisfactory results, probably because the qRT-PCR method used for scrapie was much more sensitive than the use of hybridization arrays. For such comparisons the gene expression methods should have equal sensitivity. For this reason, we compared the scrapie data with LACV and BE infections where we had data using the Super-Array qRT-PCR method. Scrapie, LACV and BE all provoked strong gliotic responses to infection, and in all three infections we found upregulation of multiple genes that probably were derived from activated astroglia and microglia. However, marked differences between scrapie and either LACV or BE were also noted. Most strikingly LACV had a strong increase in Ccr2 correlating with leukocyte infiltration, which was not seen in scrapie or BE. There was also extensive neuronal death induced by LACV, which was not observed in BE or in scrapie at the early time-points studied in these experiments. These two differences probably accounted for the larger number of upregulated genes and very high fold change values seen with LACV.
Comparison of BE and scrapie also revealed significantly different profiles of neuroinflammatory gene expression. For example, of the 26 genes upregulated in scrapie only 15 were up in BE (Table 5). Furthermore, although activated microglia were seen in BE infected mice by IHC, based on the expression levels of microglial markers, Gpr84 and Aif1, the number of activated microglia was less in BE than in scrapie. This was surprising because the BE mice used in this study were already beginning to have clinical signs and would have died in the next 5–6 days. Although microglia are a major target of BE virus infection in brain, possibly this infection might decrease the amount of microglial activation. In contrast, astroglial activation as assessed by IHC and by upregulation of Gfap and Cxcl10 appeared to be strong in both scrapie and BE infection.
The comparison of neuroinflammatory gene profiles in three scrapie strains and in two other CNS viruses in this study supports the conclusion that reactive gliosis involving astroglia and microglia is likely more diverse than predicted by simply observing the activated glial cells by pathology and IHC. Each disease has its own individual gene profile that should ultimately provide extensive information about the processes taking place. However, our information about the detailed functions of many of the neuroinflammatory cytokines and their receptors is limited. The complexity of these ligands and their receptor specificity in the setting of the central nervous system makes this a challenging problem for the future.
All mice were housed at the Rocky Mountain Laboratory (RML) in an AAALAC-accredited facility in compliance with guidelines provided by the Guide for the Care and Use of Laboratory Animals (Institute for Laboratory Animal Research Council). Experimentation followed RML Animal Care and Use Committee approved protocols 2011–04 and 2014–23.
C57BL/10 (C57) mice were originally obtained from Jackson Laboratories and have been inbred at RML for several years. Young adult male mice, weighing 26–30 g were used for all stereotactic inoculations. In order to inject a high amount of infectivity while producing a minimum of damage from the volume of inoculum, mice were inoculated with 0.5 μl of 10% scrapie brain stock strains 22L, RML, and ME7 over 2 minutes using a pump as previously described [19, 65]. These stocks had been titered previously in C57 mice and contained the following ID50 in each 0.5 microliter volume: 22L, 1.0 x 105; RML, 4.0 x 103; ME7, 8.3 x 103. Average times to clinical disease using this microinjection protocol were as follows: strain 22L, 154 dpi; strain RML, 149 dpi; strain ME7, 174 dpi. At selected time points post-inoculation, mice were euthanized by isoflurane anesthesia overdose followed by cervical dislocation. Brains were removed and immersed in 10% neutral buffered formalin (3.7% formaldehyde) for histology. For future use in western blot and RNA gene expression assays, brains were cut in half sagittally and the region of the thalamus was removed by careful gross dissection. To define the thalamus in the fresh brain the corpus callosum was used as the landmark for the dorsal border, the septum as the frontal border, a line vertical from the interpeduncular fossa as the caudal border and a final cut defining the ventral border was made between the estimated levels of the thalamus and hypothalamus. The resulting thalamic tissue (approximately 40 mg) was frozen in liquid nitrogen.
For BE virus C57 mice could not be used as they are not susceptible to this virus or to many other murine retroviruses [36]. Therefore,129S1 mice were infected within 24 h of birth by intraperitoneal injection (i.p.) with 100 μl of cell culture supernatant containing 104 focus-forming units of BE virus per mouse. BE-infected mice were observed daily and euthanized with the onset of clinical signs as described [66]. For LACV infections 3 week old C57BL/6 mice were inoculated i.p. with 103 plaque-forming units of LACV human 1978 stock in phosphate-buffered saline (PBS) in a volume of 200 μl per mouse. LACV-infected mice were observed daily for signs of neurological disease and euthanized as described [67].
Dissected brain tissue from the thalamus was homogenized in PBS to create a 20% w/v brain homogenate (BH). Homogenization was done using a mini-bead beater homogenization system for 45 seconds on the homogenate setting. For proteinase K (PK) treatment, samples were incubated with detergents and PK as follows: 20 μl of a 20% tissue homogenate was adjusted to 100 mM Tris HCl (pH 8.3), 1% Triton X-100, and 1% sodium deoxycholate in a total volume of 31 μl. Samples were treated with 50 μg/ml of proteinase K (Roche Diagnostics) for 45 minutes at 37°C. The reaction was stopped by adding 2 μl of 100 mM Pefabloc (Roche Diagnostics) and placed on ice for 5 min. An equal volume of 2X Laemmli sample buffer (Biorad, Hercules, CA) was added, and then tubes were boiled 5 minutes. Samples were run immediately or frozen at -20°C until electrophoresed on a 16% Tris-Glycine SDS-PAGE gel (Life Technologies, CA) and blotted to PVDF using a 7 minute transfer, program 3 (P3) on an iBlot (Life Technologies, CA) device. Gels were transferred to polyvinylidene difluoride membranes using the iBlot transfer system (Life Technologies). Membranes were probed with a 1:100 dilution of monoclonal human anti-PrP antibody D13 derived from cell culture supernatants produced in our laboratory from CHO cells expressing the D13 antibody construct [19] that were kindly provided by Dr. R. Anthony Williamson. Monoclonal antibody D13 recognizes residues 94–105 in PrP [68] derived from mouse, hamster and squirrel monkey, and has been extensively used for detection of PrP in immunoblots and immunohistochemistry. The secondary antibody was peroxidase-conjugated anti-human IgG, used at 1:5,000 (Sigma), and immunoreactive bands were visualized using an ECL (Thermo Scientific) detection system. Densitometry on unsaturated immunoreactive bands was performed on exposed film using the Bio-Rad ChemiDoc MP system. Adjusted volumes for immunoreactive bands were calculated by taking the total band volume and subtracting the global background using Image Lab software version 5.0 (Bio-Rad).
Brains were removed and placed in 10% neutral buffered formalin for 3 to 5 days. Whole brains were divided coronally into 4 regions: (1) olfactory bulb to Bregma including the entire striatum, (2) middle thalamic area, (3) midbrain and (4) cerebellum. These tissues were then processed and embedded in paraffin. Sections were cut using a standard Leica microtome, placed on positively charged glass slides, and air-dried overnight at room temperature. The following day slides were heated in an oven at 60°C for 20 min. For all mice serial sections were obtained in the four regions mentioned above, and multiple sections were examined to confirm the reproducibility and regional extent of the pathology.
Deparaffinization, antigen retrieval and staining were performed using the Ventana automated Discovery XT stainer. Because of the intense aggregation of PrPSc immunostaining of PrPSc requires stringent antigen retrieval using high temperatures. In the present experiments PrPSc antigens were exposed by incubation in CC1 buffer (Ventana) containing Tris-Borate-EDTA, pH 8.0 for 100 minutes at 95°C as previously described [69]. Staining for PrP was done using human anti-PrP monoclonal antibody D13 described above. For immunohistochemistry, D13 culture fluid was used at a dilution of 1:100 for 2 hours at 37°C. The secondary antibody was biotinylated goat anti-human IgG at 1:250 dilution (Jackson ImmunoResearch, West Grove, PA), and streptavidin-biotin peroxidase was used with DAB as chromogen (DAB Map kit; Ventana Medical Systems, Tucson, AZ). Hematoxylin was used as a counterstain for all slides.
In order to study the localization of PrPSc with regard to specific brain cell types, slides were dual stained with D13 followed by one of several primary antibodies listed below. Slides were first stained with D13 using the complete protocol described above, finishing with DAB application. Then, without additional antigen retrieval, the second primary antibody was applied and staining was completed as described below, finishing with Fast Red application and hematoxylin counterstaining. The overall experimental plan and number of mice examined by pathology per group is shown in Table 6. In most cases 3 or more mice per time-point were examined, but occasionally when multiple time-points were examined only 2 mice were available. The goal in this study was to look at cell specificity of PrPSc accumulation early after spread to various brain regions. This required that PrPSc density be much less dense that was seen at the clinical end-point. Therefore, mice were often examined at different times in various brain regions, and we used time-points giving the clearest cell specificity of PrPSc accumulation. The photomicrographs shown in the figures are representative of typical fields seen in replicate mice. In previous single-staining experiments, comparing D13 versus humanized monoclonal antibody D18, which recognizes PrP residues 133–157 [68], we found very similar patterns of PrPSc deposition.
The following primary antibodies were used in dual staining at the dilutions shown: Rabbit anti-Iba1 (1:2000) was a gift from Dr. John Portis, LPVD, RML, Hamilton, MT. Other antibodies were rabbit anti-glial fibrillary acidic protein (GFAP)(Dako #Z0334)(1:3500), rabbit anti-oligodendrocyte factor 2 (Olig2)(Millipore #9610)(1:50), rabbit anti-NeuN (Millipore #ABN78)(1:1000). Primary antibodies were diluted in PBS containing stabilizing protein and 0.1% Proclin 300 (Ventana Antibody Dilution Buffer). Diluent without antibody was used as a negative control. Ventana streptavidin-biotin alkaline phosphatase system (Red Map Kit, Ventana) was used to detect Iba1, GFAP, Olig2, and NeuN as described previously [70] with the exceptions that the secondary antibody was goat anti-rabbit Ig, (Biogenex, HK336-9R) and Fast Red chromogen was used. Additional dual staining was done with primary antibodies against GFAP with DAB chromogen followed by antibody against Olig2 with Fast Red chromogen. Slides were examined and photomicrographs were taken and observed using an Olympus BX51 microscope and Microsuite FIVE software. Discovery XT Staining Module software was used to design the dual stain procedures.
For mice infected with scrapie, thalamus tissue was excised and homogenized in 1.0 ml ZR RNA Buffer (Zymo Research) and stored up to 5 days at -80°C before processing. Total RNA was isolated using the Quick-RNA MiniPrep (Zymo Research), eluted with 75 ul nuclease-free water with 1 x RNase Inhibitor (SUPERase-In, Ambion), and stored at -80°C until use. For mice challenged with LACV or BE, brains were removed and total RNA isolated as previously described [66, 67].
For quantitative analysis of changes in transcription using qRT-PCR arrays, 400ng of high-quality RNA from each sample was reverse transcribed to synthesize cDNA using the RT2 First Stand Kit per manufacturer’s instructions (Qiagen). Each cDNA reaction was mixed with 2x RT2 SYBR Green Mastermix purchased from Qiagen with RNase-free water to a final volume of 1.3 ml. Ten microliters of the mixture was then added to each well of a 384-well format plate of the Mouse Inflammatory Cytokine & Receptors super array PAMM-011ZE (Qiagen). The analysis was carried out on an Applied Biosystems ViiA 7 Real-Time PCR System with a 384-well block using the following conditions: 1 cycle at 10 min, 95°C; 40 cycles at 15 s, 95°C then 1 min, 60°C with fluorescence data collection. Melting curves were generated at the end of the completed run to determine the quality of the reaction products. Raw threshold cycle (Ct) data was collected with a CT of 35 as the cutoff. CT data was analyzed using the web-based RT² Profiler PCR Array Data Analysis (http://pcrdataanalysis.sabiosciences.com/pcr/arrayanalysis.php). All Ct values were normalized to the average of the Ct values for the housekeeping genes Actb, Gapdh, and Hsp90ab1. Changes in transcription were calculated by the software using the ΔΔCT based method [71]. Statistical analysis was performed using the unpaired Student’s t-test to compare the replicate 2(-ΔCT) values for each gene in the control group versus infected groups. A mean of ≥ 2.0 fold change and p-value of ≤ 0.05 considered significant. Each treatment and control group consisted of a minimum of 3 independent RNA samples.
To determine changes in Aif1 (encoding IBA1), Il12b (encoding IL12p40), Cx3cr1, Vim (encoding Vimentin), Gpr84, and Gfap transcription during disease by qRT-PCR, 200 ng of RNA was reverse transcribed into cDNA using the RT2 Easy First Stand kit (Qiagen) as indicated by the manufacturer. The cDNA product was diluted 1:2 with nuclease-free water and stored at -20°C until use. Twenty-five microliter reactions were performed in a 384 well format using 12.5 ul RT2 qPCR Mastermix (Qiagen), 10.5 nuclease-free water, 1.0 diluted cDNA template, and 1.0 ul Primer Assay Mix. qRT-PCR conditions were as follows: 1 cycle at 10 min, 95°C; 40 cycles at 15 s, 95°C then 1 min, 60°C with fluorescence data collection. Melting curves were generated at the end of the completed run to determine the quality of the reaction products. Raw threshold cycle (CT) data was collected with a CT of 35 as the cutoff. The results were calculated using the ΔΔCT method as above, where relative amounts of RNA were normalized to the geometric mean (CT) of Gapdh and Actin. Primer Assay Mixes for Aif1, Il12b, Cx3cr1, Vim, Gfap, Gapdh, and Act were purchased from Qiagen. Specific primer sequences for Gpr84 were CTGACTGCCCCTCAAAAGAC as the forward primer and GGAGAAGTTGGCATCTGAGC as the reverse primer.
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10.1371/journal.pcbi.1000444 | Power Efficiency of Outer Hair Cell Somatic Electromotility | Cochlear outer hair cells (OHCs) are fast biological motors that serve to enhance the vibration of the organ of Corti and increase the sensitivity of the inner ear to sound. Exactly how OHCs produce useful mechanical power at auditory frequencies, given their intrinsic biophysical properties, has been a subject of considerable debate. To address this we formulated a mathematical model of the OHC based on first principles and analyzed the power conversion efficiency in the frequency domain. The model includes a mixture-composite constitutive model of the active lateral wall and spatially distributed electro-mechanical fields. The analysis predicts that: 1) the peak power efficiency is likely to be tuned to a specific frequency, dependent upon OHC length, and this tuning may contribute to the place principle and frequency selectivity in the cochlea; 2) the OHC power output can be detuned and attenuated by increasing the basal conductance of the cell, a parameter likely controlled by the brain via the efferent system; and 3) power output efficiency is limited by mechanical properties of the load, thus suggesting that impedance of the organ of Corti may be matched regionally to the OHC. The high power efficiency, tuning, and efferent control of outer hair cells are the direct result of biophysical properties of the cells, thus providing the physical basis for the remarkable sensitivity and selectivity of hearing.
| The sense of hearing is exquisitely sensitive to quiet sounds due to active mechanical amplification of sound-induced vibrations by hair cells within the inner ear. In mammals, the amplification is due to the motor action of “outer hair cells” that feed mechanical power into the cochlea. How outer hair cells are able to amplify vibrations at auditory frequencies has been somewhat of a paradox given their relatively large size and leaky electrical properties. In the present work, we examined the power conversion efficiency of outer hair cells based on first principles of physics. Results show that the motor is highly efficient over a broad range of auditory frequencies. Results also show that the motor is likely controlled by the brain in a way that allows the listener to focus attention on specific frequencies, thus improving the ability to distinguish sounds of interest in a noisy environment.
| Outer hair cells (OHC) in the mammalian cochlea are essential to the remarkable sensitivity of hearing. These highly specialized cells actively feed mechanical power into the organ of Corti and amplify its mechanical vibrations in response to sound [1]–[5]. How this is achieved at auditory frequencies is a subject of considerable debate. Five biological motor mechanisms have been described in outer hair cells that may contribute [2],[3],[5],[6]. Motors localized to the hair bundles include: actin-myosin motors associated with slow bundle movements and adaptation mechano-electrical transduction (MET) currents [7],[8]; Ca2+ sensitive reclosure or conformational change of the MET molecular apparatus associated with fast bundle movements and adaptation [9]; and electrically-driven bundle displacement that act independent of MET function [10]. Motors localized to the soma include: cytoskeletal remodeling mechanisms [11],[12] and electrically-driven changes in length [13]–[15]. The ability of each of these mechanisms to feed mechanical power into cochlea is limited by their intrinsic thermodynamic properties. As such, some of these motors can be ruled out as key to amplification of mechanical motions in the cochlea simply because they are too slow. The mammalian cochlear amplifier is extremely fast and capable of cycle-by-cycle action, in some species at frequencies exceeding 50 kHz [16],[17]. This rules out mechanisms that require cyclic phosphorylation, transport and/or protein synthesis. In non-mammalian species, that do not have OHCs or the protein prestin, bundle-based motors underlie the active amplification process [18],[19]. In mammals, the evidence indicates OHC somatic motility is a key contributor [20]–[24], and this is the motor we focus on here.
OHC somatic electromotility is driven by the MET current entering the cell and likely draws thermodynamic power from the electo-chemical potential between fluid compartments in the cochlea. The apical surfaces of OHCs are bathed in high-potassium endolymph, biased to approximately +50 to +80 mV, and their basal poles bathed in high-sodium perilymph at 0 mV reference. This endocochlear potential is maintained by the stria vacularis and associated cells [25]–[27]. When the hair bundle is displaced and MET channels open at the tips the stereocilia, ionic currents (primarily K+ and Ca2+) are driven into the OHC. A fraction of this MET current enters the apical face of the soma at the base of the stereocilia. In the absence of phosphorylation, it is likely that this current carries the thermodynamic electrical power input that drives the OHC mechanical power output. Here, we analyze how this electrochemical energy is converted into useful mechanical work by somatic electromotility using the model illustrated in Fig. 1. The current model is fundamentally piezoelectric in nature and extends concepts developed by Iwasa [28],[29] to address frequency-dependent power conversion efficiency.
New results include the force vs. velocity, and power vs. velocity curves for OHCs (c.f. skeletal muscle cells [30]), and the frequency-dependent power efficiency that arises from intrinsic limitations on impedance matching between the cell and the load. Results indicate that OHCs are broadly tuned to have maximum power efficiency at a best frequency, thus contributing to tuning and the place principle in the cochlea. Furthermore, results provide an interpretation of how efferent activation may directly attenuate and de-tune the power output of OHCs and thereby providing a means for the brain to command exquisite control over the cochlear amplifier in a frequency dependent manner.
Experimental procedures and animal care were designed to advance animal welfare and were approved by the Baylor College of Medicine animal care and use committee.
Our primary objective was to estimate what fraction of the electrical power entering the soma is converted into useful mechanical power output, and to estimate how this conversion efficiency would vary with frequency and biophysical parameters. It has not yet been technically possible to directly measure the electrical to mechanical power conversion efficiency of the OHC. The primary challenge is that one must measure the MET current, membrane potential, mechanical force generated and mechanical strain and velocity, all simultaneously and under physiologically relevant mechanical loading conditions. Therefore, we applied first principles of physics to formulate a relatively simple mathematical model of the OHC that reproduces all key published experimental data using a single set of physical parameters. The same model was then applied to compute the power conversion efficiency.
Dissipative drag from the cytoplasm and the extracellular space are unavoidable. As a first approximation we modeled the axial component of the drag acting on the plasma membrane using a version of the Navier-Stokes equations. Assuming small displacements from the resting configuration, and ignoring the convective nonlinearity, the Navier-Stokes equations reduce to(20)where is the density of the fluid, r is radial coordinate, is the effective viscosity, and is the axial velocity. To approximate the visco-elastic properties of the materials, we used a complex-valued viscosity of the form , where , is the frequency, is a material constant, and the is a parameter that determines the relative contributions of viscosity vs. elasticity of the material. When this model reduces to the standard Newtonian viscous fluid and when this reduces to the standard shear elastic solid. For biological materials ζ falls between these two extremes – e.g. for the tectorial membrane [55]. These equations account for both the visco-elastic drag and entrained fluid mass. We solved the equations to obtain the velocity field resulting when a cylinder oscillates in the axial direction with displacement . Having the velocity field, we computed the axial shear stress acting on the cylinder wall per unit axial displacement(21)where are Hankel functions, is the non-dimensional Womersley number (complex-valued), and a is the cylinder radius. With this, the damping parameter appearing in the momentum equation (Eq. 8) is . This model is approximate, but matches the viscous analysis of Tolomeo and Steel [43] if the length of the cell is much longer than the diameter, motions are axial, and the viscosity is strictly real valued, i.e. .
Model parameters were estimated from known dimensions and physical constants combined with voltage clamp and mechanical data shown in Figs. 2–3 as well as microchamber data in Fig. 4. All other results (Fig. 5–8) and voltage clamp data in Fig. 4 are model predictions and the associated data were not used to estimate parameters. The model uses a reference thickness to describe the multi-component composite lateral wall and it is important to note that some parameters cannot be independently separated from this reference thickness (e.g. , , appear as groups). Coefficients appearing in the cable equation were computed from the physical parameters listed below using: , , and . Coefficients appearing in the wave equation were computed using , and . Dimensions were based on OHCs from the guinea pig cochlea. Data in Fig. 2–3 were used to find the effective stiffness, piezoelectric coefficient, electrical permittivity and conductance of the membrane. These data are for relatively low stimulus frequencies where the intracellular axial resistance has negligible effect on the results. To estimate the axial resistance we used the corner frequency where the capacitance measured at the basal pole of the cell begins to roll off (Fig. 3). The fraction of the membrane occupied by the motor was set to 80% () and the passive component to 20% (). The overall cell compliance was estimated from the slope of the compliance vs. cell length reported by Frank et al. [48], reproduced in Fig. 2C, using as well as the gain reproduced in Fig. 4 (solid, microchamber curve). An iterative optimization routine was run to refine the initial estimates of , and to simultaneously fit data in Fig. 2–4. Specific optimized numerical parameters include: OHC radius a = 4.5e-6 m; composite mechanical stiffness C* = 1.4e6 N/m2 (based on , and ); plasma membrane conductance ; apical face membrane conductance ; basal membrane conductance ; transduction current gain ; composite reference thickness ; OHC length ; length of the active lateral wall was , and was set by requiring passive basal pole to have a passive capacitance of 7 pF; intracellular axial resistance ri = 5.76e10 Ohm/m; composite piezoelectric coefficient at rest (C/m2) at rest; plasma membrane area specific capacitance ; density ; transduction current adaptation time constant ; fluid viscosity ; and fractional viscosity coefficient . We note that the mixture fraction is not uniquely determined by currently available data and it is possible to find alternative mixture fractions and stiffness parameters that result in the same composite stiffness C*. Nevertheless, it was necessary to use a value of to simultaneously fit all of the data and explain the magnitude of voltage dependent capacitance under unloaded and zero strain conditions. Additional experiments, perhaps involving voltage-dependent capacitance measurements under controlled mechanical loads, have the potential to resolve this ambiguity and reveal more about the lateral wall motor, but are not necessary for the purpose of the present power analysis since the composite parameters would not change.
Experimental procedures and animal care were designed to advance animal welfare and were approved by the Baylor College of Medicine animal care and use committee. All physical parameters were deduced from the published literature, with the exception of the intracellular electrical resistance, . To estimate , we isolated OHCs from the guinea pig cochlea [56] and examined the frequency dependence of the input electrical impedance under whole-cell voltage clamp (Axopatch 200 B, Molecular Devices, Sunnyvale, CA). OHCs were harvested from euthanized guinea pigs. Cells were patch-clamped at the base with quartz pipettes covered with Sylgard, and hyperpolarized to minimize the voltage-dependent nonlinear capacitance. K+ and Ca2+ ion channels were blocked with the addition of (C2H5)4N(Cl), CsCl and CoCl to the bathing and/or pipette solutions [57]. The input admittance was determined with a single sinusoidal voltage (0.015 V peak to peak, 90–3200 Hz) superimposed on top of a −0.13 V holding potential after correcting for the inherent phase shifts of the amplifier [58]. 210 measurements were averaged at each frequency. The resistance and capacitance were calculated from the input admittance [57] accounting for the series resistance (∼6 Mohm, remained constant throughout experiment). Experiments were conducted at room temperature.
There are four major observations that can be drawn from the present work. The first addresses how OHCs operate at high frequencies given their electrical capacitance [9], [62], [81]–[83]. Capacitance is thermodynamically conservative and present results confirm that the ability of OHCs to supply mechanical power to the cochlea is not limited by electrical capacitance [84], even at frequencies much higher than the membrane time constant (e.g. Fig. 7). This is true because capacitance is not dissipative. Instead, present results suggest the most serious factor that may limit power output by OHCs is how well the “impedance” of the hair cell is matched to that of the cochlear partition (e.g. Fig. 6). OHCs driving against an excessively stiff cochlear partition, for example, would be inefficient.
The second observation is that OHCs may be tuned to maximize their power output at a best frequency, albeit broadly tuned. Although OHC displacement and force are quite flat over a broad range of frequencies when driven by voltage (e.g. Fig. 5, present model and published data [48]), OHC power output is tuned when one considers the mechanical power output relative to the electrical power input. The predicted tuning is dependent upon cell length and correlates with the cochlear place principle [78], thus indicating that tuning of OHCs may contribute to the sharp mechanical and afferent neural tuning in the living cochlea.
The third observation addresses how the MET channels would be expected to further tune output of the somatic motor. MET adaptation generates high-pass filtered MET currents [47],[66],[85],[86]. Since the filtering is upstream of the somatic motor it would further sharpen tuning of OHC somatic motor output by attenuating low-frequency amplification. In the context of the organ of Corti, MET adaptation would also be expected to alter the phase of the OHC force possibly to maximize power input to the cochlea near the best frequency [67] and, additionally, might introduce a non-optimal phase that would sharply attenuate cochlear gain at both low and high frequencies. Because of these factors, the influence of tuning in isolated OHCs on tuning curves in the cochlea would be expected to be even more significant than implied by the OHC motor efficiency alone (Fig. 7).
The fourth observation is that OHC somatic power output may be controlled by the brain via efferent activated ionic conductance(s). The model predicts that increasing the conductance of the basal pole would reduce OHC power output and tuning, thus providing a plausible explanation for a fast mechanism that may be used by the brain to control both sensitivity and frequency selectivity of hearing (e.g. Fig. 7).
Finally, it is important to note that the OHC somatic motor is not present in non-mammals, yet these animals also exhibit many of the properties of the mammalian cochlear amplifier [87],[88]. The MET apparatus itself is clearly a key contributor to hair bundle motility and amplification [9],[89]. In addition, there is an MET-independent component of hair bundle motility driven by voltage [10]. This voltage-dependent component has analogy to the somatic motility addressed here, and may be involved in tuning and the power stroke of hair bundle motility with potential relevance to active bundle amplification in high frequency hearing organs [90]. These hair-bundle features occur upstream of the somatic motor and the two clearly interact with each other via micromechanical environment and electrical fields [91].
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10.1371/journal.pntd.0007603 | Association of APOL1 renal disease risk alleles with Trypanosoma brucei rhodesiense infection outcomes in the northern part of Malawi | Trypanosoma brucei (T.b.) rhodesiense is the cause of the acute form of human African trypanosomiasis (HAT) in eastern and southern African countries. There is some evidence that there is diversity in the disease progression of T.b. rhodesiense in different countries. HAT in Malawi is associated with a chronic haemo-lymphatic stage infection compared to other countries, such as Uganda, where the disease is acute with more marked neurological impairment. This has raised the question of the role of host genetic factors in infection outcomes. A candidate gene association study was conducted in the northern region of Malawi. This was a case-control study involving 202 subjects, 70 cases and 132 controls. All individuals were from one area; born in the area and had been exposed to the risk of infection since birth. Ninety-six markers were genotyped from 17 genes: IL10, IL8, IL4, HLA-G, TNFA, IL6, IFNG, MIF, APOL, HLA-A, IL1B, IL4R, IL12B, IL12R, HP, HPR, and CFH. There was a strong significant association with APOL1 G2 allele (p = 0.0000105, OR = 0.14, CI95 = [0.05–0.41], BONF = 0.00068) indicating that carriers of the G2 allele were protected against T.b. rhodesiense HAT. SNP rs2069845 in IL6 had raw p < 0.05, but did not remain significant after Bonferroni correction. There were no associations found with the other 15 candidate genes. Our finding confirms results from other studies that the G2 variant of APOL1 is associated with protection against T.b. rhodesiense HAT.
| Though some work has been done on the genetics of trypanosome infections in animals, relatively little is known about the genetics of human African trypanosomiasis (HAT) infections. To test whether any variants are associated with reduced or increased risk of trypanosomiasis, 96 variants in 17 genes were genotyped in patients diagnosed with T. b. rhodesiense HAT and individuals without the disease in this study. From the 96 variants, only one variant G2 in the APOL1 gene was found to be strongly associated with protection from trypanosomiasis. The results reported here will contribute to the knowledge of the role of human genetics in disease progression, which could offer opportunities for development of much needed new diagnostics and intervention strategies.
| Human African trypanosomiasis (HAT), also known as sleeping sickness, is one of the major neglected infectious diseases. Sleeping sickness is endemic in 36 African countries and over 60 million people are at risk of being infected [1]. HAT is more prevalent in rural areas where health care is scarce and affects mainly individuals of reproductive age, increasing their poverty [2].
HAT is a vector-borne parasitic disease transmitted by tsetse flies of the genus Glossina. It is caused by two subspecies of the single-celled parasite Trypanosoma brucei: T.b. rhodesiense found in eastern and southern Africa, with reservoirs in livestock and wildlife, and T.b. gambiense found in central and western Africa, which causes the majority of human cases with the main reservoir being humans [2,3]. Sleeping sickness has two clinical stages; the haemolymphatic stage followed by the meningoencephalitic stage. The two subspecies have different rates of disease progression; T.b. rhodesiense infection is typically described as an acute disease with rapid progression to late stage and T.b. gambiense progresses more slowly [3].
Untreated HAT infections are believed to be 100% fatal, with death occurring within weeks or months of symptoms first appearing [4,5]. However, there is increasing evidence that infection by T.b. rhodesiense can result in a wide range of clinical outcomes in its human host [6–8]. Furthermore, there is evidence that individuals from non-endemic areas suffer a more severe infection than people from endemic areas [9,10]. Similar variation in disease progression is also observed in infections with T.b. gambiense [11,12]. Some infected people in Guinea and Côte d’Ivoire progressed to self-cure after refusing treatment, and other individuals in endemic foci in West Africa have shown trypanotolerance analogous to that observed in some West African cattle breeds and in mouse models [13–18].
Genetic polymorphisms in T. b. gambiense as well as the human host have been shown to contribute to different responses to infection [19–21]. Genes involved in immune responses and regulating immunity play important roles in infection outcomes. One such gene is Apo-lipoprotein-L1 (APOL1) whose variants G1 and G2 are associated with kidney disease in African Americans and have been predicted to have been selected because they provide protection against HAT [22,23]. APOL1 lyses trypanosomes by depolarizing the parasite lysosomal membrane, which leads to osmotic swelling and rupture of the lysosome and then lysis of the trypanosome [24–28]. Trypanosoma brucei rhodesiense can infect humans because they express the serum-resistance-associated (SRA) protein, which binds to the SRA-interacting domain of APOL1 resulting in the loss of APOL1 lytic function [24–30]. It has been shown that serum containing G1 and G2 alleles of APOL1 is lytic to T.b. rhodesiense in vitro, whilst the parasites are resistant to serum containing the G0 allele [22], but evidence that these alleles of APOL1 mediate resistance to parasites in vivo is less conclusive. The G2 allele has been associated with protection against T.b. rhodesiense HAT in one study in Uganda but not in another, and no associations have been found between carriage of the G1 allele and reduced risk of developing T.b. rhodesiense HAT [31,32].
Other genes have also been implicated in the response to infection with T. brucei spp. Two candidate gene association studies in the Democratic Republic of Congo (DRC) have shown association with the disease and alleles of four genes (IL6, HLA-G, IL10 and APOL1) out of the 10 genes (IL1A, IL4, IL6, IL8, IL10, TNFA, IFNG, HLA-G, HPR and APOL1) studied [21,33]. In other studies, cytokine levels have shown significant association with HAT infections, but the genetic factors regulating this response have not been identified [8,33–37]. In the present study, we investigated the role of single nucleotide polymorphisms in 17 genes (IL10, IL8, IL4, HLA-G, TNFA, IL6, IFNG, MIF, APOL1 HLA-A, IL1B, IL4R, IL12B, IL12R, HP, HPR and CFH) for association with susceptibility to HAT using a case-control study design on subjects from the northern part of Malawi.
The samples came from the Rumphi District in northern Malawi (Fig 1), where the prevalence of HAT cases is highest in Malawi. Between 2000 and 2006, 150 people were confirmed to have died of HAT in Rumphi district alone [38,39].
Cases were identified through active and passive surveys. Hospital case files were checked for previously diagnosed HAT patients and were followed up in their communities. All these cases had been treated with Suramin I.V 20 mg/kg body weight for 23 days and Melarsoprol I.V 3.6 mg/kg body weight for 23 days for the early and late stage of HAT respectively according to Malawi HAT Treatment Guidelines S1 Table. They were then followed up at 3, 6, and 12 months after discharge for review S1 Table. The active screening was conducted during the follow-up of HAT cases. After taking a history and an examination related to HAT infection, venous blood was collected in heparinized tubes and taken to the laboratory at a temperature of 4°C. Eight capillary tubes were prepared from each sample and the buffy coat was examined by microscopy. Out of 350 people screened, 243 individuals entered the study [40]. Cases were defined as individuals in whom trypanosomes were detected by microscopy in blood, lymphatic fluid or cerebral spinal fluids (CSF). Controls were defined as individuals with no signs and symptoms suggestive of HAT and no trypanosomes detected from the blood.
A total of 202 samples, 70 cases and 132 controls were genotyped. Cases and controls came from the same area, were born in the area and had been exposed to infection since birth.
The protocol was approved by the Malawi National Health Sciences Research Committee, protocol numbers NHSRC 15/4/1399 and Malawi 1213. There was also local involvement of all stakeholders, and local leaders gave approval for the study to be carried out in the area. All individuals enrolled in the study were 18 years of age or older. All individuals signed informed consent forms in their native language.
This study was one of six studies of populations of HAT endemic areas in Cameroon, Cote d’Ivoire, Guinea, Malawi, Democratic Republic of Congo (DRC), and Uganda. The studies were designed to have: 80% power to detect odds ratios (OR) >2 for loci with disease allele frequencies of 0.15–0.65; and 100 cases; 100 controls with the 96 SNPs genotyped.
This study had 132 controls, 70 cases from our study area, and had 80% power to detect an OR >2 with disease allele frequencies of 0.1–0.75 with the 96 SNPs genotyped. Power calculations were undertaken using the genetics analysis package gap in R [41–43].
DNA was extracted from whole blood (collected in heparin vacutainer tubes (BD) during survey), using Qiagen DNeasy Blood & Tissue Kit (Crawley, UK) as per the manufacturer’s instructions. Extracted DNA samples were temporarily stored at -20°C.
Genes were selected based on prior knowledge of their role in the development of HAT. The following genes were selected: IL10, IL8, IL4, HLAG, TNFA, IL6, IFNG, MIF, APOL, HLAA, IL1B, IL4R, IL12B, IL12R, HP, HPR, and CFH [20–22,31,33,36,44–53].
Ninety-six SNPs were selected for genotyping using two strategies: 1) SNPs that had been previously reported to be associated with HAT or 2) by scanning for sets of linked marker SNP (r2 <0.5) across each of IL4, IL8, IL6, HLAG, MIF and IFNG. The SNPs in this second group of genes were selected using a merged SNP dataset obtained from low fold coverage (8-10x) whole genome shotgun data generated from 230 residents living in regions (DRC, Guinea Conakry, Ivory Coast and Uganda) where trypanosomiasis is endemic (TrypanoGEN consortium, sequences at European Nucleotide Archive Study: EGAS00001002602) and 1000 Genomes Project data from African populations [54]. PLINK v1.9 package (https://www.cog-genomics.org/plink/1.9/) [55] was used to estimate linkage disequilibrium (LD) (r2) between loci and all sets of SNPs covering the gene were identified. Loci that were excluded during assay development or failed to be genotyped were not replaced and hence not all regions of each gene were linked to marker SNP. S2 Table shows the candidate genes and SNPs selected for this study.
Samples were genotyped by two commercial service providers: INRA- Site de Pierroton, Plateforme Genome Transcriptome de Bordeaux, France and LGC Genomics, Hoddesden, UK. At INRA, two sets of 40 SNP assays were designed using Assay Design Suite v2.0 (Agena Biosciences). SNPs were genotyped with the iPLEX Gold genotyping kit (Agena Biosciences) for the MassArray iPLEX genotyping assay, following the manufacturer’s instructions. Products were detected on a MassArray mass spectrophotometer and data were acquired in real time with MassArray RT software (Agena Biosciences). SNP clustering and validation was carried out with Typer 4.0 software (Agena Biosciences). LGC Genomics genotyped all SNPs that failed genotyping at INRA and some additional SNPs using the PCR based KASP assay [56].
Plink 1.9 [55] was used for data analysis and R version 3.3.1 (2016-06-21)—"Bug in Your Hair" was used for data visualization (R Foundation for Statistical Computing, Vienna Austria). The data from genotyping were converted to PLINK format and were tested for data completeness, allele frequencies, LD, and Hardy-Weinberg equilibrium (HWE). Individuals and SNP loci with more than 15% and 20% missing data respectively, were removed from the analysis. Fisher’s exact test [57] in PLINK was used for testing the association of SNPs with HAT. One of each pair of SNPs with post genotyping linkage r2 > 0.5 were excluded. This increased the power of analysis by reducing the number of tests. Multiple testing was corrected for using a Bonferroni corrected p-value of 0.00077 (0.05/65) [58] and the Benjamini Hochberg false discovery rate (FDR) was used to estimate the probability that the null hypothesis of no association should not be rejected [58,59].
Two hundred and two samples were sent for genotyping. There were 143 males (70%) and 59 females (30%) with 70 cases (35%) and 132 (65%) controls. The mean ages of the cases and controls were 45 and 41 respectively.
Ninety-six SNPs were genotyped from 17 genes (see Plink MAP and PED files S1 and S2 Data). After the data was cleaned, 26 individuals with more than 15% missing data were filtered out leaving 176; 59 cases and 117 controls. Nine SNPs with more than 20% missing data were filtered out leaving 87 (see S1 and S2 Figs). Four SNPs, which were not in HWE, were removed. A cut-off of HWE p-value of 1 x 10−8 was used and genotype scatter plots were checked for allele clusters.
To increase the power of analysis, 18 SNPs, which were linked to each other (r2 > 0.5), were excluded by pruning (by working across the loci in windows of five SNPs moving one SNP at a time and excluding one of each pair of SNPs with LD greater than r2 = 0.5). After quality control and linkage pruning, 65 SNPs were left for association analysis. See S3 Table showing filtered data, and S4 Table showing pruned SNPs.
Results of case-control studies can be confounded by population structure. Most of the cases and controls (95%) were Tumbuka speakers, however there were speakers of five other languages in the cases (3) and controls (8) (Table 1). If the minor language speakers had different allele frequencies from the Tumbuka, this could affect the results.
The Fisher’s exact test was used to compare allele frequencies in cases and controls. Allele frequencies differed at two SNPs in two genes (APOL1 and IL6) between the cases and controls. However, only rs71785313 (G2) in APOL1 (OR 0.14) remained significant after Bonferroni correction (threshold p = 0.00077) and after Benjamini-Hochberg FDR correction, as shown in Table 2. Complete results for all loci are shown in S5 Table. The data was also analysed using logistic regression with gender and age as covariates, neither of these covariates had significant effects (p > 0.05) (see S6 Table).
An association was observed at APOL1_G2 rs71785313 (Table 2) with an odds ratio of 0.14 (95% CI: 0.05 to 0.41, p = 0.00001). This indicates a substantially reduced susceptibility to T.b. rhodesiense infection for individuals that possess a G2 variant. No association was found at APOL1 G1 rs73885319 with T.b. rhodesiense infection (p = 0.80; Table 2). The remaining 15 genes did not show any statistically significant difference in the allele frequencies between cases and controls as shown in S5 Table.
The study looked at 96 SNPs in seventeen genes to test genetic association with HAT in the northern part of Malawi. The main finding of this study is that the APOL1 G2 variant was strongly associated with protection against T.b. rhodesiense infection in northern Malawi. This is the first study to show such an association in Malawi. Our study showed a seven-fold reduced susceptibility for individuals possessing the APOL1 G2 variant. This is consistent with a two-centre study in Uganda and Guinea [31] that found a five-fold reduced susceptibility to T.b. rhodesiense for individuals that possess a single copy of G2 variant but no association with the G1 haplotype and T.b. rhodesiense. However, another study in Uganda found no association between the G2 allele and T.b. rhodesiense HAT [32]. The two studies in Uganda were conducted in two very different populations. Cooper et al. [31] found an association in a population from Kabermaido District of mixed Nilotic and Bantu descent with a G2 allele frequency in controls of 14.4%, whereas Kimuda and colleagues [32] found no association in a population of Bantu descent in Busoga district with a G2 frequency of 8.6%. The G2 frequency in this study was 19.7% (Table 2), which is comparable to that in the Kabermaido population in Uganda. However, the Malawi population is also of Bantu descent and is linguistically and possibly genetically closer to the Busoga population with low G2 frequency and no association with HAT. Thus, G2 frequencies and association between G2 and HAT do not correlate with the major ethno-linguistic groups. This discrepancy may be due to random genetic drift or specific selection by HAT and/or other diseases at this locus or to variation in the SRA gene in the different foci.
There was no association between the G1 allele and HAT in Malawi, which is consistent with both previous studies on T.b. rhodesiense HAT in Uganda [31,32], but this is in contrast to studies of T.b. gambiense HAT population in Guinea where the G1 allele was protective [31,60]. An in vitro study also showed that G1 alleles are associated with less lytic potential than G2 alleles [22].
The seven-fold reduced susceptibility for individuals with APOL1 G2 variant is consistent with the in vitro evidence of lysis of T.b. rhodesiense by plasma containing the APOL1 G2 allele and a study that showed that mice with APOL1 G2 survived longer after infection with T.b. rhodesiense [22,61]. Both the G1 and G2 renal risk variants are in the SRA-interacting domain of APOL1. The two-amino acid deletion in G2 rs71785313 prevents the binding of SRA to APOL1 [22,61,62], enabling carriers of the G2 variant to lyse the parasites.
The G1 haplotype consists of two missense mutations in almost perfect linkage disequilibrium (rs73885319 and rs60910145). In this study, only rs73885319 was genotyped (Table 2 and S2 Table), but no association was found with HAT in Malawi.
In conclusion, this study has shown that host genetic polymorphisms play a role in the control of infections and morbidity in HAT. Of the 17 genes studied, only the APOL1 G2 variant showed a statistically significant association with T. b. rhodesiense infections after Bonferroni correction for multiple testing. This is the first study in Malawi to show this association and increases support for a role for this allele in disease resistance which has previously been found associated in one study but not associated in another study. Further studies will be required to determine the effect of the G1 variant on the severity of T. b. rhodesiense infections and gene expression between the cases and the controls.
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10.1371/journal.pbio.0060108 | Allele-Specific Up-Regulation of FGFR2 Increases Susceptibility to Breast Cancer | The recent whole-genome scan for breast cancer has revealed the FGFR2 (fibroblast growth factor receptor 2) gene as a locus associated with a small, but highly significant, increase in the risk of developing breast cancer. Using fine-scale genetic mapping of the region, it has been possible to narrow the causative locus to a haplotype of eight strongly linked single nucleotide polymorphisms (SNPs) spanning a region of 7.5 kilobases (kb) in the second intron of the FGFR2 gene. Here we describe a functional analysis to define the causative SNP, and we propose a model for a disease mechanism. Using gene expression microarray data, we observed a trend of increased FGFR2 expression in the rare homozygotes. This trend was confirmed using real-time (RT) PCR, with the difference between the rare and the common homozygotes yielding a Wilcox p-value of 0.028. To elucidate which SNPs might be responsible for this difference, we examined protein–DNA interactions for the eight most strongly disease-associated SNPs in different breast cell lines. We identify two cis-regulatory SNPs that alter binding affinity for transcription factors Oct-1/Runx2 and C/EBPβ, and we demonstrate that both sites are occupied in vivo. In transient transfection experiments, the two SNPs can synergize giving rise to increased FGFR2 expression. We propose a model in which the Oct-1/Runx2 and C/EBPβ binding sites in the disease-associated allele are able to lead to an increase in FGFR2 gene expression, thereby increasing the propensity for tumour formation.
| Recently, a number of whole-genome association studies have identified genes that predispose individuals to common diseases such as cancer. The challenge now is to understand how the identified risk loci contribute to disease, since the majority of these loci are located within introns (which are discarded after transcription) and intergenic regions, and therefore do not change the coding region of nearby genes. This manuscript describes how two single–base pair changes in intron 2 of the FGFR2 (fibroblast growth factor receptor 2) gene, “the top hit” of the breast cancer susceptibility study, exert their function. We find that the changes alter the binding of two transcription factors and cause an increase in FGFR2 gene expression, thus providing a molecular explanation for the risk phenotype. This is the first functional study, to our knowledge, of the risk loci identified for breast cancer in a whole-genome scan and demonstrates that these studies can be used as valid starting points for studying the underlying biology of cancer.
| FGFR2 (fibroblast growth factor receptor 2) plays a pivotal role both in mammary gland development and in cancer [1]. The FGFR2 gene encodes a transmembrane tyrosine kinase and can function as a mitogenic, motogenic, or angiogenic factor, depending on the cell type and/or the microenvironment. Mammary epithelial cells express FGFR2IIIb (including alternatively spliced exon 9), which binds FGF-7 and FGF-10, which are normally expressed by surrounding mesenchymal cells. Mouse models of mammary carcinogenesis have long established the FGF signalling pathway as a major contributor to tumorigenesis [2], and a mouse mammary tumour virus (MMTV) insertional mutagenesis screen for genes involved in breast cancer has identified FGFR2 and FGF10 [3]. In human breast cancer, the expression of FGFR2 has long been known to be elevated in estrogen receptor (ER)–positive tumours [4], which has been confirmed by data analysis performed with the ONCOMINE 3.0 array database [5,6]. Likewise both FGF-7 and FGF-10 have been found to be expressed in a proportion of breast cancers [7, 8]. Functional studies in cell lines have implicated FGFR2 as playing a role in tumourigenesis, with an alternative splicing in the C-terminal domain of FGFR2 giving rise to a more strongly transforming isoform [9]. However, as yet, nothing is known about the mechanism by which FGFR2 acts as a risk factor in predisposition to breast cancer.
We examined the functional implication of genetic variation in the FGFR2 haplotype associated with susceptibility to breast cancer and we demonstrate increased gene expression for the risk allele.
Two independent studies have identified FGFR2 as risk factor in breast cancer [10,11]. We have shown that in Europeans, the minor disease-predisposing allele of FGFR2 is inherited as a haplotype of eight single nucleotide polymorphisms (SNPs) covering a region of 7.5 kb within intron 2 of the gene [10] (Figure 1), in a haplotype block with no linkage disequilibrium with the coding region of the gene. Microarray gene expression analysis on the Nottingham City Hospital cohort, using both the Agilent [12] and the Illumina [13] platforms, indicated that FGFR2 is expressed at higher levels by tumours that are homozygous for the minor alleles than by those with the common alleles (Wilcox p < 0.05). Analysed tumours were all diploid for this region based on array-comparative genome hybridization data [14]. This correlation was independent of either ER expression or p53 mutation status of the cells. Quantitative TaqMan PCR analysis confirmed a significant increase in FGFR2 expression in rare homozygotes, as compared to common homozygotes (Wilcox p = 0.028) (Figure 2). We also examined expression of the FGFR2 ligands FGF-7, FGF-10, and FGF-22, which are usually produced by the surrounding stroma, in 45 normal breast samples as well as the microarray data on tumours, but we found no correlation with genotype. Furthermore, FGFR2 displays a very complex splicing pattern with the most commonly expressed variants of the N terminus of the gene either including exons 1, 2, and 3 or including exons 1 and 2, but lacking exon 3. Again, no correlation was observed between genotype and the presence or absence of exon 3. Thus, the risk genotype correlates with FGFR2 expression itself, rather than affecting its function through receptor-ligand interactions.
This correlation suggests that the functional SNPs map to a regulatory region within the gene, possibly by altering one or more transcription factor binding sites. Interactions between proteins from nuclear extracts and DNA were examined for the eight most strongly disease-associated alleles (Figure 1). Two of these candidate functional SNPs showed distinct binding patterns in electrophoretic mobility shift assays (EMSA). The common allele of rs7895676 (FGFR2–33) formed strong protein–DNA complexes with nuclear extracts from the breast carcinoma cell lines HCC1954 (Figure 3A) and PMC42 and from HeLa cells (unpublished data), whereas no binding was detected on the minor allele. Competition studies and supershift experiments identify the bound protein as C/EBPβ (Figure 3A). We note that the FGFR2–33 sequence has considerable homology to the C/EBPβ binding site from the interleukin 6 (IL-6) promoter [15] (Figure 3C). The heterogeneity of the observed protein–DNA complexes is most likely due to the presence of multiple C/EBPβ isoforms. For rs2981578 (FGFR2–13), both alleles give rise to a strong protein–DNA complex in HCC1954 cell extracts. However, a second more slowly migrating complex was only seen on the rarer genotype (Figure 3B). Interestingly, both alleles are able to compete for both bands, suggesting that the formation of the upper complex depends on the presence of the lower complex. Inspection of the FGFR2 DNA indicated the presence of a perfect octamer binding site immediately adjacent to the SNP, while the SNP itself lay within a sequence with homology to Runx binding sites (Figure 3C). Competition studies and incubation with specific antisera shows that both alleles bind Oct-1, while only the minor allele binds Oct-1 and Runx2 in HCC1954 nuclear extracts (Figure 3B), as well as in PMC42 cells (Figure S1).
To establish whether or not these sites were occupied in vivo, we carried out chromatin immunoprecipitation (ChIP) experiments using the ER+ breast cancer cell lines HCC70 and T47D, which are homozygous for the minor and the common FGFR2 alleles, respectively. In addition, we confirmed that these cell lines were diploid for the FGFR2 locus and only expressed the epithelial-specific isoform FGFR2IIIb [16]. The ChIP analysis was carried out on homozygous cell lines, because the SNP overlapping the C/EBPβ site lies in a repetitive region for which the different alleles could not be distinguished reliably by TaqMan PCR. A representative experiment is shown in Figure 3D. After Runx2-precipitation, the FGFR2–13 site is enriched 2-fold for the minor versus the common allele, confirming the EMSA results. Western blotting indicated that Runx2 is more abundant in T47D cells, thus confirming that differential ChIP in the two cell lines is due to the presence of the SNP. Oct-1 precipitation did not yield enrichment of FGFR2–13 for either allele. The Oct-1 epitope may either be sequestered within a higher-order complex or the antisera used do not work efficiently in a ChIP assay. On the FGFR2–33 site, we observed a 1.7-fold enrichment of C/EBPβ binding on the common allele. In addition, we observe that C/EBPβ can also bind to the minor allele, although less efficiently. Both cell lines contain comparable amounts of C/EBPβ as judged by Western blotting (unpublished data). In conclusion, both the C/EBPβ and the Runx2 binding sites are occupied in vivo.
To test whether differential protein binding could alter the ability of the susceptibility alleles to activate transcription, we multimerised oligonucleotides overlapping both the Oct-1/Runx2 and the C/EBPβ binding sites, cloned these in both orientations upstream of the luciferase reporter gene in pGL3Enh (Figure 4A), and assayed them in three breast cancer cell lines (PMC42, HCC70, and T47D). Transfections were carried out in triplicate and repeated at least twice for each cell line. A representative transfection into HCC70 cells is shown in Figure 4B (see Figure S2 for PMC42 and T47D). In all three cell lines tested, the minor allele at the Oct-1/Runx2 site stimulated transcription 2- to 5-fold over the common allele, independent of orientation, with the average being just above a 3-fold increase (p < 0.01). In contrast, the minor and common alleles of the multimerised C/EBPβ binding site did not show a consistent pattern of activation relative to each other. It varied with the cell lines and the orientation in which constructs were tested. Nevertheless, relative to the parental vector, the common allele always showed transcriptional activation. Compared to the common allele, the minor allele was either not significantly different or gave rise to a smaller degree of activation. However, in the latter case, the rare allele still activated transcription significantly above the enhancer-only construct (p < 0.01). Presumably this reflects the fact that the minor allele of FGFR2–33 still binds C/EBPβ above background levels in vivo (Figure 3D). By comparing the two different sites, we found that for Oct-1/Runx2 the minor allele was more active, while for C/EBPβ, the common site yielded higher levels of transcription in the majority of experiments. Hence their effects were opposing. We therefore assayed a synthetic construct consisting of single sites for C/EBPβ, Oct-1, and Runx2. In this arrangement, the effect of Oct-1/Runx2 clearly predominates, with the minor allele expressed at higher levels, reflecting the situation at the endogenous locus.
The data presented here lead us to conclude that the Oct-1/Runx2 binding site is the dominant determinant of differential expression between the common and minor haplotypes of FGFR2. Although Runx2 is a master regulator of osteoclast-specific transcription, Runx2 also plays an important role in mouse mammary gland–specific gene expression [17], where Runx2 activity is dependent on Oct-1 [18]. It is intriguing to note that in bone cells, overexpression of constitutively active FGFR2 leads to increased levels of Runx2 mRNA [19]. FGFR2 in turn is responsive to Runx2 in osteoclasts via the OSE2 (osteoclast specific element 2) in its promoter [20]. The description here of a Runx2 site in the FGFR2 gene that is occupied in breast cancer cells, suggests that in the presence of the minor genotype, a similar positive feedback loop could also be established in breast cells. The role of the C/EBPβ binding site on FGFR2 expression has been harder to define. The common allele binds C/EBPβ more tightly and activates transcription more strongly in most cases. Yet in a composite construct the activity of the Oct-1/Runx2 site dominates. This may be because C/EBPβ can directly bind to and synergize with Runx2 [21]. Thus, on the minor genotype, Oct-1 and Runx2 are present and able to synergize with the C/EBPβ bound (as suggested from the ChIP experiments), giving rise to higher levels of transcriptional activation. This is supported by the finding that a single copy of the C/EBPβ/Oct-1/Runx2 site gives rise to higher levels of activation than a concatemerized Oct-1/Runx2 site with six potential interaction sites (Figure 4A). A potential role for C/EBPβ in tumour etiology is supported by the observation that C/EBPβ is highly overexpressed in malignant human breast cells [22]. In conclusion, our evidence supports Oct-1/Runx2 as the probable primary determinant of activity, with C/EBPβ contributing to the risk haplotype.
The increased risk in breast cancer conferred by the FGFR2 allele is predominant for ER+ breast tumours, while there is no significant increase in risk for ER– tumours. Genome-wide analysis of ER binding sites has revealed three potential ER binding sites within the FGFR2 gene [23], and ER and Oct-1/Runx2 may cooperate to increase gene expression. This is consistent with findings that Oct and ER sites often cluster [23]. The risk conferred by the disease-associated genotype may also depend on the signalling potential of FGFR2 in ER+ cells. FGF-7 is over-expressed only in breast tumours that are ER+ [8]. Elevated levels of FGFR2 may then contribute to the establishment of an autocrine signalling loop, reducing the cell's propensity to undergo apoptosis [24]. Alternatively, paracrine signalling by mesenchymally or luminally derived FGF-7 or -10 on cells overexpressing FGFR2 may also drive cell proliferation.
To our knowledge, this is the first functional study on the risk loci recently identified for breast cancer. Our study demonstrates that SNPs identified by whole-genome scans can be used a valid starting points for studying the underlying biology of cancer. SNPs identified in other whole-genome scans for the genetic basis of complex diseases also primarily map in intronic or intergenic regions. Our observation that an identified SNP regulates the expression of the risk allele is therefore likely to be a common theme. Breast cancer is one of the most common cancers in the developed world. The FGFR2 minor allele carries only a small increase in risk and acts as part of a spectrum of risk factors. However, it has a high minor allele frequency (0.4), and FGFR2 is therefore likely to contribute to the incidence of breast cancer in many individuals.
DNA from the 170 tumour samples was genotyped using a fluorescent 5′ exonuclease assay (TaqMan) and the ABI PRISM 7900 Sequence Detection Sequence (PE Biosystems) in 384-well format. Duplicate samples were included to assess concordance and quality of genotyping. The genotyping assay was designed for rs2981582, which tags the whole haplotype block associated with the disease [10].
Analysis was performed on total RNA from breast tumour cases. cDNA was prepared with the TaqMan Reverse Transcription Reagents kit (Applied Biosystems) using random hexamers, according to the manufacturer's instructions. Expression levels were determined using a TaqMan Gene Expression Assay (Hs00240796_m1, Applied Biosystems) and normalized to four different housekeeping genes.
To assess whether there were significant statistical differences between the expression levels across the genotype groups we used a Wilcoxon test, fitted using the R statistical framework. Elsewhere, Student's t-tests were carried out using Microsoft Excel.
Breast cancer cell lines HCC1954, HCC70, T47D, and PMC42 were cultured in RPMI supplemented with 10% foetal calf serum and penicillin/streptomycin under standard conditions. These cell lines have been characterised extensively, and karyotypes are available at the Cancer Genomics Program of the University of Cambridge (http://www.path.cam.ac.uk/~pawefish).
Small-scale nuclear extracts and bandshifts were carried out as previously described [25], except that Complete Protease Inhibitors (Roche) were used. In supershift experiments, polyclonal antisera against Oct-1 (sc-232x), Runx2 (sc-10758x), and C/EBPβ (sc-150x) were obtained from Santa Cruz Biotechnology, Inc and up to 8 μl were added per reaction, unless otherwise stated. Oligonucleotides (Table S1) were annealed to complementary strands, and the resulting BamHI overhangs filled in with Klenow enzyme, using radiolabelled [α32P]dCTP (GE Healthcare, UK).
Primers were designed using Primer Express (Applied Biosystems) and Lasergene (DNA Star) to amplify regions of up to 100 bp comprising the SNPs of interest, plus one negative control (region of the genome not suspected to bind any of the transcription factors of interest) (Table S1). PCR amplification was carried out with Power SYBR Green Mastermix (Applied Biosystems), using 2 μl of precipitated and purified DNA as described [23]. The antisera were as in the EMSAs, except for C/EBPβ, which was a polyclonal serum from Abcam, UK.
The pGL3-Enhancer vector (Promega) was linearized with BglII and re-circularised in the presence of annealed oligonucleotides (Table S1). All constructs were verified by sequencing. DNA was prepared using Qiagen kits and transfected into tumour cell lines cultured in 24-well plates. Per well, 500 ng of reporter and 100 ng CMV-β-galactosidase plasmid were tranfected using 2 μl of Fugene 6 (Roche), harvested 36–48 h later and extracts prepared using 100 μl Promega lysis buffer. Luciferase and β-galactosidase activity in 25 μl was measured using Promega reagents. Results are given as ratios of luciferase over β-galactosidase activity.
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10.1371/journal.pntd.0003303 | Persisting Social Participation Restrictions among Former Buruli Ulcer Patients in Ghana and Benin | Buruli ulcer may induce severe disabilities impacting on a person's well-being and quality of life. Information about long-term disabilities and participation restrictions is scanty. The objective of this study was to gain insight into participation restrictions among former Buruli ulcer patients in Ghana and Benin.
In this cross-sectional study, former Buruli ulcer patients were interviewed using the Participation Scale, the Buruli Ulcer Functional Limitation Score to measure functional limitations, and the Explanatory Model Interview Catalogue to measure perceived stigma. Healthy community controls were also interviewed using the Participation Scale. Trained native interviewers conducted the interviews. Former Buruli ulcer patients were eligible for inclusion if they had been treated between 2005 and 2011, had ended treatment at least 3 months before the interview, and were at least 15 years of age.
In total, 143 former Buruli ulcer patients and 106 community controls from Ghana and Benin were included in the study. Participation restrictions were experienced by 67 former patients (median score, 30, IQR; 23;43) while 76 participated in social life without problems (median score 5, IQR; 2;9). Most restrictions encountered related to employment. Linear regression showed being female, perceived stigma, functional limitations, and larger lesions (category II) as predictors of more participation restrictions.
Persisting participation restrictions were experienced by former BU patients in Ghana and Benin. Most important predictors of participation restrictions were being female, perceived stigma, functional limitations and larger lesions.
| Disabilities among Buruli ulcer patients remain a problem. Previous studies revealed contractures, deformities and functional limitations in daily life after treatment. According to the International Classification of Functioning, Disability and Health, disabilities occur not only at the physical and activity level but at the participation level (participation restrictions) as well. The latter are the social consequences of the disease such as problems in relationships, going to festivals and visiting public places. This study focused on participation restrictions by using the Participation Scale among former Buruli ulcer patients and healthy persons residing in two areas endemic for Buruli ulcer in Ghana and Benin. This study showed that almost half of the former Buruli ulcer patients encountered problems in social life, especially related to employment. In addition, the results suggest that being female, perceived stigma, functional limitations and a larger lesion (category II) predict participation restrictions. These findings indicate that rehabilitation programs should not only focus on physical disabilities but also on participation after completion of medical treatment.
| Buruli ulcer (BU) is a skin condition caused by Mycobacterium ulcerans, which is the third most prevalent mycobacterial disease in immuno-competent humans, after the diseases caused by Mycobacterium tuberculosis and Mycobacterium leprae [1]. BU presents as a small nodule or a plaque sometimes accompanied by edema. At a later stage, the lesion breaks open with ulceration typically presenting with undermined edges [2]. The World Health Organization (WHO) has classified lesions as category I: lesions cross-sectional diameter of less than 5 cm; as category II: lesions of 5–15 cm and category III: lesions of >15 cm; category III also includes lesions on important sites (for example eyes) and multiple lesions. The exact mode of transmission remains unclear, though it is generally accepted that infection is associated with living close to stagnant water [3]. BU has been found in more than 30 countries predominantly with tropical or subtropical climates; the most burdened region is West Africa. In 2011, Côte d'Ivoire, Ghana and Benin reported the highest numbers of new cases [4]. In Benin, the prevalence varies from 5.4 cases/10,000 to 60.7/10,000 inhabitants depending on altitude of villages [5] while the national BU prevalence in Ghana is 20.7 cases/100,000 inhabitants [6]. Since 2005, standard medical treatment entails antimicrobial therapy sometimes complemented with surgery [7].
Prevention of Disability (POD) programs have been developed by the WHO, which are implemented in endemic countries to reduce disabilities. Essential components are wound management, and positioning and mobilization of the affected extremity. Nevertheless, studies have revealed that people still develop physical disabilities such as scarring, contractures, deformities, and sometimes require amputation [8], [9] or are otherwise left with functional limitations [10], [11]. Not only may BU lead to physical consequences, but also stigmatization is perceived by former BU patients, even years after healing [12]. Magico-religious ideas on the cause of BU, fear of contracting the disease and its visible signs are suggested to be the most important distinctive features of this stigma [13]. In other stigmatized health conditions such as leprosy and leishmaniasis, participation restrictions in social life after treatment are common [14]–[18]. Participation restrictions are defined as ‘any problem an individual may experience in involvement in life situations' [19]. For example, a person may encounter restrictions related to employment, meeting new people, visiting public places or attending social events in the community. Participants of a qualitative study have expressed that scarring and physical disabilities as a result of BU disease may result in problems with marriage and employment [20]. In addition, community members expressed persisting negative attitudes towards BU patients resulting in social exclusion as victims are believed to have no social responsibilities and should be restricted in attending social events [21]. Social problems are of particular importance because of their impact on a person's well-being and quality of life [22]. The aim of this study was to explore participation restrictions among former BU patients and to gain insight into the factors that predict participation restrictions.
From January to October 2012 data for this cross-sectional study were collected in Ghana and Benin. Eligible for inclusion were former BU patients aged at least 15 years, who were treated between 2005 and 2011, and whose treatment was completed at least 3 months before the study commenced. Medical records of the Centre de Dépistage et de traitement de l'Ulcère de Buruli de Lalo in Benin and Agogo Presbyterian Hospital in Ghana were screened for potential participants. In the absence of an address system and with no phone numbers recorded, potential participants had to be sought in the villages. In Benin a high number of potential participants were found, and therefore primary health care posts surrounding the hospital were chosen as study sites. Posts were selected if a high number of cases was found, from the medical records, in the catchment area of the post and if they were relatively easy to access (in terms of distance and road circumstances). In Ghana, former BU patients who participated in another follow-up study of the BURULICO trial in Ghana [23] were excluded. Healthy community controls without any history of BU or without a visible disability were recruited from villages located in the study area. In both countries, we aimed to include at least 50 healthy controls. Community controls representing the same age (+5/−5 years), female/male ratio and geographical location as the former BU patients were recruited.
Procedures regarding translation of the P-scale are extensively described elsewhere [30] briefly summarized the scale was translated and back translated into Twi (language in Ghana) and French (Benin).
Before data collection, in each country two native language speaking interviewers participated in a training to prevent bias during the interview. The training was provided using the available manuals; the Participation Scale Users Manual (version 6.0) and the BUFLS Manual (2012). During data collection regular discussions were held to reveal difficulties encountered during interviewing. During the interviews no specific problems were encountered with understanding the peer comparison. Former patients with BU were identified with either the assistance of a BU coordinator, a health care worker, or one of the local community volunteers. If eligible former BU patients could not be found or appeared not to be in the village, a second visit was planned to ask for study participation. To ensure privacy during the interviews, private quiet places were used to conduct the interview.
Ethical approval was granted by the Medical Ethical Review Committees of the Kwame Nkrumah University of Science and Technology, School of Medical Sciences, Komfo Anokye Teaching Hospital in Ghana (ref: CHRPE/RC/127/12) and the Ministry of Health in Benin (ref: N01961/MS/DC/SGM/DRF/SRAO/SA). Adult participants provided written informed consent. A parent or guardian of any child participant provided informed consent on their behalf.
Data analyses were performed with Statistical Package for the Social Science (SPSS) version 20.0. The cut-off for the P-scale scores was determined calculating the 95th percentile of the P-scale sum scores of healthy community controls [24]. Two outliers in Ghana were removed for this analysis. The resulting cut-off was 16, indicating that participants with scores up to 16 were categorized as not having participation restrictions and participants with scores 17 or higher were categorized as having participation restrictions. Basic features of the data were analyzed using descriptive statistics. As appropriate, Pearson's chi-square test, Fisher's exact test, Mann-Whitney U test, Kruskal-Wallis test and Spearman Ranks correlation were performed to compare for differences in socio-demographic factors and clinical aspects across countries as well as for univariate associations with P-scale sum scores. Factors significantly related (P<0.1) to P-scale sum scores were entered as potential predictors of participation restrictions in a linear regression analysis. Residuals were checked for a normal distribution. The variable: ‘visible deformity‘ was removed for analysis because of missing values (n = 41). Predictors were removed from the model when removal criterion was met (P>0.1). Interaction terms (country x sex, sex x stigma scores, sex x age, and country x stigma score) were explored, also using differences found in the previous analysis [30] between Ghana and Benin. For interpretability, age was centered at 15 years as minimum age of the BU patients was 15 years of age.
In total 121 patients were treated for BU in Agogo Presbyterian Hospital in Ghana between 2005 and 2011 of which 46 could not be found. Reasons were unknown addresses (20), unclear information on name or location (16), had died (6) or were not traced (4) resulting in participation of 75 former patients with BU in Ghana. In Benin, a total of 4 village health centers were visited resulting in 255 patients treated for BU between 2006 and 2011. In total 68 former patients with BU could be traced. Reasons why patients could not be found were not recorded. Significant differences between Ghana and Benin were found in length of time since start of treatment, type of treatment, lesion size, type of lesion, visible deformity, profession, and living situation (Table 1).
Using a cut-off value of 16, in total, 67 (47%) former patients with BU experienced participation restrictions (median 30, IQR; 23; 43) while 76 indicated no participation problems (median 5, IQR; 2;9). Median P-scale sum scores of the former BU patients were similar in Ghana and Benin (Ghana: median 13, IQR; 5;29, Benin: median 13, IQR; 4;30). Across both countries, the most frequently reported problems among former BU patients were related to employment. In addition, in Ghana former BU patients experienced mainly problems with meeting new people, giving their opinion in family discussions, long-term relationships, being socially active, respect, and recreational and social activities. In Benin, former BU patients experienced mainly problems with being socially active, giving their opinion in family discussions, going for visits outside the village, recreational/social activities, helping others, and attending major festivals and rituals. Patients in Ghana had higher scores on each item compared to the healthy community controls, except for doing household work and confidence to learn new things. In addition healthy community controls in Ghana had higher levels of participation restrictions compared to healthy controls in Benin (Figure 1 and 2).
To illustrate, a former BU patient expressed her aspiration to be the teacher of a local woman's group, but because of BU she could not effectively organize the group. Another patient expressed his wish to be the leader of his political party in the future but he did not have enough money and because of the condition he could not succeed.
In Benin, women, or patients with more than 1 lesion, a visible deformity or larger lesions scored significantly higher on the P-scale (Table 2). Furthermore, a median EMIC score of 19.5 (IQR; 10;36) was reported; women scored a median score of 19 (IQR; 9;36) and men 21.4 (IQR; 11;37). The median BUFLS was 7.9 (IQR; 0;20) and women (median 18, IQR; 3;24) scored significantly (P = .009) higher compared to men (median 0, IQR; 0;16). In Ghana, a median EMIC score of 20 (IQR; 13;53) was found; women scored a median score of 21.1 (IQR; 9;54) and men 20 (IQR; 13;54). The median BUFLS was 6.7 (IQR; 0;16) and women (median 11.8, IQR; 3;19) scored significantly (P = .043) higher compared to men (median 2.6, IQR; 0;13).
Factors significantly contributing to the regression equation were sex, functional limitations (BUFLS), perceived stigma (EMIC), age and lesion size (Table 3). The explained variance of the model was 52%. In the prediction model, females had on average higher (8.1) P-scale scores than men. As functional limitation increases by 1 point, (scale range 0–71), P-sale score increases on average with 0.6 units. As perceived stigma increases by 1 point, (scale range 0–90), P-scale score increases on average with 0.2 units. Having a category II lesion (cross-sectional diameter of 5–15 cm) increases the P-scale score on average with 7.6 points.
Post hoc analysis was performed to determine predictive value of lesion size as dichotomized variable (category I vs category II and III) on participation restrictions. Factors significantly contributing to the regression equation were similar as shown in Table 3 as well as the explained variance of the model. Having a category II or III lesion increases the P-scale score by 6.8 points (P = .010).
We showed persisting participation restrictions in almost half of the former patients with BU in Ghana and Benin. The percentage of former patients with BU with participation restrictions is less compared to previous studies among former leprosy patients positively screened for difficulties in functioning in Indonesia (about 60%) [18], but is higher compared to former leprosy patients in Bangladesh (34%) [17] and Brazil (35%) [16]. Most commonly reported problems as indicated by former BU patients related to employment. This is in line with a previous study on participation restrictions using the P-scale among a total of 20 leprosy affected persons in Nigeria [14] reporting problems in areas related to work, domestic life and interpersonal relations. And a study among recently diagnosed leprosy patients in India showed that many respondents experience restrictions in areas related to work [31]. Finally the results of our study confirm qualitative findings reporting that BU may cause problems with employment [20]. The other areas in which former BU patients experienced restrictions differed between Ghana and Benin. Predictors of participation restrictions were sex, perceived stigma, functional limitations and the size of the lesion. Women were more at risk for participation restrictions, which may be explained by sociocultural perception differences on participation restrictions between men and women. Further the difference can be explained by a different experience of the negative attitudes of community members as indicated in a previous study [21]. Furthermore it is plausible that women have more tasks and relationships as compared to men, however in Indonesia no difference in participation restrictions between men and women was found [18]. Patients affected by larger lesions may lose more muscle or joint function and as a result are more restricted in participation. In addition functional limitations may also affect people's mobility to participate in their community. Finally feeling stigmatized as a result of being a former BU patient may prevent people to interact with others in and outside the community or participate in relationships.
To our knowledge, this study was the first to use a prediction model for participation restrictions among former patients with BU as measured with the P-scale. Surprisingly duration between end of treatment and time of interview did not influence participation restrictions. In addition participation restrictions were not significantly different for category III lesions compared to category I lesions. It is plausible that the small sample size of former patients with BU with category III lesions resulted in this outcome. Therefore we performed a post hoc analysis dichotomizing small lesions (category I) and large lesions (category II and III). The results of this analysis showed that having a category II or III lesion increases the P-scale score by 6.8 points.
To establish cut-off scores the 95th percentile of the community scores was calculated. Two healthy community controls from Ghana were removed for this analysis because they presented extreme outliers, affecting cut-off tremendously (29 versus 14). The cut-offs varied slightly for Ghana and Benin (18 versus 14) indicating heterogeneity across countries. Though, we decided to calculate 1 cut-off as preferred for future use in the field.
Several study limitations should be mentioned. The groups of BU patients in Ghana and Benin were heterogeneous as many factors such as case finding activities and exclusion of potential participants due to participation in another study were beyond our control. As a result, former patients in Ghana were treated much more recently and mainly had category I lesion. Furthermore differences regarding employment related problems were found. However background information regarding these differences is not available as it was not the focus of our study, also because we did not expect these differences. In Benin, we aimed for random sampling of the potential participants, however, logistical reasons led to the decision for a convenience sample in certain villages. It is conceivable that selection bias may have occurred. Furthermore, the cross-sectional design of the study prohibits drawing causal relationships. As such, some of the statistical predictors (perceived stigma and functional limitations) of participation restrictions may also be a result of participation restrictions. This is in line with the ICF model encompassing all the dimensions of disability, showing solely bidirectional associations. Finally, due to unknown reasons visible deformity was filled out less frequently leading to missing data and its influence on the P-scale could therefore not be analyzed in the linear regression.
To conclude, we have shown persisting participation restrictions among former patients with BU, even long after treatment had finished and wounds had healed. Unfortunately the introduction of the antibiotic treatment in 2005 has not been able to prevent long-term consequences on the capability to participate in the community. The results indicate active case finding is required, as former patients with BU that presented with small lesions experienced less participation restrictions. POD programs, including stigma reduction strategies and physical and social rehabilitation are needed even after ‘successful’ completion of medical treatment. Such programs should pay extra attention to work integration. Before the development of these POD programs mixed methods studies should be performed to study local meanings of participation restrictions.
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10.1371/journal.pcbi.1002090 | An Evolutionary Trade-Off between Protein Turnover Rate and Protein Aggregation Favors a Higher Aggregation Propensity in Fast Degrading Proteins | We previously showed the existence of selective pressure against protein aggregation by the enrichment of aggregation-opposing ‘gatekeeper’ residues at strategic places along the sequence of proteins. Here we analyzed the relationship between protein lifetime and protein aggregation by combining experimentally determined turnover rates, expression data, structural data and chaperone interaction data on a set of more than 500 proteins. We find that selective pressure on protein sequences against aggregation is not homogeneous but that short-living proteins on average have a higher aggregation propensity and fewer chaperone interactions than long-living proteins. We also find that short-living proteins are more often associated to deposition diseases. These findings suggest that the efficient degradation of high-turnover proteins is sufficient to preclude aggregation, but also that factors that inhibit proteasomal activity, such as physiological ageing, will primarily affect the aggregation of short-living proteins.
| In order to carry out their biological function, proteins need to fold into well-defined three-dimensional structures. Protein aggregation is a process whereby proteins misfold into inactive and often toxic higher order structures, which is implied in about 30 human diseases such as Alzheimer's disease, Parkinson's disease and systemic amyloidosis. In earlier work it has been shown that although protein aggregation is an intrinsic property of polypeptide chains that cannot be entirely avoided, evolution has optimized protein sequences to minimize the risk of aggregation in a proteome. Here we show that this pressure is not uniform, but that proteins with a short lifetime have on average a higher aggregation propensity than long-living proteins. In addition, we show that high turnover proteins also make fewer interactions with chaperones. Taken together, these observations suggest that under normal physiological conditions the aggregation propensity of short-lived proteins does not represent a significant treat for the biochemistry of the cell. Presumably the strong dependence of these proteins on proteasomal degradation is sufficient to preclude the accumulation of aggregates. As proteasomal activity declines with age this would also explain why we observe a higher association of high turnover proteins with age-dependent aggregation-related diseases.
| Biological networks are fine-tuned to respond to narrow changes in protein concentration. The ability of a cell to maintain metabolic and signal transduction fluxes is therefore highly dependent on a tight regulation of its proteostatic network [1]. The capacity of the protein quality control system to regulate protein folding and degradation erodes with age, resulting in increased protein aggregation and aggregation-associated diseases [2], [3]. Which proteins first fall prey to misfolding is most likely a stochastic process that is modulated by both tissue-specific expression levels and environmental factors [4]. However, sensitivity to protein aggregation is also determined by intrinsic protein parameters such as the efficiency of the folding process [5], thermodynamic stability [6], [7], the aggregation propensity of the protein sequence [8], [9] and its ability to be recognized by the protein quality control system [10]. We previously showed that evolutionary forces shape protein sequences in order to minimize their aggregation propensity, by strategically placing aggregation-opposing gatekeeper residues along the sequence [11], [12]. Although this insight has been confirmed by independent studies [13], [14], [15], [16], the extent to which selective pressures mould protein sequences is most likely not uniform, but determined by the biological context in which the protein functions [17]. For instance, it has been shown that proteins with high expression levels on average have a lower aggregation propensity than proteins with lower expression levels [18]. We reasoned that proteins with high turnover rate and thus short lifetime will have, on average, lower risk of misfolding than long-living proteins. Their respective sequences should therefore also experience different selective pressures against protein aggregation. Such evolutionary pressure might have resulted in different affinities towards molecular chaperones and different implications towards aggregation-related diseases.
In order to determine the relationship between protein lifetime and protein aggregation we here combine experimental lifetime measured for 611 proteins [19] with the corresponding gene expression data in 532 healthy individuals. We also correlated experimental chaperone interaction data and structural information of these proteins to their aggregation propensity using TANGO [20], an algorithm that accurately predicts the intrinsic aggregation propensity of protein sequences. This analysis resulted in two major observations: i) short-living proteins on average are predicted to have longer and more severe aggregating regions than long-living proteins, and ii) the evolutionary enrichment of aggregation breaking gatekeeper residues is less pronounced in short-living proteins, suggesting that they experience milder selective pressure to minimize aggregation. Further, we also found significantly less interactions between short-living proteins and molecular chaperones in the IntAct database [21]. Our results suggest that under normal circumstances, protein aggregation of short-living proteins is not problematic, and thus there is little evolutionary pressure to reduce the intrinsic aggregation propensity or optimize chaperone interaction. This would turn such proteins into the Achilles' heel of the proteome in conditions where proteasomal function is significantly reduced, such as is reported for normal human ageing [22], [23], [24], [25]. In support of this hypothesis, we found that all but one of the proteins with experimentally determined turnover rates that are involved in a protein deposition disease belong to the fastest turnover rate group.
The current study focuses on short-stretch mediated protein aggregation, where specific segments of a polypeptide chain assemble into an intermolecular beta-sheet and thus nucleate aggregation. Since current knowledge in the field suggests that the short-stretch mediated protein aggregation covers the majority of disease-associated protein deposition, and no reliable prediction methods exist for alternative protein aggregation mechanisms, we feel justified to ignore alternative aggregation mechanisms such as 3D domain swapping and native protein aggregation. Like all current protein aggregation prediction algorithms, TANGO calculates intrinsic aggregation propensity of an input polypeptide sequence and returns short stretches predicted to have a high propensity to nucleate protein aggregation through the formation of intermolecular beta-sheets. These regions constitute the intrinsic aggregation propensity of the sequence in the absence of globular structure. Since these aggregation prone regions are nearly always part of the hydrophobic core when the protein resides in its native conformation, the aggregating stretches identified computationally need to become exposed by (partial) unfolding of the protein before they can actually nucleate protein aggregation. So, although three dimensional relationships that existed in the folded state are no longer relevant during assembly into an intermolecular beta-sheet, they are highly relevant to determine if a particular region is likely to become exposed in the first place. In order to estimate the likelihood that a given short polypeptide segment may become exposed by (partial) protein unfolding, we employ the FoldX force field, which calculates the contribution of each amino acid to the thermodynamic stability of the three dimensional structure of the protein, thus allowing to determine if an aggregation prone region is in a stable or less stable part of the structure.
The statistical mechanics algorithm TANGO [20] was used to determine the aggregation-prone regions in the human proteins. This resulted in an aggregation propensity (0–100%) for each residue, whereby an aggregating segment is defined as a continuous stretch of at least five consecutive residues, each with a TANGO score higher than 5%. The five positions before and after aggregation-prone regions are considered as “gatekeeping flanks”, with each P, R, K, E or D counting as gatekeepers [17]. No distinction was made between gatekeepers at the N or C terminus of the aggregating stretch.
Our dataset was composed of 532 HG-U133_Plus_2 type microarray experiments extracted from GEO (Gene Expression Omnibus) [26]. Queries were carried out using GEOmetadb module from R [27]. The dataset is composed of cancer healthy control samples only. HG-U133_Plus_2 microarrays contains probe sets of 54675 human genes per chip. All 532 chips were preprocessed in one single block using robust multichip average (RMA). RMA processing consists of three steps: background adjustment, quantile normalization and finally summarization. A list of common housekeeping genes (EIF4G2, RPL9, SFR9, GUK1, H3F3A, RHOA, ACTB) was used to confirm that the expression levels remain constant for the whole dataset. The dataset was divided into two subsets according to long-living and short-living proteins. Conversion of Affymetrix to Uniprot identifiers was done using Babelomics4 id converter [28], [29].
Structures were selected according to the following criteria: (1) 100% sequence identity with the sequence of interest, (2) crystal structure, (3) resolution at least 3 Ä. All modeling was performed using the FoldX 2.8 force field and tool suite [30], [31]. All structures were repaired using the RepairPDB command and homology models were constructed using the BuildModel command. The stability of the aggregation nucleating regions was extracted using the SequenceDetail command.
Comparison of the distributions for each parameter tested in the analysis of short versus long-living proteins was performed using Mann-Whitney and Kolmogorov-Smirnov tests.
Yen et al. developed a global stability analysis, a high throughput approach for proteome-scale protein-turnover analysis, resulting in a protein stability index (PSI) for 8000 human proteins [19]. PSI scores ranges from 1 to 7, with higher values indicating higher biological protein stability and thus slower protein turnover. To simplify the analysis, we used a low and a high cut-off value to eliminate proteins with intermediate lifetime, so that the data were split in two groups of short (PSI ≤ 2) versus long-living (PSI ≥ 5) proteins (Text S1). A number of characteristics of the aggregation propensity of these 611 proteins were determined using the TANGO algorithm [20]: i) the average aggregation propensity of the protein (total TANGO score normalized by protein length), ii) the number of aggregating segments in the protein, iii) the length of aggregating segments, and iv) the aggregation propensity of each aggregating segment. The correlation with the experimentally determined biological lifetime of the protein was tested for each individual parameter and significant differences were found (Text S1): Short-living proteins display a higher average aggregation propensity (Figure 1A), which is not caused by an increase in the average number of aggregating segments (Figure 1B), but by an significant increase in their length (Figure 1C) and aggregation propensity (Figure 1D). As previous studies have shown that long proteins on average have less effective aggregation-promoting regions than shorter proteins [32] and the average length of short and long-living proteins is respectively 263 and 357 amino acids, the aforementioned observations could also be due to the longer mean length of long-living proteins. In order to exclude this possibility, we repeated the analysis after the exclusion of proteins longer than 300 amino acids, and found that the difference in aggregation tendency between the two lifetime categories remains significant (p<0.001), showing that the observed difference in aggregation tendency is linked to the disparity in lifetime, and is independent of the difference in mean length of the proteins. This conclusion is confirmed by plotting the average aggregation tendency in function of the protein length for each lifetime category (Figure 2A). In view of the idea introduced by Vendrusculo and co-workers that protein expression levels are tuned to the solubility limit of the protein [18], we need to exclude that the difference in aggregation load in our data is simply due to a lower expression level for the fast turnover proteins. To address this, we employed publically available microarray data from the Gene Expression Omnibus (GEO) [33], corresponding to 532 healthy individuals from 62 studies to compare expression levels of the proteins in our lifetime dataset. The density plot of the normalized expression levels for all proteins from the short lifetime and long lifetime groups reveals indeed a different composition of both groups in terms of expression levels (Figure 2B). However, when we plot the length normalized aggregation score of the short and long-living proteins grouped per expression level (Figure 2C), we see that the expression level is not the determining factor in the difference in aggregation propensity between fast and slow turnover proteins. These results suggest that proteins with a short biological lifetime undergo less evolutionary pressure to minimize the burden of aggregation.
An alternative explanation for the lower sensitivity of fast turnover proteins to the evolutionary pressure against protein aggregation could be that these proteins possess native structures with inherently superior thermodynamic stability to those of proteins from the long lifetime group. Given the significant structural coverage of our dataset, i.e. there are high resolution crystallographic structures available for 127 proteins in our dataset of 611 (Text S1), we can address this question using a modeling approach. To do so we employed the FoldX force field [31] to calculate the thermodynamic stability of the aggregation nucleating regions predicted by TANGO in the corresponding crystal structures. We then plotted the average thermodynamic stability of the aggregating nucleating regions per bin of aggregation propensity according to TANGO (Figure 3A). In this plot, we observe a clear correlation between the aggregation propensity of a polypeptide stretch and thermodynamic stability of the same region in the context of its native three-dimensional structure, so that sequences with the highest aggregation propensity form the most stable parts of the protein structure under native conditions, which is in accordance with previous observations [5]. Importantly, Figure 3A reveals no significant differences between proteins with a long or a short lifetime, showing that the difference in aggregation propensity between these groups is not due to fundamental differences in protein architecture or thermodynamic stability.
It has been well established that evolutionary pressure against protein aggregation has resulted in the enrichment at the flanks of aggregation prone segments of gatekeeper residues, a term used to indicate amino acids that counteract aggregation [12], [15], [34]. This disruption of the aggregation prone stretches is achieved by a) the repulsive effect of charge (arginine, aspartate, glutamate), b) the entropic penalty for burial (arginine and lysine) or c) incompatibility with beta-structure conformation (proline) [34]. We analyzed the frequency of occurrence of gatekeeper residues in our short- and long-living protein datasets and found that the frequency of occurrence of gatekeeper residues shows a small but significant reduction in short-living proteins (Figure 3B), which indicates that the introduction of gatekeepers as an evolutionary mechanism, to minimize aggregation is less pronounced in this set. This is consistent with the observation of longer aggregating stretches since they are less frequently interrupted by aggregation breaking residues, resulting also in a higher aggregation propensity of the stretches.
A major component of the protein quality control system that evolved in all forms of cellular life to deal with the unavoidable burden of protein misfolding and aggregation is formed by the diverse families of molecular chaperones, which are a class of proteins that assist other proteins in (re)folding and disaggregation and eventually shuttle substrates to the degradation machinery [35]. In order to address the question if protein turnover rates influence the requirement of chaperone assistance of a protein, we searched the protein interaction database IntAct [21] (release March 19, 2010) for experimentally recorded interactions between proteins from our dataset and an extensive list of known human molecular chaperones (listed in Text S1). A total of 237 chaperone-binding proteins were identified, but experimentally determined protein stability was available for only 114 proteins. Based on Yen et al., we divided this set of proteins into four categories according to their PSI turnover scores: short half-life (PSI < 2), medium half-life (2 ≤ PSI<3), long half-life (3 ≤ PSI<4) and extra-long half-life (PSI ≥ 4) [19]. For each category we calculated the enrichment of chaperone-binding proteins, where enrichment is defined as PSIN/PSIT – SUMN/SUMT. PSIx is the number of proteins in a given set x, belonging to a given PSI category and SUMx the total number of proteins in a given set x. X points to the total set (T) or the (non-) chaperone-binding proteins (N). Comparison of the chaperone enrichment in short-living versus long-living proteins shows that in our limited dataset, proteins that interact with molecular chaperones are significantly enriched in the group of long-living proteins (Figure 3C). Given we observed no fundamental differences in the thermodynamic stability or protein architecture between these groups (see FoldX analysis above), this suggests that short-living proteins on average require less chaperone intervention than long-living proteins, consistent with the notion that their fast degradation rate is sufficient to protect against misfolding and aggregation.
We investigated which of the proteins in our dataset are involved in a human disease associated with protein deposition and found 16 proteins with known PSI score (Text S1). Interestingly, all but one of these proteins belong to the category of short (PSI < 2) or medium (2 ≤ PSI < 3) half-life. Although this analysis is not exhaustive, the data does suggest that the lack of evolutionary pressure to reduce aggregation in short-living proteins can backfire in circumstances were their turnover is altered.
Protein aggregation is triggered by short polypeptide stretches within a protein sequence that assemble into intermolecular beta-sheets when they become exposed to the solvent [8], [36], [37] (Figure 4). These aggregation nucleating regions can be predicted with good accuracy with biocomputational tools [20], [38], [39], [40], [41], [42], [43], [44], [45], [46], [47], [48], [49], [50], [51] and earlier work has shown that their occurrence is an inevitable consequence of the structural requirements of protein structure [52]. Globular protein architecture requires the tertiary packing of hydrophobic secondary structure elements to form a stable hydrophobic core. Unfortunately, these physicochemical parameters are also associated to a high probability for self-assembly of such secondary structure elements into β-aggregates [53], [54]. Indeed, less than 10% of globular protein domains are devoid of aggregation propensity [12]. As a consequence of these overlapping but opposing forces that govern protein folding and aggregation, protein folding is generally a very inefficient process [55], [56]. Moreover, aggregation is detrimental for the cell as misfolded proteins are inactive [57] and can acquire toxic gain-of-function [58]. Protein homeostasis is therefore tightly regulated by the protein quality control machinery of the cell.
Given the high burden of protein aggregation on the proteome, and even if aggregation propensity cannot be avoided altogether, selective pressure to minimize the aggregation propensity of protein sequences is still to be expected. Indeed, it was found that aggregation-opposing residues are enriched at specific sites along the sequence of proteins [12], [59]. These so-called aggregation-gatekeepers residues, consisting of prolines and charged amino acids, are systematically found at the flanks of aggregation-prone sequences stretches within proteins. Due to their β-breaking nature or charge they efficiently lower the aggregation propensity of hydrophobic stretches while at the same time preserving hydrophobic cores by their peripheral placement (Figure 4). Removal of gatekeepers increases aggregation and as a result gatekeeper mutations are three times more frequent in human disease mutants than in human polymorphisms [17], [60].
Selective pressure against aggregation is not homogeneous. We previously showed that enrichment of gatekeeper residues is more pronounced at the flanks of strongly aggregating sequences [12] and it was also shown that aggregation propensity inversely correlates with gene expression [18]. In this study we employed the TANGO aggregation prediction tool [20] to compare the aggregation characteristics of proteins taken from the extremes of the protein lifetime distribution from the large scale data by Yen et al [19]. We observe a significantly higher aggregation propensity in proteins with a short lifetime than in proteins with a long lifetime. Analysis of gene expression data in 532 healthy individuals excluded the possibility that the observed difference in aggregation propensity arises from differences in gene expression levels between short-living and long-living proteins. Additionally the FoldX [31] analysis of the structures from both groups of proteins clearly show that this is not a result from a superior thermodynamic stability of short lifetime proteins, but rather from a genuinely higher aggregation propensity of their protein sequence. The higher aggregation propensity of short-living proteins does not originate from a higher number of aggregating regions, but rather from the higher average length and aggregation propensity of these regions, which can be traced back to a reduction in the amount of aggregation breaking gatekeeper residues. Hence, the reduced placement of gatekeepers in short-living proteins and the resulting higher average aggregation propensity, is evidence for the fact that proteins with a fast turnover rate experience less selective pressure to minimize aggregation than proteins with a longer biological lifetime.
Moreover, a search of the IntAct database [21] revealed that there are significantly more recorded chaperone interactions for long-living proteins than short-living proteins. So, not only do short-living proteins experience milder selective pressure against aggregation, but at the same time they also interact less frequently with molecular chaperones or at least form less stable interactions of the type that can be recorded by current experimental techniques. Taken together, these data strongly suggest that the misfolding of short-living proteins is generally not affecting the fitness of the cell, as presumably the strong dependence of these proteins on proteasomal degradation suffices to avoid the accumulation of protein aggregates.
On the other hand, it is known that the efficiency of the proteasomal system erodes as a result of physiological ageing [61], [62], [63]. Under these changing conditions, proteins with a higher aggregation propensity and lacking sufficient affinity for chaperones would form the Achilles' heel of the proteome and be among the most susceptible to aggregate. In this respect it is interesting to see that some of the fast turnover proteins from the dataset are indeed associated with human diseases with a protein deposition phenotype.
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10.1371/journal.ppat.1003560 | A Compact Viral Processing Proteinase/Ubiquitin Hydrolase from the OTU Family | Turnip yellow mosaic virus (TYMV) - a member of the alphavirus-like supergroup of viruses - serves as a model system for positive-stranded RNA virus membrane-bound replication. TYMV encodes a precursor replication polyprotein that is processed by the endoproteolytic activity of its internal cysteine proteinase domain (PRO). We recently reported that PRO is actually a multifunctional enzyme with a specific ubiquitin hydrolase (DUB) activity that contributes to viral infectivity. Here, we report the crystal structure of the 150-residue PRO. Strikingly, PRO displays no homology to other processing proteinases from positive-stranded RNA viruses, including that of alphaviruses. Instead, the closest structural homologs of PRO are DUBs from the Ovarian tumor (OTU) family. In the crystal, one molecule's C-terminus inserts into the catalytic cleft of the next, providing a view of the N-terminal product complex in replication polyprotein processing. This allows us to locate the specificity determinants of PRO for its proteinase substrates. In addition to the catalytic cleft, at the exit of which the active site is unusually pared down and solvent-exposed, a key element in molecular recognition by PRO is a lobe N-terminal to the catalytic domain. Docking models and the activities of PRO and PRO mutants in a deubiquitylating assay suggest that this N-terminal lobe is also likely involved in PRO's DUB function. Our data thus establish that DUBs can evolve to specifically hydrolyze both iso- and endopeptide bonds with different sequences. This is achieved by the use of multiple specificity determinants, as recognition of substrate patches distant from the cleavage sites allows a relaxed specificity of PRO at the sites themselves. Our results thus shed light on how such a compact protein achieves a diversity of key functions in viral genome replication and host-pathogen interaction.
| Positive-stranded RNA viruses are ultimate parasites. In order to replicate their genome, they first need to invade a host cell and, with usually very limited viral genetic material, subvert the host's molecular machinery. Turnip yellow mosaic virus (TYMV) is an excellent model system for studying positive-stranded RNA virus replication. As for many such viruses, TYMV genome replication is dependent on the activity of a viral proteinase (PRO) to properly process the virus' replication molecules. We have recently established that PRO is a multifunctional enzyme and is also used by TYMV to subvert a key host defense against pathogens. We report here the atomic structure of PRO as well as new functional data on PRO's interaction with the host. Our data shed light on how PRO can perform such multiple activities despite its small size, providing TYMV with a Swiss army knife in its ongoing fight with a vastly more complex host.
| Plus-strand RNA (RNA+) viruses are the largest class of eukaryotic viruses. They include significant pathogens of humans, animals and plants [1]. From the sequencing of their genomes, it has become clear that despite a huge diversity, these viruses possess high similarities at the molecular level [2][3]. Indeed, common strategies and regulatory mechanisms have been uncovered in the replication of RNA+ viruses [4]. Thus, all RNA+ viruses studied to date synthesize new viral genomes at an intracellular membrane. There, synthesis of the viral progeny requires the establishment of specific and regulated interactions between viral proteins and different cellular factors, assembled within a replication complex. In RNA+ viruses, the replication proteins are usually synthesized as a single polypeptide chain that may be subsequently processed by viral (and sometimes also cellular) proteinases. Another common feature of RNA+ viruses is that the highly compact viral genome codes for usually multifunctional proteins.
Turnip yellow mosaic virus (TYMV) is a simple, model RNA+ virus whose replication is well characterized at the molecular and cellular levels [5][6][7]. It is included in the alphavirus-like supergroup of RNA+ viruses [3] that also comprises the animal alphaviruses (including Sindbis virus, Semliki Forest virus and Chikungunya virus) and rubiviruses (including Rubella virus). Indeed, TYMV shares with these viruses striking similarities in the organization and processing of the replication polyprotein [8]. Its 6.3-kb genome codes for three proteins, the largest of which (206K) is a polyprotein of 206 kDa that contains all the viral components of the replication machinery (Fig. S1 in Text S1). From N- to C-terminus, 206K harbors methyltransferase (MT), cysteine proteinase (PRO), helicase (HEL or 42K) and RNA-dependent RNA polymerase (POL or 66K) domains. In previous works, we established that the PRO domain is a key regulator of TYMV replication. First, its endopeptidase activity is required to proteolytically process 206K at the HEL/POL junctions to release the 66K polymerase, while a second cleavage at the PRO/HEL junction contributes to the regulation of viral RNA synthesis [8]. The PRO domain is also essential for the recruitment of 66K to the membrane replication sites [7]. Finally, we recently reported that TYMV PRO also displays an ubiquitin hydrolase (DUB) activity in vitro and in vivo, and identified 66K polymerase as a specific substrate of this activity [9]. PRO's DUB isopeptidase activity is thus also a key factor for the interaction of the virus with its host, in counteracting the ubiquitin-proteasome system and possibly subverting it into regulating availability of 66K for TYMV replication [10].
Here we describe the crystal structure of the recombinant PRO. Strikingly, PRO displays no homology to other processing proteinases from RNA+ viruses, including that of alphaviruses, and the closest structural homologs of PRO were identified as DUBs from the Ovarian tumor (OTU) family. Our crystal captures a view of TYMV PRO in its polyprotein processing mode that reveals dual substrate specificity determinants. Modelling of a PRO/ubiquitin complex, subsequent site-directed mutagenesis of PRO and enzymatic analysis of its DUB activity suggest that PRO structural elements used for specific recognition of ubiquitin overlap those used in its processing proteinase function. These findings provide a structural rationale for PRO's targeting of the diverse viral and cellular, endo- and isopeptide bonds whose hydrolysis allows TYMV to complete its replication cycle.
We report here the structure of the TYMV PRO domain to a resolution of 2 Å with a final Rfree of 20.1% (Table 1). As reported elsewhere [11], all data including 3 derivative datasets obtained by heavy atom soaks were from crystals grown in a single crystallization drop. The asymmetric unit contains a single PRO molecule packing against the next PRO along the crystallographic 31 screw axis, making up continuous PRO helices in the crystal (Fig. S2 in Text S1), and an Escherichia coli contaminant (ribosomal protein S15). S15 bridges the separate PRO helices, explaining why diffraction-quality crystals only grew from a PRO preparation heavily contaminated by S15 [11].
PRO displays a three-lobed architecture. The N-terminal lobe (in blue on Fig. 1A) comprises two short helices flanking a two-stranded, distorted β-sheet. The catalytic domain is made up by the central and C-terminal lobes (a bundle of five helices and a four-stranded β-sheet, respectively). The catalytic dyad Cys783-His869 (TYMV polyprotein numbering) lies at the interface between helix α3 (the first helix in the central lobe) and strand β6 (the last strand of the C-terminal lobe). Indeed, Cys783 is the first residue of helix α3 and His869 the first residue of strand β6 (Fig. 1B). We used the DALI server [12] (http://ekhidna.biocenter.helsinki.fi/dali_server) to seek homologs of PRO with available structures in the Protein Data Bank (PDB). Strikingly, there is no detectable homology (DALI Z-score below 2) to other processing proteinases from RNA+ viruses, including that of alphaviruses. It was previously remarked that PRO shares limited sequence similarities around the two catalytic residues with the OTU domain class of DUB enzymes and we recently reported that PRO is a functional DUB in vitro and in vivo [9]. Indeed, although no close homolog is available and the N-terminal lobe cannot be matched at all, the fold of the PRO catalytic domain is clearly the same as the core fold of the OTU1 cellular DUB (yOTU1, Saccharomyces cerevisiae, DALI Z-score 7.5, 91 residues matched) [13] and nairovirus DUB (vOTU, Bunyaviridae, DALI Z-score 6.8, 90 residues matched) [14][15][16]. These two DUBs are assigned to clan CA of papain-like proteinases in the MEROPS peptidase database scheme [17] (http://merops.sanger.ac.uk/). Although clan CA contains several viral processing proteinases from Picornaviridae and Coronaviridae, only strict DUBs (i.e. enzymes lacking endopeptidase activity) have substantial DALI Z-scores in comparisons with PRO. Indeed, the nearest homolog of PRO with reported endopeptidase activity is the bacterial Staphopain (Z-score 3.4).
A DALI superposition of yOTU1 and vOTU yields a Z-score of 12.2. This higher score is due to yOTU1 and vOTU being structurally superimposable on a significantly larger number of residues (126 residues matched by DALI). Of note, in both yOTU1 and vOTU, the segment directly upstream of the homolog of helix α3 (in green on Fig. 1C) partially covers the exit from the active site. In contrast, the catalytic dyad of TYMV PRO Cys783-His869 is completely solvent exposed (Figs. 1C, 2A and 2C). There is no pocket that could act as a stabilizer for the oxyanion intermediate in the reaction for cysteine and serine proteases. Indeed, due to the lack of a covering segment there is no counterpart for the main chain nitrogen of Asp37 of vOTU (Fig. 2C), that has been proposed to participate in formation of this oxyanion hole [15]. Furthermore, the side chain of Trp99 of vOTU, that has been shown to take part in oxyanion hole formation [16], is missing in TYMV PRO's Gly821 (Fig. 2C). Similarly, there is no candidate in PRO for a catalytic residue acting as a general acid to stabilize and activate the side chain of His869. Asp153 of vOTU, that has been shown to be implicated in the catalytic triad [16], is replaced by a serine in Tymoviridae as Trp99 is replaced by a glycine (Fig. 2C, Fig. 1B). Thus, TYMV PRO's catalytic site appears to be reduced to an exposed dyad, possibly explaining in part its poor DUB activity [9] (see below) compared to e.g. vOTU [15][14][16]. Furthermore, although the dyad itself is almost superimposable with the corresponding residues of yOTU1 and vOTU (Fig. 2C and legend thereof), the Cys783 side chain is flipped and makes no interaction with the His869 side chain. Thus the PRO active site is most likely not in its catalytically competent state in the crystal, where we caught a product release state (see below).
The PRO active site's exposure is due to the long β2–α3 loop (residues 771–782) connecting the N-terminal lobe to the central lobe coming to helix α3 from the other side of the α3–β6 interface. The β2–α3 loop threads through a cleft between helices α4 and α5 at the back of the central lobe. This results in the N-terminal lobe being apposed to the catalytic domain (Fig. 1A) but on the other side from the catalytic dyad (Fig. 2A). Important residues in this positioning of loop β2–α3 are Arg769, that participates in an extended network of interactions, including a salt bridge to Asp809 and a hydrogen bond to Pro808 at the base of α5; Pro777, that positions the main chain to make two hydrogen bonds to the indole ring nitrogen of Trp800 in α4; and Pro779, that inserts into a hydrophobic pocket lined by Trp800, Leu785 and Leu822. The pattern of conservation among Tymoviridae proteinases (Fig. 1B) indicates that this arrangement, and consequently the position of the N-terminal lobe, are very likely conserved in the family.
Remarkably, the five-residue loop between strands β5 and β6 contains two successive cis-prolines 865-Gly-Pro-Pro-867 (Fig. S3 in Text S1, Fig. 2A). Such a conformation was recently found in only 7 out of 809 Pro-Pro segments in high resolution structures of proteins [18]. Downstream of strand β6, the main chain makes a sharp turn so that the C-terminal residues of PRO 874-Lys-Arg-Leu-Leu-Gly-Ser-879 point away from the α3–β6 interface. Finally, the electrostatic potential at the surface of PRO displays three strong features (Fig. 2B): First an apolar bulge made by the two cis-prolines 866–867; second, a basic patch on the N-terminal lobe on the other side from the entry to the catalytic cleft; and third, a small acidic pocket to the side of the entry to the catalytic cleft.
The continuous helices of PRO in the crystal are formed by the insertion of the C-terminus of one molecule into the catalytic site of the next (Fig. S2 and S4 in Text S1). Thus, we have captured the N-terminal product complex resulting from the self-cleavage in trans of a viral polyprotein by its resident proteinase. The specificity of PRO is on the N-terminal (P) side of the scissile bond, while the C-terminal (P') side is not important as defined by mutagenesis studies [19][8]. This structure thus reveals the molecular determinants of PRO specificity in its processing proteinase function (Fig. 3). The specificity of PRO is defined as P5-(K/R)LX(G/A/S)(G/A/S)-P1 [8]. The molecular determinants for this are now readily assigned by examining the interactions between one PRO molecule (hereafter called “substrate”, with relevant residues with an “s” subscript) and the next (hereafter called “peptidase”, with relevant residues with a “p” subscript).
Analysis of the peptidase-substrate interface using the PISA server [20] (http://www.ebi.ac.uk/msd-srv/prot_int/pistart.html) shows that 940 Å2 (11.9%) and 825 Å2 (10.5%) of solvent-accessible surface area are buried in the complex for the substrate and peptidase, respectively. This interface is not expected to be stable in solution. Accordingly, we find that PRO solutions up to 10 mg/ml are monodisperse as measured by dynamic light scattering (not shown), with an hydrodynamic radius of 2.3 nm very close to the one calculated from the crystal structure for the PRO monomer (2.14 nm). The residues involved in the interface are mostly, but not exclusively (Fig. 3A) in and around the entry to the catalytic cleft for the peptidase and in the C-terminus for the substrate, respectively. The last five residues of the substrate 875-Args-Leus-Leus-Glys-Sers-879 are funneled in an extended beta conformation towards the catalytic dyad of the peptidase Cysp783-Hisp869 (Fig. 3B, where the peptidase residues are labeled in white and the substrate residues in black). Indeed one of the carboxyterminal oxygens and the main chain nitrogen of Sers879 are hydrogen-bonded to the main chain nitrogen and carbonyl, respectively, of Phep870. Likewise, the main chain polar atoms of Glys878 are hydrogen-bonded to the main chain polar atoms of Leup822 in the connection between helices α6 and α7 on the other side of the catalytic cleft. The net effect is a small three-stranded intermolecular beta-sheet firmly holding 878-Glys-Sers-879 in place. The other Sers879 carboxyterminal oxygen is hydrogen-bonded to the side chain of Hisp869. This is the only interaction stabilizing this side chain in the crystal (see above) and it is less well ordered than the other residues in the catalytic cleft (Fig. S4 in Text S1). Further upstream the substrate, the extended conformation of the main chain is maintained by hydrogen bonds from side chains of the peptidase. Key side chains are those of Hisp862 (that also participates in the P4 and P2 specificity, see below) and Thrp824 (Fig. 3B).
In our crystal structure, the side chain hydroxyl of Sers879 hydrogen bonds to the main chain nitrogen of the catalytic Cysp783 (Fig. 3B). The relaxed G/A/S specificity at P1 stems largely from the solvent exposure of the exit of the catalytic cleft (see above). Still, the side chain of Leup781 caps the S1 site, precluding the presence of a large side chain at position P1. Similarly, the constriction of the active site cleft at the S2 pocket is less pronounced than in yOTU1 and vOTU, where bulky residues strictly restrict specificity to GG. The S2 pocket, lined by Hisp862, Phep870 and Serp868 and occupied by Glys878 in our crystal structure, could readily accommodate a small side chain. At first sight, the sharp difference in specificity between P3 (seemingly no specificity) and P4 (strict specificity for a hydrophobic amino acid with a strong preference for Leu) [8] is somewhat surprising. Both Leus877 (P3) and Leus876 (P4) make extensive contacts to conserved, shallow hydrophobic pockets at the surface of the peptidase's central and C-terminal lobes, respectively. These contacts bury respectively 89% of Leus877's and 96% of Leus876's solvent-accessible surface area, as reported by PISA. Two major differences though are that Leus877 (P3) is buttressed only on one side and that the S3 pocket is lined by negatively charged residues (the outer edge of the acidic pocket depicted on Fig. 2B). This explains why P3 may be mutated to Ala and may naturally be either a hydrophobic (e.g. Leu), small polar (e.g. Asn) or arginine side chain, but an aspartate is never found at this position [8]. In contrast, Leus876 (P4) is sandwiched between two strictly apolar surfaces on the two C-terminal lobes of the peptidase and substrate, respectively, with Hisp862, Phep870, Valp840, Ilep847 and Serp842 on one side and Pros846 and Tyrs841 on the other side. This likely accounts for the specificity at position P4. As for the strict P5 specificity for a positively charged amino acid, it is readily explained by the fact that P5 inserts its side chain into the acidic pocket depicted on Fig. 2B. Indeed, Args875 makes salt bridges to two conserved glutamates that protrude from the central lobe of the peptidase, Glup816 in the small helix α6 overhanging the S3 pocket and Glup825 in helix α7.
Molecular recognition of the substrate by the peptidase further involves patches recessed from the C-terminus of the substrate (Fig. 3A). In the peptidase, these recognition patches are harbored by the N-terminal lobe. Thus, there is a prominent hydrophobic contact between the double cis-proline of the substrate Pros866-Pros867 and Prop733-Alap734-Prop735 at the base of helix α1 in the N-terminal lobe of the peptidase.
A second patch in this lobe is centered on Asnp760 at the tip of the extended α2–β2 loop. Asnp760 makes both a hydrogen bond to Asps790 and a stacking interaction to the Pros872-Glys873 motif that makes up the sharp turn to the C-terminal residues mentioned above. This contact is completed on one side by an electrostatic interaction between Glup759 and Lyss793. On the other side, the hydrophobic contact is taken up between the aliphatic part of the side chain of Lyss874 and Ilep847, an interaction that is continuous with the S4 pocket.
Using the HADDOCK program (see Materials and Methods), we performed a docking simulation to explore the possible binding modes of ubiquitin in association with PRO. We first defined spatial ambiguous restraints for the interaction between ubiquitin and PRO by the involvement of: 1) the C-terminal residues of Ub 2) the corresponding catalytic cleft on PRO and 3) an apolar patch on the surface of ubiquitin (hereafter referred to as the Ile44 patch) that is recognized by most ubiquitin-binding proteins [21]. The ubiquitin residues most frequently targeted in this patch are Ile44, His68, Val70, Gly47, Leu8 and Arg42 (in cyan on Fig. 4) [21]. The structures of both yOTU1 and vOTU have been reported as covalent complexes with ubiquitin [13][14][15][16]. These works have shown [14][15] that the Ile44 patch is recognized by nonhomologous regions of the two OTU DUBs due to a 75° rotation of Ub around an axis defined by the main chain of the 5 C-terminal Ub residues (Fig. 4AB). To account for this known flexibility of the C-terminal tail of Ub [21], multiple conformations were sampled prior to the rigid-body docking step so as not to bias the interface too heavily towards a given binding mode. Two clusters of solutions were found, among which only the largest cluster (which also contains the complex with the best HADDOCK score) had a binding mode consistent with the C-terminal residues of Ub in the PRO active site. This binding mode was cross-confirmed by other docking simulations using alternative methodologies, in particular without applying prior restraints between the putative binding regions (see Protocol S2 in Text S1). Further inspection of the lowest energy structure (Fig. 4C) shows that the orientation of ubiquitin is similar to that in the vOTU complex and that the predicted interface prominently involves PRO's N-terminal lobe. Indeed, the tip of the lobe's extended α2–β2 loop inserts into the patch (Fig. 4D), suggesting that Glu759 and Asn760, that participate in PROs binding (Fig. 3A), may also be involved in ubiquitin recognition. Indeed, in the docking model Glu759 would be in a position to make salt bridges to Ub His68 in the Ile patch and/or Lys6 at its periphery. Since such interactions are usually good specificity but weak affinity determinants, we further looked for hydrophobic patches on PRO's surface that could come into contact with the Ile44 patch based on the model prediction. We found two such PRO patches on either side of the Ile44 patch (Fig. 4D). One is made by Leu732/Leu765 in the N-terminal lobe. The other is centered on Ile847. Although not part of the N-terminal lobe, Ile847 seemed an excellent candidate as it is an exposed residue with a conserved hydrophobic character (Fig. 1B). This matches a known feature of interface evolution, where contacts between apolar patches are the most conserved although the residues themselves may not be [22]. Thus if the docking model was correct and Ile847 was an interface residue with the Ile44 patch, we expected a reduction in the bulk of its side chain to reduce the interaction and the substitution for a short charged residue to almost abolish it.
In view of the docking results, we assessed the DUB activity of PRO and selected mutants. All mutants described below were produced in E. coli in a soluble form and purified to homogeneity (Fig. S6 in Text S1). Dynamic light scattering analysis of the mutants showed the same results as for the wild type, indicating that they were likely properly folded.
As a deubiquitylating assay we used hydrolysis of the general substrate Ubiquitin-7-amino-4-methylcoumarin (Ub-AMC). Determination of initial velocities up to the highest Ub-AMC concentration available to us showed that the wild type hexahistidine-tagged PRO whose structure is reported here is still far from saturating conditions at the highest substrate concentration we could reach (20 µM Ub-AMC, Fig. 5A). Accordingly, we compared the wild type and mutant enzymes by determining their pseudo first-order rate constants, Kapp, which approximate kcat/Km in conditions far from saturation (Table 2). For wild type PRO, the Kapp value of 2650 M−1s−1 we find is comparable to the Kapp of 1550 M−1s−1 previously reported from initial velocity measurements of a GST-tagged version of PRO [9] and thus ∼100-fold less than the Kapp reported for vOTU [14][15][16].
A L732A/L765A mutant was not significantly affected in its Kapp (p = 0.34, Mann-Whitney rank test). On the other hand, an E759G/N760G mutant showed a significant (p<0.01) though slight (20%) reduction in Kapp, suggesting that the α2–β2 loop is indeed involved in ubiquitin recognition. Further tampering with this loop, e.g. deleting its tip by replacing 758-PENT-761 with a diglycine motif, led to no soluble PRO production, so that we could not further probe this.
We next assessed Ile847 mutants for their DUB activity. I847A is impaired in Kapp (a 10-fold deterioration), while I847D is barely active (a 150-fold reduction in Kapp). This behavior is exactly as predicted from the docking model, since I847A will reduce the size and complementarity of the PRO hydrophobic patch, while I847D will destroy its apolar character altogether. To further probe the docking model, we tested the I847A mutant initial velocity at higher substrate conditions (Fig. 5B), where the slope of the wild type curve starts to decrease (Fig. 5A). Within the same range, the I847A curve still appears linear in substrate concentration, suggesting that ubiquitin binding rather than turnover rate is impaired in the I847 mutants.
In the present work, we provide structural insights into viral polyprotein processing by a viral proteinase that cleaves at its own C-terminus. Such an event is common enough in the viral world, particularly among positive-stranded RNA viruses, and may in principle be achieved either in cis (the proteinase domain cleaves the polypeptide of which it is a part) or in trans (it cleaves another polyprotein molecule). Our structure precludes the possibility of the C-terminus of PRO looping back towards the entry to the catalytic cleft in the same molecule. Therefore, cleavage of the TYMV replication polyprotein at the PRO/HEL junction occurs only in trans. This cleavage is a regulatory event in the replication of the TYMV RNA genome. It occurs in the replication complex comprising the two products of the first cleavage: the 66K RdRp and the 140K protein. 140K harbors PRO and localization determinants to the chloroplast envelope, where it recruits 66K. There, cleavage of 140K at the PRO/HEL junction into 98K and the 42K helicase contributes to the switch to synthesis of the +strand [7][8] (Fig. S1 in Text S1). A strictly trans cleavage likely takes part in the regulation by requiring a sufficient local concentration of 140K at the chloroplast membrane and/or remodeling of this membrane into a special compartment for viral replication before synthesis of new viral genomes.
The interface in the crystal of the N-terminal complex of this trans cleavage reveals the molecular determinants for the peptidase sequence specificity. The fact that PRO proteinase specificity is confined to the P side is readily explained by the fact that the catalytic cleft ends abruptly at the catalytic dyad, leaving it completely solvent-exposed. Thus, residues on the P' side of the substrates will have little or no contact with PRO. In contrast, there are extensive interactions with the P-side residues up to P5 from both lobes making up the catalytic proteinase domain. The C-terminal β-sheet lobe thus provides a hydrophobic pocket for P4 and the central α-helical lobe an acidic pocket contributing salt bridges to the positively charged P5 and a shallow hydrophobic patch for P3. The constriction of the active site cleft at the interface between the two lobes ensures that only small residues (but not necessarily glycines) can be at positions P2 and P1.
We recently reported that 98K has DUB activity in vivo and in vitro and that this DUB activity is localized in PRO [9]. Thus, PRO recognizes at least three different substrates that differ at positions P1 (G for Ub, but A and S for the HEL/66K and PRO/HEL junctions, respectively) and specifically cleaves either endopeptide bonds (HEL/66K, PRO/HEL) or isopeptide bonds (Ub). Our crystal structure shows that the latter property is linked to an unusual solvent exposure of the active site of PRO on the P'/lysine sidechain side of the cleavage. The former property of relaxed sequence specificity is allowed by an also unusual lesser constriction of the PRO active site cleft at the P1 and P2 positions (see below). Such features imply that PRO may be more heavily dependent on the recognition of additional molecular determinants away from the active site, in order to maintain sufficient substrate affinity and most importantly, high substrate specificity.
Accordingly, the crystal structure we obtained allows us to identify two such determinants. First, an acidic pocket to the side of the entry to the active site strongly favors a positively charged residue in P5 of the substrate. Second and most important, we identify the Tymoviridae-specific N-terminal lobe of PRO as a recognition element for surface patches of the PRO/HEL substrate, as this lobe was found to recognize a signature bulge made of the two successive cis-prolines in the PRO substrate molecule. Whether the N-terminal lobe is also prominently used in recognition of the HEL/66K junction cannot be assessed at present. However, docking of the PRO/Ub complex and subsequent mutational analysis of the DUB activity of PRO suggest that PRO targets the Ile44 patch that is recognized by all characterized DUBs in part with elements also involved in recognition of PROs, such as Ile847 and possibly the α2–β2 loop. Of note, our docking model places the three residues that differ between plant ubiquitin (the natural TYMV PRO target) and human ubiquitin (that we used in modeling and functional work) on the side of ubiquitin opposite the interfaces with PRO (Fig. S7 in Text S1). This would rule out a different behavior of the natural substrate of PRO's DUB function (plant ubiquitin) compared to the readily available experimental substrates (derivatives of human ubiquitin).
Using a myc-tagged version of human ubiquitin, it was previously shown that, in contrast to other viral DUBs (e.g. vOTU), 98K is a very specific DUB whose overexpression in cells does not lead to a global deubiquitylation of cellular proteins but rather to specific deubiquitylation of 66K [9]. Several lysine side chains of 66K are polyubiquitylated in vivo [10] and the types of these ubiquitin chain linkages are presently unknown. In vitro TYMV PRO may disassemble both Lys48-linked and Lys63-linked polyubiquitin chains, albeit with weak activity [9]. In the light of our findings, one may ask whether PRO may display specificity to particular ubiquitin linkages. Specificity may be achieved in several ways, e.g. on the P'/Lys side of the catalytic cleft (Fig. 4E) either by recognizing the sequence context of the modified lysine or by positioning the Lys-linked moiety. In either case, addressing the question of specificity would require modeling a diubiquitin chain across PRO's catalytic site. Such an exercise (not shown) must be highly speculative at the moment in the absence of structures for relevant complexes of OTU DUBs [23]. We may note that, as for other OTU DUBs, the isopeptide bonds in extended linkages (such as Lys63-linked polyubiquitin) can in principle be readily accessed by PRO, but compact chain conformations (as in Lys48-linked polyubiquitin) require an extensive conformational change to expose the isopeptide bond and allow binding and cleavage by PRO [23]. But the question of linkage specificity can also be addressed by modeling a diubiquitin chain on the P side of PRO's catalytic cleft (Fig. 4E). Molecular recognition of Lys48-linked chains is poorly understood, as in their compact conformations their ubiquitin moieties interact through their Ile44 patches. It is proposed that structural flexibility allows transient access to the Ile44 patches to binding partners, and indeed a minor population of more open Lys48-linked diubiquitin has been modeled from nuclear magnetic resonance data [23]. Interestingly, placing this minor conformation onto our docking model results in PRO's N-terminal lobe being sandwiched between the catalytic domain and the Lys48-linked diubiquitin and making contact with both ot the latter's Ile44 patches (Fig. 4E, top). On the other hand, similarly placing the structure of a Lys63-linked diubiquitin predicts no interactions to the second moiety (Fig. 4E, bottom), due to the extended character of the Lys63 linkage.
PRO counteracts the 66K polymerase degradation by the ubiquitin-proteasome system through polyubiquitin removal [10][9]. Since Lys48-linked polyubiquitylation is the canonical proteasome addressing signal, simultaneous recognition by the N-terminal lobe of several ubiquitin moieties on the P side (Fig. 4E, top) could be a mechanism allowing more efficient cleavage of Lys48-linked polyubiquitin chains. It might also explain in part why PRO displays rather poor activity for a DUB (e.g. compared to vOTU [14][15][16]) in a general deubiquitylation assay using a monoubiquitin derivative [9] (this work), with a Km in the tens of micromolar range (Fig. 5A). The other obvious feature of TYMV PRO explaining its lesser activity is the minimal character of its active site. It lacks altogether two important functional elements that are present in most cysteine proteinases, including the closest relatives of PRO (clan CA, including yOTU1 and vOTU, see below): The oxyanion hole and a general acid as the third catalytic residue. Our structural work thus draws the picture of a barely complete proteinase that nonetheless effectively achieves cleavage of several endo- and isopeptide targets by combining co-localization with the targets and a versatile recognition lobe.
Among peptidases that process polyproteins from RNA viruses with a Cys/His catalytic dyad, there are two known structural clans with unrelated folds. The first is clan CA, that comprises yOTU1 and vOTU. Another is clan CN, whose type is the nsP2 proteinase of alphaviruses [24]. Alphaviruses, including Sindbis virus, Semliki Forest Virus and Chikungunya virus, are animal relatives of tymoviruses. The two virus families share many features in their replication strategies, including successive cleavages of the replication polyprotein by the resident proteinase regulating RNA+ vs −strand synthesis [8]. Nevertheless, our data clearly show that PRO is unrelated to nsP2 and assign PRO to clan CA, a result that could not be firmly established by sequence comparisons alone (http://merops.sanger.ac.uk/) [25][9]. The two other families of processing proteinases assigned to clan CA are also from positive-stranded RNA viruses: They are the coronavirus papain-like proteinases PLP1 and PLP2 [26][27] and the picornavirus leader proteinase [28]. These proteinases have also been reported to be ubiquitin hydrolases [27][29]. Yet PRO does not display detectable homology to these proteinases.
Instead, the fold of PRO's two-lobed catalytic domain is clearly a more compact version of the OTU domain fold of ubiquitin hydrolases. The least dissimilar OTU domains to PRO are those of the cellular OTU1 DUB (yOTU1), whose structure is available in complex with Ub [13], and the viral OTU domain encoded in the L protein of Crimean–Congo haemorrhagic fever virus (vOTU), whose structure has been recently reported in complex with Ub and ISG15 [14][15][16]. Thus, PRO is closest to enzymes with no endopeptidase activity.
Potential clues as to how TYMV acquired an ubiquitin hydrolase as a dual DUB/processing proteinase may be found in the family Flexiviridae of plant viruses. In this closest family to Tymoviridae, some of the replication proteins encode two peptidase domains, an OTU domain being N-terminal to the processing proteinase P [30]. One may therefore picture a scenario in which an ancestor to Tymoviridae harbored such a two-peptidase replication polyprotein. Subsequently, the OTU peptidase acquired specificity determinants allowing its use as processing proteinase and the P domain was lost. This report and previous works [13][14][15] establish that nonhomologous recognition modules have repeatedly evolved in the OTU family of DUBs, which is consistent with such a scenario. Whatever actually happened, the present diversity of specific functions performed by PRO is remarkable in a proteinase domain that is no larger (148 ordered residues) than the more specialized vOTU (162 ordered residues) or yOTU1 (170 ordered residues). Our results shed light on the molecular details that allow such a compact protein to perform a diversity of key functions in viral genome replication and host-pathogen interaction.
The production and purification of an N-terminally 6-histidine tagged PRO domain and of PRO mutants are described in details in protocol S1 in Text S1. Briefly, the coding sequence of the PRO domain (residues 728–879 of 206K) was produced with an in-frame N-terminal 6His-tag. Purification was performed with two successive chromatography steps (immobilized metal affinity chromatography followed by size exclusion chromatography). Crystallization is described elsewhere [11]. Briefly, a pool from all fractions of the size exclusion step in buffer 10 mM Tris-HCl pH 8, 350 mM Ammonium Acetate, 1 mM DTT, was concentrated to 39 mg/ml as judged by OD280 nm. Hexagonal crystals of up to 50×50×40 µm3 grew in a single vapor diffusion drop where 1 µl protein solution plus 1 µl well solution (0.1 M Hepes pH 7.5, 2.5 M Ammonium formate) was equilibrated against a 0.5 ml reservoir volume. Prior to testing, crystals were transferred for ∼30 s in 0.1 M Hepes pH 7.5, 4 M Ammonium formate, 16% glycerol and flash cooled by plunging into liquid nitrogen.
Details of the structure determination are given elsewhere [11]. Briefly, the structure was solved by MIRAS from three poor derivatives thanks to the high (69%) solvent content of the crystals. Heavy atom derivatives (HgAc2, NaI and CsCl) were obtained by soaking. Data were processed with the XDS package [31]. Initial heavy atom sites were located with SHELXD [32]. This first heavy atom model was refined, completed and pruned and initial phases were computed and improved with autoSHARP [33]. The resulting map was interpretable and a first model was built with phenix.autobuild [34]. The model was manually rebuilt with COOT [35] and refined with phenix.refine [34]. Data processing and refinement statistics are collated in table 1.
Interfaces in the crystal were assessed using the PISA server [20] (http://www.ebi.ac.uk/msd-srv/prot_int/pistart.html). Homologs of PRO were sought and superimposed with the DALI server [12] (http://ekhidna.biocenter.helsinki.fi/dali_server). Structures were displayed and figures were prepared with Pymol (www.pymol.org). Figure 1B was generated with ALINE [36].
98 monomeric structures were generated from the Ub monomer extracted from the vOTU-Ub structure by sampling and clustering 2,000 C-terminal tail conformations using the Rosetta 3.4 FloppyTail application [37]. These conformations were used as a starting ensemble for Ub in the docking process. HADDOCK v2.1 [38] [39] was used to perform the docking with standard parameters, generating 5,000 rigid-body docking conformations followed by flexible explicit solvent refinement of the best 500 structures. The solutions were clustered and the most likely model was picked (see details in Protocol S2 and Fig. S5 in Text S1).
This model was subsequently used for visualizing PRO/diubiquitin models. Lys48-linked diubiquitin was either the compact structure (PDB 1AAR) or the minor population structure (PDB 2PE9) [23]. Lys63-linked diubiquitin was PDB 2JF5. For each diubiquitin, one moiety was superimposed on the ubiquitin in the docking model. This was either the moiety with the lysine-linked C-terminus (across-cleft modeling) or the moiety with the free C-terminus (P side modeling). In across-cleft modeling, this results in major clashes of the other moiety with PRO for both Lys48-linked diubiquitin conformations and still large clashes for Lys63-linked diubiquitin. In P side modeling, this results also in unrelievable clashes for the compact Lys48-linked diubiquitin, as with all proteins binding the ubiquitin Ile44 patch. There were few clashes with the minor population Lys48-linked diubiquitin in P side modeling and none with the Lys63-linked diubiquitin.
Recombinant wild type and mutant his-PRO were generated, produced and purified as described in Protocol S1 in Text S1. Samples were concentrated to 200–1096 µM, dialyzed in 50 mM HEPES pH 8, 150 mM KCl, 1 mM DTT, 10% glycerol, aliquoted and kept at −80°C until use. DUB activity was assessed in Assay buffer (HEPES-KOH 50 mM pH 7.8, KCl 10 mM, EDTA 0.5 mM, DTT 5 mM, NP40 0.5%, DMSO 2%) using the fluorogenic substrate Ub-AMC (Boston Biochem). DMSO was adjusted to 2% in all assays to match the DMSO concentration in the highest Ub-AMC concentration tests. The rate of substrate hydrolysis was determined by monitoring AMC-released fluorescence as described previously [9] with some modifications. Assays were performed at 20°C in a temperature-controlled Perkin-Elmer LS50B spectrofluorimeter. The initial velocity V was derived from the linear increase in fluorescence at 460 nm (excitation at 380 nm) in minutes 1 to 11 after mixing in Ub-AMC.
In order to determine the Kapp, the substrate concentration was kept at a concentration below 0.5 µM where the initial velocity is linear in substrate concentration. Enzyme concentrations were 100 nM for wild type PRO, L732A/L765A and E759G/N760G, 1 µM for I847A and I847D. The apparent kcat/Km (Kapp) values were determined according to the equation V/[E] = Kapp/[S].
Subsequently V was also determined at higher substrate concentrations ranging from 1 µM to 20 µM for PRO wild type (10 nM) and Pro I847A (100 nM). Results were fitted to Michaelis-Menten kinetics by nonlinear curve fitting using Graphpad Prism (Graphpad Software inc., la Jolla, CA).
Data were expressed as the means and standard deviations of these independent experiments. All experiments were performed at least in triplicates for Kapp values and at least in duplicates for the higher substrate concentrations experiments.
|
10.1371/journal.pcbi.1004812 | Incomplete Lineage Sorting and Hybridization Statistics for Large-Scale Retroposon Insertion Data | Ancient retroposon insertions can be used as virtually homoplasy-free markers to reconstruct the phylogenetic history of species. Inherited, orthologous insertions in related species offer reliable signals of a common origin of the given species. One prerequisite for such a phylogenetically informative insertion is that the inserted element was fixed in the ancestral population before speciation; if not, polymorphically inserted elements may lead to random distributions of presence/absence states during speciation and possibly to apparently conflicting reconstructions of their ancestry. Fortunately, such misleading fixed cases are relatively rare but nevertheless, need to be considered. Here, we present novel, comprehensive statistical models applicable for (1) analyzing any pattern of rare genomic changes, (2) testing and differentiating conflicting phylogenetic reconstructions based on rare genomic changes caused by incomplete lineage sorting or/and ancestral hybridization, and (3) differentiating between search strategies involving genome information from one or several lineages. When the new statistics are applied, in non-conflicting cases a minimum of three elements present in both of two species and absent in a third group are considered significant support (p<0.05) for the branching of the third from the other two, if all three of the given species are screened equally for genome or experimental data. Five elements are necessary for significant support (p<0.05) if a diagnostic locus derived from only one of three species is screened, and no conflicting markers are detected. Most potentially conflicting patterns can be evaluated for their significance and ancestral hybridization can be distinguished from incomplete lineage sorting by considering symmetric or asymmetric distribution of rare genomic changes among possible tree configurations. Additionally, we provide an R-application to make the new KKSC insertion significance test available for the scientific community at http://retrogenomics.uni-muenster.de:3838/KKSC_significance_test/.
| The presence/absence patterns of transposed elements, so called jumping genes, provide invaluable information about evolution. Unfortunately, there is still no clear all-encompassing analysis of the statistical significance of insertion patterns, and the single existing model of insertion data is no longer sufficient for the emerging genomic era. Here, we have provided a comprehensive statistical framework for testing the significance of support for phylogenetic hypotheses derived from genome-level presence/absence data such as retroposon insertions and for evaluating such data for different evolutionary scenarios, including polytomy, incomplete lineage sorting, and ancestral hybridization. This statistical framework is especially important for high-throughput applications of current and upcoming genome projects due to its treatment of unlimited numbers of testable markers, and is embedded in a user-friendly R-application available to the scientific community online. Finally, a reliable, adaptable calculation for the significance of support for phylogenetic trees derived from retroposon presence/absence data is now available.
| In their pioneering work, Ryan and Dugaiczyk [1] first proposed using Short INterspersed Element (SINE) insertions as phylogenetic markers with the suggestion: “we submit that the chronology of divergence of primate lines of evolution can be correlated with the timing of insertion of new DNA repeats into the genomes of those primates”. Although their originally detected insertions were of no direct phylogenetic relevance, subsequent studies fostered this innovative idea, and systematically searched for retroposon insertions as genomic landmarks of phylogeny (e.g. [2],[3]).
While the current most popular use of DNA sequence comparisons to deduce phylogenetic relationships must make do with only four possible character states (ACGT), retroposon insertions can theoretically produce millions of different character states corresponding to the large number of random genomic insertion sites, and thereby requires special statistics to deal with such large numbers of character states. Important is, that the inserted element itself does not encode the character state, but rather the character state derives from the exact genomic position of the inserted element. The probabilities of two independent random insertions of the same element at the same genomic location in two unrelated lineages or the exact deletion of an orthologous element are negligible but not excludable (see also Discussion). For example, the probability of parallel SINE insertion in primates is calculated to be about 0.05% [4] and precise SINE excision to be less than 0.5% [5]. More importantly, inexact parallel insertions or deletions are easy recognizable by careful analysis of the complex structure of each individual diagnostic element insertion, enabling these loci to be excluded from further analysis. The character polarity of these markers is, in contrast to sequence data, unambiguous: presence indicates the derived state and absence the plesiomorphic condition (for additional information on the marker system see [6]). But it should also be mentioned, that presence/absence markers are, in contrast to sequence data, not universally available. Their accumulation is not clocklike, and therefore they are not suitable for calculating exact branch-length or population size. A synergistic application of both marker systems is the most efficient way to extract historical information from species.
An ideal phylogenetic marker evolves neutrally [7]. Unfortunately, such neutral or nearly neutral markers then tend to diverge beyond recognition in relatively short times and are therefore not suitable for deep phylogenetic comparisons. At the sequence analysis level, a compromise is to consider more conserved nucleotide positions (e.g., the second position of codons) taking into account that such positions are less neutral and therefore may lead to only a limited phylogenetic statement. On the other hand, slight natural selection rarely complicates phylogenetic analysis, as it usually involves only rate shifts, while “balancing selection” is a real challenge [8]. Retroposon insertions, by contrast, are unrestricted, random, almost exclusively neutral events, and therefore virtually free of any converging effects, fulfilling essentially the strict precondition of neutral evolution [9]. Due to the complex structure of inserted elements, retroposon insertions are recognizable for tens or hundreds of millions of years and are highly resistant to insertion saturation, hence resistant to post-insertional state changes. The degree of natural selection on retroposon insertions correlates with the region of insertion. Apart from the very rare cases of insertions into functionally significant structures (regulatory areas, intron boundaries, or coding sequences), the overwhelming majority of random integrations have no functional or selective importance. Any insertion, independent of where it takes place, is a unique event and post-insertional removal in a descendent lineage is easily recognizable by the highly complex traces that the insertion process leaves behind, enabling such markers to be omitted from further analysis. As explained before, mutations within an element do not compromise its phylogenetic value as a unique presence/absence marker. Diagnostic elements are extracted following strong criteria of orthology and only when they are clearly recognizable in all investigated lineages or when they can be irrefutably defined as absent are they used for phylogenetic analysis.
Another big advantage of this attractive marker system is its relative lack of conflicting data [6]. When such conflicts do arise, their origins are more easily recognized than those of simple sequence changes. One of the avoidable but still most common sources of apparently conflicting presence/absence patterns of retroposed elements is the violation of a strict definition of orthology. In most instances of mammalian retrotranspositions, the process of insertion generates specific target site duplications (TSD) of 8–30 nts flanking all inserted elements [10]. It is important to carefully compare the identity of such TSDs to the unoccupied site of distantly related reference species to clearly confirm the orthology of these loci. The consistent orientation of inserted elements and congruent element types in all analyzed species is another essential criterion for orthology. Furthermore, shared truncations of, or random indels in, elements can help to verify orthology after carefully considering potential hotspots of indels and breakpoints. In the most current investigations only loci with a clear signature of presence/absence in all investigated species (with sequence similarity >70%) are considered [3,11].
A second source of apparently conflicting presence/absence patterns in retrophylogenomics is incomplete lineage sorting during evolution, whereby polymorphic conditions of presence/absence states at the time of the formation of new species might lead to a random distribution of presence or absence states. Such character state polymorphism can similarly influence all types of polymorphic molecular or anatomical characters. Fixation starts with the appearance of an individual change in a population and continues until all individuals of the subsequent populations inherit the change, which can take several million years depending on effective population size [12] and is easily determined by t = 4Ne (where t = generations, multiplied by 25 years for humans will lead to the estimated real time and Ne is the expected ancestral effective population size, e.g, 20,000 for humans). Accordingly, for humans a fixation time of about 2 million years can be estimated. Corresponding to the neutral theory of molecular evolution, the fixation of a previously polymorphic marker depends on the size of the founder population (the smaller a population the sooner a neutral marker is fixed) and generation time (the shorter the generation time the sooner a marker is fixed). For primate populations 1–3 million years are usually sufficient to fix most markers [12,13]. Therefore, especially in rapid successive radiations and in young terminal branches, retroposed elements that entered part of a population may not yet have been uniformly fixed before the next step in speciation occurred. In most such cases, this incomplete lineage sorting leads to a random presence or absence state of markers in lineages and, due to the relative unambiguity of retroposon insertions (presence or absence) and their insertion complexity, is more easily recognized as an equal or symmetric polytomy (all three possible topologies of three related species are more or less equally supported) [14] than a simple sequence change. For example, the highly debated phylogenetic relationships among the three major placental branches Xenarthra, Afrotheria, and Boreotheria were intensively examined by two independent groups [15,16] that revealed markers for all possible variants of relationships, positive evidence supporting ancestral incomplete lineage sorting.
A third potential source of apparent conflicts in the presence/absence patterns of retroposed element insertions is ancestral hybridization, expressed by the exchange of genetic material between separated populations that are still able to reproduce with one another. After hybridization, a new lineage or mixed old lineages can evolve that carry different amounts of genetic material from both lineages. This might lead to asymmetric polytomy, as proposed for an overlapping retroposon distribution (e.g., two elements shared by guinea pig and squirrel vs. eight elements shared by mouse and guinea pig, but no elements shared between mouse and squirrel [17]).
Two other potential sources of conflicts, the exact deletion or parallel insertion of retroelements in related species, are both very rare (see also above). Lagemaat et al. [5] claimed to have found rare cases of exact deletions in young insertions with perfect recombining TSDs; however, the data are not distinguishable from those that might result from incomplete lineage sorting. Notable is, that any exact deletion or exact parallel insertion (producing the same TSDs) in individual genomes must spread over the population to finally be fixed in a lineage. So, random exact deletions or parallel insertions are very rare. For LINE1-mobilized retropositions, one can recognize a slight preference for a TT/AAAA target site motif [18] (the slash represents the cutting/insertion site) perhaps generating some slight hotspots for insertions. The distribution of such rare conflicting cases is only detectable in high-throughput computational or experimental screening for phylogenetic markers [19].
At nearly the same time that insertions of SINEs were proposed as phylogenetic markers [1], the probability of obtaining incorrect phylogenetic information due to segregation of ancestral polymorphism was intensively debated in the phylogenetic community [20] and ancestral polymorphism is now known to be common in lineage diversification [8]. The first consideration of polymorphic markers was based on the principle of Kimura’s neutral theory of molecular evolution [21]. However, in some of these early publications, the only source of phylogenetic conflicts considered was ancestral polymorphism due to incomplete lineage sorting [14,20,22]. Recently, polymorphism due to ancestral hybridization as source for conflicting phylogenetic resolutions was discussed [23,24] and illustrated at the sequence analysis level [25].
Notably, the probability of deriving incorrect phylogenetic signals from ancestral polymorphisms was first shown for rare and irreversible mutations [20], which can be adapted to the analysis of presence/absence of retroelements. Waddell et al. [26] created a criterion for supportive and/or conflicting SINE insertions to support or reject predefined phylogenetic topologies depending on a predefined prior hypothesis against polytomy due to incomplete lineage sorting. The use of this criterion became more popular with the rising popularity of the nearly conflict-free nature of presence/absence data and the increasing availability of genomic data. Nevertheless, from time to time apparently conflicting patterns were recovered and described (e.g., [27]). Unfortunately, the Waddell criterion [26] has many shortcomings that are not compatible with current requirements. For example, the restriction to only test trees limited to the support of five potential phylogenetically informative markers versus symmetric polytomies, or the requirement when testing experimental data that an equal amount of data must be testable for all three possible tree configurations of three species (e.g., for gorilla, chimpanzee, and human ideally an equal number of markers derived from all individual genomes should be screened) is often not available from in silico data. The current immense accumulation of genomic data facilitates novel multi-lineage perspectives to search for phylogenetically informative markers but also requires novel statistical models.
We should also note that not every phylogenetic reconstruction based on retroposon insertion presence/absence patterns is derived in an unbiased way (e.g., those derived from one-directional searches when just one of three genomes is available for screening; see supportable branches in red for a species A restricted search in Fig 1A–1C). Previously, we were not able to test all possible tree topologies for those derived from one-directional searches. As an example, the first systematic screenings for phylogenetically informative retroposon markers in primates [28] used the only available genome information available at the time, human. Therefore, only branches leading to human could be tested and supported (similar to the lineage leading to A in Fig 1A–1C). Other relationships apart from the human lineage could only be examined by inspecting the few additional random insertions also present by chance in the sequenced loci. The ideal situation is to independently screen for markers from two leading lineages (see Fig 1; screening from species A and B) to find all diagnostic insertions and potential conflicting markers.
To overcome the various shortcomings of previous statistical applications and to successfully analyze data that is somewhat less than ideal, we present a new statistical approach that provides a clear test system to evaluate the significance of retroposon presence/absence data and to differentiate between clear bifurcations, incomplete lineage sorting (polytomy), and ancestral hybridization scenarios. This tool is especially important for the high-throughput applications of current and upcoming genome projects due to the unlimited number of testable markers obtained. The new differentiation for one- and multi-directional searches (data from 1 or 2 and more leading species) embedded in a user-friendly R-application enables us to apply the significance test to different screening strategies, and is also suitable for those cases when genomic species representation is not optimal.
This approach dissects phylogenetic trees into series of 3 lineages and evaluates their relationships individually with the KKSC statistics. A statistical evaluation of branch support can be obtained for most such phylogenetic questions, but in the case of ancient rapid radiations leading to so-called anomaly zones with random distributions of polymorphic markers often spread over many speciation events, such a simplification will not solve conflicts between multiple groups. To find phylogenetically diagnostic presence/absence insertion signals in such zones is currently impossible (see [29], [30]), because the noise (random signals) overlays any potential useful signal. The proposed three-lineage subdivision is not adequate for such complexities, but the underlying mathematical model is being used to derive a multi-lineage application to extract hidden phylogenetic signals from a mosaic of marker information. Luckily, although such anomaly zones do exist, most phylogenetic questions are simple and easy to solve with the current strategy.
The unbiased collection of phylogenetically informative presence/absence markers by computational comparative screening (searching for presence/absence patterns in the available sequenced genomes) and/or experimental amplification of promising loci is one of the first steps in reconstructing the evolutionary relationships among species that for example can be easily supplemented by using the GPAC presence/absence finder applied on available multi-way alignments [31]. The next and essential stage is to determine the reliability of the derived presence/absence data. This includes both the careful alignment of individual loci to define the clear orthology of markers and the removal of all loci with partial deletions and non-exact parallel insertions. All verified orthologous markers are then submitted to statistical analysis to derive the support values for the branches of the given species tree. Mathematical models are necessary that consider different biological scenarios. Starting with assumptions based on a simplified situation of three existing lineages that might have arisen following three different scenarios, binary branching, polytomy, or ancestral hybridization, we provide the basic mathematical conditions to be considered (see S1 Appendix). We call the new statistics the KKSC insertion significance test.
We consider three currently existing lineages A, B, C with a common ancestry, and inspect the presence/absence patterns for retroelements inserted at orthologous genomic loci in these lineages. The following events were selected to define phylogenetically informative markers:
ω1—an orthologous retroelement is present in a genomic locus of A and B but absent in C;
ω2—an orthologous retroelement is present in a genomic locus of A and C but absent in B;
ω3—an orthologous retroelement is present in a genomic locus of B and C but absent in A.
We consider the random variable ηj as the number of events ωj (i.e., this variable reflects the number of presence/absence markers supporting the relatedness of two appointed lineages). If the total number of all markers consolidating any two lineages (n):
n=η1+η2+η3
(1)
is fixed, then, in compliance with the proposed model (see S1 Appendix, S1.8), the random variables η1, η2, η3 are distributed according a polynomial distribution:
P(η1=y1,η2=y2,η3=y3)=n!y1!y2!y3!p1y1p2y2p3y3,(y1+y2+y3=n),
(2)
where the parameters of polynomial distribution p1, p2, and p3 are determined depending on which of the three models are applied, for binary branching, polytomy, and ancestral hybridization, respectively.
Under the term C-tree we consider a scenario where at time t0 a common ancestral population separated into two isolated branches (that no longer interbreed). The first branch at time T1 (t1 = t0 + T1) subsequently separated into two lineages A and B. The second branch formed lineage C (Fig 2A).
In compliance with the proposed model (see also equations S1.38—S1.39 in S1 Appendix) we derive:
{p1=1−23Ψ(τ1)p2=p3=13Ψ(τ1),
(3)
where:
τ1=T12N1
(4)
is the drift time according to Waxman [41] (see equation S1.14 in S1 Appendix), and
Ψ(τ)=e−τ1+n1n0(τ+e−τ−1),
(5)
N1 is the average effective population size of the first branch before the split (at the period [t0, t1]), n1 is the average number of new insertions of retroelements per generation on this branch, and n0 is the average number of new insertions of retroelements per generation in an ancestral population.
It should be noted that formula (Eq 3) under condition n1 = n0 coincides with the formulations obtained by Wu [20] and corrected by Hudson [22] for a phylogenetic marker system, see also Liu [14].
Hence, the mathematical model for the C-tree corresponds to (Eq 2) under the assumption:
H1={p2=p3=1−p12,p1>13}
(6)
Accordingly we can define the assumptions for the B-tree (Fig 2C):
H2={p1=p3=1−p22,p2>13}
(7)
and the A-tree (Fig 2B):
H3={p1=p2=1−p32,p3>13}.
(8)
Thus:
P(η1=y1,η2=y2,η3=y3|Hj)=n!y1!y2!y3!pjyj(1−pj2)n−yj,pj>13,(y1+y2+y3=n).
(9)
An ABC-tree (polytomy) is the extreme form of an unresolved tree topology (Fig 2D):
H0={p1=p2=p3=13},
(10)
that is:
P(η1=y1,η2=y2,η3=y3|H0)=n!y1!y2!y3!13n,(y1+y2+y3=n).
(11)
If we assume that no other speciation scenario for A, B, and C is relevant, the parametric space for the model (Eq 2) reduces to:
Ω=H0∪H1∪H2∪H3.
(12)
Thus, to accept for example hypothesis H1, we must reject the opposite hypothesis:
H023=H0∪H2∪H3.
(13)
This leads to the fact that the data relevant for rejecting hypothesis H023 are at the same time sufficient for automatically accepting H1. An example result [27:13:0] representing relevant markers for the A, B, and C trees accordingly, will contradict the assumptions (Eq 7), (Eq 8), and (Eq 10) with a clear significance at the 5% level (in fact, even higher). This corresponds to Wu [20]. However, this result will also be inconsistent with (Eq 6) for the last two numbers [13:0] (for B and C trees). This indicates significant differences between p2 and p3 (that should be equal) that cannot be explained in either the present or previous models [20,22,26] or for coalescence models [14]. However, the skewed distribution of markers (e.g., 0 vs. 13) can be explained by ancestral hybridization [23,24]. To accommodate this, we added a simple model of hybridization that allows any combination of values of p1, p2, and p3, including the binary trees as a special case (see equations S1.40-S1.60 in S1 Appendix).
For ancestral hybridization (Fig 2E) we assume that at time t = t0 the common ancestral population separated into two isolated branches. Later, after T1 and T2 generations, subpopulations of each of the two branches separated from their parent branches (indicated by vertical lines on Fig 2E) and reproduce with one another, forming a new branch B (horizontal line, respectively; Fig 2E). The remaining two branches represent lineages A and C (Fig 2E). We will call this scenario B-fusion. In this simple scenario we ignore all events in the subpopulations before fusion, because elements inserted in genomes on these branches do not generate informative data.
The proportions of the two subpopulations in the newly joined population are denoted by γ1 and γ2 (γ1 + γ2 = 1). Then, according to the proposed mathematical model (equation S1.57 in S1 Appendix), if γ1,2 is not equal to 0 or 1 we have:
p1>p2andp3>p2.
(14)
When either γ1 or γ2 is equal to 0, we obtain an A-tree or C-tree, respectively. In the case of C-fusion (splits from A and B fuse), p1 exchanges places with p2, and in the case of A-fusion (splits from B and C fuse), p3 exchanges places with p2.
Consider the C-tree hypothesis:
H1={p2=p3=1−p12,p1>13}.
(15)
In fact, this is equivalent to the two statements:
H1+={p1>13}andH23={p2=p3}.
(16)
Therefore, H1 is accepted when both hypotheses (H1+ and H23) are supported and rejected when at least one of them is not accepted. In turn, the hypothesis H1+ is accepted when its opposite hypothesis
H¯1+={p1≤13}
is rejected. η1 is a sufficient statistic for the parameter p1, and distributes according to the binomial distribution:
P(η1=k)=(nk)p1k⋅(1−p)n−k,
(17)
where
(nk)=n!k!(n−k)!
.
Thus, if we obtain η1 = Y1, the critical region for the hypothesis
H¯1+
is the set of values greater or equal to Y1. Then:
P(η1≥Y1)=∑k=Y1n(nk)p1k⋅(1−p)n−k=Ip1(Y1,n−Y1+1),
(18)
where Ip(x,y) is an incomplete beta function, which can also be expressed by the cumulative binomial distribution function:
Pbinom(m,n,p)=∑k=0m(nk)pk⋅(1−p)n−k=1−Ip(m+1,n−m).
(19)
Thus, the significance level is defined by the formula:
SL1(Y)=max︸p≤13P(η1≥Y1)=I13(Y1,n−Y1+1).
(20)
We define the maximum probability of a Type I Error α as the probability to reject
H¯1+
in favor of H1+ when
H¯1+
is true. Thus, if SL1(Y) ≤ α, then hypothesis
H¯1+
is rejected, and hypothesis H1+ is accepted.
Note, that when testing the hypothesis H23, the conditional distribution of the random variable η2 is binomially distributed with the parameter
p=p2p2+p3
:
P(η1=k|η2+η3=m)=(mk)pk⋅(1−p)m−k,
(21)
and hypothesis H23 is equivalent to the statement:
p=12
.
When using a two-sided test, the test statistics will be max{η2,η3}. In the case that the experimental data is validated (η2 = Y2, η3 = Y3), the critical region for the hypothesis H23 is the set of values {y2 + y3 = Y2 + Y3, max{y2,y3} ≥ max{Y2,Y3}}.
Accordingly, the level of significance is:
SL23(Y)=|2I12(max{Y2,Y3}1,min{Y2,Y3}+1),ifY2≠Y3,1,ifY2=Y3,
(22)
An illustration of all outcomes for the random distribution of markers and significance areas is presented in Fig 3.
In the case of a one-directional search for markers, data are only available to support two configurations of trees, while four configurations of trees and three hybridization scenarios are possible. This information is insufficient for our full model, but for the condition in which hybridization has already been ruled out, and we assume that only bifurcating trees or polytomy are possible, we derived a simple model for comparing two random binomial-distributed variables (see equations S1.61—S1.63 in S1 Appendix).
We consider the random binomial-distributed variables η1 and η2 and testing of hypothesis
H0:p1≤12
(B-tree, A-tree, ABC-tree) against an alternative hypothesis
H1:p1>12
(C-tree). Then, when H0 is rejected because Y1 is significantly bigger than Y2, the C-tree can be accepted. In the opposite case, when Y2 > Y1, we can reject
H0:p2≤12
(C-tree, A-tree, ABC-tree) and accept the B-tree. If Y1 and Y2 are empirical values of η1 and η2, then, calculating similarly to (Eq 22), the level of significance will be:
SL(Y)=I12(Y1,Y2+1)
(23)
(Except in the situation where Y1 = Y2 and H0 is certainly accepted).
The direct calculation of probabilities for large sets of phylogenetic markers requires some extensive calculations and extended knowledge of mathematical functions. Approximations can help to derive computational scripts including the statistical test. To find the boundaries of critical areas, we can use the normal approximation:
P(η1≥Y1)≈1−F0(Y1−12−np1np1(1−p1)),
(24)
where F0(x) is the standard normal distribution function. Denoting zα as the root of the equation F0(z) = 1−α, from the condition P(η1 ≥ Y1) ≤ α and assuming
p1=13
we obtain:
Y1≥n3+12+zα2n3.
(25)
Proceeding similarly, we define the second critical area for a given level of significance α as:
|Y2−Y3|≥1+zα2Y2+Y3.
(26)
In the case of a one-sided comparison (23), the critical area is defined by the formula:
Y1≥Y1+Y2+1+zαY1+Y22.
(27)
Values for zα used in the approximated formulas (25), (26) and (27) are given in Table S1 in S2 Appendix for significance levels α<0.05, α<0.025, α<0.01, and α<0.005.
The statistical model described here was implemented in a graphical web-application available at http://retrogenomics.uni-muenster.de:3838/KKSC_significance_test/. The application is generated with the Shiny package [32] in the R language [33]. No additional software needs to be installed to use it.
Based on our proposed mathematical model presented in the Methods (see also S1 Appendix), we can calculate the critical values for the numbers of markers shared by two lineages for various schemes of phylogenetic studies. A one-directional search (genome information of only one of three species is available; e.g., Fig 1A–1C for species A) provides a very limited amount of interpretable information. The calculation is based on formulas (Eqs 23 and 27) (see Table S2 in S3 Appendix). However, interpretations of presence/absence patterns derived from one-directional searches should be made with care. The lack of a difference between two values (numbers of markers) does not necessarily reject the third possible tree configuration, which cannot be tested from this one direction, and cannot exclude a polytomy between all three possible configurations or a significantly resolved third tree hypothesis (e.g., Fig 1F; the genome of species B is necessary). On the other hand, based on our model, differences between the two smallest values indicate ancestral hybridization events. Then significant statistical differences between two values obtained in the one-directional search do not distinguish between the possible bifurcated tree and hybridization (see equations S1.61—S1.63 in S1 Appendix).
In contrast to one-directional searches, unbiased screenings (multi-directional search) from two directions (e.g., Fig 1 using genomes of species A and B), returning three values for the numbers of shared markers, provide more information for interpretation (Tables S3-S4 in S3 Appendix), based on our statistical two-step criterion (Eqs 22–23). Using our web-interface and the implemented model (Eqs 20 and 22 and 23), we can easily derive P-values for the different phylogenetic scenarios (http://retrogenomics.uni-muenster.de:3838/KKSC_significance_test/).
An example of a conflicting distribution of markers was detected when we inspected the root of placental mammals [15]. We identified a presence/absence pattern of (9:8:5) similarly supporting all three possible tree hypotheses (Epitheria, Atlantogenata, and Exafroplacentalia). Using the web application to resolve this contradiction, the user first selects the “Analysis type”, either a “multi-directional” search (for cases in which more than one reference genome were screened, as in this example), or a “one-directional” search (for cases in which a screening was performed from only one reference species). It is also possible to specify the names of the species (e.g., A: Afrotheria; B: Xenarthra; C; Boreotheria), which are used for the results table. The user then provides the numbers of markers shared by the lineages that were analyzed. For the current example of a multi-directional analysis, the Afrotheria and Xenarthra shared 8 markers, Xenarthra and Boreotheria shared 5, and Afrotheria and Boreotheria 9 markers. The table at http://retrogenomics.uni-muenster.de:3838/KKSC_significance_test/ displays statistical information about the tests. The column “test type” displays the type of the test, and P-values are calculated for the different tests based on the values presented in the third column (e.g., p = 0.5811 for the hybridization test and p = 0.293 for bifurcation test). The fourth and fifth columns display the boundaries of critical areas for p<0.05 and p<0.01, respectively. The resulting figure of the KKSC significance test highlights the most probable evolutionary scenario. Significantly supported lineages are labeled by dark spheres; hybridization is indicated by divided spheres labeled with the hybridizing lineages; and the tree located in the center of the triangle indicates an unresolved tree topology.
We have also presented an applicable approximation for an unlimited number of markers (Eqs 25–27). As can be seen in Tables S3-S4 in S3 Appendix (columns 5% and 1% borders), this approximation effectively works from the minimum number of markers, and can be used as a brief estimation of significance of ongoing experimental results without using tables or the web-interface. For example, Nishihara et al. [16] examined the root of the placental tree and found 25 retroposon insertions supporting the Epitheria hypothesis, 22 supporting the Exafroplacentalia hypothesis, and 21 supporting the Atlantogenata hypothesis. Because the total number of markers is larger than 30, the pattern (25:22:21) cannot be directly evaluated using Tables S3-S4 in S3 Appendix. Therefore, to test the significance of the support for the various hypotheses the approximation formulas or the web-interface (http://retrogenomics.uni-muenster.de:3838/KKSC_significance_test/) should be used. To test the Epitheria hypothesis: calculate the sum of the relevant supporting markers (22+21 = 43) and the difference of the two smallest values (22–21). Setting the significance level at α<0.05, from Table S1 in S2 Appendix, we have a value of
zα2=1.960
. Using equation (Eq 26) we can calculate the critical value for the difference of the two smallest values for their sum 43 and round this value up to the closest integer value:
1+1.960⋅43≈13.9=⊳14
. Thus, on the level of a significance of α<0.05, we cannot accept the hybridization hypothesis. To test the Epitheria hypothesis against polytomy we calculate the full sum (n = 25+22+21 = 68) and use equation (Eq 25). Setting the significance level at α<0.05, from Table S1 in S2 Appendix we have a value of zα = 1.645. Calculating the critical value and rounding up, we have:
0.5+68+1.645⋅2⋅683≈29.6=⊳30
. Then, because 30<33, polytomy cannot be rejected and should represent the most realistic evolutionary scenario.
We also analyzed an interesting example of asymmetric conflicts in rodents. To determine the origin of the three major rodent lineages, best represented by mouse, guinea pig, and squirrel [16], we found 8 markers shared by mouse and guinea pig to the exclusion of squirrel, but also two markers shared by guinea pig and squirrel to the exclusion of mouse, and no insertions shared by mouse and squirrel. Because the Waddell criterion is limited to only 5 markers [26], it was not possible to use it to statistically evaluate this pattern. With our new statistical models we can test this case for significance of a resolved tree topology or hybridization. In the pattern (8:2:0), the two smallest values (2:0) do not fulfill the minimum number of markers for supporting a clear hybridization scenario (see Table S3 in S3 Appendix), so the critical values cannot be calculated and we cannot yet accept hybridization (p>0.05) as a viable hypothesis. According to our web-interface, a resolved tree topology of (mouse, guinea pig), squirrel is supported at a significance level of p=0.0034. However, under our criteria, hybridization can only be significantly supported when there are 12 or more markers. This example shows that an appropriate statistical model plus a sufficient number of markers are necessary to correctly interpret hybridization signals.
Based on our mathematical model a calculation of the confidence intervals of drift time (τ) for a common ancestor of the two youngest lineages is possible (see S4 Appendix for details, examples, and simulation results).
The first phylogenetic applications of retroposon presence/absence patterns were conducted with a few hand-selected cases [34]. The clear polarity of retroposon markers, with presence as the derived condition and absence as the ancestral state, encountered little if any conflicting situations and designated retroposons as perfect, homoplasy-free markers [6]. As more and more genome data became available, seemingly conflicting patterns of markers were also obtained, requiring that we pay more careful attention to these conflicts in applying statistically meaningful tests. In addition to the conflicting retroposon presence/absence pattern at the root of placental mammals [15,16], there is also a series of conflicting retroposon presence/absence patterns in neoavian birds [29,35]. These patterns are probably due to the effects of incomplete lineage sorting because all possible phylogenetic topologies are represented more or less equally. Contradicting phylogenetic signals from retroposon presence/absence data were also detected in cichlid fishes [36] and turtles [37].
Given that we know that such conflicts reflect real evolutionary paths and not problematic data, these same conflicting patterns can provide valuable information about the first steps of new lineages after speciation. Distinguishing between equal and unequal polytomies provides unique information about potential ancient hybridization events. Retroposon insertions are very stable over time and point mutations have not critically reduced the recognizability of these signals over hundreds of millions of years. The cases of noise, introduced by parallel insertion [4] and precise deletion of retroelements [5], does not significantly influence the retroposon data, because of their rare appearance. Nevertheless, from time to time we receive an indication that parallel insertion or exact deletion cannot be completely ruled out, even if it is just a minor part of the collected data. For example, of more than 300 retroposon markers analyzed in the order Carnivora, three were highly inconsistent [4]. Although their insertion sites appeared highly orthologous, their locations in completely different parts of the phylogenetic tree clearly ruled out insertions in a common ancestor or incomplete lineage sorting in a local anomaly zone of the tree. Instead, they could be seen as real examples of parallel insertions of identical elements with identical target side duplications in distant parts of a phylogenetic tree. Likewise, the few loci containing retroposed elements under strong selective pressure do not influence presence/absence patterns, because selection does not selectively remove or insert complete copies in one or more lineages. Lineage-specific conserved versus non-conserved orthologous retroposon loci are only considered if a clear presence/absence state is recognizable in all investigated lineages. Thus, compared to other types of molecular markers, the very stable and recognizable nature of clear orthologous retroposon insertions preserves and provides important information about different scenarios of speciation events.
Initially, only SINE elements more close to the terminal mammalian branches were used as phylogenetic clade markers because they are more specific for a restricted group of species and rarely traverse the order levels in mammals [27]. Thus, retroposon presence/absence data were initially restricted to primates, rodents, lagomorphs, afrotherians, xenarthrans etc., and the interrelationships among these groups were not analyzed using SINE elements. This limitation was overcome by screening for Long INterspersed Elements (LINEs) and Long Terminal Repeats (LTRs) and using them similar to SINEs as phylogenetically informative markers [16,38]. With this expansion, it was possible to analyze deep mammalian branches. At that time, however, despite the newly available mouse genome, the human genome was still taken as the leading source of initial screening for potential informative markers. The current large number of available genomes provides numerous possibilities to further extend retroposon searches and provides excellent sources for investigating the tree of life.
Ongoing full genome screenings for retroposon presence/absence patterns can provide hundreds or even thousands of retroposon markers [3,39]. However, a subsequent clear individual confirmation of orthology by inspecting the element type and orientation, determining the exact identical insertion sites and target site duplications, and, if applicable, considering diagnostic truncations points, is essential to obtaining a noise-free dataset for further reliable investigations. One such example is presented in Doronina et al. [3], where the phylogenetic relationships of the three carnivore superfamilies (Ursoidea, Musteloidea, and Pinnipedia) were examined. Analysis based on a combined SINE and LINE dataset provided the pattern (192:74:60), where 192 markers reflected the consolidation of Pinnipedia and Musteloidea, 74 markers indicated a common ancestral branch for Ursoidea and Musteloidea, and 60 markers provided support for a Pinnipedia/Ursoidea clade. The resolved tree topology of (Pinnipedia, Musteloidea) Ursoidea was supported at a significance level of p<3.3×10−21 using the KKSC statistics (the small asymmetry of (74:60) did not indicate hybridization (p>0.2). This result confirms the most recent supertree analyses [40]. The detected zone of intense incomplete lineage sorting fits well with the proposed extensive radiation at the beginning of arctoid evolution [41].
In principle, and in addition to the branch support statistics, it is possible to calculate/simulate specific parameters of ancestral populations such as the effective populations size, but the random nature of marker fixation renders such values not as trustworthy as sequence-based calculations. Therefore, we only present some possible calculations in the S4 Appendix.
For small numbers of markers, the KKSC presence/absence statistics corresponds to the values of the previously established Waddell test [25] but returns less significant values in apparent marker conflict situations such as 3:1:0 (p = 0.111 vs. p = 0.0617, respectively) (see Table S9 in S5 Appendix). This is due to the consideration of more complex evolutionary scenarios, such as ILS and ancient hybridization in KKSC. Unfortunately, the Waddell test is only applicable for up to 5 markers. Compared to the PAUP*4.0b10 presence/absence data analysis [42] (irrev.up option of character transformation) as for example applied in Doronina et al. [2], the new statistics provides more reliable estimates of branch supports, especially for small numbers of markers. For example, in PAUP a single diagnostic insertion leads to a bootstrap value of 100, but more realistically is not significant in KKSC (possible Type I Error of the PAUP estimation). For small numbers of supporting markers, a Bayesian inference (MrBayes, Standard Discrete Model: binary; ctype irreversible; [43]), applied for example in Doronina et al. [2] lacks resolution (e.g., 2:0:0, polytomy in MrBayes). A chi-square test leads to results similar to those of KKSC. Applying the Yates’s correction for continuity (advised for small numbers) [44] to small sets of markers (1–3) leads to non-significant results. Finally, the KKSC test is the only test that not only rejects polytomy (trifuraction) but also detects hybridization signals and significantly extends the previously standard application presented by Waddell et al. [25].
Based on the principles of population genetics and the neutral theory of evolution, our statistical models create complete sets of criteria for testing all possible evolutionary scenarios for retroposon presence/absence data that are not randomly distributed during rapid radiations. One of the novelties of our model is the inclusion of a simple scenario for ancestral hybridization that is necessary for explaining asymmetric patterns of retroposon presence/absence insertions. Furthermore, our statistical criteria can be applied to any irreversible, largely neutrally evolving set of molecular markers (e.g., retroposon or indel presence/absence data) without any upper limitations on the size of the dataset. As discussed above, our new model is partially compatible with the criteria of Waddell et al. [26], but at the same time markedly enlarges the applicability for comprehensive datasets as they are generated today from genome-level analyses.
There are some natural limits in the acquisition of sufficient data and interpretation of the statistical significance using our model, mainly concerning low quality data, for example from one-directional searches (see Fig 1). For a one-directional search, we can only obtain resolution for two possible evolutionary scenarios. For the third possible tree, no data are available and consequently no safe statistical statement can be made. Furthermore, for such a limited screening, the hybridization probability cannot be calculated. A second limitation is that an evaluation of the level of hybridization is not yet available. However, one can imagine a hypothetical situation in which the relevant markers are distributed as (101:11:0), in which hybridization is supported with high significance (p<0.001), but support for the first tree topology is strong enough (101 marker) to favor this topology. One solution of this problem may be to define a tree with hybridization as a specific case and restrict hybridization cases to situations where we cannot define a clear topology, when the two highest values (Y1 and Y2) have no statistical difference (note: comparisons of the two highest values can be derived from our new web-interface (http://retrogenomics.uni-muenster.de:3838/KKSC_significance_test/) or can be performed with Eq 23 or the approximation formula 27). We intend to derive a more sensitive detection model for hybridization as soon as more retroposon presence/absence data are available for proven hybridization events, for example from plant phylogeny.
We have repeatedly stressed the need for extremely careful validation of the orthology of insertion markers and for only using those that fulfill very strict criteria. Is it possible that such strong filtering biases the dataset? Ascertainment biases can arise when filtered markers are not obtained from a random sample of the polymorphisms in the population of interest [45]. Even though our selections are very strict, they are still random. However, it should be mentioned that under special conditions an extreme reduction in the number of informative markers can occur from a large pool of potentially informative markers. For example, to validate the position of platypus in the tree of mammals by retroposon data [46], we screened ~90 thousand markers, but only three of them fulfilled all the criteria of orthology in such a deep mammalian branch. In such cases, we try to add screenings for additional types of elements active at the same time (SINEs, LINEs, LTRs etc.) to gain more information. Although the three markers were randomly selected and distributed over the full genomic expansion, it remains a theoretical possibility that they belong to a special subset of phylogenetically inconsistent loci, (e.g., a special subsets of markers that were incompletely sorted). That is why we advise, in addition to using as many sources of information as possible, it is best to screen genome-wide so as to obtain the largest number of markers possible. We recommend using optimized search criteria involving at least two different lineages in a multi-sided screening, and require a much higher burden of significance for markers resulting from a single-sided search with the warning that specific tree topologies cannot be resolved from such restricted searches.
Another current limitation is the restriction of our statistical test to combinations of three lineages, which is sufficient for most specific phylogenetic questions. Recently, however, large genome sequence analyses yielded multilevel conflicts in phylogenetic signals including many more than just three lineages with inconsistent markers [29,30,35]. We are currently in the process of developing a new statistic for specifically resolving such complex relationships resulting from extreme population expansions after bottlenecks and successive speciation periods that are much shorter than the time necessary for marker fixation (see for example the neoavian radiation 66 million years before [33,34]).
The minimum number of markers required for significant support of a selected tree hypothesis is three conflict-free markers detected via data derived from representatives of at least two or all three lineages [3:0:0], in agreement with Waddell et al. [26]. If only one representative of the three investigated lineages is available, five markers are required for significant support [5:0:0]. The statistical test that also considers conflicting patterns of markers can be taken from Table S2 in S3 Appendix (up to 30 markers can be tested) or from Table S3 together with Table S4 in S3 Appendix (up to 30 markers can be tested). In both cases significance values can be derived directly from formulas (Eqs 22 and 23) and our web-interface (http://retrogenomics.uni-muenster.de:3838/KKSC_significance_test/).
We have provided a comprehensive statistical framework for testing the significance of support for phylogenetic hypotheses derived from genome-level data and for evaluating possible retroposon presence/absence patterns for different evolutionary scenarios, including polytomy, incomplete lineage sorting, and ancestral hybridization. Finally, a reliable, adaptable calculation for the significance of support for phylogenetic trees derived from genome-wide retroposon presence/absence data is now available.
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10.1371/journal.ppat.1005342 | Evidence for Persistence of Ectromelia Virus in Inbred Mice, Recrudescence Following Immunosuppression and Transmission to Naïve Mice | Orthopoxviruses (OPV), including variola, vaccinia, monkeypox, cowpox and ectromelia viruses cause acute infections in their hosts. With the exception of variola virus (VARV), the etiological agent of smallpox, other OPV have been reported to persist in a variety of animal species following natural or experimental infection. Despite the implications and significance for the ecology and epidemiology of diseases these viruses cause, those reports have never been thoroughly investigated. We used the mouse pathogen ectromelia virus (ECTV), the agent of mousepox and a close relative of VARV to investigate virus persistence in inbred mice. We provide evidence that ECTV causes a persistent infection in some susceptible strains of mice in which low levels of virus genomes were detected in various tissues late in infection. The bone marrow (BM) and blood appeared to be key sites of persistence. Contemporaneous with virus persistence, antiviral CD8 T cell responses were demonstrable over the entire 25-week study period, with a change in the immunodominance hierarchy evident during the first 3 weeks. Some virus-encoded host response modifiers were found to modulate virus persistence whereas host genes encoded by the NKC and MHC class I reduced the potential for persistence. When susceptible strains of mice that had apparently recovered from infection were subjected to sustained immunosuppression with cyclophosphamide (CTX), animals succumbed to mousepox with high titers of infectious virus in various organs. CTX treated index mice transmitted virus to, and caused disease in, co-housed naïve mice. The most surprising but significant finding was that immunosuppression of disease-resistant C57BL/6 mice several weeks after recovery from primary infection generated high titers of virus in multiple tissues. Resistant mice showed no evidence of a persistent infection. This is the strongest evidence that ECTV can persist in inbred mice, regardless of their resistance status.
| Orthopoxviruses (OPV) cause acute infections in mammalian hosts but some OPV, including ectromelia virus (ECTV), have been isolated from tissues of several species of animals long after infection. We present evidence that ECTV causes a persistent infection in some strains of disease-susceptible mice in which infectious virus was present in the bone marrow for several weeks post-infection. While an antiviral response was generated and persisted during the entire study period, it was insufficient to eliminate virus. Both host factors and virus-encoded host response modifiers influenced virus persistence. Se veral weeks after infection, mice that had apparently recovered succumbed to disease and transmitted virus to co-housed naïve animals following immunosuppression. Significantly, infectious virus was also isolated from resistant mice that had been subjected to sustained immunosuppression several weeks post-infection. This is the strongest evidence that ECTV can persist in inbred mice, regardless of their resistance status.
| An acute viral infection can result in complete recovery of the host, death or establishment of persistence. The OPV genus is generally believed to cause acute infections. However, some members such as ECTV [1–7], monkeypox virus (MPXV) [8], cowpox virus (CPXV) [8–10] and vaccinia virus (VACV) [11,12] have been reported to persist for several weeks or months after experimental infection in a variety of animal species that show no clinical signs of disease [13]. Those reports have neither been thoroughly investigated nor their significance understood. If proven correct, they have profound implications for the ecology of OPV and the epidemiology of diseases they cause. One suggestion is that the reports may be a reflection of persistent infection within a population rather than virus persistence in individual animals [13]. VARV causes smallpox in humans but the disease was successfully eradicated through vaccination more than 35 years ago [13] without any evidence of re-emergence, implying that it does not cause persistent infections.
Despite the eradication of smallpox, there is still significant interest in the pathogenesis of OPV infections due to the potential threat of accidental or intentional release of VARV [14], the emergence of zoonotic MPXV [15–17], outbreaks of VACV infection in dairy cattle and their transmission to humans [18,19] and sporadic outbreaks of cowpox in humans and various animal species [20–22]. While outbreaks of CPXV or VACV infections in humans are not common, monkeypox is an emerging disease in West and Central Africa [17,23]. The introduction of MPXV into the United States in 2003 in a consignment of wild-caught animals from Africa established for the first time that outbreaks of human monkeypox could occur outside of the African continent [24].
Mousepox is a disease that is similar to smallpox and an excellent small animal model to study the human disease. Like the outbred human population, which exhibited varying degrees of susceptibility to smallpox [13], inbred strains of mice are either resistant or susceptible to mousepox. C57BL/6, C57BL/10, AKR and some sub-lines of 129 mice are resistant whereas A/J, DBA2, CBA/H and BALB/c mice are susceptible [4,5,25–27]. At least 4 genetic loci in the mouse genome are known to confer resistance to mousepox [27], and are associated with the generation of robust innate and adaptive immunity by the host [28–37]. Susceptible strains lack resistance alleles at these loci and the immune response generated against ECTV is weak and delayed [27,33]. In resistant strains, the potent immune response can largely overcome the effects of host response modifiers (HRM) that ECTV encodes to subvert, dampen or evade immunity, whereas in susceptible strains virus-encoded HRM can overwhelm the sub-optimal immune responses. Nonetheless, susceptible mice can control infection with mutant viruses lacking specific HRM [38–40]. BALB/c mice infected with a deletion mutant of ECTV that does not express viral IFN-γ binding protein (vIFN-γbp) overcome the infection through augmented IFN-γ production and cell-mediated immunity [38]. Although the animals apparently recover from infection, preliminary studies revealed that virus is not completely eliminated. This finding provided us with an opportunity to address OPV persistence using the ECTV model.
We report here that at sub-lethal doses of wild type (WT) or mutant ECTV infection in disease-susceptible BALB/c mice, low numbers of virus genomes persisted over several weeks despite the presence of effector CD8 T cell responses. Virus genomes were detected in several organs but only in the BM and blood beyond 37 days post-infection (p.i). Importantly, infectious virus was also demonstrable in the BM of some mice more than 100 days p.i. Contemporaneous with the presence of virus, a change in the immunodominance hierarchy of CD8 T cell responses was evident during the first 3 weeks. The capacity of virus to persist was influenced by the host immune response and virus-encoded HRM. However, treatment of mice that had apparently recovered from infection with the immunosuppressive drug cyclophosphamide (CTX) several weeks post-infection caused mousepox with high titers of virus in visceral organs. CTX-treated mice, but not untreated animals, transmitted virus to co-housed naïve mice, all of which succumbed to mousepox. An unexpected and surprising finding was that treatment of the resistant C57BL/6 mice several weeks after infection with CTX also caused mousepox with high titers of virus in organs. There is no evidence of ECTV causing a persistent infection in this strain. Our data provide robust evidence that ECTV can persist at very low levels in both resistant and susceptible strains of mice.
BALB/c mice infected with ECTV-WT at a dose of 500 PFU or greater succumb to mousepox due to uncontrolled virus replication [33,38]. When inoculated with a similar dose of ECTV-IFN-γbpΔ, this strain generates good cell-mediated immunity and antibody responses with a significant proportion of animals overcoming infection but a small subset succumbing to disease ([38] and Fig 1A). Virus was isolated from most organs as late as day 21 p.i. (Fig 1B) but was below the level of detection by viral plaque assay at later time points (Fig 1C and 1D; [38]). The more sensitive qRT-PCR assay, however, revealed the presence of ECTV-IFN-γbpΔ genomes in several organs at 37 days p.i., with genome copy numbers highest in the BM and blood (Fig 1E). The presence of virus genomes was biologically significant as infectious virus was detected in 2 of the 6 animals in the BM and in 1 of 6 animals in blood by viral plaque assay (Fig 1F). Virus genomes were also detected in several tissues of CBA/H mice (Fig 1G), another ECTV-susceptible strain, but not in the resistant C57BL/6 strain (Fig 1H). Despite the presence of virus genomes, the animals did not display any clinical signs of disease.
As animals that survived infection with ECTV-IFN-γbpΔ generate neutralizing antibody responses between days 14–37 p.i. [38], we hypothesized that ineffective virus clearance might be related to suboptimal or defective CTL responses. We therefore characterized the CD8 T cell responses to ECTV-IFN-γbpΔ during the early (day 7), intermediate (day 14) and late (day 21) phases of a primary infection. We used ECTV-WT and the highly attenuated ECTV-TKΔ, in which the viral thymidine kinase (TK) gene had been deleted, as controls for the early time point. BALB/c mice infected with ECTV-WT succumb to mousepox before day 14 whereas ECTV-TKΔ is cleared rapidly and CD8 T cell responses are not detectable at day 14 or beyond.
At day 7 p.i., ECTV-TKΔ and ECTV-IFN-γbpΔ induced CTL responses that were comparable but about 9-fold higher in magnitude than the response elicited by ECTV-WT (Fig 2A) [38]. The determinant-specific CTL response induced by ECTV-IFN-γbpΔ (Fig 2B) was also similar to that generated by ECTV-TKΔ but the response was 3-9-fold lower in ECTV-WT-infected mice (S1 Fig). The Ld-026 determinant-specific response was the strongest followed by Dd-043- and Kd-149.5-specific responses.
Each of the viruses induced similar proportions of determinant-specific IFN-γ+ CD8 T cells, with a greater proportion of cells responding to Ld-026 peptide (Fig 2C). However, in terms of numbers, ECTV-TKΔ and ECTV-IFN-γbpΔ induced significantly higher determinant-specific IFN-γ+ CD8 T cells compared to ECTV-WT (Fig 2D). The Ld-026-specific tetramer+ CD8 T cells were the predominant population (Fig 2E) followed by the Dd-043 and Kd-149.5 tetramer+ CD8 T cells. Consistent with the determinant-specific IFN-γ response, the numbers of tetramer+ CD8 T cells induced by ECTV-WT were significantly fewer compared to numbers in mice infected with the other viruses. All 3 viruses also induced similar T cell receptor (TCR) Vβ chain repertoires in CD8 T cells at day 7 p.i., with greater than 35% expressing the TCR Vβ8, followed by Vβ7, Vβ6, Vβ4 and Vβ10a (Fig 2F). Thus, although all 3 viruses induced CD8 T cells of comparable TCR Vβ repertoires, ECTV-WT induced a poor response in terms of numbers and functionality.
At 2 and 3 weeks p.i., virus-specific cytotoxic T lymphocyte (CTL) activity was demonstrable in mice infected with ECTV-IFN-γbpΔ, albeit the magnitude was about 3-fold lower than day 7 (Fig 3A). By day 21 p.i., the response was predominantly Kd-149.5-restricted (Fig 3B), associated with increased proportions (Fig 3C) and numbers (Fig 3D) of Kd-149.5-specific IFN-γ+ cells and Kd-149.5 tetramer+ (Fig 3E) CD8 T cells. Proportions (Fig 3F) and numbers (Fig 3G) of ECTV-specific (total) IFN-γ+ CD8 T cells increased at day 14 p.i., over and above day 7 p.i., and remained high at day 21. The dominance of Kd-149.5 tetramer+ CD8 T cells (S2A Fig) and Kd-149.5-restricted CTL activity at day 21 (S2B Fig) also occurs in mice infected with a sub-lethal dose (100 PFU) of ECTV-WT and is therefore not unique to ECTV-IFN-γbpΔ infection. At this dose, ECTV-WT induces similar numbers of tetramer+ CD8 T cells as mice infected with 500 PFU of ECTV-IFN-γbpΔ (S2C Fig).
The Ld-026 tetramer+ CD8 T cells were predominantly Vβ8.1/8.2+ at day 7 p.i., and despite a decline in proportions at days 14 and 21 p.i., they were still the dominant type (S3 Fig). Conversely, proportions of Kd-149.5 tetramer+ Vβ8.1/8.2+ CD8 T lymphocytes expanded gradually from day 7 and were the main type by day 21 p.i., with proportions of Vβ10a+ and Vβ11+ cells also increasing by this time. The Dd-043 tetramer+ CD8 T cells utilized the Vβ8.1/8.2 TCR repertoire early in infection but to a lesser extent at later time points. While the significance of TCR Vβ chain usage by CD8 T cells in BALB/c mice or with respect to ECTV persistence is currently unknown, it is notable that the most responsive CD8 T cell populations utilized the Vβ8.1/8.2 TCR chain.
Taken together, these data indicate the presence of activated, effector CD8 T cells and an inversion of immunodominance hierarchy of the CD8 T cell response during the first 3 weeks p.i. An investigation into the mechanism(s) that result in a change in the immunodominance hierarchy of the CD8 T cell response is beyond the scope of this study but the results are consistent with virus persistence.
CD62L, the homing receptor for lymphocytes and CD127, the IL-7 receptor α chain are both expressed at high levels on the surface of naïve CD8 T cells but are reduced following antigenic stimulation and activation. Low-level virus persistence (Fig 1E and 1F) might have been responsible for an ongoing immune response as demonstrated by reduced expression of CD62L and CD127 (S4A Fig), IFN-γ production (S4B Fig) and cytolytic activity exhibited by CD8 T cells at day 37 p.i. (S4C Fig). Significant numbers of tetramer+ CD8 T cells were also present at this late stage of infection (S4D Fig).
In ECTV-resistant C57BL/6 mice, the kinetics of viral load in organs closely parallels the CTL activity over the first 2 weeks but the response contracts and is undetectable once virus is cleared [33,34]. However, in C57BL/6 mice lacking B or CD4 T lymphocytes, ECTV causes a persistent infection and chronic stimulation of CTL activity [34]. Thus, the presence of virus (or viral antigen) appears necessary for continued stimulation of CD8 T cells. We reasoned that the presence of activated effector CD8 T cells in BALB/c mice beyond day 37 p.i. might be indicative of viral antigenic stimulation of this population and possibly virus persistence. Indeed, virus- (Fig 4A) and determinant-specific (Fig 4B) CTL responses were detectable throughout the 177-day period in ECTV-IFN-γbpΔ-infected mice. The responses were high during the first 3 weeks p.i., consistent with data in Figs 2 and 3, with a gradual decline over time but still detectable at very low levels at day 177. The kinetics of the CTL response corresponded with virus- (Fig 4C) and determinant-specific (Fig 4D) IFN-γ+ and tetramer+ CD8 T cell numbers (Fig 4E) and proportions (S5 Fig). This longer-term study confirmed that there was a change in the immunodominance hierarchy of the CD8 T cell response during the first 3 weeks p.i. The kinetics of the CD8 T cell responses closely paralleled the presence of virus genomes in the BM (Fig 4F) and blood (Fig 4G), at least over the first 60 days p.i. Virus genomes were largely below the limit of detection after this period, but low levels of genome copies were occasionally detectable in the BM of a very small number of animals. Despite our inability to detect ECTV genomes consistently beyond 60 days p.i, low-level CD8 T cell effector activity was still measurable (Fig 4A–4E), suggesting that viral antigen was likely being presented to the T cells. However, these effector cells were unable to completely clear the persistent virus infection.
Numerous mechanisms can potentially contribute to virus persistence but at least two important factors are the effectiveness of host immune response in eliminating virus and subversion of the response by virus-encoded HRM. We investigated the roles of 4 specific virus-encoded HRM known to dampen cell-mediated immunity in contributing to virus persistence. In addition, we used BALB congenic mice to assess whether some host genes associated with genetic resistance to mousepox and known to modulate cell-mediated immunity contribute to overcoming virus persistence.
Deletion of vIFN-γbp reduced viral load and increased survival rates of BALB/c mice, but virus genomes were still detectable at day 37 p.i. (Fig 1). We reasoned that deletion of additional genes encoding HRM might be more effective in clearing virus and overcoming persistence. Single deletion ECTV mutants lacking vIFN-α/βbp (ECTV-IFN-α/βbpΔ), vIL-18bp (ECTV-IL-18bpΔ) or viral serine protease inhibitor-2 (vSPI-2, an inhibitor of caspase activity) (ECTV-SPI-2Δ), a double mutant lacking vIFN-γbp and IL-18bp ECTV-IFN-γbpΔ-IL-18bpΔ), and a triple mutant lacking vIFN-γbp, vIL-18bp and vSPI-2 (ECTV-IFN-γbpΔ-IL-18bpΔ-SPI-2Δ) were used to infect BALB/c mice. Each of these HRM has been shown to dampen the host NK cell and CTL responses [38–41].
Titers of single mutant viruses were significantly lower in the liver at day 7 (Fig 5A) compared with ECTV-WT and viral load was further reduced when both vIFN-γbp and vIL-18bp were deleted (double mutant). The biggest reductions in viral load were evident in mice infected with the triple mutant or ECTV-IFN-α/βbpΔ, which were below the limit of detection. Similarly, genomes of the triple mutant and ECTV-IFN-α/βbpΔ were also below the limit of detection in the BM (Fig 5B) and blood (S6A Fig) at day 35 p.i. However, a caveat to this finding is that ECTV-IFN-α/βbpΔ was detectable in the liver at day 7 (S6B Fig) and in the BM at day 35 p.i (Fig 5C) when the inoculation dose was increased by 100- or 1000-fold. At similar or higher doses of the highly attenuated ECTV-TKΔ, virus genomes were not detectable in the BM (Fig 5C) or liver (S6B Fig). The data suggested that effective control of virus replication in the liver by day 7 significantly reduced the possibility of virus genome detection in the BM at day 35 p.i.
Of the 4 genetic loci in the mouse genome known to confer resistance to mousepox [27], resistance to mousepox 1 (Rmp-1) locus on chromosome 6 maps to the natural killer cell complex (NKC) [42] and Rmp-3 locus on chromosome 17 is linked to the major histocompatibility complex (MHC) [43], and believed to be the classical MHC class Ia Db molecule [26]. The ECTV-resistant C57BL/6 strain encodes all known resistance loci. In addition, the non-classical MHC class Ib molecule Qa-1b, bound with ECTV-derived or ECTV-induced host protein-derived peptides, can activate NK cells via the CD94-NKG2E heterodimer receptor and contribute to resistance against mousepox [44].
We speculated that BALB congenic mice encoding the C57BL/6 NKC (BALB/c.Cmv1r) [45], MHC (BALB/b) or both (BALB/b.Cmv1r) loci would control ECTV replication more effectively and potentially overcome virus persistence. Indeed, at a dose of 500 PFU ECTV-WT, 100% of BALB/b.Cmv1r mice survived infection compared with survival rates of 60% in BALB/b strain, 50% in BALB/c.Cmv1r mice and 0% in BALB/c mice (Fig 5D). The combined expression of Rmp-1 and Rmp-3 in BALB/b.Cmv1r conferred this strain a higher degree of resistance to ECTV infection compared with the other strains (Fig 5D). Nonetheless, the 4 strains of mice fully recovered from a sub-lethal dose of 100 PFU ECTV. At this dose, BALB/c.Cmv1r and BALB/b.Cmv1r mice generated stronger NK cell responses (Fig 5E) whereas BALB/c.Cmv1r, BALB/b and BALB/b.Cmv1r generated antiviral CTL responses (Fig 5F) that were higher in magnitude than BALB/c mice at day 7 p.i. The increased resistance of BALB/c.Cmv1r and BALB/b.Cmv1r congenic mice compared to the corresponding WT strains BALB/c and BALB/b, respectively) was, at least in part, due to the function of NK cells as depletion of this subset with the anti-NK1.1 mAb abolished protection (S6C Fig). In the NKC congenic strains, viral genome copy numbers were lower in the BM compared to the corresponding WT strains at day 35 (Fig 5G) and below the limit of detection in BALB/b.Cmv1r strain.
It was of interest to determine whether infection of BALB/b.Cmv1r congenic mice with the attenuated ECTV-IFN-α/βbpΔ would further reduce the level of virus replication and tip the balance in favor of the host, allowing it to overcome virus persistence. Indeed, virus genomes were below the limit of detection in the BM of BALB/b.Cmv1r mice at 35 days p.i. with 105 PFU of ECTV-IFN-α/βbpΔ, whereas virus genomes were detectable in the other mouse strains (Fig 5H). Titers of ECTV-IFN-α/βbpΔ were significantly lower in BALB/b and BALB/c.Cmv1r mice compared to WT BALB/c mice. Taken together, the data suggested that both host and viral factors impact on whether virus persists.
The ongoing immune response likely kept viral load under check but was insufficient to eliminate virus, suggesting equilibrium might have been reached between virus and the host immune response. We reasoned that perturbation of the equilibrium through immunosuppression might tip the balance in favor of the virus and allow the low-grade persistent infection to become an overwhelming one. In the results described below, mice that had been infected with ECTV 80 days previously were treated with CTX every 5 days over a period of 15 and monitored for a further 6–7 days. The ECTV-resistant C57BL/6 strains were treated every 5 days over a period of 20 and monitored for a further 8 days. We chose CTX treatment for immunosuppression since the combined depletion of CD4 and CD8 T cell subsets, NK cells, granulocytes and plasmacytoid dendritic cells with monoclonal antibodies [46] over 3 weeks was not sufficient to cause disease or result in virus recrudescence (S7 Fig).
Groups of WT BALB/c mice infected with sub-lethal doses of ECTV-WT or ECTV-IFN-γbpΔ were separated into 3 groups at 80 days p.i. (Fig 6A). Group 1 was sacrificed at day 80 p.i. to measure virus genomes in various tissues. Group 2 was left untreated but maintained for a further 3 weeks as a control for Group 3, which was treated with CTX every 5 days over a 3-week period. When some CTX-treated mice showed clear signs of disease or succumbed to mousepox 3 weeks later, these and control untreated animals (Group 2) were sacrificed to measure viral load in organs.
At 80 days p.i., virus genomes were not detected in the blood or visceral organs of mice in Group 1 except in the BM of 1 of 5 mice infected with ECTV-WT (S8A Fig) and 2 of 5 mice infected with ECTV-IFN-γbpΔ (S8B Fig). At 101 days p.i., infectious virus was isolated from the BM of some animals but not in any other organ of ECTV-WT (Fig 6B) or ECTV-IFN-γbpΔ infected mice that were not treated with CTX (Group 2) (Fig 6C). In contrast, CTX treatment of mice in Group 3 resulted in significant increases in viral load in all organs, regardless of the type of virus used for infection (Fig 6B and 6C). In a similar but separate experiment, CTX treatment over 3 weeks also resulted in high ECTV titers in organs of CBA/H mice 85 days p.i. with ECTV-WT (Fig 6D), indicating that virus persistence is not unique to the BALB/c strain.
It is of note that virus genomes were not detected in the BM of most animals sacrificed at day 80 (S8A and S8B Fig), but infectious virus was isolated from all similarly infected animals that were treated with CTX for 3 weeks (Fig 6B and 6C). Thus, failure to detect virus genomes in the BM did not imply an absence of virus persistence. This finding raised at least two relevant questions. The first is whether BALB/b.Cmv1r mice in which ECTV-IFN-α/βbpΔ was below the limit of detection at 35 days p.i. (Fig 5H) had completely cleared virus. The second is whether virus might persist in disease-resistant C57BL/6 mice, in which virus genomes were below the limit of detection at day 35 p.i. (Fig 1H). This strain is known to effectively control ECTV infection and shows no evidence of a persistent infection.
Intriguingly, infectious virus was isolated from the BM, spleen and liver of BALB/b.Cmv1r mice that had been infected with 105 PFU ECTV-IFN-α/βbpΔ and treated with CTX 80 days later (Fig 6E). Virus genomes were below the limit of detection at day 35 p.i. in this strain (Fig 5H), but sustained immunosuppression resulted in virus recrudescence. In contrast to ECTV-IFN-α/βbpΔ, we were unable to demonstrate the presence of infectious virus in BALB/c WT mice infected 80 days previously with 105 PFU of the triple mutant ECTV-IFN-γbpΔ-IL-18bpΔ-SPI-2Δ or ECTV-TKΔ and subjected to immunosuppression over 4 weeks (S9A and S9B Fig).
The most unexpected but highly significant finding was made with the ECTV-resistant C57BL/6 strain. Infectious virus was isolated from the BM, spleen and liver of C57BL/6NCrl mice following treatment with CTX at 80 days p.i. (Fig 6F). The experiment was repeated using C57BL/6J mice to determine whether our results might be unique to the C57BL/NCrl mice. The C57BL/6J mice had been infected for more than 80 days with ECTV-WT and CTX treatment resulted in the death of one animal (Fig 6G) with severe liver necrosis and high titers of virus in this and other organs (S9C Fig), a hallmark of mousepox. Virus was not recovered from the remaining 3 animals sacrificed at 35 days post commencement of treatment. Taken together, the data established that ECTV persists in both resistant and susceptible strains of mice even though they recover from the infection. However, our results also indicate that highly attenuated strains of ECTV do not persist even in the susceptible strain of mice.
It is noteworthy that infectious virus was isolated from the BM of some mice at 101 days p.i. without immunosuppression (Fig 6B and 6C). We assessed whether similarly infected but untreated mice or CTX-treated mice that harbored high titers of ECTV could transmit virus to co-housed naïve animals. Groups of BALB/c (index) mice infected with a sub-lethal dose of ECTV-WT were rested for 80 days (Fig 7A). One group of index mice was treated with CTX and the second group was left untreated. Two weeks later, each treated or untreated index mouse was co-housed separately with 2–3 naïve animals for 3 days after which the latter were removed and housed in separate cages. Index mice were sacrificed 3 days after separation due the death of 2 animals. The co-housed naïve mice were sacrificed 4 days after separation when one animal succumbed to disease.
Infectious virus was not detectable in liver, spleen or lungs of index mice that had not been treated with CTX (Fig 7B). Consistent with the preceding data (Fig 6), CTX-treated mice harbored high titers of virus in all 3 organs. Two of the 5 index mice that died at 6 days post-co-housing harbored high titers of virus (Fig 7C, boxed data) but virus was not isolated from organs of mice co-housed with non-treated index mice (Fig 7D). In contrast, animals that were housed with CTX-treated index mice harbored virus (Fig 7D) of varying titers (Fig 7E). The one co-housed naïve mouse that died on day 7 post-exposure to a CTX-treated index mouse had the highest viral load in all organs (Fig 7E, boxed data). The data established that although infectious virus may be present at low levels in the BM of some animals several months p.i., they do not transmit virus. However, under conditions of immunosuppression, virus replicates to high titers and is able to be transmitted to co-housed naïve animals.
An acute viral infection can result in complete recovery of the host with or without residual sequelae, death or establishment of virus persistence [47]. Numerous mechanisms can potentially contribute to an acute viral infection becoming persistent but two factors thought to be critical are the effectiveness of host immune response in eliminating virus and subversion of the response by virus-encoded HRM. The OPV generally cause an acute infection in their hosts. However, some OPV, including ECTV, have been reported to persist in various animal species [13]. It has been suggested that this might be a reflection of persistent infection within a population rather than virus persistence in individual animals [13]. Our data indicate that the natural mouse pathogen ECTV can persist in the individual host.
Several lines of evidence indicate that ECTV causes a low-grade persistent infection in susceptible BALB/c and CBA strains of mice. First, virus genomes were detected in several organs but most consistently in the BM and blood several weeks after a primary infection. Although the virus load was below the limit of detection by viral plaque assay in most organs, infectious virus was nonetheless detected in a small proportion of animals in the BM at 101 days p.i. Second, virus persistence resulted in chronic stimulation of CD8 T cell responses, which were readily demonstrable ex vivo. ECTV-specific CD8 T cells were activated (CD62lo and CD127lo) and functional as they were cytotoxic ex vivo, produced IFN-γ and their numbers (tetramer+) remained elevated throughout the course of the study. In contrast, in ECTV-resistant C57BL/6 mice the CTL activity is undetectable once virus is cleared [34]. Third, as seen in some models of persistent viral infections [48–52], a change in the immunodominance hierarchy of the CD8 T cell response was evident during the first 3 weeks of infection. Whether virus persistence is a cause or an effect of the switch in the immunodominance response is not known. Finally, the most definitive evidence for virus persistence was established through the use of the immunosuppressive drug CTX. Sustained immunosuppression over a 3-week period with CTX allowed ECTV to replicate to high titers and cause mousepox in all animals that had been infected 80–85 days previously. Such mice readily transmitted virus to co-housed naïve animals to cause disease.
Neither a single treatment with CTX nor the combined depletion of CD4 and CD8 T cell subsets, NK cells, granulocytes and plasmacytoid dendritic cells with monoclonal antibodies [46] every 2–3 days over a 3-week period resulted in virus recrudescence or signs of disease (S7 Fig). The requirement for sustained immunosuppression may be one reason why previous attempts to “reactivate” the disease in mice persistently infected with ECTV were unsuccessful [53]. It was evident that some animals not treated with CTX harboured low levels of virus in the BM but did not transmit virus to co-housed naïve mice. It is likely that virus was sequestered in the BM or another tissue below a threshold titer effective for transmission since animals that had recovered from infection, but not treated with CTX, did not transmit virus to cage mates. Previous reports indicate that ECTV can persist in sequestered sites of mice several weeks or months after sub-cutaneous infection without any shedding [1,2]. Virus shedding can nonetheless occur for extended periods during chronic intestinal tract infection in mice exposed to virus through the oral [3] or intra-gastric routes [4] but transmission does not occur beyond 36 days despite the presence of virus in the spleen of some animals at 95 days p.i [4]. The oral route of virus transmission can occur through cannibalism. We postulate that virus seeds and persists in the BM when viral load in visceral organs reaches a certain threshold. Further, in animals with a persistent infection, viral load in the BM or any other site needs to be above a certain threshold in order to seed visceral organs and be transmitted to other animals. Our ability to induce virus recrudescence through immunosuppression indicates that the immune system must keep virus under check and sequestered in tissues such as the BM.
Our results support a role for virus-encoded HRM in contributing to virus persistence. ECTV-encoded vIFN-γbp, vIL-18bp, SPI-2 and vIFN-α/βbp are known to down-regulate NK cell [38–41] and CTL [38,39] responses, both of which are critical for recovery of mice from infection. Most of these HRM also diminish IFN-γ production, which is critical for recovery of mice from mousepox [29,54]. Through dampening the host immune response, virus-encoded HRM impede effective virus clearance rapidly and as a consequence contribute to virus persistence. Deletion of gene(s) encoding one or two HRM reduced viral load in the liver early during the course of infection but was insufficient to overcome virus persistence. The most profound effect was evident in mice infected with the triple mutant (vIFN-γbp, vIL-18bp and SPI-2) or with ECTV-IFN-α/βbpΔ. Both viruses were cleared by day 7 in the liver and below the limit of detection in the blood and BM at day 35. However, when the inoculum dose was increased by 100- to 1,000-fold (104−105 PFU), ECTV-IFN-α/βbpΔ was not cleared in the liver at day 7 and virus genomes persisted in the BM at day 35 p.i. In contrast, the highly attenuated TK mutant virus was below the limit of detection in mice infected with 106 PFU. This data suggested that effective virus control early during the course of infection could overcome ECTV persistence. Accordingly, CTX treatment of BALB/c WT mice infected with high doses of ECTV-TKΔ or the triple mutant did not result in recrudescence of virus. This was not the case with ECTV-IFN-α/βbpΔ (discussed below), lending support to the idea that the combined actions of several HRM increase the propensity of ECTV to persist and in their absence virus is effectively cleared and does not persist.
Resistance to mousepox in C57BL/6 mice is associated with the capacity of this strain to generate robust innate and adaptive immunity [28–37,55], whereas these responses are sub-optimal in susceptible strains like BALB/c [33]. There are 4 known genetic loci in the C57BL/6 mouse genome that confer resistance to mousepox [27] whereas the BALB/c strain lacks alleles associated with resistance. In this study, BALB congenic strains that harbour resistance alleles at Rmp-1 or Rmp-3 were better able to control ECTV-WT replication and significantly reduce the numbers of virus genomes in the BM. In the BALB/b.Cmv1r mice, which encodes both Rmp-1 and Rmp-3, viral genomes were below the limit of detection, demonstrating that the combined contributions of resistance alleles at both loci were far more effective in virus control. This congenic strain also effectively controlled a high dose of the attenuated ECTV-IFN-α/βbpΔ unlike the BALB/c WT mice, signifying that both host and viral factors impact on virus persistence. Nonetheless, despite our inability to demonstrate the presence of viral genomes in the BM of BALB/b.Cmv1r mice infected with ECTV-IFN-α/βbpΔ, CTX treatment resulted in high titers of virus in organs. This finding further established that absence of viral genomes in the BM did not necessarily indicate complete virus clearance by the host and is consistent with the data obtained with C57BL/6 mice (discussed below).
We have investigated the roles of only two of four known loci that confer resistance to ECTV using the BALB congenic mice. It is conceivable that Rmp-2, which maps to the C5 complement component and Rmp-4, which maps to the selectin gene complex contribute to early virus control and potentially overcome persistence. In this regard, the C57BL/6 strain, which has alleles associated with resistance at all four loci is known to effectively clear virus with no evidence of a persistent infection. The fact that we were able to demonstrate recrudescence of virus and disease in 2 different lines of C57BL/6 mice several weeks after primary infection through immunosuppression indicates that ECTV can persist in a genetically resistant host that is immune without any clinical signs of disease or persistent infection. We do not know the mechanism(s) through which this happens or in what form(s) and where virus persists. These are fundamental questions that will need to be investigated. ECTV has been shown to persist for prolonged periods in a number of myeloma, lymphoma and hybridoma lines derived from ECTV-resistant or -susceptible mouse strains in vitro with no obvious adverse effects on the cells [56]. Further, the virus has been shown to persist in splenic dendritic cells and macrophages in BALB/c mice. These studies suggest that the virus might have a propensity to persist in certain types of cells in the BM of infected animals. Nonetheless, our finding raises the important question of whether persistence of ECTV in genetically resistant mice that are immune is unique to this virus model or if it is more widespread. There is at least one other example of persistence of virus that is generally associated with causing an acute infection in mice. Low levels of lymphocytic choriomeningitis virus (LCMV) strain WE have been shown to persist in immune C57BL/6 mice several weeks after an acute infection [57]. The authors suggest that virus persistence may in fact contribute to the maintenance of immunological memory. Although we do not have any direct evidence that ECTV persistence is necessary for maintenance of immunological memory, ECTV-WT induces a far more superior antibody response, which is life-long in C57BL/6 mice, whereas the response induced by ECTV-TKΔ wanes rapidly [33,46,54,58–60]. In the current study, we have found that ECTV-WT can persist in C57BL/6 mice whereas ECTV-TKΔ does not, even in the susceptible BALB/c strain.
Prolonged persistence of viruses (or viral nucleic acids) that cause either acute or chronic infections has been reported in humans, although in some cases persistence may be due to other underlying immunological conditions. West Nile virus has been found to persist in renal tissues of infected patients, either with chronic clinical symptoms or no symptoms, for more than 6 years [61,62]. Although measles virus is considered a prototype for viruses that cause acute infections, more recent studies indicate that viral RNA can persist in naturally infected children for months [63], significantly longer than previously thought. It has been suggested that cell-mediated immunity is involved in initial virus control and that the antibody response eventually clears measles viral RNA and prevents recurrent production of infectious virus [64] but it is not clear how long after infection this occurs. Finally, cryptic or occult infections with Hepatitis C virus (HCV) have been reported in some individuals. Occult HCV infection is characterized by the presence of viral RNA in the liver but in the absence of anti-HCV antibodies or HCV RNA in serum [65–67]. The underlying mechanisms of occult HCV are not fully understood but are believed to be multifactorial, including viral and host factors and co-infection with other pathogens. It is possible that HCV might be sequestered and replicates at low levels in immune privileged extra-hepatic sites, one consequence of which is the inability of the host to generate appropriate antiviral immunity. In individuals with occult HCV infections, virus reactivation might be expected to occur under conditions of chemotherapy or immunosuppression.
The requirement for cell-mediated immunity for early virus control and virus-specific antibody for complete virus clearance is well established for the mousepox model [33,34,55]. In C57BL/6 mice, ECTV infection becomes persistent in the absence of CD4 T cell-dependent antibody responses even in the presence of effector CD8 T-cell responses [32,34]. Hence, the role of antiviral antibody in overcoming a persistent infection in the BALB/c strain merits discussion. We have specifically only investigated the role of cell-mediated immunity in BALB/c mice but there is no question that an effective antibody response will be critical to overcoming virus persistence. Although BALB/c mice that survive an infection with ECTV-IFN-γbpΔ generate strong antibody responses [38], virus still persists, suggesting the possibility that the response may not be effective. It is evident that antibody-mediated effector mechanisms can become defective during a persistent viral infection. High levels of viral antigen-antibody complexes are generated during a persistent LCMV infection, and these have been shown to suppress Fcγ-receptor-mediated antibody effector function [68,69]. A similar defect may be operative in ECTV-infected BALB/c mice, further contributing to virus persistence. Nonetheless, in the chronic LCMV model, the viral load is high and the antiviral CD8 T cells are exhausted [70]. This does not appear to be the case with ECTV infection of BALB/c mice, in which the viral load was very low and effector CD8 T cells were demonstrable throughout the entire period of study.
In summary, we have provided compelling evidence that ECTV causes a persistent infection in some susceptible strains of mice. The results lend support to previous reports of MPXV [8], CPXV [8–10] and VACV [11,12] persistence, possibly at a population level, in a variety of animal species. We have found that virus can persist in individual animals. Our finding that virus recrudescence can occur in ECTV-resistant C57BL/6 mice following sustained immunosuppression was unexpected. Nonetheless, these results in inbred strains of mice might be relevant and have implications for virus-host ecology and virus circulation in wild populations of mice. We speculate that wild mice could be subjected to stress and immunosuppression as a consequence of either food shortages during mouse plagues or during natural disasters. The occurrence of virus recrudescence in immunosuppressed mice under those situations may be rare but could potentially lead to virus spread, including through cannibalism, to naive animals. Whether immunosuppression caused under those conditions is equivalent to immunosuppression induced by treatment with CTX is not clear but further work is needed to understand the significance of virus persistence in resistant mice.
This study was performed in strict accordance with the recommendations in the Australian code of practice for the care and use of animals for scientific purposes and the Australian National Health and Medical Research Council Guidelines and Policies on Animal Ethics. The Australian National University Animal Ethics and Experimentation Committee approved all animal experiments (Protocol Numbers: J.IG.75.09 and A2012/041). Tribromoethanol (Avertin) was used as the anesthetic (200–240 mg/kg body weight) given via intra-peritoneal injection prior to infection with virus. The respiration rate of the animals was monitored during anesthesia and recovery took place upon a warm table. Animals were euthanized by cervical dislocation.
Inbred, specific-pathogen-free female BALB/c (H-2d), BALB/b (H-2b), CBA/H (H-2k), C57BL/6 mice and BALB congenic strains C.B6-Klra8Cmv1-r/UwaJ (BALB/c.Cmv1r) [45] and B.B6-Klra8Cmv1-r/UwaJ (BALB/b.Cmv1r) were bred at the ANU Bioscience Services. The congenic strains carry C57BL/6 alleles in the NKC on mouse chromosome 6 including NK1.1. The NKC in the BALB strain lacks some activating receptors and is a known locus for resistance to ECTV. Mice were used at 6–10 weeks of age. The highly susceptible A/J strain was purchased from Animal Resources Centre, Western Australia and used in virus transmission experiments.
BS-C-1 (ATCC CCL-26), epithelial kidney cell line from African green monkey, CV-1 cells (ATCC CCL-70), fibroblast kidney cell line from African green monkey, P-815 (H-2d; ATCC TIB-64), a DBA/2 mouse-derived mastocytoma and MC57G (H-2b; ATCC CRL-2295), a C57BL/6J mouse-derived fibrosarcoma, were obtained from American Type Culture Collection (Rockville, MD). All cells were maintained in Eagle’s Minimum Essential Medium (GIBCO) supplemented with 10% fetal calf serum (Sigma-Aldrich Inc., St. Louis MO, USA), 2mM L-glutamine (GIBCO), 120 μg/ml penicillin and 200μg/ml streptomycin and neomycin sulfate (GIBCO).
The Moscow strain of wild type ECTV (ATCC VR1374), designated ECTV-WT and the vIFN-γ bp deletion mutant virus derived from ECTV-WT, designated ECTV-IFN-γbpΔ [38] were used. In addition, ECTV deletion mutant viruses lacking vIFN-α/βbp (ECTV-IFN-α/βbpΔ), vIL-18bp (ECTV-IL-18bpΔ), serine protease inhibitor 2 (ECTV-SPI-2Δ), vIFN-γbp and IL-18bp (ECTV-IFN-γbpΔ-IL-18bpΔ; double mutant), vIFN-γbp, IL-18bp and SPI-2 (ECTV-IFN-γbpΔ-IL-18bpΔ-SPI-2Δ; triple mutant) and thymidine kinase (ECTV-TKΔ) [71] were used. The mutant viruses were generated as described elsewhere [38]. All ECTV strains were propagated in BS-C-1 cells, titrated using viral plaque assay (VPA) [32] and all mutant viruses were found to replicate to levels comparable with ECTV-WT (S10 Fig). Mice were inoculated with 50, 100 or 500 PFU ECTV-WT, 100 or 500 PFU ECTV-IFN-γbpΔ and 105 or 106 PFU ECTV-TKΔ subcutaneously (s.c.) in the flank of the left hind leg (hock) under avertin anesthesia. In all animal experiments, a back titration of the virus inoculum was performed routinely to ensure consistency and the correct dose was used.
The use of CTX in experiments was authorized under the Work Health and Safety Act of 2011, Australia. Mice that had been infected with ECTV 80 days previously were injected with 240 mg/kg CTX (Sigma-Aldrich) through the intra-peritoneal route every 5 days, with a total of 3 injections over a period of 15 days and observed for a further 6–7 days. The ECTV-resistant C57BL/6 strains were similarly treated but with a total of 4 injections over a period of 20 days and observed for a further 7–15 days.
For virus transmission experiments, some index BALB/c mice were treated three times with CTX whereas controls were left untreated. Two weeks later, each treated or untreated index mouse was co-housed separately with 2–3 naïve A/J mice for 3 days after which the latter were removed and housed in separate cages. Index mice were sacrificed 3 days after separation due the death of 2 animals. The co-housed naïve mice were sacrificed 4 days after separation when one animal succumbed to disease.
As an alternative to CTX, multiple leukocyte subsets were depleted to induce immunosuppression in mice. WT C57BL/6 mice infected with 1000 PFU of ECTV-WT 80 days previously were treated with monoclonal antibodies to deplete granulocytes (clone RB6-8C5), plasmacytoid dendritic cells (clone 120G8), NK cells (clone PK136), CD4 T cells (clone GK1.5) and CD8 T cells (clone 2.43.1) as described previously [46] every 2–3 days for 3 weeks and sacrificed to measure viral load in organs.
ECTV-specific CD8 T cell determinants restricted by H-2d Ld-EVM02626–34 (Ld-026), Kd-EVM149.544–52 (Kd-149.5) and Dd-EVM043140-148 (Dd-043) [72] used in this study are shown in S1 Table. Peptides were synthesized and purified via reverse-phase HPLC at the BRF, JCSMR, Australian National University.
Direct ex vivo cytolytic activity of splenic CTL was determined at various effector-to-target ratios in 6-hr standard 51Chromium (51Cr)-release assays as described elsewhere [33]. Briefly, spleen cells from infected animals were assessed for their ability to kill 51Cr-labeled ECTV-infected, ECTV peptide determinant-pulsed or uninfected syngeneic P815 (H-2d) or MC57G (H-2b) targets. The ECTV peptide determinants used are listed in S1 Table.
Antigen-specific IFN-γ producing CD8 T cells were enumerated using intracellular cytokine staining as described elsewhere [72] using anti-CD8α-APC (clone 53–6.7) and anti-IFN-γ-PE (clone XMG1.2) (BD Biosciences). Total events for cells were acquired using a FACSCalibur flow cytometer (BD Biosciences) and analyzed using FlowJo software (Tree Star Inc.).
Tetrameric H-2d MHC class I complexes folded with Ld-026, Kd-149.5 and Dd-043 peptides (S1 Table), were used to phenotype determinant-specific CD8 T cells. Spleen cells were stained with PE-conjugated peptide-MHC class I tetramers and anti-CD8α-APC at 4°C for 60 min and subsequently washed twice with PBS containing 2% FCS before analysis. For analysis of Vβ TCR chain usage, splenocytes were stained with anti-CD8α-APC, anti-Vβ TCR-FITC screening panel of antibodies (BD Biosciences) and PE-conjugated MHC class I tetramers. Data was acquired on a LSR Fortessa flow cytometer (BD Biosciences) with BD FACS Diva software and analyzed using FlowJo Software (Tree Star, Inc).
Tissue removed aseptically from mice was stored at -80°C until processed. Virus titers, expressed as log10 PFU/gram tissue were determined on BSC-1 monolayers using the conventional viral plaque assay, as described previously [32,33,73]. Briefly, organs were weighed and homogenized in 1 ml PBS, dispersed by sonication and used to make serial 10-fold dilutions. A 100μl volume of the homogenate was plated onto monolayers of B-SC-1 cells beginning at 10−1 dilution. Undiluted organ homogenates were toxic the B-SC-1 monolayers and were not used. The limit of detection of virus was therefore 100 PFU. Viral load below the limit of detection by viral plaque assay was measured by quantitative real time PCR (qRT-PCR), as described elsewhere [46] to amplify the target sequence of the late gene ECTV-Mos-156 that encodes the viral hemagglutinin. The limit of detection of viral genomes by qRT-PCR is 10 copies. One PFU of ECTV-WT is equivalent to 275 genome copies. Genome copy numbers above 10 are considered biologically significant, as the LD50 of the highly susceptible A/J mouse strain to ECTV-WT is 0.04 PFU, i.e. 11 genome copies.
Statistical analyses of experimental data, employing parametric and nonparametric tests as indicated, were performed using GraphPad Prism (GraphPad Software, San Diego USA).
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10.1371/journal.pmed.1002640 | Metabolic syndrome and risk of Parkinson disease: A nationwide cohort study | The association of metabolic syndrome (MetS) with the development of Parkinson disease (PD) is currently unclear. We sought to determine whether MetS and its components are associated with the risk of incident PD using large-scale cohort data for the whole South Korean population.
Health checkup data of 17,163,560 individuals aged ≥40 years provided by the National Health Insurance Service (NHIS) of South Korea between January 1, 2009, and December 31, 2012, were included, and participants were followed up until December 31, 2015. The mean follow-up duration was 5.3 years. The hazard ratio (HR) and 95% confidence interval (CI) of PD were estimated using a Cox proportional hazards model adjusted for potential confounders. We identified 44,205 incident PD cases during follow-up. Individuals with MetS (n = 5,848,508) showed an increased risk of PD development compared with individuals without MetS (n = 11,315,052), even after adjusting for potential confounders including age, sex, smoking, alcohol consumption, physical activity, income, body mass index, estimated glomerular filtration rate, and history of stroke (model 3; HR, 95% CI: 1.24, 1.21–1.27). Each MetS component was positively associated with PD risk (HR, 95% CI: 1.13, 1.10–1.16 for abdominal obesity; 1.13, 1.10–1.15 for hypertriglyceridemia; 1.23, 1.20–1.25 for low high-density lipoprotein cholesterol; 1.05, 1.03–1.08 for high blood pressure; 1.21, 1.18–1.23 for hyperglycemia). PD incidence positively correlated with the number of MetS components (log-rank p < 0.001), and we observed a gradual increase in the HR for incident PD with increasing number of components (p < 0.001). A significant interaction between age and MetS on the risk of incident PD was observed (p for interaction < 0.001), and people aged ≥65 years old with MetS showed the highest HR of incident PD of all subgroups compared to those <65 years old without MetS (reference subgroup). Limitations of this study include the possibilities of misdiagnosis of PD and reverse causality.
Our population-based large-scale cohort study suggests that MetS and its components may be risk factors of PD development.
| Recent evidence has indicated that components of metabolic syndrome (MetS) may contribute to the pathophysiology of Parkinson disease (PD).
Longitudinal studies regarding the association between MetS and the development of PD are limited.
Several prospective studies investigating associations between each component of MetS and incident PD have reported inconsistent results.
We analyzed the health checkup data of the entire South Korean population aged ≥40 years provided by the Korean National Health Insurance Service between 2009 and 2012.
Multivariable Cox proportional hazards regression models were used to evaluate the association of MetS and its components with the risk of incident PD, with mean follow-up duration of 5.3 years.
Our analysis indicated that individuals with MetS had a 24% higher risk of incident PD than individuals without MetS, and each MetS component was positively associated with PD risk.
Incidence and risk of PD increased gradually with the number of MetS components individuals had.
MetS and its components might be considered risk factors for PD development.
Our results show that MetS components are positively associated with increased PD risk and that as the number of components increases, so does the PD risk.
Optimal control of MetS and its components may reduce the risk of incident PD, and this possibility warrants further investigation.
| Metabolic syndrome (MetS) refers to a cluster of several interrelated risk factors for cerebrocardiovascular diseases that result in insulin resistance. These metabolic abnormalities frequently coexist, and MetS is prevalent among patients with obesity and/or a sedentary lifestyle [1,2]. MetS prevalence has been continually increasing in recent decades, globally and in the Republic of Korea (South Korea), due to the obesity epidemic [3]. Each metabolic abnormality predicts both type 2 diabetes and cardiovascular disease, and having a cluster of the abnormalities imposes additional risk in addition to the risks associated with the individual abnormalities [1,2]. In addition, MetS increases all-cause mortality risk and the burden of healthcare costs [4]. Recent evidence has indicated that increased oxidative stress is a major characteristic of MetS-related diseases [5]. Therefore, components of MetS may contribute to the pathophysiology of Parkinson disease (PD), which also shows high levels of reactive oxygen species [6].
PD is a frequent neurodegenerative disease and is considered a leading chronic disease worldwide. PD affects 1 out of 800 individuals worldwide, and PD prevalence is expected to double to over 9 million patients by 2030 due to aging. The increasing prevalence of PD has a substantial impact on morbidity, mortality, and healthcare costs [7]. Prior studies have attempted to uncover the risk factors for incident PD. Recently, the possible role of MetS and its components in PD development has been highlighted. Growing evidence indicates that several mechanistic pathways (such as oxidative stress, lipid pathway alteration, and increased inflammation related to abnormal protein deposition) in neurodegenerative diseases including Alzheimer disease and PD share several elements with the systemic metabolic dysfunction observed in obesity and MetS [8,9]. Furthermore, anti-obesity or metabolically protective therapies have been suggested to be beneficial for patients at risk of neurodegenerative diseases [10]. Thus, we hypothesized that neurodegenerative diseases and metabolic abnormalities are linked due to their shared mechanistic pathophysiology. However, longitudinal studies of the association between MetS and the development of PD are limited. Despite several prospective studies investigating associations between components of MetS and incident PD, results have been inconsistent due to the diverse methodologies implemented. Moreover, only a few studies have exploited nationwide representative cohort data. Our study investigated the association of MetS and its components with PD development using large-scale cohort data from the whole South Korean population.
Our study was based on the entire South Korean population database provided by the National Health Insurance Service (NHIS), which is a single insurer managed by the Korean government. The NHIS provides a mandatory universal insurance system covering approximately 97% of the South Korean population; the remaining 3% with low income is covered by the Medical Aid Program. Patients subscribed to NHIS pay for 30% of their total medical expenses, and the medical providers must submit claims for reimbursement from the NHIS for the rest. In addition, the NHIS recommends that all insured individuals have a standardized health examination at least every 2 years. Hence, the NHIS retains an extensive health information dataset of approximately 50,000,000 South Koreans regarding demographics, medical treatment, procedures, disease diagnoses according to International Classification of Diseases–10th Revision–Clinical Modification (ICD-10-CM) codes, and health examinations. Since 2015, NHIS has released a dynamic, nationally representative retrospective cohort database consisting of nearly the whole South Korean population, and the database is open to all researchers whose study protocols are approved by the official review committee.
Among individuals ≥40 years old who had undergone a health examination provided by NHIS at least once between January 1, 2009, and December 31, 2012, we excluded those who had a prior diagnosis of PD during the 4 years of washout period before enrollment (n = 35,124) and those who had any missing variables (n = 674,398). The remaining 17,163,560 eligible individuals (8,215,180 men and 8,948,380 women) were included in the analyses and followed until the date of death or until December 31, 2015. Newly diagnosed PD was identified based on the ICD-10-CM code for PD (G20) and the PD registration code (V124) (the South Korean government has implemented a registration program for copayment reduction of up to 10% for rare intractable diseases including PD since 2006).
This study adhered to the tenets of the Declaration of Helsinki and was approved by the Institutional Review Board of Sahmyook Medical Center (No. SYMC IRB 1706–04). The requirement for written informed consent was waived by the review board because anonymous and de-identified information was used for analysis.
Detailed information of individuals’ demographics and lifestyle was obtained through standardized self-reporting questionnaires. Income level was dichotomized at the lower 20%. Smoking status was classified as non-smoker, ex-smoker, or current smoker. Individuals who consumed ≥30 g of alcohol per day were defined as heavy alcohol consumers [11]. Physical activity was categorized based on the frequency per week of strenuous exercise performed for at least 20 minutes (none, 1–4 times/week, or ≥5 times/week). Baseline comorbidities (hypertension, diabetes mellitus [DM], dyslipidemia, ischemic heart disease, and stroke) were identified based on the combination of past medical history and ICD-10-CM and prescription codes.
The health examination provided by NHIS includes anthropometric and laboratory measurements. Height, weight, and waist circumference (WC) were measured, and body mass index (BMI) was calculated by dividing weight (kg) by height (m) squared. Systolic and diastolic blood pressure (BP) were measured in a seated position after at least 5 minutes rest. Blood sampling was conducted after overnight fasting, and serum levels of glucose, total cholesterol, triglycerides, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and creatinine were measured. MetS was defined based on the modified criteria of the National Cholesterol Education Program Adult Treatment Panel III, while the Asian-specific WC cutoff was adopted for abdominal obesity [2,12]. Individuals with at least 3 of the following components were diagnosed with MetS: (i) WC ≥ 90 cm for men or ≥85 cm for women; (ii) serum triglycerides ≥ 1.70 mmol/l or treatment with lipid-lowering medication; (iii) serum HDL-C < 1.04 mmol/l for men or <1.30 mmol/l for women or treatment with lipid-lowering medication; (iv) systolic BP ≥ 130 mm Hg, diastolic BP ≥ 85 mm Hg, or treatment with antihypertensive medication; and (v) fasting plasma glucose ≥ 5.55 mmol/l or use of hypoglycemic agents. We defined lipid-lowering medication use as at least 1 claim per year for lipid-lowering medication prescription under ICD-10-CM code E78; however, the specific lipid-lowering medication could not be identified. Estimated glomerular filtration rate (eGFR) was calculated using the equation from the Modification of Diet in Renal Disease (MDRD) study: eGFR = 175 × serum creatinine−1.154 × age−0.203, further multiplied by 0.742 for women [13]. We defined eGFR < 60 ml/min/1.73 m2 as chronic kidney disease (CKD) [14].
Statistical analyses were conducted using SAS software (version 9.2; SAS Institute, Cary, NC, US). Baseline characteristics of study participants according to the presence of MetS are presented as mean ± standard deviation for continuous variables and number (percentage) for categorical variables. Values were compared using the independent t test for continuous variables and the chi-squared test for categorical variables. Incidence rates of PD were calculated by dividing the number of events by 1,000 person-years. Cox proportional hazards analyses were performed to evaluate the association of MetS and its components with incident PD, and hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated. Model 1 was adjusted for age and sex. Model 2 was additionally adjusted for smoking status, alcohol consumption, physical activity, and income. Model 3 was additionally adjusted for BMI, eGFR, and history of stroke. Given the competing risks of PD and death, as recommended by a reviewer, a competing risk regression model was considered using the Fine and Gray method [15]. Kaplan–Meier curves show the cumulative incidence probability of PD, and a log-rank test was performed to examine the association of the number of MetS components with the risk of PD. We also evaluated the risk of incident PD according to the number of MetS components individuals had using Cox proportional hazards analyses. Stratified analyses according to sex and age were also performed. A p-value < 0.05 was considered statistically significant. The text from our study proposal is provided in S1 Text.
Study participants were followed up until December 31, 2015, with an average follow-up duration of 5.3 ± 1.2 years. At baseline, 5,848,508 individuals (34.1% of total population) were diagnosed with MetS. Table 1 shows the baseline characteristics of the study population according to the presence of MetS. Mean age was 58.1 ± 11.0 years in the MetS group and 51.9 ± 10.3 in the group without MetS. The proportion of men was higher in the MetS group than the non-MetS group. Individuals with MetS exhibited higher mean values of cardiometabolic parameters such as BMI, WC, BP, fasting plasma glucose, serum total cholesterol, triglycerides, and LDL-C compared to those without MetS. The mean values of HDL-C and eGFR were lower in individuals with MetS than those without. The proportion of non-smokers and of people with regular physical activity was higher in the non-MetS group than the MetS group, and heavy alcohol consumption was higher in the MetS group compared to the non-MetS group. Patients in the MetS group were more likely to have a higher prevalence of hypertension, DM, dyslipidemia, CKD, ischemic heart disease, and stroke. The results of the comparison of baseline characteristics between included individuals and those who were excluded due to missing values are shown in S1 Table. Although the p-values calculated were significant except for comorbidities such as dyslipidemia and ischemic heart disease, this appears to result from the very large sample size. There seems to be no actual difference in baseline characteristics between the 2 groups; thus, we do not suspect selection bias.
A total of 44,205 individuals were diagnosed with PD during the follow-up period, and the incidence rate of PD in the MetS group was approximately 2.2 times higher than that in the non-MetS group. An increased risk of PD development was observed in the MetS group compared with the non-MetS group in all models (HR, 95% CI: model 1, 1.29, 1.27–1.32; model 2, 1.26, 1.24–1.29; model 3, 1.24, 1.21–1.27). Each component of MetS showed a similar association with incidence of PD, even after adjustment for confounding variables. Individuals with abdominal obesity or hypertriglyceridemia had approximately 13% higher risk of PD compared with those without (model 3, HR, 95% CI: 1.13, 1.10–1.16 and 1.13, 1.10–1.15, respectively). Individuals with low HDL-C had a HR of 1.23 (95% CI 1.20–1.25) for PD in model 3. High BP and fasting plasma glucose were also significantly associated with increased risk of PD (model 3, HR, 95% CI: 1.05, 1.03–1.08 and 1.21, 1.18–1.23, respectively) (Table 2). In a competing risk analysis accounting for death as a competing risk, the results were mostly the same as in the main findings in Table 2 (S2 Table).
Fig 1 and Table 3 show the longitudinal associations between the number of MetS components individuals had and PD incidence. The Kaplan–Meier curve in Fig 1 presents the incidence probability of PD according to the number of MetS components compared to the group without any components. PD incidence was positively correlated with the number of MetS components (log-rank p < 0.001). The HR for incident PD compared to people without any MetS components gradually increased with the number of components (p for trend < 0.001) (Table 3). These associations persisted even after adjusting for potential confounding variables. Individuals with 3 MetS components were at 31% higher risk of PD, and those with all 5 components were at 66% higher risk, compared to those without any components (model 3).
Fig 2 shows the combined effects of age and MetS on the incident PD risk after all potential confounding variables were adjusted for. There was a significant interaction between age and MetS on the risk of PD (p for interaction < 0.001). Compared to people aged <65 years without MetS, we observed a gradual increase in the HR of PD for individuals <65 years with MetS, those ≥65 years without MetS, and those ≥65 years with MetS, in both sexes (p < 0.001). People with both old age (≥65 years) and MetS had the highest HR of incident PD (HR, 95% CI: 1.83, 1.71–1.96 in men and 2.78, 2.60–2.98 in women).
Our population-based large-scale cohort study revealed that the incidence rate of PD was approximately 2.2 times greater for people with MetS compared to those without MetS over a 5.3-year follow-up period and that individuals with MetS had a 24% higher risk of incident PD. The presence of each MetS component was also associated with increased risk of PD development, and individuals with a higher number of MetS components were at higher risk of incident PD. These associations persisted even after adjusting for potential confounding variables. Furthermore, increased risk of PD was observed for individuals ≥65 years old compared to younger individuals (<65 years); older individuals with MetS showed the greatest risk of PD. These associations were particularly prominent in women.
Our findings suggest that MetS may be a risk factor for incident PD. Our results also show that even individual components of MetS are positively associated with increased PD risk and that as the number of components increases, so does PD risk. Therefore, our study shows important clinical implications for MetS and its components in PD development.
There is limited previous evidence regarding the impact of MetS on the risk of PD development. To the best of our knowledge, only 1 case-control study and 1 prospective study have been performed, showing mixed results. The case-control study compared 80 PD patients (with BMI ≥ 18.5 kg/m2) to 80 controls and reported that there was a lack of association between MetS and PD [16]. However, apart from the limitations deriving from the study design and the small sample size, this study was limited by an inclusion bias: the PD group included did not represent the whole patient population, and the controls were admitted to a medical center due to their excessive body weight, a condition that may be associated with other comorbidities. The prospective study of 6,641 adults based on the Mini-Finland Health Survey with a follow-up duration of 30 years reported that the adjusted relative risk of PD was 0.5 (95% CI 0.30–0.83) for individuals with MetS compared to those without [17]. This study suggested that elevated levels of serum triglycerides and fasting plasma glucose were related to low PD incidence and that there was an inverse association between MetS and PD mainly attributable to serum triglyceride concentration. Although the findings from this study are at odds with ours, the methodology followed was different from ours, with a smaller sample size and a longer follow-up duration, which likely caused unrelated changes to the patients’ health status. Our study, based on a large nationwide population cohort with a relatively short follow-up period, provides novel insight into the association between MetS and PD.
Several previous epidemiological studies have evaluated individual cardiovascular risk factors such as hypertension, DM, dyslipidemia, and obesity—which are similar to the components of MetS studied here—as risk factors for PD. These studies have produced conflicting findings, and, moreover, the underlying mechanisms that may explain the observed associations of MetS and its components with incident PD risk are currently unclear. However, a possible mechanism may be deduced from evidence regarding the association between obesity and PD risk. A 30-year follow-up study of men in the Honolulu Heart Program found that high triceps skinfold thickness in midlife is associated with future PD risk [18]. People with obesity have lower dopamine receptor availability than non-obese people; this may cause compensatory increases in turnover of dopamine, and lead to increased oxidative stress and neuronal death [18,19]. Conversely, a recent study using 2-sample Mendelian randomization reported that genetic variants known to influence BMI appear to be associated with lower risk of PD [20]. However, our findings revealed that abdominal obesity is positively associated with incident PD risk even after adjusting for BMI. The low-grade chronic and systemic inflammation prevalently observed in abdominal obesity and MetS is possibly associated with increased PD risk [21]. Additionally, animal studies proved that a high-fat diet is accompanied by dopamine-specific toxin exposure and may reduce the threshold for developing PD [22–24].
No previous cohort studies have investigated the effect of blood triglycerides and HDL-C on PD risk, except for the aforementioned Finnish prospective study [17]. A Swedish longitudinal nested case-control study reported that high blood triglyceride levels were less frequent in PD patients than controls; however, this association was attenuated after adjustment for smoking [25]. A prospective study found that high serum levels of total cholesterol and triglycerides (in men) were associated with an elevated risk of developing restless legs syndrome, which may constitute a possible preclinical marker of PD [26,27]. Additionally, the protective effects of statins on PD risk may partly explain the association of high serum triglyceride and low serum HDL-C levels with increased PD risk [28]. Meanwhile, several studies on serum total cholesterol have reported conflicting findings, and these inconsistent findings may be explained by the various time points at which cholesterol levels were measured and personality changes during the premotor phase of PD [29,30]. However, high serum triglyceride and low serum HDL-C levels are closely related to insulin resistance, which appears to occur analogously in the brains of PD patients because a defect in the insulin signaling pathway may contribute to the pathogenesis of PD [31].
The association between hypertension and PD is still unclear. In various studies, hypertension was less frequent in PD patients [32], hypertension showed no difference compared with healthy people [33], or history of hypertension was found not to be associated with PD risk [29]. However, a Finnish retrospective cohort study reported that high-normal BP and hypertension were associated with an increased PD risk in women [34]. Although the mechanisms linking hypertension and elevated BP to PD are still unclear, persistent hypertension can cause ischemic cerebrovascular lesions. Cerebral ischemia possibly activates the dopaminergic pathway due to decreased expression of nicotinic acetylcholine receptors, and plays a role in the clinical expression and deterioration of idiopathic PD symptomatology [35,36]. Long-term elevated BP likely causes hypertensive vasculopathy in brain structures, which may influence the dopaminergic cells and break the links between neurons in the substantia nigra and the putamen (striatum) [37].
Our results are supported by the fact that DM may be a risk factor for PD. Most epidemiological evidence supports the positive association between DM and PD risk, although there are discrepancies that may be explained by residual confounders [31]. A prospective cohort study suggested that type 2 DM is independently associated with PD risk after adjusting for various confounding variables [38]. Furthermore, several studies have suggested that even insulin resistance or prediabetes negatively affects the course of PD [39–41]. Pathophysiological mechanisms regarding the link between hyperglycemia and incident PD are speculative other than that they share cellular mechanisms such as mitochondrial dysfunction and decreased expression of the transcriptional regulator PPARγ coactivator 1α (PGC1α), which stimulates mitochondrial biogenesis and respiration [42,43].
Interestingly, our study revealed a combined effect of age and MetS on PD development. Aging is the most important risk factor for PD, and our study found that people with older age and MetS had the highest risk for incident PD. Even older people without MetS showed higher risk of PD development than younger individuals with MetS. In addition, the HRs were higher among women than men in all subgroups categorized according to age and MetS status. This is in contrast to previous studies that showed that males were more susceptible to PD, possibly due to the interplay between sex-specific hormones and genes [44]. However, increased oxidative stress, which is the common mechanism leading to MetS and PD, especially in postmenopausal women, may explain the reported associations [45].
In this way, several epidemiological studies and additional animal and experimental studies are consistent with our findings. However, discordances between previous studies and the current study are recognized, suggesting that residual confounding or modifying factors may modulate the association of MetS and its components with PD risk. Thus, further epidemiological, basic science, and clinical research is still needed on the relationship between MetS and PD. The current study, in which each MetS trait appears to be associated with increased incident PD risk, supports the data on the link between MetS and PD, implicating that MetS and its components may contribute to the pathophysiology of PD and act as risk factors for PD. The risk of PD may be exacerbated by metabolism-related dysfunction related to MetS, and any interventions to control MetS traits in the general population could be beneficial not only for common chronic conditions related to MetS, but also for PD.
Several limitations should be mentioned regarding the interpretation of our results. First, because the NHIS database relies on physicians’ assignment of a diagnostic code for PD, there may be a possibility of misdiagnosis of PD, which could result in under- or overestimation. Also, individuals with non-motor symptoms who were yet to be diagnosed with PD at baseline could have been more likely to participate in the NHIS health examinations, resulting in selection bias. Second, because this study was not prospectively designed, causality cannot be determined. Individuals with prior diagnosis of PD during the 4 years before enrollment were excluded to minimize the possibility of reverse causality. However, there is still a possibility of reverse causality based on the long prodromal phase of PD. Third, due to lack of data, we did not consider dietary factors that may be potentially related to MetS and its components. Fourth, the duration of MetS, which possibly influences PD risk, could not be considered. Fifth, we could not distinguish the specific lipid-lowering medications used in the diagnosis of the MetS components hypertriglyceridemia and low HDL-C. Finally, our findings from the Korean population cannot be extrapolated to other ethnicities.
Nevertheless, our study has a major strong point, because it is a very large-scale cohort study that evaluated the influence of MetS and its components on PD. Our study provides the first evidence to our knowledge that MetS and its components constitute risk factors for PD in the general population, because the NHIS database includes the entire South Korean population. Further, we had comprehensive ascertainment of coexisting illnesses, allowing for adjustment for potential confounders.
In conclusion, we found that MetS and its components are independent risk factors for PD development. Careful monitoring of neurological symptoms related to PD seems to be helpful for patients with MetS, and assessment of MetS may be considered when encountering a newly diagnosed parkinsonism. Future studies are warranted to examine whether control of MetS and its components can decrease the risk of PD development.
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10.1371/journal.pgen.1002614 | Probing the Informational and Regulatory Plasticity of a Transcription Factor DNA–Binding Domain | Transcription factors have two functional constraints on their evolution: (1) their binding sites must have enough information to be distinguishable from all other sequences in the genome, and (2) they must bind these sites with an affinity that appropriately modulates the rate of transcription. Since both are determined by the biophysical properties of the DNA–binding domain, selection on one will ultimately affect the other. We were interested in understanding how plastic the informational and regulatory properties of a transcription factor are and how transcription factors evolve to balance these constraints. To study this, we developed an in vivo selection system in Escherichia coli to identify variants of the helix-turn-helix transcription factor MarA that bind different sets of binding sites with varying degrees of degeneracy. Unlike previous in vitro methods used to identify novel DNA binders and to probe the plasticity of the binding domain, our selections were done within the context of the initiation complex, selecting for both specific binding within the genome and for a physiologically significant strength of interaction to maintain function of the factor. Using MITOMI, quantitative PCR, and a binding site fitness assay, we characterized the binding, function, and fitness of some of these variants. We observed that a large range of binding preferences, information contents, and activities could be accessed with a few mutations, suggesting that transcriptional regulatory networks are highly adaptable and expandable.
| The main role of transcription factors is to modulate the expression levels of functionally related genes in response to environmental and cellular cues. For this process to be precise, the transcription factor needs to locate and bind specific DNA sequences in the genome and needs to bind these sites with a strength that appropriately adjusts the amount of gene expressed. Both specific protein–DNA interactions and transcription factor activity are intimately coupled, because they are both dependent upon the biochemical properties of the DNA–binding domain. Here we experimentally probe how variable these properties are using a novel in vivo selection assay. We observed that the specific binding preferences for the transcription factor MarA and its transcriptional activity can be altered over a large range with a few mutations and that selection on one function will impact the other. This work helps us to better understand the mechanism of transcriptional regulation and its evolution, and may prove useful for the engineering of transcription factors and regulatory networks.
| The precise regulation of gene expression depends upon the specific binding of transcription factors to their cognate binding sites. For this process to be accurate, the sites for each factor need to be separable from all other sequences in the genome [1], [2]. Many groups have studied specific protein-DNA interactions, and while nucleotide preferences are starting to be understood at the biophysical level for some DNA binding domains [3]–[5], no universal DNA-recognition code has been discovered [6]. What has emerged is a consistent picture of binding site degeneracy. That is, for most factors there is a single consensus binding site that is bound with the highest affinity and an increasing number of lower affinity sites that vary from the consensus. At some point the degeneration is so great that all remaining sites show the same non-specific binding energy [7]–[9]. Using information theory, the amount of conservation within a set of binding sites (information content), as well as the amount of information needed to specifically locate N sites in a genome of length L, can be quantified [1], [10]. In bacteria, it has been shown that these values are identical for many factors, suggesting that the size of a factor's regulon constrains how specific it needs to be [1], [11], [12]. This relationship does not hold as well for individual transcription factors in eukaryotes though [13], [14], where gene regulation is often under the control of cooperatively acting factors [15].
Once bound to their target sequence, transcription factors can modulate the rate of expression over a range of activities. Differences in expression levels have been suggested and shown to vary with binding site strength [16]–[19]. Given this relationship, the range and continuity of binding affinities for a factor partially define the range and continuity of potential outputs for that factor [19], [20]. These outputs in turn can significantly affect the phenotype and fitness of the cell and are selected to maximize cellular gain while minimizing cost [19], [21], [22]. Therefore, there is not only a selective advantage for transcription factors to specifically recognize and bind their target sites, but to bind them with an affinity that produces the maximally fit transcriptional output. Since both specific binding preferences and transcriptional activity are dependent on the distribution of binding energies for a factor, selection on one will ultimately affect the other.
We are interested in understanding how plastic the informational and regulatory properties of a transcription factor are, and how transcription factors evolve to balance these functions. To address this, we developed an in vivo selection system in E. coli to select for functional variants of the transcription factor MarA with altered binding preferences, whose binding properties and activity could be further characterized. By functional, we mean that a variant could modulate the level of transcriptional output within a physiological range. This is in contrast to in vitro selection assays, like phage display, that generally select for high affinity binding to a single target sequence, and disregard the impact of these mutations on transcriptional activity.
To do these selections, we wanted to use a monomeric, transcriptional activator whose binding sites have been characterized and structure had been solved. MarA fit these criteria. It is a monomeric, helix-turn-helix transcription factor in the AraC family [23] that can both activate and repress transcription in E. coli [24]–[26]. It regulates the expression of approximately 20 genes involved in exporting low levels of drugs and organic solvents from the cell [24], [27]. The structure of the MarA-DNA complex suggests that specific recognition occurs through two alpha-helices that bind the major groove [28], [29]. Additionally, MarA has two homologues in E. coli, Rob and SoxS, that have similar binding preferences [30], suggesting that the MarA binding domain can be selected to recognize additional sites.
We generated a sequence logo from the 16 E. coli MarA binding sites summarized in Martin et al. [24] to visualize the natural binding preference of the protein and the relative contribution of each contacting residue to binding specificity (Figure 1). Sequence conservation follows a sine wave as seen for other transcription factors [31], [32]. MarA specifically contacts the DNA through helices 3 and 6. Bases contacted by helix 3 (red helix on structure, DNA positions to ) have a greater information content than do those contacted by helix 6 (blue helix on structure that intersects the sine wave, DNA positions to ), suggesting that helix 3 is more important for specific DNA recognition. This is consistent with alanine-scanning mutagenesis data for MarA [33].
Three residues in helix 3 (Trp42, Gln45, and Arg46) specifically contact DNA bases according to the MarA-DNA structure [28] (Figure 1). Interestingly, the structure does not predict a specific contact at position , but the sequence logo indicates a strong preference for ‘A’ at this position. The ‘C’ at position is completely conserved and only contacted by the tryptophan at residue 42, suggesting this is a highly specific amino acid.
To identify variants of MarA that have altered binding preferences, we randomized the three specifically contacting residues in helix 3 and selected for mutants that could bind a target DNA sequence and initiate transcription of the tetracycline resistance gene (tet) on the selection plasmid shown in Figure 2. Both the promoter of the tet gene and helix 3 of the MarA protein were flanked by restriction sites that allowed promoter and binding domain variants to be cloned into the plasmid (Figure 2). Functional MarA protein-binding site pairs within this system activated tet and allowed for cell survival in tetracycline. As we increased the concentration of drug, we selected for higher affinity interactions [19]. Additional parameters can affect the rate of transcriptional initiation, most notably the position of the binding site relative to the polymerase [24]. Since we vary the binding site within a fixed promoter context, our selection should just be on the strength of the DNA-protein interaction. We performed our selection in the E. coli strain N8453 (mar, sox-8::cat, rob::kan, see Materials and Methods) to prevent activation by wild type MarA, or by the MarA E. coli homologues Rob and SoxS. Expression of MarA on the plasmid was controlled by an L-arabinose inducible promoter [34].
We needed to identify a promoter that was only functional when activated to have tet expression and cell survival dependent upon MarA binding. To identify one, we randomized the of the tet promoter construct (Figure 2B) and selected for a promoter sequence that allowed cell growth on tetracycline plates with L-arabinose (induced expression of MarA) but not on plates without it (see Materials and Methods). The 6.5 bit binding site that we identified is marked in Figure 2B. The strength of this site was predicted using the model presented in [18], and is an average site compared to all sites in the genome. In a single construct, we cloned 3 in-frame and 2 out-of-frame stop codons into helix 3 of the MarA binding domain and tested if the resulting truncated protein could express tet with this promoter. At 15 g/ml tetracycline and 0.1% L-arabinose, we observed significant growth with wild type MarA, and no growth with the truncated mutant (data not shown), suggesting that in this condition activation of tet and cell survival is dependent upon binding by MarA.
The MarA regulon in E. coli includes the arcAB operon, which when over-expressed shows increased tolerance to many antibiotics including tetracylcine [35], [36]. To ensure that we are selecting for variants that directly activate tet, we performed a selection against the anti-consensus MarA binding site (the worst possible binding site according to Figure 1: CGTTTGACCCGCCAGGGCG). We could not identify any protein variants that allowed for survival in 20 or 30 g/ml tetracycline, suggesting that differential regulation of the MarA regulon is not sufficient for cell viability. This does not exclude the possibility that the over-expression of the arcAB operon may reduce the selective pressure on tet production. Selection in this system is somewhat similar to selection in a natural system, where the fitness of a binder is dependent upon the relative contribution of multiply expressed genes. We have in essence added tet to the MarA regulon. Because of the high concentration of tetracycline used for selection, the fitness gain for expressing tet is probably much greater than for any other gene that it regulates.
MarA binding domain mutants were selected against three variants of the 15.3 bit mar binding site (Figure 1) that is found upstream of the mar operon in E. coli [24]. The three target sequences we selected against are named ‘GCA’, ‘GAA’ and ‘GAC’ according to the bases present at positions , and (Figure 1 and Figure 2B). We varied these bases because they are the most highly conserved ones contacted by helix 3. Binding domain libraries were made as described in Materials and Methods. We transformed the N8453 cells with each library and selected for growth on plates at 20 and 30 g/ml of tetracycline +0.1% L-arabinose. Individual colonies were sequenced.
Sequences of viable MarA binding domain variants are shown in Table 1 and sequence logos generated from these variants are shown in Figure 3. Each binding domain is referenced by residues 42, 45 and 46. For example, wild type MarA is noted as WQR. Of the 18 sequenced binding domains selected against the MarA consensus ‘GCA’ binding site at 20 g/ml tetracycline, we identified 13 different variants, including that of the wild type protein, that could initiate tet transcription to a sufficiently high level for cell survival. Only 5 different variants were observed at 30 g/ml tetracycline, and no new variants were observed at this higher concentration as expected. Three of the 13 binding domains were represented by multiple codon sets further supporting that these variants are functional. Interestingly, only the ‘TCK’ variant selected against ‘GCA’ lacks an arginine at position 46, but it retains a positively charged lysine residue at that position.
Selection against the ‘GAA’ and ‘GAC’ binding sites showed much less variability in the number of identified functional MarA variants. We only identified two mutants that could activate the ‘GAA’ binding site and three that could activate ‘GAC’. No colonies were observed when we selected against ‘GAC’ at the higher tetracycline concentration of 30 g/ml.
We were interested in how the variability in the selected mutants compared to the natural variability at these residues. We blasted the E. coli MarA sequence against all bacterial genomes using BlastP with non-redundant protein sequences and default search parameters [37]. The top 250 hits were aligned by ClustalX [38] and sequence logos were generated using the Delila programs [39] (Figure 3, Natural). Both the natural and the experimentally selected binding domain variants show a strong preference for arginine at position 46. Interestingly, tryptophan is highly conserved at position 42 in the natural binding domains, whereas it was only observed in two selected variants (Table 1). In a similar selection for specifically contacting residues in the engrailed homeodomain by phage display, experimentally and naturally selected variability correlated well [40]. Engrailed binds a more specific set of sequences than does MarA. Therefore, natural selection on binding by engrailed is probably directed to maintain high affinity to a single or small set of sites as was experimentally selected. Conversely, MarA has probably been selected to maintain affinity to a more degenerate set of sequences, which may explain the discordance between the naturally and experimentally selected binding domains.
To identify the highest affinity MarA mutant for each of the three DNA binding sites, the protein binding domains in each library were competed against each other in liquid culture containing 30 g/ml tetracycline+L-arabinose for 24 hours. The competed cultures were mini-prepped, retransformed and individual variants were sequenced (Materials and Methods). We expected the mutant that produced the highest tet output to be represented at the highest frequency in the competed population as seen in a similar experiment [19]. We sequenced 8 individuals from each library and observed only one protein variant for each target binding site: RQR for ‘GCA’, SQR for ‘GAA’ and TRR for ‘GAC’ (Table 1, marked with ‘X’ in Best column). Interestingly, wild type MarA (WQR) was not identified as the most fit variant for its naturally evolved consensus binding site ‘GCA’.
We determined the relative affinity of wild type MarA and four selected MarA variants to 64 different binding sites using MITOMI (Figure 4). MITOMI (Mechanically Induced Trapping of Molecular Interactions) measures the relative thermodynamic association constant of a single transcription factor for a large number of DNA sequences using a microfluidics based approach. The relative amount of fluorescently-labeled protein associated with fluorescently-labeled DNA is quantified by microscopy for each binding site to determine interaction strengths [8].
The 64 sequences we measured binding to covered all combinations of bases at positions , and in the mar binding site (Figure 1). The 5 transcription factor variants chosen were wild type MarA (WQR), the most fit binder for the wild type consensus binding site (RQR), a double mutant that binds to the wild type consensus (RTR), a double mutant that activates the ‘GAA’ site (SAR), and the most fit mutant for the binding site ‘GAC’ (TRR). We did not obtain reliable binding data for SQR, the most fit mutant for ‘GAA’, and therefore did not include it in this study. For each of these five transcription factor variants, we set the binding affinity of the strongest site to 1 and scaled the strength of all other sites relative to that (Figure S1). To identify sequences that are similarly bound for each mutant, we clustered the DNA binding sites according to their relative affinities using Cluster [41] (Figure 4). Additionally, we we generated energy-based position weight matrices and logos [42] (Figure 5), and calculated the degree of similarity between all matrices as Kullback-Leibler Divergences (KLD) using the program MatCompare [43] (see Materials and Methods). A KLD generally indicates that two matrices are significantly similar, and a KLD of 0 indicates that they are identical. All measured binding affinities, position weight matrices, and pair-wise KLD values are reported in Table S1.
MITOMI data for wild type MarA are consistent with the MarA sequence logo (Figure 1). Three sequences are tightly bound, ‘GCA’‘ACA’‘CCA’, as seen in natural sites. A single mutation from a Trp at position 42 to an Arg has a dramatic effect on the binding preferences of the factor (Figure 5, KLD = 1.53). The RQR mutant still specifically recognizes ‘GCA’, but with a 1.6 fold reduced affinity relative to its most tightly bound site ‘TCC’. As with wild type MarA, RQR has a strong preference for ‘C’ at position , but overall RQR is a less specific binder; the information content () [1] for positions to is 3.03 and 2.27 bits for WQR and RQR respectively (Figure 5, Table 2). The 2.46 bit RTR logo is significantly similar to the RQR logo (KLD = 0.15), but shows a slight decrease in degeneracy at position , as well as a switch in preference for ‘G’ over ‘T’ at position . Interestingly, the RQR and RTR mutants maintained the same relative difference in affinity between the bound sequences ‘GCA’, ‘ACA’ and ‘CCA’ as wild type ( for both, data not shown), suggesting that the core binding preferences of wild type are somehow preserved in these variants although they are no longer the highest affinity sites.
SAR is the least specific of the variants ( bits). It shows a preference for ‘A’ or ‘G’ at position , and almost no preference at positions and 0. It does not strongly bind ‘GAA’, the site it was selected against. Conversely, TRR appears to only bind its selected target site ‘GAC’ (Figure S1). While TRR is specific for this sequence, the relative difference in binding strength between ‘GAC’ and the non-specific background () is much less than observed for WQR, RQR and RTR (Figure S1). As the logos in Figure 5 are generated from the calculated differences in binding energy from the strongest bound site to all single base-pair mutants (see Materials and Methods), a low would result in a logo with a weak equiprobable conservation of all non-specifically bound bases at each position as observed for TRR.
Given the MITOMI data, we can test two assumptions that underlie most thermodynamic DNA binding models: (1) that the energetic contribution of each nucleotide at each position is independent of neighboring bases and (2) that this contribution is purely additive to the overall binding affinity [7], [44], [45]. Using Scan, an information theory based program that predicts binding affinities based on an independent and additive model, we calculated the predicted affinity for each protein mutant to all 64 sequences [44], and plotted this against the corresponding measured of binding (Figure 6, see Materials and Methods). Theoretically sites with an bits are predicted to be bound non-specifically, as [9], [44].
For all mutants, except for SAR, predicted binding strength is highly correlated with actual binding for sites bits (blue sequences in Figure 6), and is poorly correlated for sites bits (red sequences in Figure 6). The experimental measurement of binding affinity for weakly bound sites has previously been shown to be less accurate than for strongly bound ones [9]. Because of this, we are not surprised by the weak correlation for the sites with an bits. If these sequences are truly bound non-specifically though, we would also expect the slope of the regression line to be 0. For WQR, RQR and RTR we observe a slightly negative slope (, and respectively), which suggests that to a small degree, binding energy does change as a function of sequence (bound specifically) for a fraction of these sites. This is evident for RQR, where sites bits lie close to the regression line for the positively bound sequences (Figure 6). We expect the specific/non-specific boundary to be closer to bits for this binding domain. Likewise, for TRR the non-specific boundary is probably at bits, but this deviation from 0 bits can be explained by the low , and subsequently biased model for TRR as previously mentioned.
To approximate the non-specific binding energy for each mutant, we determined the intercept of the positive and negative site regression lines (Table 2). SAR appears to be almost completely non-specific from the MITOMI data, and we are not confident in the identified boundary between specific and non-specific binding for this mutant.
Surprisingly, there appears to be a di-nucleotide binding preference for the RTR mutant (Figure 6). RTR binds ‘GC-C’‘GC-T’‘GC-G’‘GC-A’ and ‘TA-C’‘TA-T’‘TA-G’‘TA-A’ with almost equivalent energies between sites that have the same nucleotide at the third position ( = 0.99). A simple independent and additive model would predict that a single mutation of a ‘G’ to ‘T’ at position or a ‘C’ to ‘A’ at position would not affect the binding energy of the site. Indeed, ‘TC-C’‘TC-T’‘TC-G’‘TC-A’ and is highly correlated to the equivalent ‘GC-N’ and ‘TA-N’ sites ( = 0.84 and 0.91 respectively), but ‘GA-N’ sites are not correlated and all sites have a greater than the RTR non-specific binding threshold of 3.30 kJ/mol. This clearly violates a simple independence assumption.
To identify the in vivo binding preferences of the 5 MarA protein variants, we generated a library of selection plasmids for each mutant where positions , , , 0 and in the mar binding site were randomized (Figure 2). We transformed N8453 cells with these libraries and competed them against each other in 5 ml LB+50 g/ml tetracycline+ L-arabinose for 24 hours. The competed populations were mini-prepped and sequenced in a single sequencing reaction (Figure S2). Sequence logos were generated for all mutants as described in Materials and Methods (Figure 7). Higher affinity binding sites should be more fit and represented at a higher frequency in the competed population [19]. While the relative peak height for a given base at a given position within the chromatogram is correlated with the base frequency in the population, it can be biased by the identify of the neighboring bases. Therefore, this is a semi-quantitative representation of positional nucleotide frequency.
In vivo binding preferences identified by this selection method are consistent with our MITOMI results. The wild type MarA protein (WQR) requires a ‘C’ at position and shows a strong preference for a ‘G’ at position . Unlike the MITOMI data, there is more variability at position in the selected sites, resulting in a large Kullback-Leibler Divergence between the corresponding WQR logos of 1.67, but a decrease in KLD between WQR and the RQR and RTR mutants (Table S1). The RQR in vivo selected sites have an increased variability at positions and relative to the MITOMI data, but overall the resulting logos are nearly identical (KLD = 0.15). Similar results are observed for RTR (KLD = 0.15), which only shows a slight decrease in degeneracy at position in the experimentally selected sites. Interestingly ‘A’ is not observed at position in the RTR in vivo sites, even though ‘TAA’ is tightly bound according to the MITOMI data.
The SAR mutant shows substantially less variability in the in vivo binding site selection as compared to the MITOMI data; the for positions to = 4.66 and 0.80 bits respectively. The concentration of tetracycline used for selection, imposes an energetic minimum that the factor must bind its site above to be viable [19]. This lack of variability in the SAR in vivo binding site selection suggests that unlike WQR, RQR and RTR, few SAR sites are above this threshold (i.e. weakly bound). SAR is the only mutant to show a strong preference for ‘G’ at position , while all other mutants preferred a cytosine there. Differences in the SAR binding preferences observed in vivo and in vitro may also be accounted for by the presence of a unfavorable ‘C’ at position in the MITOMI binding site library (Figure 1), which could significantly reduce the binding affinity of all sites. TRR binds to a single site, ‘GAC’, as expected.
Interestingly, we observed a wide range of degeneracy at position , which does not appear to be directly contacted by any of the varied residues. There is a preference for ‘A’ at this position for all mutants, and it is completely conserved for SAR and TRR. We expect that the amount of observed variation at is not dependent upon specific contacts at that base, but on the energetic contribution of the rest of the binding site. That is, weak binding at positions , and by residual differences requires a base with a higher affinity (‘A’) at position for the site to be sufficiently strong in this selection. This suggests that degeneracy at a single position in a site is not completely defined by the residue that contacts it, but by the energy of the other contacts in the site.
To quantify the extent of overlap in sites specifically bound by all mutants in vivo, we calculated the predicted binding strength () of each mutant to the 64 potential binding site variants at positions to , and directly compared these affinities (Figure 8). Since the RQR logo has the lowest information content, we compared all mutants to it. Sequences that fall in the upper right quadrants in Figure 8 are predicted to be specifically bound by the two mutants compared (positive for both). Sites in the lower left are predicted to not be bound by either. The remaining quadrants contain sites that are only bound by one mutant. As the RQR and RTR logos are significantly similar (KLD = 0.14), it is not surprising that their predicted affinities are highly correlated (). Only a few sequences specifically bound by RQR are not bound by RTR (lower right quadrant) and no unique sequences are bound by RTR (upper left quadrant) suggesting that RTR is merely binding a subset of the sites bound by RQR (Figure 8A). A similar result is observed for WQR, except that it binds a further reduced subset of the specifically bound RQR sites. There is no overlap in specifically bound sites by SAR and TRR with RQR, suggesting that these bind a completely orthogonal set of sequences (Figure 8B).
To better understand how mutations in the binding domain affect the transcriptional activity of MarA, we measured the expression of tet under the control of wild type MarA (WQR) with 11 different binding sites, and under the control of RQR with 15 different binding sites using quantitative PCR (Figure 9). We chose binding sites for each variant that covered a range of binding strengths based on the MITOMI data. For convenience, we normalized the output so that the relative expression of the ‘GCA’ binding site by WQR is 1.
For the WQR binding sites, the expression data correlate well with binding site strength ( for all sites, for the 3 tightly bound sites). The non-specifically bound sites show minor variability in their measured output. The expression data for the RQR bound sites do correlate with binding affinity but not as well ( for all sites) and we observed much more variability in the non-specifically bound sites. The transcriptional output from the highest affinity RQR site is almost twice that of the strongest WQR site, suggesting that functionally this mutant can access a much larger dynamic range of outputs.
It is becoming increasingly clear that differences in transcriptional regulation are an important driving force in species diversification and evolution [46], [47]. Fine scale differences in the expression level of an individual gene can be easily achieved by mutations in transcription factor binding sites contained within the associated cis-regulatory region [19]. Larger scale effects on the transcriptional network, and subsequently cellular phenotype, can be accessed through mutations in transcription factor binding domains which will impact the expression levels of all genes within their regulons [48]. As the systematic effects of transcription factor mutations are more difficult to characterize, few experimental studies have been done to probe their evolvability [5]. Since both the informational and regulatory properties of a transcription factor are determined by its binding site energy distribution [1], [20], we developed an in vivo selection assay to select for variants with altered binding preferences that still maintain a physiologically relevant transcriptional activity. Further in vivo and in vitro characterization of a subset of these mutants revealed that a large range of binding preferences, information contents and activities could be accessed with a few mutations suggesting that transcriptional regulatory networks may be easily adaptable.
One way in which regulatory networks are believed to evolve is through the duplication of an existing transcription factor gene that is subsequently selected to recognize a unique set of targets [49], [50]. It is unclear how readily this can happen. Maerkl and Quake observed that a relatively limited range of binding preferences could be accessed by single mutations in the basic helix-loop-helix protein MAX [5]. For MarA, we observed that we could get an orthogonal regulator with two mutations. The double mutant TRR is the most dramatic example. It is absolutely specific for ‘GAC’, which no other variant specifically bound (Figure S1, Figure 8B). Likewise SAR bound its own unique set of sites that do not overlap wild type (Figure 8B). Interestingly, both SAR and TRR have a lower for their highest affinity sites compared to mutants that bind the wild type consensus sequence. This suggests that a novel regulator may emerge or be engineered relatively easily, but may be initially limited in its range of potential activities.
Gene duplication may not be the only pathway by which orthogonal regulators can evolve. WQR, RQR and RTR appear to have largely overlapping binding sites, where RTR and RQR have an incrementally increasing number of specifically bound sites (Figure 8A). This suggests that a transcription factor could evolve to have an increased or decreased information content (become more or less specific), while still maintaining the majority of its binding targets. An orthogonal regulator could potentially evolve through an intermediate with broader specificity like RQR or RTR (Figure 10). A mutation of this type would impact the relative expression levels of the genes controlled by the transcription factor, as seen in Figure 9, and initially compromise the fitness of the cell [21], but would presumably have a significant advantage over a mutation that leads to the loss of potential targets. Further selection could re-specify the transcription factor after becoming promiscuous to regulate a new set of sequences. As this broadening of specificity can be done relatively easily (WQR can be converted to RQR by a single nucleotide mutation), this pathway may be highly tractable by evolution and useful for engineering regulatory networks. As previously mentioned, the information content of a transcription factor's binding sites is highly correlated to the amount of information needed to specifically locate its binding sites in the genome for bacterial systems [1]. This suggests that as the size of a bacterial factor's regulon increases or decreases, so does the selective pressure on binding site information. The decrease in information from WQR to RTR to RQR, also suggests that a transcription factor can easily evolve to expand or contract the size of its regulon.
The overlap in binding sites between WQR, RQR and RTR may not be surprising as all were selected to bind the wild type consensus sequence ‘GCA’. The dominant feature for these three mutants is a highly conserved ‘C’ at position (Figure 5, Figure 7). One possibility is that the for this base is increased from WQR to RTR to RQR, and a stronger individual contact here compensates for a greater number of energetically unfavorable mismatches at positions and , decreasing the information content (Table 2). Interestingly, expanding the number of specifically bound sites for RQR also expands the range of transcriptional outputs nearly two fold (Figure 9). If RQR has a much greater range of potential activities, and largely similar binding preferences to wild type MarA (WQR), why is it not observed in nature? WQR has a greater information content to ratio than both RQR and RTR (Table 2), suggesting that it encodes the fewest number of specifically bound sites for its range of binding energies (Table 2). It also appears to have a large energetic gap between its three highest affinity sites and the background, which the other variants lack (Figure 4). These properties of the wild type MarA binding site distribution, and not just overall affinity, may be evolutionarily advantageous and thus selected, as an increased for all sites would decrease the likelihood of the factor binding the wrong location [51], [52], and fewer recognized sites would decrease the probability of spurious sites emerging in the genome [53]. Directly assaying the global effects of these mutations by RNA profiling and chromatin immunoprecipitation would dramatically improve our understanding of their cellular implications.
We modified the plasmid-based selection system described in [19] to select for and characterize MarA variants that have altered binding preferences (Figure 2). Griffith et al. generated an L-arabinose inducible MarA expression pBAD18 variant (pBAD18-hisMarA) [34]. We cloned the marA gene, the AraC regulated promoter and the araC gene from this plasmid into our pBR322-based selection system, allowing for us to control the expression of MarA by the addition of L-arabinose (Figure 2A). An XhoI site was introduced about 10 residues upstream of the start of helix 3 by modifying the ‘CTG’ codon encoding the leucine at residue 30 to the synonymous codon ‘CTC’ by QuickChange [54] (Figure 2). An AgeI site exists immediately downstream of helix 3. To make this a unique restriction site, we removed a second AgeI site present in a non-regulatory region upstream of the marA gene by QuickChange.
To generate variants of the MarA-activated tet promoter (Figure 2B), the selection plasmid was simultaneously digested with EcoRI and ClaI restriction enzymes for 2 hours at 37C (NEB). Inserted promoter variants and libraries were generated by DNA synthesis (Integrated DNA Technologies). We synthesized both strands of the DNA, and designed oligos to contain the appropriate overhang to be cloned into the EcoRI and ClaI sites. Digested plasmid and synthesized inserts were ligated overnight at 14C using T4 DNA ligase (NEB).
To generate binding domain variants (Figure 2C), we used a similar method. Plasmid was digested with XhoI and AgeI simultaneously for 2 hours at 37C (NEB). The digested plasmid was gel purified and ligated to complementary synthesized inserts that had XhoI and AgeI overhangs. To randomize the residues 42, 45 and 46, we synthesized the oligos with an equal mixture of all four bases at the first two positions of the codon, and an equal mixture of ‘G’ and ‘T’ at the third position of the codon to generate a more equal distribution of amino acids at each position. The ligated promoter and binding domain libraries were transformed into DH10B cells, recovered for 1 hour in LB, and plated on 100 ml LB+30 g/ml ampicillin plates. Cells were suspended from the plates in 10 ml LB and mini-prepped using the QIAquick miniprep kit (Qiagen).
To prevent activation of the tet gene by the endogenous MarA, Rob or SoxS proteins, selections were performed in the E. coli strain N8453 (mar, sox-8::cat, rob::kan variant of GC4468) prepared by J.L. Rosner and R.G. Martin and obtained from R.E. Wolf. To identify a binding site that was only functional when activated, we transformed a library with a variant of the promoter construct shown in Figure 2B that contain the mar MarA binding site (Figure 1) and a randomized hexamer. These plasmids also contain the wild type MarA protein. The library was transformed in N8453 cells by electroporation, recovered for 1 hour in 500 l LB at 37C, shaken at 225 rpm and plated on 5 g/ml tetracycline LB plates + L-arabinose. Individual colonies were picked and streaked on on 10, 15 and 20 g/ml tetracycline LB plates+/− L-arabinose. Colonies that only grew on L-arabinose containing plates were sequenced.
To identify binding domain variants that specifically bound different DNA sequences, libraries were transformed into N8453 cells by electroporation, recovered for 1 hour in 500 l LB at 37C, shaken at 225 rpm and plated on 100 ml LB plates containing 30 g/ml ampicillin + L-arabinose. Colonies that grew on the plates overnight were suspended in 10 ml LB containing 30 g/ml ampicillin + L-arabinose and grown at 37C, shaken at 225 rpm for 8 hours. 70 l of these cells were then plated on 25 ml LB agar plates containing 20 or 30 g/ml of tetracycline + L-arabinose. Individual colonies were picked, grown overnight, miniprepped by the QIAquick miniprep kit and sequenced.
To identify the binding domain for each site that could produce the most tet transcript, libraries were transformed by electroporation into N8453 cells and plated on 5 g/ml tetracycline LB plates and grown overnight. These colonies were suspended in 5 ml LB with 5 g/ml tetracycline + L-arabinose and allowed to grow in liquid culture overnight. The following morning fresh 5 ml 30 g/ml tetracycline + L-arabinose cultures were inoculated with 100 l of the overnight culture and competed for 24 h. The competed library was miniprepped by a QIAquick miniprep kit, transformed into DH10B cells and plated on 30 g/ml ampicillin plates. Individual colonies were picked, grown up overnight, miniprepped and sequenced as described above.
Binding site competitions for the 5 MarA selected variants were performed as described previously [19], except that the libraries were transformed into N84533 cells and all media contained L-arabinose. Libraries were competed in 50 g/ml tetracycline for 24 hours and sequenced on a 96 capillary 3730xl DNA Analyzer (Applied Biosystems). Nucleotide variation in the population of competed promoters was visualized using Finch TV (Geospiza Inc). To generate sequence logos from these data (Figure 7), we measured the peak height of each base at each position in a chromatogram (Figure S2), and divided this height by the summed heights of all peaks at the position to calculate a relative nucleotide frequency. A standard position weight matrix was generate from these frequencies, and represented as a sequence logo using the Delila programs [39].
MITOMI (Mechanically Induced Trapping of Molecular Interactions) was performed according to Maerkl et al. [8]. The 64 variants of the mar binding site (Figure 1) were synthesized by Integrated DNA Technologies. In vitro transcription and translation was done using the RTS E. coli HY kit (Roche). Fluorescently labeled lysines were incorporated into the protein during in vitro translation by addition of tRNA-lys-bodipy-fl (Promega). Protein and DNA fluorescence was measured using Genepix (Molecular Devices).
The of binding for each variant to each binding site was calculated using , where is the ideal gas constant, is the temperature of the experiment (295K) and is the association constant as measured by MITOMI. The of binding was calculated for each binding site by subtracting the of binding for that site from the of binding from the highest affinity site for a protein variant.
To generate the energy logos, we calculated a matrix for each variant by determining the difference in binding energy between the strongest bound site for that factor (the consensus site) and all single base-pair mutants. For example, to calculate the relative weights of each base at position for wild type MarA, we subtracted the measured binding energies of ‘ACA’, ‘CCA’, ‘GCA’ and ‘TCA’ from ‘GCA’. We used the enoLogos webserver to convert these energies into a log-likelihood matrix [42] and generated logos using the Delila programs [39]. The matrices for all logos are given in Table S1.
To quantify the similarity in binding preferences between MarA variants, we used the program MatCompare to calculate the Kullback-Leiber Divergence (KLD) between the inferred sequence logos [43]. All pair-wise KLD values are reported in Table S1. The relative affinity () of a given binding model to all DNA sequences was calculated using the information theory based program Scan [44].
A library of mar binding sites was cloned into plasmids containing either the wild type MarA protein, or the RQR mutant. The library was transformed into N8453 cells, plated on 30 g/ml ampicillin and grown overnight. Individual colonies were grown overnight in 5 ml LB+30 g/ml ampicillin. Glycerol was added to 200 l of cells to a final concentration of 20% and stored at C. The remaining culture was mini-prepped and sequenced to determine which binding site was present. 11 different binding sites covering a range of affinities as determined by MITOMI were chosen for wild type MarA and 15 were chosen for the RQR mutant. These were not the same sites for both factors.
Cultures were inoculated with the frozen samples and grown overnight in 5 ml LB cultures with 30 g/ml ampicillin and L-arabinose. A fresh 5 ml LB+30 g/ml ampicillin+L-arabinose culture was started at and grown to an . cells were added to RNAprotect Bacteria reagent (Qiagen), and RNA was purified using the RNeasy Mini kit with on-column DNase digestion (Qiagen). cDNA was made from 2 g of RNA using the Superscript III RT kit (Invitrogen). QPCR was performed with the SYBR green mix from NEB. QPCR primers specific to the tet and marA gene were both used. The relative expression of the tet gene was determined by the ratio of tet abundance over marA abundance for each sample.
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10.1371/journal.pcbi.1005578 | Robust transmission of rate coding in the inhibitory Purkinje cell to cerebellar nuclei pathway in awake mice | Neural coding through inhibitory projection pathways remains poorly understood. We analyze the transmission properties of the Purkinje cell (PC) to cerebellar nucleus (CN) pathway in a modeling study using a data set recorded in awake mice containing respiratory rate modulation. We find that inhibitory transmission from tonically active PCs can transmit a behavioral rate code with high fidelity. We parameterized the required population code in PC activity and determined that 20% of PC inputs to a full compartmental CN neuron model need to be rate-comodulated for transmission of a rate code. Rate covariance in PC inputs also accounts for the high coefficient of variation in CN spike trains, while the balance between excitation and inhibition determines spike rate and local spike train variability. Overall, our modeling study can fully account for observed spike train properties of cerebellar output in awake mice, and strongly supports rate coding in the cerebellum.
| Detailed computer simulations of biological neurons can make an important contribution to our understanding of how the brain works. In this paper we use such a model of a neuron that represents the output from the cerebellum. We can show that the inhibition this neuron type receives from Purkinje cells in the cerebellar cortex is well suited to pass a detailed time course of movement control to the output of the cerebellum. Importantly we find that this type of coding requires a population of Purkinje cells that pass the same temporal coding of spike rate to the output neurons in the cerebellar nuclei.
| Transmission of information through firing rate changes in populations of connected neurons is one of the most widely accepted principles of neural coding. In motor control, for example, cortical neurons showing firing rate changes as a function of movement direction can be said to dynamically compute the current movement direction in a population vector [1]. This representation also works well computationally in abstract neural networks, for example when the motion of handwriting control is computed in the neural engineering framework [2]. Little is known, however, about how biological neurons utilize rate codes transmitted by their typically hundreds or thousands of input synapses to control their own output firing rate, and how robust such a code is in the presence of noise, intrinsic non-linearities given by voltage-gated channels, and a balance of excitatory and inhibitory inputs. Further, it is unclear whether rate codes are equally present in inhibitory as in excitatory transmission. We addressed these questions by studying the inhibitory transmission between cerebellar cortical Purkinje cells (PCs) and their targets in the cerebellar nuclei (CN) through recordings from awake mice and detailed biophysical simulations of synaptic integration in CN neurons. Linear rate coding has been identified to represent excitatory input information from granule cell input in PCs [3,4], but the correlation of coding at the population level and its transmission to CN neurons in vivo remains unclear. We use rhythmic motor patterns and in particular the rhythmic control of respiration as a model behavior to study the transmission of rate coding in cerebellar circuits, as rhythmic respiratory rate modulation is well expressed in the spiking activity of PCs in the cerebellar vermis [5] as well as in the synaptically connected medial (fastigial) cerebellar nucleus [6], and this pathway plays a functional role in the neural control of respiration [6].
In the present study we used an updated version of a full biophysical model of CN neurons [7] to study how the population of Purkinje cell inputs expected to converge on a single CN [8] may transmit a rate coded rhythmic behavior, and whether CN model generated spike trains can account for spiking properties recorded from CN neurons in awake mice. We developed a new algorithm that allows the flexible construction of sets of artificial PC spike trains that match the statistical properties of recorded PCs while also allowing the insertion of correlations observed between pairs of recorded PCs into a larger set of PC spike trains that converge onto a single CN neuron as input. This new algorithm development was necessary because it is at this time physiologically impossible to record from and identify all the PCs that converge onto a single CN neuron. Therefore, in order to simulate a realistic range of rate-correlations and respiratory coding correlations between the ~50 PC inputs received by a single CN neuron, it is necessary to generate populations of artificial spike trains (ASTs) in which each AST matches the statistics of PC recordings (which we obtained from awake mice) while flexibly allowing the addition of specific rate co-variances between ASTs. We achieved this goal by creating an intermediate representation of spike trains as rate templates that could be manipulated algebraically to show more or less rate co-variances both for respiratory related rate changes and slow rate fluctuations. To create ASTs we could then draw gamma distributed interspike intervals from the rate template to match the template’s rate fluctuations as well as the recorded spike train statistics. To our knowledge this study presents the first such algorithm, which we expect will be generally useful for similarly minded modeling studies of synaptic integration in the awake brain.
Our CN modeling results for the first time give a full match of CN spiking properties seen in awake recordings derived from the biophysical properties of CN neurons and the statistics of their synaptic inputs. The results reveal an unexpected amplification of rate coding at the CN output compared to the PC inputs received and show a highly robust transmission of rate codes from the cerebellar cortex to the CN via inhibition in the waking condition. They also provide evidence for an involvement of intrinsic cellular dynamics in providing gain control in the transmission of rate codes.
The starting point of our analysis was a database of 21 PC, 11 mossy fiber (MF) and 16 CN recordings. These data were obtained in awake head-fixed mice with multiwire recordings while respiration was monitored using a thermistor placed in front of one nostril [5,9]. Out of 20 PCs that were analyzed for rate modulation linked to respiration, 15 (75%) showed significant rhythmic rate modulation, as indicated by a deflection of the rate change in a peri-event time histogram (PSTH) triggered on inspiratory event markers above 3 standard deviations (S2 and S3 Figs). Standard deviations were calculated from a set of 100 control PSTHs from each cell that were calculated from randomly shifted spike time series with respect to the respiratory event markers. The same analysis showed significant respiratory modulation for 10 of 16 CN neurons (63%), and 6 of 9 (67%) MF recordings. This strong representation of respiratory activity supports previous evidence that the vermal cerebellar cortex through its output connection in the medial cerebellar nucleus is involved in the adaptive control of respiration [6]. The rate modulation for different cells showed different phase relationships to respiration, and the averaged rate modulation in the PC, CN, and MF neuron population was not significant (S3 Fig), suggesting that cerebellar respiratory modulation occurs at all phases of respiration to a similar degree, though in different populations of neurons.
We also made a detailed analysis of the recorded PC, MF and CN baseline spike train statistics, in particular firing rate as a function of time, interspike-interval (ISI) distribution, coefficient of variation (CV), and local variation (LV) [10], which indicates the variability of pairs of successive ISIs (S1 Fig, Table 1 in S1 Text). Our goal was to determine whether the spike train statistics and respiratory modulation of CN neurons can be explained from the dynamics of a biophysically realistic CN neuron model [7] and the input patterns received.
To achieve our goal we first had had to design a bootstrapping method by which to extrapolate from 2 simultaneously recorded PCs to a population of ~50 PC spike trains with flexible rate covariances that converge on a single CN neuron with strong synapes [8]. We determined that our recorded PC spike trains had broad cross-correlations that were in part related to behavior [9], but did not find any millisecond precision in simple spike cross-correlations here or in previous studies [11,12]. Complex spikes were removed from the PC spike trains, and not further considered in this study. We constructed a Matlab (MathWorks, Inc.) algorithm by which we can assemble artificial spike trains (AST) closely matching properties of single recorded PCs (Fig 1). The core of this algorithm consists of building and manipulating spike rate templates (Fig 1A and 1B), which are constructed by convolving spikes recorded from a single neuron with Gaussians [13,14] (see Methods). To construct ASTs we draw gamma distributed ISIs from a distribution with a mean rate tracking a rate template, and a shape parameter kappa (κ) that is mathematically derived from the LV of the recorded spike train, where for gamma distributed events LV = 3 / (2 κ +1) [10]. To obtain an ISI distribution in an AST that matches the original recording (Fig 1C) we further had to perform a refractory period correction, as gamma distributions do not model processes with refractory periods directly (see Supplemental Methods for details). We validated our ASTs by comparing the spike train power spectrum between recorded neurons and the built-to-match ASTs (Fig 1D), and by ascertaining that the coefficient of variation (CV) and the LV of the AST also matched the recording closely (Fig 1E). An important observation was that the LV could be modeled as a static parameter as previously observed for cortical neurons [15], but the global variability of the spike train over time represented by the CV is an outcome measure that is influenced by the LV as well as the spike rate modulation over time. Using these methods we made populations of 50 PC ASTs with statistical properties and spike rate fluctuations matching our recorded PCs while also being able to flexibly control rate covariances. All 50 PC ASTs used as input to the CN model were taken from the same master rate template of a single PC with specific different manipulations of rate-covariances for different simulation runs as described below (also see Supplemental Methods for details). These AST populations were then used to analyze how convergent input from 50 PCs would influence CN spiking, and what properties of convergent input were needed to account for observed CN spike train statistics.
The biophysical CN model we used consists of 485 dendritic and one somatic compartments incorporating 9 active conductances to replicate slice CN recordings [7]. We included the modifications of ion channel voltage-dependence and density as well as synaptic kinetics described in the supplemental materials of the original publication ([7], S3 & S4 Figs), which lead to a more depolarized level of tonic depolarization (Fig 2A) and a more linear f-I curve (Fig 2B) as well as faster synaptic kinetics to more closely replicate CN slice recordings in these qualities [16,17,18,19]. In the present study we further modified the synaptic kinetics of PC->CN synapses to incorporate the experimentally determined short term depression parameters [20,21] leading to a steady state depression of around 60% for a Purkinje cell firing rate of 75 Hz (Fig 2C). The resulting spiking pattern with random excitatory and inhibitory input trains of the modified model remain similar to the original publication (Fig 2D and 2E), and are based on a balance of excitatory and inhibitory input currents with a fluctuating total synaptic current near zero (Fig 2F), which modulates the spontaneous activity of these neurons [16,22].
Next we characterized the CN model spiking output statistics for input patterns aimed to match the PC spike train statistics derived from our recorded data. We used 50 PC ASTs (48 dendritic, 2 on the soma) to match the number of strong PC inputs to converge on a CN neuron recently described [8]. We also applied 48 dendritic mossy fiber ASTs to create the required balance between excitation and inhibition [16,22]. We scanned through an array of input parameter settings that are not fully experimentally constrained, notably the size of unitary excitatory and inhibitory conductances (Gin and Gex), and the amount of rate covariances present between 50 synchronous PC inputs. The latter setting was manipulated by a shift fraction (SF), that is the proportion of rate modulation that utilized a randomly time shifted version of the master rate template. For the first set of simulations we used the PC firing rate of the template neuron (64.9 Hz) for all 50 ASTs. MF inputs to the model were also taken from a typical single recorded MF rate template, but as this study focused on the effect of rate covariances present in the PC pathway to influence CN spiking statistics we chose to use a SF of 1.0 for our baseline simulation (all MF inputs are temporally decorrelated) and an MF input rate of 20.4 Hz, which is the recorded sample mean.
The results of this input parameter scan show that using different ratios of Gin and Gex allowed us to achieve a wide range of CN output firing rates (Fig 3A), and revealed a systematic relationship between firing rates, CV and LV (Fig 3B and 3C) such that faster CN spike trains associated with a smaller Gin / Gex ratio showed a lower CV and LV despite using the same PC input spike trains. Further, the CV and LV of CN spike trains were higher for larger absolute values of Gin (Fig 3B and 3C, red traces). For a high Gin (20 nS per PC input) the PC input rate covariance also had a strong effect on the CN output CV, such that a higher input rate covariance (Fig 3B, red traces with x symbols) resulted in a higher CN spike train CV. In contrast, the LV of CN spiking was much less affected by the input rate covariances (Fig 3C). A key result of our study is given by the match of the dependencies between CV and LV of CN spike trains between our simulations (Fig 4A and 4C) and our recorded CN data sample (Fig 4B and 4D) for the full range of physiological spike rates between 10 and 70 Hz. This simulation result indicates that the statistics of the PC and MF input to the CN as derived from our PC and MF recordings can fully account for the CN spike train statistics recorded in the same state. Interestingly, the match between recordings and simulation was best for a simulated SF of 0.5, indicating that the spike train statistics in the CN recordings are most compatible with PC input that contains about 50% rate covariance. Further, the variability between our CN recordings can be explained by a possible variability in total PC input conductance amplitudes received by different CN neurons and different rate covariances between these inputs. These results for the first time fully account for spike train statistics in the awake state in a biophysically based neural simulation. While certainly other factors than the PC input statistics can influence CN spike statistics in the animal, our results demonstrate that the PC input statistics alone are sufficient to account for the full spectrum of recorded CN rates and LV as well as CV statistics and their interdependence.
Next, we aimed to incorporate the respiration related rhythmic spike rate modulation in the PC input to CN neurons in our simulations to determine whether the recorded PC respiratory modulation (Fig 5A and 5B) can explain the recorded CN modulation (Fig 5E and 5F). An important question not addressed by our recordings concerns the required level of covariance in respiratory modulation between PC inputs to a single CN to allow for the observed amplitude of CN respiratory modulation if it was solely transmitted by PC inputs. In order to create ASTs with respiratory modulation matching the recordings we again employed rate template manipulations. We determined the average rate modulation triggered by respiration in a given PC (Figs 5A, 5B, S2 and S3), and then we convolved the normalized rate modulation waveform with our master rate template at the measured time of each inspiration. We find that by drawing random gamma spike trains with refractory periods from this combined rate template we are able to create PC ASTs with respiratory rate modulation closely matching the experimental data (Fig 5C and 5D) while maintaining the spike train statistics of recorded PCs including their rate, LV, CV and power spectrum. As a proof of concept simulation we picked a specific CN recording with a peak of 36% spike rate increase during respiration (Fig 5E and 5F) and for our simulation input picked a Gin of 16 nS and Gex of 3.5 nS, which we knew from our parameter scan to result in a matching mean baseline CN simulation spike rate of ~22 Hz. We then asked the question of how many of the 50 PC inputs need to show the respiratory modulation shown in our ‘typical’ PC recording (Figs 5A, 5B, S2A–S2C and S3A–S3C) in order to generate the behavioral modulation strength seen in our ‘typical’ CN recording (Figs 5E, 5F, S2D–S2F and S3D–S3F). The results showed that a respiratory modulation in 25 PC inputs (i.e. 50% of inputs) resulted in a match with our recorded CN modulation (Fig 5E–5H).
Next, we determined the robustness and relative expression strength in the transmission of respiratory rate modulation in the PC -> CN pathway by systematically varying the number of modulated PC inputs and the strength of modulation in each input for a slow and a fast spiking CN simulation resulting from 2 different levels of excitation (Fig 6). We find that a change in the PC respiratory modulation strength is transmitted faithfully to the CN, and that respiratory modulation is well transmitted by slow or fast firing CN neurons (Fig 6A). Both the fraction of modulated PC inputs (BMF) and the strength of PC respiratory modulation (BMS) had strong effects on CN modulation (Fig 6B). At the strength of PC modulation present in our experimental sample PC (Fig 5A), a modulation of 10 of 50 PC inputs (BMF = 0.2) to the CN neuron was sufficient to result in a significant output modulation. If all PC inputs to the CN simulation were modulated using the 11.4% mean rate decrease in the PSTH trough observed in the sample PC, the CN mean PSTH peak rate increase was 25.9% at a firing frequency of 60 Hz, and 48.3% at a firing rate of 20 Hz, indicating that the respiratory modulation depth is amplified in the transmission from the PC to the CN in an inhibitory transmission. Strong respiratory modulation in the CN lead to a moderate increase in the CV of the CN spike trains (Fig 6C), while the LV was less affected (Fig 6D, Gex = L. red solid lines). We further examined the effect of global rate covariances between PC inputs on respiratory modulation (SF 1.0 vs. 0.5), and found that this manipulation of background rate covariance only had a small effect on the transmission of respiratory modulation (Fig 6B, circle vs asterisk symbols), while it had a strong effect on the overall CV of the spike train (Fig 6C). As detailed in the Supplemental Information we found that the transmission of respiratory modulation was also robust against different PC input firing rates, the presence of absence of short term depression in the PC-> CN synapses, using rate templates from a different PC, and changing the gain on template rate fluctuations (S2–S7 Figs). The key outcome of these sets of simulations was that the inhibitory PC inputs on tonically active CN neurons provide a sensitive and accurate means of transmitting a rate code related to controlling behavior, and that the strength of this rate transmission is highly dependent on the fraction of inputs modulated with the same time course. Further, the transmission of rate modulation is robust in the face of common background rate modulation in the input and operates well for the full range of observed CN firing rates (10–70 Hz). Importantly our simulations demonstrate that the observed respiratory modulation in CN neurons can be fully explained by the measured rate modulation in PCs if at least 20% convergence of similarly modulated PCs onto single CNs is present.
Earlier anatomical estimates of the number of PC inputs on CN neurons [23] were much higher than given by the recent physiological assessment [8]. While the recent work can account for the larger number of boutons anatomically observed by positing multiple boutons per PC input to a CN neuron, we were interested to know what the consequence of using 500 instead of 50 inputs would be for matching our recorded CN data from awake mice. We created 500 ASTs using the same rate template as previously for 50, but we divided the unitary synaptic conductance by 10 to arrive at a similar average conductance waveform (Fig 7A). A notable difference in the total conductance of 500 inputs was that high frequency fluctuations were much diminished due to averaging over 500 instead of 50 random processes. Notably, this had a large effect on the output spike rate from the CN simulation (Fig 7B and 7C), which was diminished for 500 inputs from 63 Hz to 20 Hz for a high level of excitation, and from 20 Hz to near zero for a low level of excitation. This dramatic difference illustrates the high importance for fast input conductance fluctuations in triggering individual sodium action potentials, a property not seen in integrate and fire neurons. We have previously also observed this finding in dynamic clamp experiments of CN neurons in brain slices [16]. Despite the large decrease in CN spike rate for 500 PC inputs, the respiratory modulation remained strong (Fig 7D–7F), and the absolute values of respiratory spike rate increases were nearly the same for a 20 Hz spike rate with 500 PC inputs than they were with a 60 Hz spike rate with 50 PC inputs with the same spike train properties (Fig 7E and 7F Gex: high, dashed lines). These findings again show that inhibitory synaptic transmission is a highly robust carrier for a behavioral event related rate code. The main computational outcome of using 500 instead of 50 PC inputs was that much more excitatory input is needed in order to match the spike rates in the model with those recorded in awake mice.
Finally, we asked the question whether the intrinsic active currents of CN neurons make an important contribution to the spiking statistics and respiratory modulation in our simulations. While a full treatment of this question falls outside the scope of this study, we used manipulations of the density of the calcium dependent potassium current (SK) to see what contributions this modulatory current makes to CN coding properties in awake mice. In previous work we and others have shown that this current is present in CN neurons and that blocking it with apamin causes bursting, bistability and pronounced spike rate increases with depolarization [18,24,25]. Further, stochastic excitatory and inhibitory input patterns in the CN model lead to strong fluctuations in SK current [26]. The involvement of this important modulatory current in synaptic integration in the awake animal remains unknown, however. Our default simulation made to match typical CN slice recordings from 14-21d old rats had a somatic SK density of 2 S / m2 and a dendritic density of 0.6 S / m2. We varied these densities for SK densities between 0 and 8 S / m2 in the soma and proportionally 0 to 2.4 S / m2 in the dendrites (Fig 8). When SK was absent, comparing simulation Vm traces for 0 vs 8 nS SK density with the same synaptic input, we find a much reduced spike-afterhyperpolarization (example indicated by blue arrow in Fig 8A) and much stronger spike rate modulation for a given input rate modulation (Fig 8A, see 0.7 to 0.9s for a period of decreased inhibitory conductance), as should be expected from the biophysical properties of this potassium current that is activated via the calcium inflow with each action potential. Not surprisingly, there is also a systematic decrease in overall spike rate with increasing SK density (Fig 8B) and a decrease in CV (Fig 8C). The LV on the other hand shows a non-monotonic dependency on gSK, with a maximum near 4 nS (Fig 8D). While SK is known to regularize spike trains [27],this usually refers to the CV. The low LV when SK is absent is probably due to the high local regularity during periods of high frequency firing, but we did not further examine this effect. The effect of SK density on respiratory rate change transmission was also strong (Fig 8E and 8F). With increasing gSK the CN output PSTH rate modulation with the same input was much diminished. This result indicates that SK is well suited to dampen the transmission of behaviorally related rate changes. Interestingly SK appears to be downregulated in adult rodents [25], suggesting that as the cerebellum matures the gain of rate change transmission may be increased. Overall the strong effect of SK on spiking statistics and synaptic transmission of rate changes shows the high importance of intrinsic neuronal properties on the transmission of behaviorally related rate codes.
Our study posed the general question on how rate codes can be transmitted by inhibitory synaptic inputs using the cerebellar cortex to cerebellar nuclei projection as a paradigmatic example. This inhibitory connection is particularly interesting in that it conveys the entire output from the cerebellar cortex, and the cerebellar cortex is commonly thought to be involved in coding detailed temporal aspects of motor behavior [28,29,30]. Therefore, detailed temporal information has to be transmitted through the inhibitory cerebellar cortico-nuclear pathway. However, cerebellar research has generated conflicting ideas on whether this information is transmitted by a rate code [3,4] or by a temporal code triggered by input synchronicity [8,31]. While the distinction between rate and temporal codes can be blurry at intermediate values of temporal precision, one would generally take neural algorithms depending on coincidence detection [32], synfire chains [33] or input synchrony to detect patterns [34] as examples of a temporal code, while spike rate modulation at the time scale of the behavior controlled (which could be quite fast for saccades for example) represents a rate code. Our findings with respect to the coding of respiration in mice in this study are fully supportive of the rate coding model in the control of cerebellar output, as rate was smoothly varying on the time scale of the behavior observed. Our findings substantiate the concept that inhibitory synaptic transmission can convey such information with high accuracy in tonically active neurons. Nevertheless, it is entirely possible that a temporal code is multiplexed with this rate code, and would be triggered by specific events, such as motor errors. In the cerebellum such an event in particular is likely to be coded by highly synchronous climbing fiber firing [3,4], which could result in rebound activity in the CN [7,35,36,37]. This pathway should be analyzed carefully in future modeling work, but an experimental database of simultaneous cerebellar cortical and nuclei recordings in behaving animals while assessing climbing fiber synchrony is not yet available. For simple spike activity in cerebellar cortex in our mouse preparation we previously described an absence of synchronized spiking or synchronized pauses with respect to respiration and licking [5], or sensory activation in anesthetized rats [12]. Our modeling results in the present study show that indeed such coincident PC simple spike inputs to a CN neuron are not required to explain the observed rate, regularity or respiratory modulation of our CN recordings. Instead, we found that rate coding of PCs is fully sufficient to account for observed CN spiking properties, but that a substantial correlation in the rate modulation between PCs projecting to the same CN neuron is required.
We used a detailed biophysical CN neuron model to perform our investigation, which allows us to address the question of how much intrinsic active properties of CN neurons are important in decoding synaptic input. Interestingly, in the present input scenario of a time-varying balance of excitatory and inhibitory input the strong rebound firing capabilities of the model, which match experimental findings [7], did not come into play as significant de-inactivation of the rebound currents (T-type Calcium and persistent Na conductances) through strong hyperpolarization did not occur. Nevertheless, our investigation of the role of the SK conductance in the present study shows that the neurons’ active properties are highly significant in decoding synaptic input. Specifically, the SK conductance in CN neurons is known to cause prolonged spike-afterhyperpolarizations and regularize spontaneous spiking [17,24], which after block of SK current with apamin becomes highly bursty [17,24]. In a previous dynamic clamp study we showed that bursting is suppressed with a baseline of inhibitory and excitatory input conductance, but that the gain of responses to input modulation was increased when SK was low [25]. This role of SK controlling the gain of the synaptic response function was confirmed in our present modeling study for input conditions of respiratory spike rate modulation in the awake mouse. Recordings from slices of rodents at different ages suggest that SK is downregulated as animals become adult [25], thus perhaps allowing a greater CN output modulation by input fluctuations as the cerebellum learns to code for specific behaviors. However, even in the adult mouse the amount of SK current observed in single CN neurons may be highly variable as is typically observed for voltage-gated currents [38], and possibly serve as a gain control mechanism on the synaptic coding function of behavioral spike rate modulation that could be regulated through intrinsic plasticity. Such SK plasticity has not been studied in the CN, but is known to occur in other cell types [39].
While our results support the notion that modulated PC input on CN neurons is sufficient to explain observed CN spike train statistics and respiratory modulation, we do not wish to imply that mossy fiber inputs to the CN are irrelevant or ineffective in this regard. In our previous dynamic clamp studies in CN brain slice recordings we have shown that PC input alone can control CN spike rate and regularity in the presence of tonic excitation, which is required to achieve a necessary balance between excitation and inhibition [16]. However, when the MF input is also modulated in the dynamic clamp input to mimic in vivo input conductances [22], the MF activity can also control CN spiking, and that MF and PC triggered modulation of CN spiking is roughly additive. Nevertheless, we found that due to the high proportion of slow NMDA conductance in MF input to CN neurons [40,41] that CN spike train irregularity is predominantly caused by PC input transients [22]. Our current results lead to the prediction that the contribution of MF input to respiratory rate modulation would critically depend on the amount of respiratory rate-covariance in the MF inputs to a CN neuron. MF respiratory modulation, similar to PC modulation, shows a variety of phase relationships to respiration (S2 and S3 Figs), and therefore a mechanism to strengthen MF convergence with similar modulation on single CNs would be required. A detailed exploration of the required MF input parameters in order to be effective is outside of the scope of the present study, but will be undertaken in the future. Another implication that we do not wish to be taken from our modeling study is that the respiratory modulation transmitted from the PC to the CN does in fact control respiration. In fact, our working hypothesis is that baseline respiration is not controlled by the cerebellum, but that the observed coordination of different orofacial rhythms such as licking, swallowing, whisking with respiration [9] is effected through the connection of the medial cerebellar nucleus to the respective rhythm generators [42]. Establishing the functional role of cerebellar output on the coordination of these rhythm generators and the ability of the cerebellum to delay or advance the respiratory cycle when needed will require new experimental studies where these rhythms are challenged and the output of the cerebellum is optogenetically manipulated.
Any given neuron in the brain typically receives synaptic input from hundreds of other neurons. For the synaptic transmission of a rate code it is therefore critically important to understand what number of these inputs need to be rate co-varying, in order for a robust transmission of behaviorally related information. This point is closely related to the question of how population coding is instantiated in the brain, as enough neurons need to be participating in the same coding process so that their convergent connections on a target population would transmit a rate code accurately. To our knowledge this study is the first that quantifies the answers to these questions in the framework of a biophysically accurate model to match a data set recorded in awake animals. We find that in the cerebellum where 50 PCs converge onto a single CN neuron, transmission of significant rate modulation required about ~10 (20%) rate covarying PC inputs, while ~25 (50%) PC inputs resulted in an outcome matching one of the stronger CN rate modulation amplitudes found in our experimental data. This code was found to be robust against interference from both correlated and uncorrelated background noise. These modeling results make a strong experimental prediction that populations of PC neurons converging to single CN neurons need to show a larger shared behavioral rate modulation than is present in a random sample of single recorded PCs. While such data are not yet available, advances in calcium imaging at single cell resolution in combination with transsynaptic retrograde labeling may allow verification of this prediction in the near future.
We undertook a careful effort to characterize the global and local spike train statistics through assessing CV, LV, and power spectra. A considerable theoretical literature has been devoted to the significance of neuronal variability and its use to determine the statistical properties of spike trains and their functional relevance [10,43,44,45,46]. In particular, the presence of CV values greater than 1.0 that is characteristic of random Poisson processes has piqued the interest of theorists, and such values were present in some of our recordings. Previous work has related such high variability to spike initiation non-linearities [46,47] or dendritic coincidence detection [46,48], because an integrator over many random inputs would result in a very high degree of regularity in the output. However, our study suggests an alternative mechanism, by which high CV values result from rate covariances in the population of PC inputs to the model neuron. These input rate-covariances lead to constantly changing firing rates in the output as well, which increases the CV. A hallmark of this effect is that local spike train variability of 2 successive ISIs (LV) is much less affected and the outcome values of LV are smaller than the CV, unlike in random processes. Therefore, our data and simulations indicate that the assumption of a stationary statistical process underlying neuronal spike trains should be abandoned for the awake condition. Our method of using firing rate templates with specific proportions of co-variance to drive output spiking indeed capture the observed spiking irregularity of data from awake animals well. Parameterizing the degree of these covariances needed to match recorded spike train statistics allowed us to estimate of the required population rate covariance in the behaving animal, and such modeling can therefore shed some light on potential population coding properties in the brain. Another extensive line of theoretical and modeling work has focused on ‘balanced state’ networks, where inhibition and excitation are matched [44,49,50,51]. These recurrent networks of integrate and fire neurons can show Poisson irregularity in firing [50], and a CV > 1 when co-varying sensory rate fluctuations are transmitted [51]. The required balance between excitation and inhibition in this network state is similar to the balance of excitation and inhibition needed in synaptic input applied to CN neurons with dynamic clamping in order to result in irregular firing patterns with random input spike trains [16,22], a property well replicated in our model [26]. The present study extends this work to the awake state and demonstrates that these concepts fully suffice to explain the spike train statistics recorded in awake alert mice when rate covariance between inputs is added.
Animals. Experiments were performed on male and female adult C57BL/6J (B6) mice (18–25 g; The Jackson Laboratory). All mice used in this study were raised and all experiments were performed in accordance with procedural guidelines approved by the University of Tennessee Health Science Center Animal Care and Use Committee under protocol # 13–077. Details about surgical procedures to implant a head post and a recording chamber over the cerebellum were previously published [5,52]. During recording mice were head-fixed to a metal holder and the body was loosely covered with a plastic tube to limit body movements. Respiratory behavior was monitored with a thermistor (Measurement Specialties) placed in front of one nostril. Breathing cycles were measured as increasing and decreasing temperature changes caused by exhale and inhale movements, respectively. Peaks and troughs in the respiratory signals corresponded to the ends of expiration and inspiration cycles, respectively. Trough times were detected from the analog thermistor output sampled at 1 KHz and used throughout this study as respiratory event markers for respiratory spike train modulation and as alignment for respiratory peri-event histograms.
Up to seven recording electrodes (glass-insulated tungsten/platinum; 80 μm O.D.; impedance, 3–7 MΩ) were inserted acutely into the cerebellum during each recording session using a computer-controlled microdrive (System Eckhorn; Thomas Recording). Vermal Purkinje cells were identified by recording depth, a high spontaneous activity rate, and the presence of complex spikes [53]. Mossy fibers were identified using previously described criteria based on granular layer identification and spiking characteristics [54]. Single unit recordings from CN neurons were identified by electrode depth, the electrode passing through an area without spiking activity (i.e. the white matter embedding the CNs) before reaching the nucleus, and finally by the presence of sustained spiking (~10–70 Hz) without the occurrence of complex spikes. Recording locations were verified by placing small electrolytic lesions during the last 2 recording days and anatomical reconstruction from 50 μm coronal sections with a cresyl violet staining to align lesion sites with stereotaxic atlas coordinates [55]. Spikes were sorted off-line using Spike2 software (Cambridge Electronic Design) and only neurons with a clear refractory period in the ISI histogram and stable spike size over at least 45 s were used for further analysis. This resulted in a data set of 21 PCs, 11 MF, and 16 CN neurons.
Spike trains were aligned on respiratory event markers (end of inspiration) to create a respiratory PSTH. A confidence interval (z-score) to determine significant modulation was constructed by shuffling the respiratory event times 100 times and creating a shuffled PSTH for each instance. Respiratory modulation exceeding the 95% confidence percentile for multiple data points in sequence was deemed significant. The amplitude of modulation was scored by the area under the largest peak or trough of the modulation after baseline subtraction and was scaled to units in spikes, thus yielding a measure of the number of spikes adding or missing in the PSTH peak or trough compared to the shuffle predictor.
Using Matlab (MathWorks, Inc.) we designed an algorithm to create artificial spike trains (AST) that could replicate the observed spike trains statistics and respiratory modulation. We could not directly use experimentally recorded spike trains to drive our CN simulation input because we had at most triple simultaneous PC recordings whereas 50 simultaneous spike trains are needed as input to the model. We used cross correlation and spike covariance analysis [5] to determine the types of cross-correlation and rate covariance present between pairs of simultaneous spike trains and designed an algorithm that could extrapolate these properties to larger spike train populations. Our algorithm uses as core concept the method of rate templates, which are constructed from recorded spike trains by convolving each spike with a Gaussian (see Supplemental Methods, the full Matlab algorithm is available on ModelDB). In the next step of the algorithm we drew gamma distributed spike trains using a mean ISI tracking our rate template and using the shape parameter κ (kappa) experimentally determined by the LV from our recordings (see Results). This method allowed us to add respiratory rhythmic modulation in spike trains in a flexible way, by convolving rate templates with a gain-scaled version of the mean peri-stimulus time histogram (PSTH) triggered by each cycle of respiration. In order to convolve rate templates and respiratory PSTH functions independent of absolute firing rates, both rate functions were normalized to 1.0 before combining them, and the resulting combined rate function was scaled back to the desired mean firing rate of the output AST. Our new method of creating ASTs is quite general and could be used to incorporate any other known rate changes related to behavior. We expect that this method will be of general use in the neural simulation community.
In this study we utilized our existing 486 compartment model including the updates to the voltage dependent conductances and synapses described in the Supplemental Information of the original publication and previously shown to replicate CN firing with stochastic synaptic input patterns applied by dynamic clamping [26]. This model has a set of 6 voltage gated and 1 calcium dependent conductance to match the spike shape, spontaneous firing, and responses to depolarization/hyperpolarization of slice CN recordings closely. It also includes 2 inactivating inward conductances that control rebound bursting after strong hyperpolarization [7]. These rebound conductances were present in the model used here, but due to the lack of strong hyperpolarizations with the input patterns constructed to match the waking condition they remained largely inactivated and rebound firing was not observed. In the present study, we included one further model update by incorporating a detailed version of the short term plasticity rules in the PC synapses on CN neurons experimentally determined [20,21]. The model depression rule is based upon the rate dependent release probabilities at multiple release sites as estimated by Telgkamp and Raman, 2004. These STD rules required a re-write of the Genesis 2.3 synchan object base code as they could not be achieved with existing synaptic mechanisms in Genesis. The new C base code as well as the updated model definition are available in ModelDB, https://senselab.med.yale.edu/modeldb/ShowModel.cshtml?model=229279.
Synaptic inputs were modeled as a dual exponential alpha function with rise and decay time constants matching voltage clamp recordings in slices (see detailed explanation in supplemental materials, [7]). Each spike from our PC ASTs triggered a unitary IPSC with a peak amplitude controlled by the Gin parameter. The range of Gin used was between 2 and 20 nS for parameter scans, and values of 4 or 16 nS were used in most simulation runs exploring respiratory rate modulations. This compares to an average IPSC size of 9.4 nS with minimal (single axon) stimulation in slices and an observed experimental range of 1–25 nS. Our excitatory MF inputs triggered both an NMDA and an AMPA EPSC, as mixed currents have been found in experiments [40,41,56]. 48 inhibitory and 48 excitatory synapses were distributed randomly across the 485 dendritic compartments, and 2 inhibitory synapses were placed on the soma. There was no somatic excitation as excitatory synapses are not observed on the soma of CN neurons [57,58]. Each synapse was connected to one AST input spike train.
Simulations were run in batches on a Linux cluster, where each batch completed a matrix of parameter settings. All simulations were run to produce 115s of output data. Binary and spike event output files from simulation batches were put into a Pandora database format and analyzed with custom made Matlab scripts. Power spectra were determined using functions from the Chronux Matlab toolbox (http://chronux.org/).
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10.1371/journal.pcbi.1003414 | Competition for Antigen between Th1 and Th2 Responses Determines the Timing of the Immune Response Switch during Mycobaterium avium Subspecies paratuberulosis Infection in Ruminants | Johne's disease (JD), a persistent and slow progressing infection of ruminants such as cows and sheep, is caused by slow replicating bacilli Mycobacterium avium subspecies paratuberculosis (MAP) infecting macrophages in the gut. Infected animals initially mount a cell-mediated CD4 T cell response against MAP which is characterized by the production of interferon (Th1 response). Over time, Th1 response diminishes in most animals and antibody response to MAP antigens becomes dominant (Th2 response). The switch from Th1 to Th2 response occurs concomitantly with disease progression and shedding of the bacteria in feces. Mechanisms controlling this Th1/Th2 switch remain poorly understood. Because Th1 and Th2 responses are known to cross-inhibit each other, it is unclear why initially strong Th1 response is lost over time. Using a novel mathematical model of the immune response to MAP infection we show that the ability of extracellular bacteria to persist outside of macrophages naturally leads to switch of the cellular response to antibody production. Several additional mechanisms may also contribute to the timing of the Th1/Th2 switch including the rate of proliferation of Th1/Th2 responses at the site of infection, efficiency at which immune responses cross-inhibit each other, and the rate at which Th1 response becomes exhausted over time. Our basic model reasonably well explains four different kinetic patterns of the Th1/Th2 responses in MAP-infected sheep by variability in the initial bacterial dose and the efficiency of the MAP-specific T cell responses. Taken together, our novel mathematical model identifies factors of bacterial and host origin that drive kinetics of the immune response to MAP and provides the basis for testing the impact of vaccination or early treatment on the duration of infection.
| Mycobacterium avium subsp. paratuberculosis (MAP) is the causative agent of Johne's disease, a chronic enteric disease of ruminants such as sheep and cows. Due to early culling and reduction in milk production of affected animals, MAP inflicts high economic cost to diary farms. MAP infection has a long incubation period of several years, and during the asymptomatic stage a strong cellular (T helper 1) immune response is thought to control MAP replication. Over time, Th1 response is lost and ineffective antibody response driven by Th2 cells becomes predominant. We develop the first mathematical model of helper T cell response to MAP infection to understand impact of various mechanisms on the dynamics of the switch from Th1 to Th2 response. Our results suggest that in contrast to the generally held belief, Th1/Th2 switch may be driven by the accumulation of long-lived extracellular bacteria, and therefore, may be the consequence of the disease progression of MAP-infected animals and not its cause. Our model highlights limitations of our current understanding of regulation of helper T cell responses during MAP infection and identifies areas for future experimental research.
| Mycobacterium avim subsp. paratuberculosis (MAP) infects intestine of ruminants (e.g., cattle and sheep) and causes a chronic inflammatory disease called Johne's disease (JD) [1], [2]. Due to reduction of milk production and early culling of diseased animals, JD causes a significant economic loss to animal industries [3], [4]. MAP has also been suspected as a causative agent of Crohn's disease, an inflammatory bowel disease in human [5].
Infection of animals occurs mainly through ingestion of materials contaminated with MAP-containing feces [6]. After the ingestion MAP bacilli reach intestine of the animal, are taken up by M cells and enterocytes, and are engulfed by submucosal macrophages [7]–[10]. MAP survives in resting tissue macrophages by inhibiting phagosome maturation [11]–[15]. At late stages of JD, MAP-infected animals shed bacilli in their feces thereby completing the infection cycle.
MAP infection follows a lengthy latent and sub-clinical period in which the infection is difficult to diagnose [1], [2]. Current research efforts focus on developing tools that aide in early detection of the infection before the infected animals start shedding MAP into the environment [16]. Vaccines are available for ovine and bovine JD [17]. Although the vaccines reduce or delay clinical symptoms and shedding of MAP into feces, they do not prevent new infections [17]. The lack of progress in vaccine development is in part due to poor understanding of the nature of the protective immune response against MAP infection [18]. In this respect, experimental infections of animals with MAP have been carried out in several studies examining the kinetics of MAP-specific immune responses [18]–[20]. These studies demonstrated that animals with paucibacillary lesions (at early stages of the infection) are likely to express a cell mediated (Th1-type) immune response measured by the expression of IFN- [21]. This response is likely to be protective against intracellular pathogens since IFN- can induce intracellular killing of MAP by macrophages [22], [23], and infected animals with a dominant Th1 response have very few lesions [20]. At later stages of the infection animals with multi-bacillary lesions express predominantly a Th2-type immune response that is measured by the presence of MAP-specific IgG1 antibodies, production of which is driven by IL-4 or IL-10 producing CD4 T cells [18]–[20]. Although high levels of MAP-specific antibodies are detected in animals in late stages of the disease, these antibodies do not appear to be protective and may even be detrimental by increasing uptake of extracellular bacteria by macrophages [24]. Thus, experimental infections of sheep with MAP suggest a switch from dominance of the MAP-specific Th1 immune response in the early stages of the disease to a predominantly Th2 response at later stages of the disease (Figure 1A).
More detailed analysis of the kinetics of MAP-specific Th1 (IFN-) and Th2 (antibody) responses in experimentally infected sheep revealed that the majority of animals do not display the “classical” Th1/Th2 switch (Figure 1B–D). Around 50% of infected animals have combined Th1/Th2 responses (Figure 1B&C) while the minority of animals (11%) show only Th1 response (Figure 1D). Reasons for such different patterns of the kinetics of Th1/Th2 responses are not well understood.
Th1 and Th2 subsets of helper CD4 T cell responses are defined by a set of cytokines they secrete and transcription factors that drive the development of each subset. Both Th1 and Th2 effectors differentiate from naïve CD4 T cells depending on the type of cytokines in the environment and the stimulating antigen [25], [26]. Interleukin 12 (IL-12), IFN-, and strong antigenic stimulation upregulate expression of a transcription factor T-bet in naïve CD4 T cells that in turn drives differentiation of T cells into Th1 effectors. IL-4 and weak antigenic stimulation upregulate expression of a transcription factor GATA-3 that in turn drives differentiation of naïve T cells into Th2 effectors. Differentiated effector T cells themselves also start producing cytokines. Th1 effectors produce proinflammatory cytokines such as tumor necrosis factor- (TNF-) and IFN-. These cytokines activate macrophages to kill intracellular bacteria [13]. Th2 effectors produce a different group of cytokines such as IL-4, IL-5, IL-6, IL-10, and IL-13. Th2 effectors and these cytokines direct B cells to produce MAP-specific antibodies [26]. There is a competition between Th1 and Th2 responses as observed in in vitro experiments [27]–[29]. Cytokines produced by Th1 cells inhibit differentiation of naïve CD4 T cells into Th2 cells and vice versa [30].
Mathematical modelling has influenced the current understanding of Th1/Th2 cell differentiation [31]–[36]. These models can be divided into three categories (i) models that describe different T cell phenotypes induced by transcription factors that govern the molecular mechanism for lineage selection and maintenance [31]–[33], [37]–[39], (ii) differentiation of naïve T cells into a mixed population of Th1 and Th2 effectors in response to cytokines induced by antigen-presenting cells and each T cell subset [34]–[36], [40], [41], and (iii) regulatory network reconstruction with a repertoire of molecular and cellular factors that control Th cell differentiation [42]–[45]. As far as we know, only limited modelling work has been done on dynamics of Th1/Th2 responses to specific pathogens, such as viruses (e.g., human immunodeficiency virus) and mycobacterial pathogens (e.g., Mycobacterium tuberculosis). Most of mathematical models did not specify pathogen to simulate differentiation of naïve CD4 T cells into different Th cell subsets [31]–[36], [38], [41], [45].
The switch from a Th1 to a Th2 immune response in MAP-infected animals often occurs together with signs of clinical disease [20], [21]. Mechanisms underlying this switch are still poorly understood, in particular it is unclear 1) which factors contribute to the timing of the switch, 2) whether the timing of the switch can be regulated, and 3) whether the switch is the driver of infection to the clinical stage or it is just a consequence of the progression to clinical disease. To address these questions, we developed a mathematical model of the immune response to MAP infection. We use this model to understand and identify conditions under which switch from Th1 to Th2 immune response occurs during MAP infection. We specifically consider two hypotheses: 1) switch is driven by accumulation of extracellular bacteria that in turn skew differentiation towards the Th2 response, and 2) switch is caused by exhaustion/suppression of Th1 response and concomitant rise of Th2 response. We investigate the conditions under which these mathematical models give rise to the Th1 to Th2 switch. We show that the following factors strongly influence Th1/Th2 switching dynamics: the mechanism by which MAP-specific Th cells are maintained at the site of infection (continuous differentiation from naïve T cells or local proliferation), rate at which Th1 response is exhausted, longevity of extracellular bacteria, and the efficiency at which immune responses cross regulate each other.
To study factors that may contribute to the dynamics of MAP and MAP-specific Th1 and Th2 responses we propose a novel mathematical model. The model is based on the current biological understanding of basic properties of mycobacterial infections [20], [21], [46], and experimental and theoretical understanding of Th1 and Th2 effector differentiation from naïve T cells [28], [30], [33], [38] (see introduction). The model includes interactions between extracellular MAP bacteria (), macrophages () (target cells), naïve CD4 T cells (), and the two subsets of the MAP-specific immune response, Th1 () and Th2 () cells (Figure 2). Infection is initiated by extracellular bacteria at the dose . Macrophages internalise extracellular bacteria and get infected at a rate giving rise to infected macrophages (). There is still uncertainty in the literature on how macrophages are maintained at local sites such as the gut [47]. In the model we assume that during infection, macrophages are supplied from progenitor monocytes that are recruited from the blood to the site of infection at a rate . Infected macrophages burst at a rate releasing bacteria into the extracellular environment. Th1 effectors remove infected macrophages at a rate and intracellular bacteria are killed in this process. Effector CD4 T cells activate macrophages to kill intracellular bacteria and help with the generation of the MAP-specific CD8 T cell response which in turn clears infected macrophages [48]. Extracellular bacteria are cleared at a rate . Some extracellular bacteria are taken up by macrophages and are destroyed at a rate . Furthermore, given available experimental data [24] we assume that MAP-specific antibodies (Th2 response) are ineffective at eliminating extracellular bacteria. Uninfected and infected macrophages have death rates of and , respectively.
Selective differentiation of naïve CD4 T cells into either Th1 or Th2 effectors is established during priming caused by interaction of major histocompatibility complex (MHC)-specific peptide complexes on antigen-presenting cells and T-cell receptors [25], [26]. It is generally believed that Th1 responses are generated against intracellular pathogens such as viruses while Th2 responses are generated against extracellular pathogens such as extracellular bacteria [40], [49]–[51]. Factors that influence priming and differentiation of CD4 T cells include the dose and type of antigen, co-stimulatory molecules and/or antigen presenting cells, and the cytokine environment present during priming [25], [38]. In our model, MAP-specific naïve CD4 T cells (Th0) are produced at a rate continuously from the thymus [52] and decay at rate . Th0 cells are recruited into the Th1 and Th2 immune responses at per capita rates and , respectively. Recruitment rates depend on the density of infected macrophages and extracellular bacteria. We make the simplest assumption that Th1 response is driven by the density of infected macrophages , and Th2 response is driven by the density of extracellular bacteria . Following recruitment, naïve CD4 T cells undergo a program of division and differentiation resulting in a large population of MAP-specific effectors. Therefore, in the model Th1 effectors are produced at a rate and Th2 effectors are produced at a rate where and are the parameters determining the magnitude of clonal expansion of the Th1 and Th2 responses, respectively. Th1 and Th2 effectors decay at rates and , respectively. With these assumptions (Figure 2) the basic mathematical model is given by the following system of differential equations (see the Supplemental Information (Text S1) for the basic properties of the model and derivation of the basic model reproduction number, ):(1)(2)(3)(4)(5)(6)
Very little quantitative detail regarding MAP infection of ruminants is available, and therefore, most of the parameters of our mathematical model are unknown. Information on immunological responses to MAP infection was collected from different ruminant species when there was no strong contradictory findings among the species. Thus, our model is not for a particular ruminant. We used several different strategies to provide some estimates for the model parameters. Some parameters have been estimated from published experimental data (Table 1). Also, we have carried out literature search to obtain general acceptable ranges for other parameters. Well established biological knowledge was used to estimate cell populations in the sheep gut (Table 2). Where information about a parameter was not found, estimates were used that enable observation of cell kinetics within an acceptable range of the estimated cell population per in the gut. Yet, it still should be emphasized that most of our parameters are only educated guesses. Therefore, to determine which parameters in the model impact the most the time of the Th1 to Th2 switch we also performed standard parameter sensitivity analysis [53].
From many experiments it is well known that Th1 and Th2 responses cross-inhibit each other [28], [38], [46], [54]; in particular Th1 cytokines generally suppress differentiation of naïve CD4 T cells into Th2 effectors and vice versa [30], [38], [55], [56]. However, most of the information on cross-inhibition comes from in vitro studies under strong polarising conditions and it remains unknown if such cross-inhibition also occurs during infections in vivo. In our basic mathematical model we neglected the possibility of cross-inhibition and investigated whether Th1/Th2 switch could be achieved if Th1 and Th2 responses are not directly cross-suppressive. Surprisingly, this model was able to predict accumulation and loss of the Th1 response and thus the switch from the dominant Th1 to dominant Th2 response during the infection (Figure 3A).
In the model, the phenomenon occurs due to the following steps. Due to high infectivity of free bacteria and a large population of resident macrophages, many macrophages become infected and very few extracellular bacteria exist. The large population of infected macrophages leads to generation of a Th1 response which, however, lacks the ability to eliminate the infection [57], [58]. Infected macrophages produce new bacteria which in turn infect newly arriving macrophages. A quasi equilibrium is established. Because Th1 response is unable to clear extracellular bacteria and because in this simulation extracellular bacteria are relatively long lived (Table 1), extracellular bacteria accumulate over time. The increase in free bacteria then skews differentiation of Th0 cells toward Th2 phenotype, and this process indirectly suppresses generation of Th1 response which starts to decline over time. Therefore, the two assumptions are sufficient to drive the switch of the initially dominant cellular (Th1) response to antibody (Th2) production. These assumptions are 1) the generation of Th1 response is driven by density of infected macrophages while the generation of the Th2 response is driven by free bacteria (see Eqns. (1)–(6)), and 2) extracellular bacteria are long lived. Indeed, increasing the death rate of extracellular bacteria () effectively removes the Th1/Th2 switch whereby both responses are able to persist and Th1 response remains dominant (Figure 3B). This occurs because if extracellular bacteria are cleared rapidly, density of the bacteria remains proportional to the density of macrophages and thus, Th2 response never outgrows the initially dominant Th1 response.
Further insights into the dynamics of the Th1/Th2 switch can be obtained by calculating the dynamics of the ratio of the density of Th1 to Th2 response, (see the Derivation of the Th1/Th2 ratio equation Section in the Supplemental Information (Text S1) for the derivation of the equation for dynamics). The dynamics of the ratio in the model is given by(7)
When under assumption of a quasi steady state for () we find that , and therefore for the ratio to slowly change over time, the ratio of infected macrophages to free bacteria should change from a value more than one to a value less than one. This in general occurs when extracellular bacteria are long lived outside of macrophages [59] and the number of bacteria released per infected macrophage is relatively low [60]. However, Eqn. (7) also shows that if the decay rate of the Th1 response is much greater than that of Th2 cells (i.e., ), the Th1/Th2 switch may still occur at high rates of clearance of extracellular bacteria (results not shown).
Experimental infections of sheep with MAP showed four different patterns of the immune response development: the so-called classical Th1/Th2 switch (Figure 1A), delayed Th1/Th2 switch (Figure 1B), a combined Th1/Th2 response (Figure 1C) and a Th1 only response (Figure 1D) [20]. We investigated whether our basic mathematical model can reproduce these experimental patterns. To compare model predictions with experimental data, we normalised the predicted Th1 and Th2 response by their maximum value reached in infection. Note that this is different from the Th1/Th2 dynamics shown in Figure 3 where cell populations are not normalised. To avoid over-fitting, we selected parameters for fitting the data predicted as most important for determining timing of Th1/Th2 switch using sensitivity analysis (Figure S1 and the sensitivity analysis Sections in the Supplemental Information (Text S1)). These include parameters that drive infection dynamics (, ) and parameters that control differentiation and recruitment of effector T cells (, , , and ). The least squares method was employed using the patternsearch function in MATLAB.
As our results show, the model can relatively well reproduce all major patterns of Th1/Th2 dynamics in MAP-infected sheep (Figure 4). The major parameters that determine the type of the response is the initial bacterial dose and parameters determining the kinetics of the immune response.
Timing of the Th1 and Th2 switch was further investigated by varying the initial bacterial inoculation dose and the burst size of infected macrophages (Figure 5). Both of these parameters can be manipulated experimentally. Increasing the initial bacterial dose resulted in a faster switch, but a very large increase in the dose is needed to observe a noticeable decrease in the switch times (Figure 5A&B). Increasing the dose size results in more macrophages being initially infected leading to a rapid depletion of uninfected macrophages and generation of the Th1 immune response. Rapid growth of the population of infected macrophages leads to accumulation of extracellular bacteria. Early and rapid growth of the bacterial population pushes for early Th2 immune response development, resulting in an early Th1/Th2 switch. Importantly, increasing the burst size is more effective at reducing the time of the Th1/Th2 switch than equivalent increase in the initial bacterial dose (Figure 5C). Both of these predictions can be tested by infecting animals with different initial doses or with MAP strains that differ in virulence.
Progression of MAP infection in ruminants often occurs concomitantly with a switch from dominance of the MAP-specific Th1 immune response to a dominance of a Th2 response. Previous studies have shown that animals with paucibacillary lesions are likely to express a cell mediated, Th1-type, immune response that is protective against intracellular bacteria, while a Th2-type response is generally detected in animals with multi-bacillary lesions [18]–[20]. In this study, we have developed the first mathematical model of the helper T cell response to MAP and have analysed mechanisms that influence the dynamics of Th1 to Th2 switch during disease progression in MAP-infected animals. A number of interesting results emerged from the analysis of the model.
First, the model is able to simulate two main infection outcome scenarios, (i) elimination of infection, this is associated with an initial strong Th1 immune response (Th1 only response), (ii) infection persistence (or latency), this is marked by both a Th1 and a Th2 response with high expression of a Th1 response over a Th2 response (classical and delayed switch). Second, if the extracellular bacteria are not readily removed by the host's innate immune system, simulations show a Th1/Th2 switch which is characterized by accumulation of long-lived extracellular bacteria. Third and finally, the basic model was able to explain different patterns of the dynamics of MAP-specific Th1 and Th2 responses as was observed in experimental infections of sheep (Figures 1 and 4). These results from the basic model suggest that Th1/Th2 switch may be a result of disease progression rather than the cause.
We find that in our basic mathematical model the longevity of extracellular bacteria is one of the key factors driving Th1/Th2 switch and disease progression in MAP-infected animals (Figure 3). Long survival of extracellular bacteria also naturally explains increased shedding of bacteria in animals with JD. Of note, extended models that assume a relatively short survival time of extracellular bacteria do not predict accumulation of MAP over the course of infection (see Section Alternative models in the Supplemental Information (Text S1)). Estimation of the average survival time of MAP in extracellular environment in the host will allow further refinement of our mathematical model. Such measurements may also suggest which additional mechanisms need to be involved to explain kinetics of Th1/Th2 responses in MAP-infected animals.
Several other mechanisms may influence the likelihood and the kinetics of the Th1/Th2 switch including inhibition of Th0 cell differentiation by Th1 and Th2 responses, proliferation of effector T cells at the site of infection, and exhaustion of protective Th1 responses due to exposure to the antigen (see Section Alternative models in the Supplemental Information (Text S1)). The first two mechanisms, differentiation inhibition and local proliferation can dramatically alter the course of MAP-specific Th1 and Th2 responses depending on the strength of inhibition and sensitivity of Th1/Th2 cells to the local antigen concentrations. Therefore, future experimental studies should focus more on these dynamic processes and determine whether they occur in MAP-infected animals.
Interestingly, we found that inhibition of effector functions of Th1 cells by Th2 response (and vice versa) did not influence the kinetics of the Th1/Th2 switch in the basic mathematical model when clearance rate of extracellular bacteria is high (results not shown). In part, this is because in the basic model the Th1/Th2 switch is driven by the accumulation of extracellular bacteria and reduction of the efficacy of MAP-specific Th1 cells does not influence bacterial loads, and therefore, does not impact the kinetics of the Th1/Th2 switch.
Although our mathematical model considers a number of important biological processes and illustrates the wealth of different scenarios for the dynamics of MAP-specific Th1 and Th2 responses, several potentially important biological details have been ignored in the model. First, we did not consider the dynamics of MAP-specific Th17 cells and regulatory T cells which are thought to play important role during intestinal infections in mice and humans [61]. There is, however, limited evidence that these subsets play a critical role in control of MAP replication but more experimental data in this area need to be collected. Secondly, in our mathematical model we ignored so-called “cellular plasticity” of effector CD4 T cell responses where effectors with a particular phenotype (e.g., Th1) may potentially convert into effectors of another phenotype (e.g., Th2) [62]. Exact mechanisms of how such cellular plasticity is regulated especially during chronic infections are still unknown. In our mathematical model we assumed only “population plasticity” where the change in the phenotype of MAP-specific T cells occurs due to differences in the rates of differentiation, proliferation and death at the site of infection [62]. Additional experimental studies will be needed to determine if conversion of protective MAP-specific Th1 cells into detrimental Th2 cells actually occurs during MAP infection.
Thirdly, to keep the model simple, we only captured the role of Th1 cells (CD4 T cells) to represent the cell mediated response. There is evidence of accumulation of CD8 T cells in MAP infection [63], which also secrete IFN- and can lyse infected macrophages. However, whether their involvement contribute significantly in the control of MAP is yet to be clearly demonstrated. Mycobacteria mainly resides within vacuoles of infected cells and therefore most of bacterial antigens are presented on MHC-II molecules (which are recognised by CD4 T cells). It was postulated in the study of Chiodini and Davis [64], that CD8+ T cells may be key in the development of the protective immunity through modulating the regulatory activity of T cells, although the exact mechanism of this cooperation was not presented.
Also, in this study we did not include the role of M-cells and enterocytes, which are essential in the establishment of MAP infection as entry vehicles to pass the bacteria to professional phagocytes (mucosal macrophages). There is potential to understand better the host-pathogen immune interactions using a spatial model that captures pathogen and immune response components (both innate and adaptive) at different locations and stages of disease. However, currently there is limited quantitative data to parameterise a spatial compartmentalised model. Furthermore, infection progression and priming of the adaptive immune response is mostly centred on macrophages, which (i) have the capacity to stimulate the immune response and (ii) harbor MAP for a long time period. Apart from phagocytic properties, macrophages also present antigens to CD4+ cells in the context of MHC-II molecules. Hence, the main modeling assumptions used in this current study and the choice of a simple model that does not include different compartments.
Our study suggests that Th1/Th2 switch in MAP infection can be explained through (i) different regulation of Th cell differentiation, (ii) bacteria accumulation, (iii) proliferation and differentiation inhibition of T cells, and (iv) Th1 immune exhaustion. Some of these results are echoed in previous mathematical modeling studies. For instance, the studies [34], [35] showed that when effectors fail to clear the antigen, the initially dominant Th1 response is lost and Th2 response arises. The studies [31], [33], [37]–[39] showed that cytokines that are present at the site of infection can influence the direction of naïve CD4 T cell differentiation, and therefore may determine the shift in the effector dominance. This mechanism was shown to be regulated by specific transcription factors such as T-bet, GATA-3, FoxP3 and ROR-t which directs differentiation of naïve T cells into specific effector subsets [62]. It should be noted that previous mathematical models of Th1/Th2 regulation focused on cross regulation of Th1/Th2 cell responses based on cell to cell interactions via Th1/Th2 cytokines [34]–[36], [41]. To the best of our knowledge our study is the first to model Th1/Th2 dynamics in MAP infection (see the discussion for Th1/Th2 insights from previous mathematical studies).
In summary, our JD immunology models indicated that the following factors can determine the timing of Th1/Th2 switch: (i) bacteria dose size and the burst size of infected macrophages, (ii) longevity of extracellular bacteria, (iii) degree of competition between Th1 and Th2 responses, and (iv) Th1 immune exhaustion. Testing these model predictions using more detailed experimental data that can be obtained from infecting animals with (i) MAP-strains of different virulence, (ii) different initial doses of MAP, and (iii) measuring the fraction of intracellular vs. extracellular MAP in tissues in vivo will help identify the mechanism controlling the kinetics of Th1/Th2 immune responses. JD is associated with slow disease progession in cattle, therefore experiments using small ruminants such as sheep and goats that are associted with relatively fast disease progression are recommended to make the experiments less expensive and to allow for reasonable time to collect the required data.
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10.1371/journal.ppat.1002980 | The Respiratory Syncytial Virus Polymerase Has Multiple RNA Synthesis Activities at the Promoter | Respiratory syncytial virus (RSV) is an RNA virus in the Family Paramyxoviridae. Here, the activities performed by the RSV polymerase when it encounters the viral antigenomic promoter were examined. RSV RNA synthesis was reconstituted in vitro using recombinant, isolated polymerase and an RNA oligonucleotide template representing nucleotides 1–25 of the trailer complement (TrC) promoter. The RSV polymerase was found to have two RNA synthesis activities, initiating RNA synthesis from the +3 site on the promoter, and adding a specific sequence of nucleotides to the 3′ end of the TrC RNA using a back-priming mechanism. Examination of viral RNA isolated from RSV infected cells identified RNAs initiated at the +3 site on the TrC promoter, in addition to the expected +1 site, and showed that a significant proportion of antigenome RNAs contained specific nucleotide additions at the 3′ end, demonstrating that the observations made in vitro reflected events that occur during RSV infection. Analysis of the impact of the 3′ terminal extension on promoter activity indicated that it can inhibit RNA synthesis initiation. These findings indicate that RSV polymerase-promoter interactions are more complex than previously thought and suggest that there might be sophisticated mechanisms for regulating promoter activity during infection.
| Respiratory syncytial virus (RSV) is a major pathogen of infants with the potential to cause severe respiratory disease. RSV has an RNA genome and one approach to developing a drug against this virus is to gain a greater understanding of the mechanisms used by the viral polymerase to generate new RNA. In this study we developed a novel assay for examining how the RSV polymerase interacts with a specific promoter sequence at the end of an RNA template, and performed analysis of RSV RNA produced in infected cells to confirm the findings. Our experiments showed that the behavior of the polymerase on the promoter was surprisingly complex. We found that not only could the polymerase initiate synthesis of progeny genome RNA from an initiation site at the end of the template, but it could also generate another small RNA from a second initiation site. In addition, we showed that the polymerase could add additional RNA sequence to the template promoter, which affected its ability to initiate RNA synthesis. These findings extend our understanding of the functions of the promoter, and suggest a mechanism by which RNA synthesis from the promoter is regulated.
| Respiratory syncytial virus (RSV) is the major cause of respiratory tract disease in infants and young children worldwide, causing 3.4 million cases of severe acute lower respiratory infection, and between 66,000 and 199,000 deaths per annum [1]. As yet, there is no vaccine available to prevent RSV disease, or effective antiviral drug to treat it [2], [3].
RSV has a single stranded, negative sense RNA genome and is classified in the Order Mononegavirales, Family Paramyxoviridae. In general terms, RSV shares the strategy for gene expression and genome replication that is used by all non-segmented negative strand (NNS) RNA viruses [4]. The RSV genome acts as a template for transcription, to generate subgenomic mRNAs, and RNA replication, to generate an antigenome RNA. The antigenome in turn acts as a template for genome RNA synthesis (reviewed in [5]). Both the genome and antigenome RNAs are encapsidated with multiple copies of nucleoprotein (N) as they are synthesized, such that each N molecule binds seven nucleotides (nts) [6]. These RNAs are never completely uncoated and so it is this N-RNA structure that acts as a template for the viral RNA dependent RNA polymerase (RdRp). To perform transcription and replication, the RdRp engages with promoter sequences that lie at the 3′ ends of the genome and antigenome RNAs [7]. The 44-nt leader (Le) promoter region at the 3′ end of the genome is responsible for directing initiation of mRNA transcription and antigenome synthesis, and the 155-nt trailer complement (TrC) promoter at the 3′ end of the antigenome directs genome RNA synthesis [8]. The organization of the Le and TrC promoters has been studied extensively using the RSV minigenome system [9]–[12]. These studies indicate that the minimal promoters are located within nts 1–11 of the genome and antigenome termini, with additional downstream sequences required for production of full-length RNA products.
Although the promoter regions of RSV and other NNS RNA viruses have been thoroughly mapped and the viral proteins involved in RNA synthesis have been identified, a detailed understanding of the molecular mechanisms underlying transcription and genome replication initiation lags significantly behind that of other RNA viruses. In part, this is due to the lack of tractable assays for studying polymerase behavior. Although the minigenome system is a valuable tool, because it is an intracellular assay it is largely limited to studying the final, stable products of these processes, and cannot be used to examine unstable RNA intermediates. It is also not possible to manipulate intracellular conditions to isolate specific steps in RNA synthesis initiation. In addition, from a drug discovery perspective, it is an expensive and time-consuming assay that is not readily applicable to a high-throughput screening approach. Study of positive strand RNA viruses has been helped enormously by the development of in vitro assays that reconstitute RNA synthesis using purified components e.g. [13]. Using this approach, template sequences, the polymerase and available substrates can be manipulated to perform detailed mechanistic analyses. A major hurdle to applying this approach to the NNS RNA viruses is that the natural template for their RdRp is encapsidated RNA. Although there are some reports indicating that it is possible to reconstitute N-RNA complexes in vitro for the rhabdoviruses [14]–[16] attempts to reconstitute RSV N-RNA complexes in vitro have been unsuccessful. However, available data indicate that the N protein must be locally and transiently displaced to allow the RdRp to engage the RSV RNA template in its active site [6], [17], suggesting that it might be possible to use a naked RNA oligonucleotide to recapitulate the events that occur once N protein has been locally removed from the promoter. This approach was recently applied to studying RNA synthesis initiation by the RdRp of another NNS RNA virus, vesicular stomatitis virus (VSV) [18], and now we show that it is possible to utilize this technique for studying RSV RNA synthesis initiation. Importantly, experiments with this assay, combined with analysis of RSV RNA generated in infected cells, revealed that the RSV RdRp has a far more complex behavior on its promoter than previously realized, or than has been described for VSV, with the capability of initiating RNA synthesis from two different sites on the promoter, and extending the 3′ end of the TrC RNA using a back-priming mechanism.
To enable detailed analysis of the mechanisms involved in RSV RNA synthesis initiation, an assay was developed in which RSV RNA synthesis was reconstituted in vitro using isolated components. To date, the only recombinant NNS virus RdRps that have been expressed and purified in functional form are those of VSV, Chandipura and Sendai virus [18]–[23]; the purification of recombinant RdRp of RSV or any other human pathogens in the paramyxovirus family has not been described. Therefore, a strategy for purification of recombinant RSV RdRp from baculovirus infected insect cells was developed. Based on previous studies it was known that the catalytic domain for RSV RNA synthesis is located in domain III of the 250 kDa large (L) protein [24], [25], and that in infected cells, L forms a complex with the viral phosphoprotein (P), which is thought to act as a bridge between the L protein and the N protein of the nucleocapsid template [26]–[28]. Purification of the RSV L protein proved challenging for two major reasons. First, numerous attempts to express L without P were unsuccessful, indicating that whereas the VSV, Chandipura and Sendai virus L proteins can be expressed in isolation, in the case of RSV, the P protein might be necessary to stabilize L. Second, expression of L protein using the RSV gene sequence resulted in very poor expression of full-length L protein. This problem was overcome by using a codon-optimized version of the L open reading frame. By co-expressing codon-optimized L with P, it was possible to purify microgram quantities of L/P complex to near homogeneity. Figure 1B shows characteristic examples of isolated L/P complexes, with the bands corresponding to the correct migration pattern for full length L and P indicated. Note that the 27 kDa P protein has previously been shown to migrate anomalously [29], [30]. Analysis of these and other bands from a representative gel by excision, trypsin digestion and mass spectrometry, determined that the bands indicated with an asterisk or dots contained L and P specific polypeptides, respectively. The smaller L fragment may arise as a consequence of premature translation termination or proteolytic cleavage of the full length L protein. The relative abundance of this band compared to full-length L protein varied depending on the preparation. The P protein is known to be differentially phosphorylated and to exist as a highly stable oligomer [30], which could account for the multiple P bands present. The band migrating between 70 and 80 kDa was also consistently observed and identified as Hsp70 and/or HSC70 by Western blot analysis (Figure 1C). Hsp70 has previously been shown to affect RSV RdRp activity in an assay involving an infected cell extract [31], but its relevance to RSV RdRp function in the in vitro RNA synthesis assay described here is not yet known. Because the L/P preparations were not completely pure, and because there was variation in the relative levels of full-length and truncated L proteins, the experiments described in Figures 1 and 2 were performed with three independent preparations of wt and mutant L/P complexes and essentially identical results were obtained with each preparation.
To determine if the isolated RdRp was capable of performing RNA synthesis on a naked RNA template, L/P complexes were incubated with an RNA oligonucleotide representing the 3′ terminal 25 nts of the TrC promoter (Figure 1A) in the presence of all four NTPs and an [α-32P]ATP label. Although the M2-1 protein has been shown to bind P and RNA and affect transcription of mRNAs longer than ∼200 nts, it was not included in these experiments because it has been shown to have no effect on either transcription or replication initiation [32]. RNA products were analyzed by denaturing gel electrophoresis alongside a molecular weight ladder corresponding to nts 1–25 of the anticipated Tr RNA product, followed by autoradiography (Figure 1E). A number of labeled products were detected, ranging from 8 to 23 nts in length, with dominant bands of ∼8–10 nts and 21 nts (Figure 1E, lane 2). Some products longer than 25 nts could be detected at a very low level, and these are discussed in the following sections. No products were observed in reactions containing an RdRp preparation in which the L protein contained a substitution in the catalytic GDNQ motif, LN812A [25] (Figure 1D; Figure 1E, lane 3), confirming that the RNA synthesis activity observed was that of the RSV RdRp. It should be noted that the bands of the molecular weight ladder do not align perfectly with the products of the RSV RdRp. For the smaller RNAs this might be in part because the RNA transcripts in the ladder contained a monophosphate group at the 5′ terminus, whereas the terminal triphosphate was removed from the products of the RSV RdRp with calf intestinal phosphatase. In addition, the ladder was designed to represent RNA initiated from +1 of the TrC promoter, but as described below, it is likely that most or all of the RSV RNA synthesis products were generated from a +3 initiation site, and so the sequences and migration patterns might not have been identical. Importantly, reactions containing an RNA template consisting of the complement of the promoter sequence (i.e. the 5′ terminal 25 nts of Tr) did not yield RNA products (Figure 1E, lane 4). This finding shows that the isolated L/P complex had RNA synthesis activity with specificity for an RNA template containing RSV promoter sequence.
To determine if similar results were obtained with a different NTP label, reactions were performed, as described above, using [α-32P]GTP rather than [α-32P]ATP. In this case, the in vitro RNA synthesis reaction also resulted in products of 8–10 and 21 nts in length (Figure 2A, lane 2; note that these bands are faint in this experiment due to the relatively low NTP concentration; see Figure 3). However, dominant products of 26, 27 and 28 nts were also detected, specifically in reactions containing wt RSV RdRp and the TrC RNA. The fact that these products were larger than the input template suggested that they might have been generated as a result of the RdRp adding nts to the 3′ end of the template RNA, as has been shown for a number of other viral RdRps in in vitro reactions [33]–[43]. To test this possibility, reactions were performed with GTP as the only NTP source, to prevent de novo RNA synthesis from the TrC promoter. Under these conditions, a 26 nt band was observed (Figure 2B, lane 3). This result indicated that the 26 nt band was the result of nt addition to the 3′ end of the TrC template and was not a product of de novo RNA synthesis. In addition, RNA containing 3′ puromycin (PMN) in place of the 3′ hydroxyl group was tested in a reaction containing all four NTPs. The presence of 3′ PMN should abrogate 3′ terminal nt addition, while not preventing the ability of the RdRp to use the RNA as a template. The 3′ PMN TrC RNA generated significant levels of the RNAs≤23 nts, but the 26–28 nt RNA products were not detected (Figure 2C, lane 3). These results show that the RNA products smaller than 25 nts were generated by de novo RNA synthesis from the promoter, whereas the products longer than 25 nts were generated by addition of nts to the 3′ end of the template. In summary, the data presented in Figures 1 and 2 show that the RSV RdRp had two distinct RNA synthesis activities in vitro: one in which it used the TrC RNA as a template for de novo synthesis of RNA products, yielding a dominant product of 21 nts, minor products of 22 and 23 nts, and a series of smaller RNAs, and another in which it added additional nts to the 3′ end of the TrC RNA to generate products of 26–28 nts in length. Having identified these activities, we set out to examine the mechanisms by which they occurred.
As a step towards optimizing the RNA synthesis assay, the NTP concentration in the reaction was varied from 200 µM to 1 mM of each NTP. At 200 µM NTP concentration, the de novo RNA synthesis products could be barely detected, whereas the 3′ extension products were produced at a relatively high level (Figure 3, lane 2). As the NTP concentration was increased, RNA synthesis became much more efficient (Figure 3, compare lanes 2, 3, and 4). These data show that de novo RNA synthesis and 3′ extension are differentially affected by NTP concentration, with de novo RNA synthesis depending on a higher NTP concentration than 3′ nt addition.
Experiments were performed to characterize the initiation and termination sites of the products of de novo RNA synthesis. During RSV infection, the TrC promoter directs synthesis of genome RNA, which is the full-length complement of the antigenome. Therefore, it would be expected that the RdRp would initiate RNA synthesis from the 3′ terminal nt of the TrC promoter, the +1 position, and continue RNA synthesis to the end of the template to generate a 25 nt product. The finding that the major de novo RNA synthesis product from the 25 nt TrC template was 21 nts in length indicated that the RSV RdRp either initiated internally and/or failed to extend to the end of the template RNA. To identify the initiation site(s), the RNA synthesis reaction was performed without UTP. As shown in Figure 1A, the first A residue in the template is at position +14, so omission of UTP should inhibit the RdRp from continuing RNA synthesis beyond nt 13. Reactions were performed with either [α-32P]ATP or [α-32P]GTP as a label (Figure 4, panels A and B, respectively). In both cases, omission of UTP resulted in a dominant band of 11 nts in length, and another band of 13 nts. However, products longer than 13 nts, including the 21 nt band, were still detectable, particularly in reactions containing [α-32P]ATP (Figure 4A and B, lane 2; note that there are more A than G residues in the Tr product which greatly increases the sensitivity of the [α-32P]ATP label). The presence of these bands suggested that either the NTP stocks were impure, or that the RdRp had poor fidelity in this assay, allowing it to insert an alternative NTP instead of UTP. Products less than 11 nts in length could also be detected, but their abundance was not affected by the presence or absence of UTP, indicating that these were premature termination products, rather than RNA initiated from downstream sites (Figure 4A and B, compare lanes 1 and 2). The fact that the 11 nt product was dominant specifically in reactions lacking UTP indicated that the RSV RdRp could initiate RNA synthesis opposite the position +3 of the TrC template. On the other hand, the 13 nt product could either be RNA that was initiated at +1 and terminated at the first A in the template at position +14, or RNA initiated at +3 and extended to the second A in the template at position +16, due to misincorporation of an NTP opposite position +14.
Therefore, as a second step to identify the initiation sites of the 11 and 13 nt products, reactions were performed using [γ-32P]ATP or [γ-32P]GTP as a label. A [γ-32P]NTP label can only be incorporated into the 5′ terminal nt of the product. Thus, it would be expected that RNA initiated at +1 would incorporate [γ-32P]ATP, whereas RNA initiated at +3 would incorporate [γ-32P]GTP. Despite multiple experiments with different NTP concentrations, it was not possible to clearly detect incorporation of [γ-32P]ATP into RNA synthesis products (data not shown). In contrast, RNA products labeled with [γ-32P]GTP were readily detected. In reactions lacking UTP, a product of 11 nts could be detected (Figure 4D, lane 2), providing confirmatory evidence that the RdRp could initiate opposite the C residue at position +3. A 13 nt band could also be detected (Figure 4D, lane 2). This suggested that the 13 nt RNA was generated if the RdRp initiated at position +3 and then terminated when it reached position +16. These data indicate that under these in vitro assay conditions, the majority of detectable RNA transcripts were initiated at nt +3.
Having identified that RNA was initiated at position +3, it was possible to deduce how far it could be extended. In reactions containing a [γ-32P]GTP label and all four NTPs, a product of 21 nts was generated, although smaller amounts of 22 and 23 nt products could also be detected (Figure 4C, lane 2). This indicated that the RdRp frequently paused or terminated at nt 23, with less frequent extension to the end of the template.
In summary, the data from these experiments show that during de novo RNA synthesis, the RdRp initiated from +3, and that while initiation at +1 might have occurred, RNA initiated from this site was not readily detectable. The data also show that the RdRp tended to pause or terminate at nt 23. In addition, the data suggest that the RSV RdRp had low fidelity under these assay conditions.
Although initiation at the +3 site of the TrC promoter has been observed previously in experiments using the RSV minigenome system [44], it has never been described during RSV infection and the size of the RNA generated from this site has not been determined precisely. Examination of the TrC sequence showed that positions +3 to +12 are almost identical to the gene start signal sequence that lies at the beginning of the RSV L gene (Figure 5C), suggesting that initiation at +3 could occur by a mechanism analogous to transcription initiation at the gene start signals that lie internally on the RSV genome. To determine if the +3 initiation site is used during infection, RNA purified from wt RSV infected cells was analyzed by primer extension using TrC-sequence specific primers. Analysis using a primer that hybridized at positions 13–35 relative to the 5′ end of the Tr sequence clearly identified two bands, corresponding to initiation at positions +1 and +3 (Figure 5A, left panel, lane 4). This finding was consistent with the results obtained with the in vitro RNA synthesis assay, and indicates that nt +3 is a bona fide initiation site. Analysis with a primer that hybridized to positions 32–55 of Tr detected RNA initiated from +1 but not from +3, indicating that whereas the RNA initiated from +1 could be elongated, the RNA generated from the +3 initiation site was not extended far enough to hybridize to this primer (Figure 5A, right panel, lane 4). To determine the size of the RNA generated from the +3 site more precisely, RNA from RSV infected cells was also analyzed by Northern blotting with a probe specific to nts 5–32 of Tr, using conditions optimized for examination of RNA of 10–500 nts in length. This analysis identified an apparently abundant RNA transcript of ∼21–25 nts (Figure 5B, lane 2). This length is consistent with the primer extension analysis of the RNA generated from the +3 site, although the data do not exclude the possibility that some of the small RNA was initiated at +1. These data show that the RSV TrC promoter has the unusual property of having two closely positioned initiation sites, one at +1 that is required to generate genome RNA, and another at +3 that yields small RNA transcripts.
The data presented in Figure 2 show that in addition to generating newly synthesized RNA, the RSV RdRp could add nts to the 3′ end of the TrC RNA. Experiments were performed to determine which nts could be added, and to establish if they were added in a specific order. Reactions were performed containing each NTP label, either alone, or in combination with the other unlabeled NTPs. As described above, incubation with GTP in the absence of other NTPs showed strong incorporation into a 26 nt band, but no detectable incorporation into longer RNAs (Figure 6C, lane 3). If other NTPs were included in the reaction, a 27 nt band could be detected (Figure 6C, lane 2). This indicated that a different nt was added after the G to generate the 27 nt RNA. Labeled CTP was also incorporated into a 26 nt band in the absence of other NTPs, and yielded dominant bands of 26 and 28 nts when all four NTPs were present (Figure 6B, compare lane 3 with lane 2). In contrast, when UTP was used as a label, no incorporation was detected with UTP alone, but a 27 nt band was dominant when the other NTPs were present and a 28 nt band could be faintly detected (Figure 6D, compare lane 3 with 2). Similarly to the results shown in Figures 1 and 4, ATP showed only very weak incorporation into RNA longer than 25 nts, either in the presence or absence of other NTPs (Figure 6A). These data suggest that nts were incorporated onto the 3′ end of the TrC RNA with some specificity. Based on these data it can be deduced that either a G or C residue could be added to the −1 position at the 3′ end of the TrC RNA; a U residue could only be efficiently added after G, resulting in the 27 nt bands detected with either the GTP or UTP label, but not detectable with a CTP label; a C residue could then be added to the U to generate the 28 nt band, detected with CTP, and UTP, and to a lesser extent with a GTP label (see also Figures 2, 3 and 4). Thus, the sequence of nts most frequently added to the 3′ end of the TrC RNA was G, GU, GUC, or C only; other nt sequences, such as an A tract, might also have been added to a lesser extent. This experiment also revealed that ATP and CTP could be incorporated into the 3′ end of the Tr sense RNA also (Figure 6A and B, lane 5), but the CTP label showed that this occurred less frequently than addition to the 3′ end of the TrC RNA (Figure 6B, compare lanes 3 and 5).
The mechanism by which the nts were added to the TrC RNA was investigated. There were two potential mechanisms by which 3′ nt addition could occur: terminal transferase activity, or back-priming (also known as template dependent priming). In back-priming, the 3′ end of the RNA interacts with an internal sequence to form a hairpin structure, and the RdRp adds nts to the 3′ terminus using the folded RNA as a template [38], [45]. Visual inspection of the TrC RNA sequence showed there was possibility for two alternative hairpin loop structures to form in which nt 1U could base pair with either nts 14A or 16A, and nt 2G could base pair with either nts 13C or 15C. Pairing of nts 1 and 2 with 13 and 14 and extension by one to three nts would allow the RdRp to add a G, GU, or GUC, to the 3′ end of the TrC RNA by using nts 15C-17G as a template, whereas pairing of nts 1 and 2 with 15 and 16 would allow the RdRp to add a C (Figure 7A). This model was consistent with the results shown in Figure 6. To investigate this model, nts 1 or 14 and 16 in the 25 nt TrC RNA were substituted (Figure 7A) and NTP incorporation at the 3′ end of the RNA was examined using either a GTP or ATP label. Substitution of position 1U with an A caused a significant decrease in the levels of the 26–28 nt RNAs, suggesting that the identity of the 3′ terminal nt was significant for 3′ nt addition to occur (Figure 7B, compare lanes 1 and 2). Surprisingly, substitution of positions 14A and 16A with U residues did not block 3′ nt addition, but caused an alteration in the number and sequence of incorporated nts, with A being added, and G only being incorporated into longer products (Figure 7B, compare lanes 1 and 3, and 4 and 6). Thus, disruption of possible base-pairing between the 3′ terminus and nts 13 and 14, or 15 and 16 did not prevent 3′ addition, but altered the sequence of added nts. These results show that modification of the 3′ end of the TrC RNA involves an internal sequence, consistent with a back-priming mechanism, rather than terminal transferase activity.
Having shown that nts were added to the 3′ end of the TrC RNA in vitro with some specificity, it was of interest to determine if this occurred during RSV infection. In the context of an RSV infection, the TrC sequence is at the 3′ end of the antigenome. To our knowledge, no one has previously identified additional sequences at the 3′ terminus of the RSV antigenome. However, antigenome 3′ terminal sequences are rarely determined directly, but instead are inferred from the genome sequence [7], [46]–[48]. In one paper in which antigenome RNA was analyzed, only a small number of individual clones were sequenced [49]. Thus, prior sequencing analyses did not exclude the possibility that nts are added to the TrC region of a subpopulation of antigenome RNAs during RSV infection. To examine this possibility, antigenome RNA from RSV infected cells was tailed with either A or C residues, transcribed into cDNA by 3′ rapid amplification of cDNA ends (3′ RACE) and sequenced. Direct sequence analysis of the cDNA population showed that there was a mixed population of sequences, with a significant proportion of antigenomes containing additional nts of G, U and/or C at the −1, −2, and −3 positions relative to the 3′ end of the TrC promoter, respectively (Figure 8B, left panels). Sequencing of individual cDNA clones showed that while 10/19 clones contained wt antigenome sequence with no additional nts, 7/19 clones contained a 3′ G, 3′ UG, or 3′ CUG at the end of the antigenome (Figure 8C; note that 2/19 clones did not fall into either category). These sequence additions are consistent with a back-priming event involving interaction of nts 1, 2 and 13, 14 of the TrC RNA and extension by 1–3 nts in a template dependent manner, as illustrated in Figure 8A (left panel). Examination of the Le promoter sequence at the 3′ end of the genome showed that it also has the potential to form a secondary structure that could be used to direct back-priming. Indeed, in this case, a significantly stronger secondary could be formed than by the TrC sequence (Figure 8A, right panel). However, analysis of the same RNA preparation using Le specific probes showed that there was no additional sequence at the 3′ end of the Le promoter in the genome RNA (Figure 8B, right panels), demonstrating that the 3′ end of the Le is unmodified. These findings suggest that in addition to being able to use the TrC RNA as a promoter, the RdRp also facilitates a back-priming event to allow a precise sequence of nts to be added to the 3′ end of the antigenome.
The presence of additional sequence at the 3′ end of almost half of the antigenome sequences that we examined indicated that the 3′ extension plays a role in RSV replication. The only known function of antigenome RNA is as a template for RNA synthesis. Therefore, we examined if the additional nts at the 3′ end of the TrC sequence affected promoter activity. The 1–25 TrC RNA template was compared to a “+CUG” RNA template, which contained 1–25 nts of TrC sequence and a 3′ CUG extension, using the in vitro RNA synthesis assay. Both RNA templates contained a 3′ terminal PMN group to ensure that neither was subject to further 3′ modification. Analysis of the RNA generated from these templates showed that the presence of a 3′ terminal CUG extension was highly deleterious to RNA synthesis, indicating that the 3′ extension inhibited access of the RdRp to the promoter (Figure 9, compare lanes 2 and 3). We considered the possibility that the extension might increase initiation from the +1 position, but there was no evidence of incorporation of a [γ-32P]ATP label into RNA synthesized from the +CUG template (data not shown). Thus, these data indicate that the 3′ terminal extension can inhibit antigenome promoter activity.
This is the first report of purification of functional RSV polymerase from a recombinant source and the first time that an assay of this kind has been applied to study initiation of paramyxovirus RNA synthesis. Experiments with this assay revealed that the RSV RdRp is capable of two different RNA synthesis activities: initiation of de novo RNA synthesis from the TrC promoter, and 3′ extension of the RNA by a back-priming mechanism. Analysis of RNA isolated from RSV infected cells provided confirmatory evidence that there is a +3 initiation site within the TrC promoter, and the antigenome RNA can be modified by 3′ extension. These findings suggest that the interactions between the RSV RdRp and the promoter are more complex than previously thought. They also demonstrate clear differences between RSV and the prototype NNS RNA virus, VSV, but a surprising similarity with the more distantly related Borna disease virus, which has also been shown to elongate the 3′ ends of replicative RNAs [50], [51]. These results indicate that while there might be transcription and genome replication paradigms that can be applied to all the Mononegavirales, the details of these processes should be considered on a case-by-case basis for each virus.
It is accepted that normally the RSV template RNA is encapsidated with N protein. However, the fact that our experiments showed that the RSV polymerase was able to recognize the RNA in a sequence specific manner in the absence of N, and modify the TrC RNA apparently by using an RNA secondary structure, reveals insight into the molecular details of the polymerase-template complex. These findings suggest that although the antigenome RNA is normally encapsidated with N protein, there are occasions during the RSV replication cycle when the RdRp can interact with the RNA directly. The ability of the RSV RdRp to recognize the promoter in the absence of N protein is not necessarily surprising, as prior studies have shown that there is no requirement for the RSV promoter to be in phase with N protein [52]. Likewise, the VSV RdRp has also been shown to be able to recognize a specific initiation signal on a naked RNA template [18], and also does not follow an integer rule [53]. The situation might be different for other NNS RNA viruses, which require their genomes to be a particular integer length [54]–[60]. In these cases, the promoter is clearly recognized in the context of N protein [61]–[63] and it might be that the polymerase would either not recognize naked RNA as a template, or that there would be little or no sequence specificity on naked RNA. While RdRps are known to be error prone, the data obtained from the –UTP experiments suggest that the RdRp might have particularly low fidelity in this system (Figure 4A), which would not be tenable during infection. It is possible that during RSV infection, the N-RNA template opens to allow the RdRp to make direct contacts with the promoter RNA and initiate RNA synthesis, but that because RNA elongation is dependent on release of the RNA from the downstream N molecules, the RdRp structure is slightly altered, allowing for greater accuracy.
The TrC promoter would be expected to direct RNA synthesis initiation from the +1 position, to yield the genome RNA, and RNA initiated from +1 could be readily detected in RSV infected cells. However, RNA initiated at +3 was also detected. Primer extension analysis showed that this RNA was truncated within a short distance from the promoter and consistent with this, RNA transcripts of 21–25 nts in length could be readily detected in infected cells. The function of the small RNA initiated from +3 is not yet known. However, previous studies suggest that Tr-specific RNA might play a role in subverting the cellular stress granule response [64], [65]. If this RNA does play a functional role, it would indicate that the TrC promoter is not limited to initiating RNA replication, but also has a role in RSV transcription, albeit directing synthesis of a small RNA transcript rather than mRNA.
In this study, we showed that initiation at +3 occurs by a de novo initiation mechanism, and apparently does not depend on prior initiation at +1 (Figure 4). This is consistent with previous minigenome experiments that showed that mutations that inhibited initiation at +1 augmented initiation at +3 [44]. In contrast, we were unable to convincingly demonstrate initiation at the +1 position in the in vitro assay by incorporation of a [γ-32P]ATP label, despite numerous experiments aimed at optimizing NTP concentration for +1 initiation. A band of 25 nts in length could occasionally be detected (e.g. Figure 9, lane 2) indicating that a low level of initiation at +1 might occur. Failure to detect +1 initiation using [γ-32P]ATP could reflect differences in the NTP concentrations required for initiation at +1 versus +3. It was possible to detect +3 initiation with [γ-32P]GTP by using a relatively low concentration of unlabeled GTP in the reaction so that the proportion of labeled GTP in the total GTP pool was not too low. If initiation at +1 required a particularly high concentration of ATP, it might be impossible to identify conditions that allow +1 initiation, without out-competing the [γ-32P]ATP label with unlabeled ATP. It is also possible that initiation at +1 requires different conditions, or an additional factor that is missing in the in vitro assay, or that this initiation event was too inefficient to be detected with a [γ-32P]ATP label. In previous minigenome studies, we showed that if deletions or substitutions were introduced at position +1U of the TrC or Le promoter, almost all the detectable replication product was restored to wt sequence in a single round of replication, indicating that during initiation at the +1 site, the initiating NTP was selected independently of the 3′ terminal nt of the template [44], [66]. Based on these findings, we proposed that the RSV RdRp becomes preloaded with a primer for initiation at +1 [66]. If this model were correct, it would be expected that ATP might be required at a particularly high concentration to generate a primer and/or that other factors might also be involved. Thus, it is not surprising that the +1 initiation event could not be detected or reconstituted as readily as +3 initiation. It is unusual for an RdRp initiate from two sites within the same promoter region. One explanation for how this occurs is that the RdRp binds to a sequence within nts 3–11 of the promoter and either recruits GTP to initiate at position +3, or is preloaded with a 5′ AC or 5′ ACG primer to initiate RNA synthesis from position +1.
An in vitro RNA synthesis assay was also recently established for the VSV RdRp [18]. The VSV study utilized the Le, rather than the TrC promoter sequence, preventing direct comparison with the results obtained here. However, the RSV Le promoter also contains a gene start-like sequence at nts 3–12, and has been shown to direct RNA synthesis initiation from positions +1 and +3 in the minigenome system [66]. In contrast, the VSV RdRp only initiated RNA synthesis at the +1 position on the wt template, and unlike the situation with RSV, RNA initiated at this site could be readily detected using a [γ-32P]ATP label [18]. Thus, the existing data suggest a significant difference in the functional properties of the RSV and VSV promoters, and in their mechanisms for RNA synthesis initiation.
In the experiments shown here, there were dominant RNA synthesis products of ∼8–10 nts (e.g. Figure 1E). The reason why RNAs ∼8–10 nts in length were generated at a relatively high level is likely due to abortive synthesis in which the RdRp failed to escape from promoter and released the nascent RNA transcript. This is a common feature of initiation by RNA polymerases, which has been well documented [67]. In addition, the RdRp was inefficient at extending transcripts to the end of the template, frequently halting RNA synthesis at nt 23 of the TrC RNA (e.g. Figure 1E). Examination of RNA synthesis products from two shorter templates indicated that in one case the RdRp was able to extend to the end of the template, whereas in another it terminated either at the terminal or penultimate nt (data not shown). Therefore, it is possible that the polymerase was influenced by the 5′ terminal sequence of the template, as has been shown for two bromovirus replicases [68]. The RdRp might also terminate due to a termination signal or inherent instability at this position, as the short RNAs detected in RSV infected cells were ∼21–25 nts in length (Figure 5).
The data also revealed that the RSV RdRp could add nts to the 3′ terminus of the TrC RNA both in vitro and during RSV infection, apparently using a back-priming mechanism (Figures 2, 6, 7 and 8). These results are similar to findings for Borna disease virus, in which it has been shown that nts are added to both the genome and antigenome RNAs with an apparent 100% efficiency [51]. The finding that RSV shares a back-priming activity with Borna disease virus is surprising, as Borna disease virus is somewhat distinct from RSV and the other NNS RNA viruses. Interestingly, in the experiments described here, none of the RNA oligonucleotides tested in the in vitro assay possessed a stable secondary structure that would allow base-pairing between the 3′ terminus and an internal sequence, as predicted by Mfold analysis [69]. Furthermore, there was no evidence for addition of nts to the 3′ end of the RSV genome, despite the 3′ end of the Le region having the potential to form an inherently stronger RNA secondary structure (Figure 8A and B). Therefore, the RNA secondary structure to facilitate back-priming on the antigenome presumably is stabilized by the RdRp (or an associated protein) at a point when the antigenome RNA is not fully encapsidated. One possible model is that nt addition to the 3′ end of the TrC sequence occurs as the RdRp completes synthesis of the antigenome RNA. In this scenario, when the RdRp reaches the end of the antigenome, it folds the RNA into the back-priming structure and adds the additional 3′ nts before the nascent RNA becomes completely encapsidated. Alternatively, the 3′ end of the antigenome might become unencapsidated prior to RdRp binding the promoter. Then depending on RdRp orientation when it accesses the promoter, it could either modify the 3′ terminus or initiate de novo RNA synthesis.
It remains somewhat unclear what role the 3′ terminal extension plays in RSV replication. Unfortunately, because of the multifunctional nature of the TrC promoter in directing RNA synthesis and encapsidation, it is probably not possible to generate a mutant virus in which the 3′ terminal extension activity is ablated without also affecting other aspects of genome replication. Therefore, we can only speculate on the significance of antigenome 3′ terminal extension during RSV infection. In the case of Borna Disease virus, the function of the additional nts at the genome and antigenome ends is to compensate for cleavage of the 5′ ends of replication products, which allows the virus to avoid detection by RIG I [70]. It is unlikely that 3′ nt addition fulfills a similar function in the case of RSV, as the data suggest that only a subpopulation of replication products are modified (Figure 8). Furthermore, RIG I binds to RSV RNAs and is activated early in RSV infection [71], [72]. A second possibility is that having additional sequence and a double stranded RNA structure at the ends of the RSV RNA provides some protection of the promoter sequence from cellular exonucleases. However, if this were the case, it is not clear why there was heterogeneity in the antigenome population. We have also investigated if back-priming activity could allow repair of a template in which the 3′ terminal nt was deleted, but we were unable to detect incorporation of labeled UTP into this template in the in vitro RNA synthesis assay, indicating that deletion nt 1 of the TrC promoter prevents the back-priming event from occurring (data not shown). However, it is possible that repair can occur through a back-priming mechanism in a cellular environment. Finally, it is possible that the 3′ terminal additions are part of a regulatory mechanism to modulate promoter activity. Consistent with this idea, the data shown in Figure 9 indicates that the 3′ terminal extension inhibits RNA synthesis, at least in the context of the in vitro assay. It is important to note that in this assay the RNA was naked, and so the effect of the 3′ extension might not reflect the situation in RSV infected cells. For example, in the in vitro assay, the 3′ extension is likely to have created an RNA secondary structure that prevented access of the RdRp to the promoter. In contrast, in infected cells the antigenome RNA would be expected to become completely encapsidated in N protein, eliminating RNA secondary structure. Comparison of wt and mutant templates containing the extensions using the minigenome system (in which the template RNA does become encapsidated) indicated that, the 3′ terminal UG and CUG additions slightly increased RNA synthesis from both the +1 and +3 positions, while a G extension had no effect (data not shown). However, we also found that the 3′ termini of the input wt and mutant minigenome templates had the potential to be modified in the transfected cells, and so it is not clear how much weight can be attributed to these results. Nonetheless, it is interesting to speculate that the ability of the RdRp to add nts to the antigenome terminus and the effect of the nt extension are linked by the encapsidation status of the antigenome RNA. For example, one possibility is that 3′ terminal extension occurs when encapsidation of the newly synthesized antigenome RNA lags behind RNA synthesis. In this scenario, the putative hairpin structure formed by 3′ terminal extension might function to prevent initiation of de novo RNA synthesis until the antigenome RNA becomes fully encapsidated, at which point it might become an even more efficient promoter.
In summary, the findings presented here indicate that the behavior of the RSV RdRp at the TrC promoter sequence is more complex than previously realized, directing initiation of RNA synthesis from two sites, and having the capacity to add nts to the 3′ end of the antigenome template by a back-priming mechanism. We speculate that the advantage of having greater complexity in polymerase-promoter interactions is that it might offer an opportunity for temporal or environmental regulation of RSV RNA expression.
A codon-optimized version of the RSV (strain A2) L protein ORF was chemically synthesized (GeneArt) and the mutant LN812A was generated by QuickChange site-directed mutagenesis (Aligent Technologies). RSV L or LN812A, were cloned in the pFastBac Dual vector (Invitrogen) together with the RSV A2 P ORF, which was tagged with a hexahistidine sequence, separated from the ORF by a tobacco etch virus (TEV) protease cleavage site. Recombinant baculoviruses were recovered using the Bac-To-Bac system (Invitrogen) and used to infect Sf21 cells in suspension culture. RSV L/P protein complexes were isolated from cell lysates by affinity chromatography, TEV protease cleavage, and size exclusion. Isolated L/P complexes were analyzed by SDS-PAGE and PageBlue staining (Fermentas) and the L protein concentration was estimated against bovine serum albumin reference standards. The bands migrating between 160 to 250 kDa and ∼35 and 50 kDa were confirmed to be RSV L and P polypeptides by excising each band and performing trypsin digestion followed by liquid chromatography, tandem mass spectrometry (LC/MS/MS). The MS/MS spectra were analyzed using SEQUEST software using the RSV A2 sequence as a reference. The identity of a band migrating between 70 and 80 kDa was determined by performing SDS-PAGE and Western blot analysis using an hsp70/HSC70 specific antibody (Santa Cruz).
Codon optimized wt and LN812A ORFs were introduced under the control of the T7 promoter in pTM1. Minigenome RNA analysis was performed using plasmid MP-28, which encodes a replication competent dicistronic CAT minigenome template [10]. Minigenome transcription and RNA replication were reconstituted in BSR-T7 cells as described previously [66]. Minigenome specific RNAs were analyzed by Northern blot using CAT-specific probes, as described previously [73].
RNA oligonucleotides representing nts 1–25 of either wt or mutant TrC promoter sequence or its complement (Tr sequence) were purchased (Dharmacon). All oligonucleotides contained an –OH group at the 3′ terminus, unless stated otherwise, and an –OH group at the 5′ terminus. The RNA oligonucleotides were combined with RSV L/P (containing ∼100 ng of L protein) in transcription buffer containing 50 mM Tris HCl at pH 7.4, 50 mM NaCl, 5 mM MgCl2, 5 mM DTT, 40 U RNase inhibitor, and NTPs, including either 1 µl of [α-32P]ATP, CTP, GTP or UTP (as indicated in the legend) or [γ-32P]GTP (∼10 µCi), in a final volume of 50 µl. The RNA and NTP concentrations used for each experiment are indicated in the figure legends. Reactions were incubated at 30°C for 3 h, heated to 90°C for 3 min to inactivate the RdRp and cooled briefly on ice. Reactions containing [α-32P]NTP were combined with 10U calf intestinal alkaline phosphatase, incubated at 37°C for 1 h and RNA was isolated by phenol-chloroform extraction and ethanol precipitation. Reactions containing [γ-32P]GTP were combined with 7.5 µl 10% SDS, 0.5 µl 500 mM EDTA and 10 µg proteinase K and incubated at 45°C for 45 min before phenol-chloroform extraction and ethanol precipitation. The RNA was analyzed by electrophoresis on a 20% polyacrylamide gel containing 7 M urea in tris-borate-EDTA buffer, followed by autoradiography. On each autoradiogram, the nt lengths of the RNA products were determined by comparison with a molecular weight ladder generated by alkali hydrolysis of a 32P end-labeled RNA oligonucleotide representing the anticipated 25 nt Tr RNA product. This marker is shown in Figure 1E, Figure 2, Figure 3, Figure 6, and Figure 9, and the same marker was used for the remaining experiments. The bottom of each gel is cropped to eliminate the non-specific signal from unincorporated radiolabeled NTPs that were not always efficiently removed during RNA purification and electrophoresis.
HEp-2 cells were infected with RSV at an MOI of 5 or mock infected and incubated at 37°C for 17 h. Total intracellular RNA was isolated using Trizol (Invitrogen). Primer extension reactions were carried out as described previously [44] using primers that hybridized at positions 13–35 or 32–55 relative to the genome 5′ terminus. The sizes of the labeled cDNA products were compared to 32P end labeled DNA oligonucleotides of sequence and length equivalent to cDNAs corresponding to RNAs initiated at positions +1 and +3 on the antigenome. The method for detecting small genome sense RNA by Northern blot analysis was adapted from a protocol described by Varallyay and co-workers [74]. Briefly, total intracellular RNA was subjected to electrophoresis in a 6% urea-acrylamide gel alongside low-range ssRNA and miRNA molecular weight standards (NEB). The lanes containing the molecular weight standards were excised from the gel prior to Northern transfer, stained with ethidium bromide and the standards were detected with UV light. The remainder of the gel was transferred to Nitran-N positively charged Nylon membrane (Sigma-Aldrich) using the Whatmann TurboBlotter downward capillary transfer system (Sigma-Aldrich) in 8 mM NaOH, 3 mM NaCl. Following transfer, blots were neutralized in 6× SSC and UV-crosslinked. Blots were prehybridized for 1 h in 5× Denhardt's Solution, 6× SSC, 0.1% SDS, and 0.01% NaPPi at and hybridized for 12–18 h with a 32P end-labeled locked nucleic acid modified DNA oligonucleotide specific to nts 5–32 (relative to the 5′ terminus) of genome sense RNA (5′- GAGATATTAGTTTTTGACACTTTTTTTC - 3′) in the same buffer at 62°C. Blots were washed with 6× SSC twice for 15 minutes at room temperature, and twice for 10 minutes at 62°C, and the RNA was detected by autoradiography.
3′ RACE and sequence analysis of antigenome and genome RNA (Figure 8) was performed using RNA isolated from infected cell extracts enriched for RSV ribonucleoprotein (RNP) complexes [75]. Briefly, 8×106 HEp2 cells were infected at an MOI of 5. At 17 h post infection, the supernatant was replaced with media containing 2 µg/ml actinomycin D and cells were incubated at 37°C for a further 1 h. Following an ice cold PBS wash, cells were treated for 1 min with PBS supplemented with 250 µg/ml lyso-lecithin, on ice. Cells were scraped into 400 µl of ice cold Buffer A (50 mM tris-acetate pH 8, 100 mM K-acetate, 1 mM DTT, 2 µg/ml actinomycin D), disrupted by repeated passage through an 18G needle and incubated on ice for 10 min. Following centrifugation at 2400× g for 10 min at 4°C, the resulting pellet was disrupted in 200 µl of ice cold Buffer B (10 mM tris-acetate pH 8, 10 mM K-acetate, 1.5 mM MgCl2, 1% triton X-100) by repeated passage through an 18G needle and then incubated on ice for 10 min. The sample was centrifuged and the resulting pellet was disrupted in 200 µl of Buffer B supplemented with 0.5% deoxycholate, 1% tween 40 as described above. Following a 10 min incubation on ice and a repeat centrifugation, the supernatant enriched for viral RNPs was collected and RNA was extracted using Trizol (Invitrogen). The purified RNA was tailed with either A or C residues using E. coli poly A polymerase (NEB), followed by heat inactivation of the enzyme, according manufacturer's instructions. First strand cDNA synthesis was performed using primers 5′ GAGGACTCGAGCTCAAGCATGCATTTTTTTTTTTTTTT, or 5′ GAGGACTCGAGCTCAAGCATGCATGGGGGGGGGGGGGGG, which hybridized to the poly A or poly C tail, respectively, and Sensiscript reverse transcriptase (Qiagen), according to manufacturer's instructions. To determine the sequence of the antigenome 3′ terminus, purified cDNA was PCR amplified using primer SLNQi 5′-GAGGACTCGAGCTCAAGC and a TrC specific primer Tr1: 5′-GCAGCACTTTTAGTGAACTAATCC. The resulting product was subjected to a second round of hemi-nested PCR using primer SLNQi and primer Tr2: 5′-GCAGTCGACCATTTTAATCTTGGAG. PCR products were gel purified and either sequenced directly or cloned into a pGEM vector for sequencing of individual cDNA clones. Analysis of the genome 3′ terminus was performed as described above, using the same cDNA preparation and primer SLNQi, but with NS1 specific primers: 5′-GCACAAACACAATGCCATTC and 5′-GCAGTCGACGTATGTATCACTGCCTTAGCC.
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10.1371/journal.pcbi.1006627 | A stochastic framework to model axon interactions within growing neuronal populations | The confined and crowded environment of developing brains imposes spatial constraints on neuronal cells that have evolved individual and collective strategies to optimize their growth. These include organizing neurons into populations extending their axons to common target territories. How individual axons interact with each other within such populations to optimize innervation is currently unclear and difficult to analyze experimentally in vivo. Here, we developed a stochastic model of 3D axon growth that takes into account spatial environmental constraints, physical interactions between neighboring axons, and branch formation. This general, predictive and robust model, when fed with parameters estimated on real neurons from the Drosophila brain, enabled the study of the mechanistic principles underlying the growth of axonal populations. First, it provided a novel explanation for the diversity of growth and branching patterns observed in vivo within populations of genetically identical neurons. Second, it uncovered that axon branching could be a strategy optimizing the overall growth of axons competing with others in contexts of high axonal density. The flexibility of this framework will make it possible to investigate the rules underlying axon growth and regeneration in the context of various neuronal populations.
| Understanding how neuronal cells establish complex circuits with specific functions within a developing brain is a major current challenge. Over the last past years, enormous progress has been done to precisely resolve brain anatomy and to dissect the mechanisms controlling the establishment of precise neuronal networks. However, due to the extreme complexity of the brain, it is still experimentally difficult to investigate in vivo how neurons interact with each other and with their physical environments to innervate target territories during development. Here, we have developed a framework that integrates a dynamic 3D mathematical model of single axonal growth with parameters estimated from neurons grown in vivo and simulations of entire populations of growing axons. The emergent properties of our model enable the study of the mechanistic principles underlying the growth of axonal population in developing brains. Specifically, our results highlight the impact of mechanical interactions on both individual and collective axon growth, and uncover how branching regulate this process.
| In vivo, neurons extend processes that must navigate through the extremely dense and complex environment of developing brains to find their targets and assemble into functional networks. This environment provides the diffusible extracellular cues that guide neuronal processes to their destination by orienting their growth and promoting their extension or retraction [1–6]. As revealed by recent work, axons are not only capable of sensing and responding to external chemical signals, but are also guided by local changes in the mechanical properties of their surrounding [7–9]. Beyond providing chemical or mechanical cues orienting axon navigation, the confined and crowded environment of the brain also imposes spatial constraints on the organization and growth of neuronal projections. Thus, strategies to optimize growth in the context of increasing numbers of neurons have been developed across evolution. These include organization of neurons into populations that coordinate to grow and innervate target territories [10, 11]. Although studies have revealed that inter-neuron coordination and interactions are particularly important in the context of a developing population [10, 12–15], axon growth has so far been mainly studied in vitro on isolated neurons, or in vivo on whole populations of neurons. However, to fully understand population growth, one needs to understand the behavior of single constituent neurons, and how they interact and influence themselves to produce global growth. This is experimentally not trivial, as tools to reproducibly visualize and manipulate axon-axon interactions in vivo in populations of growing axons are either lacking or heavy to implement.
To overcome these difficulties, we have developed a 3D dynamic mathematical framework that generates simulations yielding realistic single cell morphologies and accurately reproduces the process of axon growth in a population context. Although models taking into account different aspects of axon competition have been described [16–21], 3D models considering spatial constraints and mechanical neuron-neuron interactions are so far rare [22–25]. Torben-Nielsen and De Schutter, for example, elaborated a framework for context-aware neuron development where growth rules are mainly phenomenological [23]. Zubler et al. proposed a model of neuron growth based on physical forces between objects and diffusion of substances through the extracellular domain [24]. Vanherpe et al. proposed a framework for the development of non-intersecting tubular-like structures in confined spaces, which highlighted the dependence of axon elongation and final morphology on spatial boundaries and axonal density [25]. Together, these models stressed the importance of considering space-embedded processes and interactions with the cellular environment when studying neuronal morphologies. However, they have not, or could not, use parameters estimated from real data, and did not showcase explicative or predictive aspects of their models.
In this work, we developed a flexible framework that can integrate data from biological samples with mathematical modeling to uncover the principles underlying axon growth in a population context. First, we proposed a 3D stochastic model for the growth of individual axons which relies on parameters that can be estimated from data. This model can be implemented with branch formation, and applied to the simulation of individual axons. Second, we simulated the growth of populations of axons, letting them grow simultaneously, in a spatially constrained environment where they compete for space and change their path when encountering obstacles.
To test our model on biological data, we took advantage of a database of confocal images representing single mature axons grown in the context of Drosophila brains. Strikingly, our framework generated a range of growth and arborization patterns similar to that observed in Drosophila brains, when implemented with parameters estimated from in vivo data, providing a mechanistic explanation for the origin of observed morphological diversity. Furthermore, modulating the capacity of axons to form branches influenced axon growth. At the population level, branching axons grew more efficiently than non-branching ones. At the single-cell level, non-branching axons were out-competed by branching ones, generating defective growth profiles similar to those observed in vivo upon inactivation of imp, a gene known for its role in axon growth and branching. While the importance of branching has so far been mostly considered in the context of establishing connections with partners [26, 27], our results thus suggest that branching may also be a strategy adopted during development to overcome spatial competition and optimize growth in contexts of high axonal density. Together, the simple, explicative and predictive framework we have developed enables mechanistic studies of the principles governing the collective growth of axons in realistic spatially constrained environments.
Here, we propose a simulation framework for the collective growth of axonal extensions. Individual 3D axonal morphologies are generated by a mathematical model for single axonal path generation, and an algorithmic implementation of branch occurrence. Time is also implemented algorithmically, and interactions between individuals are considered via volume exclusion. Parameters can be estimated from real data, which allows the model to be not only generative, but also explicative of key aspects of the growth process, and predictive.
To mimic the crowded environment of the brain, we implemented via volume exclusion the mechanical and spatial constraints imposed by surrounding tissues an cells, which can occur upon interaction with another axon, or with the edges of the cavity ξ in which axons grow. We considered two possibilities for each artificial time point. If space permitted, axon tips grow with a maximal speed of v m a x = n m a x Δ ρ Δ t during tj. If, on the contrary, axon tips encounter another axon, or the geometrical limits of ξ before accomplishing nmax steps, they will retract the last few (nr) steps realized during tj (Fig 1C). Then, they will try to regrow in another direction until nmax steps are accomplished (Fig 1C, CASE A). However, if a second obstacle is encountered within the same time frame (Fig 1C, CASE B), axon tips will stop their growth after the retraction of the last nr steps, and will try again growing only in the next time point (tj+1). Such an alternance of growth and repulsion steps during the axonal elongation process has been described in the literature [17, 23, 25]. Gallo and colleagues ([7]), for example, observed that neurons growing under mechanical constraints sample repeatedly the obstacles they encountered, until they find a free way. Furthermore, Schier and colleagues ([32]) mentioned the existence of inter-neuron contacts during development, proposing the so-called growth and repulsion mechanism. For the sake of computational time, as well as to mimic real timing constraints imposed by the developmental program, each axon tip has a limited global number of trials before it stops growing (counter). The maximum value of trials upon interaction (countermax) should be estimated or fixed. The axonal diameter d may be measured from data or adapted from already published values. Finally, the shape and dimensions of the growth cavity ξ should be adapted to the case under study (see Table 1).
In order to handle volume exclusion, the growth of each axon is simulated sequentially in each time point. After each step, the algorithm checks that the new position of the axon tip is at least one diameter (d) away from every other axonal structure or cavity limits (ξ). If this condition gets false (mechanical obstacle), the algorithm proceeds as described in the previous paragraph.
To understand the importance of considering mechanical interactions and confinement when studying axons growing as a population in vivo in brains, we applied our model to Mushroom Body (MB) γ neurons. These neurons project stereotypically in each hemisphere of the Drosophila brain (Fig 4A). MB γ axons (about 650 in total) fasciculate proximally to form a dense fiber projecting ventrally: the peduncle (Fig 4B). More distally, adult axons de-fasciculate to innervate the so-called medial lobe where they form branches of various lengths, with at least one reaching the distal tip of the medial lobe (Fig 4C, red arrows and [33]). Remarkably, a range of arborization patterns is typically observed within populations of genetically identical γ neurons. As described previously by Rubin and colleagues [34], MB γ axons establish contact with specific sets of input and output neurons projecting to functional and anatomical compartments distributed along the medial lobe (Fig 4D).
To quantitatively analyze the morphology of individual axons within the population, we genetically labeled single MB γ neurons and imaged their adult arborization patterns by confocal microscopy. We then reconstructed the skeletons of each individual axonal trees (n = 43; available at NeuroMorpho.org), and included them all in the same reference lobe after normalization (Supporting information and S3A–S3D Fig). As shown in S3C and S3D Fig, reconstructed axons spread over the entire reference lobe and exhibited a wide range of morphologies, indicating that our database is representative of the variety of growth patterns that γ axons may adopt. Analyzing the average directionality of individual axonal segments along the medial lobe revealed that MB γ axons are strongly oriented toward the midline (S3E Fig). Furthermore, measuring the deviation angles of branches from the branch they emerge, revealed that branches could be classified into two groups with distinct properties. While long (>10 μm) branches were mainly oriented relatively parallel to the lobe axis (Fig 4E), sometimes reaching the distal end of the medial lobe (Fig 4C), short branches (<10 μm) exhibited no bias in directionality (Fig 4E). These observations suggested that long and short branches (that we named type I and type II branches respectively) may be generated via distinct mechanisms.
To better understand how branches are generated and thus implement them in our framework, we imaged in real-time maturing brains expressing GFP in single γ neurons. MB γ neurons are born during embryogenesis and early larval stages but then undergo developmental remodeling [34–38], such that growth and branching of adult axonal trees occur during metamorphosis. Although live-imaging at this stage did not allow us to follow the entire growth of γ axons, it enabled us to dynamically record parts of this process. Consistent with our analysis of fixed samples, two main types of branches were observed in movies: very dynamic short branches (type II, asterisks), and more static longer branches (type I, arrows) (S1 Video and S4A Fig). Type II branches exhibited series of growth and retraction events and typically measured between 2 and 10 μm (80% between 2 and 5 μm and 18% between 5 and 10 μm, n = 484, see S4B Fig). Longer type I branches had a dynamic activity restricted to branch tips, and were on average longer than 10 μm. We thus included both types of branches in our model, such that two scenarios can occur at the end of each tj. In the first one, the axon generates a type I branch that will then elongate following the previously described rules in Fig 1. In the second one, no type I branch is generated. A type II branch is then formed if its distance to the previous branch is higher or equal to a random number from the Poisson distribution with parameter λb. This branch will appear and disappear with different uniform random angles and get stabilized if it establishes a contact with another branch. Its length is drawn from the distribution measured from data and described previously. As type II branches are quite short and dynamics, we did not consider their volume in our model, but only their formation and stabilization.
Beyond enabling quantitative description of axonal trees, reconstructions of real axons allowed us to directly estimate, or calibrate, all the morphological and spatial parameters from individual axons grown in vivo as a population (see values in Table 2). Temporal parameters were arbitrarily fixed and invariant for all the experiments.
α and β: To estimate these parameters, we considered that each kth reconstructed axon of length M can be represented by the sequence θ i k, applying Eq 2 to each step of length Δρ. It can be shown that the variance of θ i k is
σ θ i k 2 = σ 0 2 ∑ i = 1 M - 1 γ 2 i = σ 0 2 1 - γ 2 M 1 - γ 2 (5)
where
γ = α α + β ; σ 0 2 = 1 2 ( α + β ) . (6)
When i → ∞ we obtain the expression
σ θ ∞ k 2 = σ 0 2 1 - γ 2 . (7)
It can also be shown (Supporting information) that the variance of the difference θ i k - θ i - 1 k (for i → ∞) is
σ Δ θ ∞ k 2 = 2 σ 0 2 ( 1 - γ ) 1 - γ 2 . (8)
To obtain the estimations α ^ k and β ^ k, we assume that the axons are long enough, and calculate σ θ ∞ k 2 ^ and σ Δ θ ∞ k 2 ^, from where we get γ ^ and σ 0 2 ^ to finally apply Eq (9) α ^ k = γ ^ 2 σ 0 2 ^ , β ^ k = 1 2 σ 0 2 ^ - α ^ k . (9)
and obtain the estimates of the model parameters for each neuron k. To estimate the parameters of a population containing K axons, we consider the distributions of parameters (α ^ k , β ^ k) obtained from individual axons using Eq 9. However, the distributions of the estimated values were not well described by their mean or median. Thus, we chose the couple of values (α ^ , β ^) that maximized the similarity between the distribution of parameters estimated from data and that obtained from simulations with different values of (α, β), taking into account axon-axon interactions (S4 Fig and Supporting information). The estimated values were: α ^ = 7 . 45 and β ^ = 1 . 67 (Table 2).
Δρ: was set to 1 μm, to be consistent with the general axonal diameter in the images, and avoid oversampling (see Supporting Information and Table 2).
ψ: the external attractive field was estimated based on the observed directionality of real axons, as neither the identity nor the source of the cue(s) guiding the growth of γ axons are currently known. We placed the attractive source at the end of the medial lobe, and evaluated different gradient geometries by comparing the similarity between real axon orientation and field directionality (S6A–S6C Fig and Supporting information). The source configuration maximizing this similarity was selected for further analysis, see Table 2.
d and Numax: the axonal diameter was estimated from published electron microscopy images [39], and optimized by simulations (see S6E Fig and Supporting information). The selected diameter value corresponds to the first elbow of the obtained logistic function (d = 0.23 μm). Nmax = 650 was obtained from the work of [34] (Table 2).
ξ and Xmax: in vivo, adult γ axons grow as a population, in a confined environment defined by surrounding neuronal and glial cells (Fig 4A and [34]). To consider mechanical constraints that underlie axon growth in the crowded environment of a maturing brain, we imposed a spatially-restricted environment mimicking MB lobe geometry, and defined based on our confocal images as well as on the work of Aso et al. [34] (Supporting information and S6E Fig).
Pb, ω, λb and bl: We first estimated the probability Pb as the mean of main axon lengths (93 μm) divided by nmax to obtain -roughly- the number of time points (tj) it takes to grow. By simply dividing the mean number of type I branches per axon (2.25) by this number, we obtained Pb = 0.15. Based on our observations of real samples, we then hypothesized that branches (types I and II) are born initially with uniform random angles (ω), and that type I branches can create type II branches but not type I (i.e. bl = 1 for type I branches and 2 for type II (Table 2)).
Using our model and the parameters estimated from data, we simulated entire populations of γ axons. To estimate the capacity of axons to successfully grow, and extend until the extremity of the medial lobe, we defined a “stopping region” of about 20 μm-wide at the end of the medial lobe (Fig 5A). Although real axons do not all sharply stop at the extremity of the medial lobe (midline), they all reach this region. We thus considered as non-elongated axons those that did not reach this region. In this condition, about 10% of simulated γ axons (n = 3 simulations) failed to elongate properly.
To assess the validity of our branch occurrence hypothesis (random uniform), we then analyzed the distribution of normalized type I branching point numbers along real lobe axes (Fig 5B, orange bars). This revealed that, in vivo, type I branching points are not uniformly distributed throughout the lobe, but rather peak in the most central part. Such a distribution correlates with that of axon density (or occupation rate) along the medial lobe (Fig 5B, green line). This observation led us to consider an alternative hypothesis in which branch occurrence may be favored in regions of high density and increased spatial competition.
Thus, we proposed that branching condition is true when the axon tip encounters mechanical obstacles (branching upon contact) during a given time interval (CASE B in Fig 1C). Considering such a mechanical branching, we performed new simulations of entire γ axon populations, and observed a reduction of the percentage of non-elongated axons (4.9 ± 0.22%; n = 3 simulations). To more precisely assess the similarity between real and simulated axons in this condition, we compared the distributions of main axon lengths (defined in Supporting information), as well as the distributions of distances traveled within the medial lobe (defined by the distances from the lobe entry point to the end point projected along the medial lobe axis, scheme in Fig 5A). As shown in Fig 5C, the distribution profiles of real and simulated axons were very similar.
In term of branching, simulated γ axons had on average 2.0 ± 0.01 type I branches (n = 3 simulations; standard deviation σ = 1.68), a number very close to that of real axons (2.25 with a standard deviation of 1.1). Furthermore, they exhibited morphologies matching those of real axons (Fig 5C).
To quantify the overall similarity between simulated and real axonal trees, we used the distance between trees developed by [40]. This measurement takes into account the length, the morphology, and the directionality of main axons, as well as branching characteristics. As shown in Fig 5D and S7B Fig, the distribution of distances between all pairs of real and simulated axons is close to that of distances between all pairs of real axons and, remarkably, is significantly closer to that obtained with axons simulated using the random branching hypothesis (p value between random and mecha of 3.3e−15). To then determine if our model accurately reproduces intra-population variability, we compared the distances between each couple of real axons to those between each couple of simulated ones (Fig 5E and S7C Fig). The distributions for real axons and axons simulated with mechanical branching were close, while that for axons simulated with random branching was significantly different (p value between random and mecha of 9.6e−7). This analysis highlighted that our model recreated morphological features that were not initially imposed by the model, but rather emerged from its rules.
Together, integrating axon-axon interactions in our model generated populations of γ axons with a realistic range of morphologies. Furthermore, this revealed that branching in response to physical constraints increases the chance that axons successfully elongate, and reproduces the intra-population variability of morphologies observed for real axons.
So far, we have considered in our simulations that the whole population of axons follows the same rules, and have analyzed the emergent collective phenomena. We next wondered what would happen if the properties of only a single neuron would be altered in a context where the rest of the population grew according to our model. To address this question, we performed 45 independent simulations where a single axon unable to generate type I branches grew among a population of surrounding axons capable of branching. As shown by the distributions of axon lengths and traveled distances of single non-branching neurons, a bimodal behavior emerged with about half of the simulated single axons failing to grow properly (Fig 6A). This proportion is much higher than that observed in condition where all axons in the population do not form branches (10% in this condition).
The bimodal axon growth distribution we observed for non-branching single neurons was reminiscent of the growth pattern described for single neurons mutant for the imp gene [41, 42]. To quantitatively compare distributions, we took advantage of a collection of 45 confocal images obtained from Drosophila brains in which axons mutant for imp grew in the context of an otherwise wild-type population. These neurons, labeled with GFP, were reconstructed, and the distribution of their axon length and traveled distance plotted. Strikingly, as shown in Fig 6B, the bimodal distribution profiles of real imp mutant axons were very similar to those displayed in Fig 6A, with about half of the mutant individual axons failing to reach the extremity of the medial lobe. Furthermore, the morphology of reconstructed imp mutant axons was very similar to that of individual simulated axons (S8B and S8C Fig). In both cases, indeed, few short side branches were observed along the main axon, and a mixture of short and long axons was observed (compare Fig 6A and 6B, lower panels and see [41]). These results thus suggest that branching deficiency might be the primary defect induced by imp inactivation, resulting in axon growth defects exacerbated by a competition with surrounding wild-type neurons. They also demonstrate the biological relevance of our mathematical framework, and its capacity to propose a mechanistic interpretations of in vivo phenotypes. An interesting prediction of our model is that the defective growth of individual non-branching axons can be rescued by increasing their apparent growth speed. Indeed, increasing nmax in non-branching axons surrounded by branching axons increased their chance to properly elongate, both in the context of a theoretical cylindric volume (S9A Fig), and in the context of an in vivo environment (increasing nmax by five fold reduces the percentage of non-elongating non-branching axons to 5%; not shown). In contrast, increasing nmax did not significantly affect elongation success in a homogeneous population of growing axons (S9B Fig).
Our model of individual axon growth relies on two main parameters estimated from in vivo data: α and β. To investigate how differences in parameter values affect the system, we separated the wild type in vivo data set in two random halves, and estimated the parameters separately in each population. Similar values of α and β were obtained for the two populations (S5B” Fig). We then simulated the growth of γ axon populations using these two pairs of parameters, and obtained a growth efficiency of above 95% in each case (data not shown). This reveals the stability of the model in response to small changes in the parameter values, as well as the parameter estimation coherence within the data set.
To further test the robustness of the model, we then calculated the percentage of the γ axon population that fail to elongate in simulations of collective growth upon larger variations in the α and β values (see resulting map in Fig 7A). Remarkably, the combination of parameter values estimated from data, and used in our model (α = 7.45 and β = 1.67) is close to the theoretical combination that minimizes the percentage of axons showing defective growth (αo βo) (Fig 7A).
Interestingly, we noticed that the αo βo combination, while optimal for axon growth efficiency, generated axons with a reduced complexity (Fig 7B; to be compared with Fig 5C), and a lower number of type I branches (1.6 +/- 1.4 vs 2.25 +/- 1.1 for real axons). As further illustrated by the measure of intra-population morphological variability (Fig 7C), the population of axons simulated with these values was more homogeneous than the real one, failing to reproduce the diversity of axon morphologies observed in vivo (p value between axons simulated with estimated parameters and αo βo of 1.5e−44). Thus, the pair of parameters estimated from real data does not only represent an optimal combination ensuring efficient growth, but also generate biologically-relevant morphological diversity and complexity.
In this work, we have proposed a stochastic model for 3D axon growth and branching that takes into account spatial constraints imposed by the environment, as well as physical interactions between neighboring axons of the growing population. As shown, the proposed model is not only generative of axonal morphologies, but also explicative and predictive.
A major strength of this model is that it relies on parameters that are related to the biological process of axon growth, and can be estimated or calibrated from data or literature (when available).
Individual axon path, for example, is modeled by a succession of discrete steps, governed by a Gaussian Markov Chain with two parameters that are linked to the physiological and mechanical properties of individual axons. Specifically, it embeds a first term reflecting the axonal rigidity (α), and a second term modeling the attraction provided by an external field (β). Thus, the model includes three main influences on axon growth: rigidity, attraction towards a target area and randomness (representing not only the inherent stochasticity of the biological process, but also the presence of other surrounding cells). Similar ways of modeling axonal growth have already been described in previous work [43–47], considering different mathematical formulations. Most of previous models, however, were developed in 2D, and did not allow the estimation of parameters from data. Furthermore, they relied on many parameters, and sometimes parameters without direct biological meaning. Our 3D model overcomes these limits, in part because it is invariant to data spatial sampling, which is an important advantage when dealing with discrete models and discrete datasets of possibly different spatial resolutions. Classically, the parameters of such models can be estimated from data with second order statistics. However, we observed that the proposed model is broken when axons growing in a population context hit surrounding cells or other axons. Therefore, we proposed a scheme that alternates population simulation and parameter estimation to take into account these environmental constraints, and accurately estimate the parameter values. Remarkably, the parameters we thus estimated from real neurons generated populations of axons that not only recapitulated, but also predicted the properties of real axons growing in vivo in the context of the Drosophila brain.
Model parameters can be divided into three articulated sets (morphological, spatial and temporal), which enables both flexible and step-specific dissections of the axon growth process, and the study of a wide-range of growth patterns. Highlighting the versatility of our model, a variety of morphologies could be simulated by adapting parameter values (Fig 2).
Another major strength of our model is that it can provide mechanistic interpretations of neuron behavior. For example, the diversity in γ axonal trees observed in vivo is well reproduced and explained by our model that generates collections of neurons with unique morphology. Part of the variability observed after simulation is triggered by intrinsic factors, including the intrinsic stochasticity of the axon growth process modulated by the properties of the axons. Increasing axon sensitivity to the attractive field, indeed, generated populations of neurons with reduced morphological heterogeneity (Fig 7B and 7C), suggesting that differences in intrinsic properties may partly explain the various degrees of morphological variance observed in vivo in different populations. Interactions with the surrounding environment also largely contribute to the variability in axon morphology observed in our model. In particular, mechanistic constraints imposed by other γ neurons growing synchronously and competing for space define final axon paths and impact on the formation of branches. Remarkably, previous work has shown that, like mammalian brain structures, MBs exhibit a unique degree of flexibility in their organization, with neurons establishing plastic synapses and receiving unstructured rather than stereotyped inputs [48–50]. Thus, establishing a dense network of non-stereotypic axonal branches may be an optimal strategy for MB γ neurons to perform their described integration function, and in particular contextualize novel sensory experiences to provide adapted output behavior [51, 52].
An interesting finding of our work is that γ axons forming side branches grow more efficiently than axons unable to branch. Indeed, populations of branching axons reach the end of the medial lobe with a higher overall frequency than non-branching axons. Furthermore, populations of branching axons also complete overall growth more rapidly (half the time to complete growth at 95% compared to non-branching axons), which could be beneficial in the context of maturing brains subjected to the timing constraints of developmental programs. The importance of branching is also visible at the single cell level, as simulating the growth of individual non-branching axons in a competition context revealed that axons unable to form branches are out-competed by branching neighbors. This prediction fits with the growth defects observed in single imp mutant neurons grown in vivo in a wild-type environment.
How does axon branching promote elongation? The main advantage of forming branches is likely to provide neurons with the possibility to overcome local mechanical hindrances preventing the growth of axonal processes by re-deploying their axonal trees into less spatially constraint regions. Such a process may be compared to the “selective branching” model proposed to explain the oriented growth of terminal axonal arbors in response to guidance cues [53, 54]. In both cases, forming branches that can optimally respond to external cues, and thus exhibit preferential growth, represents an efficient means to regulate the elongation of axon arbors, and to adapt it to variations of the local chemical or mechanical environment.
Another finding of this study is the importance of axon-axon mechanical interactions when considering in vivo axonal growth. Such interactions were shown in different contexts to underlie the establishment of functional neuronal architecture, by promoting the self-organization of axonal arrays and their incorporation into interconnected fibers and circuits, or by defining adapted target innervation patterns [15]. Axon-axon fasciculation, for example, promotes the sorting of axons into bundles, thus facilitating the coordinated long-range navigation of axon populations [15, 55]. Axon-axon repulsion, in contrast, is essential in tiling strategies, where branches must maximize the surface they cover and minimize overlap between neighboring arbors [32, 56]. In Drosophila MBs, γ axons de-fasciculate when leaving the peduncle, and then innervate the medial lobe at high density, filling it with projections [34, 57]. Axons growing in such a crowded environment thus compete with others for space, and must evolve strategies to grow optimally.
Here, we have shown that axon branching in response to mechanical obstacles is more favorable to overall axon elongation than random branching. Indeed, forming new branches “on demand” may be an optimal strategy to provide responses adapted to local spatial constrains while preventing crowding of the growth space. Of note, we have here considered that the limits of the growth cavity and the neighboring growing axons represent the main source of mechanical obstacles, but the presence of connecting neurons extending their processes to reach γ axons is likely to also play a role. Adult γ axons, indeed, establish synapses with distinct populations of MB output neurons along the medial lobe, and also receive direct inputs from specific groups of modulatory neurons [34, 58, 59]. Thus, a possibility is that the increased density of branch points observed in the central part of the lobe (Fig 5B) may also reflect the presence of a high density of afferent and efferent neurons extending branches in this region. In the future, it will be interesting to implement in the model the presence of external processes extending to connect to γ neurons.
To date, the molecular and cellular mechanisms that may trigger formation of new branches in response to mechanical hindrance are unknown, but a possibility is that branching may be induced by axon growth cone pausing. Indeed, real-time imaging of cultured sensorimotor neurons has revealed that new branches were formed at sites where growth cones had paused, shortly after they resumed their growth [60]. Pausing was proposed to enable accumulation of material such as cytoskeletal components required for branch initiation [61]. An alternative hypothesis is that branching may be triggered by a branch-promoting signaling induced upon contact with neighboring neurons. Such a signal may be mediated by transmembrane signaling molecules present at the surface of neighboring cells or via synaptogenesis, as both were shown to induce branch formation [62–64].
In conclusion, the new principles that emerged from our model may underlie the growth and functionality of various populations of axons. For example, cerebellum-like structures, sometimes described as the vertebrate counterparts of MBs, also consist of large collections of equivalent cells that project axon-like processes into densely-packed parallel fibers [65, 66]. In the future, it will also be interesting to apply our model to vertebrate brain interconnected structures with different neuro-architectures, and to explore its relevance during both developmental and regenerative axon growth.
MARCM clones were generated as described by Wu and Luo [67], using the following fly stocks: hsp-flp, tub-Gal80, FRT19A; 201YGal4,UAScGFP; FRT19A and FRT19A imp7. Brains were dissected at the adult stage, and stained with anti-GFP (molecular probes life technology; ref A11122) and anti-FasciclinII (1D4, DSHB) primary antibodies, revealed by respectively anti-rabbit Alexa 546 and anti-mouse Cy5 secondary antibodies (see [41] for a detailed procedure). Brains were mounted in propyl-galate mounting medium, and imaged with an inverted Zeiss LSM 710 confocal microscope equipped with a 40X/1.1 NA water objective. Z sections were taken every 0.6 to 0.9 μm, with a xy pixel size of 0.09 μm.
Axons continue growing if i) they have not reached the end of the medial lobe, ii) their counter is smaller or equal to a fixed maximum value, and, iii) no other type I branch from the same neuron has reached the extremity of the medial lobe. The value of the counter is incremented by two at each time point tj if the axon fails to elongate nmax steps. Axons finding too many mechanical obstacles along their way will thus reach the maximum counter value before reaching the lobe extremity. The simulation is completed when there is no growing axon left.
Branching may occur at every time point tj after elongation. Type I branch origin can be random (with uniform probability Pb) or upon contact (after encountering two mechanical constraints in the same time point). In the first case (random branching), the branching point is placed at a step performed during tj and selected randomly, while in the second one (branching upon contact) it is placed at the axon tip. We estimated the Poisson parameter λb based on the distances between branching points in our data set. A type I branch (random or upon contact) effectively emerges if a random uniform number from zero to one is less or equal than the value of the Poisson probability for the distance from the tip to the nearest branching point. To place a type II branch, a random distance is drawn from the Poisson distribution. If it fits (randPoisson ≤ DPB (Distance to the Previous Branch)) the branch is placed at that distance from the last branch. Both types of branches initially emerge with a random uniform angle. Type II branches measure between 2 and 10 μm, and appear and disappear randomly during all the simulation until they contact another branch tip or branching point and get stabilized. If they do not stabilize by the end of the simulation they get lost. Type I branches grow following the same rules as main axons.
Main axons were automatically defined using a previously described algorithm ([42] and Supporting information). For visualization, main axon length were projected to the xy plane (in 2D) to avoid the bias due to the low resolution of confocal images in z, and to the compression of the sample along the z axis. 3D length distributions are shown in S7A and S8A Figs. The simulation code is written in MATLAB and is provided as an annotated source code.
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10.1371/journal.ppat.1005602 | Macropinosomes are Key Players in Early Shigella Invasion and Vacuolar Escape in Epithelial Cells | Intracellular pathogens include all viruses, many bacteria and parasites capable of invading and surviving within host cells. Key to survival is the subversion of host cell pathways by the pathogen for the purpose of propagation and evading the immune system. The intracellular bacterium Shigella flexneri, the causative agent of bacillary dysentery, invades host cells in a vacuole that is subsequently ruptured to allow growth of the pathogen within the host cytoplasm. S. flexneri invasion has been classically described as a macropinocytosis-like process, however the underlying details and the role of macropinosomes in the intracellular bacterial lifestyle have remained elusive. We applied dynamic imaging and advanced large volume correlative light electron microscopy (CLEM) to study the highly transient events of S. flexneri’s early invasion into host epithelial cells and elucidate some of its fundamental features. First, we demonstrate a clear distinction between two compartments formed during the first step of invasion: the bacterial containing vacuole and surrounding macropinosomes, often considered identical. Next, we report a functional link between macropinosomes and the process of vacuolar rupture, demonstrating that rupture timing is dependent on the availability of macropinosomes as well as the activity of the small GTPase Rab11 recruited directly to macropinosomes. We go on to reveal that the bacterial containing vacuole and macropinosomes come into direct contact at the onset of vacuolar rupture. Finally, we demonstrate that S. flexneri does not subvert pre-existing host endocytic vesicles during the invasion steps leading to vacuolar rupture, and propose that macropinosomes are the major compartment involved in these events. These results provide the basis for a new model of the early steps of S. flexneri epithelial cell invasion, establishing a different view of the enigmatic process of cytoplasmic access by invasive bacterial pathogens.
| Shigella flexneri is an intracellular bacterial pathogen and the causative agent of bacillary dysentery. It possesses the ability to invade and propagate within human cells by injecting bacterial effector proteins directly into host cells. Shortly after entry within a vacuole, S. flexneri induces vacuolar rupture and escapes into the host cytosol via an unknown mechanism. Using large volume correlative light electron microscopy (CLEM) and dynamic microscopy we studied discrete and highly transient steps of S. flexneri early invasion in detail. We provide the first 3D high resolution view of the S. flexneri invasion site and of vacuolar rupture itself. We find that vesicles formed at the invasion site due to injected bacterial effectors, termed macropinosomes, are functionally involved in vacuolar rupture and come into direct contact with the bacterial containing vacuole during this process. This unique and surprising pathogenic strategy stands in stark contrast to other invasive pathogens that induce direct lysis of their surrounding vacuole via the action of destabilizing bacterial proteins.
| The lifestyle of intracellular bacterial pathogens is generally divided into the following steps: contact and entry into the host cell, residence within a vacuole, escape into the cytosol or establishment of a membrane encased niche, and cell-to-cell spreading [1]. Some intracellular bacterial pathogens, called invasive bacteria, such as Yersinia pseudotuberculosis, Listeria monocytogenes, Salmonella enterica and Shigella flexneri, are able to induce their own entry into nonphagocytic host cells [2]. Invasive bacteria are thought to subvert various host endocytic pathways during their life cycle, allowing the establishment of their specific intracellular niche and evasion of host immunity [3],[4].
S. flexneri is a medically important pathogen [5] that uses a type III secretion system (T3SS) to translocate bacterial proteins called effectors into the host cell [6],[7]. Induction of macropinocytosis has been proposed as the invasion strategy for S. flexneri entry into non-phagocytic epithelial cells [2]. In short, effectors released upon cell contact induce major rearrangements of the host cell cytoskeleton, mainly polymerization of actin filaments to form bundles supporting membrane projections termed “ruffles” [8],[9],[10],[11]. This leads to the formation of large membrane protrusions, which form a pocket enclosing the bacteria and facilitating entry [12]. Such ruffles appear similar to those described for macropinocytosis, a classical non-selective cellular uptake mechanism of molecules into large, irregular shaped vesicles, termed macropinosomes, formed by the collapse and fusion of ruffles with the plasma membrane [13]. In this classic model [2], entry and macropinosome formation represent a single process. However, evidence exists that bacterial entry and membrane ruffling are associated with different bacterial effectors and host responses during S. flexneri invasion: for example, host vinculin is recruited specifically to entering bacteria [14], Rho-GTPase isoforms are recruited differentially to either entering bacteria or membrane ruffles [8], and the bacterial effector IpgD was shown to regulate ruffling morphology and is not required for bacterial entry [15],[16]. Entry has been proposed to occur initially via effector mediated contact of S. flexneri [17] to cholesterol rich lipid raft membrane domains [18] and to be mediated by specific receptors [19],[20] suggesting entry is akin to receptor mediated phagocytosis. In the case of Salmonella enterica, an invasive,T3SS-employing pathogen which shares many common aspects with S. flexneri entry into host cells, it was hypothesized that Salmonella containing vacuole and macropinosomes may be distinct, as they are sorted into different intracellular routes [21]. Thus, it appears that the classic model for S. flexneri invasion may be too simplistic, and a revised model could include two parallel processes: (i) bacterial entry and (ii) membrane ruffling, whose precise biological role in invasion has not been studied in detail.
Upon entry, S. flexneri reside within bacteria containing vacuoles (BCVs), followed by BCV rupture and escape into the cytosol within 10 minutes, a step crucial for the intracellular growth of S. flexneri [22]. Initially, it was thought that bacterial effectors, such as the translocator proteins IpaB and IpaC, induce S. flexneri vacuolar rupture [23]. For instance, IpaB was shown in vitro to oligomerize and insert into the plasma membrane of target cells, forming cation selective ion channels involved in vacuolar rupture [24]. In contrast, we recently showed that S. flexneri subverts host cell pathways for BCV rupture [25]. A siRNA screen aimed at identifying host proteins involved in vacuolar rupture yielded multiple hits related to membrane trafficking, including EEA1, SNX1, VAMP2, and the small Rab GTPases Rab4, Rab5 and Rab11. Rab5 and Rab11 were recruited to the invasion site, with Rab11 localized to a large area surrounding invading bacteria, but not to the forming BCV. Rab11 knockdown caused a strong delay in vacuolar rupture timing, providing a functional link between membrane trafficking at the invasion site and vacuolar rupture. Additionally, the bacterial effector IpgD, a PI(4,5)P2 phosphatase, was shown to regulate Rab11 recruitment and to be required for efficient vacuolar rupture [25]. How this subversion of host cell pathways by S. flexneri relates to bacterial entry and vacuolar rupture has remained unclear.
In this work, we applied dynamic imaging and advanced large volume correlative light electron microscopy (CLEM) in the form of focused ion beam/ scanning electron tomography (C-FIB/SET) to reveal key features of the pathogenic strategy employed by S. flexneri. We analyze the architecture of the S. flexneri invasion site in detail and reveal that it contains two distinct compartments, the BCV and macropinosomes. We demonstrate that Rab11 is recruited directly to macropinosomes and its activity is required for efficient vacuolar rupture. Finally, we reveal that the BCV and macropinosomes come into direct contact at the onset of vacuolar rupture. Our results represent a major step forward in understanding the mechanism of S. flexneri escape to the cytosol, and provide the basis for an updated model of S. flexneri invasion into epithelial cells.
Macropinosomes were previously observed at the invasion site of Salmonella enterica in epithelial cells using phase contrast microscopy and fluorescent dextran added to the infection media, acting as a fluid phase marker [21]. We set out to examine macropinosome formation at the S. flexneri invasion site by measuring internalization of fixable dextran conjugated to Alexa Fluor-647 (Fig 1A). HeLa, Caco-2 and NRK cells were infected for 30 minutes with wild-type dsRed expressing bacteria, added together with the fluorescent conjugate. This was followed by washes to remove non-internalized dextran, fixation and staining for actin to visualize the site of invasion, and DNA to visualize cell nuclei. Multiple dextran containing vesicles were observed surrounding S. flexneri at the invasion site to a similar extent in all host cell types used. The size distribution of the vesicles was quantified in HeLa cells using automated software. Vesicles are heterogeneous in size, with the majority of vesicles having a diameter less than 1μm and a fraction of larger vesicles observed. As S. flexneri is generally thought to enter cells within a pocket formed by large membrane protrusions [12], we expected the BCV to also contain detectable fluid phase marker. Surprisingly, bacteria were never observed within dextran positive vesicles, implying S. flexneri containing vacuoles exclude most extracellular fluid. The observed vesicle size heterogeneity and uptake of extracellular fluid are reminiscent of features classically associated with macropinosomes [26], suggesting S. flexneri is surrounded by macropinosome-like vesicles (here on referred to simply as ‘macropinosomes’, see discussion) at the invasion site but does not reside within them.
As the bacterial effector IpgD has been previously implicated in regulation of ruffling morphology during S. flexneri invasion [15],[27], we examined its role in macropinosome formation (Fig 1B and 1C). Confocal microscopy of HeLa cells infected by wild-type and ΔipgD strains in the presence of dextran Alexa Fluor-647 for 30 minutes revealed a reduction in the number of macropinosomes surrounding bacteria when using the mutant strain (Fig 1B). Automated quantification of the number of macropinosomes formed per bacterium revealed that macropinosome formation was reduced by around 60% when using the IpgD mutant (Fig 1C). Infection with ΔipgD/IpgD strain showed partial complementation of vesicle formation, in accordance with previously reported results [25] (S1 Fig). We conclude that macropinosome formation at the S. flexneri invasion site is regulated by the bacterial effector IpgD.
Intracellular bacterial pathogens are normally thought to subvert pre-existing host endocytic vesicles during invasion (see introduction), however our study indicated that nascent macropinosomes appear to be a major endomembrane component at the invasion site of S. flexneri. We examined the relative contribution of pre-existing endocytic vesicles to the endomembrane content at the invasion site (Fig 1D). To this end, we performed sequential labeling experiments, pre-loading cells with dextran Alexa Fluor-488 for two to three hours prior to infection to allow uptake by the host endocytic compartment. This was followed by extensive washes and infection in the presence of dextran Alexa Fluor-647. Extensive labeling of punctate dextran Alexa Fluor-488 endomembrane structures was observed throughout the cells. Strikingly, we could not detect accumulation of dextran Alexa Fluor-488 containing vesicles around invading bacteria, while dextran Alexa Fluor-647 vesicles were highly enriched around bacteria. Furthermore, we did not detect any co-localization of the two differently colored dextrans around the entering bacteria at the measured time-point. Quantitative image analysis revealed 92% of total vesicle volume (i.e. combined volume of vesicles labeled by either dextran Alexa Fluor-488 or dextran Alexa Fluor-647) at the invasion site is occupied by dextran Alexa Fluor-647 containing vesicles. This indicated that the local environment around invading S. flexneri is occupied by newly formed macropinosomes, without significant recruitment of pre-existing host endocytic vesicles, arguing against active subversion of pre-existing host endocytic pathways by S. flexneri during invasion.
Since fine structural details of the S. flexneri invasion site are obscured by the resolution limit of standard light microscopy, we applied CLEM in the form of correlative large volume focused ion beam/scanning electron tomography (C-FIB/SET) to examine the S. flexneri invasion site in detail [28] (Fig 2). This emerging technique combines 3D fluorescent and large volume electron tomography into a single correlated data set, providing three features ideal for investigation of S. flexneri invasion: First, discreet and highly transient (in the order of minutes) stages of invasion can be targeted using fluorescent microscopy with stage-specific fluorescent markers prior to tomography acquisition. Thus, precise access to early invasion (before BCV rupture) and to the vacuolar rupture event itself can be gained. Secondly, the large cellular volumes (in the order of 1000 μm3) acquired using this technique allow visualization of entire invasion sites, accounting for multiple bacteria, vesicles and other cellular structures present at the site. The BCV’s and other vesicles’ membranal integrity and their connectivity to other compartments can be examined in 3D from all axes, providing structural information not easily accessible by classic serial sectioning and tomography approaches [29]. Finally, the fluorescent labeling correlated in 3D to the tomography data (with bacteria observed by light and electron microscopy acting as alignment fiducials) provides molecular specificity to features observed within the tomography volume, albeit within the light microscopy diffraction limit (see materials and methods). Overall, 15 C-FIB/SET data sets of invasion sites were acquired for this study. As fluorescence microscopy of the S. flexneri invasion site revealed that BCVs exclude detectable fluorescent dextran and are surrounded by dextran containing vesicles in all our experiments, we first set out to examine the architecture of the early invasion site in detail using C-FIB/SET. A detailed description of the correlative workflow for the data set analyzed in Fig 2 is presented (Fig 2A, S1 Movie): First, a S. flexneri invasion site was imaged by confocal microscopy. As a tight actin enrichment around bacteria was previously shown to appear exclusively prior to vacuolar rupture [30], a site containing bacteria with this feature (as indicated by phalloidin staining) was chosen, thereby providing a clear indication of early invasion (Fig 2A, inset). After sample processing for electron microscopy, a FIB/SET data set was acquired at the exact same location. The two data sets were then combined into a single correlated volume, providing a full 3D view of the entire S. flexneri invasion site from all axes. Membrane ruffles are observed at the cell surface while BCVs and smaller vesicles are seen in the cytosol.
We examined the structure of the BCV in early invasion in detail and delineated the typical BCV structural motifs (Fig 2B, S2 Movie). The data in Fig 2B is representative of all datasets acquired during early invasion in our study. We found that the bacterial cytosol and membrane are surrounded by a layer of low electron density. Based on annotation from previous ultrastructural studies we associate this layer with bacterial lipopolysaccharides (LPS) (e.g. [31]). Furthermore, this layer is also present in rupturing BCVs missing parts of their BCV membrane (see below), indicating it does not represent the vacuole lumen. These bacterial structures are tightly enclosed by the vacuole membrane. An additional electron dense layer observed only around bacteria surrounded by phalloidin stain (as identified by fluorescence microscopy) is consequently identified as actin. Finally, we observe multiple small vesicles surrounding the BCV, but not contacting it nor each other. In all data sets observed, BCVs contained a single bacterium (often in division), were structurally uniform and tightly surrounding the bacterium inside. We were not able to detect a luminal BCV space around the bacteria in any of our data sets. Overall, our high resolution correlated view of the early S. flexneri invasion site reveals the BCV is a tight, uniform compartment, structurally distinct from the surrounding heterogeneous macropinosomes. We conclude that the BCV and macropinosomes present at the early invasion site represent two distinct compartments.
Our two color dextran labeling experiments (see Fig 1D), indicated that the local environment around invading S. flexneri is occupied by macropinosomes formed in situ during invasion without significant recruitment of pre-existing host endocytic vesicles. We applied C-FIB/SET to correlate the total vesicle population at the invasion site with dextran Alexa Fluor-647 labeling (added during infection) (S2 Fig). FIB/SET analysis reveals the entire vesicle population around invading S. flexneri irrespective of the fluorescent probe, while the dextran Alexa Fluor-647 label provides an indication of the volume occupied by vesicles formed during invasion. In the two datasets presented (S2A and S2B Fig), 95% and 99% of vesicles found at the invasion site reside within the fluorescent dextran label (S2C Fig). In data set B, five of the largest vesicles, found near the surface of the cell, lie outside of the dextran labeling, most likely representing late forming macropinosomes formed after removal of dextran in the washing phase. This result confirms that newly formed macropinosomes are the major compartment at the S. flexneri invasion site, in agreement with our two color dextran labeling experiments (Fig 1D).
We have recently shown that the bacterial effector IpgD is required for efficient vacuolar rupture [25]. As IpgD is also a regulator of macropinosome formation (see Fig 1B and 1C), we hypothesized these two processes may be functionally related. In order to test this hypothesis, we performed two bacterial effector mutant library screens aimed at identifying the relationship between the number of macropinosomes at the invasion site and vacuolar rupture timing (Fig 3A and 3B, selected hits are presented. For screen details see materials and methods. Full screen results are provided in S3A Fig). First, the library was screened for the number of macropinosomes formed at the invasion site using quantitative image analysis of fluorescent dextran containing vesicles (Fig 3A). Various effectors alongside IpgD were found to affect macropinosome formation, including IpgB1, IpgE (a chaperone for IpgD [15]) and IpgB2. Secondly, the library was screened for vacuolar rupture timing using dynamic microscopy (Fig 3B). We found an inverse correlation between the number of macropinosomes formed at the invasion site and the delay in vacuolar rupture timing, i.e. the fewer macropinosomes found at the invasion site, the longer rupture is delayed. Of particular interest is the effector IpgB1, the strongest hit in both screens. IpgB1 was shown to be involved in the early stages of S. flexneri invasion, is associated with membrane ruffles, and can induce membrane ruffling via stimulation of Rac1 and Cdc42 activities. Like IpgD, it has no known function in disrupting membrane integrity or pore formation to our knowledge [32]. Thus, our mutant screens results revealed that vacuolar rupture efficiency is correlated to macropinosome availability. We also studied the effect of drugs known to inhibit macropinocytosis and actin dynamics on macropinosome formation and vacuolar rupture timing. Amiloride, a Na(+)/H(+) exchange inhibitor known to cause indirect inhibition of macropinocytosis [33] did not inhibit macropinosome formation and entry at low doses and was cytotoxic at higher doses. As we have previously identified via high-content siRNA screening that Arp2/3 subunits are among host proteins involved in S. flexneri uptake and vacuolar rupture [25] we tested CK-666, an Arp2/3 complex inhibitor that does not stimulate dissociation of preformed actin branches in vitro [34]. CK-666 inhibited macropinosome formation without inhibiting bacterial entry, and significantly delayed vacuolar rupture timing (S3B Fig). The selective inhibition of macropinosome formation by CK-666 may be due to the drug having a stronger effect on ruffle formation, a process requiring massive actin re-arrangement, as opposed to the more small scale actin re-arrangement required for bacterial uptake. Overall, our results demonstrate that vacuolar rupture timing is dependent on the availability of macropinosomes, indicating that membrane ruffling and macropinosome formation may be functionally linked to vacuolar rupture.
We therefore set out to examine the cell biology underlying the relation between macropinosomes and vacuolar rupture in detail using dynamic microscopy, functional assays and structural analysis. First, we examined the spatial and temporal proximity of macropinosomes to the rupturing BCV. We performed live microscopy of S. flexneri invasion in cells transfected with a marker of phosphatidylinositol 3-phosphate (PI3P), 2XFYVE-GFP, used for labeling macropinosomes, and galectin-3-mOrange used for labeling vacuolar rupture. PI3P has been previously reported to be associated with early macropinosome maturation, in particular with the cup closure step [35]. We found that 2XFYVE-GFP was partially co-localized with dextran positive vesicles at the invasion site, this partial co-localization likely due to the transient nature of its recruitment to forming macropinosomes [35] (S3C Fig). Galectin-3-mOrange is commonly used as a vacuolar rupture marker as it specifically labels the BCV only after loss of vacuole integrity [36]. 3D multi-channel images were acquired every 30 seconds (Fig 3C and 3D, S3 Movie) and the resulting movies were quantified by counting the number of events where macropinosomes were found bordering (within 1μm) the rupturing BCV, as indicated by the first appearance of a galectin-3-mOrange signal (Fig 3E left). In 73% of all vacuolar rupture events analyzed (n = 30 events in three independent experiments), macropinosomes were found bordering the BCV, with 50% of events containing more than one bordering vesicle. This number most likely underrepresents such events as not all macropinosomes are labeled with 2XFYVE-GFP. In order to assess whether macropinosome proximity to the BCV occurs upstream of rupture or is a product of rupture, we performed high temporal resolution imaging with multi-channel confocal stacks acquired every 5 seconds (S3D Fig, S4 Movie). This allowed clear detection of bordering macropinosomes at the onset of the galection-3-mOrange signal appearance and therefore at the onset of vacuolar rupture. Data quantification (Fig 3E, right) showed that in 92% of the subset of vacuolar rupture events containing bordering macropinosomes (n = 36 events in three independent experiments), bordering occurred at least one frame before the onset of rupture, indicating this proximity is present upstream of vacuolar rupture. In all movies acquired, rupturing BCV’s (i.e. labeled with galectin-3-mOrange) were never co-labeled with 2XFYVE-GFP, and 2XFYVE-GFP positive vesicles were never co-labeled with galection-3-mOrange, implying macropinosome proximity to the rupturing BCV does not result in membrane fusion or ruptured macropinosomes (see discussion).
Rab-GTPases act as markers for macropinosome formation and maturation [13],[37] and have been previously shown to be recruited to the S. flexneri invasion site to various degrees [25]. We examined the recruitment of Rabs to the invasion site in the context of dextran labeled macropinosomes and invading bacteria (S4A Fig). Cells transfected with Rab4-GFP, Rab5-GFP, Rab7-GFP and Rab11-GFP were infected with wild-type fluorescent bacteria for 30 minutes in the presence of dextran Alexa Fluor-647. Rab4-GFP was not recruited to the invasion site, in agreement with previous studies [25], while other Rabs examined were recruited to macropinosomes at the invasion site to various degrees but not around invading bacteria. As Rab11’s association with macropinosomes has not been previously demonstrated in other systems to our knowledge, and its recruitment has been previously shown to be prevalent at the invasion site and is required for efficient vacuolar rupture [25], we decided to examine its association with macropinosomes in detail.
While Rab11 is classically associated with recycling endosomes [38],[39], our results suggested that the major compartment at the invasion site is composed of macropinosomes and that host endosomes are not recruited during invasion (Fig 1D, S2 Fig). Furthermore, our fixed invasion experiments (S4A Fig) revealed some macropinosomes directly surrounded by Rab11-GFP. Taken together, these results suggest that during invasion Rab11 can associate directly with newly formed macropinosomes via a non-classical pathway that does not involve recycling endosomes. In order to test this hypothesis we performed dextran pulse chase experiments combined with live imaging of cells transfected with Rab11-GFP and galectin-3-mOrange (Fig 4A–4C, S5 Movie). Cells were infected with wild type S. flexneri in the presence of dextran Alexa Fluor 647 for 22 minutes. At that point the cells were quickly washed to remove non-internalized dextran from the media and live imaging was initiated. Cells were imaged in 3D at 60s intervals. The time point for dextran washes (t = 0) was chosen so that invasion sites already containing internalized dextran but prior to vacuolar rupture (as indicated by galectin-3-mOrange) could be captured. Thus the relation between dextran containing macropinosomes and Rab11-GFP recruited to the invasion site could be examined during live imaging of early invasion. A representative experiment is presented. We found that Rab11 is directly and dynamically recruited to macropinosomes prior and during vacuolar rupture, conforming the non-classical association between macropinosomes and Rab11 at the S. flexneri invasion site. As expected, live imaging also revealed that rupture events occur in close proximity to macropinosomes (in agreement with Fig 3C–3E).
Given the non-classical association between macropinosomes and Rab11 occurring during S. flexneri invasion, we examined the functional role of Rab11 in relation to macropinosome formation and vacuolar rupture. We employed a functionally impaired Rab11: Rab11S25N-GFP, a GDP-locked dominant negative (Rab11 DN) [40]. We found that it did not inhibit macropinosome formation or bacterial entry, and unlike Rab11-GFP, was not recruited to macropinosomes (Fig 4D left, S4B and S4C Fig). Next we examined the functional role of Rab11 in vacuolar rupture. We transfected cells with Rab11-GFP or Rab11 DN and galectin-3-mOrange and performed live imaging of S. flexneri invasion to determine vacuolar rupture timing (Fig 4D right, S6 Movie, S7 Movie showing representative experiments). We found that vacuolar rupture is significantly delayed when using the Rab11 DN. Our results indicate that while Rab11 is not required for macropinosome formation or bacterial entry, its activity is required for recruitment to macropinosomes and efficient vacuolar rupture. We hypothesize that Rab11’s role in vacuolar rupture is manifested through its well described function in vesicle trafficking regulation [39] acting uniquely on macropinosomes in the case of S. flexneri invasion (see discussion).
Overall our effector mutant screens, live imaging and functional studies reveal that macropinosomes at the invasion site are the target for direct recruitment of Rabs, they border the BCVs at the onset of vacuolar rupture, and their formation and trafficking are required for efficient vacuolar rupture. Together, these results provide for the first time evidence that vacuolar rupture involves two cellular compartments, the BCV and macropinosomes.
We hypothesized that macropinosomes are directly implicated in vacuolar rupture via physical contacts with the BCV. Such contacts are obscured when using fluorescence microscopy due to the limited resolution of light microscopy. We therefore applied C-FIB/SET to image the very short-lived and highly dynamic rupturing event in detail, acquiring correlative data sets of invasion sites containing BCVs labeled with galectin-3-mOrange to indicate rupture (Fig 5). C-FIB/SET is particularly suited for the unambiguous identification of compartmental contact points at the invasion site, as it provides information in all three axes and within a large cellular volume likely to contain multiple contacts occurring at various orientations. Strikingly, in cells infected with the wild-type strain (Fig 5A, S8 Movie) rupturing BCVs were found in direct contact with multiple surrounding vesicles. A partly dissociated BCV membrane emanating into the cytosol from the contact point between a macropinosome and a BCV was observed, with most of it still attached to the bacterium (Fig 5A, white arrows, yellow). Smaller intraluminal vesicles within the large macropinosome were commonly observed at the interface between the macropinosome and the BCV (Fig 5A, black arrowhead). As IpgD is required for efficient rupture [25], we examined its involvement in the formation of macropinosome-BCV contact points. Infection with a ΔipgD strain revealed a reduction in the number of vesicles in contact with the BCV, yet macropinosome-BCV contact points morphologically identical to the wild-type were still always observed (Fig 5B, S9 Movie), indicating that IpgD regulates macropinosome availability but does not impact the formation of macropinosome-BCV contacts during rupture. In the rupture events presented here, most of the BCV membrane is still present (Fig 5B, yellow), and rupture is observed at the opposite side of the bacterium in relation to the contact points (Fig 5B, black arrows). We conclude that the BCV and surrounding macropinosomes come into direct contact during vacuolar rupture. Thus, our functional, dynamic and structural analysis (Figs 3, 4 and 5) suggest that macropinosomes are required for efficient vacuolar rupture and that macropinosome-BCV contacts are involved in this process.
We report here that macropinosomes are a central compartment in S. flexneri invasion and that they are implicated in S. flexneri vacuolar rupture. Our results lay the ground for a new model of the early steps of S. flexneri invasion into epithelial cells (Fig 6A): Invasion begins with two distinct processes occurring in conjunction: bacterial entry into a tight BCV and the regulated formation of macropinosomes via membrane ruffling. Then, host Rab GTPases are recruited directly to nascent macropinosomes, followed by direct contact between macropinosomes and the BCV at the onset vacuolar rupture. Host endomembranes are not recruited to the invasion site, indicating invasion occurs without subversion of pre-existing host endocytic pathways or pre-existing endomembrane compartments. Macropinosomes are revealed to be key players in the process of vacuolar rupture, as indicated by functional experiments demonstrating that the efficiency of vacuolar rupture is dependent on macropinosome availability and Rab11 activity (acting in direct association with macropinosomes). This new model provides a conceptual framework that accommodates previous studies [8],[14],[15],[16],[25], and has predictive power in regard to the possible biological role of membrane ruffling and macropinosomes in other pathogenic systems [11],[21]. For example, various observations made regarding the bacterial effector IpgD are put into biological context with our work: While IpgD is not required for bacterial entry [15], it is known to promote ruffling [15], is required for macropinosome formation (Fig 1B and 1C), Rab11 recruitment to the invasion site and efficient vacuolar rupture [25],[27]. These activities are now placed in sequence, as ruffling, macropinosome formation, Rab GTPase recruitment to macropinosomes and vacuolar rupture represent a cascade of causally connected events occurring during S. flexneri invasion (Fig 6B).
Vesicles induced by S. flexneri share some similarities with classically described macropinosomes formed in non-pathogenic systems [13],[41]. Their formation in the context of membrane ruffling, size heterogeneity and uptake of fluid phase marker resemble that of macropinosomes (and micropinosomes, accounting for smaller vesicles formed at the invasion site). However, their formation is induced via the action of bacterial effectors that modulate other cellular pathways as well, and thus may affect vesicle biochemistry. Indeed, our results demonstrate that S. flexneri induced macropinosomes are associated with Rab11, a typical marker for the recycling endosomal pathway [38],[39], not known to label macropinosomes in other systems. Macropinosome trafficking in the absence of pathogens is cell type dependent, and while professional phagocytizing cells direct macropinosomes into the lysosomal pathway [42], in some non-professional phagocytizing cells, macropinosomes were reported to recycle to the plasma membrane, with little or no interaction with endosomal vesicles [41],[43],[44]. It may be that the macropinosome-BCV contacts are a product of “misdirected” trafficking towards the BCV that we like to term “internal recycling”, instead of recycling to the plasma membrane, as both compartments present similar membranal features.
Intracellular pathogens are generally thought to engage and subvert existing housekeeping endomembrane system pathways, such as the lysosomal or recycling pathway [38]. This pathogenic strategy is a hallmark of Salmonella invasion into epithelial cells, with many studies demonstrating complicated subversion patterns induced by Salmonella during invasion [4],[45]. In stark contrast, we report here a pathogenic strategy that does not exploit a pre-existing host pathway. Instead, S. flexneri induces the formation and trafficking of macropinosomes formed in situ in a “local pathogenic pathway” that involves the recruitment of Rab GTPases to macropinosomes, and does not involve recruitment of host endomembranes. Thus at each S. flexneri invasion site a local trafficking pathway is initiated without it being normally present in the cell. This pathway is subsequently exploited by the pathogen for vacuolar escape. The strategy employed by S. flexneri represents a divergence from existing paradigms of pathogen- host interactions centered on the successive pathogen subversion of established trafficking pathways [3]. In resemblance to other pathogenic systems, such as viral infection [46], macropinosomes are revealed to be a key constituent of the invasion process.
The establishment of a new model for S. flexneri invasion presented here hinged on the utilization of the emerging technique of C-FIB/SET. First, C-FIB/SET was used to demonstrate that S. flexneri enters cells within a tight BCV (Fig 2), likely explaining the lack of detectable fluorescent dextran labeling around bacteria when studied by light microscopy (Fig 1). This result strongly supports a model where bacterial entry occurs via a phagocytosis- like mechanism leading to tightly enveloped bacteria, and not, as is classically suggested (see introduction), via a macropinocytosis- like entry process—which would result in spacious, heterogeneous BCVs [2] (see Fig 6). In this “two parallel pathways” invasion model, the biological function of membrane ruffling and the resulting macropinosomes is related to the downstream step of vacuolar rupture, and not, as previously thought, to bacterial entry. Second, by specifically targeting rupturing vacuoles (as indicated by galectin-3-mOrange fluorescent labeling) for FIB/SET acquisition, this enigmatic and transient event became reliably and reproducibly accessible to 3D ultrastructural investigation for the first time, revealing macropinosome-BCV contacts and emanating dissociated membranes. The biological observations obtained through the application of C-FIB/SET in our study not only led to the emergence of new biological concepts regarding S. flexneri invasion, but act to demonstrate the power of this technique to investigate complex biological arenas containing multiple transient or rare events, complex 3D cellular organization and intricate structural interfaces.
We reveal a functional relationship between macropinosomes and the process of vacuolar rupture, mediated by the activity of Rab11. Macropinosome formation and trafficking ultimately result in macropinosome—BCV contact points formed at the onset of vacuolar rupture. How these contact points may drive membrane destabilization and eventual vacuolar rupture remains an open question. Our results lay the ground for future in depth investigations of the molecular mechanism and underlying biophysics driving this process, however initial insights into the mechanism of rupture can be gained from several observations: Firstly, in both fixed and dynamic light microscopy experiments, macropinosomes were never labeled with galectin-3-mOrange during vacuolar rupture. As BCV membranes become coated with galectin-3 at the loss of vacuolar integrity [36], fused or ruptured macropinosomes (i.e. with inner membranes exposed to the BCV or cytoplasm) would be expected to be labeled with galectin-3 as well. Furthermore, rupturing BCVs were never labeled with fluorescent dextran (fixed and pulse-chase experiments) or 2XFYVE-GFP (live microscopy; even during time-lapse studies with very high temporal resolution), as would be expected had dextran flowed from fusing macropinosomes into the collapsing BCV during rupture or if lipids were exchanged. These two observations argue against macropinosome -BCV fusion or macropinosome rupture as the underlying process facilitating BCV rupture, suggesting an altogether different mechanism is in place. Secondly, IpgD regulates macropinosome formation as well as rupture timing [25]. Infections with the ΔipgD strain exhibit overall fewer macropinosomes in contact with rupturing BCVs, while the typical macropinosome-BCV contact point is still present in every rupturing BCV examined (Fig 4B). A decrease in the amount of contacts may limit damage to the BCV, explaining the observed delay in vacuolar rupture when using this strain and hinting at a cumulative damaging effect of macropinosome-BCV contacts. A decrease in macropinosome trafficking towards the BCV when using Rab11 KD or Rab11 DN would result in a similar effect. One possible mechanism could involve the formation of intraluminal vesicles (ILVs) pinched off from the BCV membrane and into maturing macropinosomes, causing increased membrane tension and eventually vacuolar rupture. Such vesicles are often observed at the interface between the BCV and connected macropinosome (Fig 5A, black arrowhead). The molecular machinery driving the formation of ILVs has been described in endosomes and phagosomes, but not in macropinosomes thus far to our knowledge [41]. Finally, the involvement of multiple bacterial effectors in vacuolar rupture (Fig 3A and 3B), without any single effector mutant inducing a complete arrest of rupture, implies that vacuolar rupture is not dependent on a single bacterial effector as described in other systems [47]. While other invasive bacterial pathogens employ direct lysis of their surrounding vacuole via bacterial proteins inserted directly into the membrane [48],[49], S. flexneri vacuolar rupture is most likely a complex process that involves multiple bacterial and host molecules acting to facilitate macropinosome formation, trafficking, contacts with the BCV and membrane destabilization. Future studies using imaging, biochemical and biophysical approaches will allow further elucidation of this critical step in the growth of S. flexneri.
Macropinosomes have been identified at the invasion site of intra-cellular bacterial pathogens years ago in seminal works by S. Falkow and others [11],[21],[50]. However, since then, their precise role in intra-cellular bacterial invasion has remained poorly understood and their importance overlooked. We report here a central role for macropinosomes in the pathogenic strategy of S. flexneri, and predict that this compartment is exploited by other invasive bacteria as well, possessing various biological functions in the context of pathogenicity yet to be discovered.
The following S. flexneri strains were used: M90T AfaI [22], expressing the adhesin afaI (Figs 1B, 2, 3C–3E and 4A–4C), the invasive ΔipgD AfaI [30] (Figs 1B and 5B), M90T (Figs 1C and 4D), ΔipgD (1C) and M90T expressing dsRed (Fig 1A and 1D). For the two mutant library screens (Fig 3A and 3B) all S. flexneri strains used were kindly provided by JR. Rohde (Dalhousie University): the strains do not express afaI and are a part of a pwR100 collection [51]. The parent strain for this collection is a streptomycin-resistant strain of S. flexneri serotype 5a (M90T-Sm). Growth medium was supplemented with ampicillin (50 μg/ml) for all strains except for ΔipgD supplemented with tetracycline (5 μg/ml). All bacterial strains were cultured on tryptic casein soy broth (TCSB) agar plus 0.01% Congo red with 20mg/mL agar at 37°C.
Human epithelial HeLa cells (clone CCL-2 from ATCC) and NRK fibroblasts (Clone CRL- 1570 from ATCC) were cultured in Dulbecco’s modified Eagle’s medium (DMEM) supplemented with 10% (vol/vol) fetal bovine serum (FBS) at 37°C, 5% CO2. Caco-2 TC7 were kindly provided by P. Sansonetti (Institut Pasteur) and cultured as above except with 10% CO2. (Fig 1A, HeLa cells were used for all other experiments).
For invasion experiments, overnight bacterial cultures were inoculated at a 1/100 dilution in TCSB with the appropriate antibiotic if required and grown to an optical density of ≈ 0.4 at 600 nm (OD600). Prior to infection, bacteria were washed with PBS and resuspended in EM buffer (120 mM NaCl, 7 mM KCl, 1.8 mM CaCl2, 0.8 mM MgCl2, 5 mM glucose, 25 mM HEPES, pH 7.3) and incubated with poly-L-lysine for 15 min if they did not express AfaI. For imaging of fixed samples, bacteria were added to cells at MOI 30 (Figs 1A, 1C and 3A) or MOI 20 for all other fixed experiments and allowed to adhere for 10 minutes at RT. Samples where then incubated at 37°C for 30 minutes, washed three times in PBS and fixed with cold PFA 4% followed by staining with DAPI and Phalloidin. Dextran Alexa Fluor-488 and Alexa Fluor-647, 10,000 MW fixable (Life Technologies) were added to EM buffer and placed with cells for two to three hours before infection for sequential labeling assay (Fig 1D) or added during infection at a final concentration of 0.5μg/ml (all other experiments). For rupture timing screen (Fig 3B) and live imaging (Figs 3C–3E and 4A–4C) bacteria were added at a MOI of 50.
HeLa cells were seeded into 96-well glass bottom plates (Greiner) at a density of 7000 cells per well 24 h prior to transfection. The cells were then transfected as described [25] with Rab11-GFP (Fig 4A–4C), 2XFYVE-GFP (Fig 3C–3E), galectin-3-mOrange (Figs 3C–3E, 4 and 5) or Rab11S25N-GFP, a Rab11 GDP-locked dominant negative (DN) (Fig 4D, kindly provided by B. Goud (Institut Curie)) using X-tremeGENE 9 reagent (Roche) for 24–48 h, according to the manufacturer’s instructions. For mutant library rupture timing screen (Fig 3B) cells were transfected with Actin-mOrange [30] and galectin-3-GFP [36] in an identical manner.
Fixed samples were imaged using a Perkin Elmer UltraView spinning disc confocal microscope, with a 60X/ 1.2NA water objective and a Z step size of 0.3 μm. For time lapse microscopy, cells were imaged with a confocal microscope (for experiments in Figs 3C–3E and 4A–4C) at 37°C with a 40X / 1.3 NA oil objective. Every 60, 30 or 5 seconds depending on experiment, a stack of 6 z-planes (step size of 0.5μm) was acquired sequentially in two or three channels using a 488nm, 561nm and 640nm laser depending on experiment. For mutant library rupture timing screen (Fig 3B) and Rab11 DN experiments (Fig 4D) cells were imaged at 37°C using a Nikon Ti-E wide-field microscope with a 20X air objective. Imaging was performed with excitation at 465 to 500 nm and 540 to 565 nm. Images were acquired every 90 or 120 seconds (depending on the exposure time) for 1 to 2 hours. In each screen experiment two mutants and one control (wild-type strain) were screened and images of 10 positions per strain acquired. For the Rab11 DN experiments 8 positions per condition were acquired with WT and IpgD as controls.
Sample preparation and acquisition were performed as previously described [25]. In short, HeLa cells were cultured on MatTek dishes with finder grid (MatTek Corporations). Infections were performed as described above. Samples were fixed with 0.1% Glutaraldehyde and 4% paraformaldehyde for 30 minutes at room temperature. After high resolution confocal microscopy imaging (using 60X objective) positions of interest were marked using phase contrast and fluorescence with 10X and 20X objectives. Samples were then fixed overnight with 2.5% Glutaraldehyde in 0.1 M cacodylate buffer followed by fixation in 2.5% glutaraldehyde + 0.4% Tannic Acid pH 7.2 in 0.1 M cacodylate buffer for 30 min at RT. Samples were stained with 1% OsO4 in DDW for 30 min at 4°C. Samples were dehydrated in graded ethanol series and embedded in Epon followed by FIB/SET performed in a Helios Nanolab Dual beam (FEI) at the electron microscopy unit at the Weizmann Institute of Science (Israel). XY pixel size range was 6.5–8.3 nm and slice thickness 10 nm. Data was aligned using ImageJ (http://imagej.nih.gov/ij/). Amira and Avizo (FEI) were used for 3D visualization, data correlation, manual segmentation and supporting movies. Internalized bacteria were used as correlating fiducials. Inverted contrast is presented. Overall 15 C-FIB/SEM datasets of S. flexneri invasion sites were acquired, 11 of WT strain and 4 of ΔipgD strain.
Macropinosome size distribution (Fig 1A) was quantified using ICY (http://icy.bioimageanalysis.org/). “Spot detector” plugin was applied to segment dextran containing vesicles in 3D confocal stacks. A spherical assumption was used to extract vesicle diameter. 2300 vesicles from three separate experiments were analyzed. WT vs. ΔipgD strain vesicles/bacterium (Fig 1C) was quantified using CellProfiler (http://www.cellprofiler.org/). Macropinosomes were counted only when found in actin labeled ruffles that contained at least one bacterium. Quantification is based on six separate experiments including in total 1186 cells, 1877 bacteria and 4330 vesicles. Unpaired t test was used for significance. Vesicle volume distribution (Fig 1D) was performed using a custom ICY protocol. 34 invasion sites in two experiments were chosen for analysis. Shigella-dsRed, dextran Alexa Fluor-488 and Alexa Fluor-647 signals were segmented in 3D. The sum of volumes of all vesicles of each type within 30 voxels of bacteria in each invasion site was calculated and normalized to the total vesicle volume at the site. Overall 476 vesicles were used in analysis. The mutant library macropinosome formation screen (Fig 3A) was quantified using CellProfiler in an identical manner to the WT vs. ΔipgD strain vesicles formation assay described above. Results were obtained from two independent experiments, each containing at least 30 invasion sites, with 907 invasion sites analyzed in total. For mutant library rupture timing screen and Rab11 DN experiments (Figs 3B and 4D), time lapse series were visually analyzed with Fiji (http://fiji.sc). For rupture timing screen three independent experiments per strain were performed, at least 50 invasion events per strain were measured, with 791 events in total. For Rab11 DN experiments three independent experiments per condition were performed, with 946 rupture events analyzed in total (WT 351, IpgD 193, DN 402). Vacuolar rupture timing was measured as the time interval between the beginning of ruffle formation and the appearance of a Galectin-3 localized signal. Statistical analysis was performed in GraphPad Prism software v6. The difference between WT and mutants, or DN rupture timing was evaluated using one-way ANOVA. p < 0.05 was considered as significant: *p<0.05, **p<0.01, ***p<0.0001, and ****p<0.0001. Time lapse microscopy (Fig 3C–3E) was manually quantified using Fiji, with bordering event counted when 2XFYVE-GFP and galectin-3-mOrange signal where found within 6 pixels distance of each other. Analysis of macropinosomes bordering at the onset of BCV rupture was performed by manually counting all instances of 2XFYVE-GFP positive vesicles present at the rupture site at least one frame before galectin-3 cage appearance (positive hit) in contrast to vesicles appearing only during or after galectin-3 cage appearance (negative hit). 36 events were imaged in eight independent experiments All quantitative data in the manuscript is presented as mean, with error bars presented as s.d.
Supporting figures materials and methods can be found in S1 Text.
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10.1371/journal.ppat.1006224 | ALPK1 controls TIFA/TRAF6-dependent innate immunity against heptose-1,7-bisphosphate of gram-negative bacteria | During infection by invasive bacteria, epithelial cells contribute to innate immunity via the local secretion of inflammatory cytokines. These are directly produced by infected cells or by uninfected bystanders via connexin-dependent cell-cell communication. However, the cellular pathways underlying this process remain largely unknown. Here we perform a genome-wide RNA interference screen and identify TIFA and TRAF6 as central players of Shigella flexneri and Salmonella typhimurium-induced interleukin-8 expression. We show that threonine 9 and the forkhead-associated domain of TIFA are necessary for the oligomerization of TIFA in both infected and bystander cells. Subsequently, this process triggers TRAF6 oligomerization and NF-κB activation. We demonstrate that TIFA/TRAF6-dependent cytokine expression is induced by the bacterial metabolite heptose-1,7-bisphosphate (HBP). In addition, we identify alpha-kinase 1 (ALPK1) as the critical kinase responsible for TIFA oligomerization and IL-8 expression in response to infection with S. flexneri and S. typhimurium but also to Neisseria meningitidis. Altogether, these results clearly show that ALPK1 is a master regulator of innate immunity against both invasive and extracellular gram-negative bacteria.
| Epithelial cells line internal body cavities of multicellular organisms. They represent the first line of defense against various pathogens including bacteria and viruses. They can sense the presence of invasive pathogens and initiate the recruitment of immune cells to infected tissues via the local secretion of soluble factors, called chemokines. Although this phenomenon is essential for the development of an efficient immune response, the molecular mechanism underlying this process remains largely unknown. Here we demonstrate that the host proteins ALPK1, TIFA and TRAF6 act sequentially to activate the transcription factor NF-κB and regulate the production of chemokines in response to infection by the pathogens Shigella flexneri, Salmonella typhimurium and Neisseria meningitidis. In addition, we show that the production of chemokines is triggered after detection of the bacterial monosaccharide heptose-1,7-bisphosphate, found in gram-negative bacteria. In conclusion, our study uncovers a new molecular mechanism controlling inflammation during infection by gram-negative bacteria and identifies potential targets for treatments aiming at modulating inflammation during infection.
| Intestinal epithelial cells (IECs) are not considered to be professional immune cells. However, they play an important role in immuno-surveillance and contribute to the initial phase of inflammation after infection by invasive bacteria or viruses. They can sense the presence of pathogens and orchestrate, together with resident macrophages, the recruitment of immune cells to sites of infection. IECs sense highly conserved pathogen-associated molecular patterns (PAMPs) via pathogen recognition receptors (PRRs) including Toll-like (TLRs) and NOD-like receptors (NLRs). They also detect cellular stress-induced danger-associated molecular patterns (DAMPs) produced during infection. All these sensing mechanisms result in complex signal transduction cascades regulating the expression of proinflammatory genes coding for cytokines, chemokines and antimicrobial peptides.
Shigella flexneri is an enteroinvasive bacterium responsible for shigellosis, an acute intestinal inflammation in humans [1]. After ingestion of contaminated food or water, bacteria reach the large intestine and cross the intestinal barrier by transcytosis through M-cells. Once in the submucosal area, they utilize a type III secretion (T3S) apparatus to induce apoptosis in macrophages and invade IECs from their basolateral side. A T3S apparatus is a syringe-like nanodevice enabling the injection of bacterial effector proteins into target cells [2]. Once effectors have translocated into cells, they can subvert the cellular activities of central host factors to favor bacterial internalization. Shigella bacteria then escape the internalization vacuole, multiply within the cytoplasm and use actin-based motility to spread from cell-to-cell within the intestinal epithelium. It has been proposed that the main PRR involved in the direct recognition of S. flexneri is the NLR NOD1 [3]. This receptor recognizes a component of the peptidoglycan called D-glutamyl-meso-diaminopimelic acid that is part of the gram-negative bacterial cell wall [4]. Upon recognition, NOD1 oligomerises and interacts with the receptor-interacting serine/threonine-protein kinase 2 (RIP2) [5]. This protein associates with the transforming growth factor (TGF)-β-activated kinase 1 (TAK1), and the TAK1 binding protein 1 and 2 (TAB1 and 2) complex. This process leads to the phosphorylation, ubiquitination and degradation of the inhibitory κB (IκB), the nuclear translocation of the NF-κB transcription factor and the transcription of pro-inflammatory genes including the gene coding for interleukin-8 (IL-8). TAK1 is also involved in the activation of the MAPKs JNK, p38 and ERK, which are important for the activation of the transcription factor AP1 [6] and histone H3 phosphorylation. In addition, S. flexneri infection can also be sensed indirectly via the production of DAMPs. For instance, Dupont et al. found that the membrane vacuolar remnants produced after vacuolar lysis are detected by host cells and that the signals produced contribute to inflammation [7]. In particular, the accumulation of diacylglycerol around the bacterial entry site and within membrane remnants activates NF-κB via a mechanism dependent on the CARD–BCL10–MALT1 complex and TRAF6 [8]. Interestingly, S. flexneri possesses a number of tools downregulating the immune response of infected cells. In particular, several type III effectors interfere with the NF-κB and MAPK pathways to reduce IL-8 expression. For instance, OspG reduces the nuclear translocation of NF-κB by preventing IκB ubiquitination and degradation [9]. OspF reduces transcription via its phosphothreonine lyase activity towards p38 and ERK1/2 and its subsequent impact on chromatin remodeling [10].
Although bacteria manipulate the inflammatory response of infected cells, a massive influx of polymorphonuclear cells is observed in tissues infected with S. flexneri [11]. ATP, released by intestinal epithelial cells after infection by S. flexneri, contributes to this inflammation [12]. In addition, a previous study by our laboratory showed that innate immunity during S. flexneri infection is potentiated by a gap junction-mediated mechanism of cell-cell communication between adjacent epithelial cells [13]. We observed NF-κB and MAP kinase activation in uninfected cells located in the proximity of cells containing bacteria and showed that these bystander cells produced large amounts of inflammatory cytokines including IL-8 and tumor necrosis factor alpha (TNFα). IL-8 was also largely produced in bystander cells after infection with Salmonella typhimurium and Listeria monocytogenes [13, 14], suggesting that potentiation of innate immunity by cell-cell communication is a common host response to different bacterial infections. This phenomenon also occurs during viral infections. First, Patel et al. found that recognition of viral double stranded DNA leads to type I interferon expression in bystander cells via a gap junction-mediated mechanism [15]. More recently, it has been shown that anti-viral immunity can spread via the diffusion of cGMP-AMP through gap junctions; cGMP-AMP then binds to the receptor STING localized at the endoplasmic reticulum, which subsequently induces anti-viral gene expression [16].
Although the control of innate immunity has important physiological consequences during bacterial infection, the molecular basis of its regulation remains poorly understood. Here we performed a genome-wide RNAi screen and identified the proteins TIFA and TRAF6 as critical factors for the control of IL-8 expression during S. flexneri infection. We show that threonine 9 (T9) and the forkhead-associated domain (FHA domain) of TIFA are both important for the oligomerization of TIFA occurring in infected and bystander cells. This process is required for the subsequent oligomerization of TRAF6 and the activation of NF-κB. We demonstrate that TIFA/TRAF6-dependent IL-8 expression is triggered by the bacterial metabolite heptose-1,7-bisphosphate (HBP). In addition, we identify alpha-kinase 1 (ALPK1) as the critical kinase controlling TIFA oligomerization and show that ALPK1 controls innate immunity in response to the invasive bacteria S. flexneri and S. typhimurium as well as to the extracellular pathogen Neisseria meningitidis.
In order to characterize the signaling pathways controlling inflammation during infection of epithelial cells by enteroinvasive bacteria, we systematically searched for proteins regulating IL-8 expression following S. flexneri infection. For this purpose, we developed a high throughput assay that monitors IL-8 expression at the single-cell level using fluorescence microscopy (Fig 1A) and performed a genome-wide RNAi screen. HeLa cells, an epithelial cell line commonly used in S. flexneri infection assays, were infected for 3.5 hours with the ΔvirG mutant of S. flexneri as previously described [17]. This mutant is unable to perform actin-based motility [18] and forms large intracellular microcolonies, which are easily detectable by automated image analysis (Fig 1B and S1 Fig). Background signals from remaining extracellular bacteria were minimized by engineering S. flexneri to express the dsRed protein only once it is intracellular [19]. dsRed expression was restricted to cytosolic bacteria by placing dsRed under the transcriptional control of the glucose 6-phosphate transporter uhpt promoter, which is only upregulated once bacteria are in the presence of glucose 6-phosphate [20]. Cells were then treated with monensin to trap IL-8 in intracellular compartments. After fixation, cells were stained for DNA, F-actin and IL-8 and visualized by immunofluorescence. In agreement with previous work [13], IL-8 expression was largely restricted to uninfected cells located in the proximity of infected cells (Fig 1B and S1 Fig), confirming the importance of bystander cell activation in the control of inflammation during S. flexneri infection [13, 21]. In order to identify proteins involved in the control of IL-8 expression, the assay was run in a high throughput setup to screen a commercially available genome-wide library made up of pools of 4 siRNAs per gene. Total cell number, infection rates and IL-8 measurements were extracted for all targeted genes using CellProfiler (see Materials and Methods, S1 Table). As expected from previous work [22] [23], pools targeting NF-κB p65 and TAK1 had strong inhibitory effects on IL-8 expression (S1 Table), validating the approach and the experimental setup of the screen. TIFA and TRAF6 were found amongst proteins whose depletion strongly inhibited IL-8 expression after S. flexneri infection, and were thus selected for further validation and molecular characterization (Fig 1C, S1 Table). TRAF6 mediates signaling from members of the TNF receptor superfamily as well as the Toll/IL-1 family [24]. Interestingly, a previous publication had already reported that TRAF6 was involved in the activation of NF-κB in S. flexneri-infected cells [8]. TIFA is a 20-kDa protein that was first identified as a TRAF6-interacting protein in a yeast two-hybrid screen [25]. It contains a FHA domain, known to bind phosphothreonines and phosphoserines, and a consensus TRAF6-binding motif [26]. In TNFα signaling, it is involved in the oligomerization of TRAF6, which is required for downstream activation of NF-κB [27]. Very recently, it has been reported that TIFA is involved in the inflammatory response triggered following the detection of heptose-1,7-bisphosphate (HBP), a metabolite present in gram-negative bacteria [28]. HBP can be secreted or released upon bacterial lysis and internalized by eukaryotic cells via endocytosis. In order to exclude possible off-target effects in the RNAi screening data and confirm the specific implication of TIFA and TRAF6 during S. flexneri infection, silencing of these two genes was repeated with an independent set of siRNA sequences. While infection remained comparable (S2 Fig), this independent approach confirmed a dramatic inhibition of IL-8 after S. flexneri ΔvirG infection of cells depleted for TIFA and TRAF6 (Fig 1D and 1E). Similar results were obtained upon infection with wild-type bacteria (Fig 1F and 1G) as well as in HEK293 cells (Fig 1H and 1I), showing that the contribution of TIFA and TRAF6 was not restricted to infections with the ΔvirG mutant or with HeLa cells. Altogether, these data show that TIFA and TRAF6 play an essential role in the control of inflammation in S. flexneri infection of epithelial cells and confirm that RNAi screens are valuable tools to identify new players in a given cellular pathway.
Since a published report indicated that TRAF6 was involved in the activation of NF-κB in S. flexneri-infected cells [8], we tested whether TIFA was also required for this process. The activation of NF-κB was monitored by following the nuclear translocation of the p65 subunit in conditions where nearly all cells were infected with S. flexneri. Interestingly, p65 translocation was reduced both in TRAF6 and TIFA-depleted cells (Fig 2A and 2B), showing that these proteins were required to activate NF-κB in infected cells. When cells were infected at a lower MOI (Fig 2C and 2D), a reduction of NF-κB translocation was also found in bystander cells, showing that the depletion of TIFA and TRAF6 had an impact on NF-κB activation in both cell types. The role of TIFA in NF-κB activation was more broadly tested using stimuli other than S. flexneri infection. In contrast to TRAF6, depletion of TIFA failed to inhibit NF-κB activation induced by phorbol 12-myristate 13-acetate (PMA) (Fig 2E), showing that TIFA is not systematically involved in pathways activating NF-κB and that TRAF6 can also function independently of TIFA. Depleting TIFA and TRAF6 had no significant effect on TNFα-induced NF-κB activation (Fig 2F) but partially inhibited activation induced by the NOD1 ligand C12-iE-DAP (Fig 2G). Together, these results show that TIFA is not involved in the intrinsic machinery of NF-κB activation. Instead, we found TIFA to be implicated in at least two signaling pathways that link bacterial infection to inflammation.
TIFA contains a FHA domain (Fig 3A), a widespread signaling unit that recognizes phosphorylated threonine and serine residues and binds proteins intra- and inter-molecularly [29]. Huang et al. showed that when TIFA is unphosphorylated at the threonine 9 position, it exists as an intrinsic dimer [27]. Upon TNFα stimulation, T9 is phosphorylated by an unknown kinase and FHA-pT9 binding occurs between different dimers forming large TIFA oligomers. This mechanism leads to the subsequent oligomerization of TRAF6 and activation of NF-κB. In order to characterize the mode of action of TIFA during S. flexneri infection, we investigated the contribution of T9 and the FHA domain. For this purpose, we measured IL-8 expression after infection of cells that were first depleted for TIFA by RNAi and then transfected with siRNA-resistant wild-type or mutated TIFA cDNA constructs. As expected, we found that wild-type TIFA was able to significantly rescue IL-8 expression (Fig 3B and 3C). In contrast, TIFA mutated at T9 (T9A mutant) or within the FHA domain (RKN mutant) failed to restore IL-8 expression. The same result was observed with the TIFA E178A mutant [27], which is unable to bind TRAF6 (Fig 3B and 3C). Altogether, these results show that T9, the FHA domain and E178 are all essential for TIFA activity suggesting that, as in TNFα signaling, the pT9-FHA interaction and the ability to bind TRAF6 are necessary to induce IL-8 expression during S. flexneri infection.
In order to better characterize the role of TIFA in S. flexneri infection of epithelial cells, we monitored its subcellular localization. For this, cells were transfected with a TIFA cDNA construct and TIFA was visualized after infection by immunofluorescence using a TIFA-specific antibody. In the absence of infection, the protein was uniformly distributed in the cytoplasm and the nucleus (Fig 4A). Following infection with S. flexneri, punctate structures, likely corresponding to large TIFA protein oligomers [27], were formed. These structures were still visible in S. flexneri-challenged cells after several hours (Fig 4A and 4B). TIFA oligomers were found in both infected and bystander cells, suggesting that TIFA was functionally active in both cell types during infection. A co-staining between TIFA and NF-κB p65 showed that TIFA oligomers formed as early as 15 minutes post-infection and seemed to even precede NF-κB activation as visible in some cells (Fig 4C). TIFA oligomerization was also observed following infection of the Caco-2 cell line (S3 Fig), revealing that this process is also a relevant host response to S. flexneri infection in human colonic cells.
The role of the FHA-pT9 interaction and TRAF6 binding in the mechanism of TIFA oligomerization was investigated in cells transfected with the different TIFA mutants. Neither the T9A nor the RKN mutant was able to form oligomers (Fig 4D), indicating that the FHA-pT9 interaction was necessary. In contrast, the E178A mutant formed oligomers (Fig 4D), demonstrating that binding to TRAF6 was not required for TIFA oligomerization. Extrapolating these data to the IL-8 rescue experiment (Fig 3B and 3C) suggests that TIFA oligomerization and binding to TRAF6 are both required to induce IL-8 expression after S. flexneri infection. These results further suggested that, in line with published data on TNFα signaling [27], TIFA also induces the oligomerization of TRAF6 and the subsequent activation of NF-κB following S. flexneri infection. This hypothesis was tested by determining whether TIFA and TRAF6 co-localized after infection. The localization of both proteins was first visualized in S. flexneri-infected HeLa cells co-transfected with TIFA-myc and TRAF6-Flag cDNA constructs. As shown in Fig 4E, TRAF6 was also found in punctate structures both in infected and bystander cells. Furthermore, these structures were perfectly co-localized with TIFA oligomers. The same result was obtained upon infection of Caco-2 cells (Fig 4F). Interestingly, the E178A TIFA mutant that is unable to bind TRAF6 did not co-localize with TRAF6 (Fig 4E). The absence of TRAF6 oligomers in these cells showed that the formation of these structures was dependent on the ability of TIFA to bind TRAF6. The interaction between TIFA and TRAF6 was further addressed by co-immunoprecipitation in cells transfected with TIFA-myc and TRAF6-Flag (Fig 4G). A weak signal was detected in uninfected cells showing some TIFA-TRAF6 interaction under basal conditions whereas their interaction was strongly enhanced upon S. flexneri infection. As expected, this interaction was not observed when cells were transfected with the E178A TIFA mutant (Fig 4G), confirming that TIFA and TRAF6 interact in a TIFA E178-dependent manner. Altogether, these results show that S. flexneri infection induces the formation of co-localizing TIFA and TRAF6 oligomers and that the TIFA-TRAF6 interaction depends on E178 of TIFA.
To elucidate the mechanism triggering the activation of the TIFA/TRAF6 pathway, we tested whether TIFA was also involved in the induction of the IL-8 response observed after Listeria monocytogenes and Salmonella typhimurium infections. Like S. flexneri, these two enteroinvasive bacteria induce the secretion of the inflammatory cytokine IL-8. In both cases, IL-8 expression is potentiated via cell-cell communication between adjacent epithelial cells [13]. Depletion of neither TIFA nor TRAF6 had an impact on L. monocytogenes-induced IL-8 production (Fig 5A) and TIFA failed to form oligomers after infection (Fig 5B). In contrast, the depletion of either TIFA or TRAF6 abolished IL-8 expression after S. typhimurium infection (Fig 5C), while TIFA formed oligomers in both infected and bystander cells (Fig 5B). Since S. flexneri and S. typhimurium are both gram-negative, these results suggested that TIFA/TRAF6-dependent IL-8 expression was specifically triggered during gram-negative bacterial infections. We hypothesized that this innate immune response was induced by the recognition of HBP, a recently identified PAMP present in gram-negative bacteria [28]. HBP is a phosphorylated metabolic intermediate of lipopolysaccharide biosynthesis, produced from D-glycero-D-manno-heptose-7-phosphate by the HldE enzyme [28] (S4A Fig). The role of HBP in the induction of IL-8 expression was directly tested by measuring IL-8 production in response to infection with a S. typhimurium mutant deleted for the hldE gene (ΔhldE) and which expressed the dsRed protein under the uhpT promoter. Data showed that infection with the ΔhldE mutant, which is unable to synthesize HBP, failed to induce IL-8 production both in infected and bystander cells (Fig 5D and 5E). As expected, infection with bacteria deficient for the enzymes GmhB (ΔgmhB) or WaaC (ΔwaaC), which act downstream of HldE in the ADP heptose biosynthetic pathway [30] (S4A Fig), induced strong IL-8 expression (Fig 5D and 5E, S4B Fig). The same experiment was repeated with S. flexneri mutants. Interestingly, the ΔhldE and ΔwaaC mutants were dramatically more invasive than wild-type or ΔgmhB bacteria (Fig 5G and S4C Fig). However, at all multiplicities of infection tested, the absence of HBP led to a complete inhibition of IL-8 expression (Fig 5F and 5H). As with S. typhimurium, the ΔgmhB and Δwaac mutants induced massive IL-8 expression, indicating that the expression of IL-8 was dependent on bacterial synthesis of HBP (Fig 5F and 5H, S4D Fig). In addition, infection with the S. flexneri and S. typhimurium ΔhldE mutants failed to induce the oligomerization of TIFA (Fig 5I, S5A and S5B Fig). Finally, multiplex cytokine analysis showed that S. flexneri infection of HeLa cells induced the secretion of IL-6, IL-1β, IFNγ, IL-8 and TNFα in an HBP-dependent manner (S6A Fig). Furthermore, the induction of IL-8 and TNFα observed in Caco-2 cells after S. flexneri infection was also largely dependent on HBP (Fig 5J and S6B Fig). Altogether, these results show a causal link between HBP, the oligomerization of TIFA/TRAF6, the activation of NF-κB and inflammatory cytokine expression. They also show for the first time, that HBP is a critical PAMP that triggers inflammation in epithelial cells during infection by at least two invasive gram-negative pathogens, S. typhimurium and S. flexneri.
The observation that TIFA oligomerization was dependent on T9 and the FHA domain of TIFA suggested that at least one kinase was involved upstream of TIFA to control IL-8 expression. In order to identify kinase candidates, an RNAi screen targeting each gene of the human kinome with three individual siRNAs, was performed. TAK1, known to be involved in S. flexneri-induced NF-κB activation downstream of TRAF6 and RIPK2 [8], was the strongest negative hit (S2 Table, Fig 6A). We tested whether this kinase could also control TIFA oligomerization during infection. Although depleting TAK1 completely abrogated IL-8 production (Fig 6A and 6B, S2 Table), TIFA oligomers were still visible in infected and bystander cells (Fig 6B), confirming that TAK1 was implicated downstream of TIFA. The second strongest hit was ALPK1. ALPK1 belongs to the atypical kinase group [31] and is poorly characterized. It is a component in apical transport of epithelial cells [32]. Furthermore, polymorphism in the alpk1 gene is associated with type 2 diabetes, dyslipidemia, gout and chronic kidney disease [33–36]. Strikingly, the alpk1 and tifa genes are direct neighbors on human chromosome 4 [37], suggesting that they may be co-regulated and part of a common cellular pathway. ALPK1 was thus further investigated for its implication in S. flexneri infection and TIFA-dependent innate immunity. First, the role of ALPK1 in IL-8 production after S. flexneri infection was confirmed by intracellular IL-8 staining (Fig 6C) and ELISA (S7A Fig). The secretion of IL-6, IL-1β, IFNγ and TNFα was reduced in ALPK1-depleted cells (S7B Fig), showing that ALPK1 is a master regulator of S. flexneri-induced inflammatory cytokine expression, a process largely triggered in response to HBP (S6A Fig). Since TIFA and TRAF6 regulated S. flexneri-induced NF-κB activation, we investigated the role of ALPK1 in this process. Western blot experiments performed on uninfected and infected cells revealed that depleting ALPK1 reduced the degradation of the inhibitor of NF-κB, IκBα, in infected cells (Fig 6D). In agreement, ALPK1 depletion also impaired the nuclear translocation of NF-κB after S. flexneri infection without significantly affecting bacterial entry (Fig 6E and 6F and S2 Fig). Altogether, these results suggested that ALPK1 was a promising candidate for the control of TIFA-dependent innate immunity. The role of ALPK1 in this process was directly addressed by several means. First, depletion of ALPK1 prevented the formation of TIFA oligomers both in infected and bystander cells during S. flexneri infection (Fig 6G and 6H). Second, in rescue experiments, whereby cells were transfected with control or ALPK1 siRNA and then transfected with the empty vector pEYFP or a siRNA-resistant full-length ALPK1-YFP cDNA construct (Fig 6I and 6J), overexpression of YFP-ALPK1 did not induce the formation of TIFA oligomers in the absence of infection, indicating that this process was tightly regulated. Notably, when ALPK1-depleted cells were transfected with full-length YFP-ALPK1, TIFA oligomerization was restored in a large fraction of infected and bystander cells. This result excluded the possible contribution of RNAi off target effects and unambiguously established the role of ALPK1 in S. flexneri-induced TIFA oligomerization. Interestingly, transfection of a siRNA-resistant cDNA construct deleted for the kinase domain of ALPK1 (YFP-ALPK1-ΔK) failed to rescue TIFA oligomerization (Fig 6I and 6J), showing that the kinase domain of ALPK1 was necessary for the induction of TIFA oligomerization after S. flexneri infection. Finally, the role of ALPK1 on the TIFA-TRAF6 interaction was investigated by co-immunoprecipitation experiments. Data showed that the TIFA-TRAF6 interaction induced upon S. flexneri infection was strongly reduced in ALPK1-depleted cells, demonstrating that this interaction was ALPK1-dependant (Fig 6K). Altogether, these results showed that ALPK1 is a master regulator of cytokine expression during S. flexneri infection and that TIFA oligomerization depends on the kinase domain of ALPK1.
As S. flexneri-induced TIFA oligomerization occurred in response to HBP (Fig 5I), we tested whether ALPK1 was involved in this process. Cells were stimulated with lysates from S. flexneri containing an empty pUC19 vector or expressing the HBP-synthesizing enzyme HldA from N. meningitidis [28]. As expected, the lysate from HldA-overexpressing bacteria was more potent at inducing IL-8 expression than those of wild-type bacteria (Fig 7A and 7B). Interestingly, depletion of ALPK1 prevented the oligomerization of TIFA (Fig 7C) as well as IL-8 production (Fig 7A and 7B) in response to both lysates, showing that ALPK1 controlled the oligomerization of TIFA following HBP recognition. As with TIFA, depletion of ALPK1 failed to inhibit IL-8 expression and NF-κB activation observed after L. monocytogenes infection (S8A and S8B Fig), suggesting a specific implication in infection by invasive gram-negative bacteria. Furthermore, ALPK1 was not required to activate NF-κB in response to PMA (S9A Fig) or TNFα (S9B Fig). As with TIFA and TRAF6, depleting ALPK1 had a moderate but significant effect on C12-iE-DAP-induced NF-κB activation (S9C Fig). The role of ALPK1 was further characterized in the inflammatory response triggered by Neisseria meningitidis, an important gram-negative extracellular human pathogen. This bacterium is responsible for meningitis and other forms of meningococcal diseases including meningococcemia, a case of life-threatening sepsis [38]. Upon infection with this pathogen, HBP can be secreted or released by lysing bacteria [28]. We confirmed that treating HeLa cells with N. meningitidis lysate induced TIFA oligomerization (Fig 7D and 7E) and IL-8 expression (Fig 7F). Furthermore, depleting either TIFA or TRAF6 prevented IL-8 expression (Fig F). Interestingly, we found that TIFA oligomerization and IL-8 expression were both completely abrogated in ALPK1-depleted cells (Fig 7D, 7E and 7F), showing that ALPK1 also controls the innate immune response to N. meningitidis infection (Fig 7G). Altogether, these results show that HBP is a key bacterial PAMP sensed by epithelial cells during infection by both invasive and extracellular gram-negative bacteria and that TIFA/TRAF6-dependent innate immunity against HBP is controlled by ALPK1.
An RNAi screen implicated TIFA and TRAF6 in the control of IL-8 expression after S. flexneri infection. We show that these two proteins act upstream of NF-κB p65 activation in infected and bystander cells. In particular, we provide evidence demonstrating that S. flexneri induces the oligomerization of TIFA and TRAF6 in infected and bystander cells in a FHA/T9-dependent manner. In cells expressing a TIFA mutant unable to bind TRAF6, the formation of TRAF6 oligomers was not observed, showing that the TIFA-TRAF6 interaction is necessary to trigger TRAF6 oligomerization. Given that TRAF6 oligomerization has been shown to increase its E3 ubiquitin ligase activity [39], our data suggest that TIFA works as an adaptor protein promoting TRAF6 oligomerization and thereby NF-κB activation and inflammatory gene expression (Fig 7G). In infected and bystander cells, TIFA oligomers are distributed evenly throughout the cytoplasm. They appear within minutes of infection and are still visible four hours post infection. Co-staining of TIFA and lysosomal-associated membrane protein 1 (LAMP1) in S. flexneri-infected cells revealed that TIFA/TRAF6 oligomers are not localized to lysosomes (S10 Fig). More work is needed to determine whether these aggregation platforms are associated with other subcellular structures or whether they freely diffuse in cells.
We show that during S. flexneri and S. typhimurium infection, the TIFA/TRAF6 pathway is activated in response to the bacterial monosaccharide HBP, present in gram-negative bacteria. Indeed, we found that the ΔhldE mutants of S. flexneri and S. typhimurium, which are unable to synthesize HBP, fail to induce the oligomerization of TIFA and the production of IL-8. These results open up a new avenue to understand the molecular processes controlling inflammation in bacterial infection and highlight the central role of HBP during infection by invasive bacteria. In contrast to the study by Gaudet et al. [28], the production of IL-8 in response to S. flexneri and S. typhimurium infection is unlikely due to the simple mechanism of HBP endocytosis. Indeed, we previously demonstrated that noninvasive S. flexneri bacteria failed to induce IL-8 expression [13]. This point was further confirmed by Lippmann et al. who showed that the expression of IL-8 in bystander cells requires bacterial internalization [21]. Mechanisms explaining how HBP could therefore be detected within minutes of bacterial invasion have to be envisioned. Although there is, to our knowledge, no evidence in the literature for the release of metabolites via type III secretion, we cannot exclude the possibility that HBP may be directly secreted into the host cytoplasm via the injectisome. An alternative mechanism would consist in the cellular uptake of HBP during the process of bacterial internalization. A study using dynamic imaging and advanced large volume correlative light electron microscopy recently reported that two distinct compartments are formed during the first step of bacterial invasion: the bacterial containing vacuole (BCV) and surrounding macropinosomes [40]. Whereas the membrane of the BCV tightly surrounds the bacterium, macropinosomes are heterogeneous in size and contain significant volumes of extracellular fluid [40]. HBP, released from residual secretion or bacterial lysis, may be engulfed by infected cells via the BCV or macropinosomes and released into the cytoplasm shortly after membrane rupture. The small molecular size of HBP (370 Da) should allow its diffusion to adjacent cells via gap junctions leading to TIFA oligomerization and IL-8 expression in bystander cells (Fig 7G). Alternatively, HBP sensing in infected cells may lead to the production of a second messenger that could diffuse to bystander cells and activate the ALPK1/TIFA/TRAF6 pathway. In the case of S. typhimurium, the complete rupture of the internalization vacuole is a rare event. In most cases, bacteria remain inside Salmonella-containing compartments. Interestingly, a recent study shows that early Salmonella-containing compartments are leaky and that autophagy proteins promote the repair of endosomal membranes damaged by the type III secretion system 1 [41]. In this context, HBP may leak out of these early compartments, be released into the cytoplasm of infected cells and induce IL-8 expression both in infected and bystander cells, as observed previously [13]. We showed that secretion of inflammatory cytokines after S. flexneri infection of epithelial cells in vitro is largely HBP-dependent, which supports a central role of HBP in the control of innate immunity in S. flexneri infection. More work is needed to determine the exact contribution of HBP in in vivo infection where other PAMPs, including peptidoglycan-derived peptides and LPS, have previously been shown to play a role [4, 42].
Our results show that TIFA’s activity in S. flexneri-induced IL-8 expression is dependent on residue T9 and the FHA domain of TIFA. As the interaction between these two features occurs once T9 is phosphorylated and is required to trigger TIFA oligomerization, we searched for a kinase acting upstream of TIFA oligomerization in bacterial infection. We identified the kinase ALPK1 in a human kinome RNAi screen. Strikingly, the genes coding for ALPK1 and TIFA are immediate neighbors on human chromosome 4 [37]. Gene neighborhood is conserved across several species including coelacanth, xenopus, chicken and mouse, suggesting that both genes may be co-regulated and the encoded proteins part of a same cellular pathway. We show that depleting ALPK1 strongly reduced NF-κB activation and the production of several cytokines including IL-8, TNFα, IL-1β, IFNγ and IL-6 after S. flexneri infection. IL-8 production was also reduced after S. typhimurium infection. ALPK1 depletion completely prevented the formation of TIFA oligomers after S. flexneri infection, a process triggered in response to HBP sensing. TIFA oligomerization was restored by overexpressing a siRNA-resistant full length ALPK1 construct. In contrast, overexpressing a construct deleted of the kinase domain of ALPK1 failed to do so, showing that the kinase domain of ALPK1 is essential for the regulation of TIFA oligomerization. In addition, co-immunoprecipitation experiments revealed that the TIFA-TRAF6 interaction is dependent on ALPK1. All these results demonstrate that ALPK1 is involved in the early signaling cascade controlling inflammation following cellular invasion by gram-negative bacterial pathogens. Furthermore, we show that ALPK1 is also implicated in the control of inflammation after stimulation with N. meningitidis lysates, indicating that this kinase acts as a master regulator of innate immunity to both invasive and extracellular gram-negative bacteria. ALPK1 is an atypical kinase belonging to the α-kinase family that recognizes phosphorylation sites in the context of an alpha-helical conformation [31]. The fact that T9 is not in this environment is not sufficient to exclude that ALPK1 can directly phosphorylate TIFA. Indeed, it has been shown that members of this protein family can also phosphorylate substrates independently of a helical conformation [31]. More experiments are required to elucidate the mode of action of ALPK1 in the activation of the TIFA/TRAF6 pathway. In addition, it will be informative to determine whether HBP can directly bind to ALPK1 or whether this new bacterial PAMP binds to a yet unknown pathogen recognition receptor able to activate ALPK1 and trigger TIFA oligomerization. Interestingly, by sensing the presence of HBP, a metabolite of the LPS biosynthetic pathway, such a receptor would constitute a new specific sensor for the presence of gram-negative bacteria.
In conclusion, we show that ALPK1 is a master regulator of innate immunity against both invasive and extracellular gram-negative bacteria. This kinase acts in response to the detection of HBP to activate the TIFA/TRAF6 pathway. By regulating the expression of inflammatory cytokines, this new signaling pathway is critical to orchestrate the initial host immune response and limit bacterial dissemination within infected tissues. It may also contribute to the control of intestinal homeostasis by regulating the molecular cross-talk taking place between gram-negative bacteria of the microbiota, the intestinal epithelium and the immune system.
HeLa (American Type Culture Collection) and HEK293 (American Type Culture Collection) cells were cultured in Dulbecco’s modified Eagle’s (DMEM) medium supplemented with 10% FCS and 2 mM Glutamax-1. Caco-2 cells (American Type Culture Collection) were cultured in MEM, 20% FCS and 1% non-essential amino acids. Transfection of siRNAs was carried out using RNAiMAX (Invitrogen). HeLa cells, seeded in 96-well plates (6,000 cells/well), were reverse transfected with 20 nM siRNA according to the manufacturer’s instruction. Cells were used 72 hours after transfection. siRNAs against TIFA (s40984), TRAF6 (s14389) and ALPK1 (s37074) were from Ambion and TAK1 from Dharmacon. For cDNA transfection, HeLa cells were seeded in a 96-well plate at a density of 12,500 cells/well. The next day, cells were transfected with 80 ng of plasmid using Fugene 6 (Roche) according to the manufacturer’s instruction. Wild-type, T9A, E178A and the RKN TIFA cDNA constructs [27] were kindly provided by Prof. M.D. Tsai (Institute of Biological Chemistry, Academia Sinica, Taiwan). They were made TIFA siRNA (s40984) resistant by the introduction of 3 silent point mutations within the recognition site of the siRNA. Point mutations were introduced by overlapping PCR using primers TIFA_BamHI_F, TIFA_R2, TIFA_F2, TIFA_XbaI_R and TIFA_EA_XbaI_R (listed in S3 Table). The resulting PCR products were digested with BamHI and XbaI and ligated into pcDNA3. A YFP-ALPK1 construct was kindly provided by Pr R. Jacob (Marburg University, Germany). It was made siRNA (s37074)-resistant by the introduction of 5 silent point mutations at positions 761-762-763-767-768 by directed mutagenesis (Agilent Technology). A mutant deleted for the kinase domain of ALPK1 was generated by introducing a stop codon at position 3059 before the kinase domain by directed mutagenesis. All primers used in directed mutagenesis are listed in S3 Table.
For TIFA and ALPK1 rescue experiments, Hela cells were first reverse transfected with TIFA or ALPK1 siRNAs (s40984, s37074 respectively). After 48 hours, they were transfected with the different siRNA-resistant TIFA cDNA constructs or siRNA-resistant full length or kinase domain-deleted YFP-ALPK1. As a negative control, cells were transfected with the empty vectors pcDNA or pEYFP. Wild-type Flag-TRAF6 cDNA was a gift from John Kyriakis (Addgene plasmid # 21624) [43].
The M90T wild-type Shigella flexneri strain and the icsA (virG) deletion mutant have been previously described [44]. The Salmonella typhimurium 12023 strain expressing pKD46 was provided by J. Guignot (Institut Cochin, Paris, France) and the EGDe.PrfA Listeria monocytogenes strain stably expressing GFP [45] was provided by Prof. P. Cossart (Institut Pasteur, Paris, France). All Shigella and Salmonella strains were transformed with the pMW211 plasmid and constitutively express the dsRed protein [13]. When specifically mentioned, bacteria were alternatively transformed with a variant of pMW211 expressing dsRed under the control of the uhpT promoter (PuhpT::dsRed) [17]. For Neisseria meningitidis, a piliated capsulated Opc- Opa- variant of serogroup C strain 8013 named 2C43 was used. The hldA deficient mutant was obtained as previously described in [46].
S. flexneri M90T and S. typhimurium 12023 deletion mutants were generated by allelic exchange using a modified protocol of lambda red-mediated gene deletion [47]. Briefly, to obtain the S. flexneri M90T and S. typhimurium hldE (ΔhldE), gmhB (ΔgmhB) and waaC (ΔwaaC) deletion mutants, the kanamycin cassette of the pkD4 plasmid was amplified by PCR with the primers listed in S3 Table. The purified PCR product was electroporated into the wild-type strains expressing the genes for lambda red recombination from the pKM208 (for S. flexneri mutants) or pKD46 (for S. typhimurium mutants) plasmids [48]. Recombinants were selected on TSB or LB plates containing 50 μg ml-1 of kanamycin. Single colonies were screened by PCR.
S. flexneri M90T overexpressing the hldA gene from Neisseria meningitidis was generated as follows. The hldA gene was amplified by PCR from a bacterial lysate with the primers listed in S3 Table. After gel purification (Macherey-Nagel), the PCR product was digested with EcoRI and HindIII, and ligated into EcoRI/HindIII-digested pUC19 (Life Technology). The ligation product was used to transform Top10 E. Coli. pUC19-HldA was purified and used to electroporate S. flexneri M90T. As a control, S. flexneri M90T was also electroporated with the pUC19 empty vector.
Bacterial lysates were prepared as described in Gaudet et al. [28]. Briefly, 1 OD600 unit of bacteria from an overnight culture was centrifuged, resuspended in 100 μl PBS and boiled for 15 mins. Bacterial debris were removed by centrifugation at 13 000 rpm for 10 mins. Supernatants were collected and protein concentration was measured by BCA assay (Interchim) for normalization. Lysates were then treated with RNAse A (10 μg/ml), DNAse I (20U) (both Roche) and proteinase K (100 μg/ml) (Sigma-Aldrich). Samples were boiled for a further 5 minutes, centrifuged and the supernatant passed through a 0.22 μm filter. Lysates were stored at -20°C.
S. flexneri, S. typhimurium and L. monocytogenes were used in exponential growth phase. Shigella and Salmonella were coated, or not, with poly-L-lysine prior to infection. Cells seeded in 96-well plates, were infected at indicated MOIs in DMEM supplemented with 10 mM Hepes and 2 mM glutamax-1. After adding bacteria, plates were centrifuged for 5 minutes and placed at 37°C for indicated time periods. Extracellular bacteria were killed by gentamicin (100 μg/ml). For stimulation experiments, cells were incubated with PMA (Sigma), C12-iEDAP (Invivogen) and TNFα (R&D Systems) at indicated concentrations. For intracellular IL-8 measurements, monensin (50 μM) was added together with gentamycin to block IL-8 secretion. Infection and stimulation assays were stopped by 4% PFA fixation.
The screening methodology has already been described [17]. Briefly, RNA interference (RNAi) directed against the human genome was achieved using the commercially available genome-wide siRNA library from Dharmacon (pools of 4 siRNAs/gene). The human kinome RNAi screen was performed with the Ambion library made of three individual siRNAs per gene. All experiments were conducted in a 384-well plate format. In addition to gene-specific siRNAs, all plates contained general siRNA controls for transfection efficiency (e.g. Kif11), positive control siRNAs known to affect inflammation after S. flexneri infection (TAK1, p65 NF-κB) and non-targeting siRNAs. In each experiment, 25 μl of RNAiMAX/DMEM (0.1 μl/24.9 μl) mixture was added to each well of the screening plates containing 1.6 pmol siRNA diluted in 5 μl RNase-free ddH2O. Screening plates were incubated at room temperature (RT) for 1 hour. Following incubation, 600 HeLa CCL-2 cells were added per well in a volume of 50 μl DMEM/16% FCS, resulting in a final FCS concentration of 10%. Plates were incubated at 37°C and 5% CO2 for 72 h prior to infection. For infection, S. flexneri M90T ΔvirG pCK100 (PuhpT::dsRed) were harvested in exponential growth phase and coated with 0.005% poly-L-lysine (Sigma-Aldrich). Afterwards, bacteria were washed with PBS and resuspended in assay medium (DMEM, 2 mM L-Glutamine, 10 mM HEPES). 20 μl of bacterial suspension was added to each well with a final MOI of 15. Plates were then centrifuged for 1 min at 37°C and incubated at 37°C and 5% CO2. After 30 min of infection, 75 μl were aspirated from each well and monensin (Sigma) and gentamicin (Gibco) were added to a final concentration of 66.7 μM and 66.7 μg/ml, respectively. After a total infection time of 3.5 hours, cells were fixed with 4% PFA for 10 minutes. Liquid handling was performed using the Multidrop 384 (Thermo Scientific) for dispension steps and a plate washer (ELx50-16, BioTek) for aspiration steps. For immunofluorescent staining, cells were washed with PBS using the Power Washer 384 (Tecan). Subsequently, cells were incubated with a mouse anti-human IL-8 antibody (1:300, BD Biosciences) in staining solution (0.2% saponin in PBS) for 2 hours at RT. After washing the cells with PBS, Hoechst (5 μg/ml, Invitrogen), DY-495-phalloidin (1.2 U/ml, Dyomics) and Alexa Fluor 647-coupled goat anti-mouse IgG (1:400, Invitrogen) were added and incubated for 1 hour at RT. The staining procedure was performed using the Biomek NXP Laboratory Automation Workstation (Beckman Coulter).
Microscopy was performed with Molecular Devices ImageXpress microscopes. MetaXpress plate acquisition wizard with no gain, 12 bit dynamic range, 9 sites per well in a 3×3 grid with no spacing and no overlap and laser-based focusing was used. Robotic plate handling was used to load and unload plates (Thermo Scientific). A 10X S Fluor objective with 0.45NA was used.
Data analysis was performed using the computational infrastructure described in [17]. Cell counts, rates of infection and IL-8 positive cells were quantified as described in [17]. In brief, intensity and texture features were extracted from bacterial and IL-8 images. Based on these features, cells were scored for infection and IL-8 expression using CellClassifier and supervised machine learning using a Support Vector Machine based binary classifier [49]. Measurements were normalized for plate-to-plate variations and population context dependency as described in [17].
After fixation, cells were permeabilized in 0.1% Triton X-100 for 10 minutes, incubated in PBS supplemented with 0.5% BSA for 2 hours and then overnight at 4°C with different combinations of primary antibodies. NF-κB p65 localization was visualized by using a mouse monoclonal anti-p65 antibody (Santa Cruz Biotechnology, USA), TIFA was visualized with a polyclonal rabbit anti-TIFA primary antibody (Sigma-Aldrich), and LAMP1 was visualized with an anti-mouse anti-LAMP1 (Abcam). Cells were then stained with Alexa 647- or Alexa 488-conjugated secondary antibodies (Invitrogen, Carlsbad, USA). DNA and F-actin were stained with Hoechst and FITC-phalloidin, respectively. The production of IL-8 was measured by immunofluorescence using an anti-human IL-8 antibody in 0.2% saponin in PBS (BD Pharmingen, San Jose, USA) 4 hours post infection.
Images were automatically acquired with an ImageXpress Micro (Molecular devices, Sunnyvale, USA). Image analysis was performed using the custom module editor (CME) of MetaXpress. Briefly, cell nuclei were identified by the "autofind blobs" function of the CME. Nuclei were then extended by 6 pixels to define the cellular mask of each cell that was used to measure bacteria and IL-8 signals. Bacteria and IL-8 signals were both detected with the "keep marked object" function of the CME based on minimal/maximal size requirements and intensity threshold. Cells showing IL-8 signals above threshold were defined as IL-8 positive. Quantification of NF-κB activation was performed with the "translocation enhanced" module of MetaXpress (Molecular Devices, USA) that automatically identifies the nuclei and cytoplasmic compartments from a Hoechst image. Quantification was done by measuring the intensity ratio of p65 in the nucleus and the cytoplasm in several thousand cells per well and in three wells per condition. Cells showing nuclear/cytoplasmic p65 intensity ratio above a threshold ratio were defined as NF-κB positive cells.
Cells were plated in 6-well plates (180 000 cells/well), transfected or not with 20 nM siRNA and/or 2.4 μg cDNA and infected according to the experiment. After infection, cells were washed twice in ice cold PBS with gentamicin (100 μg/ml), lysed in RIPA buffer supplemented with inhibitors of proteases (Promega) and phosphatases (Thermofisher Scientific), incubated on ice for 30 minutes and subsequently centrifuged at 4°C for 30 minutes at 16,000g. The BCA Protein Assay kit (Interchim) was used to determine protein concentration. 15–20 ug of protein was subjected to SDS-polyacrylamide gels and electroblotted onto nitrocellulose membranes. For immunoprecipitation (IP), cell lysates were incubated with an anti-myc antibody (9E10, Santa Cruz) overnight. Protein A/G-coated beads (ThermoFisher) were then added for 2 hours and washed six times in Mac Dougall buffer. Cell lysates and IPs were diluted in Laemmli buffer containing SDS and β-mercaptoethanol, boiled for 6 minutes and subjected to SDS-PAGE. Immunoblotting was performed using primary antibodies diluted in phosphate buffered saline containing 0.1% Tween and 5% nonfat dry milk. HRP-conjugated secondary antibodies were purchased from GE Healthcare or Cell signaling technology or ThermoFisher Scientific. The blots were developed with an enhanced chemiluminescence method (SuperSignal West Pico Chemiluminescent substrate, Thermofisher Scientific).
IL-8 secretion was measured by ELISA in the supernatant of HeLa and Caco-2 cells infected with S. flexneri for 6 hours. The cell-free supernatants from triplicate wells were analyzed for their IL-8 content using the commercial ELISA kit (eBioscience). The secretion of additional cytokines including TNFα, IL-1β, IL-6 and IFNγ was measured using the Cytokine Human Magnetic 10-plex Panel for Luminex Platform (Life Technologies).
Data are expressed as mean ± standard deviation of triplicates samples as indicated. p values were calculated with a two-tailed two-sample equal variance t-test.
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10.1371/journal.pntd.0002992 | Cross-sectional Study of the Burden of Vector-Borne and Soil-Transmitted Polyparasitism in Rural Communities of Coast Province, Kenya | In coastal Kenya, infection of human populations by a variety of parasites often results in co-infection or poly-parasitism. These parasitic infections, separately and in conjunction, are a major cause of chronic clinical and sub-clinical human disease and exert a long-term toll on economic welfare of affected populations. Risk factors for these infections are often shared and overlap in space, resulting in interrelated patterns of transmission that need to be considered at different spatial scales. Integration of novel quantitative tools and qualitative approaches is needed to analyze transmission dynamics and design effective interventions.
Our study was focused on detecting spatial and demographic patterns of single- and co-infection in six villages in coastal Kenya. Individual and household level data were acquired using cross-sectional, socio-economic, and entomological surveys. Generalized additive models (GAMs and GAMMs) were applied to determine risk factors for infection and co-infections. Spatial analysis techniques were used to detect local clusters of single and multiple infections.
Of the 5,713 tested individuals, more than 50% were infected with at least one parasite and nearly 20% showed co-infections. Infections with Schistosoma haematobium (26.0%) and hookworm (21.4%) were most common, as was co-infection by both (6.3%). Single and co-infections shared similar environmental and socio-demographic risk factors. The prevalence of single and multiple infections was heterogeneous among and within communities. Clusters of single and co-infections were detected in each village, often spatially overlapped, and were associated with lower SES and household crowding.
Parasitic infections and co-infections are widespread in coastal Kenya, and their distributions are heterogeneous across landscapes, but inter-related. We highlighted how shared risk factors are associated with high prevalence of single infections and can result in spatial clustering of co-infections. Spatial heterogeneity and synergistic risk factors for polyparasitism need to be considered when designing surveillance and intervention strategies.
| In Coast Province, Kenya, infections with Schistosoma haematobium, Plasmodium spp., filarial nematodes, and geohelminths are common, resulting in high levels of both single infections and polyparasitism. The long-term effect of these infections, separately or in combination, has a major impact on human health and on the economic welfare of affected populations. The transmission dynamics of these parasitic infections can be linked to shared risk factors that often overlap in space. We studied human and environmental factors driving transmission and the resulting spatial pattern of infections in six communities, using cross-sectional, socio-economic and entomological surveys. Single and co-infections were widespread in the communities, and were associated with environmental, demographic and socio-economic risk factors, including distance of community from the coast, sanitation and human age and crowding. The spatial patterns of single and co-infections were heterogeneous among and within communities, with overlapping clusters of single and multiple infections in areas where houses with lower socio-economic status and more crowding were located. The heterogeneities among and within communities can provide important insights when designing surveillance and intervention strategies when planning appropriate surveillance and control strategies targeting polyparasitism.
| In coastal Kenya, multiple parasite species infect human populations and their transmission dynamics can significantly overlap. In this ecological setting, transmission of Schistosoma haematobium, Plasmodium spp., filarial nematodes, and geohelminths is common, resulting in high levels of concurrent human urinary schistosomiasis, malaria, hookworm infection and/or ascariasis, as well as pockets of lymphatic filariasis [1], [2], [3], [4], [5]. Because of their combined long-term effects, these infections appear to play a significant but, as yet, incompletely defined synergistic role in the causation of chronic clinical and sub-clinical human disease and poverty [6], [7], [8]. In this context, transmission patterns and risk factors for these diverse parasitic infections often appear to be linked and to overlap extensively [9], [10], [11]. We hypothesized that people living in areas where environmental factors allow for coincident transmission of several parasites would have a much higher chance of suffering from multiple concurrent infections. Although the interaction between parasites [12], [13] is still not fully understood, now in the era of integrated parasite control programs, it is important to define those factors that enhance risk of co-infection. This challenge has been approached by several studies that investigated the complexity of multi-parasite ecology, focusing on heterogeneities in infection risk across physical and social space, and over time [12], [14], [15], [16], [17], [18].
Building on our earlier studies of schistosomiasis, we hypothesized that environmental factors are the key determinants of transmission potential for these parasites, and that these interact with demographic and socio-economic factors to determine the observed spatial/demographic patterns of parasitic disease. While this in itself is not a new concept [19], recent research on parasite eco-epidemiology indicate that these effects need to be reconsidered on multiple levels–individual, household, village, and district-wide– both separately for each parasite, and for the combined suite of infections [18], [20], [21], [22].
Although ‘wormy villages’ have been described empirically in the past [23], new advances in diagnostic technology have increased test sensitivity and specificity for these parasites, revealing that in endemic areas, chronic parasitic infection with Schistosoma spp. [24], Plasmodia spp. [25], and/or filaria [26] are much more common than previously thought. In holoendemic areas such as coastal Kenya or Papua New Guinea, malaria prevalence, as detected by PCR is 60–75%, more than double the previous estimates of 20–33% by blood smear microscopy, with ≥10% carrying two or more malaria species [25], [27]. This finding dramatically changes our concept of malaria as a chronically prevalent disease, and substantially alters estimates of attributable risk for critical infection-associated morbidities such as anemia [28]. Similarly, advances in filaria antigen detection techniques indicate that past community surveys have underestimated prevalence of filariasis by 40% [29], while standard screening techniques for S. haematobium have probably missed 50–60% of low level infections with this parasite [30]. These findings indicate the need to carefully re-evaluate the risk of infection and parasite-related morbidity in exposed populations.
The role of the environment is assumed to be critical for vector-borne and soil-transmitted parasite transmission, although the relative non-linear impact of individual environmental factors has not been well-quantified [31], [32]. In contrast to person-to-person contagion of viruses and bacteria, there is a difference between a person's exposure to parasites and her or his ultimate level of parasite infections and related diseases, which is often governed by continued residence in the high- risk environment.
Previous studies performed in sub-Saharan (or tropical) countries [9], [16], [33] have pointed to the need for adopt novel quantitative approaches that take into account the issue of scale when investigating the interactions of physical and social space with the risk for poly-parasitism. Our study's aim was to detect spatial and demographic patterns of transmission and infection for schistosomiasis, malaria, filariasis, and soil-transmitted helminths (STH) in coastal Kenya through integration of parasitological data with landscape, land use, and socioeconomic risk factors. Our project is one of the few studies which use socio-ecological data and spatial analysis techniques to examine a large spectrum of co-infections affecting people living in coastal Kenya. By combining remotely sensed and directly measured environmental factors with new aspects of social geography, along with implementation of new diagnostics methods and the use of advanced statistical tools, our analysis provides new insights into polyparasitism that can inform the design and application of more effective, population-based control strategies.
Ethical approval and oversight for this study was jointly provided by the Institutional Review Board of the University Hospital Case Medical Center of Cleveland (Protocol 11-07-45) and by the Ethical Review Committee of the Kenya Medical Research Institute (KEMRI) (Non-SSC Protocol 087). All residents of the study villages were eligible for inclusion as participants in the study if they were permanent residents of the selected study communities, and aged 5 years or above. Written informed consent was obtained from the subject or, for minors, his or her parent, prior to participation.
We conducted this six-village study across four different environmental settings within Kwale County, Coast Province, Kenya during 2009–2011 ([34], [35], [36], [37]). Village selection was aimed at creating a stratified sample of different environments across the County, covering an estimated population of 12,000 people. The required study size was estimated based on the likely prevalence of major co-infections in the area, as reported in previous smaller surveys [5], [38]. The ecological settings were: a. estuary (Jego), b. coastal plain (Magodzoni, Nganja, and Milalani), c. coastal slope (Vuga), and d. inland semi-arid (Kinango) areas (Figure 1, Tables 1). In terms of its demographics and developmental metrics, Kwale County is representative of other rural districts of Kenya (and sub-Saharan Africa) that are burdened by polyparasitism [39]. To optimize participation and limit participation bias, each village survey included preliminary informational meetings, followed by demographic census, including household location by GPS or remotely sensed visual imaging as detailed in our previous schistosomiasis study in Msambweni [40], and their enumeration. At each household, an adult informant was interviewed on household SES using an established, validated questionnaire administered in the local languages (Kidigo or Kiswahili) [36], [41], [42].
Consenting participants were tested for infection exposure, current infection, and current infection intensity as follows: Current malaria infection was detected initially by rapid antigen-detection card technique (ICT Diagnostics, Australia), and later confirmed and quantified by PCR [27]. In our analysis, an ICT-positive status was the basis for assigning malaria infection; hookworm, Trichuris, and Ascaris infections were detected and quantified by standard Kato-Katz stool examination (two duplicate smears) of a single stool specimen [43]; The presence of Wuchereria bancrofti infection (lymphatic filariasis, LF) was detected by circulating antigen detection (Binax, Portland, ME); S. haematobium infection was detected and quantified by Nuclepore urine filtration technique from a single midday urine [44], [45]. Infected subjects received standard anti-parasite treatments at the time of the survey according to the Ministry of Health guidelines.
Mosquito trapping was performed longitudinally over four years (April 2009–April 2013) in all eight study villages. For the period of April 2009 to December 2010, mosquito collections were performed once every 4 weeks using three different methods: Pyrethrum Spray Catch (PSC), Clay pots and Prokopack aspirator. Indoor collections by PSC were performed from April 2009 through December 2010 in 10 randomly selected houses, while outdoor collections by clay pots were performed from April 2009 through August 2010 in 10 randomly selected houses (discontinued due to poor catch).
Mosquito collections using Prokopack aspirator [46] were started in March 2010 and continued through December 2010 in 5 randomly selected houses. Mosquito collections from January 2011 to March 2011 were inconsistent, with only 7 mosquito collection efforts conducted out of the possible 24 for both PSC and Prokopack aspirator. No mosquito collections were performed in January and most of February 2011 due to logistical difficulties. For the period of April 2011 to April 2013, mosquito collections were performed once every 8 weeks in all the eight study villages using PSC and Prokopack aspirator in 10 randomly selected houses for each mosquito collection method.
Mosquito collection by all methods always started at 06:00 h and ended no later than 10:00 h. For PSC catches of indoor resting mosquitoes, houses were sprayed with 10% pyrethrins dissolved in kerosene using the method described by Mutuku and others [34]. Mosquito collection using clay pots and Prokopack aspirator were performed as described by Maia and others [47] and Odiere and others [48].
We evaluated the socio-economic standing (SES) of each individual and assigned an SES score based on a set of factors related to asset ownership and the physical characteristics of their home. We considered variables related to ownership of land, house, and durable assets (e.g., radio, motor vehicle, television) (Table 2). Given the heterogeneity among the studied communities in the range of these factors, we adjusted the SES scale for each village. Economic inequity was also estimated based on house characteristics (e.g., number of rooms used for sleeping and building materials) and on access to utilities and infrastructure (e.g. sanitation facility and source of water).
The SES score was calculated using Multi Correspondence Analysis (MCA) [49]. MCA is a multivariate method developed for exploring datasets with discrete quantitative values that can be used to create a weight index based on the variance explained by each included variable. The MCA weight index is similar to the one calculated using principal component analysis (PCA) [49], both using a set of linear combinations to account for variability in the data. While PCA is based on variance-covariance matrix, MCA uses a scaling of the Pearson's chi-squared statistic [50]. In calculating the SES scores, we only considered the first MCA linear combination that explained the greater part of the data's variability, then used this score to categorize households of each village into four ordinal groups (Poorest, Poor, Rich, Richest), based on quartiles.
A set of logistic regressions based on generalized additive models (GAMs) [51] was created to analyze the effect of demographic variables, SES, village setting, use of bednets, and entomological measures on presence or absence of parasite infection in the surveyed populations. We also performed a generalized additive mixed model (GAMM) [51] to calculate how these variables were associated with individual co-prevalence of two or more infections. Because we used the number of co-occurring infections of different parasites (poly-parasitism), the GAMM was performed based on a Poisson distribution and, to account for data over-dispersion, individual ID was entered as random effect [52]. In both GAMs and GAMMs, age was included as a non-linear predictor represented by a smooth function [51].
In addition to analyzing the aggregate data for the six villages, we also performed the same modeling analyses for the village of Milalani, the most heavily parasitized village and the one with the highest prevalence of poly-parasitism.
Given the presence of clusters of single and multiple infections, we tested (using Moran's I) whether the same spatial autocorrelation pattern persisted in model residuals, which would indicate a spatial bias, as applied by Dormann et al, 2007 [53].
The spatial patterning of prevalence of individual parasite infections and of poly-parasitism was quantified with the Getis' Gi*(d) local statistic [54], using the inverse distance as the spatial weight. Significance was evaluated by comparing observed values with values expected under the null hypothesis of complete spatial randomness (based on 999 Monte Carlo permutations of location status). We also applied the Gi*(d) to analyze the spatial clustering of greater household crowding and lower SES [54].
Fisher's least significant difference (LSD) test [55] was applied to determine significant differences in prevalence of infections, SES, sanitation, sources of drinking water, house structure, and mosquito infestation between villages. Wilcoxon signed-rank test was performed to evaluate difference in mean number of female mosquitoes collected per house between communities. The Spearman's nonparametric correlation coefficient, ρ, was applied to test a possible association of spatial co-occurrence of clustering of high prevalence of co-infection with of clustering of lower SES and household crowding. We applied this test at the house level to determine whether households included in co-infection clusters were also part of SES or crowding clusters.
All geographic data were stored in a Geographic Information System (GIS) using Quantum GIS (QGIS) software [56] georeferenced using Universal Transverse Mercator (UTM) Zone 37 South, datum WGS84. Spatial analysis tests were performed using Easyspat (Bisanzio et al. in prep.), an open-source software based on PySal libraries written in Python language [57]. All other analyses and data cleaning were performed using R software [56].
The environmental characteristics of the six villages are summarized in Table S1 in Text S1. The distance from the coastline ranged from 1.7 to 31.2 Km (Fig. 1). Mean elevation was highest in Kinango (186.2 meters above sea level), and lowest in Jego (15.1 meters). Annual mean temperature and annual rainfall was negatively correlated with distance to the sea and with elevation.
Demographic, SES and sanitation attributes are shown in Tables 1 and Table S1 in Text S1 along with participation rates in each village. Participation was incomplete in every village, ranging from 45–74% of eligible residents. Overall, 56% of those eligible completed their full participation in the laboratory testing. Adult female, who are more often at home, had higher rates of participation than adult males (overall M∶F ratio = 0.56). Both of these may have biased our estimates of infection prevalence. Residents of Kinango owned more assets than inhabitants of the other villages. Kinango and Vuga had a significantly higher percentage of houses with both cement floors and iron roofs (Fisher's LSD, p<0.05), and also had a significantly lower number of houses without a sanitation system (Fisher's LSD, p<0.05), and the highest proportion of households with access to a public source of drinking water (Fisher's LSD, p<0.05). Kinango had the highest percentage of households with their own source of drinking water (17.9%, Fisher's LSD, p<0.05).
Average SES was lowest in Jego, which had the lowest proportion of houses with access to sanitation and its inhabitants owned the fewest assets. Milalani, Nganja and Vuga levels of SES, sanitation, and sources of drinking water were intermediate between Jego and Kinango. There was no significant difference in education level between villages (Fisher's LSD, p>0.05).
Entomological data are shown in Table 2. A total of 32,982 female mosquitoes were collected during April 2009–April 2013. Culex spp. females were by far the most abundant (31,116; 94.6% of all mosquitoes), followed by An. gambiae (988; 3.1%) and An. funestus (878; 2.9%). Culex spp. mosquitoes also were collected in a significantly higher proportion of households than all other mosquitoes (Table 2, Fisher's LSD, p<0.05).
In Milalani and Nganja, the percentage of houses infested with Culex spp. was significantly higher than in the other four villages (Fisher's LSD, p<0.05). The abundance and presence of Culex spp. was significantly lower in Magodzoni (Wilcoxon test, Fisher's LSD, p<0.05). An. funestus was significantly more abundant in Jego and Magodzoni, and An. gambiae was more abundant only in Jego (Table 2, Wilcoxon test, p<0.05; Fisher's LSD, p<0.05).
The prevalence of infections is presented in Figures 1 and 2 and supplemental Tables S2 and S3 in Text S1. The most common infections among tested individuals were S. haematobium (26.0% overall prevalence) and hookworm (21.4%). Co-infection by these two parasites was the most common co-infection (6.3%), significantly more than expected by random chance (the product of single parasite infection prevalences) (Figure 1, Table S2 in Text S1). Prevalence of malaria, filariasis, and Trichuris infections were similar and significantly less frequent than S. haematobium and hookworm infections (Fisher's LSD, p<0.01). Ascaris infection was by far the least common (prevalence of only 0.3%), and was excluded from most of the analyses. Overall, 18.8% of the population was infected by more than one parasite. Two individuals were co-infected by all of the five different parasites.
Prevalence of infections in the six villages was significantly different for all parasites other than the rare Ascaris. In Kinango the prevalence of overall parasitic infections was lowest (Fisher's test, p<0.05), with significantly lower prevalence of hookworm and Trichuris infections (Fisher's LSD, p<0.01), but not of schistosomiasis. Kinango also had the lowest prevalence of multiple infections. In Vuga, Nganja, and Kinango prevalence of malaria was similar and significantly lower than in the other three villages. People living in Milalani had significantly higher prevalence of infections compared with the other villages (Fisher's LSD, p<0.05). Prevalence of co-infection in Kinango, Vuga, and Magodzoni was significantly lower than the other three villages (Fisher's LSD, p<0.05, Figure 2, Table S3 in Text S1).
For the aggregated data from all six villages, results from the GAMs and GAMMs (Table 3), showed that demographic factors (age, gender, and education), SES, and household characteristics (construction, use of bednets, water source, sanitation, and number of inhabitants) were significantly associated with parasite infections and co-infections. Males had a higher risk of being infected by all parasites other than Schistosoma, for which gender did not have a significant effect. Males were also more likely to be infected with more than one parasite species. Lower SES, lack of access to sanitation, and absence of a safe source of drinking water were associated with a higher risk of infection and co-infection, although SES was not associated with filariasis or Trichuris infection.
Age was an important factor affecting all single and co-infections (Figures 3 and 4, Figures S2 through S7 in Text S1). Aggregated prevalence data for all villages showed a reduction in malaria rates after age 19, an increase in filaria prevalence with age, a peak in S. haematobium prevalence for ages 10–19, an increase in hookworm infections with age, and a decline in Trichuris infections after age 19 (Figures S2–S6 in Text S1). In Milalani and Nganja, hookworm infections declined in the 10–19 age group before rising again in the older age groups. Overall, children and young adults were more likely to be infected with malaria parasites, S. haematobium, and Trichuris, while adults were more likely to be infected with filariasis and hookworms. Polyparasitism (two or more infections) was highest in the 10–19 age group. The smooth function, obtained from GAM, of age association with infection presence (Figure 3) provided a good fit to the results of the association of age with prevalence, with the exception of hookworms (Figure 3, panel D), which was less prevalent in ages 12–23. This overall effect was primarily a result of the age-patterns in Nganja and Milalani, where hookworm infections were most common.
Village and environmental factors were major independent correlates of infection. After controlling for demographic factors and SES, living in Kinango was protective with regard to several single infections and for co-infections. However, as shown by univariate analysis, living in Kinango was associated with increased risk of S. haematobium infection.
Results from the GAM applied to the entomological data (Table 4) showed no significant correlation between presence or number of collected mosquitoes and malaria or filariasis cases. However, not surprisingly, bednet use was protective against malaria.
When the GAM and GAMM were applied to Milalani alone (Table 5), very few covariates (other than living in Milalani per se) were correlated with infection status. SES was significantly associated with the risk of being infected by any of the parasites only with lower risk of being infected by hookworms. The use of bednets was again a protective factor for malaria. Sanitation and access to safe source of drinking water were protective only against Trichuris and against poly-parasitism, which in Milalani was mostly the result of multiple helminthic infections. These results are consistent with the health status of the Milalani community, where the most common infections and co-infections were due to S. haematobium and STH. Age was again associated with single infections or co-infections (Figure 4), similar to the results obtained for the multi-village models.
Results based on the Gi*(d) test showed that high density households and lower income were clustered in all villages (Figure 5). In Vuga and Kinango, the clustering pattern of both density and low income overlapped. Clusters of high household density were often located near the main road (Figure 5, Figure S1 in Text S1). In contrast, people with lower SES were usually clustered away from the main roads (Figure 5, Figure S1 in Text S1). Low-income houses were clustered closer to sites that were suitable for snail hosts of S. haematobium (Figure 5 and Figure S1 in Text S1).
The spatial distribution of prevalence of single parasitic infections and of poly-parasitism in each village was significantly clustered (Figures 6 and 7). Hot spots of diseases were not confined to a particular area in the villages, but, rather, overlapped each other (Gi*(d) test, p<0.05). Malaria and schistosomiasis hot-spots co-occurred in the same locations in all villages except for Magodzoni. In Milalani and Jego there was only one malaria cluster per village, whereas the highest number of discrete malaria clusters (n = 3) was detected in Vuga. Clusters of the various helminthic infections overlapped in all villages, and were located near the main roads in all communities, with the exception of Kinango. In Kinango, almost all the spatial clusters of infections and co-infections were found in one area far from main roads and confined to the eastern part of the community. The exception was filariasis, which was clustered in the northwest part of the community. Not surprisingly, clusters of high levels of co-infection prevalence (Figure 7) were located where the highest numbers of single infection clusters were also found (Figure 6). Table 6 presents values and significance levels of Spearman's ρ used to describe correlation between disease hot spots and clusters of high population density and of lower SES.
In several villages clusters of single infections and of co-infections were significantly correlated with the presence of clusters of high household density and of low SES (Table 6). Spatial correlation of single infection hot-spots with high household density clusters, when it was significant (with malaria in Jego, Kinango and Vuga, with S. haematobium in Magodzoni and Kinango, with hookworm infection in Kinango and Vuga, and with Trichuris in Milalani/Nganja), was always positive. Spatial clusters of households with lower income were significantly correlated with occurrence of infection. The correlation was either positive, mostly in Kinango (malaria, S. haematobium) and in Milalani/Nganja (hookworm and Trichuris), or negative (with malaria, filariasis, S. haematobium, and hookworms in Magadzoni, and with hookworm in Jego). The results were similar for co-infections, with significant positive correlations with high household density in all villages except for Vuga. Lower SES was positively correlated with poly-parasitism in Kinango and Milalani/Nganja and negatively in Jego and Magodzoni. Magodzoni was the only village in which there was no correlation between poly-parasitism clustering and either high household density or lower SES.
Simultaneous and sequential transmission of multiple parasites, and the resultant chronic/recurrent infections, are facts of life in many underdeveloped rural areas. They represent a significant, but often poorly recognized health and economic burden for affected populations [58], [59]. The chronic inflammatory process associated with long-term parasitism contributes to anemia and undernutrition [60], [61], [62] which, in turn, can lead to growth stunting, poor school performance [63], [64], poor work productivity [6], and continued poverty [6], [7]. Recently, a clear interest in integrated parasite control systems that can simultaneously target multiple NTDs is emerging. These national and international programs aim to create control systems based on knowledge from epidemiological analyses, such as the present study, that are performed to investigate the dynamics of multi-parasite transmission [18], [58], [65], [66], [67], [68].
Our study was focused on analyzing co-infections by several parasites and identifying factors associated with increased risk of polyparasitism. Our findings demonstrate that most infections have common risk factors (i.e., sanitation, SES, age), which increase the risk of co-infections for inhabitants of specific communities. Similar results have been showed by studies performed to investigate co-infection of malaria and hookworm in schoolchildren in coastal Kenya [17]. However, we have also shown that risk factors are not the same in each community. This may be associated with differences in environmental and population characteristics recorded in the villages. Integrating data from demographic, socio-economic, and behavioral surveys with spatial pattern of disease occurrence, we did not identify a particular risk area where all infections were clustered; rather, we were able to highlight co-infection hot-spots. We saw that individuals in the 8–16 age group were at high risk of exposure to malaria, schistosomiasis, and Trichuris, but not for filariasis and hookworms, which mostly affected adults.
Infection status of villages involved in our study was consistent with findings reported in previous studies performed in coastal Kenya [69], [70]. In this region, malaria, schistosomiasis, and STH are widespread throughout many communities affecting a high portion of population. Our results indicate that the most common infection was with STH, of which hookworm showed the highest prevalence. In our communities A. lumbricoides was rarely detected with an overall prevalence of 0.3%. It is well documented that this parasite infects only a few individuals in coastal Kenya, but that in other parts of Southern Kenya it reaches a prevalence of ∼20% [69], [70].
Prevalence of single and multiple infections were heterogeneous between the communities comprising this study. This coarse spatial pattern was associated with elevation, climatic, environmental and SES factors which affect diffusion and persistence of parasites [14], [18], [70], [71]. Similar spatial heterogeneity (at a larger scale) was highlighted by an extensive study examining co-infections across East Africa [17]. On a finer scale, within communities, our spatial analyses detected hot spots for each of the parasitic infections that we studied. Similar spatial heterogeneity at household level has also been reported for schistosomiasis in Kenya [1], [40], malaria in Mali [72], filariasis in Tanzania [73], and STH in Brazil and Panamá [14], [71]. We recorded co-infection clusters more often in those locations where several hot-spots of single infections overlapped, emphasizing the increased risk of polyparasitism where increased risk for individual infections is locally combined. In some villages, clusters of malaria, filariasis, and schistosomiasis overlapped or occurred near each other. These co-occurring clusters were found near aquatic habitats favorable for Anopheles mosquitoes and snails. Similarly, Mboera, et al. have reported that, in Tanzania, children living in proximity to rice fields are infected or co-infected with malaria and helminths more often than children living in dry areas [74].
We found a significantly higher prevalence of double infections than the rate predicted by the local prevalence of each individual parasite. This result could be due to a synergic effect of common risk factors (e.g., SES, sanitation) and parasite spatial distribution as shown by our analyses. Previous studies have also shown that the prevalence of co-infection prevalence could be increased by the interaction between helminths and P. falciparum [16], [33]. However, there are still conflicting reports on this topic, and the mechanisms underlying this possible interaction are still not completely understood [75].
Absence of spatial autocorrelation in residuals of each model showed that variables used to perform the analysis describe well the spatial pattern of the mono and co-infections. This result indicates that the spatial heterogeneity at village level is related with characteristics of households and their inhabitants. Previous studies have also shown that environmental factors are less likely than demographic and socioeconomic condition to capture spatial pattern of infection and co-infections at village scale [16], [33].
In our study, the two communities of Nganja and Milalani were contiguous and did not show a significant difference in prevalence of parasite infections and co-infection, with the exception of malaria. Many fewer Anopheles spp. females were collected in Nganja than in Milalani, and this difference could be explained by the presence of more larval sites in Milalani and the short dispersal distance of vector mosquitoes [76]. However, we did not find a significant correlation between mosquito abundance (or presence) and malaria or filarial infections. These results were likely affected by the limitations of the methods used to collect mosquitoes—based on our long-term surveillance for the present study, we previously detailed [77] that when abundance of mosquitoes is low, it is difficult to obtain a representative sample of local mosquito populations using classical sampling methods.
We found a marked relationship between urbanization, socio-economic development, and STH infections. Kinango, Vuga, and Magodzoni had lower STH prevalence compared to the other villages. These three communities averaged higher SES levels compared with the communities in the southern part of study area. Prevalence of STH in a community has been shown to be negatively correlated with the number of houses with a dirt floor, and positively associated with lack of access to good sanitation [14], [71], both of which impact parasite contact with humans and their spread in human environment. We found both single infections and co-infections to be associated with SES. These findings are consistent with results showed by similar studies investigating helminth and malaria mono and co-infection in sub-Saharan Africa [17], [33]. People who were classified as poor had higher risk of being infected with multiple infections. Within a community, the association of SES with disease prevalence was observed in Kinango's spatial pattern of infection: with the exception of filariasis, all single and co-infections were clustered in the less developed area of Kinango where a hot-spot of low SES was detected. In contrast, when we applied GAMs to the Milalani data community, there was no strong association between SES and parasite prevalence (with the exception of hookworm prevalence which was significantly lower among those with the highest SES). Milalani is a poor rural community, where the range in SES subdivision is less marked than in other communities. Such a limited association of SES with STH prevalence was also reported from Indian rural communities where SES did not vary significantly [78].
Children and young adults under age 20 were at higher risk for single infections and co-infections, with the exception of filariasis, which was more often detected in people over 20. A mass drug administration (MDA) had been performed in the study area in 2003 as part of the National Programme for Elimination of Lymphatic Filariasis (NPELF) [70], consisting of treatment with diethylcarbamazine citrate (DEC) and albendazole, which also has a de-worming effect for STH. This prior administration of albendazole may also have reduced the prevalence of hookworms in Milalani and Nganja residents, where prevalence was lowest in the 10–19 age group. Following drug administration, re-infection with Trichuris and Ascaris is rapid (less than one year to reach pre-treatment prevalence), but is longer for hookworm [79], which may explain why we did not find a similar likely effect of NPELF on Trichuris infection. School-based de-worming campaigns were performed in our study area once a year during 2005, 2008, and 2011. This national program had a less coverage than 2003 campaign. However, the treatment frequency adopted during the last campaigns did not have an important impact on long term prevalence and intensity of helminths in coastal Kenya [80].
Although children are at highest risk for most infections and co-infections, surveillance and control strategies need to target both children and adults both to reduce transmission and improve health status [69]. The challenge of polyparasitism is increasingly recognized as manifested by: 1) increasing appreciation of the health and social burden of chronic/recurrent infections [59], [81], [82]; 2) more sensitive diagnostics indicating that concurrent polyparasitism is much more prevalent than previously thought [18], [27]; and 3) new inexpensive approaches to treatment and transmission control becoming increasingly more accessible [58], [83], [84]. Until recently, conventional wisdom about parasites has been that light parasitic infections are mostly ‘asymptomatic’–meaning that they do not provoke symptoms that require medical attention [85]. However, new studies of immunopathology of infection and chronic disease formation [61], [62], [86], [87], indicate that the presence, as well as the intensity of infection, drives morbidity due to infection [7]. Under-recognized ‘subtle’ morbidities such as malnutrition, anemia, and poor school performance have been shown to be significant correlates of individual helminthic or protozoan infection [6], [59], [61], [62], [64], and concern is growing about the combined health effects of multiple concurrent parasite infections [67]. This is an important issue to take in consideration in coastal Kenya, where ∼50% of our study population was positive for at least one parasite and ∼20% were burdened with co-infections. These estimates are also bound to be underestimates since our parasitological methods (egg detection) are less than optimal diagnostic tools. It is possible, though, that this high parasitic burden has a major impact on the health status of the low-income populations, and limits the potential development of the region. The combined impact of these endemic infections has not been well studied, and may prove to have additive or more complex non-linear interactive effects. Consequently, optimal control strategies may require local reduction of both transmission (preventing infection) and disease manifestations through the integrated targeting in concert of one, some, or all of these parasitic infections. A key challenge for reducing transmission of these infections is the diversity in exposure and transmission routes, across multiple levels, including spatially and temporally. In this scenario, the results of our study should prove quite helpful in designing an integrated drug distribution plan for coastal Kenya. As previously shown [68], more cost-effective integrated systems can be impacted by increasing knowledge about the total infectious burden of target population. This should be achievable through the use of detailed disease mapping [68]. The prediction maps of co-distribution of NTDs in the literature were often developed using data collected for a specific demographic group such as schoolchildren from few schools in a specific district [10], [11], [17]. However, these data do not give a full picture of the actual health status of the population which should be targeted by a national control program. Our study highlighted how prevalence of single and multiple infections differed between age groups. We also pointed out that infection and co-infection prevalence of each village was not well represented by the overall parasitic prevalence. Our findings point to the need for applying the appropriate spatial scale and sampling strategy when designing and planning a survey system, and are especially relevant when drawing NTDs maps for the planning of effective MDAs in specific territories.
In terms of strengths and limitations, our study benefitted from the spatially diverse and long-term data that underlie it, from our familiarity and established relations with the communities that comprise the study population and with the study area. Our long-term association and rapport with the communities provided us with the local support necessary to enroll and collect the extensive data necessary for such an encompassing study. Our study is unique in the integration of environmental, demographic, socio-economic risk factors and entomological data with a broad parasitological outcomes. Our study also benefitted from the successful application of up to date spatial and multivariate techniques, such as spatial clustering, MCA, GAM, GAMM and a range of non-parametric tests. Like most field-based populations studies, we have encountered variable response rate in the different communities and lower rates of participation by adult males, which may have biased our results. Even with our more sensitive detection techniques, some infections have been missed, and, as a result, we could not always separate the relative contribution of the different risk factors for infection and co-infection. In particular, the environmental factors of distance from the sea, elevation and rainfall were highly correlated and their separate role could not be assessed. Given our very large database and our sophisticated yet cautious analytical approach, we are confident with regard to the significance of our findings and their implications.
We have shown how several protozoan and helminthic parasites are widespread in southern coastal Kenya. In villages with high prevalence of helminthic infections (schistosomiasis and STH) and malaria, co-infections were clustered in areas where environmental and human risk factors (i.e., low SES, poor sanitation, age, and presence of water bodies) came together to enhance the combined transmission of several parasites. The challenge of polyparasitism is increasingly recognized through i) our increasing appreciation of the health and social burden of chronic/recurrent infections [59], [81], [82]; ii) more sensitive diagnostics, which indicate that concurrent polyparasitism is much more prevalent than previously thought [18], [27]; and iii) new inexpensive approaches to treatment and transmission control that are increasingly more accessible [58], [83], [84]. We underline the heterogeneities among and within communities that need to be taken into account when planning appropriate surveillance and control strategies that target polyparasitism. Although children are at highest risk for most infections and co-infections, surveillance and control strategies need to target children and adults, both to reduce transmission and to reduce parasite-related disease burden [69].
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10.1371/journal.pntd.0000868 | Quantifying the Burden of Rhodesiense Sleeping Sickness in Urambo District, Tanzania | Human African trypanosomiasis is a severely neglected vector-borne disease that is always fatal if untreated. In Tanzania it is highly focalised and of major socio-economic and public health importance in affected communities.
This study aimed to estimate the public health burden of rhodesiense HAT in terms of DALYs and financial costs in a highly disease endemic area of Tanzania using hospital records.
Data was obtained from 143 patients admitted in 2004 for treatment for HAT at Kaliua Health Centre, Urambo District. The direct medical and other indirect costs incurred by individual patients and by the health services were calculated. DALYs were estimated using methods recommended by the Global Burden of Disease Project as well as those used in previous rhodesiense HAT estimates assuming HAT under reporting of 45%, a figure specific for Tanzania.
The DALY estimate for HAT in Urambo District with and without age-weighting were 215.7 (95% CI: 155.3–287.5) and 281.6 (95% CI: 209.1–362.6) respectively. When 45% under-reporting was included, the results were 622.5 (95% CI: 155.3–1098.9) and 978.9 (95% CI: 201.1–1870.8) respectively. The costs of treating 143 patients in terms of admission costs, diagnosis, hospitalization and sleeping sickness drugs were estimated at US$ 15,514, of which patients themselves paid US$ 3,673 and the health services US$ 11,841. The burden in terms of indirect non-medical costs for the 143 patients was estimated at US$ 9,781.
This study shows that HAT imposes a considerable burden on affected rural communities in Tanzania and stresses the urgent need for location- and disease-specific burden estimates tailored to particular rural settings in countries like Tanzania where a considerable number of infectious diseases are prevalent and, due to their focal nature, are often concentrated in certain locations where they impose an especially high burden.
| Sleeping sickness (human African trypanosomiasis - HAT) is a disease transmitted by tsetse flies and is always fatal if left untreated. The disease occurs in foci affecting poor communities with limited access to health service provision and as such the disease is often left undiagnosed, mistaken for more common afflictions. Even if diagnosed, sleeping sickness is costly to treat, both for health services and patients and their families in terms of costs of diagnosis, transport, hospital care, and the prolonged period of convalescence. Here we estimate the health burden of the “acute form” T. b. rhodesiense sleeping sickness in Urambo District, Tanzania in terms of Disability Adjusted Life Years (DALYs), the yardstick commonly used by policy makers to prioritize disease management practices, representing a year of healthy life lost to disease. In this single district, the burden of the disease over one year was estimated at 979 DALYs and the estimated monetary costs to health services for the 143 treated patients at US$ 11,841 and to the patients themselves at US$ 3,673 for direct medical costs and US$ 9,781 for indirect non-medical costs. Sleeping sickness thus places a considerable burden on the affected rural communities and health services.
| Sustainable solutions to most of the neglected tropical infections, which include human African trypanosomiasis (HAT) [1], remain illusory in most of the poor rural communities living in third world countries. Whilst significant, but still insufficient, recourses have been directed towards research and control of those diseases which are capable of making sizeable contributions to global pandemics such as HIV, Avian influenza, H5N1 and most recently H1NI, political bias and a myriad of other factors have led woefully inadequate resources being allocated to dealing with less high profile diseases [2]. Poor countries are thus left alone struggling to control a handful of endemic ‘neglected’ infectious diseases [3]. Many of these diseases occur in clusters within the same individuals the so-called “polyparasitic” [3], [4] and often alongside other conditions, including the major killer diseases [5]. Frustratingly, amongst the neglected tropical diseases, some diseases are more neglected than others both locally and internationally. Furthermore, many neglected disease are also zoonotic [6], affecting both humans and animals and imposing a dual burden on human and animal health. As such these diseases have a major impact on rural livelihoods by contributing towards the increasing the level of poverty in most of these communities, which are already poor. Sleeping sickness or human African trypanosomiasis (HAT) is a classic example of a neglected disease. The ‘acute’ form of HAT found in eastern Africa and caused by Trypanosoma brucei rhodesiense is zoonotic and needs to be addressed from human and animal perspectives. Some authors [7], [8] have notably argued that the burden of the rhodesiense form of HAT can be significantly reduced by treating domestic livestock reservoirs as this can play an important role in controlling HAT in humans. This has significant cost implications for rural medical and veterinary services.
The public health burden for HAT has been estimated at 1,609,000 DALYs with 50,000 annual deaths [9], although there are many methodological and data issues that deserve greater consideration when making such estimates for HAT [10]. The HAT figure none-the-less seems small when compared with the standard (discounted at 3% and age-weighted) DALY burden in Africa of (46.7 million due to HIV/AIDS (Human immunodeficiency virus/Acquired Immune Deficiency Syndrome), 30.9 million from malaria and 10.8 million from tuberculosis [8]. However, HAT, like many other neglected diseases, is highly focalized, and its burden therefore needs to be considered in relation to the affected localities. There has been a dramatic reduction in the number of reported HAT cases, which declined to 15 and to 17,000 cases per year [11] in the mid-2000s from nearly 40,000 at the end of the 1990s, but major challenges still exist in predicting future disease trends. With only six years remaining to the year targeted by World Health Organization (WHO) for HAT elimination, the disease situation is still unclear in about one third of the countries where it is endemic [11]. Accurate estimates of incidence and effective control of HAT, especially of the chronic West and Central African form caused by T. b. gambiense, depend on active surveillance which is expensive and requires a high level of organization. Among vector-borne diseases in Africa, HAT ranks second for mortality and fourth in terms of disability adjusted life years (DALYs), but the fact that this is a severely under-reported disease [12] has largely prevented accurate assessment of its true burden.
In the United Republic of Tanzania, HAT is a disease of major public health and socio-economic importance in affected rural communities, where it continues to impose a serious threat to the 4–5 million people exposed to it. Between 1996 and 2006, 2748 cases were reported in Tanzania [11], [13]. This figure represents an average of about 250 cases of T. b. rhodesiense reported annually, and over 40% of the global total. More than 95% of the cases were reported from just three regions in the western part of the country. Tanzania still has several active HAT foci which have been persistent for over 80 years. The disease, which was effectively controlled in early 1960s, made a dramatic re-emergence in some parts of the country during the 1990s [14], [15], due to a lack of adequate and sustained control activities. Here we attempt to quantify the burden of zoonotic HAT imposed in rural Tanzania in recent years.
Between 2000 and 2007, five rural districts of Tanzania, Kigoma, Kasulu, Kibondo, Urambo and Mpanda, all located in the western part of the country, reported cases of sleeping sickness. Out of the five districts, Urambo was purposefully selected for two main reasons; first it recorded the highest number of cases, and second it had the best record-keeping for HAT. Thus, our study relates to the burden of HAT in the high incidence district of Urambo.
Urambo District (Figure 1) is situated in Tabora region, which is in the western part of the country between latitude 4° 00″–5° 53″S and longitude 30° 00′–32° 37″ E. It occupies 25,995 km2 with a total population of 369,329 in 2002 [16]. The district shares a common border with Mpanda, Kigoma rural and Kasulu districts where HAT is also endemic. The predominant ethnic groups are Nyamwezi and Sukuma with some Fipas. Kinyamwezi is the most widely spoken language, although Swahili, the national language, is also used. Subsistence farming is widely practiced, the main crops being tobacco, rice and maize.
All the patients included in the present study were diagnosed and treated in Kaliua Health Centre. Three cases were diagnosed during active surveillance activities carried out in Urambo district, which were also referred to Kaliua for treatment. The remaining patients presented passively. Kaliua is a missionary health facility that was selected by the district health authorities to manage all HAT cases in Urambo district, due to its location. It is within the disease catchment area and is easy accessible by train to patients from all affected villages. Information about HAT patients diagnosed and treated in Kaliua between 2000 and 2007 was obtained from hospital registers. Patient records were categorized by sex, age cohort, stage of the disease, dates of admission and discharge, initial and final diagnosis; treatment provided (this was done with the intention of confirming the stage of the disease), duration of hospitalization and finally the outcome of the disease. The distinction between first/early stage patients (where the parasite is circulating in the blood and lymph system) and second/late stage (where it has passed through the blood brain barrier into the cerebro-spinal fluid) determines what treatment patients are given. For T. b. rhodesiense, first stage patients receive Suramin and second stage patients Melarsoprol [17]. It was not possible to obtain disease stage data for 22 of the patients (as this was not recorded by the care team and further records were unavailable). However, all these un-staged patients recovered and were discharged from the hospital. Therefore, using the conservative assumption that there are fewer non drug-related complications and there is no increased non drug-related mortality in the early stage of the disease, the 22 patients were assumed to be early stage.
Population, incidences and number of deaths were grouped into 5 year age cohorts for ages 5 to 79, and into <1 year, 1–4 years, and 80+ years. Data were manipulated in Microsoft Excel 2003 and analysed in a fully stochastic framework run using the @Risk software package (Palisade, Newfield, NY, USA version 5.0), as described in [7]. The methodology captures both uncertainty and annual variability in the estimates. Both life expectancy data and the template for the DALY calculation were obtained from WHO website (http:/www.who.int/evidence). The life table used in this study was Tanzania-specific [8].
The DALYs calculations for HAT in Tanzania used an adapted stochastic approach [8]; 10,000 Monte-Carlo simulations were run using, at each iteration, input values drawn from the data of the same year per iteration. Final output data was provided as means with 95% confidence intervals.
The total DALY score for each cause-age-sex group were calculated as the sum of non-fatal burden (years of life lived with disability - YLD) and the burden of premature mortality (years of life lost - YLL). To calculate the DALY score for T. b. rhodesiense HAT in Urambo, we applied a 3% discount rate, both with and without age-weighting. The information required for DALY estimations is shown in Table 1. The population data for Urambo District used in the current calculations was projected for the year 2004 from the 2002 Tanzania population and housing census data by applying the estimated annual population growth rate of 4.8% per annum [16] using the standard compound growth rate formula to obtain an estimate of 406,235.
To calculate YLL for Urambo, standard global burden of disease (GBD) methods were followed, using the age categories described above. All other information required for the calculation is shown in Table 1. The total number of reported deaths and estimated under-reporting for Tanzania are described elsewhere [18]; we used under-reporting rates of 0% (no under-reporting), and the figure 45% under-reporting (that is for every 100 reported cases, a further 45 remained un-reported), which had already been derived [16].
As previously published [8], we use a disability weighting of 0.21 for early stage HAT and 0.81 for late stage episodes based on standard definitions [19]. Previous DALY estimates for T. b. rhodesiense used a weighting of 0.35 (originally devised for T. b. gambiense infection); we also ran our model with this value, for comparison. Although long term effects, sequelae, from HAT are known to exist [20] the data to support a calculation of these for this sample did not exist and therefore sequelae were not included.
AIDS has a significant impact on burden of disease estimates. It is estimated that life expectancy for Tanzanians fell from 54 years in 1990 to 45.9 years in 2004 [21] and that the incidence of AIDS will reduce life expectancy in Tanzania by 17% during 2000–05, by 14% during 2010–2015 and by 7% during 2045–50 [22]. The disease that has already increased mortality by 11 percent has also resulted in welfare losses equivalent to 47.2 percent of GDP [23]. In realizing this importance, if we assume that HIV pandemic did not occur, then the burden due to most of diseases would probably have been different from what we observe today. In an attempt to capture and compare this component of calculations, we decided to recalculate DALYs using the same set of data but with the assumption that HIV pandemic did not occur. We therefore re-calculated the DALY score using the life expectancy of Tanzanians for the year 1990, when HIV infection was not very severe in the country.
The costs to the health system per HAT patient were estimated from four components i) the product of the total recorded hospital stays due to HAT in days multiplied by an estimated daily cost for hospital services plus ii) the estimated cost of diagnosing HAT patients plus iii) a value for the drugs used to treat HAT less iv) the amounts that patients paid towards these costs. The total number of days of hospitalisation due to HAT in 2004 at the Kaliua Health Centre was 3601, or a little more than 25 days per parasitologically confirmed HAT patient. Kaliua Health Centre being a missionary hospital, patients are only charged a nominal amount of Tanzanian Shillings (TZS) 1000 (approximately US$ 1), per every patient per night bed occupancy [24], plus a one off payment of TZS 500 (approximately US$ 0.5) for initial laboratory tests on admission. The true cost of these tests would be higher. Here we assume that, in line with the figures cited in [17] the total cost would be at least US$1. This hospitalisation charge is also low, compared, for example with costs for Uganda [7] which were estimated at US$ 2 per day's hospitalisation for HAT, in itself a low figure. The US$ 2 figure was retained as an estimate of the true cost of hospitalisation, half of which was covered by the patients' nominal fee. Thus we conservatively assumed that the fees charged to patients for hospitalisation and admission covered half the actual cost to the health services. Charges for other services such as additional laboratory investigations and treatment incurred by individual patients (depending on the clinical presentation and severity of the condition) were not included in this part of the study. The local currency was converted using the 2004 rates taken from the Bank of Tanzania, during which US$ 1was equivalent to TZS 1000.
Turning to drugs, all the drugs used to treat HAT cases in Tanzania are provided free of charge by WHO. Drugs are bought by WHO from the manufacturers at a greatly subsidised price. The Tanzanian Ministry of Health and Social Welfare is then responsible for making these drugs available to all treatment centres. Finding an appropriate value for the drugs is thus complex, so the compromise of using the costs at the levels paid by WHO is used here. Accordingly the costs for the 30 early and 113 late stage cases were estimated and added to the costs, using the rate of US$ 35 for early and US$ 63 for late stage estimated by WHO.
Other additional costs were difficult to quantify and were excluded from the current estimates since they vary greatly according to each individual presentation. These include drugs to treat individual presentations such as fever, anaemia, pain, adverse drug events and all concomitant conditions. However, transport to and from the hospital, living costs during hospital stay and costs to cover living expenses for one accompanying person were regarded as patients' non-medical or indirect costs and were estimated. All HAT patients require assistance with daily living activities, such as meal preparation, shopping and housework.
The costs borne by patients were firstly, the direct medical costs in terms of the US$ 1 fee per day's hospital stay and the US$ 0.50 admission fee and secondly indirect, non-medical costs. These were estimated in terms of transportation and living costs incurred by each patient during the entire duration of hospitalization. Data used in this part of study were provided by relatives of the patients who were admitted at Kaliua Health Centre during the course of the study and also patients who recovered from HAT who were followed up in their homes in a separate study conducted towards the end of 2007. Travel costs were estimated using reasonable rail fares (roads in most of the disease endemic villages are impassable during the rainy season and the only reliable means of transport is through the railway system; all HAT-affected villages were accessible by train).
The village of origin for all 143 patients were obtained from the hospital register. Standard travel costs were estimated based on the information provided by both patient and relatives, the majority reported that they travelled on the third class coach; therefore third class fare information for all destinations was obtained from the station master at Kaliua railway station. The basis for the estimates is shown in Table 2. The 143 patients spent a total of US$ 479.50 on transport from their home village to Kaliua Health Centre. This is equivalent to US$ 3.35 per patient per single trip to Kaliua, or US$ 6.70 for a return trip. HAT patients also require assistance with food preparation and personal care, meaning that each patient required one accompanying adult person (over 18 years) for TZS 600 per day to buy meals for their relative from a local restaurant. Since the majority of patients were from villages located far from Kaliua we estimated this using the modest costs of ordering food from a nearby restaurant. We assumed that the accompanying person spent the same amount per day on meals.
The living costs for an accompanying person were estimated at the rate TZS 500 per person per night spent in a local guesthouse (US$ 0.50). All other expenses such as costs required for general care and personal hygiene or any other basic needs for the patient were not included in the current estimates as they varied from one patient to another.
Permission to conduct this study in Tanzania was obtained from the Research Ethics Sub-Committee of the National Institute for Medical Research as well as the District Health Authorities of United Republic of Tanzania.
Between 2000 to 2007, a total of 521 HAT cases were diagnosed and treated at Kaliua Health Centre as follows; 29 patients were diagnosed in 2000, 38 cases in 2001, 58 cases in 2002, 98 cases in 2003, 143 cases in 2004, 83 in 2005, 33 in 2006 and 39 cases in 2007. The highest number of cases of HAT was observed in the year 2004, and this year was selected for the study. As an example, some of the other conditions diagnosed at Kaliua for the year 2004 are shown in Figure 2.
The overall DALYs burden due to T. b. rhodesiense HAT in Urambo is the sum of all the YLLs and YLDs estimated from the model using annual age specific data for both reported and estimated (at 0.45 (95% CI: 0.36–0.53) unreported deaths, and non age weighted discounted YLL (DYLL) as shown in Table 3. The model also estimated that premature mortality due to HAT was responsible for 190.1 (95% CI: 17.5–250.0) years of life if we assume that all HAT cases were reported. When 45% under-reporting with age weighting and without age weighting are taken into account then HAT was responsible for 1030.5 (95% CI: 201.9–1747.3) and 610.8 (95% CI: 17.5–938.4) YLLs respectively. Table 3 shows the results of years of life lost for each age group. The model also estimated that T. b. rhodesiense HAT was responsible for 22.0 (95% CI: 0–63.8) years of life lived with disability in Urambo district, when no age weighting and no under-reporting was applied. When age weighting was added then this result increases to 25.5 (95% CI: 0–67.4) YLDs. A full breakdown of age specific years lived with disability in Urambo district is shown in Table 4.
From the above YLL and YLD results, if we assume that all HAT cases were reported and no age weighting was applied then the DALY score for Urambo was 215.7 (155.3–287.5), when age weighting is applied the result increased to 281.8 (95% CI: 209.1–362.6). If 45% under-reporting and no age weighting was applied then the result was 622.5 (95% CI: 155.3–1098.9), however, when 45% under-reporting and age weighting is applied then the DALY score was 978.9 (95% CI: 201.9–1870.8). Age specific DALY scores are detailed in Table 5. If a disability weight of 0.35 is applied (25) then the DALY burden would be 573.5 (95% CI: 147.5–1002.1).
If we assume that DALY burden were estimated in the absence of AIDS pandemic, as shown in Table 6, then the DALYs with no under-reporting would have been 205.1 (95% CI: 147.5–273). When 45% under-reporting and no age weighting was applied, then the DALY burden would have increased to 585.7 (95% CI: 175–1029.8) and 761.6 (209.3 –
Between January and December 2004, 143 T. b. rhodesiense HAT cases were parasitologically confirmed at Kaliua Health Centre, out of them 30 cases were early while the other 113 were late stage sleeping sickness cases. These patients stayed in hospital for a total of 3601 days, resulting in mean hospital stay per patient of 25 days. Following the discussion on costs above, the costs of hospital stays were estimated for all 143 patients at the conservatively low rate of TZS 2000 (US$ 2) per patient per night giving a cost of US$ 7202, and an estimated US$ 1 per patient for initial diagnosis, coming to US$ 143. Applying as the cost of the drugs to treat the disease the values estimated by WHO, 30 early stage and 113 late stage patients would cost US$ 1050 and US$ 7119 respectively to treat, Thus the total cost to the health services, including WHO, would be estimated at US$ 15,514. Of this, the patients themselves would contribute US$ 3601 (US$1 per day's hospitalisation) plus US$ 71.50 (US$0.50 admission fee per patient), making a total of US$. 3672.50 Subtracting this sum, the net cost to the health services would thus be US$ 11,841.50. These figures should be regarded as low estimates.
Apart from the costs incurred by every individual patient on admission costs, each patient required an additional of US$ 63.40 which were indirect non-medical costs to cover their travel costs, meals and accommodation for one accompanying person during their 25 day stay in hospital. Other costs such as costs incurred by health providers were not estimated in this study since Kaliua is a missionary hospital and most of the care providers are based on voluntary basis. Table 2 shows how these indirect costs were estimated for each individual patient. For the total number of 143 patients at Kaliua this would then come to US$ 9781.20.
Findings of the present study show that the re-emergence of HAT in Urambo district continues to impose a significant burden on health care systems as well as communities affected by the disease. The study utilized datasets from hospital records combined with estimates of under-reporting of T. b. rhodesiense HAT in the district. The disease is severely neglected and does not appear among the top priority diseases in any of the disease endemic districts of Tanzania. Notably, the sporadic nature of zoonotic HAT results in cases being recorded as other conditions in hospital reports, health-care seeking for HAT can be prolonged and frustrating [25] and many cases are not reported at all because the affected patients fail to reach health facilities. This study demonstrates the importance of conducting disease-specific burden assessments in local settings [10] as they provide useful epidemiological data that can be very useful in the planning, prioritisation and proper allocation of limited resources in rural areas.
The present study showed that nearly ¾ of HAT cases presented to health facilities in late stage of the disease, and almost all (98%) most presented passively. Late stage presentation has serious consequences; delays in seeking care result in reduced chance of complete cure and late presentation increases the risk of drug-associated adverse effects and the chances of treatment failure which can both result in death [26]. Late stage HAT patients suffer a much greater burden per episode and enormous stigma as the disease may be mistaken for AIDS because both diseases share clinical similarities. One explanation for late hospital presentation is that patients spent much of the early stage seeking health support from alternative sources and only after failing to recover do they decide to seek referral medical attention [25], [26]. There are no effective traditional treatments for HAT and HAT drugs are not available in any pharmacies.
Evidence shows that people often seek basic health services from more than one source, including local drug stores and occasionally traditional healers [25], [26]. These studies observed that about 20% of the communities living along the Tanzanian coast used no health services at all - an interesting finding since communities living along the coast may be considered to have a higher level of awareness on health matters and also a better health services as compared to most of the rural communities in remote areas of western Tanzania where the present study was conducted [27].
This paper presents the first assessment of the burden of rhodesiense HAT for Tanzania and only the second for East Africa. Some studies on the burden of other neglected diseases and zoonoses have been undertaken for Tanzania including the burden of brucellosis [28]. Its findings which are consistent with ours, both diseases show high burden that impact on local health systems as well as communities.
Estimating disease specific burdens for individual conditions can be useful, particularly when such studies use local data and can be applied at the local level. Assessments do not require much additional resource at the local level, while the results can inform local systems with regard to disease prioritisation. Improvements in record keeping should be a priority for local health facilities wishing to improve the evidence-base for resource-allocation decisions.
Previous burden of disease studies conducted in Tanzania estimated that the country loses 10 million years of life annually through illness and death. This amounts to four months of life lost per year for every man, woman and child in the country [29]. Our study also shows that families incur very substantial costs to maintain their sick family members in the health system while being treated for HAT. HAT patients require assistance with almost all activities of daily living [25], and the majority of the villages from which HAT patients arise are located far from the treatment centres. The distance to health facilities has been associated with serious delays in seeking health care services [25], [30]. It is clear that families with HAT patients will require substantial financial capacity to cover transport costs as well as maintain their patients in hospital for a prolonged duration.
The majority of affected communities are poor and have other priority health conditions to attend to (infectious diseases as well as non communicable diseases). Evidence from other rural areas of Tanzania suggests that some communities could not afford a user fee even as small as TZS 500 (approximately US$ 0.50) which represents the charge for a basic laboratory investigation [31]. The majority of families living in rural communities live below the poverty line. Studies conducted in rural districts of Tanzania during 1990–1996 suggested that 26% of rural communities were subsisting on less than US$ 1 per person per day [32]. For families in rural communities to spend such huge amount of money for diagnosis of a family member is a serious financial burden. That burden is likely to be more severe if the patient is the bread-winner, as other family members will need either to do their work on the farm or produce earnings to cover the gap caused by the sick family member - not an easy task for rural settings where employment is almost non-existent. Even if a HAT patient obtains treatment, full recovery takes several months before a patient can return to productive activities. In some cases patients may develop serious long term or even permanent complications.
Several other authors have associated household income and disease burden. A study conducted in Tanzania suggests that the majority of the rural population suffers the combined burden of diseases as most of the socio-economic activities carried at the household level does not provide sufficient resource to cover their basic health needs [33]. In Kenya, households could not afford to buy a mosquito net to protect themselves or their family members from malaria [34]. The first study of its kind to look at patients' costs for HAT [35] estimated that patients spent the equivalent 1.5 months of income on costs, rising to nearly a quarter of a year's income if their time off work were included. For HAT, in the Democratic Republic of Congo (DRC) it was found that household incomes were so limited and the costs of HAT treatment was considered to be so high that this was likely to compromise the timely receipt of medical care [35]. In DRC, patients' average financial costs came to 5 months of income and were as high as 10 months for some individuals. For families living on less than US$ 1 per day in Tanzania, the cost of US$ 68.40 of indirect non-medical costs plus US$ 25.50 of direct medical costs per patient represents a similar proportion of income.
Patients and their family members with in a household often take a long time to prepare for receiving health care and, in most cases, such preparations depend on the solidarity of family members and sometimes even community support [36]. Similarly, household members take time to ready themselves and to mobilize resources, before they seek HAT treatment on behalf of or accompany [12], [36]. In DRC it was observed that despite the communities' acceptance of active screening, the introduction of a ‘symbolic’ card fee was a major obstacle to treatment-seeking behaviour [37]. This is likely to cause very serious consequences for the household and may even compromise other family commitments such as provision of basic education to school children and even food provision to the rest of the family members.
Our study also observed that the high disease burden of HAT was largely attributed to high levels of disease under-reporting in Urambo, which suggested that for every reported case of HAT nine went un-reported [18]. Previous under-reporting studies in Uganda estimated that for every 10 reported cases of HAT, 7 went unreported, and, since untreated patients die, this meant that for every HAT death reported to health systems, twelve others went undetected [12]. Our studies also observed that HIV/AIDS pandemic may have contributed to increase the burden of HAT in Tanzania. Despite health reforms in Tanzania, expecting districts to move from managing diseases to managing health systems from an equity perspective is thought to be unrealistic [38]. The poorest societies carry the heaviest burden of diseases and it is therefore difficult for any health system to target the very poor accurately.
A recent study [32] observed that poorer families were less active and effective in seeking health care than their relatively wealthier counterparts even in rural societies that were assumed to be uniformly poor. Despite Tanzania having the higher health care seeking rates than many countries in sub-Saharan Africa, health inequalities between the very poor and wealthier families were obvious [38]. Members of better off families had higher chances of obtaining suitable treatment once ill, than did those from poor families.
There are some inherent limitations of using the decision tree model for under-reporting in Tanzania that was originally designed for Uganda. The data used to construct that model were derived from an area with better health coverage and better equipped health facilities [11] than in the area of Tanzania where this study was conducted, where most health facilities do not have even basic diagnostic equipment such as microscopes.
One of the major control challenges for HAT is the resurgence of both the gambiense and rhodesiense forms of the disease, which has been observed for rhodesiense in Tanzania as well as in many other disease endemic countries of sub-Saharan Africa. Some of the recent examples of these resurgences have been reported in several countries [14], [15], [39], [40], and there have also been reports of the occurrence of new foci of HAT in many areas of sub-Saharan Africa including Uganda and Tanzania [41], [42]. This resurgence was linked to a lack of adequate knowledge regarding disease trends as well as of the control options in many of the affected communities as well among some policy makers [7], [43]. This in turn was linked to the lack of sufficient resources to sustain regular surveillance activities. This stresses the urgent need for disease specific burden estimation in such communities, as it will allow proper allocation of the limited available resources in poor communities. Zoonotic HAT affects both rural communities as well as the animals. Previous studies observed that the lowest level of effective health seeking behaviour was observed among livestock keepers [30]. Control needs to be targeted at both humans and animals and needs to be a shared responsibility between medical and veterinary sectors [44].
Being one of the most neglected diseases, in most cases HAT is not considered an important disease in most of the affected communities as there is a tendency for policy makers to rank the disease according to their importance in the communities simply by looking at national mortality figures. Findings from our study demonstrated that HAT consumes a very significant proportion of workforce resource, time and hospital space. Also the results of this study demonstrated the importance of conducting disease-specific burden studies, particularly in rural settings rather than generalizing using regional or national figures, reinforcing the conclusions of earlier work [10]. There is a need to target the limited available resources more efficiently so as prevent future outbreaks. In deciding areas requiring prioritisation it is important to use estimates from community perspectives. Local burden of disease estimates are important aspects as they provide good epidemiological data which can be used for timely planning and proper resource allocation in most of the local health care settings and inform disease prioritisation in rural settings.
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10.1371/journal.pmed.1002505 | Risk and surrogate benefit for pediatric Phase I trials in oncology: A systematic review with meta-analysis | Pediatric Phase I cancer trials are critical for establishing the safety and dosing of anti-cancer treatments in children. Their implementation, however, must contend with the rarity of many pediatric cancers and limits on allowable risk in minors. The aim of this study is to describe the risk and benefit for pediatric cancer Phase I trials.
Our protocol was prospectively registered in PROSPERO (CRD42015015961). We systematically searched Embase and PubMed for solid and hematological malignancy Phase I pediatric trials published between 1 January 2004 and 1 March 2015. We included pediatric cancer Phase I studies, defined as “small sample size, non‑randomized, dose escalation studies that defined the recommended dose for subsequent study of a new drug in each schedule tested.” We measured risk using grade 3, 4, and 5 (fatal) drug-related adverse events (AEs) and benefit using objective response rates. When possible, data were meta-analyzed. We identified 170 studies meeting our eligibility criteria, accounting for 4,604 patients. The pooled overall objective response rate was 10.29% (95% CI 8.33% to 12.25%), and was lower in solid tumors, 3.17% (95% CI 2.62% to 3.72%), compared with hematological malignancies, 27.90% (95% CI 20.53% to 35.27%); p < 0.001. The overall fatal (grade 5) AE rate was 2.09% (95% CI 1.45% to 2.72%). Across the 4,604 evaluated patients, there were 4,675 grade 3 and 4 drug-related AEs, with an average grade 3/4 AE rate per person equal to 1.32. Our study had the following limitations: trials included in our review were heterogeneous (to minimize heterogeneity, we separated types of therapy and cancer types), and we relied on published data only and encountered challenges with the quality of reporting.
Our meta-analysis suggests that, on the whole, AE and response rates in pediatric Phase I trials are similar to those in adult Phase I trials. Our findings provide an empirical basis for the refinement and review of pediatric Phase I trials, and for communication about their risk and benefit.
| Phase I cancer clinical trials aim to determine the safety of a new drug, and present a high risk of serious adverse events with limited prospect of therapeutic benefit.
National and international regulations establish limits on allowable risk for research involving children.
Little is known about the level of risk and benefit in pediatric Phase I trials in oncology.
We designed this systematic review with meta-analysis to establish the risk and benefit associated with pediatric Phase I studies in oncology, and to compare our results with those reported for Phase I adult studies in the literature.
We systematically searched for pediatric Phase I cancer studies published between 1 January 2004 and 1 March 2015.
We identified 170 studies with 4,604 patients meeting eligibility criteria.
The pooled overall objective response rate was 10.29%, with a response rate of 3.17% for solid tumors and 27.90% for hematological malignancies.
The overall rate of clearly reported fatal (grade 5) adverse events was 2.09%.
The average grade 3/4 adverse event rate per person was equal to 1.32.
Serious (grade 3, 4, and 5) adverse event and response rates for pediatric Phase I cancer studies were similar to those reported for adult studies.
These data help to inform the assessment and ethical evaluation of, and communication about, risk for pediatric Phase I cancer protocols.
| Despite enormous strides in treating pediatric malignancies, childhood cancer remains the fourth leading cause of death in US children aged 1–18 years [1]. Historically, many pediatric malignancies were treated by adjusting the dosages of anti-cancer drugs that were proven effective in adults [2,3]. However, many pediatric tumors differ histologically from those of adults. Also, children’s physiology may substantially change drug pharmacokinetics and pharmacodynamics [4,5]. As a consequence, new cancer treatments must generally be validated in pediatric populations.
Phase I trials in oncology aim at establishing dose, safety, and preliminary evidence of efficacy of new cancer drugs. Participants generally have advanced cancer and have exhausted standard therapeutic options. Because Phase I trials expose patients to unproven drugs and involve a high degree of uncertainty about risk, the ethical oversight approaches have been widely debated [6–12]. In pediatric trials, participants cannot legally provide informed consent, thus adding an additional challenge to the conduct and ethical evaluation of protocols [13–22]. As with adults, Phase I trials in children present risks of serious toxicity and limited prospect of benefit, and patients are potentially exposed to levels of drug that are inactive [2,11,23,24]. Longer survival times of children can be associated with possible later side effects of cancer therapy, including secondary cancers. Several practices are designed to maximize the therapeutic prospect of Phase I pediatric cancer trials, including prior testing in adults and testing within a narrower dose range [2,4,25].
Little is known about the risk and benefit for pediatric Phase I trials and how well these trials comport with the ethical expectation that such studies offer a favorable balance of risk and therapeutic benefit. In 2005, Lee et al. suggested that the proportion of pediatric Phase I monotherapy trial participants experiencing drug-related fatalities was 0.5%, and the objective response rate was 9.6% [26]. Since 2005, major new drug classes have emerged, as have novel dosing regimens intended to improve risk/benefit balance [11,24,25,27–29]. In what follows, we used systematic review and meta-analysis to establish the risk/benefit balance for contemporary pediatric Phase I cancer studies and to appraise the value of practices aimed at improving the risk/benefit balance of Phase I studies in oncology.
Our protocol was prospectively registered in PROSPERO (CRD42015015961) [30]. We followed PRISMA guidelines (S1 PRISMA Checklist).
We systematically searched Embase and PubMed for articles and abstracts published between 1 January 2004 and 1 March 2015, using strategies that included key words and suggested MeSH and Emtree entry terms, their synonyms, and closely related words. Searches were not limited by language. The starting date of our search period was determined by the timing of the last study to our knowledge presenting data on the efficiency of pediatric Phase I trials in oncology [26]. The full search strategies were checked using the Canadian Agency for Drugs and Technologies in Health peer-review checklist; our literature search strategies and a flow diagram are presented in S1 Table and S1 Fig.
We included pediatric cancer Phase I studies, defined as “small sample size, non-randomized, dose escalation studies that defined the recommended dose for subsequent study of a new drug in each schedule tested” [31], as well as Phase I/II reports containing results of Phase I studies provided separately. We defined “minors” as individuals below the age of 21 years. Inclusion criteria were as follows: (1) all or most participants (over 50%) were less than 21 years old and the study was indicated as pediatric or results for pediatric participants were reported separately; (2) any malignancy (e.g., solid or hematological); and (3) assessment of chemotherapy (cytotoxic drugs) and/or targeted therapy (targeted therapy was defined as monoclonal antibodies or small molecules or antibody drug conjugates [32]). We excluded reports for studies involving (1) topical only or regionally delivered drugs (i.e., delivered directly to the tumor without any systemic effects or minimal systemic effects); (2) only the pharmacokinetics and/or pharmacodynamics of a tested treatment; (3) nonpharmacological modalities (e.g., surgery, radiotherapy, gene therapy, stem cell therapy, or any of these combined with pharmacological therapies); or (4) supportive care without anticancer agents or with other interventions not falling under targeted therapy, chemotherapy, or combined therapy categories (such as antiviral agents or nonspecific immunotherapy). All inclusion and exclusion criteria were defined prospectively in the protocol [30]. They are also listed in S2 Table.
We created and piloted an extraction form, and on the basis of the pilot we refined the form and prepared the final version. Data were extracted from each publication independently by 2 reviewers (MW, MMB, MK, RRJ, AW, JP, AS, JWM, KS, MTW). All reviewers received training prior to extraction. Disagreements were resolved by discussion, and when necessary a third person, an arbiter, was involved (MMB, DN). An experienced methodologist and experienced experimental oncologist had supervisory roles (MMB, DN). In the case of duplicate publications for the same study, the results from full publication and, if possible, the most recent version were used in the extraction. Data were extracted using Google Forms. From each study, we extracted data related to study design, funding, reason for stopping the trial, patient characteristics, intervention, outcomes, and the timing of pediatric testing relative to adult testing. Because Phase I cancer studies do not generally have comparator arms or measure survival endpoints, we used objective response rate and the number of patients receiving recommended dose as proxies for therapeutic benefit [33–37].
We defined objective response rate as the proportion of participants with partial or complete response as defined by authors of the included studies; for hematological malignancies, we considered any of the various methods of measuring response (e.g., cytogenetic, molecular, or flow criteria) as acceptable. For acute leukemias, we did not count partial responses in our assessment of objective response, since anything short of complete response is not considered a benefit for these malignancies. Toxicity and adverse events (AEs) (grade 3, 4, or 5 drug-related events) were measured as defined by the Common Toxicity Criteria version 2.0 and revised versions (Common Terminology Criteria for Adverse Events version 3.0 and version 4.0).
Because large differences were observed in response rate between solid tumors and hematological malignancies, our meta-analysis was also stratified by type of cancer. For 9 studies that included both solid tumors and hematological malignancies, patients were separated for response analysis.
Pooled response rate and fatal (grade 5) AE rate were calculated within each stratum when more than 1 study provided data using meta-analytic methods. Modeling with random effects and the restricted maximum likelihood (REML) estimator was used to account for between-study heterogeneity. I2 statistics were calculated to provide a measure of the proportion of overall variation attributable to between-study heterogeneity. Differences in response rate and grade 5 AE rate between categories of type of therapy, number of drugs, and number of types of malignancies were assessed using the Q test for heterogeneity in meta-regression. Pooled response and grade 5 AE rate were calculated for categories of publication year (2004–2006, 2007–2009, 2010–2012, and 2013–2015) to assess changes over time. p-Values for trend in response and grade 5 AE rate between 2004 and 2015 were obtained from meta-regression. Meta-analysis was conducted using the metafor package (R version 3.2.3); p < 0.05 was considered statistically significant.
The average number of grade 3/4 AEs per person with 95% confidence interval was estimated using a Poisson regression model. In cases where a fatal event was not clearly described as treatment-related, we excluded it from our estimations of grade 5 AE rate. In order to compare risk and benefits, we analyzed a cohort of studies where both drug-related deaths (grade 5 AEs) and response were clearly reported.
Our search identified a total of 7,061 citations for review. A total of 170 unique studies met full eligibility for extraction. Our sample included 74 studies of targeted drugs (43.53%), 72 studies testing classic chemotherapy (42.35%), and 24 studies testing a combination of the two (combined therapy) (14.12%). A full list of drugs tested is shown in S3 Table.
Table 1 summarizes the characteristics of the studies in our sample. The vast majority reported Phase I trials only (155 trials, 91.18%), and 15 studies reported the results of Phase I and Phase II trials (8.82%). According to references provided by the authors, most of the pediatric studies were initiated following completion of the corresponding studies in adults (111 studies, 65.29% of all trials). However, 57 studies (33.53%) did not report adult studies as having been completed. One hundred twenty-eight studies (75.29%) included only patients with solid tumors, and 33 only with hematological malignancies (19.41%). The vast majority of the studies, 144 (84.71%), used conservative dosing strategies, where the initial dose increase was <100%; 4 (2.35%) trials used aggressive dosing designs, where at least the first 2 doses increased by 100%; and another 4 trials used a “modified Fibonacci” dosing strategy (defined as a dose increased by 100% then 66% and 50%). The majority of the studies (74.12%) recommended Phase II trials, and 6 (3.53%) recommended against further testing. The majority of corresponding authors were affiliated with North American institutions (81.18%).
Baseline characteristics of the 4,604 enrolled patients are provided in Table 2. In 139 studies the median age of participants was below 21 years, and in the remaining 31 studies median age was not reported. In all studies included, pediatric participants were the majority. Patients’ performance status at baseline was difficult to assess as only 32 studies reported these data, and the studies used 3 different scales, depending on the age of enrolled patients (Karnofsky, Lansky, or WHO/Zubrod scale).
We defined objective response as the surrogate clinical benefit because objective response—the main read-out of treatment response used in Phase I trials—does not always predict improved survival. Objective response rates were reported in 167 of the 170 trials. There were 406 objective responses reported among 4,349 patients enrolled in the 167 trials (Table 3). The pooled overall response rate across all malignancies was 10.29% (95% CI 8.33% to 12.25%; I2 = 74.49%; Tau2 [estimate of between-study variance] = 0.0007). The response rate for solid tumors among 3,569 patients was 3.17% (95% CI 2.62% to 3.72%), while the response rate for hematological malignancies among 780 patients was significantly higher: 27.90% (95% CI 20.53% to 35.27%); p < 0.001. Response rates varied according to the type of therapy used, significantly so in solid tumors (p = 0.0045), while in case of hematological malignancies this relation was at the limit of statistical significance (p = 0.1047). Higher response rates were observed in combined therapy trials: 44.12% (95% CI 26.30% to 61.94%) for hematological malignancies and 6.44% (95% CI 3.82% to 9.05%) for solid tumors. Response rates were similar for solid tumors tested with classical chemotherapy (6.39%; 95% CI 4.60% to 8.17%) and combined therapy (6.44%; 3.82% to 9.05%).
We also found significant differences in response rate related to the number of drugs used per study, regardless of the type of therapy (S4 Table). The response rate was higher in all studies where 2 or more drugs were tested in comparison to single-drug studies (Tables 1 and S3). The highest relative difference between response rates was identified in solid tumors. For cancers treated with 1 drug, the response rate was 2.49% (95% CI 1.88% to 3.11%), while for cancers treated with 2 or more drugs, it was 10.54% (95% CI 7.61% to 13.46%); p < 0.001. Another significant difference between responses was related to the number of types of malignancies included in a study. The response rate was much higher in all interventions where 3 or fewer types of cancers were treated in comparison to the studies with 4 or more types of malignancies. The highest relative difference between responses was again identified in solid tumors. When 3 or fewer types of malignancies were included in a study, response rate was 15.01% (95% CI 6.70% to 23.32%). When 4 or more different malignancies were included in a study, response rate was 2.85% (95% CI 2.28% to 3.42%); p < 0.001.
We did not find significant linear time trends in objective response rates (p = 0.25 for solid tumors, p = 0.64 for hematological malignancies) (Fig 1). Table 3 shows details of response rates and fatal (grade 5) AE rates in different therapy subgroups.
A total of 70 of the 170 trials reported fatal (grade 5) AEs. We observed 37 grade 5 AEs clearly reported among 1,838 patients (Table 3), suggesting an overall grade 5 AE rate of 2.09% (95% CI 1.45% to 2.72%; I2 = 0.0%; Tau2 = 0). This included 14 patients with solid tumors (1.85%; 95% CI 1.14% to 2.56%) and 23 patients with hematological malignancies (4.04%; 95% CI 2.18% to 5.89%). Differences in AE rates by type of therapy were not statistically significant. Grade 3/4 AEs were reported in 129 studies among 3,547 patients, with an average rate per person of 1.32 (95% CI 1.28 to 1.36). This included on average 1.34 (95% CI 1.22 to 1.47) grade 3/4 AEs per person with solid tumors and 1.22 (95% CI 1.12 to 1.31) grade 3/4 AEs per person with hematological malignancies. The highest average grade 3/4 AE rate per person was identified in patients with hematological malignancies tested with combined therapies: 2.75 (95% CI 2.46 to 3.07). We did not find significant linear time trends in fatal (grade 5) AEs (p = 0.9 for solid, p = 0.7 for hematological) (Fig 1). However, there was a significant difference in grade 5 AE rate (p = 0.02) for hematological malignancies between 2004–2006 (1.03%; 95% CI 0.10% to 2.87%) and 2007–2009 (7.79%; 95% CI 2.52% to 13.06%).
For direct risk and benefit evaluation, we identified a cohort of 66 studies out of the 170 where both objective responses and grade 5 AEs were reported (S5 Table). For sensitivity analysis, we calculated response rates in the subgroup of 66 studies and compared them with response rates in the rest of the 101 studies where objective responses were reported. There were no statistical differences between these 2 groups in the case of solid tumors (2.97% versus 3.31%, p = 0.54) and hematological tumors (26.74% versus 29.42%, p = 0.81). We also calculated the grade 5 AE rates in the subgroup of 66 studies, and the majority of the results were almost identical as in the 70 studies where grade 5 AEs were reported. We found that higher response rates were associated with higher grade 5 AE rates in hematological malignancies. We did not find this relationship in solid tumors.
Our findings suggest that, on average, 1 in 10 children who enroll in pediatric Phase I trials experience objective response, while 1 in 50 die from drug-related AEs.
Because pediatric Phase I cancer trials enroll populations that lack competence to provide informed consent, these trials are generally pursued in a manner that maximizes their therapeutic prospect and reduces their risk. For example, they are generally pursued only after adult trials have clarified toxicity and appropriate dosing, and they generally test a narrower dose range. Despite this, our findings suggest that pediatric Phase I studies have similar drug-related serious (grade 3, 4, and 5) AE and response rates as adult studies. In S6 Table we compare our results with 6 similar reviews of adult Phase I cancer trials and 1 review of trials in pediatric populations. Despite the differences in methods applied in these studies, the pooled overall response rate for all types of cancers (solid and hematological) in our study was similar to that presented in meta-research with adults (10.6%) [38] and much higher than that in another study (2.95%) [39]. The pediatric response rate in our study for solid tumors, 3.17% (95% CI 2.62% to 3.72%), was slightly lower than that in adult solid tumor trials (3.8%) [40] and much lower than results presented in a smaller study (7.2%) [41]. We should further note that our aggregate objective response estimate for pediatric studies does not appear to have been driven by a small number of Phase I trials with large dose expansion cohorts. Only 44 trials involved dose expansion cohorts. Response rates for these trials did not differ from those not having dose expansion cohorts (p = 0.10), nor did we observe an obvious relationship between higher response rate and higher number of patients in expansion cohorts (Spearman’s rank correlation coefficient R = −0.08, p = 0.7; see S2 Fig).
The overall death rate calculated in our systematic review was also higher in comparison with non-pediatric trials, though the size of the difference may be caused by differences in the calculation method [38,40] (S6 Table). Despite an evolution in new treatments and study methods, we did not find linear time trends in risk and benefit across the time period of our analysis.
The number of patients receiving doses recommended for subsequent testing can be interpreted as another proxy of therapeutic value for Phase I trials [38], though it should be noted that, on the one hand, a minority of drugs completing Phase I studies are ultimately proven safe and effective, while, on the other hand, doses lower than those recommended can still be therapeutic (if suboptimal). Overall, 32% of the patients received the recommended dose and 39% received doses below that recommended (weighted mean). Designs intended to increase the number of patients receiving the recommended dose [2,28,42–45] were uncommon.
We found a significantly higher overall response rate in hematological malignancies than in solid tumors. This likely reflects different criteria used to assess response, differences in the biology of these malignancies, and that the former typically enroll a more homogeneous set of indications. The response rate was also higher in all interventions where 2 or more drugs were tested in comparison to the single-drug studies. The response rate was higher in all interventions where 3 or fewer types of malignancies were treated in comparison to the studies with 4 or more malignancies. This possibly indicates that studies where patients with a wider variety of malignancies are enrolled are based on a weaker research hypothesis regarding the efficacy of the tested agent against the specific malignancy. The average grade 3/4 AE rate per person was 1.32, which means that the typical patient was exposed to at least 1 major side effect of a therapy.
Our findings should be interpreted in light of the following limitations. First, the trials analyzed in our review were very heterogeneous. We used broad inclusion criteria to summarize the global response rate and risk. To reduce heterogeneity, we separated therapy types (chemotherapy, targeted agents, and combined therapies) and cancer types (solid tumors and hematological malignancies). We also explored this heterogeneity using meta-regression. Second, we relied only on published data and on the quality of reporting. Many current studies illustrate discrepancies between clinical trial registry records and published articles [46–49]. Moreover, we identified serious issues with reporting in our set of 170 analyzed trials. For instance, the poor quality of outcome reporting did not allow us to meta-analyze grade 3/4 AEs, and we were able to pool only the average number of grade 3/4 AEs per patient. Third, there was no explicit information about treatment-related deaths (grade 5 AEs) in 58.82% of studies—a figure that is surprising given the goal of Phase I trials. The low number of clearly reported treatment-related grade 5 AEs is an important limitation of our data synthesis. Fourth, response rates were used as a surrogate for benefit in our study. On the one hand, response rates could be a sensitive measure of benefit in the context of pediatric malignancies, given their rapid progression. On the other hand, the relationship between response rates and patient-centered outcomes like quality of life or survival is variable [33–37]. Moreover, eventual drug approvals are usually based on survival data from randomized controlled trials, and only about 6.7% to 9.6% of drugs tested in oncology will eventually be registered [50,51]. Better measures of benefit, like progression-free or overall survival, are typically not available in Phase I trials. Our measure of safety did not consider potential downstream effects, like secondary malignancies.
In adult Phase I cancer research, there is a lively debate as to whether access to treatments through trials is therapeutic [6–9,11,24,52]. This debate has particular significance for pediatric trials, since national and international policies generally require that interventions in trials presenting greater than minor increase over minimal risk must “hold out the prospect of direct benefit for the individual subject” and that “the relation of the anticipated benefit to the risk is at least as favorable to the subjects as that presented by available alternative approaches” [53]. Although experimental treatments in Phase I studies that deliver active drug doses clearly meet the first condition, the favorability of risk against benefit in comparison with alternative treatment options is subject to interpretation and may vary depending on the trial. Our data, coupled with careful ethical analysis, provide an empirical basis for further discussions about the therapeutic status of Phase I trials in children. In particular, they provide evidence for refining risk/benefit balance in Phase I trials and identifying those studies that present greater challenges for meeting standards of acceptable risk in children. They also provide a basis for clearer communications about risk and benefit to patients and their guardians.
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10.1371/journal.pgen.1002444 | Adaptive Evolution of the Lactose Utilization Network in Experimentally Evolved Populations of Escherichia coli | Adaptation to novel environments is often associated with changes in gene regulation. Nevertheless, few studies have been able both to identify the genetic basis of changes in regulation and to demonstrate why these changes are beneficial. To this end, we have focused on understanding both how and why the lactose utilization network has evolved in replicate populations of Escherichia coli. We found that lac operon regulation became strikingly variable, including changes in the mode of environmental response (bimodal, graded, and constitutive), sensitivity to inducer concentration, and maximum expression level. In addition, some classes of regulatory change were enriched in specific selective environments. Sequencing of evolved clones, combined with reconstruction of individual mutations in the ancestral background, identified mutations within the lac operon that recapitulate many of the evolved regulatory changes. These mutations conferred fitness benefits in environments containing lactose, indicating that the regulatory changes are adaptive. The same mutations conferred different fitness effects when present in an evolved clone, indicating that interactions between the lac operon and other evolved mutations also contribute to fitness. Similarly, changes in lac regulation not explained by lac operon mutations also point to important interactions with other evolved mutations. Together these results underline how dynamic regulatory interactions can be, in this case evolving through mutations both within and external to the canonical lactose utilization network.
| Differences in gene regulation underlie many important biological processes and are thought to be important for the adaption of organisms to novel environments. Here we focus on the regulation of a group of well-studied genes, the lac operon, that control the utilization of lactose sugar, and we examine how their regulation changes during the adaptation of populations of Escherichia coli bacteria to environments that differ only in the presence of lactose. We find that lac operon regulation is altered in almost all populations that evolve in the presence of lactose and identify two classes of mutations that explain a large part of this change and that confer significant fitness benefits. Interestingly, our study indicates that other mutations, lying outside of the commonly recognized control region, cause new regulation of the lac operon. Together these findings reinforce the importance of changes in gene regulation during evolution and suggest that the biological basis of these changes can be complex and involve novel interactions between genes.
| Changes in gene regulation are an important and common cause of adaptation. Support for this comes from bioinformatic evidence that changes in regulatory elements are associated with presumably adaptive phenotypic changes (reviewed in [1], [2]), comparative experimental studies [3] and from experimental evolution studies, which often find regulatory changes occurring during adaptation to novel environments [4]–[12]. Indeed, in some of these cases direct links have been established between regulatory changes and adaptation [6]–[8], [10]. These experiments directly demonstrate that small and local regulatory changes can significantly contribute to adaptation. In most cases, however, the physiological basis for selection of regulatory changes is unknown.
Previously, we described the evolution of populations of Escherichia coli in defined environments that differed only in the number and presentation of the limiting resource [13]. Populations were evolved in environments supplemented with a single limiting resource or combinations of two limiting resources either presented together or fluctuating daily. These populations adapted to the environments in which they were evolved and this adaptation was, at least to some extent, environment-specific [13]. Here, we focus on a subset of 24 populations that evolved in environments supplemented with glucose and/or lactose and examine changes in the regulation of the lac operon in these populations.
Several attributes make the lac operon a good candidate in which to observe regulatory changes and relate them to their physiological and fitness effects. First, the costs and benefits of lac operon expression are environmentally dependent. Expression of the lac operon is necessary for utilization of lactose for growth, but expression in the absence of lactose can impose a significant cost [14]–[16]. Second, the molecular components of the lactose utilization network are well characterized and their activity can be measured in vivo. The ability to assay changes in the lac regulatory network ‘output’ provides a means to identify and test activity of evolved regulatory changes. Third, the lac operon has been the subject of much theoretical work, leading to the development of mathematical models to explain important features of lac operon physiology [14], [17]–[22] and evolution [16], [23], [24]. Fourth, the utility of the lac operon for examining evolution of regulatory changes has been demonstrated experimentally. For example, lac operon constitutive [25], [26], loss of function [14] and duplication [11] mutants have been recovered following growth in different selective environments, demonstrating that lac operon regulation is evolutionarily flexible and can be a target of selection. It has even been possible to predict the evolution of regulatory changes on the basis of their expected fitness effects. Dekel and Alon (2005) used a cost-benefit analysis to predict the optimum expression level of the lac operon in different inducer concentration environments. They found that populations selected in environments containing a high level of gratuitous inducer, but various concentrations of lactose, generally evolved to regulate expression of the lac operon to the predicted level.
In this study, we examine changes in lac operon regulation associated with selection in environments differing in the presentation of its natural substrate and inducer, lactose, and repressor, glucose. In addition to quantitatively characterizing the changes that have occurred, we examine the genetic and demographic basis for selection of different modes of lac regulation. We find that regulatory changes in the lac operon evolved in many replicate populations selected in environments containing lactose. Much, but not all, of these changes were due to mutations in the LacI repressor or its major operator binding site within the lac promoter. By themselves, these mutations conferred significant fitness benefits in all of the evolution environments that contained lactose. We also present evidence for interactions between lac mutations and mutations in genes outside of the canonical lac utilization network and show that these interactions impact lac operon regulation and fitness. Finally, operator and repressor mutations fixed at different frequencies in different selective environments, although the selective basis of this is currently not known.
Previously we reported propagation of 24 replicate populations of E. coli B REL606 for 2000 generations in one of four environments that differed only in the concentration and/or presentation of glucose and lactose [13]. Environments used were glucose (Glu), lactose (Lac), glucose and lactose presented simultaneously (G+L) or alternating daily between glucose and lactose (G/L). To determine whether regulation of the lac operon had changed during the evolution of our experimental populations, we screened ≥1000 clones from each population on LacZ indicator plates (see Materials and Methods). All six Glu populations had LacZ phenotypes that were indistinguishable from the ancestor. By contrast, in all other evolution environments at least some replicate populations showed clear changes in LacZ activity (Lac, 3 of 6; G+L, 6 of 6; G/L, 6 of 6) (Table 1, Figure 1A). Some of these populations also had within-population variation in LacZ activity and colony morphology. To facilitate molecular and physiological studies of these changes we identified and isolated 46 clones (at least one from each evolved population) that encompassed the range of LacZ activity and morphology types present across all populations (Table 1). These clones were used in all subsequent analyses.
To characterize evolved changes in lac operon regulation, we used a dual fluorescence reporter system to independently quantify with single cell resolution the activity of the major transcriptional regulators of the lac operon, LacI and CRP. LacI and CRP bind the lac promoter at different locations, repressing and activating transcription of the lac operon, respectively (Figure 1B). The ancestor and each of the 46 evolved clones were transformed with Plac-GFP (LacI and CRP reporter) and PlacO(-)-RFP (CRP reporter) constructs. For each strain we used flow cytometry to measure the population distribution of steady-state GFP and RFP expression over a range of thiomethyl-galactoside (TMG) concentrations (TMG is a non-metabolizable inducer of the lac operon). The Plac-GFP reporter captured several key features of lac regulation. The inducer response of the ancestor is ultra-sensitive, showing a sharp transition from low to high expression states as a function of TMG concentration and is bimodal, with populations showing a mix of non-induced and fully induced cells at intermediate levels of TMG [19], [27], [28] (Figure 1C, Figure 2). The CRP-only reporter (PlacO(-)-RFP) shows a unimodal distribution with a constant mean over the range of TMG concentrations, confirming that CRP activity of the ancestor is independent of LacI activity and lac operon expression state (Figure 1C). To facilitate comparisons between the ancestral and evolved genotypes, we quantified three aspects of lac regulation: the TMG concentration required for half maximal population expression (TMG½ Max), the range of TMG concentrations causing a bimodal population response and the fully induced (maximum) steady state level of lac expression. For the ancestor, TMG½ Max is 25 µM, the range of bimodality is between 15–30 µM TMG, and the level of Plac-GFP at full induction (100 µM TMG) is ∼84 RFU (Figure 2).
Evolved changes in the inducer response profiles were common and fell into three broad classes (Figure 2). 1) Constitutive: operationally defined as mean Plac-GFP expression varying by less than 2-fold across the range of tested TMG concentrations. Constitutive clones were observed in at least some populations of each of the treatments containing lactose (Lac, G+L and G/L), but were not observed in any of the Glu evolved populations. 2) Lower threshold/graded response: increased sensitivity to inducer with TMG½ Max values ranging from 2 µM to 8 µM. In addition, all but one clone with a lower induction threshold (G+L2-1) also evolved a graded response, with a unimodal distribution of Plac-GFP expression that increased continuously as a function of TMG concentration. 3) Bimodal: bimodal induction response differing from the ancestor by generally being less sensitive to the inducer, with TMG½ Max values ranging from 25 µM to 50 µM TMG (Figure 2). Higher inducer thresholds were observed for all clones evolved in Glu as well as some clones evolved in Lac and G/L environments. Representative inducer response curves of each class are shown in Figure 3A and the distribution of response types across environments is shown in Figure 3B. Interestingly, clones with a lower threshold response were found exclusively in populations evolved in the G+L environment, fixing in four of the six populations. This pattern is unlikely to occur by chance (Fisher's exact test omitting the polymorphic G+L population, P = 0.002). By contrast, populations evolved in the G/L environment were almost exclusively composed of clones with constitutive lac expression and populations evolved in Lac had either bimodal or constitutive regulation. All clones evolved in Glu showed a bimodal response type (Figure 2, Figure 3B).
The level of Plac-GFP expression at maximum induction (100 µM TMG) was higher than the ancestor in 36 of the 46 evolved clones (Figure 2). To examine this observation in more detail, we repeated our measurements of Plac-GFP expression in each evolved clone, but this time only in the presence of 100 µM TMG, which enabled us to include all clones in duplicate in the same experimental block (Figure 4A). We again saw a strong trend toward an increase in lac expression with 40 of 46 clones having a mean reporter expression level greater than the ancestor. Furthermore, the mean change in expression level of clones evolved in lactose containing environments was significantly higher than the ancestor (two tailed t-test with unequal variance: Lac, P = 0.008; G+L, P = 0.004; G/L, P<0.001) but not significantly different for clones evolved in the Glu environment (P = 0.242). To verify that changes in the level of our GFP reporter accurately reflected changes in the expression level of the native lac operon, we used a direct assay to measure the lac promoter activity of a focal evolved clone G+L3-1, which shows an approximate 2-fold increase in maximal Plac-GFP expression [29]. We found that expression from the lac promoter was significantly higher in the G+L3-1 clone than the ancestor and this increase agreed with our estimate based on flow cytometry (Figure 4B). Measurements taken at two time-points during exponential growth gave similar promoter activity estimates for all strains tested, indicating that promoter activities are representative of the lac system at steady state.
Lastly, we measured the expression level of the CRP activity reporter (PlacO(-)-RFP) as a function of inducer concentration. All evolved clones showed a unimodal distribution with a mean response that was independent of inducer concentration, suggesting that, similar to the ancestor, evolved clones maintained predominantly non-cooperative interactions between the LacI and CRP regulators and that CRP activity is independent of lac expression level (Figure S1).
To determine the genetic basis of changes to lac regulation we first sequenced the main lac regulatory regions, lacI and Plac, of each evolved clone (excluding G/L5 clones whose lac regulatory region could not be amplified by PCR). We found that all but one clone classified as constitutive had either a deletion or insertion of a 4 bp sequence within the lacI gene, which results in a frame shift (Figure 5). This region of lacI has three 4 bp direct repeats and is known to be a mutational hotspot, accounting for ∼75% of all spontaneous lacI null mutations [30], [31]. Constitutive clone G/L4-3 had a nonsynonymous mutation in lacI conferring a leucine to glutamine change at residue 71. This mutation is predicted to cause a severe defect in the ability of LacI to repress lac expression [32]. In addition, we found that all clones that evolved a lower induction threshold contained a single base pair substitution in the primary LacI repressor binding site of the lac promoter (lacO1). We identified three unique lacO1 mutations, two of which occurred twice in independent G+L populations (Figure 5). Previous work has demonstrated that all three lacO1 mutations can reduce the binding efficiency of LacI to the operator, thereby reducing repression of the lac operon in the absence of a specific inducer [33]–[36]. Lastly, populations in the bimodal inducer response class did not have any mutations within lacI or the lac promoter region, even though other aspects of their lac regulation, such as the region of bimodalilty and TMG½Max values, were altered (Figure 2).
The inducer response profiles presented above used a GFP reporter controlled by the ancestral Plac promoter. It is possible that this promoter does not accurately reflect the activity of the mutant promoters that evolved in the G+L isolated strains. To investigate the effect of this difference we constructed a version of the Plac-GFP reporter with the lacO1G11A mutation found in the G+L3 and G+L5 populations (PlacO1-GFP) and used it to generate inducer response profiles in the G+L3-1 clone (Figure S2A). Inducer response profiles for G+L3-1 with PlacO1-GFP are qualitatively similar to those obtained with the Plac-GFP reporter, showing a graded inducer response and a lower induction threshold. These characteristics were confirmed with β-Gal enzymatic assays that directly examined mean LacZ activity (Figure S2B).
The lacI insertion/deletion and lacO1 mutations are clearly good candidates to explain the evolved changes in lac operon regulation, but additional mutations may also be influential. To test the effect of the identified mutations on lac expression, we added the evolved lacI 4 bp deletion allele and the lacO1G11A mutation individually into the ancestral reporter strain. If these mutations play a major role in determining the evolved regulatory change, we expect these constructed strains to have inducer profiles similar to those of the evolved strains from which the mutations were isolated. Indeed, the lacI deletion recapitulated the inducer response profiles found in all constitutive clones, causing Plac-GFP expression levels to become independent of TMG concentration (Figure 6A, lacIΔTGGC versus Figure 3A, Constitutive). Similarly, adding the lacO1G11A mutation into the ancestral background recreated the lower threshold/graded inducer response associated with evolved clones harboring mutations in lacO1 (Figure 6A, lacO1G11A versus Figure 3A, Lower threshold). The TMG½Max value for the lacO1G11A reconstructed strain was 4 µM, which is similar to the TMG½Max of evolved clones with this mutation (G+L3-1, 8 µM; G+L5-1, 4 µM), demonstrating that the lacO1 mutation is the primary cause of evolved changes in inducer sensitivity.
The lacI and lacO1 mutations can explain many, but not all, of the lac regulation changes seen in the evolved strains. Specifically, neither mutation confers the increase in maximal expression that was seen in most evolved clones (Figure 6B, Figure 4B). To conclusively establish that additional evolved mutations impacted lac regulation in the G+L3-1 evolved clone, we replaced the lacO1 mutation with the ancestral operator sequence. This strain maintained the ∼2-fold increase in maximum Plac-GFP expression level relative to the ancestor, indicating that mutations outside the canonical lac operon regulatory network contribute to evolved changes in lac regulation (Figure 6B). Whole genome sequencing of G+L3-1 found 6 additional mutations (in the genes or gene regions rbsDACB, ECB_00822, fabF, sapF, mreB and malT), none of which are in genes previously characterized as affecting lac operon expression.
That multiple lacO1 and lacI mutations arose independently in replicate populations and affect a trait of relevance in the evolution environments suggests that they confer a selective advantage. Further, the presence of lacO1 mutations exclusively in the G+L evolved populations suggests that they confer a greater advantage in this environment than do lacI mutations. To test these predictions we introduced the lacI and lacO1 mutants individually into the ancestor and measured the fitness of these constructed strains relative to the ancestor in each of the four evolution environments. We found that lacO1 and lacI mutations conferred a fitness benefit in all environments containing lactose (mean relative fitness effect and 2-tailed t-test: Lac environment. lacI: 8.9%, P<0.001; lacO1: 7.9%, P<0.001. G+L environment. lacI: 8.4%, P<0.001; lacO1: 8.0%, P = 0.001. G/L environment. lacI: 6.6%, P<0.001; lacO1: 2.2%, P = 0.02). By contrast, both mutations imposed a small fitness cost in the glucose environment, consistent with them not being observed in Glu populations (Glu environment: lacI: −4.0%, P<0.001; lacO1: −1.8%, P = 0.16) (Figure 7). Intriguingly, despite the lacO1 mutations being significantly overrepresented among G+L populations, they did not confer a greater fitness advantage in this environment. Similarly, lacI mutations did not confer a greater advantage in Lac or G/L environments, where they were dominant. To address the possibility that some non-transitive interaction could complicate our indirect comparison of the relative fitness benefits of the two mutations, we also performed direct competitions between the two constructed strains. Again, we found that the fitness of the lacI mutant was not significantly different from that of the lacO1 mutant in any environment (fitness of lacI relative to lacO1, 2-tailed t-test: Glu environment: 0.5%, P = 0.69; Lac environment: 0.4%, P = 0.60; G+L environment: −0.5%, P = 0.39; G/L environment: 2.1%, P = 0.05) (Figure 7).
To examine the basis of the fitness effects conferred by the lacI and lacO1 mutations, we quantified their effect on population growth dynamics, focusing on the transitions between glucose and lactose utilization that are encountered in the G+L and G/L environments (environments in which lacO1 and lacI mutants predominated). We found that the lacI and lacO1 mutations significantly decreased the lag phase, relative to the corresponding ancestral alleles, following a shift from growth on glucose to growth on lactose (a part of the G/L environment) (Figure 8A, Table 2). Furthermore, both lacI and lacO1 mutations eliminated the diauxic lag phase measured for the ancestor when switching from glucose to lactose utilization in the G+L environment. Neither mutation had a significant effect on lag time following a shift from growth in lactose to glucose, indicating that the change in lag time was specific to lactose utilization. The maximum growth rate constant (μMax) for lacI and lacO1 mutants was not significantly different from that of the ancestor, except during growth on lactose in the G+L environment (Figure 8A, Table 2). In this case, both lacI and lacO1 mutants had significantly higher growth rates than the ancestor. In agreement with fitness measurements, strains containing the lacI and lacO1 mutations show no significant differences in the length of lag phases or maximum growth rates in any of the environments tested. To decrease experimental noise in these experiments, we used higher sugar concentrations than present in the evolution experiment. Analysis of growth dynamics using the exact evolution environments yielded qualitatively similar results (Figure S3).
In contrast to the lacI mutant, the lacO1 mutant can repress lac expression to some degree (Figure 2, Figure 3A). To examine how this difference in regulation translates to the evolution environment, we measured LacZ activity of the ancestor and the lacI and lacO1 mutants in the G+L environment (Figure 8B). The ancestor shows the anticipated LacZ expression profile, with activity decreasing 97% during growth on glucose and increasing back up to the initial level of activity after switching from growth on glucose to lactose. Interestingly, lacI and lacO1 mutants showed indistinguishable LacZ activity profiles, with LacZ activity much higher than the ancestor at all time points during growth in G+L medium. These results suggest that the relatively low levels of lactose present in the G+L environment induce the lacO1 mutant lac operon, even in the presence of glucose concentrations sufficient to prevent induction of the ancestral lac operon. Analysis of the steady state levels of lac expression during growth in DM+Glu (2 mg/mL) with and without lactose (87.5 µg/mL) supports this conclusion, with repression of lac expression only occurring in the absence of lactose (Figure S4). In summary, in the ancestral background, lacO1 and lacI mutations are indistinguishable with respect to their effect on fitness, growth dynamics and lac expression dynamics in the G+L environment.
A possible explanation for the success of lacO1 mutations in the G+L populations despite them not conferring any advantage relative to more frequent lacI mutations is that they interact synergistically with other mutations that fixed during the evolution of these populations [37]. To test this, we compared the fitness advantage conferred by lacO1 and lacI alleles in the ancestral background relative to the advantage they confer in the genetic background of evolved clone G+L3-1, which substituted a mutation in lacO1 during evolution in the G+L environment (Figure 5). If epistasis was important in selection of lacO1 alleles in the G+L environment, we predicted that the fitness advantage conferred by the lacO1 mutation would be significantly larger in the background of this evolved clone than in the ancestral background, and that this positive effect will be less pronounced for the lacI mutation. The lacO1 mutation did confer a bigger benefit in the evolved background (two tailed t-test, fitness in evolved background minus fitness in ancestral background = 4.4%, P = 0.03) (Figure 9). However, a similar effect was seen for the lacI mutation (two tailed t-test, fitness in evolved minus fitness in ancestral background = 4.8%, P<0.001) and there was no significant difference in the fitness conferred by the lacI and lacO1 mutations in the evolved background when they were directly competed against one another (lacI versus lacO1 in evolved background, relative fitness difference = 1.3%, P = 0.07). Therefore, epistatic interactions increase the fitness effect of both the lacI and lacO1 mutations in the evolved background, but do not explain the enrichment of lacO1 mutations in populations evolved in the G+L environment.
In the absence of a measurable difference in the fitness conferred by lacI and lacO1 mutations, what could explain our finding that lacOI mutations only occurred in populations evolved in the G+L environment? If the lacI and lacO1 mutations occurred with equal probability, the distribution of mutation types over selection environments we observed is unlikely to have occurred by chance (Fisher's exact test omitting the polymorphic G+L population, P = 0.002). In fact, our observations are even more unlikely than this test implies because the lacO1 mutation will almost certainly occur at a much lower rate than the lacI mutation. The frequency of lacI null mutations in the E. coli strain used in this experiment is ∼3×10−6 per generation (Hana Noh and TFC unpub. obs.), which is in good agreement with a previous measurement for E. coli K12 [31]. By contrast, we conservatively estimate the lacO1 mutation frequency to be <3×10−9 per generation (Materials and Methods). Without some unique advantage, it is difficult to see how lacO1 mutations could reach high frequency in any population, let alone predominate as in the G+L populations. The ∼1000 fold difference in mutation frequency does, however, provide an explanation for the absence of lacO1 mutations in Lac or G/L environments.
We considered the possibility that cross-contamination could be responsible for the occurrence of identical lacO1 mutations in two of the G+L populations, which would reduce the number of independent lacO1 populations to three. This is unlikely since replicate populations were not propagated in adjacent wells, and no evidence for cross contamination was found during the course of the experiment (see Materials and Methods). Furthermore, the environmental association remains significant even if only unique lacO1 mutations are considered (Fisher's exact test omitting one polymorphic population and populations with non-unique lacO1 mutations, P = 0.006).
Finally, it is possible that the statistical association between the lacO1 mutation and the G+L evolution environment, despite its high significance, is nevertheless spurious. In this case, repeat evolutionary experiments of the ancestor in the same G+L and G/L environments (where selection of lacI mutations was most consistent) would not be expected to lead to significant mutation-environment association. To test this, we began 12 ‘replay’ populations in each of the G+L and G/L environments, founding each population with the same ancestor as used in the original experiment. Every 100 generations, we examined the frequency of lacI and lacO1 mutations in each population using LacZ indicator plates and by sequencing select clones. Although lacO1 mutants were detected in the majority of G+L replay populations, their frequencies never rose above 4% in any one population (Table S1). In contrast, lacI mutants rose to high frequency, accounting for >98% of clones in all populations by 400 generations. Replay experiments in the G/L environment followed a similar trend, although lacO1 mutations were detected in only 2 of the 12 populations over the course of the experiment (Table S2). In summary, despite being highly improbable, the failure of lacO1 mutations to establish in our replay experiments suggests that their enrichment over competing lacI mutations in the original G+L populations may have occurred by chance.
We sought to test whether evolution in environments that differed only in the availability and presentation of lactose selected for changes in the regulation of the lac utilization network. Our analysis of inducer response profiles found three broad classes of lac regulation change among evolved clones. Two of these classes, constitutive expression and a lower threshold/graded inducer response, represent substantial changes from ancestral regulation and were observed only in populations evolved in environments containing lactose. Sequencing of lac regulatory regions in evolved clones uncovered mutations in the lac repressor (lacI) and the primary lac operator (lacO1) that correlated with the constitutive and the lower threshold/graded inducer response, respectively. Addition of these mutations to the ancestor demonstrated that they explained many, but not all, of the broad scale changes in regulation we observed and that, by themselves, they can confer fitness benefits in environments containing lactose. These fitness benefits were relatively large, representing 20%, 27% and 28% of the total mean fitness improvement in the Lac (lacI mutation), G+L (lacO1 mutation) and G/L (lacI mutation) evolved populations, respectively [13]. Together these results indicate that regulatory changes were common, complex — occurring both within and outside of the recognized lac regulatory elements — and adaptive.
Extensive previous study of lac operon regulation offers the opportunity to connect the genetic and phenotypic changes we observed. Twenty-one of the 22 lacI mutants we identified mapped to a mutational hotspot within lacI [30], [31]. These mutations generate a frameshift in the coding sequence that results in expression of a nonfunctional repressor, which provides a good explanation for the complete loss of negative regulation we observed in lacI mutants. Similarly, the three lacO1 mutations we identified in the G+L evolved populations have been reported as reducing the binding affinity of LacI for this binding site [33]–[35]. Consistent with the mutations reducing, but not completely preventing, LacI binding, strains containing lacO1 mutations are able to repress the lac operon, but are induced at much lower TMG concentrations than the ancestor. lacO1 mutations also conferred a graded response to increasing inducer concentration, which contrasted with the canonical bimodal response of the ancestor. The same regulatory outcome was demonstrated by Ozbudak et al. (2004), who found that decreasing the effective concentration of LacI by providing extra copies of lacO1 binding sites resulted in a graded unimodal induction of the lac operon [19]. The similarity in regulatory changes suggests that the graded induction we observe is a consequence of decreased LacI-lacO1 affinity, reducing the effective concentration of LacI repressor. More generally, our results support the concept that small changes in the activity of cis-regulators have the potential to transform the output of a regulatory network between binary and graded responses [38].
Both lacI and lacO1 mutations were shown to confer significant fitness benefits in the three lactose containing evolution environments (Lac, G+L and G/L). Analysis of the growth dynamics of strains containing only these mutations indicated that a large part of this benefit is due to a reduction in lag phase when switching from glucose to lactose utilization. Interestingly, when added to the ancestor, both lacO1 and lacI mutations abolished the diauxic lag that separates glucose and lactose growth phases during growth in the G+L environment. This phenomenon is well documented for lacI mutants [39], but to the best of our knowledge has not been demonstrated for lacO1 mutants. In the case of lacI mutants, constitutive expression of the lac operon primes the cell for utilization of lactose as soon as glucose resources are exhausted. In contrast to lacI mutants, lacO1 mutants are still capable of repressing lac expression in the absence of inducer. However, when grown in media containing both glucose and lactose the lacO1 mutation essentially phenocopies a lacI mutant, causing constitutive lac expression. Evidently glucose-mediated blockage of lactose import through LacY (inducer exclusion) is insufficient to prevent lactose accumulating in cells to a concentration sufficient to allow lac operon induction in lacO1 mutants [39], [40].
The loss of lac repression in lactose (3/6), but not glucose (0/6), evolved populations is consistent with the ‘use it or lose it’ hypothesis [23], [24]. This hypothesis proposes that negative regulation will be maintained during evolution in environments in which gene products, in this case the LacI repressor, are used because mutants that lose the repressor will needlessly express the lac operon and be selected against. If a repressor is seldom used, as in the lactose evolution environment, loss of function mutations will not be effectively selected against and can fix through genetic drift. However, in its simplest form, this mutation accumulation mechanism does not capture the dynamics of the regulatory changes we see. First, loss of the lacI repressor actually confers a benefit during growth on lactose, so that underlying mutations will increase in frequency faster than expected if they were neutral. Second, repressor mutations also occurred in environments where glucose was just as common as lactose. Analysis of growth curves suggest a mechanism for this; lac repressor mutants were able to quickly begin growth following a switch from glucose to lactose. Fitness measurements indicated that this advantage outweighed the cost of unnecessary lac expression during growth in glucose.
Given the large benefit conferred by lacI mutations in the Lac environment, it is interesting that only three of the six Lac populations were enriched for lacI mutations. We identify two possible explanations for this observation. First, clonal interference may have resulted in lacI mutations being outcompeted by higher effect beneficial mutations. Second, populations that did not enrich lacI mutations may have fixed alternative mutations that genetically interact with lacI mutations to reduce their fitness benefit. To distinguish between these possibilities, we are continuing the evolution experiment and tracking the frequency of lacI mutations in the Lac populations. In addition, we are examining the fitness benefit conferred by lacI mutations when introduced into clones from Lac evolved populations that did not fix lacI.
We can explain why lac mutations occurred only in lactose containing selective environments. A second layer of environment specificity is less clear; why do lacO1 mutations only reach high frequency in the G+L environment? The lacI and lacO1 mutations had no differential effect on fitness in either the ancestor or an evolved background and conferred indistinguishable growth dynamics in all evolution environments. Without a selective advantage over lacI mutations it is difficult to understand how lacO1 mutants were fixed in 4 of 6 G+L populations, especially considering that the rate of lacI mutations is likely on the order of 1000-fold greater than for lacO1 mutations. In the absence of a plausible mechanism to explain enrichment of lacO1 mutations in the G+L environment, we investigated whether environment-specific selection of lacO1 mutants was reproducible. This was not the case, with all 12 of the independent replay populations selected in G+L eventually fixing (>98%) lacI mutations. It remains formally possible that subtle differences in media or experimental conditions during competition assays or the replay evolution experiments could affect the fitness advantage experienced by lacO1 mutants in focal G+L evolved populations. However, taken at face value, the different outcome between replay and primary populations suggests that, notwithstanding mutation rate differences and the strong statistical association between environment and mutation type, the enrichment of lacO1 mutations over lacI mutations in the G+L environment might have occurred by chance.
Models incorporating interactions between key regulatory elements can successfully predict aspects of lac operon regulation [18], [19]. Nevertheless, recent studies demonstrate that regulation of the lac operon is evolutionarily plastic, such that interactions can arise or be altered to fine tune regulation and better fit E. coli to its environment [11], [14], [41]. By characterizing changes in regulation without a priori assumptions as to the nature of regulatory changes or the mutations causing them, we were able to identify evolved clones with changes in lac regulation that are likely due to novel interactions with mutations in genes outside of the canonical lac operon. Two results support this conclusion. First, we identified numerous clones with maximal steady state expression levels of the lac operon that were higher than the ancestor. Further examination of evolved clone G+L3-1 indicated that the increase in maximal lac expression level could not be explained by the lacO1 mutation present in this clone. Second, the fitness benefit conferred by the lacO1 mutation in this same evolved clone was significantly greater than in the ancestor, indicating that one or more evolved mutations interact with the lacO1 mutation to determine its effect on fitness. Whole genome sequencing of G+L3-1 identified six additional mutations, however, none of these mutations mapped to the lac operon or genes known to directly impact CRP-cAMP activity. It seems likely, therefore, that one or more mutations in the G+L3-1 clone have directly or indirectly led to new regulatory control of the lac operon.
Is there an optimal level of lac expression in each of the three lactose environments? Dekkel and Alon (2005) found that, in the presence of a gratuitous inducer, lac operon expression evolved to a level predicted on the basis of a cost-benefit analysis, dependent on the concentration of lactose in the selection environment [14]. Our results support the idea that maximal expression level is a plastic feature of the lac operon and can be tuned to best fit the environment. At this time, however, we do not know the genetic or molecular basis for the widespread increase in maximum lac expression observed in many evolved clones. Possible ‘local’ explanations include: increases in the level of cAMP, mutations in the lacZYA genes that affect mRNA stability, or changes in DNA supercoiling that increase lac operon transcription. It is also possible that changes in lac maximum expression reflect an alteration in some global process. For example, changes in the function or concentration of ribosomes could affect expression of all genes. In future work we aim to identify the evolved mutations that are responsible for changes in maximum lac expression and then construct strains that will allow us to test the adaptive value of different expression levels as well as probe the underlying molecular mechanisms.
An additional widespread change in lac regulation was that evolved clones displaying bimodal inducer responses tended to also have higher induction thresholds (TMG½Max) than the ancestor. This trend was not environment specific, occurring in clones isolated from Glu, Lac and G/L evolved populations. However, the parallel and large-scale increases in induction threshold observed for Glu-evolved clones suggests that this change in lac regulation is a direct or correlated response to adaptation. The mechanistic bases of increases in induction threshold are currently not understood, but could be the result of both direct and indirect mechanisms. For example, reduction in the activity and/or concentration of the permease LacY could increase the concentration of extracellular inducer required to achieve intracellular levels of inducer sufficient to inactivate LacI. In glucose evolved populations, changes in LacY activity may result from mutations in the PTS system that optimize glucose transport but lead to elevated levels of unphosphorylated EIIAglc, which is a known inhibitor of LacY activity [40]. Alternatively, higher growth rates of evolved strains will also tend to decrease the steady state intracellular concentration of inducer thereby increasing the external concentration required for induction of the lac operon. Further study will be required to discern between these and other hypotheses.
In summary, we have identified and characterized widespread changes in lac operon regulation that occurred during selection of replicate populations in different lactose containing environments. In our view, the most important aspects of our findings are how common these changes were and that they likely involve mutations both within and outside of the set of genes that are recognized as regulating the lac operon. Identification of these changes will provide a rare insight into how regulatory networks can be rewired in response to an environmental change.
The ancestral strains used for experimental evolution studies were E. coli B REL606 (ara−) and an otherwise isogenic ara+ derivative, REL607 [42]. For routine culturing, cells were grown in lysogeny broth (LB) medium [43]. Davis minimal (DM) medium was used for experimental evolution and subsequent analysis of evolved clones [42]. Sugars were added to base DM medium at the following concentrations to make single and mixed resource environments: glucose (Glu) 175 µg/mL, lactose (Lac) 210 µg/mL, and Glucose+Lactose (Glu+Lac) 87.5 µg/mL and 105 µg/mL, respectively. These concentrations were chosen to ensure that each environment supports approximately the same stationary phase density of bacteria (∼3.5×108 cfu/mL) [13]. Strains were grown at 37°C unless otherwise stated. T medium contains 1% Bacto tryptone, 0.1% yeast extract and 0.5% sodium chloride. Antibiotics were used at the following concentrations: chloramphenicol (Cm), 20 µg/mL; kanamycin (Km), 35 µg/mL; streptomycin (Sm), 100 µg/mL.
Evolved populations were screened for qualitative changes to lac operon regulation using TGX medium, which consisted of T agar plates supplemented with 0.5% glucose and 30 µg/mL of the colorimetric LacZ substrate 5-bromo-4-chloro-3-indolyl-beta-D-galactopyranoside (X-Gal). On this medium, the degree of blue coloration correlates with the degree of LacZ activity and, therefore, lac operon expression. Clones representing the diversity in LacZ activity and colony morphology within each evolved population were recovered, scored and stored for future analysis. TGX plates enabled us to distinguish between the ancestor and clones with lacO1 and lacI mutations (mutations that we subsequently identified in evolved populations), which appear white, faint blue and dark blue, respectively.
We developed a dual fluorescent reporter system that enabled us to independently monitor LacI and CRP activity with single cell resolution. In this system the native lac promoter (containing LacI binding sites O1 and O3), coupled with an optimized ribosomal binding site, drives expression of the fast maturing GFP derivative, GFPmut3.1 [44]. This Plac-GFP module was cloned into a mini-Tn7 delivery vector, to make pTn7-Plac-GFP, and integrated into the chromosome in a site-specific manner [45].
Expression of GFP from the Plac-GFP reporter depends on both LacI and CRP activity. To isolate these effects we developed a second reporter to monitor the contribution of CRP to lac operon expression independent of LacI. To do this, we constructed a version of the lac promoter (PlacO(-)) with defined mutations in the O1 and O3 operators which have been shown to prevent binding of the repressor LacI while maintaining the integrity of the major binding site for CRP [46]. This synthetic promoter was used to drive expression of the red shifted fluorescent protein DsRed express2 [47]. This reporter was cloned into a stable low copy plasmid (∼5 copies per cell) to generate pRM102-3. Control experiments confirmed that the introduced mutations abolished LacI binding while retaining promoter response to cAMP dependent activation of CRP (Figure S5). This reporter system differs from a previously published system in three important ways [19]. First, the fluorescent signal is bright enough to allow analysis by flow cytometry. Second, the CRP reporter is a derivative of the lac promoter that has the LacI binding sites deleted, rather than an unrelated reporter subject to regulation by CRP. Third, DsRed express2 shows low cytotoxicity relative to other commonly used RFP's [47].
The Tn7-Plac-GFP reporter was integrated into the chromosome of focal evolved clones by tri-parental mating with the MFDpir (pTn7-Plac-GFP) donor strain and the MFDpir (pTSN2) helper strain [48]. Strains were grown overnight in LB and washed once in LB before being resuspended in 1/10 volume LB. Recipient, donor and helper strains were mixed at a 4∶1∶1 ratio and incubated on an LB plate for 3 hrs. Transconjugants were selected on LB plates supplemented with Km and Sm. A PCR assay was used to confirm that the mini-Tn7 had inserted into the single characterized chromosomal integration site [49]. Finally, all reporter strains were screened to ensure that they retained the LacZ phenotype that formed the basis of their initial selection from evolved populations.
Strains were inoculated from glycerol stocks into 500 µL LB media in 2 mL deep-well plates (Phenix Research) and grown overnight at 37°C on a microplate shaker at 750 rpm (Heidolph Titramax 1000). After overnight growth, cultures were diluted 1∶1000 into 500 µL DM+0.4% glycerol and incubated for a further 24 hrs at 37°C. To determine the inducer response of each strain, overnight cultures were diluted 1∶1000 into separate wells of a 96-well plate containing DM+0.4% glycerol supplemented with TMG at concentrations ranging from 0 to 100 µM and incubated at 37°C for a further 15–18 hrs. This time period encompassed approximate steady state reporter expression in ancestral and evolved clones (Figure S6). TMG induces the lac operon by binding and inactivating the LacI repressor. Unlike the natural inducer, allolactose, TMG is metabolically stable, which is advantageous for quantitative studies because it allows accurate control of TMG concentrations through the course of the experiment. Importantly, import of TMG into the cell is dependent on the lactose permease LacY, which is not true of other commonly used synthetic inducers such as Isopropyl β-D-1-thiogalactopyranoside (IPTG).
Flow cytometry was performed with a FACScalibur (BD Biosciences) equipped with a high throughput sampler. PMT voltages for the flow cytometer were set as follows: SSC-H - E02, FSC-H 580 V, FL1-H 800 V and FL2-H 800 V. The threshold was set at 250 on the SSC-H channel. For each expression assay, a total of 25,000 events were captured at a rate of 1000–3000 events/s. Data was acquired in log mode with no hardware compensation. We examined day-to-day reproducibility by measuring ancestral inducer response curves on 5 separate days using independent cultures (Figure S7). The level and distribution of Plac-GFP expression in response to TMG was similar between replicates, indicating that our protocol for measuring inducer response profiles was robust.
Routine analysis of the flow cytometry data and plotting of inducer response profiles was performed in R (version 2.12) using the Bioconductor packages FlowCore and FlowViz [50]–[52]. To control for cross talk between GFP and RFP reporter detection, a compensation matrix was calculated using the appropriate single reporter control strains and used to correct flow cytometry data post acquisition. To minimize detection noise and enrich for cells of similar size, all data was filtered with FlowCore's norm2Filter on the FSC-H and SSC-H channels using the default settings. This typically resulted in retention of 50–60% of all collected events.
For quantitative analysis of inducer responses, flow cytometry data were processed using Matlab (Mathworks, Inc.). An elliptical gate corresponding to a Mahalanobis distance of 0.5, centered at peak cell density in the FSC-SSC coordinates, was used to minimize the effects of varying cell size and granularity on the resulting fluorescence histograms. A custom bimodality detection algorithm (to be described elsewhere) was applied to determine the region of bimodality for each histogram. Inducer sensitivity was determined as the point where the population-mean of GFP expression was halfway between baseline and saturation.
Assays were carried out as described by Zhang and Bremer (1995) with modifications [53]. Specifically, 1–2 mL of cell culture was pelleted and resuspended in 250 µL unsupplemented DM medium to remove any remaining lactose. Cell concentration was estimated by measuring absorbance at OD600 with a microplate reader (Tecan). Cells were permeabilized by mixing 20 µL of cell suspension with 80 µL of permeabilization solution (100 mM Na2HPO4, 20 mM KCl, 2 mM MgSO4, 0.8 mg/mL cetyl-trimethylammonium bromide (CTAB), 0.4 mg/mL sodium deoxycholate, 5 µL/mL β-mercaptoethonal). After incubation for 10 minutes at room temperature, measurement of LacZ activity was initiated by adding 150 µL of o-nitrophenyl-β-D-galactoside (ONPG, 4 mg/mL) and mixing. Yellow color development was stopped by addition of 250 µL 1 M sodium carbonate and the reaction time recorded. Samples were centrifuged to remove cell debris and absorbance at OD420 was measured for 200 µL of the supernatant. LacZ activity (Miller units) was calculated as (1000×OD420)/(volume (mL)×OD600×reaction time (min)).
Strains were inoculated into LB medium from freezer stocks, incubated overnight at 37°C and then diluted 1∶100 into DM medium supplemented with Glu, Lac or G+L at the concentrations used in the original evolution experiment. Strains were grown overnight and diluted 1∶100 into fresh DM media and incubated for a further 24 hrs to allow them to become physiologically adapted to their resource environment. To measure growth dynamics, a 1∶100 dilution of pre-conditioned culture was inoculated into 200 µL of DM supplemented with Glu, Lac or G+L in a clear 96-well plate. Concentration of sugars was either the same as in the evolution environments or to facilitate higher cell densities and correspondingly more precise OD measurements, were as follows: Glu, 0.2 mg/mL; Lac, 1.8 mg/mL; G+L, 0.2 mg/mL & 1.5 mg/mL, respectively. Incubation and optical density measurements were performed with a Bioscreen C plate reader (Oy Growth curves AB Ltd) at 37°C with continuous shaking and OD600 measured at 5 min intervals. The maximal growth rate constant (μMax) of each strain was calculated by linear regression of the plot of ln(OD600) versus time (hrs) using a sliding window of 10 data points. The steepest of these slopes was used to calculate μMax with units hrs−1. Lag time was calculated by extrapolating the μMax regression line to its intersection with OD600 = 0.06. Extrapolating to the initial density of individual growth curves would have been preferable, however, we found that these measurements were quite variable. To account for this we adopted the approach described by Friesen et al. (2004) where a constant reference density is used, assuming that starting biomass is similar for all strains [54]. For strains showing diauxic growth, we analyzed both growth phases separately to derive μMax-1 and μMax-2. The diauxic lag phase (lag-2) was calculated by determining the difference between the times when the regression lines for μMax-1 and μMax-2 intersect with the OD600 value coinciding with the end of the first growth phase.
Assays were performed as described by Kuhlman et al. (2007) [29]. Briefly, steady state levels of LacZ activity were measured for strains grown in DM+0.2% glucose supplemented with a saturating concentration of the gratuitous inducer IPTG (1 mM). β-Gal assays were performed as described above except that color development was followed over time by measuring absorbance at OD420 and linear regression used to fit a line of best slope to the plot of OD420 vs time (min). LacZ activity (Miller units) was calculated as (1000×slope)/(assay volume (mL)×OD600). Doubling rate was measured for each strain in the experimental conditions by linear regression of log2(OD600) plotted against time, with the steepest of these slopes designated as the maximum doubling rate (doublings/hr). Promoter activity was calculated as the product of LacZ activity (Miller units) and doubling rate (hrs−1). To confirm that the inducer concentration we used was sufficient to completely inactivate LacI, we also measured expression from a lacI null mutant. This mutation caused a similar expression increase as induction with 1 mM IPTG, indicating that this level of inducer was sufficient to fully induce the lac operon.
Constructs and approaches used for the manipulation of each mutation were as follows. The PlacO1 and araA- mutations were introduced using a suicide plasmid approach that has been described previously [6]. Briefly, PCR products containing the relevant evolved alleles were separately cloned into pDS132 [55]. Resulting plasmids were introduced into recipients by conjugation and CmR cells (formed by chromosomal integration of the plasmid) were selected. Resistant clones were streaked onto LB+sucrose agar to select cells that lost the plasmid (which carries the sacB gene conferring susceptibility to killing by sucrose). These cells were then screened for the presence of the evolved alleles by a PCR-RFLP approach using the enzyme HaeIII (araA-) or on LacZ indicator medium (PlacO1). Putative allelic replacements of the evolved ara- and lacO1 alleles were confirmed by sequencing.
The lacI(-) mutation was obtained by isolating spontaneous mutants of relevant strains that could grow on minimal media supplemented with P-Gal as the only carbon source and confirmed by sequencing [56]. We isolated independent lacI(-) mutants that had either insertion or deletion mutations in a previously identified mutational hotspot [57]. Preliminary experiments indicated that these mutant types had identical fitness in each of the environments used here. For this reason, we used only the deletion mutant in the experiments reported in Results.
The fitness of constructed strains was measured relative to the ancestor strain used to found the evolution experiment or directly to each other. Competing strains contained opposite Ara marker or lac regulation types, which allowed them to be distinguished on tetrazolium arabinose (TA) [42] or LB+X-Gal indicator medium, respectively. Before each fitness assay, competing strains were grown separately for one complete propagation cycle in the environment to be used in the assay so that they reached comparable cell densities and physiological states. Following this step, competitors were each diluted 1∶200 into the assay environment. A sample was taken immediately and plated on indicator plates to estimate the initial densities of the competing strains. At the end of the competition a further sample was plated to obtain the final density of each competitor. The fitness of the test strain relative to the reference strain was calculated as ln(NT2/NT0)/ln(NR2/NR0), where NT0 and NR0 represent the initial densities of the test and reference strains, respectively, and NT2 and NR2 represent their corresponding densities at the end of the competition with correction for the number of transfer cycles the competition occurred over. Competitions were generally carried out over two transfer cycles. All assays were carried out with at least four-fold replication unless reported otherwise in Results.
To estimate the per generation frequency of loss of function lacO1 mutations, we assume that mutation of the lacO1 region is random and equally likely for each of the 21 bp that define lacO1. Using a mutation rate of 5×10−10 bp/generation [58] the probability of generating a single substitution in lacO1 is ∼1×10−8 per generation. However, only a subset of these mutations will severely compromise LacI binding. A survey of the literature indicates that approximately 16 single base pair substitutions within lacO1 have been reported to reduce the affinity of LacI by >90% and/or reduce the effective repression of LacZ expression >90% [33], [34]. Based on this, we conservatively estimate that a third of the possible 63 single base pair substitutions will severely compromise LacI binding, giving a mutation frequency of ∼3×10−9 per generation. This is likely an overestimate since only three lacO1 alleles were selected during evolution, two of which were selected twice in independent populations, indicating that relatively few of the possible lacO1 mutations may actually confer an advantage in the evolution environments. In addition, the lacO1 alleles selected during evolution have been reported to reduce the affinity for LacI by >98%, further reinforcing the stringency of our operational criteria for estimating the number of possible lacO1 mutations [33], [36].
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10.1371/journal.ppat.1006435 | A systemic macrophage response is required to contain a peripheral poxvirus infection | The goal of the innate immune system is to reduce pathogen spread prior to the initiation of an effective adaptive immune response. Following an infection at a peripheral site, virus typically drains through the lymph to the lymph node prior to entering the blood stream and being systemically disseminated. Therefore, there are three distinct spatial checkpoints at which intervention to prevent systemic spread of virus can occur, namely: 1) the site of infection, 2) the draining lymph node via filtration of lymph or 3) the systemic level via organs that filter the blood. We have previously shown that systemic depletion of phagocytic cells allows viral spread after dermal infection with Vaccinia virus (VACV), which infects naturally through the skin. Here we use multiple depletion methodologies to define both the spatial checkpoint and the identity of the cells that prevent systemic spread of VACV. Subcapsular sinus macrophages of the draining lymph node have been implicated as critical effectors in clearance of lymph borne viruses following peripheral infection. We find that monocyte populations recruited to the site of VACV infection play a critical role in control of local pathogenesis and tissue damage, but do not prevent dissemination of virus. Following infection with virulent VACV, the subcapsular sinus macrophages within the draining lymph node become infected, but are not exclusively required to prevent systemic spread. Rather, small doses of VACV enter the bloodstream and the function of systemic macrophages, but not dendritic cells, is required to prevent further spread. The results illustrate that a systemic innate response to a peripheral virus infection may be required to prevent widespread infection and pathology following infection with virulent viruses, such as poxviruses.
| Prior to the eradication of variola virus, the orthopoxvirus that causes smallpox, one-third of infected people succumbed to the disease. Despite many complications, smallpox vaccination using vaccinia virus enabled a successful eradication of the disease. Following smallpox eradication, vaccinia (the smallpox vaccine) remains a widely used vaccine vector, so any information about the immune response to the vector can help engineer safer vaccines, or treatment, following complications of immunization. During natural infection, orthopoxviruses spread from a peripheral site of infection to become systemic. This study elucidates the early requirement of innate immune cells to control spread of the smallpox vaccine vector after a peripheral infection. We report that systemic populations of cells, rather than those recruited to the site of infection, are responsible for preventing virus dissemination. The viral control mediated by these cell subsets presents a potential target for therapies and rational vaccine design.
| A large number of viruses infect the host at the periphery and spread systemically through the lymphatic system to cause disease. This is the same mechanism by which many viruses of concern to human and animal health such as orthopoxviruses (variola virus, monkeypox virus), enteroviruses (polio, coxsackie), Aphthovirus (foot-and-mouth disease), Rubivirus (rubella), Flavivirus (Yellow Fever, Dengue, West Nile), Rubulavirus (mumps), Morbillivirus (measles), Varicelovirus (chickenpox), and others, spread and cause disease [1, 2]. When a pathogen breaches the epidermis, an ideal innate immune response attacks the infectious agent and keeps the infection localized to the initial site of inoculation, so the host does not risk a fulminant, disseminated infection. Here, we investigate the cellular mechanisms responsible for preventing widespread dissemination following dermal virus infection.
A number of potential checkpoints exist to stop or blunt the spread of virus following peripheral infection. Recruitment of innate immune cells, such as neutrophils or monocytes/macrophages, to the site of infection (in this case, the skin) could restrict or slow the spread of virus. However, cellular recruitment can take hours to days so a rapidly replicating virus could spread prior to migration of innate immune cells to the site of infection. After inoculation, infectious virus quickly enters the lymphatic system and empties into the draining lymph nodes (D-LN). Particles carried by lymph first enter the subcapsular sinus (SCS) of a D-LN where they are taken up by CD169+ SCS macrophages, [3]. Infection of SCS macrophages may be vital to prevent the spread of virus and is important for efficient activation of the immune system. SCS macrophages are optimized for virus uptake and antigen presentation to B cells, fulfilling a function during peripheral viral infection that is analogous to the role of metallophilic marginal zone (MZ) macrophages in the spleen during viremia [4]. CD169+ macrophages in LN and spleen may even support limited replication of some viruses, which can be important for providing sufficient viral antigen to quickly activate antiviral immunity [4–7]. If not internalized by SCS macrophages, virus may be internalized by or infect less specialized macrophages in the medullary sinuses [8] (akin to the splenic MZ macrophages that border the red pulp). If both of these populations of macrophages are absent, inactive, or overwhelmed, the assumption is that virus may enter the bloodstream, allowing a systemic infection [9, 10].
Systemic macrophage populations that are in close contact with the bloodstream, particularly those in the MZ of the spleen, but also in the liver or kidney, are targets of many bloodborne viruses [11–18]. Infection of MZ macrophages is thought to be important for production of Type-I interferon (IFN) [18], IL-1 [14], induction of T cells [16], or antibody [5] responses. The MZ macrophages also they function to “soak up” bloodborne virus to prevent additional spread [15]. In addition, myeloid cell populations in other organs, such as the liver, may also “soak up” bloodborne virus [12, 19–24]. However, none of the published data have utilized a manner of targeting macrophages within a specific organ without bystander effects upon other organs. Therefore, macrophages at the site of infection, in the D-LN, and in the splenic MZ or other blood rich organs may all play a role in controlling the systemic spread of virus, but the relative contributions of macrophage populations at each checkpoint following peripheral virus infection have not been assessed. Here, we will dissect the role of macrophages at different spatial checkpoints following dermal infection with the orthopoxvirus Vaccinia virus (VACV).
Poxviruses are a group of viruses that infect primarily through the skin (e.g. cowpoxvirus, monkeypoxvirus, and ectromelia virus (ECTV), which causes mousepox). A poxvirus that is currently endemic in the human population is molluscum contagiosum virus (MCV), the third most prevalent viral skin infection worldwide [25]. VACV is a poxvirus that can establish localized skin infection in a wide range of mammals and is widely used as a backbone for viral vaccine vectors. VACV is most effective as a vaccine when delivered via damage to the epidermis [26], and as a pathogen is adapted to the skin, as revealed by a number of viral immunomodulatory molecules that facilitate virus replication only during skin infection [27]. VACV remains localized to the skin in mice with intact immune systems [28]. However, if VACV does reach internal organs it replicates profusely, particularly in the ovaries of female mice [29], a pattern of tropism for epithelial and steroidogenic cells that resembles more virulent poxviruses [30]. This makes VACV an ideal, relevant model to study the factors that determine whether a peripheral infection will remain peripheral or become disseminated, as individual components of the immune system can be ablated and the spread of VACV measured. Monocytes migrate to the site of VACV infection in the skin [31, 32] and become infected with VACV at this site [33]. In addition, we [34], and others [33] have shown that VACV draining from the skin preferentially infect SCS macrophages in the D-LN. However, replicating VACV primarily remains restricted to the skin after dermal infection, displaying a relatively mild, localized pathogenesis [28] that may be related to the fact that VACV replicates poorly, if at all, in macrophages or DC, either murine or human [35–37]. In fact, both VACV and MCV infections are notable for the large numbers of skin-resident DC that leave the skin and migrate to the D-LN, but this migration of infected DC does not help the virus disseminate [38–40]. Other studies have suggested that infected monocytes [41] or neutrophils [42] may fulfill a “Trojan horse” role during dermal VACV infection. However, we previously established that mice depleted of phagocytes allow VACV dissemination from skin to the internal organs, spread that would not be possible if phagocytes were the primary carriers of the virus [32].
In our previous work we used multiple depletion methods to examine the role of myeloid cell populations in spread of VACV following dermal infection. Although we found that each manipulation of myeloid cell populations had profound effects upon local pathology in the skin following dermal VACV infection, only clodronate-loaded liposomes (CLL) administration allowed the spread of large quantities of VACV from the ear to the ovaries, the primary site of VACV replication [32]. Therefore, CLL uniquely depleted a population, or a combination of populations, of cells that are required to prevent systemic VACV spread. CLL relies upon the phagocytic property of a cell to internalize toxic liposomes, leading to cell death. In contrast, other mechanisms of myeloid cell depletion target either cells in which specific promoters are active, or cells with certain proteins displayed upon the cell surface. Therefore, the mechanism of targeting by CLL is unique, and CLL may deplete populations that are not depleted by other cell-depletion methodologies. Here, we expand upon our previous work by using additional methodologies to deplete myeloid cell populations, none of which replicate the ability of CLL to allow systemic spread of VACV, to identify the spatial checkpoint(s) at which VACV spread is contained.
In this study we use two complementary genetic mouse models to study the transient depletion of myeloid cells, primarily macrophages and neutrophils, during VACV infection. Both models utilize a system in which expression of a suicide receptor is driven by a “lineage-specific” promoter, and ligation of that receptor can allow ablation of the population expressing the receptor. First, we use MaFIA (Macrophage Fas-Induced Apoptosis) mice, which express a suicide receptor driven by the CD115 promoter [43]. Injection of the drug AP20187 causes dimerization of the suicide receptor and induction of apoptosis in cells in which the CD115 promoter, which drives expression of the M-CSF receptor, is or has been active. As a second approach, we use LysMcre:iDTR mice, which express a high-affinity diphtheria toxin (DT) receptor in cells that express Lyz2 (the gene for lysozyme 2) [44–46]. Administration of DT depletes cells in which the Lyz2 promoter is or has been active, primarily granulocytes, but also monocytes, macrophages, and alveolar type II cells. Each of these systems allows effective and transient depletion of the target populations. Each of these promoters was chosen due to activity in particular myeloid cell populations, but use of a single promoter to target cell populations often means that other “off-target” populations may be depleted. In particular, both the Lyz2 and CD115 promoters have been described to be active in subpopulations of DC [47–49].
In order to control for the effects of the MaFIA and LysMcre:iDTR mouse models upon DC populations we used two additional models. In CD11ccre:iDTR mice, the expression of the DT receptor is driven by the CD11c promoter, that drives expression of an integrin subunit that is primarily active within DC. Thus, DT administration to CD11ccre:iDTR mice allows transient deletion of DC, as well as other cells that express CD11c (Table 1). In addition, we used Batf3-/- mice, which have a germline deletion for the Batf3 transcription factor. Batf3-/- mice lack the CD8+ subset of DC, which we have previously shown to be important in the innate immune response to ectromelia virus (ECTV), a virus related to VACV that is the causative agent of mousepox [50].
In order to target cell populations with certain proteins displayed on the cell surface we also used antibody-mediated depletion to remove populations of neutrophils (anti-Ly6G) and T cells (anti-Thy1 (CD90)). In each case we titrated doses of antibody carefully to ensure that depletion was >90% effective at all sites examined (site of infection, D-LN and spleen). Antibody remains in the injected mouse for a number of days, allowing continual depletion. However, again, the use of a single antigenic target allows depletion of unintended cell populations that may express that target. In this case, some T cell subsets express Ly6G and some keratinocytes and epithelial cells express Thy1. Therefore, care must be taken when interpreting results obtained following antibody depletion.
A further mechanism used to investigate the role of myeloid cells is genetic deletion of chemokine receptors required for a particular cell population to reach its intended site of action. An example used in this study is mice lacking the chemokine receptor CCR2, which is required for monocyte precursors to leave the bone marrow and enter the circulation, as well as for recruitment of inflammatory monocytes to some sites of inflammation [51]. Another example is mice lacking CX3CR1, a chemokine receptor expressed by resting monocytes (and many other cells, including T lymphocytes), which binds a ligand released from sites of inflammation. In CX3CR1-/- mice the “circulating” or “patrolling” monocytes are unable to reach sites of tissue damage where they may be needed to replenish tissue-resident macrophages [52]. The disadvantage of using such chemokine receptor knockouts is that they have global effects, and rarely target individual populations of cells, potentially giving a global developmental defect that must be considered when interpreting experimental results.
Each of these mechanisms of examining the role of individual myeloid cell populations has advantages and disadvantages, but it is clear that a global perspective must be taken when interpreting results, rather than reliance upon a single depletion methodology (Table 1). Here we use multiple methodologies to gain a comprehensive picture of the role of myeloid cell populations in preventing the systemic spread of VACV following a peripheral infection. In using multiple methodologies we can eliminate the off target effects of any single approach. This is particularly important because the efficiency and specificity of these cell depletion methodologies have been established in the steady, or non-infectious disease, state. VACV infection induces a significant c-kit-dependent alteration in the hematopoietic stem cell compartment of the bone marrow, as myeloid cell precursors are released into the circulation [53] where they can produce atypical myeloid cells at the site of infection [32]. Therefore, an in-depth study of the effects of myeloid cell depletions or deficiency in the context of a VACV infection is warranted. Here, we utilize these depletion or deficiency models compared to CLL treatment to examine which cell populations at which anatomical location are required to prevent VACV spread following dermal infection. We find, surprisingly, the local macrophage populations do not play an exclusive role in preventing VACV dissemination. Rather, systemic macrophages are infected with bloodborne VACV and are depleted uniquely by CLL, indicating that these cells play a role in preventing systemic spread of VACV following dermal infection.
In our previous studies on mice examining intradermal VACV infection, we observed that systemic depletion of phagocytes with clodronate-loaded liposomes (CLL) allowed the virus to spread from the ears, becoming a disseminated infection detectable at high levels in internal organs [32]. To gain an insight into the mechanism by which phagocytes prevented VACV spread, we initially investigated at which point VACV could establish a systemic infection. To do this, we utilized the peripheral nature of our model to remove the initial site of infection at various times after inoculation. Mice were given a dose of CLL i.v. and then infected with 104 pfu of VACV in the center of each ear pinna. The site of infection was removed at different times post-infection. Five days post-infection, the level of virus replication in the ovaries was similar whether the ear pinnae had been left intact for 1 hour, 24 hours, or the entire 5 days post-infection (Fig 1A). Even when ear pinnae were removed 10 seconds post-infection, some virus drained from the skin and later reached the ovaries, although not as much as if the pinnae were left intact for an hour or more (Fig 1A). This shows that a small intradermal inoculum of VACV is sufficient to establish disseminated infection within an hour. As CLL-depleted mice reliably showed titers in the ovaries on the level of 108 VACV pfu/mouse, in subsequent figures we chose to display extremely low values as “< 104 pfu”, the amount of the inoculum. We were concerned that the injection of a bolus of fluid intradermally may allow VACV to bypass natural checkpoints involved in preventing spread. Therefore, we compared the ability of CLL treatment to allow the systemic spread of VACV following intradermal infection to VACV administered to skin damaged by scarification. Systemic CLL treatment allowed spread of VACV, irrespective of whether the virus was infected i.d. or via scarification (Fig 1B). Therefore, the ability of CLL treatment to allow systemic spread of VACV does not depend upon injection of fluid to bypass immune checkpoints.
To examine when CLL-depleted cells are required to prevent disseminated infection, we infected mice intradermally and gave them a single dose of CLL i.v. at different times pre- or post-infection. We then determined the level of VACV replication in the ovaries 5 days post-infection. CLL given on the day of infection, or the next day, allowed VACV to spread to the ovaries (Fig 1C). Although virus was detectable in the ovaries of some untreated mice, the levels of VACV were ~4 orders of magnitude lower than those seen with CLL depletion (Fig 1C). However, by 4 days post-infection VACV spread following CLL administration was reduced over 10,000 fold compared to mice treated with CLL just prior to infection (Fig 1C). Therefore, despite the intensive virus replication occurring in the skin 4 days after infection, with VACV titers peaking at day 5 [32, 41], depletion of phagocytes only allowed virus to drain from the ear and establish systemic infection for 3 days post-infection. This finding indicates that the initial VACV inoculum, and replicating VACV at early time points following infection, can disseminate throughout the body in the absence of phagocytes depleted by systemic CLL treatment.
After establishing that the day of primary infection is the crucial moment for viral dissemination, we investigated how long phagocytes responsible for blocking VACV spread are absent after CLL depletion. We gave mice a single dose of CLL i.v., then infected with VACV at times ranging from 4 hours to 16 days post-depletion and assayed for the presence of VACV in the ovaries 5 days post-infection. Mice infected 5 days post-depletion still had uncontrolled dissemination of VACV to ovaries, and even at 7–11 days post-depletion not all mice were able to block virus dissemination (Fig 1D). Since VACV is still able to disseminate 24 hours post-infection, this indicates that it takes an absolute minimum of 6 days to reconstitute the crucial phagocyte population that prevents virus spread.
To examine the role of phagocytic cells at the site of infection, we sought to establish conditions that deplete myeloid cell populations in the ear following dermal VACV infection. We first examined the efficacy of two depletion methodologies at removing CD11b+ phagocytes at the site of infection. First, we administered AP20187 to MaFIA mice to target cells that express, or have expressed, CD115 [43]. Cells were extracted from VACV-infected ear pinna at a time when inflammation and viral replication are still increasing (day 5 of infection), and analyzed by flow cytometry. When total myeloid cells were counted (FSCHi CD11b+ CD90.2- CD19- NK1.1-), AP20187 treatment eliminated the vast majority of myeloid cells (Fig 2A). We have previously characterized the infiltration of classical inflammatory monocytes (CD11b+Ly6C++ Ly6G-) and a distinct population of atypical CD11b+Ly6C+ Ly6G+ “tissue-protective” monocytes to the site of VACV infection [32]. MaFIA mice treated with AP20187 before and during infection saw a 95% decrease in recruited macrophages or inflammatory monocytes (Fig 2B) and a 90% decrease in Ly6G+ cells (Fig 2C). In contrast, CLL treatment was much less effective at reducing local macrophage numbers (Fig 2B) and increased numbers of both total CD11b+ cells and tissue-protective Ly6G+ cells (Fig 2A and 2C). As a second approach we administered diphtheria toxin (DT) to LysMcre:iDTR mice. We found that DT administration in LysMcre:iDTR mice reduced the number of CD11b+ phagocytes to a similar degree as AP20187 treatment in MaFIA mice (Fig 2D). Additionally, inflammatory macrophage (Fig 2E) and Ly6G+ cell (Fig 2F) populations were reduced >80% in the VACV-infected ear. Therefore, both the MaFIA and LysMcre:iDTR mouse models provide mechanisms to examine the role of locally recruited phagocytes relative to CLL treatment.
As the MaFIA and LysMcre:iDTR mouse models were substantially more efficacious at depleting local myeloid cell populations in the infected ear, we first examined the local pathogenesis caused by VACV infection. After intradermal infection with VACV and sustained cell depletion, the MaFIA and LysMcre:iDTR mouse models had larger skin lesions than mock-depleted littermates (Fig 2G and 2H). We have previously demonstrated that depletion of Ly6G+ cells causes a similar increase in lesion size in VACV-infected mice [32]. Therefore, we initially attributed the enhanced tissue damage to depletion of these tissue-protective cells in MaFIA and LysMcre:iDTR mice. However, we also observed markedly enhanced lesions in VACV-infected mice treated with CLL (Fig 2I), indicating that the Ly6C++ inflammatory macrophages at the site of infection can also modulate the pathogenesis following VACV infection. To specifically ablate the role of Ly6C++ inflammatory macrophages at the site of infection, we used CCR2-/- mice in which inflammatory monocytes are completely ablated from the site of VACV infection (Fig 2K). In CCR2-/- mice as well, the lesion size was considerably enhanced compared to wild-type mice (Fig 2J). However, the recruitment of Ly6G+ cells In CCR2-/- mice was unaffected (Fig 2K), indicating that classical inflammatory macrophages as well as Ly6G+ myeloid cells [32] are needed to limit local tissue damage following VACV infection. Damage at the site of infection, and subsequent spread beyond the ear, could be affected by alterations in local VACV replication. We have previously shown that neither CLL nor AP20187 treatment have a large impact upon local VACV replication [32]. Here we expand that observation to show that DT treatment of LysMcre:iDTR also did not dramatically increase or decrease VACV replication locally (Fig 2L), also indicating that changes in local tissue pathogenesis may be due to changes in the immune response, rather than VACV replication.
Inflammatory macrophages recruited to the site of VACV infection clearly affect local pathogenesis, so we next sought to examine the ability of these cells to block the systemic dissemination of VACV following dermal infection. As before, we found that targeting phagocytes with CLL allowed virus dissemination from the site of infection and massive replication (107–108 pfu/mouse) in the ovaries (Fig 3A). However, targeting myeloid cells for depletion in LysMcre:iDTR mice treated with DT did not allow systemic spread of VACV, as replicating virus was not detected in the ovaries of DT-treated mice (Fig 3A). Similarly, AP20187 treatment of MaFIA mice did not facilitate spread of VACV to the ovaries (Fig 3B). To directly examine the role of inflammatory macrophages in preventing the spread of VACV from the ear, we infected CCR2-/- mice with VACV intradermally in the ear pinnae. In addition, we also infected mice lacking the chemokine receptor CX3CR1. A previous report has implicated CX3CR1+ DC in control of VACV following an intranasal infection [54]. However, neither CCR2-/- nor CX3CR1-/- mice demonstrated marked systemic spread of virus following dermal VACV infection (Fig 3C), indicating that, unlike in the intranasal infection model, locally recruited macrophage populations are unlikely to play a role in restricting VACV to the skin. We have previously described a requisite role for Ly6G+ cells in protection of the ear from tissue damage following VACV infection. To examine whether tissue protective Ly6G+ cells were required to prevent systemic dissemination of VACV we depleted these cells with anti-Ly6G antibody, which effectively depletes systemic and infiltrating Ly6G+ cells [32]. Similar to the MaFIA and LysMcre:iDTR mouse, both of which effectively deplete Ly6G+ cells at the site of VACV infection, we found that targeted depletion of Ly6G+ cells did not allow systemic spread of VACV (Fig 3D). Ly6G+ cells have been proposed to interact with CD8+ T cells at the site of infection to modulate local VACV replication in the ear [41]. Therefore we also examined the effect of depletion of Thy1+ T cells upon the spread of VACV from the ear. As with depletion of Ly6G+ cells, T cell depletion also failed to allow systemic spread of VACV (Fig 3D). Therefore, CLL treatment appears unique in its ability to facilitate systemic spread of VACV from a peripheral site of infection.
Macrophage populations have been implicated in the prevention of systemic disease and death following challenge with ECTV, the causative agent of mousepox [55]. In contrast to VACV, ECTV causes disseminated infection but is usually survived by C57BL/6 mice. After intradermal infection with ECTV, LysMcre:iDTR mice depleted with DT (Fig 3E) and MaFIA mice depleted with AP20187 (Fig 3F) both succumbed to lethal infection, similar to the phenotype we observed with CLL depletion [55]. However, CCR2-/- mice were no more susceptible to ECTV mortality than wild-type mice (Fig 3G). Therefore, myeloid cell populations can be crucial for efficient immunity to poxvirus challenge, although the role that they play is likely different depending on the nature of the viral challenge, and potentially by the ability of a virus to replicate within myeloid cell populations.
Lymph node macrophages, in particular SCS macrophages, have a demonstrated role in restricting the spread of some viruses following peripheral infection [4–8]. Therefore, we examined depletion of myeloid cell populations in the D-LN following either a treatment that is permissive (systemic CLL) or non-permissive (DT treatment of LysMcre:iDTR) for VACV spread following intradermal infection, in an effort to find a uniquely depleted cell population to which we could attribute the function of restricting VACV spread. Naïve LysMcre:iDTR mice were given a single injection of CLL or DT, and myeloid cells in the D-LN were analyzed the following day (Fig 4A–4F). DT treatment reduced the populations of D-LN macrophages (Fig 4A), Ly6C++ monocytes (Fig 4B) neutrophils (Fig 4C), and CD8+ DC (Fig 4D) populations, as well as, to a lesser extent (<50%) bulk DC (Fig 4E) and CD11b+ DC populations (Fig 4F). Systemic CLL administration, which allows spread of VACV, depleted bulk DC (Fig 4E) and CD11b+ DC populations (Fig 4F) to a similar extent to the minor depletion with DT, which does not allow spread. However, although systemic CLL treatment did partially deplete D-LN macrophages (Fig 4A), Ly6C++ monocytes (Fig 4B), and CD8+ DC (Fig 4D) populations to a statistically significant degree, the depletion was never greater than 50%, i.e. DT was much more effective at depleting these populations than CLL. Therefore, there is no discernable population in the D-LN that is depleted more effectively by CLL, which allows disseminated infection, than by DT that does not allow a systemic infection to be established. These data indicate that it is unlikely that D-LN resident macrophages play an exclusive role in preventing dissemination of VACV following dermal infection.
Although systemic treatment with CLL failed to deplete myeloid cell populations in the D-LN, it has been published that local treatment with CLL can deplete SCS macrophages, thus allowing systemic dissemination of virus following peripheral vesicular stomatitis virus infection [4, 56]. Therefore, we examined the ability of systemic or local CLL administration, or DT treatment, to deplete CD169+ SCS macrophages or SIGN-R1+ medullary macrophages by microscopy. In vehicle-treated mice, CD169+ SCS macrophages form an intact barrier around the entire LN and SIGN-R1+ macrophages populate the medulla and are excluded from B and T cell areas (Fig 4G). DT treatment partially reduced CD169 staining, but systemic CLL did not deplete SCS macrophages to a discernable degree (Fig 4G). DT treatment also slightly reduced SIGN-R1 staining, and systemic CLL treatment increased depletion of medullary macrophages (Fig 4G). However, local administration of CLL greatly reduced staining of both CD169 and SIGN-R1, with the barrier around the LN clearly disrupted (Fig 4G). Therefore, we examined whether local CLL administration versus systemic CLL administration would allow dissemination of VACV to the ovaries 5 days post-infection. We injected CLL at the base of the ears on the day of infection or 1 day post-infection, timepoints at which an intravenous dose of CLL reliably permits VACV to spread. However, VACV was detected in the ovaries in only a small proportion of mice (2 of 14) in which CLL was delivered locally, compared to 100% of the mice given intravenous CLL in the same experiment (Fig 4H). Finally, we examined whether CLL treatment altered levels of VACV found in the D-LN at the peak of virus replication, on d5 post-infection. As previously published, levels of VACV in the D-LN were much lower than at the primary site of infection [27, 28], but were unaffected by systemic CLL treatment (Fig 4I). Taken together, these data support the conclusion that VACV drainage through the D-LN is unaffected by D-LN-resident myeloid cell populations, which are unlikely to play an exclusive role in preventing disseminated VACV infection.
Our data support the model that VACV moves through the lymph node and enters the blood stream, so we sought to mimic these conditions and examine the role of systemic macrophage populations in control of VACV dissemination. Intradermal infection inoculates 2x104 pfu per mouse, not all of which stays in the skin. In previous studies, we found that less than half of the initial inoculum is recovered from the infected ear at 1 day post-infection [57], suggesting that a considerable dose of VACV reaches the lymphatics and circulation. This could explain the occasional establishment of disseminated infection even in mice with intact phagocytes. When 105 pfu VACV was injected i.v., ~ 85% of mice experienced uncontrolled viral replication in the ovaries 5 days later (Fig 5A). However, when the inoculum was reduced to 1000 pfu VACV in the same injection volume, the virus never established infection in the ovaries in multiple experiments (Fig 5A). Infection with 1000 pfu i.v. may lead to fewer viral particles reaching the circulation than i.d. infection with 2x104 pfu, so this finding demonstrates that dissemination of low levels of VACV in the blood, similar to those moving from a site of dermal infection, was blocked.
To ascertain whether systemic macrophages become infected with VACV-GFP in order to block dissemination, we inoculated mice with 105 pfu VACV i.v. and examined which cells in the spleen were infected 24 hours post-infection. We only observed infected GFP+ cells in the MZ of the spleen, the area known to filter the blood (Fig 5B). Of the infected cells, the majority (~70%) stained for CD169, denoting the metallophilic MZ macrophage population (Fig 5B and 5C) [58]. A smaller population (~15%) of SIGN-R1+ MZ macrophages was also infected. Therefore, it is possible that these MZ macrophage populations can “soak up” VACV in the circulation to prevent subsequent spread.
Many resident tissue macrophage populations can “filter” the blood of particles, and splenic MZ macrophages have a demonstrated role in restricting the spread of some viruses following systemic infection [11–18]. Therefore, as a model for the role of systemic macrophages that filter the blood, we examined depletion of splenic myeloid cell populations in order to find a splenocyte population depleted by CLL, but not by DT treatment of LysMcre:iDTR mice, to which we could attribute the function of restricting VACV spread. When spleens of LysMcre:iDTR mice were analyzed following CLL or DT treatment, we found that systemic CLL treatment was markedly better at ablating splenic myeloid cell populations than DT (Fig 6A–6F). DT treatment reduced the numbers of macrophages (Fig 6A), monocytes (Fig 6B), bulk DC (Fig 6E) and CD11b+ DC (Fig 6F) in the spleen significantly vs. controls, but depletion was never greater than 50%. In contrast, CLL depleted populations of macrophages (Fig 6A), monocytes (Fig 6B) and CD8+ DC (Fig 6D) by ~85%, and populations of bulk DC by ~70% of control levels (Fig 6E). Depletion of CD11b+ DC was similar with DT or CLL treatment (Fig 6F) and, although DT treatment reduced the number of neutrophils, the permissive CLL treatment actually increased numbers of these cells (Fig 6C), which supported our observation that systemic depletion of these cells with anti-Ly6G antibody did not allow systemic spread of VACV (Fig 3D). From these data, it is clear that DT treatment does not deplete the majority of a number of myeloid cell populations in the spleen, several of which are almost ablated by CLL treatment.
We sought to confirm that the populations of MZ macrophages that become infected with VACV are depleted by permissive systemic CLL treatment versus the non-permissive treatments, namely dermal CLL treatment and DT treatment of LysMcre:iDTR mice. We isolated spleens from treated or control mice 5 days post-depletion with DT or CLL, and stained cryosections with antibodies to CD169 and SIGNR1. Treatment with DT reduced staining with anti-SIGN-R1, indicating depletion of MZ macrophages. However, staining with anti-CD169 was only marginally reduced, indicating that the metallophilic MZ macrophages, which are the primary target of VACV in the spleen, remain in DT treated mice (Fig 6G). These results are consistent with the flow cytometry data, which show a ~45% reduction in the number of macrophages in the spleen of DT treated mice. In contrast, systemic, but not local, treatment with CLL completely ablated staining with both antibodies (Fig 6G), indicating ablation of both populations of MZ macrophages. This is consistent with the ~90% reduction in the number of macrophages in the spleen of CLL treated mice. Together with the flow cytometry analysis, these data indicated a markedly enhanced ability of the permissive systemic CLL treatment to deplete splenic macrophages, particularly metallophilic MZ macrophages, when compared to the non-permissive DT and local CLL treatments.
We have previously demonstrated that DC populations, primarily plasmacytoid DC and CD8+ DC, are essential for mice to survive ECTV infection [50]. Others have also shown that recognition of ECTV infection by DC is essential [59]. DC were sensitive to CLL depletion and, to a much lesser extent, to DT depletion (Fig 6E). Therefore, we examined the spread of VACV to the ovaries after a dermal infection of mice in which DC were depleted. We treated CD11ccre:iDTR mice with DT using a regimen we have previously characterized during ECTV infection, and which confers lethal susceptibility to ECTV infection [50]. DT treatment of CD11ccre:iDTR mice failed to allow spread of VACV following dermal infection (Fig 7A). The CD11b+ DC subpopulation was depleted by DT and CLL to a similar extent (Fig 6F), so it is unlikely that this cell population is required to prevent the spread of VACV. However, CD8+ DC were the major DC population depleted by CLL treatment, but not signficiantly by DT treatment (Fig 6D), so we used VACV to intradermally infect Batf3-/- mice, which lack CD8+ DC [60]. However, as seen in Fig 7B, VACV infection did not disseminate in Batf3-/- mice. This indicates that it is not DC, nor the specialized splenic CD8+ DC population that restricts dissemination of VACV.
Next, we examined whether the monocyte/macrophage populations that are preferentially depleted by CLL treatment prevented the ability of small quantities of VACV in the bloodstream to spread systemically. To ensure that we were examining the clearance of bloodborne virions by systemic macrophage populations, we inoculated mice with either high (105 pfu) or low (103) dose VACV intravenously, or with 104 pfu i.d., as in our previous experiments. The high dose VACV i.v. overcame the immune system and spread to the ovaries in the majority of mice, but both low dose i.v. and i.d. VACV never reached the ovaries (Fig 7C). However, when mice were pre-depleted with CLL, infection with 1000 pfu i.v. or 104 pfu i.d. resulted in the spread of equally high levels of VACV to the ovaries 5 days post-infection (Fig 7C). Therefore, CLL-depleted cells, likely systemic macrophages that filter blood, are essential to prevent disseminated VACV infection.
Finally, we examined whether systemic macrophages can impair the dissemination of VACV prior to the virus reaching the ovaries. To do so, we measured virus titers in a “filtering organ”, the spleen, versus the target organ (the ovaries) at various times after infection of CLL-depleted mice with 1000pfu VACV i.v.. On the day of infection titers from each organ were undetectable, but by 2 days post-infection titers in the spleens of all mice approached 106 pfu (Fig 7D). In contrast, only one of eight VACV-infected mice showed detectable titers in the ovaries 2 days after infection (Fig 7D). By 5 days post infection, however, all the CLL-depleted mice showed levels of VACV in the ovaries that surpassed those in the spleen, where titers appear to have reached a plateau soon after 2 days post-infection (Fig 7D). These results are consistent with VACV in the bloodstream being filtered by systemic macrophages prior to spread of VACV to the target organ, the ovaries.
Many viruses of importance to human health such as variola (smallpox), monkeypox, polio, coxsackie, rubella, yellow fever, dengue, West Nile, mumps, measles, varicella (chickenpox), lymphocytic choriomeningitis virus, vesicular stomatitis virus, herpes simplex 1, and many more, penetrate their hosts through disruptions of epithelial surfaces and disseminate stepwise to distant organs following a lympho-hematogenous route [1, 2, 61]. However, the majority of studies examining the immune response to viruses that infect in this manner focus on immunity following i.v. or i.p. infection. Following these routes of infection, the virus has access to populations of cells that it may not normally encounter upon infection via a natural route. The geographical restriction of infection to a single site is an important factor in shaping the nature of the subsequent response. In this study, we examined the role of myeloid cells at three distinct spatial checkpoints, namely: 1) the site of infection, 2) the draining lymph node, and 3) systemically by organs that filter the blood, in control of VACV systemic dissemination. We find that local myeloid cell populations do not play an exclusive role in preventing disseminated VACV infection and, surprisingly, that D-LN resident macrophage populations are not the only point of control in restricting the spread of VACV. Rather, our data supports a model in which VACV spreads from the site continually for 4 days post-infection, and virus can move past the D-LN, into the bloodstream where further spread is prevented by systemic macrophage populations with access to the bloodstream.
The prevailing theory at present is that the major spatial checkpoint in preventing the spread of virus after a peripheral infection resides with the SCS macrophages in the D-LN [4–7] or, in the absence of SCS macrophages, the medullary macrophages of the D-LN [8]. We have previously demonstrated that SCS macrophages and other myeloid cell populations in the D-LN are infected with VACV within hours following dermal infection [34]. Thus, VACV spreads via the lymphohematogenous route (via the lymph node into the blood). However, here our data clearly show D-LN macrophage populations were not necessary to prevent systemic dissemination of VACV. The most compelling evidence that D-LN macrophages have a vital role in preventing spread of peripheral virus infections comes from administration of CLL locally following infection with VSV [4], MVA [62], a Rabies virus-based vector [63] or MCMV [7], implicating macrophage populations in the D-LN as requisite to prevent virus spread or initiate an adaptive immune response. However, local administration of CLL failed to confer the ability of VACV to spread systemically to the ovaries, indicating that the D-LN is not a vital checkpoint in the prevention of VACV spread. The fundamental difference between our observation and those with other viruses could be explained by the use of replication deficient viruses in a number of the previous publications [4, 62, 63]. Under conditions where the immune system only has to control spread of input loads of virus, the D-LN macrophage populations are likely to be able to internalize sufficient virus to prevent spread. In contrast, our full replicative virulent Western Reserve VACV can produce up to 109 pfu (from 104 pfu input) in the ear at the peak of replication [57] and the D-LN macrophages are likely consistently saturated with high concentrations of virions. Alternatively, the proposal from Moseman et al that the role of CLL-depleted SCS macrophages is to become infected and produce Type-I IFN [56] may indicate why VACV spread is not prevented by D-LN macrophages. All of the viruses for which D-LN macrophages have been described to prevent systemic spread are very sensitive to Type-I IFNs. However, VACV encodes a significant number of immunomodulatory molecules that block Type I IFN induction, action or signaling [64], and we have previously found that dermal VACV infection poorly induces Type I IFN production [57]. Indeed, VACV infection can restore the ability of VSV to replicate in the presence of IFN [65]. In contrast, MVA, a non-replicating attenuated vector derived from VACV, lacks many of the genes that modulate Type-I IFN [66]; this increased IFN-sensitivity, combined with reduced replicative ability, may account for an enhanced role for D-LN macrophages in MVA infection. Highly virulent viruses that express numerous mechanisms of evading the Type-I IFN response, such as VACV or ECTV, may bypass D-LN macrophages on their way to establishing a systemic infection.
It is often thought that one of the major roles of the innate immune response is to restrict replication of a pathogen at the original site of infection prior to the recruitment of adaptive immune cells that can then eliminate the infection. After dermal VACV infection, replicating virus is constrained at the site of infection, most often the ear pinnae [28]. This route mimics the natural route of infection with the majority of poxviruses, and also the major route of immunization used during smallpox vaccination or with many VACV-based vectors. Virus enters the dermis, stimulating resident somatic and immune cell populations to recruit neutrophils and monocytes/macrophages that can attack the pathogen [31, 32], and also triggering DC migration away from the area of infection to present antigen to naïve T cells [38–40]. Multiphoton microscopy studies have revealed that a “granuloma-like” structure forms following dermal VACV infection, with a virus infected core containing monocytes and other myeloid cell populations surrounded by a layer of adoptively-transferred CD8+ T cells that kill infected cells leaving the core [41]. However, we have shown that CD8+ T cells do not begin to accumulate at the site of infection prior to day 5 post-infection [32]. Here we show that the initial VACV inoculum and virus produced prior to day 4 post-infection spreads systemically when mice are treated with CLL, but after that time point the virus remains restricted to the skin. Therefore, it appears that the anatomy of the innate and adaptive response prevents VACV spread from the initial site of infection only at 4 or more days post-infection.
The identity of the cell population(s) that are required to prevent VACV dissemination is unknown. Hickman et al postulated that CD8+ T cell killing of infected monocytes leaving the viral lesion in the skin prevents virus dissemination [41]. However, replication of VACV in monocytes was extremely low (<2 pfu/cell) [41], and continual depletion of T cells did not allow spread of virus to the ovaries (Fig 3D). Infiltrating Ly6G+ myeloid cells are also not required to prevent VACV dissemination ([32] and Fig 3D), although depletion of both CD8+ T cells and Ly6G+ myeloid cells did increase local replication dramatically [41]. Indeed, the only treatment described to allow VACV dissemination following dermal challenge with the low doses of VACV we use here is CLL administration [32]. Therefore, before we began this study we presumed that CLL-depleted monocyte/macrophages at the site of infection acted locally to prevent VACV dissemination. However, macrophages at the site of infection do not exclusively restrict VACV spread as neither blockade of the recruitment of monocyte/macrophages (in CCR2-deficient mice), nor depletion of bulk local myeloid cells (via in LysMcre:iDTR or MAFIA mice), allowed VACV spread following dermal infection.
Although monocyte/macrophages at the site of infection do not appear to restrict VACV spread, they do appear to have a dramatic impact upon local pathogenesis and tissue damage. Despite only a minor impact on local VACV replication (1.5–3 fold [32]), we have shown that local macrophages do control the lesion size at the site of infection. This is likely a role of recruited monocyte/macrophage populations, as the phenotype of CLL-treated mice is reproduced in CCR2-/- mice that lack recruitment of inflammatory monocytes. Besides a role for macrophages following VACV infection, the simultaneous depletion of Ly6G+ cells and CD8+ T cells demonstrated that local VACV replication in the ear is not self-limiting, but is controlled by innate and adaptive effector cells acting in concert [41]. Dual depletion strategies examining depletion of macrophages along with depletion of other cell types, such as Ly6G+ cells or CD8+ T cells, have not been explored, but may reveal a cooperative effect on the control of local VACV replication. The mechanisms used by recruited monocyte populations to control the lesion size are also unknown, but are displayed at 7–8 days post-infection, a time point significantly after monocyte infiltration has peaked [32]. Ly6G+ myeloid cells, acting through a reactive oxygen species (ROS)-dependent mechanism, limit the extent of tissue damage, offering the possibility that Ly6G+ myeloid cells and recruited monocytes act in concert to accomplish this task. Whether this is via enhanced ROS production, or production of skin specific wound healing factors such as IL-22, remains a focus of ongoing investigation.
Our data clearly demonstrate that systemic CLL-depleted populations are required to prevent further VACV dissemination once the virus has entered the bloodstream. It is currently not technically feasible to deplete individual macrophage populations in particular organs. Multiple populations in secondary lymphoid organs or non-lymphoid organs may be required to prevent VACV dissemination. Indeed, it is possible that myeloid cells at the site of infection, in the D-LN, and systemically, all act together to prevent fulminant infection. If we administer small doses of VACV to bypass the role of the D-LN, metallophilic MZ and MZ macrophages in the spleen quickly become VACV-infected, as they do with numerous other bloodborne viruses [11–18]. CLL-depleted macrophages, such as metallophilic MZ macrophages in the spleen (which are not depleted by DT), Kupffer cells in the liver or juxtaglomerular macrophages of the kidney, may play a role in both preventing VACV spread to the ovaries and protection of the spleen from ongoing VACV replication. Non-productive infection of “suicide macrophages” that filter either lymph or the blood has been proposed as a means to reduce virus spread [4, 15], and may be the mechanism that allows protection of the ovaries. VACV replicates poorly, if at all, in monocyte/macrophages, in contrast to ECTV which replicates rapidly and effectively in macrophages and DC. The different capabilities of these related poxviruses to replicate within macrophage populations may explain why ECTV can spread rapidly and cause death in susceptible mice. Indeed, following ECTV infection it is the DC populations, rather than macrophages, that are required for survival [50, 59] and this may reflect an ability to replicate within macrophages. Therefore, our data supports VACV infection of “suicide macrophages” in organs that filter the blood, thus removing VACV virions from the circulation and reducing dissemination. If this is the case, the applicability of these findings may be limited to viruses that do not replicate effectively in macrophage populations. Alternatively, infection of populations of systemic macrophages may be important for production of Type-I interferon [18, 67], IL-1 [14] or induction of T cell [16] or antibody [5] responses. Under these circumstances, the applicability of the data shown here may be much broader. However, it is not technically possible to deplete these subsets specifically in a single organ at this time so a full description of the role of the required CLL-depleted cell type is not possible to delineate.
It has recently become clear that the myeloid cell compartment is exemplified by its plasticity, and that the local environment dramatically alters the phenotype and function of cells [68, 69]. In the context of our own studies, it is clear that macrophages present at the infection site, in the D-LN or at distal sites, have dramatically different roles during VACV infection. Therefore, it is no longer appropriate to just assign a role to “macrophages”, and we await the technology to study the role of individual phenotypic and spatially situated populations. Our studies also demonstrate that even injection of relatively low levels of virus that appear to replicate only locally can lead to systemic distribution of virions prior to initiation of an adaptive immune response. Normally these systemically distributed virions are contained by the innate immune response, in this case, by macrophage populations. However, if large doses of virus are inoculated systemically, the role of the innate response is not revealed. Therefore, it is essential to infect via natural routes with relevant doses of virus in order to fully elucidate the breadth of any immune response during virus infection and thereby gain insight into potential checkpoints that may be manipulated clinically.
C57BL/6 mice were purchased from Charles River Laboratories. CCR2−/− (catalog no. 004999), MaFIA (catalog no. 005070), Batf3-/- (catalog no. 013755), CX3CR1gfp/gfp (catalog no. 005582), LysM-cre (catalog no. 004781), CD11c-cre (catalog no. 008068) and iDTR (catalog no. 007900) mice were purchased from Jackson Laboratory and subsequently bred at the Hershey Medical Center. LysMcre:iDTR and CD11ccre:iDTR mice were heterozygotes derived from crossing purebred LysM-cre or heterozygous CD11ccre with purebred iDTR mice. All knockout mouse strains were on the C57BL/6 background after a minimum of 12 backcrosses.
All animals were maintained in the specific-pathogen-free facility of the Hershey Medical Center and treated in accordance with the National Institutes of Health and AAALAC International regulations. All animal experiments and procedures were approved by the Penn State Hershey IACUC (Animal Welfare Assurance # A3045-01) that follows the Office of Laboratory Animal Welfare PHS Policy on Humane Care and Use of Laboratory Animals, 2015.
VACV (strain Western Reserve) stocks were produced in 143B TK− cell (American Type Culture Collection, ATCC) monolayers and ECTV (strain Moscow) stocks were produced in L929 cell (ATCC) monolayers [70]. VACV was further purified by ultracentrifugation through a 45% sucrose cushion. For intradermal infection with VACV, mice were anesthetized using ketamine-xylazine and injected in each ear pinna with 104 pfu in a volume of 10 μl [28]. For intravenous (i.v.) infection with VACV, mice were injected in the tail vein with the dose shown in a volume of 400 μl. For intradermal infection with ECTV, mice were injected in the right rear footpad with 3,000pfu. For infection via scarification, a droplet of 106 pfu was placed on the ear and the ear scratched 20x through the droplet of virus with a 27 gauge needle. The droplet of virus was then removed.
To assess dermal pathogenesis, ear thickness was measured using a 0.0001-m. dial micrometer (Mitutoyo), and lesion progression was measured using a ruler [32]. To analyze the presence of replicating virus, organs were harvested, subjected to three freeze-thaw cycles in HBSS, ground in a Dounce homogenizer, and sonicated prior to a conventional plaque assay [32].
To deplete phagocytes, mice were injected i.v. with doses of 200–250 μl clodronate-loaded liposomes (CLL) in PBS, or 25 μl i.d in the center of the ear pinnae. Cl2MDP (or clodronate) was from Roche Diagnostics GmbH, Mannheim, Germany. Liposomes were prepared using Phosphatidylcholine (LIPOID E PC, Lipoid GmbH) and cholesterol (Sigma) [71]. For depletion of CD115-expressing cells, MaFIA mice were injected intraperitoneally (i.p.) with AP20187 (kind gift of Ariad Pharmaceuticals), diluted in sterile water containing 4% ethanol, 10% PEG-400, and 1.7% Tween immediately before injection. We followed the prescribed MaFIA injection regimen of 10 μg/g AP20187 on days -4, -3, -2, and -1 pre-infection, and 1 μg/g AP20187 on the day of infection and every third day thereafter [32]. For depletion of tissue-resident CD115-expressing cells, mice were injected i.p. with 50 μg/g AFS98 in HBSS/0.1%BSA on days -4, -2, and 0 pre-infection. For depletion of LysM-expressing cells, LysMcre:iDTR mice were injected i.p. with diphtheria toxin (DT) (Sigma D0564), diluted in PBS. For a single depletion, mice were injected with 100 ng/g DT. Sustained depletion involved multiple injections of 40 ng/g DT.
VACV-infected ear pinna were cut into strips and digested in 1 mg/ml collagenase XI (Sigma C7657) for 60 min at 37°C. Spleens and LN were minced and digested in 1 mg/ml collagenase D (Roche) for 40 min at 37°C. Live cells were blocked and stained on ice in 2.4G2 cell supernatant containing 10% normal mouse serum (Gemini Bio-Products 100–113). To stain cells for flow cytometry we used antibodies to F4/80 (clone BM8) and B220 (RA3-6B2) from eBioscience, Ly6G (clone 1A8) and CD45 (30-F11) from BioLegend, and Ly6C (clone AL-21), CD45.2 (104), CD11c (HL3), CD8α (53–6.7), CD11b (M1/70), CD19 (1D3), CD90.2 (53–2.1), and NK1.1 (PK136) from BD. CD19, CD90.2 and NK1.1 antibodies were all labeled with biotin to aid in gating out lymphocytes. Phycoerythrin (PE)-Cy7-streptavidin (BD) was used to label biotin-conjugated antibodies. Sample acquisition was performed with an LSRII or Fortessa flow cytometer (BD), and data were analyzed with FlowJo software (TreeStar). In order to compile data across many experiments, data are expressed as % of the mean number of cells in untreated mice.
Spleens and LN from naïve mice were harvested and embedded in Tissue-Tek OCT (Sakura Finetek), then rapidly frozen by immersion in liquid nitrogen-cooled 2-methyl butane, and kept at -80°C overnight. Cryostat sections (10–12 μm) were cut at -20°C, mounted on glass slides, air-dried for 2–3 hours, fixed for 10–15 minutes in cold acetone, air-dried again for 30 minutes, and stored at -80°C. Slides were warmed to room temperature and stained in 1x TBS with 5% BSA and 0.1% Tween 20. Sections were stained with antibodies to SIGNR1 (22D1), secondary labeled with mouse anti-hamster Alexa Fluor 488 (1:100 dilution). CD169 was visualized with the clone MOMA-1 (Abcam), biotinylated and secondary labeled with streptavidin-PE (1:200 dilution). Microscope and software used to analyze microscopy were from Leica Microsystems (Buffalo Grove, IL).
To visualize infection of systemic macrophages with VACV we infected mice with 10,000 pfu VACV-GFP (strain Western Reserve, with no gene deletions). Six hours later spleens were harvested, flash frozen, sectioned (10 μm) and fixed in 2% paraformaldehyde in PBS (pH 7.4). Sections were then stained with antibodies to CD169 and SIGN-R1 as above.
Data were graphed and analyzed using Prism software (Graphpad). All data are expressed as mean ± standard error of mean. Means were compared using either an unpaired students t-test or two-way ANOVA as applicable. Survival curves were analyzed using a Log-rank test. Significance between groups was determined by p-value below 0.05, and is displayed as; * = p<0.05; ** = p<0.01; *** = p<0.001.
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10.1371/journal.ppat.1006967 | KSHV induces immunoglobulin rearrangements in mature B lymphocytes | Kaposi sarcoma herpesvirus (KSHV/HHV-8) is a B cell tropic human pathogen, which is present in vivo in monotypic immunoglobulin λ (Igλ) light chain but polyclonal B cells. In the current study, we use cell sorting to infect specific B cell lineages from human tonsil specimens in order to examine the immunophenotypic alterations associated with KSHV infection. We describe IL-6 dependent maturation of naïve B lymphocytes in response to KSHV infection and determine that the Igλ monotypic bias of KSHV infection in vivo is due to viral induction of BCR revision. Infection of immunoglobulin κ (Igκ) naïve B cells induces expression of Igλ and isotypic inclusion, with eventual loss of Igκ. We show that this phenotypic shift occurs via re-induction of Rag-mediated V(D)J recombination. These data explain the selective presence of KSHV in Igλ B cells in vivo and provide the first evidence that a human pathogen can manipulate the molecular mechanisms responsible for immunoglobulin diversity.
| Kaposi sarcoma herpesvirus (KSHV) infection of human B cells is poorly understood. KSHV infection in humans is heavily biased towards B cells with a specific subtype of antibody molecule (lambda light chain rather than kappa light chain). This has been a conundrum in the field for years because there is no known physiological distinction between B cells with different light chains that might provide a mechanism for this bias. Here, we develop a novel system for infecting B cells from human tonsil with KSHV and tracking how the virus alters the cells over time. Using this system, we demonstrate a number of KSHV-driven alterations in B cells, including the fact that KSHV infection of kappa light chain positive B cells drives them to become lambda light chain positive by re-inducing recombination events that are normally restricted to B cell development in the bone marrow. We believe that this study is the first demonstration that a virus can alter immunoglobulin specificity via direct infection of B cells.
| Kaposi sarcoma herpesvirus (KSHV), also called human herpesvirus 8 (HHV-8) is the most recently discovered human herpesvirus, and infection with this virus is linked to the development of KSHV-associated malignancy including Kaposi sarcoma, primary effusion lymphoma (PEL) and multicentric Castleman disease (MCD), particularly in the absence of adequate immune surveillance (e.g. HIV disease). [1–4]. Although the association of KSHV infection with pathological lymphoproliferation is well established, very little is known about the early stages of KSHV infection in B lymphocytes and how the virus drives pathology in this niche. Moreover, our understanding of the pathogenesis of MCD is specifically hampered by the lack of an experimental model system. As a human herpesvirus with highly restricted tropism, KSHV does not lend itself to animal models, and the murine homolog of KSHV, MHV68, while extensively used to study the immune response to gamma-herpesvirus infection, lacks many homologs of KSHV proteins, and fails to recapitulate key features of human disease entities, including MCD[5]. Previous studies have performed infection of human B cells with KSHV[6–8] and have observed features consistent with MCD phenotypes during in vitro infection[9], but no studies to date have combined high infection efficiency with culture methods allowing detailed longitudinal immunophenotyping of lymphocytes during de novo KSHV infection.
During B lymphocyte development, establishment of tolerance in the bone marrow involves the sequential production of immunoglobulin light chain rearrangements by V(D)J-recombination orchestrated by the lymphoid lineage-specific recombinase activating gene (RAG) protein products, Rag1 and Rag2[10]. This phenomenon, termed B cell receptor (BCR) editing begins with rearrangements of the immunoglobulin κ (Igκ) locus, and rearrangements in the immunoglobulin λ (Igλ) locus occur only when Igκ rearrangements fail to produce a functional, non-autoreactive BCR. As such, in the human peripheral B cell repertoire Igκ-expressing B lymphocytes outnumber their Igλ counterparts, and the Igκ/Igλ ratio increases as a function of age[11]. Interestingly, restriction of KSHV infection to Igλ positive B lymphocytes is a long-recognized feature of MCD in vivo[12,13] and, although PEL are generally immunoglobulin negative those that express surface immunoglobulins are frequently Igλ [14]. Infection of total tonsil-derived B lymphocytes in vitro with KSHV has also shown that infected cells are biased towards Igλ+ cells[9]. This phenomenon is counter-intuitive given the relative abundance of Igκ lymphocytes as infection targets and the fact that the two subsets should be physiologically indistinguishable. As such, the restriction of KSHV infection to Igλ+ lymphocytes remains a conundrum in the field.
In the current study, we developed an in vitro model system to determine phenotypic changes associated with KSHV-infection of primary B lymphoyctes from human tonsil tissue. During infection of naïve B lymphocytes, we observe IL-6 dependent acquisition of an IgD+CD27+ immunophenotype, termed variously natural effector[15,16], or marginal zone-like (MZL) B lymphocytes in the literature. Our experiments further reveal that KSHV infection induces de novo Igλ expression in Igκ tonsil lymphocytes, resulting in skewed Igλ variable gene usage and significant numbers of isotypically included (Igλ+Igκ+) lymphocytes. We demonstrate that Igλ expression in originally Igκ+ lymphocytes during infection is associated with the re-induction of V(D)J recombination, a phenomenon termed BCR revision. Importantly, these data provide an explanation for restriction of KSHV infection to Igλ+ lymphocytes in vivo, and represent the first evidence that a lymphotropic human pathogen can induce BCR rearrangements in mature peripheral lymphocytes.
For this study, we developed a protocol for flow sorting (S1A Fig) and in vitro infection of tonsil-derived primary B lymphocytes with BAC16 recombinant KSHV that met two important criteria: (1) stable infection of sufficient cell numbers to provide phenotypic data by flow cytometry (FCM) and (2) maintenance of pre-sort immunophenotypes in mock-infected controls. Concentrated preparations of cell free recombinant BAC16 KSHV virions were able to infect a variety of B lymphocyte lineages in both bulk infection (Fig 1A and 1B) and isolated subsets flow sorted from human tonsil (S1B Fig), as evidenced by GFP reporter expression from the BAC16 KSHV genome. Co-culture with gamma-irradiated feeder cells expressing FcgRII (CDw32) allowed survival for 2–3 weeks in culture and good maintenance of immunophenotypes in many primary B lymphocyte lineages including naïve (CD19+, CD38low, IgD+, CD27-) and memory (CD19+, CD38low, IgD-, CD27+) cells (S1C Fig). When total B lymphocytes were used for infections, the proportion of specific lineages expressing GFP generally paralleled the overall percentage of the same lineage in corresponding mock-infected cultures, thus revealing no significant bias towards infection of a particular lineage (Fig 1B). Moreover, we were able to validate viral gene expression in total B lymphocyte infections by RT-PCR (Fig 1C). In these assays, KSHV transcript levels generally increased over time in the infected cultures, which is consistent with our observation of increased numbers of GFP+ cells over the same timecourse (Fig 1A). LANA expression >5-fold above the limit of detection was observed for 3/4 tonsil samples, 4/4 samples displayed greater than 5-fold increases in ORF59 and greater than 10-fold increases in K8.1 expression were observed in 3/4 tonsil samples. Taken together, these data indicate that tonsil-derived B lymphocytes are susceptible to recombinant KSHV infection in vitro and that there is consistent early lytic gene expression and frequent late lytic gene expression associated with these infections, which may indicate a mixture of lytic and latently infected cells in these mixed B lymphocyte cultures.
Having established a robust infection model, we sought to characterize immunophenotypic changes induced by KSHV infection in B lymphocytes. We used naïve B lymphocytes for the remainder of the experiments described herein because they displayed two features ideal for tracking virus-induced phenotypic changes over time. First, naïve cells were highly susceptible to KSHV infection (Fig 1D and S1B Fig) with variable levels of infection based on the virus stock and tonsil sample used, but generally showing infection rates >10% by 7 days post-infection. Second, uninfected cells robustly maintained their pre-sort immunophenotype over time in culture (S1C Fig). Moreover, previous histological studies have shown data consistent with KSHV infection of naïve B lymphocytes in MCD [12], and infection of the related Epstein-Barr virus in humans (EBV) is thought to fist occur in naïve B cells in the oral mucosa [17].
In our KSHV-infected naïve B lymphocyte cultures we observed several reproducible immunophenotypic shifts. There were consistently elevated levels of IL-6 in culture supernatants (Fig 2A), We employed a species-specific cytokine assay in order to ensure that cytokines were being secreted by B cells and not the murine feeder cells. In contrast, levels of IL-2, IL-4, IL-10 and TNFα remained unchanged in the same supernatants. Concurrently, we observed upregulation of CD27 during KSHV infection producing a substantial population of cells with an IgD+CD27+ immunophenotype (Fig 2B & 2C), termed variously natural effector or marginal zone-like (MZL) B lymphocytes in the literature[15,16]. These results are consistent with a previous study demonstrating both IL-6 production and CD27 acquisition in tonsil lymphocytes infected with PEL-derived wild-type KSHV[9]. Interestingly, although statistically significant trends in IgD+CD27+ subsets were observed in both GFP+ and GFP- populations in KSHV-infected cultures compared to mock-infected cultures, the stronger effect occurred in the GFP- (bystander) population (Fig 2B and 2C). Circulating MZL B cells are currently the subject of considerable interest and debate, and are thought to represent a T cell-independent, extrafollicular maturation pathway[18]. Moreover, both IL-6 secretion and MZL B lymphocyte phenotypes are seen in KSHV-infected cells in primary cases of MCD[12,19]. Thus, our in vitro infection system recapitulates key features of KSHV infection in vivo and MCD pathogenesis, supporting the conclusion that KSHV infection may drive an extrafollicular maturation pathway.
At early times post infection, we observe that KSHV infects both Igκ+ and Igλ+ naïve B cells equally (Fig 2D, top right panel). However, later in infection we see profound shifts in immunoglobulin light chain expression. Specifically, we found a substantial loss of Igκ+Igλ- lymphocytes coincident with the emergence of an isotypically included (Igκ+Igλ+) subset and an increased proportion of Igκ-Igλ+ cells (Fig 2D & 2E). Interestingly, both the GFP+ and GFP- cells in the infected cultures lost Igκ expression, indicating that the phenotype may be influenced by a soluble factor.
Because IL-6 signaling is known to be a critical driver of pathogenesis in MCD[19], we determined whether the immunophenotypic alterations we observe in vitro were a result of IL-6 signaling. Neutralization of either hIL-6 or the gp130 receptor had no effect on KSHV-mediated shifts in Ig light chain expression (Fig 3A). However, we observed that the emergence of the MZL immunophenotype (IgD+CD27+) in our infected cultures was highly correlated with the magnitude of IL-6 induction (Fig 3B), and neutralization of hIL-6 or its receptor abrogated the induction MZL phenotypes in KSHV-infected cultures (Fig 3C), indicating that the induction of hIL-6 by KSHV drives B cell maturation. Moreover, cells in which light chains were modified by KSHV (isotypically included and revised) did not display alterations in CD27 expression, suggesting that CD27 acquisition and light chain revision are mutually exclusive phenomena in our experiments.
In order to determine whether the loss of Igκ+ lymphocytes in KSHV infected cultures is due to selective toxicity or phenotypic shift we performed experiments in which Igκ+ naïve lymphocytes were purified by flow sorting and subsequently infected with KSHV. The full gating scheme for this analysis is provided in S2A Fig. In these experiments, mock-infected Igκ+ cultures displayed no significant shift in Igκ expression over the experimental time course. In contrast, we observe a rapid and profound shift of KSHV-infected Igκ+ lymphocytes to Igκ+Igλ+, which is followed by the emergence of a small but reproducible Igκ-Igλ+ population at later timepoints (Fig 4A and Fig 4B, bottom panel). This phenotype is robust, and the shift for each light chain population (Igκ+, Igκ+Igλ+ and Igλ+) is statistically significant compared to mock infected samples (Fig 4B). As with our unsorted experiments, we observe that GFP- cells in the KSHV infected culture also undergo a significant shift to Igκ+Igλ+ (Fig 4A, middle panels). Interestingly, although both populations display isotypic inclusion, the kinetics are slower in the GFP- population. This bystander effect could be due to a soluble factor other than IL-6 or direct interactions between infected and uninfected cells. Alternatively, the revised GFP- cells could represent a population of lymphocytes in which KSHV infection was aborted at a stage prior to GFP expression. Similar experiments performed in flow sorted Igλ lymphocytes revealed no alterations in light chain expression (S2B Fig). Moreover, we have identified six lymph node biopsies from four HIV+ patients with AIDS-related lymphadenopathy (ARL), which did not have histological features of MCD but had rare KSHV-positive cells. In these cases, like in MCD, the infected cells were Igλ+ (S3 Fig). Thus, the Igλ bias of KSHV infection in vivo is a feature of KSHV infection itself, rather than a phenotype specific to MCD. Taken together, these data support the conclusion that the Igλ-bias observed in KSHV infected cells in vivo is a result of immunophenotypic shift after infection rather than a bias towards infection of Igλ+ lymphocytes.
We next sought to determine whether V(D)J recombination drives the emergence of Igλ expression in KSHV-infected Igκ+ B lymphocytes using the diagnostic BIOMED2 primer set[20]. This is a standard assay to document the presence of V(D)J rearrangements revealing polyclonal and monoclonal B cells. We were able to detect polyclonal V-J genomic rearrangements in the Igλ locus in flow sorted Igλ lymphocytes and in KSHV-infected Igκ+ lymphocytes, but not in mock-infected Igκ+ lymphocytes. This can be appreciated by the intensity of the smear, as expected for a polyclonal B cell population (Fig 5A). We further verified the expression of functionally rearranged Igλ mRNA sequences in KSHV-altered Igκ lymphocytes by nested RT-PCR. For these experiments, we flow sorted Igκ+ lymphocytes, infected them with KSHV and then performed a secondary sort at 7 days post-infection, capturing single cells that were GFP+ and had transitioned to Igκ-Igλ+. cDNA was made from these single cells and Igλ transcripts were amplified by nested RT-PCR. We used the amplification of Igλ transcripts from mock infected Igλ+ controls, which had been sorted for Igλ expression, then mock-infected, cultured and post-sorted in parallel with KSHV samples as a technical control for our ability to detect Igλ expression in single cells for each experiment. Three independent experiments of ≥96 single cells per condition using three distinct tonsil specimens revealed significant numbers of KSHV-modified Igκ lymphocytes expressing Igλ transcripts (Fig 5B). Similarly, we were able to validate the isotypically included (Igκ+Igλ+) population by single cell RT-PCR (Fig 5C). Expression of the lymphocyte-specific recombinases Rag1 and Rag2 is normally restricted to immature B lymphocytes in the bone marrow and the expression of these proteins regulates V(D)J recombination and BCR editing during B lymphocyte development[10]. In order to determine whether KSHV-associated BCR revision was Rag-mediated we performed nested RT-PCR for Rag transcripts and were able to detect both RAG1 and RAG2 mRNA at early time points post-infection (Fig 5D). This result was reproducible in six tonsil specimens at time points <6 hours post-infection. However, due to the very early induction of RAG transcripts which was consistently observed prior to the expression of GFP in our infected cultures, we were unable to determine whether expression of Rag proteins was restricted to KSHV infected cells or was more widespread within the culture. We hypothesized that if V(D)J recombination was being re-induced in these cultures, limiting the repair of Rag-mediated double stranded DNA breaks would result in apoptosis. Indeed, pharmacological inhibition of DNA-PKcs (a critical enzyme in the double stranded break repair pathway used in V(D)J recombination) resulted in selective toxicity in KSHV-infected cultures (S4 Fig). Taken together, these data clearly demonstrate the re-induction of V(D)J recombination activity in mature primary B lymphocytes during de novo KSHV infection.
In order to explore the specific nature of the Igλ rearrangements in KSHV-infected B lymphocytes, we used cDNA from flow sorted single cells analyzed in Fig 5B to sequence Igλ variable regions from control (Mock Igλ+) and KSHV-revised (Igκ+ at infection, GFP+ Igκ-Igλ+ at 7dpi) lymphocytes and determined IGLV gene usage using IgBLAST. As expected[21], control Igλ clones were biased towards IGLV gene families 1–3 with few clones utilizing IGLV4-10. In contrast, KSHV-modified Igλ+ cells displayed increased usage of upstream IGLV family genes including a significant number of clones from the VL4 gene family (Fig 5E). Interestingly, despite enriching for functional rearrangements by sorting Igλ-expressing lymphocytes, more of the KSHV-modified Igλ clones we sequenced were non-productive rearrangements (defined as out-of-frame rearrangements or those incorporating a premature stop codon): (6/20) in KSHV-modified samples compared to the Igλ controls (2/17). This increased rate of non-productive rearrangements (although not statistically significant based on the low numbers of antibody clones) is consistent with the high error rate for V(D)J recombination[22], and further supports the conclusion that de novo V(D)J recombination is occurring in these cultures. Moreover, the high prevalence of non-productive Igλ rearrangements in KSHV-infected Igκ cells sorted for Igλ expression suggests that mechanisms of allelic exclusion may not be functional in this system.
Our data demonstrating that KSHV induces BCR revision during de novo infection of human B lymphocytes is the first evidence that a lymphotropic human pathogen can alter immunoglobulin specificity by direct infection of B lymphocytes. These results could imply that KSHV infection can shape the immune repertoire. MYC-activating translocations involving the immunoglobulin loci present in Burkitt’s lymphoma (BL) were once thought to be a product of EBV-mediated re-induction of V(D)J recombination [23]. While infection of B cells with EBV has been shown to induce expression of RAG1 and RAG2[24], these were not detected in early passage lymphoblastoid cells[25] or most EBV-associated lymphomas[26]. Moreover, there is no apparent bias towards Igλ expression in EBV-associated tumors or EBV-infected cells in vivo as is seen with KSHV infection. However, the phenotypic shifts to Igλ we observe during infection in vitro explain the longstanding enigma of KSHV restriction to Igλ+ lymphocytes in primary patient specimens. Certainly, the implications of KSHV-mediated alteration of the human immunoglobulin repertoire are potentially far-reaching and deserve further study. KSHV-mediated BCR editing might represent a novel immune-evasion mechanism directed at altering the adaptive immune response to infection. This hypothesis is consistent with clinical evidence that antibody responses to KSHV in infected human populations are inconsistent and highly variable over time[27–29].
For this study, we rely on a new ex vivo culture model for primary B lymphocytes in which mouse L cells expressing FCγRII/CDw32 receptor are able to maintain lymphocyte immunophenotypes in isolated B lymphocyte lineages over time. The choice of these feeder cells was largely empirical based on trial and error, and the robust maintenance of naïve B lymphocytes in this system was surprising because naïve cells lack IgG expression and thus should not bind CDw32. This discrepancy certainly deserves further study as it may hint at an uncharacterized quorum sensing mechanism present in lymphoid organs. Although the data presented herein uses primarily naïve B lymphocytes, the target cell type(s) for KSHV infection in vivo are still a subject of debate [30]. Indeed, we show that multiple lineages are susceptible to KSHV infection both in bulk (Fig 1B) and sorted cultures (S1B Fig). Certainly, additional studies into the specific biology associated with KSHV infection of a variety of B cell lineages are warranted. Moreover, our use of tonsil lymphocytes derived from routine tonsillectomy may raise questions about the contribution of the baseline inflammatory state and the presence of co-pathogens in inflamed tonsil tissue to KSHV infection. There is epidemiological evidence that KSHV transmission is inefficient[31] and the prevalence of KSHV infection in the developing world is suggestive of a correlation between the presence of co-pathogens and KSHV transmission. As such, we would suggest that lymphocytes from inflamed tonsil specimens represent a relevant model for KSHV transmission.
Based on these results, we propose that KSHV infection represents an interesting experimental method for perturbing human B cell physiology ex vivo. A robust in vitro infection and culture system, coupled with our ability to manipulate the KSHV genome represents a unique opportunity to use viral subversion of B cell biology to investigate obscure aspects of human immunology. For example, our observation that KSHV-infected cells acquire MZL B cell immunophenotypes dependent upon IL-6 signaling provides novel insight into the development of this enigmatic lineage, and investigations of the KSHV-mediated mechanisms driving the emergence of MZL B cells from naïve progenitors could reveal previously undiscovered pathways regulating extrafollicular B cell maturation in humans. Moreover, BCR revision in human immunology remains highly controversial[32–35], and our current model system using human cells provides a method to study the cell biology underlying this phenomenon.
Interestingly, both isotypic inclusion and BCR revision have been implicated as potential drivers of autoimmune disease[36–38]. This is particularly interesting given that HIV disease[39] and KSHV-associated lymphoproliferative diseases[40–42] frequently co-present with autoimmune manifestations. Although it would be difficult to determine whether KSHV infection plays a direct role in promoting autoimmune disease in humans, study of the B cell-specific pathways targeted by KSHV to affect BCR revision could provide critical insights into the pathogenesis of autoimmune diseases in which BCR revision plays a role.
IL-6 and gp130 neutralizing antibodies were from R&D systems. NU7441 was from Sigma. FCM antibodies were from BD and are detailed below. CDw32 L cells (CRL-10680) and control L cells (CRL-2648) were obtained from ATCC and were cultured in DMEM supplemented with 10% FBS (Atlanta Biologicals) and 50μg/ml gentamicin. For preparation of feeder cells, L cells were trypsinized and resuspended in 30ml of media in a 50ml conical tube and irradiated with 3000 rad using a gamma source. Irradiated cells were then counted and cryopreserved until needed for experiments.
iSLK[43] bearing BAC16[44] recombinant KSHV WT (BAC16 producer cells were kindly provided by Ashlee Moses, OHSU) were grown in DMEM supplemented with 10% FBS, 50μg/ml gentamicin, 250μg/ml G418, 1μM Puromycin, and 1.2mg/ml Hygromycin B. Eight confluent T175 flasks were induced for 72 hours with 3mM Sodium Butyrate and 2μM Doxycycline. Culture supernatants were clarified twice by centrifugation at 2000rpm for 5 minutes and 7000rpm for 15 minutes. Virus was pelleted out of clarified supernatants over a 25% sucrose cushion by ultracentrifugation at 22,000 rpm for 90 minutes. Virus pellets were resuspended in a total of 2ml TNE buffer and stored at -80°C.
De-identified primary human tonsil specimens were obtained after routine tonsillectomy from the Weill Cornell/New York Presbyterian Immunopathology Laboratory Biorepository with approval from the Institutional Review Board of Weill Cornell Medical College. Lymphocytes were extracted by dissection and masceration of the tissue in RPMI media. Lymphocyte-containing media was passed through a 70μm filter and pelleted at 400g for 7 minutes. RBC were lysed for 5 minutes in RBC lysing solution (0.15M ammonium chloride, 10mM potassium bicarbonate, 0.1M EDTA). After dilution to 50ml with RPMI, lymphocytes were filtered through a 0.4μm filter, counted and pelleted a second time. Aliquots of 1(10)^8 cells were resuspended in 1ml of freezing media containing 90% FBS and 10% DMSO and cryopreserved. For experiments, lymphocytes were thawed rapidly at 37°C, diluted to 10 ml with 10% RPMI and pelleted at 400g for 5 minutes. Cells were resuspended in 1ml RPMI with 20% FBS and 100μg/ml Primocin (Invivogen) and incubated at 37°C for 4–18 hours. For experiments utilizing magnetic cell sorting, untouched total B cells (Miltenyi Cat# 130-091-151) or naïve B cells (Miltenyi Cat# 130-091-150) were isolated according to manufacturer instructions. For experiments utilizing fluorescence activated cell sorting (FACS), lymphocytes were washed 1x with PBS and resuspended in PreSort Buffer (BD Biosciences) containing antibodies to CD19 (BD Cat# 340720, 16μl/1(10)^6 cells), Immunoglobulin Lambda Light Chain (BD Cat# 561379, 4μl/1(10)^6 cells) and Immunoglobulin Kappa Light Chain (BD Cat# 561319, 4μl/1(10)^6 cells) and incubated on ice for 15 minutes. Cells were washed 2x with PreSort Buffer, resuspended in 2ml PreSort Buffer and sorted on a Custom Order FACSAria Cell Sorter. For infection with KSHV, 1(10)^6 lymphocytes were pelleted into 12x75mm round bottom tubes and resuspended in 400μl serum free RPMI containing TNE (Mock) or KSHV. Doses of virus used for infections were calculated based on per cell infection volume required to achieve 20% infection of HUVEC at 48 hours based on GFP expression, and correspond to 0.1–5 genomes/cell depending upon the specific virus preparation. Cells were centrifuged at 1000rpm in inoculating medium for 30 minutes at 4°C and transferred to 37°C for a further 30 minutes. 100μl FBS (20% of the final culture volume) and Primocin were added and cells with inoculum were transferred to gamma-irradiated mouse L CDw32 feeder cells. At 3 days post-infection, the media was replaced with fresh 20% RPMI+Primocin, removing residual virus inoculum and cells were fed with fresh media every 3 days over the experimental timecourse
At indicated times post-infection a proportion of lymphocyte cultures representing ~200,000 cells were pelleted at 400g for 3 minutes into 96-well round bottom plates. Cells were washed once with PBS and resuspended in 200μl PBS containing (0.4ng/ml) fixable viability stain (BD Cat# 564406) and incubated at room temperature for 10 minutes. Cells were pelleted and resuspended in 100μl cold PBS without calcium and magnesium containing 5% FBS, and 0.1% Sodium Azide (FACS Block) and incubated on ice for 15 minutes after which 100μl cold PBS containing 0.5% FBS and 0.1% Sodium Azide (FACS Wash) was added. Cells were pelleted and resuspended in FACS Wash containing B cell phenotype panel as follows for 15 minutes on ice: (volumes indicated were routinely used for up to 0.5(10)^6 cells and were based on titration of the individual antibodies on primary tonsil lymphocyte specimens) Ig Lambda Light Chain-V450 (2μl), CD19-PE (8μl), CD38-PECy7 (3μl, BD Cat# 560667), IgD-PerCP Cy5.5 (2.5μl, BD Cat# 561315), CD138-APC (4μl, BD Cat# 347207), CD27-APC H7 (2.5μl BD Cat# 560222), Ig Kappa Light Chain-Alexa700 (2μl). After incubation, 100μl FACS Wash was added and pelleted lymphocytes were washed with a further 200μl of FACS Wash prior to being resuspended in 200μl FACS Wash for analysis. Data was acquired on a BD LSR2 Flow Cytometer and analyzed using FlowJo software.
For viral gene expression assays, primary naïve lymphocytes from four independent tonsil specimens were magnetically sorted and infected with KSHV or mock-infected. 1e6 cells were harvested at indicated timepoints and total RNA was extracted using Directzol RNA Miniprep Kit (Zymo Research) according to manufacturer instructions. A second DNase step was performed on 50ng total RNA using Ambion DNA free Kit (Cat #AM1906) and cDNA was synthesized from 50ng total RNA using Thermo High Capacity cDNA synthesis kit. 3μl of cDNA was used for duplicate RT-PCR reactions with Taqman Fast Advanced Mastermix (Cat #: 4444556). Primer and probe (FAM/BGH) sequences were as follows (5’ to 3’): LANA Fwd: GCCTATACCAGGAAGTCCCA, LANA Rev: GAGCCACCGGTAAAGTAGGA, LANA Probe: ACACAAATGCTGGCAGCCCG K8.1 Fwd: TGCTAGTAACCGTGTGCCAT, K8.1 Rev: AGATGGGTCCGTATTTCTGC, K8.1 Probe: TGCGCGTCTCTTCCTCTAGTCGTTG; ORF59 Fwd: TTAAGTAGGAATGCACCCGTT, ORF59 Rev: GGAAGCCGGTGGTAGGAT, ORF59 Probe: CCAGGCTTCTCCTCTGTGGCAA.
Primary naïve lymphocytes were flow sorted based on light chain expression and infected with KSHV, as above. At 4 days post-infection lymphocytes were harvested and genomic DNA was extracted using Wizard SV kit protocol (Promega Cat #A2360) and eluted in 2 x 75-μL water containing 1 μL RNase A. 12.5-ng and 40-ng of extracted genomic DNA was used as PCR template for mock samples and KSHV-infected samples, respectively. 50-μL of Platinum Supermix Hifi (Thermo Cat # 12532) was used, in addition to 0.5-μL of each 10 μM Lambda Forward and Reverse primers (Forward: Vl1/2–5' ATTCTCTGGCTCCAAGTCTGGC 3' and Vl3- 5' GGATCCCTGAGCGATTCTCTGG 3'; Reverse: Jl1/2/3–5' CTAGGACGGTGAGCTTGGTCCC 3'). The PCR program was as follows: 7-minutes at 95°C, 50 cycles of 30-seconds at 95°C, 30-seconds at 64.7°C, and 30-seconds at 72°C, followed by 10-minutes at 72°C [20]. The PCR products from the first amplification were used for a second identical PCR amplification using 2-μL of previous reaction as template. The PCR products from the re-amplification reaction were analyzed on a 2% agarose gel.
Single cells were harvested by flow sorting into 96-well PCR plates containing 4μl of RNA lysis buffer (0.5x PBS+10mM DTT+4U SUPERas-In (Thermo Cat #AM2694)). Plates were sealed and stored at -80°C. cDNA was synthesized directly in wells using Thermo High Capacity cDNA synthesis kit (Cat #4368814). PCR primers for amplification of immunoglobulin light chains were from Tiller et. al.[45]. Outer RT-PCR reactions were assembled with Illustra PureTaq Ready-to-go PCR beads (GE Cat #27-9557-02), mixtures of 0.2μl of each 10μM outer primer and 3μl of single cell cDNA in total 25μl reaction volumes. Cycling parameters for outer PCR were 50 cycles of 30-seconds at 95°C, 30-seconds at 60°C for Igλ or 58°C for Igκ, and 55-seconds at 72°C, followed by 5-minutes at 72°C. Nested PCR was assembled with 15μl 2x Phusion Flash Mastermix (Cat # F548L), 0.05μl of each 10μM inner primer mixture and 3μl of outer PCR reaction in 30μl total reaction volume. Primer sequences were adapted from[45,46]. Cycling parameters for nested PCR were 40 cycles of 10-seconds at 95°C, 15-seconds at 60°C for Igλ or 58°C for Igκ, and 10-seconds at 72°C. PCR products were analyzed on 2% agarose gel or purified for Sanger sequencing by isopropanol precipitation.
Naïve tonsil lymphocytes were magnetically isolated and infected as above. 2(10)^6 mock or KSHV-infected lymphocytes were harvested into 400μl Tri-Reagent at 4 hours post-infection and total RNA was extracted using Directzol RNA Miniprep Kit (Zymo Research) according to manufacturer instructions. A second DNase step was performed on 50ng total RNA using Promega RQ1 DNase kit (Cat #M6101) and cDNA was synthesized from 50ng total RNA using Thermo High Capacity cDNA synthesis kit. Outer RT-PCR reactions were assembled with 30μl of Platinum Hifi Supermix, 0.5μl of each 10μM outer primer (RAG1 Forward: 5’-AAGGAGAGAGCAGAGAACAC-3’, RAG1 Reverse: 5’-GTCCCAACTCAGCCATTGTT-3’, RAG2 Forward: 5’-AGTCAGCCTTCTGCTTGC-3’, RAG2 Reverse: 5’- AGGCAGCTTGGAGTCTGAAA-3’) and 3μl of cDNA. Cycling parameters for outer PCR were 40 cycles of 30-seconds at 95°C, 30-seconds at 53°C, and 45-seconds at 68°C, followed by 5-minutes at 68°C. Nested PCR was assembled with 30μl Platinum Hifi Supermix, 0.5μl of each 10μM inner primer (RAG1 Forward: 5’-TTCTGCCCCAGATGAAATTC -3’, RAG1 Reverse: 5’-CTGGACAAGGCTGATGGTCA-3’, RAG2 Forward: 5’-TCTCTGCAGATGGTAACAGTCAG-3’, RAG2 Reverse: 5’-CTACCTCCCTCCTCTTCGCT-3’) and 3μl of outer PCR reaction. Cycling parameters for nested PCR were 50 cycles of 30-seconds at 95°C, 30-seconds at 64°C for RAG1 and 63°C for RAG2, and 30-seconds at 68°C, followed by 5-minutes at 68°C. For GAPDH the nested PCR reaction was performed with conditions identical to RAG2 using 3μl cDNA. PCR products were visualized on 2% agarose gel
200μl supernatants were harvested from primary cell cultures at various times post-infection, clarified by centrifugation and stored at -80°C. Supernatants were thawed and subjected to bead-based immunoassay (BD CBA Cat# 561521) according to manufacturer’s instructions. Briefly, standard curve samples were prepared and processed together with experimental samples. Human IL-6 Capture beads (BD Cat# 558276) were incubated with supernatants for 2 hours at room temperature, then detection reagent was added and incubated for another 2 hours at room temperature followed by two washes. Samples were then incubated for 1 hour at room temperature with enhanced sensitivity detection reagent, washed twice and data was acquired by FCM using a BD LSR2 flow cytometer and analyzed using FlowJo software. Two technical replicates were performed for each sample. Standard curves and absolute cytokine values were calculated using R software.
Dual immunohistochemistry for KSHV LANA and immunoglobulin light chains was performed as previously described[12].
Data plots and statistical analysis were performed in R software[47] using ggplot2[48] and RColorBrewer[49] packages. Additional statistical analysis was performed on aggregate data using R packages car[50], lme4[51], lmerTest[52]. Specific methods of statistical analysis and resulting values for significance are detailed in the corresponding figure legends.
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10.1371/journal.pntd.0007056 | The Accuracy of Histopathological and Cytopathological Techniques in the Identification of the Mycetoma Causative Agents | Mycetoma is a devastating neglected tropical disease, caused by various fungal and bacterial pathogens. Correct diagnosis to the species level is mandatory for proper treatment. In endemic areas, various diagnostic tests and techniques are in use to achieve that, and that includes grain culture, surgical biopsy histopathological examination, fine needle aspiration cytological (FNAC) examination and in certain centres molecular diagnosis such as PCR. In this retrospective study, the sensitivity, specificity and diagnostic accuracy of grain culture, surgical biopsy histopathological examination and FNAC to identify the mycetoma causative organisms were determined. The histopathological examination appeared to have better sensitivity and specificity. The histological examination results were correct in 714 (97.5%) out of 750 patients infected with Madurella mycetomatis, in 133 (93.6%) out of 142 patients infected with Streptomyces somaliensis, in 53 (74.6%) out of 71 patients infected with Actinomadura madurae and in 12 (75%) out of 16 patients infected with Actinomadura pelletierii. FNAC results were correct in 604 (80.5%) out of 750 patients with Madurella mycetomatis eumycetoma, in 50 (37.5%) out of 133 Streptomyces somaliensis patients, 43 (60.5%) out of 71 Actinomadura madurae patients and 11 (68.7%) out of 16 Actinomadura pelletierii. The mean time required to obtain the FNAC result was one day, and for the histopathological examinations results it was 3.5 days, and for grain it was a mean of 16 days. In conclusion, histopathological examination and FNAC are more practical techniques for rapid species identification than grain culture in many endemic regions.
| In mycetoma endemic regions, the medical and health settings are commonly suboptimal, and only a few diagnostic tests and techniques are available. That had badly affected the patients’ proper diagnosis and management and thus the late presentation of patients with advanced disease. In this retrospective study, the experience of the MRC on the common in use diagnostic tests in the period between 1991 and 2018 is presented. In this study, the sensitivity, specificity rates and diagnostic accuracy of grain culture, surgical biopsy histopathological examination and FNAC to identify the mycetoma causative organisms were determined. The histopathological examination appeared to have better sensitivity and specificity. Furthermore, the grain culture identification needs high experience, it is the tedious procedure, and cross-contamination is common hence misdiagnosis is frequent. It can be concluded that histopathological examination and FNAC are more practical techniques for rapid species identification than grain culture in many endemic regions with poor diagnostic setting.
| Mycetoma is a chronic granulomatous subcutaneous inflammatory infection, endemic in subtropical and tropical regions, but it is reported globally [1, 2]. It is characterised by a painless subcutaneous swelling, multiple sinuses formation and a discharge that contain grains [3, 4]. The clinical presentation can give a clue to the diagnosis, but without further diagnostic testing it will lead to misdiagnosis and inaccurate treatment [5]. Mycetoma can be caused by different bacteria (actinomycetoma) or fungi (eumycetoma) [6, 7]. More than 70 different micro-organisms were reported to cause this infection, and hence it is essential to identifying the causative agents to the highest level of resolution which in turn will contribute to choosing appropriate treatment [8, 9]. In endemic regions, the most commonly used tools are culturing of the grains, surgical biopsy followed by histopathological examination and fine needle aspiration cytological (FNAC) examination [10, 11].
Currently, culturing the grains culture is still considered to be the golden standard for species identification in many centres [12, 13]. However, this technique is tedious, time-consuming due to the slow growth rate and it needs expert microbiologists to identify the causative agents based on the macroscopic appearance of the isolates. Furthermore contamination is common. Patients on medical treatment may have non-viable gains, and hence it is difficult to identify the causative organism [14, 15].
To overcome these difficulties, histological examination is often used complementary to culture. In a histopathological examination, it is easy to discriminate between fungal and bacterial causative agents [16, 17]. However, identification to the species level is more challenging and considered far from reliable [18, 19]. At the Mycetoma Research Centre (MRC), University of Khartoum, Khartoum, Sudan FNAC is a common tool to identify the causative organisms. It is less invasive and time-consuming compared to the histopathological and culture techniques [20, 21]. However, to the best of our knowledge there was no study performed in which the sensitivity and specificity of the two techniques for the identification of the mycetoma causative organisms were compared. With this background, this study was conducted at the Mycetoma Research Centre were 8500 confirmed mycetoma patients were seen and treated. In this retrospective study, the records of these patients were reviewed, and patients who undergone the three diagnostic tests were included.
Following the Mycetoma Research Centre Institutional Review Board ethical approval, all the histopathological, cytological and microbiological reports of the patients seen in the Mycetoma Research Centre over a 27-year period (January 1991 to January 2018) were reviewed.
The data were collected in the pre-designed data collection sheet. The patient demographic characteristics, results of the three techniques were collected.
In this study, only patients in whom the causative organisms were identified by culture and had undergone both a fine needle aspirate for cytological examination and deep-seated excisional biopsy for histopathological examination were included. (Fig 1).
The number of true-positive (TP), false-positive (FP), true-negative (TN), and false-negative (FN) test results was calculated for each technique and was compared to the culture which considered as our gold standard method. According to these results, sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were calculated for each test. Accuracy was calculated as the proportion of true results (both true positives and true negatives) among the total number of cases.
The grains were obtained by surgical biopsy and/or FNAC. For the latter, a 25-gauge needle was inserted into the lesion, and aspirates were taken. The yield of grains was assessed visually by the number and size of grains obtained. If the yield was low, a second aspiration was taken with a 23-gauge needle. When excessive bleeding from the lesions was encountered, a 27-gauge needle was used. The obtained sample usual splited into two parts; one was transported immediately to the microbiology department for culturing, the other part was sent to the histology department for histopathological and cytological.
The mycetoma grains were washed three times in sterile normal saline. When a fungus was expected based on the clinical data accompanied the request form and the grains color and consistency (fungal grains tend to be hard and are either white, yellow and black in color according to the causatives agent), the grains were cultured on Sabouraud dextrose agar with gentamicin sulphate (400 μg/ml) for fungal grains at 37 °C. When an actinomycete was expected according to the clinical data and ultrasound report as well as the grain color and consistency (actinomycetoma grains tend to be soft and smooth) grains were cultured on Blood agar, Colombia agar, Glucose yeast extract agar, Brain-heart infusion, Löwenstein—Jensen agar and Modified Sabouraud agar supplemented with 0.5% yeast extract. After that the plates were incubated at 37°C for 7 to 14 days.
When a fungus was grown on the Sabouraud plate, it was identified based on its macroscopic and microscopic morphology. Table 1 demonstrate the characteristic of different eumycetoma causatives agent for macroscopically and microscopically identification.
Macroscopically the appearance of M. mycetomatis colonies is quite variable. At the beginning the colonies tend to be white, and upon maturing they change to yellow or brown. Some strains of M. mycetomatis are able to produce a brown pigment in the culture. The texture varies from smooth, flat or heaped. Madurella mycetomatis is differentiated from T. grisea by its ability to grow at temperatures up to 40°C and its inability to assimilate sucrose (Table 2).
When colonies are obtained, presumptive species identification is based on macroscopical and microscopical appearance of the species. Typical colonies of Nocardia spp and Streptomyces spp are dry to chalky in consistency, usually folded. The color will range from yellow to gray white. A. madurae and A. pelletieri strains produce cream- and red-pigmented mycelia respectively and lack aerial filaments on initial isolation. Ziehl-Neelsen staining is used to determine if the isolate is acid fast. Nocardia spp will stain positively and Actinomadura spp will stain negatively.
Different biochemical tests will be performed to identify the causative agent to the species level. These include the degradation of adenine, casein, and hypoxanthine; growth on adonitol; aesculin hydrolysis; glycerol; glycogen; D-raffinose; L-rhamnose; D-turanose; D-xylose; and L-aspartic acid (Tables 3 and 4).
The aspirate was allowed to air dry and was stained using Diff-Quick stain. The stained aspirates were examined by an expert histopathologist for the presence of the following cytomorphological features: smears cellularity, the host inflammatory tissue reaction, the presence and types of the causative organisms’ grains. Species identification was based on species-specific criteria. In general, M. mycetomatis grains can be either small or large, are light to dark brown in colour and have irregular outlines and a crushing artefact when stained with hematoxylin and eosin (H&E) (Fig 2A).
S. somaliensis grains are difficult to see in H&E stained sections, they stain bright pink to hazy pink in colour, are often oval to irregular shaped and can be as aggregates (Fig 2C).
A. madurae grains are small oval shaped, and it stained pink to red colour in H&E and tend to be as one mass without any fractures. A. pelletierii grains are small rounded to oval shaped, and they stained deep blue in H&E stained sections and tend to be fractured.
All patients underwent surgical biopsy under anaesthesia, which was fixed in 10% formalin and processed further into paraffin blocks. 3-5-μm sections were obtained and stained with H&E. In our Histopathological laboratory the histopathologist issued the report with the species name according to the following criteria i.e. species specific criteria which have been used by all of them. M. mycetomatis grains tend to be large, light to dark brown in colour with irregular outlines. They tend to fracture when sections are cut. M. mycetomatis has two different types of grains, and these are the filamentous and vesicular. The filamentous type, is the most common type and consists of brown septated and branched hyphae that may be slightly more swollen towards the edges (Fig 3D).
S. somaliensis grains are rounded to oval in shape, with homogenous appearance in tissue sections. They appear faint yellow in unstained sections, and the grains are not well stained with H&E. Moreover, as a result of sectioning they may show longitudinal cracks, the filaments are fine (measured between 0.5–2 μm in diameter), closely packaged and embedded in cement matrix (Fig 3B).
A. pelletierii grains are small, round to oval in shape and semicircular and sickle like shapes have been observed as well. The filamentous structures are pretty difficult to be detected. However, a careful and meticulous examination of the periphery of the grains may show some of them. A. pelletierii grains stain deep violet with H&E, which is very characteristic and allows the definitive diagnosis without a need for culturing techniques (Fig 3C).
A. madurae grains ranged from yellow to white. Therefore, it can be difficult to discriminate them from the surrounding fat. Histologically the grain size ranges from small to large. The large grains have a characteristic variegated pattern. The periphery of the grain is opaque, homogenous and deep purple when stained with H&E stain, while the centre is less densely stained. Additionally, the periphery of the grains shows an eosinophilic material (Fig 3A). Smaller grains are more homogeneous and are difficult to distinguish from A. pelletierii. However, even the small grains of A. madurae have a more deeply stained purple fringe, which is not seen in A. pelletierii.
In this study, 991 patients out of 7940 patients were eligible and were included in the analysis. Their ages ranged between 5 and 75 years old. The majority were males 737 (74.3%), and most of them were students 327 (32.9%) and farmers 167 (16.8%). The majority of the patients (837 out of 991), gave a history of discharge that contained grains and the majority of these grains were black (565; 57%)) followed by yellow (104;10.5%), white (60; 6.1%) and red grains (14; 1.4%). In this cohort, the majority of patients, (72.6%) had no history of local trauma, only 191 (19.3%) patients did recall a local trauma and the remaining 73 (7.4%) patients were not certain.
Based on the culture reports of the grains, in 750/991 (75.6%) of the patients the mycetoma was caused by M. mycetomatis, in 142/991 (14.4%) it was caused by S. somaliensis, in 71/991 (7.16%) it was caused by A. madurae and in 16/991 (1.6%) it was caused by A. pelletieri. In 11 patients no growth was reported from the grains obtained during the sample collection. The time to growth differed case by case and ranged between 5 and 28 days.
In this study, out of the 991 mycetoma cases, the correct species identification was obtained for 912 cases using histopathological examination. Using FNAC, the correct diagnosis was obtained in 708 cases. The histopathological examination confirmed the diagnosis of M. mycetomatis in 714 of 750 cases with 95.2% sensitivity, 95.4% specificity and diagnostic accuracy of 95.3%. For FNAC only 604 out of 750 M. mycetomatis cases were identified, resulting in a sensitivity of 80.5%, a specificity of 88.4% and a diagnostic accuracy of 82.4%.
Out of 142 S. somaliensis cases, 133 were also identified with histopathological examination with 93.7% sensitivity, 98.9% specificity and diagnostic accuracy of 98.2%. With FNAC only 50 out of 133 S. somaliensis cases were identified, resulting in a sensitivity of 35.2%, a specificity of 99.3% and a diagnostic accuracy of 90.1%.
53 out of 71 cases with A. madurae identification were identifuied by histopathological examination, with a sensitivity of 74.7%, 99.5% specificity and diagnostic accuracy of 97.7%. FNAC identified 43 out of 71 cases with a sensitivity of 60.6%, specificity of 94.4% and diagnostic accuracy of 91.9%.
For A. pelletierii out of 16 cases; 12 were also identified with histological examination with 75.0% sensitivity, 100% specificity, and diagnostic accuracy of 99.6%. For FNAC a sensitivity of 68.8%, a specificity of 99.7% and a diagnostic accuracy of 99.2% were obtained.
With the histopathological examination, false negative result was reported in 36/750 M. mycetomatis cases, 9/142 S. somaliensis cases, 18/71 A. madurae cases and 4/16 A. pelletieri cases. To determine the false negative results reasons, the histopathological slides were re-examined. There were various reasons for the false negative, and that included the absence of mycetoma histopathological architecture resulted in overlooking the causative agent (Fig 4).
Furthermore, in some blocks, the grains were absent; either because the tissue was not homogenously infected by the causative agent and that the part which was taken for histology or the section contained no grains. This latter might be overcome by examining multiple sections at different depths of the histology blocks especially when inflammation and necrosis are noted.
False positive results were obtained in 28 of the cases. This was attributed to the presences of numerous structures that can mimic the appearance of M. mycetomatis and that included vegetables, synthetic fibres and algae which can resemble fungal hyphae and calcification (Fig 5).
In overall, using histology correct species identification was obtained in the majority of cases. The mean time to identify the culture isolates was 16 days (range 5 to 28 days), for histology it was 3.5 days (range 2 to 5 days), and for cytology, it was one day (range 1 to 2 days). This demonstrated that reliable species identification using histology was obtained in 92.0% of cases within an average time reduction of 13.5 days, for cytology this was 71.4% of cases with time reduction of 15 days, indicating that adding histology or cytology to the diagnostic techniques used for species identification resulted in an earlier start of treatment.
The accurate identification of mycetoma causative agents is considered the cornerstone for the initiation of appropriate therapy. Hence a rapid and accurate diagnostic tool to achieve the definitive species identification is considered a critical part in patient treatment and management [21–23]. Different laboratory techniques for species identification are in use, including culture, histopathology, [7, 25], FNAC [8, 24], serological assays and imaging [26–29] as well as different molecular diagnostic tools [30–35]. However, not all these assays are available in endemic regions. In the Mycetoma Research Centre, culturing of the grains, histopathology and FNAC are routinely performed and have been used for the past 27 years. In this communication we have used the data collected for the last 27 years to assess the sensitivity, specificity and diagnostic accuracy of histopathology and cytology in the identification of mycetoma causative agents in comparison to the current golden standard: culturing.
This study showed that the histopathology was more accurate to FNAC in terms of species identification. Our results are in line with that reported previously by Yousif and colleagues [36]. They reported 90.9% agreement when histopathology was compared to FNAC for the diagnosis of M. mycetomatis (90.9%) while for actinomycetoma causative agents it was only 60%. The lower diagnostic agreement of actinomycetoma causative agents could have been caused by morphological similarities of these microorganisms. Furthermore, both techniques are operator dependent and need intensive training and experience which could have its reflections on the accuracy.
Mycetoma can be caused by more than 70 different causative agents [37], but the distribution of these species is not everywhere the same which could cause differences in diagnostic accuracy in different regions. In some of the mycetoma endemic regions, mycetoma is caused by closely related species. Morphologically these organisms may look similar which could cause a challenge in the identification of these organisms. In Mexico, the most common causative agents are Nocardia brasiliensis and Nocardia asteroides [37], two closely related species which are difficult to differentiate from each other based on histopathology [38, 39]. In Senegal, the most common causative agents of eumycetoma are M. mycetomatis and Falciformispora senegalensis which both can cause black grain mycetoma [37, 40]. In the black grains of F. senegalensis, the centre is non-pigmented, and the cement is absent, whereas at the peripheries the grains are dark coloured and brown cement is present. However, this is also seen in black grains of Trematosphaeria grisea and certain grains of M. mycetomatis. Hence an expert pathologist is needed to differentiate between these organisms [41].
The study performed here was a retrospective study, looking back at the records of the Mycetoma Research Centre for the past 27 years. During that time molecular identification of the causative agents was not performed and culture was considered the golden standard. Recently in the study conducted by Borman and colleagues demonstrated that using morphological identification, misidentifications occurred in many cases [42]. Out of 28 previously identified Trematosphaeria grisea isolates, 22 were, other fungal species [42]. For actinomycetoma causative organisms, misidentifications also have been described. In 2008, Quintana and associates demonstrated that half of the S. somaliensis isolates obtained from Sudan appeared to be Streptomyces sudanensis [43]. Furthermore, next to A. madurae and A. pelletieri also Actinomadura latina was described [44]. Therefore a current ongoing study is including molecular diagnosis to determine the true etiology and predictive value of culture, FNAC and histology. With the introduction of molecular diagnosis in our centre we already made the first step in this respect.
In this study, the sensitivity of histopathological technique was superior to that FNAC for all species tested. In that study they studied the performance of FNAC in comparison to histology in 19 different mycetoma patients. Out of these 19 patients, five patients had to be excluded due to inadequate aspirated materials. From the 14 remaining patients, 10 were diagnosed as M. mycetomatis with histopathology, and 4 were actinomycetoma. With this limited number of patients they could conclude that FNAC could identify the causative agent in 9 out of 10 M. mycetomatis patients. One patient identified by histology could not be identified with cytology, again confirming that histology was superior to FNAC in respect to species identification [15]. A result confirmed in our current study, as in our study 146 patients with M. mycetomatis mycetoma were missed with FNAC. However, of the three different identification methods used, FNAC was the most rapid and resulted in species identification within 1 day, instead of 3.5 days for histology or 16 days for culture. FNAC is a simple and rapid diagnostic technique which can be used at the one-stop diagnosis clinic and in epidemiological and field surveys. However, it has many limitations: it is an operator dependent technique can be painful and can lead to deep-seated bacterial infections. FNAC is less invasive than a deep-seated biopsy, as only a small puncture hole is obtained. With a deep-seated biopsy a larger area of the lesion is removed thereby also exposing a larger part of the lesion to secondary bacterial infections and creating a bigger risk for dissemination of the infection. Currently, deep-seated biopsies are only performed to obtain a diagnostic sample, not to reduce the burden of infection at the site of the lesion. This, because the lesion can be extensive, even when on the outside only a small lesion is seen. At the moment the fine needle aspirate is often taken blindly without guidance of ultrasound imaging which creates a risk that the operator might miss the pockets which contains grains. With the use of the ultrasound-guided aspiration, the diagnostic yield of the technique will improve which in its turn could enhance the number of cases in which positive species identification might be obtained.
The grains culture remains in many centres the cornerstone for the diagnosis of mycetoma. moreover, morphological identification of mycetoma causatives agent may be some times be difficult to achieved due to the overlapping and similarities encountered between different species as demonestrated in Table 1, However culture is a time-consuming procedure and an experienced microbiologist is needed to identify the organisms to the species level [45]. Cross-contamination is a common problem.
Recently an identification scheme of eumycetoma causative agents has been published which was based on the pysiological properties of the causative agent, indicating that it is possible to use physiological properties to identify eumycetoma causative agents more reliably [1]. These include culturing at 37°C and growth on actidione, L-sorbose, glycetol, potassium 2-keto-gluconate, methyl-D-glucopyranoside, inositol and D-sorbitol. However, these tests are more time consurming and delay the identification of the causative agent which will potentially leed to delay in patient treatment. However, by complementing culturing with histology or FNAC a preliminary identification might be obtained earlier. Especially, since the treatment of mycetoma infections is dependent on the causative agent. Actinomycetoma is treated differently than eumycetoma. [22].
Correct identification to the species level will influence clinical decision making. The first diagnostic discrimination needed is the distinction between actinomycetoma causative agents and eumycetoma causative agents, since this would implicate either antibacterial treatment or a treatment based on surgery and antifungal treatment. In this situtation fine needle aspiaration cytology and histopathological examination were found to be highly sensitive in the discrimination between actinomycetoma from eumycetoma based on the morphological characteristics; therefore, and based on our experience we recommended the used of FNAC in a low resources centers like in the rural centers were there is no Histopathology laboratories.
For the actinomycetoma causative agents, it is currently not known if the choice of antibacterial agent is dependent on the causative agents and studies are needed to evaluate if differrent treatment regimens are needed for each of the different bacterial causative agents. When this appears to be the case, identification to the species level becomes essential. For eumycetoma causative agents, surgery is always combined with itraconazole. However, Medicopsis romeroi and Madurella fahalii, were not susceptibile towards itraconazole in vitro. This would indicate that discrimination between these species and the susceptible species would be mandatory and could potentially effect clinical management. Clinical evaluation of such cases and the effect on the therapeutic success rates are needed. However, before such a study can be performed proper species identification needs to be obtained.
In summary, the Cytopathologist/Histopathologist need to be aware of the many mimics that can look like the mycetoma causatives agent, and we highly recommended that the pathologists after issuing the diagnosis should recommend correlation with microbiology or provide a cautionary statement to advise clinicians of the limitations of identifying organisms with histopathologic/cytopathologic examination.
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10.1371/journal.pgen.1002777 | Stretching the Rules: Monocentric Chromosomes with Multiple Centromere Domains | The centromere is a functional chromosome domain that is essential for faithful chromosome segregation during cell division and that can be reliably identified by the presence of the centromere-specific histone H3 variant CenH3. In monocentric chromosomes, the centromere is characterized by a single CenH3-containing region within a morphologically distinct primary constriction. This region usually spans up to a few Mbp composed mainly of centromere-specific satellite DNA common to all chromosomes of a given species. In holocentric chromosomes, there is no primary constriction; the centromere is composed of many CenH3 loci distributed along the entire length of a chromosome. Using correlative fluorescence light microscopy and high-resolution electron microscopy, we show that pea (Pisum sativum) chromosomes exhibit remarkably long primary constrictions that contain 3–5 explicit CenH3-containing regions, a novelty in centromere organization. In addition, we estimate that the size of the chromosome segment delimited by two outermost domains varies between 69 Mbp and 107 Mbp, several factors larger than any known centromere length. These domains are almost entirely composed of repetitive DNA sequences belonging to 13 distinct families of satellite DNA and one family of centromeric retrotransposons, all of which are unevenly distributed among pea chromosomes. We present the centromeres of Pisum as novel “meta-polycentric” functional domains. Our results demonstrate that the organization and DNA composition of functional centromere domains can be far more complex than previously thought, do not require single repetitive elements, and do not require single centromere domains in order to segregate properly. Based on these findings, we propose Pisum as a useful model for investigation of centromere architecture and the still poorly understood role of repetitive DNA in centromere evolution, determination, and function.
| During cell division, DNA packed in chromosomes must be perfectly distributed between daughter cells. Centromeres play a crucial role in this process. Current centromere biology maintains that stable chromosomes can be either monocentric, with one functional domain located at a single position, or polycentric, with multiple domains located along the entire chromosome. We found that pea chromosomes are different, exhibiting very large single centromeres containing multiple functional domains, thus representing a novel intermediate type of centromere. We demonstrate that all of the functional centromere domains in the pea are tightly associated with clusters of distinct satellite DNA families, indicating their role in centromere evolution. Our results support the idea that the tandem organization of repeating units, but not their primary sequences, is an important co-determinant of functional centromere domains.
| Centromeres are chromosome domains that are essential for faithful chromosome segregation during cell division. It is maintained that stable chromosomes can be either monocentric, possessing centromere activity within a cytologically distinguishable primary constriction, or polycentric (holocentric), lacking a primary constriction and exhibiting centromere activity over nearly the entire chromosome length. It is assumed that polycentric chromosomes have arisen multiple times during evolution because they are present in independent eukaryotic lineages. However, the mechanism of transition from monocentric to polycentric chromosomes is not yet known, nor has any intermediate between the two types been documented.
A functional centromere domain is currently defined as a chromosomal region upon which a kinetochore assembles and to which microtubules of the mitotic spindle are attached [1]. One of the fundamental inner kinetochore proteins is a centromere-specific histone H3 variant, referred to as CenH3 (CenpA in animals, CID in Drosophila melanogaster, Cse4 in Sacharomyces cerevisiae or HCP3 in Caenorhabditis elegans) [2]. Contrary to canonical histone H3, which is extremely conserved in all eukaryotes, CenH3 shows considerable variability between species [2]. Since it is present in the functional centromere of all eukaryotes studied so far, it has become a universal marker of centromeric chromatin. It has been shown that functional centromere domains of monocentric chromosomes are composed of small intermingling subunits, 10 to 50 Kbp in length, containing nucleosomes with either CenH3 or canonical H3 histones [3], [4]. During chromosome condensation, it is postulated that the CenH3-containing subunits accumulate toward the poleward face of the centromere and are the foundation for a single compact kinetochore complex [4]–[6]. While the CenH3-containing chromatin forms a single domain localized within primary constriction of the monocentric chromosomes, it is distributed as contiguous loci in a linear axis over nearly the entire length of the polycentric ones [7]–[9].
In our previous work we found that chromosomes of the pea (Pisum sativum) exhibit unusually long primary constrictions containing multiple clusters of distinct families of satellite DNA [10]. This contrasts with most species investigated so far, that exhibit short primary constrictions and often a single family of satellite DNA that is common to all centromeres of a given karyotype. Although the role of satellite DNA for centromere function is not yet fully understood, the centromere domains tend to be established upon its arrays [11]. In order to uncover how the size and DNA sequence composition of primary constrictions are related to the organization of the functional centromere domains in pea chromosomes, we employed molecular and cytological techniques combined with next-generation sequencing of genomic and ChIP-enriched DNA followed by bioinformatics analysis. We demonstrate that the elongated constrictions in pea chromosomes exhibit 3–5 explicit CenH3-containing regions, a centromeric novelty. We introduce this as a meta-polycentric organization, representing the first example of an intermediate between monocentric and polycentric centromeres. We show that these domains are tightly associated with clusters of 13 distinct families of satellite DNA, indicating an important role of satellite DNA in centromere function and evolution.
Although CenH3 is, with few known exceptions [12]–[15], encoded by a single gene in diploid species [16], in the pea genome we identified two divergent copies, designated as CenH3-1 and CenH3-2 (GenBank accession numbers JF739989 and JF739990), sharing only 55% identity. CenH3-1 and CenH3-2 proteins differ both in length and sequence, being composed of 123 and 119 amino acid residues, respectively, and having 72% identity (Figure 1A). Transformation experiments using pea hairy-root tissue cultures expressing constructs containing cDNA fragment coding either of the CenH3 variants fused with yellow fluorescence protein (YFP) gene demonstrated that, despite the sequence differences, both CenH3 variants target all 14 centromeres in diploid nuclei (Figure 1B–1C). Immunodetection experiments further showed that both CenH3 variants completely co-localized in interphase and mitotic chromosomes (Figure 1E–1M). In sharp contrast to other investigated species, including Vicia faba [17], [18] which is a close relative of the pea, we found that primary constrictions of all pea chromosomes contain not a single but multiple functional centromere domains (Figure 2). These domains were best distinguished in prophase and prometaphase chromosomes exhibiting three to five domains that are clearly separated by chromatin blocks lacking CenH3. With increasing condensation in late metaphase and anaphase, the domains came very close to one another or merged into a single extended layer at the poleward face of the primary constriction. However, even in fully condensed chromosomes, the CenH3 within the layer was not evenly distributed, showing intermingling fluorescent signal spots of varying intensity in a row, resembling a string of beads (Figure 2F–2G). The chromosome segments delimited by the two most distant domains were roughly estimated to represent 9.5–18.8% of individual chromosomal DNA which corresponds to 69–107 Mbp (Table S1). To verify that all CenH3-containing domains are indeed sites of kinetochore formation, we carried out the simultaneous immunodetection of CenH3 and tubulin, a protein of the mitotic spindle which is also localized to the kinetochore [19]. Tubulin signals detected in the kinetochore colocalized with those of CenH3, indicating that all CenH3-containing regions are truly functional centromere domains (Figure 2H and Figure 3).
We selected chromosome 3 for the investigation of the centromere structure and the CenH3 distribution with higher resolution using correlative light fluorescence microscopy (LFM) and field emission scanning electron microscopy (FESEM). In this approach, CenH3-containing regions were detected with FluoroNanogold, allowing for the investigation of the same chromosomes with both techniques (Figure 4A–4C). Three distinct and strongly labeled regions, located on either side of the longitudinal chromosome axis, were detected using both LFM and FESEM imaging (Figure 4A–4B). Secondary electron (SE) imaging of the primary constriction revealed longitudinally oriented fibrillar structures interspersed with chromomeres in the range of about 200 nm in diameter (Figure 4C). Backscattered electron (BSE) detection of CenH3 markers showed that labeled regions are composed of discrete multiple signals (Ø 10–15 nm) from markers near the surface, as well as diffuse regions from markers in the interior of the centromere (Figure 4B). Subsequent investigation with a dual beam focused ion beam (FIB) and FESEM system allowed direct visualization of CenH3 markers in the centromere interior (Video S1). Measured by 5 nm milling steps, markers occurred between 10 nm and approx. 200 nm from both poleward centromere surfaces (Figure S1). High resolution 3-D reconstruction of the CenH3 distribution revealed that very few of the CenH3 signals actually occur at the chromosome surface (Video S2), indicative of other kinetochore factors at the chromatin-microtubule interface.
In order to uncover DNA sequence composition of the functional centromere domains, we carried out chromatin immunoprecipitation sequencing (ChIP-seq) which produced approximately 9.5 and 19.7 million 35 nt long reads for ChIP and its input control sample, respectively. As the whole pea genome sequence is not yet available, we employed as a reference sequences that were obtained by paired-end Illumina sequencing of the pea nuclear DNA at 0.48× coverage (20.5 million reads, 100 nt in length; all deep sequencing data related to this study have been deposited into the Sequence Read Archive under the study accession number ERA079142 (http://www.ebi.ac.uk/ena/data/view/ERA079142)). Sequences associated with CenH3 were identified based on the ratio between ChIP and input sequences mapped either to sequence clusters representing the most abundant repeats of the pea genome or to each reference read (Figure S2). The latter approach revealed a total of 354 717 reference reads (1.73%) showing at least 10-fold enrichment which were grouped into sequence clusters based on their mutual similarity, as described previously [20]. Further analysis of the clustered sequence data revealed that a vast majority (99%) of the ChIP-enriched sequences belongs to 13 distinct families of satellite DNA (Table 1) and one family of Ty3/gypsy retrotransposon belonging to the CRM clade of chromoviruses [21]. This data suggests that functional centromere domains are established almost exclusively upon repetitive DNA sequences. These repeats differed considerably from one another, not only in their primary sequences but also in the size of repeating units and abundance in genome (Table 1, Figure 5 and Dataset S1). The association of all these repeats with functional centromere domains was confirmed using fluorescence in situ hybridization (FISH) combined with immunodetection of CenH3-1 (Figure 4D–4F and Figure 6) which allowed the assignment of each CenH3-containing domain to some of the identified satellites. These experiments also showed that only the repeats with a high ChIP/input ratio are specific to functional centromere domains, while those with lower ChIP/input ratio (e.g. PisTR-B and TR-12) are localized predominantly outside of these domains (Table 1, Figure 6 and data not shown). In addition to the ChIP-enriched satellites, we included in these experiments three families of satellite DNA (TR-2, 4, and 5) that are known to occupy primary constrictions but showing no ChIP enrichment (ChIP/input<1.1), which indeed localized outside of CenH3-containing regions (Figure 6D–6E and 6M–6N). Contrary to most other species that possess a relatively high level of sequence homogenization among all centromeres [22] DNA sequence composition of the centromere domains in the pea varied between chromosomes as well as between individual domains of the same chromosome. The only exception was chromosome 2 that contains a single centromeric satellite family (TR-11, Figure 6F).
It has already been shown that functional centromere domains of monocentric chromosomes are composed of intermingling subunits, 10 to 50 Kbp in length, containing nucleosomes with either CenH3 or canonical H3 histones [3], [4]. Although it is not yet well understood how the centromeric chromatin folds during chromosome condensation in mitosis, all current models postulate that CenH3-containing subunits are brought together toward the poleward face of the centromere to form a single compact kinetochore [4]–[6]. As the size of the subunits is relatively small, they can be observed only at the finest resolution of chromatin fiber but not at the level of condensed mitotic chromosomes. Thus, none of the current models allow for large intermingling domains at the poleward side of mitotic chromosomes, as are observed in the pea. On the other hand, the high resolution 3-D distribution of CenH3 in individual centromere domains (Video S2) resembles that postulated for single centromere domains of previously investigated centromeres [6].
From a molecular point of view, therefore, the pea chromosomes have multiple centromere domains, yet they have only one primary constriction at metaphase. Chromosomes with two or more functional centromeres are usually unstable due to the formation of anaphase bridges leading to chromosome breakage. One exception is when the two centromeres are physically so close that they are able to fuse into a single centromere without disturbing mitosis [23]. The maximum distance between two centromeres that still allows faithful segregation of dicentric chromosomes was estimated to be about 20 Mbp [24]. Taking into account the size of the chromosome segments delimited by the two outermost functional centromere domains (Table S1) and the total number of these domains in individual chromosomes, the distance between any two domains is likely to be either below this limit or not exceed it considerably. This probably allows the multiple domains to act in concert, assuring that pea chromosomes are stable during mitosis, behaving as functional monocentrics.
The high diversity of DNA sequence composition of functional centromere domains observed in the pea is unprecedented, but it concurs with the notion that centromeres are determined rather epigenetically (for review see [25]). On the other hand, similarly to most other species investigated thus far [11], all of the centromere domains in the pea are made up of satellite DNA, indicating that the tandem organization of repeating units co-determines centromere domains. This converges with the recently proposed role of repetitive DNA in centromere function relying on a formation of covalently closed DNA loops made by inter-repeat homologous recombination [26]. However, the tandem arrangement of the repeating units is clearly not the only precondition for a DNA sequence to function as a centromere because some clusters of satellite DNA located within the primary constrictions are not associated with CenH3.
The structure of large pea centromeres is reminiscent of holocentric, also called polycentric, chromosomes that exhibit numerous discrete centromere domains extending over nearly the entire length of the chromosome [7]. As with the pea, the centromere domains congregate during mitosis to form a composite, linear-like kinetochore [7], [9], [27]. The sizes of segments of pea chromosomes delimited by the outermost functional centromere domains (Table S1) approach or even exceed the size of entire polycentric chromosomes of some species, including C. elegans (14–21 Mbp) and Luzula nivea (155 Mbp on average) [28], [29]. A portion of centromere domains in Luzula nivea is composed of scattered clusters of satellite LCS1 [30], suggesting that satellite DNA is an important centromere determinant in at least some holocentric chromosomes. Remarkably, the LCS1 satellite has a similarity to the RCS2 (CentO) which is the major centromeric satellite of monocentric chromosomes of some Oryza species [31].
Although the mechanism of transition from monocentric chromosomes to polycentric ones is not yet known and may differ between organisms, a conceivable scenario for Luzula nivea could be that it occurred as a consequence of spreading of centromere-competent satellite(s). If this is the case, then pea chromosomes with multiple distinct clusters of CenH3-associated satellites might represent an intermediate “meta-polycentric” type between monocentric and polycentric chromosomes. However, it has been postulated that centromere expansion causes deleterious effects which in turn create pressure for its suppression, possibly by changes in key factors such as CenH3 or Cenp-C [32]. This explains why the centromere expansion is not an infinite process and why the size of centromeres of most eukaryotic species remains limited to relatively small chromosome domains. Therefore, we assume that pea centromeres are more likely to be or to have already been suppressed in their expansion rather than continue their spreading further into noncentromeric regions. It is tempting to speculate that the presence of two CenH3 genes in the pea is somehow related to the unusual centromere structure. However, it is impossible to conclude from the available data whether the ancient duplication of CenH3 genes and their diversification occurred before or after the centromere expansion. Further research is necessary to fully understand the cause and effects of these unusual features of pea centromeres. Establishing the pea as a new model organism for centromere investigation will contribute to a better understanding of centromere chromatin organization and dynamics during the cell cycle as well as the still elusive role of repetitive DNA in centromere evolution, determination and function.
All experiments were performed using pea (Pisum sativum) cultivar Carrera. Seeds were obtained from Osiva Boršov (Boršov nad Vltavou, Czech Republic).
Search for CenH3 gene was done using sequence data obtained from next-generation sequencing of the pea transcriptomes of roots, leaves and flowers (about 72.2 million 50 nt long reads generated by SOLiD sequencing, unpublished data) and the pea genome at the depth corresponding to 0.48× coverage (about 20.5 million 100 nt long reads generated by Illumina sequencing). It revealed two different variants of CenH3 genes, designated as CenH3-1 and CenH3-2. Long fragments of the CenH3 genes were amplified using PCR. CenH3-1 was amplified with primers PN_ID317 (AAA AGC GAA ATT GAA AAT CAA AAT CTG) and PN_ID320 (GAC TCA TTT TAA ATT CTC ATT CTC ATT CTC ATT) while the CenH3-2 was amplified with primers PN_ID321 (AGT CGC TCT CTG TGT ACA CAA ACT TAA AG) and PN_ID324 (GTT CCA AGA ATT TTA CTT TCC AGA TAG ATA CTT A). Each 30 µl PCR contained 1× PCR buffer, 0.2 mM dNTP, 0.3 µM of each primer, 2% (w/v) DMSO, 0.3 U LA DNA polymerase (Top-Bio, Prague, Czech Republic) and 150 ng pea genomic DNA. The reaction profile consisted of a denaturation step (94°C/60 s) followed by 35 cycles of 94°C/15 s, 61°C/30 s and 68°C/3 min. Amplified fragments were cloned into the pCR4-TOPO plasmid vector (Invitrogen, Carlsbad, CA). Consensus sequences derived from sequencing of three randomly selected clones of each CenH3 variant have been deposited in GenBank under accession numbers JF739989 and JF739990.
The coding regions of the CenH3-1, CenH3-2, and canonical H3 genes were obtained by RT-PCR amplification. Total RNA was isolated either from leaves of the pea (CenH3-1 and CenH3-2) or Medicago truncatula (H3) using Trizol reagent (Invitrogen, Carlsbad, CA) and treated with DNase I (Ambion, Austin, TX). First strand synthesis was achieved with a SuperScript III First-Strand Synthesis System for RT–PCR kit (Invitrogen, Carlsbad, CA), following the manufacturer's recommendations and employing random hexamers as primers. A sample of 5 ng of the resulting cDNA was used as template for a 25 µl PCR containing 1× PCR buffer, 0.2 mM dNTP, 0.2 µM of each primer (PN_ID76: ATG GGT AGA GTT AAG CAC TTC C and PN_ID69: CCA AAG TCT TCC TAT TCC TGT AAG for CenH3-1, PN_ID313: ATG GCG AGA GTT AAA CAA ACA and PN_ID314: CCA AGG TCT TCC TAT CCC G for CenH3-2, PN_ID93: ATG GCA CGT ACC AAG CAA ACT G and PN_ID95: AGC GCG CTC ACC ACG GAT for H3), 1.5 mM MgCl2 and 1 U Platinum Taq polymerase (Invitrogen, Carlsbad, CA). The amplification regime consisted of an initial denaturation step (94°C/3 min), followed by 35 cycles of 94°C/30 s, 55°C/50 s and 72°C/60 s and a final extension of 72°C/10 min.
RT-PCR amplified fragments encoding for CenH3 and H3 histones were cloned into the pCR8/GW/TOPO entry vectors using pCR8/GW/TOPO TA Cloning Kit (Invitrogen, Carlsbad, CA). The fragments in appropriate orientation were subsequently recombined into destination vector pEarleyGate104 (obtained from TAIR; http://www.arabidopsis.org/), allowing for C-terminal fusion with YFP. The recombination reaction was carried out using Gateway LR Clonase II Enzyme Mix (Invitrogen, Carlsbad, CA) according to the manufacturer's instructions. Nucleotide sequences of all constructs were verified by sequencing.
Transgenic hairy root cultures expressing the reporter gene were obtained by transformation of P. sativum plants by Agrobacterium tumefaciens C58C1 carrying both hairy root inducing plasmid pRiA4 and pEarleyGate104 vector possessing either of the constructs. The transformation was performed by injecting Agrobacterium suspension into stems of 7-days-old seedlings cultivated in vitro on 50% Murashige and Skoog medium (Duchefa, Haarlem, Netherlands). The seedlings were grown at 20°C (16 h photoperiod). After 3–4 weeks of cultivation, hairy roots emerging from the inoculation sites were excised and placed on solid Gamborg B5 medium (Duchefa, Haarlem, Netherlands) supplemented with ticarcillin (500 mg/l) and cefotaxime (200 mg/l) for elimination of bacteria, and glufosinate ammonium (10 mg/l) for selection of lines carrying the YFP constructs. Hairy root cultures were grown in Petri dishes at 24°C in the dark and transferred to fresh B5 medium once a month.
The images of transgenic cells expressing the constructs of CenH3 and H3 (both YFP at C-terminal end) were captured using confocal microscope Olympus FV1000 and processed in FW10-ASW software.
Nuclei were isolated from 10 g of young leaves as described previously [10]. The isolated nuclei were centrifuged at 400 g for 5 min at 4°C and resuspended in 3 ml micrococcal nuclease (MNase) buffer (10% sucrose, 50 mM Tris-HCl pH 7.5, 4 mM MgCl2, 1 mM CaCl2). The chromatin suitable for ChIP was prepared by digestion of the nuclei with MNase (150 units of the enzyme per 3 ml of nuclei) for 40–60 min at 37°C. The reaction was stopped by adding 0.5 M EDTA to a final concentration of 20 mM and samples were centrifuged at 13,000 g for 5 min at 4°C. The supernatant containing well digested chromatin was saved (fraction 1) while the pellet containing poorly digested chromatin was resuspended in 200 µl MNase buffer and redigested with 15 units of MNase for 5 min at 37°C. The reaction was stopped with EDTA and centrifuged as described above. The supernatant was mixed with the fraction 1 and a 200 µl aliquot was taken from the chromatin sample for DNA isolation to serve as an input control sample. The rest of the mixture was diluted with the same volume of ChIP incubation buffer (50 mM NaCl, 20 mM Tris-HCl pH 7.5, 5 mM EDTA, 0.2 mM phenylmethylsulfonyl fluoride, 1× protease inhibitor cocktail (Sigma-Aldrich, St. Louis, MO)). ChIP was done using Immunoprecipitation Kit – Dynabeads Protein G (Invitrogen, Carlsbad, CA) according to manufacturer's instructions with some modifications. The ChIP was preceded with a precleaning step; 2 ml of the chromatin were mixed with Dynabeads Protein G from 50 µl of the stock, incubated on a rotator for 4 h at 4°C, and finally separated from the beads using a magnet. Antibody binding was done for 2 h at 4°C in 200 µl of Ab binding and washing buffer containing magnetic beads from 50 µl and 30 µg of the antibody to CenH3-1. The beads with bound antibody were mixed with the precleaned chromatin and the mixture was incubated with rotation overnight at 4°C. Immunoprecipitated complexes were washed 4×5 min using 200 µl of the washing buffer. Elution of the chromatin was done using 2×100 µl of preheated elution buffer (1% sodium dodecyl sulfate, 0.1 M NaHCO3) for 30 min at 65°C. DNA from the ChIP and input control samples was isolated using ChIP DNA Clean and Concentrator Kit (Zymo Research, Irvine, CA). Sequencing of the input and ChIP DNA was done using Illumina technology producing 36 nt long reads (Creative Genomics, Shirley, NY).
Most immunostaining and FISH experiments were done using chromosomes isolated from root tip meristem cells synchronized using 1.25 mM hydroxyurea and blocked at metaphase using 15 µM oryzalin or 10 µM APM as described previously [33]. The squash preparations were made in 1× phosphate-buffered saline (PBS) buffer by squashing synchronized root tip meristems fixed in 4% formaldehyde for 25 min and digested with 2% cellulase and 2% pectinase in 1× PBS for 85 min at 28°C. To avoid potential influence of the synchronization on signal patterns of CenH3, we employed also squash preparations made of nonsynchronized meristems which produced the same results. Affinity purified polyclonal antibodies to peptides designed from CenH3-1 (GRV KHF PSP SKP AAS DNL GKK KRR CKP GTK C) and CenH3-2 (TPR HAR ENQ ERK KRR NKP GC) histones were custom-produced (Genscript, Piscataway, NJ) in rabbit and chicken, respectively. Commercially available antibodies included rabbit antibody to GFP (Invitrogen, Carlsbad, CA; catalog number A11122) and mouse antibody to α-tubulin (Sigma-Aldrich, St. Louis, MO; catalog number T6199). Prior to incubation with either antibody, the slides were incubated in PBS-T buffer (1× PBS, 0,1% Tween 20, pH 7,4) for 30 min at room temperature (RT). The slides were incubated with primary antibodies diluted in PBS-T overnight at 4°C. Dilution ratios were as follows: 1∶1000–5000 for both CenH3 antibodies, 1∶500 for YFP antibody, and 1∶50 for antibody to α-tubulin. Following two washes in 1× PBS for 5 min, the antibodies were detected by anti-rabbit-Rhodamine Red-X-AffiniPure (1∶500, Jackson ImmunoResearch, Suffolk, UK; catalog number 111-295-144), anti-chicken-DyLight488 (1∶500, Jackson ImmunoResearch; catalog number 103-485-155), anti-mouse-FITC (1∶100, Abcam, Cambridge, UK; catalog number ab6785) or anti-rabbit-Alexa488-NanoGold (Nanoprobes, Yaphank, NY) in PBS-T buffer for 1 h at RT. After final washes of PBS, the slides were counterstained with 4′,6-diamino-2-phenylindole (DAPI) and mounted in Vectashield mounting medium (Vector Laboratories, Burlingame, CA). In double immunodetection experiments, the two primary or secondary antibodies were incubated together and appropriate control experiments were performed to exclude non-specific binding.
For a combined detection of the CenH3 proteins and the satellite repeats, the immunodetection procedure was followed by FISH. After CenH3-1 detection and washing, the slides were immediately postfixed in 4% formaldehyde in 1× PBS for 10 min at RT, and dehydrated in series of 70% and 96% ethanol, 5 min at RT each. Chromosome denaturation was carried out in a PCR buffer (Promega, Madison, WI) supplemented with 4 mM MgCl2 for 2 min at 94°C. The preparation of hybridization probes, hybridization conditions, and probe detection were set up as described by Macas et al. [10]. The chromosomes were examined using a Nikon Eclipse 600 microscope. Images were captured with a DS-Qi1Mc cooled camera and analyzed by NIS Elements 3.0 software (Laboratory Imaging, Praha, Czech Republic).
The chromosome sizes in Mbp were estimated from relative chromosome lengths of individual chromosomes and haploid genome size of 4 300 Mbp [34] using following formula: genome size×relative chromosome length/100. The relative chromosome lengths were taken from Neumann et al. [35]. Centromere size was estimated using chromosomes stained with DNA-binding fluorescent dye DAPI as a proportion of integrated fluorescence density within the segments delimited by the two outermost CenH3-containing regions compared to that of whole chromosome. The measurements were done on mitotic chromosomes at prometaphase to metaphase.
Reads from ChIP and input sequencing were trimmed at both 5′ and 3′ end, leaving 31 bp sequences which were further subjected to quality filtering (reads containing more than one base with a quality lower than 20 were removed). This left 9,515,830 and 19,699,136 high quality reads in the ChIP and input data sets, respectively. In order to get a suitable reference needed for the identification of ChIP-enriched sequences, we sequenced the pea nuclear genome at about 0.48× coverage (20,527,392 reads 100 bp in length) using Illumina technology producing pair end reads. All sequence data has been deposited to the Sequence Read Archive under the study accession number ERA079142 (http://www.ebi.ac.uk/ena/data/view/ERA079142). The mapping of the ChIP and input reads to the reference sequences was done using PatMaN program [36], allowing for up to two differences between the query and the hit, both of which could be indels with a maximum total size of four bases. The ChIP enrichment was calculated as a ratio between a proportion of ChIP and input reads mapped to a reference. This was done using two approaches. The first approach employed clusters calculated due to computational limitations from only 2 million randomly selected reference sequences as described previously [20]. The clusters grouped together reads derived from individual repeat families or their fragments. Top 1000 clusters with the highest genome representation were used to determine their ChIP-enrichment. In this approach the ChIP and input reads were mapped to the reference sequences present in the clusters. ChIP or input sequence read was assigned to a given cluster if it had a hit to at least one reference read from that cluster. It should be noted that each read could be assigned to only a single cluster. Reads with equal similarity to reference sequences from more than one cluster were assigned to the one with a higher genome representation. The ChIP enrichment values were calculated from the total number of reads assigned to individual clusters. Advantage of this approach was that it allowed to determine ChIP-enrichment values for all major repeat families present in the pea genome. On the other hand, it missed all single and low-copy sequences.
The other approach relied on determination of the ChIP enrichment values for each of 20,527,392 reference sequences. Only those showing the ChIP enrichment of at least 10 were selected to build the clusters as described in Novák et al. [20]. Thus, these clusters were made only of sequences putatively derived from CenH3-associated regions, providing about 10-fold deeper coverage as compared to the clusters build from 2,000,000 reads. In addition, this analysis involved all available sequences regardless of their repetitiveness.
Type of ChIP-enriched repeat families was determined by their similarity to previously characterized repeats [10] and by their graph shape [20]. Tandem arrangement of novel satellite repeat families was confirmed by PCR using primers directed outwards from a putative monomer instead towards each other (data not shown). In such PCR design, a product of expected size can be obtained only if monomer sequences have head-to-tail tandem organization. The only non satellite centromere repeat, the Ty3/gypsy retrotransposon belonging to the CRM clade, was determined by high level of similarity to a full-length element described recently [21].
Prior to FESEM, immunolabeled (Alexa488-NanoGold, see above) specimens were washed in 100% ethanol to remove mounting medium, washed in distilled water, and silver enhanced for 4 min according to the manufacturer's instructions (HQ Silver, Nanoprobes, Yaphank, NY). After washing again in distilled water, specimens were dehydrated in acetone, critical point dried from CO2, cut to size and mounted onto aluminum stubs. Specimens were carbon-coated by evaporation (Balzers high vacuum evaporator BAE 121, Liechtenstein) to a layer of 3–5 nm for orthogonal (top-view) FESEM and examined at 10–30 kV with an Hitachi S-4100 field emission scanning electron microscope equipped with a Everhard-Thornby chamber secondary electron (SE) detector and a YAG-type back-scattered electron (BSE) detector (Autrata). SE and BSE images were recorded simultaneously with DigiScan hardware and processed with Digital Micrograph 3.4.4 software (both Gatan, Pleasanton, CA).
Dual beam focused ion beam/field emission scanning electron microscopy (FIB/FESEM) investigations were performed on a Zeiss Auriga CrossBeam Workstation, a field emission scanning electron microscope equipped with a Gallium ion beam, in-lens, chamber SE and EsB detectors (Carl Zeiss, Germany) as described in Schroeder-Reiter et al. [37]. For FIB/FESEM sectioning the specimens were carbon-coated to 10 nm for stability. Milling steps were defined at 5 nm. The electron beam voltage was 1 kV; the EsB grid was set at 900 V. In the cut-and-view mode FESEM images were recorded using a ratio of 70% BSE to 30% SE signal detection. Specimens were tilted to an angle of 54°; image recordings were tilt-compensated.
Marker molecules were quantified by counting the number of signal spots per milled centromere section. Animation of FIB milling and partial alignment functions were performed with ImageJ (Rasband, W.S., ImageJ, U. S. National Institutes of Health, Bethesda, MD, http://rsb.info.nih.gov/ij/). Segmentation and labeling of signals and chromatin, 3D reconstructions, and animations were achieved using Amira software (Visage Imaging, Richmond, Australia).
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10.1371/journal.pgen.1000222 | The HLH-6 Transcription Factor Regulates C. elegans Pharyngeal Gland Development and Function | The Caenorhabditis elegans pharynx (or foregut) functions as a pump that draws in food (bacteria) from the environment. While the “organ identity factor” PHA-4 is critical for formation of the C. elegans pharynx as a whole, little is known about the specification of distinct cell types within the pharynx. Here, we use a combination of bioinformatics, molecular biology, and genetics to identify a helix-loop-helix transcription factor (HLH-6) as a critical regulator of pharyngeal gland development. HLH-6 is required for expression of a number of gland-specific genes, acting through a discrete cis-regulatory element named PGM1 (Pharyngeal Gland Motif 1). hlh-6 mutants exhibit a frequent loss of a subset of glands, while the remaining glands have impaired activity, indicating a role for hlh-6 in both gland development and function. Interestingly, hlh-6 mutants are also feeding defective, ascribing a biological function for the glands. Pharyngeal pumping in hlh-6 mutants is normal, but hlh-6 mutants lack expression of a class of mucin-related proteins that are normally secreted by pharyngeal glands and line the pharyngeal cuticle. An interesting possibility is that one function of pharyngeal glands is to secrete a pharyngeal lining that ensures efficient transport of food along the pharyngeal lumen.
| To make an organ, cells must be instructed to be part of a common structure yet must also be assigned specific roles or identities within that structure. For example, the stomach contains a variety of different kinds of cells, including muscles, nerves, and glands. This same complexity is seen even in relatively simple organs, like the pharynx (foregut) of the nematode C. elegans. The pharynx is a neuromuscular organ that pumps in food (bacteria) from the environment. This organ is relatively simple (containing only 80 cells) yet contains five distinct kinds of cells. How these different cells are specified is unclear but likely involves combinations of developmental regulators known as transcription factors. Here, we examine one cell type, the pharyngeal glands, and identify a key regulator of their development, the transcription factor HLH-6. Interestingly, HLH-6 is closely related to a mammalian transcription factor, Sgn1, which is involved in development of mammalian salivary glands, suggesting that C. elegans pharyngeal glands are evolutionarily related to mammalian salivary glands. A further connection is that the pharyngeal glands of C. elegans appear to be required for efficient feeding, possibly by secreting mucin-like proteins that ensure the smooth passage of food along the digestive tract.
| An important question in the study of organ development is how different cells are instructed to become part of a common structure and yet are also specified to have a distinct identity within that structure. This problem is well-illustrated in the pharynx of the nematode C. elegans. The pharynx is a small (80 cells) neuromuscular organ that pumps food (bacteria) in from the environment and initiates digestion (Figure 1A). It contains five different cell types (muscles, epithelia, neurons, marginal cells and glands) that are not restricted by their lineal origins. Recruitment of cells to the pharynx involves the “organ identity factor” PHA-4 (the C. elegans FoxA ortholog), which is required for cells to adopt a pharyngeal identity [1]–[3]. Available data supports a model in which PHA-4 directly regulates most or all genes that are expressed in the pharynx [4]. However, PHA-4 alone cannot be responsible for all aspects of organ development and must function with other factors to control the various sub-programs of pharyngeal organogenesis, such as specification of the distinct cell types. Aside from the involvement of PHA-4, little is known about the specification and development of any of the distinct pharyngeal cell types, though regulators of pharyngeal muscle development have been identified [5]–[8].
In this work, we chose to examine development of the pharyngeal glands, one of five cell types in the pharynx [9]. We chose this cell type for three reasons: first, nothing is known about regulation of gland gene expression nor about the specification of the glands (aside from the general involvement of PHA-4). Second, the function of the glands in C. elegans is poorly understood, although proposed roles include initiation of digestion, molting of the pharyngeal cuticle and resistance to pathogenic bacteria [9]–[12] and the digestive tract glands of parasitic nematodes are known to play crucial roles in host-parasite interactions (reviewed in [13]). Third, several genes with gland-specific expression have been identified, based on a combination of microarray and in situ hybridization data [14]–[16].
The pharyngeal glands are five cells in the posterior bulb of the pharynx with cellular projections that open into the pharyngeal lumen at discrete points along the length of the pharynx [9] (Figure 1A). The glands are further divided into two sub-groups, g1 and g2, based on their appearance in electron micrographs, though the significance of these sub-types is not known.
Given recent advances in computational biology and genomics, one powerful approach to exploring the issue of cell type specification is to identify a group of co-expressed (and presumably co-regulated) genes and from this group identify shared regulatory elements. These elements can then be used as tools for determining and characterizing the relevant trans-acting factor(s).
Here we identify both a cis-acting regulatory element (PGM1) and the corresponding trans-acting factor (HLH-6) that are together necessary and sufficient for pharyngeal gland-specific gene expression. We further show that elimination of HLH-6 results in the loss of a subset of pharyngeal glands, disrupted function of the remaining glands and defects in feeding that lead to partial starvation. Based on our analysis of hlh-6 mutants, we propose that one function of pharyngeal glands is to assist in the transport of food through the pharyngeal lumen. The glands secrete mucin-like proteins that line the pharyngeal lumen, which possibly lubricate the tract to ensure efficient passage of bacteria. These results not only demonstrate an important function of the pharyngeal glands, but also illustrate evolutionary conservation of foregut gland function, as both C. elegans pharyngeal glands and a component of the vertebrate foregut, the salivary glands, have roles in ensuring efficient transport of food through the front end of the digestive tract [17].
To investigate regulation of pharyngeal gland development, we first searched for cis-regulatory elements in the promoters of gland-expressed genes. Co-expressed genes often share common cis-acting regulatory elements, and identification of elements required for gland expression could lead to the identification of the corresponding trans-acting factors. We began with a list of fourteen confirmed and probable gland-specific genes, based on previous work [14] (Table 1). Twelve of these fourteen genes are predicted to encode proteins whose only recognizable features are a signal peptide and multiple copies of the ShK motif, a cysteine-rich sequence first described in metridin toxin from the sea anemone [18]. Proteins containing only ShK motifs appear to be gland-specific, while proteins containing ShK motifs in the presence of other recognizable domains (such as astacin in NAS-14 or tyrosinase in TYR-1) are not gland-specific [14]. We modified the original list of fourteen genes by excluding one gene (C14C6.5) that contains motifs in addition to ShK and also lacks supporting expression data. We also added one gene (T10B10.6) that encodes an ShK protein and is expressed solely in pharyngeal glands according to available in situ hybridization data [15],[16] (Table 1). We will refer to ShK-encoding genes with confirmed gland-specific expression as phat genes, for pharyngeal gland toxin-related.
To verify the quality of the list of fourteen genes, we constructed GFP or YFP reporters for four of the genes (two of which were previously reported; [14]) and found that all four were expressed specifically in pharyngeal glands (Figure 1 and Table 1). Of the four genes, three (B0507.1, phat-1, and phat-3) were expressed in all five glands (Figure 1C,E,I), while phat-5 was only expressed in the two anterior-most glands, the left and right g1A cells (g1AR and g1AL; Figure 1G). Previous reports have suggested that the g1AR and g1P cells are fused [9], yet we see no passage of phat-5-expressed YFP from g1AR to g1P, suggesting either that YFP is restricted from diffusing between these cells or that the two cells are not fused.
By searching the upstream 500 bp (relative to the ATG) of the fourteen gland genes using the Improbizer program [14] for shared sequence motifs, we identified one candidate gland-specific cis-acting element, which we named PGM1 (for Pharyngeal Gland Motif 1; Figure 1B). This size of promoter was justified because many of the gland genes have neighboring genes within 500 bp upstream, consistent with the observation that C. elegans promoters are generally small [19],[20]. PGM1 was the only motif identified by Improbizer that had a position weight matrix score higher than any of the motifs generated in control runs (See Materials and Methods), suggesting that it might be a functional regulatory element. In addition, PGM1 appeared to be enriched in the promoters of gland-expressed genes, as these promoters were four times more likely to contain significant occurrences of PGM1 (12/14 = 86%) than a control set of promoters from pharyngeal (but not gland-specific) genes (20/96 = 21%) (Table S1).
Analysis of PGM1 in the context of pharyngeal gland-specific promoters demonstrated that PGM1 was required for expression. Site-directed mutations in PGM1 sequences eliminated expression of phat-1 and phat-3 reporters, and greatly reduced expression of B0507.1 and phat-5 reporters (Figure 1C–J). The promoter of phat-5 has one other potential occurrence of PGM1 that could account for its residual activity (at −118 bp; Figure S1). The B0507.1 promoter has no other apparent PGM1 sequences, suggesting that the remainder of its expression is dependent on an as yet unidentified cis-regulatory motif. Together, these results suggested that PGM1 is necessary for the high level expression of a subset of genes in pharyngeal glands. We queried other gland-expressed genes to determine whether they also required PGM1 for expression. We analyzed the expression of two genes that were not part of our original data set, but that were reported to be expressed in glands: pqn-8 and lys-8 [21],[22]. The pqn-8 reporter was expressed exclusively in pharyngeal glands whereas the lys-8 reporter was expressed in pharyngeal glands and the intestine, as reported (Figure 1K, M). Mutation of a PGM1 sequence in the pqn-8 promoter completely abolished expression (Figure 1 K–L). The lys-8 promoter had three potential PGM1 sites at −180, −452 and −581 bp relative to the ATG (Figure S1). Two of these sequences (at −180 and −452) are not required for expression in pharyngeal glands (data not shown), while mutation of the third site (−581 bp) resulted in a loss of expression (Figure 1M–N).
Not all pharyngeal gland genes contain identifiable PGM1 sequences. In a search for additional pharyngeal gland genes based on in situ hybridization data [15],[16], we identified Y8A9A.2 as a probable gland-expressed gene that does not contain a PGM1 sequence in its promoter. Expression in pharyngeal glands was verified with a transcriptional Y8A9A.2::GFP reporter containing 2000 bp of upstream sequence (relative to the ATG) (Figure S2). This reporter does not contain any sequence that resembles a PGM1 site, suggesting that its expression is PGM1 independent or that there is an occurrence of PGM1 that is too divergent to be recognizable. Based on further analysis (below), Y8A9A.2::GFP expression is likely to be PGM1-independent.
Closer examination of PGM1 revealed that it contains an E-box (CAnnTG), the consensus binding site for basic helix-loop-helix (bHLH) transcription factors [23]. Mutations that specifically disrupt the E-box sequence eliminate PGM1 activity (Figure 1). However, the E-box is not sufficient for PGM1 activity: mutation of sequence flanking the E-box in the phat-1 reporter resulted in a significant loss of expression (data not shown), suggesting that an extended sequence is required for activity. Alignment of the functionally defined PGM1 sequences revealed an extended consensus of CAnvTGhdYMAAY (where V = A, C or G, H = A, C or T, D = A, G or T, M = A or C, and Y = C or T; Figure 2A). This extended consensus is present in all 12 of the 14 genes in our initial list that contained PGM1 (Figure 2A). The functionally defined consensus may represent either an extended binding preference for the relevant trans-acting factor or the juxtaposition of binding sites for two (or more) distinct factors.
Given that PGM1 is necessary for expression of many genes in pharyngeal glands, we next asked whether PGM1 was also sufficient for gland expression. Indeed, three tandem copies of the PGM1 sequence from phat-3 placed upstream of a “promoter-less” reporter (to make the “3×PGM1” construct) was sufficient to activate pharyngeal gland expression in 78% (31/40) of transgenic animals (Figure 2B–C). A fraction of these animals (7/31) also showed weak expression in the I3 pharyngeal neuron, a sister cell of the g1P gland [24]. These results indicate that PGM1 is a pharyngeal gland-specific enhancer element, and further suggests that PGM1 is a binding site for one or more transcription factors that function in pharyngeal glands.
Given the apparent extended consensus sequence for PGM1, we performed additional enhancer tests to determine what portions of PGM1 were required for its activity. We first tested a version of the 3×PGM1 plasmid in which all three copies of the E-box were changed from CAnnTG to AAnnTG. This construct (3×PGM1ΔE) showed no expression in transgenics, indicating (as above) that the E-box was required for PGM1 activity (Figure 2D). We next tested an enhancer in which sequence flanking the E-box was altered (3×PGM1Δflank) and found that this sequence was also required for PGM1 activity (Figure 2E), demonstrating that the E-box is necessary but not sufficient for PGM1 activity.
Since PGM1 activity is dependent on an E-box sequence, our search for the relevant trans-acting factor(s) began with bHLH proteins. bHLH proteins typically bind to DNA as heterodimers, composed of a ubiquitous “Class I” subunit and a tissue-restricted “Class II” partner (reviewed in [25]). In C. elegans, the sole Class I bHLH is encoded by hlh-2 [26], which is expressed in many cells throughout development, including the glands. To identify the relevant Class II bHLH, we examined data from microarray experiments that identified candidate pharynx-expressed genes [4],[27], including three Class II bHLHs: hlh-3, hlh-6 and hlh-8. Both hlh-3 and hlh-8 are expressed exclusively in non-pharyngeal tissue (in neurons and muscles, respectively; [28]) suggesting that they are false positives with respect to the microarray data and are thus unlikely to function through PGM1. At the time of our analysis, hlh-6 was uncharacterized and was therefore a candidate PGM1 trans-acting factor.
To examine the involvement of hlh-6 in PGM1 activity, we first determined the expression of a transcriptional reporter that included almost all intergenic sequence (1175 bp of 1190 bp) between hlh-6 and its nearest upstream neighbour, T15H9.2. We found that hlh-6::YFP was expressed strongly and specifically in the pharyngeal glands (98% of transgenics), with occasional (12%), weak expression in the pharyngeal neuron I3 (Figure 3). Expression was first detectable shortly after the terminal cell division that gives rise to pharyngeal glands (bean stage embryos) and persisted throughout the life cycle in all five pharyngeal glands. Because PGM1 and hlh-6 both appear to be active in pharyngeal glands and because PGM1 contains a bHLH binding site, we hypothesized that HLH-6 is the cognate trans-acting factor for PGM1.
We determined that HLH-6 is required for PGM1 activity by demonstrating that PGM1-dependent reporters were not expressed in hlh-6(tm299) mutants. The deletion mutant hlh-6(tm299) (generously provided by S. Mitani; [29]) is a probable null, as it removes 595 bp from hlh-6, including all but one nucleotide from the second intron, resulting in a frameshift (Figure 3A). The mutation is homozygous viable (see Materials and Methods), which allowed us to examine gland reporter expression in these mutants. We found that expression of 6/6 gland reporters (phat-1, phat-3, phat-5, B0507.1, pqn-8 and lys-8) was significantly reduced in hlh-6 animals (Figure 4A,C; Figure S2). For example, only 26% of hlh-6 mutants had visible phat-1::YFP expression (n = 65), and this expression was significantly weaker than the expression seen in 100% of wild type animals. Four of the other gland reporters showed a similar loss of expression in hlh-6 mutants. Expression of the B0507.1 reporter was less affected than the others, consistent with it being only partially PGM1 dependent. Likewise, expression of Y8A9A.2::GFP, which lacks an identifiable PGM1 sequence, was unaffected in hlh-6 mutants (Figure S2). There is thus a perfect correlation between PGM1-dependent gene expression and hlh-6-dependent gene expression, implying that HLH-6 is acting directly on the reporters rather than earlier in the pathway of gland specification.
To confirm that loss of reporter expression was due to the hlh-6 mutation, we performed transgenic rescue with either genomic hlh-6 or an hlh-6 “minigene”. The genomic fragment contains hlh-6 and 2030 bp upstream of the ATG (including 840 bp of the upstream neighbour, T15H9.2) and 60 bp downstream of the predicted stop codon. The minigene construct consists of 568 bp of promoter sequence fused to hlh-6 cDNA containing a synthetic intron (Figure 3). The 568 bp promoter fragment is only active in pharyngeal glands [30], so the hlh-6 minigene is expressed only in pharyngeal glands. Both genomic and minigene versions of hlh-6 rescued phat-1 reporter expression in hlh-6 mutants (Figure 4C).
Together, the above three lines of evidence indicate that the bHLH transcription factor encoded by hlh-6 functions through PGM1. First, PGM1 activity depends on an E-box, the canonical binding site for bHLH transcription factors. Second, the expression patterns of hlh-6 and the PGM1 enhancer are identical. Third, hlh-6 is required for PGM1-dependent reporter activity.
Given that hlh-6 was required for expression of PGM1-dependent genes, we next examined hlh-6 mutants to determine the effect on pharyngeal gland development using our hlh-6 reporter. Expression of hlh-6 is not critically dependent on hlh-6, though hlh-6 shows weak autoactivation [30]. An integrated hlh-6::YFP reporter is expressed in 100% of hlh-6 mutants (Figure 4C). However, in 84% of hlh-6 mutants (n = 90), expression was observed in only three gland cells, rather than the expected five (Figure 4B). This finding was verified with a nuclear-localized fluorescent reporter (data not shown). Based on the position and morphology of expressing cells, it appeared that the three g1 glands (g1AR, g1AL and g1P) were present, while the two g2 cells were either missing or failed to express all gland reporters (hlh-6::YFP, phat-1::YFP, B0507.1::GFP, et al.).
The apparent absence of g2 glands in hlh-6 mutants could be explained by three possibilities: first, the g2 glands may undergo apoptosis; second, the cells may be mis-specified and adopt an alternate fate; third, the cells may persist as undifferentiated cells. The sister cells of the g2 glands undergo apoptosis in normal development [24] and so we tested whether blocking apoptosis with a mutation in ced-3 would restore g2 glands. Strong loss-of-function mutations in ced-3 result in the survival of all cells that normally undergo programmed cell death [31],[32]. However, only 9% of hlh-6; ced-3 double mutants (n = 32) expressed the hlh-6::YFP reporter in g2 cells, comparable to the expression in hlh-6 mutants, indicating that g2 glands are not restored by preventing apoptosis.
To address the possibility that g2 glands adopt an alternate cell fate, we performed nuclear counts in the back half of the posterior pharyngeal bulb where the g2 cells are normally located using a pha-4 reporter, which is expressed in all pharyngeal nuclei except for some pharyngeal neurons [3]. There are 11 pharyngeal cells in this region (four muscles, three marginal cells, three glands and one neuron), 10–11 of which express pha-4 post-embryonically (expression in the pharyngeal neuron in the posterior bulb is variable). We expected that hlh-6 mutants would either have a wild type number of PHA-4-expressing cells or an average loss of ∼1.6 such cells (because ∼80% of hlh-6 mutants do not have visible g2 cells). There was a significant decrease in pha-4::GFP::HIS2B expressing cells between wild type and hlh-6 mutants (9.1 vs. 7.8, respectively, p<0.05), suggesting that either the presumptive g2 cells do not express pha-4::GFP::HIS2B or the cells are not present. Consistent with these cells not having a pharyngeal identity, we did not observe an increase in the numbers of other pharyngeal cell types, demonstrating that the presumptive g2 cells have not adopted an alternate pharyngeal identity (Figure S3). In the course of these nuclear counts, we also observed that the numbers of other types of pharyngeal nuclei were not affected in hlh-6 mutants. In particular, pm6 cells, which are lineally-related to the g2 glands, were present and expressed the correct markers (data not shown). This suggests that the hlh-6 mutation specifically affects glands and does not act in the differentiation of other pharyngeal cell types, as expected given the expression pattern of hlh-6.
The failure of the presumptive g2 cells to express any tested pharyngeal reporters implies that these cells were not present in hlh-6 mutants. To explore this possibility, we followed the lineages that give rise to g2 in hlh-6 mutant animals. In eight cases (73%), the immediate precursor to the g2 cell (MSnapapa) failed to undergo its terminal division, but remained in its usual position within the embryo (Figure 5). In one case, the grandmother of g2 failed to divide. Such a lineage defect would prevent formation of one of the pm6 muscles, though we do not see a loss of pm6 cells in hlh-6 mutants. In the remaining two cases (18%), the g2 precursor underwent its normal division. Thus, in 82% of cases, the g2 cell failed to be generated, consistent with our observation that 84% of hlh-6 mutants do not express hlh-6 in g2 cells. Interestingly, PHA-4 expression is lost in the arrested g2 precursors, based on our counts of pha-4::GFP::HIS2B nuclei, yet PHA-4 must be normally expressed earlier in this lineage (i.e., in the g2 grandmother MSnapap), as no other pharyngeal cells (e.g., pm6 cells, which are cousins of the g2s) were missing. Formally, this result indicates that hlh-6 is required for maintenance of pha-4 expression in g2 cells, though the nature of this regulation is unclear.
In addition to a loss of gland gene expression and defects in gland development, hlh-6 animals display a variety of characteristics that indicate a starvation phenotype: partially penetrant larval arrest, slow growth, smaller body size and decreased brood size among those surviving to adulthood. On average, 32% (n = 105) of hlh-6 mutants arrest as L1 larvae. The anterior pharyngeal lumen of arrested larvae is stuffed with bacteria (Figure 6A–B), indicating a failure of these animals to properly transport food along the pharyngeal lumen. Animals that develop beyond the L1 stage also exhibit signs indicative of starvation. First, hlh-6 mutants are consistently smaller than wild-type worms of the same chronological age (Figure 6C). Adult hlh-6 mutants are roughly half the length of wild-type adults (635±210 µm vs. 1202±124 µm, n = 23 and 14 respectively). hlh-6 mutants also grow more slowly than control strains, taking more than twice as long to reach sexual maturity compared to controls (6.6±1.7 days vs. 3.1±0.4 days after embryos were collected, n = 22 and 22; Figure 6D). As adults, hlh-6 mutants have dramatically smaller broods, laying an average of 11.9±15.4 eggs throughout their lifetime (n = 21) compared to the congenic control rol-6 unc-4 strain (116.5±25.7 eggs, n = 22; Figure 6E). All aspects of the hlh-6 mutant phenotype were rescued by either the hlh-6 genomic fragment or the “minigene” constructs described previously (data not shown and Figure 6C–E), indicating that the phenotypes result from a loss of hlh-6 activity in the pharyngeal glands. The larval arrest, small size, slow growth and low brood size are all characteristic of starvation and are observed in other mutants that are feeding defective, such as the eat mutants and animals with abnormal pharynx morphology [33],[34].
To further verify that hlh-6 mutants are starved, we stained animals with the lipophilic dye Nile Red, which detects intestinal fat stores [35]. We consistently observed increased fat stores in hlh-6 mutants compared to control strains (Figure 7A,C). Increased fat stores are observed with other feeding defective strains (e.g., tph-1; [35],[36]), reflecting a metabolic response to decreased nutrient availability or uptake. Other starvation mutants, however, such as pha-2 and pha-3, have more severe feeding defects and exhibit decreased fat stores, possibly because food uptake is too low to provide nutrients to store as fat [34]. These results suggest that hlh-6 mutants may not be as severely starved as pha-2 and pha-3 mutants. However, because Nile Red staining does not always correlate with fat levels, further investigation is required to verify this interpretation [37]. Increased fat storage in response to starvation requires the activity of the transcription factor DAF-16/FoxO (reviewed in [38]). Accordingly, daf-16(RNAi) suppressed the increased fat storage of hlh-6 mutants, indicating that the starvation response of hlh-6 animals acts through the canonical DAF-16-dependent pathway (Figure 7B,D). Control feeding with GFP(RNAi) did not affect Nile Red staining.
The starvation of hlh-6 mutants can be rescued by providing an alternate food source. C. elegans are usually grown by feeding with the E. coli strain OP50, though feeding with the strain HB101 can rescue the starvation phenotype of some eat mutants, which appears to be easier for C. elegans to eat [39]. We found that the mutants grown on HB101 were not starved, exhibiting wild type growth rates and a suppression of larval arrest (Figure 6D and data not shown). Two factors that affect the ability of different food sources to rescue eat mutants are bacterial cell size and the relative “stickiness” of the cells [39]. HB101 and OP50 cells are the same size (2.8±0.7 µm and 3.0±0.4 µm, respectively), but OP50 are more adhesive compared to HB101 [39].
Mutations that affect feeding generally do so by affecting the rhythmic contractions of pharyngeal muscle, resulting in decreased or arrhythmic pharyngeal pumping and therefore “inefficient” feeding. Such mutations affect either pharyngeal muscle morphology and/or function (e.g. pha-2, eat-2; [33],[40]) or the neurons that innervate the muscles (e.g. eat-4 and ceh-28; [41],[42]). hlh-6 differs from other genes involved in feeding as hlh-6 functions in pharyngeal glands. Consistent with hlh-6 not acting in either pharyngeal muscle or neurons, we find that hlh-6 mutants had normal pharyngeal pumping with respect to both rate and rhythm of the muscle. Control animals (rol-6 unc-4) had an average of 169±39 pumps per minute (n = 20) and hlh-6 mutants (rol-6 hlh-6 unc-4) had an average of 156±42 pumps per minute (n = 19). Likewise, peristaltic contractions of the pharyngeal isthmus were also normal, with both control and mutant strains showing an average of one isthmus contraction per four pharyngeal pumps. These findings indicate that hlh-6 mutants are defective in some other aspect of food transport for which the glands are required.
Because some gland genes are expressed independently of hlh-6, hlh-6 mutants might be only partially impaired with respect to gland activity. To examine the effect of complete loss of pharyngeal glands, we genetically ablated the glands using an hlh-6::egl-1 transgene, which activates expression of the pro-apoptotic gene egl-1 in pharyngeal glands [43]. Induction of egl-1 is sufficient to induce apoptosis in other cells, such as pharyngeal neurons [44]. We assayed the presence or absence of glands using an integrated phat-1::YFP reporter and followed the presence of the hlh-6::egl-1 transgene with an intestine-specific mTomato marker [45]. Transgenic animals that lacked pharyngeal glands were viable but showed delayed growth and development, with 39% (n = 23) larval arrest, comparable to hlh-6 mutants (data not shown). These results suggest that the pharyngeal glands of C. elegans are primarily involved in efficient feeding and that in the absence of hlh-6, glands are entirely nonfunctional with respect to growth and fecundity.
By analogy to foregut glands in other organisms, we postulated that pharyngeal glands could function in feeding by one of three ways: first, glands may secrete digestive enzymes required for efficient feeding; second, glands may produce secretions that coat food to ensure its passage along the lumen; third, glands may produce secretions that line the lumen and prevent adhesion of food. The first possibility, that the glands produce digestive enzymes, was suggested in part by the fact that the gland-expressed gene lys-8 is predicted to encode a lysozyme [22]. However, the ability of HB101 bacteria to rescue the starvation phenotype of hlh-6 animals suggests that glands are not required for digestion of food.
The other two possibilities, in which the glands lubricate the pharyngeal lumen, were suggested by the ability of a less sticky food source (HB101) to rescue hlh-6 starvation. As noted, the majority of known gland-expressed genes are predicted to encode secreted proteins that contain multiple copies of the ShK domain. Interestingly, this family of proteins is similar to a group of secreted mucins from the parasitic nematode Toxocara canis [46]–[48]. The T. canis mucins are defined by multiple copies of the ShK domain (sometimes referred to as the SXC domain), a signal sequence and stretches of Ser/Thr-rich (probable sites of glycosylation). We find that, like the T. canis proteins, the PHAT proteins contain stretches of Ser/Thr-rich sequence between their ShK domains (Figure S4) and many of these Ser/Thr sites are predicted to be sites for O-linked glycosylation [49]. The PHAT proteins may therefore function as mucin-like proteins.
We found that a representative PHAT protein, PHAT-5, lines the pharyngeal lumen, consistent with the protein having a mucin-like function. We examined the subcellular location of PHAT-5 using a phat-5::mCherry fusion expressed under the control of the hlh-6 promoter. The PHAT-5::MCHERRY fusion protein was visible in discrete puncta throughout the cell bodies of the glands, as well as along their extensions (Figure 8A). In live animals, these puncta could be seen to traffic along the extensions, suggesting that the protein had been packaged into secretory vesicles. More importantly, the PHAT-5:: MCHERRY fusion protein was found along the lumen of the pharynx, indicating that the protein had been secreted from the glands (Figure 8A–C). The fusion protein had a discrete anterior boundary, extending as far as the cheilostom groove in the buccal cavity (Figure 8B,C), the boundary between the epidermal cuticle and the pharyngeal cuticle [50], suggesting that PHAT-5 is specifically associated with pharyngeal cuticle. In addition, PHAT-5 fusion protein remained associated with shed pharyngeal cuticle, arguing that the protein forms part of the lining of the pharyngeal lumen (Figure S6). No protein was seen to co-localize with bacteria in the pharynx lumen, suggesting that PHAT-5 does not coat food particles.
To investigate whether the glands of hlh-6 mutants are functionally impaired, we examined whether PHAT-5::MCHERRY could be secreted by the glands of hlh-6 mutants. phat gene expression is absent from hlh-6 animals, so we expressed the PHAT-5 fusion under the control of the hlh-6 promoter, which remains active in hlh-6 mutants. The hlh-6::phat-5::mCherry construct was expressed in pharyngeal glands, but no protein was seen at the pharyngeal lumen, likely reflecting a functional defect in the hlh-6 glands (Figure 8D–F). No rescue of the hlh-6 phenotype by hlh-6::phat-5::mCherry was observed. Punctate signal was observed in the gland ducts and in live animals these puncta appeared to migrate along the ducts as in wild type, suggesting that vesicles were still present and capable of being transported within the glands. The hlh-6 mutants are therefore defective either in secretion of the PHAT-5 protein or in retention of this protein at the pharyngeal lumen. To distinguish between these possibilities we expressed PHAT-5::MCHERRY in pharyngeal muscles (using the myo-2 promoter; [51]) to investigate the localization of PHAT-5 independent of gland function. In wild type animals, pharyngeal muscle could secrete PHAT-5::MCHERRY. Signal was seen lining the pharyngeal cuticle in addition to puncta throughout the muscles (Figure 8G–I). In hlh-6 mutants, some signal was visible on the luminal surface, but we also observed significant signal in the intestinal lumen (though not associated with cell surfaces), which was not observed in wild type animals (Figure 8J–M). This result suggests that while PHAT-5::MCHERRY can associate with the pharyngeal cuticle in hlh-6 animals, this association is less stable, resulting in the movement of the fusion protein along the digestive tract. This observation is consistent with the hypothesis that the pharyngeal lining is defective in hlh-6 mutants, likely due to the absence of other gland-secreted proteins, including the other PHAT proteins. No rescue of the hlh-6 phenotype by myo-2::phat-5::mCherry was observed.
Based on our findings, we propose that HLH-6 regulates a battery of pharyngeal gland-expressed genes in C. elegans and is required for both differentiation and function of the glands. While some glands are present in hlh-6 mutants, they are non-functional, as the removal of pharyngeal glands phenocopies the loss of hlh-6. The pharyngeal glands are essential for efficient feeding and appear to play a role in facilitating the transport of bacteria along the pharyngeal lumen, though they are not involved in regulation of pharyngeal pumping. These findings illustrate a previously unknown role for the pharyngeal glands in efficient feeding and demonstrate that aspects of both foregut gland development and function are evolutionarily conserved.
We identified both a cis-regulatory element and trans-acting factor that are required for expression in pharyngeal glands, though it is presently not known whether the two components interact directly. There are two lines of evidence that support the hypothesis that HLH-6 interacts directly with PGM1. First, the PGM1 motif contains a functional E-box (Figure 1), and bHLH proteins (like HLH-6) bind to E-boxes. Second, PGM1 activity requires HLH-6 (Figure 4). A formal possibility is that HLH-6 acts upon a second bHLH that in turn binds to PGM1, as seen with the cascades of neurogenic and myogenic bHLH factors [52],[53]. However, no other C. elegans class II bHLH is known to be expressed in pharyngeal glands, though some hlh genes remain uncharacterized.
As with other bHLH proteins, HLH-6 probably functions as a dimer, most likely with the broadly-expressed Class I protein HLH-2 [26]. However, HLH-6 appears to require an additional non-bHLH factor that functions through the YMAAY sequence found in PGM1. Three lines of evidence indicate that HLH-6 requires additional factor(s) to activate gland gene expression. First, the YMAAY sequence is required for PGM1 activity, but is unlikely to represent an extended binding sequence for HLH-6, as solved bHLH-DNA structures indicate contact of bHLH proteins up to but not beyond three bases outside of the E-box [54],[55], while the YMAAY sequence extends beyond this limit. Second, ectopic expression of HLH-6 (±HLH-2) is not sufficient to activate ectopic expression of a gland-expressed marker (data not shown), suggesting that an additional factor is required to induce target gene expression. Third, we tested whether HLH-6 (±HLH-2) could bind to PGM1 in vitro using electrophoretic mobility shift assays (EMSA), but were unable to detect an interaction (Text S1 and Figure S5), though we are able to detect interactions between other bHLH dimers and E-box-containing sequences. Thus, the YMAAY sequence likely represents a binding site for an additional factor. This factor may be limiting with respect to activation of gland genes in vivo and binding to PGM1 in vitro. Precedence for such a model comes from studies of mammalian Mash1, which must form a complex with the POU domain transcription factor Brn2 in order to bind to specific target sequences [56]. Similarly, the pancreatic determinant PTF1 is a complex of the bHLH Ptf1a with a ubiquitous Class I bHLH and the mammalian Su(H) ortholog RBP-J [57]; the PTF1 complex binds to a composite DNA sequence consisting of an E-box and a Su(H) site [58].
Involvement of an additional factor may explain the specificity of PGM1 activity. A general question in transcription factor biology is how specificity of response is achieved. For example, the E-box of PGM1 could be recognized by any of the numerous bHLH factors expressed in the various tissues of C. elegans, yet it is only activated in pharyngeal glands (Figure 2). One solution to this problem is that related transcription factors distinguish between different binding sites based on subtle differences within the core DNA sequence. For example, different MyoD-containing bHLH dimers have well-characterized binding site preferences [59],[60], as do the C. elegans bHLH factor Twist/HLH-8 [28],[61] and the Drosophila bHLHs atonal and scute [62]. However, given that binding of bHLH factors to E-boxes may be somewhat promiscuous in vitro, an additional approach to ensure specific response is the involvement of spatially restricted co-factors. Tertiary interactions between bHLH dimers and non-bHLH co-factors are known to affect dimerization and activity [63],[64]. In our case, a cofactor may recognize the YMAAY portion of PGM1 and be required for transcriptional activation of target genes.
The FoxA transcription factor PHA-4 is required for specification of pharyngeal cells, including glands [1]. One question, then, is the regulatory relationship between PHA-4 and the HLH-6 gene battery. We have shown in other work that HLH-6 is a probable direct target of PHA-4, so PGM1-dependent genes are at least indirectly regulated by PHA-4 [30]. However, previous work suggested that most or all pharyngeal genes are directly regulated by PHA-4 [4]. Consistent with this idea, we find candidate PHA-4 binding sites in the regulatory regions of all seven gland genes analyzed in addition to the PGM1 motif (Figure S1). Furthermore, a deletion of the phat-1 promoter that removes a predicted PHA-4 binding site drastically reduces but does not eliminate reporter expression and does not affect the pattern of expression (data not shown). Similar results are seen with the PHA-4 sites in other promoters (e.g. myo-2; [4]). PHA-4 may regulate gland-specific gene expression both directly and indirectly, consistent with the proposed model of PHA-4 action. This type of feed-forward transcriptional regulation is also observed in other developmental pathways, such as the myogenic cascade of bHLH transcription factors [53].
hlh-6 mutants have multiple defects in gland differentiation, yet still produce g1 (and occasionally g2) gland-like cells and express at least some gland-specific markers (such as B0507.1 and Y8A9A.2; Figure 4, Figure S2). Therefore, different factors activate expression of different gene batteries in pharyngeal glands, as occurs in body wall muscles and in the excretory cell of C. elegans [65],[66]. It will be interesting to identify more HLH-6-independent genes to determine whether the function of that gene battery is distinct from the role of the HLH-6-dependent gene battery; that is, are the different functions of the cells parsed out in an interpretable manner?
bPrevious work suggested a role for pharyngeal glands in feeding, based on analysis of the kel-1 gene [11]. KEL-1 is detected in pharyngeal glands and kel-1 mutants arrest as early larvae and fail to reach adulthood, in contrast to hlh-6 mutants and gland-ablated animals which are starved but viable. One possible explanation for the difference in phenotypes is that kel-1 function is not limited to pharyngeal glands. In fact, available in situ hybridization data for kel-1 indicates that the message is broadly expressed throughout embryogenesis, with no apparent enrichment in glands [15],[16]. Thus, loss of kel-1 likely affects cells in addition to the pharyngeal glands.
The defects in hlh-6 mutants are consistent with HLH-6 playing a role in differentiation of gland cells rather than their specification. The g1 cells still have several “gland-like” features in hlh-6 mutants: the cell bodies are located in the terminal bulb, they express some gland-specific markers and the cells send projections to the appropriate positions within the pharynx. However, these cells are not fully functional as ablation of all the gland cells is no more severe than loss of hlh-6 alone, indicating that the residual glands in hlh-6 mutants contribute little, if any, wild type function. The g2 gland defect is more pronounced as these cells fail to differentiate in hlh-6 mutants, apparently arresting as precursor cells with an uncertain identity. These cells lose expression of pha-4::GFP::HIS2B, suggesting that they fail to retain pharyngeal identity. A similar loss of pha-4 reporter expression in seen in tbx-2 mutants, which fail to produce anterior pharyngeal muscle [8]. An interesting possibility is that successful differentiation of pharyngeal cells (into specific cell types) is required for maintenance of pha-4 expression and pharyngeal identity. A similar loss of cell identity may occur in unc-120; hlh-1; hnd-1 triple mutants, in which presumptive body muscles are found in their normal position within the embryo yet do not adopt a muscle identity nor do they adopt an alternate (non-muscle) fate [66]. In contrast, C. elegans neurons that lose specific sub-type identities retain their neuronal identity [67].
Although we do not detect hlh-6 expression in the g2 precursors of wild-type animals, it must be expressed at this time, as hlh-6 activity is required for division of the precursors and g2 development can be rescued by transgenic hlh-6(+). The relevant expression is likely to be too weak to be detectable.
The pharyngeal glands are required for efficient feeding (Figures 6 and 7). A compelling model is that the glands secrete material that coats the pharyngeal lumen to prevent food from adhering to the pharyngeal cuticle. Support for this model comes from three lines of evidence. First, hlh-6 mutants are feeding defective yet have normal pharyngeal pumping. Second, the starvation phenotype of hlh-6 mutants is rescued by feeding with a different (less sticky) food source. Third, the lining of the pharynx differs in hlh-6 mutants as shown by the inability of the secreted PHAT-5::MCHERRY protein to adhere tightly to the pharyngeal lumen as it does in wild type (Figure 8). Many of the HLH-6-dependent gland genes encode mucin-like proteins, at least one of which (PHAT-5) lines the pharyngeal cuticle. Although we did not demonstrate that PHAT's are responsible for lubrication, we propose a speculative model in which gland secretion of the mucin-related PHAT proteins act to lubricate the pharyngeal lining, comparable to some aspects of mucin function in other organisms [17].
The positioning of the gland duct openings at discrete points along the length of the pharyngeal lumen could also be explained by a requirement for thorough lining of the lumen, as mosaic animals that express a PHAT-5 fusion in only a subset of glands show incomplete coverage of the pharyngeal lining. For example, when a PHAT-5::YFP fusion is expressed in only the g1P cell, which opens at the anterior end of the pharynx, fluorescent signal is detectable at high levels at the anterior end of the pharynx but decreases posteriorly, becoming undetectable before the terminal bulb (Figure S6). Likewise, expression in only the g1A cells results in signal near the middle of the pharynx that fades towards the anterior and posterior extremes. Thus, secretion from all five glands may be required for complete lining of the pharyngeal lumen.
An interesting finding is that both the regulation (by bHLH factors) and function (feeding) of foregut glands appears to be evolutionarily conserved. The closest mammalian homolog of HLH-6 is Sgn1, a bHLH required for normal salivary gland development in the mammalian foregut [68]. In addition, development of salivary glands in the Drosophila foregut depends on the combined activity of forkhead (the ortholog of PHA-4) and sage (a salivary gland expressed bHLH) [69], although sage is not the closest homolog to hlh-6. Database searches have found other genes encoding proteins with high similarity to HLH-6, including the Ash2 gene, which is expressed in the digestive tract glands of the jellyfish P. carnea [70], and related sequences from the genomes of parasitic nematodes. Gland function in parasitic nematodes is critical for parasitism, suggesting a conserved function of foregut glands in the processing or passage of food [46],[71]. Targeting gland development or function may offer a new strategy for controlling these parasitic species.
Standard nematode handling conditions were used [72]. The hlh-6(tm299) II allele was kindly provided by S. Mitani [29]. Presence of the tm299 deletion was followed by genomic PCR with oligonucleotides oGD65 (5′ CATAACCGGTATCATAGCATTATTACTCGAAT 3′) and oGD97 (5′ TTATACATTTGAGAATGGGGTCTACTCGAC 3′). The original hlh-6(tm299)-bearing chromosome contains a linked larval lethal mutation (let-x) to the left of hlh-6. hlh-6 was outcrossed five times and the arms of LG II were replaced by selecting appropriate recombinants tested for the presence of hlh-6(tm299) by PCR. First, we placed unc-4 in cis with let-x hlh-6 and then selected Rol non-Daf recombinants from let hlh-6 unc-4/rol-6(e187) daf-19(m86) to obtain +rol-6 hlh-6 unc-4. Because this strain is Rol Unc, in all subsequent functional assays a rol-6 unc-4 strain was used as a control.
All transcriptional reporters were made by PCR amplification of promoter fragments from genomic DNA, followed by cloning into either the pPD95.77 or pPD95.77-YFP vectors (gifts from A. Fire), which contain the coding sequences for gfp and yfp, respectively. Mutations in occurrences of PGM1 in the promoters were subsequently made by PCR-based site-directed mutagenesis [73].
Enhancer constructs were built using synthetic oligonucleotides that were cloned into pPD95.77. Use of this vector for enhancer assays was established previously [67].
The 750 base pair phat-5 cDNA was amplified from a cDNA library provided by R. Barstead using primers oGD570 (5′ aaggtacccATGGTGAGCAAGGGCGAG 3′) and oGD571 (5′ ccgaattcTTACTTGTACAGCTCGTCCATGCC 3′). The product was digested with enzymes KpnI and EcoRI (restriction sites in the oligonucleotides are underlined), and cloned in-frame to YFP or mCherry [45]. The phat-5::YFP fusion was placed under the control of the lys-8 promoter, while the hlh-6 minimal promoter was sub-cloned from min-hlh-6::YFP [30] in front of the phat-5::mCherry fusion to create the hlh-6::phat-5::mCherry construct. The myo-2::phat-5::mCherry plasmid was cloned using the myo-2 promoter from plasmid pSEM474 [4].All clones were verified by restriction digests and sequencing. Details of plasmids and cloning strategies are available upon request.
For rescue of hlh-6 mutants, we subcloned a 3398 bp PstI-XbaI fragment of fosmid WRM066cG05 that contains hlh-6(+) into pBlueScriptII(SK+). The “minigene” construct was created by amplification and subcloning of the hlh-6 cDNA from a library provided by R. Barstead. The cDNA was ligated to a 568 bp fragment of the hlh-6 promoter that is active in pharyngeal glands [30]. A synthetic intron was cloned in to a blunt-ended KpnI site of the hlh-6 cDNA using annealed primers oGD198 (5′ GgtaagtttaaacagatatctactaactaaccctgattatttaaattttcagTAC 3′; intron sequence in lower case) and oGD199 (5′ GTActgaaaatttaaataatcagggttagttagtagatatctgtttaaacttacC 3′).
The hlh-6::egl-1 plasmid was constructing by PCR amplification of egl-1 from genomic N2 DNA using primers oGD531 (5′ caccaccggtatgctggtaagtctagaaattatt 3′) and oGD532 (5′ ttcacggccgcacatctggtgttgcaggc 3′). The amplified product was digested with AgeI and EagI and cloned downstream of a 747 bp fragment of the hlh-6 promoter. Design of this construct was based on previous work [44].
Reporter DNA was injected at 5–30 ng/µL together with 50 ng/µL pRF4 (rol-6(su1006)), which confers a dominant Roller phenotype [74], and 20–45 ng/µL pBS II (SK+) to a total DNA concentration of 100 ng/µL. For some analyses, we included 20 ng/µL of an intestine specific reporter (elt-2::GFP::LacZ, ges-1::mRFP::His2B or elt-2::mTomato::HIS2B) that served as an independent marker for transgenic arrays when scoring expression [75]. For injections with enhancer constructs, 50 ng/µL of the construct was injected with 50 ng/µL pRF4 into N2 animals. For hlh-6::phat-5::mCherry, 40 ng/µL was injected while myo-2::phat-5::mCherry was injected at 5 ng/µL, because the myo-2 promoter is very strong and can be toxic at higher concentrations. Except where noted, a minimum of two independent transgenic lines were analyzed for each construct.
The integrated hlh-6 reporter ivIs10 [hlh-6::YFP ges-1::mRFP::His2B rol-6(su1006)] and integrated phat-1::YFP reporter ivIs12 [phat-1::YFP elt-2::GFP::LacZ rol-6(su10060] were generated by gamma-ray-induced integration of extrachromosomal arrays carried in a wild-type background [76]. The pha-4::GFP::HIS2B reporter was provided by Dr. Susan Mango as an integrated array (SM496), which was crossed into the GD211 strain.
To induce cell death in glands, the hlh-6::egl-1 construct was injected at 20 ng/µL with 30 ng/µL elt-2::mTomato::HIS2B and 50 ng/µL pBS II (SK+) into a strain carrying an integrated phat-1::YFP reporter (GD139 ivIs12; see above). Doubly transgenic animals were identified based on the Rol phenotype of GD139 (100%) and the presence of red intestinal fluorescence. Animals lacking visible YFP expression (indicating a loss of glands) were then analyzed for survival and growth.
For rescue of hlh-6, both the genomic fragment and the minigene were injected at 50 ng/µL with 30 ng/µL of phat-1::YFP and 20 ng/µL elt-2::GFP::LacZ into N2 animals. These arrays were subsequently crossed into GD211.
We used the Improbizer program [14]; available at http://www.soe.ucsc.edu/̃kent/improbizer/) to search for possible gland-specific regulatory elements. We initially searched for motifs occurring once per sequence, using the input sequence as background. The motif presented here (PGM1) was obtained with a search for a motif size of six. Searches for motifs of larger sizes (8–20 bases) recurrently found variations of PGM1. Other parameters of Improbizer were used at their default settings. We also performed control runs in which the input gene sequence was randomized and searched and found that only PGM1 obtained an Improbizer score greater than the scores of ten or more control runs.
To find probable occurrences of PGM1 in other promoters (as in pqn-8 and lys-8), we used the Improbizer sister program, Motif Matcher (www. http://www.cse.ucsc.edu/̃kent/improbizer/motifMatcher.html), which searches for top-scoring matches to the Improbizer-generated position weight matrix.
Lineages of embryos from hlh-6/mC6g heterozygotes or hlh-6 homozygotes were examined using a 4D-microscope [77]. The genotype of hlh-6/mC6g progeny was determined after recording by the presence or absence of GFP, which marks the mC6g balancer chromosome. The identities of cells was determined by lineaging backwards using the data base SIMI°Biocell.
All animals were grown on OP50 except for the OP50-GFP bacteria used to visualize the stuffed pharynx and the HB101 bacteria used to rescue the hlh-6 mutant; all bacterial strains were provided by the Caenorhabditis Genetics Center. OP50-GFP was grown on NGM plates containing 100 µg/mL ampicillin and HB101 was grown on NGM plates containing 200 µg/mL streptomycin.
For measurement of body length, embryos laid over a one hour period by gravid adults were collected from and grown at 25°. Larvae were removed from plates and transferred to slides at the indicated times. Pictures were taken at 400× magnification and the lengths of the animals were measured using ImageJ (http://rsb.info.nih.gov/ij/) as described previously [34]. Greater than twenty animals were analyzed for each genotype at each time-point. For measuring time to reach adulthood, single eggs were placed on plates and followed at 24 hour intervals until the animal reached adulthood. For brood sizes the number of eggs laid was counted throughout the lifetime of each animal.
The intestinal fat stores of the hlh-6 mutants were measured using the dye Nile Red (Sigma N-3013) as described [35]. Briefly, L4 animals of the indicated genotype were transferred to plates with 0.05 µg/mL Nile Red and allowed to grow for 24 hours before being scored using conventional fluorescence microscopy. At least fifteen animals were observed for each genotype and one animal that represents the average level of fluorescence per each genotype is shown. daf-16(RNAi) was performed by “feeding RNAi” using an available daf-16 dsRNA-expressing bacterial strain [78],[79]. Adults were placed on the RNAi plates and their progeny were transferred to RNAi-Nile Red plates for scoring.
For pharyngeal pumping assays, L4 animals were transferred to fresh plates and grown for 24 hours before scoring. Pumping was counted under a dissecting microscope at 100× magnification.
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10.1371/journal.pgen.1002335 | Mutations in a Guanylate Cyclase GCY-35/GCY-36 Modify Bardet-Biedl Syndrome–Associated Phenotypes in Caenorhabditis elegans | Ciliopathies are pleiotropic and genetically heterogeneous disorders caused by defective development and function of the primary cilium. Bardet-Biedl syndrome (BBS) proteins localize to the base of cilia and undergo intraflagellar transport, and the loss of their functions leads to a multisystemic ciliopathy. Here we report the identification of mutations in guanylate cyclases (GCYs) as modifiers of Caenorhabditis elegans bbs endophenotypes. The loss of GCY-35 or GCY-36 results in suppression of the small body size, developmental delay, and exploration defects exhibited by multiple bbs mutants. Moreover, an effector of cGMP signalling, a cGMP-dependent protein kinase, EGL-4, also modifies bbs mutant defects. We propose that a misregulation of cGMP signalling, which underlies developmental and some behavioural defects of C. elegans bbs mutants, may also contribute to some BBS features in other organisms.
| Bardet-Biedl syndrome (BBS) is a genetically heterogeneous, multisystemic disorder. Defects to the cilium, an evolutionarily conserved organelle, cause ciliopathies, a growing class of diseases that includes BBS. BBS proteins are involved in the vesicular transport of proteins to the cilium and in the process of intraflagellar transport. Here we show that, in addition to sensory defects, Caenorhabditis elegans bbs mutants exhibit reduced body size and delayed developmental timing. The reduced body size phenotype is not fully recapitulated by IFT mutants, suggesting that BBS proteins may have additional functions beyond bridging IFT motors. We further identified that the loss of function mutations in the soluble guanylate cyclase complex, GCY-35/GCY-36, results in a suppression of these defects. Interestingly, GCY-35/GCY-36 influences the body size through a cGMP-dependent protein kinase EGL-4 in a group of body cavity neurons. BBS proteins, on the other hand, function through a non-overlapping set of ciliated sensory neurons to influence cGMP signalling in the body cavity neurons. In conclusion, this study reveals a non-cell autonomous role for sensory cilia in regulating cGMP signalling during development. We propose that aberrant cGMP signalling, essential for a number of cellular processes, may also contribute to some ciliopathy features in other systems.
| The cilium plays diverse cellular functions in metazoans which include imparting motility, enabling sensory processes and regulating the activity of cell signalling pathways during development [1]. The biogenesis and maintenance of this evolutionarily conserved organelle relies on intraflagellar transport (IFT) – the bidirectional transportation of diverse cargo proteins along the microtubule-based axoneme. Defective IFT or ciliary dysfunction result in ciliopathies, a growing class of pleiotropic human diseases with overlapping clinical features, some being of significant morbidity [2]. Bardet-Biedl syndrome (BBS, OMIM 209900) is an autosomal recessive and genetically heterogeneous ciliopathy with hallmark clinical features that include photoreceptor degeneration, renal abnormalities, obesity, cognitive impairment, and digit and genital anomalies [3]. To date, sixteen genes are associated with BBS [3]–[5]; of these, eight function mostly as a conserved protein complex (BBSome) [6] to regulate vesicular sorting and packaging [7], IFT [8]–[9], as well as cilium maintenance and function (reviewed in [10]).
Animal models have been instrumental in deciphering the physiological functions of BBS proteins [2], [10]. Initial characterization of Caenorhabditis elegans BBS orthologues led to the discovery of BBS proteins as ciliary components, associating ciliary defects with the loss of BBS protein function [11]–[13]. The loss of BBS-7 and BBS-8 led to shortened cilia, reduced uptake of a lipophilic dye (DiI) by the cilium, and defective chemo- and thermotaxis [13]–[14].
Murine Bbs mutants recapitulate several human BBS features including photoreceptor degeneration, renal anomalies and obesity [15]–[16]. Additionally, these models led to the identification of new features such as neural tube closure defects [17], anosmia [18], and behavioural, mechano- and thermosensory deficits [14] that expanded the diagnostic features of human ciliopathies. Morpholino-mediated knockdown of bbs in zebrafish led to developmental phenotypes such as dorsal thinning, poor somitic definition and Kupffer's Vesicle malformation [19]–[21], while defects characteristic of ciliopathy such as delayed retrograde melanosome transport [20] and vision impairment [22] also manifested.
In addition to a role in sensory transduction, the primary cilium functions as a signalling ‘apparatus’ to regulate development [1]. For example, IFT-dependent localization of Sonic Hedgehog (Shh) receptors to primary cilia is required for Shh signalling [23]–[24]. Disrupting IFT components IFT172, TG737/Polaris and the motor KIF3A in the mouse resulted in phenotypes typical of Shh mutants [25]. Similarly, defective planar cell polarity (PCP) signalling [17], [26] and/or aberrant Wnt signalling [27]–[28] were associated with the inactivation of BBS, Polaris or KIF3A components. These, and others studies [29]–[30] suggest that the cilium may modulate multiple signalling pathways in a tissue-specific manner. Aberrant PCP, Shh and Wnt signalling have been implicated in underlying a number of ciliopathy features, such as neural tube closure, polydactyly and obesity [17], [22], [31]–[32]. The pathology of other features such as photoreceptor degeneration, remains largely unexplained, indicating the presence of unidentified cellular processes that are regulated by the cilium.
C. elegans BBS orthologues are exclusively expressed by 60 ciliated neurons. Localizing at the base of cilia, they undergo active IFT, and their absence results in the destabilization of IFT and sensory defects [8]. C. elegans sensory neurons play key roles in multiple developmental processes [33]. Some chemosensory mutants exhibit a reduced body size [33], indicating that sensory function may influence this developmental process. Another key regulator for body size is the cGMP-dependent protein kinase (PKG) EGL-4 [33]–[36]; a loss of EGL-4 function leads to increased body size that is genetically epistatic to that of chemosensory mutants [33]. The mechanisms for sensory neuron-mediated body size regulation, however, remain to be fully elucidated. In the present study, in order to identify additional cilium-regulated signalling events in C. elegans, we carried out the phenotypic characterization of bbs mutant animals, and identified genetic modifiers that associate aberrant cGMP signalling with a subset of bbs features.
We performed a thorough phenotypic and behavioural assessment of severe or complete loss-of-function (lf) mutants for the C. elegans bbs-1, -2, -7, -8 and -9 genes (Table S1). Consistent with previous reports on bbs-7 and -8 [13], all examined bbs mutants exhibited a failure in the uptake of a lipophilic dye DiI by sensory neurons (Figure 1A), confirming a common structural and functional deficit in the sensory cilia. In addition to sensory defects, we identified three novel bbs-associated phenotypes: decreased body size, altered dwelling/exploration behaviour and delayed developmental timing.
Despite grossly normal body morphology, bbs mutants shared a reduced body length by ∼11–28% when compared to wild-type animals. Defects were visible during early larval stages and persisted throughout adulthood (Figure 1B and Figure S1). In these analyses, we defined a 3.5% or greater difference as biologically relevant in body size change, as this was the upper range for the coefficient of variance in young adult wild-type populations. The reduced body length is caused by the loss of BBS function, as it was fully rescued by introducing a wild-type genomic or cDNA copy of bbs into the respective mutants (Figure 1C). A decrease in body width was also characteristic of bbs mutants (Figure 1D). By DAPI staining of nuclei we did not observe differences in tissue and cell numbers between wild-type and bbs-7 animals (data not shown). The overall decrease in body size is thus best attributed to a smaller, averaged cell size in bbs mutants.
bbs double and triple mutants exhibited smaller body sizes that were no more severe than the smallest bbs single mutant. Although small differences between the body length of bbs single, double and triple mutant strains were observed as animals aged, the effects, however, were not additive (Figure 1B), consistent with BBS proteins functioning in the same biological processes to regulate ciliary development and function [6]–[7], [37].
We further examined the body size of a number of IFT mutants, including che-2, -3, and -11, as well as osm-3, -5, -6 and klp-11, all of which display defects to cilia structure and abnormal dye filling (dyf). Only some exhibited decreases in body size; among them, che-11 mutants exhibited the most significant decrease (by 11% in young adulthood), but not as severely as in age-matched bbs mutants (Figure S2A). Notably, a loss of the IFT motors, KLP-11 and OSM-3 kinesins, and CHE-3 dynein had little or no effect on body size. Therefore while sensory neurons affect body size, the dyf phenotype, caused by defective IFT transport, is not indicative of severe body size defects. The BBSome, exclusively expressed in ciliated sensory neurons, has a greater influence in the regulation of body size, indicating a role beyond bridging IFT motors for BBS proteins in these neurons.
C. elegans exhibits a defined developmental time course [38]. Multiple bbs mutant strains exhibited slower larval development (Figure 1E), resulting in a 2.3–6.2 hour delay between the first (L1) and last (L4) larval stage. During foraging, C. elegans exhibits a combination of dwelling and exploration/roaming behaviours that are altered in some chemosensory defective mutants [33]. Multiple bbs mutants showed a 56% to 76% decrease in overall movement, or roaming (Figure 1F) when compared to wild-type animals. This behavioural change does not reflect a general loss of locomotor activity, as bbs mutant animals exhibit normal locomotion during roaming. These additional phenotypes support a notion that the C. elegans cilia regulate cellular processes in addition to taxis behaviours.
Unlike all other bbs strains, MT3645 bbs-7(n1606) (received from the Caenorhabditis Genetics Center) displayed body size, roaming behaviour and developmental timing characteristics of wild-type animals. Upon genetic outcrossing, we re-isolated a homozygous bbs-7(n1606) strain that exhibited phenotypes characteristic of other bbs mutants. These defects were fully rescued by the expression of the wild-type genomic copy of bbs-7 (Figure 1D). We concluded through genetic analyses that a single modifying locus from the original MT3645 strain rescued a subset of the bbs-7 mutant endophenotypes (Figure 2).
We mapped this modifier allele, hp433, to gcy-35 (Materials and Methods), a gene encoding the α subunit of a soluble guanylate cyclase (sGC). sGC proteins are composed of a heme/NO binding (HNOB), a heme/NO binding associated (HNOBA), and a GC catalytic domain (reviewed in [39]). They are heterodimeric complexes consisting of α and β subunits to catalyze the conversion of GTP to cGMP. hp433 results in a frame-shift in the coding sequence and a premature termination codon in the HNOBA domain, causing a truncation of the GC catalytic domain (Figure 2A). We also saw that a deletion allele in the GC domain of gcy-35, ok769, functions as a recessive suppressor of bbs-7 mutants (Figure 2A and 2B).
While both gcy-35(lf) alleles exhibit similar body length to that of wild-type animals, they suppressed the significant body length defects of multiple bbs mutants (Figure 2B). In contrast, gcy-35(lf);dyf mutants showed little or no improvement to body length (Figure S2B). The consistent suppression observed in bbs mutant animals advocated strongly for the further investigation of gcy-35(lf) as an epistatic suppressor of bbs-mediated phenotypes. gcy-35 also modified the bbs endophenotypes in developmental timing and roaming. Both the developmental timing from the first (L1) to the last (L4) stages, and roaming scores of gcy-35 mutants were comparable to that of wild-type (Figure 2C and 2D). The developmental timing of gcy-35;bbs-7 and gcy-35;bbs-2 mutants was identical to that of wild-type animals, whereas gcy-35;bbs-8 animals showed a partial improvement over that of bbs-8 animals (Figure 2C). Roaming defects of bbs-2, bbs-7, and bbs-8 mutants were partially suppressed by gcy-35 (Figure 2D).
Other bbs phenotypes, such as shortened cilia and defective DiI uptake (Figure 2E and data not shown), were not rescued by gcy-35(lf) mutants. These results suggest that the ciliary structures remain impaired in gcy-35;bbs mutants, and that the cellular pathways regulating body size, developmental timing and roaming behaviours either function genetically downstream of, or differ from those involved in sensation.
GCY-35 and its partner GCY-36 form a heterodimeric sGC that modulates C. elegans behaviour in response to ambient oxygen concentrations [40]–[41]. gcy-36(db66) (lf) mutants [40] rescued the body size defect of bbs-7 mutants (Figure 2B). Moreover, gcy-35;gcy-36;bbs-7 animals exhibited a body size no different from either gcy-35;bbs-7 or gcy-36;bbs-7 (Figure 2B), consistent with GCY-35 and GCY-36 also functioning as a heterodimeric sGC to regulate body size.
We examined in which neurons this sGC influences body size using bbs mutants. Both GCY-35 and GCY-36 are expressed in the ciliated body cavity sensory neurons AQR and PQR, and a non-ciliated body cavity neuron URX; GCY-35 is additionally expressed in the non-ciliated ALN, PLN, and SDQ neurons [41]. AQR, PQR and URX expression of wild-type GCY-35 in gcy-35;bbs-7 animals reverted body size similarly to that of bbs-7 mutants, whereas GCY-35 expression in ALN, PLN, and SDQ had no effect (Figure 3A). Similarly, the expression of a GFP-tagged GCY-36 in AQR, PQR and URX neurons in bbs-7;gcy-36 animals was also sufficient to revert body size close to that of bbs-7 mutants (Figure 3A). This suggests a specific requirement of GCY-35 and GCY-36 in body cavity neurons to regulate body size.
Among them, URX appears to be the most essential neuron, as restoring GCY-35 in URX (and other neurons not normally expressing gcy-35/gcy-36) by an exogenous promoter showed a partial, but significant reversion of the body size in gcy-35;bbs-7 animals (Figure 3A). Similarly, GCY-36 is most essential in URX: we conducted a mosaic analysis of bbs-7;gcy-36 animals carrying a functional GFP::GCY-36 transgene, and we observed expression of GFP::GCY-36 in URX of all rescued animals (Figure 3B). URX expression of the GCY-35/GCY-36 sGC is therefore essential, although not fully sufficient, to regulate the body size of bbs mutants.
Finally, if the body cavity neurons contribute to the small size phenotype of bbs mutants, their ablation by the transgene qaIs2241(Pgcy-36::EGL-1) [42] in a bbs background should also have a rescuing effect. While both qaIs2241 and gcy-35;qaIs2241 animals exhibited normal body sizes, bbs-7;qaIs2241 mutants showed a significantly increased body size when compared to bbs-7 animals (Figure 3C), further supporting that these neurons modulate the body size of bbs mutants.
Our genetic analyses indicate that BBS proteins negatively regulate sGC-mediated signalling activity. We examined if bbs mutants exhibit an elevated expression and/or expanded localization of sGC in these neurons. As reported [40], a functional GFP::GCY-36 localized largely to the soma and along the dendrites of AQR, PQR and URX. We did not observe significant changes in its expression or localization in bbs mutants (Figure S3), nor did we see a loss of ciliary localization as reported for the loss of a putative isoprenylation signal at the C-terminus [40]. bbs mutations did not perturb, at a gross level, the expression and localization of GFP::GCY-35 either (Figure S3). We did observe a high degree of morphological variability in the dendritic endings of URX, AQR, and PQR neurons as previously reported [43] in both wild-type and bbs mutants. However, given the morphological variability, we cannot exclude the possibility of subtle alterations in the cilium length of bbs mutants. Therefore, while AQR, PQR and URX neurons regulate the body size through a process that involves sGC activity, it does not appear to result directly from altered sGC protein level or subcellular localization.
cGMP is a key secondary messenger (reviewed in [44]). In C. elegans, cGMP activates a heteromeric cGMP-gated ion channel TAX-2/TAX-4 for oxygen sensation and other sensory processes [45]. cGMP also activates a PKG, EGL-4, to regulate olfactory adaptation, life span, behavioural states and body size [33]–[34], [46]–[47]. Specifically, egl-4(lf) mutants exhibit a large body size, and are epistatic to the reduced body size and roaming behaviour of some sensory mutants; whereas a constitutively active, gain-of-function (gf) egl-4 mutation, causes a small body size [36]. The shared effects of bbs and egl-4 suggested that EGL-4 could be a downstream effector of sGCs in body size regulation.
We examined the egl-4 mutants (Figure 4) for their genetic interactions with bbs-7 and gcy-35 mutants. egl-4(lf) alleles were epistatic to bbs-7, gcy-35 and bbs-7;gcy-35 for body size and developmental timing (Figure 4B and 4C). egl-4(gf) also exhibited an epistatic effect to bbs-7 and bbs-7;gcy-35 mutants, although egl-4(gf);bbs-7 and egl-4(gf);bbs-7;gcy-35 mutants may exhibit a slightly more severe phenotype than egl-4(gf) that was only distinguished by statistical analyses (Figure 4B). By contrast, lf mutants for another cGMP effector, the cGMP-gated cation channel subunit TAX-2 and TAX-4, did not exert body size suppression for bbs-7 (Figure S4). Moreover, some TGF-β signalling mutants exhibited altered body size [48]–[50]. While EGL-4 was proposed to function genetically downstream of TGF-β signalling [33], body size defects of several TGF-β mutants exhibited additive effects in bbs-7 or bbs-7;gcy-35 backgrounds (Figure 4D), suggesting that TGF-β signalling and BBS-mediated body size regulation likely operates in an additive or parallel manner. Taken together, these results confirm a specific genetic relationship between bbs-7, gcy-35 and egl-4 for cilia-mediated body size regulation and developmental timing.
To further test if the body size influence of GCY-35/GCY-36 sGC functions through EGL-4, we expressed egl-4 cDNA harbouring the ad450(gf) mutation in AQR, PQR and URX neurons of gcy-35;bbs-7 animals. If GCY-35 regulates body size through activating EGL-4, a constitutively activated EGL-4(ad450) in these neurons should abrogate the body size suppression by the loss of gcy-35. Indeed, this transgene reverted animals to a bbs-7-like body size (Figure 4E). Wild-type animals expressing the same transgene did not exhibit changes in body size (Figure 4E). Not only is this result consistent with elevated cGMP signalling contributing to the reduced body size in bbs mutants, it further suggests that GCY-35/GCY-36 regulates body size through EGL-4 in the body cavity neurons.
To investigate whether ciliary functions regulate body size strictly through body cavity neurons like GCY-35/GCY-36, we expressed a functional BBS-7 or GFP::BBS-2 in the AQR, PQR and URX neurons of bbs-7 or bbs-2 mutants, respectively. The small body size of respective bbs mutants was not rescued (Figure 5A). We did, however, observe a complete rescue of the body size with a pan-neuronally expressed GFP::BBS-2 in bbs-2 mutants (Figure 5A). Moreover, we observed a full or partial rescue of the body size defects when a functional BBS-7 was expressed in at least two non-overlapping groups of sensory neurons AWB, AWC, AWA, ADF and ASH, or, ADL, ADF, ASH, PHA and PHB (Figure 5A). All these neurons have sensory cilia exposed to outside of the body cavity. Therefore, while the GCY-35/GCY-36 sGC functions through body cavity neurons to regulate body size, restoring ciliary function in these neurons alone does not sufficiently reduce cGMP signalling to restore body size. Alternatively, other sensory neurons that input onto the body cavity neurons, may serve to regulate body size. Regardless, the observation that BBS proteins can influence body size through different groups of sensory neurons is reminiscent of the previously reported observation that the body size of egl-4(lf) mutants could be rescued by restoring EGL-4 expression in non-overlapping sets of sensory neurons [33].
That multiple, non-overlapping and non-body cavity ciliated sensory neurons (CSNs) regulate body size suggests a cumulative effect on cGMP signalling-mediated body size regulation by multiple sensory neurons, or, a predominating effect by body cavity neurons in cilia-mediated body size regulation through GCY-35/GCY-36. The first model predicts that additional GCs in other sensory neurons would epistatically suppress body size. The C. elegans genome encodes 7 sGCs and 27 receptor-like GCs (rGCs) [51]. Single loss-of-function mutations in other sGCs, GCY-31, 32, 33, 34 and 37 all failed to alter bbs-7 body size defects (Figure S5A and data not shown). Of 16 rGC mutants tested, four exhibited modifying effects, two very mildly suppressing and two significantly exacerbating the smaller body size of bbs-7 animals, but none suppressed bbs-7 mutant phenotypes comparably to gcy-35/gcy-36 (Figure 5B and Figure S5A). We examined the effect of several double and triple rGC mutants on bbs-7 body size to explore the possibility that multiple rGCs function redundantly [52] but we did not observe obvious suppression effect in these additional mutants (Figure S5A).
We further examined the combinational effect of gcy-35 and other rGC mutants on bbs-7, including a mildly suppressing allele (gcy-4(tm1653)), two exacerbating alleles (gcy-7(tm901) and gcy-16(ok2538)) and three “neutral” alleles (gcy-23(ok797), gcy-28(tm2411) and gcy-25(tm4300)). We did not observe a significant body size improvement between gcy-35;bbs-7 and these triple mutants (Figure S5B). Therefore with the caveat that we have not exhausted the examination of all single or combinational GC mutants, GCY-35/GCY-36, through the body cavity neurons, act uniquely as a predominating effector for BBS-mediated body size regulation.
In the present study, we show that C. elegans bbs mutants exhibit reduced body length, delayed development and altered roaming pattern, in addition to known sensory defects. These endophenotypes depend, fully or in part, on the GCY-35/GCY-36 sGC complex, through its effector EGL-4 PKG, in the AQR, PQR and URX body cavity neurons. On the other hand, body size can also be regulated via multiple, non-overlapping sets of non-body cavity sensory neurons. We propose that the loss of C. elegans BBS function in ciliated sensory neurons leads to non-cell autonomous, aberrant cGMP-PKG signalling in body cavity neurons, which contributes to abnormal body size and delayed development.
Ciliated sensory neurons transduce environmental cues into behavioural responses. In C. elegans bbs mutants, defective IFT and ciliary functions are reflected by chemosensory and thermosensory deficits [13]–[14]. Given the restricted expression of C. elegans BBS proteins in sensory neurons, the additional bbs endophenotypes such as developmental timing, body and inferred cell size, and roaming indicate that in addition to sensory perception, sensory neurons also participate in developmental regulation in a non-cell autonomous manner. These bbs endophenoptypes are not recapitulated by several dyf/IFT motor mutants, further implying that BBS proteins affect sensory neuron function in addition to their role in IFT.
While all bbs mutants share these endophenotypes, they exhibit small differences in the severity of phenotypic expression that could be attributed to specific allelic effects. Alternatively, BBS proteins could possess certain degrees of unknown functional specificity. This may not be so surprising given the difference in phenotypic expression among BBS patient populations [53]–[55], as well as the observation that tissue-specific BBS isoforms are responsible for some syndromic features [22], [56].
The involvement of primary cilia in signalling during development [57] also positions them to affect development in a non-autonomous fashion. For example, mouse BBS proteins are required in the hypothalamus to regulate leptin receptor trafficking and to prevent the onset of obesity [31]. Ciliary dysfunction therefore contributes to increased adiposity partly in a non-cell autonomous manner. The additional phenotypes of C. elegans bbs mutants, highlights the global and non-cell autonomous consequence of sensory ciliary dysfunction, which may also account for some phenotypic features in other ciliopathy models.
Previous studies established that the GCY-35/GCY-36 sGC can regulate oxygen sensation through either the body cavity neurons, or another group of neurons [40]–[42]. Activated by oxygen, this complex catalyzes the conversion of GTP to cGMP, which subsequently activates the cGMP-gated cation channel TAX-2/TAX-4 to initiate hyperoxic avoidance responses [45]. Additional sGCs can act in body cavity neurons or other neurons under specific hypoxic conditions [45], [58].
GCY-35/GCY-36 modifies body size through a mechanism partly divergent from that of hyperoxic avoidance. GCY-35 is only necessary and sufficient in body cavity neurons that either have ciliated dendrites [59] or express some ciliated neuron-specific genes [60]. Furthermore, the loss of EGL-4, but not TAX-2 or TAX-4, suppresses the body size defects of bbs and dyf mutants. The loss of TAX-2 and TAX-4, in fact, slightly exacerbated bbs phenotypes (Figure S4), which may reflect an increased cGMP pool for EGL-4 activation or the loss of a potential EGL-4 phosphorylation target [35]. As well, despite some sGCs having overlapping expression profiles with GCY-35/GCY-36, other oxygen-responsive sGC mutants failed to suppress bbs-7 body size defects under standard culture conditions - possibly due to low activity under normoxia. Therefore, body cavity neurons, through GCY-35/GCY-36 activity, participate in developmental regulation through an alternate cGMP effector.
EGL-4 is present fairly ubiquitously, but the activation of EGL-4 in sensory neurons exerts a dominant influence on body size [33]. The genetic epistasis of both egl-4(lf) and egl-4(gf) alleles over that of bbs and bbs;gcy-35 argues in favour of BBS proteins and EGL-4 functioning through a shared cellular pathway to regulate body size and developmental timing. Expression of EGL-4(gf) in the body cavity neurons of gcy-35;bbs-7 mutants specifically alleviated the rescuing effect on body size, suggesting that increased EGL-4 activity, driven by increased availability of cGMP in body cavity neurons, contributes to the body size defects of some ciliary mutants (Figure 5C).
The body size defects of ciliary mutants are rescued by non-overlapping sets of sensory neurons. However, restoring BBS function in body cavity neurons is insufficient to rescue the observed body size defects, giving rise to a possibility that the effect of cGMP signalling by body cavity neurons is indirectly moderated by a non-cell autonomous function of BBS proteins in ciliated sensory neurons. Furthermore, that URX, a pair of non-ciliated neurons, play a necessary role in this suppression indicates that BBS proteins are not directly influencing body size in these neurons. Our genetic analyses of the modifying effect of other GC mutants also support this scenario, as we have not found additional GC mutants that potently restore the body size of bbs mutants.
These results do not exclude the possibility that other GCs function redundantly in non-body cavity sensory neurons to influence body size through EGL-4/PKG (Figure 5D). The overexpression of egl-4(gf) in body cavity neurons was incapable of further reducing the body size of gcy-35;bbs-7 animals beyond that of bbs-7 mutants. This is in concordance with the ablation of body cavity neurons, which did not phenocopy the large body size of EGL-4 loss of function mutants, suggesting additional neuronal groups influence body size through EGL-4/PKG signalling. This study, however, establishes body cavity neurons as a predominating cGMP/PKG effector in body size regulation, and the ciliated sensory neurons as playing a key role in moderating cGMP signalling of these effector neurons.
Mechanisms on how dysfunctional ciliary sensory neurons lead to elevated cGMP/PKG signalling in these neurons are unknown. The body cavity neurons, AQR, PQR and URX do not receive extensive or direct synaptic inputs from sensory neurons where BBS proteins are sufficient to rescue body size. The non-cell autonomous effect of ciliated sensory neurons therefore suggests a potential involvement of indirect synaptic inputs, or other forms of neuronal communications, such as peptidergic and/or hormonal signalling between these neuronal groups. For example, body cavity neurons express the C. elegans homologue of the neuropeptide NPY receptor [61], making their activity susceptible to modulation by neuropeptides, some of which could be secreted by sensory neurons [62]. Sensory neurons also secrete insulin/IGF-like ligands, some of which may systematically affect neuronal states [63]–[64]. Indeed, insulin and leptin have been shown to regulate the activity of specific hypothalamic neurons [65]–[66]. Speculatively, C. elegans BBS proteins could affect the secretion of multiple signals by ciliated sensory neurons to regulate cGMP/EGL-4 signalling in the body cavity neurons.
While aberrant PCP, Shh and Wnt signalling underlie a number of ciliopathy features, the biology behind other ciliopathy features such as photoreceptor degeneration, and reduced body size in Bbs mice [15] remains unexplained. cGMP signalling plays key roles in biological processes such as phototransduction, axonal guidance, and synaptic plasticity (reviewed in [67]–[68]). PKGs have also been implicated in photoreceptor degeneration and dwarfism [69]–[70]. It is worth exploring the involvement of cGMP signalling in the underlying pathology of BBS and other ciliopathy features.
All strains were maintained on NGM plates at 20°C. C. elegans bbs, gcy and egl-4 strains were obtained from the CGC. CX7102 was obtained from the Bargmann lab. Genotypes for all strains are listed in Text S1.
bbs-7(n1606);hp433 mutants were outcrossed twice against N2 by selecting animals that were genotyped for n1606 mutation, but exhibited normal body size. The hp433 mutation was crossed into bbs-7(ok1351) mutants and mapped based on the suppression of small body size and roaming defects using the SNP markers in the CB4856 strain, which placed it at a 93.5 kb interval between the SNPs pkp1133 and uCE1-1426. We conclude that hp433 encodes gcy-35 by: 1) Injection of three overlapping fosmids covering gcy-35, T04D3.5, and T04D3.t2, reverted the body size suppression in hp433; bbs-7 animals. A fragment of WRM641cB09 that encompassed a truncated gcy-35, but complete T04D3.5 and T04D3.t2 failed to revert the hp433 suppression; A genomic fragment containing only gcy-35 fully reverted the suppression. 2) gcy-35(ok769) animals shared the same synthetic phenotypes and genetic interactions with bbs-7 as hp433, while hp433;bbs-7(ok1351) animals also failed to complement gcy-35(ok769);bbs-7(ok1351). 3) Sequencing of gcy-35 identified a 2 bp deletion in exon 8.
See Text S1.
|
10.1371/journal.pgen.0040034 | Unintentional miRNA Ablation Is a Risk Factor in Gene Knockout Studies: A Short Report | One of the most powerful techniques for studying the function of a gene is to disrupt the expression of that gene using genetic engineering strategies such as targeted recombination or viral integration of gene trap cassettes. The tremendous utility of these tools was recognized this year with the awarding of the Nobel Prize in Physiology or Medicine to Capecchi, Evans, and Smithies for their pioneering work in targeted recombination mutagenesis in mammals. Another noteworthy discovery made nearly a decade ago was the identification of a novel class of non-coding genes called microRNAs. MicroRNAs are among the largest known classes of regulatory elements with more than 1000 predicted to exist in the mouse genome. Over 50% of known microRNAs are located within introns of coding genes. Given that currently about half of the genes in mouse have been knocked out, we investigated the possibility that intronic microRNAs may have been coincidentally deleted or disrupted in some of these mouse models. We searched published murine knockout studies and gene trap embryonic stem cell line databases for cases where a microRNA was located within or near the manipulated genomic loci, finding almost 200 cases where microRNA expression may have been disrupted along with another gene. Our results draw attention to the need for careful planning in future knockout studies to minimize the unintentional disruption of microRNAs. These data also raise the possibility that many knockout studies may need to be reexamined to determine if loss of a microRNA contributes to the phenotypic consequences attributed to loss of a protein-encoding gene.
| To determine the function of a gene, it is often informative to first disrupt the expression of that gene through targeted recombination or the insertion of gene trap cassettes. In our study, we point out that these approaches may be confounded by the presence of small non-coding elements known as microRNAs. MicroRNAs constitute one of the largest classes of regulatory elements, and over 50% of known microRNAs have been identified within an intron of a coding gene. Disruption of a gene could therefore also result in the disruption of microRNAs in the region. In this study, we searched databases of gene-trapped cell lines as well as previously published knockout studies and report almost 200 examples where microRNA expression may have been unintentionally disrupted. Our results are of broad interest and importance because they raise the possibility that a number of protein function studies may need to be reexamined to determine whether the loss of a microRNA may have contributed to the phenotype previously attributed to the loss of a protein.
| In the mouse, stable disruption of a gene is typically accomplished using gene trap mutagenesis or targeted homologous recombination. We wish to communicate the overlooked possibility of unintentionally disrupting microRNA (miRNA) genes along with a targeted gene. Because miRNAs play key roles in many cellular processes, the unintended ablation of these species may have significant consequences that complicate the interpretation of gene knockout studies.
Given that many miRNAs are located within introns of longer coding transcripts, we reasoned that a gene trap disrupting a host gene could also alter miRNA expression in one of two ways. The trapping cassette could either ablate miRNA expression with a terminal polyadenylation sequence (Figure 1A) or overexpress an miRNA via an internal promoter (Figure 1B). To determine the potential extent of these unintended changes in miRNA expression, we compared the genomic position all mouse gene traps listed in the International Gene Trap Consortium (IGTC) [1] to the loci of 367 annotated mouse miRNA genes as well as candidate miRNA genes computationally identified by Berezikov et al., 28% of which have been validated to date [2,3]. In the cases where an miRNA was located within an intron of a host gene, we identified any gene traps which inserted within the host gene transcript and upstream of the miRNA. Using the same set of annotated and candidate miRNAs, we next identified all protein-coding genes with an miRNA located within the transcribed loci, in either the sense or the antisense orientation. We cross-referenced these genes with all homologous recombination studies listed in the Mouse Genome Informatics (MGI) database (v. 3.54) [4] to assemble a list of studies where the miRNA and coding gene were potentially co-ablated (Table S1). The boundaries of the deleted loci were bioinformatically verified for each study.
Our analysis of the IGTC database revealed 98 annotated or candidate miRNAs potentially misregulated in 420 gene trap cell lines (Table S2). A study of the slit3 gene [5] is an example of a potential unintended double-knockout scenario produced from a gene trap cell line. To ablate slit3, the authors used a trap located upstream of exon 6, which produced a truncated slit3 mRNA. Mir-218–2 is located within intron 14 of slit3, and the potential loss of mir-218–2 expression may contribute to the phenotype resulting from the loss of functional slit3.
The analysis of the MGI database yielded a small but significant number of studies where miRNAs may have been unintentionally disrupted (Table S1). In addition to 20 studies where an annotated or candidate miRNA was completely ablated by the targeting strategy (Figure 1C), there were also numerous studies describing the deletion of regions immediately upstream (78 cases) or downstream (55 cases) of a miRNA (Figure 1D), or in the promoter of the host gene (4 cases). MiRNAs have been shown to be transcribed in conjunction with a host transcript or from an independent promoter [6]. Therefore, the disruption of host promoters or of regions adjacent to miRNAs may compromise promoter and/or enhancer sites for these miRNAs.
While 71 of the studies in our analysis were published prior to the expansion of the miRNA field in 2002, the fact that 90 were published since may indicate that miRNAs in targeted loci continue to be overlooked. To avoid inadvertent double-knockout scenarios, we wish to alert investigators to consider non-coding elements in the locus to be deleted. Because not all non-coding elements have been annotated, it may be preferable to employ methods that minimize the deletion of endogenous DNA. We also wish to raise the interesting possibility that a number of studies may need to be reevaluated to dissociate the consequences of ablating an miRNA from the consequences of ablating the targeted gene. |
10.1371/journal.pntd.0007625 | Ultrasound-guided minimally invasive autopsy as a tool for rapid post-mortem diagnosis in the 2018 Sao Paulo yellow fever epidemic: Correlation with conventional autopsy | New strategies for collecting post-mortem tissue are necessary, particularly in areas with emerging infections. Minimally invasive autopsy (MIA) has been proposed as an alternative to conventional autopsy (CA), with promising results. Previous studies using MIA addressed the cause of death in adults and children in developing countries. However, none of these studies was conducted in areas with an undergoing infectious disease epidemic. We have recently experienced an epidemic of yellow fever (YF) in Brazil. Aiming to provide new information on low-cost post-mortem techniques that could be applied in regions at risk for infectious outbreaks, we tested the efficacy of ultrasound-guided MIA (MIA-US) in the diagnosis of patients who died during the epidemic.
In this observational study, we performed MIA-US in 20 patients with suspected or confirmed YF and compared the results with those obtained in subsequent CAs. Ultrasound-guided biopsies were used for tissue sampling of liver, kidneys, lungs, spleen, and heart. Liver samples from MIA-US and CA were submitted for RT-PCR and immunohistochemistry for detection of YF virus antigen. Of the 20 patients, 17 had YF diagnosis confirmed after autopsy by histopathological and molecular analysis. There was 100% agreement between MIA-US and CA in determining the cause of death (panlobular hepatitis with hepatic failure) and main disease (yellow fever). Further, MIA-US obtained samples with good quality for molecular studies and for the assessment of the systemic involvement of the disease. Main extrahepatic findings were pulmonary hemorrhage, pneumonia, acute tubular necrosis, and glomerulonephritis. One patient was a 24-year-old, 27-week pregnant woman; MIA-US assessed the placenta and provided adequate placental tissue for analysis.
MIA-US is a reliable tool for rapid post-mortem diagnosis of yellow fever and can be used as an alternative to conventional autopsy in regions at risk for hemorrhagic fever outbreaks with limited resources to perform complete diagnostic autopsy.
| Reliable mortality information is of paramount importance to establish sound public health policies. Autopsy is an important tool not only for determining the cause of death, but also for the detection of novel diseases. In the last decades, we have been globally identifying an unprecedented number of emerging infections. Therefore, there is great interest in the development of less invasive and low-cost tools for the accurate post-mortem diagnosis in fatal cases. Minimally invasive autopsy (MIA), conceived as targeting diagnostic biopsies of key organs by needle puncture, has been proposed as an alternative to conventional autopsy (CA) for the determination of cause of death in developing countries. In this research, we tested the efficacy of MIA in the post-mortem diagnosis of 20 patients with suspected or confirmed yellow fever who died during the recent epidemic of yellow fever that occurred in Brazil. There was a perfect agreement between MIA and CA in determining the cause of death (hepatic failure) and main disease (yellow fever) in all patients with confirmed yellow fever. This finding indicates that MIA can be used as an alternative to CA in regions at risk for infectious disease outbreaks with limited resources to perform conventional autopsies.
| In the last decades, we have been globally identifying an unprecedented number of emerging infections [1]. As a result, considerable effort is currently being made by several actors from governmental and non-governmental institutions for better preparedness for the next expected emerging threats to public health worldwide [2–4]. Therefore, there is great interest in the development of tools for the early detection of infectious outbreaks for the establishment of appropriate measures [2,5].
Autopsy has been an important and indisputable diagnostic tool for the detection of novel diseases. In the past years, our group has performed autopsy studies to describe new aspects of human pathology of emerging infectious diseases, such as measles, leptospirosis, and influenza A(H1N1)pdm09 [6–8]. However, the global distribution of facilities capable of performing the procedure is markedly uneven. New strategies for collecting post-mortem tissue samples are necessary, particularly in areas where outbreaks of infectious diseases are occurring and where the identification of the causative agent as well as its effects on target organs is a public health priority [4,5,9].
Minimally invasive autopsy (MIA) has been proposed as an alternative to conventional autopsy, conceived as targeting small diagnostic biopsies by needle puncture of key organs, with or without the guidance of any imaging technique. The use of computed tomography (CT) and CT-angiography, associated with needle biopsy, has been proposed as feasible for diagnosis of common causes of death [10,11]. However, these techniques can only be performed in centers of excellence, requiring substantial budgets. More recently, ultrasound-guided tissue sampling (MIA-US) has also been tested, with promising results [12–18]. This methodology represents a portable, rapid and low-cost post-mortem technique, which may be especially useful in countries where mortality data are largely unavailable [2,13–18].
Viral hemorrhagic fevers (VHFs) are severe viral infections that may present as hemorrhagic disease with fatal multi-organ failure. In endemic areas, they can cause long-lasting epidemics with great impact on human morbidity and mortality. Yellow fever (YF), in particular, is a re-emerging disease, endemic in tropical regions of South America and sub-Saharan Africa. It is a mosquito-borne flavivirus-induced VHF, with a high case-fatality rate, clinically manifested as hepatic dysfunction, renal failure, coagulopathy, and shock [19]. The investigation of deaths related to VHFs should put MIA-US into perspective among other potential diagnostic strategies.
From the end of 2017 to mid-May 2018, we experienced a YF epidemic in the southern region of Brazil. During this period, 498 autochthonous confirmed YF cases were registered in the state of Sao Paulo, with 176 deaths (fatality rate: 35.4%). A substantial part of the fatalities (80 cases) was referred to the autopsy service at Sao Paulo University Medical School. Aiming to provide new information on low-cost post-mortem techniques that could be applied in at-risk regions in Brazil as well as worldwide, we tested the efficacy of MIA-US in the post-mortem diagnosis of 20 patients who died with suspected or confirmed YF during the recent epidemic in Brazil.
This prospective observational study was approved by the University of Sao Paulo School of Medicine Internal Review Board (CAAE protocol number: 18781813.2.0000.0068). Informed written consent was obtained from the next of kin.
From January 23, 2018 to February 27, 2018, 20 deceased patients with suspected or confirmed YF underwent MIA using ultrasound-guided percutaneous core needle biopsy. Conventional autopsy (CA) was performed subsequently by a distinct group of pathologists, blinded to the MIA-US results.
Fig 1 illustrates the sequence of the autopsy procedures.
All the patients included in the study fulfilled the following criteria: 1) patients died with suspected or confirmed yellow fever; 2) an autopsy was requested by the clinician; 3) death occurred during business hours, when interventional radiologists were available to perform the MIA-US; 4) informed consent to perform the autopsy was given by a family member.
The case definition of YF employed during the epidemic period was established by the Brazilian Ministry of Health and the Health Department of the State of Sao Paulo. Suspected cases referred to those patients who had a sudden onset of high fever associated with jaundice and/or hemorrhages, who lived or had visited areas with cases of YF, YF epizootics in non-human primates, or isolation of yellow fever virus (YFV) in vectors, regardless of the vaccine status for YF, during the preceding 15 days. Confirmed cases referred to those patients who had compatible clinical presentation and laboratory confirmation by at least one of the following methods: positive serum IgM (MAC-ELISA) (performed in 10 of 20 patients); detection of YFV-RNA by RT-PCR in blood samples (performed in 17 of 20 patients); and histopathology compatible for YF hepatitis with detectable YF antigen in tissues by immunohistochemistry (wild, SP strain, hyperimmune, IAL–SP, Brazil) [20]. In all of the cases, viral hepatitis (A, B, and C) and dengue fever were excluded by serology and/or RT-PCR.
The procedures were performed at the “Image Platform in the Autopsy Room”, a research center in the University of Sao Paulo Medical School, located next to the Autopsy Service of Sao Paulo University (https://pisa.hc.fm.usp.br/). Radiologists and pathologists were aware of the information provided by the next of kin prior to autopsy.
All US examinations were performed by interventional radiology physicians, who were trained to perform gray-scale post-mortem US during a period of 8 weeks. In total, fifteen exams were necessary in order to achieve thorough ultrasound imaging and good quality biopsy samples. We used a SonoSite M-Turbo portable ultrasound (Fujifilm, Bothell, WA, USA) with broadband and multifrequency transducers: C60x (5–2 MHz Curved) and HFL38X (13–6 MHz Linear) and DICOM medical images.
Tissue samples from the liver (at least two samples), lungs (three samples from each lung), both kidneys (one sample each), spleen (one sample), and heart (one sample) were collected under ultrasound guidance using a large core (14-gauge) needle biopsy. A final MIA-US diagnosis was provided by conjunct analysis of US and biopsy data, blinded to CA results.
The autopsies were performed following the Letulle technique, where all the organs are removed en masse, requiring dissection of each organ [21]. Tissue samples were collected from all body systems. Both biopsy and autopsy samples were submitted for routine histological examination, and stained with hematoxylin-eosin (H&E). Brown-Brenn, Ziehl-Neelsen, and Grocott stains were used to detect bacteria, acid-fast bacilli, and fungi, respectively, when required.
Eleven liver biopsy samples and all autopsy liver samples were submitted for reverse transcriptase-polymerase chain reaction (RT-PCR) for detection of YFV-RNA. Liver samples measuring 1 cm3 were stored at −70°C. The tissue was macerated, the nucleic acid extraction was performed using the TRIzol reagent (Life Technologies, Carlsbad, CA, USA), and carried out according to the manufacturer’s instructions. Molecular detection of YFV was performed using the AgPath-ID One-Step RT-PCR Reagents (Ambion, Austin, TX, USA) with specific primers/probe previously described [22]. To identify cases of adverse vaccine response (i.e., fatal cases associated with the vaccine virus) we used primers/probe specific for the vaccine virus [23]. qRT-PCR reactions consisted of a step of reverse transcription at 45°C for 10 min, enzyme activation at 95°C for 10 min, and 40 cycles at 95°C for 15 s and 60°C for 45 s for hybridization and extension with the use of ABI7500 equipment (Thermo Fisher Scientific, Waltham, MA, USA).
All biopsy and autopsy liver samples were submitted for IHC for detection of YFV antigens as previously described [24]. Briefly, after antigen retrieval using Tris-EDTA at pH 9.0, the sections were incubated overnight with the primary antibody (goat anti-human IgM polyclonal anti-arbovirus, provided by Institut Pasteur de Dakar, Dakar, Senegal, 1:20,000) at 4°C [25]. The slides were incubated with biotinylated secondary antibody (Reveal–Biotin-Free Polyvalent HRP DAB, Spring Bioscience, cod. SPD-125) and chromogen (Dako Liquid DAB+ Substrate Cromogen System, Dako, cod. K3460) and counterstained with Harris-hematoxylin (Merck, Darmstadt, Germany). The primary antibody was tested in liver samples from patients with virus hepatitis (A, B, and C), herpes virus, cytomegalovirus, adenovirus, dengue virus, Treponema pallidum, Leptospira, and atypical mycobacteria infections, obtained from our autopsy archive. The IHC reaction resulted negative in all these samples.
We compared the MIA-US diagnosis with the CA diagnosis of the cause of death and main disease. In addition, we performed a descriptive analysis of the concordances and discrepancies between the diagnoses of MIA-US and CA in all organs analyzed.
All patients who died during the yellow fever epidemic that fulfilled the inclusion criteria were included in the study (n = 20).
Of the 20 patients who died with suspected (n = 11) or confirmed YF (n = 9), 17 had their diagnosis confirmed after autopsy by histopathological and molecular analysis. A total of nine in ten patients had positive serology. Serum YFV-RT-PCR was performed in 17 patients, and 16 patients showed positive results. However, eight patients died before their serum RT-PCR results were obtained, and their diagnoses were established after autopsy.
YF was not confirmed in three patients, as described below. Interestingly, MIA-US could determine the main disease and cause of death in these three patients.
First we present the results of the 17 patients with confirmed YF.
The patients comprised 13 men and 4 women, with a median age of 47 (24–86) years. Fourteen patients died at our institution and three were referred for autopsy. Sixteen patients lived or traveled to the epizootic areas of the city. One patient lived in urban area and had YF vaccine-associated viscerotropic disease (YEL-AVD), which was confirmed by liver RT-PCR. Six patients received YF vaccination 0–3 days before death; only one of them presented with YEL-AVD. One patient was a 24-year-old, 27-week pregnant woman. The median timespan between the occurrence of symptoms and hospital admission was 4 days (1–15) and that between the occurrence of symptoms and death was 8 days (6–21). The most frequent associated clinical conditions were alcoholism (n = 7), smoking (n = 5), and systemic arterial hypertension (n = 4). One patient had undergone liver transplantation three days before death for hepatic failure due to YF. The primary symptoms upon hospital admission were fever, jaundice, myalgia, anorexia, abdominal pain and hemorrhagic phenomena. The main alterations in the laboratory data were related to acute liver failure, as well as renal dysfunction and metabolic acidosis.
Table 1 presents detailed clinical data of the 17 patients.
Adequate biopsy samples from the liver (100%), kidneys (94%), and lungs (88%) were obtained in majority of patients. More limited samples were obtained from the spleen (82%) and heart (76%).
Liver biopsies showed panlobular hepatitis with severe steatosis and midzonal apoptotic bodies, which were characteristic of fatal YF, in all samples analyzed (100%). All liver samples were submitted for IHC and 11 liver samples were submitted for RT-PCR for detection of YFV. IHC revealed positive results in 16 samples (94%), and RT-PCR revealed positive results in all 11 samples (100%).
Lung samples showed pulmonary involvement (73%) that included alveolar hemorrhage (67%) and pneumonia (60%). Pulmonary aspergillosis was confirmed in a single lung biopsy. Kidney alterations included acute tubular necrosis (100%) and a mesangial proliferative glomerulonephritis (63%). Spleen biopsies showed lymphoid hypoplasia (93%), splenitis (86%), and hemophagocytosis (43%). Heart biopsies showed interstitial edema (85%) and fiber hypertrophy (54%). In the pregnant woman, MIA-US could be used to assess the placenta and provided adequate placental tissue for histological analysis.
The cause of death in the 17 patients was hepatic failure. Associated organ-specific hemorrhages and/or hemorrhagic shock was present in 16 patients. One patient presented with septic shock.
All patients presented with panlobular hepatitis with severe steatosis and midzonal apoptotic bodies. IHC was positive in 15 (88%) of 17 liver samples, and RT-PCR was positive in 17 (100%) liver samples. One patient had undergone liver transplantation after developing YF-induced hepatic failure and the graft was infected. One patient presented with YEL-AVD, confirmed by liver and spleen RT-PCR. Other autopsy liver findings were hepatomegaly, ischemic centrilobular necrosis, and alcoholic cirrhosis (two cases). Ascites was detected in 10 (59%) patients. Table 2 presents detailed liver findings at MIA-US and CA for the 17 patients.
At the respiratory system, alveolar hemorrhage (94%) and pneumonia (53%) were the main pulmonary autopsy findings. Pleural effusion (41%), diffuse alveolar damage (41%), and bronchoaspiration (18%) were also observed. Two patients presented with fungal pneumonia (one aspergillosis and one mucormycosis) and associated pulmonary necrosis secondary to mycotic thrombus. Two patients presented with pulmonary embolism. The main kidney alterations were acute tubular necrosis (94%) and mesangial proliferative glomerulonephritis (88%). Other kidney findings were interstitial fibrosis, nephrosclerosis, hypertensive nephropathy, vascular thrombosis, and pyelonephritis. Spleen findings included lymphoid hypoplasia (100%), splenitis (94%) and cytophagocytosis (50%). One patient underwent splenectomy. The primary cardiac changes at autopsy included fiber hypertrophy (76%), interstitial edema (71%), myocardiosclerosis (65%), and coronary atherosclerosis (65%).
Autopsy also showed organ alterations that were not assessed by MIA-US, such as pancreatic changes (ischemic pancreatic changes, peripancreatic steatonecrosis, and alcoholic pancreatic interstitial fibrosis), central nervous system (CNS) changes (cerebral edema, perivascular hemorrhage, and cerebral herniation), gastrointestinal (GI) bleeding, skin perivascular inflammation, lymphoid hypoplasia in lymph nodes, and bone marrow with erythroid depletion and hemophagocytosis.
One patient was a 19-year-old man who died due to perforated appendicitis and sepsis. Purulent fluid leaked from the abdominal cavity when the radiologist performed US-guided liver and kidney biopsies, indicating an intra-abdominal infection. Lung biopsy in this case showed secondary acute lung injury with intense pulmonary hemorrhage. One patient was an 87-year-old woman with sepsis due to bacterial pneumonia and pyelonephritis, both observed respectively on lung and kidney US-guided biopsies. The third patient was a 14-year-old boy with acute myeloid leukemia. Liver and kidney samples from MIA-US showed infiltration of atypical myeloid CD34 positive cells (blasts).
The present results demonstrate the effectiveness of MIA-US in the post-mortem diagnosis of the main disease (YF) and cause of death (hepatic failure) in a group of patients evaluated during the epidemic of YF that occurred in early February 2018 in Sao Paulo, Brazil. We found a 100% concordance between MIA-US and CA for the diagnosis of the main disease and cause of death, which validated our initial proposition that this diagnostic method could be a rapid and viable alternative to CA in determining post-mortem diagnosis of a VHS.
The excellent correlation between MIA-US and CA in this group of patients may be explained by some factors. First, the acknowledged accuracy of ultrasound to guide needles into the viscera affected by this viral disease, especially the liver, kidneys, and lungs. Second, important technical aspects were applied to obtain tissue samples, involving the use of the portable ultrasound equipment and Tru-Cut 14G needles. Third, procedures were performed by interventional radiologists after a training period. The ultrasound guidance, ensuring the researchers about the organ or region that underwent needle biopsies, favored good quality samples in most patients, thus allowing precise histopathologic diagnosis to be made. Technical limitations were observed in the access to the spleen and heart of some patients, due to their anatomical locations, i.e. the heart being partially obscured by the sternum and the spleen by the rib cage and intestinal gas distension. The procedure turned out to be a simple and reliable method of post-mortem tissue sampling that could be applied in remote areas after adequate training.
Our results are in concert with those of other researchers [12–18,26,27]. In the series by Fariña et al. [12], there was an 83% diagnostic concordance between MIA-US and CA. Castilho et al. [14] reported a success rate of 83% for the determination of cause of death by MIA-US in a study of 30 patients with a predominance of infectious diseases. Recently, Martinez et al. [15] compared MIA-US and CA in 30 patients and obtained an 89.5% concordance for the detection of an infectious agent directly involved in the cause of death. Conversely, Cox et al. [13] found a 57% concordance between MIA-US and CA for infectious major diagnoses, mainly present in the lungs (63%), liver (44%), and spleen (34%). Result differences among studies possibly relate to different patient populations and different causal mixes.
Our results further showed that MIA-US is also a reliable tool to obtain samples with sufficient quality for molecular studies and microorganism identification and to assess the systemic involvement of the disease. Besides typical hepatic changes observed on histological examination, IHC and/or RT-PCR allowed definite viral detection in 94% of the patients. These results encourage further development of local technologies in endemic regions, which might provide access to real-time research data, improving epidemic preparedness.
In addition to the liver, the main affected organs at CA were the lungs and kidneys [19]. Lung biopsies showed that pulmonary hemorrhage and suppurative pneumonia were important complications of YF infection, significantly contributing to death. Besides the more prevalent bacterial pneumonia, MIA-US was able to detect severe pulmonary aspergillosis in one patient, demonstrating an immunosuppressive state associated with fatal YF. Shock-induced acute tubular necrosis was related to kidney dysfunction and could be detected in all kidney biopsies. Interestingly, in one patient with pyelonephritis, the etiologic agent (Aspergillus spp.) could be identified at MIA-US but not at CA. YF-induced glomerulonephritis could be observed in most kidney biopsies [24]. One of the 17 patients was a 24-year-old, 27-week pregnant woman. In this specific case, MIA-US was able to assess the placenta and provided adequate placental tissue for analysis. Therefore, we believe that MIA-US can be an important tool to study maternal deaths and maternal-fetal transmission.
The limitations of MIA-US in the present study were related to changes observed at autopsy that were not assessed by MIA-US. These mainly included changes related to GI tract and CNS. Shock-induced ischemic pancreatic alterations, steatonecrosis, and gastrointestinal bleeding, as well as cerebral edema, were common and important findings that certainly contributed to death. Therefore, for diseases that primarily affect the CNS and/or GI tract, strategies to include assessment of these organs should be considered.
In the past years, we have been globally identifying an unprecedented number of emerging infections [1,4,5,18]. This phenomenon is probably associated with a complex interaction of factors such as human behavior, environmental changes and vector proliferation worldwide [1]. Precise post-mortem diagnosis during the first periods of an emerging epidemic would represent an improvement in identifying the specific etiologic agent, with substantial impact in disease monitoring. For instance, it is quite plausible that if post-mortem brain sampling had been performed in the early cases of microcephaly in Northeastern Brazil, the identification of Zika virus infection could have been made earlier [28]. Moreover, protective measures could have been taken more effectively. However, such measures are hindered by the lack of autopsies in areas prone to be affected by emerging infectious agents. In addition, because of its minimal invasiveness, MIA-US may represent a safer procedure to health authorities who investigate the emerging infectious diseases of high (Ebola, for instance) or unknown lethality.
In this scenario, our results indicate that MIA-US represents a portable, low-cost post-mortem technique that can be useful for the rapid monitoring and surveillance of infectious diseases spread and outbreaks. The adoption of consolidate protocols, associated with international partnerships to use the resources of molecular diagnosis to regions where such resources are scarce, may, theoretically, expand the horizons of surveillance of global infectious threatens.
In conclusion, our results show that MIA-US is a reliable tool for the post-mortem diagnosis of YF and can be used as an alternative to conventional autopsy in regions at risk for viral hemorrhagic fever of different etiologies.
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10.1371/journal.ppat.1006400 | Structural insights into reptarenavirus cap-snatching machinery | Cap-snatching was first discovered in influenza virus. Structures of the involved domains of the influenza virus polymerase, namely the endonuclease in the PA subunit and the cap-binding domain in the PB2 subunit, have been solved. Cap-snatching endonucleases have also been demonstrated at the very N-terminus of the L proteins of mammarena-, orthobunya-, and hantaviruses. However, a cap-binding domain has not been identified in an arena- or bunyavirus L protein so far. We solved the structure of the 326 C-terminal residues of the L protein of California Academy of Sciences virus (CASV), a reptarenavirus, by X-ray crystallography. The individual domains of this 37-kDa fragment (L-Cterm) as well as the domain arrangement are structurally similar to the cap-binding and adjacent domains of influenza virus polymerase PB2 subunit, despite the absence of sequence homology, suggesting a common evolutionary origin. This enabled identification of a region in CASV L-Cterm with similarity to a cap-binding site; however, the typical sandwich of two aromatic residues was missing. Consistent with this, cap-binding to CASV L-Cterm could not be detected biochemically. In addition, we solved the crystal structure of the corresponding endonuclease in the N-terminus of CASV L protein. It shows a typical endonuclease fold with an active site configuration that is essentially identical to that of known mammarenavirus endonuclease structures. In conclusion, we provide evidence for a presumably functional cap-snatching endonuclease in the N-terminus and a degenerate cap-binding domain in the C-terminus of a reptarenavirus L protein. Implications of these findings for the cap-snatching mechanism in arenaviruses are discussed.
| Arenaviruses occur worldwide and can cause severe, often fatal hemorrhagic fever in humans. Vaccines and effective treatments are not available. Arenaviruses replicate in the cytoplasm of infected cells and since they cannot synthesize cap-structures they use a mechanism called cap-snatching to steal cap structures from host mRNAs for viral transcription. This mechanism is an attractive drug target, as it is essential for virus replication and virus specific. However, the arenaviral components of this mechanism are poorly defined compared to influenza virus, the prototypic cap-snatching virus. We present the first crystal structures of two putative components of the California Academy of Sciences arenavirus cap-snatching machinery, namely the isolated N- and C-termini of the viral RNA polymerase (L protein). The N-terminus harbors what looks like a functional cap-snatching endonuclease. The L protein C-terminus, despite complete sequence divergence, shows overall structural similarity to the C-terminal region of influenza virus polymerase PB2 subunit, suggesting a common evolutionary origin. A domain clearly related to the PB2 cap-binding domain is present, although cap-binding could not be biochemically demonstrated. The determined structures provide the basis for future research to unravel the details of the arenavirus cap-snatching mechanism and its potential as a target for drug development.
| The family of arenaviruses is divided in two genera: mammarenaviruses and reptarenaviruses. With the notable exception of Tacaribe virus, rodents are described as the natural reservoirs for mammarenaviruses. Reptarenaviruses have only been found in captive snakes [1]. Some arenaviruses such as Lassa virus (LASV), Junin virus and Machupo virus, can cause severe human disease with hemorrhagic and neurological symptoms. To date, the only drug available for treatment of arenavirus infections is ribavirin, which presumably targets viral replication [2].
Arenaviruses are enveloped particles that contain two single stranded negative sense RNA segments. The two genome segments code for four viral proteins, the nucleoprotein (NP), the glycoprotein-precursor, the small matrix protein Z and the large > 200 kDa L protein which harbors the viral RNA-dependent RNA polymerase. The minimal viral components for genome replication and transcription are the viral RNA, NP, and the L protein [3]. The L protein synthesizes two distinct RNA species: (i) the antigenomic and genomic RNA as products of genome replication and (ii) the shorter capped viral mRNAs during transcription. To initiate viral transcription, the L protein presumably uses a process called cap-snatching. It is assumed that the L protein cleaves host cell mRNAs downstream of the 5'-cap structure and uses this short capped RNA as a primer for viral mRNA synthesis. Consistent with this hypothesis 4–5 non-templated nucleotides are found at the 5'-ends of viral mRNAs and there is an endonuclease in the N-terminal region of the L protein [4–7]. The prototype of cap-snatching viruses is influenza virus [8], which harbors an endonuclease in the PA subunit of the viral polymerase as well as a cap-binding site in the PB2 subunit [9–11]. Given the phylogenetic relatedness and similarities in the replication cycle of orthomyxoviruses and arenaviruses—both are segmented negative strand RNA viruses—it is reasonable to assume that the arenavirus L protein harbors a cap-binding site as well, although there is no direct evidence for this [12]. Previous functional data obtained with a LASV replicon system suggested that the cap-binding site might be located in the C-terminus of the L protein [13].
To further characterize the cap-snatching machinery of arenaviruses, we attempted to solve the structure of N- and C-terminal domains of L proteins of various arenaviruses. Eventually, we have been successful with the L protein of the California Academy of Sciences virus (CASV), which is a reptarenavirus. Here we present the crystal structures of the two terminal domains of the CASV L protein: the cap-snatching endonuclease in the N-terminus and the 326 C-terminal residues, which, by analogy to LASV, might play a role in transcription [13]. The active site of the endonuclease is nearly identical to other related enzymes, suggesting that reptarenaviruses use a cap-snatching mechanism for mRNA synthesis. The C-terminal domain is structurally related to the influenza virus PB2 protein and features a putative non-functional cap-binding site. We speculate about its role in the cap-snatching mechanism of arenaviruses and discuss our data in the context of available structural and functional data from other segmented negative strand RNA viruses.
To obtain soluble protein fragments of the C-terminal domain, we cloned and tested more than 120 different L protein fragments from 20 arenavirus species covering a wide phylogenetic spectrum for soluble expression in Escherichia coli (see S3 Table). Fifteen percent of the proteins were initially soluble. Soluble candidates were purified by nickel affinity and size exclusion chromatography and tested for stability. About five percent of the fragments were monodisperse and stable and used for crystallization trials. Optimization of expressed fragments using bioinformatics, limited proteolysis, and thermal stability assays led to the C-terminal 326 amino acids of the CASV L protein (residues 1721–2046; residue numbering refers to the full-length L protein) with N-terminal His-tag as best candidate for structure determination.
After His-tag cleavage, the purified seleno-methionine-labelled protein was successfully crystallized and the structure was solved using the single anomalous dispersion method. The protein (called CASV L-Cterm) crystallized in space group P212121 with two molecules per asymmetric unit and the structure could be refined to a resolution of 2 Å (Fig 1A and 1B, S1 Table). Except for residues 1748, 1762 and 1768 in chain A and the region comprising residues 2034–2040 in chain B, clear electron density was observed for the structure. The protein crystallized as a dimer, which is not fully symmetric. The only notable difference between the monomers lies in the flexible loops connecting the two domains described below. This dimeric form is also observed in solution as revealed by size-exclusion chromatography and SAXS measurements (Fig 1C, S1A Fig). The protein monomer is U-shaped and consists of two separate domains, (i) a mainly α-helical domain (domain 1) composed of residues 1721–1793 and 1894–2046 with a long C-terminal tail and (ii) a domain (domain 2) consisting of a large β-sheet as well as one long and two short α-helices (residues 1794–1894) (Fig 1B, blue and green respectively). The second domain is inserted into the sequence of the first one and both domains are connected by two long flexible linkers with barely any additional contacts.
In the crystallized dimer the two U-shaped monomers interlock with each other to form a ring with a hole in the middle with a buried surface area of approximately 3000 Å2 between the monomers. The most intensive intermolecular contacts are between the very C-terminal 40 residues of each chain (buried surface area 1100 Å2).
To identify known structural homologs of our structure we used the DALI program for protein structure comparison [14] and performed the search with the whole monomer and with the two domains separately. For the mainly α-helical domain 1, no meaningful hit could be identified. The results included a variety of proteins such as exportins, importins, protein phosphatases, cytoskeleton-associated proteins, glutathione S-transferase as well as the eIF4G subunit of eukaryotic translation initiation factor 4F. All these hits had very low Z-scores (< 4.6) and no convincing structural similarity to L-Cterm.
Interestingly, for L-Cterm domain 2 the list contained the cap-binding domain of influenza virus PB2, which was also found when using the full monomer of CASV L-Cterm as search model. Other hits for domain 2 were acetyltransferases, sulfatases, methyltransferases, β-lactamases, and TATA-box binding proteins, again with relatively low Z-scores (< 5.0).
Despite a complete lack of sequence homology CASV L-Cterm and influenza PB2 show a remarkable similarity in overall domain architecture and sub-domain topology (Figs 2 and 3, influenza virus PB2 domains are drawn according to structure from ref. [15]). First, part I of CASV L-Cterm domain 1 (residues 1721–1790) is similar to the mid-domain of influenza virus PB2. Both are composed of four α-helices that are followed by a loop connecting with L-Cterm domain 2 or the PB2 cap-binding domain, respectively (Figs 2B and 3). Second, L-Cterm domain 1 part II (residues 1896–1924) is similar to the link region of PB2; both comprise a three-stranded β-sheet (Figs 2B, 2D and 3). Third, L-Cterm domain 1 part III (residues 1925–2046) corresponds to PB2 627-domain. Both regions comprise an α-helical bundle followed by a four-stranded small β-sheet, albeit in different orientations (Fig 2D). Only the acidic C-terminal tail of CASV L-Cterm (see also S2 and S10 Figs) is absent in influenza, which instead has a small domain containing the terminal nuclear localization sequence.
Most importantly, the highest degree of similarity was seen between the L-Cterm domain 2 and the PB2 cap-binding domain (Fig 2C). Both are formed by an antiparallel β-sheet packed against 3–4 α-helices. PB2 has a β-hairpin structure inserted between two strands of the β-sheet, which is lacking in domain 2 of L-Cterm. The latter features only a long loop at the homologous position (Figs 2C and 3). In PB2, the cap is bound in between F404 protruding from the end of the long helix (Fig 4A, right panel, helix shown in light green) and H357 located in the β-hairpin. Domain 2 of L-Cterm also contains an aromatic residue (Y1872) at the end of the homologous long helix (Fig 4A, left panel) pointing in the same direction as the F404 in PB2. As the β-hairpin is absent in the CASV L-Cterm, there is no homologue for the histidine residue. A possible candidate in L-Cterm to form an aromatic sandwich as seen in PB2 [9] could be W1818 that protrudes from the second β-strand. However, this residue is not in a conformation to form an aromatic sandwich as seen in PB2. The hypothetical conformational changes needed for W1818 side chain to get engaged in such an interaction are not possible in our structure, as P1810 from a neighboring loop tightly interacts with W1818 and holds the loop and thus the side chain of W1818 in place (Fig 4C). In conclusion, L-Cterm domain 2 is structurally similar to the PB2 cap-binding domain, although the typical aromatic sandwich for cap-binding is not complete.
Besides the structural organization of the isolated domains, their arrangement in the primary structure is conserved between influenza virus and CASV (Figs 2A and 3): in both PB2 and L-Cterm the cap-binding domain and domain 2, respectively, are inserted in the polypeptide chain at similar positions via two flexible linkers.
To test whether the CASV L-Cterm might bind to cap-structures despite an unfavorable arrangement of the aromatic residues in the crystal, we conducted several experiments using the cap-analogue m7GTP. First, the cap-analogue was soaked into the CASV L-Cterm crystals. However, electron density did not appear in the cavity formed by Y1872, F1806, and W1818, i.e. in the position expected by comparison to PB2 (Fig 4A and 4B). Instead, the cap-analogue was bound to F1839 at the periphery of the β-sheet in between the two CASV L-Cterm monomers. There was no second aromatic residue found in any symmetry related molecule suggesting m7GTP was not bound by an authentic cap-binding site. In fact the observed electron density was neither strong nor covering the full m7GTP molecule (Fig 4B). As mentioned, the dimeric form of the protein in the crystal is not fully symmetric and we found the m7GTP only bound between domain 2 of chain A and domain 1 of chain B, where the interface is slightly more open compared to the interface between domain 2 of chain B and domain 1 of chain A. We also tested the cap-binding ability of CASV L-Cterm in m7GTP-agarose pull-down assays. Whereas PB2 and eukaryotic initiation factor 4E (eIF4E), a eukaryotic cap-binding protein, bound to m7GTP-agarose, we could not detect binding of CASV L-Cterm (S6A Fig). Additionally, we could not observe an effect of m7GTP on the thermal stability of CASV L-Cterm or binding of CASV L-Cterm to capped RNA in a radioactive gel shift assay (S7 and S6B Figs).
The dimer formation observed for CASV L-Cterm both in solution and in the crystal is presumably an artifact due to expression of the isolated C-terminal fragment of the L protein and not existent in the context of the full-length L protein. As the putative cap-binding site is close to the dimer interface, we tested whether the presence of L-Cterm domain 1 and/or the dimerization of CASV L-Cterm may prevent the protein from binding to m7GTP by locking the protein in a non-natural conformation. To this end, we attempted to block dimerization of L-Cterm. We analyzed the dimer interface and designed a mutant protein in which the C-terminal 14 residues are lacking (deltaC). These mostly negatively charged residues interact with a positively charged patch on the second molecule (S10 Fig), forming one third of the dimer interface. The deltaC construct was indeed purely monomeric according to SAXS measurements (S1C Fig), however, it did not bind to m7GTP-agarose (S6A Fig) and was not thermally stabilized by m7GTP (S7 Fig). Although weak binding to RNA was observed in gel shift assays, this affinity was not cap-specific (S6B and S6C Fig). Therefore, no further experiments were conducted with this fragment.
To further substantiate that L-Cterm domain 1 has no influence on the conformation of L-Cterm domain 2, we crystallized and solved the structure of the isolated domain 2 (Fig 5A, S1 Table). This structure was refined to a resolution of 1.8 Å. CASV L-Cterm domain 2 also crystallized as a dimer but—due to absence of domain 1—with a completely different and much smaller interface compared to CASV L-Cterm. The protein also appeared as a dimer in solution as shown by SAXS (Fig 5B and S1B Fig). Superimposition of the isolated Cterm domain 2 with its counterpart in the full CASV L-Cterm structure shows only small differences in the loop upstream of W1818 and no major rearrangement of potential cap-binding side chains, even though B-factors are relatively high around the putative cap-binding site (Fig 5C and 5D). Co-crystallization of the domain with m7GpppG, m7GTP, GTP or ATP did not result in additional electron density.
Again, we did not detect binding to m7GTP-agarose of the isolated CASV L-Cterm domain 2 (S6A Fig) nor a thermal stabilization of the protein by m7GTP (S7 Fig). Assuming that the cap-structure alone might not be sufficient for binding, we also carried out binding experiments in a native gel using capped RNA. We detected a shift of the RNA with PB2, but not with L-Cterm domain 2 (S6B Fig).
As neither a monomeric form of CASV L-Cterm (deltaC) nor a dimeric form with a different dimer interface (domain 2) binds m7GTP, we conclude that the dimerization of the protein and the presence of domain 1 are not responsible for the lack of cap-binding activity.
The cap-snatching mechanism has been proposed and characterized so far only for mammarenaviruses based on (i) sequencing results showing 4–5 non-templated nucleotides at the 5' end of viral mRNAs and (ii) structural and functional data demonstrating the existence of an endonuclease in the N-terminus of the L protein [4, 5, 16]. Therefore, we aimed to provide additional evidence for a cap-snatching machinery in reptarenaviruses. We focused on the N-terminus of the L protein, where the endonuclease should be located.
In a sequence alignment of arenavirus L protein N-termini, the key active site residues of the endonuclease were found to be highly conserved across the virus family, even in reptarenaviruses (S8 Fig). Therefore, we expressed and purified the first 205 residues of the CASV L protein as N-terminally His-tagged protein. As expected from the metal-dependent enzymatic mechanism of viral endonucleases, thermal stability assays showed a concentration dependent stabilization of the protein by manganese ions with an increase in melting temperature of up to ~10°C at a concentration of 10 mM manganese (protein concentration in the assay 4.2 μM) (Fig 6D). After His-tag cleavage, the protein was crystallized and the crystals diffracted to a resolution of 1.9 Å. Molecular replacement using any of the three known arenavirus endonuclease structures or their subdomains as search models was not successful. Therefore we expressed the protein with seleno-methionines and crystallized it after His-tag cleavage in the presence of manganese ions. Phases were determined using the single anomalous dispersion method and used to solve the structure with the dataset from the better diffracting native crystals. The structure was refined to a resolution of 1.9 Å. The native protein crystallized in space group P212121 with four molecules per asymmetric unit. The structures of the four molecules are very similar with the only difference in the C-terminal 15 residues, which are not visible in all molecules (RMSD between 0.227 and 0.317 Å). The CASV endonuclease has basically the same fold as endonucleases from LASV, Pichinde virus (PICV), and lymphocytic choriomeningitis virus (LCMV) (Fig 6A and 6B, S1 Table) even though the amino acid sequence of this protein is hardly conserved among these viruses (identity ranging between 20 and 55% and similarity ranging between 54 and 79%, S11 Fig). Slight differences between the structures were observed in the long α-helix parallel to the β-sheet (Fig 6A and 6B, α-helix shown in orange), which is separated into two helices in CASV endonuclease domain compared to the other structures, as well as in the helical region shown in green, which is composed of four to six helices of different length and orientation. RMSD between the structures is in the range of 1.372 Å (CASV vs. LCMV) to 1.856 Å (CASV vs. LASV). The highly conserved residues of the endonuclease active site are positioned as in other arenavirus endonuclease structures (Fig 6E). The electrostatic surface potential of CASV endonuclease is also comparable to the other endonuclease structures with positively charged patches next to the negatively charged active site cavity (Fig 6C). We also tested for endonuclease activity using our previously established RNA cleavage assay [17], however, we did not observe enzymatic activity of the isolated domain (S9 Fig).
Cap-snatching was first discovered in influenza virus [8]. The structures of the individual domains responsible, namely the endonuclease in PA and the cap-binding domain in PB2, have been solved [9–11]. From the structure of the complete influenza polymerase a mechanism for cap-snatching and cap-dependent transcription has been proposed [18]. The cap-snatching mechanism is an attractive drug target, because the corresponding functional domains of the polymerase are both essential and virus specific. After the identification of non-templated host-derived sequences at the 5' ends of mRNAs of other segmented negative strand RNA viruses cap-snatching was proposed to be a common mechanism in these viruses [4, 6, 7, 19–24]. However, in contrast to the endonuclease, which has recently been shown to be located at the very N-terminus of the L protein of mammarena-, orthobunya-, and hantaviruses using structural and molecular biological techniques [5, 16, 17, 25, 26], the cap-binding domain has not been identified in any arena- or bunyavirus so far.
We solved the structure of the 326 C-terminal residues of a reptarenavirus L protein. Despite the lack of any significant sequence homology, the domains of this 37-kDa fragment are structurally similar to the cap-binding and adjacent domains of influenza virus PB2 [15]. Both proteins share a common architecture with respect to the linear arrangement of the domains and of the secondary structure elements. The highest degree of similarity is observed between the PB2 cap-binding domain and domain 2 of L-Cterm. Comparison of these two domains led us to identify a potential cap-binding site in L-Cterm. However, this site does not feature the typical sandwich arrangement of two aromatic residues [27]. While one aromatic residue (Y1872) is in a similar position as its putative homologue in PB2, the hairpin, which provides the second aromatic residue in PB2, is missing in CASV. Several attempts to biochemically or structurally verify the presence of a functional cap-binding site failed. In addition, we solved the crystal structure of the corresponding endonuclease in the N-terminus of the reptarenavirus L protein. It shows a typical endonuclease fold as found in other segmented negative strand RNA viruses and an active site topology that is essentially identical to that of known mammarenavirus endonuclease structures [5, 10, 17, 26, 28].
The main question arising from these data is whether the L protein of CASV—and by inference the L protein of other arenaviruses—contains a functional cap-snatching machinery as described for influenza virus polymerase? There is clear evidence from experiments with replicon systems for LASV and LCMV that the endonuclease at the N-terminus of the L protein is essential for virus transcription [5, 25]. The structures obtained for LASV and LCMV endonuclease domains, specifically the conformation of the active sites, indicate the existence of a functional enzyme, even though catalytic activity of the isolated domains is absent or poor compared to the endonucleases of influenza virus or bunyaviruses [5, 10, 17, 26]. The conserved active site topology in the CASV endonuclease structure and the stabilization of the protein by Mn2+ are strong arguments for the presence of a functional endonuclease in the L protein of reptarenaviruses, even though, identical to the isolated endonuclease domain of LASV, nuclease activity was undetectable biochemically [26]. As shown for the influenza virus endonuclease, an activation of the enzyme in the context of the complete L protein is conceivable, partly due to enhanced RNA binding [15]. Unfortunately, we cannot provide functional data for the involvement of the CASV endonuclease in viral transcription, as replicon systems for reptarenaviruses are not available. Nevertheless, in conjunction with available evidence from mammarenaviruses [5, 16, 25, 26] we consider the structural data provided here sufficient to claim the existence of a cap-snatching endonuclease in reptarenaviruses, even without biochemical proof.
In contrast to the endonuclease, both structural and biochemical data suggest that the putative cap-binding site in the C-terminus of CASV L protein is not functional. The data obtained with a dimerization deficient mutant and the isolated domain 2 of L-Cterm exclude that the interaction between domains 1 and 2 at the dimerization interface accounts for the absence of a functional cap-binding site.
We could also neither demonstrate binding of C-terminal L protein fragments of mammarenaviruses to m7GTP or capped RNA nor the thermal stabilization of these proteins by m7GTP (shown for a soluble LASV L-Cterm fragment in S6 and S7 Figs) indicating that the inability to bind cap-structures is not specific for CASV.
In a previous study, we have identified several amino acid residues in the C-terminus of LASV L protein that are critical for viral transcription but dispensable for genome replication [13]. However, the presence of a cap-binding site could not be inferred, as no motif exists to facilitate its identification at sequence level [27]. To correlate this functional data from LASV with our atomic structure of CASV L-Cterm, we attempted to align the primary sequences of both proteins. Unfortunately, this was not feasible due to the extremely low sequence conservation in the C-terminus of arenavirus L proteins (S12 Fig). Therefore, we used predicted secondary structures of LASV and other arenavirus L protein C-termini [29–31] together with the determined secondary structure from the influenza virus PB2 and CASV L-Cterm crystal structures as a guidance to propose a sequence alignment of these viruses (S2 Fig). Although this alignment has to be interpreted with caution, it facilitated inference of LASV counterparts to CASV L protein residues potentially involved in cap-binding and vice versa (S3 Fig). Specifically, residue F2042 in LASV L protein appeared to be the best homolog candidate to Y1872 in CASV L protein and F404 in influenza virus PB2. We tested various LASV L protein mutants with exchanges at this and adjacent positions in the LASV minireplicon system (S3 and S4 Figs, S2 Table). Most importantly, F2042 in LASV L protein could be replaced by the polar and hydrophilic serine without any effect on the transcriptional activity of the L protein. This phenotype is not compatible with a function of this residue in an aromatic sandwich for cap-binding. In addition, several New World arenaviruses lack an aromatic residue in the region corresponding to F2042 in LASV L [13]. On the other hand, the selective defect in transcription observed with LASV L protein mutants W1915E, E2041L, E2041K, and F2042D (S4 Fig) supports our previous findings that the C-terminus of arenavirus L protein is somehow involved in viral transcription [13]. According to the sequence alignment in S3 Fig, residues implicated in LASV transcription map to various regions of both domains 1 and 2 of CASV L-Cterm (S5 Fig). A possible explanation for the transcription defective phenotype of respective mutants is that these residues play a role in the structural integrity of the C-terminus or in interactions with other viral or cellular factors involved in viral transcription. In summary, the CASV L-Cterm structure, the LASV minireplicon data as well as the cap-binding and thermal shift assays collectively point to the absence of a functional cap-binding site in this region.
The clear structural similarities between influenza virus PB2 and CASV L-Cterm are consistent with the phylogenetic relatedness of influenza virus and arenaviruses. The cap-binding function might have been lost during arenavirus evolution, while the domain might have gained or maintained other functions in virus transcription [13]. A similar situation was proposed for Thogoto virus, an insect transmitted orthomyxovirus. Thogoto virus polymerase PA and PB2 subunits contain domains structurally similar to the endonuclease and cap-binding domains of influenza virus polymerase but with amino acid substitutions in both active sites that render them functionally inactive [32]. The hypothesis of a non-functional cap-binding site in CASV would imply that the cap-snatching mechanism of reptarenaviruses, and perhaps arenaviruses in general, is divergent from that of influenza virus. There are indeed significant differences in the transcription initiation between both virus families. Influenza virus depends on nuclear RNA polymerase II as provider of capped host cell RNA [33]. As arenaviruses replicate in the cytoplasm, they must have acquired a different source of cellular capped RNAs. This could involve cellular cap-binding proteins [34], which may substitute for a cap-binding domain in the L protein. Additionally, more than 50% of the arenavirus L protein has neither been structurally characterized nor assigned a distinct function. Thus it is still possible that a different cap-binding site could be present even in the L protein, although in the corresponding region of bunyavirus L protein, no cap-binding domain is apparent [28]. Arenavirus NP has also been proposed as a cap-binding protein [35] although this hypothesis could not be confirmed using the LASV minireplicon system [36] and in the crystal structure of the NP-RNA complex the suggested cap-binding site was shown to be an RNA binding site [37].
An alternative and speculative hypothesis is that the potential cap-binding site in CASV might be able to adopt alternative configurations; the binding site may switch between active and inactive conformations. These may, for example, correspond to transcription and replication mode of the L protein, respectively. The putative cap-binding site in CASV L-Cterm, inactive in isolation, might become activated in the physiological RNP context as a result of interactions with other parts of the L protein, other viral proteins such as NP or Z [38–40], cellular factors, virus RNA and/or host cell RNA. A hypothetical viral or cellular partner could induce a conformational change, which facilitates the formation of a functional cap-binding site. Binding of viral RNA also has a considerable effect on the configuration of the cap-binding and endonuclease domains in the context of the complete influenza virus polymerase complex [15, 41]. Moreover, induced fit is not unknown in cap-binding proteins: for example, the cap-binding side chains of eIF4E undergo significant rearrangement upon ligand binding [42].
In conclusion, we solved the structures of the isolated N- and C-termini of CASV L protein. The N-terminus harbors a presumably active cap-snatching endonuclease, which is structurally similar to its homologs from mammarenaviruses. The C-terminus shows structural similarity to the influenza virus cap-binding protein PB2, although the cap-binding site is not functional in the isolated domain. Our data provide insight into possible scenarios of transcription initiation in arenaviruses. Future experiments in the context of the full-length L protein may elucidate the detailed mechanisms.
Based on an alignment of arenavirus L protein C-terminal sequences, we designed L protein expression constructs of different lengths for 20 arenavirus species covering the full phylogenetic spectrum. All sequences were cloned into pOPINF vectors [43] using the In-Fusion HD EcoDry Cloning Kit (Clontech). Solubility of fragments was assessed in a medium-throughput setup with different E. coli strains, autoinduction medium and small-scale His-tag purification and the expression and purification subsequently optimized for soluble proteins. The CASV L-Cterm and domain 2 were expressed in E. coli strain BL21 Gold (DE3) (Novagen) at 17°C overnight using TB medium and 0.5 mM isopropyl-β-D-thiogalactopyranosid for induction. After pelleting, the cells were resuspended in 50 mM Tris, pH 8.0, 300 mM NaCl, 10 mM imidazole, 0.5 mM phenylmethylsulfonyl fluorid, 0.4% (v/v) triton X-100 and 0.025% (w/v) lysozyme and subsequently disrupted by sonication. The protein was purified from the soluble fraction after centrifugation by Ni affinity chromatography. A buffer containing 50 mM imidazole was used for the washing steps and another buffer with 500 mM imidazole for the elution of the protein. Affinity chromatography was followed by size exclusion chromatography (Superdex 200, 50 mM Tris, pH 7.5, 150 mM NaCl, 10% glycerol, 2 mM dithiothreitol) and removal of the N-terminal His-tag by a GST-tagged 3C protease at 4°C overnight. Furthermore, the protein was purified by anion exchange chromatography (loading buffer: 50 mM Tris, pH 7.5, 100 mM NaCl, elution with salt gradient up to 1M NaCl) and a second size exclusion chromatography (see above). Purified proteins were concentrated using centrifugal devices, flash frozen in liquid nitrogen, and stored in aliquots at –80°C.
Based on an alignment of arenavirus L protein N-terminal sequences, we designed L protein constructs of different lengths for CASV endonuclease. Cloning procedures, solubility testing, and large-scale expression was essentially done as described for CASV L-Cterm constructs. After pelleting, the cells were resuspended in 50 mM Na-phosphate, pH 6.8, 300 mM NaCl, 10 mM imidazole, and Complete protease inhibitor EDTA-free (Roche). E. coli were disrupted by sonication and the protein was purified by Ni affinity chromatography from the soluble fraction after centrifugation. A buffer containing 50 mM imidazole was used for the washing steps and the protein was eluted by a buffer containing 100 mM Na-phosphate, pH 6.8, 300 mM NaCl and 250 mM imidazole. The His-tag was removed by incubation with a GST-tagged 3C protease at 4°C overnight with simultaneously dialyzing against 20 mM Tris pH 7.5, 100 mM NaCl, 1mM EDTA and 2.5% glycerol. Furthermore, the protein was purified by anion exchange chromatography (elution with salt gradient up to 1M NaCl) and size exclusion chromatography (Superdex 200, 20 mM Na-phosphate, pH 6.0, 300 mM NaCl, and 5% glycerol). Purified proteins were concentrated using centrifugal devices, flash frozen in liquid nitrogen, and stored in aliquots at –80°C.
Protein expression was done in M9 minimal medium [44] supplemented with 1 mM MgSO4, 0.4% glucose, 0.0005% thiamine and 200 μM FeSO4 at 17°C overnight. Incorporation of seleno-methionine was achieved by metabolic inhibition of methionine biosynthesis in E. coli prior to addition of seleno-methionine and induction with 1 mM isopropyl-β-D-thiogalactopyranosid. Cells were harvested and the labelled protein was purified as described but in presence of 5 mM β-mercaptoethanol for Ni affinity purification and 10 mM dithiothreitol for the remaining purification steps.
The CASV L-Cterm protein was produced with seleno-methionine labelling. Protein crystals grew at 12 mg/ml protein concentration in 37% Jeffamine ED-2001, 2 mM TCEP and 100 mM HEPES pH 7.1 in a sitting drop vapor diffusion setup at 20°C. L-Cterm domain 2 crystallized in presence of 100 mM Tris, pH 7.9, 1.3 M trisodium citrate at 10 mg/ml protein concentration by sitting drop vapor diffusion at 20°C. Crystals were flash frozen in liquid nitrogen with 30% glycerol as cryo protectant. Datasets for CASV L-Cterm were obtained at the ID29 beamline of the ESRF, Grenoble, France. Data for L-Cterm domain 2 crystals were collected at beamlines P13 and P14 of PETRA III at Deutsches Elektronen Synchrotron (DESY), Hamburg, Germany. Datasets were processed with iMosflm [45]. Phases for the CASV L-Cterm structure were determined using the single anomalous dispersion method and PHENIX AutoSol [46] and then used to solve the structure with a new dataset from better diffracting crystals. The L-Cterm domain 2 structure was solved by molecular replacement with the CASV L-Cterm structure using residues 1794–1894 and PHASER [47]. Both structures were refined by iterative cycles of manual model building in Coot [48] and computational optimization with PHENIX [46]. Visualization of structural data was done using PyMOL (PyMOL Molecular Graphics System, Version 1.7 Schrödinger, LLC.) and UCSF Chimera [49]. Electrostatic surfaces were calculated using PDB2PQR and APBS [50, 51].
The CASV endonuclease protein was produced as a native protein (Endonative) and with seleno-methionine labelling (EndoSeMet), respectively. Protein crystals of the Endonative protein grew at 10 mg/ml protein concentration in 20% PEG 200, 2.5% PEG 3000, and 100 mM MES, pH 5.7, whereas the EndoSeMet protein crystallized in presence of 2% 2-propanol, 8% PEG 4000, 7 mM MnCl2 and 100 mM Na-citrate, pH 5.4, at 8 mg/ml protein concentration. Crystals were obtained in a sitting drop vapor diffusion setup at 6–8°C. Crystals were flash frozen in liquid nitrogen with 30% PEG 400 (Endonative) or 20% ethylene glycol (EndoSeMet) as cryo protectants. Datasets for both proteins were collected at beamlines P13 and P14 of PETRA III at DESY, Hamburg. Datasets were processed with iMosflm [45] and the EndoSeMet structure was solved by the single anomalous dispersion method using PHENIX AutoSol [46]. The Endonative structure was solved by molecular replacement with the EndoSeMet structure using only chain A and PHASER [47]. Refinements, visualization of structures and calculation of electrostatic surface potentials was done as for CASV L-Cterm.
The thermal stability of CASV endonuclease was measured by thermofluor assay [52]. The assay contained a final concentration of 4.2 μM of the endonuclease protein, 100 mM Tris, pH 7.5, 150 mM NaCl, SYPRO-Orange (final dilution 1:1000) and either 10 mM EDTA, various concentrations of MnCl2 or no further additives.
Thermal stability of CASV L-Cterm proteins, LASV L-Cterm and Influenza virus PB2 was tested in presence and absence of m7GTP, GTP and ATP. The final protein concentration in these assays was between 4 and 17 μM (CASV L-Cterm 5.3 μM, L-Cterm deltaC 5.6 μM, L-Cterm domain 2 17.0 μM, LASV L-Cterm 4.1 μM and PB2 10.6 μM). Reactions were carried out in 100 mM Tris, pH 7.5, 150 mM NaCl and SYPRO-Orange.
Proteins were incubated overnight at 4°C or for 2 h at 20°C at a concentration of 50 μg/ml with m7GTP-agarose or blank agarose (both Jena Bioscience), respectively, in a buffer containing 50 mM Tris, pH 7.5, 150 mM NaCl, 10% glycerol, and 0.005% Tween 20. Agarose beads were washed extensively with the mentioned buffer and SDS sample buffer was added to the beads for subsequent SDS-PAGE analysis.
A 40mer polyA RNA substrate was produced by in vitro transcription and radioactively labelled by capping with capping enzymes (Cellscript) and α32P-GTP. In parallel a synthetic polyA 40mer RNA was labelled with T4 polynucleotide kinase (New England Biolabs) and γ32P-ATP. RNA substrates were subsequently purified with a Microspin G25 column (GE Healthcare). Reactions containing 5 pmol of protein and 0.4 pmol total RNA (fraction of radioactively labelled RNA was constant in all reactions and adjusted to facilitate proper detection) were set up in presence of 0.5 U/μl RNasin (Promega), 20 mM HEPES, pH 7.3, 70 mM KCl, 5 mM MgCl2, 0.7 mM dithiothreitol, 15% glycerol and 0.7 μg/μl bovine serum albumin, and incubated for 45 min at 20°C. Samples were subjected to native gel electrophoresis using 4% polyacrylamide Tris-borate-EDTA gels and 0.5-fold Tris-borate buffer. The temperature of the gel during electrophoresis was kept low. Signals were visualized by phosphor screen autoradiography using a Typhoon scanner (GE Healthcare).
Small angle X-ray scattering (SAXS) measurements were performed after size exclusion chromatography in the respective buffers mentioned in the protein purification procedures with different protein concentrations (typically 0.5–5 mg/ml). Data was collected at the SAXS beamline P12 of PETRA III storage ring of the DESY, Hamburg, Germany [53]. Using a PILATUS 2M pixel detector at 3.1 m sample distance and 10 keV energy (λ = 1.24 Å), a momentum transfer range of 0.01 Å–1 < s < 0.45 Å–1 was covered (s = 4π sinθ/λ, where 2θ is the scattering angle). Data were analyzed using the ATSAS 2.6 package [54]. The forward scattering I(0) and the radius of gyration Rg were extracted from the Guinier approximation calculated with the AutoRG function within PRIMUS [55]. GNOM [56] provided the pair distribution function P(r) of the particle, the maximum size Dmax and the Porod volume. Ab initio reconstructions were generated with the program DAMMIF [57]. Ten independent DAMMIF runs were superimposed by SUPCOMB [58] and averaged using the program DAMAVER [57]. The average excluded volume was extracted from the final pdb-file. Structures were visualized using UCSF Chimera.
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10.1371/journal.pcbi.1005759 | Modeling the adenosine system as a modulator of cognitive performance and sleep patterns during sleep restriction and recovery | Sleep loss causes profound cognitive impairments and increases the concentrations of adenosine and adenosine A1 receptors in specific regions of the brain. Time courses for performance impairment and recovery differ between acute and chronic sleep loss, but the physiological basis for these time courses is unknown. Adenosine has been implicated in pathways that generate sleepiness and cognitive impairments, but existing mathematical models of sleep and cognitive performance do not explicitly include adenosine. Here, we developed a novel receptor-ligand model of the adenosine system to test the hypothesis that changes in both adenosine and A1 receptor concentrations can capture changes in cognitive performance during acute sleep deprivation (one prolonged wake episode), chronic sleep restriction (multiple nights with insufficient sleep), and subsequent recovery. Parameter values were estimated using biochemical data and reaction time performance on the psychomotor vigilance test (PVT). The model closely fit group-average PVT data during acute sleep deprivation, chronic sleep restriction, and recovery. We tested the model’s ability to reproduce timing and duration of sleep in a separate experiment where individuals were permitted to sleep for up to 14 hours per day for 28 days. The model accurately reproduced these data, and also correctly predicted the possible emergence of a split sleep pattern (two distinct sleep episodes) under these experimental conditions. Our findings provide a physiologically plausible explanation for observed changes in cognitive performance and sleep during sleep loss and recovery, as well as a new approach for predicting sleep and cognitive performance under planned schedules.
| Sleep loss is known to cause significant decrements in cognitive performance, but the physiological mechanisms responsible for this response are not well understood. Computational models have been developed to predict how individuals will cognitively perform under acute or chronic sleep loss, but they currently lack an explicit physiological foundation, and do not specifically predict sleep timing. Adenosine is hypothesized to be an important mediator in the effects of sleep loss, as it is a sleep-promoting substance that accumulates in the brain during wakefulness. We developed a mathematical model of the adenosine system in the brain and showed that it can parsimoniously account for not only changes in cognitive performance during acute sleep deprivation, chronic sleep restriction, and recovery, but also changes in sleep patterns during long-term recovery. The model thus provides a quantitative link between complex whole-organism behaviors and underlying molecular and physiologic mechanisms.
| When sleep is restricted, cognitive performance declines, recovering again when adequate sleep is obtained. The dynamics of performance decline and recovery depend on the timescales over which sleep loss occurs. During 1–2 nights of sleep deprivation (continuous wakefulness), cognitive performance declines rapidly, and then returns to baseline after 1–2 nights of recovery sleep [1,2]. However, when sleep restriction is chronic (i.e., multiple nights of insufficient sleep), such as 1–2 weeks of 3–6 hours of sleep per night, performance declines steadily on a timescale of weeks or longer [3,4], and performance remains significantly impaired even after 2–3 nights of recovery sleep [5].
Mathematical models have been developed to describe the effects of different sleep schedules on physiology and cognitive performance. In 1984, Daan et al. [6] developed a mathematical model, called the “two-process model”, to describe the effects of regular sleep schedules and sleep deprivation on EEG slow-wave activity, which is one marker of “sleep debt”. The two-process model assumes that sleep is regulated by two independent processes: a circadian process, which describes the approximately 24-hour rhythm in sleepiness, and the sleep homeostatic process, which describes the tendency to accrue sleep debt and become sleepier the longer one is awake. The dynamics of the sleep homeostatic process consist of exponential saturation towards an upper threshold during wake, with a time constant of ~20 h, and exponential decay towards a lower threshold during sleep, with a time constant of ~4 h in young adults. Variants of the two-process model have also been used to describe changes in cognitive performance with sleep loss [7–12]. This whole family of models, however, fails to describe the long-timescale changes in cognitive performance that occur under chronic sleep restriction [3,5,13,14]. This is because the models lack any time constants longer than ~20 h, so the effects of any particular sleep regime (restriction or recovery) rapidly saturate.
To address this problem, extensions of the two-process model were developed [15–18]. These models include an additional long-timescale process that modulates the upper and/or lower saturation thresholds for the sleep homeostatic process. Adding this additional degree of freedom allows the models to capture changes in cognitive performance under both acute sleep deprivation and chronic sleep restriction. These long-timescale model processes are, however, ad hoc, and not based on physiology. Additional insights and opportunities for intervention design could be gained if these models were based on the physiological processes that underlie the sleep homeostatic process, including the adenosine system in particular.
Sleep-promoting substances, including adenosine, accumulate in multiple regions of the brain during wakefulness [19]. In addition, adenosine A1 receptors in the brain are up-regulated by sleep loss [20,21]. McCauley et al. [17] noted that the dynamics of their model “could be a mathematical representation of the interaction between a neurotransmitter or neuromodulator and its receptor, with the density of both changing dynamically across time awake and time asleep”; they identified the adenosine system as a probable candidate [22]. However, no explicit link was made between their model and the underlying physiology; we show below that their model’s dynamical structure differs from our model of the adenosine system.
Understanding the physiological basis for cognitive impairments associated with sleep restriction is important, given that approximately 30% of the adult US population sleeps less than 7 hours per night, which is below the 7–9 hour range recommended by the National Sleep Foundation [23]. Moreover, impaired cognition due to sleep loss is associated with errors and accidents [24]. Here, we develop an explicit mathematical model of the adenosine system, with the goal of testing the hypothesis that dynamic changes in the concentrations of both adenosine molecules and receptors can account for changes in cognitive performance and sleep patterns under acute sleep deprivation, chronic sleep restriction, and recovery from chronic sleep restriction. Our model is tested against two previously published experimental data sets: (i) psychomotor vigilance test (PVT) data during acute and chronic sleep restriction, and recovery from chronic sleep restriction; and (ii) sleep durations during long sleep opportunities in individuals recovering from low-level chronic sleep restriction.
We first develop a pharmacokinetic model of the adenosine system, including the dynamics of the concentrations of adenosine molecules and adenosine receptors. We use this model to give a new physiological definition of the sleep homeostatic process, and then link this process to performance on the psychomotor vigilance test (PVT). Methods used to estimate parameter values are then described. A schematic of the model and its variables is shown in Fig 1.
Extracellular adenosine concentration increases during wakefulness and decreases during sleep in multiple brain regions, including the basal forebrain where its action is important to sleep regulation [25]. Time-courses for increase and decrease of adenosine concentration have not been precisely characterized, but we can make reasonable physiological assumptions; see e.g., [26]. Specifically, we assume adenosine is produced at a constant rate in wakefulness and at a constant (but lower) rate in sleep, due to the lower (on average) brain metabolism during sleep [27]. We also assume that adenosine follows first-order pharmacokinetics, i.e., it is cleared at a rate proportional to its concentration in both wake and sleep, with clearance being faster during sleep due to active removal of metabolites [28]. Adenosine concentrations can vary between different brain regions [19]; here we calibrate the model against data collected from the basal forebrain, given its important role in sleep regulation [25]. We do not model regional differences in concentrations within the brain.
These assumptions yield the following ordinary differential equation for total adenosine concentration,
χdAtotdt=μ−Atot.
(1)
The solution of this is exponential towards the saturation value μ with a time constant χ. The values of χ and μ depend on sleep/wake state, which is a binary input to the model (i.e., the model can be either awake or asleep at any given time). Across sleep/wake transitions, we demand continuity of Atot.
Total adenosine concentration includes: concentration of unbound molecules, denoted Au; concentration of molecules bound to A1 receptors, denoted A1,b; and concentration of molecules bound to A2A receptors, denoted A2,b. We do not consider A2B or A3 receptors here, due to their much lower affinity for adenosine [29]. Thus,
Atot=Au+A1,b+A2,b.
(2)
Concentrations of the different pools of adenosine depend on the availability of A1 and A2A receptors. For receptor type n (where n is 1 or 2A, abbreviated by 2), we denote total receptor concentration by Rn,tot. Total receptor concentration includes: concentration of unbound receptors, denoted Rn,u; and concentration of bound (occupied) receptors, denoted Rn,b, which by definition equals An,b. Thus,
R1,tot=R1,u+R1,b,
(3)
R2,tot=R2,u+R2,b.
(4)
We use mass-action kinetics to describe the rates of binding and unbinding at each receptor type,
dAudt=−k1,bAuR1,u−k2,bAuR2,u+k1,uR1,b+k2,uR2,b,
(5)
dR1,bdt=k1,bAuR1,u−k1,uR1,b,
(6)
dR2,bdt=k2,bAuR2,u−k2,uR2,b.
(7)
Parameters kn,b and kn,u are rate constants for binding and unbinding at receptor type n, respectively. The equilibrium conditions for Eqs (5)–(7) can be written in terms of ratios of rate constants,
Kd1=k1,uk1,b=AuR1,uR1,b,
(8)
Kd2=k2,uk2,b=AuR2,uR2,b.
(9)
These ratios are the dissociation constants for each reaction and have units of concentration. The value of Kdn can be interpreted as the concentration of unbound adenosine, Au, for which there are equal numbers of bound and unbound receptors: Rn,b = Rn,u.
It has been experimentally observed that the concentration of A1 receptors increases when sleep is restricted and adenosine concentrations are elevated, and decreases to normal following recovery [30]. We hypothesize here that this phenomenon reflects the dynamics of a (homeostatic) cellular response to maintain stable levels of A1 receptor occupancy. When adenosine levels are elevated, a greater fraction of A1 receptors will be occupied. To return to a homeostatic level of occupancy, more receptors must be synthesized. This is a physiologically reasonable hypothesis, as receptor occupancy rates could be sensed and integrated on a per-cell basis. We model these dynamics as:
λdR1,totdt=R1,b−γR1,tot,
(10)
where 0 < γ < 1 is the target occupancy fraction, with equilibrium at R1,b/R1,tot = γ, and λ is a time constant that determines how quickly receptors are up-regulated or down-regulated in response to a change in occupancy.
We assume that R2,tot is fixed, because only A1 receptors have been convincingly demonstrated to up-regulate in response to chronic sleep restriction [30].
The evolution of Atot and R1,tot occurs on the timescale of hours to weeks, which is much slower than the chemical rate constants. The system can therefore be timescale separated by assuming Eqs (5)-(7) are at equilibrium (i.e., they are quasistatic). We can then solve Eqs (8) and (9) for A1,b and A2,b in terms of Atot, R1,tot, and R2,u (the concentration of unbound A2A receptors, which can be assumed to be approximately constant and is estimated below),
A1,b=12[Atot+R1,tot+Kd11−β−(Atot+R1,tot+Kd11−β)2−4AtotR1,tot]
(11)
A2,b=β(Atot−A1,b),
(12)
where
β=R2,uR2,u+Kd2,
(13)
Substituting Eq (11) into Eq (10) gives a closed two-dimensional system for Atot and R1,tot that depends on the values of Kd1 and Kd2, which are known from experiment, and does not depend on the values of the individual kn,b and kn,u parameters.
Human cognitive performance and sleep depend on both the circadian process and the sleep homeostatic process [6]. Here, we model the sleep homeostatic process as the concentration of bound A1 adenosine receptors, R1,b. In this respect, our model differs from previous models, which have usually treated the sleep homeostatic process per se as a predictor for EEG slow wave activity or cognitive performance. We choose bound receptor concentration as the relevant sleep homeostatic variable over total receptor concentration or total adenosine concentration, because it is the downstream consequence of binding that mediates physiological effects. The sleep homeostatic process could also depend on R2,b, as well as other sleep-promoting molecules and receptors, such as cytokines [31], but we choose the adenosine model here as the most parsimonious basis for a computational model, and first probe its explanatory power.
We model the circadian process by a sinusoid with a period of 24 hours. This is a reasonable assumption for individuals who are entrained to the 24-hour day during chronic sleep restriction or recovery, or for individuals who are undergoing a brief acute sleep deprivation under constant conditions [32], which are the experimental conditions modeled here. Under more complicated scenarios, such as shifting time-zones, a dynamic circadian model should be used to account for changes in amplitude and phase [33].
We assume that the overall influence of the circadian and homeostatic processes on sleep and performance is represented by a linear combination of the two processes, which we call the overall sleep drive,
D=R1,b+acosω(t−ϕ),
(14)
where a > 0 is the circadian amplitude, ω = (2π/24)h-1, and ϕ is the clock-time (modulo 24, where zero is midnight) at which the circadian process maximally promotes sleep. This is typically near the core body temperature minimum in the early hours of the morning [33]. Using D, we can predict when the model is sleepy (high values of D) or alert (low values of D).
There is evidence of nonlinear interactions between the circadian and homeostatic processes [34,35], and these have been introduced in some models [8,36]. Here, we choose to start with the linear form, with the possibility of extending the model in future.
As a metric for cognitive performance, we use the number of lapses (defined as responses slower than 500ms) on the 10-minute PVT task. This metric is bounded. It is not possible to have fewer than 0 lapses, and the length of the test imposes a maximum possible number of lapses. Thus, we choose a sigmoid function for converting D into an estimate of PVT lapses,
P=pmax1+e(Dmid−DDs),
(15)
where pmax is the maximum possible number of lapses, Dmid is the value of D for which the half-maximum value of lapses is achieved, and Ds determines the width of the sigmoid. The variables D and P are both continuous functions of time. For comparison with experiments, we sample P at times when a PVT test was performed.
In this section, we describe how the model parameters are fit and how the model outputs are compared to experimental data. We first estimate physiological ranges for a subset of the model parameters and the mathematical relationships between others. Due to the nature of the model and data, we then fit all the parameter values using an iterative approach. For Experiment 1, the dependent variable is PVT lapses, and sleep/wake timing is given as an input to the model via Eq (1). Values of all model parameters are fit at this stage, with the exception of λ, which takes a nominal value. This is valid due to the fact that the model predictions for Experiment 1 are only weakly dependent on λ. The model is then applied to simulate Experiment 2. For this experiment, the dependent variable is daily sleep duration. Two new parameters, Dsleep, and Dwake, are introduced to allow the model to make automatic transitions between sleep and wake (i.e., the model no longer requires sleep/wake timing as an input). The values of the three parameters λ, Dsleep, and Dwake are thus fit to Experiment 2, with all other parameters taking the values previously obtained by fitting to Experiment 1. Finally, the values of Dmid and Ds are recalibrated to ensure the model still optimally fits data from Experiment 1. Details of this fitting procedure are provided below.
In the adenosine system model, some parameters can be estimated from existing data. The dissociation constants for Kd1 and Kd2 for A1 and A2A receptors are 1-10nM and 100–10,000nM, respectively [37]. The fact that Kd1 ≈ Kd2/100 means that A1 receptors have much higher affinity for binding adenosine molecules (i.e., equivalent binding at 1/100 the concentration).
The time constants in Eqs (1) and (10) can be estimated based on the time-course of sleep homeostasis. The hypothesis we wish to test with our model is that variations in Atot on timescales of hours to days can account for short timescale variations in sleep homeostatic pressure such as acute sleep deprivation, whereas variations in R1,tot on timescales of weeks to months can account for long timescale effects of chronic sleep restriction. The value of λ must therefore be large. The longest inpatient chronic sleep restriction experiment to date lasted 3 weeks, with large decreases in PVT performance between weeks 1 and 2, followed by non-significant decreases in PVT performance between weeks 2 and 3 [3]. This suggests λ is on the order of 1–2 weeks. The dynamics of the model in Experiment 1 are found to be relatively insensitive to the value of λ, so we use a nominal value of 300 h (the fit value ends up being close to this initial guess). The value of λ is then refined by fitting the model to Experiment 2.
Since R1,tot is slowly varying, it can be considered approximately constant on a timescale of hours or shorter. On this timescale, only Atot significantly varies, so the dynamics of sleep homeostatic pressure described by the two-process model should approximately correspond to the dynamics of Atot described by Eq (1). Thus, we use the time constants of the original two-process model, χwake = 18.18 h and χsleep = 4.20 h [6].
Biochemical data also give us typical values for the concentrations R1,tot, R2,tot, and Au, which can be used to establish approximate quantitative relationships between some of the model’s parameters. In the human cerebral cortex, R1,tot, is approximately 600nM, and R2,tot, is approximately 300nM, with some variation in both between brain regions [38]. In mammals, microdialysis measurements of extracellular unbound adenosine concentration, Au, report concentrations around 30nM [19]. Since the dissociation constant for A2A receptors is large compared to physiological concentrations of Au, it is reasonable to assume R2,u ≈ R2,tot in Eq (13).
In an individual who is well rested (i.e., not sleep restricted) and keeping a regular daily sleep/wake cycle, Au will make daily oscillations about a stable level in response to sleep/wake cycles, causing daily oscillations in R1,b = Ab,1, following the relationship described in Eq (11). In steady state, Eq (10) can be written <R1,b> = γ(<R1,b> + <R1,u>), where <∙> denotes expected value, and Eq (8) (valid only at equilibrium) can be rewritten in term of its time average as <R1,b>=Kd1−1<Au><R1,u>[1+h.o.], where h.o. denotes the time average of higher order terms (multiplicative cross products). Combining these yields:
γ≈〈Au〉〈Au〉+Kd1.
(16)
Given Au is typically around 30nM and Kd1 is 1-10nM, this gives an estimated value of 0.50–0.91 for γ.
Using Eqs (2) and (11)–(13), the parameters Atot, Au, Kd1, and β are related by
2Au=(1−β)(Atot−R1,tot−Kd11−β+(Atot+R1,tot+Kd11−β)2−4AtotR1,tot)
(17)
Rearranging for Atot gives
Atot=Au(Au+Kd1+R1,tot(1−β))(Au+Kd1)(1−β).
(18)
Given values of Kd1 and β, we use the typical value of Au ≈ 30nM to estimate a typical value of Atot. Finally, we relate the value of Atot to the parameters μwake and μsleep in Eq (1). During wake, the solution of Eq (1) as a function of time into the wake episode is
Atot,wake(t)=μwake+(Atot,wake(0)−μwake)e−t/χwake.
(19)
Similarly, during sleep, the solution of Eq (1) as a function of time into the sleep episode is
Atot,sleep(t)=μsleep+(Atot,sleep(0)−μsleep)e−t/χsleep.
(20)
For an individual who keeps a regular 24-hour sleep/wake cycle with a block of T hours of sleep per day, continuity of Eqs (19) and (20) require that
Atot,wake(24−T)=Atot,sleep(0),
(21)
Atot,sleep(T)=Atot,wake(0).
(22)
Combining these conditions gives the initial values of Atot at the beginning of each sleep and wake episode, respectively,
Atot,sleep(0)=μwake(1−eT−24χwake)+μsleep(1−e−Tχsleep)eT−24χwake1−eT−24χwake−Tχsleep,
(23)
Atot,wake(0)=μsleep(1−e−Tχsleep)+μwake(1−eT−24χwake)e−Tχsleep1−eT−24χwake−Tχsleep.
(24)
For a human with a typical schedule (T = 8 h), substituting the numerical values of χwake and χsleep in Eqs (21) and (22), and averaging these, we obtain an approximate estimate of a typical total adenosine concentration in the model,
Atot=0.36μwake+0.65μsleep.
(25)
Given values of Kd1 and Kd2 within their respective physiological ranges, we use Eq (18) to estimate Atot. For each value of Atot we then obtain a range of values for μwake and μsleep using Eq (25). We require μwake > μsleep > 0.
In the performance model described in Eq (15), the parameter pmax can also be estimated. In the standard 10-minute PVT, trials occur every 2–10 seconds. Inter-trial intervals are drawn uniformly randomly from this time interval, with an average inter-trial interval of 6 seconds. For a typical response time of δ, the number of trials per PVT is 600/(6 + δ). The theoretical maximum number of lapses would occur in an individual who responded in exactly 500ms on each trial, giving 92 lapses. In reality, lapses are often much longer than 500ms [39]. Individuals subject to a combination of acute sleep deprivation and severe chronic sleep restriction approach 4 seconds as a median response time [3]. This suggests a theoretical ceiling of pmax ≈ 60 lapses.
The first test of the model is whether it can account for PVT data collected in humans undergoing acute sleep deprivation or different levels of chronic sleep restriction. In Experiment 1 four groups of healthy young adults were exposed to different conditions of sleep restriction [4]. One group (n = 13) underwent acute sleep deprivation for 88 h. The other groups underwent chronic sleep restriction for 13 nights (4 h time in bed per night for n = 13, 6 h time in bed per night for n = 13, and 8 h time in bed per night for n = 9), followed by 2 recovery nights (8 h time in bed per night). Average sleep times per night during the chronic sleep restriction were approximately 3.7 h, 5.5 h, and 6.8 h, for the 4 h, 6 h, and 8 h time in bed conditions, respectively. In each condition, participants awoke at the same time of 7:30am, which we plotted as 8am for convenience. Prior to beginning the experiment, all four groups had three baseline nights with 8 h time in bed. In the 5 nights prior to entering the laboratory, participants reported getting an average of 7.8 h sleep per night.
During wakefulness, participants completed 10-minute PVT tests every 2 hours. Group-average PVT lapses were reported for each experimental condition in McCauley et al. [17], beginning 4 hours after awakening each day to avoid effects of sleep inertia on performance. We used these data (recorded manually from the previous paper) as our performance metric, P. The same data set was used previously to develop a model of the effects of chronic sleep restriction on human performance [17] and a similar data set [5] was used to develop another model [18]. It is therefore an important first test of our physiological model.
Model parameter values were chosen within the estimated ranges given in Table 1 to achieve a least-squares fit to the experimental data. This optimization was performed numerically using the Levenberg-Marquardt algorithm for global convergence. The implementation used was the nlinfit function in Matlab (version R2014A, Natick MA, USA). The optimization was initialized using parameter values that fell within physiological ranges.
The model was initialized by simulating a schedule with 7.8 h sleep, matching the sleep duration participants reported getting prior to the inpatient schedule. This schedule was repeated until convergence to a limit cycle was achieved. Three baseline nights were then simulated with 7.0 h sleep per night, matching the average sleep duration participants achieved during baseline inpatient conditions. Actual sleep durations were then simulated for each condition. For consistency between all conditions, we chose to simulate recovery nights in the same manner as baseline nights, with 7.0 h sleep per night. Morning awakenings are all plotted as occurring at 8am.
For reference, we also plotted the predictions of the McCauley et al. model. For these, we used the published equations and initial conditions.
In Experiment 2, 16 healthy young adults lived under “long night” conditions for 28 days [40]. During these days, they were required to spend 14 hours per night (6pm to 8am) in bed in a completely dark room, with no activities allowed, besides using the bathroom. Participants were free to sleep for as much of this time as they liked, and sleep was recorded with polysomnography. In the week prior to this, the participants were given 8 h time in bed per night, beginning around midnight. During this week, they averaged approximately 7 h total sleep per night, and thus likely had some residual sleep debt. While it was not primarily designed for this purpose, the experiment can be viewed as a long-term recovery from chronic low-level sleep restriction. This is extremely valuable, since most chronic sleep restriction experiments have involved a week or less of recovery, making it difficult to determine the timescale of recovery. The experimental data are strongly suggestive of a slow recovery process from an accrued sleep dept; individuals slept an average of 10.3 h across nights 1–3, 9.1 h across nights 4–7, 8.7 h across nights 8–14, 8.7 h across nights 15–21, and 8.2 h across nights 22–28. Interestingly, some individuals developed “split” sleep patterns, in which they had two main nighttime sleep bouts with a period of awakening in the middle. This finding has been used as empirical support for the historical claim that humans in pre-industrial times had split sleep patterns [41].
The estimation and fitting methods described above for Experiment 1 provide values for all parameters of the adenosine model, except λ. The length of Experiment 2 allows us to accurately fit the value of this parameter. In addition, we introduce two new parameters, Dsleep, and Dwake that allow the model to generate its own sleep/wake patterns. During times when sleep is allowed by the schedule, the following rules are used to determine sleep/wake transitions,
{D>Dsleep:TransitiontosleepifcurrentlyawakeD<Dwake:Transitiontowakeifcurrentlyasleep
This is motivated by the two thresholds used for sleep/wake transitions in the two-process model [6].
The values of λ, Dsleep, and Dwake were estimated by least-squares fitting the model’s daily total sleep durations to the experimental group-average daily total sleep durations for days 1–28 of Experiment 2. The Levenberg-Marquardt algorithm was found to perform poorly in this application, due to many points in parameter space achieving similarly good fits to the data. We therefore finely gridded parameter space to find the optimal values of λ, Dsleep, and Dwake, each to at least 3 significant figures.
The model was initialized by simulating a schedule with 7 h sleep per day, beginning at midnight. This schedule was repeated until convergence to a limit cycle was achieved. The experimental protocol was then simulated by allowing sleep between 6pm and 8am each night. More specifically, the model was forced to be awake from 8am to 6pm each day, and then freely selected sleep and wake times in the interval between 6pm and 8am each night using the thresholds described above. These conditions were maintained for 50 days, allowing the model’s behavior in the first 28 days to be compared to data and allowing us to observe the model’s predicted longer-term behavior.
Finally, the parameters Dmid and Ds were recalibrated against Experiment 1 data to yield their final values, resulting in modest changes in both parameters. The Levenberg-Marquardt algorithm was again used. This recalibration was necessary, because the values of λ, Dsleep and Dwake determine the model’s natural sleep duration during baseline and the level of initial sleep homeostatic pressure. The PVT function parameters must therefore be adjusted to allow the model to still optimally fit Experiment 1, while maintaining the same outputs for Experiment 2 (because the PVT function parameters do not affect sleep/wake outputs). All other parameters therefore remained fixed at their previously fit values.
Values for all parameters except λ, Dsleep, and Dwake were first found by fitting the model to PVT data in Experiment 1 (Table 1); the three other parameters were then fit using Experiment 2, as described above. Finally, Dmid and Ds were recalibrated against Experiment 1, resulting in modest changes in both parameter values (Table 1). To illustrate the model’s essential dynamics, we show the time evolution of adenosine concentrations (unbound and total) and receptor concentrations (bound and total) in Fig 2 for two simulated experimental conditions: 4 days of acute sleep deprivation and 8 days of chronic sleep restriction with 4 hours time in bed per night. These two conditions are chosen because they affect the sleep homeostatic process in different ways. Under acute sleep deprivation, the model predicts that total adenosine concentration rapidly saturates to a higher level (Fig 2B) and, on a slower timescale, total A1 receptor concentration progressively increases (Fig 2D), in response to the elevated adenosine concentration. Under chronic sleep restriction, there is a smaller increase in total adenosine concentration (Fig 2B). The progressive increase in total A1 receptor concentration is thus slower, occurring at about half the rate as it does under acute sleep deprivation (Fig 2D). The overall effect of each condition on sleep homeostatic pressure and cognitive performance can be assessed by examining the concentration of bound A1 receptors (Fig 2C). This reveals that 8 days of chronic sleep restriction causes a similar cognitive impairment to 1–2 days of acute sleep deprivation, in line with experimental results [4,5]. However, it takes about 4 days of acute sleep deprivation to generate the same up-regulation in total A1 receptor concentration as does 8 days of chronic sleep restriction.
Within the physiological constraints discussed in Materials and Methods, the model achieves a good fit to the PVT data from Experiment 1, with an adjusted R2 value of 0.66. The same adjusted R2 value was obtained both with the initial fit to Experiment 1 and with the final (recalibrated) parameter values. Values for fit parameters are in Table 1. Notably, values for Kd1 and Kd2 are both at the lower end of the allowed (physiological) range. This suggested that a better fit may exist outside of the range. Relaxing the lower bounds on Kd1 and Kd2, a best fit was achieved at Kd1 = 0.011 nM and Kd2 = 5.0 nM, with a slightly better adjusted R2 value of 0.73, but this solution was discarded on the grounds of physiological constraints. The system’s ability to capture PVT performance to similar accuracy both inside and outside the empirically-observed ranges for Kd1 and Kd2 suggests that these ranges exist due to other biological constraints.
Fits to each of the experimental conditions are shown in Fig 3. The model performs especially well in fitting the 4-h time in bed and 8-h time in bed conditions. Some minor discrepancies between model and data are also observed. Under acute sleep deprivation, there is a slight mismatch in circadian phase; this is likely due to (i) the model fitting an average circadian phase to all conditions, (ii) drift away from a period of 24 hours under constant conditions, and (iii) data being restricted to certain circadian phases in the other three conditions. There is also a tendency for the model to underestimate PVT lapses in the 6-h time in bed condition. This same issue was faced by McCauley et al. when they fit their model to the same dataset; they attributed this to one outlier participant in the 6-h group who was unusually sensitive to the effects of sleep restriction [17]. The predictions of the McCauley et al. model are shown in Fig 3 for reference, since our model’s parameters were fit to the exact same dataset. In general, the models closely agree under these simulated conditions. Both models predict a characteristic within-day variation in performance, with a sudden decline in performance in the final hours of awakening. This corresponds to the onset of the circadian night, as the circadian phase of maximal alertness is passed and homeostatic sleep pressure continues to build. This is consistent with dependence of PVT performance on circadian phase [3]. For PVT data, we find adjusted R2 = 0.66. Using the McCauley et al. model, which has a similar number of total parameters and no explicit physiological constraints, we find adjusted R2 = 0.70 on the same data set.
The same model used to simulate PVT lapses in Experiment 1 can also account for changes in sleep duration and timing during recovery from chronic sleep restriction in Experiment 2. Depending on the value of λ and the separation between the thresholds Dsleep and Dwake, the model was found during the fitting procedure to generate a variety of different sleep patterns, from one sleep bout per night to multiple sleep bouts per night. Smaller values of λ and smaller separations between the thresholds favored more sleep bouts per night, due to the shorter time required to transit between thresholds. This finding is consistent with previous results found in the two-process model [6] and physiological models of mammalian sleep [26,42].
Fig 4 shows the model’s optimal fit to Experiment 2. In general, the model and data closely agree, with a root mean square error of 0.36 h. The model underestimates sleep duration on the first night by 1.3 h, then is within 0.7 h of the experimental data on all subsequent nights. During the recovery process, the model exhibits both monophasic sleep (one sleep bout per night), and biphasic sleep (two sleep bouts per night). This is interesting, since both sleep patterns were experimentally observed in different participants in Experiment 2. Some participants consistently had one sleep bout throughout the experiment, others consistently had two sleep bouts, while others alternated between one and two sleep bouts on different nights [40]. This suggests that the human population may span the region of parameter space that encompasses these two different modes of sleeping, in agreement with evidence that some humans historically had a split sleep pattern [41]. We note, however, that the model’s biphasic sleep patterns in Fig 4 are a transient response to long nights, following a period of insufficient sleep. After approximately 30 days, sleep timing spontaneously shifts considerably later and consolidates back into a monophasic pattern (Fig 4B) as the system returns closer to the well-rested equilibrium state. This prediction cannot be compared to data, since the experiment ended after 28 days. The observed sleep/wake patterns can be better understood by observing the change in total sleep drive in Fig 4C and 4D. During most of days 1–20, when sleep pressure remains relatively high, the main sleep bout occurs early and there is time after the main sleep bout for the sleep drive to reach the upper threshold again, resulting in a second sleep bout. This results in a spike and wave shape for D. Later, when sleep pressure is relatively dissipated, the main sleep bout occurs later and there is no longer time for a second sleep bout (i.e., sleep becomes monophasic).
The adenosine system has been proposed as a putative mechanism for changes in cognitive performance and sleep with sleep restriction [43,44]. In this paper, we developed a physiologically-based model of the brain’s adenosine system and showed that its dynamics capture changes in cognitive performance and sleep during acute sleep deprivation, chronic sleep restriction, and recovery from chronic sleep restriction.
Our physiological model performs similarly well to the best existing phenomenological models, which are based on the two-process model. Each of these models includes a fast homeostatic variable and a slow homeostatic variable. It was previously proposed that the variables of two-process-based models could therefore represent adenosine concentration and adenosine receptor concentration [17]. If so, a variable transformation should link phenomenological models to a physiological model. In investigating this, however, we found that our model is structurally different from two-process-based models. As illustrated in Fig 5, all existing two-process-based models include a dependence of the fast variable (Process S) on the slow variable, since the slow variable modifies the asymptotic behavior of Process S. Cognitive performance is then assumed to be a function of the fast variable. While these fast and slow variables have previously been interpreted as possible elements of the adenosine system based on pharmacokinetic principles [17,22], this mathematical structure is notably distinct from our model, in which the slow variable (A1 receptor concentration) responds to the fast variable (adenosine concentration), and the fast variable’s dynamics are independent of the slow variable. Cognitive performance in our model is then dependent on bound A1 receptors, which functionally depends on both the fast and slow variables. Due to these differences in model structure, the models will differ in their predictions in certain settings, such as protocols involving alternating cycles of sleep restriction and recovery, as we recently demonstrated [45]. Finding and testing such conditions will therefore be important to distinguishing which model structures are closest to the underlying biology.
A further important mathematical distinction exists between our model and the prior McCauley et al. model. Whereas our model’s dynamics are always convergent, the McCauley et al. model’s predictions are divergent in the long-time limit for sleep durations shorter than a critical duration [17]. This feature of convergence improves the McCauley et al. model’s performance on schedules that involve very short sleep opportunities (<4 h), which is one condition in which our model should next be tested. In a physiological context, we expect dynamics to converge, since it is unclear how to physiologically interpret divergence. The PVT output of our model is also a bounded function (sigmoid) of the physiological variables, implying a ceiling for performance impairment, reflective of the fact that there is a theoretical limit on the number of possible lapses per test. We note that our model is similar in the long-time limit to a previous phenomenological approach that assumed a ceiling on lapse probability [46]; one difference being that our model output is total number of lapses per test rather than lapse probability per trial. While our model is designed to test a hypothesis related to the adenosine system, there are many other important sleep-promoting molecules in the brain. These include cytokines, nitric oxide, and prostaglandins. As more data come to light, it may be necessary to extend our model to include other ligand/receptor systems and potential interactions between these systems. A sensible next step would be modeling the dynamics of A2A receptors, which are involved in mediating effects of caffeine [47]. Previously we developed a physiological model of the neuronal systems that regulate sleep and alertness [32], including the effects of caffeine on subjective alertness and sleep [48]. The effects of caffeine were also recently included in another model of cognitive performance [49]. Both these models of caffeine, however, currently use a sleep homeostatic process that is not directly linked to adenosine receptor dynamics [50]. Integrating neuronal sleep models with the adenosine system model developed here is therefore an important future goal with multiple applications.
In experiments involving rodents, there is some evidence of an allostatic response to chronic sleep restriction, meaning the homeostatic set-point may change in response to chronic sleep restriction. Specifically, EEG slow-wave activity in male F344 rats is increased by sleep restriction on the first day, but this elevation disappears as sleep restriction continued on days 2–5. Moreover, when the animals recover, slow-wave activity falls below baseline levels [51]. Such a response could be explained by a decrease in the rate of adenosine production [52]. The existence of an allostatic response is, however, disputed. Leemburg et al. [53] found slow-wave activity was consistently elevated during both sleep restriction and recovery in male WHY rats, although this conflict could be due to strain differences. A decrease in slow-wave activity during chronic sleep restriction has not been reported in humans, but slow-wave activity notably shows little to no change during chronic sleep restriction, despite declining cognitive performance [4]. This outcome could be explained by different receptors mediating different functions (slow-wave activity vs. cognitive performance) and responding differently to sleep restriction. Indeed, there is evidence of A2A receptor down-regulation in some brain regions following sleep deprivation [30]. This would be consistent with the finding that selective A2A agonists promote non-rapid-eye-movement sleep, while selective A1 agonists do not [54]. However, there is significant functional overlap between receptor types, since A1 receptors also mediate slow-wave activity changes in response to sleep restriction [43], so it cannot be as simple as A1 receptors controlling cognitive performance and A2A receptors controlling slow-wave activity; a more complex model is needed.
Our model has limitations and raises new questions. We have tested the model against two datasets, but in each case, we used group-average data, similar to other chronic sleep restriction models in their initial development [17,18]. Accounting for individual differences is particularly valuable, given large and stable differences between individuals in their vulnerability to sleep restriction on specific tasks [55,56]. Ramakrishnan et al. recently reported fitting a two-process-based model on an individual basis, but only after excluding the 3 most variable participants from a sample of 18 [57]. Since our model is based on underlying physiology, it could potentially be used to identify candidate mechanisms that account for individual differences. Ultimately, these mechanisms could be empirically tested using adenosine receptor imaging techniques [58].
In summary, we present the basis for a new model that is the first to explicitly and quantitatively link the sleep homeostatic process, cognitive performance, and sleep patterns to an underlying physiological and molecular biological mechanism. Although we are constrained by physiology in posing this model and fitting its parameter values, it performs similarly to the best existing phenomenological ad hoc models for PVT performance, which are free of such constraints. In addition, it advances beyond most of these models in accurately predicting sleep. The model could therefore be used to generate one-step predictions of both sleep and performance, rather than common two-step approaches in which sleep patterns are first derived and performance then predicted [59]. Our findings provide a physiologically plausible basis for observed changes in cognitive performance and sleep under a variety of experimental conditions. Critically, our model’s structure sheds light on the potential origin of empirical observations that sleep homeostasis involves dynamics on both short and long time scales.
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10.1371/journal.ppat.1006449 | MoEnd3 regulates appressorium formation and virulence through mediating endocytosis in rice blast fungus Magnaporthe oryzae | Eukaryotic cells respond to environmental stimuli when cell surface receptors are bound by environmental ligands. The binding initiates a signal transduction cascade that results in the appropriate intracellular responses. Studies have shown that endocytosis is critical for receptor internalization and signaling activation. In the rice blast fungus Magnaporthe oryzae, a non-canonical G-protein coupled receptor, Pth11, and membrane sensors MoMsb2 and MoSho1 are thought to function upstream of G-protein/cAMP signaling and the Pmk1 MAPK pathway to regulate appressorium formation and pathogenesis. However, little is known about how these receptors or sensors are internalized and transported into intracellular compartments. We found that the MoEnd3 protein is important for endocytic transport and that the ΔMoend3 mutant exhibited defects in efficient internalization of Pth11 and MoSho1. The ΔMoend3 mutant was also defective in Pmk1 phosphorylation, autophagy, appressorium formation and function. Intriguingly, restoring Pmk1 phosphorylation levels in ΔMoend3 suppressed most of these defects. Moreover, we demonstrated that MoEnd3 is subject to regulation by MoArk1 through protein phosphorylation. We also found that MoEnd3 has additional functions in facilitating the secretion of effectors, including Avr-Pia and AvrPiz-t that suppress rice immunity. Taken together, our findings suggest that MoEnd3 plays a critical role in mediating receptor endocytosis that is critical for the signal transduction-regulated development and virulence of M. oryzae.
| During the interaction between the rice blast fungus Magnaporthe oryzae and the host, the pathogen employs a series of receptors and sensors at the plasma membrane to recognize host surface cues and to activate signal transduction pathways required for appressorium formation and pathogenicity. We found that MoEnd3-mediated endocytosis is responsible for internalization of non-canonical GPCR Pth11 and the sensor MoSho1 to endosomal compartments. This is important for activating the downstream Pmk1 MAPK pathway to control appressorium formation and penetration. MoEnd3 is regulated through phosphorylation by the actin-regulating kinase MoArk1. In addition, MoEnd3 has a role in establishing effector secretion required for suppressing rice immunity. Our studies provide evidence that endocytosis is required for normal signaling and virulence in M. oryzae.
| The rice blast fungus Magnaporthe oryzae produces an infectious structure called the appressorium that enables it to penetrate host plant cells and initiate infection [1]. During the interaction between the pathogen and the host, the fungus secretes numerous effectors into the host that suppress plant immunity [2–5]. Previous studies have shown that G-protein/cAMP signaling is important in the perception of host surface cues by M. oryzae and during invasion of host tissue [6, 7]. M. oryzae contains three distinct Gα subunit proteins: MagA, MagB and MagC as well as a highly conserved cAMP-dependent signaling pathway, which consists of the adenylate cyclase Mac1, the regulatory subunit of protein kinase A Sum1, and the catalytic subunit of protein kinase A cPKA [6, 8]. cPKA activation is responsible for appressorium differentiation. In addition, the non-canonical G-protein coupled receptor (GPCR) Pth11 is known to function upstream of G-protein/cAMP signaling [9, 10]. Moreover, the MAP kinase cascade comprised of Mst11 (MAPKKK), Mst7 (MAPKK), and Pmk1 (MAPK) is also involved in the regulation of appressorium formation [11]. Furthermore, MoMsb2 and MoSho1 function as two upstream sensors of the MAP kinase cascade [12]. Deletion of either MoMSB2 or/and MoSHO1 resulted in a significant reduction in appressorium formation. Intriguingly, the expression of a dominant active MST7 allele partially suppressed the defects exhibited by the ΔMomsb2 mutant [12].
Recently, endosomal compartments were discovered to function as signaling platforms by anchoring the components of G-protein/cAMP signaling. The various signaling components then interact within the endosomal compartments for sustaining signaling [13]. Endosomal compartments contain early and late endosomes. Proteins internalized from the cell surface target early endosomes to undergo a sorting process, by which they are either recycled back to the plasma membrane or sent to late endosomes for degradation. Previous studies have shown that disruption of phosphoinositide PI3P synthesis on the endosomal membrane or inhibition of the conversion of early endosomes into late endosomes by MoVPS39 gene deletion disrupts the endosomal localization of Pth11, MagA, Mac1 proteins, and a regulator of G protein signaling MoRgs1 thereby leading to an inhibition in appressorium formation [13]. However, despite these important findings, the mechanism by which Pth11 or other receptors proteins enter intracellular compartments to activate signal transduction in M. oryzae is still unclear.
Endocytosis is a conserved intracellular transport process in which membrane proteins, lipids, or other macromolecules are transported to endosomal compartments. During endocytosis, endocytic proteins are recruited to endocytic sites and interact with actin cytoskeleton to drive vesicle maturation and scission [14]. In Saccharomyces cerevisiae, the Eps15 homolog (EH) domain-containing proteins Pan1p and End3p are important members of endocytic proteins and depletion of Pan1p or End3p severely impairs endocytosis and actin organization [15–17]. When vesicles are mature, endocytic proteins and actin components simultaneously dissociate from the vesicle membrane, thereby promoting efficient endocytosis [18]. The Ark1p/Prk1p actin-regulating kinases are implicated in this dissociation process [19, 20]. Ark1p/Prk1p phosphorylates Pan1p and other proteins to promote their dissociation [20, 21]. Deletion of Ark1p and Prk1p results in aggregation of endocytic proteins and actin cytoskeleton in the cytoplasm, which prevents endocytosis [22].
We previously found that MoArk1 has conserved functions in regulating endocytosis and that MoArk1 is required for appressorium turgor generation and penetration in M. oryzae. This study suggested that endocytosis plays an important role in the pathogenesis of the rice blast fungus [23]. Here we continued to investigate the mechanism that links MoArk1-regulated endocytosis to fungal pathogenesis. We identified a MoArk1-interacting protein MoEnd3 by mass spectrometry analysis and characterized its function. We found that MoEnd3 is an endocytic protein and mediates the endocytic transport of GPCR Pth11 and sensor MoSho1. This transport could trigger downstream Pmk1 phosphorylation for autophagy, appressorium formation and penetration. In addition, we identified that MoEnd3 function is regulated by MoArk1-dependent phosphorylation at Ser-222. Finally, we demonstrated that secretion of the MoEnd3-regulated effectors is directly linked to host immunity suppression.
MoArk1 is an actin-regulating kinase homolog required for endocytosis, growth, development, and full virulence of M. oryzae [23]. To explore the mechanism by which MoArk1 regulates these processes, we employed protein co-immunoprecipitation (Co-IP) to identify putative MoArk1-interacting proteins. By expressing the MoARK1:FLAG construct and using FLAG beads to isolate MoArk1:FLAG-interacting proteins followed by mass spectrometry analysis, we found several proteins potentially important for endocytosis and actin cytoskeleton, including homologues of the clathrin heavy chain, amylase-binding protein AbpA, Arp2/3 complex subunit proteins, endocytosis and cytoskeletal organization proteins, vesicular integral-membrane protein Vip36, and F-actin-capping proteins (S1 Table). Additional proteins co-precipitated with MoArk1 also include the dynamin-A homologue MoDnm1 that regulates peroxisomal and mitochondrial fission through interactions with MoFis1 and MoMdv1 [24].
We identified MGG_06180.6 as an endocytic protein homolog to S. cerevisiae End3p (30% amino acid sequence identity) and characterized its function. To confirm the interaction between MoEnd3 and MoArk1, we employed the yeast two-hybrid assay that demonstrated the interaction. Transformants expressing AD-MoEnd3 and BD-MoArk1 constructs showed β-galactosidase activity on SD-Leu-Trp-His-Ade plates (Fig 1A). In addition, we performed in vitro protein binding and bimolecular fluorescence complementation (BiFC) assays that further substantiated the MoEnd3 and MoArk1 interaction (Fig 1B and 1C). In the BiFC assay, fluorescence appeared in the cytoplasm of the conidia and 24 h appressorium of the strain co-expressing MoEnd3-YFPN and MoArk1-YFPC constructs, but not in controls (Fig 1C).
To characterize MoEnd3 functions, a ΔMoend3 mutant was obtained (S1 Fig) and characterized. No significant differences were observed between the ΔMoend3 mutant and the wild-type Guy11 strain in colony diameter (on CM, MM, SDC and OM medium plates) or conidia production (S2 Table). However, when the ΔMoend3 mutant was crossed to the tester strain TH3 (MAT1-1), no perithecia were observed after 3 weeks (S2 Fig), suggesting that MoEnd3 is dispensable for vegetative growth and conidiation but not sexual reproduction.
To examine whether MoEnd3 is required for endocytosis, we stained the cells with the lipophilic dye FM4-64 and observed its internalization. After 1 min of staining, the dye appeared in the cytoplasm of hyphal tips in Guy11 and the complemented strain, but the dye remained at the plasma membrane of the ΔMoend3 mutant (Fig 2A). At 15 min, the dye was most intense in the hypal tip of Guy11 and the complemented strain, while was near invisible in the cytoplasm of ΔMoend3. Only at 30 min, when some dye internalization was observed in ΔMoend3. The fluorescence intensity of the dye was quantified using the ImageJ software (Fig 2B), and this quantification is consistent in suggesting that MoEnd3 is required for normal endocytosis.
Since the End3 endocytic protein regulates endocytosis through the coordination of the F-actin assembly at endocytic sites in S. cerevisiae [25], we examined whether MoEND3 deletion impairs F-actin organization using the Lifeact:RFP marker [26]. A toroidal-shaped F-actin network could be observed in 80.4% of the mature appressoria produced by wild-type Guy11 (Fig 2C). By comparison, ΔMoend3 displayed an aberrant distribution of F-actin in 98.8% of appressoria, as demonstrated by a line-scan analysis. It is known that the actin patch that associates with plasma membrane corresponds to endocytic sites [27]. In the conidia of Guy11, a lot of punctae-like cortical actin patches were observed in the cytoplasm of conidia (Fig 2D). However, aggregated, instead of punctae-like, actin structures were observed in nearly 96.3% of ΔMoend3 conidia (Fig 2D). In addition, many actin patches displayed polarized distributions at the hyphal tip regions of Guy11, whereas they were rarely seen at the hyphal tip region of ΔMoend3 (Fig 2E).
To further examine whether MoEnd3 is associated with F-actin, the MoEnd3:GFP fusion protein and Lifeact:RFP were co-expressed in the ΔMoend3 mutant and localizations of the GFP and RFP fusion proteins were observed by confocal fluorescence microscopy. We found that MoEnd3:GFP co-localized with the F-actin network in appressoria after 6 and 12 h of incubation (Fig 1D). In conidia and the hyphal tips, MoEnd3:GFP patches were found at the plasma membrane and were co-localized with actin patches (Fig 1D). However, we still observed some regions only showed MoEnd3:GFP or Lifeact:RFP, likely due to that End3 protein arrives endocytic sites or disassembles from there earlier than F-actin, as suggested in studies involving S. cerevisiae End3p [16, 27].
We then examined whether MoEnd3 interacts with F-actin protein MoAct1 by performing yeast two-hybrid and in vitro protein binding assays. Consistently, both assays demonstrated an interaction occurred between MoEnd3 and MoAct1 (Fig 1E and 1F), supporting that MoEnd3 could coordinate actin assembly through a direct interaction with F-actin.
On hydrophobic surfaces, the ΔMoend3 mutant showed delayed appressorium development compared with Guy11 and the complemented strain (Fig 3A and 3B) and this delay became indistinguishable after 24 h. However, the germ tubes of ΔMoend3 were elongated and the appressoria were smaller in size and not fully developed (Fig 3C and 3D). The incipient collapse assay [28] showed that the collapse rate of appressoria of ΔMoend3 was significantly higher than Guy11 and the complemented strain (Fig 3E), suggesting that MoEnd3 contributes to appressorial turgor generation.
We further observed translocation and degradation of glycogen and lipid required for turgor generation during conidia germination and appressoria development. Iodine solution and Nile red were used to stain the glycogen and lipid bodies, respectively. At 0 h, the glycogen and lipids were abundant in conidia (S3 Fig). In Guy11, the glycogen and lipids were translocated from conidia to nascent appressoria and were rapidly degraded in conidia after 6 h. They were completely degraded in over 60% of conidia after 12 h and in 90% of the mature appressoria after 24 h. In ΔMoend3, the degradation of glycogen in conidia and its translocation to appressoria occurred more slowly, and this was coupled with the delayed appressorium formation. After 12 h, glycogen and lipids in conidia were not translocated or degraded. After 24 h, they remained in almost 50% of conidia. These results suggested that MoEnd3 is required for an efficient translocation and breakdown of glycogen and lipids.
To further test the role of MoEnd3 in pathogenesis, conidial suspensions were sprayed onto susceptible rice seedlings (Oryza sativa cv. CO-39). After 7 days of inoculation, ΔMoend3 produced significantly fewer lesions than control strains. The lesions produced by ΔMoend3 were also smaller and less expansive, in contrast to the fully expanded necrotic lesions produced by Guy11 and the complemented strain (Fig 3F). Similar results were obtained in barley leaf infection assay after 5 days (Fig 3F). To further validate the reduction in virulence of ΔMoend3, we performed penetration assays using detached barley leaf. By observing 100 appressoria for each strain at 24 hpi and classifying their invasive hyphae (IH) into 4 types (type 1, no hyphal penetration; type 2, IH with one or two branch; type 3, IH with at least three branch, but the IH are short and less extended; type 4, IH that has numerous branches and fully occupies a plant cell), we found that in Guy11 and the complemented strain, nearly 80% of appressoria were type 3, in contrast to that 52.3% were type 1 and 38.1% were type 2 in ΔMoend3 (S4 Fig). In the penetration assays using rice tissues, 90.2% of appressoria of Guy11 and the complemented strain displayed extended IH growth, whereas less than 10% of ΔMoend3 appressoria formed IH, which were arrested in individual rice cells and did not extend to neighboring cells (Fig 3G). These results indicated that MoEnd3 is required for full virulence.
Pth11 is a non-canonical GPCR that functions upstream of the G-protein/cAMP pathway for surface sensing in M. oryzae [9]. Once proper surface clues were sensed by M. oryzae, Pth11 and cAMP signaling components, such as MagA and MoRgs1, are anchored on the endosomal compartments to sustain the transduction of cAMP signaling [13]. In addition, membrane sensors MoMsb2 and MoSho1 are responsible for recognition of surface signals and activation of the downstream MAPK cascade consisting of Mst11-Mst7-Pmk1 [12]. Both the cAMP pathway and the Pmk1-MAPK cascade are known to regulate appressorium formation and penetration.
In mammalian cells, endocytosis transports membrane receptors or sensors to endosomes so that these receptors and sensors interact with signaling proteins to activate and amplify signal transduction [29]. We examined whether Pth11, MoMsb2, and MoSho1 are transported by endocytosis. We expressed Pth11:GFP, MoMsb2:GFP, and MoSho1:GFP in Guy11 and observed their co-localization with FM4-64 in germ tubes following conidia incubation on hydrophobic surfaces for 3 h. This stage is crucial for pathogen to sense surface clues and initiate appressorium development. We observed that signal of Pth11:GFP and MoSho1:GFP, but not MoMsb2:GFP, was primarily accumulated in regions also labeled by FM4-64 (Fig 4A, 4B and 4C).
Rab5 GTPase and Rab7 GTPase are known to bind with early endosomes and late endosomes, respectively [30]. To determine whether FM4-64 stained regions in germ tubes are endosomes or vacuoles, co-localizations of FM4-64 with GFP:Rab5 or GFP:Rab7 and vacuole marker CMAC were observed in germ tubes (S5 Fig). We found that most of FM4-64 was localized to GFP:Rab5 labeled regions (S5A Fig) and rarely co-localized with GFP:Rab7 (S5B Fig). In addition, CMAC-marked vacuoles did not appear in the germ tubes but only in the conidia. These observations revealed that internalized FM4-64 localizations in germ tube are likely to be early endosomes. Considering our finding that Pth11:GFP and MoSho1:GFP were co-localized with FM4-64, we proposed that most of Pth11 and MoSho1 are localized to early endosomes of the germ tubes.
To further demonstrate that Pth11 and MoSho1 are internalized by endocytosis, we used actin inhibitor Latrunculin B (LatB) that inhibits endocytosis [14] and determined the effect of LatB on Pth11 and MoSho1. We found that LatB inhibited Pth11:GFP and MoSho1:GFP internalization and enriched them at plasma membrane (Fig 4E and 4F). In addition, exposure to Lat B for 30 min resulted in 91.5% of germinated conidia being unable to form appressorium (Fig 4D).
Next we determined the role of MoEnd3 in endocytosis of Pth11:GFP and MoSho1:GFP. We found that most of the Pth11:GFP and MoSho1:GFP signals remained at the plasma membrane of the germ tubes in ΔMoend3 (Fig 4G and 4H), and this pattern is similar to that of Pth11:GFP and MoSho1:GFP in Guy11 treated with LatB. We further compared ΔMoend3 and Guy11 in the endocytosis rate of Pth11 and MoSho1 by fluorescence recovery after photobleaching (FRAP), a technique that measures the mobility of fluorescent proteins. We intended to bleach fluorescence from the regions where Pth11:GFP or MoSho1:GFP were accumulated in germ tubes and the recovery of fluorescence can reflect the rate of endocytosis. Considering newly synthesized proteins can be delivered from Golgi to endosomes, we treated germinated conidia (3 h) with cycloheximide to inhibit protein biosynthesis, which may prevent Golgi resident Pth11:GFP or MoSho1:GFP from entering endosomes. We also treated germinated conidia with benomyl for 10 min to inhibit endosomes trafficking via microtubule [31, 32]. In the FRAP assay, we bleached 90% of fluorescence of a region using 488 nm light. For Pth11:GFP, 72.7 ± 4% of fluorescence was recovered at post-photobleach 35 s in Guy11, compared with 16.1 ± 0.8% in ΔMoend3 (Fig 4I and 4J). In addition, the recovery level of MoSho1:GFP in ΔMoend3 (27.5 ± 3.1%) was significantly lower than that in Guy11 (78.8 ± 7.9%) at post-photobleach (Fig 4K and 4L). Collectively, these results suggested that MoEnd3 is important for endocytosis of Pth11 and MoSho1.
It is clear that the Mst11-Mst7-Pmk1 MAPK pathway is required for appressorium formation and function [11]. Since ΔMoend3 showed defects in appressorium formation, penetration and endocytosis of Pth11 and MoSho1, we tested the hypothesis that Mst11-Mst7-Pmk1 signaling could also be affected in ΔMoend3. We extracted proteins and performed Western blot analysis and found that there was no difference in the expression of Pmk1 (42-kDa) between ΔMoend3 and Guy11 (Fig 3H bottom panel). By using the phosphor-MAPK antibody, Pmk1 phosphorylation was detected at all stages except conidia in Guy11 (Fig 3H bottom panel). However, a reduced Pmk1 phosphorylation level was detected in the ΔMoend3 appressoria following 16 h of incubation. This finding suggested that MoEnd3 affects Pmk1 phosphorylation during appressorium development.
Previous studies showed that the constitutively activated MST7S212D T216E allele restores normal Pmk1 phosphorylation and appressorium formation in the Δmst11 and Δmst7 mutant strains [11]. To confirm that MoEnd3 affects Pmk1 phosphorylation, we introduced the MST7S212D T216E allele into ΔMoend3 and found that it too suppressed the defect of ΔMoend3 in appressorium formation (Fig 3H upper panel). Interestingly, 50% of conidia of the ΔMoend3/MST7S212D T216E strain appeared to form appressoria after 6 h of incubation on hydrophobic surfaces, whereas no appressoria were formed in ΔMoend3. There were no significant differences in the formation rate between Guy11 and the ΔMoend3/MST7S212D T216E strain after 10 h (Fig 3I and 3J). Moreover, ΔMoend3 only formed a small number of lesions on rice leaves (Fig 3K and 3L). In contrast, the ΔMoend3/MST7S212D T216E strain produced many typical lesions (Fig 3K and 3L). Further, penetration assays using rice tissues were conducted by observing 100 appressoria for each strain and classifying their IH into 4 types (type 1, no hyphal penetration; type 2, IH with less than two branches; type 3, IH with at least two branches, but the IH are short and less extended; type 4, IH that fully occupies a plant cell and moves into neighboring cells). We found that 84.2% of appressoria from the ΔMoend3/MST7S212D T216E strain could penetrate the rice cells (Fig 3M). In contrast, less than 10% of appressoria from ΔMoend3 could penetrate the host. These results suggested a function link between MoEnd3 and Pmk1 by showing that elevating Pmk1 phosphorylation level could significantly suppress the defect of ΔMoend3 in appressorium formation and infection.
Nuclear degradation in conidia is essential for appressorium development and penetration, which is also the consequence of autophagy following mitosis and nuclear migration [33]. To test if MoEnd3 has a role in autophagy, an RFP-labeled H1 histone protein (H1:RFP) was expressed in both Guy11 and the ΔMoend3 mutant, and nuclei were visualized following conidia germination on the hydrophobic surface. ΔMoend3 displayed successive nuclear divisions, with no breakdown of nuclei in conidia or germ tubes at 24 h (Fig 5A). We also expressed H1:RFP in the Δpmk1 mutant and found that nuclei failed to degrade (Fig 5A), consistent with previous study [33]. Thus, it is likely that the defect in nuclear degradation in ΔMoend3 is due to the defective Pmk1 phosphorylation.
We then determined whether deletion of MoEND3 affects autophagy by culturing mycelia in liquid minimal medium with reduced nitrogen (MM-N) in the presence of the proteinase B inhibitor phenylmethylsulfonyl fluoride (PMSF) for 4 h and observing hyphal vacuoles under a electron microscope. Autophagosomes were observed in the vacuoles of Guy11 but not ΔMoend3 (Fig 5B). The GFP:MoATG8 construct can be used as a functional marker for monitoring the delivery of vesicles to vacuoles and the breakdown of autophagosomes, and normal autophagy cannot easily hydrolyze free GFP protein cleaved from GFP:MoAtg8 [24, 34, 35]. We monitored autophagy using GFP: MoAtg8 in both Guy11 and the ΔMoend3 mutant. GFP was observed in 76.7% of vacuoles of Guy11, but 15.2% in ΔMoend3 (Fig 5C and 5D).
Interestingly, the expression of the MST7S212D S216E allele promoted GFP:MoAtg8 to enter the 68.3% of vacuoles in ΔMoend3. This phenomenon was further examined by the GFP:MoAtg8 proteolysis assay. Total proteins were extracted from strains expressing GFP:MoAtg8 following 0, 2 and 5 h of nitrogen starvation. The full-length GFP:MoAtg8 (41-kDa) and cleaved free GFP were detected (Fig 5E). In Guy11, the level of full-length GFP:MoAtg8 decreased as the time of nitrogen starvation increases. This was not observed in the ΔMoend3 mutant. Meanwhile, the expression of the MoMST7S212D S216E allele accelerated the breakdown of GFP:MoAtg8 in ΔMoend3 (Fig 5E). Based on these results, we concluded that MoEnd3 is important for autophagy, and autophagy defect in ΔMoend3 is possibly caused by a defect in Pmk1 phosphorylation.
Given that MoEnd3 interacts with MoArk1, a serine/threonine protein kinase, we tested whether the activity of MoEnd3 is regulated by MoArk1 through protein phosphorylation. Mn2+-Phos-tag SDS PAGE was thus performed to detect the phosphorylation of MoEnd3. Phosphorylated proteins in Mn2+-Phos-tag SDS PAGE are visualized as slower migrating bands compared with the corresponding unphosphorylated proteins [36]. We extracted the MoEnd3:GFP protein from the ΔMoend3/MoEND3:GFP strain. Then the protein was treated with phosphatase or phosphatase inhibitor, and was separated in Mn2+-Phos-tag SDS PAGE followed by analysis with the GFP antibody. The band of MoEnd3:GFP treated with the inhibitor migrated slower than that treated with phosphatase (Fig 6A), indicating that phosphorylation occurs in MoEnd3:GFP. In contrast, the band of MoEnd3:GFP from the ΔMoark1/MoEND3:GFP strain migrated as fast as that of the unphosphorylated MoEnd3:GFP protein treated with phosphatase (Fig 6A), indicating that MoEnd3 phosphorylation is dependent on MoArk1.
Additionally, mass spectrometry was used to identify potential phosphorylated site(s) in MoEnd3. In the strain expressing MoARK1, one MoEnd3 peptide containing a phosphorylated Ser-222 was detected (Fig 6B), in contrast to none found in the MoARK1 deletion strain. We expressed the MoEnd3 Ser-222 to Ala allele linked to GFP in ΔMoend3 and examined the phosphorylation level of MoEnd3S222A:GFP protein using Mn2+-Phos-tag SDS PAGE. The result showed that the band of MoEnd3S222A:GFP migrated as fast as the band of MoEnd3:GFP extracted from the ΔMoark1/MoEND3:GFP strain (Fig 6C), suggesting that MoEnd3S222A:GFP is a unphosphorylated protein and MoEnd3 Ser-222 is a specific site for MoArk1-mediated phosphorylation.
In S. cerevisiae, Ark1p/Prk1p kinases initiate phosphorylation to inhibit endocytic protein functions and promote disassembly of endocytic proteins at the late stage of endocytosis [19]. To further determine whether MoEnd3 function is regulated by MoArk1-mediated phosphorylation at Ser-222, the constructs of the constitutively unphosphorylated MoEnd3 S222A and phosphomimetic MoEnd3 S222D mutants were introduced into ΔMoend3, ΔMoark1, and Guy11, respectively. Endocytosis was observed following 5 min of hyphal exposure to FM4-64. We found that MoEND3S222A and MoEND3S222D expressions could not restore endocytosis to ΔMoend3 and ΔMoark1 (Fig 6D and 6E). However, we noticed that MoEND3S222A expression mildly promoted endocytosis. But the MoEND3S222D expression impaired endocytosis in Guy11, and showed no rescue effect on endocytosis in ΔMoend3 and ΔMoark1, suggesting the constitutively phosphorylated MoEnd3 interferes with normal MoEnd3 function.
We further extracted proteins from appressoria or germinated conidia incubated for 8 h expressing MoEND3S222A and MoEND3S222D and performed Western blot analysis using the phosphor-Pmk1 antibody. We found that MoEND3S222A expression could elevate Pmk1 phosphorylation levels to some degree in ΔMoend3 and ΔMoark1, in contrast to MoEND3S222D that was unable to induce Pmk1 phosphorylation in ΔMoend3 (Fig 6F). In addition, the appressorium formation assay showed the ΔMoend3/MoEND3S222A strain, but not the ΔMoend3/MobEND3S222D strain, had a higher appressorium formation rate than ΔMoend3 after 10 and 16 h of incubation (S6 Fig). Pathogenicity assay showed only MoEND3S222A expression could partially rescue virulence ofΔMoend3 and ΔMoark1. Taken together, we concluded that the function of MoEnd3 is negatively regulated by MoArk1-dependent Ser-222 phosphorylation and that this regulation is important for endocytosis, Pmk1 phosphorylation, and virulence.
Plants protect themselves against pathogens by evolving multiple layers of innate immunity, which is often associated with the hypersensitive response (HR), reactive oxygen species (ROS) accumulation, and the induction of pathogenesis-related (PR) genes [37, 38]. We hypothesized that small lesions and limited IH growth by ΔMoend3 are likely the results of the mutant being unable to suppress the host defense system. We thus measured host ROS production and HR induction using 3, 3’-diaminobenzidine (DAB) and Trypan blue staining, respectively [39–41] and found significant ROS accumulation or HR occurring at 36 hpi in over 50% of rice cells infected by ΔMoend3, compared with less than 20% by Guy11 and the complemented strains (S7A, S7B, S7C and S7D Fig).
Diphenyleneiodonium (DPI) functions as a flavoenzyme inhibitor that prevents the activation of NADPH oxidases necessary for ROS generation in plants [41, 42]. When treated with DPI, 51.7% of rice cells infected by ΔMoend3 displayed improved IH grow that 36 hpi and these IH were able to spread to neighboring cells (S7E Fig), indicating that IH growth of ΔMoend3 was arrested by strong plant defense reaction. We examined the transcript levels during the early stages of infection (0–36 hpi) of four rice pathogenesis-related (PR) genes (PR1a, PAD4, CHT1 and AOS2) involved in the salicylic acid and jasmonic acid pathways [5, 42, 43] by qRT-PCR and results indicated significantly higher transcription levels of all PR genes elicited by ΔMoend3 infection than by Guy11 infection (S7F Fig).
During the early stages of infection, M. oryzae is believed to secrete effector proteins to suppress PTI and facilitate its own growth within rice tissues. The strong immunity triggered by ΔMoend3 led us to hypothesize that the mutant may be impaired in effector secretion. To test whether ΔMoend3 is defective in the secretion of AvrPib and AvrPi9 effectors, conidial suspensions were sprayed onto rice LTH (a universally susceptible rice variety), LTH-Pib (LTH harboring resistant gene Pib), and LTH-Pi9 (LTH harboring resistant gene Pi9). Guy11 produced many typical virulent-type lesions on LTH and tiny dark-brown HR-type lesions (a highly resistant response) in LTH-Pib and LTH-Pi9 (Fig 7A and 7D). The virulent-type lesions are larger than 1 mm in diameter and are considered virulent because conidia will be produced from this type of lesions under high humidity condition [44]. In contrast, the HR-type lesions are smaller than 1 mm, cannot produce conidia, and considered avirulent. ΔMoend3 still could produce virulent-type lesions in LTH, but the lesions were much less than those produced by Guy11, and ΔMoend3 induced the resistant response in LTH-Pib and LTH-Pi9, similar to Guy 11 (Fig 7A and 7D). These results suggested that MoEnd3 is dispensable for AvrPib and AvrPi9 triggered host immunity.
To test other effectors that are not contained in Guy11, such as Avr-Pia and AvrPiz-t, constructs containing genes encoding Avr-Pia and AvrPiz-t were introduced into Guy11 and ΔMoend3. Conidial suspensions of Guy11/Avr-Pia and ΔMoend3/Avr-Pia were sprayed onto LTH and LTH-Pia (LTH harboring resistant gene Pia). Guy11/Avr-Pia was found to have normal infection in LTH and induce aresistant response in LTH-Pia. However, ΔMoend3/Avr-Pia produced typical lesions in LTH-Pia and LTH, suggesting Avr-Pia secretion may be affected in ΔMoend3 (Fig 7B and 7D). Similarly, ΔMoend3/AvrPiz-t was unable to cause a strong resistant response in LTH-Piz-t in comparison to Guy11/AvrPiz-t (Fig 7C and 7D), suggesting that MoEND3 deletion also inhibits AvrPiz-t function.
Avr-Pia and AvrPiz-t are cytoplasmic effectors that are preferentially accumulated in the biotrophic interfacial complex (BIC) and translocated to the rice cell cytoplasm [45]. We fused Avr-Pia and AvrPiz-t with GFP, expressed them in Guy11 and ΔMoend3, and observed their localizations at the early stage of infection. In the cells infected by Guy11, Avr-Pia:GFP and AvrPiz-t:GFP accumulated in over 95% of BIC structures adjacent to primary hyphae (Fig 7E and 7F), in contrast to the cells infected by ΔMoend3 in which less than 10% of BICs contained Avr-Pia:GFP and AvrPiz-t:GFP (Fig 7E and 7F).
To further demonstrate the requirement of MoEnd3 for secretion of Avr-Pia and AvrPiz-t, but not AvrPib and AvrPi9, we observed effector secretion with the strains co-expressing Avr-Pia:GFP and AvrPiz-t:GFP with AvrPib:RFP or AvrPi9:RFP. For Guy11, we found about 95% of BICs containing AvrPib:RFP or AvrPi9:RFP appeared with Avr-Pia:GFP and AvrPiz-t:GFP (S8A, S8B, S8C and S8D Fig). For ΔMoend3, more than 90% of BICs showed the presence of AvrPib:RFP or AvrPi9:RFP, but less than 10% of BICs with AvrPib:RFP or AvrPi9:RFP containing Avr-Pia:GFP and AvrPiz-t:GFP. Moreover, RT-PCR analysis for Avr-Pia and AvrPiz-t during infection showed that MoEND3 deletion did not inhibit their expression (S9 Fig), which ruled out the possibility that this secretion defect of ΔMoend3 was caused by the inhibition of effector gene expression.
Interestingly, the expression of the MST7S212D S216E allele in ΔMoend3 was unable to induce a resistant response in rice harboring resistant genes (Fig 7B, 7C and 7D) and to enrich Avr-Pia:GFP and AvrPiz-t:GFP in BICs (Fig 7E and 7F), suggesting that the two effector secretion may be not directly regulated by Pmk1-MAPK. Moreover, the DAB staining assay indicated that ΔMoend3/MST7S212D S216E failed to suppress ROS responses as effectively as Guy11 (S10 Fig), implying that the expression of the MST7S212D S216E allele still cannot restore effector secretion required for suppressing rice innate immunity. Taken together, we concluded that MoEnd3 facilitates secretion of effectors such as Avr-Pia and AvrPiz-t, but not Avr-Pib and Avr-Pi9, though a pathway independent of Pmk1 phosphorylation.
Endocytosis is employed by eukaryotic cells to constitutively internalize plasma membrane-associated proteins, lipids, and other molecules for regulating many key cellular functions. In M. oryzae, this process is closely linked to fungal physiology and pathogenicity [23, 24, 45–47]. Our current studies provide evidence further supporting this conclusion. Our results show that in addition to having an important role in mating and virulence, MoEnd3-mediated endocytosis is also important for transport of the GPCR Pth11 and the membrane sensor MoSho1. Significantly, MoEND3 deletion delayed endocytosis of Pth11 and MoSho1, resulting in delayed appressorium development. Similar to phenotypes in the strains lacking cPKA [48], the appressoria produced by ΔMoend3 strains showed impaired turgor pressure, inefficient mobilization of glycogen and lipids, and a defect in host penetration. Additionally, we found that MoEnd3 function affects the Pmk1 MAPK signaling pathway. Collectively, our findings support that endocytosis is required for receptor-mediated signaling, development and pathogenesis in M. orzae.
Our findings are consistent with observations in other model organisms. For example, in mammalian cells, activation of plasma membrane receptors including receptor tyrosine kinases and GPCR by external agonists is followed by the endocytic receptor transport to the endosome. In the endosome the internalized receptors can interact with key components of various signaling pathways to activate specific signal transduction pathways [49, 50]. Furthermore, in the biotrophic plant pathogen Ustilago maydis, studies of tSNARE Yup1 revealed that endocytosis controls GPCR Pra1-mediated signaling. Yup1 is co-localized with Rab5-marked early endosomes. A temperature-sensitive mutation of yup1 blocked the fusion of endocytic vesicles with early endosomes and the endocytic recycling pathway [51]. These defects result in depletion of the pheromone receptor Pra1 from the plasma membrane and disruption in pheromone-mediated signal transmission to downstream effectors that would normally trigger pathogenic development [51].
Autophagic cell death in the conidium is necessary for appressorium formation and infection. Previous studies have shown that a Δpmk1 mutant is blocked in autophagic nuclear degradation [33]. We found that constitutively activated Mst7 could accelerate autophagy in the ΔMoend3 mutant. This supports the hypothesis that the severely delayed nuclear degradation and autophagy in ΔMoend3 was caused by a defect in Pmk1-MAPK signaling. This is in agreement with several other studies that also found that MAPK signaling is involved in the autophagic process. In mammalian cells, members of the MAPK family including MAPK1/ERK2, MAPK8/JNK, MAPK14/p38a and MAPK15 are involved in the control of autophagy [52–54]. In S. cerevisiae, the Slt2-MAPK and Hog1-MAPK signaling pathways were found to be required for mitophagy and pexophagy [55].
Additionally, mammalian and yeast Ark1p/Prk1p serine/threonine kinases initiate phosphorylation of endocytic and actin cytoskeleton components to control endocytosis [19]. We previously reported that MoArk1 regulates endocytosis and pathogenicity and is localized to actin patches in M. oryzae [23]. Here, we demonstrated that MoEnd3 function is regulated by MoArk1 through protein phosphorylation. We further found that neither of the constitutively phosphorylated nor unphosphorylated form of MoEnd3 could properly function in endocytosis, Pmk1 phosphorylation or virulence. Strikingly, the unphosphorylated MoEnd3 could still function to partially suppress the defects of ΔMoend3 and ΔMoark1. This is in contrast to the constitutively phosphorylated MoEnd3, which was completely inactive.
M. oryzae secretes effectors, such as Slp1, into rice cells to suppress host immunity [56]. IH growth of ΔMoend3 was found to be arrested suggesting that it was inhibited by a robust host immune response. This could be due to ΔMoend3 being unable to secrete effector molecules. Indeed, we found that the secretion of Avr-Pia and AvrPiz-t was impaired in ΔMoend3. This finding is in accordance with our earlier studies in which we found that Qc-SNARE MoSyn8 is required for Avr-Pia and AvrPiz-t secretion [45]. However, the secretion of AvrPib and AvrPi9 was not affected in ΔMoend3, suggesting that secretion of these effectors may involve mechanisms independent of MoEnd3. Moreover, when Pmk1 phosphorylation was activated by expressing the MST7S212D T216E allele in ΔMoend3, the secretion of Avr-Pia and AvrPiz-t was still impaired, suggesting that these mechanisms are also independent of Pmk1 signaling. It would be interesting to identify such mechanisms in future studies.
Previous studies indicated that there are two distinct effector secretion systems functioning in M. oryzae [2]. The cytoplasmic effectors such as Pwl2 are preferentially accumulated in BIC, and their secretion depends on the t-SNARE protein MoSso1 and exocyst components MoExo70 and MoSec5. The secretion of apoplastic effectors, such as Bas4, follows the Golgi-dependent secretion pathway [2]. Some studies also indicated that endocytosis and exocytosis/secretion are obligatorily coupled [57, 58]. In S. cerevisiae, the perturbation of She4p affects endocytosis and defects in endocytosis result in a slow motion of exocytic vesicles during polarity establishment [59]. This decreased exocytosis could reflect in defects in endocytic recycling of components required for membrane fusion, including certain SNARE proteins [59]. Therefore, it is likely that MoEnd3-mediated endocytosis affects secretion of certain effector proteins and that delayed endocytosis in ΔMoend3 could also affect movement of certain exocytic vesicles required for transporting effector proteins. Ultimately, inhibition of effector secretion could attenuate M. orzae pathogenicity.
In summary, our studies demonstrate that the endocytic protein MoEnd3 is required for blast fungus growth and development, endocytic transport of pathogenic GPCRs, interaction with the rice host, and pathogenicity. Together with MoArk1, MoEnd3 exhibits a regulatory function for multiple processes, including appressorium development and function, autophagy, Pmk1 MAPK transduction, and signaling and regeneration of Pth11 and MoSho1 (Fig 8). Given that endocytosis is closely coupled with exocytosis, MoEnd3 could have additional roles in facilitating effector secretion to suppress host defenses.
M. oryzae Guy11 was used as the parental wild type strain in this study. All strains were cultured on complete medium (CM) agar plates. Liquid CM medium was used to prepare the mycelia for DNA and RNA extraction. For conidia production, strains were maintained on straw decoction and corn (SDC) agar media at 28°C for 7 days in the dark followed by 3 days of continuous illumination under fluorescent light [42].
Plugs of mutant and the wild type strain Guy11 (MAT1-2) and the mating partner strain TH3 (MAT1-1) were point inoculated 3 cm apart on oatmeal agar medium and incubated at 20°C under constant fluorescent light for 3 to 4 weeks [60].
The MoEND3 deletion mutant was generated using the standard one-step gene replacement strategy [61]. First, two approximate 1.0 kb of sequences flanking of MoEND3 (MGG_06180) were amplified with two primer pairs MoEND3-F1/MoEND3-R1, MoEND3-F2/MoEND3-R2, the products of MoEND3 were digested with restriction endonucleases (EcoRI and SalI, SpeI and SacII) and ligated with the HPH cassette released from pCX62. The protoplasts of wild type Guy11 were transformed with the vectors for targeted gene deletion by inserting the hygromycin resistance HPH marker gene cassette into the two flanking sequences of the MoEND3 gene. For selecting hygromycin-resistant transformants, CM plates were supplemented with 250 μg/ml hygromycin B (Roche, USA).
To generate complementary construct pYF11-MoEND3, the gene sequence containing the MoEND3 gene and 1.0 kb native promoter was amplified with MoEND3-comF/MoEND3-comR. Yeast strain XK1-25 was co-transformed with this sequence and XhoI-digested pYF11 plasmid. Then the resulting yeast plasmid was expressed in E. coli. To generate the complementary strain, the pYF11-MoEND3 construct containing the bleomycin-resistant gene for M. oryzae transformants screen was introduced into the ΔMoend3 mutant [61].
EcoRV was used to digest the genomic DNA from wild-type strain Guy11 and the ΔMoend3 mutant. The digest products were separated in 0.8% agar gel and were hybridized with the MoEND3 gene probe. The probe was designed according to the disruption strategy and was amplified from Guy11 genomic DNA using primers MoEND3-InterF/MoEND3-InterR. To confirm MoEND3 replacements, labeled MoEND3 probe was used to hybridize the EcoRV-digested genomic DNA from the ΔMoend3 mutant and wild-type Guy11. The copy number of HPH gene in the ΔMoend3 mutant was detected using labeled HPH fragments that amplified from the plasmid of pCB1003 with primers FL1111/FL1112. The whole hybridization was carried out according to the manufacturer’s instruction for DIG-High Prime [61].
Conidia were harvested from 10-day-old SDC agar cultures, filtered through three layers of lens paper and re-suspended to a concentration of 5×104 spores/ml in a 0.2% (w/v) gelatin solution. Two-week-old seedlings of rice (cv. CO39) and 7-day-old seedlings of barley (Hordeum vulgare cv. Four-arris) were used for pathogenicity assays. For spray inoculation, 5 ml of a conidial suspension of each treatment were sprayed onto rice with a sprayer. Inoculated plants were kept in a growth chamber at 28°C with 90% humidity and in the dark for the first 24 h, followed by a 12 h/12 h light/dark cycle. Lesion formation in rice and barley was observed after 7 and 5 days, respectively [60].
For infection assay with rice tissues, conidia were re-suspended to a concentration of 1×105 spores/ml in a 0.2% (w/v) gelatin solution. 3-week-old rice cultivar CO-39 was inoculated with 100 μl of conidial suspension on the inner leaf sheath cuticle cells and incubation under humid conditions at 28°C. The leaf sheaths were observed under Zeiss Axio Observer A1 inverted microscope at 36 hpi. For barley epidermis penetration assays, conidia were suspended to a concentration a concentration of 5×104 spores/ml in a 0.2% (w/v) gelatin solution. Droplets (10 μl) of conidial suspension were placed on detached barley leaf epidermis. The barley epidermis was observed under Zeiss Axio Observer A1 inverted microscope at 24 hpi.
Conidia were harvested from 10-day-old cultures, filtered through three layers of lens paper, and re-suspended to a concentration of 5×104 spores/ml in sterile water. For appressorium formation assay, droplets (30 μl) of conidial suspension were placed on plastic cover slips (Fisher Scientific, St Louis, MO, USA) under humid conditions at 28°C [62]. Appressorium turgor was determined by cell collapse assay using a 1–4 molar concentration of glycerol solution. The percentages of conidia germinating and conidia forming appressoria were determined by microscopic examination of at least 100 conidia. To visualize glycogen and lipid, KI solution and Neil red were used as described [48]. All the samples were observed under Zeiss Axio Observer A1 inverted microscope (40×).
For DAB staining assay, rice tissues infected by strains at 36 hpi were stained with 1 mg/ml DAB (Sigma-Aldrich) solution (pH 3.8) for 8 h and destained with an ethanol/acetic acid solution (ethanol/acetic acid = 98:2, v/v) for 1 h. For Trypan blue staining assay, rice tissues infected by strains at 36 hpi were stained with a 2.5 mg/ml Trypan blue solution for 1 h and destained in 2.5 g/ml lactophenol for 1 h. For evaluating the growth of IH in ROS-suppressed rice sheath, a conidial suspension (1×105 spores/ml) treated with 0.5 μm DPI was inoculated into the rice sheath for 36 h. All the samples were observed under Zeiss Axio Observer A1 inverted microscope (40×).
For detection of the rice PR gene transcription during infection stage, total RNA samples were extracted from plants inoculated with the wild-type strain or mutant at 0, 24, 48, and 72 hpi. Transcription of elongation factor 1a gene (Os03g08020) was used as endogenous control in O. sativa. For detection of AVR-Pia and AVRPiz-t transcription during infection stage, total RNA samples were extracted from the strains at 24 and 48 hpi. Transcription of actin gene (XP 003719871.1) was used as endogenous control. The qRT-PCR was run on the Applied Biosystems 7500 Real Time PCR System with SYBR Premix Ex Taq (Perfect Real Time, Takara, Japan). Normalization and comparison of mean Ct values were performed as previously described [42].
Bait constructs were generated by cloning MoARK1 and MoACT1 full-length cDNAs into pGBKT7, respectively. MoEND3 full-length cDNA was cloned into pGADT7 as the prey construct. The prey and bait constructs were confirmed by sequencing analysis. The yeast strain AH109 was transformed with the bait and prey constructs as the description of BD library construction & screening kit (Clontech, USA). The Trp+ and Leu+ transformants were isolated and assayed for growth on SD-Trp-Leu-His-Ade medium [63].
The MoEND3-YFPN plasmid was generated by cloning the MoEND3 gene with a native promoter into the vector pHZ65 containing hygromycin-resistant gene. The MoARK1 gene with a native promoter was cloned into the vector pHZ68 containing bleomycin-resistant gene to generate the MoEND3-YFPC plasmid. The two plasmids were introduced into protoplasts of wild type Guy11. Transformants resistant to both hygromycin and bleomycin were isolated and examined using fluorescence microscopy (Zeiss Axio Observer A1 inverted microscope, 40×).
To construct the plasmids of GST-MoEND3, His-MoARK1 and His-ACT1, full-length cDNA of MoEND3 was amplified and inserted into the vector pGEX4T-2, and full-length cDNAs of MoARK1 and MoACT1 were amplified and inserted into the vector pET-32a, respectively. Then these plasmids were expressed in E. coli strain BL21 (DE3) and bacterial cells were collected and treated by lysis buffer (10 mM Tris-HCl [pH 7.5], 150 mM NaCl, 0.5 mM EDTA, 0.5% Triton x-100). To confirm expression of the GST or His fusion proteins, bacterial lysates were separated by SDS-PAGE gel followed by Coomassie blue staining. In the binding assay for His-MoArk1 and GST-MoEnd3, bacterial lysate containing His-Ark1 protein was incubated with 30 μl Ni-NTA agarose beads (Invitrogen, Shanghai, China) for 1 h at 4°C. Then the beads were washed for five times, incubated with bacterial lysate containing GST-MoEnd3 for 1 h at 4°C, washed for five times again and boiled for elution. The elution was probed with His and GST antibodies (Abmart, Shanghai, China). In the binding assay for His-MoAct1 and GST-MoEnd3, bacterial lysate containing GST-MoEnd3 protein was incubated with 30 μl GST agarose beads (Invitrogen, Shanghai, China) for 1 h at 4°C. Then the beads were washed for five times, incubated with bacterial lysate containing His-MoAct1 for 1 h at 4°C and boiled for elution. The elution was probed with His and GST antibodies (Abmart, Shanghai, China).
To construct plasmids of MoARK1:FLAG, PTH11:GFP, MoMSB2:GFP, MoSHO1:GFP, MST7S212D T216E (RP27 promoter), MoEND3:GFP, MoEND3S222A:GFP, MoEND3S222D:GFP, Lifeact:RFP (RP27 promoter), H1:RFP, Avr-Pia:GFP, AvrPiz-t:GFP, AvrPi9:RFP and AvrPib:RFP, their gene fragments were amplified with primers listed in S3 Table and inserted into pYF11 plasmid by transformation with yeast XK1-25 strain. Yeast transformants were isolated from the SD-Trp plates and resulting constructs were amplified by expression in E. coli.
FM4-64 (Molecular Probes Inc., Eugene, OR, USA) was solved in distilled water to a final concentration 5 μg/ml. For assaying with hyphae, strains were grown on CM liquid medium for 16 h at 28°C. Before observation, hyphae were washed with distilled water and strained with FM4-64 on glass slide. For assaying with germinated conidia, conidia were inoculated on the coverslips with hydrophobic surface. After 3 h, the dye was added to the conidia for 10 min. Then samples were washed with distilled water. Latrunculin B (LatB) (Cayman, USA) is stocked in DMSO in a concentration of 25 mg/ml. Conidia incubated on the coverslips with hydrophobic surface were treated with LatB (final concentration 0.1 μg/ml) for 30 min, while the controls were treated with 5% DMSO. Then samples were washed with distilled water. Cycloheximide (MedChemExpress, USA) was solved in distilled water and the germinated conidia were treated with a final concentration 10 μg/ml for 10 min. Then samples were washed with distilled water. Benomyl (Aladdin, Shanghai, China) was solved in 0.1% DMSO and added to germinated conidia with a final concentration 1 μg/ml. Then the samples were washed with distilled water. All the samples were observed under a fluorescence microscope (Zeiss LSM710, 63× oil). The filter cube sets: GFP (excitation spectra: 488 ± 10 nm, emission spectra: 510 ± 10 nm), FM4-64 (excitation spectra: 535 ± 20 nm, emission spectra: 610 ± 30 nm). Exposure time: 800 ms.
The conidial suspensions (1×105 conidia/ml in a 0.2% gelatin) were injected into rice sheath from 3-week-old rice seedlings (cv. CO39). The BICs in the infected rice cells were observed using fluorescence microscopy (Zeiss Axio Observer A1 inverted microscope, 40×) at 24 hpi and the images were captured immediately. The filter cube sets: GFP (excitation spectra: 488 ±10 nm, emission spectra: 510 ± 10 nm), RFP (excitation spectra: 561 ± 10 nm, emission spectra: 610 ± 10 nm). Exposure time: 800 ms.
About 150 to 200 mg of mycelia were ground into powder in liquid nitrogen and resuspended in 1 ml of extraction buffer (10 mM Tris-HCl [pH 7.5], 150 mM NaCl, 0.5 mM EDTA, 0.5% Triton x-100) with fresh added 1 mM PMSF and 10 μl of protease inhibitor cocktail (Sigma, Shanghai, China). Total proteins were separated on a 12% SDS-PAGE gel and transferred to nitrocellulose membranes. The p44/42 MAPK (Erk1/2) antibody (Cell Signaling Technology, USA) was used to detect endogenous Pmk1 expression. The phospho-p44/42 MAPK (Erk1/2) (Thr202/Tyr204) antibody (Cell Signaling Technology, USA) was used to detect phophorylated Pmk1.
Thegerminated conidia with 3 h of incubation were treated with cycloheximide and benomyl as described. FRAP were performed using a fluorescence microscope Zeiss LSM710. Regions containing Pth11:GFP and MoSho1:GFP in germ tube were selected for photo-bleaching. The photobleaching was carried out using an Argon-multiline laser at a wavelength of 488 nm with 90% laser power and 150 iterations in ROI. Images were acquired with 2% laser power at a wavelength of 488 nm every 5 sec. For quantitative analyses, fluorescence intensity was measured using the ZEISS ZEN blue software and fluorescence recovery curves were fitted using following formula: F(t) = Fmin + (Fmax − Fmin)(1-exp−kt), where F(t) is the intensity of fluorescence at time t, Fmin is the intensity of fluorescence immediately post-bleaching, Fmax is the intensity of fluorescence following complete recovery, and k is the rate constant of the exponential recovery [64]. Mobile Fraction was calculated as the following formula: Mf = (Fend − F0)/(Fpre − F0), where Fend is the stable fluorescent intensity of the punctae after sufficient recovery, F0 is the fluorescent intensity immediately after bleaching, and Fpre is the fluorescent intensity before bleaching [65].
The MoEND3:GFP fusion construct was introduced into ΔMoend3 and ΔMoark1 mutants, respectively. The proteins extracted from mycelium were resolved on 8% SDS-polyacrylamide gels prepared with 50 μM acrylamide-dependent Phos-tag ligand and 100 μM MnCl2 as described [36]. Gel electrophoresis was run at 80 V for 3–6 h. Prior to transfer, gels were equilibrated in transfer buffer containing 5 mM EDTA for 20 min two times and then in transfer buffer without EDTA for 10 min. Protein transfer from the Mn2+-phos-tag acrylamide gel to the PVDF membrane was performed overnight at 80 V at 4°C, and then the membrane was analyzed by Western blotting using the anti-GFP antibody.
To identify phosphorylation sites of targeted proteins, samples were separated on 10% SDS PAGE. The gel bands corresponding to the targeted protein were excised from the gel, reduced with 10 mM of DTT and alkylated with 55 mM iodoacetamide. In gel digestion was carried out with the trypsin/lys-c mix (Promega, USA) in 50 mM ammonium bicarbonate at 37°C overnight. The peptides were extracted using ultrasonic processing with 50% acetonitrile aqueous solution for 5 min and with 100% acetonitrile for 5 min. The extractions were then centrifuged in a speed to reduce the volume. A liquid chromatography–mass spectrometry (LC–MS) system consisting of a Dionex Ultimate 3000 nano-LC system (nano UHPLC, Sunnyvale, CA, USA), connected to a linear quadrupole ion trap Orbitrap (LTQ Orbitrap XL) mass spectrometer (ThermoElectron, Bremen, Germany), and equipped with a nanoelectrospray ion source was used for our analysis. For LC separation, an Acclaim PepMap 100 column (C18.3 μm, 100 Å) (Dionex, Sunnyvale, CA, USA) capillary with a 15 cm bed length was used with a flow rate of 300 nL/min. Two solvents, A (0.1% formic acid) and B (aqueous 90% acetonitrile in 0.1% formic acid), were used to elute the peptides from the nanocolumn. The gradient went from 5% to 40% B in 80 min and from 40% to 95% B in 5 min, with a total run time of 120 min. The mass spectrometer was operated in the data-dependent mode so as to automatically switch between Orbitrap-MS and LTQ-MS/MS acquisition. Survey full scan MS spectra (from m/z 350 to 1800) were acquired in the Orbitrap with a resolution r = 60,000 at m/z 400, allowing the sequential isolation of the top ten ions, depending on signal intensity. The fragmentation on the linear ion trap used collision-induced dissociation at a collision energy of 35 V. Protein identification and database construction were processed using Proteome Discoverer software (1.2 version, Thermo Fisher Scientific, Waltham, MA, USA) with the SEQUEST model. MS/MS-based peptide identifications were accepted if they could be established at greater than 95.0% probability, as specified by the Peptide prophet algorithm.
Gene sequences can be found in the GenBank database under the following accession numbers: MoEND3 (MGG_06180), MoARK1 (MGG_11326), MoACT1 (MGG_03982), PTH11 (MGG_05871), MoMSB2 (MGG_06033), MoSHO1 (MGG_09125) and MST7 (MGG_00800).
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10.1371/journal.pgen.1004015 | Dual Regulation of Gene Expression Mediated by Extended MAPK Activation and Salicylic Acid Contributes to Robust Innate Immunity in Arabidopsis thaliana | Network robustness is a crucial property of the plant immune signaling network because pathogens are under a strong selection pressure to perturb plant network components to dampen plant immune responses. Nevertheless, modulation of network robustness is an area of network biology that has rarely been explored. While two modes of plant immunity, Effector-Triggered Immunity (ETI) and Pattern-Triggered Immunity (PTI), extensively share signaling machinery, the network output is much more robust against perturbations during ETI than PTI, suggesting modulation of network robustness. Here, we report a molecular mechanism underlying the modulation of the network robustness in Arabidopsis thaliana. The salicylic acid (SA) signaling sector regulates a major portion of the plant immune response and is important in immunity against biotrophic and hemibiotrophic pathogens. In Arabidopsis, SA signaling was required for the proper regulation of the vast majority of SA-responsive genes during PTI. However, during ETI, regulation of most SA-responsive genes, including the canonical SA marker gene PR1, could be controlled by SA-independent mechanisms as well as by SA. The activation of the two immune-related MAPKs, MPK3 and MPK6, persisted for several hours during ETI but less than one hour during PTI. Sustained MAPK activation was sufficient to confer SA-independent regulation of most SA-responsive genes. Furthermore, the MPK3 and SA signaling sectors were compensatory to each other for inhibition of bacterial growth as well as for PR1 expression during ETI. These results indicate that the duration of the MAPK activation is a critical determinant for modulation of robustness of the immune signaling network. Our findings with the plant immune signaling network imply that the robustness level of a biological network can be modulated by the activities of network components.
| Robustness of a network is defined by how consistently it performs upon removal of some of its components. It is a common strategy for plant pathogens to attack components of the plant immune signaling network in an attempt to dampen plant immunity. Therefore, it is crucial for the plant immune signaling network to have a high level of robustness. We previously reported that the robustness level of the plant immune signaling network is higher during Effector-Triggered Immunity (ETI) than Pattern-Triggered Immunity (PTI). Here we discovered a molecular switch that determines two robustness levels during ETI and PTI. Salicylic acid (SA) is a major plant immune signal molecule that regulates many immune-related genes. SA-independent alternative mechanisms also regulated the majority of SA-responsive genes during ETI but not PTI. One of the SA-independent mechanisms was mediated by prolonged activation of MAP kinases (MAPKs). MAPK activation was prolonged during ETI but transient during PTI. Thus, the duration of MAPK activation switches the robustness level of the plant immune signaling network. Our findings imply that the robustness level of a biological network can be modulated by activities of its components.
| How network properties, such as robustness against network perturbations, emerge from biological networks has been a central question in systems biology [1], [2]. Possible modulation of network robustness in a biologically relevant context and mechanisms underlying the modulation are areas of study that have rarely been explored.
Innate immunity, in which defense responses are induced through signaling events initiated by recognition of pathogen attack, composes a major part of plant immunity [3]. PAMP/Pattern-Triggered Immunity (PTI) and Effector-Triggered Immunity (ETI) are modes of plant innate immunity defined by the way pathogens are detected [4], [5]. PTI is triggered by recognition of microbe/pathogen-associated molecular patterns (MAMPs/PAMPs) by the cognate pattern-recognition receptors (PRRs), which are typically receptor-like kinases or receptor-like proteins [6]. For example, Arabidopsis thaliana FLS2 is the PRR for flg22, an elicitor-active epitope of flagellin from Gram-negative bacteria [7]. While most non-adapted pathogens cannot overcome PTI, adapted pathogens deliver effectors into the plant cell that manipulate plant cell functions to facilitate their infection by, for instance, interfering with PTI signaling [8], [9]. ETI is triggered by specific recognition of effectors by resistance (R) proteins, which are often nucleotide-binding leucine-rich repeat (NB-LRR) proteins [10]. For example, the Arabidopsis intracellular NB-LRR R proteins RPS2 and RPM1 indirectly recognize perturbations of the PTI signaling component RIN4 by the effectors AvrRpt2 and AvrRpm1/AvrB, respectively, of a Gram-negative bacterial pathogen, Pseudomonas syringae [3]. In addition to proteinaceous effectors, some P. syringae strains deliver coronatine, which is a jasmonic isoleucine mimic, in order to suppress plant immunity [11]. Recently, it was shown that coronatine suppresses immune responses dependent on salicylic acid (SA) as well as independent of SA [12], [13]. Thus, there are evolutionary arms races between hosts and pathogens. Pathogens evolve much faster than hosts, rapidly changing effector repertoires, thereby changing points of attack in host immune networks. As hosts cannot match the speed of pathogen evolution, it is important that hosts develop robust immune networks that remain functional in the face of effector attack. Mechanisms underlying network robustness are thus a critical aspect of immunity.
SA is a signal molecule controlling a major portion of immunity against biotrophic and hemibiotrophic pathogens, including P. syringae [14]. SID2 encodes a key enzyme for SA biosynthesis in response to pathogen infection [15]. In Arabidopsis sid2 mutants, pathogen-induced SA accumulation is almost undetectable [14]. Hundreds of genes are transcriptionally regulated by SA signaling, mediated mainly by a positive regulator of SA signaling, NPR1 [14]. PR1 is one SA-inducible gene used as a canonical SA marker [14].
Arabidopsis has 20 mitogen-activated protein kinases (MAPKs) [16], and four of them, MPK3, MPK4, MPK6 and MPK11, have been described as immune signaling components [17]. MPK3 and MPK6 are associated with immune responses, such as reactive oxygen species (ROS) production, ET production/signaling, phytoalexin production and cell death [17]. For instance, ethylene production is positively controlled by dual regulation of enzymes (ACS) synthesizing the ethylene precursor 1-amino-cyclopropane-1-carboxylic acid. MPK6 stabilizes ACS2 and ACS6 by their phosphorylation, and MPK3 and MPK6 control gene expression through a transcription factor, WRKY33, which is activated by the MAPKs [18], [19]. The same cascade is required for production of a phytoalexin, camalexin, by controlling expression of a biosynthetic gene, PAD3 [20]. A double mutant deficient in MPK3 and MPK6 is embryonic lethal but the single mutants are viable, suggesting functional redundancy between them in development [21]. MPK3 phosphorylates the bZIP type transcription factor VIP1 whose phosphorylation is required for its nuclear translocation [22]. Transient over-expression of VIP1 led to weak induction of PR1 in Arabidopsis protoplasts although involvement of SA in this PR1 induction is not known [23].
The overall spectra of induced defense responses are overlapping between PTI and ETI whereas the kinetics and intensity of the responses seem different [4], [24]. In Arabidopsis, knocking out the hub genes of four major signaling sectors abolished 80% of flg22-triggered PTI (flg22-PTI) and AvrRpt2-triggered ETI (AvrRpt2-ETI), indicating extensively shared signaling network machinery between PTI and ETI [25]. Relationships among these signaling sectors are part compensatory and part synergistic in flg22-PTI but are predominantly compensatory in AvrRpt2-ETI, which explains a high level of robustness in the ETI level against network perturbations [25]. Single mutations (dde2, ein2, pad4 and sid2) weakly but significantly compromised flg22-PTI but not AvrRpt2-ETI while the quadruple mutation largely abolished both. These observations demonstrated differences in the robustness of the highly overlapping signaling networks during the two modes of plant immunity. However, the molecular mechanism controlling modulation of the network robustness is not known.
Here we report a molecular mechanism that affects the robustness of the plant immune signaling network. Although Arabidopsis MPK3 and MPK6 are activated during both PTI and ETI, the duration of the activation was much longer during ETI than PTI. Only sustained activation of the MAPKs supported expression of a majority of SA-responsive genes in the absence of SA. The roles of MPK3 and SA signaling during AvrRpt2-ETI were compensatory, contributing to network robustness against perturbations during ETI. Our findings demonstrate that a biologically important differential network property, robustness, can emerge from duration of the activity of a network component.
We previously reported that ETI is more robust against network perturbations than PTI due to a higher level of network compensation [25]. We hypothesized that this compensation occurred at the level of gene regulation. To test this hypothesis, we examined expression of a canonical SA marker gene, PR1, during ETI. Transcriptional induction of PR1 was completely dependent on SID2, which is a key SA biosynthetic enzyme, and hence completely dependent on SA signaling during PTI [26]. We found that PR1 induction was only partially dependent on SID2 and NPR1 at a late time point of 24 hours post inoculation (hpi) with ETI-triggering P. syringae pv. tomato DC3000 (Pto) strains expressing the effectors AvrRpt2 (Pto AvrRpt2) or AvrRpm1 (Pto AvrRpm1) (Figure 1A and Figure S1). While AvrRpt2 and AvrRpm1 are recognized by the CC-type NB-LRR proteins RPS2 and RPM1, AvrRps4 is recognized by the TIR-type NB-LRR protein RPS4 [3]. We also observed SID2- and NPR1-independent PR1 induction during AvrRps4-triggered ETI although induction levels were lower compared to AvrRpt2- and AvrRpm1-ETI (Figure S1). In contrast, PR1 induction was completely dependent on SID2 in the case of the non-ETI triggering Pto strain carrying an empty vector (Pto EV). Inoculation of the ETI-triggering strains at a high dose can trigger a form of programmed cell death called a hypersensitive response (HR) [3]. The inoculation dose used in this experiment was relatively low (OD600 = 0.001), and we did not observe a macroscopic HR within 24 hpi. To test the possibility that the SA level increased independently of SID2 during ETI, we measured the SA level in these tissues. The increased SA accumulation was completely dependent on SID2 in all conditions (Figure 1B). These results indicate that some SA-independent mechanism(s) can activate PR1 during ETI. At an earlier time point of 6 hpi, only SA-dependent PR1 induction was observed with all three strains (Figure 1A), suggesting that this SA-independent mechanism(s) during ETI requires more than 6 hours to be effective.
SA-independent mechanism(s) for PR1 induction during ETI prompted us to investigate the possibility that other SA-responsive genes can also be transcriptionally regulated in an SA-independent manner during ETI. For this purpose, mRNA profiles were analyzed using a whole genome DNA microarray. Leaves of wild type (Col) or sid2 plants were inoculated with water (mock), Pto hrcC, Pto EV, or Pto AvrRpt2, and were collected at 24 hpi for mRNA profiling. The Pto hrcC strain is deficient in the type III secretion system used to transport effectors into plant cells. It elicits the PTI response by presenting various MAMPs [11]. Among 2828 genes that were significantly up- or down-regulated (with q values<0.01 and more than 2-fold changes) in both Pto EV and Pto AvrRpt2 infection in Col, regulation of 187 genes showed strong SID2-dependence in Pto EV infection (Figure 2A and Table S1). These genes are designated SA-responsive genes hereafter. Remarkably, regulation of most SA-responsive genes, including PR1, at 24 hpi with Pto AvrRpt2 is largely SID2-independent although SA contributes to their full expression, indicating that SA-independent signaling mechanism(s) can regulate most SA-responsive genes during AvrRpt2-ETI. The SID2-dependency of gene regulation after Pto hrcC inoculation was similar to that after Pto EV inoculation, although the overall extent of up- or down-regulation was lower, and distinct from that after Pto AvrRpt2 inoculation (Figure S2 and Table S2). Thus, initiation of ETI appears to be the key for activation of this SA-independent mechanism(s).
We hypothesized that a kinetic difference in activation of network components is responsible for activation of SA-independent mechanism(s). A prior study suggested that the duration of MPK3 and MPK6 activation is longer during ETI than non-ETI [27]. We compared the duration of MAPK activation in ETI and PTI. When wild-type seedlings in a liquid medium were treated with the PTI inducer flg22, activation of the MAPKs was observed after 10 min and returned to the basal level within one hour (Figure 3A), confirming previous observations [28]. The possibility that flg22 was rapidly degraded in the liquid culture was excluded since the MAPKs were activated similarly when fresh seedlings were placed in the liquid medium containing flg22 that had been incubated with other seedlings for 3 hours (Figure 3A). Thus, MAPK activation is truly transient after flg22 treatment. We employed transgenic seedlings carrying an estradiol-inducible AvrRpt2 transgene (XVE-AvrRpt2) to measure MAPK activation during ETI in the absence of PTI. The MAPKs were activated by three hours and remained active for at least 7 hours after estradiol treatment (Figure 3B). This sustained MAPK activation was ETI-specific as no such activation was observed in the rps2 mutant background, which lacks the corresponding receptor (Figure 3C). PR1 induction during AvrRpt2-ETI was independent of SA in XVE-AvrRpt2 transgenic seedlings (Figure S3), which is consistent with the results obtained using adult leaves inoculated with a Pto strain expressing AvrRpt2 (Figure 1). Similar trends in MAPK activation duration were observed when adult leaves were inoculated with Pto strains: sustained activation of the MAPKs was observed with Pto AvrRpt2 in a manner dependent on the R gene RPS2, but not with the strains that do not trigger ETI (Figure 4). While the amounts of activated MPK3 and MPK6 were similar during AvrRpt2-ETI triggered in XVE-AvrRpt2 transgenic plants (Figure 3), there was more activated MPK3 than activated MPK6 during AvrRpt2-ETI triggered by Pto AvrRpt2 (Figures 4, S4 and S5), suggesting that MPK3 plays a major role during AvrRpt2-ETI in bacterial infection. We also observed sustained MAPK activation during AvrRps4-ETI although levels of activation were weaker compared to AvrRpt2-ETI (Figure S4). Since there are 20 MAPKs in Arabidopsis [16], we determined the identities of the activated MAPKs. Indeed, the activated MAPKs during AvrRpt2- and AvrRps4-ETI were MPK3 and MPK6 (Figure S4). Previously, Beckers et al (2009) reported that an SA analog, benzo(1,2,3,)thiadiazole-7-carbothioic acid S-methyl ester (BTH), induced priming of MPK3 activation by inducing expression of MPK3 [29]. In contrast, sustained activation of MPK3 during AvrRpt2-ETI was independent of SA (Figure S5). The sustained activation was not due to an increased amount of MPK3 as we did not observe obvious changes in the MPK3 protein level during AvrRpt2-ETI (Figure 3). Taken together, our data show that sustained activation of the MAPKs is SA-independent and occurs during ETI but not during non-ETI responses.
To test if sustained activation of MPK3 and MPK6 can induce PR1 in an SA-independent manner, transgenic plants expressing constitutively active forms of MKK4 (MKK4DD) or MKK5 (MKK5DD) under the control of a dexamethasone (DEX)-inducible promoter were employed (DEX-MKK4DD and DEX-MKK5DD). MKK4 and MKK5 are MAP kinase kinases, whose activated forms phosphorylate and activate MPK3 and MPK6 [17]. DEX-induced expression of MKK4DD or MKK5DD leads to sustained activation of MPK3 and MPK6 (Figure S6) [30]. Induction of PR1 was observed 9 hours after DEX treatment (Figure 5A), suggesting that sustained activation of MPK3 and MPK6 is sufficient for induction of PR1. Induction of FRK1 is thought to be a good marker for activation of MPK3 and MPK6 [31] and was observed 3 hours after DEX treatment while PR1 was not (Figure 5B). FRK1 was strongly induced 30 minutes after flg22 treatment [32], and the induction did not require SA accumulation (Figure S7). Thus, although transient MAPK activation of MPK3 and MPK6 is sufficient for FRK1 induction, sustained MAPK activation is necessary and sufficient for SA-independent PR1 induction. The sustained activation of MPK3 and MPK6 by DEX-induced MKK4DD or MKK5DD did not increase the level of SA (Figure 6A). Furthermore, a wild-type-like PR1 induction 24 hours after DEX treatment was observed in plants deficient in SID2 or NPR1 (Figure 6B). Since PR1 induction was not observed in a DEX-inducible ß-glucuronidase (GUS, an arbitrary reporter gene) line after DEX treatment, PR1 induction was not caused by the DEX-inducible system or DEX but by induced expression of MKK4DD or MKK5DD. Although MPK4 was activated as well as MPK3 and MPK6 during PTI and ETI (Figure 4; [17]), expression of MKK4DD or MKK5DD does not lead to strong activation of MPK4 [30]. Therefore, it is unlikely that MPK4 plays a role. We conclude that sustained activation of MPK3 and/or MPK6 causes PR1 induction in an SA-independent manner.
We tested whether mpk3 and mpk6 single mutations had effects on PR1 induction by MKK4DD or MKK5DD expression. PR1 induction was unaffected in mpk6 but strongly reduced in mpk3 plants (Figure S8A). MKK4DD induction was also strongly reduced in mpk3 plants (Figure S8B), so the reduction of PR1 induction in DEX-MKK4DD/mpk3 may be due to reduction of MKK4DD expression. MKK5DD induction in DEX-MKK5DD/mpk3 was reduced compared to DEX-MKK5DD/Col yet 10 times higher than MKK4DD induction in DEX-MKK4DD/mpk3 while PR1 induction was similarly compromised in both plant lines. Thus, these results suggest that MPK3 is required for SA-independent PR1 induction conferred by forced MKK5 activation while MPK6 is dispensable.
We tested whether sustained activation of MPK3 and/or MPK6 also regulates other SA-responsive genes. Leaves of the DEX-MKK4DD transgenic lines in wild type (Col) or sid2 backgrounds were treated with DEX or mock control and were collected for mRNA profiling at 24 hours after treatment. The transcriptomic changes caused by DEX treatment were very similar between Col and sid2 (Figure S9 and Table S3), indicating that gene regulation by sustained activation of the MAPKs is mostly independent of SA. Therefore, only the mRNA profile from the DEX-MKK4DD sid2 line was included in the following analysis. The heatmap in Figure 2A shows that a majority of the SA-responsive genes responded in the DEX-treated DEX-MKK4DD sid2 line similarly to sid2 plants during AvrRpt2-ETI: most up-regulated or down-regulated SA-responsive genes in sid2 during AvrRpt2-ETI were up-regulated or down-regulated, respectively, in the DEX-treated DEX-MKK4DD sid2 line. This suggests that sustained activation of the MAPKs regulates a majority of SA-responsive genes in an SA-independent manner during AvrRpt2-ETI.
Three gene clusters were selected for further analysis (Clusters I–III in Figure 2A). The expression level changes of genes in each cluster were averaged and shown in Figure 2B–D. Clusters I and III include genes up- or down-regulated, respectively, in a SID2-independent manner during AvrRpt2-ETI and by sustained activation of the MAPKs. Thus, these genes appear to be regulated by sustained activation of the MAPKs during ETI. Cluster II includes genes that were up-regulated in a largely SID2-independent manner during ETI but not up-regulated by sustained activation of the MAPKs. Thus, up-regulation of the Cluster II genes during ETI is supported by a mechanism(s) other than the mechanism mediated by the MAPKs. When the GO terms associated with the clusters were examined, Cluster I, but none of the other clusters, was enriched with genes related to biological stresses (response to biotic stimulus, P = 2.8×10−5; response to other organism, P = 1.1×10−4; multi-organism process, P = 5.9×10−4). The results imply that genes induced by both SA and the MAPKs are important for biological stress responses. The regulatory trends for the clusters were confirmed by qRT-PCR analysis of one gene from each cluster (Figure S10).
We investigated if compensation between MPK3/MPK6 and SA signaling could be detected in the PR1 expression level during ETI. Leaves of wild type (Col), mpk3, mpk6, sid2, mpk3 sid2 and mpk6 sid2 plants were inoculated with Pto AvrRpt2 or Pto AvrRpm1, and PR1 expression levels were determined 24 hpi (Figure 7A). While PR1 expression was compromised in sid2 but not in mpk3 or mpk6 during AvrRpt2-ETI, it was compromised in mpk3 sid2 more than in sid2 (blue bar), suggesting compensation between MPK3 and SID2 on PR1 expression during AvrRpt2-ETI. To quantify the level of compensation between MPK3 and SID2 on PR1 expression, a signaling allocation analysis was applied [25]. In this analysis, the effects of the genes and their interactions were estimated for contribution to the PR1 expression level after inoculation. We estimated the individual contribution of MPK3 on the PR1 expression level as the difference in expression levels between sid2 and mpk3 sid2, that of SID2 as the difference in PR1 expression levels between mpk3 and mpk3 sid2 and their combined contribution as the difference in PR1 expression levels between the wild type and mpk3 sid2. The value of the genetic interaction between MPK3 and SID2 was calculated by subtracting the sum of the individual contributions of MPK3 and SID2 from their combined contribution. Their combined contribution in the wild type was less than the sum of the individual contributions of SA and MPK3, which is signified by the negative interaction between them. We previously defined this less-than-additive combined contribution as compensation [25]. Such compensation was observed for AvrRpt2-ETI (Figure 7B, top). Thus, signaling mediated by MPK3 and SA is compensatory on PR1 expression during AvrRpt2-ETI. No significant effects of MPK6 or the interaction (MPK6:SID2) on PR1 expression were detected during AvrRpt2-ETI (Figure 7A and B). No significant effects of MPK3, MPK6 or their interactions (MPK3:SID2 and MPK6:SID2) on PR1 expression (Figure 7A and B, red bar) or resistance (Figure S11) were detected during AvrRpm1-ETI, suggesting a divergence in the mechanisms that modulate network robustness between different cases of ETI.
A similar trend was observed with the effects of MPK3 and MPK6 on bacterial resistance in AvrRpt2-ETI (Figure 7C). AvrRpt2-ETI is defined as the difference in in planta growth of Pto EV and Pto AvrRpt2 on a log10-scale [25]. The compensation between MPK3 and SID2 was clear from the signaling allocation analysis, as both had positive effects and their interaction was negative (Figure 7D, left). We did not detect significant effects of MPK6 or the interaction (MPK6:SID2), although we observed a similar pattern to the case of MPK3 (Figure 7D, right). Thus, compensation of SA signaling by a signaling mechanism involving MPK3 exists in inhibition of bacterial growth, as well as in PR1 expression, during AvrRpt2-ETI.
Lethality of the double mutants mpk3 mpk6 [21] does not allow us to determine combined contributions of MPK3 and MPK6 to compensation of SA signaling during ETI. It is possible that MPK6 is not a major factor in SA signaling compensation during ETI and that a signaling mechanism(s) other than that involving MPK3 or MPK6 is important during AvrRpm1-ETI. Nonetheless, these results clearly demonstrate that at least during AvrRpt2-ETI, SA signaling can be compensated by MPK3-mediated signaling in regulation of SA-responsive gene expression and that this compensation increases the robustness of the network output. This allows immunity to be maintained even if the major network sector, SA signaling, is compromised.
In this study, we identified a mechanism that can increase the robustness of the plant immune signaling network during AvrRpt2-ETI. Our results demonstrate that (1) MPK3 and MPK6 are activated in a sustained manner during ETI and in a transient manner during non-ETI; (2) Transient MAPK activation during non-ETI such as PTI does not contribute to SA-independent regulation of the SA-responsive genes; (3) Sustained MAPK activation activates an SA-independent alternative mechanism that regulates the SA-responsive genes; (4) SA-independent alternative mechanisms which regulate the SA-responsive genes were activated during AvrRpt2-ETI; (5) SA signaling compensation by the signaling sector involving MPK3 contributes to increased robustness against network perturbations during AvrRpt2-ETI (Figure 8).
A prior study implied that the duration of MPK3 and MPK6 activation is longer during ETI compared to during non-ETI upon P. syringae infection [27]. However, it did not rule out the possibility that the effector AvrRpt2 caused sustained MAPK activation through a mechanism independent of recognition of AvrRpt2 via RPS2. We clearly demonstrated that sustained MAPK activation occurs when ETI is triggered (Figures 3 and 4). The duration of MPK3 and/or MPK6 activation is the determinant for activation of the SA-independent alternative mechanism to regulate the SA-responsive genes: only sustained MAPK activation results in activation of the alternative mechanism. One potential cause of the differential activation duration is rapid turnover of PTI receptors, PRRs. FLS2 is rapidly degraded and disappears within one hour upon exposure to flg22 [33], [34]. Although turnover rates of other PRRs are not known, if many PRRs turn over rapidly upon activation, this could explain transient activation of the MAPKs by Pto hrcC (Figure 4), which presents multiple MAMPs [11]. The turnover rates of R proteins, the ETI receptors, upon their activation are largely unknown. Whether turnover rate is involved or not, this hypothesis that the duration of MAPK activation and, consequently, the robustness of the network can be tuned to each receptor is attractive because it would enable network robustness to be evolutionarily adapted according to what pathogen-derived signals are recognized by the receptors. Another potential but not mutually exclusive cause of the differential activation duration is involvement of protein phosphatases that dephosphorylate and inactivate the MAPKs: activation of the MAPKs may be negatively regulated by a phosphatase(s) during non-ETI responses while the phosphatase may be inactivated during ETI, resulting in the sustained activation of the MAPKs. Multiple types of such phosphatases including MAPK phosphatases are known in Arabidopsis [35]. Differential regulation of these phosphatases during ETI and non-ETI responses may explain the differential duration of MAPK activation.
Switching of downstream signaling by differential duration of MAPK activation is known in animals and yeast [36]–[38]. In one case, it is explained by nuclear translocation of a MAPK that occurs only after its sustained activation [36]. In this way, sets of substrates available to the MAPK are distinct between its transient and sustained activation, which could lead to distinct downstream signaling. In plants, it has also been reported that MAPKs are translocated to the nucleus upon stimulation [39], [40]. Investigation of potential subcellular localization changes of Arabidopsis MPK3 and MPK6 during PTI and ETI will provide insight into this possibility. Another appealing explanation is involvement of a feed-forward network motif [41]. For example, activation of a transcription factor TF-X may mediate the alternative mechanism regulated by sustained MAPK activation. The activation of TF-X may require signal Y in addition to active MPK3 and/or MPK6. Signal Y may be slowly generated as a consequence of the activation of the MAPKs (e.g., 5 hours). The MAPKs would need to be activated for a long time to simultaneously have both signal Y and the active MAPKs to activate TF-X and regulate the SA-responsive genes. In either scenario, discovery of the signaling components downstream of the sustained MAPK activation will be the key to elucidate the mechanism that decodes duration of MAPK activation. Multiple transcription factors, such as TGAs, WRKYs, TBF1 and VIP1 [14], [22], [23], [42]–[44], are involved in regulation of PR1. These transcription factors may provide a good starting point for a search for the decoding mechanism.
Pto produces the small molecule coronatine, which is a molecular mimic of the JA-Ile conjugate and promotes virulence by suppressing SA signaling [13]. Pto is highly virulent on Arabidopsis plants while ETI-triggering strains of Pto, such as Pto AvrRpt2, are much less virulent. Nevertheless, coronatine could suppress SA signaling. Therefore, SA-independent alternative mechanism(s) to regulate expression of the SA-responsive genes, such as that mediated by the MAPKs, may have a substantial role against perturbation of the immune signaling network by coronatine. This hypothesis is consistent with our observation that loss of both MPK3 and SA led to increased susceptibility to Pto AvrRpt2 (Figure 7).
Pto DC3000 possesses type III effectors which directly or indirectly suppress MAPK activation [45]–[48]. However, we observed sustained activation of MPK3 and MPK6 during AvrRpt2-ETI when AvrRpt2 was delivered from Pto DC3000 (Figure 4). We speculate that the amounts of such MAPK-inhibiting type III effectors delivered and/or the kinetics of their delivery are not optimal to effectively suppress MAPK activation when the type III effectors are delivered from Pto DC3000, which represents a relatively natural context.
The effector HopAI1 from Pto DC3000 can physically interact with and inactivate MPK3 and MPK6 by removing the phosphate group from phosphothreonine via a phosphothreonine lyase activity [45]. HopAI1 also targets MPK4 and decreases MPK4 activity [48]. Decreased MPK4 activity appears to be monitored by the NB-LRR protein SUMM2, resulting in triggering ETI. Overexpression of HopAI1 in wild-type Col-0 plants but not summ2 mutant plants leads to dwarfism and constitutive activation of immune responses [48]. However, Pto DC3000 does not trigger SUMM2-mediated ETI. Consistently, HopAI1 of Pto DC3000 is disrupted by an insertion in its promoter region [49]. Thus, the amount of HopAI1 delivered from Pto DC3000 appears insufficient for effective inhibition of MPK3 and MPK6 activation during AvrRpt2-ETI.
Another effector, HopF2, from Pto DC3000 can also suppress activity of MPK3, MPK4 and MPK6 by targeting the upstream MKK5 and likely other MKKs as well [46], [47]. When overexpressed in plants, HopF2 interferes with AvrRpt2-ETI by inhibiting AvrRpt2-mediated RIN4 degradation [50]. Again, the reason that HopF2 cannot suppress sustained activation of MPK3 and MPK6 triggered by AvrRpt2 when it is delivered from Pto DC3000 (Figure 4) is likely insufficient HopF2 or inappropriate timing of its delivery. Delivery of AvrRpt2 may precede that of HopF2 [50].
One enigma is why plants need to make the robustness of the immune signaling network lower during PTI when the network itself has the capacity to be highly robust. If the network output during PTI were as robust as during ETI, the chance that “true” pathogens will overcome PTI would be much lower. We speculate that the lower robustness during PTI is selected through evolution as trade-offs with other requirements. Many MAMPs are shared among pathogens and benign microbes and provide low quality information about pathogen attack. It is probably not adaptive for plants to respond to a MAMP with strong and sustained immune responses similar to those during ETI since in many cases, plants encounter benign microbes and ETI-type responses cost fitness. A strategy apparently selected is to respond weakly first and wait to intensify the response until further information increases the probability that a true pathogen is present [24]. In contrast, since effectors are a hallmark of true pathogens and provide high quality information, during ETI plants can induce rapid and strong immune responses with a very low chance of needless fitness costs.
The signaling sector activated by sustained activation of the MAPKs during ETI and the SA signaling sector can regulate the common set of genes. This is one of the mechanisms underlying robustness of the immunity level against network perturbations during ETI. This modulation of the network robustness is controlled by signaling kinetics of a network component. Our findings imply that properties of biological networks can be modulated through network component activities.
Free SA measurement, MAP kinase assays, bacterial growth assays and the signaling allocation analysis were performed as described previously [25], [26].
Arabidopsis plants were grown in a controlled environment at 22°C with a 12 h photoperiod and 75% relative humidity. Arabidopsis thaliana accession Col-0 was the background of all mutants used in this study. Arabidopsis mpk3-1 (SALK_151594) [21], mpk6-2 (SALK_073907) [18], npr1-1 [51], rps2 101C [52] and sid2-2 [15] were previously described. We generated the double mutants mpk3 sid2 and mpk6 sid2 by standard genetic crosses. Estradiol-inducible AvrRpt2 transgenic lines [53] and the DEX-MKK4DD and -MKK5DD transgenic lines [30] were previously described. We crossed DEX-MKK4DD and -MKK5DD into the mutant backgrounds mpk3, mpk6, npr1, sid2 and vip1. Primers and restriction enzymes used for screening of the mutants are listed in Table S4.
Flg22 peptide was purchased from EZBiolab Inc (Westfield, IN, USA). Estradiol (E8875) and DEX (D1756) were purchased from Sigma (Saint Louis, MO, USA).
Pto DC3000 strains (or water for mock) or 2 µM DEX (or 0.1% ethanol for mock) were infiltrated into leaves of 4-week-old plants. Leaves were collected at the indicated time points. Total RNA isolation and qRT-PCR analysis were carried out as described previously [54], [55]. The following models were fit to the relative Ct value data compared to Actin2 using the lme function in the nlme package in the R environment: Ctgytr = GYTgyt+Rr+εgytr, where GYT, genotype:treatment:time interaction, and random factors; R, biological replicate; ε, residual; Ctgyr = GYgy+Rr+εgytr, where GY, genotype:treatment interaction; Ctgtr = GTgt+Rr+εgtr, where GT, genotype:time interaction. The mean estimates of the fixed effects were used as the modeled relative Ct values and visualized as the relative log2 expression values and compared by two-tailed t-tests. For the t-tests, the standard errors were calculated using the variance and covariance values obtained from the model fitting. Primers used in the study are listed in Table S4.
Four-week-old Arabidopsis Col-0 and sid2 leaves were infiltrated with Pto hrcC, Pto pLAFR (EV), Pto AvrRpt2 or water (mock). Independently, leaves of four-week-old DEX-MKK4DD plants in Col-0 or a sid2 background were infiltrated with 2 µM DEX or 0.1% ethanol (mock). Samples were collected at 24 hpi. Total RNA was extracted as described previously [26] and profiled using the NimbleGen DNA microarray (A. thaliana Gene Expression 12×135K array TAIR9.0) following the manufacturer's protocol (Roche Applied Science, Indianapolis, IN, USA). Three independent experiments (biological replicates) were performed. The microarray data were submitted to Gene Expression Omnibus (Accession, GSE40555). Probe signal values were subjected to the robust multi-array average (RMA) summarization algorithm [56] using the standard NimbleGen software to obtain the expression level values of the transcripts. Among transcripts of a single gene, those with higher expression values were selected as the representative transcripts of the genes. The following models were fit to log2 expression values using the lmFit function in the limma package in the R environment: Sgyr = GYgyt+Rr+εgyr, where S, log2 expression value, GY, genotype:treatment interaction, and random factors; R, biological replicate; ε, residual. The eBayes function in the limma package was used for variance shrinkage in calculation of the p-values and the Storey's q-values were calculated from the p-values using the qvalue function in the qvalue package. First, genes whose expression was up-regulated or down-regulated (q values<0.01 and more than 2 fold change) in both Pto EV and Pto AvrRpt2-infected Col compared to mock were selected (2828 genes). Second, SID2-dependent genes in Pto EV infection (inductions/suppression in sid2 are less than 20% compared to Col) were selected (187 “SA-responsive” genes) for the clustering analysis. Heatmaps were generated by CLUSTER [57] using uncentered Pearson correlation and complete linkage, and visualized by TREEVIEW [57].
The accession numbers for the Arabidopsis genes discussed in this article are as follows: Actin2 (At2g18780), Chitinase (At1g02360), CHS (At5g13930), FRK1 (At2g19190), MKK4 (At1g51660), MKK5 (At3g21220), MPK3 (At3g45640), MPK4 (At4g01370), MPK6 (At2g43790), NPR1 (At1g64280), RPM1 (At3g07040), RPS2 (At4g26090) and SID2 (At1g74710).
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10.1371/journal.pntd.0004006 | Community-Effectiveness of Temephos for Dengue Vector Control: A Systematic Literature Review | The application of the organophosphate larvicide temephos to water storage containers is one of the most commonly employed dengue vector control methods. This systematic literature review is to the knowledge of the authors the first that aims to assess the community-effectiveness of temephos in controlling both vectors and dengue transmission when delivered either as a single intervention or in combination with other interventions. A comprehensive literature search of 6 databases was performed (PubMed, WHOLIS, GIFT, CDSR, EMBASE, Wiley), grey literature and cross references were also screened for relevant studies. Data were extracted and methodological quality of the studies was assessed independently by two reviewers. 27 studies were included in this systematic review (11 single intervention studies and 16 combined intervention studies). All 11 single intervention studies showed consistently that using temephos led to a reduction in entomological indices. Although 11 of the 16 combined intervention studies showed that temephos application together with other chemical vector control methods also reduced entomological indices, this was either not sustained over time or–as in the five remaining studies—failed to reduce the immature stages. The community-effectiveness of temephos was found to be dependent on factors such as quality of delivery, water turnover rate, type of water, and environmental factors such as organic debris, temperature and exposure to sunlight. Timing of temephos deployment and its need for reapplication, along with behavioural factors such as the reluctance of its application to drinking water, and operational aspects such as cost, supplies, time and labour were further limitations identified in this review. In conclusion, when applied as a single intervention, temephos was found to be effective at suppressing entomological indices, however, the same effect has not been observed when temephos was applied in combination with other interventions. There is no evidence to suggest that temephos use is associated with reductions in dengue transmission.
| Dengue remains largely uncontrolled globally. Prevention and control relies on vector control methods, and good case management is key to reducing mortality. For vector control several methods exist, including biological and chemical interventions, and environmental modifications. The application of the organophosphate larvicide temephos to water storage containers is one of the most commonly employed dengue vector control methods. This systematic literature review assesses the community-effectiveness of temephos in controlling both vectors and dengue transmission when delivered either as a single intervention or in combination with other interventions. 27 studies were included in this systematic review (11 single intervention studies and 16 combined intervention studies). All 11 single intervention studies showed consistently that using temephos led to a reduction in entomological indices. Although 11 of the 16 combined intervention studies showed that temephos application together with other chemical vector control methods also reduced entomological indices, this was either not sustained over time or–as in the five remaining studies—failed to reduce the immature stages. Temephos was found to be effective at suppressing entomological indices, however, the same effect has not been observed when temephos was applied in combination with other interventions. There is no evidence to suggest that temephos use is associated with reductions in dengue transmission.
| Vector control remains the only available intervention to prevent and control the transmission of dengue[1].Various vector control strategies aiming at controlling the principal vector of dengue, Aedes aegypti, are currently used with the intention of preventing the occurrence of dengue, or controlling outbreaks. These vector control measures often include the application of chemical or biological agents for the control of immature and adult mosquito stages, or environmental control methods that target mosquito breeding sites[2]. These vector control measures can be applied as single interventions or in combination.[3]. However, the efficacy and community-effectiveness of vector control strategies in terms of reductions in dengue transmission remain unclear, as previous systematic reviews have reported regarding the application of single intervention methods such as peridomestic space spraying and the use of Bacillus thuringiensis israelensis[4,5].
One of the most commonly employed methods for dengue vector controlis the use of the organophosphorous compound temephos (commercial name Abate) as a larvicide. Its use has been documented since 1965[6] in ponds, marshes and swamps at a dosage of 0.1–0.5 kg/ha for vector control in general, although fewer studies exist in relation to Ae. aegypti. Per the WHO Pesticides Evaluation Scheme, temephos can be used safely in potable water when the dosage does not exceed 56–112 g/ha (5.6–11,2 mg/m2) or 1 mg/l [7]. Moreover, the WHO hazard classification of temephos is “U”, meaning it is unlikely to cause acute hazard under conditions of normal usage[8].Temephos is a widely preferred tool for several reasons, including its ease and simplicity of application, selective killing of mosquito larvae and its long lasting effect when compared to traditional oil application methods[6].Temephos is commercially available in standardised preparations such as emulsifiable concentrates, dilute solutions, dusts and granules, including slow release formulations. It can be applied in different ways depending on the site and rate of application required. It can be delivered by hand or by injection through drip system devices or power sprayers. Temephos sand granules can be applied to household water storage containers of varying capacity by using a calibrated plastic spoon in order to administer a consistent dosage of 1ppm(1 ppm = 10−6 = 1 parts per million = 0,0001%) [9].
Temephos has widely been considered a cornerstone for controlling immature forms of Ae. aegypti yet while its efficacy has been demonstrated under laboratory conditions, comparable levels of efficacy are not necessarily replicated under field conditions[10].For the purpose of this article, efficacy under field conditions, including the use in ongoing control programmes, is referred to as community-effectiveness.
This systematic literature review assesses the community-effectiveness of temephos for controlling dengue vectors and dengue disease transmission when delivered in the field as a single intervention or in combination with other interventions.
This systematic literature review follows the reporting guidelines described in the PRISMA statement[11].A comprehensive systematic literature search protocol was developed and agreed on by the authors consisting of six databases (PubMed, WHOLIS, GIFT, CDSR, EMBASE, Wiley).An additional review of grey literature,—screening the reference lists of the included publications as well as asking stakeholders about relevant literature—was performed, including theses, unpublished data, and other reports. The search was conducted until 15 June 2013. All sources fulfilling the predefined inclusion criteria and exclusion criteria were cross checked for additional references and these were included if again fulfilling the inclusion and exclusion criteria.
No language restrictions were applied and abstracts of publications in languages other than English were translated. The search included all studies irrespective of the year of publication.
The literature search strategy was based on four categories1) dengue vectors (Aedes aegypti or Aedes albopictus),2) vector control intervention (temephos),3) dengue disease, and 4) dengue prevention and control. The search was conducted using the appropriate Medical Subject Heading (MeSH) terms followed by the Boolean operator “OR” for terms within each of the 4 categories, “AND” between categoriescombined with “free text” terms. The terms used for the 'vector' category included Aedes aegypti and Aedes albopictus. The search terms for the ‘vector control intervention’ category included insect control, vector control, mosquito control, larvicides, temephos, temefos and Abate. For the 'dengue disease' category, the terms dengue, dengue fever, DF, dengue hemorrhagic (haemorrhagic) fever, DHF, dengue shock syndrome and DSS were used. These terms were used in different combinations together with the terms prevention and control in order to broaden the search.
All titles and abstracts of potentially relevant articles were initially screened for the relevance of the research question and irrelevant articles as well as duplicates were excluded. The remaining articles that fulfilled the inclusion criteria were assessed, extracted and analysed using the full text of the study. The processes were conducted by two independent reviewers in consensus.
The inclusion criteria for this review were as follows: (i) Studies or programmes conducted with aim to prevent/control dengue; (ii) Studies with quantitative outcomes such as Breteau Index (BI), Container Index (CI), House Index (HI), larval mortality indicated by pupal skins, average number of positive containers per house, pupal index, indoor resting density, ovitrap index or dengue incidence; (iii) Community-effectiveness studies; iv) Peer reviewed studies with the study designs such as Randomised Control Trials (RCT), Cluster-Randomised Controlled Trials (CRCT), Non Randomised Control Trials (NRCT), Before and After studies, Studies with an Intervention and a Control area(Intervention studies); (v) Studies where temephos was used as a single intervention or in combination with other interventions.
Exclusion Criteria were: (i) Studies based in laboratory or semi-field settings; (ii) Studies where temephos was not used alone or as part of a combination intervention; (iii) Studies without clearly specified outcome measures; (iv) Cross-sectional studies, case series, reports, letters, newspaper articles, lectures, conference reports or abstracts.
After applying exclusion criteria, all included articles were categorised as either single or combination intervention studies and tabulated in an evidence table and stratified by type of study. Information on the study setting, objectives, design/sample size and study period as well as outcome measurements, main results and conclusions has been extracted.
Although considering the study quality by reflecting the study types as weighting tool for the discussion, no articles were excluded because of quality, taking the relative scarcity of relevant material into consideration.
To ensure the quality of this systematic review, the tool for assessment of systematic reviews (AMSTAR) was used[12].
The systematic literature search generated 18,439 potentially relevant citations. After screening by title and abstract, application of the inclusion criteria and removal of duplicates, 54 studies were retrieved for full text evaluation. After the final application of the inclusion and exclusion criteria, 20 articles were included and 7 further articles were added from the cross references and grey literature for a final total of 27included studies (Fig 1).
Of the27 studies, 14 were conducted in the Americas, 10 in South East Asia, one in Europe and two in the Western Pacific. Nine of the South East Asian studies were conducted in Thailand (Three by one researcher:Y.H.Bang).Even though no language restrictions were applied, all 27 articles retrieved were in English. All studies were publishedbetween1971 and 2012, of which seven studies were published between 2000 and 2012.
Of the 27 studies[13–39], 11 studies used temephos as a single intervention (Group A), while 15 studies used temephos in combination with other vector control methods (Group B). Only one study [21] tested temephos both alone and in combination, and this study has been included in the combination intervention group. The studies are described in detail according to the two groups(Tables 1 and 2).
Studies on the efficacy of temephos were not assessed and only its community-effectiveness was analysed. However, temephos is used routinely in many parts of the world as a part of dengue vector control activities, and hence this systematic review focused on evidence for its community-effectiveness in the manners in which it has been applied routinely. Although we used a broad search strategy we could have missed potentially relevant studies. We also assume some publication bias towards studies demonstrating a positive effect.
The variability of the outcome measures encountered and the different larval and pupal indices used also limit the comparability of the studies, especially when relating outcomes to dengue transmission[40]. Moreover, very few studies monitored the changes in reported cases of dengue. Among the combined intervention studies, only four studies clearly stated which interventions were found to be effective, while the rest of the studies failed to disaggregate the data.
Overall, a diverse picture emerged with regards to the community-effectiveness of temephos reported in the 27 reviewed studies. Whilst the single intervention studies showed consistently that using temephos led to a reduction of entomological indices, this did not always hold true when applying temephos in combination with other interventions. For the latter group, three studies [30, 31, 33] clearly stated that temephos application along with other chemical vector control methods failed to reduce the larval and pupal indices, and while a further 10 studies reported a post-intervention reduction in immature mosquito stages, the results were either not sustainable over time or the coverage was not complete. The reasons for this can be manifold, and this has important implications for dengue control programmes and raises further questions: were the reasons operational, since when applying combined interventions the focus may perhaps shift from quality to quantity, or are there yet unknown interactions between the different interventions? Or was it simply because of limited resources? A further implication for dengue control programmes is the unknown epidemiological impact of temephos-based interventions, only one study linked basic knowledge of dengue to a reduction of DHF [25].
The effectiveness of temephos interventions depends on many factors related to the quality of delivery and maintenance of the intervention, including water turnover rate and type of water, as well as environmental factors such as organic debris, temperature and exposure to sunlight. This suggests that quality control and the suitability of application sites are key to the ultimate success of the intervention. However, the reviewed studies reporting the residual effect of temephos showed a duration between two and three months, confirming similar findings upon which operational guidelines have been based[9]. This recommended periodicity could have practical implications, however, as frequent re-treatment led to reluctance of temephos use in one of the reviewed studies[22]. Another study[19] suggested that a combined method of cyclic mass treatment at three month intervals with targeted treatment of new habitats between the mass treatments would offer optimal control.
The timing of the use of temephos is another factor to consider: dengue epidemics tend to occur in warm, humid and rainy seasons, favouring the growth of mosquito populations[41]. Hence, the timing of temephos applications can influence the efficacy of dengue vector control, as demonstrated by studies [14,18,23]which suggested that temephos application at the beginning of the rainy season was most likely to control the occurrence of an epidemic, while two studies [16,20]reported the successful control of Aedes species during both the dry and wet seasons.
The method of temephos application recommended by WHO is the use of calibrated plastic spoons to apply the appropriate doses to water-holding containers [9]. However, this review has shown that many different formulations and methods of temephos application can be successfully used, including zip-lock bags [13]which had longer residual effects and were cheap and easy to apply. The application of temephos using spoons [13,18]was reported to create an unpleasant odour, taste and turbidity-a disadvantage not seen with temephos zeolite formulations [15]. Using corncorb grit granules with a blower [21]was cheaper, required less manpower and was effective due to its dispersion. In the Ae. Aegypti elimination programme carried out on an island, many temephos delivery systems were successfully applied, such as emulsion paint on walls, perifocal application of untreated areas, larviciding of water containers[22]. In one of the reviewed studies [21], temephos was also found to be effective when it was applied at half the recommended dosage.
Coverage of potential breeding sites is a recurrent issue: inaccessibility of breeding sites for temephos treatment can be important in cases such as leaf axils, brick or rock holes, miscellaneous containers such as cans, bottles, tiers, flower vases etc. Another aspect of coverage is the question of distance between the treated household and untreated areas. In a study related to Ae. aegypti dispersal, Reiter et al. [42] showed that maintaining a treated barrier zone of 50–100 meters around the house of a dengue case is unlikely to be effective, as Ae. aegypti can fly much further to oviposit. This was evident in one of the studies [16]where there-infestation of a treated area occurred despite the maintenance of a barrier zone of 87 houses.
Operational aspects, such as cost, supplies, time and labour efforts are further issues limiting the potential effectiveness of temephos application [19,29],as is low community acceptability(such as reluctance to use temephos in drinking water)[15, 24,34, 36, 37]. These factors are very important considering that for any community based programme to be successful, it needs to be widely accepted by the people with whom the intervention is being carried out[43],and that programmes need to reflect the "felt needs" of the people in order to maintain their interest, motivation and long-term engagement[44].
No conclusive evidence was attained regarding which intervention was the most effective, nor was it possible to come to a conclusion regarding which combinations of interventions formed the best package in terms of effectiveness, feasibility, cost and sustainability. For the group of studies failing to show effectiveness of temephos combined with other interventions, the failure was attributed to false complacency arising from the perception that temephos was sufficient to control the vector, neglect of source reduction activities [22] and low acceptability of temephos application in potable water[36]. This highlights the importance of community participation, as has been proposed with "Communication-for-Behavioural-Impact" (COMBI) efforts[45].The findings of this study on community participation were similar to those of a systematic review conducted by Heintze et al.[46],which suggested that community-based control strategies implemented together with other interventions were able to reduce classical Aedes larval indices, but they were unable to disentangle the effect of different interventions and community participation.
The role of entomological surveillance is another important factor: WHO recommends the implementation of an integrated dengue surveillance and outbreak preparedness system[47]. The importance of implementation and maintenance of a surveillance programme was highlighted in study [22], which reported the importation of viable Ae.aegypti eggs through a crate of household articles and through trade delivered via the ports.
In conclusion, this review presents more questions than answers regarding the community-effectiveness of temephos for dengue vector control. While there is little doubt concerning the effectiveness of temephos in controlling Aedes breeding sites, the same level of effectiveness was not clear from the studies using temephos combined with other interventions. No conclusive evidence has been shown regarding the impact of temephos interventions on dengue transmission.
This review highlights that although temephos is one of the most widely used interventions against dengue vectors worldwide, its effectiveness can vary greatly. The lack of data relating reductions in entomological indices to reductions in dengue transmission remains a significant knowledge gap in the area of dengue epidemiology and vector control efficacy. Integrated surveillance activities may need to be implemented along with vector control interventions, in order to address this knowledge gap.
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10.1371/journal.pntd.0000291 | Hookworm-Related Anaemia among Pregnant Women: A Systematic Review | Hookworm infection is among the major causes of anaemia in poor communities, but its importance in causing maternal anaemia is poorly understood, and this has hampered effective lobbying for the inclusion of anthelmintic treatment in maternal health packages. We sought to review existing evidence on the role of hookworm as a risk factor for anaemia among pregnant women. We also estimate the number of hookworm infections in pregnant women in sub-Saharan Africa (SSA).
Structured searches using MEDLINE and EMBASE as well as manual searches of reference lists were conducted, and unpublished data were obtained by contacting authors. Papers were independently reviewed by two authors, and relevant data were extracted. We compared haemoglobin concentration (Hb) according to intensity of hookworm infection and calculated standardised mean differences and 95% confidence intervals. To estimate the number of pregnant women, we used population surfaces and a spatial model of hookworm prevalence.
One hundred and five reports were screened and 19 were eligible for inclusion: 13 cross-sectional studies, 2 randomised controlled trials, 2 non-randomised treatment trials and 2 observational studies. Comparing uninfected women and women lightly (1–1,999 eggs/gram [epg]) infected with hookworm, the standardised mean difference (SMD) was −0.24 (95% CI: −0.36 to −0.13). The SMD between women heavily (4000+ epg) infected and those lightly infected was −0.57 (95% CI: −0.87 to −0.26). All identified intervention studies showed a benefit of deworming for maternal or child health, but since a variety of outcomes measures were employed, quantitative evaluation was not possible. We estimate that 37.7 million women of reproductive age in SSA are infected with hookworm in 2005 and that approximately 6.9 million pregnant women are infected.
Evidence indicates that increasing hookworm infection intensity is associated with lower haemoglobin levels in pregnant women in poor countries. There are insufficient data to quantify the benefits of deworming, and further studies are warranted. Given that between a quarter and a third of pregnant women in SSA are infected with hookworm and at risk of preventable hookworm-related anaemia, efforts should be made to increase the coverage of anthelmintic treatment among pregnant women.
| Anaemia affects large numbers of pregnant women in developing countries and increases their risk of dying during pregnancy and delivering low birth weight babies, who in turn are at increased risk of dying. Human hookworm infection has long been recognized among the major causes of anaemia in poor communities, but understanding of the benefits of the management of hookworm infection in pregnancy has lagged behind the other major causes of maternal anaemia. Low coverage of anthelmintic treatment in maternal health programmes in many countries has been the result. After systematically reviewing the available literature we observed that increasing hookworm infection intensity is associated with lower haemoglobin levels in pregnant women. We also estimate that between a quarter and a third of pregnant women in sub-Saharan Africa are infected with hookworm and at risk of preventable hookworm-related anaemia. However, all identified intervention studies showed a benefit of deworming for maternal or child health and we argue that increased efforts should be made to increase the coverage of anthelmintic treatment among pregnant women.
| Anaemia is a major factor in women's health, especially reproductive health in developing countries. Severe anaemia during pregnancy is an important contributor to maternal mortality [1], as well as to the low birth weight which is in turn an important risk factor for infant mortality [2]–[3]. Even moderate anaemia makes women less able to work and care for their children [4]. The causes of anaemia are multi-factorial, including diet, infection and genetics, and for some of the commonest causes of anaemia there is good evidence of the effectiveness of simple interventions: for example, iron supplementation [5], long-lasting insecticide nets and intermittent preventive treatment for malaria [6]–[7].
Hookworm infection has long been recognized among the major causes of anaemia in poor communities [8], but understanding of the benefits of the management of hookworm infection in pregnancy has lagged behind the other major causes of maternal anaemia. An epidemiological study in 1995 highlighted the paradox presented to public health workers that an estimated one-third of all pregnant women in developing countries were infected with hookworm and yet, in the absence of safety data, the appropriate advice then current was to avoid the use of anthelmintics in pregnancy [9]. Furthermore, the lack of an acceptable intervention constrained the development of evidence-based understanding of the impact of hookworm infection on maternal anaemia [10]. These issues were addressed directly by de Silva and colleagues [11], who analysed the safety profile of some 20 years of mebendazole use in antenatal clinics in Sri Lanka. In 2002, WHO published new guidance indicating that pregnant women should be treated for hookworm infection, ideally after the first trimester [12]. This immediately provided the opportunity for improved service delivery, and also encouraged studies to assess the contribution of hookworm to anaemia in pregnancy and the impact of treatment, some of which have been undertaken since 2002. These provide a rich new source of data to help inform public health decision making, and in this paper we present a systematic review of hookworm as a risk factor for anaemia among pregnant women. We also estimate the extent of the problem of hookworm among pregnant women living in sub-Saharan Africa, where hookworm remains an intractable reproductive health problem.
A systematic search of published articles was undertaken in July 2007 and repeated again in October 2007. The online databases MEDLINE (1970–2007) and EMBASE (1980–2007) were used to identify relevant studies, using the Medical Subject Headings (MSHs) pregnancy or pregnant AND hookworm, Necator americanus, Ancylostoma duodenale, intestinal parasites, geohelminths or soil-transmitted helminths AND an(a)emia, h(a)emoglobin or h(a)ematocrit. All permutations of MSHs were entered and each search was conducted twice to ensure accuracy. The search did not exclude non-English language papers. The abstracts of returned articles were then reviewed, and if they did not explicitly investigate the association between hookworm and anaemia, they were discarded. Potentially useful articles were retrieved. We also reviewed reference lists of identified articles and hand searched reviews. Where suitable papers did not provide information in a relevant format, authors were emailed and requested to provide relevant summaries of data. SB undertook the literature search and scanned the results for potentially relevant studies, retrieved the full article, and contacted authors. SB and PJH independently assessed every relevant paper, with no disagreements arising, and SB used a pre-formed database to abstract information.
We followed the reporting checklist of the Meta-analysis of observational studies in epidemiology (MOOSE) group [13]. The primary outcome analysis was haemoglobin concentration (Hb), and our hypothesis was that haemoglobin concentration is associated with the intensity of hookworm infection. Data without quantitative measures of Hb and hookworm infection intensity were excluded. No distinction could be made between the two different hookworm species, Necator americanus and Ancylostoma duodenale, because none of the studies used specific methods to differentiate the species, and routine coprology is unable to do this. Studies had to be based on at least 30 individuals. No scoring of quality of studies was undertaken. However, a description of statistical methods employed, including whether adjustment for potentially confounding variables, is provided. For randomised controlled trials, information is provided on key components of study design as recommended by the CONSORT statement [14].
Data were stratified according to the intensity of infection, based on thresholds recommended by WHO: light (1–1,999 epg); moderate (2,000–3,999 epg); and heavy (4000+ epg). Estimates of Hb were assessed for each intensity category and differences between categories were presented as a standardized mean difference (SMD) and 95% confidence interval. These were calculated with a random-effects model according to the DerSimonian and Laird method [15]. Heterogeneity was assessed by the I2 test with values greater than 50% representing significant heterogeneity. When heterogeneity between studies was found to be significant, pooled estimates were based on random-effect models and the Hedges method of pooling. Results were displayed visually in forest plots. Bias was investigated by construction of funnel plots and by the statistical tests developed by Begg & Mazumdar [16] and Egger et al. [17]. Analysis was performed using the ‘metan’ and related functions in STATA version 10 (College Station, TX).
We attempted to estimate the number of pregnant women infected with hookworm in hookworm-endemic countries in sub-Saharan Africa. To estimate the number of pregnant women, we used population data from the Gridded Population of the World (GPW) version 3.0 β [18]. GPW3.0β is a global human population distribution map derived from areal weighting of census data from 364,111 administrative units to a 2.5′×2.5′ spatial resolution grid. Country-specific medium variant population growth rates and proportions of the female population aged 15–49 years available from the United Nations Population Division – World Population Prospects [UNPD-WPP] database [19] were used to project this age cohort of the population total to 2005 using ArcView (Environmental Systems Research Institute Inc., CA, USA). The number of pregnant women was estimated separately for each country from the crude birth rate (number of births over a given period divided by the person-years lived by the population over that period); this will be an under-estimate as it does not include women experiencing miscarriages and stillbirths, which are not routinely reported. Hookworm prevalence was defined on the basis of an existing model which uses satellite-derived climatic factors to predict the geographical distribution and prevalence of hookworm among school-aged children [20]. In the absence of relevant empirical data, we assume that infection prevalence is equivalent in school-aged populations and pregnant women; this is probably an under-estimate since hookworm prevalence is generally higher in adult populations [21]. We also assume that no large-scale hookworm control has been undertaken. Extractions of population at risk by prevalence of hookworm were then conducted in ArcView 3.2.
Our literature searches identified 105 citations and from this list 30 potentially relevant research studies were identified; the remaining citations were either research studies among non-pregnant women, reviews or editorials. Of these 30 potentially relevant studies, 19 were determined to be eligible, including 13 cross-sectional studies, 2 randomised controlled trials, 2 non-randomised treatment trials and 2 observational studies.
13 studies presented observational data on the relationship between hookworm infection and haemoglobin concentration: eight from Africa, three from Asia and two from Latin America. The characteristics of the cross-sectional studies included are presented in Table 1. The data were stratified according to the intensity of infection. In four of the studies, none of the woman included had an intensity of infection that exceeded 2,000 epg; in eight studies women had an infection intensity that exceeded 4,000 epg. Comparing uninfected women and women lightly (1–1,999 epg) infected with hookworm, the standardized mean difference (SMD) in Hb was −0.72 (95% CI: −1.26 to −0.18) (n = 13), indicating that even women lightly infected with hookworm have lower Hb levels than uninfected women. However, there was variation in the differences observed and examination of forest plots suggested heterogeneity of effect, which was statistically significant (I2 score of 72.9%). This was explained by inclusion of the study by Rodríguez-Morales et al. [22] which collated data from nine states across Venezuela. Omitting this study from the analysis, the SMD between women uninfected and lightly infected was −0.24 (95% CI: −0.36 to −0.13) (Figure 1). Omission of other studies made little or no difference to the overall effect. There was slight evidence of bias using the Egger test (p = 0.008) and the Begg test (p = 0.07): the relatively small study by Ayoya et al. [23] in Mali showed evidence of effects that differed from the larger studies. Heavy hookworm infection was also significantly associated with a lower Hb level compared to light infection: the standardized mean difference in Hb was −0.57 (95% CI: −0.87 to −0.26) (n = 7) (Figure 2). No evidence of bias was observed.
Our literature search identified two randomised controlled trials (RCTs) on the impact of anthelmintic treatment in pregnancy, two non-randomised intervention trials, and two observational studies (Table 2). All the studies showed a benefit of deworming for maternal or child health, but since a variety of outcomes measures were employed it is difficult to compare study findings quantitatively. Both RCTs had clear objectives, provided sample sizes calculations and undertook analyses adjusted for potentially confounding factors, but only the RCT in Peru [24) presented a flow of participants through each stage and baseline characteristics of each group, and stated there was adequate concealment of assignment of participants. Of the two RCTs, only the one in Sierra Leone demonstrated a statistically significant benefit of treatment, with the decline in Hb during pregnancy 6.6 g/L less in women treated with albendazole compared to untreated women [25]. The study also showed an additive impact of deworming and iron-folate supplementation, with 13.7 g/L less decline in Hb over the course of pregnancy compared to controls. The RCT in Peru found no impact of treatment on Hb or mean birthweight but showed a significant decrease in the prevalence of very low birthweight with anthelmintic treatment [24]. In the Sierra Leone RCT, no adverse pregnancy outcomes were found to be linked to albendazole. The RCT in Peru found no significant difference between the mebendazole and placebo groups in the frequency of miscarriages, malformations, stillbirths, early neonatal deaths and premature babies [24],[26].
The two non-randomised intervention trials presented data on the impact of anthelmintic treatment on Hb. A study in Cote d'Ivoire included 32 pregnant women treated with pyrantel pamoate and showed that the prevalence of hookworm decreased by 93% and Hb increased by 6 g/L over the course of the pregnancy [27]. A study in Sri Lanka also showed that treatment increased Hb in pregnant women, and found that providing both mebendazole and iron supplementation had a greater impact on Hb than iron supplementation alone [28]. The observational study in Nepal compared women who had received anthelmintic treatment to those who did not, and found that treatment had significant beneficial effects on severe anaemia, birthweight and infant mortality [29]. The other observational study on pregnant women, in India, also found that co-administration of mebendazole and iron supplementation resulted in improved Hb [30].
Using GPW3.0β population estimates and country-specific age-sex structures, we estimate that in 2005 there were 148 million women of reproductive age (15–49 years) in hookworm endemic countries in SSA. Overlaying this surface with our model of hookworm prevalence we estimate that 37.7 million women of reproductive age are infected with hookworm. On the basis of number of live births occurring in SSA, we estimated that the number of pregnant women in SSA in 2005 was 25.9 million of which approximately 6.9 million were infected with hookworm.
That human hookworm infection results in intestinal blood loss which, in turn, can contribute to anaemia is well-established [8]. What has remained unclear and hindered public health policy and planning is the extent to which hookworm is associated with anaemia during pregnancy. The results of our systematic review quantify this relationship and confirm that heavy intensities of hookworm infection are associated with lower levels of haemoglobin than light infection intensities. This finding corroborates previous studies among school-aged children that show a relationship between infection intensity and haemoglobin [31]–[33].
Over forty years ago, Roche & Layrisse [31] in their seminal study on hookworm anaemia identified four conditions necessary to show an association between hookworm infection and Hb: a large sample size; quantitative measures of haemoglobin and hookworm infection; sufficient variation in infection levels; and few other competing causes of anaemia. These conditions are also relevant to interpreting the current results: in particular, the absence of estimates of hookworm intensity resulted in the exclusion of studies, some of which, reported no association between hookworm infection and the risk of anaemia [34]–[36]; while others reported a significant association [37]–[38]. Anaemia in developing countries has multiple causes, including micro-nutrient deficiencies, infectious diseases and inherited disorders [39], and as such, the observed relationship between Hb and hookworm infection may be confounded by other causes of anaemia. Furthermore, residual confounding may exist among studies which did not adjust for socio-economic status, which may lead to an overestimation of association. However, nine of the 13 studies undertook some form of analysis which adjusted for potential confounding variables, including dietary intake, gestation age, and co-infections (Table 1), thereby adding weight to the observed associations; only four studies adjusted for socio-economic status.
The contribution of hookworm infection to maternal anaemia is such that all women of child-bearing age could benefit from periodic treatment in hookworm endemic areas, and that women harbouring the heaviest infections are likely to benefit most. Previously, a systematic review of randomised controlled trials investigating the impact of anthelmintic treatment on haemoglobin among school-aged children concluded that treatment against intestinal nematode infections resulted in an increase in haemoglobin of 1.71 g/L (95% confidence intervals 0.70–2.73) [40]. However, there were a number of important omissions in the study, including the failure to distinguished between different helminth species or account for intensity of infection, which may have under-estimated the true treatment effect [41]. The treatment studies among pregnant women reported here found that albendazole was effective in reducing the decline in haemoglobin that typically occurs during pregnancy [25], but that the effect was less apparent with mebendazole [24]. This may reflect the lower efficacy of mebendazole versus albendazole in treating hookworm infection [42],[43]. However, there is a trade-off between efficacy and safety since mebendazole is poorly absorbed from the gut whereas albendazole is turned into a sulfoxide metabolite that gets widely distributed in the tissues. In addition to drugs used, there are other potential reasons accounting for the difference in the observed impact of anthelmintic treatment on haemoglobin. These include higher intensities of hookworm among women in Peru than among the women in the Sierra Leone study. In addition, different underlying aetiologies of anaemia may be relevant, such differences in iron deficiency anaemia and malaria and schistosome transmission intensity [39]. Finally, although we did not quantitatively assess the quality of the studies, reporting of the RCT in Sierra Leone was incomplete and it is possible that there were methodological differences that were associated with observed treatment effects [14].
Despite the potential benefits of anthelmintic treatment during pregnancy, few countries have included deworming in their routine antenatal care (ANC) programmes, with only Madagascar, Nepal and Sri Lanka doing so routinely. It is suggested that a fear of adverse birth outcomes as well as a lack of safety data, especially country-specific data, represents a barrier for many ministries of health including anthelmintics into their ANC programmes. The evidence from the RCTs included in this review found no evidence of an increased risk of adverse events following treatement. This is consistent with other observational studies which have investigated the safety of mebendazole in pregnant women (for a recent review of studies, see [26]). We feel that the findings of the present paper make clear that hookworm in pregnancy is prevalent and important, and we strongly encourage that a substantial review of the safety evidence is undertaken, perhaps by WHO and its partners.
The finding that co-administration of deworming and iron supplements has a greater impact on haemoglobin than deworming alone supports the assertion that deworming is unlikely to replenish iron stores in the short term, and needs to be combined with iron supplementation, particularly among populations whose diets is low in bioavailable iron [10]. In addition, a review of the impact of malaria-related anaemia among pregnant women in sub-Saharan Africa suggested that over a quarter of cases of severe anaemia were attributable to malaria [44], while other evidence shows that anaemia burden can be reduced effectively by anti-malarial intermittent preventive treatment (IPT) [7]. An effective package to improve maternal anaemia should therefore ideally include IPT, iron supplementation and anthelmintic treatment. Interestingly, a recent case control study of the causation of severe anaemia in young children in Malawi also concludes that hookworm has tended to be overlooked as a causal factor [45]. The value of combining deworming with micronutrient supplementation for children has previously been emphasized [46].
We found only slight evidence of publication bias, and this is likely to be less important than the numerous other factors that may introduce heterogeneity [17], such as transmission of malaria and schistosomiasis, iron and nutritional intake, diagnostic accuracy in quantifying Hb and hookworm intensity. Furthermore, hookworm species may be important but in the reported studies, no distinction was made between N. americanus and A. duodenale because of the practical difficulties of differential diagnosis. Pathological studies indicate that A. duodenale causes greater blood loss than N. americanus [47], with epidemiological studies among Zanzibari schoolchildren suggesting that A. duodenale is associated with an increased risk of anaemia [48]. Thus, where hookworm is exclusively A.duodenale, such as in Nepal [49], the observed effect on maternal anaemia might be greater.
In 1995, Bundy and colleagues estimated that in low income countries, 44 million (35.5%) out of 124 million pregnant women were infected with hookworm [9]. Here we estimate that 6.9 million (26.7%) out of 25.9 million pregnant women in SSA are infected with hookworm. Our current estimates are more precise since they are the first to explicitly include the fine spatial variation in distribution of both infection and population. They suggest that the earlier methodology may have overestimated the proportion of pregnant women infected. On the other hand, the reliance on infection prevalence data from surveys of schoolchildren, in the absence of data from adult women, means that both estimation procedures are likely to result in under-estimates. Nonetheless, the estimates suggest that between a quarter and a third of pregnant women in sub-Saharan Africa are infected with hookworm and therefore at risk of preventable hookworm-related anaemia.
In conclusion, this systematic review presents evidence that increasing hookworm infection intensity is associated with lower haemoglobin levels in pregnant women in poor countries. The chronic and recurring nature of hookworm infection throughout the reproductive years means that it may have a chronic impact on the iron status of infected women, potentially contributing to their morbidity and mortality and that of their children. In many developing countries it is policy that pregnant women receive anthelmintic treatment but in practice coverage rates are often unacceptably low. We suggest that efforts are made to increase the coverage of anthelmintic treatment and iron supplementation, with, where appropriate, intermittent preventive treatment for malaria.
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10.1371/journal.pntd.0003270 | A One Health Framework for the Evaluation of Rabies Control Programmes: A Case Study from Colombo City, Sri Lanka | One Health addresses complex challenges to promote the health of all species and the environment by integrating relevant sciences at systems level. Its application to zoonotic diseases is recommended, but few coherent frameworks exist that combine approaches from multiple disciplines. Rabies requires an interdisciplinary approach for effective and efficient management.
A framework is proposed to assess the value of rabies interventions holistically. The economic assessment compares additional monetary and non-monetary costs and benefits of an intervention taking into account epidemiological, animal welfare, societal impact and cost data. It is complemented by an ethical assessment. The framework is applied to Colombo City, Sri Lanka, where modified dog rabies intervention measures were implemented in 2007. The two options included for analysis were the control measures in place until 2006 (“baseline scenario”) and the new comprehensive intervention measures (“intervention”) for a four-year duration. Differences in control cost; monetary human health costs after exposure; Disability-Adjusted Life Years (DALYs) lost due to human rabies deaths and the psychological burden following a bite; negative impact on animal welfare; epidemiological indicators; social acceptance of dogs; and ethical considerations were estimated using a mixed method approach including primary and secondary data. Over the four years analysed, the intervention cost US $1.03 million more than the baseline scenario in 2011 prices (adjusted for inflation) and caused a reduction in dog rabies cases; 738 DALYs averted; an increase in acceptability among non-dog owners; a perception of positive changes in society including a decrease in the number of roaming dogs; and a net reduction in the impact on animal welfare from intermediate-high to low-intermediate.
The findings illustrate the multiple outcomes relevant to stakeholders and allow greater understanding of the value of the implemented rabies control measures, thereby providing a solid foundation for informed decision-making and sustainable control.
| Successful rabies control generates benefits in terms of improved human and animal health and well-being and safer environments. A key requirement of successful and sustainable rabies control is empowering policy makers to make decisions in an efficient manner; essential to this is the availability of evidence supporting the design and implementation of the most cost-effective strategies. Because there are many, at times differing, stakeholder interests and priorities in the control of zoonotic diseases, it is important to assess intervention strategies in a holistic way. This paper describes how different methods and data from multiple disciplines can be integrated in a One Health framework to provide decision-makers with relevant information, and applies it to a case study of rabies control in Colombo City, Sri Lanka. In Colombo City, a new comprehensive intervention was initiated in 2007 based on vaccination, sterilisation, education, and dog managed zones. Results showed that for the four year time period considered, the new measures overall cost approximately US $ 1 million more than the previous programme, but achieved a reduction in dog rabies cases and human distress due to dog bites, reduced animal suffering and stimulated a perception of positive changes in society. All these achievements have a value that can be compared against the monetary cost of the programme to judge its overall worth.
| The One Health paradigm aims to effectively manage complex risks affecting human, animal, and environmental health by forging new interdisciplinary partnerships and collaborations. Rabies, an acute progressive encephalomyelitis with almost 100% case fatality rate caused by viruses in the genus Lyssavirus, is a zoonotic disease that is responsible for an estimated 55,000 human deaths, tens of millions of human exposures, and substantial animal losses annually [1]. It requires a generalised approach if it is to be managed effectively and efficiently [2].
While One Health thinking has come into vogue, systematic integration of various disciplines such as biological, environmental, social, and health sciences to manage health more holistically is often complicated by interdisciplinary and intersectoral barriers to effective collaboration [3]. One major challenge is the paradigm debate caused by the philosophical assumptions that guide the collection and analysis of quantitative (post-positivist) and qualitative (constructivist) data which may be viewed differently by disciplines. It has been suggested that using both approaches in the same study provides, in combination, a superior understanding of research problems than either approach alone [4]. Another important barrier is the current institutional architecture in which public funds are allocated to specific ministries thereby hindering development of joint public health programmes, which in the case of zoonotic diseases can result in a fragmented approach to control.
The most important vector for maintenance of rabies virus and transmission to humans is the domestic dog, with over 90% of human cases attributable to dog bites. The tools to eliminate rabies from animal populations exist, yet relatively few countries are currently rabies-free placing a major strain on public health budgets. Nearly all human rabies deaths occur in developing countries because they are lacking the resources and capacity to provide both adequate pre-exposure prophylaxis and post-exposure prophylaxis (PEP) in humans and effective management of the virus in animal populations. The World Health Organisation estimates that the annual cost of rabies may be in excess of US $6 billion per year including an estimated US $1.6 billion for PEP [5]. Where rabies control has been successful, efforts have been based on quarantine in an advantageous geographical location (e.g. United Kingdom) or the systematic mass vaccination of domestic and wild host populations (e.g. mainland Europe). In the long term, controlling rabies in the dog population through mass dog vaccination has been shown to be more cost-effective than human PEP alone [6]. The World Health Organisation, the World Organisation for Animal Health, and the Food and Agriculture Organisation of the United Nations acknowledge the need for intersectoral collaboration to manage rabies [5]. However, the systematic control of rabies in animal populations requires financial resources, and the technical capacity to plan, implement and evaluate the vaccination campaign; aspects that are often lacking in affected countries.
Sustaining control demands political, societal and financial backing to maintain the campaign as well as the logistic and human resource capacity to deliver vaccine, and knowledge of, and access to, target populations. On-going collection of data through surveillance systems to monitor and evaluate the economic and technical efficiency of campaigns is necessary to ensure objectives are being achieved, and surveillance must be continuous following eradication to detect re-emergence of the virus promptly. Many of these components need the active support of the public in affected areas. In many countries where rabies is endemic these requisite criteria are not met, and interventions against other diseases are given a higher priority. As a result rabies is considered a neglected disease.
Modern science tends to abstract phenomena and reduce reality into smaller portions that can be easily understood and, as much as possible, be expressed in mathematical terms. While these mathematical abstractions are critical in modelling the dynamics of disease in a population and to assess the effectiveness of interventions, they do not provide an understanding of the support for rabies control measures in society nor do they shed any light on wider-reaching issues such as ethical concerns or animal welfare, in short, they oversimplify reality. For example, anecdotal evidence suggests that some people are not supportive of rabies control measures such as dog culling and actually jeopardise the process by hiding or moving their dogs. Thus, both reductionist in-depth studies, as well as collaboration with other disciplines are needed to understand and plan sustainable and publicly acceptable control programmes.
Many projects have focused on individual components of rabies impact, for example the use of pre-exposure prophylaxis and PEP in humans [7]–[10], the effectiveness of different strategies for dog vaccination [11], [12], willingness-to-pay for dog vaccination [13] and the indirect costs of rabies exposure [14]. However, they have all been assessed independently. Assessed in conjunction, they provide important insights into the positive and negative consequences of rabies management and build a robust basis for informed decision-making.
This paper proposes a generic framework for the assessment of rabies interventions encompassing a wide range of positive and negative consequences and local conditions in order to assess economic efficiency and illustrates its use by applying it to the rabies control programme in Colombo City, Sri Lanka.
In Colombo City, canine rabies has been endemic for several decades. The national anti-rabies strategy aims to protect people who are exposed and those at risk of contracting the disease, establish dog population immunity and to control the dog population. A well regulated system of PEP is in place, limiting the average number of human rabies cases between 1995 and 2011 to 0.65 per year in a city of 650,000 (unpublished data, Veterinary Department of Colombo Municipal Council). The Veterinary Department of Colombo Municipal Council used to combat rabies through culling of roaming dogs via carbon monoxide and carbon dioxide poisoning in a gas chamber and vaccination of owned dogs, but canine rabies cases continued to persist in the city. From 2007 to 2012, following cessation of culling by Presidential decree in 2006, a modified comprehensive intervention to control rabies was implemented, which included mass vaccination of dogs, targeted sterilisation of both owned and unowned dogs, education of children and adults in bite prevention and rabies awareness, and development of dog managed zones in public areas. The stakeholders involved in the intervention hypothesised that the new measures would lead to a decrease in the number of dog rabies cases, an associated reduction in the administration of PEP to people, an increased acceptance of dogs in society, and overall a positive net value of the intervention in Colombo City. The aim of this case study was to assess the economic value of the intervention explicitly taking into account monetary and non-monetary consequences resulting from the change in rabies prevalence, animal welfare and social acceptance.
The survey in 2007 found 23 dog bites in 1,063 household members or an annual incidence rate of 0.0216. The survey in 2010 found 8 dog bites in 559 household members or an annual incidence rate of 0.0143. The difference in incidence rate in 2007 and 2010 was not significant (p = 0.3105, significance level set at 5%). Extrapolating these dog bite incidence rates to the total population of Colombo City of 642,163 in 2007 and 644,450 in 2010, respectively, resulted in the following inputs for the economic assessment: 13,871 annual dog bites for the baseline scenario and 9,216 annual dog bites for the intervention. These figures were multiplied by four to estimate the total number of dog bites for a four year period, which resulted in 55,484 and 36,864 dog bites for the baseline scenario and the intervention, respectively. The average number of human deaths for the four year duration of the intervention and the baseline scenario, respectively, was three human deaths each for the four year period. The national hospital reported that in May 2006, 131 people sought care following dog bites and in May 2011, 160 people were recorded. These monthly figures were multiplied by 48 to estimate proxies for the number of people seeking medical attention for dog bites in Colombo City for the baseline scenario (n = 6,288) and the intervention (n = 7,680), respectively. The estimated rate of reporting was 0.11 for the baseline scenario and 0.21 for the intervention, respectively.
The number of dog rabies cases was 19 for 2007 (proportionally estimated from annual figure for the period June to December), 17 in 2008, 20 in 2009, 10 in 2010, and 2 in 2011 (until June). For the baseline scenario, the estimated average number of dog rabies cases per year was 43, i.e. 172 for the four year duration. The number of dogs culled with a mixture of carbon monoxide and dioxide in the exhaust fumes produced by a freestanding combustion engine was zero in the intervention due to the presidential decree in 2006 that stopped the elimination of dogs and an estimated 9,384 in the baseline scenario for the four years. Field data from Colombo City collected by the BPT from 5 July to 13 August 2011 during 24 vaccination sessions in 12 different wards (total dogs vaccinated = 658) showed that a mean 28% (SD = 21.9%) of the total dogs vaccinated were held by people from the community (owner or other people) and the remaining dogs were caught in a net for vaccination. Using this proportion to estimate the number of dogs in the situation ‘dogs held by owner and vaccinated’ resulted in 36,300 dogs for the intervention and 25,013 dogs for the baseline scenario for the four years. The number of dogs in the situation ‘catch in net and vaccinate’ was estimated at 10,740 for the four years of intervention. The number of dogs sterilised in the intervention during the four years was 5,323 in total based on records from the Blue Paw Trust.
Table 4 summarises the additional investment and the additional outcomes in monetary and non-monetary terms resulting from the intervention when compared with the baseline scenario over a time period of four years. The overall costs of the intervention were US $1.03 million, which was the sum of the additional investment of US $818,851 for the control measures in the animal health sector and the additional US $215,064 spent on monetary human health costs. The net benefits from the intervention were 738 DALYs averted resulting from the reduction in dog bites, increased acceptance of roaming dogs in society and improved animal welfare. The detailed findings are presented below.
Table 5 illustrates the total costs incurred for dog rabies control activities for the intervention from different organisations involved (Sri Lankan government, Blue Paw Trust). Table 6 lists the total costs incurred by the Sri Lankan government for dog rabies control in the years 2002 to 2006 which reflect the control costs in the baseline scenario. In the intervention, the largest proportion of the total costs was staff costs (33%), followed by implementation costs (21%), other costs (19%), and planning and preparation costs (11%). In the baseline scenario, the costs for implementation activities contributed most (about 92%) to the total annual costs in all years. The difference in costs between the baseline scenario and the intervention over a time period of four years was US $818,851.
The total human health cost per dog bite was estimated at US $159 without using immunoglobulin, US $163 with equine immunoglobulin and US $39 for the people who only needed medical care, but not vaccination. The total human health costs for the four years of intervention and the baseline scenario were US $1,179,925 and US $964,861, respectively (Table 4). The difference between the two was US $215,064.
The total DALYs lost for the four years related to psychological distress were 1,461 for a total 36,864 dog bites in the intervention and 2,199 for a total 55,484 dog bites in the baseline scenario, respectively. The total DALYs lost for a four year period related to human deaths were 83.97 for both the intervention and the baseline scenario with three human deaths each. The total number of DALYs averted in the intervention period as compared to the baseline scenario for the four year period was 738.
The sensitivity analyses on the input variables that determined the outcomes “difference in monetary human health costs” and “DALYs averted” over the four years are illustrated in Figures 2 and 3. For the outcome “difference in monetary human health costs” the most influential variables were the number of people bitten and seeking treatment in the intervention (outcome changed by 82%) and the baseline scenario (outcome changed by 67%), respectively, followed by the overhead cost per hospital visit (outcome changed by 13%) and the proportion of people presented with dog bites receiving PEP (outcome changed by 11%). All other input variables caused changes in outcome of 1% or less (Figure 2). The difference in monetary human health costs when varying the two most influential inputs number of people bitten and seeking treatment in the intervention and baseline scenario, respectively, between −30% and +30% from the base is shown in Table 7. The results demonstrate by how much the inputs need to change for the intervention to create a benefit in terms of monetary human health costs. When keeping the base value for the baseline scenario constant, a reduction of the intervention input by at least 20% would lead to a monetary benefit in the human health sector. The additional expenditures for the intervention spent by the animal health sector could be recovered by monetary human health benefits if, ceteris paribus, the input people seeking treatment in the intervention was 950 (12% of the base value) or the input people seeking treatment in the baseline scenario was 13,026 (207% of the base value).
For the outcome “DALYs averted” the most influential variables were the number of dog bites in the baseline scenario (outcome changed by 45%) and in the intervention (outcome changed by 30%), respectively, followed by the DALYs lost per dog bite due to psychological distress (outcome changed by 15%). The DALYs lost per human rabies death did not influence the outcome (Figure 3).
Table 8 and Table 9 illustrate the score per situation without taking into account dog numbers and the score per situation taking into account dog numbers. For the intervention, the qualitative estimates ranged between very low and high. For the baseline scenario, the estimates ranged between very low and very high. The overall score was estimated as low-intermediate for the intervention and intermediate-high for the baseline scenario.
Table 10 summarises the overall acceptance scores for the baseline scenario and the intervention among dog owners and non-dog owners derived from the two surveys. The Kruskal-Wallis rank test to compare different groups showed that the differences between the four groups of dog owners and non-dog owners were statistically significant (p = 0.001). The post-hoc Wilcoxon rank-sum tests yielded a significant difference between dog owners and non-dog owners in 2007 (z = 8.22, p<0.0001), dog owners and non-dog owners in 2010 (z = 3.836, p = 0.0001), and non-dog owners in 2007 and 2010 (z = −2.71, p = 0.0068). There was no significant difference between all participants in the baseline scenario and the intervention (z = −0.938, p = 0.35).
Of the 61 focus group participants, 53 were women and 8 were men. There were 17 housewives and 28 who did not indicate their professions. The rest of the occupations included salesmen, students, nursery teachers, garment makers, an architect and business people. When asked about dog-related issues in the past, the groups described significantly more problems for the past than the present, specifically past problems 7.8±1.5 and present problems 3.3±1.2 (Wilcoxon test, p<0.01). Figure 4 illustrates the number of dog related problems reported by the nine focus groups. Significantly fewer groups mentioned rabies and breeding or puppies as problems at present than in the past (Mc Nemar's test, p<0.05). The stark decrease in the perception of rabies as a problem was explained by workshop participants as being due to possession of knowledge about the disease and knowing what to do when bitten by a dog.
The population control measures mentioned by participants were sterilisation, vaccination, shelter, re-homing, treatments, birth control injection, dumping, education, and awareness campaigns. The highest preference across all groups was given to sterilisation, vaccination and education. None of the groups mentioned culling as a means of population control.
All focus groups indicated that their behaviour following a dog bite had changed. Many groups reported the application of Murunga (a local plant) in the past, but would nowadays wash the wound with soap and running water and go to a hospital to seek treatment.
The mean acceptable total number of roaming dogs reported in the vicinity (i.e. street) was 2 (SD 2, range 0 to 10). There was a significant difference in levels of roaming dogs reported for the past and the present across all focus groups (p<0.001) (Table 11). There was no significant difference in the total number of roaming dogs reported by income levels (p = 0.184), whether the household reared dogs (p = 0.708), gender (p = 0.535), and occupation of participants (p = 0.696).
The economic analysis showed that the use of an additional US $818,851 in the animal health sector to combat rabies and manage the dog population in Colombo City had both negative and positive consequences in society when contrasting the intervention and the baseline scenario. Non-monetary benefits included an increase in the acceptance of roaming dogs among non-dog owners and dog owners, a reduction in animal suffering, and 738 DALYs averted. The increased acceptance of roaming dogs and the DALYs averted increased well-being of society. While reducing animal suffering overall, the intervention strategy at the same time compromised animal welfare (e.g. due to sterilisation or catching in a net). Negative consequences included an increase of US $215,064 in human health costs related to seeking health care following dog bites. Hence, there was a net cost to society in monetary terms of US $1.03 m and a net benefit in non-monetary terms. The lower number of estimated dog bites and the improvement in reporting of bites and treatment of people indicated that the risk to people of contracting rabies was decreasing. The intervention was shown to be effective, as the official number of dog rabies cases decreased from an average of 43 cases per year (2001 to 2005) to just two cases in the first six months of 2011.
Ethical aspects relating to the rights and fairness approach in dogs and humans as well as the virtue approach in people included the following:
In people:
In dogs:
The judgement if the good of the intervention outweighed the harm (the utilitarian approach) and if it best served the community as whole and not just some members (the common good approach) depends on how decision-makers prioritise ethical issues. It might be argued that the avoidance of animal suffering and the increased well-being of people justified the net monetary cost of the strategy. Others might attribute more weight to monetary values resulting from the control activities.
The article proposes a comprehensive framework for assessing multiple aspects of rabies control and combining them in an economic analysis. It is composed of five components (epidemiological, economic, social, animal welfare and ethical assessments) that are all interlinked to guide decision-making and the allocation of resources. While almost all parts were covered individually in previous studies, to the authors' knowledge there are no publications on rabies control that cover all these aspects in the spirit of One Health and link them in an economic analysis. The advantage of the framework is its comprehensive nature that provides decision-makers with a wide array of information that they need to be able to take informed decisions on disease management. However, it requires capacity in multiple disciplines, extensive data collection and an acknowledgment of the multi-factorial processes of decision-making. Similar elements essential for One Health decision making have also been identified by others. For example, a framework published after this study was conducted for the estimation of the economic costs of zoonoses [31] conceptually linked epidemiological and economic models and placed them in the context of wider risk management strategies including assessment of the context, hazard identification, risk assessment, capacity building and communication. The approach proposed here can be considered as an expansion of the risk assessment and risk management steps described in the other framework, whilst providing more detail on a specific disease (i.e. rabies) and the associated effects.
The comparison of additional costs with both monetary and non-monetary outcomes required presenting the results in an unconventional way. On the one hand, this presentation allowed reflecting the complexity of the real world and the various economic consequences related to a decision. On the other hand, the combination of negative monetary and positive non-monetary outcomes made the interpretation more challenging than a conventional net present value or cost-benefit ratio. Cost-benefit analysis is an approach that is intuitively appealing, because it assesses the positive and negative consequences of a strategy in a common unit, generally money. Cost-effectiveness analysis uses the same basic approach, but presents the outcome of a strategy in non-monetary units. The selection of an appropriate measure of effectiveness is critical, and must be in accordance with the control objective. A “CEA is only as valid as its underlying measures of effectiveness and cost” [32], but unlike in health economics, where attempts have been made to harmonise CEA methodologies and encourage comparability of studies [33], there are no specific guidelines available yet for its application in animal health. Currently, due to variability of interests, approaches, designs, capacity and resource availability of organisations involved in rabies control, any incremental cost-effectiveness analyses going beyond human health will vary depending on the outcome measures defined. If the scientific community was to find an agreement on a standardised approach to measure outcomes of rabies control in an integrated way, the economic efficiency of such control measures could be compared internationally and the best approach chosen. As long as there is no standardisation of effectiveness measures for rabies or disease control in general, the variety in outcomes will make a meta-analysis difficult or even impossible. The presented framework is a starting point that may help to create awareness and stimulate discussion.
A range of approaches were used in the case study to cover the multifaceted control measures implemented which were expected to decrease the number of dog rabies cases, to reduce the number of PEP applied to people, to increase acceptance of dogs in society, and to generate a positive net value overall. The case study illustrates the various components of the proposed framework in a developing country context. Because of the limited availability of resources for the case study, secondary data were used whenever possible and where primary data collection was necessary, low-cost approaches were considered for data collection. While the case study is subject to various limitations as described below, it provides information for Sri Lankan stakeholders involved in rabies control on the profitability and cost-effectiveness of the implemented intervention and demonstrates the advantages and challenges of the proposed framework.
Importantly, the number of dog rabies cases was drastically reduced during the time of the intervention to only two in the last six months of the study period compared to a previous high number of dog rabies cases (an average of 43 per year in the period of 2001 to 2005). This indicated that high enough vaccination coverage was achieved and that good progress was being made towards the elimination of rabies in the years 2014–2015, the specified long term target. Given that rabies is still prevalent in other parts of the island, it is important to continue intervention and surveillance efforts in Colombo City to maintain the favourable situation until rabies can be eliminated island-wide.
One critical variable in the estimation of monetary and non-monetary human health consequences was the number of dog bites. While the number of people seeking health care following a dog bite derived from data from the national hospital showed an increase from 2006 to 2011, the numbers derived from the two surveys in 2007 and 2010 showed a decrease in the number of dog bites. There are four possible explanations for this increase: 1) people were more aware of rabies prophylaxis and went to the hospitals more often, 2) there was a better system in place to record dog bites in hospitals, 3) there were effectively more dog bites, and 4) unknown factors related to the two months of data provided caused a fluctuation in numbers (a comprehensive data set for the entire period of 2006 to 2011 was not available). Given the fact that the intervention substantially decreased the number of dog rabies cases in the population, an increase in the number of dog bites seems highly unlikely. This hypothesis is corroborated by the survey and focus group data. Because the survey data showed a decrease in the number of dog bites and the focus groups an increase in disease awareness, it is most likely that the increase in the number of registered dog bites was due to a higher number of people seeking medical advice in case of dog bites. The analysis of the focus groups demonstrated that people's reaction following a dog bite had changed. All focus groups reported that they would now wash the wound with soap and water and go to the hospital to receive PEP. Also, the development of a better system to record bites in hospitals in recent years was expected to have had a positive impact on the number of registered cases (personal communication Dr Obeyesekere).
The difference between the number of dog bites collected from the national hospital and the number estimated from the surveys provided an indication of the rate of under-reporting. The estimated reporting rates indicated an improvement in dog bite reporting in the intervention compared to the baseline scenario. This observation further confirmed the increased rabies awareness of people in the community. However, it also showed that a considerable part of the population did not seek medical attention after being bitten by a dog. As long as rabies is not eradicated from the dog population, people should constantly be informed about the appropriate behaviour in case of a dog bite.
The increase of registered dog bite cases in health centres caused an increase in human health costs. For the savings in monetary human health costs to cover the additional investment made in the animal health sector, the number of people seeking treatment following dog bites would have to be reduced drastically as shown in the sensitivity analysis. It is expected that the number of people seeking medical advice will remain high or increase despite a reduction in dog bites, because the on-going intervention activities constantly promote disease awareness. Only elimination of rabies from the dog population will allow reducing the provision of PEP after dog bites. As long as rabies is endemic in the dog population, people bitten by rabies-suspect animals should get a thorough assessment by health professionals and PEP, as recommended by World Health Organisation guidelines. The only way to reduce public health costs in a rabies endemic situation is to find cheaper and equally effective methods of PEP. The public health sector has already initiated such cost savings by using intradermal vaccines and only administering immunoglobulin in priority cases following a sound history taking and assessment.
Remarkably, there was a considerable reduction in the number of problems listed in all focus groups. Nearly all groups reported that there had been a reduction in rabies, barking, puppies and breeding behaviour and dog fights since the implementation of the intervention. Thus, dogs were perceived more favourably by people, because they looked healthier and showed reduced breeding and nuisance behaviour. Moreover, some focus group participants indicated that their fear of rabies had decreased drastically, because of their improved knowledge of the disease. The selection of participants was performed independently by the community liaison officers in collaboration with community leaders and therefore not influenced by the staff of the BPT. Because the community liaison officers did not receive fixed criteria about socio-economic status of participants, it is likely that ‘high’ socioeconomic groups represented more the middle level, as those at the truly high end did not have the time or interest to participate and were not known well to the community leaders. To promote open sharing of thoughts and concerns, the facilitator made sure to create a comfortable atmosphere and assured participants that the data would be handled anonymously and that their answers did not have any negative consequences for them. However, it is still possible that a few participants may have felt that a less than positive evaluation would result in discontinuation of the project. While such behaviour introduces bias into the results, it also reflects the social desirability of the project, i.e. a community wanting the project to continue is in itself an indication of the degree of perceived success. A source of bias that could not be controlled was the imbalance in gender representation in the focus groups. Only a few men were able to join the focus groups, which was due to the fact that all groups met during the day when the men were at work.
While a variety of approaches are available to assess animal welfare (e.g. welfare assessment protocols for commercial livestock), there are no guidelines in place for the systematic assessment of the impact of rabies and its control on animal welfare. Therefore, we developed a qualitative approach to assess defined situations related to rabies and its control that may negatively affect animal welfare. The assessment was a combination of field data, scientific literature, logical reasoning and professional judgment. Importantly, the scores attributed to the different situations were relative and not absolute. The development of an absolute scoring system would require systematic measurement of physiological and behavioural parameters, which was not within the scope of this project. Taking into account the numbers of dogs in the situation, the highest score (‘very high’) was attributed to the situation culling dogs via carbon monoxide and carbon dioxide poisoning using the exhaust fumes of a combustion engine, and the lowest scores to the situation of holding dogs by the owner or people from the community, and vaccination. Thus, replacing the culling of dogs by other intervention strategies reduced animal suffering. Because none of the focus groups mentioned culling of dogs as an intervention strategy for rabies or population control, it is most likely that the avoidance of culling dogs not only promotes animal welfare, but also the well-being of people in society who care for the dogs.
The ethical assessment helped guide the interpretation of the results. However, it did not attribute weights to the different criteria analysed. Such weights were expected to differ among decision-makers depending on the political agenda, local norms and customs, available resources, experience and personal preferences.
Further benefits that were not quantified in the analysis and remain open to further research include a potential reduction of rabies cases in other animals, promotion of responsible dog ownership and thus better animal welfare, and the decrease of fear in the human population.
This case study explicitly took into account a range of factors that impact on the value of rabies control measures. By combining different monetary and non-monetary aspects, it not only provided information about the impact of rabies control on monetary public health costs, but also important insights about non-monetary effects, particularly animal welfare and social acceptability that were not only valuable outcomes in themselves, but also helped to explain and support some of the other findings. For example, the epidemiological data on the number of dog rabies cases as well as the information from the surveys on dog bites and the focus groups on disease awareness provide an explanation for the increase in human health costs. Linkages between the individual components could be more formalised by for example making social assessments an integral part of epidemiological analysis.
The proposed framework provides a first proposal for looking at rabies control in a holistic way and covers multiple facets that inform decision-making. The framework is expected to help planning impact evaluations of rabies control so that future data collection protocols can take into account not only the health costs, but also consider factors like social acceptance and animal welfare. It thereby helps to conduct integrated assessments for zoonotic disease control and can be further developed to address more complex One Health challenges.
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10.1371/journal.pcbi.1006637 | Dynamical anchoring of distant arrhythmia sources by fibrotic regions via restructuring of the activation pattern | Rotors are functional reentry sources identified in clinically relevant cardiac arrhythmias, such as ventricular and atrial fibrillation. Ablation targeting rotor sites has resulted in arrhythmia termination. Recent clinical, experimental and modelling studies demonstrate that rotors are often anchored around fibrotic scars or regions with increased fibrosis. However, the mechanisms leading to abundance of rotors at these locations are not clear. The current study explores the hypothesis whether fibrotic scars just serve as anchoring sites for the rotors or whether there are other active processes which drive the rotors to these fibrotic regions. Rotors were induced at different distances from fibrotic scars of various sizes and degree of fibrosis. Simulations were performed in a 2D model of human ventricular tissue and in a patient-specific model of the left ventricle of a patient with remote myocardial infarction. In both the 2D and the patient-specific model we found that without fibrotic scars, the rotors were stable at the site of their initiation. However, in the presence of a scar, rotors were eventually dynamically anchored from large distances by the fibrotic scar via a process of dynamical reorganization of the excitation pattern. This process coalesces with a change from polymorphic to monomorphic ventricular tachycardia.
| Rotors are waves of cardiac excitation like a tornado causing cardiac arrhythmia. Recent research shows that they are found in ventricular and atrial fibrillation. Burning (via ablation) the site of a rotor can result in the termination of the arrhythmia. Recent studies showed that rotors are often anchored to regions surrounding scar tissue, where part of the tissue still survived called fibrotic tissue. However, it is unclear why these rotors anchor to these locations. Therefore, in this work, we investigated why rotors are so abundant in fibrotic tissue with the help of computer simulations. We performed simulations in a 2D model of human ventricular tissue and in a patient-specific model of a patient with an infarction. We found that even when rotors are initially at large distances from the fibrotic region, they are attracted by this region, to finally end up at the fibrotic tissue. We called this process dynamical anchoring and explained how the process works.
| Many clinically relevant cardiac arrhythmias are conjectured to be organized by rotors. A rotor is an extension of the concept of a reentrant source of excitation into two or three dimensions with an area of functional block in its center, referred to as the core. Rapid and complex reentry arrhythmias such as atrial fibrillation (AF) and ventricular fibrillation (VF) are thought to be driven by single or multiple rotors. A clinical study by Narayan et al. [1] indicated that localized rotors were present in 68% of cases of sustained AF. Rotors (phase singularities) were also found in VF induced by burst pacing in patients undergoing cardiac surgery [2, 3] and in VF induced in patients undergoing ablation procedures for ventricular arrhythmias [4]. Intramural rotors were also reported in early phase of VF in the human Langendorff perfused hearts [5, 6]. It was also demonstrated that in most cases rotors originate and stabilize in specific locations [4–8].
A main mechanism of rotor stabilization at a particular site in cardiac tissue was proposed in the seminal paper from the group of Jalife [9]. It was observed that rotors can anchor and exhibit a stable rotation around small arteries or bands of connective tissue. Later, it was experimentally demonstrated that rotors in atrial fibrillation in a sheep heart can anchor in regions of large spatial gradients in wall thickness [10]. A recent study of AF in the right atrium of the explanted human heart [11] revealed that rotors were anchored by 3D micro-anatomic tracks formed by atrial pectinate muscles and characterized by increased interstitial fibrosis. The relation of fibrosis and anchoring in atrial fibrillation was also demonstrated in several other experimental and numerical studies [8, 11–14]. Initiation and anchoring of rotors in regions with increased intramural fibrosis and fibrotic scars was also observed in ventricles [5, 7, 15]. One of the reasons for rotors to be present at the fibrotic scar locations is that the rotors can be initiated at the scars (see e.g. [7, 15]) and therefore they can easily anchor at the surrounding scar tissue. However, rotors can also be generated due to different mechanisms, such as triggered activity [16], heterogeneity in the refractory period [16, 17], local neurotransmitter release [18, 19] etc. What will be the effect of the presence of the scar on rotors in that situation, do fibrotic areas (scars) actively affect rotor dynamics even if they are initially located at some distance from them? In view of the multiple observations on correlation of anchoring sites of the rotors with fibrotic tissue this question translates to the following: is this anchoring just a passive probabilistic process, or do fibrotic areas (scars) actively affect the rotor dynamics leading to this anchoring? Answering these questions in experimental and clinical research is challenging as it requires systematic reproducible studies of rotors in a controlled environment with various types of anchoring sites. Therefore alternative methods, such as realistic computer modeling of the anchoring phenomenon, which has been extremely helpful in prior studies, are of great interest.
The aim of this study is therefore to investigate the processes leading to anchoring of rotors to fibrotic areas. Our hypothesis is that a fibrotic scar actively affects the rotor dynamics leading to its anchoring. To show that, we first performed a generic in-silico study on rotor dynamics in conditions where the rotor was initiated at different distances from fibrotic scars with different properties. We found that in most cases, scars actively affect the rotor dynamics via a dynamical reorganization of the excitation pattern leading to the anchoring of rotors. This turned out to be a robust process working for rotors located even at distances more than 10 cm from the scar region. We then confirmed this phenomenon in a patient-specific model of the left ventricle from a patient with remote myocardial infarction (MI) and compared the properties of this process with clinical ECG recordings obtained during induction of a ventricular arrhythmia.
Our anatomical model is based on an individual heart of a post-MI patient reconstructed from late gadolinium enhanced (LGE) magnetic resonance imaging (MRI) was described in detail previously [20]. Briefly, a 1.5T Gyroscan ACS-NT/Intera MR system (Philips Medical Systems, Best, the Netherlands) system was used with standardized cardiac MR imaging protocol. The contrast –gadolinium (Magnevist, Schering, Berlin, Germany) (0.15 mmol/kg)– was injected 15 min before acquisition of the LGE sequences. Images were acquired with 24 levels in short-axis view after 600–700 ms of the R-wave on the ECG within 1 or 2 breath holds. The in-plane image resolution is 1 mm and through-plane image resolution is 5 mm. Segmentation of the contours for the endocardium and the epicardium was performed semi-automatically on the short-axis views using the MASS software (Research version 2014, Leiden University Medical Centre, Leiden, the Netherlands).
The myocardial scar was identified based on signal intensity (SI) values using a validated algorithm as described by Roes et al. [21]. In accordance with the algorithm, the core necrotic scar is defined as a region with SI >41% of the maximal SI. Regions with lower SI values were considered as border zone areas. In these regions, we assigned the fibrosis percentage as normalized values of the SI as in Vigmond et al. [22]. In the current paper, fibrosis was introduced by generating a random number between 0 and 1 for each grid point and if the random number was less than the normalized SI at the corresponding pixel the grid point was considered as fibroblast.
Currently there is no consensus on how the SI values should be used for clinical assessment of myocardial fibrosis and various methods have been reported to produce significantly different results [23]. However, the method from Vigmond et al. properly describes the location of the necrotic scar region in our model as for the fibrosis percentage of more than 41% we observe a complete block of propagation inside the scar. This means that all tissue which has a fibrotic level higher than 41% behaves like necrotic scar.
The approach and the 2D model was described in detail in previous work [24–26]. Briefly, for ventricular cardiomyocyte we used the ten Tusscher and Panfilov (TP06) model [27, 28], and the cardiac tissue was modeled as a rectangular grid of 1024 × 512 nodes. Each node represented a cell that occupied an area of 250 × 250 μm2. The equations for the transmembrane voltage are given by
C m d V i k d t = ∑ α , β ∈ { - 1 , + 1 } η i k α β g gap ( V i + α , k + β - V i k ) - I ion ( V i k , … ) , (1)
where Vik is the transmembrane voltage at the (i, k) computational node, Cm is membrane capacitance, ggap is the conductance of the gap junctions connecting two neighboring myocytes, Iion is the sum of all ionic currents and η i k α β is the connectivity tensor whose elements are either one or zero depending on whether neighboring cells are coupled or not. Conductance of the gap junctions ggap was taken to be 103.6 nS, which results in a maximum velocity planar wave propagation in the absence of fibrotic tissue of 72 cm/s at a stimulation frequency of 1 Hz. ggap was not modified in the fibrotic areas. A similar system of differential equations was used for the 3D computations where instead of the 2D connectivity tensor η i k α β we used a 3D weights tensor w i j k α β γ whose elements were in between 0 and 1, depending both on coupling of the neighbor cells and anisotropy due to fiber orientation. Each node in the 3D model represented a cell of the size of 250 × 250 × 250 μm3. 20s of simulation in 3D took about 3 hours.
Fibrosis was modeled by the introduction of electrically uncoupled unexcitable nodes [29]. The local percentage of fibrosis determined the probability for a node of the computational grid to become an unexcitable obstacle, meaning that for high percentages of fibrosis, there is a high chance for a node to be unexcitable. As previous research has demonstrated that LGE-MRI enhancement correlates with regions of fibrosis identified by histological examination [30], we linearly interpolated the SI into the percentage of fibrosis for the 3D human models.
In addition, the effect of ionic remodeling in fibrotic regions was taken into account for several results of the paper [31, 32]. To describe ionic remodeling we decreased the conductance of INa, IKr, and IKs and depending on local fibrosis level as:
G Na = ( 1 - 1 . 55 f 100 % ) G Na 0 , (2) G Kr = ( 1 - 1 . 75 f 100 % ) G Kr 0 , (3) G Ks = ( 1 - 2 f 100 % ) G Ks 0 , (4)
where GX is the peak conductance of IX ionic current, G X 0 is the peak conductance of the current in the absence of remodeling, and f is the local fibrosis level in percent. These formulas yield a reduction of 62% for INa, of 70% for IKr, and of 80% for IKs if the local fibrosis f is 40%. These values of reduction are, therefore, in agreement with the values published in [33, 34].
The normal conduction velocity at CL 1000 ms is 72 cm/s (CL 1000 ms). However, as the compact scar is surrounded by fibrotic tissue, the velocity of propagation in that region gradually decreases with the increase in the fibrosis percentage. For example for fibrosis of 30%, the velocity decreases to 48 cm/s (CL 1000 ms). We refer to Figure 1 in Ten Tusscher et al [25] for the planar conduction velocity as a function of the percentage fibrosis in 2D tissue and 3D tissue.
The geometry and extent of fibrosis in the human left ventricles were determined using the LGE MRI data. The normalized signal intensity was used to determine the density of local fibrosis. The fiber orientation is presented in detail in the supplementary S1 Appendix.
The model for cardiac tissue was solved by the forward Euler integration scheme with a time step of 0.02 ms. The numerical solver was implemented using the CUDA toolkit for performing the computations on graphics processing units. Simulations were performed on a GeForce GTX Titan Black graphics card using single precision calculations.
The eikonal equations for anisotropy generation were solved by the fast marching Sethian’s method [35]. The eikonal solver and the 3D model generation pipeline were implemented in the OCaml programming language.
Rotors were initiated by an S1S2 protocol, as shown in the supplementary S1 Fig. Similarly, in the whole heart simulations, spiral waves (or scroll waves) were created by an S1S2 protocol.
For the compact scar geometry used in our simulations the rotation of the spiral wave was stationary, the period of rotation of the anchored rotor was always more than 280 ms, while the period of the spiral wave was close to 220 msec. Therefore, we determined anchoring as follows: if the period of the excitation pattern was larger than 280 ms over a measuring time interval of 320 ms we classified the excitation as anchored. When the type of anchoring pattern was important (single or multi-armed spiral wave) we determined it visually. If in all points of the tissue, the voltage was below -20 mV, the pattern was classified as terminated. We applied the classification algorithm at t = 40 s in the simulation.
In the whole heart, the pseudo ECGs were calculated by assuming an infinite volume conductor and calculating the dipole source density of the membrane potential Vm in all voxel points of the ventricular myocardium, using the following equation [36]
E C G ( t ) = ∫ ( r → , D ( r → ) ∇ → V ( t ) ) | r → | 3 d 3 r (5)
whereby D is the diffusion tensor, V is the voltage, and r → is the vector from each point of the tissue to the recording electrode. The recording electrode was placed 10 cm from the center of the ventricles in the transverse plane.
Twelve-lead ECGs of all induced ventricular tachycardia (VT) of patients with prior myocardial infarction who underwent radiofrequency catheter ablation (RFCA) for monomorphic VT at LUMC were reviewed. All patients provided informed consent and were treated according to the clinical protocol. Programmed electrical stimulation (PES) is routinely performed before RFCA to determine inducibility of the clinical/presumed clinical VT. All the patients underwent PES and ablation according to the standard clinical protocol, therefore no ethical approval was required. Ablation typically targets the substrate for scar-related reentry VT. After ablation PES is repeated to test for re-inducibility and evaluate morphology and cycle length of remaining VTs. The significance of non-clinical, fast VTs is unclear and these VTs are often not targeted by RFCA. PES consisted of three drive cycle lengths (600, 500 and 400 ms), one to three ventricular extrastimuli (≥200 ms) and burst pacing (CL ≥200 ms) from at least two right ventricular (RV) sites and one LV site. A positive endpoint for stimulation is the induction of any sustained monomorphic VT lasting 30 s or requiring termination. ECG and intracardiac electrograms (EG) during PES were displayed and recorded simultaneously on a 48-channel acquisition system (Prucka CardioLab EP system, GE Healthcare, USA) for off-line analysis.
Fibrotic scars can not only anchor the rotors but can dynamically anchor them from a large distance. In the first experiments we studied spiral wave dynamics with and without a fibrotic scar in a generic study. The diameter of the fibrotic region was 6.4 cm, based on the similar size of the scars from patients with documented and induced VT (see the Methods section, Magnetic Resonance Imaging). The percentage of fibrosis changed linearly from 50% at the center of the scar to 0% at the scar boundary. We initiated a rotor at a distance of 15.5 cm from the scar (Fig 1, panel A) which had a period of 222 ms and studied its dynamics.
First, after several seconds the activation pattern became less regular and a few secondary wave breaks appeared at the fibrotic region (Fig 1, panel B). These irregularities started to propagate towards the tip of the initial rotor (Fig 1, panel C-D) creating a complex activation picture in between the scar and the initial rotor. Next, one of the secondary sources reached the tip of the original rotor (Fig 1, panel E). Then, this secondary source merged with the initial rotor (Fig 1, panel F), which resulted in a deceleration of the activation pattern and promoted a chain reaction of annihilation of all the secondary wavebreaks in the vicinity of the original rotor. At this moment, a secondary source located more closely to the scar dominated the simulation (Fig 1, panel G). The whole process now started again (Fig 1, panels H-K), until finally only one source became the primary source anchored to the scar (Fig 1L) with a rotation period of 307 ms. For clarity, a movie of this process is provided as supplementary S1 Movie.
Note that this process occurs only if a scar with surrounding fibrotic zone was present. In the simulation entitled as ‘No scar’ in Fig 1, we show a control experiment when the same initial conditions were used in tissue without a scar. In the panel entitled as ‘Necrotic scar’ in Fig 1, a simulation with only a compact region without the surrounding fibrotic tissue is shown. In both cases the rotor was stable and located at its initial position during the whole period of simulation. The important difference here from the processes shown in Fig 1 (Fibrotic scar) is that in cases of ‘No scar’ and ‘Necrotic scar’ no new wavebreaks occur and thus we do not have a complex dynamical process of re-arrangement of the excitation patterns. We refer to this complex dynamical process leading to anchoring of a distant rotor as dynamical anchoring. Although this process contains a phase of complex behaviour, overall it is extremely robust and reproducible in a very wide range of conditions. In the second series of simulations, the initial rotor was placed at different distances from the scar border, ranging from 1.8 to 14.3 cm, to define the possible outcomes, see Fig 2. Here, in addition to a single anchored rotor shown in Fig 1H we could also obtain other final outcomes of dynamical anchoring: we obtained rotors rotating in the opposite direction (Fig 2A, top), double armed anchored rotors which had 2 wavefronts rotating around the fibrotic regions (Fig 2A, middle) or annihilation of the rotors (Fig 2A, bottom, which show shows no wave around the scar), which normally occurred as a result of annihilation of a figure-eight-reentrant pattern. To summarize, we therefore had the following possible outcomes:
Termination of activity A rotor rotating either clockwise or counter-clockwise A two- or three-armed rotor rotating either clockwise or counter-clockwise
Fig 2, panel B presents the relative chance of the mentioned activation patterns to occur depending on the distance between the rotor and the border of the scar. We see, indeed, that for smaller initial distances the resulting activation pattern is always a single rotor rotating in the same direction. With increasing distance, other anchoring patterns are possible. If the distance was larger than about 9 cm, there is at least a 50% chance to obtain either a multi-armed rotor or termination of activity. Also note that such dynamical anchoring occurred from huge distances: we studied rotors located up to 14 cm from the scar. However, we observed that even for very large distances such as 25 cm or more such dynamical anchoring (or termination of the activation pattern) was always possible, provided enough time was given.
We measured the time required for the anchoring of rotors as a function of the distance from the scar. For each distance, we performed about 60 computations using different seed values of the random number generator, both with and without taking ionic remodeling into account. The results of these simulations are shown in Fig 3. We see that the time needed for dynamical anchoring depends linearly on the distance between the border of the scar and the initial rotor. The blue and yellow lines correspond to the scar model with and without ionic remodeling, respectively (ionic remodeling was modelled by decreasing the conductance of INa, IKr, and IKs as explained in the Methods Section). We interpret these results as follows; The anchoring time is mainly determined by the propagation of the chaotic regime towards the core of the original rotor and this process has a clear linear dependency. For distant rotors, propagation of this chaotic regime mainly occurs outside the region of ionic remodelling, and thus both curves in Fig 3 have the same slope. However, in the presence of ionic remodelling, the APD in the scar region is prolonged. This creates a heterogeneity and as a consequence the initial breaks in the scar region are formed about 3.5 s earlier in the scar model with remodeling compared with the scar model without remodeling.
To identify some properties of the substrate necessary for the dynamical anchoring we varied the size and the level of fibrosis within the scar and studied if the dynamical anchoring was present. Due to the stochastic nature of the fibrosis layout we performed about 300 computations with different textures of the fibrosis for each given combination of the scar size and the fibrosis level. The results of this experiment are shown in Fig 4. Dynamical anchoring does not occur when the scar diameter was below 2.6 cm, see Fig 4. For scars of such small size we observed the absence of both the breakup and dynamical anchoring. We explain this by the fact that if the initial separation of wavebreaks formed at the scar is small, the two secondary sources merge immediately, repairing the wavefront shape and preventing formation of secondary sources [37].
Also, we see that this effect requires an intermediate level of fibrosis density. For small fibrosis levels no secondary breaks are formed (close to the boundary of the fibrotic tissue). Also, no breaks could be formed if the fibrosis level is larger than 41% in our 2D model (i.e. closer to the core), as the tissue behaves like an inexcitable scar. For a fibrosis > 41% the scar effectively becomes a large obstacle that is incapable of breaking the waves of the original rotor [37]. Close to the threshold of 41% we have also observed another interesting pattern when the breaks are formed inside the core of the scar (inside the > 41% region) only and cannot exit to the surrounding tissue, see the supplementary S1 Movie.
Finally, note that Fig 4 illustrates only a few factors important for the dynamical anchoring in a simple setup in an isotropic model of cardiac tissue. The particular values of the fibrosis level and the size of the scar can also depend on anisotropy, the texture of the fibrosis and its possible heterogeneous distribution.
To verify that the dynamical anchoring takes place in a more realistic geometry, we developed and investigated this effect in a patient-specific model of the human left ventricle, see the Method section for details. The scar in this dataset has a complex geometry with several compact regions with size around 5-7 cm in which the percentage of fibrosis changes gradually from 0% to 41% at the core of the scar based on the imaging data, see Methods section. The remodeling of ionic channels at the whole scar region was also included to the model (including borderzone as described the Fibrosis Model in the method section). We studied the phenomenon of dynamical anchoring for 16 different locations of cores of the rotor randomly distributed in a slice of the heart at about 4 cm from the apex (see Fig 5).
Cardiac anisotropy was generated by a rule-based approach described in details in the Methods section (Model of the Human Left Ventricle). Of the 16 initial locations, shown in Fig 5, there was dynamical anchoring to the fibrotic tissue in all cases, with and without ionic remodeling. After the anchoring, in 4 cases the rotor annihilated. The effect of the attraction was augmented by the electrophysiolical remodelling, similar as in 2D.
A representative example of our 3D simulations is shown in Fig 5. We followed the same protocol as for the 2D simulations. The top 2 rows the modified anterior view and the modified posterior view in the case the scar was present. In column A, we see the original location of the spiral core (5 cm from the scar) indicated with the black arrow in anterior view. In column B, breaks are formed due to the scar tissue, and the secondary source started to appear. After 3.7 s, the spiral is anchored around the scar, indicated with the black arrow in the posterior view, and persistently rotated around it. In the bottom row, we show the same simulation but the scar was not taken into account. In this case, the spiral does not change its original location (only a slight movement, see the black arrows).
To evaluate if this effect can potentially be registered in clinical practice we computed the ECG for our 3D simulations. The ECG that corresponds to the example in Fig 5 is shown in Fig 6. During the first three seconds, the ECG shows QRS complexes varying in amplitude and shape and then more uniform beat-to-beat QRS morphology with a larger amplitude. This change in morphology is associated with anchoring of the rotor which occurs around three seconds after the start of the simulation. The initial irregularity is due to the presence of the secondary sources that have a slightly higher period than the original rotor. After the rotor is anchored, the pattern becomes relatively stable which corresponds to a regular saw-tooth ECG morphology. Additional ECGs for the cases of termination of the arrhythmia and anchoring are shown in supplementary S2 Fig. For the anchoring dynamics we see similar changes in the ECG morphology as in Fig 6.
The dynamical anchoring is accompanied by an increase of the cycle length (247 ± 16 ms versus 295 ± 30 ms). The reason for this effect is that the rotation of the rotor around an obstacle –anatomical reentry– is usually slower than the rotation of the rotor around its own tip—functional reentry, which is typically at the limit of cycle length permitted by the ERP.
In the previous section, we showed that the described results on dynamical anchoring in an anatomical model of the LV of patients with post infarct scars correspond to the observations on ECGs during initiation of a ventricular arrhythmia. After initiation, in 18 out of 30 patients (60%) a time dependent change of QRS morphology was observed. Precordial ECG leads V2, V3 and V4 from two patients are depicted in Fig 7. For both patients the QRS morphology following the extra stimuli gradually changed, but the degree of changes here was different. In patient A, this morphological change is small and both parts of the ECG may be interpreted as a transition from one to another monomorhpic ventricular tachycardia (MVT) morphology. However, for patient B the transition from polymorphic ventricular tachycardia (PVT) to MVT is more apparent. In the other 16 cases we observed different variations between the 2 cases presented in Fig 7. Supplementary S3 Fig shows examples of ECGs of 4 other patients. Here, in patients 1 and 2, we see substantial variations in the QRS complexes after the arrhythmia initiation and subsequently a transformation to MVT. The recording in patient 3 is less polymorphic and in patient 4 we observe an apparent shift of the ECG from one morphology to another. It may occur, for example, if due to underlying tissue heterogeneity additional sources of excitation are formed by the initial source. Overall, the morphology with clear change from PVT to MVT was observed in 5/18 or 29% of the cases. These different degrees of variation in QRS morphology may be due to many reasons, namely the proximity of the created source of arrhythmia to the anchoring region, the underlying degree of heterogeneity and fibrosis at the place of rotor initiation, complex shape of scar, etc.
Although this finding is not a proof, it supports that the anchoring phenomenon may occur in clinical settings and serve as a possible mechanism of fast VT induced by programmed stimulation.
In this study, we investigated the dynamics of arrhythmia sources –rotors– in the presence of fibrotic regions using mathematical modeling. We showed that fibrotic scars not only anchor but also induce secondary sources and dynamical competition of these sources normally results their annihilation. As a result, if one just compares the initial excitation pattern in Fig 1A and final excitation pattern in Fig 1L, it may appear as if a distant spiral wave was attracted and anchored to the scar. However, this is not the case and the anchored spiral here is a result of normal anchoring and competition of secondary sources which we call dynamical anchoring. This process is different from the usual drift or meandering of rotors where the rotor gradually changes its spatial position. In dynamical anchoring, the break formation happens in the fibrotic scar region, then it spreads to the original rotor and merges with this rotor tip and reorganizes the excitation pattern. This process repeats itself until a rotor is anchored around the fibrotic scar region. Dynamical anchoring may explain the organization from fast polymorphic to monomorphic VT, also accompanied by prolongation in CL, observed in some patients during re-induction after radio frequency catheter ablation of post-infarct scar related VT.
In our simulations the dynamics of rotors in 2D tissue were stable and for given parameter values they do not drift or meander. This type of dynamics was frequently observed in cardiac monolayers [38, 39] which can be considered as a simplified experimental model for cardiac tissue. We expect that more complex rotor dynamics would not affect our main 2D results, as drift or meandering will potentate the disappearance of the initial rotor and thus promote anchoring of the secondary wavebreaks. In our 3D simulations in an anatomical model of the heart, the dynamics of rotors is not stationary and shows the ECG of a polymorphic VT (Fig 6).
The dynamical anchoring combines several processes: generation of new breaks at the scar, spread of breaks toward the original rotor, rotor disappearance and anchoring or one of the wavebreaks at the scar. The mechanisms of the formation of new wavebreaks at the scar has been studied in several papers [15, 37, 40] and can occur due to ionic heterogeneity in the scar region or due to electrotonic effects [40]. However the process of spread of breaks toward the original rotors is a new type of dynamics and the mechanism of this phenomenon remains to be studied. To some extent it is similar to the global alternans instability reported in Vandersickel et al. [41]. Indeed in Vandersickel et al. [41] it was shown that an area of 1:2 propagation block can extend itself towards the original spiral wave and is related to the restitution properties of cardiac tissue. Although in our case we do not have a clear 1:2 block, wave propagation in the presence of breaks is disturbed resulting in spatially heterogeneous change of diastolic interval which via the restitution effects can result in breakup extension. This phenomenon needs to be further studied as it may provide new ways for controlling rotor anchoring processes and therefore can affect the dynamics of a cardiac arrhythmia.
In this paper, we used the standard method of representing fibrosis by placement of electrically uncoupled unexcitable nodes with no-flux boundary conditions. Although such representation is a simplification based on the absence of detailed 3D data, it does reproduce the main physiological effects observed in fibrotic tissue, such as formation of wavebreaks, fractionated electrograms, etc [22]. The dynamical anchoring reported in this paper occurs as a result of the restructuring of the activation pattern and relies only on these basic properties of the fibrotic scar, i.e. the ability to generate wavebreaks and the ability to anchor rotors, which is reproduced by this representation. In addition, for each data point, we performed simulations with at least 60 different textures. Therefore, we expect that the effect observed in our paper is general and should exist for any possible representation of the fibrosis. The specific conditions, e.g. the size and degree of fibrosis necessary for dynamical anchoring may depend on the detailed fibrosis structure and it would be useful to perform simulations with detailed experimentally based 3D structures of the fibrotic scars, when they become available.
Similar processes can not only occur at fibrotic scars, but also at ionic heterogeneities. In Defauw et al. [42], it has been shown that rotors can be attracted by ionic heterogeneities of realistic size and shape, similar to those measured in the ventricles of the human heart [43]. These ionic heterogeneities had a prolonged APD and also caused wavebreaks, creating a similar dynamical process as described in Fig 1. In this study however, we demonstrated that structural heterogeneity is sufficient to trigger this type of dynamical anchoring.
It is important to note that in this study fibrosis was modeled as regions with many small inexcitable obstacles. However, the outcome can depend on how the cellular electrophysiology and regions of fibrosis have been represented. In modeling studies, regions of fibrosis can also be represented by coupled elements with a fixed resting potential or with detailed fibroblast models, or by smoothly varying but reduced diffusion [44]. However, different in-silico studies [14, 45, 46] on modeling fibrosis in AF demonstrated that in AF, the reentrant activities co-located at the borders of fibrotic regions, although different methodologies were used to model fibrosis. For example, in a reecent modeling study by Morgan et al [45], rotors also stabilize in the border zones of patchy fibrosis in 3D atria, although fibrosis was modeled with myocyte-fibroblast coupling.
These results agree with multiple experimental and clinical studies, which also showed co-localization of rotors and fibrosis [5, 7, 8, 11–14]. Our results suggest that the possible mechanisms for the fact that such patterns are so abundant is due to dynamical anchoring. Therefore, we expect that different methodologies in modeling studies would give rise to similar results.
In our simulations the dynamics of rotors in 2D tissue were stable and for given parameter values they do not drift or meander. Although this type of dynamics was observed in cardiac monolayers [39, 47], the size of the myocardial cell in cultured monolayer is usually smaller than myocytes in myocardium, and they do not have a cylindrical form. Moreover, the gap junctions in cell cultures are usually found circumferentially, whereas in vivo gap junctions are found mostly at intercalated disks [48]. As these differences may affect the stabilty of the rotors, i.e. cause more complex dynamics, and it may also affect the results of 2D studies.
Another possible effect by which fibrotic scars can influence rotor dynamics is the electrotonic influence from the fibrotic scar region. Indeed, electrotonic effects are well known in cardiac electrophysiology and can strongly affect the heterogeneity of cardiac tissue and the susceptibility to arrhythmias [49]. It was also estimated that, in many models of cardiac cells, the spatial length of the electrotonic effects is of the order of 0.5-1cm [50, 51]. In our case we see dynamical anchoring for spirals located as far as 10-12 cm, which is far beyond these values. In addition, we also observe the same effect in case if we do not have ionic remodelling in the scar region (Fig 3), and thus in that case AP of all cells are the same (up to some possible boundary effects). Therefore, we think that the electrotonic influence from the scar is unlikely to be a main determinant of the dynamic anchoring. However, a heterogeneity around the scar has some effect on the anchoring process which can also be seen in Fig 3.
Although the dynamical anchoring reported in this paper will always bring the rotor to the scar region, the precise location of the rotor inside this region was not studied here. This question requires additional investigation, which is currently being performed in our research group. The first preliminary results indicate that in case of a scar with a complex structure multiple anchoring sites are possible. Their location depends on several factors, such as the location of the initial rotor, the presence and the extent of the ionic remodelling. In this work, to describe ionic remodeling we decreased the conductance of INa, IKr, and IKs as explained in the Methods Section. Identification of the specific features of the anchoring region and its delineation is of great importance as it may have implications for the treatment of the related arrhythmias. It would also be of value to quantify the excitation patterns in terms of number of re-entrant sources during the dynamic anchoring process, which can be done using the methodology in Panfilov et al [52] and or Vandersickel et al [53].
The most common mechanism of scar-related VT is due to slow conduction through a surviving channel in the scar. In this manuscript we did not model it explicitly. This is because we wanted to investigate a general phenomenon which can occur in cardiac tissue in the presence of fibrosis and did not try to reproduce specific geometries of the scar and slow conducting channels. It would be interesting to perform similar studies based on detailed reconstructions of infarction scars, such as [54] and see if the presence of the slow conduction channel(s) would affect the process of anchoring. However, note that complex patterns of fibrosis similar to those studied in our paper were observed for example in patients with non-ischaemic cardiomyopathy [55]. This type of substrate can also result in monomorphic ventricular tachycardia typical for anchored rotational activity.
In this paper, we considered the case of a pre-existing rotor and focused on its interaction with the fibrotic scar. The formation of a rotor can occur via multiple possible mechanisms (e.g. [15–18]) which we did not take into account. This is because we wanted to address the process at a stage common for all mechanisms. This assumption is idealized, and it would be more natural to consider the complete sequence of transition from the sinus rhythm to rotor formation and then to its interaction with the scar. This is because such interaction process can potentially influence the process of dynamic anchoring. However, such interactions will add additional levels of complexity to the problem on top of the effects studied in our paper and may be specific for each particular mechanism. Therefore we decided to focus on this later stage of an already existing rotor, which is common for all these mechanisms. This is a limitation of our approach and it should be addressed in subsequent studies for each of the mechanisms of generation of the initial rotor.
We have studied the dependency of dynamical anchoring to the scar on size and fibrotic content of the scar in a simplified situation: the scar was of circular shape, the fibrosis was modelled as diffuse fibrosis and had a constant level within the entire scar (Fig 4). It would be of value to extend such studies and consider different shapes of the scar and study the possible effects of this shape on the anchoring. Additionally, we could consider a non-homogeneous distribution of fibrosis inside the scar and study it with and without of ionic heterogeneity. Furthermore, it would be valuable to study the possible effects of different texture of fibrosis like interstitial and patchy patterns on the dynamical anchoring. Also, in this paper we have always considered an initial rotor rotating in a homogeneous part of the tissue. It would be important to study more realistic tissue setups, where fibrosis is not only present around the scar but also at distant locations where the rotor is present. We plan to address these shortcomings in subsequent research. Our 2D simulations are performed for rotors which have stationary rotation. As stationary rotation of free non-anchored rotors is unlikely to occur in the whole heart, these results account for an idealized situation and may change if the rotor rotation is not stationary. However, as we discussed only a qualitative relation between the data we think that our general interpretation is acceptable. Clinical ECG recordings used in our paper were taken after ablation. It would be important to compare pre-ablation ECGs showing arrhythmia dynamics in patients with collected LGE MRI images. Unfortunately such clinical information was not available to us.
Most of our simulations were performed with a space step of 250 microns. Although such step is widely used in computational studies on cardiac propagation it is larger than the typical size of cardiac cells. Therefore, it would be of value to perform simulations with a smaller space step (e.g. 100 microns). However, as 2D studies performed here use a generic representation of cardiac fibrosis we do not expect qualitative changes of the results obtained in this paper.
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10.1371/journal.ppat.1005880 | The Matrix Protein of Nipah Virus Targets the E3-Ubiquitin Ligase TRIM6 to Inhibit the IKKε Kinase-Mediated Type-I IFN Antiviral Response | For efficient replication, viruses have developed mechanisms to evade innate immune responses, including the antiviral type-I interferon (IFN-I) system. Nipah virus (NiV), a highly pathogenic member of the Paramyxoviridae family (genus Henipavirus), is known to encode for four P gene-derived viral proteins (P/C/W/V) with IFN-I antagonist functions. Here we report that NiV matrix protein (NiV-M), which is important for virus assembly and budding, can also inhibit IFN-I responses. IFN-I production requires activation of multiple signaling components including the IκB kinase epsilon (IKKε). We previously showed that the E3-ubiquitin ligase TRIM6 catalyzes the synthesis of unanchored K48-linked polyubiquitin chains, which are not covalently attached to any protein, and activate IKKε for induction of IFN-I mediated antiviral responses. Using co-immunoprecipitation assays and confocal microscopy we show here that the NiV-M protein interacts with TRIM6 and promotes TRIM6 degradation. Consequently, NiV-M expression results in reduced levels of unanchored K48-linked polyubiquitin chains associated with IKKε leading to impaired IKKε oligomerization, IKKε autophosphorylation and reduced IFN-mediated responses. This IFN antagonist function of NiV-M requires a conserved lysine residue (K258) in the bipartite nuclear localization signal that is found in divergent henipaviruses. Consistent with this, the matrix proteins of Ghana, Hendra and Cedar viruses were also able to inhibit IFNβ induction. Live NiV infection, but not a recombinant NiV lacking the M protein, reduced the levels of endogenous TRIM6 protein expression. To our knowledge, matrix proteins of paramyxoviruses have never been reported to be involved in innate immune antagonism. We report here a novel mechanism of viral innate immune evasion by targeting TRIM6, IKKε and unanchored polyubiquitin chains. These findings expand the universe of viral IFN antagonism strategies and provide a new potential target for development of therapeutic interventions against NiV infections.
| Nipah virus (NiV) is a zoonotic paramyxovirus causing severe respiratory and encephalitic illness with case fatality rates of 40 to 90%. The host type-I interferon (IFN-I) system protects against viral infections; however, to establish productive infection NiV has developed mechanisms to evade these host antiviral responses. An important component of the IFN system is the IKKε kinase, which is directly involved in IFN-I production and IFN-I signaling. The activity of the IKKε kinase is regulated by unanchored K48-linked polyubiquitin chains, a novel form of ubiquitin that is not covalently attached to any protein and can induce activation of kinases by promoting protein oligomerization. These unanchored polyubiquitin chains that activate IKKε are generated by the E3-ubiquitin ligase TRIM6. Here we demonstrate that the matrix structural protein (M) of NiV, which is important for virus assembly and budding, also has IFN-I antagonist functions and interferes with the host antiviral response. We found that NiV-M interacts with TRIM6 and promotes its degradation. Consequently, association of unanchored polyubiquitin chains with IKKε is reduced leading to impaired IKKε activation and ineffective IFN responses. Since the matrix protein is present in the virions and is released immediately after virus entry into the cell, this provides an efficient mechanism to escape the host antiviral response. These data may help explain the highly pathogenic potential of these viruses.
| Innate immune responses are initiated when conserved features of microbial pathogens referred to as pathogen-associated molecular patterns (PAMPs) are recognized by host pattern-recognition receptors (PRRs), such as Toll-like receptors (TLRs) and retinoic acid-inducible gene I (RIG-I)-like receptors (RLRs) [1, 2]. During viral infections, single or double-stranded RNAs generated by viruses can be recognized by the endosomal TLR3 or the cytoplasmic RIG-I/Melanoma Differentiation-Associated gene 5 (MDA-5) [3, 4]. When activated, TLR3 signals through the adaptor protein Toll-IL-1R (TIR) domain-containing adaptor-inducing IFNβ (TRIF). On the other hand, RIG-I and MDA-5 utilize the adaptor protein MAVS localized in the mitochondrial membrane. TLR and RLR signaling requires activation of multiple signaling components converging at the level of the serine/threonine kinases TANK binding kinase-1 (TBK1) and IκB kinase-ε (IKKε) [5, 6], which phosphorylate the transcription factors IFN regulatory factor 3 (IRF3) and IRF7 [7, 8]. This promotes the nuclear accumulation of IRF3 and IRF7, triggering the expression of type-I IFNs (IFN-I) [3, 4, 6]. Pathogen recognition by TLRs and RLRs also results in activation of the nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB), which promotes the induction of various chemokines and cytokines including interleukin-6 (IL-6) and tumor necrosis factor (TNFα) [9, 10]. NF-κB is also important for the optimal production of IFN-I [5–8]. Secreted IFN-I bind to the heterodimeric type I IFN receptor (IFNAR), thus activating the (Janus kinase) JAK1 and TYK2 kinases, which phosphorylate the transcription factors STAT1 (signal transducer and activator of transcription) and STAT2. Together STAT1 and STAT2 with IRF9 form the IFN stimulated gene factor 3 (ISGF3) that translocates to the nucleus for induction of numerous IFN-stimulated genes (ISGs), triggering an antiviral state [11]. Importantly, formation of the ISGF3 complex and induction of the full breadth of ISGs also requires STAT1 phosphorylation by IKKε, which plays a non-redundant role in the IFN signaling pathway [12–14].
The Tripartite Motif (TRIM) family of proteins constitutes over 70 proteins, which are characterized by the presence of a RING, B box, and a coiled-coil domain (collectively called RBCC), has been implicated in innate immune signaling pathways by acting as E3-Ubiquitin ligases [15–19]. Recently, we have shown that an unprecedented large number of TRIMs positively regulates innate immune responses [19, 20]. In-depth molecular characterization focused on TRIM6, which we showed is important in both IFN-I production and IFN-I signaling pathways. Specifically, TRIM6 together with the E2-conjugase UbE2K synthesize unanchored K48-linked polyubiquitin chains that activate IKKε, culminating in the induction of a subset of ISGs essential for the antiviral response [13].
To successfully replicate in a host cell, viruses have devised mechanisms to evade the host IFN response by inhibiting key components of the pathway leading to either reduced IFN production or reduced ISG induction. Some members of the family Paramyxoviridae have a RNA-editing mechanism to produce alternative proteins from the P gene, namely V and W, that have been shown to have IFN antagonistic activities [21]. For example, the rubulavirus V proteins target STATs for proteasome-mediated degradation [22, 23]; the V proteins of henipaviruses sequester STAT1 and prevent their activation [24, 25]; and measles virus V protein blocks IFN-induced STAT1/2 nuclear translocation by an unknown mechanism [26].
Nipah virus (NiV) is a newly emerging and a highly pathogenic zoonotic paramyxovirus that causes fatal diseases in humans [27, 28]. NiV P, V and W proteins have been demonstrated to block IFN-I signaling and to bind STAT1 [24, 29]. W protein of NiV inhibits the host IFN response by sequestering STAT1 in the nucleus [30–32] and by blocking virus and TLR3-dependent ISG induction and TBK1/IKKε-mediated IRF3 activation [33]. However, although NiV is known to inhibit IFN responses during infection, studies have shown that mutations in NiV-P or its gene products fail to abolish inhibition of STAT activation and recombinant viruses harboring these mutations are not attenuated in IFN competent cells [34–37]. Therefore, additional NiV proteins may also be able to inhibit RIG-I-mediated IFN production.
Here, we show that the matrix protein of NiV (NiV-M) has a role in IFN antagonism and that a conserved lysine residue (K258) in the bipartite nuclear localization signal (NLS) of NiV-M is essential for this activity. Interestingly, this residue has been implicated in the ubiquitin-regulated nucleo-cytoplasmic shuttling of NiV-M in the early stages of Ni-V infection [38]. Mutation of the conserved lysine to either alanine (K258A) or arginine caused defects in nuclear import or export respectively; both mutants had decreased levels of ubiquitination and budding defects [38, 39]. We show that K258 is essential for NiV-M to counteract IFN-I mediated responses, as a K258A mutant failed to inhibit IFN-I production and the induction of ISGs via disrupting IKKε activation. Mechanistically, this is achieved by NiV-M-induced degradation of both endogenous and overexpressed TRIM6, which we had previously reported to be involved in activating IKKε via endogenous K48-linked unanchored polyubiquitin chains [13].
Nipah virus encodes for four viral proteins (P, C, V, W) with IFN antagonist functions when used in overexpression studies [34, 40]. Although it is now clear that NiV is able to inhibit IFN responses during virus infection, recombinant NiV with mutations in the P, C, V or W genes are not significantly attenuated in IFN-competent cells [34–37], suggesting that NiV encodes for additional viral proteins with ability to antagonize the IFN system. The NiV matrix protein, which is required for virus budding and assembly, has been shown to traffic through different cellular compartments before being targeted to the cellular membrane for virus assembly [38, 39], raising the question as whether it may have non-structural functions. We hypothesized that NiV-M is a viral product with novel innate immune antagonist functions. To test this hypothesis, the ability of NiV-M to inhibit different components of the type-I IFN pathway was investigated (Fig 1A). Exogenous NiV-M expression reduced Sendai virus (SeV)-induced IFNβ promoter activation in a reporter assay (Fig 1B). In contrast, NiV-V protein, which is known to inhibit IFN signaling but not IFN production, did not affect IFNβ induction (Fig 1B). In line with these results and a role of IFNβ in inducing antiviral responses, SeV RNA replication levels were increased in M expressing cells (S1 Fig). NiV-M also blocked IFNβ induction mediated by TRIF and MAVS (Fig 1C and 1D), albeit with different potencies. TRIF and MAVS act as adaptor proteins for the TLR3 and RIG-I pathways respectively (see diagram in Fig 1A). Since MAVS and TRIF dependent signaling pathways converge downstream at the level of the TBK1/IKKε kinases, these results suggest that NiV-M may target shared signaling factors downstream of these adaptor proteins. Consistent with this hypothesis, NiV-M also inhibited TBK-1- and IKKε-dependent IFN induction in a dose-dependent manner (Fig 1E and 1F). In contrast, NiV-M did not inhibit the IRF3-induced IFNβ reporter activation (Fig 1G), suggesting that NiV-M blocks IFN induction by acting at the level of the TBK-1/IKKε kinases.
To further validate these results and ensure that NiV-M has a biological function in relevant primary innate immune cells, experiments were performed in primary human monocyte-derived dendritic cells (hMDDC), which are targets of NiV infection [41]. To this end, lentiviruses encoding the NiV-M protein and the well-characterized IFN antagonist VP35 protein from Ebola virus [42, 43] were generated as previously described [42, 44], and their ability to inhibit IFN responses was examined. Monocytes from healthy donors were cultured with GMCSF and IL-4 for 5 days followed by lentiviral transduction [42, 44]. The efficiency of lentiviral transduction was monitored by flow cytometry (Fig 2A). hMDDC were then challenged with the Cantell strain of SeV, which activates the RIG-I pathway via its known production of viral-defective interfering particles (DI), leading to induction of IFNβ and pro-inflammatory cytokines. IFNβ mRNA expression peaked at 4 hr post-infection (p.i.) and was induced about 600-fold (Fig 2B, left panel). NiV-M-expressing cells potently inhibited IFNβ induction as compared to empty lentiviral control vector (Fig 2B, top left panel), whereas SeV RNA replication was increased (Fig 2B, bottom left panel). Consistent with the previously reported role of VP35 on RIG-I function [45], induction of the pro-inflammatory cytokines TNFα and IL-12p40, were reduced in VP35 expressing cells. In contrast, NiV-M expression had only minor effects on TNFα and IL-12p40 expression levels as compared to the control lentiviral vector (Fig 2B, top and bottom right panels). Since these pro-inflammatory cytokines require activation of the NF-kB transcription factor and these signaling pathways diverge downstream of MAVS/TRIF (see diagram in Fig 1A), these results are consistent with our reporter assays and support a role of NiV-M downstream of the adaptor proteins and towards the TBK-1/IKKε signaling axis. Furthermore, consistent with reduced IFNβ induction in NiV-M-expressing cells, the induction of IFN-stimulated genes ISG54 and MxA was also attenuated as compared to control cells (Fig 2C).
Taken together these data indicate that NiV-M specifically acts as a potent antagonist of IFNβ and antiviral responses but does not play a significant role in inhibition of pro-inflammatory cytokines in cell lines and relevant primary innate immune cells.
NiV-M travels to the cell membrane where it is required for viral assembly and budding during viral replication [38]. However, the NiV-M protein traffics from the cytoplasm to the nucleus before reaching the cell membrane. Translocation to the nucleus requires a conserved lysine residue (K258) in the bipartite nuclear localization signal (NLSbp) of the NiV-M protein, and a lysine to alanine mutant (K258A) results in retention of NiV-M in the non-membrane cytoplasmic fraction [38]. To further understand the mechanism by which NiV-M inhibits IKKε activity, we tested whether NiV-M-K258A mutant has the ability to inhibit IFN induction. IKKε-mediated IFN induction was attenuated approximately 50% in the presence of low concentrations of NiV-M-WT. In contrast, NiV-M-K258A did not inhibit IFN induction in these conditions (Fig 3A). Although high concentrations of M-K258A marginally inhibited IFN induction, these effects are significantly attenuated as compared to NiV-M-WT (Fig 3A). Similar effects were observed when cells were activated with TBK-1; however, TBK-1 appeared to be more sensitive to the inhibitory effects of M as compared to IKKε, and higher concentrations of M-K258A were able to inhibit IFN induction (Fig 3B).
Since the K258A mutant of NiV-M is retained in the non-membrane cytoplasm fraction [38], it could be that its loss of IFN antagonist function is due to failure to reach the cytoplasmic/membrane compartments where the IFN-I signaling components localize (for example, MAVS localizes to the mitochondrial membrane; [46]). To test this possibility, we utilized two different M-K258A fusion proteins that contain membrane-targeting signals (L10-K258A and S15-K258A) and have been previously shown to rescue M-K258A trafficking to the cytoplasmic membranous fractions [38]. Strikingly, ectopic expression of either L10-K258A or S15-K258A rescued the loss of inhibitory effects of NiV-M-K258A on TBK-1-induced IFNβ expression, especially at low dose of M expressing vectors (Fig 3B). Of note, all NiV-M proteins were expressed in similar levels and TBK-1 levels were not affected in the presence of NiV-M-WT or mutant M proteins (Fig 3B, bottom panel). These results suggest that NiV-M requires trafficking to cytoplasmic/membrane fractions for antagonism of IFN-I responses.
It appeared that NiV-M-K258A had stronger inhibitory effects on TBK-1 as compared to IKKε (compare NiV-M-WT to NiV-M-K258A in Fig 3A and 3B). Therefore, the K258A mutant provides a better tool to study the mechanism by which NiV-M inhibits IFN induction mediated by IKKε. Furthermore, we recently elucidated the detailed molecular mechanism by which IKKε is activated [13]. Therefore, we focused our study on the IKKε kinase.
We have shown that NiV-M inhibits IKKε but not IRF3-mediated IFN induction (Fig 1F and 1G), suggesting that M targets the IKKε kinase. In support of this, co-immunoprecipitation (coIP) studies demonstrated that both NiV-M-WT and the K258A mutant efficiently interacted with IKKε when coexpressed in HEK293T cells (Fig 3C). Although NiV-M did not disrupt binding of IKKε to IRF3, IKKε-mediated phosphorylation of IRF3 was reduced in the presence of WT-NiV-M, while only minor effects were observed in the presence of NiV-M-K258A (Fig 3C, pIRF3 row), suggesting that NiV-M-WT inhibits IKKε activity and that the K258 residue is required for this effect. Consistent with this, autophosphorylation of IKKε on T501 (a marker of IKKε activation) was reduced in the presence of NiV-M-WT as compared to NiV-M-K258A or the empty vector control (Fig 3C, quantifications shown in S2 Fig).
We recently reported that IKKε activity is regulated by unanchored K48-linked polyubiquitin chains, which are not covalently attached to any protein [13]. These polyubiquitin chains interact with IKKε and promote its oligomerization and autophosphorylation on IKKε-T501 (see diagram in Fig 3D; [13]). NiV-M-WT reduced association of IKKε with K48-linked unanchored polyubiquitin chains to a greater degree than M-K258A (Fig 3C and 3E). In line with these results, IKKε oligomerization was also reduced in the presence of NiV-M-WT as compared to empty vector or the NiV-M-K258A mutant (Fig 3E). It is important to note that expression of NiV-M does not reduce the total cellular pool of K48-linked polyubiquitin chains, which include covalently ubiquitinated proteins and unanchored polyubiquitin chains (see Fig 3C, panel K48-linked ubiquitin, WCE). Together these results indicate that NiV-M-WT blocks activation of IKKε by suppressing its specific association with unanchored K48-linked polyubiquitin chains, which in turn inhibits downstream IKKε oligomerization and IRF3 phosphorylation.
To test whether other henipavirus matrix proteins also have IFN antagonist functions, we examined the ability of the matrix proteins of Hendra virus (HeV), Ghana virus (GhV) and Cedar virus (CedV) to inhibit IFNβ induction in our reporter assays and their ability to bind IKKε in coIP assays. GhV is a novel African bat henipavirus that is divergent from NiV and HeV (~ 60% sequence identity in the M gene compared to ~90% identity between NiV-M and HeV-M) [47, 48]. CedV, which is the most recent member of the genus Henipavirus, has been reported to be non-pathogenic in ferrets and guinea pigs and has reduced ability to inhibit IFN signaling, presumably by absence of its P gene products [49, 50]. HeV-M, GhV-M and CedV-M significantly inhibited IKKε-induced IFN promoter activation (Fig 4A). Furthermore, the matrix proteins of these three henipaviruses also interacted with IKKε (Fig 4B). These results demonstrate that the matrix proteins of henipaviruses have IFN antagonist functions. The conservation of NLSbp in the henipavirus matrix proteins suggests that the NLS region is potentially important for this function (Fig 4C) [38].
We have shown that NiV-M expression results in reduced levels of the unanchored K48-linked polyubiquitin chains that specifically associate with IKKε (Fig 3C). This effect could be due to either interference of IKKε binding to unanchored polyubiquitin chains or by inhibition of the enzyme that synthesizes these polyubiquitin chains.
We recently demonstrated that TRIM6, a member of the TRIM E3-ubiquitin ligase family of proteins, catalyzes the synthesis of unanchored K48-linked polyubiquitin chains that specifically associate with IKKε promoting its oligomerization and activation for downstream signaling [13]. We hypothesized that NiV-M blocks IKKε activation by inhibiting the TRIM6-mediated synthesis of unanchored polyubiquitin chains. To this end, we first tested whether NiV-M interacts with TRIM6 in coIP assays. In line with our hypothesis, NiV-M efficiently interacted with TRIM6 (Fig 5A). To map the region of TRIM6 binding to NiV-M, we used deletion mutants of TRIM6 expressing the N-terminal RING (R), B box and coiled-coil (CC) domains or the C-terminal SPRY domain (diagram in Fig 5B) and tested interaction with NiV-M. The C-terminal SPRY domain of TRIM6 specifically interacted with NiV-M-WT, whereas a mutant lacking only the SPRY domain lost the ability to interact with NiV-M (Fig 5C), indicating that this interaction required the C-terminal SPRY domain of TRIM6. Since IKKε also interacts with the SPRY domain of TRIM6 [13], we asked whether NiV-M competes with IKKε for TRIM6 binding. In agreement with this possibility, coIP studies showed that the interaction between TRIM6 and IKKε is decreased in the presence of both NiV-M-WT and NiV-M-K258A (S3 Fig), suggesting that inhibition of IKKε activity can be explained in part by interference of TRIM6-IKKε binding by NiV-M, and that this interference does not depend on the NiV-M K258 amino acid.
To further confirm binding of TRIM6 with NiV-M, we performed co-localization studies by confocal microscopy (Fig 5D). As previously reported [13, 51], TRIM6 localizes in punctate cytoplasmic bodies. In contrast NiV-M-WT showed membrane localization as well as some nuclear and cytoplasmic localization (Fig 5D), as previously shown [38]. However, upon NiV-M and TRIM6 coexpression, a fraction of NiV-M colocalized with TRIM6 in cytoplasmic bodies. The NiV-M K258A mutant, which does not translocate to the nucleus, also appeared to colocalize with TRIM6 (Fig 5D). Importantly, a K258R mutation on NiV-M, which translocates to the nucleus but does not exit the nucleus [38], did not colocalize with TRIM6 (Fig 5D). It is important to note that cells coexpressing NiV-M-WT and TRIM6 appeared to have lower levels of TRIM6 and in many cases it was difficult to observe the characteristic punctate localization of TRIM6 (Fig 5D, see next section for more details).
To test whether NiV-M binding to TRIM6 has functional relevance, we examined the ability of NiV-M to inhibit IFNβ promoter activation by the constitutively active 2CARD domain of RIG-I [RIG-I(2CARD)] [3, 4] in TRIM6 knockdown cells. As expected, NiV-M inhibited RIG-I(2CARD)-induced IFNβ promoter activation in non-targeting siRNA control cells (Fig 5E). TRIM6-targeting siRNA cells showed a markedly reduced IFNβ promoter activity upon RIG-I(2CARD) transfection compared to control cells, as we previously reported [13]; however there was a minor but still detectable IFNβ induction by RIG-I(2CARD) stimulation. This residual IFNβ induction was not significantly affected by exogenous NiV-M expression (Fig 5E). Reconstitution of TRIM6 rescued the RIG-I(2CARD)-induced IFNβ reporter activity in TRIM6 knockdown cells and this induction was almost completely blocked in the presence of NiV-M (Fig 5E), demonstrating that M inhibits IFN induction by a TRIM6-dependent mechanism.
In addition to its role in phosphorylation of IRF3, the IKKε kinase has been shown to play a non-redundant role in the type-I IFN signaling pathway and is required for the expression of a subset of IKKε-dependent ISGs [14]. As we previously reported [13], ectopic expression of TRIM6 enhances the IFNβ-induced ISG54 reporter activity (ISG54 is an IKKε-dependent ISG) (Fig 5F). In support for a role for NiV-M in inhibition of the TRIM6-IKKε axis in the IFN signaling pathway, NiV-M-WT but not the M-K258A was able to inhibit TRIM6-mediated IFNβ-induced ISG54 reporter activity in a dose-dependent manner (Fig 5F). Importantly, the levels of TRIM6 protein were also reduced by NiV-M-WT in a dose-dependent manner, whereas TRIM6 protein levels were less affected in the presence of NiV-M-K258A (Fig 5F, immunoblot). These results suggest that NiV-M inhibits IFN responses by reducing TRIM6 protein expression. NiV-M did not have a general effect on inhibition of the IFN signaling pathway because, in contrast to the effects observed on the ISG54 reporter, NiV-M did not inhibit the IFNβ-induced ISG15 reporter activity, as ISG15 is not an IKKε-dependent ISG (Fig 5G, and [13, 14]. The NiV-V protein, which is known to target STAT1 for antagonism of IFN signaling [24, 25], reduced ISG15 reporter activity and was used as a positive control for this experiment (Fig 5G).
Taken together these data indicate that NiV-M acts as an antagonist of both the IFN production and IFN signaling pathways by targeting the E3-ubiquitin ligase TRIM6 and consequently blocking the activation of IKKε.
Our results show that TRIM6 and NiV-M interact in coIP assays and colocalize in cytoplasmic bodies (Fig 5A–5D). While performing these experiments we noticed a reduction of TRIM6 protein levels in the presence of NiV-M-WT (see Fig 5A, 5D and 5F), suggesting that TRIM6 may be targeted for degradation. Evidence included a very low number of cells in which TRIM6 and NiV-M-WT were detected together in the colocalization studies. In the rare instance in which NiV-M-WT and TRIM6 were detected in the same cell, TRIM6 levels appeared lower and did not exhibit its characteristic punctate localization as compared to the TRIM6 dots observed when expressed alone (Fig 5D). The effect of NiV-M on TRIM6 expression levels was confirmed in biochemical assays in which expression of NiV-M-WT clearly reduced the levels of ectopically expressed TRIM6 in a dose-dependent manner. This effect on TRIM6 levels was significantly attenuated in the presence of the NiV-M-K258A mutant (see immunoblot in Fig 5F and intensity of the TRIM6 dots in 5D, M-K258A).
To rule out possible artifacts of TRIM6 overexpression, the levels of endogenous TRIM6 were quantified in the presence of NiV-M-WT or NiV-M-K258A. In agreement with our observations, endogenous TRIM6 protein levels were also reduced in NiV-M-WT expressing cells, whereas the K258A mutant did not affect TRIM6 protein levels (Fig 6A). Similar results were observed in coIP assays, and proteasome inhibition with MG132 did not recover TRIM6 protein (Fig 6B), suggesting that NiV-M promotes TRIM6 degradation by a proteasome-independent mechanism.
To validate these observations in the context of live Nipah virus infections, we generated recombinant NiV (rNiV) lacking the M protein (rNiV-ΔM) and compared the levels of endogenous TRIM6 during rNiV-WT and rNiV-ΔM infections. To facilitate detection of infected cells by immunofluorescence, both WT and ΔM rNiVs were engineered to express GFP. Since the M protein is required for virus budding and assembly, rNiV-ΔM was initially rescued in HEK293 cells stably expressing the M protein in trans [38, 39]. However, rNiV-ΔM could be passaged on Vero cells in the absence of exogenous M, albeit with much delayed replication kinetics compared to WT rNiV-GFP (S4A Fig), in agreement with a recent study [52]. In rNiV-WT infected cell lysates, both nucleocapsid and M proteins could be detected as early as 24 h.p.i, whereas no M protein could be detected in rNiV-ΔM infected cell lysates at any of the time points tested, even though by 48 h.p.i, virus replication was evident by the presence of nucleocapsid protein (S4B Fig). These results also confirm that our rNiV-ΔM virus is truly M-deficient.
Next, we compared the effects of these rNiVs on endogenous TRIM6 expression. Consistent with a role of M in TRIM6 degradation, cells infected with rNiV-WT (GFP+ cells, Fig 7A) expressed lower levels of TRIM6 protein as compared to uninfected cells (GFP- cells, Fig 7A). In contrast, cells infected with rNiV-ΔM showed similar levels of TRIM6 protein expression as compared to uninfected cells (Fig 7A). Furthermore, the number and intensity of TRIM6 cytoplasmic dots also appeared to be reduced as compared to non-infected cells, while no significant differences were observed in rNiV-ΔM infected versus non-infected cells (Fig 7B). In line with these results, TRIM6 expression and dot formation was excluded from areas with high levels of matrix protein in rNiV-WT infected cells (Fig 7C). These data demonstrate that NiV-M reduces the protein levels of TRIM6 during virus infection.
Taken together, our data shows that NiV matrix protein inhibits IFN responses by targeting the E3-ubiquitin ligase TRIM6 during infection or ectopic expression in cell lines and primary innate immune cells. Reduction in TRIM6 expression correlates with reduced unanchored polyubiquitin chains that specifically associate with IKKε and have been previously shown to be required for IKKε activation, ultimately resulting in impaired induction of IFN-mediated antiviral responses (Fig 8).
In this study we demonstrate that the matrix protein of NiV inhibits the antiviral IFN system by targeting the E3-ubiquitin ligase TRIM6, which is required for the synthesis of unanchored polyubiquitin chains that activate the IKKε kinase for IFN production. Our findings indicate that the NiV-M protein plays a role in the context of virus replication and in primary innate immune cells. This is supported by three lines of evidence: i) lentiviruses expressing NiV-M strongly inhibited SeV-induced IFNβ induction but not pro-inflammatory cytokines in hMDDC, ii) infections with WT NiV reduce expression of TRIM6 protein and the formation of TRIM6-cytoplasmic bodies, iii) infections with a recombinant NiV lacking M protein rescue TRIM6 protein levels.
We have shown that a K258A mutation of NiV-M, which is impaired in cytoplasm-nuclear trafficking ([38] and Fig 5D), has reduced ability to inhibit IFN-I responses. In contrast, the mutants L10-K258A-M and S15-K258A-M, which recover trafficking to cytoplasmic membrane fractions [38], recovered the ability to antagonize IFN-I responses (Fig 3B). Since NiV-M-K258A can still interact with TRIM6 in coIP and colocalization studies and is able to compete with IKKε for TRIM6 binding, our data suggest that the reduced ability of M-K258A to inhibit IFN-I is due to cellular mislocalization, resulting in failure to target TRIM6 for degradation. Most likely, the K258 amino acid on NiV-M is not required for interaction with TRIM6, and is instead required for targeting M protein to the cytoplasmic compartment where the TRIM6—IKKε “signalosome” assembles, and NiV-M may recruit other factors required for TRIM6 degradation. We previously showed that TRIM6 recruits IKKε to “ubiquitin-rich” bodies in the cytoplasm and that unanchored polyubiquitin is important for the formation of these structures [13]. Although these TRIM6-ubiquitin-rich structures have not been well characterized, it is unlikely that they contain membranous structures. Some studies on TRIM5α, a close relative of TRIM6, suggest that some of these cytoplasmic bodies may contain components of the aggresome system [53, 54]. An alternative possibility is that ubiquitination of NiV-M on the K258 amino acid, which has been shown to be important for trafficking of M [38] may also be responsible for recruiting other factors involved in TRIM6 degradation. These possibilities are currently under investigation.
Our results show that NiV-M targets TRIM6 for degradation. However, we have so far not been able to elucidate the precise pathway involved in TRIM6 degradation. Experiments using the proteasome inhibitor MG132 [55] and the lysosome inhibitor chloroquine [56] did not appear to rescue endogenous levels of TRIM6 protein in the presence of NiV-M (Fig 6B and S8 Fig), suggesting that NiV-M targets TRIM6 for degradation by a proteasome- and lysosome-independent mechanism. We are currently investigating potential pathways of TRIM6 degradation using other inhibitors that could potentially recover IFN responses by blocking TRIM6 degradation. If successful, inhibitors that rescue TRIM6 expression and IFN responses may have potential clinical use as antivirals to NiV infections.
Importantly, NiV-M not only impaired TRIM6-mediated IFN induction upon stimulation of the RIG-I pathway in primary human DCs and in cell lines, but also impaired IFNβ signaling. These results are in line with the multiple roles described for IKKε and TRIM6 in both the IFN induction and signaling pathways [8, 13, 14].
Our data indicates that NiV-M can inhibit IFN induced by both TBK-1 and IKKε when ectopically expressed. However, in our previous studies we did not find interaction of TRIM6 with TBK-1 or effects on TBK-1 activation in TRIM6 knockdown cells, suggesting that the mechanisms by which NiV-M inhibits TBK-1 and IKKε are different. One possibility is that NiV-M is able to inhibit other TRIM proteins responsible for TBK-1 activation.
Henipaviruses are zoonotic pathogens that have been found in Asian fruit bats of the Pteropus genus, without causing evident disease. Henipaviruses have also been reported to inhibit innate immune pathways in bats to establish infections in their natural reservoir [35]. Interestingly, TRIM6 is conserved across mammalian species including bats of the genus Pteropus (S5 Fig). Human TRIM6 shares relatively high homology with bat TRIM6 (80–84% amino acid identity, S6 Fig). Importantly the cysteine and histidine residues in the RING domain, which are important for TRIM6 E3-ubiquitin ligase activity, are conserved between species, suggesting that TRIM6 is active in bats. The C-terminal SPRY domain of human TRIM6, which is responsible for M interaction, also shares 80–84% homology. Although it is difficult to predict whether NiV-M is still able to interact and inhibit TRIM6-medaited antiviral responses in bats, it is tempting to speculate that M proteins of henipaviruses may also be able to antagonize IFN responses in the bats, especially because other factors of the RIG-I pathway and specifically TBK1 and IKKε (97% and 80% identity respectively), are also relatively conserved in bat species (S7 Fig). Therefore, TRIM6 and the IKKε signalosome may be highly relevant in antiviral responses in the natural reservoir of henipaviruses.
The matrix protein is a viral structural protein with functions in virus assembly and budding. Our findings have important implications on the efficiency of NiV replication. The fact that the matrix protein is contained in the virion and is released immediately upon virus entry gives the virus a tool to fight the host antiviral response early during virus replication and before the IFN response is activated, giving an advantage to the virus. Pharmacological approaches could be developed to target NiV-M interactions with TRIM6 or the use of inhibitors to block TRIM6 degradation promoted by M.
In summary, here we report an additional paramyxovirus protein with IFN antagonist function. The matrix protein of Nipah virus inhibits both the IFN induction and the IFN signaling pathways by promoting degradation of the E3-ubiquitin ligase TRIM6, which synthesizes unanchored polyubiquitin chains required for IKKε activation and induction of an efficient antiviral response (Fig 8). This is the first example of a structural protein of henipaviruses with IFN antagonist functions.
HEK-293T, HeLa, A549, Vero E6 (CRL1586), and Vero (CCL-81) cells were all purchased from the American Type Culture Collection (ATCC). All cells were maintained in Dulbecco's Modified Eagle's Medium (DMEM) supplemented with 10% fetal bovine serum (FBS), 2 mM L-glutamine and 1% penicillin-streptomycin (Gibco-BRL). Sendai virus (SeV; Cantell strain) was obtained from Charles River Laboratories and propagated in 10-day old pathogen-free embryonated chicken eggs (Charles River Laboratories; North Franklin, CT). For infection, 293T cells were incubated with SeV for 2hrs at 37°C, and then the medium was changed to complete growth medium. The Nipah virus (NiV) strain Malaysia (kindly provided by the Special Pathogens Branch, CDC, Atlanta) was propagated in VeroE6 cells. Stock virus titer was determined by plaque assay in Vero-CCL81 cells. For infection, confluent monolayers of 293T cells (seeded in 24-well-plates) were infected with NiV (MOI 0.01 to 1.0) for 1 hr at 37°C, and then fresh medium containing 2% FBS, 100 U/mL penicillin and 100 μg/mL streptomycin was added. Cells were harvested at 24 h.p.i. in 100μl PBS, lysed in 6x SDS-PAGE Laemmli buffer and incubated for 15 min at 95°C. All work with live virus was carried out under Biosafety Level 4 (BSL4) conditions in the Robert E. Shope BSL4 Laboratory, UTMB.
The NiV-M, NiV-K258A, K258R, 3XFlag-tagged NiV-M and mutant K258A expression constructs have been described before [38]. The NiV-V expression construct was made by PCR amplification of the NiV-V open reading frame (ORF) and insertion into the pcDNA3.1(+) vector with an N-terminal HA tag. The TRIF, MAVS, TBK-1 and IKKε expression constructs as well as pIFNβ_fLuc, ISRE_fLuc reporters were kindly provided by Dr. Genhong Cheng and have been described previously. The IRF3 expression construct was provided by Dr. Ren Sun. Reporter plasmids expressing firefly luciferase under the control of the ISG54-ISRE and the IFNβ promoter were described previously [4, 57], and were a generous gift from Dr Garcia-Sastre (Mount Sinai, NY). FLAG-tagged RIG-I(2CARD) was previously described [58]. The reporter plasmid carrying the Renilla luciferase gene (REN-Luc/pRL-TK) was purchased from Promega. The HA-TRIM6 plasmid was kindly provided by Andrea Ballabio [51]. All sequences were confirmed by sequencing analysis (Genewiz, NJ) and at the UTMB molecular genomics core facility.
Transient transfections were performed with TransIT-LT1 (Mirus), Lipofectamine 2000 or RNAiMax (Invitrogen) according to the manufacturers' instructions. Rabbit anti-NiV-M antibody has been described previously [38]. Rabbit anti-phospho-IRF3 (Ser396) antibody (4D4G) and mouse anti-IRF3 (3F10) antibody were from Cell Signaling and Immuno-Biological Laboratories, respectively. Rabbit anti TRIM6 (N term) antibody, rabbit and mouse anti c-myc antibody, mouse and rabbit anti-FLAG antibodies, rabbit anti-HA antibody, mouse anti-β-tubulin and mouse anti-β-actin antibodies were from Sigma. Rabbit anti phospho IKKε antibody (T501) was purchased from Novus Biologicals. Rabbit monoclonal anti-ubiquitin Lysine 48 (K48, clone Apu2) was purchased from Millipore. Rabbit anti GST antibody (OTI4G1) was from Bethyl Laboratories. Fluorescently labeled secondary antibodies for imaging: Alexa Fluor 488 goat anti mouse, Alexa Fluor 488 donkey anti rabbit, Alexa Fluor 555 goat anti mouse, Alexa Fluor 555 donkey anti rabbit, goat anti mouse Alexa Fluor 633, were purchased from ThermoScientific (Life Technologies). Directly conjugated antibodies towards Flag/DYKDDDK tag rabbit (Alexa 555) and HA tag mouse (Alexa 488) were from ThermoScientific and Cell Signaling Technologies.
HEK293T cells were transfected in 24 (50X103 cells per well) or 96-well plates (10X103 cells per well) (Falcon, Becton Dickinson, NJ) with 10–50 ng of IFNβ/ISG54 ISRE reporter plasmid, 4–20 ng of Renilla luciferase and 2–50 ng plasmids using TransIT-LT1 (Mirus), at a ratio 1:3. Empty vector was used to ensure that the plasmid concentration in each well was the same. 24 h later, cells were lysed and dual-luciferase assay was performed according to the manufacturer's instructions (Promega, Madison, WI, USA). Percentage inhibition was calculated first by normalizing the Luciferase values by Renilla values (fLuc/rLuc). Then, in each graph the positive control which has an activator (SeV, IFNβ, TRIF etc.) but no inhibitor (i.e. NiV-M or NiV-V) was set to 100% and everything else was normalized to this control sample.
Transfected 293T cells were harvested in RIPA lysis buffer containing 50 mM TRIS, pH8.0, 280 mM NaCl, 0.5% [v/v] NP40, Glycerol 10%, protease inhibitor cocktail [Roche and supplemented with 5 mM N-ethylmaleimide (NEM) and Iodoacetamide as deubiquitinase inhibitors. Cell lysate was clarified by microcentrifugation at 14,000rpmi for 20 min. One tenth of the aliquot from clarified lysate was taken and added to 2x Laemmli buffer with β-ME and stored at -20°C for western blots (Whole cell extracts, [WCE]). To the rest of the lysate mouse anti-FLAG or anti-HA antibody cross-linked to agarose beads (EZ View Red Anti-FLAG M2 or EZ View Red Anti-HA Affinity Gel Sigma) were added and rotated on a bench top shaker overnight at 4°C. The next day, beads were extensively washed, and the bound proteins were eluted by boiling for 10 min in Laemmli loading buffer.
For immunoblotting, proteins were resolved by SDS-polyacrylamide gel electrophoresis (7.5% or 4–15% SDS-PAGE) and transferred onto a PVDF membrane (Immobilon-P Millipore or BioRad Laboratories). The following primary antibodies were used: anti-Ub-K48 (1:1,000), anti-Flag (1:3,000) (Sigma), anti-HA (1:5,000) (Sigma), anti-GST (1:2,000) (Bethyl Laboratories), anti-myc (1:2,000) (Sigma), anti-NiV M (1:3000), anti TRIM6 N term (1:1000), pIKKε (1:500), anti pIRF3 (1:1,000), anti IRF3 (1:3000), anti actin/tubulin (1:5000).
Immunoblots were developed with the following secondary antibodies: ECL anti-rabbit IgG horseradish peroxidase conjugated whole antibody from donkey, and ECL anti-mouse IgG horseradish peroxidase conjugated whole antibody from sheep (GE Healthcare; Buckinghamshire, England). The proteins were visualized by an enhanced chemiluminescence reagent (Pierce).
Transient knockdown of endogenous TRIM6 in human 293T cells, seeded in 96-well plates, was achieved as described before [13]. Briefly, by transfection of 10 pmol of non-targeting control or an siRNA specific for TRIM6 (Life technologies, sleath RNAi TRIM6-specific sequence targeting the 5’-UTR region of transcript variant 2; sense: GCUGCUUCAAGUCCUUGGCUCUGAU and antisense: AUCAGAGCCAAGGACUUGAAGCAGC), with RNAiMAX (Invitrogen) according to the manufacturer’s instructions. Rescue of knockdown was achieved by transfecting TRIM6 encoding plasmid using TransIT-LT1 (Mirus) 24h post silencing. Because the siRNA targets the untranslated region of TRIM6, these siRNA sequences do not attenuate TRIM6 expression from the expression vector upon transfection. TRIM6 knockdown and rescue efficiency were determined by western blotting and real time PCR using specific primers as described before [13].
QPCR was done as previously described [44]. In brief, total RNA was extracted from hMDDCs with TRIzol reagent (Sigma). cDNA was prepared by using a SuperScript III first-strand synthesis system (Invitrogen). Relative gene expression was determined by using PerfeCTa SYBR green FastMix (Quanta Biosciences, Inc.) with a Bio-Rad CFX96 instrument. CXF Manager software (Bio-Rad) was used to analyze the relative mRNA expression levels by the change in the threshold cycle (ΔCT), with the RPS11 gene serving as a reference mRNA to which the results were normalized. The copy number for RPS11 was based upon a standard curve generated by using an RPS11-containing plasmid.
For colocalization studies of TRIM6 and NiV-M or mutants, HeLa cells were seeded into Lab-Tek II 8-well chamber slides (CC2 Glass slide, Nunc; Rochester, NY). After 12–16 h, 300-700ng of plasmids harboring NiV-M, NiV-K258A, NiV-K258R, HA-TRIM6 or empty vector backbone were transfected with Lipofectamine 2000 (Invitrogen) at a ratio 1:1. Six hours later, media was replaced and 16–24 h later, cells were washed with PBS, fixed with 4% paraformaldehyde, permeabilized with 0.5% NP-40 (v/v) in PBS, and blocked with 0.5% BSA 0.2% fish gelatin in PBS for 1h (blocking solution). For NiV-M or its mutants, cells were stained with primary rabbit anti-M antibody (1:1000) [38] and for HA-TRIM6, anti-HA antibody (1:200) Alexa Fluor 488 (prepared in blocking solution) overnight at 4°C. The next day, cells were washed 3 times with blocking solution and secondary antibody donkey anti-rabbit Alexa-Fluor 555 (Invitrogen) diluted in blocking buffer along with DAPI (1:2000) were used to visualize the proteins. The slides were imaged on a Zeiss LSM 510 confocal microscope in the UTMB optical imaging core at a magnification of 63×. For non-confocal imaging, slides were observed and imaging was done on Bio-Tek Cytation5 plate reader with fluorescence microscope.
For the live viral infection experiments, cells were infected with recombinant Nipah virus expressing EGFP (rNiV-EGFPNP) [59] (depicted in the text as NiV-WT) or rNiV-EGFPNP lacking the matrix protein (rNiV-EGFPNP delta M, depicted in the text as NiV-ΔM) at an MOI of 0.1. 24-48h post infection, cells were fixed with 10% formalin for 24 hr and removed from the BSL4. Cells were washed extensively with PBS, permeabilized with 0.5% NP-40 (v/v) in PBS, and blocked with 0.5% BSA 0.2% fish gelatin in PBS for 1h (blocking solution).
Cells infected with rNiV-EGFPNP or rNiV-EGFPNP delta M were stained for TRIM6 using rabbit anti-TRIM6 N-term antibody (Sigma) (1:200) overnight at 4°C, washed 3X with blocking buffer the next day and stained with secondary donkey anti rabbit Alexa Fluor 555 (1:500) and counterstained with DAPI (1:2000) for 1h (Fig 7A and 7B). Alternatively, cells were stained for anti-NiV-M antibody [38] (1:1000) along with mouse anti TRIM6 antibody (1:100) overnight at 4°C followed by three washes the next day. Cells were then stained with secondary donkey anti rabbit Alexa Fluor 555 (1:500) and goat anti-mouse Alexa Fluor 633 and counterstained with DAPI (1:2000). After extensive washing with PBS, cells were mounted using Vecta shield mounting medium and imaged on a Zeiss LSM 510 confocal microscope in the UTMB optical imaging core and Bio-Tek cytation5 (Fig 7C).
Cells were manually counted, included or excluded by inspection to ensure that all cells included in the final scoring had the cell boundary correctly defined. ImageJ software was used to calculate ratio between NiV-M and TRIM6 mean fluorescence intensities (MFI). For this quantification, individual cells were selected using the freehand selection tool in imageJ software following the borders of each individual cell based on TRIM6 staining. Over 50 cells were selected for quantification in ImageJ software. A minimum cutoff intensity level was applied to ensure NiV-M expression was sufficient.
The T7-driven rNiV-ΔM rescue construct was derived from rNiV with firefly luciferase between the N and P genes [59]. The M gene was deleted via replacement by EGFP. HEK293T cells were transfected with helper plasmid encoding NiV-M (0.5μg) to facilitate budding of mature virions. Four hours later, cells were transfected with helper plasmids encoding codon-optimized T7 polymerase (1μg), NiV-N (1μg), NiV-P (0.8μg), NiV-L (0.2μg), and full-length rNiV-ΔM (3.5μg) using TransIT-LT1 reagent. Supernatants were collected on day 5, and rescued virus was then propagated through infections of NiV-M-inducible 293-pTRE3G-M cells. To generate these cells, codon-optimized NiV-M in pTRE3G (Clontech) was introduced into HEK293 Tet-On 3G cells (Clontech) and single cell-cloned for doxycycline-inducible expression of NiV-M. Propagation in G418 maintained the Tet-on transactivator, and hygromycin maintained pTRE3G-NiV-M, which was stably integrated along with a linear hygromycin marker (via co-transfection). M expression was induced by the addition of 30ng/ml doxycycline prior to infection with rescued rNiV-ΔM. Supernatant was then collected 5 days post-infection. Virus titers were determined by standard plaque assays in Vero cells. The lack of M expression was confirmed by Western blot. (S4 Fig).
Human MDDCs were generated from CD14+ cells purified from concentrated leukocytes of healthy human donors (New York Blood Center), as described previously [42, 44]. Briefly, peripheral blood mononuclear cells (PBMCs) were isolated by Ficoll density gradient centrifugation and CD14+ cells were purified by using CD14 microbeads. CD14+ cells, were incubated at 37°C for 5 days in DC medium (RPMI containing 4% human AB serum, 2 mM L-glutamine, 1 mM sodium pyruvate, 100 U/ml penicillin–100 μg/ml streptomycin, and 55 μM β-mercaptoethanol) supplemented with 500 U/ml human granulocyte-macrophage colony-stimulating factor and 500 U/ml human interleukin-4 (hIL-4; PeproTech). MDDCs were transduced by spinoculation at 1,850 rpm with lentiviruses and Vpx-VLPs for 2.5 h and then cultured in fresh medium for 72 h. Transduced MDDCs were harvested to assess expression by flow cytometry and Western blotting or were used in subsequent experiments.
The leukocytes from healthy human donors were obtained from the New York Blood Center. These samples are anonymous blood bank donor samples. This constitutes exempt research and does not require IRB review.
Statistical analysis was performed with Prism (Version 5.0, GraphPad Software) using Student’s paired t test or in defined cases two-way ANOVA with Bonferroni post-test were used. *p < 0.05; **p < 0.01; ***p < 0.001, ****p < 0.0001
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10.1371/journal.pgen.1002429 | MAPK/ERK Signaling Regulates Insulin Sensitivity to Control Glucose Metabolism in Drosophila | The insulin/IGF-activated AKT signaling pathway plays a crucial role in regulating tissue growth and metabolism in multicellular animals. Although core components of the pathway are well defined, less is known about mechanisms that adjust the sensitivity of the pathway to extracellular stimuli. In humans, disturbance in insulin sensitivity leads to impaired clearance of glucose from the blood stream, which is a hallmark of diabetes. Here we present the results of a genetic screen in Drosophila designed to identify regulators of insulin sensitivity in vivo. Components of the MAPK/ERK pathway were identified as modifiers of cellular insulin responsiveness. Insulin resistance was due to downregulation of insulin-like receptor gene expression following persistent MAPK/ERK inhibition. The MAPK/ERK pathway acts via the ETS-1 transcription factor Pointed. This mechanism permits physiological adjustment of insulin sensitivity and subsequent maintenance of circulating glucose at appropriate levels.
| Insulin signaling is an important and conserved physiological regulator of growth, metabolism, and longevity in multicellular animals. Disturbance in insulin signaling is common in human metabolic disorders. For example insulin resistance is a hallmark of diabetes and metabolic syndrome. While the core components of the insulin signaling pathway have been well established, the mechanisms that adjust the insulin responsiveness are only known to a limited extent. Here we present results from a genetic screen in Drosophila that was designed to identify regulators of cellular insulin sensitivity in an in vivo context. Surprisingly, we discovered cross-talk between the epidermal growth factor receptor (EGFR)–activated MAPK/ERK and insulin signaling pathways. This regulatory mechanism, which involves transcriptional control of insulin-like receptor gene, is utilized in vivo to maintain circulating glucose at appropriate levels. We provide evidence for a regulatory feed-forward mechanism that allows for dynamic transient responsiveness as well as more stable, long-lasting modulation of insulin responsiveness by growth factor receptor signaling. The combination of these mechanisms may contribute to robustness, allowing metabolism to be appropriately responsive to physiological inputs while mitigating the effects of biological noise.
| The insulin signaling pathway is a highly conserved regulatory network coordinating animal metabolism and growth with nutritional status. In mammals, energy metabolism is regulated by insulin, and tissue growth by insulin-like growth factors (IGFs), through their respective receptors (for review see [1]). Drosophila has a single insulin-like receptor protein (InR), which is activated by a family of insulin-like peptides (ILPs) and mediates physiological responses related to both metabolism and growth (for review see [2]). InR stimulation leads to activation of the phosphatidylinositol 3-kinase (PI3K)/AKT pathway. AKT is recruited to the plasma membrane through phosphatidylinositol-3,4,5-triphosphate (PIP3), which is generated by phosphorylation of PI-4,5-P2 by PI3K (for review see [3]). Membrane-recruited AKT is activated through phosphorylation by PDK1 and by TOR complex 2 [4]–[6]. AKT has several downstream effectors, including FOXO, a Forkhead transcription factor. AKT-mediated phosphorylation promotes retention of FOXO in the cytoplasm, thereby limiting FOXO activity [7]. AKT also promotes the activity of TOR complex 1 by phosphorylating two of its upstream regulators, TSC2 and PRAS40 [8]–[11].
Insulin signaling is involved in homeostatic regulation, gradually adjusting physiological processes in response to variable nutritional conditions. This tuning mode of regulation differs from many developmental signaling pathways, which produce a limited range of outputs (e.g. cell fate). Therefore, it is perhaps not surprising that cellular insulin sensitivity is modulated by input from other signaling pathways. For example, TORC1-regulated S6 kinase (S6K) inhibits expression of the IRS scaffold proteins, which are recruited to activated insulin/IGF receptors, thereby making cells more insulin resistant [12], [13]. Inflammatory signals, on the other hand, are known to cause insulin resistance by c-Jun N-terminal kinase-mediated phosphorylation of the IRS proteins (for review see [14]). This is likely to contribute to the pathogenesis of type 2 diabetes.
While regulation of insulin sensitivity is clearly of physiological importance, identifying novel regulatory mechanisms through genetic screens has been challenging due to the need for a sensitive readout that is also amenable to large-scale screening. The Drosophila eye provides such a system. Because insulin signaling limits FOXO activity, overexpression of FOXO can challenge the regulatory capacity of the insulin pathway, creating a sensitized genetic background [6], [15]. Using this strategy to identify in vivo modulators of insulin pathway activity, we have uncovered a novel regulatory mechanism influencing insulin sensitivity. We show that the extracellular signal-regulated kinase (ERK)/MAP kinase signaling pathway (for review see [16]) influences cellular insulin responsiveness controlling the expression of the insulin-like receptor (inr) gene. This transcriptional regulation is mediated through the Ets-1 orthologue, Pointed, a transcription factor regulated by the MAPK/ERK pathway [17]. This mechanism provides a means for integration of signaling input via the Epidermal growth factor receptor (EGFR)-regulated MAPK/ERK with insulin-like signaling to control systemic glucose homeostasis.
To identify novel modulators of insulin-like signaling we screened for modifiers of FOXO overexpression, which produces small rough eyes. This phenotype has earlier been shown to respond to changes in insulin-like signaling in a highly sensitive manner [6], [15]. As insulin-like signaling is a known regulator of growth, we focused on screening RNAi lines that had earlier shown tissue undergrowth in a wing-based screen ([18], Figure S1). Our screen identified the PI3K gene, which serves as a positive control. The screen also identified kinase suppressor of ras (ksr) as an enhancer of the FOXO gain-of-function phenotype. Downregulation of ksr by RNAi enhanced the FOXO phenotype, but on its own, did not reduce eye size (Figure 1A, quantified in Figure 1B). The lack of an obvious eye phenotype resulting from ksr depletion alone presumably reflects the magnitude of KSR downregulation generated with the GMR-GAL4 driver during the phase of eye imaginal disc growth. Enhancement of the FOXO overexpression phenotype was also observed when one copy of the ksr gene was removed (Figure S2). Removing one copy of the ksr gene on its own did not reduce eye size, indicating the utility of the sensitized background to identify subtle modulators of pathway activity.
FOXO is regulated at multiple levels, including nuclear localization [7]. FOXO is nuclear in cells devoid of growth factors, but upon insulin stimulation FOXO accumulates in the cytoplasm through AKT-mediated phosphorylation ([7]; Figure 1C) As expected, RNAi-mediated depletion of PI3K limited the insulin-induced shift toward cytoplasmic FOXO. Depletion of KSR by RNAi produced a similar effect (Figure 1C; representative images in Figure S3).
Does KSR act via AKT or does a parallel pathway override AKT-mediated FOXO regulation? To address this, we monitored insulin-induced activation of AKT. Depletion of KSR suppressed insulin-induced phosphorylation of the activating ‘hydrophobic motif’ site S505 on AKT (Figure 2A). The known functions of KSR are related to MAPK/ERK activation [19], [20]. To ask if changes in the canonical MAPK/ERK pathway would explain the KSR effect on insulin signaling, we depleted D-MEK (MAP kinase kinase). This produced an effect comparable to that of KSR (Figure 2B). Insulin-induced AKT activation involves increase in the level of plasma membrane phosphoinositide PIP3 through the activity of PI3K [3]. To assess PIP3 levels, we used a GFP-linked pleckstrin homology (PH) domain from GRP1 [21]. Insulin treatment induced prominent membrane accumulation of the GRP1-PH domain, which was prevented by depletion of PI3K (Figure 2C). Similarly, RNAi-mediated depletion of KSR reduced membrane localization of GRP1-PH in response to insulin.
To test whether other downstream targets of AKT besides FOXO were affected by MAPK/ERK inhibition, we analyzed phosphorylation of S6K, a target of TORC1 [22]. Insulin-induced phosphorylation shifts some of the S6K protein into a ladder of slower migrating forms [e.g. [23]], which was reduced by depletion of KSR or D-MEK (Figure 2D). In both experiments the effects were comparable to depletion of PI3K. To test, whether MAPK/ERK signaling affected PI3K activity independent of insulin, we overexpressed activated PI3K in the absence of insulin stimulation and monitored AKT phosphorylation. In this setting, knockdown of KSR had no influence on pathway activity (Figure S4). In sum, these data suggest that reduced MAPK/ERK activity lowers sensitivity to insulin stimulation, but does not hamper PI3K from activating AKT.
The data above suggested that MAPK signaling regulates the insulin-like pathway above the level of PI3K. We therefore sought to monitor InR expression level as well as activation making use of a shift in its electrophoretic mobility caused by phosphorylation (Figure 3A, lanes 1 and 4). Surprisingly, we found that depletion of KSR led to a reduction in the total level of InR protein. This was observed in both insulin-treated and untreated cells. Depletion of PI3K did not produce a comparable effect. To confirm that InR expression is regulated by the canonical MAPK/ERK pathway, we silenced the expression of Raf and D-MEK, which also showed reduced InR expression (Figure 3B). Increasing MAPK/ERK pathway activity acts in the opposite direction: depletion of Gap1, the GTPase activator of RAS, led to activation of MAPK/ERK signaling visualized by phospho-specific antibody against the active form of ERK (Figure 3C) as well as elevated InR expression (Figure 3B). Thus, regulation of InR appears to be a specific MAPK/ERK pathway effect and InR levels are sensitive to both positive and negative changes in the MAPK/ERK activity.
How does MAPK/ERK signaling affect InR expression? Using quantitative RT-PCR we observed a significant reduction in the levels of the mature inr mRNA and the unspliced inr primary transcript upon KSR depletion in S2 cells (Figure 3D). The reduction in inr primary transcript levels upon KSR depletion suggests that MAPK/ERK activity is likely to regulate inr transcription. If so, we would expect transgene-directed expression of inr to be refractory to the effects of MAPK pathway modulation. This proved to be the case. A transfected version of inr under control of a heterologous promoter was insensitive to KSR depletion (Figure 4A). As an in vivo test we used GMR-Gal4 as a heterologous promoter to direct expression of a UAS-InR transgene in the eye. Under these conditions, KSR depletion did not enhance the FOXO overexpression phenotype (Figure 4B, 4C). The insensitivity of transgene-directed InR to the effects of KSR depletion is consistent with the hypothesis of a mechanism involving control of endogenous inr transcription.
pointed (pnt) encodes an ETS-1 transcription factor that is activated by MAPK/ERK through phosphorylation of a conserved threonine residue, T151 [24], [25]. We tested its involvement in the regulation of inr expression by depletion of Pointed from S2 cells by RNAi. This caused a prominent reduction of inr mRNA (Figure 5A). To narrow down the regulatory region of the inr gene we systematically analyzed >20 kb upstream of the inr coding region by preparing a series of luciferase reporter constructs (Figure S5), which lead to identification of a minimal cis-regulatory region of 0.8 kB (Figure 5B). Overexpression of Pointed-P2 in S2 cells increased reporter activity directed by this 0.8 kB region (Figure 5C). This fragment contained one site perfectly matching the consensus Pointed binding site (5′-(C/G)(A/C/G)GGA(A/T)(A/G)-3′; [26]). Mutating the consensus site reduced the ability of Pointed to induce reporter expression (Figure 5C), suggesting binding to this site contributes to Pointed-mediated regulation of inr gene expression. Reciprocally, Pointed depletion in S2 cells led to a decrease in the level of InR protein (Figure 5D), and to a reduction of insulin-induced AKT S505 phosphorylation (Figure 5E).
To further test this relationship in vivo, we asked whether reducing pointed levels would influence the severity of the FOXO overexpression phenotype. Removing one copy of the pointed gene using three independent alleles modestly but significantly enhanced the FOXO overexpression phenotype in the eye (Figure 5F, Figure S6). These findings suggest that the MAPK/ERK pathway acts via the Ets-1 transcription factor Pointed to control cellular insulin sensitivity.
To begin to explore the physiological role of regulation of InR levels by the MAPK/ERK pathway in vivo, we performed a survey of larval tissues and found that KSR depletion led to significant reduction of inr transcript levels in imaginal discs and larval fat body, Drosophila equivalent of liver and adipose tissue (Figure 6A). KSR depletion also led to a reduction of InR protein levels (Figure 6B) as well as nuclear FOXO accumulation in the larval fat body (Figure 6C). MAPK/ERK signaling can be regulated by a variety of receptor tyrosine kinases (RTKs). We next made use of pumpless-GAL4 to manipulate RTK activity. Pumpless is active mainly in the fat body, but also other tissues, such as parts of larval gut. Inhibition of Epidermal growth factor (EGF) signaling by expression of a dominant negative form of EGFR (dnEGFR) led to downregulation of inr mRNA (Figure 6D) and protein levels (Figure 6E) in the isolated fat body. This suggests that EGFR is a physiologically relevant upstream regulator of MAPK/ERK-mediated control in Inr expression in vivo.
Systemic regulation of InR activity has been shown to influence metabolic homeostasis [reviewed in [27]]. In this context, the effects of KSR depletion on inr expression and FOXO localization in fat body were suggestive of a link to energy metabolism. To ask whether reduction of inr to half of normal levels was sufficient to cause a metabolic disturbance, we made use of larvae carrying one copy of the deletion Df(3R)BSC678, which fully removes the inr gene (Figure 5B). Quantitative RT-PCR was used to confirm that inr mRNA levels were reduced to ∼50% in these animals (Figure 7A). Notably, this result indicates that there is little or no feedback from InR signaling on inr expression, and suggests that there is limited output from InR via the MAPK pathway in Drosophila. In addition, we observed that flies with modestly reduced inr levels showed impaired capacity to limit FOXO activity in the eye (Figure S7).
Larvae lacking one copy of the inr gene showed no significant change in levels of stored glycogen and triglycerides or trehalose (Figure S8), a circulating disaccharide synthesized by the fat body through glycogenolysis ([28]). However, levels of circulating glucose in the hemolymph were substantially increased (Figure 7B), suggesting compromised clearance of dietary glucose from the circulation. To ask whether this phenotype could be achieved by independent genetic means, we used pumpless-GAL4 to drive expression of a UAS-inrRNAi transgene to deplete InR (Figure 7C). These animals showed elevated glucose in their hemolymph, verifying that regulation of InR levels is physiologically important in vivo in maintaining levels of circulating glucose. This conclusion was further supported by the finding that InR overexpression modestly, but significantly, decreased levels of circulating glucose (Figure S9). These findings are consistent with what has been reported in flies in which the ability to produce insulin-like peptides was compromised by genetic ablation of the insulin-producing neurosecretory cells [29]. These flies showed a prominent metabolic change at the level of circulating glucose levels [29]. Similarly, insulin signaling in mammals regulates glucose uptake and reduced insulin sensitivity is linked to hyperglycemia, metabolic syndrome and type-2 diabetes [30], [31].
To test whether MAPK/ERK-regulation of InR expression is involved in maintaining systemic glucose homeostasis, we assessed the effects of KSR RNAi. Depletion of KSR led to elevated levels of circulating glucose (Figure 7D). If the effect of KSR depletion is due to reduced InR levels, we would expect restoring inr expression under Gal4 control to lower glucose levels toward normal. This proved to be the case (Figure 7D). As expected, expression of a dnEGFR using pumpless-GAL4 driver also resulted in elevated circulating glucose (Figure 7E). The glucose levels were restored by simultaneous overexpression of Pointed, which is in agreement with the view that Pointed acts as a downstream effector of the pathway (Figure 7F). These observations suggest a physiological role for EGFR-MAPK/ERK-Pointed activity in control of glucose homeostasis via regulation of InR levels.
The insulin signal transduction pathway is regulated by cross-talk from several other signaling pathways. This includes input from the amino-acid sensing TOR pathway into regulation of insulin pathway activity by way of S6 kinase regulating IRS [12]–[14]. Signaling downstream of growth factor receptors has also been linked to regulation of insulin signaling [32], [33]. The active form of the small GTPase Ras can bind to the catalytic subunit of PI3K and promote its activity. Expression of a form of PI3K that cannot bind Ras allows insulin signaling, but at reduced levels [33]. The work reported here provides evidence for a second mechanism through which growth factor receptor signaling through the MAPK/ERK pathway modulates insulin pathway activity. Transcriptional control of inr gene expression by EGFR signaling may provide a means to link developmental signaling to regulation of metabolism. In this context, we noted a statistically significant correlation between EGFR target gene sprouty and inr gene expression at different stages during Drosophila development (Figure S10).
Several steps of the insulin pathway can be regulated by phosphorylation. Given that the MAPK/ERK pathway is a kinase cascade, a priori, the possibility of phosphorylation-based interaction between these pathways would seem likely. However, this appears not to be the case. Acute pharmacological inhibition of the MAPK/ERK pathway proved to have no impact on insulin pathway activity (Figure S11). Thus short-term changes in MAPK/ERK pathway activity do not seem to be used for transient modulation of insulin pathway activity. Instead, the MAPK/ERK pathway acts through the ETS-1 type transcription factor Pointed to control expression of the inr gene. Transcriptional control of inr suggests a slower, less labile influence of the MAPK pathway. Taken together with the earlier studies [32], [33], our findings suggest that growth factor signaling can regulate insulin sensitivity by both transient and long-lasting mechanisms.
Why use both short-term and long-term mechanisms to modulate insulin responsiveness to growth factor signaling? The use of direct and indirect mechanisms that elicit a similar outcome is reminiscent of feed-forward network motifs [34]. Although these motifs are often thought of in the context of transcriptional networks, the properties that they confer are also relevant in the context of more complex systems involving signal transduction pathways. In multicellular organisms, feed-forward motifs are often used to make cell fate decisions robust to environmental noise [35]. Our findings suggest a scenario in which a feed-forward motif is used in the context of metabolic control, linking growth factor signaling to insulin responsiveness. In this scenario, growth factor signaling acts directly via RAS to control PI3K activity and indirectly via transcription of the inr gene to elicit a common outcome – sensitization of the cell to insulin. This arrangement allows for a rapid onset of enhanced insulin sensitization, followed by a more stable long-lasting change in responsiveness. Thus a transient signal can both allow for an immediate as well as a sustained response. The transcriptional response also makes the system stable to transient decreases in steady-state growth factor activity. We speculate that this combination of sensitivity and stability allows responsiveness while mitigating the effects of noise resulting from the intrinsically labile nature of RTK signaling. As illustrated by our data, failure of this regulation in the fat body leads to elevated circulating glucose levels, likely reflecting impaired clearance of dietary glucose from the circulation by the fat body. Maintaining circulating free glucose levels low is likely to be important due to the toxic effects of glucose [28]. In contrast, circulating trehalose, glycogen or triglyceride levels showed no significant change in animals with reduced InR expression, suggesting that these aspects of energy metabolism can be maintained through compensatory mechanisms in conditions of moderately impaired insulin signaling.
Earlier studies by Puig and coworkers have shown that the transcription of the inr gene is under dynamic control [36], [37]. Activation of FOXO in the context of low insulin signaling leads to upregulation of inr transcription, thus constituting a feedback regulatory loop. Thus, InR expression appears to be under control of two receptor-activated cues, which have opposing activities: inr expression is positively regulated by the EGFR-MAPK/ERK module, but negatively regulated by its own activity on FOXO. In the setting of this study, the cross-regulatory input from the MAPK/ERK pathway was found to dominate over the autoregulatory FOXO-dependent mechanism. If conditions exist in which the FOXO-dependent mechanism was dominant, we would expect to observe a limited potential for crossregulation by the MAPK/ERK pathway. Whether Pointed and FOXO display regulatory cooperativity at the inr promoter is an intriguing question for future study.
UAS-InR, pntΔ88, pnt07825, UAS-Pnt-P2, ksrS-627, Df(3R)Exel6186, Df(3R)ED6076 and Df(3R)BSC678 flies were obtained from the Bloomington Stock Center. UAS-RNAi-PI3K, UAS-RNAi-KSR and UAS-RNAi-InR lines were from the Vienna Drosophila RNAi center. pUAST-FOXO-GFP flies were provided by Aurelio Teleman. pUAST-dnEGFR flies were provided by Pernille Rørth. pntT5 flies were provided by Christian Klämbt.
S2 cells were grown at 25°C in SFM (Gibco) supplemented with L-glutamine. dsRNA was prepared using MegascriptT7 (Ambion) with the following templates: PI3K, nucleotides 358–857 of Pi3K92E coding sequence (FBpp0083348); ksr, nt 2224–2710 (FBpp0078413); D-MEK, nt 961–1191 of the ORF plus the first 83 nt of the 3′UTR (FBtr0071313); Raf, nt 522–912 (FBpp0110324);GAP1, nt 153–646 (FBpp0076096); pnt, nt 1541–1957 (718AA isoform, FBpp0088658); GFP, nt 17–633 of EGFP2, was used as control. S2 cells were treated with 37 nM dsRNA. Cells were transfected using effectene reagent (QIAGEN) with pMT-GAL4, pUAST-FOXO-GFP or pUAST-Myc-Dp110CAAX, or pMT-GFP-PH or pMT-InR-Flag or pMT-Pnt-P2. 0.7 mM CuSO4 was used to induce FOXO, Dp110, GFP-PH, InR or Pnt expression after transfection. The following primers were used to clone InR-Flag into pMT vector with EcoRI, NotI and XhoI sites by fusion: 5′-GGTACCTACTAGTCCAGTGTGGTGGAATTCATGTTCAATATGCCACGGGGAGTGAC-3′; 5′- TTCGAAGGGCCCTCTAGACTCGAGCGGCCGCTTACTTGTCATCGTCGTCCTTGTAGTCCGCCTCCCTTCCGATGAATCCA-3′; 5′- ACGTTGCGCTCGAGCCAGAGCTCGA-3′ and 5′- TCGAGCTCTGGCTCGAGCGCAACGT-3′. The primers used to clone pntp2 into pMT-Myc by SLIC at EcoRI site were: forward, 5′-AGTGCAACTAAAGGGGAATTCATGGAATTGGCGATTTGTAAAACAG-3′; reverse, 5′- GATAAGCTTCTGCTCGAATTCATCCACATCTTTTTTCTCAATCTTAAG-3′. The primers used to clone the inr gene regulatory region into pGL3-Basic at HindIII and XhoI sites were: forward, 5′- GCGTGCTAGCCCGGGCTCGAGTGAGAGTTTCATGTGTCAGA -3′; reverse, 5′- AAGCTTACTTAGATCGCAGATGTTAATTGCACAGCAAGCTC-3′. The primers used to mutate the predicted Pnt consensus site with QuickChange II XL kit were: forward, 5′-GAGAATGCCGGAGATGAAGACGCGAACGAAGATGAAGTCGATG-3′; reverse, 5′- CATCGACTTCATCTTCGTTCGCGTCTTCATCTCCGGCATTCTC-3′.
For FOXO-GFP localization, live S2 cells were imaged using a Leica SP5 confocal microscope. Images were taken of random fields within 15 min after 10 µg/ml insulin boost for 30 min and scored for GFP localization (scoring was done ‘blind’). For GFP-PH images were taken within 10 min after 10 µg/ml insulin boost for 5 min. The ratio of membrane to cytoplasmic GFP levels was measured as pixel intensity along the white line as indicated in Figure 2C (left panel). For fat body FOXO immunofluorescent staining, newly hatched 1st instar larvae were seeded at 50/vial and reared at 25°C. Wandering 3rd instar larvae were dissected. Tissues were fixed in PBS with 4% paraformaldehyde at room temperature for 20 min. Anti-FOXO antibody [36] was used at 1∶1000 dilution. Fat body connected with salivary gland was imaged using a Zeiss LSM700 confocal microscope.
Cells were homogenized in SDS sample buffer, boiled and resolved by SDS-PAGE before transfer to nitrocellulose membranes for antibody labeling. Antibodies to phospho-S505-AKT, AKT, P-InR and Myc were from Cell Signaling Technology. Anti-Kinesin was from Cytoskeleton. Phospho-ERK antibody was from Sigma. Anti-S6K is described in [38]. Anti-dInR is described in [37].
Total RNA was extracted from S2 cells using QIAGEN RNeasy Mini Kit and treated with On-Column DNase (QIAGEN RNase-Free DNase Set) at room temperature for 15 min to eliminate genomic DNA contamination. Reverse transcription to synthesize the first strand used oligo-dT primers and Superscript RT-III (Invitrogen). PCR was performed using POWER SYBR GREEN Master Mix (Applied Biosystems) and analyzed on Applied Biosystems 7500 fast real-time PCR system. Results were normalized to Kinesin mRNA, and rp49 was used as a control. The following primers were used: Kinesin-f, 5′-GCTGGACTTCGGTCGTAGAG-3′; Kinesin-r, 5′- CTTTTCATAGCGTCGCTTCC-3′; rp49-f, 5′- GCTAAGCTGTCGCACAAA-3′; rp49-r, 5′- TCCGGTGGGCAGCATGTG-3′; InR-f, 5′- CTGGTGGTGCTGACAGAGAA-3′; InR-r, 5′- GCAGCTGACAACTGGCATTA-3′; pri-InR-f, 5′- CAAGAGACAGCAACAAAAGG-3′; pri-InR-r, 5′- GCTTGCATGTGTTGGTGAGC-3′; KSR-f, 5′- AGCCGAGCGAAGATTGTAAA-3′; KSR-r, 5′- TCCCGATACATGCCTACACA-3′; pnt-f, 5′- CGATGCGAATGCCTACTACACG-3′; pnt-r, 5′- TGCTGGTGTTGTAGCCTGAAC-3′.
Newly hatched 1st instar larvae were seeded at 50/vial and reared at 25°C. Hemolymph was extracted from wandering stage 3rd instar larvae. 2 µl of pooled hemolymph was diluted with 8 µl Tris buffered saline (pH 6.6) and incubated at 70°C for 5 min before clarification by centrifugation at 20 000×g for 1 min. Glucose was measured in 6 µl supernatant using the GAGO-20 kit (Sigma) and normalized to the same amount of TBS as blank control. For trehalose measurement, supernatant was incubated with 7.5 µg trehalase (Sigma) overnight at 37°C and measured using GAGO-20 kit as well. For glycogen and triglyceride measurements, 3rd instar larvae were homogenized using Sartorius Potter-S tissue homogenizer in water with 0.05% Tween. Supernatant was collected after 5 min of heat inactivation at 70°C and centrifugation at 13000 rpm for 3 min. Glycogen and protein levels were measured using Glycogen assay kit (Bio Vision) and Bio-Rad protein assay reagent, respectively. Triglyceride was measured using Sigma Triglyceride kit. Data were normalized to total protein.
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10.1371/journal.ppat.1005340 | A Trypanosomatid Iron Transporter that Regulates Mitochondrial Function Is Required for Leishmania amazonensis Virulence | Iron, an essential co-factor of respiratory chain proteins, is critical for mitochondrial function and maintenance of its redox balance. We previously reported a role for iron uptake in differentiation of Leishmania amazonensis into virulent amastigotes, by a mechanism that involves reactive oxygen species (ROS) production and is independent of the classical pH and temperature cues. Iron import into mitochondria was proposed to be essential for this process, but evidence supporting this hypothesis was lacking because the Leishmania mitochondrial iron transporter was unknown. Here we describe MIT1, a homolog of the mitochondrial iron importer genes mrs3 (yeast) and mitoferrin-1 (human) that is highly conserved among trypanosomatids. MIT1 expression was essential for the survival of Trypanosoma brucei procyclic but not bloodstream forms, which lack functional respiratory complexes. L. amazonensis LMIT1 null mutants could not be generated, suggesting that this mitochondrial iron importer is essential for promastigote viability. Promastigotes lacking one LMIT1 allele (LMIT1/Δlmit1) showed growth defects and were more susceptible to ROS toxicity, consistent with the role of iron as the essential co-factor of trypanosomatid mitochondrial superoxide dismutases. LMIT1/Δlmit1 metacyclic promastigotes were unable to replicate as intracellular amastigotes after infecting macrophages or cause cutaneous lesions in mice. When induced to differentiate axenically into amastigotes, LMIT1/Δlmit1 showed strong defects in iron content and function of mitochondria, were unable to upregulate the ROS-regulatory enzyme FeSOD, and showed mitochondrial changes suggestive of redox imbalance. Our results demonstrate the importance of mitochondrial iron uptake in trypanosomatid parasites, and highlight the role of LMIT1 in the iron-regulated process that orchestrates differentiation of L. amazonensis into infective amastigotes.
| Leishmaniasis is a serious parasitic disease that affects 12 million people worldwide, with clinical manifestations ranging from self-healing cutaneous lesions to deadly visceralizing disease. A vaccine is not available, and new and less toxic drugs against this protozoan parasite are urgently needed. Following introduction into vertebrate hosts during a sand fly blood meal, Leishmania parasites undergo extensive changes in morphology and metabolism that are critical for adaptation to life inside host macrophages and replication as amastigotes. Earlier studies identified major events that occur during amastigote differentiation, but the signaling mechanism initiating this process remained poorly understood. Previously we demonstrated a novel role for the reactive oxygen species (ROS) H2O2 in initiating amastigote differentiation, a process proposed to be dependent on iron availability inside the parasite’s mitochondria. In this study we identify LMIT1, a Leishmania transmembrane protein that functions as a mitochondrial iron transporter and is conserved in other trypanosomatid protozoan parasites. Reduced LMIT1 expression impairs mitochondrial function in the infective amastigote stage, abolishing parasite virulence. Our findings identify LMIT1 as a promising new drug target, and support the conclusion that iron-dependent ROS signals generated in the mitochondria regulate differentiation of virulent Leishmania amastigotes.
| Leishmania spp., parasitic protozoa from the Trypanosomatidae family, cause a broad spectrum of human diseases collectively referred to as leishmaniasis. An estimated 12 million people worldwide are infected with these parasites, with another 350 million at risk of infection [1]. If not treated, the visceral form of leishmaniasis can cause high mortality. With no efficacious vaccines, the emergence of resistance to existing drugs and the lack of less toxic and affordable treatments, the identification of new targets for therapeutic development is urgently needed.
While alternating between mammalian and sand fly hosts Leishmania parasites experience extreme changes in environment [2]. In mammals, Leishmania replicate inside acidic parasitophorous vacuoles (PV) of macrophages as oval-shaped amastigotes with a very short flagellum. When ingested by sand flies during a bloodmeal, amastigotes transform into flagellated promastigotes that replicate in the insect’s digestive tract. As they mature, promastigotes cease to replicate, transform into infective metacyclic stages and migrate to the fly proboscis, from where they are reintroduced into a mammalian host during a blood meal. To adapt to these distinct environmental conditions, Leishmania undergo extensive morphological changes and metabolic retooling, orchestrated through genome-wide changes in gene expression and post-translational modifications [2, 3]. A shift to pH, temperature, oxygen and nutrient conditions similar to those encountered inside mammalian macrophages has been successfully used to induce promastigote to amastigote differentiation in axenic culture [4, 5]. However, the signaling pathway driving the generation of virulent amastigotes, the most important life cycle form in human infections, is still poorly understood.
Recent developments in redox biology revealed a novel role of reactive oxygen species (ROS), specifically H2O2, as a signal for differentiation [6]. While the high levels of ROS generated during oxidative stress cause damage to DNA, proteins and lipids, more subtle variations in ROS levels can be involved in signaling pathways that initiate biological processes. Mitochondria-generated ROS is tightly controlled, and its low level modulation has been implicated in regulation of aging, autophagy, immunity and cell fate determination, particularly the transition between cell growth and differentiation [7]. In agreement with these findings, recent work from our laboratory implicated iron-dependent ROS signaling as a trigger for amastigote differentiation in L. amazonensis [8]. It was suggested that iron deprivation causes “leakage” of electrons from the mitochondrial respiratory chain, generating superoxide radicals that are broken down into H2O2, the signaling molecule for differentiation, in a reaction catalyzed by iron dependent superoxide dismutase (FeSOD) [8]. Since all trypanosomatid SODs utilize iron as an essential co-factor, these initial studies could not distinguish between the role of mitochondrial FeSODA [9] and the glycosomal FeSODB (equivalent to cytoplasmic SOD in higher eukaryotes [10]). To test the hypothesis that mitochondria are the major site where iron-dependent H2O2 responsible for inducing differentiation of infective amastigotes is generated, we investigated the mechanism by which iron enters this organelle in L. amazonensis.
Regulation of iron levels is critical for maintaining the mitochondrial redox balance. The ability of iron to transition between various oxidation states makes it an ideal redox-active cofactor, which is utilized by virtually all organisms [11]. Iron uptake into mitochondria is essential for the synthesis of two important prosthetic groups, iron-sulfur clusters (ISC) and heme, which are required for the functioning of numerous biochemical processes including the electron transport chain (ETC). However, free ferrous (Fe++) iron reacts with oxygen or nitrogen compounds to generate highly toxic reactive radicals via the Fenton reaction. Hence, mitochondrial iron import must be tightly regulated, and coordinated with demand [12]. In this study we identify and functionally characterize the Leishmania Mitochondrial Iron Transporter-1 (LMIT1), a transmembrane protein with mitochondrial localization that has strong similarity with mitoferrin, a demonstrated mitochondrial iron transporter in several organisms. Our results show that LMIT1 is required for normal mitochondrial function, and is a critical determinant of virulence in L. amazonensis.
BLAST homology searches of the TriTryp database identified several members of the mitochondrial carrier family as possible homologs of yeast mitochondrial iron transporter protein mrs3 or human mitoferrin-1. The highest identity (E = 3e-38) with both human mitoferrin-1 and yeast mrs3 was observed for LmxM.08_29.2780 (L. mexicana), LinJ.29.2890 (L. infantum), LmJ.29.2780 (L. major) and Tb927.3.2980 (T. brucei). Using Leishmania mexicana sequence information, the gene fragment LMIT1, coding for a 291 amino acid mitoferrin homolog, was amplified from the Leishmania amazonensis genome. ClustalW analysis using protein sequences of yeast mrs3, human mitoferrin-1 and their trypanosomatid homologs showed a high degree of conservation (~31% identity and 50% similarity with yeast mrs3; ~33% identity and 48% similarity with human mitoferrin-1) (Fig 1A). Three copies of the Px(D/E)xx(K/)c(K/R) motif and the critical substrate contact point II residues M-N, unique signatures for mitochondrial carrier proteins coordinating iron transport [13], were also conserved in the trypanosomatid proteins. Secondary structure analysis using TMpred (http://www.ch.embnet.org/software/TMPRED_form.html) predicted six transmembrane helices with the signature motifs located at the end of each odd-numbered helix, similar to what was suggested for mrs3 and human mitoferrin [13, 14].
In contrast to mitoferrin homologs from other organisms, all three trypanosomatid MIT1 proteins lacked any identifiable mitochondrial targeting signals. To investigate this issue, promastigotes of L. amazonensis were transfected with an episomal plasmid encoding LMIT1-3xFLAG, and subjected to sub-cellular fractionation after solubilization with increasing concentrations of digitonin, a method previous validated for generating mitochondrial fractions [15, 16]. Western blot using antibodies against the cytoplasmic enzyme adenylosuccinate lyase [17] and the mitochondrial protein Ldp27 [16] demonstrated that full separation of cytoplasmic and mitochondrial proteins was achieved with 1 mg/ml digitonin (Fig 1B). The LMIT1-3xFLAG protein co-fractionated along with Ldp27 in the mitochondria-enriched fraction. To further confirm the localization of LMIT1-3xFLAG, anti-FLAG antibodies were used to stain the parasites after labeling with the mitochondrial marker MitoTracker Red. As shown in Fig 1C, the tagged version of LMIT1 was targeted to the parasite’s mitochondria.
To investigate the function of LMIT1, we performed complementation assays on yeast strains lacking the mitochondrial iron carrier proteins mrs3 and mrs4. This double deletion strain has a severe growth defect under low iron that can be restored by mrs3 expression [18, 19]. As expected, under limited iron availability (regular growth medium containing the iron chelator BPS) a plasmid driving mrs3 expression rescued the growth of Δmrs3Δmrs4 yeast more efficiently than the strain transformed with empty pYES2 vector. Importantly, Δmrs3Δmrs4 yeast cells expressing LMIT1 grew at rates comparable to cells complemented with mrs3 (Fig 1D). Since no differences were observed when all three yeast strains were grown in regular iron-containing medium, this result indicates that the rescue of Δmrs3Δmrs4 growth in low iron can be attributed to the ability of L. amazonensis LMIT1 to promote iron import into mitochondria.
Given the high sequence identity between Leishmania LMIT1 (LmxM.08_29.2780) and T. brucei MIT1 (Tb927.3.2980), we performed conditional RNAi-mediated knockdown of the TbMIT1 gene in T. brucei to further investigate the importance of this gene in mitochondrial function. Mitochondrial metabolism differs significantly between the two stages of T. brucei lifecycle. The procyclic form has fully functional mitochondria that house the oxidative phosphorylation machinery involved in ATP generation, primarily through proline catabolism [20]. In contrast, in the bloodstream form ATP generation occurs mainly through the breakdown of glucose within the glycosome, a peroxisome-related organelle [21, 22]. Bloodstream stages lack the respiratory complexes III and IV that are present in procyclics, and although complex I appears to be present, there is no evidence that it contributes to electron transport activity [23]. Thus, the lack of TbMIT1 expression was predicted to have a stronger impact in procyclics when compared to bloodstream forms.
Tetracycline-induced RNAi knockdown of TbMIT1 in procyclics caused an initial decline in parasite growth by day 3, when compared to cultures grown without the drug (Fig 2A). By days 4 and 5 cell numbers had further declined and morphologically abnormal cells with signs of degeneration (such as extensive vacuolation) were increasingly abundant (Fig 2A and 2Ed). Estimation of TbMIT1 transcript levels by quantitative real time PCR (qPCR) confirmed a reduction of >90% 48 h after tetracycline induction (Fig 2C). By day 3 major changes could also be seen in the ultrastructure of mitochondria, with swelling, lower electron density and loss of cristae (Fig 2Ee–2Eh) evident when compared to mitochondria of uninduced cells (Fig 2Ea–2Ec). The mitochondria of tetracyclin-induced procyclics also frequently contained dark electron dense aggregates and membrane whorls that were previously described as disrupted cristae associated with dysfunctional mitochondria [24]. No obvious abnormalities were noticed in the ultrastructure of other organelles such as the nucleus, endoplasmic reticulum, flagellum, and acidocalcisomes, suggesting that disruption of MIT1 function specifically affected the mitochondria of T. brucei procyclic forms. In contrast, no significant difference in growth rate was observed between tetracycline-induced and un-induced bloodstream forms subjected to the same RNAi procedure (Fig 2B). Consistent with the limited mitochondrial functionality observed in bloodstream forms, in this life-cycle stage TbMIT1 transcript levels were significantly lower (>5 fold) than in procyclics (Fig 2C and 2D).
Unlike T. brucei, Leishmania has fully functional mitochondria in all life cycle stages [16]. To understand the role of LMIT1, we proceeded to generate L. amazonensis strains lacking both LMIT1 alleles through homologous recombination, using knockout constructs carrying drug-resistance gene cassettes flanked by 5’ and 3‘ UTR regions of the LMIT1 gene (S1A Fig). Replacement of a single LMIT1 allele was achieved using either the PHLEO or NEO drug resistance gene cassettes. Allelic integration of the knockout construct into the desired gene locus was confirmed by PCR using specific primers (S1B Fig), and the resulting LMIT1/Δlmit1 strain showed the expected reduction in LMIT1 transcripts (S1D Fig). However, repeated attempts to replace the second allele to generate a LMIT1 null strain were unsuccessful. Even when strains resistant to both Phleomycin and Neomycin were obtained and replacement of the second allele with the targeting drug-resistance marker was confirmed by PCR, amplification of the LMIT1 gene was still possible with primers specific for its coding region, suggesting the occurrence of gene duplication, as previously reported for other essential Leishmania genes [25].
LMIT1/Δlmit1 L. amazonensis promastigotes showed a normal log phase of growth, but reached stationary phase at a lower cell density (~4–5 x 107/ml) when compared to wild type (~8 x107/ml) (Fig 3A). This defect was partially restored when LMIT1 was episomally expressed to compensate for the loss of the single LMIT1 allele (LMIT1/Δlmit1+LMIT1). This result suggested that LMIT1 plays a role in promastigote survival as they reach the stationary phase of growth, when nutrients (including iron) are depleted from the medium. Ferrozine assays revealed that whole cell lysates of promastigotes that had entered stationary phase (day 4) had a significantly higher iron content when compared to wild type (WT) or complemented LMIT1/Δlmit1+LMIT1 cells, indicating a possible over-accumulation of iron in the cytoplasm due to reduced iron transport into mitochondria. However, at this life-cycle stage (stationary phase promastigotes) no difference was observed in the iron content of mitochondria-enriched fractions from the three strains (Fig 3B).
We previously reported that culture of wild type L. amazonensis under low iron conditions promotes ROS generation and a reduction in the ability of promastigotes to sustain replication. Under these conditions, the parasites increased their rate of iron uptake, upregulated activity of the ROS detoxification enzyme FeSOD and entered a differentiation pathway that resulted in the generation of amastigote forms. In contrast, parasites lacking the Leishmania plasma membrane iron transporter LIT1 failed to upregulate FeSOD activity and accumulated high levels of superoxide, which resulted in massive cell death. These findings led us to suggest that mitochondria is the major site where ROS is generated during cellular stress, and that iron entry into this organelle is essential for activation of the mitochondrial FeSODA and generation of the signaling agonist H2O2 [8]. To test whether LMIT1 corresponds to the mitochondrial iron transporter involved in this process, we treated promastigotes from wild type, LMIT1/Δlmit1 and LMIT1/Δlmit1+LMIT1 strains with increasing concentrations of the mitochondrial superoxide generator menadione, and assessed whether loss of one LMIT1 allele affected the parasite’s ability to survive. LMIT1/Δlmit1 promastigotes were more sensitive to menadione when compared to wild type, and LMIT1/Δlmit1+LMIT1 showed an intermediate phenotype (Fig 3C).
When grown in iron depleted media, LMIT1/Δlmit1 promastigotes grew at a faster rate reaching higher concentrations (~3.5 x107/ml) relative to wild type (~1.4 x107/ml) on day 2, followed by a sudden decline in population size after day 3 (Fig 3D). This pattern of initial accelerated growth followed by cell death is similar (but not identical) to what was earlier observed with L. amazonensis promastigotes lacking the plasma membrane iron transporter LIT1 [8]. Microscopic analysis of viable cells in these iron-depleted cultures revealed that while about 30% (day 4) and 55% (day 5) of the wild type parasites had transformed into amastigote-like rounded forms lacking an evident flagellum, as previously described [8]. In contrast, most cells in LMIT1/Δlmit1 cultures retained their promastigote-like morphology with long flagella, with only ~20% appearing as round/aflagellate forms on days 4 and 5 (Fig 3E). The complemented strain LMIT1/Δlmit1+LMIT1 showed an intermediate phenotype in both assays, confirming the ability of LMIT1 to partially rescue the altered responses of LMIT1/Δlmit1 promastigotes to iron depletion. Collectively, these data suggest that iron import into mitochondria by LMIT1 plays an important role in detoxifying superoxide radicals and in iron/ROS-induced differentiation of promastigotes into infective amastigotes.
Next, we examined if loss of a single LMIT1 gene affected differentiation of L. amazonensis promastigotes into metacyclic forms, the insect stages derived from stationary phase promastigotes that initiate infections in vertebrates. Although LMIT1/Δlmit1 promastigote cultures reached stationary phase at a lower density (Fig 3A) metacyclogenesis appeared to proceed normally, as indicated by the similar percentage of metacyclic forms isolated from wild type, LMIT1/Δlmit1 and LMIT1/Δlmit1+LMIT1 cultures after selective promastigote agglutination with the 3A.1 mAb antibody [26, 27] (Fig 4A). Scanning electron microscopy analysis showed no significant variations in the morphology of metacyclics purified from the three strains (Fig 4B).
To check for possible mitochondrial functional abnormalities in metacyclic promastigotes, stationary phase parasites were treated with JC-1, a dye that exhibits membrane potential-dependent accumulation in mitochondria. A reduction in mitochondrial membrane potential (ΔΨm), a signature of impending mitochondrial failure, is indicated by a decrease in the 530/590 nm fluorescence emission ratio of JC-1. Although a slight reduction in ΔΨm was observed for LMIT1/Δlmit1 parasites when compared to the wild type and add-back LMIT1/Δlmit1+LMIT1 strains, the difference was not statistically significant (Student’s t-test p = 0.47) (Fig 4C). In contrast, a strong ΔΨm drop was observed for all three strains following treatment with CCCP, a mitochondrial membrane potential uncoupler (Fig 4C). Antimycin A, a complex III respiratory chain inhibitor, caused a partial reduction in ΔΨm in the wild type and add-back LMIT1/Δlmit1+LMIT1 strains and a more severe effect in the LMIT1/Δlmit1strain (Fig 4C), suggesting that the mitochondrial function of LMIT1 single knock-out stationary phase promastigotes is more vulnerable to stress. However, the aconitase activity in mitochondria-enriched fractions prepared from wild type, LMIT1/Δlmit1 and LMIT1/Δlmit1+LMIT1 stationary phase promastigotes was very similar (Fig 4D), indicating that the functionality of Fe-S cluster proteins remained unaffected at this life-cycle stage. Labeling with the mitochondria specific stains MitoTracker Green (which localizes to mitochondria independently of the membrane potential) and MitoTracker Red CMXRos (that accumulates in mitochondria through a mechanism dependent on membrane potential) showed no distinguishable differences in the shape and volume of mitochondria between stationary phase promastigotes from the three strains (S2 Fig). TEM analysis also showed normal ultrastructure, including the presence of normal cristae, in mitochondria from wild type and LMIT1/Δlmit1 parasites (Fig 4E). When purified metacyclics were used to infect bone marrow derived mouse macrophages (BMM), LMIT1/Δlmit1 parasites showed a strong defect in intracellular replication as amastigotes, a defect fully rescued in the LMIT1/Δlmit1+LMIT1 strain (Fig 4F). This result indicates that high levels of LMIT1 protein are likely to be necessary for the intracellular differentiation of metacyclic promastigotes into amastigotes capable of replicating inside PVs of macrophages.
The results discussed above showed that LMIT1/Δlmit1 promastigotes are defective in iron/ROS-dependent axenic differentiation into amastigotes (Fig 3E), and also cannot replicate as intracellular amastigotes after infecting macrophages (Fig 4F). Next, we examined whether loss of one LMIT1 allele also affected the axenic differentiation of LMIT1/Δlmit1 promastigotes into amastigotes using the classical differentiation protocol based on shifting late log phase promastigote cultures (pH 7.4 at 26°C) to conditions mimicking the macrophage intracellular environment (pH 4.5 at 32°C). After an initial lag period of 48 h the wild type parasites replicated steadily in amastigote medium, with a doubling time of ~14 h (Fig 5A). LMIT1/Δlmit1 cells showed a longer delay (up to 72 h) and very little growth subsequently, reaching cell densities about 6 fold lower than what was observed with the wild type strain at 96 h. At the 48 h time point >90% of the wild type population developed the characteristic amastigote morphology (rounding of the body and shortening of flagella) (Fig 5B). In contrast, only ~55% of viable LMIT1/Δlmit1 cells underwent a similar morphological change during this period (Fig 5B and 5C). The few LMIT1/Δlmit1 parasites that succeeded in acquiring the amastigote morphology appeared to be able to replicate axenically, as indicated by the small cell population increase observed by 96 h (Fig 5A). The complemented LMIT1/Δlmit1+LMIT1 parasites also initiated replication after the first 48 h, and >80% of the parasite population acquired the amastigote morphology (Fig 5A–5C). Staining with reporter dyes indicated that after loss of one LMIT1 allele LMIT1/Δlmit1 parasites were still viable by 48 h, based on fluorescein diacetate labeling (Fig 5D). However, by 48 h amastigote membrane integrity started to get compromised, with ~25% of the LMIT1/Δlmit1 parasites staining positive for propidium iodide (PI), compared to less than 3% in wild type or LMIT1/Δlmit1+LMIT1 cultures (Fig 5D). To avoid any effects of reduced viability, determinations of iron content and aconitase activity were performed in parasites induced to differentiate for only 24 h, incubation at pH 4.5 and 32°C. Ferrozine assays revealed a significant reduction in the iron content of mitochondrial fractions from LMIT1/Δlmit1 axenic amastigotes, when compared to wild type and LMIT1/Δlmit1+LMIT1 parasites (Fig 5E). Consistent with this result, there was also a significant reduction in activity of the Fe-S cluster enzyme aconitase in mitochondrial fractions of LMIT1/Δlmit1 axenic amastigotes, compared to wild type or the LMIT1/Δlmit1+LMIT1 complemented strain (Fig 5F). Assessment of mitochondrial membrane potential with the JC-1 dye also revealed a significant drop in ΔΨm in 48 h LMIT1/Δlmit1 cultures when compared to wild type, a phenotype that was rescued in the complemented LMIT1/Δlmit1+LMIT1 strain (Fig 5G). The few LMIT1/Δlmit1 parasites that appeared able to transform into amastigotes by 96 h were incapable of replicating intracellularly in macrophages when compared to wild type, and this defect was partially reversed in the LMIT1/Δlmit1+LMIT1 strain (Fig 5H). Taken together, these data indicate that L. amazonensis amastigotes, either generated intracellularly or axenically, require high levels of LMIT1 expression for maintenance of normal mitochondrial function, replication and survival.
The higher sensitivity of LMIT1/Δlmit1 promastigotes to the mitochondrial superoxide generator menadione (Fig 3B) suggested that mitochondrial iron uptake mediated by LMIT1 plays an important role in activation of mitochondrial FeSODA for detoxification of harmful radicals. Given the strong requirement for LMIT1 function in amastigotes, we investigated whether the upregulation of FeSOD activity normally seen during axenic amastigote differentiation [5] was also affected in the LMIT1/Δlmit1 strain. SOD activity was measured in whole extracts of parasites collected at different time points following the pH and temperature shift. Wild type parasites showed a steady increase in SOD activity up to 48 h, after which the activity remained constant (Fig 6A). On the other hand, FeSOD activity in LMIT1/Δlmit1 parasites was slightly higher than wild type at the onset of the differentiation process (0 h), but did not increase by 48 h and was lower by 72 h, possibly as a consequence of reduced viability of the cells at that late time point (Fig 5A). The complemented LMIT1/Δlmit1+LMIT1 strain showed a smaller increase in FeSOD activity when compared to wild type, but reached levels significantly higher than LMIT1/Δlmit1 parasites by 48–72 h (Fig 6A). The lack of FeSOD upregulation in LMIT1/Δlmit1 parasites undergoing axenic amastigote differentiation correlated with ultrastructural alterations in mitochondria, which appeared enlarged, with lower electron density and containing dense aggregates, when compared to wild type cells (Fig 6B). Other cellular structures including the nucleus, endoplasmic reticulum, acidocalcisomes, lipid droplets and flagellum appeared normal in LMIT1/Δlmit1 parasites, providing further evidence that reduction in LMIT1 expression levels has a specific deleterious effect on mitochondria, an effect likely to be related to oxidative damage resulting from reduction in iron availability to activate FeSODA.
To determine how deletion of one LMIT1 allele affected the ability of L. amazonensis to infect mice, purified metacyclic promastigotes were injected into footpads of C57BL/6 mice, and cutaneous lesion development was followed for 11 weeks. As expected, the wild type strain induced the formation of progressive lesions during this period. In contrast, no detectable lesions were observed in the mice injected with LMIT1/Δlmit1 or with LMIT1/Δlmit1+LMIT1 parasites (Fig 7A). Since lesion size is only an indirect reflection of the actual numbers of parasites in the tissues, after the mice were sacrificed 11 weeks after infection footpads were removed and the parasite load was estimated by the limiting dilution method. Consistent with the marked reduction in lesion development, the parasite burden in animals infected with LMIT1/Δlmit1 parasites was >105 fold lower than in mice infected with the wild type strain. Moreover, despite the lack of detectable lesions in mice injected with the LMIT1/Δlmit1+LMIT1 strain, the tissue load data revealed a 10–100 fold, statistically significant increase in parasite numbers for the add-back strain, when compared to LMIT1/Δlmit1 (Fig 7B). Western blot analysis showed comparable levels of episomally expressed LMIT1-HA protein in LMIT1/Δlmit1+LMIT1 parasites injected into footpads and subsequently recovered from the tissues, indicating that there was no loss of LMIT1 expression in vivo in the complemented strain (Fig 7B, inset). In agreement with this result, when inoculated into the more susceptible Balb/c mouse strain LMIT1/Δlmit1+LMIT1 metacyclics were more efficient than LMIT1/Δlmit1 in inducing lesions over a 13 week time period (Fig 7C) and showed a >109 fold higher tissue parasite load (Fig 7D). These results suggest that the LMIT1 protein must be expressed at a minimum threshold level to allow the parasites to overcome the stringent conditions encountered in mouse tissues, and survive and replicate intracellularly as amastigotes. The inability to fully restore virulence through episomal expression of LMIT1 was not surprising, considering that a lack of robust complementation is a common observation in transgenic Leishmania [28, 29].
To our knowledge, this study is the first demonstration that functional mitochondrial iron transporters homologous to mitoferrin are expressed in trypanosomatid parasites. The Leishmania LMIT1 protein localizes to the parasite’s mitochondria, rescues the growth defect of a mitoferrin-deficient yeast strain, and its close ortholog Tb927.3.2980 in T. brucei is essential for the survival of procyclic forms but dispensable in bloodstream stages that have rudimentary mitochondria [22]. In agreement with the importance of mitochondrial function in all life cycle stages of Leishmania [16, 30], loss of one LMIT1 allele caused promastigote growth defects, defective amastigote development and severe loss of virulence. Our results are consistent with earlier studies in other systems that identified a direct correlation between rates of mitochondrial iron uptake and increased mitochondrial activity [31–33].
In vertebrates, import of iron into mitochondria occurs via two mitochondrial transporters termed mitoferrin-1 (Mfrn1) and mitoferrin-2 (Mfrn2) [34]. Both carriers are located in the inner mitochondrial membrane. Mfrn1 is expressed at high levels in erythroid cells and is essential for erythropoiesis, while Mfrn2 is ubiquitously expressed and essential for viability in the absence of Mfrn1 [32]. Loss of Mfrn1 in zebrafish and in mice results in severe reduction of mitochondrial iron in erythroid progenitor cells, and in impaired heme and ISC synthesis [31, 32]. In yeast, the mitoferrin homologs mrs3p and mrs4p are required for iron import into mitochondria [19], and the poor growth of deletion mutants in low iron can be corrected by expression of vertebrate mitoferrins [18, 31, 35]. Drosophila has a single mitoferrin gene that causes partial lethality when deleted and impaired spermatogenesis when expressed at low levels [36]. Our inability to generate viable L. amazonensis LMIT1 null mutants suggests that mitochondrial iron import is also essential in promastigotes of L. amazonensis.
The reduction in growth rate observed as Leishmania promastigotes reach stationary phase may be attributed to increased oxidative stress, resulting from the elevated levels of mitochondrial respiratory chain activity observed at this stage [16]. Thus, an early increase in ROS stress might explain why LMIT1 single knockout promastigotes reach stationary phase prematurely, when compared to wild type. A reduced demand for mitochondrial activity due to cessation of promastigote division may have allowed for sufficient mitochondrial function to support development of metacyclics in LMIT1 single knockout strains. The subsequent drastic reduction in virulence observed after LMIT1 single knockout metacyclics were used to infect C57BL/6 mice or bone-marrow macrophages suggests that the mitochondrial iron requirement varies during the Leishmania life cycle, being particularly important during differentiation to the amastigote form.
L. amazonensis promastigotes lacking one LMIT1 allele were more sensitive to menadione, a drug that causes accumulation of superoxide anion radicals in mitochondria. This phenotype was most likely due to a role of LMT1 in promoting iron entry into mitochondria and activation of FeSODA, the Leishmania SOD isoform that catalyzes breakdown of superoxide within mitochondria [9]. Supporting this view, LMIT1 single knockout strains did not upregulate FeSOD activity when cultivated under axenic conditions that promote promastigote-amastigote differentiation (elevated temperature and acidic pH), a response normally observed in wild type strains [4]. Differentiation into axenic amastigotes was also impaired, and associated with ultrastructural changes in mitochondrial morphology. LMIT1 single knockout parasites also did not undergo a normal transition from promastigote to amastigote when grown in iron-depleted media, a process that we previously showed to be regulated by iron uptake and FeSOD-dependent H2O2 generation [8]. Thus, our study strengthens the conclusion that mitochondria represent the major site where superoxide anion is generated and then converted into H2O2 [6, 7], which can act as a signaling molecule for differentiation of infective amastigote stages [8].
Extensive evidence indicates that access to iron is critical for Leishmania, particularly for intracellular amastigotes residing within macrophage PVs. Mutations in Nramp1, a macrophage late endosomal iron transporter, increase host susceptibility to Leishmania infections by inhibiting iron removal from PVs [37]. Conversely, L. amazonensis lacking the plasma membrane ferrous iron transporter LIT1 have a strong defect in intracellular replication and virulence [38]. Infection with L. amazonensis also inhibits expression of the macrophage iron exporter ferroportin, a process that facilitates amastigote replication by increasing the availability of iron in the cytosol [39]. The present study significantly expands our understanding of Leishmania iron acquisition mechanisms, by identifying LMIT1 as a mitoferrin-like transporter that promotes iron entry into the parasite’s mitochondria. This conclusion is strengthened by our results showing that LMIT1 is required for normal functioning of the TCA cycle, the respiratory chain, and for upregulation of FeSOD activity.
The conservation of MIT1 in several trypanosomatid genomes suggests a common mechanism of mitochondrial iron import despite variations in the pathways by which iron initially enters the parasites. Leishmania express the plasma membrane proteins LFR1 (ferric iron reductase—[40]) and LIT1 (ferrous iron transporter—[38]) which promote iron delivery directly into the parasite’s cytosol. In contrast, the bloodstream form of T. brucei acquires iron by endocytosis of holotransferrin through transferrin receptors (Tfr) in the flagellar pocket. Once internalized, holotransferrin is handled similarly as to what occurs in mammalian cells, with translocation of soluble iron from endosomes to the cytosol through the action of an endosomal ferric reductase and a divalent cation transporter [41]. How T. brucei procyclics acquire iron is not yet fully understood, since transferrin uptake is absent in these insect forms [42]. The possible existence in T. brucei procyclics of a Leishmania-like machinery for iron uptake has been suggested, based on presence of several putative ZIP domain divalent metal ion transporters, a ferric reductase (LFR1 homolog) and evidence for iron uptake from ferric complexes via a reductive mechanism [43]. Our identification of the highly conserved MIT1 protein suggests that regardless of the mechanism by which iron enters cells, all trypanosomatids import iron from the cytosol into mitochondria utilizing a mitoferrin-like transporter, similarly to what occurs in vertebrates.
Tb927.3.2980, a T. brucei gene previously identified in a genome-wide mitochondrial carrier inventory and designated as TbMCP17, was proposed to correspond to a mitochondrial iron transporter based on its similarity to mitoferrin. However, TbMCP17 expression and functional information was not available in that initial study [14]. Our inducible RNAi assays now validate the role of Tb927.3.2980 (here designated as TbMIT1) as the T. brucei mitoferrin-like mitochondrial iron transporter, by demonstrating that it is essential for viability of the procyclic form. Our assays showed a delay between the loss of TbMIT1 transcripts (day 2) and appearance of a survival/growth phenotype in T. brucei procyclics (day 4). This may be attributed to a long half-life of the TbMIT1 protein, or to a gradual accumulation of mitochondrial damage that ultimately led to mortality. This latter scenario is in agreement with the limited role of mitochondria in energy transduction reported for African trypanosomes [44, 45]. Leishmania, in contrast, is heavily reliant on mitochondrial activity throughout its life cycle and particularly as amastigotes, which undergo stringent metabolic adjustments to life inside the PV [16, 46]. Intracellular amastigotes are more dependent on the TCA cycle and mitochondrial respiration than on glycolysis for energy production [47]. A functional electron transfer chain was also proposed to be important for maintenance of a neutral intracellular pH (6.5–7.4) in amastigotes replicating in the acidic PV of macrophages (pH 4.5) [3, 48]. Furthermore, the iron-dependent TCA cycle enzyme aconitase is increasingly required to synthesize glutamate, a precursor of the anti-oxidant molecule trypanothione, as transporter-mediated uptake of this amino acid is downregulated in amastigotes [30]. Thus, taken together with the strict iron requirement for activity of FeSODA within mitochondria, these earlier findings are fully consistent with the phenotype we observed in LMIT1 single knockout mutants, which are partially deficient in that mitochondrial iron transporter.
It will be interesting to determine if the 40% decrease in FeSOD activity that we detected in LMIT1 single knockout parasites undergoing axenic differentiation can be attributed to changes in mitochondrial FeSODA, without a contribution of the glycosomal FeSODB isoform. In other eukaryotes it is possible to biochemically distinguish between mitochondrial and other cellular SODs, based on the different metals used as cofactors. This distinction is not possible with trypanosomatid enzymes, as both SODs require exclusively iron for activity. However, the mitochondrial alterations we observed in single knockout LMIT1 parasites incubated at elevated temperature/low pH resemble the previously reported consequences of iron depletion [49] or ROS damage [50], increasing the likelihood that lack of iron to activate mitochondrial FeSODA is a major event underlying the phenotype of LMIT1 single knockout mutants. Studies in yeast revealed a similar role for mitoferrin in activating mitochondrial FeSOD of bacterial origin [31]. Further supporting this view, superoxide generation generated by mitochondrial hyper-polarization during exposure of L. infantum to elevated temperature could be countered by overexpression of mitochondrial FeSODA [51]. In this study, by identifying and characterizing LMIT1 as a mitochondrial iron importer, we established a direct connection between iron uptake, mitochondrial redox balance and the development of virulence in Leishmania, significantly expanding future options for controlling these serious human infections.
All animal work was conducted in accordance with the guidelines provided by National Institutes of Health for housing and care of the laboratory animals and performed under protocol # R-11-73 approved by the University of Maryland College Park Institutional Animal Care and Use Committee on January 14, 2015. The University of Maryland at College Park is an AAALAC-accredited institution.
L. amazonensis (IFLA/BR/67/PH8) was kindly provided by Dr. David Sacks (Laboratory of Parasitic Diseases, NIAID, NIH). Promastigotes were maintained in vitro at 26°C in M199 media (pH 7.4) supplemented with 10% heat inactivated FBS, 0.1% hemin (Frontier scientific; 25 mg/ml in 50% triethanolamine), 10 mM adenine (pH 7.5) 5 mM L-glutamine and 5% penicillin-streptamycin [38]. Differentiation of axenic amastigotes was initiated by mixing promastigote cultures (~2-4x107/ml) with equal volumes of acidic amastigote media (M199 conaining 0.25% glucose, 0.5% trypticase and 40 mM sodium succinate pH 4.5) and elevating the temperature to 32°C. Differentiated amastigotes were maintained in amastigote media at 32°C. Parasite viability was ascertained by fluorescent microscopy using fluorescein diacetate (FDA; Sigma-Aldrich) in combination with propidium iodide (PI; Sigma-Aldrich) as described previously [8].
To determine sensitivity to menadione, promastigotes from log-phase cultures (~2x107/ml) were seeded at 4x105/ml with or without increasing concentrations of menadione. Following incubation at 26°C for 48 h, parasites were counted using a hemocytometer.
Iron depleted media was prepared as described earlier [8]. To quantify growth and differentiation in iron-depleted media, mid-log phase L. amazonensis promastigotes (2x107 / ml) were harvested by centrifugation and resuspended in iron-depleted media at final concentrations of 1x106 or 4x106/ml. Cell growth was measured by microscopic counting of FDA stained cells at different times, as indicated in the experiments. Ability to differentiate was estimated as the percentage of promastigotes with long flagella (undifferentiated) versus rounded forms with short flagella (differentiated) parasites via phase contrast microscopy. At least 200 viable cells per sample were scored.
Cell culture- T. brucei bloodstream BSF-SM cells and procyclic 29–13 cells [52], which stably express T7 polymerase and Tet repressor, were used for all experiments. Procyclics were cultured in SM9 media containing 15 μg/ml G418 (Gibco) and 50 μg/ml hygromycin (Invitrogen) at 27°C and bloodstream form were maintained at 37°C in HMI-9 medium containing 15 μg/ml G418 as described [53]. Both media were supplemented with 10% tetracycline free FBS (Atlanta Biological).
Generation of RNAi cell lines- A 464 bp gene sequence targeting the T. brucei mitoferrin gene (Tb927.3.2980) for RNAi-mediated knockdown was identified using RNAit software [54] and amplified from T. brucei genomic DNA using the following oligonucleotides FD: TbMIT1-HindIII (GAAAGCTTAGGAAGTTGCGGGAGATTACA); RV: TbMfn-XbaI (GATCTAGAACCTGAAACAAGAACACGGG) (introduced HindIII and XbaI restriction sites are indicated as italicized and underlined nucleotides). The amplified gene fragment was then cloned into p2T7-fla1 [55] by replacing the fla1 gene using corresponding restriction sites to create the RNAi construct pTbMIT1-KD. The resulting plasmid linearized with NotI enzyme was electroporated into procyclic or bloodstream forms of T. brucei and stable transfectants (2T7/MIT1) were obtained by limiting dilution in 96-well plates with Phleomycin (2.5 μg/ml) for selection as described [53]. Selected clonal lines were then assessed for gene knockdown by qPCR.
RNAi mediated knockdown- dsRNA synthesis was induced by the addition of 1 μg/ml tetracycline to cultures of clonal cell lines at 1X106/ml (procyclic) or 1x105/ml (bloodstream) starting concentration. Cells growing in presence or absence of tetracycline were counted daily using a hemocytometer and diluted to the initial starting concentrations. To confirm knockdown of mitoferrin transcripts total RNA isolated from 1x107 cells and qPCR was performed as described [8] using the following primers:
Tb-Mfn-FD1 (CTCTCTTTGCCCACCACTATTT) and Tb-Mfn-RV1 (CACCACCCAAGTATGCAAGA) for TbMIT; Tb-18srRNA-FD (CGGAATGGCACCACAAGAC) and Tb-18srRNA-RV (TGGTAAAGTTCCCCGTGTTGA) for 18s rRNA.
A 3.3 kbp DNA fragment containing the mitoferrin-like gene (MIT1) and its flanking sequences was PCR amplified from L. amazonensis genomic DNA using primers (FD Mitoferrin ORF+UTR- ACAACGCCGTTCGCGACGAT and RV Mitoferrin ORF+UTR- ATGCTACGCGGGATTCGCGG) developed based on the L. mexicana (LmxM.08_29.2780) nucleotide sequence (http://tritrypdb.org) and cloned into pCR2.1-TOPO vector (Invitrogen) to obtain plasmid pLamMIT1, which was then sequenced to obtain the L. amazonensis specific nucleotide sequence.
To create LMIT1 deleted mutants, the L. amazonensis LMIT1 open reading frame (ORF) was genetically targeted for removal by homologous recombination using gene deletion constructs containing the Phleomycin resistance gene ble (PHLEO) or Neomycin phosphotransferase (NEO). Sequences upstream and downstream of the MIT1 ORF were cloned using the following primers containing SfiI restriction enzyme sites (underlined) following a previously described method to rapidly generate knock-out constructs [56]: LMIT1 5’SfiI-A:FD- GAGGCCACCTAGGCCCGGTGCGCCTGTAG and LMIT1 5’SfiI A:RV- GAGGCCACGCAGGCCGCCCTGCATGCGCG to amplify 5’ sequence; LMIT1 3’SfiI-A:FD–GAGGCCTCTGTGGCCTCAACGTGAAGCGC and LMIT1 3’SfiI-A:RV–GAGGCCTGACTGGCCGCAGGCCATCCG for 3’ UTR. Following a four-part ligation using PCR amplified 5’ and 3’ flanking sequences, drug resistance cassettes and the plasmid backbone, positive clones were identified by analyzing SfiI restriction digests of plasmid DNA samples and confirmed by sequencing with specific primers as described [56]. The targeting fragment was liberated by PacI digestion, gel purified and used to transfect L. amazonesis promastigotes by electroporation as previously described [38]. LMIT1 single knockout clones were isolated based on the ability of transformants to grow on agar plates containing Phleomycin (50 μg/ml) or neomycin (50 μg/ml) and analyzed by PCR to verify integration of the drug cassette in the desired location.
For generation of a rescue plasmid expressing LMIT1 with C-terminal hemaglutinin (HA) tag, a two-step PCR amplification strategy was employed. In the first round, a 873 bp fragment of the MIT1 ORF was amplified from pLamMIT1 with primers (FD-Mitoferritin HA: AACCCGGGACATATGTCTGGCAGCACCTCACC (SmaI site underlined) and RV-Mitoferritin HA: AGCGTAGTCTGGGACGTCGTATGGGTAAAGCAAGAGACTCTTGT) that allowed for removal of the endogenous stop codon and introduction of an in-frame HA tag. The PCR product was used as template in a second round amplification using FD-MIT1 as sense and RV:HA TAG2: TTGGATCCTTAAGCGTAGTCTGGGACGTCGTATGGTAAGCGTAG (BamHI site underlined) as antisense primers. Similarly, a LMIT1 fusion protein with three FLAG tag copies at the C-terminus was generated by PCR amplifying the MIT1 gene using FD-MIT1 as sense and RV-MIT1-3xFLAG: TTGGATCCCTCACTTGTCATCGTCATCCTTGTAATCCTTGTCATCGTCATCCTTGTAATCCTTGTCATCGTCATCCTTGTAATCCAAGAGACTCTTGT (BamHI site underlined) as antisense primers. The final PCR products were digested with BamHI and SmaI and cloned into pXG-SAT (courtesy of Prof. S. Beverley, Washington University). Transfected Leishmania clones were selected in plates containing 50 μg/ml Nourseothricin (Jena Biosciences) and expression of HA or 3xFLAG-tagged mitoferrin was confirmed by immunoblot.
The Saccharomyces cerevisiae strain Δmrs3Δmrs4-1 (MAT α his3Δ1 leu2Δ0 met15Δ0 ura3Δ0 Δmrs3Δmrs4::kan MX4) generated by double deletion of the mitochondrial ion transporter proteins mrs3 and mrs4 in the BY4741 [18] background was used in this assay. Cells were grown in CSM medium that includes yeast nitrogen base, amino acids and glucose. Iron-limited media was prepared by the addition of cell impermeable iron chelator bathophenanthroline disulfonate (BPS) at 50 μM final concentration.
Yeast MRS3 and codon optimized L. amazonensis LMIT1 gene synthesized for expression in S. cerevisiae (Integrated DNA Technology) were cloned into pYES2/CT (Invitrogen) with a galactose inducible promoter using BamHI and XhoI sites. Resulting plasmids and the vector alone (as control) were used for transformation of Δmrs3Δmrs4-1 using the lithium acetate method [57] and transformants were selected on 2% w/v glucose SC (-Ura) plates. Four to five colonies from each transformation were picked and re-streaked in SC (-Ura) plates containing 2% w/v raffinose as sugar source. Prior to spotting, the cells were cultured in liquid 2% w/v raffinose SC (-Ura) media for 16–18 h for further glucose depletion. Cells were then washed and resuspended into water to a final concentration of 0.2 OD (A600). Ten-fold dilutions of each transformant were prepared and 10 μl of each dilution was then spotted on 2% w/v galactose SC (-Ura) plates with or without 50 μM BPS and incubated at 30°C for 4 days prior to imaging.
Estimation of superoxide activity in whole cell extracts was performed as described earlier [8]. Briefly, 2x108 promastigotes were harvested, washed twice with PBS and resuspended in hypotonic buffer (5 mM Tris–HCl pH 7.8, 0.1 mM EDTA, 5 mM phenylmethylsulfonyl fluoride and 1x complete mini, EDTA-free protease inhibitor cocktail (Roche)) at a final concentration of ~5x108 cells/ml. The parasites were flash-frozen in liquid nitrogen and stored at -80°C freezer until use. To prepare lysates, frozen cells were subjected to three freeze-thaw cycles alternating between liquid nitrogen and a 37°C water bath, lysis was confirmed under the microscope, the lysates were centrifuged at 12,000g for 30 min at 4°C and supernatants carefully collected. Protein contents were determined using a BCA protein assay kit (Thermo Scientific). Superoxide dismutase (SOD) activity in whole cell extracts was measured using the SOD Assay Kit-WST (Dojindo Molecular Technologies, Inc.) according to the manufacturer’s protocol. Standard curves were generated using known concentrations of horseradish superoxide dismutase (Sigma-Aldrich).
Promastigotes were incubated with 200 nM MitoTracker Red CMXRos (Invitrogen) for 30 min at 25°C, followed by fixation with 4% paraformaldehyde and attachment to poly L-lysine coated slides (multitest 8-well; MP Biomedicals). After quenching with 50 mM NH4Cl for 1 h the cells were permeabilized with PBS 0.1% triton for 15 min, blocked with PBS 5% horse serum and 1% bovine serum albumin (BSA) for 1 h at room temperature (RT) and incubated with mouse anti-FLAG (F1804, Sigma) 1:500 dilution in PBS-1% BSA for 1 h followed by anti-mouse IgG AlexaFluor 488 (InVitrogen) 1:5000 dilution in PBS-1% BSA for 1 h and staining with 2 μg/ml DAPI for 1 h. Slides were mounted with ProLong Gold antifade reagent (Invitrogen), images were acquired through a Deltavision Elite Deconvolution microscope (GE Healthcare) and processed using Volocity Suite (PerkinElmer).
Mitochondrial membrane potential (ΔΨm) was measured using the MitoProbe JC-1 assay kit (Invitrogen). Mitochondrial import of the JC-1 lipophilic cationic dye depends on the mitochondrial potential and is independent of the size or shape of the organelle. The green fluorescence (emission 530 nm) of the monomeric dye at low concentration changes to red (emission 590 nm) as it accumulates and forms aggregates. Mitochondrial potential is thus determined by calculating the ratio of 590nm/530nm fluorescence readings, which provides an accurate quantitation of the amount of dye imported into the mitochondria. Promastigote cells (1x107) were incubated with 10 μM JC-1 for 15 min at 27°C, washed and resuspended in PBS. Fluorescence measured at 530 and 590 nm using a SpectraMax M5e microtiter plate reader (Molecular Devices) was used to determine the ΔΨm (530/590 ratio).
To visualize mitochondria and assess their membrane potential, 1x107 promastigotes were incubated with 0.1 μM MitoTracker Red CMXRos (Invitrogen) and 0.2 μM MitoTracker Green (Invitrogen) for 30 min at 27°C. After washing the cells with PBS, fluorescence for each dye was measured in a fluorimeter per manufacturer’s protocol (MitoTracker Red: excitation at 579 nm and cut-off filter at 599 nm; MitoTracker Green: excitation at 490 nm and cut-off filter at 516 nm). The stained promastigotes were also were placed in glass-bottom dishes (MatTek corporation) for live imaging on a Nikon Eclipse Ti inverted microscope with a 100x NA 1.4 objective (Nikon) equipped with a Hamamatsu C9100-50 camera and mCherry and FITC filters. Acquired images were analyzed with the Volocity Software Suite (PerkinElmer).
Mitochondria isolation and fractionation was done as described previously [15, 16] using 5x108 promastigotes from stationary phase culture or parasites undergoing axenic differentiation into amastigotes (pH 4.5 at 32°C temperature). The cells were washed three times with MES (20 mM MOPS pH7.0, 250 mM sucrose and 3mM EDTA) and resuspended in 500 μl of MES supplemented with 1 mg/ml digitonin and protease inhibitor cocktail (Roche). Following 5 min incubation at RT, cell suspensions were centrifuged for 5 min (10,000g at 4°C). The supernatant was collected as the cytoplasmic fraction. The pellet was washed once with MES buffer and used for further analysis as the mitochondrial fraction.
Estimation of intracellular iron content was performed using a colorimetric ferrozine-based assay described previously [8] using 108 parasites. The mitochondrial iron load was determined using mitochondrial-enriched pellets as described earlier. Briefly, whole cells or mitochondrial fractions were lysed with 100 μl of 50 mM NaOH followed by addition of 100 μl 10 mM HCl. 100 μl of iron-releasing solution (prepared by mixing equal volumes of 1.4 M HCl and 4.5% potassium permanganate) was added to the lysates followed by incubation at 60°C for 2 h and addition of 30 μl iron detection reagent containing 6.5 mM ferrozine, 6.5 mM neocuproine, 2.5 M ammonium acetate and 1 M ascorbic acid in water. After 30 min of incubation at RT, 280 μl of each sample was transferred to a 96-well plate, and absorbance at 550-nm wavelength was measured. Iron contents were determined from standard curves generated using ferric chloride solutions of known molarity (0–75 μM).
To measure aconitase activity, the mitochondrial pellet was washed twice with ice-cold 50 mM Tris pH 7.4 buffer containing 0.2 mM sodium citrate, resuspended in 0.2mM sodium citrate and briefly sonicated for 20 s. The suspension was then assayed using the Bioxytech Aconitase-340 assay kit (Precipio Biosciences). Aconitase activity in the mitochondrial lysate converted citrate into isocitrate, which was further converted into α–ketoglutarate by isocitrate dehydrogenase present in the assay mix, with concomitant formation of NADPH from NADP+ the rate of which was measured by monitoring the increase in absorbance at 340nm.
For transmission EM (TEM), cells were fixed in 2.5% (v/v) glutaraldehyde in 0.1M sodium cacodylate buffer, pH 7.4 for 60 min and post-fixed with osmium tetroxide in the same buffer for 1 h at RT. Following subsequent standard dehydration steps, the cells were embedded in Spurr’s resin mixture and thin sections were prepared with Reichert Ultracut E. Final images were obtained using Zeiss EM10 CA microscope.
For scanning EM (SEM), parasites fixed in 2.5% (v/v) glutaraldehyde in 0.1M sodium cacodylate buffer, pH 7.4 for 60 min and attached to poly-L-lysine coated coverslips were rinsed briefly with PBS, fixed with 0.1M cacodylate buffer, pH 7.4, treated with osmium tetroxide for 1 h, acetone dehydrated and critical point dried from CO2. After sputter coating with Au/Pd, the preparations were imaged in an Amray 1820D scanning electron microscope.
A total of 1 × 105 BMMs from C57/BL6 mice (Charles River Laboratories) prepared as previously described [58] were plated on glass coverslips in 3 cm dishes 24 h prior to experiments. Infective metacyclic forms were purified from stationary promastigote cultures (7-day old) using the m3Ab monoclonal antibody as described earlier [38]. Attached BMMs were washed with fresh RPMI 1640 and infected with purified metacyclics at 1:5 multiplicity of infection (MOI), or with axenically transformed amastigotes at 1:1 MOI in RPMI supplemented with 10% FBS. After allowing for invasion (1 h for amastigotes and 3h for metacyclics) macrophages were washed three times in PBS and incubated for the indicated times at 34°C. Coverslips were fixed in 4% PFA after 1 (baseline infection) 24, 48, and 72 h of incubation, permeabilized with 0.1% Triton X-100 for 10 min, and stained with 10 μg/ml DAPI for 1 h. The number of intracellular parasites was quantified by scoring the total number of macrophages and the total number of intracellular parasites per microscopic field (100× N.A. 1.3 oil immersion objective, Nikon E200 epifluorescence microscope) and the results were expressed as intracellular parasites per 100 macrophages. At least 300 host cells, in triplicate, were analyzed for each time point. The data were analyzed for statistical significance using an unpaired Student’s t test (p< 0.05 was considered significant).
Six-week-old female C57BL/6 or Balb/c mice (n = 5 per group) were inoculated with 1 X106 purified metacyclics [38] from WT, LMIT1/Δlmit1 and LMIT1/Δlmit1+LMIT1 stationary phase cultures in the left hind footpad in a volume of 50 μl PBS. Lesion progression was monitored weekly by measurements with a caliper (Mitutoyo Corp., Japan), and expressing the data as the difference between the left and right hind footpads. The parasite tissue load was estimated in infected tissue collected from footpads of sacrificed mice11 weeks post infection using a limiting dilution assay [59].
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10.1371/journal.pgen.1007462 | The Hsp70 co-chaperone Ydj1/HDJ2 regulates ribonucleotide reductase activity | Hsp70 is a well-conserved molecular chaperone involved in the folding, stabilization, and eventual degradation of many “client” proteins. Hsp70 is regulated by a suite of co-chaperone molecules that assist in Hsp70-client interaction and stimulate the intrinsic ATPase activity of Hsp70. While previous studies have shown the anticancer target ribonucleotide reductase (RNR) is a client of Hsp70, the regulatory co-chaperones involved remain to be determined. To identify co-chaperone(s) involved in RNR activity, 28 yeast co-chaperone knockout mutants were screened for sensitivity to the RNR-perturbing agent Hydroxyurea. Ydj1, an important cytoplasmic Hsp70 co-chaperone was identified to be required for growth on HU. Ydj1 bound the RNR subunit Rnr2 and cells lacking Ydj1 showed a destabilized RNR complex. Suggesting broad conservation from yeast to human, HDJ2 binds R2B and regulates RNR stability in human cells. Perturbation of the Ssa1-Ydj1 interaction through mutation or Hsp70-HDJ2 via the small molecule 116-9e compromised RNR function, suggesting chaperone dependence of this novel role. Mammalian cells lacking HDJ2 were significantly more sensitive to RNR inhibiting drugs such as hydroxyurea, gemcitabine and triapine. Taken together, this work suggests a novel anticancer strategy-inhibition of RNR by targeting Hsp70 co-chaperone function.
| Ribonucleotide reductase (RNR) is a key enzyme in the synthesis of DNA and inhibition of RNR leads to cellular sensitivity to radiation. As such, RNR is a well-validated therapeutic target for a variety of diseases including cancer. Anti-RNR drugs are effective but are associated with a range of side effects in patients. Our previous work had identified that the Hsp90 and Hsp70 molecular chaperone proteins regulate RNR. The specificity and activity of Hsp70 and Hsp90 are regulated by “co-chaperone” proteins. We examined RNR activity in cells lacking individual co-chaperones and identified the Ydj1/HDJ2 protein as a novel regulator of RNR in yeast and human cells. Importantly, we demonstrate that inhibiting HDJ2 sensitizes cells to currently used anticancer drugs.
| Heat Shock Protein 70 (Hsp70) is a well-conserved, highly expressed molecular chaperone protein. While Hsp70 assists both in the folding of newly synthesized proteins and denatured proteins (“clients”), it also targets damaged proteins for degradation by the proteasomal system [1–3]. Many housekeeping proteins require Hsp70 for stability, making Hsp70 essential for cell viability (2). Cancer cells require Hsp70 to maintain the function of unstable oncoproteins and as such are “addicted” to chaperone function and Hsp70 is often found to be overexpressed in breast and prostate cancers [4–6]. Small molecule inhibitors of chaperones have been developed and assessed for their ability to inhibit cancer cell proliferation in vivo and in vitro. Despite promising data in vitro, chaperone inhibitors have met with limited success in clinical trials due to inherent toxicity of a drug that targets an essential cellular protein [7].
The activity of Hsp70 is regulated by a suite of co-chaperone proteins comprising mainly of Hsp40s and nucleotide exchange factors (NEFs) that assist in the stimulation of ATPase activity and the transfer of clients to Hsp70 for folding [1, 8, 9]. They are a heterogeneous group that can be characterized by the presence of a remarkably conserved 70 amino acid J-Domain [1, 8, 9]. In yeast the major Hsp70 isoform Ssa1 is activated by two related Hsp40s, Ydj1 and Sis1 [10, 11]. Although these two proteins are somewhat functionally redundant, Sis1 is essential for cell viability whereas Ydj1 is not [10, 11]. The study of the behavior of chimeric Ydj1-Sis1 constructs has revealed that the C-terminus of these proteins is the determining factor in client binding and functional distinctiveness [12]. Interestingly, the C-terminus of Ydj1 contains a CAAX farnesylation motif that targets a small population of Ydj1 to the outer surface of the Endoplasmic Reticulum [11, 13]. While both the regulation and function of this targeting remains ill defined, it appears that it is required for interaction with Hsp90 and select client proteins [14]. Ydj1 controls the maturation and stability of a number of Ssa1 client proteins, particularly kinases [15]. Interestingly, Ydj1 also stabilizes several proteins involved in transcription and thus indirectly controls the expression of proteins at the transcriptional level [16, 17].
There are 47 Hsp40s expressed in humans, distributed across the cytoplasm, nucleus, ER and mitochondria and many of these are well conserved with their yeast counterparts [1, 8, 9]. The human homologue of Ydj1 is HDJ2 (also known as DNAJA1), a protein implicated in regulating HIV replication as well as cancer cell growth [18]. Each Hsp40 binds to a specific set of client proteins, thus offering the potential for selective inhibition of tumorigenic vs WT cells [19, 20]. There are no specific Hsp40 inhibitors in clinical trials and it is only recently that Hsp40s have been considered as possible drug targets, possibly due to the lack of characterization of many Hsp40 isoforms. Several dihhydropyrimidines are able to inhibit Hsp40-stimulation of Hsp70 including MAL3-39, MAL3-101 and 116-9e [21]. Recently a novel Hsp40 inhibitor, C86 was identified that promotes androgen receptor degradation offering a novel way to inhibit castration-resistant prostate cancer [22].
Upon DNA damage stress, there is a large remodeling of Hsp70 and Hsp90 complexes and tight association with the ribonucleotide reductase (RNR) complex [23, 24]. The RNR complex catalyzes the production of deoxyribonucleotides (dNTPs) required for DNA repair and S-phase progression. RNR is well conserved between yeast and humans and are comprised of a large (R1) subunit and a small subunit (R2) [25–28]. R1 (R1 in vertebrates, Rnr1/Rnr3 in yeast) forms the catalytic domain while R2 (R2B/R2 in vertebrates, Rnr2/Rnr4 in yeast) acts as regulatory subunits. Although Rnr2 and Rnr4 share sequence homology, only Rnr2 contains the key ligands for tyrosyl radical cofactor [27]. As a result, Rnr2 is essential for cell viability whereas Rnr4 is not. In vitro studies have demonstrated a role for Rnr4 in assisting with Rnr2 folding [29]. The fully active RNR complex comprises of a Rnr1 homodimer and Rnr2-Rnr4 heterodimer, with each subunit tightly controlled at the level of expression and cellular localization in response to both cell cycle stage and DNA damage [25]. One of the most highly induced genes in response to DNA damage is a low activity Rnr1 isoform Rnr3. RNR3 expression is controlled in a Mec1 kinase checkpoint-dependent manner via Crt1 derepression [30, 31]. Because of its high inducibility in response to DNA damage, RNR3 expression can be used as a measure of DNA damage response pathway activity [32].
RNR is a well-validated anticancer target. Since RNR is required for DNA repair and DNA replication, inhibition of RNR slows cell proliferation and eventually results in S-phase arrest. Hydroxyurea (hydroxycarbamide, HU) was the first small-molecule RNR inhibitor characterized and was approved for clinical use in 1967 [33, 34]. RNR inhibitors are particularly effective when used in conjunction with radiation or other DNA damaging agents [35–37]. Recent studies have demonstrated that pretreatment of cancer cells with either Hsp70 or Hsp90 inhibitors causes destabilization of the RNR complex, sensitizing cells to RNR inhibitors such as HU or gemcitabine [23, 38]. Little is known about the composition of RNR-Chaperone complex, particularly the co-chaperones involved in this process. In this study, we identify Ydj1 and Hdj2 as regulators of RNR activity in yeast and humans, respectively. Moreover, we demonstrate the feasibility of sensitizing cancer cells to RNR inhibitors such as HU by suppressing HDJ2 function.
Hsp90 and Hsp70 mediate the cellular response to DNA damage by regulating RNR activity. Given that co-chaperones mediate many of the client binding functions in Hsp70 and Hsp90, we sought to uncover candidate co-chaperones that may regulate RNR function. We screened 28 yeast co-chaperone knockout strains (BY4742 background) for cellular sensitivity to Hydroxyurea (HU). While the majority of knockouts showed no significant difference to WT, cells lacking Ydj1, Erj5, Scj1 and to a lesser degree Zuo1 showed increased sensitivity to HU (Fig 1A; S1 Fig). Erj5, Scj1 and Zuo1 are highly specialized co-chaperones that function in the ER and Ribosome respectively. Given that the RNR subunits are not present in either the ER or ribosome under standard conditions, we decided to focus our efforts of understanding the role of Ydj1 on RNR activity.
HU is a potent activator of the DNA damage response pathway, triggering transcription of DNA repair enzymes. In an effort to determine whether Ydj1 controls transcriptional output of the DNA damage response, we compared expression of β-galactosidase driven by a DNA-damage responsive promoter (RNR3 promoter-lacZ) in HU-treated WT and ydj1Δ cells. Consistent with the increased HU sensitivity of ydj1Δ, cells lacking Ydj1 were severely compromised for RNR3 transcription, suggesting a role for Ydj1 in activation of the DNA damage response (Fig 1B).
The Ydj1 protein comprises of four functional domains, the N-terminus consisting of the conserved helical J-domain responsible for binding to Hsp70 and stimulating its ATPase activity [8, 39]. In contrast, the C-terminus of Ydj1 consists of two distinct regions, a Zinc-finger like domain and substrate binding domain. These N and C-terminal sections of Ydj1 are tethered together via a flexible linker region rich in glycine and phenylalanine (known as the G/F region). To query the structural requirement for Ydj1-mediated HU resistance, we analyzed an array of Ydj1 C-terminal truncations their ability to suppress the HU growth defect of ydj1Δ yeast cells. For these experiments, we utilized JJ160, a ydj1Δ strain originating from the stress-sensitive W303 background (S2 Fig. and [16]). The Ydj1 C-terminal truncations displayed a gradual loss of functionality with regard to mediating HU resistance (Fig 2A). While cells expressing Ydj1 lacking the complete or part of the CTDII domain are somewhat sensitive to HU (Ydj1 (1–206) and Ydj1 (1–363)), additional depletion of CTDI as shown for Ydj1(1–134) completely abolished growth at 150mM HU (Fig 2A). Several Ydj1 mutants have been characterized affecting specific facets of Ydj1 function. Two such mutants, G153R and G315D are able to promote GR and ER activity in the absence of hormone when expressed in yeast and are unable to properly fold client proteins such as v-SRC [16, 40]. We examined G153R and G315D for their specific functionality with regard to HU resistance. While cells expressing G315D were moderately sensitive to HU, G153R cells were unable to grow even at 150mM HU (Fig 2A). Taken together, this suggests that both CTDI and CTDII domains play an important role in Ydj1-mediated HU resistance.
Ydj1 has high homology to another J-protein Sis1. Both possess an N-terminal J domain that binds and regulates Ssa1 [12]. Although highly related, Sis1 and Ydj1 have overlapping yet distinct functions in the cell. Cells lacking Ydj1 are viable, whereas those lacking Sis1 are not [12, 41]. Several studies have attempted to dissect the distinct roles of Ydj1 and Sis1 by creating and expressing Ydj1-Sis1 chimeras [42, 43]. To complement the truncation data in Fig 2B, we asked whether a selection of Ydj1-Sis1 fusions could provide the same function as Ydj1 in mediating the cellular response to HU in yeast. While replacement of either the Ydj1 J-domain or G/F domain with the equivalent Sis1 region alone had little impact, replacement of both resulted in HU sensitivity. This suggests that while the C-terminus of Ydj1 is critical for the response to HU, the N-terminus is also required and that Sis1 and Ydj1 are functionally distinct for this role.
While the majority of Ydj1 is localized to the cytoplasm, a fraction exists bound to the cytoplasmic side of the endoplasmic reticulum [13, 44, 45]. This localization is achieved through farnesylation of a “CAAX box” motif on the C-terminus on Ydj1 at C406 [44]. Disruption of Ydj1 farnesylation prevents the interaction of Ydj1 with Hsp90 and client proteins [14]. We queried whether loss of Ydj1 farnesylation impacted cellular resistance to HU. ydj1Δ cells expressing either WT Ydj1 or C406S were plated on media containing 150mM or 200mM HU. C406S yeast cells were partially compromised for HU resistance, being more sensitive than WT cells but more resistant than ydj1Δ cells to HU (Fig 2C.
Given that RNR stability is supported by Hsp90 and Hsp70 in yeast and mammalian cells, we wondered whether Ydj1 may be performing a similar role. We integrated GFP-epitope tags onto RNR subunits queried the total levels of Rnr1, Rnr2 and Rnr4 protein in WT and ydj1Δ cells. While Rnr1 levels were unchanged, Rnr2 levels were significantly compromised in cells lacking Ydj1 under both unstressed and hydroxyurea treated cells (Fig 3A, S3 Fig). Rnr4 levels were affected in a less dramatic manner; loss of Ydj1 decreased Rnr4 only in HU-treated cells (Fig 3A). Protein levels in cells are balanced by both rate of transcription and protein degradation. Ydj1 has been shown to bind to transcriptional machinery and thus indirectly regulates the transcription of an undetermined number of genes [16, 17]. To determine whether the decreased RNR subunit expression observed in ydj1Δ cells was a result of altered transcription, we quantified RNR1, RNR2 and RNR4 mRNA expression in WT and ydj1Δ cells using RT-qPCR. Both RNR2 and RNR4 transcription were partially decreased in cells lacking Ydj1 (Fig 3B). This does not appear to be a global effect as the expression of RNR1 remained unchanged in the absence of Ydj1 (Fig 3B). To determine whether the protein stability of Rnr1, Rnr2 and Rn4 had also been compromised ydj1Δ cells, we examined the half-life of these proteins by transcriptional shut-off experiments. While Rnr1 and Rnr4 stability were unchanged in ydj1Δ cells, Rnr2 stability was substantially lowered (Fig 4A). To corroborate this result, we examined rate of Rnr2 loss in WT and ydj1Δ cells treated with cycloheximide, a translational inhibitor. In agreement with the previous result, Rnr2 loss was accelerated in ydj1Δ, suggesting increased instability of Rnr2 protein (Fig 4A). We reasoned that if the sole reason for the HU-sensitive phenotype of ydj1Δ cells was destabilization of Rnr2, then massive overexpression of Rnr2 may allow ydj1Δ cells to grow on HU-containing media. We examined the effect of substantially overexpressing Rnr2 in ydj1Δ cells using a multicopy MET25 promoter driven plasmid (S4A Fig.). Interestingly, cells remained sensitive to HU (Fig 4C). We analyzed the levels of Rnr2 from these cells and still observed a noticeable decrease in Rnr2 levels ydj1Δ cells compared to WT (S4B Fig). Taken together, these results imply that the HU sensitivity seen in ydj1Δ is a result of decreased Rnr2 and Rnr4 levels, controlled at both the level of transcription and protein degradation.
Previous studies have demonstrated that Hsp90 and Hsp70 bind RNR components [23]. Given that Ydj1 binds both Hsp70 and Hsp90 and plays a role in RNR activity, we sought to determine whether Ydj1 interacted with Rnr2 in yeast. We immunoprecipitated Rnr2 from cells and probed for the presence of Ydj1. Ydj1 was detected as an interactor of Rnr2 in both unstressed and HU-treated cells (Fig 5A). Ribonucleotide reductase is an important chemotherapeutic target in cancer. We considered the possibility that R2B, the human homologue of Rnr2, might similarly interact with HDJ2 (human Ydj1). To examine this, we transfected HEK293 cells with HIS-epitope tagged R2B, purified the R2B complex and probed for associated HDJ2. Interaction of the RNR subunit with HDJ2 was observed, consistent with a conserved role for the co-chaperone (Fig 5B). In contrast to the stress-regulated interaction previously observed between RNR and Ssa1/Hsp82 [23, 24], the interaction between Rnr2/R2B and Ydj1/HDJ2 remained constant between untreated and HU-treated cells (Fig 5A and 5B).
Hsp40-related co-chaperones bind to Hsp70 via conserved J-domains. The J-domain is a 70-amino acid sequence consisting of four helices and a loop region between helices II and III that contains a highly conserved tripeptide of histidine, proline, and aspartic acid (the HPD motif). This region while not required for client protein binding, is absolutely essential for Hsp70-Hsp40 interaction, stimulation of the Hsp70 ATPase and release of substrates post-folding [46, 47]. While several co-chaperones have chaperone-independent activities, Hsp40s typically function through activation of Hsp70. We reasoned that Ydj1’s role in supporting RNR function would occur through its ability to bind Ssa1. We queried whether Ydj1 unable to bind Ssa1 (HPD motif mutant, Ydj1-D36N) could suppress the HU sensitive phenotype of ydj1Δ cells. Cells expressing Ydj1-D36N were HU-sensitive, suggesting Ssa1-Ydj1 interaction is critical for RNR activity (Fig 6A). To examine the possibility that the HU sensitivity of Ydj1-D36N cells was caused by lowered Rnr2 abundance, we compared Rnr2 levels in WT Ydj1 and Ydj1-D36N cells. As predicted, Rnr2 levels were lower in cells where the Ssa1-Ydj1 interaction had been disrupted (Fig 6B).
We carried out a parallel experiment in mammalian cells, utilizing a novel small molecule disruptor of Hsp70-Hsp40 interactions, 116-9e [48]. Treatment of HEK293 cells with 116-9e for 72 hours resulted in decreased R2B levels as detected by Western Blot (Fig 6C).
Hsp40s are responsible for delivering client proteins to Hsp70s for folding [8, 49]. To understand how HDJ2-R2B and HSP70-R2B were impacted by 116-9e, we purified either R2B or HSP70 from HEK293 cells treated with 116-9e. While 116-9e promoted the loss of HSP70-R2B and HSP70-HDJ2 interaction, the R2B-HDJ2 interaction persisted (Fig 6D and Fig 6E). These results, taken together suggest the Ssa1-Ydj1/Hsp70-HDJ2 interaction is critical for RNR subunit stability and resistance to RNR inhibiting drugs.
Having established that either loss of Ydj1/HDJ2 compromises RNR activity in both yeast and mammalian cells, we considered whether this regulation might be used to sensitize cancer cells to HU. We examined the difference in drug sensitivity of WT HAP1 cells compared to HAP1 HDJ2 knockout cells created via CRISPR disruption. HDJ2 KO cells were markedly more sensitive than WT cells, with a 60% decrease in IC50, from 180μM to 80μM (Fig 7A). These results suggested that 116-9e (an inhibitor of HDJ2) may be synergistic with HU. We queried the difference in drug sensitivity of cells exposed to HU and DMSO compared to cells exposed to a combination of 116-9e and HU. The combination treatment was highly synergistic, promoting a decrease in apparent IC50 of HU from 140μM to 89μM (Fig 7B). While HU has been widely utilized as an anticancer drug it has a short half-life in the body, relatively low affinity for RNR and cells tend to resistance over time. Triapine is a next generation RNR inhibitor, possessing high potency in cell and enzyme-based assays. We examined the difference in drug sensitivity of cells exposed to triapine and DMSO compared to cells exposed to a combination of 116-9e and triapine. The combination treatment was also synergistic, promoting a decrease in apparent IC50 of HU from 27nM to 19nM (Fig 7C). To determine synergy in a more quantitative manner, we calculated drug synergy (CI values) between 116-9e and either HU or triapine across a broad range of concentrations. In both cases, 116-9/HU and 116-9e/triapine demonstrated significant synergy (CI<1) across a range of doses (Fig 7D and 7E). These data clearly suggest that HDJ2 inhibition is a promising strategy to sensitize cells to RNR inhibitors.
Hsp70 and Hsp90 bind a wide variety of client proteins, regulating many important signaling processes. Several studies have linked chaperone function to the DNA damage response and recently RNR activity [50–54]. Given that co-chaperones direct the activity and specificity of Hsp70 and Hsp90 it is logical that a subset of co-chaperones are responsible for supporting RNR activity. In this study, we identified 4 co-chaperones as being important for HU resistance; Ydj1, Scj1, Erj5 and Zuo1. Scj1 and Erj5 are ER-localized Hsp70 co-chaperones that bind to the ER-specific Hsp70 isoform Kar2. Kar2 and its co-chaperones are responsible for ER-folding and degradation of proteins (ERAD). Given their spatial separation from RNR components, these co-chaperones may support RNR activity indirectly or may have totally separate roles in DNA damage signaling. Zuo1 is a ribosome-associated chaperone that activates the ATPase activity of Ssb1 and Ssb2, making it likely that Zuo1 influences the transcription of RNR subunits or regulators. We hope to shed light on the role of these proteins in the DNA damage response in future studies.
Ydj1 is a well-characterized Hsp40 responsible for mediating a large proportion of Hsp70 and Hsp90 effects in yeast. We show here that loss of Ydj1 results in lowered RNR expression via both transcription and protein stability. RNR2 and RNR4 mRNA expression is decreased in ydj1Δ cells, although this effect seems to be relatively specific given that RNR1 expression remains unchanged. It will be interesting to unravel the role of Ydj1 in global transcription in greater detail in future studies. In conjunction with decreased RNR expression, loss of Ydj1 significantly destabilizes Rnr2 (Fig 8). Again, although Ydj1 assists in the maturation and stability of numerous proteins in the cell [15], the effects here were specific. While Rnr2 half-life was lowered by loss of Ydj1, the half-lives of Rnr1 and Rnr4 remained unchanged. Rnr2 and Rnr4 hetero-dimerize in vivo and in vitro and they each support the others folding [27]. This may explain why overexpression of Rnr2 alone in ydj1Δ cells fails to suppress their HU-sensitive phenotype.
While the majority of co-chaperone function is mediated by interaction with chaperones such as Hsp90 and Hsp70, it is not unknown for co-chaperones to have chaperone-independent activities [55]. Ydj1 binds to Ssa1 via the conserved HPD region, and here we demonstrate that this interaction is required for Ydj1-mediated HU resistance. This is consistent with our previous studies identifying a role for Hsp90 and Hsp70 in regulating Rnr2 and Rnr4 [23]. These data suggest that either Ydj1 transports and transfers RNR components to Ssa1 or exists in a complex with RNR and Ssa1 to maintain RNR activity.
Ydj1 is a well-characterized Hsp40 responsible for mediating a large proportion of Hsp70 and Hsp90 effects in yeast. Ydj1 exists as a dimer in yeast and contains several functional elements such as the J-domain, G/F domain, Zinc-finger-like domains and C-terminal domain (CTDII). It is interesting to note that the same C-terminal Ydj1 truncations that result in HU-sensitivity correspond with a previously observed loss of ability of cells to sustain high-temperature growth [16]. To tease apart the role of the Ydj1 N-terminus in HU resistance, we utilized chimeras of Ydj1 and its paralog Sis1. Although Sis1 and Ydj1 possess the ability to bind Ssa1, loss of Sis1 is lethal for yeast, whereas loss of Ydj1 is not [41]. Although individual replacement of either the N-terminal domain or G/F domain with the equivalent Sis1 domain had minimal impact on HU resistance, replacement of both domains simultaneously (SSY chimera) increased the cells sensitivity to HU to a level almost equivalent to ydj1Δ cells. In addition, while ydj1Δ cells are HU sensitive, they are not as sensitive as rnr4Δ cells and are viable (unlike rnr2Δ cells), suggesting that loss of Ydj1 does not result in total loss of RNR function. This is in agreement with our data that shows partial but not complete destabilization of RNR levels in ydj1Δ cells. One potential explanation for this is that the related Sis1 co-chaperone is partially functionally redundant with Ydj1 and can contribute to some degree in RNR activity, particularly given the data shown in Fig 2B.
It is compelling that Ydj1 farnesylation is required for HU resistance, as it is not required to bind all client proteins and only a small proportion of Ydj1 is farnesylated at any one time [14]. Farnesylation anchors Ydj1 to the exterior face of the endoplasmic reticulum (ER) where it functions to prevent the passage of aggregated proteins from mother to daughter cells during cell division [45]. While no previous connection between ER function and RNR activity has been identified, it is interesting to note in this study cells lacking of either of two ER specific co-chaperones Erdj5 and Scj1 are also HU sensitive. Although beyond the scope of this study, the interplay between ER co-chaperones and the DNA damage will be interesting to explore further.
Suggesting broad conservation of the yeast mechanism, we demonstrate here that human Ydj1 (HDJ2) and human RNR2 (R2B) physically interact and that this interaction is independent of HU treatment. This is interesting given our previous findings that RNR subunit interaction with both Hsp70 and Hsp90 increase under replicative stress [23, 24]. Our data suggest that both the Ydj1 (yeast) and HDJ2 (human) co-chaperones may recruit and transfer Rnr2/R2B to their respective chaperones (Ssa1/Hsp70) for folding, as loss of co-chaperone function or inability to form a productive chaperone-co-chaperone complex promotes Rnr2/R2B degradation (Fig 8).
Yeast lacking Ydj1 display a destabilized RNR complex and corresponding sensitivity to HU. In turn, we demonstrate that we can sensitize a cancer cells to HU and the more potent RNR inhibitor triapine by either CRISPR-mediated gene knockout of HDJ2 or by inhibiting HDJ2 with 116-9e. HU was the first small-molecule RNR approved in 1967. HU and other agents, including the nucleoside analog gemcitabine (Gemzar) and triapine, remain important agents in cancer chemotherapy. These agents are commonly combined with radiotherapy and/or genotoxic chemotherapy, which potentiate RNR inhibitors via exposing the requirement for dNTPs in DNA repair [25, 33]. It would be highly desirable to identify agents that can enhance the therapeutic benefit of RNR inhibitors without incurring additional toxicity.
Several studies have demonstrated the antitumor potential of small molecule inhibitors of chaperones, particularly Hsp90 [56]. Despite promising in vitro results several potent Hsp90 inhibitors such as 17-AAG have failed clinical trials due to solubility and toxicity issues [7]. Creating clinically-relevant Hsp70 inhibitors is also challenging, given that Hsp70 is responsible for both the stabilization and degradation of client proteins, many of which are required for cell viability in healthy cells. The ‘holy grail’ of chaperone-based translational research is how to modulate chaperone function in cells such that cancer cells are selectively targeted over healthy tissue.
An alternative strategy may be the targeting of specific co-chaperones, particularly Hsp40s. By replacing the dichlorobenzyl functionality of 115-7c (an Hsp70 activator) with a bulkier diphenyl group, Wisen et al. were able to create 116-9e, an inhibitor capable of specifically inhibiting the Hsp70-Hsp40 interaction in both yeast and mammalian cells [48]. We demonstrate for the first time that 116-9e has the ability to sensitize cancer cells to anticancer agents such as triapine and HU through destabilization of R2B. Interestingly, a recent study identified a novel Hsp40-binding molecule C86 as being capable of destabilizing the androgen receptor, a driver of metastasis in castration-resistant prostate cancer [22]. While we anticipate future studies to examine the dosing and timing required to optimize cancer cell inhibition, the results of both this study and that of [22] demonstrates the validity of destabilizing select client proteins in cancer through Hsp70 co-chaperone inhibition.
Yeast cultures were grown in either YPD (1% yeast extract, 2% glucose, 2% peptone) or grown in SD (0.67% yeast nitrogen base without amino acids and carbohydrates, 2% glucose) supplemented with the appropriate nutrients to select for plasmids and tagged genes. Escherichia coli DH5α was used to propagate all plasmids. E. coli cells were cultured in Luria broth medium (1% Bacto tryptone, 0.5% Bacto yeast extract, 1% NaCl) and transformed to ampicillin resistance by standard methods. For tagging the genomic copy of RNR1, RNR2 and RNR4 with a GFP epitope at the carboxy-terminus, the pFA6a-GFP(S65T)-His3MX6 plasmid was used. A full table of yeast strains and plasmids that were used can be found in S1 Table. For serial dilutions, cells were grown to mid-log phase, 10-fold serially diluted and then plated onto appropriate media using a 48-pin replica-plating tool. Images of plates were taken after 3 days at 30°C. 200mM HU was used for serial dilutions and to stress yeast cells, a concentration established in [57].
For RNR3-lacZ fusion expression experiments, cells were grown overnight in SD-ura media at 30°C and then re-inoculated at OD600 of 0.2–0.4 and then grown for a further 4 hours. Cells were treated with 150 mM or 200 mM HU for 3 hours and then RNR3-lacZ fusion assays were carried out as described previously [58]. Briefly, protein was extracted through bead beating and protein was quantitated via Bradford assay. The b-Galactosidase reaction containing 50 μg of protein extract in 1 ml Z-Buffer (30) was initiated by addition of 200 μl ONPG (4 mg/ml) and incubated at 28°C until the appearance of a pale-yellow color was noted. The reaction was quenched via the addition of 500 μl Na2CO3 (1M) solution. The optical density of the reaction was measured at 420nm. β-Gal activity was calculated using ((OD420 x 1.7)/(0.0045 x protein x reaction time)), where protein is measured in mg, and time is in minutes. The mean and standard deviation from three independent transformants were calculated.
BY4742 WT or ydj1Δ cells transformed with either pGAL1-HA-Rnr1, 2 or 4 plasmid were grown to mid-log phase in YP Gal medium (1% yeast extract, 2% galactose, 2% peptone). Transcription of pGAL1-HA-Rnr1, 2 or 4 was shut off by addition of 2% glucose to cultures. Aliquots of cells were collected at 0, 2 and 4 hours after addition of glucose. Cell lysates from these samples were analyzed by Western Blotting for stability of RNR subunit (HA antibody) and loading control (GAPDH).
For cycloheximide experiments, BY4742 WT and ydj1Δ cells expressing endogenous promoter GFP-tagged Rnr2 were grown to exponential phase in YPD media and then treated with 200 μg/ml cycloheximide for 6 hours to halt protein translation. Cell lysates were obtained and analyzed via SDS-PAGE/Western Blotting for GFP-Rnr2 (GFP antibody) and a GAPDH loading control (GAPDH antibody).
Protein extracts were made as described (Kamada et al., 1995). 20 μg of protein was separated by 4%–12% NuPAGE SDS-PAGE (Thermo). Proteins were detected using the following antibodies; anti-HIS tag (QIAGEN #34670), anti-GFP (Roche #1814460), Anti-FLAG tag (Sigma, #F1365), anti-GAPDH (Thermo #MA5-15738), anti-Ydj1 (StressMarq #SMC-166D), anti-R2B (SCBT, #sc-376963), anti-HDJ2 (Thermo #MA512748).
Blots were imaged on a ChemiDoc MP imaging system (Bio-Rad). After treatment with SuperSignal West Pico Chemiluminescent Substrate (GE). Blots were stripped and re-probed with the relevant antibodies using Restore Western Blot Stripping Buffer (Thermo).
Cells transformed with control pRS313 plasmid or the pRS313 plasmid containing HIS-tagged Rnr2 were grown overnight in SD-HIS media, and then reinoculated into a larger culture of selectable media and grown to an OD600 of 0.800. The cells were then either unstressed or stressed with 200 mM HU for four hours. Cells were harvested and FLAG-tagged proteins were isolated as follows: Protein was extracted via bead beating in 500 μl binding buffer (50 mM Na-phosphate pH 8.0, 300 mM NaCl, 0.01% Tween-20). 200 μg of protein extract was incubated with 30 μl anti-Flag M2 magnetic beads (Sigma) at 4° C overnight. Anti-Flag M2 beads were collected by magnet then washed 5 times with 500 μl binding buffer. After the final wash, the buffer was aspirated and beads were incubated with 65 μl Elution buffer (binding buffer supplemented with 10 μg/ml 3X FLAG peptide (Apex Bio)) for 1 hour at 4° C, then beads were collected via magnet. The supernatant containing purified FLAG-Rnr2 was transferred to a fresh tube, 25 μl of 5x SDS-PAGE sample buffer was added and the sample was denatured for 5 min at 95° C. 20 μl of sample was analyzed by SDS-PAGE.
Quantitation of yeast RNR transcription was carried out as in [59]. Briefly, yeast cells were grown overnight in YPD media at 30°C, re-inoculated at OD600 of 0.2–0.4 and then grown for a further 4 hours. Cells were treated with 200 mM for 2 hours and total RNA was extracted from cells using a GeneJet RNA extraction kit. Total RNA (1 μg) was treated with 10 units of RNase-free DNase I (Thermo) for 30 min at 37°C to remove contaminating DNA. DNAse I activity was stopped by adding 1 μL of 50 mM EDTA and incubating at 65°C for 10 minutes. cDNA synthesis was carried out by iScript reverse transcriptase (BioRad) on aliquots of 1 μg RNA. The single-stranded cDNA products were used in qPCR on an ABI Fast 2000 real-time PCR detection system based on SYBR Green fluorescence. Sequences of oligo pairs (same as used in [59]) are listed in S1 Table. Signals of RNR1, RNR2 and RNR4 were normalized against that of ACT1 in each strain and the resulting ratios in WT cells were defined as onefold.
HEK293T cells were cultured in Dulbecco’s modified Eagle’s minimal essential medium (DMEM; Invitrogen, Carlsbad, CA, USA) supplemented with 10% fetal bovine serum (FBS; Invitrogen), 100 U/ml penicillin (Invitrogen) and 100 μg/ml streptomycin (Invitrogen). All cell lines were incubated at 37 C in a 5% CO2 containing atmosphere. For 116-9e treatment, HEK293 cells were treated with 116-9e (#E1036, Sigma) at 40 μM concentration and kept in incubator at 37°C and 5% CO2 for 72 hours. After 72h cells were washed with 1X PBS and total cell extracts were prepared using Mammalian Protein Extract Reagent (Thermo).
HAP1 cells and HDJ2 Knockout cells were obtained from Horizon Biosciences and were cultured in Iscove’s Modified Dulbecco’s Medium (IMDM) supplemented with 10% fetal bovine serum (FBS; Invitrogen), 100 U/ml penicillin (Invitrogen) and 100 μg/ml streptomycin (Invitrogen). For IC50 calculations, HAP1 cells and HDJ2 Knockout were seeded in triplicate in 96-well white bottom Nunc plates in growth media at 20% confluency 1 day prior to initiation of drug treatment. On Day 1 of treatment, cells were treated with a two-fold serial dilution of Hydroxyurea (400μM to 1.56 μM). After 72 h, cell viability was measured using Promega CellTiter-Glo cell viability assay on a Synergy H1 plate reader. Similarly, cells were treated with either DMSO (control) or 40 μM of 116-9e in combination with a ten-fold serial dilution of either HU (400μM to 1.56 μM) or triapine (250μM to 0.0005 μM). After 72 h, cell viability was measured using Promega CellTiter-Glo cell viability assay on a Synergy H1 plate reader.
HEK293T cells were either un-transfected or transfected with plasmids for expression of HIS-tagged proteins using Lipofectamine 3000 (Thermo). After 48 hours, the cells were washed with 1XPBS and total cell extract was prepared from the cells using M-PER (Thermo) containing EDTA-free protease and phosphatase inhibitor cocktail (Thermo) according to the manufacturer's recommended protocol. Protein was quantitated using the Bradford Assay. His-tagged proteins were purified as follows: 200 μg of cell lysate was incubated with 30 μl of His-Tag Dynabeads (Invitrogen) with gentle agitation for 20 minutes at 4° C. Dynabeads were collected by magnet then washed 5 times with 500 μl Binding/Wash buffer. After final wash, buffer was aspirated and beads were incubated with 65 μl Elution buffer (300 mM imidazole, 50 mM Na-phosphate pH 8.0, 300 mM NaCl, 0.01% Tween-20) for 20 min, then beads were collected via magnet. The supernatant containing the purified HIS-tagged protein complex was transferred to a fresh tube, 15 μl of 5x SDS-PAGE sample buffer was added and the sample was denatured for 5 min at 95° C. 20 μl of sample was analyzed by SDS-PAGE and Western Blotting.
HAP1 cells were seeded in triplicate in 96-well white bottom Nunc plates in growth media at 20% confluency 1 day prior to initiation of drug treatment. On Day 1 of treatment, cells were treated with DMSO (control) and serial dilutions of Hydroxyurea and 116-9e. After 72 h, cell viability was measured using Promega CellTiter-Glo cell viability assay on a 96-well plate reader. The combination index was calculated using the Chou-Talalay method using CompuSyn software [60].
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10.1371/journal.pcbi.1005584 | Inferring repeat-protein energetics from evolutionary information | Natural protein sequences contain a record of their history. A common constraint in a given protein family is the ability to fold to specific structures, and it has been shown possible to infer the main native ensemble by analyzing covariations in extant sequences. Still, many natural proteins that fold into the same structural topology show different stabilization energies, and these are often related to their physiological behavior. We propose a description for the energetic variation given by sequence modifications in repeat proteins, systems for which the overall problem is simplified by their inherent symmetry. We explicitly account for single amino acid and pair-wise interactions and treat higher order correlations with a single term. We show that the resulting evolutionary field can be interpreted with structural detail. We trace the variations in the energetic scores of natural proteins and relate them to their experimental characterization. The resulting energetic evolutionary field allows the prediction of the folding free energy change for several mutants, and can be used to generate synthetic sequences that are statistically indistinguishable from the natural counterparts.
| Unlike most natural proteins that are made with apparently random strings of amino acids, repeat-proteins are formed with tandem stretches of similar elements. The statistical description for these occurrences can be captured with a simple energetic model that accounts for evolutionary mechanism that gave rise to these proteins. The resulting energetic model can be used to infer folding stability and can generate sequences that are indistinguishable from the natural ones.
| Repeat proteins are composed of tandem repetitions of similar structural motifs of about 20 to 40 amino acids. Under appropriate conditions, these polymers fold into elongated, non-globular structures (Fig 1). It is apparent that the overall architecture is stabilized mainly by short range interactions, in contrast to most globular protein domains that usually adopt very intricate topologies [1]. In their natural context, repeat proteins are frequently found mediating protein-protein interactions, with a specificity rivaling that of antibodies [2–4]. Given their structural simplicity and potential technological applications, repeat-proteins are a prime target for protein design, with very successful examples for a variety of topologies [5–7]. Most of the current design strategies target the creation of rigid native structures with desired folds that, although beautiful, often lose biological functionality [8]. It is becoming clear that the population of ‘excited states’ is crucial for protein function [9], and thus tackling energetic inhomogeneities in protein structures may be crucial for understanding how biological activities emerge [10]. The challenge thus relies in finding an appropriate description for the ‘energy’ of each system, a daunting task for large molecular objects such as natural proteins.
In principle, the natural variations observed for proteins of the same family must contain information about the sequence-structure mapping. A simple model that just takes into account the frequency of each amino acid in each position is insufficient to capture collective effects, yet, for some architectures it is surprisingly good for the synthesis of non-natural repeat-proteins by ‘consensus’ design [11–14]. It is apparent that in the case of repeat proteins the local signals play inordinately large roles in the energy distribution, just as expected from their topology [15] and hence, small heterogeneities can be propagated from the local repeat units to higher orders affecting the overall structure and dynamics [16, 17]. Thus, collective effects may be approximated as small perturbations to local potentials, simplifying the energetic description of complex natural systems [18].
In the last years new methods to analyze correlated mutations across a family of proteins have arisen (mfDCA [19], plmDCA [20, 21], Gremlin [22] to name a few). The main hypothesis behind these methods is that biochemical changes produced by a point mutation should be compensated by other mutations (along evolutionary timescales) to maintain protein viability or function. These methods can also be used to disentangle relevant direct correlations from indirect ones. They are very successful at predicting spatial contacts and interactions for many protein topologies [23–27]. Nevertheless, these methods do not take into account the chemical nature of the amino acids, which can be codifying inhomogeneities in the energetic distribution that are crucial for the activity of repeat-proteins [28, 29]. On this basis, different approaches have been proposed recently to include chemical details in the correlation analyses [30], trying to predict folding stability [31], conformational heterogeneity [23, 32, 33], mutational effect in the interaction in two-component signaling proteins [27] or the global effect on antibiotic resistance from sequences of β-lactamases [34, 35]. As many other tools, these were optimized to perform well on globular proteins, and their application to repeat proteins is not straightforward. Besides the point-mutation mechanism, repeat proteins are believed to evolve via duplication and rearrangement of repeats [36], resulting in an inherent symmetry which usually confounds sequence analyses [17]. Making use of this symmetry, we have previously proposed a specific version of mfDCA and plmDCA for repeat proteins [37]. In this work we develop an alternative ‘evolutionary field’, able to disentangle biases generated by the repetitive nature of these proteins and which explicitly includes the information of the amino acids that compose a protein. We show that it is able to reflect biochemical properties of the analyzed proteins. We take advantage of the elongated and repetitive structure of these proteins (Fig 1) to extract as much information as possible from the data, and apply the general ideas on three specific families, ankyrin repeats (ANK), leucine-rich repeats (LRR) and tetratricopeptide-like repeats (TPR).
To study the co-occurrence of mutations in a sequence alignment of a particular protein family, [39] proposed a Hamiltonian or energy expression which resembles a Potts model:
E ( s → ) = - ∑ i = 1 L h i ( a i ) + ∑ i = 1 L ∑ j = i L J i j ( a i , b j ) (1)
where the set of {hi(ai)} parameters, one for each amino acid in each position, accounts for a local propensity of having a specific residue on a particular site of the protein, and the set of {Jij(ai, bj)} indicates the strength of the ‘evolutionary’ interaction between each possible amino acid in every pair of positions along the protein. There are q = 21 possible values of ai and bj, one for each amino acid and one for the gaps included on the multiple sequence alignments. This expression is evaluated on a particular sequence on an alignment, and the summations go over the L columns of the alignment. A sequence is more favorable or more energetic if it gets lower values of E ( s → ). It can be expected that the population of sequences follows a Boltzmann distribution P ( s → ) = 1 Z e - E ( s → ) [40]. The parameters are thus fitted to reproduce the frequencies of occurrence of each amino acid in each position (fi(ai)) and the joint frequencies of amino acids (fij(ai, bj)) in an alignment of natural sequences used as input:
f i ( a i ) = ∑ a k , k ≠ i P ( s → ) (2) f i j ( a i , b j ) = ∑ a k , k ≠ i , j P ( s → ) (3)
Nevertheless, for repeat proteins there is another feature we want to capture with an evolutionary energy: the high identity of amino acids constituting consecutive repeats, arisen by the repetitiveness of these families and probably a signature of their evolutionary mechanisms (Fig 2).
Therefore, we propose the following model for repeat proteins:
E ( s → ) = - ∑ i = 1 L h i ( a i ) + ∑ i = 1 L ∑ j = i L J i j ( a i , b j ) - λ I d ( s → ) (4)
This expression is designed to be applied in sequences constituted by two repeats. λId is a parameter that aims at reproducing the probabilities of the percentage of identity (%Id) between consecutive repeats in natural proteins (pid). Basically, it accounts for higher order correlations not captured by the pairwise terms. For a given sequence we calculate the %Id of the adjacent repeats and sum the parameter λId corresponding to that %Id value. When the correct parameters are obtained, this equation can be used to produce an ensemble of sequences consistent with the constraints (fi(ai), fij(ai, bj) and pid). We work with pairs of repeats as it is the minimum unit that includes the interaction between repeats and the possibility of measure sequence identity between consecutive repeats. In the following section we will show the convergence of the method and the relevant information that can be obtained from it. For further details about the procedure to assign values to the parameters, please refer to Methods section.
We construct an alignment of pairs of repeats for each family: ANK (PFAM id PF00023, and final alignment of 20513 sequences of L = 66 residues each), TPR (PFAM id PF00515, and final alignment of 10020 sequences of L = 68 residues each) and LRR (PFAM id PF13516, and final alignment of 18839 sequences of L = 48 residues each). See Methods for further details of construction. We measure fi(ai), fij(ai, bj) and pid. Using a gradient descent procedure we obtain a set of parameters in eq 4 which are able to reproduce fi(ai), fij(ai, bj) and pid. In principle, the number of parameters is large: Lq hi parameters, ( L q ) 2 - L q 2 Jij parameters and L 2 + 1 λId. For example, for pairs of ANK repeats this means 1386 hi, 959805 Jij and 34 λId. To reduce the number of free parameters to fit we use a L1-regularization which fixes to zero those parameters which do not contribute significantly to fit the frequencies. This regularization allows us to set to exactly zero between 85 and 91% of the Jij parameters which, when they are free to vary, only reach small values (S3 Fig). We bound the maximum error permitted in the frequency estimations to 0.02. Refer to Methods for more details.
In the three families studied, the parameters obtained allow us to generate ensembles of sequences which reproduce natural fi(ai), fij(ai, bj) and pid (Fig 2A). Notice that most frequencies are fitted with an error of an order of magnitude lower than the maximum bound imposed (S2 Fig).
The pid distributions are also very well reproduced (Fig 2B). Not only the general shape, but also the populated long tail for highly similar repeats. It is not possible to obtain the same distribution only by fitting amino acid frequencies fi(ai) and fij(ai, bj), it is mandatory to explicitly include the pid by including the parameters λId (S1 Fig), suggesting that higher order correlations must be accounted for describing these systems.
Once the set of parameters {hi(ai), Jij(ai, bj), λId} is obtained, it can be used to score any sequence of L amino acids via eq 4. In this section we test if this measure is capable of distinguishing polypeptides that fold in a three dimensional structure similar to members of the repeat protein family from those that do not.
We calculate the distribution of energies of different sets of sequences (Fig 3). The ensembles of natural sequences of each protein family used to learn the parameters have a unimodal distribution of energies centered around -100 (Fig 3, red lines). These distributions are clearly differentiated from the energies of random chains of residues (Fig 3, yellow lines), which constitute a basic negative control for our model.
For a positive control we evaluate designed proteins which have been experimentally synthesized. For the ANK family, we consider the library of repeat sequences built by Plückthun’s laboratory [13] (green lines, Fig 3A). This library was constructed by fixing on each repeat 26 positions out of 33 to the most frequent residue in the multiple sequence alignment. This resulted in a set of sequences that have small variations with respect to the ANK consensus (the sequence with the most frequent amino acid in each position). In our expression, they score a very low energy distribution, overlapping with the most negative tail of the distribution of natural sequences. It is notable that consensus designed ANK have been shown experimentally to be extremely stable. For the TPR family, consensus designed was done by Regan’s laboratory [11, 12]. All pairs of repeats synthesized have the same amino acid sequence, and it’s energy score is indicated by a green full square in Fig 3B. Again, the designed sequence matches values at the most left side of the energy distribution of natural sequences, and coincidentally reports high folding stability. From it, other variants with few point mutations to improve binding to a specific ligand have been synthesized. As shown in empty green squares [41] and diamonds [42] in Fig 3B, they have higher energy, but still in the left most side of natural sequences distribution. Recently, a different design strategy was done [43]. Based on a non-repetitive protein, but similar to TPR fold, they put togheter various repetitions of the fold, using TPR loops to link them. They obtained a three-repeats protein whose pair of repeats energy are represented on triangles on Fig 3B. This time, they match natural sequences distribution in higher values.
Finally, for the LRR family we contrast with the library of proteins designed by Plückthun’s group based on the consensus sequence [14]. The repetition they considered has 57 amino acids, which includes two types of repeats, one of 28 residues and the other one of 29. As the repeat we are using for LRR is 24 residues long, we aligned both definitions and evaluated the library removing the amino acids not matching our definition. Again, their scores form a narrow distribution, but this time it is not placed on the most favorable side of the natural sequences distribution (Fig 3C). Coincidentally, selected species studied do not show such a high folding stability as the ANK library did.
With these parameters, we are able to generate an ensemble of sequences which are in agreement with the constraints used, via a Monte Carlo simulation (see Methods). The distribution of energies of these simulated sequences matches the natural sequences energies distribution with remarkable accuracy. Moreover, we randomly choose 100 sequences from the natural ensemble and 100 sequences from the simulated one, perform a Smith-Waterman pairwise alignment all against all, calculate the pair similarity using BLOSUM62 matrix and used it as a distance method to plot a dendogram of the sequences (S4 Fig). Both species appear interspersed, showing that it is not possible to distinguish a natural sequence from a constructed one. Also, we tested familiarity to the ANK family as defined in [44] and found overlapping distributions for both species (S5 Fig). Therefore, simulated sequences represent possible variants to natural repeats. The wide distribution of natural proteins suggests that it should be possible to engineer sequences with more variable repeats, more dissimilar among neighbors and to the consensus than the ones published up to date.
Are there any invariant properties shared by low energy sequences? Given that repeat-proteins may evolve by other mechanisms besides point substitutions, we analyze if low energy sequences are constituted by highly similar repeats and if they are close to consensus sequences.
On Fig 4A we show the relation between the %Id between the repeats and the energy of the sequence. It is evident that low energy sequences are constructed by pairs of highly similar repeats. This could be a transitive effect: if low energy sequences are very similar to the consensus sequence, and the consensus sequence is formed by two identical repeats, we would be seeing that more similarity between repeats causes lower energies. We can see that it is not the case (Fig 4B). We plot the %Id to the consensus against the energy of each sequence. The consensus was calculated with the most frequent amino acid in each position on sequences used as input. We can see that there is no evident correlation between the energy and the similarity to the consensus. Thus, low energy sequences that differ from the consensus one may be constructed. Also, there are no sequences which get a high %Id to the consensus. We conclude that there are different repeats which have low energies within a protein family, and not only the consensus sequence.
Consensus designed ANK proteins are very stable upon chemical and thermal denaturation [13], and, as shown in Fig 3 also score a very low evolutionary energy according to eq 4. Can we quantify the relationship between the stability and the evolutionary energy?
A potential test can be performed by comparing to experiments in which the effect of point mutations was evaluated. These incorporate one, two or three point mutations in natural proteins, and characterize the unfolding free energy ΔG of the wildtype and the mutated variant. A higher ΔG reports a more stable protein. We compare the change in the ΔG between the mutated and the wildtype protein (ΔΔG), and the difference of energy for their sequences according to eq 4.
Although the energy expression is learned for pairs of repeats, we can easily extend it to an array of repeats making use of the elongated structure of repeat proteins in which only adjacent repeats interact. From our expression we have parameters assigned to intra-repeat positions (hi with i = 1 … L 2 and Jij with i,j= 1 … L 2), and inter-repeat interactions (Jij with i = 1 … L 2 and j = L 2 + 1 … L, and λId). Then for each repeat we can assign an internal energy ∑ i = 1 L / 2 h i ( a i ) + ∑ i = 1 L / 2 ∑ j > i L / 2 J i j ( a i , b j ) and a interaction energy ∑ i = 1 L / 2 ∑ j = L / 2 + 1 L J i j ( a i , b j ) + λ I d, which of course depends on the amino acids constituting each repeat.
On Fig 5A, we show the comparison between ΔΔG and the evolutionary energy calculated using Eq 4, done for three different ANK proteins: IκBα [45, 46], Notch [47] and p16 [48]. It should be noted that different experimental techniques return different values for ΔG for the same protein, non overlapping within experimental error, pointing that other factors contribute to the experimental quantification of ΔΔG. A linear fit returns R2 ≈ 0.61. From 152 mutations we analyzed, 124 (82%) are predicted favorable when the mutation stabilized the folding of the structure, and unfavorable when they have also been measured to destabilize. The predictions that deviated the most are mutations in Notch from Serine to Proline, which is a structural disruptor, and were not considered in the linear fit. A comparison against FoldX [49] predictions can be found on S6 Fig.
On Fig 5B, we show reported mutations on pp32 [50], a protein belonging to LRR family. Again, measurements with different methods report different values of ΔΔG. The linear fit returns a poor R2 ≈ 0.21, but 30 (75%) mutations are both predicted and reported unstabilizing.
A similar comparison was performed by [31] for small globular proteins with an expression related to Eq 1. To reduce the number of interaction parameters Jij(ai, bj) they explicitly used structural information and set to zero all interactions between positions which are not in contact in the native structure. In contrast, we use a L1-regularization to fix to zero those parameters which do not contribute significantly to the fitting process and obtain Jij(ai, bj) = 0 and Jij(ai, bj) ≠ 0 in all pairs of positions, regardless they are supposed to be in contact or not in the 3D structure.
Are the obtained parameters related to structural properties of these proteins? Local fields, hi(ai), should account for the local propensity of each amino acid in each position, and therefore are expected to be related to fi(ai). Fig 6A shows that the inferred hi(ai) parameters are different from the initial condition ln(fi(ai)) for the ANK family; that is, the values obtained for the parameters that account for higher order correlations are relevant. In red we highlight the points related to the consensus amino acid in each position. All of these residues have a strong local field associated to them, justifying why the construction of sequences with these amino acids results in foldable proteins. We also show a contact map of two ANK repeats (PDB id: 1N0R) on Fig 6B: gray background indicates that the two positions given by x and y axis are in contact in the native structure, and white that they are not. On the upper triangle of the figure and in blue crosses, we mark the positions involved in the highest Jij parameters, i.e. those which imply higher coupling. A darker blue indicates that there are more Jij (more combinations of amino acids) between those positions. Most of the highest Jij match a pair of positions in contact in the 3D structure, or two which correspond to the same residue in the adjacent repeat patterns, i.e. i-th position in the first repeat and position j = i+33 in the second repeat. In red crosses we show the lowest Jij, that mark a negative constraint. Again, a darker red means that there are more Jij with low values between those positions. It is apparent that these also involve mostly residues in contact, but shows that other regions are responsible for negative design.
We propose a statistical model to account for fine details of the energy distribution in families of repeat proteins using only the sequences of amino acids. The model consists of a specialization of a Potts model to account for the local and pair-wise interactions and an extra term that includes higher order correlations, accounting for the similarity between consecutive repeats. The model is constrained by evolutionary characteristics of the families of proteins: we measure the frequencies of amino acids, co-occurrence of amino acids and the identity between repeats in extant natural proteins. To statistically define these quantities it is necessary to have a large set of sequences, which we showed are currently available for several repeat-protein families [37]. No information about the native folded conformation is required. The computation of the evolutionary energy field is computationally demanding, mostly due to long times spent in rigorous Monte Carlo simulations, but once the fitting is done the parameters can be used to score individual sequences fast and easily.
We studied three popular repeat protein families: ANK, TPR and LRR. After pre-processing of the alignments, we had enough sequences (≈ 20500, 10000 and 18800 respectively) to fit the model to pairs of repeats of each family. We scored the evolutionary energy of all natural sequences in PFAM, and it allowed us to clearly distinguish between natural proteins and random sequences of amino acids: the first have energy values < -50 and show a large spread while all random sequences have energy values ≈ 0. We evaluated designed repeat proteins which have been shown to fold and found that they score within the natural sequences distribution of energies. For the ANK and TPR families, these designed proteins have been shown to be highly stable upon thermal and chemical denaturation and, coincidentally, they are located at the most favorable side of the energy distribution of natural proteins, suggesting that the evolutionary energy score can be related to folding stability.
The energetic model can be used in Monte Carlo simulations to generate sequences that agree with the natural constraints of a given protein family. This ensemble of simulated sequences matches the amino acid frequencies, the identity between repeats and also the energy distribution of natural proteins. We found this set of simulated sequences is statistically indistinguishable from natural counterparts. Thus, the proposed model can be used as a tool to design repeat-protein sequences that have all the natural characteristics evaluated to date. Repeat proteins bind to other polypeptides and are candidates for specific binder scaffolds. Designed repeat proteins have been successfully synthesized and adapted to biomedical applications. Nevertheless, consensus design limits the possible variants as only a small proportion of residues are free to vary. Furthermore, they are extremely stable. Including coupling information can wide the possible sequences that can be studied, and could lead to more malleability of the designed molecules. Moreover, the stability change upon single point mutation can be well predicted by the model using just sequence information. For the ANK family, evolutionary energy variations correlate with the experimental values with an R2 ≈0.6. This improves FoldX [49] performance, which additionally requires a reference structure. Moreover, from the 152 experiments analyzed, the 82% predicts the direction of the stability change upon a point mutation. For the LRR family, the correlation is considerably lower, but 75% of the mutations are both predicted and reported in the available bibliography as destabilizing. For both the simulated sequences and for natural counterparts, we found that the similarity between consecutive repeats correlates with lower energy values, and that these are not necessarily similar to the consensus sequence of the family, pointing out that duplication of stretches of sequences may well be an important factor in the evolution of these systems [51].
The existence of a simple and reliable energy function to score the ‘evolutionary energy’ of repeat-proteins can be used to trace the biological forces that acted upon their history, and to explore to which extent these conflict with the physical necessities of the systems [52]. Mapping the energy inhomogeneities along the repeat-arrays may allow us to infer the population of excited states in these proteins, many of which have been related to their physiological mechanisms.
Multiple sequence alignments of repeats were obtained from PFAM 27.0 [53]. The aligned sequences usually have misdetected initial and final residues. The amino acids at the ends of the repeat-detection do occur in the polypeptide chains (they are not actual deletions) and incorporating them improves the statistics of the real sequences. We completed these positions with the amino acids present on the actual proteins using the provided headers on the alignment and crossing information with UniProt database [54]. This leads to a reduction on the number of gaps in our alignments, which usually derives into noisy predictions in correlation analyses [31]. After, we created the alignment of pairs of repeats, joining sequences of repeats which are consecutive in a natural protein. Finally, we removed insertions from the alignments by deleting positions which have gaps in more than 80% of the sequences in the alignment.
Our model fits the occurrence of amino acids in every position, which we call the marginal frequency of residue ai at position i of the alignment and denote fi(ai), and the joint occurrence of two amino acids ai and bj simultaneously at two different positions of the alignment, fij(ai, bj). To avoid biases by the overrepresentation of some proteins in the database, we used CD-HIT [55] to cluster sequences at 90% of identity and chose a representative sequence from each cluster. Finally, we computed by counting the fi(ai) and fij(ai, bj), and divided by the total number of sequences.
From the same alignment explained in Frequencies calculations, for a sequence which has L residues constituting two consecutive repeats, the %Id between the repeats is the number of amino acids in positions i and i + L 2, for i = 1 … L 2 which are exactly the same. Gaps are treated as an amino acid. Once we have the values for all sequences in an alignment, we define pid as the proportion of sequences within the alignment with the same %Id between repeats.
Given a set of parameters hi, Jij, λId and Eq 4, we use a Monte Carlo procedure and the Metropolis criterion to generate an ensemble of N sequences of length L each. We initiate with a random string of L residues. At each step, we produce a point mutation in any position. If this mutation is favorable, i.e. the energy is lower than that of the original sequence, we accept the mutation. If not, we accept the mutation with a probability of e−ΔE, where ΔE is the difference of energy between the original and the mutated sequence. When accepted, the mutated sequence is used as the original one for next step. We add one sequence to our final ensemble every t steps (we used t = 1000).
Our model is proposed to reproduce fi(ai), fij(ai, bj) and pid from the alignment of natural sequences. To learn the set of parameters hi, Jij, λId which reproduce them, we used a gradient descent procedure. In each step, an ensemble of N = 80000 sample sequences was produced via Monte Carlo using as energy the expression 4 and the trial parameters. We measured its marginal, joint frequencies and pid and updated the local parameters according to:
h i t + 1 ← h i t - ϵ s f i ( a i ) - f i m o d e l ( a i ) (5)
As the number of parameters for coupling is large (= 212L2), we used a regularization L1 to force to 0 those parameters which are not contributing significantly to the modeled frequencies. Then, we update these parameters by:
Jijt+1←0ifJijt=0and|fi(ai,bj)-fimodel(ai,bj)|<γϵjfij(ai,bj)-fijmodel(ai,bj)-γsign(fij(ai,bj)-fijmodel(ai,bj))ifJijt=0and|fij(ai,bj)-fijmodel(ai,bj)|>γJijt+ϵjfij(ai,bj)-fijmodel(ai,bj)-γsign(Jijt)ifJijt+ϵj(fij(ai,bj)-fijmodel(ai,bj)-γsign(Jijt)·Jijt>00ifJijt+ϵj(fij(ai,bj)-fijmodel(ai,bj)-γsign(Jijt)·Jijt<0(6)
Finally, the parameters λId are updated according to:
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)
(7)
We iterated until the maximum difference between the predicted frequencies and the natural sequences was below 0.02. This value was chosen according to the robustness of the frequencies estimations on the available data. We calculated the frequencies on half of the available sequences and compared the results to the frequencies counts on all the available sequences. The largest differences were slightly below 0.02. We believe that this maximum error thus reflects the actual error in the data and it is not reasonable to ask the model for more accuracy than that of the data itself. The code was written in C++ and is available at GitHub: https://github.com/proteinphysiologylab/2017_Espadaetal.
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10.1371/journal.pcbi.1005818 | The role of Allee effect in modelling post resection recurrence of glioblastoma | Resection of the bulk of a tumour often cannot eliminate all cancer cells, due to their infiltration into the surrounding healthy tissue. This may lead to recurrence of the tumour at a later time. We use a reaction-diffusion equation based model of tumour growth to investigate how the invasion front is delayed by resection, and how this depends on the density and behaviour of the remaining cancer cells. We show that the delay time is highly sensitive to qualitative details of the proliferation dynamics of the cancer cell population. The typically assumed logistic type proliferation leads to unrealistic results, predicting immediate recurrence. We find that in glioblastoma cell cultures the cell proliferation rate is an increasing function of the density at small cell densities. Our analysis suggests that cooperative behaviour of cancer cells, analogous to the Allee effect in ecology, can play a critical role in determining the time until tumour recurrence.
| Mathematical models of propagating fronts have been used to represent a wide variety of biological phenomena from action potentials in neural cells to invasive species in ecology and epidemic spreading. Here we show that when such models are used to predict the effects of external perturbations the results can be very sensitive to certain details of the local dynamics. For example, the post resection recurrence of tumour growth depends strongly on the density dependence of the proliferation of cancer cells. This suggests that targeting the cooperative behaviour of cancer cells could be an efficient strategy for delaying the recurrence of diffuse aggressive brain tumours.
| The growth of a malignant tumour is driven by the uncontrolled proliferation of cancer cells, and their invasion into healthy tissue. While the primary therapy often involves the surgical removal of the tumour, unfortunately, the surgery often leaves a small population of cancer cells infiltrated into the surrounding tissue. After a remission period of variable duration, the surviving cancer cells can initiate the recurrence of the disease. This is a particularly serious concern for glioblastoma brain tumours characterised by a diffuse tumour boundary within a complex, heterogeneous and relatively soft brain tissue [1, 2].
A major recent retrospective MRI study has shown that 77% of glioma patients relapsed centrally within 2 cm of the original tumour mass, 18% patients relapsed more than 4 cm from the original enhancement and 4% relapsed within the contralateral hemisphere [3]. The median relapse time was 8 month for local relapses, and progressively longer for distant relapses. The median time for contralateral relapses increased almost two-fold, to 15 months.
At the macroscopic level, invasive cancers with a diffuse boundary such as glioblastoma can be described by mathematical models specifying the spatial and temporal changes in tumour cell density [4–9]. Models of tumour invasion often utilise travelling front solutions of the Fisher-Kolmogorov type reaction-diffusion equation [10–12]. Predictive quantitative models of tumour growth have been proposed as a potential tool for patient specific computational optimisation of treatment strategies such as localised radio- and combinatory chemotherapies [13–19]. In combination with diagnostic imaging, such models aim to forecast the spatial and temporal progression of the disease taking into account the heterogeneity of the tumour and the tissue environment [13, 17].
To understand the dynamics that controls the initiation of recurrent tumour growth, in this paper we investigate, using quantitative models, how surgical removal of the tumour affects its delayed recurrence. In particular, we aim to identify key parameters of tumour cell populations that determine how much the progression of cancer can be delayed by surgical resection. We show that a density dependent proliferation of the cancer cells [20], particularly at low cell densities, has a key impact on predicting the time until tumour recurrence.
We consider a population dynamics model of glioma invasion in which the population density of cancer cells within a tissue is determined by the balance of proliferation, motility and cell death. Tumour cells are known to engage in a rich variety of motility [21]. Yet, as we discuss below, available experimental data suggest that at long time scales cancer cell movement is random and well approximated as a diffusion process, similar to the behaviour observed in cell cultures [22]. Thus, tumour spreading at a tissue scale is thought to be well described by a reaction-diffusion equation of the form:
∂ C ∂ t = ∂ ∂ x ( D ∂ C ∂ x ) + C r ( C ) (1)
where C(x, t) is the density of cancer cells at location x and time t. The diffusivity of the cells D characterises their random motility, and the function r(C) describes the balance of the rate of proliferation by cell division and cell death rate. In the simplest, and typically used, form of Eq (1) the environment is steady and homogeneous (D and f are independent of x and t) and the proliferation term is the logistic function
r ( C ) = ρ ( 1 - C K ) . (2)
where ρ is the maximum population growth rate. Expression (2) assumes that, on average, the balance of proliferation and death rates of cells, r(C), decreases with the cell density and vanishes when the density reaches the carrying capacity K. This behaviour reflects—in a simplistic form—limitations of both biochemical resources and cell size as the cell density increases [13, 17, 23, 24].
Eq (1) with the logistic proliferation term (2) is the well known Fisher-Kolmogorov (FK) equation. The FK equation has travelling front solutions of the form C(x, t) = C0(x − vt) where v is the propagation velocity and C0 is the stationary population density profile of cancer cells, as seen in a reference system co-moving with the front [25–27]. For sufficiently localised initial conditions (e.g. with nonzero values restricted to a finite region) the asymptotic front speed is 2 D ρ and the characteristic front width is D / ρ.
Following the surgical intervention reactive gliosis appears at the site of surgery. In the majority of the cases for a couple of months the resected area remains tissue free as evidenced by follow-up imaging [28]. As such the cell spreading into this area can substantially be delayed. Thus, in our model it is natural to represent tumour resection (or other localised primary treatments such as radiation therapy) by resetting cancer cell density C to zero in the region where C is higher than a predefined detection threshold δ. Back-propagation of the tumour into the area from which it was removed can be also prevented by no-flux boundary conditions imposed at the contour of the threshold density. The modified cell density profile is then used as initial condition for the same reaction-diffusion equation to generate the post-resection dynamics in the altered spatial domain. We find that our results are quite insensitive to whether or not the resected domain remains available for repopulation.
Numerical solutions of Eq (1) with the logistic growth term (2) and resection are shown for a one dimensional system in Fig 1. Surprisingly, we find that the resection does not lead to any detectable delay of the propagation of the front: The post-resection front initiated by the truncated, low cell density tail of the cancer cell distribution coincides with the unperturbed original front (see also S1 Movie). This behaviour appears to be independent of model parameters including the resection threshold δ.
To explain this counterintuitive behaviour we note that in the logistic proliferation term (2) the cancer-free equilibrium state C = 0 corresponding to healthy tissue is linearly unstable. Therefore the FK front is a so called “pulled front” [27, 29], where the dynamics of the low cell density leading edge is not affected even by the complete removal of the population behind the front. The complete absence of a delay in front propagation, however, questions the suitability of FK equation to represent radical medical interventions, which are expected to delay the progression of cancer.
Since after resection the density of cancer cells is low everywhere, the recurrence of the tumour is mainly determined by the survival and proliferation of cancer cells at low cell densities. To gain a qualitative insight into the density dependence of the cancer cell proliferation rate, we performed a series of in vitro experiments.
Glioblastoma cells were grown and imaged in sparse cultures for at least 4 days. Cultures were seeded at low cell densities ranging from 3 to 100 cells/mm2, corresponding to an area confluency (coverage) between 0.5 and 20% (see S3 and S4 Movies). We evaluated a time-lapse image sequence s in terms of As(t), the total area covered by the cells as a function of time t (Fig 2, see Methods for further details).
The growth rate bs(T) characterising the time period T ≤ t ≤ T + ΔT was obtained as a linear fit of the corresponding As(t) values:
A s ( t ) = b s ( T ) t + c o n s t . (3)
We have chosen the duration of the time period as ΔT = 30h, sufficiently short for the linear approximation (3) to hold, and sufficiently long to detect slow changes in the area covered by cells (Fig 2a).
The density-dependent average growth rate per cell, r(C) = f(C)/C, was obtained as
r ( A ) = 〈 b s ( T ) / A ( T ) 〉 s , T : A s ( T ) ≈ A (4)
where the 〈…〉 average was calculated over parallel cultures s and time intervals for which the initial As(T) coverage was sufficiently close to A.
Experimental results from two glioblastoma cell lines suggest that in the low density regime the cell growth rate r(C) increases with the population density C while it decreases at larger densities (Fig 2). This non-monotonous behaviour is in contrast with the logistic model which assumes a monotonously decreasing growth rate. In ecology such behaviour is known as the Allee effect [30, 31], and can arise as a result of some sort of cooperative behaviour among individuals that becomes less efficient at low population density. In cultures of cancer cells such cooperative behaviour can likely arise due to autocrine growth factors, diffusive signalling molecules produced and secreted by cells that enhance growth and proliferation of other cells [32]. Mathematical and computational models of cellular mechanisms leading to the development of Allee effect in the context of tumour growth has been described in recent studies [33, 34], and properties of travelling front solutions in a model of tumour invasion with strong Allee effect was studied in [35].
Motivated by our experimental observations of non-monotonous density-dependent survival and proliferation of tumour cells, we replace the logistic growth rate (2) with a quadratic net cell proliferation rate
r ( C ) = ρ ( C K + β ) ( 1 - C K ) . (5)
We choose this functional form as being the simplest that describes a non-monotonous density dependent proliferation. The Allee effect can be categorised by the sign of the parameter β as “strong” when β < 0, or “weak” otherwise. In the case of strong Allee effect the spatially uniform population dynamics is bistable and there is a critical density CT = −βK below which the growth rate is negative. For β > 0, the case of weak Allee effect, the cell reproduction rate increases with cell density, but it is always positive and there is no critical survival density.
The existence of a minimal density required for the survival of cancer cells would imply that the tumour can be eliminated completely if the resection threshold is sufficiently low (δ < −βK). This is, however, very rare in the case of glioblastoma [36]—suggesting that this disease exhibits a weak Allee effect: 0 ≤ β ≪ 1.
We used the cell proliferation function (5) and repeated the tumour growth and resection simulations in one dimension (Fig 3). According to our expectations, in the modified model the resection can indeed substantially delay the propagation of the tumour (see also S2 Movie). Fig 4 shows the integral of cancer cell population density C(x, t) within the area outside the resection, for different values of the resection threshold δ. Note, that after resection there is a lag phase during which the total number of cancer cells is almost constant. The lag phase is followed by a sharp transition to a linear increase indicating a front moving with constant speed. From this graph we can determine the length of the remission period, τ, as a delay relative to the original unperturbed front. The remission period thus increases substantially as the resection threshold is reduced.
The dependence of the remission period length τ on the resection threshold δ (Fig 5) is qualitatively different depending on the type of Allee effect considered. In the case of strong Allee effect, the delay time becomes infinite at a finite critical resection threshold. In the borderline case when β = 0 we find a power law behaviour where the delay is inversely proportional to the square of the resection threshold. For weak Allee effect with β > 0 the remission period length appears to follow a power law similar to the β = 0 case for larger values of the threshold δ, and crosses into a logarithmic function when the resection threshold is low.
In order to explain the inverse quadratic power law dependence of the recurrence delay on the resection threshold δ for the case β = 0, we look for approximate solution of Eq (1) using the exponential tail [37] of the truncated front δ exp(−ax), where x > 0, a = ρ / D, as initial condition after resection. Since we are considering the low density tail of the cancer cell distribution left from the resected tumour (C(x)/K ≪ 1), we can neglect the limitation of growth due to the finite carrying capacity. First we compare the relative magnitude of the diffusion and proliferation terms right after resection by substituting the initial concentration into the Eq (1) ∂ C ∂ t ≈ δ ρ e - a x + ρ δ 2 K e - 2 a x (6)
From this we can see that if the resection threshold is small, δ/K ≪ 1, then at this initial state the diffusion term dominates over cell proliferation. This is also visible in the numerical simulations, which show that the population density peak at the resection boundary quickly decreases at the beginning as the cancer cell population is dispersed (S2 Movie).
Although the full diffusion-proliferation equation cannot be solved explicitly, we can use this observation regarding the dominance of diffusion, and find an approximate solution valid for the initial period of time, by neglecting the proliferation term. The solution of the diffusion equation using the exponential post-resection profile as initial condition is
C ( x , t ) = δ 2 [ 1 - e r f ( ρ t - x 2 D t ) ] e - a x + ρ t . (7)
For large t, that is relevant in the small resection threshold limit (δ → 0), the argument of the error function is dominated by the first term, and using the asymptotic form of the complementary error function 1 - e r f ( x ) ≈ ( 1 / π ) e - x 2 / x we obtain the following approximation:
C ( x , t ) ≈ δ 2 π ρ t e - x 2 4 D t . (8)
In Eq (8) we recovered a Green’s function of the diffusion equation in which the total population size remains constant.
Now we can use this approximate solution and substitute it back into the full diffusion-proliferation equation, to compare again the relative magnitudes of the diffusion and proliferation terms.
∂ C ∂ t ≈ D δ 2 π ρ t e - x 2 4 D t ( x 2 4 D 2 t 2 - 1 2 D t ) + D 2 δ 2 4 K π t e - x 2 4 D t ( x 2 2 D 2 t 2 ) (9)
At the time when the contribution of cell proliferation becomes comparable to the diffusion term, the purely diffusive approximation breaks down. At this point the proliferation of cancer cells becomes non-negligible since diffusion is no longer efficient enough to disperse the cancer cell population to keep their density low at which reproduction is slow as imposed by the Allee effect. This leads to a sudden rapid increase of total cell mass initiating tumour recurrence represented by a new propagating front. Thus we can use the time needed for the proliferation term to reach the magnitude of the diffusion term as an estimate for the remission time, τ. Using x = 0 as a reference point where the cancer cell density is maximal, and balancing the two terms at t = τ we obtain
1 2 τ δ 2 π ρ τ = ρ δ 2 π ρ τ ( δ 2 K π ρ τ ) (10)
which leads to
τ=1πρ(Kδ)2, (11)
in agreement with the numerically observed τ(δ) ∼ δ−2 for the case β = 0 which represents an upper limit for the finite recurrence time with weak Allee effect β > 0.
We have shown that the model of tumour invasion based on logistic cell proliferation cannot describe the delayed progression of cancer due to resection and therefore it may not describe correctly the typical outcome of clinical interventions that substantially reduce population density of tumour cells. We propose that the key element, that determines the time until tumour recurrence, is the Allee effect, which results from positive cooperative behaviour of the cancer cells.
The Allee effect at the level of a tumour cell population may reflect diverse processes at the cellular level. A number of signalling pathways that include autocrine components, such as TGFalpha/EGF/EGFR, PDGF/PDGFR, HGF/SF and CXCL12/CXCR4 ligand/receptor systems, have been identified in glioma and glioblastoma [32]. Thus, glioblastoma cells can both produce the diffusive factor and respond to its presence with the appropriate receptors that activate cell proliferation. In addition, interactions between tumour cells and the surrounding stromal cells may also depend on the concentration of growth supportive paracrine factors and thus on the local cell density [38]. Furthermore, the matrix remodelling capacity, including the deposition of fibrillary collagen that promote glioma cell invasion, is also influenced by the density of tumour cells [39]. Finally, multicellular spheroid models of tumour growth often exhibit resistance against various treatment modalities [40].
Our mathematical model that includes the Allee effect provides the following insight into the dynamics of the tumour cell population: After resection the proliferation of cancer cells is very slow therefore their distribution is mainly determined by random motility which spreads the cells into the low density regions faster than they could reproduce leading to progressively lower densities. The process is eventually halted by the density distribution of the cells near the resection boundary becoming almost uniform in space. Without a cell density gradient random cell motility cannot further reduce cell density and the slow proliferation eventually catches up and leads to the recurrence of the invasion front. In accord with this analysis, the radiologically and histologically assessed cellularity, i.e. the density of tumour cells in the tissue, is one of the most important histological prognostic factors in glioblastoma multiforme—more predictive than the total tumour burden or proliferation index of the surgical specimen [41–43]. A counterintuitive prediction of our analysis is that reduced cell motility would promote an earlier local recurrence of the disease. Experimentally, this hypothesis could be tested by comparing the migratory activity of patient derived glioma cells and the time of recurrence using a major glioblastoma cohort in order to decrease the impact of other potential confounding factors like genetic background or extent of resection.
Glioblastoma cells are known to follow extracellular matrix rich structures, myelinated tracks and tissue inhomogeneities such as blood vessels or white matter tracts (axon bundles). However, only 20-30% of glioma recurrence is non-local (occurs at a distance greater than 2 cm from the original tumor centroid) [3, 44]. Thus, remissions clearly involving directed cell migration in great excess to local diffusivity happen, but our simple model representing cell motility as a diffusive process deals with the majority of cases. While to the best of our knowledge there is no single tracking data available for glioblastoma cells in situ or in brain slice explants, in the latter experimental model cells often spread in a spatially isotropic pattern that appears to be consistent with a diffusive spreading [45, 46]. Thus, while glioma motion may be anisotropic and directed at sub-millimetre scales, the complexity of the brain tissue may result in an approximate diffusive spreading at larger scales.
The diffusion term of Eq (1) may also incorporate density-dependent effects. The random motility of cancer cells may also depend on the local cell density, hence affecting the diffusion parameter D. When D vanishes for small population densities, the diffusive FK fronts are replaced by compact fronts with a well defined boundary [47, 48]. Similarly, expansion of an adhesive tumour mass without substantial random motility would be described by an advection term. Although, such generalisations are likely to be relevant for other malignancies, the diffuse infiltrate characteristics of glioblastoma are best explained by a diffusive process with a finite D at vanishing densities.
Recent improvements in imaging technology offer the promise of treatments specifically optimised both for the individual patients and tumours at the specific locations. We demonstrated that predictive models of tumour progression, necessary to evaluate and design such treatments, must include the Allee effect of tumour cell population dynamics. In this paper we considered a highly simplified one-dimensional model. In reality the strongly non-uniform tissue environment distorts the shape of the tumour and influences the cell’s ability to migrate. Although this will not modify the main qualitative observations regarding the relationship between tumour recurrence time and the Allee effect, such inhomogeneities and tissue anisotropies need to be taken into account when optimising treatment modalities in a patient specific manner. While surgery always aims to remove most of the tumour cells, our results indicate that interfering with autocrine feedback regulation of growth control at low cell densities may effectively prolong remission after surgery. As areas with maximal cell densities (and not the total tumour burden) determine remission time, radiotherapy optimisation must also critically depend on the Allee effect.
Two human glioblastoma cell lines (U87 and GBM1) were investigated in this study. U87 is a standard cell line from American Type Culture Collection (ATCC, HTB-14), GBM1 was established from a giant cell variant of glioblastoma multiforme at the National Institute of Neurosurgery in Budapest, Hungary as described previously [22]. Cell lines were maintained and studied in Dulbecco’s Modified Eagle Medium (DMEM, Lonza) containing L-glutamine, supplemented with 10% fetal bovine serum (Invitrogen) and penicillin-streptomycin-amphotericin B (Lonza). Cells were grown in non-precoated culture dishes at 37°C in a humidified, 5% CO2, 95% air atmosphere. Confluent cultures were washed twice with PBS (Invitrogen) and incubated with trypsin-EDTA (Sigma) to obtain cell suspensions. Cells were seeded in low densities (3, 10, 30 cells/mm2) into 35 mm Petri dishes (Greiner).
Time-lapse recordings of the cell cultures were performed on a computer-controlled Leica DM IRB inverted microscope equipped with a Marzhauser SCAN-IM powered stage and a 10x N-PLAN objective with 0.25 numerical aperture and 17.6 mm working distance. The microscope was coupled to an Olympus DP70 colour CCD camera. Cell cultures were kept in a stage-top mini incubator at 37°C in humidified 5% CO2 atmosphere. Phase contrast images were collected every 10 minutes from each microscopic field for durations up to 3-4 days.
Recorded phase-contrast images were analysed by segmentation and particle image velocimetry (PIV) algorithms implemented in Octave and Python. To detect cell occupied area a global threshold was applied to the local standard deviation of intensity on each image [49]. The code used for segmentation and confluency calculation are available at http://github.com/aczirok/cellconfluency.
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10.1371/journal.ppat.1006495 | A virulence-associated filamentous bacteriophage of Neisseria meningitidis increases host-cell colonisation | Neisseria meningitidis is a commensal of human nasopharynx. In some circumstances, this bacteria can invade the bloodstream and, after crossing the blood brain barrier, the meninges. A filamentous phage, designated MDAΦ for Meningococcal Disease Associated, has been associated with invasive disease. In this work we show that the prophage is not associated with a higher virulence during the bloodstream phase of the disease. However, looking at the interaction of N. meningitidis with epithelial cells, a step essential for colonization of the nasopharynx, we demonstrate that the presence of the prophage, via the production of viruses, increases colonization of encapsulated meningococci onto monolayers of epithelial cells. The analysis of the biomass covering the epithelial cells revealed that meningococci are bound to the apical surface of host cells by few layers of heavily piliated bacteria, whereas, in the upper layers, bacteria are non-piliated but surrounded by phage particles which (i) form bundles of filaments, and/or (ii) are in some places associated with bacteria. The latter are likely to correspond to growing bacteriophages during their extrusion through the outer membrane. These data suggest that, as the biomass increases, the loss of piliation in the upper layers of the biomass does not allow type IV pilus bacterial aggregation, but is compensated by a large production of phage particles that promote bacterial aggregation via the formation of bundles of phage filaments linked to the bacterial cell walls. We propose that MDAΦ by increasing bacterial colonization in the mucosa at the site-of-entry, increase the occurrence of diseases.
| Bacteriophages are bacterial viruses, which in some cases encode for virulence factors and increase bacterial virulence. Comparative genomic of several strains of Neisseria meningitidis, a major human pathogen, identified the presence of an 8kb prophage in strains belonging to invasive clonal complexes. The analysis of this filamentous bacteriophage, designated MDA for Meningococcal Disease Associated (MDAΦ) did not reveal any obvious virulence factors responsible for an increase invasiveness of strains carrying this prophage. Using our animal model mimicking the septicemic phase of the neisserial invasive diseases, we demonstrate that the presence of the MDAΦ is not associated with a higher virulence, but we show that the bacteriophage particles, by promoting bacteria-bacteria interactions, increase the biomass of bacteria colonizing a monolayer of epithelial cells. These data suggest that the increased invasiveness mediated by the MDAΦ bacteriophage is likely to be due to a better ability of the bacteria to colonize the nasopharyngeal mucosa.
| Neisseria meningitidis (Nm) is a commensal bacterium commonly carried asymptomatically in the human nasopharynx. In a small proportion of colonized people, the bacteria invade the bloodstream from where they cause septicaemia and/or meningitis after crossing the blood brain barrier. Most meningococcal diseases are caused by bacteria belonging to only a few of the phylogenetic groups that constitute the population structure of this genetically variable organism [1]. Numerous virulence factors are expressed by meningococci. The capsular polysaccharide, the iron chelation systems [2] and the factor H binding protein are required by the bacteria to survive in the extra cellular fluids [3]. The type IV pili and Opa proteins are important for bacterial host cell interaction and allow nasopharyngeal colonization [4]. When bacteria are encapsulated, type IV pili are the sole bacterial attribute able to aggregate bacteria and to initiate the interaction with host cells. None of these virulence factors is specific of disease isolates and these bacterial attributes are also found in bacteria belonging to clonal complexes associated with a carrier state.
In order to get insights into the genetic basis responsible for the differences in pathogenic potential, a whole genome comparison using a collection of meningococci of defined pathogenic potential was performed. This study brought to light a sequence of 8 kb, designated MDA for Meningococcal Disease Associated island, which is associated with an increase ability of invasive disease [5, 6]. Subsequent studies have demonstrated that the MDA island encodes a functional filamentous prophage, designated MDAΦ, able to produce infectious filamentous phage particles [7] (S1 Fig highlights the organization of the MDAΦ genome). However the mechanism by which the MDAΦ prophage increases bacterial invasiveness remains unknown.
Horizontally transferable mobile elements (plasmids, transposons, genetics islands and bacteriophages) are responsible for the acquisition of novel properties by bacteria, such as antibiotic resistances, detoxification of heavy metals, or virulence factors [8, 9]. Filamentous bacteriophages are part of these horizontally mobile elements [10]. CTXΦ of Vibrio cholerae, which encodes the cholera toxin, can transduce non-toxigenic strains into toxigenic strains, contributing to the emergence of new pathogenic V. cholera clones. The Pf bacteriophages of Pseudomonas aeruginosa are involved in the formation of biofilm by inducing cell death and the subsequent release of bacterial DNA [11]. Moreover, the Pf bacteriophages inside the Pseudomonas biofilm on acellular surfaces interact with the extracellular matrix and enhance biofilm formation by increasing adhesion and tolerance to desiccation and antibiotics [12]. Recently, Secor and colleagues have shown that Pf4 bacteriophages of P. aeruginosa promote bacterial adhesion to mucine and reduce the inflammatory response [13]. Other effects of filamentous bacteriophages include horizontal gene transfer (VPIΦ of V. cholerae), increase of motility (RSS1Φ of Ralstonia solanacearum, SW1Φ of Shewanella piezotolerans) and formation of host morphotypic variants (Cf1tΦ of Xanthomonas campestris, Pf4Φ and Pf6Φ of P. aeruginosa) [10].
In this work we demonstrate that the presence of the MDAΦ prophage in meningococci is not associated with virulence during the septicemic phase of the disease. On the other hand, we show that phage particles increase colonization of encapsulated bacteria onto epithelial cells. Our data suggest that this effect is mediated by a large production of phage particles within the biomass of colonizing bacteria that promote bacterial aggregation via the formation of bundles of phage filaments. We propose that the production of MDAΦ phage particles increases the occurrence of disease by promoting bacterial colonization in the nasopharynx.
As mentioned above, the presence of the MDAΦ prophage in the genome of a Nm strain is associated with increased invasiveness [5, 6]. We aimed at determining whether its presence could increase the virulence during the septicemic phase of meningococcemia. We used a previously described experimental model of meningococcemia [14] and compared the course of infection of a wild type (WT) strain with that of an isogenic MDAΦ deleted variant. This model uses SCID mice grafted with human skin. The vascularisation inside the human skin remains of human origin even though it connects with the mice vessels. This model addresses the two events associated with the clinical presentation of meningococcemia, i.e. (i) the growth in the bloodstream and the extra cellular fluids, and (ii) the interaction with the microvessels, responsible for the thrombotic/leakage syndrome and the meningeal invasion.
Grafted-mice were injected IV with either the WT strain or an isogenic derivative deleted of the MDAΦ prophage (ΔMDA), as described in the material and methods section. Results, reported S2A Fig, did not show any significant difference in the course of infection induced by the two strains. We then performed competition experiments by infecting intravenously three grafted-mice with an equal quantity of the WT strain and the ΔMDA strain (S2B Fig). The number of bacteria in the bloodstream was determined at 1 and 18 hours after infection and the number of bacteria colonizing the graft at 18 hours [14]. The latter is directly correlated with the ability of the bacteria to interact in vivo with endothelial cells. The competitive index was calculated as described in the material and methods section. In all cases, the competitive index was close to one and no statistical difference was observed.
Since the ability to resist human complement is not addressed in the above mouse model and considering that one of the phage encoded protein, MDAORF6, has recently been implicated in the resistance to normal human serum when expressed simultaneously with other homologous proteins [15], we compared the ability of the WT strain and that of the ΔMDA strain to resist to complement containing human serum. The number of surviving bacteria after 30 min of contact with 60% of human serum was determined. Control experiments using heat-inactivated human serum and an isogenic non-capsulated strain were performed. The deleted MDA mutant was as resistant as the WT strain to complement containing human serum (S3 Fig). This result is consistent with the previously published results [15] which showed that an effect on the complement resistance was observed only when all homologous proteins of MDAORF6 were simultaneously deleted. Altogether these results ruled out a role of the MDAΦ prophage in the virulence of strain Z5463 during the septicemic phase of meningococcal infection.
Considering the above results, we hypothesized that the presence of the phage does not confer an advantage to bacteria during the septicemic phase of the disease but in the nasopharynx. A large number of meningococci in this location may be responsible for a higher translocation rate of bacteria in the bloodstream and/or a better dissemination of the bacteria among a population, which in turn increases the number of meningococcal diseases by amplifying the number of carriers. To test this hypothesis, we assessed the ability of the WT strain and that of the MDA deleted isogenic strain to interact with a monolayer an epithelial cell line derived from a pharyngeal tumor, the FaDu cells. Initial experiments were performed during a short period of time, and results, reported S4 Fig, did not show any difference between two isogenic isolates, carrying or not a MDAΦ prophage. Considering that, in the nasopharynx, the site-of-entry of meningococci, adherent bacteria are subject to a flow, due to the presence of ciliated cells [16], we assessed the ability of bacteria to colonize a monolayer during a long period of time (18 hours) under constant flow in order to be closer to the in vivo situation [17]. Two isogenic fluorescent strains, Z5463gfp and Z5463gfpΔMDA were used to quantify the biomass of bacteria adhering onto the monolayer (see Material and methods). Results are shown Fig 1A and 1B. The biomass covering the monolayers formed by the MDAΦ deleted strain was constantly reduced by 40 to 50% when compared to that of the parental strain. It should be pointed out that this difference was not explained by a difference in growth rate of the two strains as the doubling time of these strains was identical (S5 Fig). It should be pointed out that a similar phenotype was observed using another epithelial cell line, a monolayer of Calu-3 cells (cells from a lung adenocarcinoma) (S6 Fig).
The above results showing an increased colonization onto epithelial cells of the isolate containing an MDAΦ prophage were surprising in light of the in vivo data that did not show a selective advantage of the MDAΦ producing strain inside the skin graft where bacteria interact with endothelial cells. We subsequently determined the ability of the phage to promote bacterial colonization onto a monolayer of endothelial cells using the same conditions as above for epithelial cells. As shown Fig 1C and 1D, the WT and MDAΦ deleted strains showed the same level of colonization onto endothelial cells. A possible explanation for this discrepancy observed with the two cell types was the different cross talk observed when meningococci infect endothelial and epithelial cells, as this has been previously suggested [18]. To test this hypothesis, experiments were also performed using fixed monolayers (i.e. pretreated with a solution of 4% paraformaldehyde). As shown Fig 1C, the WT strain colonized significantly less a monolayer of fixed endothelial cells when compared to that of living cells. On the other hand, colonization of the MDAΦ deleted strain was dramatically reduced onto fixed cells when compared to that of the WT strain. In contrast, data obtained on a monolayer of fixed FaDu epithelial cells were similar to those obtained on living cells (Fig 1A and 1B). Altogether these results are consistent with the in vivo data which did not show any advantage to prophage containing strains when adhering onto the microvessels of the skin graft and clearly showed that the ability of the prophage to increase bacterial colonization is specific of epithelial cells and depends upon the bacteria host cell cross talk.
We next assessed whether the above phenotype observed onto FaDu cells was a consequence of the production of phage particles or a consequence of the presence of prophage encoded genes. Experiments were performed using strains deleted in two genes that have been shown to be important for phage replication and the production of viruses, MDAorf1 and MDAorf9 (S1 Fig) [5, 7]. Strains Z5463gfpΔorf1 and Z5463gfpΔorf9 described in the material and methods section are unable to produce replicative cytoplasmic forms of the phage, and infectious particle, even though they carry a prophage in their genome. As shown S7 Fig, these strains colonized a monolayer of FaDu cells at a level identical to that of a prophage deleted strain. Altogether these results are in favour of a direct role of the virus particles in the observed phenotype.
Consistent with the above results, was the intense labelling of the major capsid protein, MDAORF4, inside the biomass covering the monolayer of cells (Fig 2A). This suggested that phage production occurred during bacterial colonization of the epithelial cells. To confirm this point, phage DNA and phage proteins were monitored during bacterial colonization of the epithelial monolayer. The quantity of circular MDAΦ DNA per chromosome increases as a function of time (Table 1), and consistently the amount of MDAORF10 and MDAORF5 proteins (S8 Fig) increased in a proportion higher than expected from the growth of the biomass (see panel C S8 Fig). Considering the above results showing the absence of phenotype onto endothelial cells, we aimed at precising the phage production onto this cell type. The quantification of the circular MDAΦ DNA per chromosome in the biomass covering endothelial cells reveals the absence of production of MDAΦ during the formation of the biomass under flow on endothelial cells (Table 1). On the other hand onto fixed endothelial cells, the quantity of circular MDAΦ DNA per chromosome increased significantly (Table 1). Altogether, these results are consistent with the above reported data showing that the prophage does not provide a selective advantage to bacteria colonizing endothelial cells.
Altogether these results suggest that the increased colonization observed during interaction onto epithelial cells was associated with the production of viruses.
We next aimed at determining the mechanism by which the production of MDAΦ increases the biomass of bacteria onto epithelial cells. We first tested the hypothesis that the addition of exogenous bacteriophages during bacterial adhesion to a culture of a phage-deleted strain could mimic the phenotype observed. Bacteriophages were prepared as described in the material and methods section, and the ability of strain Z5463gfpΔMDA to form a biomass on cells was determined in the presence of 1012 bacteriophages per mL in the supernatant. Results are presented Fig 3. No significant difference was observed with or without the presence of exogenous bacteriophages, this strongly suggests that the phage has to be produced locally by the bacteria to increase the formation of the biomass.
The production of a phage by a bacterial population is possibly associated with bacterial lysis and subsequent release of extra cellular DNA (eDNA) that in turn can participate in the formation of a biofilm [19]. Even though this hypothesis was unlikely considering that filamentous phages such as MDAΦ do not have a lytic cycle, we ruled out a possible role of extra cellular DNA (eDNA) by quantifying the biomass covering the monolayer after having added DNAse in the culture media to degrade possible eDNA in the extra cellular matrix. The final concentration of DNAse was 1 μg/mL corresponding to that routinely used to degrade eDNA in extra cellular matrix [20]. As shown Fig 4, the biomass of colonizing bacteria was identical regardless of the presence of DNAse.
Type IV pili have long been identified as being the main bacterial attribute promoting both the formation of bacterial aggregates and the initial interaction of encapsulated meningococci onto host cells [21]. Considering the above results suggesting a direct role of viral particles, we determined the localisation of both pili and bacteriophages inside the biomass. To be able to visualize both, MDAΦ and pili, we used a derivative of strain Z5463 which was modified in order to express a pilin variant from strain 2C4.3, designated SB. The pili encoded by this variant can be labelled by a monoclonal antibody, 20D9. The construction of this strain designated Z5463(SB-aph3’) has been previously reported [7]. Bacteria colonizing a FaDu epithelial cell monolayer in a laminar flow chamber were harvested by aspiration as described in the material and methods section. This step removed most of the bacteria colonizing the epithelial cells, leaving only bacteria strongly adhering to the epithelial monolayer. Both adhering bacteria and bacteria peeled off the epithelial cells were labelled using the 20D9 monoclonal antibody and a polyclonal antibody directed against the MDAORF4, the major capsid protein [7]. As shown Fig 5A, bacteria obtained from the aspiration were heavily labelled by the polyclonal antibody against the major phage capsid protein, but almost no labelling was obtained using the 20D9 anti-pili monoclonal antibody. In contrast, bacteria still interacting with the epithelial monolayer were piliated but were not associated with bacteriophages. A similar experiment was performed using a prophage-deleted isogenic derivative. Only bacteria adhering strongly to epithelial cells were piliated whereas those peeled off the monolayer were not piliated (Fig 5B). These results suggested that bacteria close to the apical surface of the epithelial monolayer were piliated but not surrounded by bacteriophages whereas those located in the upper layers of the biomass produced large amount of bacteriophages but were not piliated. This finding was confirmed by performing XZ sections of an infected epithelial monolayer. Results are shown Fig 5C. After 18 hours of interaction onto a monolayer of epithelial cells (i) piliated bacteria are found close to the apical surface of the epithelial cells, in these layers very little labelling of bacteriophages is visible, and (ii) in the upper layers, bacteria are non piliated but surrounded by large amount of bacteriophages. It should be pointed out that similar XZ sections performed on infected living endothelial cells revealed the absence of MDAΦ particles at the surface of the biomass (Fig 5C). On the other hand, onto fixed endothelial cells, the XZ sections confirmed a large production of MDAΦ particles in the upper layers of the biomass (Fig 5C). Regardless of the cell type studied, the presence of type 4 pili is always restricted to bacteria associated with the apical surface of cells. These results are consistent with the data reported above showing that the prophage does not provide a selective advantage to bacteria colonizing endothelial cells.
Electron microscopy combined with immunogold labelling using both the anti-pilin 20D9 monoclonal antibody and anti-MDAORF4 polyclonal antibody were performed on bacteria obtained as above by aspiration. Results are reported Fig 6. Images shown Fig 6A confirmed that, using these labellings, we could visualise independently type IV pili and bacteriophages surrounding bacteria. It should be pointed out that the morphology of the bacteriophages is indistinguishable from that of pili. Numerous bacteriophage filaments remained linked to the cell wall. This is consistent with the fact that being a filamentous phage, the release of MDAΦ is not a consequence of bacterial lysis but the consequence of a secretion through the outer-membrane. The labelling of the aspirated biomass confirmed that in the upper part of the biofilm most of the meningococci were producing bacterial bacteriophages and not type IV pili (Fig 6B). In addition they showed (i) that numerous viruses remained associated with the bacterial cell wall, and (ii) that phage-phage interactions occurred leading to the formation of bundles of viruses that are able to connect bacteria (Fig 6B and 6C). The ability of this phage to bundle was confirmed on an immunofluorescence staining of a phage preparation using the anti MDAORF4 polyclonal antibody that showed that MDAΦ phage particles are able to form large bundles of filaments (Fig 3A). Altogether these data suggest that, in the upper layers of the biomass colonizing the biofilm, numerous bundles of viruses embed bacteria (Figs 5C, 6B and 6C) and that these bundles by interacting with phage particles still associated with the cell wall increase bacteria/bacteria connections (Fig 6C). The lack of complementation by exogenous phage suggests that the phenotype observed is specific of phage producing bacteria.
Similar experiments were performed, as described in the material and methods section, with pilE derivatives of strains Z5463gfp and Z5463gfpΔMDA. Type IV pili is the main bacterial attribute allowing the interaction of encapsulated meningococci with host cells, and as expected, very few non-piliated derivatives of strain Z5463gfpΔMDA were interacting with the monolayer. On the other hand a larger biomass of the non piliated derivative of strain Z5463gfp which carries the prophage, was colonizing the monolayer of host cells, even though the thickness of the biomass was reduced compared to that of a wild type piliated strain (Fig 7A and 7B). Electron microscopy performed on the harvested biofilm confirmed that the prophage containing strain was able to produce large amount of phage particles that can remain associated to the bacterial cell wall, and provide interbacterial connection via phage/phage interactions (Fig 7C).
The presence of the bacteriophage MDAΦ has been associated with hypervirulent clonal complexes of N. meningitidis [5],[6]. The initial hypothesis suggested the presence of virulence factors encoded by the prophage giving a selective advantage during the bloodstream phase of the disease. Our data obtained with our animal model mimicking the septicemic phase of the neisserial invasive diseases do not support this hypothesis. On the other hand, our results suggest that the virulence factor encoded by the prophage is the phage particle itself promoting bacterial aggregation when the bacteria interact with epithelial cells. The production of phage in the nasopharynx is therefore likely to increase the biomass of bacteria at the site of entry. This increased biomass could in turn increase the frequency of bacterial dissemination in the bloodstream and/or the dissemination of the bacteria inside a population, which, by increasing the number of carriers, is responsible for a higher rate of diseases. These hypothesis are consistent with previous results reported for Streptococcus pneumoniae, where an association between an increased density of nasopharyngeal colonization has been associated with a higher rate of invasive pneumococcal pneumonia [22].
Capsulated meningococci interact with epithelial cells via their type IV pili. Following this initial interaction, bacteria divide and, as previously reported, repress the transcription of the major pilin subunit PilE [23], retract their pili, and loose piliation [24], leading to the formation of layers of piliated bacteria directly in contact with the apical surface of the host cells, and bacteria loosing their pili are washed away from the monolayers by the shear stress. These previous reports are consistent with the data shown with the MDAΦ deleted strain. On the other hand, with the WT parental strain the biomass is thicker than expected, as bacteria in the upper layers are non piliated and included in a network of MDA phage particles which form large bundles able to connect bacteria most likely via interactions with filamentous bacteriophages remaining associated with the outer membrane of bacterial cells as they extrude through the cell wall (Fig 8). The mechanism responsible for the segregation of phage producing strains could resemble the one identified in Neisseria gonorrhoeae for mixed population of piliated and unpiliated bacteria [25], where non-piliated bacteria segregate from those that are piliated. The authors suggested that this cell-sorting was under the control of active forces which rely on similar physical principles as those observed in developing embryos.
A striking observation is the fact that this effect of the phage on bacteria colonization is specific of epithelial cells. Indeed, the prophage does not provide a selective advantage for bacterial colonization onto endothelial cells. Meningococci induce signalling pathways on epithelial and endothelial cells that are known to be different [18]. On endothelial cells, adhesion of N. meningitidis leads to the recruitment of the junctional components [26] and to the formation of microvilli like structures which allow bacterial protection from shear stress [27]. We hypothesized that the cross talk between bacteria and endothelial cells could be responsible for our observation, the fact that fixed endothelial cells behave like epithelial cells support this hypothesis. The molecular mechanisms responsible for this are unknown, a possible explanation is to consider that the formation of microvilli on the apical surface of endothelial cells by protecting the bacteria from shear stress prevent the production of phage particles, which may not be the case onto epithelial cells.
Interestingly, Sigurlàsdòttir and colleagues have shown that, after initial adhesion on epithelial cells, lactates, produced by host cells, initiate rapid dispersal of microcolonies of N. meningitidis [28]. In our model of adhesion under flow conditions, the permanent renewal of the medium prevents the increase of lactates concentration and the subsequent dispersal of bacteria.
Another striking result is the fact that meningococci seem to produce either pili or bacteriophages. Indeed, type IV pili and bacteriophages labelled very rarely the same bacterium. This is somewhat consistent with previous results showing that some of the type IV pilus machinery is used by the phage for its secretion, especially the secretin PilQ which is used to extrude the pilus fiber and the phage filament through the outer membrane [5]. This suggests that production of pili and bacteriophages are co-regulated and mutually exclusive. Indeed as already mentioned following initial adhesion, piliation is down regulated by the inhibition of transcription of the major pilin subunit and pilus retraction [23, 24, 29]. It is likely that these regulatory pathways control the induction of bacteriophage production.
Altogether our data by demonstrating that MDAΦ increase the colonization of bacteria specifically onto epithelial cells suggest that the increase invasiveness observed by strain carrier this prophage may be a consequence of a high bacterial load at the site-of-entry which in turn increase the chance of translocation in the bloodstream and/or the dissemination of the bacteria in a population and the number of carriers.
Z5463, formerly designated C396, is a serogroup A strain isolated from the throat of a patient with meningitis in The Gambia in 1983 (gracefully provided by J. Parkhill [30]). The genomic sequence of this strain is deposited in the PubMLST website ([Neisseria PubMLST:17882] [31]).
Neisseria were grown at 37°C in 5% CO2 on GC medium base (Difco) containing Kellogg’s supplements [32], or in GC-liquid medium [1.5% proteose peptone (Difco); 0.4% K2HPO4, 0.1% KH2PO4, 0.1% NaCl, with 12 μM FeSO4 and Kellog’s supplements].
Kanamycin (Km) was used at a concentration of 200 μg/mL, spectinomycin (Sp) at 75 μg/mL, erythromycin (Em) at 3 μg/mL and chloramphenicol (Cm) at 5 μg/mL.
Serum survival was performed as previously described [15, 33] with minor modifications. Bacterial strains were grown overnight on GC medium base plates and then cultures in chemically defined medium (CDM) supplemented with 1 mg/mL of Cohn fraction IV from human serum (Sigma-Aldrich). The CDM was Catlin 6 medium modified to contain 5.5 mM glucose, 4 mM D,L-lactate, 50 μM cysteine and 150 μM cystine [15]. Cultures were grown until an optical density at 600nm (OD600nm) of 0.6. A 1/600 dilution of this broth was realized in 60% of NHS (Normal Human Serum) or hiNHS (heat-inactivated Normal Human Serum) and incubated at 37°C. Normal Human Serum AB-type (PAA Laboratories) used was handled in a manner to preserve complement activity [34] or heat-inactivated at 56°C during 30 min. The percentage of bacteria surviving at 30 min was determined. Each assay was performed in triplicate.
The list of strains and mutants used in this study is reported in S1 Table. Mutants Z5463ΔMDA and orf1 were previously described [5, 7].
Z5463(SB-aph3’) was obtained following transformation of DNA of strain Nm 8013 SB-aph3’ [35] into Z5463 and selecting for Km resistance [7].
Z5463 expressing a green fluorescent protein (GFP) under IPTG-inducible promoter was obtained by transformation of plasmid pAM239 [36], the resulting strain was designated Z5463gfp. Z5463gfpΔMDA, Z5463gfpΔorf1 and Z5463gfpΔorf9 were obtained following transformation of Z5463gfp with DNA of strains Z5463ΔMDA, Z5463Δorf1 and Z5463Δorf9 respectively [7].
Z5463gfpΔpilE and Z5463gfpΔMDAΔpilE were obtained following transformation of the strains Z5463gfp and Z5463gfpΔMDA by the DNA of Z5463ΔpilE (erythromycin resistant) described by Meyer and collaborators [7].
To engineer a non-capsulated strain, a mutant of the gene lipA was generated. A previously described mutation [37] was amplified by PCR and introduced into Z5463 by transformation.
The experimental procedures described in this paper were conformed to the European ethical legislation (Directive 2010/63/EU) and with the guidelines established by the French regulations (Décrets 87–848, 2001–464, 2001–486 and 2001–131). The experimental protocol was approved by the Comité d'Expérimentation Animale de l'Université Paris Descartes (France, project number CEEA 12–030).
Six weeks old CB17/Icr-Prkdcscid (Severe Combined Immunodeficiency: SCID) female mice were obtained from Charles River Laboratories (Saint Germain sur l'Arbresle, France). Human skin grafts were obtained anonymously from surgical waste from patient undergoing plastic surgery at Groupe Hospitalier Paris Saint-Joseph (Paris, France). According to the French legislation, the patients were informed of the research finality and their non-opposition was orally received.
CB17/Icr-Prkdcscid/IcrIcoCrl female mice were grafted with human skin as described by Join-Lambert et al. [14]. Nm strains were grown overnight at 37°C on GCB agar plates prepared without iron and supplemented with deferoxamine (Desferal, Novartis) at a final concentration of 15 μM. Bacterial colonies were harvested and cultured in RPMI with 1% bovine serum albumin medium and 0.06 μM deferoxamine with gentle agitation to reach the exponential phase of growth. Bacteria were then resuspended in physiological saline. All mice received 10 mg of human holotransferrin (R&D Systems) administered intraperitoneally just before infection.
For the single infection model, six grafted mice were infected IV with 107 CFU of WT strain or the ΔMDA isogenic strain. The injected dose is just below the LD50. The numbers of CFU in blood and in the graft were determined at 1 and 18 hours.
To obtain a competitive index, three grafted mice were infected intravenously with a mix of 5.106 CFU of each strain. The numbers of CFU in the blood and in the graft were determined. The number of CFU corresponding to the ΔMDA strain was obtained by determining the number of spectinomycin resistant colonies. The competitive index was calculated by the ratio of [log (UFCZ5463ΔMDA)/log (UFCWT) in the blood or in the graft] on [log(UFCZ5463ΔMDA)/log(UFCWT) of the inoculum].
The pharynx carcinoma-derived FaDu epithelial cell line and the lung adenocarcinoma-derived Calu-3 epithelial cell line were obtained from the American Type Culture Collection. HDMEC (Primary Human Dermal Microvascular Endothelial Cells) were purchased from Promocell. FaDu cell lines were grown in Ham F-12 medium (PAA Laboratories) supplemented with 10% fetal calf serum (FCS; PAA Laboratories), 20 mM HEPES (PAA Laboratories) and 1% penicillin-streptomycin-amphotericin (PSA; PAA Laboratories). Calu-3 cell lines were grown in Opti-MEM (Gibco) supplemented with 5% fetal calf serum (FCS; PAA Laboratories). HDMEC were grown in ECM (Endothelial Cell Medium with supplements provided by the manufacturer, Promocell), 20 mM HEPES (PAA Laboratories) and 1% penicillin-streptomycin-amphotericin (PSA; PAA Laboratories). Cells were grown at 37°C in a humidified incubator under 5% CO2.
The cells were fixed using a solution of PBS-4% paraformaldehyde (PFA) during 20 min.
Phage preparation was performed as previously described [7]. Bacteria were pelleted from 200 mL of an overnight culture in GC-liquid medium. After filtration at 0.45 μm, the supernatant was treated for 3 h at 20°C with DNase I and RNase A, 25 μg/mL each. Phage was precipitated by addition of 10% NaCl and 20% polyethylene glycol 6000, and overnight incubation at 4°C. The phage was then pelleted by centrifugation at 11,000 g for 30 min, and resuspended in PBS 1X and added directly to the cell medium at a final concentration of 10% during the bacterial colonization onto epithelial monolayers. The concentration of phage was determined by real time PCR using a preparation of DNA of strain Z5463Δorf1 as standard.
Short-term adhesion of meningococci to FaDu cells was performed as described previously [38], with minor modifications. The 24 well plates were seeded with 105 cells per well.
Before the assay, bacteria grown on GCB agar plates were adjusted to a specific OD600nm and then incubated for 2 h at 37°C in prewarmed culture cell specific medium. The number of CFU in the inoculum was determined. Cells were infected with 1 mL of bacterial suspension in cell culture specific medium. After 30 min of contact, unbound bacteria were removed by three washes with 1 mL of cell culture medium and the infection was pursued for 6 h. The number of adherent bacteria was determined at 30 min, 3 h and 6 h.
Long-term colonization of bacteria was performed under flow conditions using FaDu, Calu-3 or HDMEC as previously described [17]. Laminar flow chamber experiments were performed on disposable flow chambers composed of six independent flow channels (μ-Slide VI 0.4 purchased from Ibidi, surface area 0.6 cm2 per channel) coated with 5 μg of rat tail collagen type I/cm2. Cells were seeded in the six channels at a density of 0.3x105/cm2 and incubated for 7 days at 37°C in 5% CO2 until confluent. A microscopic examination of the cell layers was performed before each flow assay and only channels with a uniformly confluent layer were used. Prior to infection, cell monolayers grown in μ-Slide were stained with cytoplasmic Cell Tracker Orange CMTMR (Life Technologies) according to the manufacturer’s instructions. The GFP-expressing strains were grown during 2 hours with agitation. The OD600nm was then determined and each strain was adjusted to an OD600nm of 0.1 for the piliated strains and 0.3 for the non-piliated strains. 60 μL of this suspension was used to inoculate triplicate channels of a μ-Slide. Bacteria were allowed to adhere to the monolayers for 1.5 hours without flow for the piliated strains and for 2 hours for the non-piliated strains. At 1.5 or 2 hours postinfection, a continuous flow of cell medium containing 3 μg of vancomycin/mL and, when necessary, 1 mM IPTG (isopropyl-β-D-thiogalactopyranoside) was applied for 18 hours at a constant flow rate of 0.04 mL/min for the piliated strains and 0.02 mL/min for the non-piliated strains using a syringe pump (Harvard Apparatus). The flow chamber was placed in an incubator at 37°C with 5% CO2 throughout the experiment.
When indicated, DNAse I (Roche) used at a final concentration of 1 μg/mL in the cell medium [20].
The Ibidi μ-Slide flow chambers allow direct observation with inverse microscopy through its transparent plastic bottom. All microscopic observations and image acquisitions were performed on a Leica SP5 confocal microscope. Images were obtained using a x40/1.3 Plan Apo oil objective lens. At the time of confocal acquisition, the cells were examined using red channel to assess the integrity of the monolayer. Three-dimensional biofilm structures reconstructions were generated using the IMARIS software package (Bitplane AG). Biofilm development was quantified with the COMSTAT computer program using biomass and average thickness parameters [39]. The results are expressed as a percentage of the biofilm produced by the WT strain, which is set to 100%. Values represent the means of three independent experiments, with the acquisition of at least six image stacks per each channel.
In some experiments, after 18 hours of incubation, biofilms were aspirated from the flow chamber using a large gauge needle syringe and used for immunofluorescence or immunogold labelling. In this case most of the biofilm was removed for analysis from the surface of the monolayers, leaving a single layer of bacteria which remained adherent to the apical surface of the cells.
The N-terminal domains of MDAORF4 were detected using purified rabbit polyclonal antibodies raised against peptides H2N-DGFDAAAIGTQVANV-COOH [7].
Type 4 pili was detected using the 20D9 monoclonal antibody that is specific for the SB pilin variant of strain 2C4.3 [40].
MDAORF5 was detected using rabbit polyclonal antibodies against peptides H2N-CINFLKDMGKVGTD-COOH and H2N-CVTEEGKIIRPERVGD-CONH2 [7].
MDAORF10 was detected using rabbit polyclonal antibodies against peptide H2N-FYQFRHGEPHKLINQE-COOH.
MDAΦ was detected using IF staining as previously described [38]. Briefly, aspirated biofilms or biofilms on Ibidi chamber were fixed on coverslips for 20 min with a solution of PBS-4% paraformaldehyde (PFA). After 2 washes in PBS, samples were incubated with PBS-NH4Cl during 5 min. Then samples were washed twice with PBS-0.1%Triton-1%BSA and incubated for 1 hour with the same solution. MDAΦ were stained using the anti-ORF4 N-ter antibody, used at 1/50 dilution and type IV pilin using the 20D9 antibody used at 1/1,000 dilution. While the bacteria were stained with DAPI (4′,6-Diamidine-2′-phenylindole dihydrochloride) solution at 100 ng/mL, the secondary antibodies, used at 1/400 dilution in PBS-0.1%Triton- 1% BSA, were a goat anti-rabbit antibody labelled with Alexa Fluor 488 (Molecular Probes Life tech) and a goat anti-mouse antibody labelled with Alexa Fluor 546 (Molecular Probes Life tech).
Oligonucleotides were designed using the Primer Express software (PE Applied biosystems) to obtain amplicons of the same size (S2 Table). Real-time PCR was run on an ABI Prism 7700 sequence detection system (Perkin-Elmer Biosystems) using SYBR Green PCR Master Mix (PE Biosystems), according to the manufacturer’s instructions.
Data analyses for a relative quantification of gene DNA were performed by the comparative Ct (threshold cycle) method according to the manufacturer’s instructions (user bulletin 2 for the ABI PRISM sequence detection system) and published data [41]. The parameter Ct is defined as the cycle number at which fluorescence (which is proportional to the quantity of DNA in the tube during the exponential phase of the PCR) passes the fixed threshold. The relative amount of target after normalization to a chromosomal gene pgm, is obtained by 2(Ctorf5—Ctpgm).
Preparation of protein samples, SDS-PAGE separation, transfer to membranes and immunoblotting were performed using standard molecular biology techniques [42]. Detection of immobilized antigens was performed by chemiluminescence using ECL Plus detection reagents (Amersham).
For quantification, we normalized the signal of each western-blot on the number of bacteria used for the protein preparation. All values were then normalized on the corresponding signal of NADP glutamate dehydrogenase.
Immunogold labelling of the MDAΦ and the pili were performed as previously described [7]. After aspiration from the Ibidi chambers of the biomass using a syringe with a large gauge needle, biofilms were resuspended in PBS-4% PFA and adsorbed to the grids for 15 min. The grids were then rinsed twice in PBS and placed sequentially onto drops of the following reagents at room temperature: PBS-50 mM NH4Cl (5 min), PBS-5% normal goat serum (5 min), and then the anti-ORF4 N-ter antibody diluted 1/50 in PBS-0.2% gelatine for the MDA or the anti-PilE 20D9 monoclonal antibody diluted 1/100 in PBS-0.2% gelatine for the pili (for 60 min). After five washes in PBS-0.2% gelatine, the grid was placed for 60 min on a drop of goat IgG anti-rabbit IgG conjugated to 8-nm-diameter gold particles and donkey IgG anti-mouse IgG conjugated to 12-nm-diameter gold particles diluted 1/60 in PBS-0.2% gelatine. The grids were then subjected to five washes in PBS-0.2% gelatine, fixed in PBS-1% glutaraldehyde (15 min), and washed twice in distilled water. The grids were then treated with phosphotungstic acid, air-dried and viewed. Image acquisition was performed with a JEOL 1011 transmission electron microscope. For the immunogold labelling of the biofilm of the Z5463gfpΔMDAΔpilE mutant, the goat IgG anti-rabbit IgG was conjugated to 18-nm-diameter gold particles.
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10.1371/journal.pcbi.1003095 | Fibrin Networks Regulate Protein Transport during Thrombus Development | Thromboembolic disease is a leading cause of morbidity and mortality worldwide. In the last several years there have been a number of studies attempting to identify mechanisms that stop thrombus growth. This paper identifies a novel mechanism related to formation of a fibrin cap. In particular, protein transport through a fibrin network, an important component of a thrombus, was studied by integrating experiments with model simulations. The network permeability and the protein diffusivity were shown to be important factors determining the transport of proteins through the fibrin network. Our previous in vivo studies in mice have shown that stabilized non-occluding thrombi are covered by a fibrin network (‘fibrin cap’). Model simulations, calibrated using experiments in microfluidic devices and accounting for the permeable structure of the fibrin cap, demonstrated that thrombin generated inside the thrombus was washed downstream through the fibrin network, thus limiting exposure of platelets on the thrombus surface to thrombin. Moreover, by restricting the approach of resting platelets in the flowing blood to the thrombus core, the fibrin cap impaired platelets from reaching regions of high thrombin concentration necessary for platelet activation and limited thrombus growth. The formation of a fibrin cap prevents small thrombi that frequently develop in the absence of major injury in the 60000 km of vessels in the body from developing into life threatening events.
| To restrict the loss of blood following rupture of blood vessels, the human body rapidly forms a clot consisting mainly of platelets and fibrin. However, to prevent formation of a pathological clot within vessels (thrombus) as a result of vessel damage or dysfunction, the response must be regulated, and clot formation must be limited. Our previous studies demonstrated that as a laser-induced thrombus stabilized in mice, the ratio of fibrin to platelets at the thrombus surface increased significantly. Stabilized non-occluding thrombi were observed to be covered by a fibrin network (‘fibrin cap’). In the present work the role of the fibrin network in protein transport is examined by integrating experiments in microfluidic devices with the hemodynamic thrombus model. The study reveals permeability of the fibrin network and protein diffusivity to be important factors determining the transport of blood proteins inside the thrombus. It is shown that the fibrin network does not dramatically limit the diffusion of thrombin but impairs flowing platelets in blood from reaching regions of high thrombin concentration thus, reducing the probability they are activated and stably integrated into the thrombus. This novel, counter-intuitive mechanism suggests that a fibrin network formed at early stages of thrombus initiation can prevent normally asymptomatic thrombi from developing into pathological clots.
| Damage or inflammation to the blood vessel wall initiates the development of intravascular clots (thrombi). Uncontrolled growth of thrombi can result in occlusion of the blood vessel, starving tissues in the flow field of nutrients and oxygen. Furthermore, clot fragments washed away from the thrombus (emboli) may lodge in the vasculature of the lungs and brain causing life threatening conditions such as pulmonary embolism and ischemic stroke. Thromboembolic disease is a major biomedical problem with 900,000 cases per year in the USA leading to 300,000 deaths [1].
We have reported [2]–[5] that following laser-induced injury to a few cells lining the vessel wall small non-occlusive thrombi initially grow rapidly by accumulating platelets. After several minutes, growth ceases and the thrombi stabilize. The cessation of growth occurs as the surface composition of the developing thrombus changes. While actively growing during the first few minutes following injury the surface is composed primarily of platelets. As the thrombus stabilizes the surface composition changes to one composed primarily of a fibrin network containing cellular and platelet clusters (Figure 1).
We previously developed multi-scale models of clot formation taking into account all main components including hemodynamics, molecular signaling leading to platelet activation and coagulation biochemical reactions taking place in blood and on surfaces of platelets [4], [6]. A combination of experimental approaches, image analysis and multi-scale modeling was used for formulating a novel biological hypothesis of a fibrin cap being capable of stopping blood clot growth by interfering with the transport of thrombin to the thrombus surface. Thrombin is a potent platelet activator and is required for fibrin generation [4], [7], [8]. By interfering with thrombin transport to the thrombus surface, we hypothesize the fibrin cap can limit further growth.
Because thrombin is primarily generated by coagulation reactions concentrated on the surface of activated platelets in the thrombus [9], it is important to study the movement of thrombin through the fibrin network to determine if the network limits the distribution of thrombin to affect thrombus growth. Conceivably, the fibrin network forms a diffusion and permeability barrier and prevents the transport of thrombin to the thrombus surface.
This paper examines this hypothesis by studying in detail protein advection and diffusion to describe thrombin transport through fibrin networks (fibrin cap). It integrates computational and experimental analysis to show how the formation of a fibrin network limits thrombus growth. Network permeability and protein diffusion are the important factors determining the transport of proteins through the fibrin network of thrombi. We report on the experimental measurements of these factors and the development of a thrombus hemodynamics model incorporating them. We show that networks formed under physiological concentrations of fibrinogen and thrombin do not appreciably affect the diffusion of thrombin indicating the fibrin network does not provide a diffusion barrier. However, it is demonstrated that in permeable fibrin network, thrombin generated by platelets is rapidly washed downstream by advection. Only a thin band upstream and above the source of thrombin maintains an appreciable thrombin concentration, similar to the distribution expected in the absence of a fibrin network. However, the pore size in a newly formed sparse network is small enough to exclude resting platelets in flowing blood from approaching the thrombus core. The resting platelets in flowing blood contacting the thrombus therefore do not reach regions with sufficiently high thrombin concentration necessary for their activation and stable incorporation into the developing thrombus. Thus, the development of a sparse, permeable fibrin network on the thrombus surface may provide a mechanism to limit continued growth of small thrombi. The network does not provide a transport barrier for thrombin but rather a shield blocking resting platelets from approaching sites of thrombin generation. Our results identify a novel mechanism and reinforces the concept that the spatial separation of sites of factor activation and factor activity are important to understanding thrombogenesis. The suggested mechanism may prevent small thrombi, which frequently form as a result of activation or damage of a few endothelial cells, from growing into symptomatic occlusive clots.
Since thrombin is critical for continued thrombus growth, we developed a thrombus hemodynamics model to compute the thrombin concentration at different positions in the thrombus. The model assumes thrombin is generated on the surface of activated platelets in the thrombus core and is transported through the fibrin network that surrounds the core both by advective flow of fluid permeating through the fibrin network and by diffusion through the fibrin gel. To calibrate the model, we experimentally determined fibrin clot permeability and thrombin diffusion rates through a fibrin network. Permeability was determined by perfusing liquid through fibrin gels formed with a physiological range of fibrin concentrations (0.5 to 4.0 mg/ml). Protein diffusion rates were determined using Fluorescence Recovery after Photobleaching (FRAP) [10] protocols (Figure 2A). In addition, we also examined the diffusion rates of larger molecules, Fab IgG (MW = 50 kDa), in fibrin networks to assess the effect of the protein size on its diffusion. The experimentally determined measurements of permeability, protein diffusivity and the fibrin network density were then used for predictive analysis in the thrombus hemodynamic model.
Before quantifying the transport of proteins in fibrin networks, protein diameter distributions were measured using Dynamic Light Scattering (DLS) (Figure 2B). The mean hydrodynamic diameter of thrombin and Fab IgG solutes were found to be 4.1 nm and 11.2 nm, respectively. These are in a remarkable agreement with x-ray diffraction results, reporting the size of packing orthorhombic cells to be (4.5 nm4.5 nm5.0 nm) and (8.06 nm7.22 nm18.76 nm) for thrombin [11]–[13] and IgG Fab complexes [14], respectively.
Permeability of the fibrin networks was quantified from Darcy's equation using liquid flow rate and pressure gradient measurements across the fibrin networks. Our measurements showed a substantial decrease of the fibrin network permeability (Darcy's constant) by three orders of magnitude, as the fibrinogen concentration increased from 0.2 mg/mL to 4 mg/mL. A power law fit was used to obtain an explicit relationship between the permeability, , and the fibrin volume fraction, . This gave , with (Figure 3A). does not equal zero because it was difficult to collect consistent data for clots prepared at fibrinogen concentrations lower than 1 mg/mL. Under slight hydrostatic pressure the clot fibers deformed, broke near wall regions, and bundled together, severely disrupting the network structure. Meanwhile, clots prepared with fibrinogen concentrations higher than 1 mg/mL were stable to imposed pressure. The measured permeability was in the range from 1 to 1000 . This means that the ratio of , where is the protein hydrodynamic radius, is much smaller than 1 and therefore, the effect of hydrodynamic hinderance on diffusion (Equation (2) in Text S2) can be neglected.
From the relation obtained by Diamond and Anand [15], [16],(1)the fiber radius, , was found using the measured and values. The calculated fiber radii ranged from 150 nm to 500 nm (Figure 3B), which is in good agreement with the fiber thicknesses evaluated directly from confocal images and with previously reported values of hydrated fiber bundles [16], [17] of coarse gels. The average fiber radius was found to decrease from approximately 450 nm to 200 nm when fibrinogen concentration increased from 1 mg/mL to 4 mg/mL.
The values of the diffusion coefficients and mobile fractions of thrombin and Fab IgG were obtained by fitting experimental data obtained by FRAP experiments (Text S1) using Levenberg-Marquardt curve-fitting [18]. The resulting diffusion coefficients are plotted as a function of fibrinogen concentration in Figure 4. For comparison, data reported by Stewart et al. 1988 [19] for BSA molecules and by Spero et al. 2010 [20] for 228 nm and 526 nm diameter PEG-coated particles are also shown. The diffusion coefficient of thrombin and Fab IgG in solution in the absence of fibrinogen were found to be 1102.6 /s and 400.9 , respectively. By determining the corresponding molecular diameters, one can show that these are in a very good agreement with our DLS measurements. Indeed, from the Stokes-Einstein relation , it follows that . According to our FRAP measurements, the ratio of the diffusion coefficients of thrombin to that of IgG in liquid is = 2.86. Taking 4.1 nm, yields the hydrodynamic diameter for IgG 11.2 nm, which is in agreement with the value given by DLS (Figure 2B).
There are no noticeable changes in the diffusion coefficient of thrombin and Fab IgG up to a fibrinogen concentration of 2 mg/mL and 0.4 mg/mL, respectively, indicating no diffusion retardation. However, the increase of fibrinogen concentration to 4 mg/mL reduced diffusivity of Fab IgG by 22% and diffusivity of thrombin by 13%, indicating that fibrin network structure impedes the molecular diffusion due to smaller gel pore size. Pore size between fibers was estimated from microscope images to vary from 10 m at 0.4 mg/mL of fibrinogen to less than the microscope resolution of 0.2 m at 4 mg/mL of fibrinogen. These estimates did not consider pores between fibrils whose dimensions were beyond the microscope resolution limit. Our results also showed that the noticeable changes of thrombin diffusivity occurred when fibrinogen exceeded 2 mg/mL. This correlates with measurements of thrombin adsorption [21], which demonstrated the thrombin absorption increase between 2 mg/mL and 4 mg/mL of fibrinogen.
Comparison of the diffusion coefficients obtained from FRAP measurements with diffusion models (Figure 4) shows that the extended Ogston's model [22] fit the data well, whereas the Johnson model given by Equation (3) in Text S2, overestimates protein diffusion. Here, the diffusivity is plotted as a function of , where is calculated according to Equation (3). The model fitting parameters are given in Table 1.
One of the factors determining thrombus growth is the spatial distribution of hemostatic activators such as thrombin which causes activation of platelets and fibrin polymerization. Based on our observations that stabilized thrombi are covered by fibrin [2] and using our in vitro fibrin permeation and protein diffusivity measurements, we built a hemodynamics model to study the effect of the fibrin network on thrombin transport. In the model (Figure 6A), the geometry of a small thrombus comprises a central non-permeable core of radius consisting of densely packed activated platelets and a permeable fibrin network covering the thrombus core. The exterior shape of a thrombus is approximated by a hemisphere of radius . The porous structure of the fibrin cap permits blood flow within the cap, and therefore the transport of proteins includes both diffusion and advection.
To determine which mechanism drives protein transport in a thrombus and how it depends on permeability of the network and protein size we compared advective and diffusive fluxes within the fibrin cap. We calculated the flow field for a small thrombus with dimensions, and using solution for the stream function (Equations (13) and (14) in Text S3). We found that the variation of fibrin cap permeability from high to low values noticeably changes the flow through the cap. Highly permeable cap () hardly affects the flow outside the thrombus core (Figure 6C). As the permeability of the cap decreases down to its minimum value of 3 , the cap performs almost as a non-permeable shell restricting flow within the thrombus (Figure 6B).
In the vicinity of the non-permeable thrombus core, the flow velocity tends to zero according to no-slip boundary conditions, which leads to the formation of a diffusion layer near the surface of the core (Figure 7A). The thickness of this diffusion-prevailing layer can be found from the balance between advective and diffusive fluxes, , where is the thickness of the diffusion layer, is the protein concentration on the core surface, is the protein diffusivity, and is the local advection velocity. In terms of the Peclet number, , this condition means = 1, which yields the thickness of the diffusion layer profile, . Therefore, linearly depends on the protein diffusion coefficient and inversely proportional to the local advection velocity , which in turn, depends on the fibrin cap permeability (Equation (14) in Text S3). Our calculations showed that in high permeable networks the thickness of the thrombin diffusion layer was about 4% of the thrombus radius , whereas for low permeable networks ranged from 10% to 20% of . The larger Fab IgG molecules have 2x lower diffusivity resulting in a 2x thinner diffusion profile than thrombin. This points out the importance of considering effects of protein size on diffusion retardation. The presence of multiple protein molecules inside the thrombus will result in different diffusion layer profiles and therefore, different spatial distributions of proteins inside the thrombus.
This paper provides justification for a previously unrecognized possible mechanism limiting growth of small asymptomatic thrombi. The mechanism suggests that the fibrin network overlaying a thrombus observed in mice limits the thrombus growth by affecting the transport of proteins and by separating resting platelets in flowing blood from regions of high thrombin concentration generated by the thrombus core. Our analysis integrating experiments on protein diffusion and fluid permeation with the hemodynamic thrombus model revealed that permeability of the fibrin network and protein diffusivity are important factors affecting transport of blood proteins inside the thrombus.
We found that over a physiologically relevant range of fibrinogen concentrations (0.5 to 4 mg/mL) fibrin networks do not seem to form a strong diffusion barrier and that they minimally restrict the diffusion of thrombin. It was shown that the diffusivity of thrombin in the fibrin network can be hindered by 13%, whereas diffusivity of bigger molecules, Fab IgG, can be reduced by as much as 22%. We also found that the fibrin network permeability, , decreases by three orders of magnitude when fibrinogen concentration increases from 0.5 to 4 mg/mL. This essentially reduces advective flow in thrombi. It should be noted that although FRAP permits measuring diffusivity of proteins in a biopolymer network environment, some limitations exist when studying diffusion in extracellular matrices. As was recently shown [26], in cases where there is cellular accumulation and degradation of the protein diffusant, if diffusion is fast enough, overall FRAP kinetics tends to reflect the time scale of uptake and degradation rather than diffusion. These effects can be important when performing FRAP of thrombin diffusing through platelet aggregates composing a thrombus core. Thrombin binding to platelets' (GP) Ib-V-IX receptors [27] makes the fluorescence recovery a complex function of both binding and diffusion and therefore, requires proper quantitative interpretation [28]. In this case, one should consider Fluorescence Correlation Spectroscopy [29] as an alternative to the FRAP method to measure molecular diffusion [26].
The experimentally obtained metrics characterizing diffusive and advective transport inside the clot were incorporated into a thrombus hemodynamical model to run predictive simulations. We showed that the size of diffusive and advective regions strongly depends on the permeability, , of the fibrin cap and the diffusion coefficient, , of the protein. We demonstrated that the presence of a permeable fibrin cap limits thrombin accumulation on the thrombus surface to its downstream zone. The thrombin concentration on the upstream thrombus surface and in the region above the core are insufficient to support activation of resting platelets in flowing blood near the thrombus. In a high permeable cap, advective flow washes thrombin produced on the surface of the platelets in the thrombus core downstream, thus limiting continued growth except at the very downstream surface of the thrombus. Meanwhile, a low permeable fibrin cap impenetrable to platelets can delay platelet exposure to thrombin and, subsequently, reduce platelet activation and aggregation.
The structural composition of thrombi can vary depending on the vessel type (venous versus arterial), the nature of the vessel injury, and physiological and hemodynamic conditions. Recently, [30], [31] studied thrombus development in mouse cremaster arterioles and did not observe fibrin accumulation on the luminal thrombus surface that we see on stabilized small thrombi formed in venules. There are several differences in the injury models that might account for the reported difference in thrombus structure. Arterial thrombi form at higher shear rates and are platelet rich, while venous thrombi are fibrin rich. Additionally, the injury model reported by Stalker et al [31] involves vessel rupture and clot formation in the extravascular space while in the mesentery model thrombi formed in the absence of detectable bleeding. Thus, the extent of injury and exposure of blood to the internal layers of the vessel wall is different in the two injury models. In injuries in which we induce bleeding we observe rapid fibrin accumulation at the base of the thrombus, similar to what was observed in [31].
Recently, using thrombin-sensitive platelet binding sensor (ThS-Ab), Welsh et al. [30] identified thrombin rich regions in the developing thrombus. The sensor binds CD41 on platelets and following thrombin cleavage of the ThS moiety generates a fluorophore. According to this study, the highest levels of thrombin arose between 40 and 160 seconds nearest the injury site where fibrin colocalized and where the thrombus was most mechanically stable. Low thrombin activity was detected away from the vessel wall in the thrombus and was below the minimum detection limit in the outer most, 10 micron thick, layer of the thrombus. Although thrombus composition is different in arterioles, these results are in agreement with our simulations for venous thrombi, showing that thrombin concentration decreases from the thrombus interior to its surface, down regulating thrombus growth.
Stalker et al. [31] and Voronov et al. [32] reported the transport of different size molecules through platelet-reach thrombi. It was shown [31] that greater packing density in the core facilitated contact-dependent signaling and limited entry of plasma-borne molecules visualized with fluorophores coupled to dextran and albumin. Using Lattice Boltzmann method to simulate the flow through a reconstructed thrombus and Lagrangian Scalar Tracking with Brownian motion transport to model diffusion, transport of different factors was simulated in [32]. Modelling was performed for an input average lumen blood velocity of 0.478 cm/s, which resulted in 0.2 mm/s mean flow rate within the thrombus outer shell. The shell was calculated to be 100-fold more permeable than the thrombus core with core permeability of . These results support our assumption that platelet core in our thrombus model can be considered impermeable relative to the fibrin cap with permeability in the range from to .
In [32] the effective diffusion coefficients of FX, ADP and Ca were calculated and the averaged tortuosity, defined as the ratio of the diffusion coefficient in pure liquid phase to that in the clot, was found to be from 2 to 2.5. Meanwhile, our measurements provide the maximum achievable tortuosity corresponding to 4 mg/mL fibrinogen concentration to be around 1.14 for thrombin and 1.28 for Fab-IgG. Although these experimentally derived tortuosity values and tortuosity values calculated in [32] were obtained for different thrombus models and experimental conditions, it is likely the platelet clots hinder molecular diffusion to a greater extent than fibrin networks due to smaller platelet clot pore sizes. Our findings as well as results in [32] demonstrate that both fibrin cap and platelet-rich clots do not present a strong diffusion barrier for the relevant molecules, whereas, permeability can greatly affect protein transport in thrombi.
Overall, our results complement recent studies on thrombus development and emphasize the importance of spatial distributions of sites of factor activation and factor activity in regulating thrombogenesis. Our findings suggest that timely formation of a fibrin network on the surface of a developing small thrombus could limit its further growth. The proposed mechanism can prevent small blood clots from becoming large pathological thrombi creating life threatening emboli.
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10.1371/journal.pbio.1001602 | Distributive Conjugal Transfer in Mycobacteria Generates Progeny with Meiotic-Like Genome-Wide Mosaicism, Allowing Mapping of a Mating Identity Locus | Horizontal gene transfer (HGT) in bacteria generates variation and drives evolution, and conjugation is considered a major contributor as it can mediate transfer of large segments of DNA between strains and species. We previously described a novel form of chromosomal conjugation in mycobacteria that does not conform to classic oriT-based conjugation models, and whose potential evolutionary significance has not been evaluated. Here, we determined the genome sequences of 22 F1-generation transconjugants, providing the first genome-wide view of conjugal HGT in bacteria at the nucleotide level. Remarkably, mycobacterial recipients acquired multiple, large, unlinked segments of donor DNA, far exceeding expectations for any bacterial HGT event. Consequently, conjugal DNA transfer created extensive genome-wide mosaicism within individual transconjugants, which generated large-scale sibling diversity approaching that seen in meiotic recombination. We exploited these attributes to perform genome-wide mapping and introgression analyses to map a locus that determines conjugal mating identity in M. smegmatis. Distributive conjugal transfer offers a plausible mechanism for the predicted HGT events that created the genome mosaicism observed among extant Mycobacterium tuberculosis and Mycobacterium canettii species. Mycobacterial distributive conjugal transfer permits innovative genetic approaches to map phenotypic traits and confers the evolutionary benefits of sexual reproduction in an asexual organism.
| Bacteria reproduce by binary fission, generating two clones of the original; this restricts the genomic diversity of the population, which brings with it inherent evolutionary drawbacks. This problem can be eased by conjugation, which transfers DNA from a donor to a recipient bacterium. Understanding the potential of conjugal DNA transfer for generating genetic diversity is necessary for estimating gene flow through populations and for predicting rates of bacterial evolution. The influence of chromosomal conjugal DNA transfer on mycobacterial diversity has not been previously addressed. Here, we determine and compare the complete genome sequences of independent progeny from bacterial matings between defined donor and recipient strains of Mycobacterium smegmatis. We find the resulting hybrid bacteria to be extremely diverse blends of the parental strains, reminiscent of the genetic mixing that occurs through meiotic recombination in sexual organisms. This novel mechanism of conjugation can create genome-wide mosaicism in a single event, generating segments of donor DNA that range from small (∼0.05 kb) to large (∼250 kb), widely distributed around the recipient chromosome. We exploit this mixing by using genetic tools originally developed for finding mammalian disease genes to locate the genes that confer a donor phenotype in M. smegmatis. We speculate that similar genomic mosaicism observed in pathogenic mycobacteria arose from conjugation between ancestral progenitor strains.
| Sexual reproduction in eukaryotes promotes genetic diversity by increasing gene flow through a population, permitting both the loss of mutant genes and the acquisition of functionally distinct gene alleles. The diversifying potential is further enhanced by crossover events that create new mosaic recombinant meiotic products, which in turn may impart new functionalities not present in either parent. In contrast, bacterial fission provides rapid clonal expansion to fill an environmental niche, but lacks the evolutionary advantages of sexual reproduction. Horizontal gene transfer (HGT) mitigates the diversification constraints of asexual reproduction by mediating limited gene flow through the population. The fundamental forms of HGT include transformation, transduction, and conjugation. Conjugation is considered a major contributor to HGT, as it can transfer more extensive segments of DNA between different species and even kingdoms [1]–[4].
Conjugation describes the unidirectional transfer of DNA from a donor to a recipient, and requires cell–cell contact. Conjugal processes are traditionally plasmid encoded, or encoded by a discrete genetic element integrated into the chromosome. Transfer proteins are generally classified into those that establish and maintain mating-pair formation or those responsible for DNA transfer [5],[6]. These latter proteins recognize and nick the unique origin of transfer (oriT) on the plasmid and guide the DNA into the recipient cell. oriT is cis-acting, and thus, when recombined into the chromosome, it can mediate transfer of chromosomal DNA, as first described for E. coli Hfr strains [7]. DNA transfer in M. smegmatis displays all of the hallmarks of conjugation: it requires stable and extended contact between a donor and a recipient strain, it is DNase resistant, and the transferred DNA segments are incorporated into the recipient chromosome by homologous recombination [8]. While the process clearly meets the traditional definition of conjugation, the similarities with the classical E. coli Hfr system end there [9]–[13]. Mycobacterial conjugation is chromosome—not plasmid—based, and bioinformatic and genetic studies have yet to identify a genetic element that might mediate transfer [14],[15]. In E. coli, Hfr transfer always initiates at the sole plasmid-encoded oriT site, and the DNA is transferred in a 5′ to 3′ direction, such that only genes proximal and 3′ to oriT are inherited at high frequencies [10],[16]. By contrast, in M. smegmatis, all regions of the chromosome are transferred with comparable efficiencies as demonstrated by equivalent transfer of a kanamycin-resistance marker regardless of its chromosomal location [11]. This position independence is consistent with the presence of multiple, but ill-defined, initiation sites [17].
Transposon mutagenesis screens provided initial insights into the genetic requirements of transfer [14],[15]. These studies established a prominent role for the Type VII secretion apparatus, ESX-1, in both donor and recipient activity. ESX-1 clearly plays different roles in each cell type. ESX-1 donor mutants are hyperconjugative, suggesting secretion plays a role in negatively regulating transfer activity [15]. By contrast, recipient strain ESX-1 mutants do not receive donor DNA [14]. Although these studies provided novel insights into the functional roles of ESX-1, they did not provide insights on the transfer mechanism, or define what determines the mating type of a cell (either donor or recipient).
Here, as an alternative approach, we examined the products of DNA transfer to better understand this process and its contributions to mycobacterial evolution. We used next-generation sequencing to determine the parental inheritance profiles in transconjugant M. smegmatis progeny. The genomic sequence of each of the M. smegmatis parental strains has been determined, and the abundant single nucleotide polymorphisms between the two strains indicated that the transferred segments comprising the transconjugant genomes could be mapped with precision. We found that the parental contributions to the transconjugants were much more complex than expected, indicating a surprisingly major role for conjugal DNA transfer in generating genomic diversity. The blending of the parental genomes is reminiscent of that seen in the meiotic products of sexual reproduction. This comparison is validated by our use here of genomic approaches previously developed and applied in sexual reproduction systems to define candidate genes for conjugal mating identity.
To provide a selectable marker for chromosomal DNA transfer, a kanamycin resistance gene (Kmr) was integrated in the chromosome of mc2155, the standard laboratory and conjugal donor strain of M. smegmatis. Donor mc2155 derivatives that differed in their Kmr insertion site were mated to an apramycin-resistant (Apr) recipient strain, mc2874 (Figure 1A). mc2874 is an independent isolate of M. smegmatis that we have used as a standard recipient strain [8],[18]. Apramycin resistance was episomally encoded to avoid inheritance biases caused by selecting for this gene on the recipient chromosome. From matings between these strains, 12 independent KmrApr F1 progeny were isolated, and the DNA sequences of their genomes were determined (sequence data deposited in the EBI/ENA database at http://www.ebi.ac.uk/ena/data/view/ERP002619). Our comparative sequence analyses of the parental strains had shown that the circular mc2155 and mc2874 genomes are collinear, and that they contained abundant single nucleotide polymorphisms (SNPs; averaging one per 56 bp) providing a clear distinction between parental DNA origins (Figures 1A and S1). Individual sequence reads from each transconjugant were aligned with the donor strain genome to identify all transferred donor segments. When evaluating transconjugant sequences, we conservatively required the presence or absence of two consecutive recipient SNPs to define a boundary between recipient and donor sequence tracts, respectively (Figure S2). Donor segments replaced the corresponding recipient sequences, as evidenced by a concomitant localized loss of recipient-specific SNPs in transconjugants. Unique segments of transferred donor DNA, predicted by alignment analyses in transconjugants, were confirmed by conventional PCR and Sanger sequencing (Table S1). Two transconjugants had 11 regions that were merodiploid (approximately equal contributions of donor and recipient SNPs). As this was a resequencing and not a de novo sequencing strategy, we cannot determine the precise architecture and location of these regions. These regions did not contain repetitive elements, though it is possible that integration occurred at nonsynonymous sites via microhomology or through mechanisms not requiring homology.
The most striking observation from an alignment of our initial set of 12 transconjugant genomes with the parental genomes was that the transconjugant genomes were broadly mosaic, containing at least two, and as many as 21, separate tracts of cotransferred mc2155 DNA embedded in an mc2874 background (Figure 1B and Table S2). These separate segments of DNA were acquired in a single cell–cell transfer event, as determined in earlier studies [11]. To our knowledge, this degree of genome-wide diversity is unprecedented in genetic transfer events between bacteria. This contrasts directly with the iconic plasmid-transfer systems in which a single segment of donor DNA linked to oriT is inherited [10],[19]. Therefore, we refer to mycobacterial conjugation as distributive conjugal transfer to distinguish it from oriT-mediated transfer.
As expected, all transconjugant progeny acquired the selected Kmr gene, along with variable amounts of flanking mc2155 DNA (Figure 1B, Kmr, green segments embedded in yellow recipient DNA). Surprisingly, 5-fold more mc2155 DNA was co-inherited in segments that were not selected, and these segments were distributed around the genome with no obvious regional biases (Figure 1B, alternating blue and magenta improve visual discrimination between adjacent tracts; Table S2). The 12 transconjugant genomes analyzed contained from 57 kb to 679 kb (of 6.9 Mb) of mc2155-derived sequence. The sizes of the donor segments varied >1,000-fold, ranging from 59 bp to 226 kb (Figure S3 and Table S2), with an average size of 33.8 kb, and a mean of 10 tracts per genome (Table 1).
Some regions showed intricate microcomplexity of multiple inherited segments separated by short intervals of recipient DNA (Figure 1C and highlighted in Table S2). Note that the single-nucleotide discrepancies (colored SNPs) derive from parental inheritance, not de novo mutation (see reciprocal parental reference sequence alignments in Figure 1C). These likely resulted from a combination of repair and recombination events occurring between the recipient chromosome and a single molecule of introduced donor DNA, as some segments are separated by only a few base pairs. Regardless of the mechanism, the net effect was to create a localized composite blend of parental contributions at the nucleotide level.
The image in Figure 1B shows the extent of mc2155 DNA transferred to recipients when selecting for a single event: acquisition of the gene encoding Kmr. Based on the distributive nature of transfer, we reasoned that we could employ secondary screens of the transconjugants to map any additional genetic trait regardless of its linkage to the Kmr gene. Tracking parental SNPs within a group of individual transconjugants exhibiting a given phenotype should identify those shared SNPs (and parental genes) associated with that phenotype. We have previously observed that a subset of transconjugants become donors, suggesting that these progeny acquired a donor-conferring locus [11]. We hypothesized that an unbiased genome-wide mapping approach would identify a shared segment of mc2155 DNA among those progeny encoding this trait. Transconjugants derived from crosses of the differentially marked donor strains were screened for donor ability, and 10 independent donor-proficient transconjugants were identified. We note that mating identity is a mutually exclusive phenotype, and transconjugants exhibit transfer efficiencies comparable to parental strains ([11] and Table S3). Genomic DNA from each donor-proficient transconjugant was prepared and its sequence determined. Comparative sequence analysis showed that all donor-proficient transconjugants, regardless of the location of the Kmr gene in the parent, shared only one segment of mc2155 DNA (Figure 2A and Table S4), with the smallest region of overlap encompassing coordinates 74,522 to 119,788 bp (Figure 2B). This result is consistent with transfer of a single 45 kb locus (mid) that is sufficient to switch mating identity from recipient to donor in these transconjugants.
This region is not simply a hot spot for integration of acquired DNA, since the 12 recipient-proficient (i.e., did not become donors) transconjugants in Figure 1B were not similarly enriched for this segment of mc2155 DNA (compare Figures 1B and 2A, and see below). Closer examination of the region acquired by donor-proficient transconjugants established that they all had inherited a minimal segment of DNA encompassing the mc2155 esx1 locus (Figure 2B, 74,600–107,334 bp, esx1D, where the subscript differentiates donor or recipient origin). The esx1 locus encodes a Type VII secretion system [20],[21]. The encoded ESX-1 apparatus assembles in the cell membrane and secretes a specific set of proteins, which, in M. tuberculosis, are essential for pathogenicity [22]–[24]. Proteins secreted by ESX-1 lack a signal peptide that would aid in their identification, and the most notable substrate is a heterodimer of two small proteins, EsxB and EsxA. Other proteins encoded within the esx1 locus and elsewhere in the genome are also secreted through ESX-1, some of which are co-dependent on EsxBA secretion. The functions of most of the proteins encoded by esx1 genes are unknown, but the overall composition of the esx1 loci between the parental mc2155 and mc2874 strains are similar (see below). Although our previous transposon mutagenesis studies have shown that ESX-1 plays an important role in the process of DNA transfer in both donor and recipient strains, mating-type identity is not reversed in ESX-1 mutants [14],[15]. Therefore, the role of ESX-1 in determining mating identity was quite unexpected, and underscores the utility of a “change-of-function” mapping approach.
While all of the donor-proficient transconjugants inherited an intact esx1D locus, none of the recipient-proficient F1 strains did. Notably, four of the F1 recipient-proficient strains were derived from the Km0.1 parent, in which only 15 kb separate esx1D and the selected Kmr gene. Despite this tight linkage, distributive conjugal transfer readily segregated the Kmr gene and intact esx1D locus when appropriately screened, thereby augmenting the mapping resolution (Figure 1B, Table S2, and below). Helpfully, one of these recipient-proficient transconjugants (Km0.1c) inherited parts of esx1D, excluding these esx1 genes from mid candidacy (0064–0068 and 0077–0083, Table S2). These negative correlations affirm the functional dependence of the donor trait on the mid genes of esx1D and demonstrate the robust nature of distributive conjugal transfer in generating the level of genetic diversity necessary for our mapping analyses.
In classical genetic studies, fine mapping of a genetic determinant can be achieved by performing successive backcross introgression analyses to genetically purify a locus in a recipient background. We reasoned a similar strategy would achieve two goals: (1) discard mc2155 parental genes not required for the donor transfer trait and (2) further narrow the key conjugal mid gene region. Six F1 donor recombinants were backcrossed with mc2874 recipient derivatives that were marked with a different episomally encoded antibiotic resistance gene (Hygr or Apyr) in successive generations. Introgression entailed co-selection for Kmr transfer and the recipient marker to identify transconjugants at each generation (Nx), and then screening progeny for donor proficiency (Figure 3). Comparative analyses of genomes of three donor-proficient strains showed a purifying selection of the donor-conferring locus and Kmr genes in an otherwise recipient genome (Figure 4, Table S4). In each case, the majority of the F1 mc2155 DNA was lost. For example, the F1 parent of Km0.1BCb contained 19 mc2155 segments totaling over 869 kb, yet following six backcross generations this DNA was trimmed to three segments totaling 110 kb, most of which encompassed the selected mid and Kmr genes (79 kb, Table S4).
As expected, backcross matings also resulted in recipient-proficient progeny, several of which were also sequenced (Figure 3). Coincident with a reversal of mating identity, the esx1D locus failed to transfer. One recipient strain, Km0.8BC, retained only 75 kb of mc2155 DNA of the 920 kb originally present in the F1 parent (Figure 5, Table S4). Analyses of two recipient-proficient strains derived from independent F1 Km6.9 parents further refined the region of interest. Km6.9BCa included donor genes 0055D–0067D and 0079D–0083D and Km6.9BCb contained genes 0072–0075D (Figures 5 and 6, Table S4). Thus, these esx1D genes are insufficient to confer a donor phenotype. Taken together, the mapping data identify esx1 genes in 0068D–0071D and/or 0076D–0078D as being critical for determining mating identity. Ongoing studies requiring multiple, precise, targeted gene swaps will identify the key gene(s).
While most esx1 gene products are highly conserved among mycobacterial species, M. smegmatis proteins 0069, 0070, and the N-terminal two-thirds of 0071 have notably low amino acid identity between donor and recipient orthologs (Figure 6 and Figure S4) [14] and are therefore good candidates for switching mating identity. The proteins encoded from this region are not predicted to contain an obvious motif or domain that would provide mechanistic insight into their role in conjugation. However, the location of the mid genes within esx1 suggests that the encoded proteins modify ESX-1 structure or function, to perhaps affect cell–cell communication or physically mediate DNA transfer.
We used next-generation sequencing to examine transconjugant genomes and found that mycobacterial conjugation generates highly mosaic genomes created by a robust distributive conjugal transfer process. Transconjugants acquired large amounts of donor DNA (some exceeding one-fourth of the transconjugant genome; Table S4, Km4.5a), in varied segment sizes (spanning four orders of magnitude) that were distributed around the genome. We exploited these characteristics of distributive conjugal transfer (DCT) to map mating identity genes of M. smegmatis.
Hfr transfer in E. coli is initiated from the unique oriT and results in transfer of a single segment of the donor chromosome [9],[19],[25]. Thus, while the recipient acquires new genetic information, that new information is limited to DNA immediately adjacent and 3′ to oriT (Figure 7, left). Genetic analyses and an understanding of the RecBCD recombination machinery suggest that a single segment is integrated into the recipient chromosome via a recombination event occurring at each end of the transferred DNA molecule [16]. To our knowledge, whole genome sequencing has not been reported for Hfr– transconjugants, preventing a detailed comparison of the two conjugation systems. Thus, our study provides the first genome-wide analysis of bacterial conjugal transfer. In contrast to oriT-mediated transfer, the complex inheritance profiles exhibited by mycobacterial transconjugants suggest stochastic co-transfer from multiple origins, as previously predicted [17]. Based on our genome sequence data, we speculate that random chromosomal DNA fragments are generated in the donor, some of which are co-transferred into the recipient strain where they replace recipient sequences through homologous recombination. An alternative scenario is that a single large DNA molecule is transferred, which is processed into smaller segments before their integration into the recipient chromosome by homologous recombination. This scenario seems less likely as we would have expected to identify some transconjugant progeny containing exceedingly large chunks of donor DNA (3–4 Mb) integrated into the chromosome. These would have resulted from recombination close to the ends of the transferred molecule, before creation of small segments. This latter scenario is also less consistent with our previous observations, which indicated that the donor chromosome contained multiple initiation sites and that the efficiency of gene transfer was location-independent. We have considered examining boundary sequences to determine whether they provide insight on the mechanism of conjugation. However, there are multiple factors influencing boundary regions, which together prevent a unifying mechanistic insight. For example, the actual breakpoints generated by conjugation are almost certainly lost as the boundaries are driven by the requirement for homology and by different recombination mechanisms mediating integration, as evidenced by inheritance of both regions of microheterogeneity and single large integration events.
Mycobacteria encode multiple nonredundant recombination pathways (RecBCD, AdnAB, and nonhomologous end-joining), but are not known to encode a mismatch repair system [26],[27]. We postulate that homologous recombination mediated by AdnAB is likely responsible for the simple crossover events, which is consistent with the absolute requirement for RecA in DCT [17]. However, this form of homologous recombination alone seems insufficient to explain regions of microcomplexity. The clustered proximity of recombinant tracks indicates that an imported donor segment initially encompassed the entire region, but the mechanism underlying the internal mosaicism is unclear. Characterization of the mechanism and the enzymes behind this process will require careful directed approaches using defined recombination mutants.
Every facet of the transfer process contributes to the genetic complexity of the transconjugants (Figure 7). The large number and distributive character of the transferred segments, combined with the microcomplexity in some tracts, makes each transconjugant uniquely different from the others, as well as from the parental strains. The widely varied sizes of the transferred segments allows transconjugants to acquire both major changes, potentially bringing in entire operons encoding biological pathways, and minor nucleotide substitutions that provide subtle diversity, which could, for example, modify the activity or interaction specificity of an enzyme. Multiple pan-genomic changes that typically accompany evolution of bacteria are assumed to be a serial accrual of HGT and spontaneous mutation events (Figure 7). By contrast, a single step DCT event between two single cells generates a transconjugant strain that is a mosaic blend of the parental genomes, and not merely an incrementally altered derivative. Thus, distributive conjugal transfer provides an unparalleled mechanism for quickly generating tremendous genetic diversity, which rivals that seen in sexual reproduction [28].
Recent genome-wide studies of naturally competent strains provide an interesting contrast between the progeny of transformation and conjugation [29]–[32]. In these studies, nonselected segments of DNA were also observed around the recipient chromosome and thus contribute to variation. Microcomplexity in these segments suggested that, as for DCT, integration of transformed DNA was mediated by both recombination and/or repair machinery. However, the nonselected segments were significantly smaller (1–4 kb, depending on the species) than those described here, which average 49 kb and can be as large as 249 kb (Table S4, Km4.5b: 6,942,375–202,798). The limitation on recombination sizes in pneumococci correlated with an underrepresentation of large insertions, which together argued that transformation led to genome reduction and was unlikely to act as a mechanism for uptake of accessory loci [29]. The large DNA segments acquired via DCT, in contrast, facilitates inheritance of novel operons and genes. For example, one large recombination tract introduced a contiguous stretch of ∼55 kb of nonhomologous donor-derived DNA into the transconjugant chromosome (Km6.9b). Perhaps an example more functionally pertinent to our work was an insertion–deletion exchange observed in the divergent mid candidate region of esx1 in transconjugants switched to donors (Figure S5).
We have demonstrated conjugal DNA transfer in additional naturally derived M. smegmatis strains [8], indicating a broader presence for mycobacterial distributive conjugal transfer. The rough-colony morphology members of the Mycobacterium tuberculosis complex (MTBC) exhibit extremely low genetic variation, suggesting that they do not undergo HGT, are evolutionary young, and resulted from a recent clonal expansion [33]. However, there is now convincing evidence for HGT among M. canettii, and other smooth-colony MTBC strains, which display genome-wide mosaicism, although the precise mechanism(s) of HGT are unknown [34],[35]. Based on sequence comparisons, it was proposed that M. canettii strains are extant members of a genetically diverse MTBC progenitor species, M. prototuberculosis, whose members underwent frequent HGT [34],[36],[37]. The unspecified HGT process underlying that mosaicism is presumed to result from a series of sequential transfer events. However, based on our studies, distributive conjugal transfer involving the ancestral M. prototuberculosis offers a plausible and parsimonious explanation for the remarkably similar mosaicism observed among the extant M. canettii. We could envision that distributive conjugal transfer in M. prototuberculosis rapidly incorporated the necessary blend of parental genotypes that drove the emergence of the pathogenic, rough-colony morphology species, like M. tuberculosis, allowing their subsequent clonal expansion. Moreover, if DCT drove these postulated HGT events, the evolutionary clock for M. tuberculosis is likely much shorter because of the capacity of DCT to generate genome-wide mosaicism in a single step. Given the widespread nature of conjugation, we speculate that distributive conjugal transfer also occurs in other bacteria, conferring similar evolutionary benefits.
The characteristics of mycobacterial distributive conjugation suggested to us that tools developed for mammalian genetics could be applied here. Using a eukaryotic-style genome-wide association mapping approach, we mapped the mating identity locus (mid) for mycobacterial conjugation (Figure 7). Similarly, we applied a backcross introgression strategy to refine the mapping and to purge extraneous mc2155 sequence (Figure 7). The purifying selection of successive backcross generations effectively introgressed the mc2155 mid locus into the mc2874 background; this created a strain that was nearly isogenic to the mc2874 parent strain, but which now functioned as a conjugal donor. We note that the hybrid esx1 loci produced by distributive conjugal transfer have not been disabled (as in transposon mutagenesis screens), and still encode functional ESX-1 secretory apparatuses that secrete the major ESX-1 substrates (Figure S6). The un-annotated theoretical proteins encoded by the mid candidate genes bear no overt resemblance to those known to be involved in conjugation in other bacteria. Their association with the esx1 locus suggests that Mid proteins modify the ESX-1 secretion system, are secreted by ESX-1, or interact with other ESX-1–secreted substrates. The next step in their functional assessment will likely result from an extension of this work to identify which protein(s) or protein motifs are necessary and sufficient to impart conjugal sex identity. Interestingly, orthologs for the mid candidate genes are found in the sequenced genomes of other environmental mycobacteria, suggesting a possible ongoing role for distributive conjugal transfer in gene flow between mycobacteria. Orthologs of these mid candidates are not apparent in the esx1 locus of M. tuberculosis, consistent with our speculative model that the MTBC represents a clonally expanded product of distributive conjugal transfer, not necessarily an active participant in this process. Nevertheless, recent evidence from genome sequencing comparisons indicates that some form of genetic exchange has occurred between M. tuberculosis and M. canettii [35].
While we applied DCT to map mid genes, in principle any genetic trait that differs between the parental strains can be mapped using this genome-wide mapping strategy. For example, mc2155 and mc2874 grossly differ in colony morphology, biofilm formation, and phage susceptibility, any of which could have been scored as a change of function in the recipient and mapped by DCT. Similarly, biochemical differences between these strains could be discerned through simple, high-throughput assays. We recognize that more traditional approaches for mutagenic loss-of-function mapping [38],[39] will remain important in mycobacterial studies, but this new application of conjugation now allows any phenotype that differs between a mating pair to be unambiguously mapped.
Our analysis of distributive conjugal transfer (DCT) in M. smegmatis has practical and conceptual ramifications. It brings new tools to mycobacteriology, including those traditionally used exclusively in eukaryotic genetics. It also shows how bacterial evolutionary time scales can be compressed by generating incredible genetic diversity in a single step. Identifying the necessary components, such as esx1 and mid, will help to elucidate the mechanism, to allow modification of the system, and to computationally identify bacteria that actively participate in DCT—or engineer them to do so. Our previous finding of DCT in a mixed biofilm [40] underscores the importance of predicting how prevalent DCT may be in nature, for a more accurate interpretation of metagenomic datasets and to model gene flow through bacterial populations. Regardless of these secondary ramifications, our primary finding of the tremendous genomic variation generated by DCT takes a significant step toward bringing the evolutionary benefits of sexual reproduction to bacteria.
M. smegmatis donor strains were derivatives of the laboratory strain, mc2155 [41]. Each derivative has a KmR gene inserted at a unique location in the chromosome [11], which was mapped by DNA sequencing the flanking DNA and alignment to the mc2155 genome sequence (http://cmr.jcvi.org/tigr-scripts/CMR/GenomePage.cgi?org=gms), or the draft genome of the recipient (GenBank CM001762). The recipient strain mc2874 [18],[42] was transformed with a plasmid encoding either apramycin or hygromycin resistance to allow counterselection against the donor. M. smegmatis strains were cultured at 37°C in Trypticase Soy Broth with 0.05% Tween80, or on Trypticase Soy Agar (TSA) plates. Antibiotics were added at 100 µg/ml (apramycin), 100 µg/ml (hygromycin), and 10 µg/ml (kanamycin). DNA transfer experiments were carried out as described previously selecting for dual-resistant transconjugants [8]. To allow selection in the reiterative backcrosses, the recipient strain was alternated between that encoding either apramycin or hygromycin resistance. Each independent transconjugant was assayed in subsequent mating experiments to determine whether they were donor or recipient, in parallel with positive controls. As we have observed previously [8], this phenotype was mutually exclusive. Donor transfer frequencies were determined based on the average of three, independent mating experiments as described previously [8]. Zero transconjugants were obtained with recipient strains, below the sensitivity threshold of one event per 108 cells [8].
Transconjugants were colony purified, and genomic DNA was prepared and then subjected to whole-genome DNA sequence analysis at the Institute for Genome Sciences (IGS), U. Maryland, using paired-end Illumina technology. The sequence coverage for each genome was between 50-fold for F1 progeny and ∼1,000-fold for backcross strains. Sequence reads were mapped to the mc2155 reference sequence by IGS. Single nucleotide polymorphisms (SNPs) or sequence gaps were identified using the Integrative Genomics Viewer (IGV) sequence viewer [43] to define genomic regions of different parental origins. Boundaries of recipient- and donor-derived segments were recorded as the last recipient SNP observed with a minimum of two consecutive SNPs defining parental identity (Figure S2). A donor segment unique to each transconjugant was identified to confirm accuracy of the aligned sequence reads. Primers were designed to specifically amplify these segments, and the amplified products were cloned and sequenced (Table S1) to confirm that donor SNPs had been inherited by the recipient. A compilation of the donor and recipient segments from each transconjugant was projected onto the circular mycobacterial donor chromosome reference sequence, arranged as concentric circles of a Circos plot [44], with color optimization guided by ColorBrewer (Cynthia Brewer, The Pennsylvania State University). Collinearity of the donor and recipient genome was determined using Mauve, a program that was also used to identify SNPs and in/dels [45],[46]. All sequence data have been deposited at the European Nucleotide Archive at http://www.ebi.ac.uk/ena/data/view/ERP002619.
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10.1371/journal.pgen.1002472 | Genome Engineering in Vibrio cholerae: A Feasible Approach to Address Biological Issues | Although bacteria with multipartite genomes are prevalent, our knowledge of the mechanisms maintaining their genome is very limited, and much remains to be learned about the structural and functional interrelationships of multiple chromosomes. Owing to its bi-chromosomal genome architecture and its importance in public health, Vibrio cholerae, the causative agent of cholera, has become a preferred model to study bacteria with multipartite genomes. However, most in vivo studies in V. cholerae have been hampered by its genome architecture, as it is difficult to give phenotypes to a specific chromosome. This difficulty was surmounted using a unique and powerful strategy based on massive rearrangement of prokaryotic genomes. We developed a site-specific recombination-based engineering tool, which allows targeted, oriented, and reciprocal DNA exchanges. Using this genetic tool, we obtained a panel of V. cholerae mutants with various genome configurations: one with a single chromosome, one with two chromosomes of equal size, and one with both chromosomes controlled by identical origins. We used these synthetic strains to address several biological questions—the specific case of the essentiality of Dam methylation in V. cholerae and the general question concerning bacteria carrying circular chromosomes—by looking at the effect of chromosome size on topological issues. In this article, we show that Dam, RctB, and ParA2/ParB2 are strictly essential for chrII origin maintenance, and we formally demonstrate that the formation of chromosome dimers increases exponentially with chromosome size.
| Vibrio cholerae, the causative agent of cholera in humans, has two circular chromosomes of uneven size, each with distinct maintenance requirements. This is in contrast to classical, Escherichia coli–centric bacterial models of a single chromosome. In this study, we took advantage of V. cholerae's atypical genome structure to address important biological issues related to the maintenance of multipartite genomes. We further used V. cholerae to determine how genome architecture and genetic organization affects the odds of topological difficulties arising during replication. Our approach consisted of performing massive genome rearrangements to create various synthetic mutants of V. cholerae with nearly identical genetic backgrounds. We created mutants of V. cholerae with a single chromosome, with two chromosomes of equal size, or with identical origins of replication. To do so, we developed a genetic engineering tool based on the multiplexing of two site-specific recombination systems to allow efficient and directional manipulations of any DNA segment. In this study, we show that Dam, RctB, and ParA2/ParB2 are only essential for chrII origin maintenance, and we demonstrate that the odds of forming chromosome dimers exponentially increases with chromosome size.
| Bacteria were long thought to have a simple genome architecture based on a unique circular chromosome, and it is only in the late 1980s that the first prokaryote with multiple chromosomes, Rhodobacter sphaeroides, was characterized [1]. Since this seminal observation, many other species possessing multiple circular or linear chromosomes have been characterized across numerous bacterial lineages [2]. More than 80 multipartite bacterial genomes have been sequenced, propagating various hypotheses to explain their extant nature and posing fundamental questions about the selective benefit of such a genome architecture.
Numerous studies have established the cholera pathogen, Vibrio cholerae, as the model for bacteria with multipartite genomes [3]. The genome of V. cholerae N16961 consists of two circular chromosomes, a primary 2.96 Mbp chromosome (chrI) and a secondary 1.07 Mbp chromosome (chrII). V. cholerae's genes are asymmetrically distributed between the two chromosomes [4]. ChrI has low interspecies sequence variability and harbors many genes coding for essential biosynthetic pathways. ChrII contains many more species-specific genes, unknown ORFs and proportionally fewer essential genes [4]–[5]. Furthermore, V. cholerae's particular genomic organization and genetic disparity is consistent within the Vibrionaceae family [6]–[8]. The unusual genome structure of V. cholerae has inspired numerous studies to better understand the mechanisms and purposes of maintaining such a genomic organization, resulting in an impressive body of experimental data [9]–[20]. To date, however, and despite the impressive collective effort of the cited studies along with other research on chromosome and plasmid maintenance systems, the mechanisms coordinating the maintenance of multiple chromosomes are largely unknown. In tackling such pervasive yet fundamental questions, we decided to construct a unique genetic tool allowing targeted massive chromosomal rearrangements in proteobacteria. We applied this powerful technique to answer two outstanding questions. Firstly, we addressed the specific case of the essentiality of Dam methylation in V. cholerae. Secondly, we focused our genetic system on more general questions concerning bacteria with circular chromosomes by examining the effect of chromosome size and genetic distribution on topological issues.
Unlike eukaryotic organisms, where chromosomes are managed by common machineries which coordinate up to 90 chromosomes [21], V. cholerae has evolved a relatively complex and highly targeted strategy involving interplay of specific and common machineries for the maintenance of each chromosome. Replication of each V. cholerae chromosome is controlled by a unique initiator molecule [11]. ChrI replication is initiated at oriI by DnaA, the common initiator of chromosomal DNA replication in most bacteria [11], while chrII replication is regulated at a plasmid-like oriII by the Vibrio-specific factor, RctB [22]–[23]. ChrII is nonetheless replicated only once per cell generation, unlike plasmids, which are not generally linked to the cell cycle [24]. Both E. coli and V. cholerae are members of a mono-phyletic clade of the gamma-proteobacteria defined by the acquisition of the dam-seqA-mutH genes ensuring restriction of chromosome replication initiation to once per cell cycle and probe mismatch repair of replication errors [25]. Dam methylates the palindromic GATC sequence on both strands, which become transiently hemi-methylated after replication. The origin of replication and other regions with clusters of GATC sites become sequestered after replication by SeqA for up to one third of the cell cycle, which serves to preclude new initiations of replication [25]. Both V. cholerae chrI and chrII origins have GATC methylation sites [12] and their sequestration by SeqA contributes to limiting initiation of DNA replication to only once per cell cycle [9]. Replication of the larger chrI is initiated significantly before chrII so as to insure replication is terminated synchronously, suggesting a coordinating mechanism which has yet to be explained [16]. Whereas Dam is not an essential factor in E. coli, V. cholerae mutants lacking Dam methylation are not viable [26], implying the existence of differences in replication regulation between the two organisms. Methylation by CcrM, the counterpart of Dam in the α-proteobacteria, is also known to be essential for the viability of bacteria with multipartite genomes [27]–[29]. For these reasons, it was strongly suspected that the crucial role of Dam in V. cholerae could be related to its atypical genome arrangement, and Dam appeared to be a good candidate to investigate the coordinated replication initiation of the two chromosomes. In vitro studies showed that Dam methylation of RctB binding sites increases RctB binding and possibly serves a critical function in chrII replication [9]. The requirement for Dam in order to initiate replication at oriI was first studied in vivo using plasmids and monitoring the transformation efficiency of plasmids driven by oriI. These plasmids failed to transform E. coli dam mutants suggesting that Dam was essential for oriI replication initiation [12]. A reciprocal experiment involving oriC-plasmids and a mutant of E. coli where oriC was substituted by V. cholerae oriI (ΔoriC::oriI) showed that oriC-plasmids failed to transform E. coli ΔoriC::oriI Δdam [9]. Confronted with this last result and knowing that Dam is not essential in E. coli, it was hypothesized that the additional oriI-plasmid copies out-competed replication from chromosomal oriC, thus creating incompatibility conditions where Dam was required for viability of the transformants [9]. To prevent plasmid-mediated competition, the Dam requirement of V. cholerae oriI was directly assessed on the chromosome in E. coli ΔoriC::oriI [9], [30]. Two conflicting experiments, differing from the manner in which oriC was substituted by oriI, showed Dam methylation to be either required for the viability of E. coli ΔoriC::oriI [30] or not [9]. Therefore, the question of whether Dam was essential or dispensable for replication initiation of V. cholerae oriI remained unresolved. Reciprocal experiments consisting of testing oriII Dam requirement in a E. coli chromosomal context could not be tested because all attempts to replace oriC with oriII were unsuccessful [9].
After replication, partitioning of the resulting homologous chromosomes is fundamental to maintain genome stability [31]. In V. cholerae, the segregation of oriI and oriII are mediated by distinct partition factors, ParA1/B1 for chrI and ParA2/B2 for chrII [13]. ParA/B partitioning activity requires centromere-like, cis-acting sites called parS, which are bound by ParB to form a nucleoprotein complex that is a target for the ParA ATPase protein [32]. ParA1/B1 are chromosomal-like and mediate an asymmetric segregation of oriI [14]. On the other hand, ParA2/B2 are plasmid-like and carry out a symmetric segregation of oriII [20]. ParA1/B1 are not essential for chrI segregation, indicating that other factors contribute to the segregation of chrI [14] while ParA2/B2 are essential for chrII segregation and cell viability [20]. Many other bacteria with multipartite genomes have integrated distinct plasmid-like origins of replication and partitioning mechanisms to maintain their secondary chromosomes [3], which supports the hypothesis that secondary chromosomes were originally acquired as megaplasmids.
V. cholerae uses an interesting combination of mechanisms derived from both chromosomes and plasmids for the maintenance of chrII. In contrast to the above-mentioned plasmid-like mechanisms, terminal segregation of both chrI and chrII is controlled by a common bona fide chromosomal maintenance system involved in the generation of monomeric chromosome substrates for partitioning [19]. Circular chromosomes convey specific topological problems, such as the formation of dimeric chromosomes, which threatens the partition of genetic information to daughter cells (for a review, see [33]). Chromosome dimers are a side-product of homologous recombination associated with recombinational DNA repair between replicating or newly replicated circular chromosomes [33]. If an odd number of crossovers occur between sister strands, chromosome dimers are formed and must be resolved into monomers to allow chromosome segregation. This process is carried out by the combined action of the site specific tyrosine recombinases XerC and XerD that introduce an additional crossover at dif, a 28 bp site located opposite of the origin of replication [33]. V. cholerae carries two distinct recombination sites, dif1 and dif2, located in the terminus region of chrI and chrII, respectively [19]. Resolution of chromosome dimers of chrI and chrII links chromosome segregation to the late stages of cell division via the septal protein FtsK [19].
The presence of multiple chromosomes has posed challenges for in vivo studies of chromosome maintenance in bacteria as it is difficult to attribute observed phenotypes to a specific chromosome. To circumvent this issue, we designed a strategy based on specific genome rearrangements to directly study biological systems in their endogenous host. We developed a genetic tool based on two distinct site-specific recombination machineries, which allow targeted, oriented and reciprocal DNA exchanges throughout the genome. We used V. cholerae as a bi-chromosomal bacterial model to show the power of our genetic tool and how its use can help address important biological questions. Using this strategy, we examined the requirement of Vibrio-specific essential factors involved in chromosome maintenance for which functions could not be strictly attributed to a specific chromosome. We also investigated the correlation between chromosome size and the rate of formation of chromosome dimers that are the inevitable by-products of frequent recombination associated with recombinational DNA repair. To address all these questions, we created a mutant of V. cholerae with all its genetic content reorganized onto a single chromosome. We further refined our study by making additional chromosomal rearrangements to individually decipher each biological issue. In this article, we show that Dam, RctB and ParA2/ParB2 are only essential for chrII origin maintenance. We further demonstrate that the odds of forming chromosome dimers exponentially increases with chromosome size.
We generated a mutant of V. cholerae with all its genetic content reorganized onto a single chromosome. To do so, we fused chrI with chrII in a calculated and conservative manner respecting known criteria for chromosome organization and maintenance. Prokaryotic genomes show intolerance towards various chromosome rearrangements such as inversions or relocations of DNA fragments [34]–[44]. Nevertheless, bacterial chromosomal structure can be drastically altered [45]–[48] provided that organizational features are respected (for reviews [49]–[51]). The fused chromosome was constructed to conserve the “ori-ter” axial symmetry, gene synteny, strand bias and the polarities of the original replichores. Replication of the fused chromosome initiates at oriI of chrI and finishes in the terminus of chrII near dif2. The single fused chromosome carries exclusively chromosomal-like attributes for replication and chromosome segregation (oriI, ParA1/B1, dif2), like other mono-chromosomal bacteria. By initiating replication at oriI, we conserve the replication-associated gene dosage on chrI [10]. Lastly, comparative genomics has shown that the ter region of chrI is flexible and would likely tolerate the integration of the 1 Mbp chrII [7], [52].
To perform the above-mentioned genome rearrangements, we developed a genetic tool which allows efficient and directional manipulations of any DNA segment. It involves two site-specific recombination systems which normally promote precise excision of the temperate phage genomes, λ and HK022, from their chromosomal location [53]. We used λ and HK022 integrases (Intλ and IntHK), their respective excision factors (Xisλ and XisHK) and their associated left and right excision sites (attRλ/attLλ and attRHK/attLHK). Unlike other site-specific recombination systems used for precise genome manipulation such as Cre/loxP [54] or Flp/FRT [55], the λ and HK022 recombination reactions have the calculated advantage of being directionally controlled, as the presence of the Xis excision factors orientates the catalytic reactions in one direction. This characteristic is very useful for two reasons: first, it insures that the mutant strain will not revert to the wild-type configuration after chromosomal rearrangement. Second, the newly formed sites (attB/P) react poorly with the substrate sites (attR/L) [56]. Therefore the same system can be reused in the mutant strain to perform new rearrangements at other positions by integrating new attR/L sites. In theory, this system could be used an infinite number of times in the same strain.
To fuse the two chromosomes of V. cholerae, each partner attL and attR sites specific to the same integrase were inserted on separate chromosomes: attRHK/attLλ were inserted at the junction between the two replichores in the terminus region of chrI and attLHK/attRλ were placed flanking [parA2/B2-oriII-rctA/B] in the origin region of chrII (Figure 1A). The consecutive recombination reactions between attRHK/attLHK and attRλ/attLλ sites, upon expression of Int and Xis, led to the fusion of chrI with chrII (Figure 1B, 1C). To visualize chromosomal rearrangement events, we used a colorimetric screen based on recombination-dependent reconstitution and expression of the lacZ gene (Figure 1E). We obtained a stable MonoCHromosomal V. cholerae mutant strain (MCH1) with a single chromosome of the expected 4 Mbp size (Figure 1F) observable by pulsed field gel electrophoresis (PFGE). MCH1 cells attain a generation time of 29 minutes when grown in fast-growing conditions (Table S1). Under the microscope, MCH1 fixed cells are indistinguishable from N16961 wild-type (WT) (Figure 1G) and the counting of viable cells forming microcolonies confirmed that MCH1 incurs no increase in the rate of mortality compared to the WT (data not shown). We measured the DNA distribution in exponentially growing cultures by flow cytometry and compared these distributions with modeled distributions (Figure S1). Whereas WT has a replication pattern which can be successfully modeled by assuming that chrII initiates late and terminates at approximately the same time as chrI as previously described [16], our analysis of MCH1's replication pattern was consistent with a single chromosome replicated at a constant rate (Figure S1).
We have taken a radical genetic approach by rearranging the genome of V. cholerae to investigate the specific biological functions of RctB and ParA2/B2. Since chrII is indispensable, these factors, essential for chrII initiation and partition, are ultimately essential for cell viability [11], [20], [22]. However, an additional role in the maintenance of chrI could never be formally tested due to the essentiality of their functions for chrII perpetuation. Recombinational fusion of the two chromosomes in MCH1 resulted in the excision of an 8 kb circular molecule carrying [parA2/B2-oriII-rctA/B-aph] (Figure 1C). The excised molecule encoded a functional aph gene conferring kanamycin resistance to the parental strain of MCH1, MV127. This circular molecule was readily lost in absence of selection observable by the absence of kanamycin resistance in MCH1 cells (Figure 1D). Loss of the 8 kb molecule was further confirmed by PCR, showing an absence of amplification of parB2 and rctB loci from MCH1 genomic DNA, while these loci could normally be amplified from MV127 genomic DNA (data not shown). Loss of the 8 kb molecule was surprising since it harbored the oriII origin of replication and a centromere-like parS2-B site (within rctA) [57] along with associated replication (rctA/B) and partitioning (parA2/B2) factors that should allow it to replicate autonomously in the cell. We have no experimental evidence that could explain this loss, but it could be the result of partition-mediated incompatibility [58] between parS2 sites located on separate entities, the fused chromosome and the 8 kb circular molecule. Yet, by physically linking chrII to chrI in MCH1, we placed replication and partitioning of chrII under the control of chrI machinery rendering chrII factors for replication initiation (RctB) and partitioning (ParA2/B2) non-essential.
Most of the centromere-like parS2 sites are located near oriII, ensuring its partition, but a functional parS2 site, parS2-1, was found located near the chrI terminus [57]. Therefore, ParA2/B2 could have an important function for the segregation of the terminus region of chrI. Under the microscope, MCH1 cells are indistinguishable from WT (Figure 1G). Nucleoid staining with DAPI shows no evident segregation or division problems that would be easily detectable by the presence of anucleoid cells, filaments and chromosomes trapped in the septum of division (Figure 1G). Our approach allowed us to readily demonstrate that the essential functions of RctB and ParA2/B2 in V. cholerae are strictly limited to chrII maintenance.
All previous in vivo Dam studies were undertaken in E. coli, where Dam is not essential. Here we investigate the essential function of Dam directly in V. cholerae to eliminate confusion arising from extrapolated results from E. coli. MCH1 enabled us to test the essentiality of Dam in replication initiation, since it only carries a single origin of replication, oriI. We deleted dam in MCH1 and the WT. Deletion of dam was done in the presence of pGD93, a complementing temperature sensitive replicating plasmid expressing V. cholerae dam under the control of an arabinose-inducible (permissive conditions) and glucose-repressible (restrictive conditions) promoter [9]. In the presence of Dam, both WTΔdam-[pGD93] and MCH1Δdam-[pGD93] grew normally (Figure 2A). Under restrictive conditions when Dam was depleted, WTΔdam colonies were hardly visible (Figure 2B) confirming the essentiality of Dam in V. cholerae. MCH1Δdam, on the other hand, grew and formed colonies under restrictive conditions (Figure 2B), indicating that Dam is no longer essential. This result demonstrates that initiation of replication at oriI doesn't require Dam. To more precisely characterize the role of Dam, we created a second mutant of V. cholerae where we maintained two distinct chromosomes but placed replication of chrII under the control of an additional copy of oriI, since Dam is not essential to initiate replication at oriI. To substitute oriII with oriI, we used the dual site-specific recombination tool previously described (Text S1). We generated a mutant of V. cholerae carrying two Identical Chromosomal-like oriI Origins (ICO1). The rctB deletion did not affect the viability of ICO1 confirming that its essential function was only required for replication initiation of oriII. We further tested the essentiality of Dam in ICO1, as described previously, and found that ICO1Δdam cells were viable under restrictive conditions (Figure 2B) demonstrating that Dam is no longer essential when chrII replication is initiated at oriI. Therefore, we can assert that Dam is required for replication initiation of chrII from oriII only.
It was previously thought that the critical function of Dam in V. cholerae could be related to its atypical genome arrangement. However, Dam is also essential in Yersinia pseudotuberculosis and Aeromonas hydrophila [59], bacteria with single chromosomes and members of the gamma subdivision of proteobacteria with V. cholerae. Therefore, the essential function of Dam could possibly be unrelated to the management of a multipartite genome. It is known that DNA methylation exerts an effect on diverse bacteria via its role as a global regulator of gene expression. In E. coli, many genes involved in DNA mismatch repair, SOS response, motility, host-pathogen interactions or cell cycle regulation are mis-regulated in the absence of Dam [59]. Thus, the role of DNA methylation in diverse cellular processes via gene expression regulation could also explain Dam's essential function in V. cholerae. The viability of MCH1Δdam mutants allowed us to rule out the potential role of Dam as an essential global regulator of gene expression since they have nearly the same genetic background as the WT where dam deletion is lethal. We further demonstrated in MCH1 that Dam was not required for initiation of replication at oriI. We tested the essentiality of Dam in the mutant ICO1, which carries two chromosomes both initiated at oriI. Since ICO1Δdam is viable, this precisely defined oriII as the region where Dam executes its essential function. This result substantiates earlier in vitro work showing that RctB preferentially binds methylated oriII [9]. We propose that in absence of Dam, GATC sites in oriII do not become methylated, preventing the binding of RctB to oriII and therefore precluding chrII replication initiation and maintenance which is fatal to the cell.
Formation of dimeric chromosomes is a particular problem associated with the circularity of bacterial chromosomes. We used V. cholerae as a bacterial model to determine how genome architecture affects the odds of topological difficulties during replication by assaying the effect of chromosome size on the rate of chromosome dimer formation. Very few cells carrying a dimer are expected to yield viable progeny in the absence of resolution. Inactivation of chromosome dimer resolution in E. coli results in ∼15% cell death per generation, which corresponds to the estimated rate of chromosome dimers formed at each cell generation [60]–[61]. We measured the fitness defect of a dif mutant by growth competition experiments, in which the growth of the mutant strain was directly compared to the growth of its parent (Figure 3B) to quantify the rate of dimers formed on a dif-carrying chromosome. In V. cholerae WT, 8.8% of dimers per cell per generation are formed on the 3 Mbp chrI (Δdif1) and 3.4% of dimers are formed on the 1 Mbp chrII (Δdif2) when grown in rich LB media (Figure 3A, 3B). In MCH1, 12.5% of dimers per cell per generation are formed on the 4 Mbp chromosome (Δdif2) under the same growth conditions (Figure 3A, 3B). These results suggested that dimer formation increases with replicon size. To strengthen our interpretation, we decided to construct an additional mutant of V. cholerae with two equally sized chromosomes of 2 Mbp and measure the rate of dimer formation on each chromosome. We transferred 1 Mbp from chrI to chrII by swapping the 1.05 Mbp DNA fragment evenly surrounding dif1 with the 0.12 Mbp DNA fragment evenly surrounding dif2, resulting in the exchange of dif1 and dif2 using the genetic tool described above (Text S1, Figure S2). We obtained a mutant of V. cholerae with Equally Sized Chromosomes (ESC1 with chrI/II and chrII/I) observable by PFGE (Figure S2D). A measure of the rate of chromosome dimers formed on the two 2 Mbp chromosomes was performed in ESC1. Our results show that 4.9% of dimers per cell per generation are formed on the 2 Mbp chrI/II (Δdif2) and 4.3% of dimers are formed on the 2 Mbp chrII/I (Δdif1) (Figure 3A, 3B). We plotted the rate of chromosome dimer formation as a function of chromosome size and observed a linear relationship between chromosome size and the logarithm of the frequency of dimer formation (r2 = 0.97) (Figure 3C, Methods). This result indicates that chromosome dimer formation increases exponentially with the size of the chromosome. In ESC1, the two equally sized chromosomes, chrI/II and chrII/I, have an asymmetric distribution of genes, specific machineries for their respective maintenance, distinct terminus regions and, very certainly, distinct chromosome structure, and yet the probability of dimer formation for each chromosome is essentially equivalent (Figure 3A). This implicated chromosome size as the primary influence on the rate of dimeric chromosome formation in an identical genetic background.
Homologous recombination involves a Holliday junction intermediate which is resolved by the RuvABC complex leading to either crossover or non-crossover potential products with only crossovers leading to the formation of chromosome dimers [62]. In E. coli, the RuvABC pathway is biased towards generating non-crossover products [63]–[64]. Since this bias can vary between species, it is not possible to infer the effects of genome architecture on the formation of chromosome dimers by direct comparison between bacteria with single and multiple chromosomes or between bacteria with multiple chromosomes of different sizes. V. cholerae allowed us to modify the size of the chromosomes by transferring DNA from one chromosome to the other, with minimal modifications of the genetic background.
We tested the effect of DNA distribution between multiple chromosomes on the total rate of chromosome dimer formation. To do so, we measured the fitness defect of xerC mutants to obtain a quantification of the total rate of chromosome dimers formed in the cells (Figure 3D). As a consequence of more dimer formation in WT compared to ESC1, we observed that a xerC deletion had a greater effect on WT than on ESC1 (15.5% in WT , 10.8% in ESC1). The unequal (WT) or equal (ESC1) genetic distribution influences the chances for chromosome dimers to arise. Based on this result, it might be considered surprising that the extant WT genome configuration has been selected and all vibrios characterized to date have been shown to possess two unequally sized chromosomes [8]. This suggests that dimer formation has little impact on the selection of DNA distribution on multiple chromosomes.
One possible explanation for V. cholerae's unequally sized replicons and distinct replication initiation mechanisms may lie in adjusting the balance between genes found on separate chromosomes in response to drastic changes in growth conditions [10], [17], [65]. Gene dosage tends to shape chromosome organization of fast-growing bacteria, favoring placement of genes involved in translation and transcription near the origin of replication [65]. Differential gene dosage depends on replication rate, chromosome size and doubling time. This effect is particularly important for V. cholerae with its two chromosomes of uneven size and extremely short generation time. Indeed, when V. cholerae growth rate increases, origin-proximal loci of chrI are amplified by up to four copies per cell, yet origin-proximal loci of chrII never total more than two copies per cell [17]. Consistent with its larger size, gene dosage effects on chrI are greater than on chrII [10], [16]. Differently sized replicons may thus be selectively advantageous as a means to allow for a more nuanced gene dosage effect. This is certainly the case for the vibrios, where a higher abundance of growth-essential and growth-contributing genes are located near the origin of replication of chrI coupled with a dearth of such genes on chrII. This theory lends itself well to further investigation using our genetic engineering tools.
We developed a site-specific recombination-based engineering tool, which provides us with a powerful means to massively reorganize in principle any prokaryotic genome provided that necessary host factors are present. This genetic tool consists in harnessing the λ and HK022 recombination systems to perform a large panel of genome reorganizations. By controlling the location and the orientation of each partner recombination site, we can obtain a large variety of genome rearrangements, such as chromosome fusion (e.g. MCH1), transfer and exchange of DNA fragments (e.g. ESC1), deletion, insertion, inversion or substitution of DNA (e.g. ICO1). Thanks to the construction and analysis of various synthetic mutants, we were able to tackle important biological issues on chromosome maintenance in V. cholerae. We showed that Dam, RctB and ParA2/ParB2 factors are essential for chrII maintenance. We further revealed that the odds of forming chromosome dimers exponentially increase with the size of a circular chromosome.
Our construction of mutants with massive genome rearrangements demonstrates the incredible plasticity of prokaryotic genomes. All of these genomic mutants conserved the rapid growth characteristic of vibrios, although with a slightly extended generation time (Table S1) that may be linked to their alternative genomic structure. This is currently under investigation. Recent advancements in the field of synthetic biology have demonstrated that the de novo creation of artificial genomes is now an attainable objective [66]. The recent assembly of the 580 kb genome of Mycoplasma genitalium starting from chemically synthesized oligonucleotides [67] and the successful demonstration that one can maintain and engineer a bacterial genome in a yeast and then transfer it to a bacterial recipient cell to generate an engineered bacterium [68] pave the way for many applications previously thought to be out of reach [69]. The current understanding of bacterial genomic organization and its connection with precise phenotypic properties is insufficient to propose an optimized genome arrangement to the field of synthetic biology. MCH1 is by far the closest isogenic mono-chromosomal model that can be used to make comparisons with the bi-chromosomal V. cholerae N16961 strain. A previous work has been done in Sinorhizobium meliloti, in which spontaneous fusions of the three natural replicons occurs at low frequency through recombination between repeated sequences in the genome [70]. In these experiments, the three different fused molecules all conserved their functional origins of replication, and the resulting fusion was reversible, rendering the results inconclusive in terms of the relationship between growth advantage and genome organization. On the contrary, the single chromosome of our engineered MCH1 is stable, contains only a single origin and terminus of replication and therefore provides us with a powerful new tool to investigate the selective advantage(s) of the characteristic multipartite genome organization of vibrios. New insights into bacterial genome organization and determination of how genomes are arranged can help us to design more optimized chromosomes, which will undoubtedly open novel developments in the field of synthetic biology.
Bacterial strains and plasmids used in this study are listed in Table S2. Cells were grown at 37°C in Luria broth. Antibiotics were used at the following concentrations: ampicillin, 75 µg/mL; chloramphenicol, 25 µg/mL for E. coli and 5 µg/mL for V. cholerae; kanamycin 25 µg/mL; spectinomycin 100 µg/mL; zeocin 25 µg/mL. Diaminopimelic acid was used at 0.3 mM, X-Gal (40 µg/mL); IPTG(1 mM); arabinose (0.2%) and glucose (1%).
DNA cassettes containing the att recombination sites were transferred from a plasmid vector to the chromosome by two homologous recombination steps. To provide homology for integration, two 500 bp regions spanning the point of insertion were amplified from N16961 chromosomal DNA by PCR. The amplified fragments were cloned into an R6K γ-ori-based suicide vector, pSW7848 that encodes the ccdB toxin gene under the control of an arabinose-inducible and glucose-repressible promoter, PBAD. The sequences containing the att recombination sites of interest were then cloned between the two chromosomal fragments. For cloning, Π3813 was used as a plasmid host [71]. For conjugal transfer of plasmids to V. cholerae strains, E. coli β3914 was used as the donor [71]. Selection of the plasmid-borne drug marker resulted in integration of the entire plasmid in the chromosome by a single crossover. Elimination of the plasmid backbone resulting from a second recombination step was selected for by arabinose induction of the ccdB toxin gene.
V. cholerae N16961 El Tor strain deleted for lacZ was used to create the mono-chromosomal MCH1 strain [4], [72]. Following the above-mentioned cloning and genome engineering procedures, four attR/L sites were inserted at precise chromosomal loci near dif1 on chrI and near oriCII on chrII using pSW7848-derivitave KO vectors pMP36 (attRλ), pMP42 (attLλ), pMP35 (attRHK), pMP49 (attLHK) (Table S2). First, [attRλ-3′lacZ-FRT-aph-FRT] was inserted downstream of rctB on chrII using pMP36 in N16961ΔlacZ generating strain MV122. The aph cassette was excised using pCP20 for expression of Flp recombinase that catalyses recombination between the two FRT sites [73]–[74]. After Flp-mediated recombination, a single FRT site remained near oriCII and the strain became sensitive to kanamycin, MV122Δaph. Second, [attLλ-5′lacZ-FRT-aph-FRT] was inserted upstream of dif1 on chrI using pMP42 in MV122Δaph generating strain MV124. The aph cassette was excised using pCP20, generating the mutant MV124Δaph. We checked MV124Δaph by PCR to make sure that undesirable recombination events between the remaining FRT site on chrII with FRT sites on chrI didn't occur. Third, dif1 was replaced by [attRHK-FRT-aph-FRT] using pMP35, yielding strain MV125. To insert attRHK close to dif1, it was necessary to delete dif1 to prevent site-specific integration of a dif1-carrying KO-vector mediated by the endogenous V. cholerae XerC/D recombinases [19]. The aph cassette was not excised, this antibiotic resistance cassette serving as a reporter to follow the subsequent loss of the excised 8 kb circular molecule resulting from the fusion of chr1 with chr2. Fourth, [attLHK] with no antibiotic resistance cassette was inserted downstream of parB2 using pMP49 generating mutant MV127 (Figure 1A).
A temperature-sensitive replicating vector pMP6 expressing [intλ-xisλ , intHK-xb isHK] was conjugated into MV127. Donor cells β2163 [pMP6] and recipient cells (MV127) were conjugated for one hour at 30°C and plated on LB-agar at 30°C supplemented with ampicillin, X-Gal and IPTG to select for pMP6 and monitor recombination events between attLλ and attRλ. Reconstitution and expression of the β-galactosidase encoding gene led to appearance of blue cells when grown in presence of X-Gal and IPTG. After 36 hours of growth at 30°C, blue quarters appeared within single white conjugant colonies (Figure 1E). From blue/white colonies of mixed population, cells were grown at 30°C in LB in presence of ampicillin to enrich for chromosome rearrangements. Cells were plated on LB supplemented with X-Gal, IPTG to monitor attLλ and attRλ recombination events and incubated at 42°C to cure pMP6. All selected colonies were completely blue. Ten clones were isolated and tested by PCR using primers flanking both recombined attBλ and attBHK sites to verify that recombination occurred between all four recombination sites. All tested blue clones also had recombined attRHK×attLHK. Fusion of the two chromosomes resulted in the excision of an 8 kb circular molecule. In absence of antibiotic pressure that selected for this 8 kb circular molecule (aph gene formerly located in the terminus region of chrI), the molecule was rapidly lost. All remaining and undesired FRT and attP sites were excised within the 8 kb molecule and subsequently lost. The resulting mutant carries a single circular chromosome, free of antibiotic resistance cassettes and containing only two short 50 bp attB sites that delimit chrI from chrII. Genomic stability of the mutant was established over 1000 generations carried out during a long-term evolution experiment.
The preparation of genomic DNA embedded in agarose gels and the protocol for PFGE was performed as previously described [8], [75].
WT, MCH1 and ICO1 strains were deleted for dam using pGD121 knock-out vector in the presence of pGD93 (Dam complementing vector) and then depleted for Dam as previously described [9].
The proportion of cells that a mutant strain deficient in dimer resolution fails to produce at each doubling time of its parent can be measured by growth competition experiments. Growth competitions of V. cholerae strains are described in [19]. V. cholerae cells were grown at 37°C with a 10−3 dilution in LB media every 8–12 h. Colony-forming units (CFUs) of mutant and parental cells in the cultures were determined by plating on appropriate antibiotic plates. These numbers were used to calculate the number of generations of the parent cells between each time points and the CFUs ratio of mutant versus parent cells at each time point. This ratio varies exponentially with the number of generations. The coefficient of this exponential is a good estimation of the rate of dimer formation [19]. Following this method, we estimated the rate of dimer formation for each mutant in three independent experiments. In Figure 3C, the relationship between the rate of dimer formation and the logarithm of chromosome size has a very high R2 (>0.9) with no significant departure from linearity (P value = 0.1827), which indicates a strong linear relationship between the two variables. The slope is significantly different from zero (P value<0.0001) and the confidence interval for the slope is 95%.
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10.1371/journal.pgen.1005011 | Pervasive Variation of Transcription Factor Orthologs Contributes to Regulatory Network Evolution | Differences in transcriptional regulatory networks underlie much of the phenotypic variation observed across organisms. Changes to cis-regulatory elements are widely believed to be the predominant means by which regulatory networks evolve, yet examples of regulatory network divergence due to transcription factor (TF) variation have also been observed. To systematically ascertain the extent to which TFs contribute to regulatory divergence, we analyzed the evolution of the largest class of metazoan TFs, Cys2-His2 zinc finger (C2H2-ZF) TFs, across 12 Drosophila species spanning ~45 million years of evolution. Remarkably, we uncovered that a significant fraction of all C2H2-ZF 1-to-1 orthologs in flies exhibit variations that can affect their DNA-binding specificities. In addition to loss and recruitment of C2H2-ZF domains, we found diverging DNA-contacting residues in ~44% of domains shared between D. melanogaster and the other fly species. These diverging DNA-contacting residues, found in ~70% of the D. melanogaster C2H2-ZF genes in our analysis and corresponding to ~26% of all annotated D. melanogaster TFs, show evidence of functional constraint: they tend to be conserved across phylogenetic clades and evolve slower than other diverging residues. These same variations were rarely found as polymorphisms within a population of D. melanogaster flies, indicating their rapid fixation. The predicted specificities of these dynamic domains gradually change across phylogenetic distances, suggesting stepwise evolutionary trajectories for TF divergence. Further, whereas proteins with conserved C2H2-ZF domains are enriched in developmental functions, those with varying domains exhibit no functional enrichments. Our work suggests that a subset of highly dynamic and largely unstudied TFs are a likely source of regulatory variation in Drosophila and other metazoans.
| The phenotypic differences observed between closely related organisms are thought to be due largely to changes in regulatory networks. Changes in transcriptional networks can occur via mutations in cis binding sites, for which there are numerous known examples, as well as via binding specificity variation in transcription factors (TFs), a less studied phenomenon that has been observed primarily in multi-gene families. Though large-scale experimental studies ascertaining the extent to which TFs contribute to regulatory network variation across organisms are lacking and would be time-consuming, computational methods can begin to address this challenge. Here, we present a systematic, large-scale analysis of DNA-binding specificity evolution in TF orthologs by computationally leveraging specific features of Cys2-His2 zinc finger proteins, the largest class of TFs in animals and major components of their regulatory programs. We find not only that divergence of DNA-binding residues in 1-to-1 orthologous C2H2-ZFs is pervasive but also that these changes show evidence of functional constraint and occur in a gradual, evolutionarily viable manner. We conclude that the diversification of orthologous TFs has most likely played a major and largely unstudied role in gene regulatory network evolution in metazoans.
| Differences in regulatory networks have been proposed to be one of the major determinants of the phenotypic variations observed across organisms [1]. There are two ways by which regulatory networks evolve: changes in cis or trans. The predominant view is that regulatory evolution results mainly from the gain and loss of binding sites in cis-regulatory regions because incremental, evolutionarily viable steps can occur [2–5]. Mutations in transcription factors (TFs), on the other hand, can affect the expression of multiple genes and are thought therefore to be more likely to have detrimental consequences [6–9]. Nevertheless, case studies of specific biological systems have revealed instances of regulatory divergence stemming from TF variation. These variations include gene loss as well as gene duplication where the subsequent paralogs exhibit gain and loss of effector domains, changes in interactions with other regulatory proteins, or novel TF binding potential [10–15]. Specific cases of variations in non-duplicated TFs are also known; an example of 1-to-1 orthologous plant TFs with differing binding specificities was recently discovered [16], along with a homeodomain TF in animals where the addition of a functionally important transcriptional repressor domain is found in insect orthologs [17, 18]. However, a large-scale experimental study ascertaining the extent to which TF variation may contribute to overall regulatory network evolution is still lacking; it would require determining DNA-binding specificities or genomic occupancies for numerous TFs across a diverse set of organisms. Computational methods can begin to address this challenge by leveraging specific features of TFs.
TFs come in distinct structural classes based upon their incorporation of various DNA-binding domains. For many of these domains, the amino acids conferring DNA-binding specificity are known. This provides a platform to assess TF variation via comparative sequence analysis. The Cys2-His2 zinc finger (C2H2-ZF) TFs in particular are an excellent system to probe for variation, as C2H2-ZF domains have a conserved modular structure with binding specificity conferred largely by four DNA-contacting residues within the domain’s alpha-helix [19]. Further, they constitute the largest group of TFs in higher metazoans [20], making up nearly half of all annotated TFs in human, and are major participants in regulatory programs. A C2H2-ZF domain can specify a wide range of three or four base pair targets, and tandem arrays of these domains bind contiguous DNA sequences, giving C2H2-ZF genes the ability to recognize an incredibly diverse set of motifs [21]. These features of C2H2-ZFs allow us to make binding specificity predictions of reasonably high quality for this TF family [22–26].
Previous evolutionary analyses of C2H2-ZF genes revealed a dichotomy in conservation patterns of this family. Tandemly-duplicated C2H2-ZF paralogs exhibit differences in their C2H2-ZF and effector domain counts and can be highly dynamic across short evolutionary distances [27]. The subset of C2H2-ZF KRAB repressor regulators in particular have undergone recent, rapid expansion and divergence in primates and show evidence of adaptive evolution in their DNA-binding domains in human [13, 28–30]. However, such divergence has been found primarily in extremely recent and often species-specific expansions of C2H2-ZFs [31]. In contrast, examples of single-copy 1-to-1 orthologous C2H2-ZF genes have been shown to be highly conserved across large evolutionary distances [27, 32–35]. Prdm9, a C2H2-ZF gene that mediates homologous recombination but is not known to be a TF, is a notable exception to this trend, and is highly dynamic between and within species despite being single-copy [36–40]. Several other orthologous C2H2-ZF genes have been found to diverge across vertebrates, primarily through the gain and loss of C2H2-ZF domains [27, 31]. More generally, however, it is widely believed that 1-to-1 orthologous TFs tend to maintain their DNA-binding specificities whereas paralogous TFs are free to vary [41].
In this paper, we analyze 1-to-1 orthologous C2H2-ZF TFs across closely related species. We leverage the well-understood binding interface of C2H2-ZFs to evaluate DNA-binding specificity changes resulting from C2H2-ZF variation. We focus on C2H2-ZFs in the 12 sequenced Drosophila species (phylogenetic tree in Fig. 1A), as these species benefit from relatively high-quality assembled genomes [42]. Further, as a result of their ∼45 million years of evolutionary divergence [43], they exhibit extensive regulatory variation [44, 45] and diversity in terms of morphology, physiology and ecology [46]. The flies are an ideal model organism set for our study because they have several hundred C2H2-ZF genes which are found in well-established orthologous relationships. This is in contrast to primate genomes where large-scale species-specific expansions complicate 1-to-1 orthology determination.
To assess change, we consider only C2H2-ZF genes that are in 1-to-1 orthologous relationships between D. melanogaster, which we use as a reference species, and each of the 11 other fly species. We find evidence of functional modifications to DNA-binding potential in a significant proportion of these genes. Furthermore, these changes often result in increasingly diverse predicted DNA-recognition motifs as evolutionary distance from D. melanogaster increases, implying that C2H2-ZF DNA-binding specificities may evolve gradually in evolutionarily viable steps. Our findings challenge the assumption that 1-to-1 orthologous TFs are always highly conserved and provide evidence that binding specificity modifications in single-copy TFs may play an important role in the regulatory evolution of Drosophila and other higher metazoans.
The initial step of our framework to assess variation in C2H2-ZFs was to assemble groups of orthologs (orthogroups) of C2H2-ZF genes across the 12 fly species (Fig. 1A). We identified all C2H2-ZF domains and sequences in these species using Pfam [47] and HMMER [48] and determined 1-to-1 orthogroups from existing Flybase [43] annotations. We then augmented this set using the UCSC Genome Browser [49] whole genome fly alignment, resulting in a dataset of all C2H2-ZF sequences in the Drosophila species (Methods M1–M3).
C2H2-ZF domains are known to primarily work in tandem to specify DNA motifs [50] (S1B Fig.), and so we include only those C2H2-ZF genes with 2+ C2H2-ZF domains in our analysis; we refer to these genes as poly-ZF. Tandem C2H2-ZF domains that are separated by canonical linkers—stretches of 5 to 12 amino acids, most often matching the expression TGE[K|R]P[F|Y]X (S1C Fig.)—have the strongest structural evidence for DNA binding [19, 21]. We refer to all domains that are bordered by at least one canonical linker, as defined above, to be “canonically linked.” In D. melanogaster, of the 329 genes with at least one C2H2-ZF domain, 283 have multiple C2H2-ZF domains, and 246 of those contain canonically linked domains.
We found from 319 to 366 genes with at least one C2H2-ZF domain in each of the 12 Drosophila species, 263 to 308 of which were poly-ZF (Fig. 1B, cols. 1–2), in accordance with previous studies’ findings [13, 21]. We found 278 (98.2%) poly-ZF genes in D. melanogaster with a 1-to-1 ortholog in at least one other fly species, and 165 (59.4%) of these were in 1-to-1 relationships across all species. These 278 1-to-1 orthologous poly-ZFs constitute 36.9% of the estimated 753 TFs in D. melanogaster [51]. In each non-melanogaster species, 72.4% to 88.8% of poly-ZF genes had a 1-to-1 ortholog in D. melanogaster (Fig. 1B, col. 3). In the non-melanogaster poly-ZF genes with 1-to-1 orthologs in D. melanogaster, we identified 1000+ C2H2-ZF domains per species (Fig. 1B, col. 4) that are used for comparative analysis in further steps of our framework.
We first assessed the loss and gain of C2H2-ZF domains across our orthogroups, as the number and arrangement of C2H2-ZF domains likely affects the binding specificity of each poly-ZF gene. D. melanogaster domains are considered “lost” in each non-melanogaster species without a corresponding aligned domain; D. melanogaster domains with no aligned domains in any of the other fly species are ignored because they most likely are species-specific D. melanogaster gains. Conversely, domains from non-melanogaster sequences that did not align back to a D. melanogaster domain are considered “gains” with respect to the reference.
The loss and gain of C2H2-ZF domains was recently identified as the major source of divergence in vertebrate ZF paralogs and orthologs [31]. We quantify this phenomenon in 1-to-1 orthologous TFs in D. melanogaster, where we find that between 2.4% and 10.3% of domains were lost in the other fly species (Fig. 2A), and between 0.8% and 2.9% of domains from non-melanogaster species were gained with respect to the reference (Fig. 2B). A notable 24.8% of all non-reference poly-ZF genes in 1-to-1 orthologous relationships with a D. melanogaster gene have lost or gained a C2H2-ZF domain with respect to the reference. 75.6% of gains or losses occur outside of or at an end of an array of canonically linked domains. The proportion of domains lost and gained in the non-melanogaster species with respect to the reference increases as the phylogenetic distance from D. melanogaster increases. When considering only canonically linked C2H2-ZF domains, we see the same overall phylogenetic trends, albeit at a lower level.
We note that D. melanogaster benefits from more complete sequencing coverage in comparison to the the other fly genomes [42], and relatively poor coverage and subsequent inaccurate sequence assembly would result in a greater number of unidentified or misidentified domains in those genomes. D. sechellia, D. simulans, and D. persimilis, which exhibit the greatest relative C2H2-ZF domain loss (Fig. 2A), also have the lowest relative coverage: 4.9x, 2.1x, and 4.1x, respectively, compared to between 8.4x and 11.0x for the other species. For this reason, the C2H2-ZF domain gains relative to D. melanogaster are especially noteworthy, while some of the apparent domain losses, especially from D. sechellia, D. simulans, and D. persimilis, may be due to incomplete assemblies.
Binding specificity may also be altered as a result of deviations in the DNA-contacting, specificity conferring residues in positions -1, 2, 3, or 6 of the C2H2-ZF domain [52] (Fig. 3A). As expected, with the exception of structurally constrained position 4, these four functional sites are more conserved than the neighboring, non-DNA-contacting residues within the domain’s alpha-helix. However, these functional sites still show substantial divergence (Fig. 3B). We consider an aligned domain in any non-reference fly species to be “diverged” if at least one of its residues from positions -1, 2, 3, or 6 has diverged from the D. melanogaster reference. Of the > 98% of domains from poly-ZF genes that aligned between the non-reference sequences and their orthologs in D. melanogaster, we observe from 6.3% of domains diverged (in D. sechellia, last common ancester [LCA] with D. melanogaster ∼2 Mya) to a substantial 31.5% of domains diverged (in D. mojavensis, LCA with D. melanogaster ∼45 Mya) (Fig. 3C). These divergent domains are not confined to a small subset of genes: across the 11 non-reference fly species, 19.5% to 62.4% of poly-ZF genes with 1-to-1 orthologs in D. melanogaster contain at least one divergent C2H2-ZF domain. Moreover, as with the proportion of domains lost and gained with respect to the reference, the proportion of domains diverged steadily increases as phylogenetic distance from D. melanogaster increases. The same trend with slightly lower overall divergence is observed in the subset of canonically linked domains. Of the 37.6% of domains situated in the middle of canonically linked arrays, 15.3% contain divergent binding residues. Of the remaining domains outside of or flanking canonically linked arrays, 25.1% contain divergent binding residues. Arrays of canonically linked domains appear to be under stricter constraints than singleton domains are (S2A-C Fig.). Altogether, changes in these DNA-contacting residues are substantially more frequent than the complete gain or loss of C2H2-ZF domains.
We next aimed to ascertain whether and how the variation we observe in poly-ZF orthologs changes binding specificity, as it is possible that distinct assignments of binding residues still specify the same overall recognition motif [26, 61]. We predicted the specificity of each C2H2-ZF domain with a predictor [24, 62] that utilizes a linear support vector machine based on an expanded structural model (Fig. 3A); this method is referred to as SVM. Since no method can predict binding specificity perfectly and consensus predictions are more likely to be correct (S1 Text, S2 Table), we compared the SVM predictions to those produced by an independent predictor referred to as ML that uses a probabilistic recognition code generated via maximum likelihood [22], and a random forest based predictor referred to as RF [25]. We calculate the average Pearson correlation coefficients (PCCs) across positions b1 through b4 between SVM predicted position weight matrices (PWMs) and ML and RF PWMs, and consider only the subset of SVM predictions with average PCCs > 0.25 to either of the corresponding ML or RF predictions (S4 Fig.). Of the 17734 aligned binding domains from all 12 fly species, 87.3% passed this confidence threshold; thus, overall there is good agreement between the independent methods on predicted DNA-binding specificities. Results using alternate confidence thresholds of PCC > 0.0, PCC > 0.5 and PCC > 0.75 are found in S3 Table.
We compared the SVM-predicted PWM for each divergent domain in a non-melanogaster species to the predicted PWM for the corresponding, aligned domain in its D. melanogaster ortholog by calculating the average PCC across positions b1 through b4. Overall, 74.2% of divergent domains over the 11 flies exhibit a PCC < 0.25 from their reference domain in at least one predicted position (S5A Fig.). In six non-reference fly species, 100% of all divergent domains exhibit a PCC < 1 from their reference domains in at least one predicted position. Of the remaining five species, < 1% of divergent domains do not show a significant change in predicted specificity in any position compared to their aligned D. melanogaster reference domains. Many domains from non-melanogaster species exhibit a diverged specificity from the reference in more than one predicted position (S5B Fig.). Overall, this analysis suggests that the divergent binding residues within C2H2-ZF domains likely result in changed DNA-binding specificities.
We next set out to determine whether the variation we observe in poly-ZF DNA-binding residues may result in changes in regulatory network topology. To experimentally test this, we would need experimentally-determined binding specificities and/or genomic occupancies for many poly-ZF genes across the fly species. Although we do not have TF binding data for non-melanogaster flies, there are poly-ZF TFs for which binding specificities or genomic binding locations have been experimentally determined in D. melanogaster.
We first sought to use chromatin-immunoprecipitation (ChIP) data. Of the 12 D. melanogaster poly-ZFs with associated ChIP data from modENCODE [64], five poly-ZFs—three of which exhibit divergences in their DNA-contacting residues and two of which are completely conserved—did not have associated PWMs representing their binding specificities available in the Fly Factor Survey [65], JASPAR [66], or public Transfac [67] databases, thereby precluding any efforts to determine whether these TFs bind in the other fly genomes. The remaining seven poly-ZFs are conserved TFs involved in development; thus, we would not be able to compare how the diverged and conserved poly-ZF genes in this set differ with respect to the loss of binding sites in the non-reference fly genomes. Because most ChIP studies have been carried out at various developmental stages in D. melanogaster and because, as we show in the next section, conserved poly-ZFs are enriched for developmental functions whereas diverged poly-ZFs are not, it is not surprising that few divergent poly-ZFs have associated ChIP data or specific binding at these developmental stages.
We next compiled experimentally-determined binding specificities for 52 fly poly-ZF TFs from the Fly Factor Survey, JASPAR, and public Transfac databases (Fig. 6A) and computationally mapped their binding sites using fimo [68] in the 2000 base pair promoter regions upstream of known genes in D. melanogaster. To obtain a subset of high-confidence binding site predictions in D. melanogaster, we required that the sites be conserved in the four most closely related species—D. sechellia, D. simulans, D. yakuba, and D. erecta. For each TF, we next examined whether high-confidence D. melanogaster binding sites are lost in the remaining seven fly species, and whether orthologous promoter regions are no longer bound in these species. In each species, we compare the fraction of binding sites lost for those TFs with completely conserved DNA-contacting residues across their 1-to-1 orthologs with the fraction lost for those TFs exhibiting some divergence in their DNA-contacting residues as compared to their D. melanogaster orthologs (Methods M4). We note that various features of a TF (e.g., its function) influence the extent to which its binding sites and targets vary across organisms; thus, we compare the conserved and divergent groups of TFs in aggregate.
We find that single-copy poly-ZF orthologs with divergent DNA-contacting residues are significantly more associated with a loss of bound promoter regions than are completely conserved poly-ZF orthologs (p < 1e-9 across all species, Wilcoxon test; Fig. 6B). Changes between the sets of genes predicted to be regulated by D. melanogaster poly-ZFs and the sets of genes predicted to be regulated by their orthologs in other species, therefore, are more common and pronounced when those orthologs show divergences in their DNA-binding domains. When examining individual binding sites that were predicted to be bound by a given D. melanogaster poly-ZF gene, we find that divergent poly-ZFs are significantly more associated with a loss of binding sites than are conserved single-copy poly-ZFs (p < 1e-6 across all species, Wilcoxon test; Fig. 6C). We note that relaxing our criterion for making high-confidence binding site predictions in D. melanogaster by requiring conservation in fewer species does not substantially alter our findings at either the level of promoters or binding sites (S6 Fig.). Altogether, these results suggest that the binding landscapes of divergent poly-ZFs are more different from their D. melanogaster orthologs than are those of conserved poly-ZFs, and subsequently that regulatory network topologies have most likely been affected by variation in 1-to-1 orthologous poly-ZFs.
Do divergent poly-ZF genes exhibit distinct biological functions from the set of conserved poly-ZF genes? To answer this question, we divided the genes from our analysis into two main sets: conserved and diverged. The first set contained 82 poly-ZF genes from D. melanogaster with completely conserved DNA-contacting residues across all its orthologs; 28 (34.1%) had orthologs in all other fly species, and 64 (78.0%) contained canonically linked domains. The second set contained 181 D. melanogaster poly-ZF genes with a diverged C2H2-ZF domain in 2+ orthologs; 81 (44.8%) had orthologs in all 11 other fly species, 155 (85.6%) contained canonically linked domains, and 144 (79.6%) contained a divergent canonically linked domain.
Previously, binding site turnover has been shown via ChIP experiments to be an essential component in regulatory network variation across closely-related organisms [79–83] and even across individuals of the same species [84, 85]. Here we present an analysis suggesting that divergence of orthologous TFs also plays a role in regulatory variation.
Over half of the single-copy, poly-ZF 1-to-1 gene orthogroups in Drosophila exhibit variation with respect to the number and arrangement of DNA-binding C2H2-ZF domains and the composition of specificity-conferring residues within these domains. Variations within these specificity-determining positions are known via structural studies to influence the binding specificities of the proteins in which they are found. These mutations’ conservation across phylogenetic clades, low rate of evolution, and rapid fixation as determined by their lack of overlap with population polymorphisms further demonstrate their functional importance. Additionally, predicted specificities of C2H2-ZF domains increasingly diverge as evolutionary distance from the reference D. melanogaster increases, offering evidence that specificity-altering trans changes are feasible and occur in evolutionarily viable steps even in non-duplicated orthologs.
Though C2H2-ZF binding to RNA [86] or protein [87] rather than or in addition to DNA has been observed, several lines of evidence suggest that a large fraction of the domains in our study bind DNA. We focus on only those genes with multiple C2H2-ZF domains, a requirement for specific DNA recognition. Even when we limit our analysis to canonically linked domains, which have the strongest structural evidence for DNA-binding, we observe the same overall divergence trends. Some DNA-binding C2H2-ZFs may regulate processes other than transcription; however, GO term enrichment analysis and co-domain presence suggests that many of these poly-ZFs are regulating transcription and gene expression and are likely interacting with other protein co-factors. Altogether, this suggests that a substantial set of the divergent poly-ZF genes included in our analysis are DNA-binding TFs. However, it is also possible that the likely specificity-altering mutations we see in these DNA-binding TFs may leave overall gene expression unaffected. There are cases of divergent cis-regulatory sequences that do not confer a change in gene expression [88–93], review by [94], as sometimes these binding site changes are accompanied by complementary TF changes [95]. Compensatory change may occur for some of the diverging poly-ZF TFs we observe. For those poly-ZFs with experimentally-derived PWMs in D. melanogaster, however, we see that TF orthologs across the other fly species with diverged DNA-contacting residues are associated with significantly fewer conserved binding sites and bound promoter regions than are TF orthologs with completely conserved DNA-binding domains. This suggests that the substantial trans variations must result in, at minimum, modulated expression changes, as multiple cis mutations co-occurring with and counteracting each trans specificity change would be extremely unlikely.
Poly-ZFs in D. melanogaster that diverge across the flies appear to have several notable characteristics. They tend to have limited functional annotations and are less essential than conserved poly-ZF genes. Further, they tend to be more broadly expressed, albeit at lower levels, than poly-ZF genes whose binding specificities are conserved. Intriguingly, a substantial fraction of diverging poly-ZF genes contain ZAD domains, and the vast majority of all ZAD-containing poly-ZFs diverge in their DNA-contacting residues. Uncovering the functional roles of diverging poly-ZFs, especially those containing ZAD domains, may be a particularly promising avenue for future work.
Earlier work on C2H2-ZF genes in vertebrates has established the plasticity of this class of DNA-binding domains and the potential role these genes may play in shaping species-specific regulatory networks. In particular, the human C2H2-ZF genes that contain KRAB repressor domains have been studied in depth [28, 32, 96, 97]. The KRAB C2H2-ZF family of proteins are unique to tetrapods and have undergone major species-specific segmental and tandem duplications in mammals and primates [98]. Paralogous KRAB-ZF genes residing in these clusters exhibit frequent pseudogenization, loss and gain of binding domains, and evidence of positive selection acting on the DNA-contacting residues within these domains [13, 29, 32, 97]. These findings on paralogous genes are consistent with the long-standing belief that gene duplication followed by subsequent diversification is the primary means by which otherwise conserved genes can accrue functional divergences [99]. Where attempts have been made to identify and evaluate orthologs across species containing these expansions of KRAB-ZFs, orthologs have been found to either be deeply conserved or to exhibit differences in C2H2-ZF domain count rather than in the identities of DNA-binding residues, though a few cases of variation in DNA-binding residues have been previously reported [27, 28, 31]. We note that the plasticity of domains within these expanded C2H2-ZF gene families in vertebrates does not necessarily imply that C2H2-ZF domains in other organisms will have similar properties. Indeed, we see far fewer losses and gains of domains in 1-to-1 C2H2-ZF orthologs in flies as compared to what has been observed in C2H2-ZF gene expansions in primates, and we observe a relatively higher rate of divergence in specificity-conferring residues. It remains to be seen if divergences within DNA-contacting residues are also prevalent in single-copy orthologs of other TF families.
Although prior research has recognized the possibility of TF variation occurring in multi-gene families, it has long been thought that single-copy TFs are under stringent conservation, as loss or change of function mutations in these genes could not be masked by the functional gene products of paralogs and would thus have catastrophic effects. We cannot, of course, rule out the possibility that ancient transient gene duplications and losses have complicated the detection of 1-to-1 orthologs in Drosophila. However, our large-scale results on 1-to-1 C2H2-ZF orthogroups in flies are consistent with a recent experimental case study of specificity divergence of a single-copy TF in plants [16]. Here, binding specificities of 1-to-1 orthologs of the plant TF LEAFY (lfy) were analyzed across algal, moss, and plant species, and three distinct binding preferences were found. The lfy ortholog in hornworts was dubbed a “promiscuous intermediate” as it recognizes all three binding motifs with various preferences. This intermediate, which is not accompanied by a definitive ancestral gene duplication event [100, 101], highlights a means by which TF binding specificity can evolve in single-copy genes. The gradual TF variation we observe may also give rise to such analogous TF intermediates.
In conclusion, we propose that variation in 1-to-1 orthologous TFs can shape regulatory network evolution. Changes in TFs need not be catastrophic. Rather, single amino acid mutations in DNA-contacting positions may result in overall TF binding of similar targets with varying affinities. Such variations provide the opportunity for gradual evolution of binding specificity. We propose that these changes in single-copy TFs may be substantial contributors to overall regulatory evolution in Drosophila and in other metazoans in general.
Translated protein sequences for the 12 sequenced fly species—D. melanogaster (build r6.01), D. sechellia (r1.3), D. simulans (r1.4), D. yakuba (r1.3), D. erecta (r1.3), D. ananassae (r1.3), D. pseudoobscura (r3.2), D. persimilis (r1.3), D. willistoni (r1.3), D. mojavensis (r1.3), D. virillis (r1.2), and D. grimshawi (r1.3)—were downloaded from FlyBase [43], version FB2014_04. Additional D. simulans sequences were downloaded from the Andolfatto Lab site [102]. To identify C2H2-ZF genes, HMMER’s hmmsearch (versions 2.3.2 [48] and 3.0 [103]) was run on each translated protein file using 12 Pfam HMMs [47], which were selected based upon their similarity to and presence in the same clan as the consensus C2H2-ZF profile (S1B Fig.), zf-C2H2 (PF00096)—zf-C2H2 (PF00096), zf-C2H2_2 (PF12756), zf-C2H2_6 (PF13912), zf-C2H2_jaz (PF12171), zf-C2HC_2 (PF13913), zf-H2C2_5 (PF13909), zf-met (PF12874), zf-met2 (PF12907), zf-BED (PF02892), zf-U1 (PF06220), GAGA (PF09237), DUF3449 (PF11931). Any protein sequence containing at least one HMMER hit with a bit score above the specified gathering domain threshold for that HMM was considered.
C2H2-ZF domains themselves were identified from these proteins as any HMMER hit matching the regular expression CX2, CX8, ΨX2HX3, [H|C], where Ψ is a large, hydrophobic amino acid. Hits that did not match this expression and thus no longer have the structure necessary to bind DNA are considered degenerate, and are not identified as domains. HMMER hits below the corresponding bitscore thresholds but which matched this regular expression were retained in these proteins because C2H2-ZFs are known to occur in tandem, and therefore we are more confident about all C2H2-ZF domains which co-occur with at least one high scoring domain. All C2H2-ZF domains can be found in S5 Table.
Where possible, the longest protein splice form per gene containing all C2H2-ZF domains was selected to represent each gene. If no single protein isoform contained all domains present in the gene, a minimal set of proteins which together include all unique C2H2-ZF domains was selected to represent the gene.
A list of pairwise orthologs to D. melanogaster was downloaded from FlyBase and from the Andolfatto Lab build of D. simulans [102], and orthogroups were constructed from overlaps of these orthologs. Those orthogroups containing at least one D. melanogaster poly-ZF gene were selected. Of 13273 total original orthogroups, 272 had at least one D. melanogaster poly-ZF gene.
D. melanogaster poly-ZF orthogroups with sequences missing from one or more species were augmented according to the 15 insect whole genome alignment (WGA) from the UCSC Genome Browser [49]. A missing species is defined as any species not present in the orthogroup but present in the phylogenetic subtree rooted at the most recent common ancestor of those species that are present in the orthogroup. For each of the 52 orthogroups containing at least one missing species, known protein sequences were aligned to the UCSC 15-insect WGA using BLAT [104]. Where possible, sequence(s) from the missing species were extracted from the section of the alignment with the best hits and aligned back to their corresponding translated protein files using BLAT again. Gene IDs of proteins with BLAT hits with an e-value cutoff of 0.001 were extracted and, when they were not present in pseudogene lists, were added to the corresponding orthogroups. Through this process, 13 of the orthogroups with missing species were augmented with at least one new gene.
All 1-to-many (i.e., one gene from D. melanogaster but more than one gene from at least one other species) orthogroups were truncated such that only those species with a single gene in the original orthogroup were included in the new orthogroup. In this manner, our analysis was restricted to variation in 1-to-1 orthologs.
A gene tree was constructed from a multiple alignment for each many-to-many orthogroup using T-Coffee, version 10 [105]. Each of these gene trees was then reconciled with the phylogenetic species tree for the 12 Drosophila species using Notung, version 2.8 [106]. For each input pair of gene and species trees, the reconciled tree output by Notung is marked with the most parsimonious duplication and loss events along ancestral branches, such that branches of the gene tree now coincide with speciation events of the species tree. Each subtree of the reconciled Notung tree was considered separately as a new potential orthogroup.
Potential orthogroups that contained fewer or greater than one D. melanogaster gene were discarded. All remaining potential orthogroups were truncated as before where necessary, such that only genes that were found to be 1-to-1 with a single D. melanogaster gene were retained. Potential orthogroups containing sequences from at least two species were extracted as new 1-to-1 orthogroups. Six original orthogroups were reconciled using Notung in this manner. All augmented and reconciled orthogroups can be found in S6 Table.
We initially obtained binding specificity motifs, represented as PWMs, for 62 D. melanogaster poly-ZF genes from the FlyFactorSurvey, JASPAR, and public Transfac databases. There are 96 binding specificity motifs for these 62 genes, as different isoforms or subsets of binding domains may correspond to distinct motifs (e.g., peb_Z1-3 and peb_Z5-7). For cases of duplicate binding motifs, we preferentially selected the PWM generated from SOLEXA sequencing over SANGER sequencing, and the longer PWM over the shorter. To exclude binding motifs that are non-specific, we discarded PWMs with fewer than six columns exhibiting information content (IC) > 0.5. To exclude binding motifs of low complexity (e.g. poly-A motifs), we discarded PWMs where > 80% of columns with IC > 0.5 correspond to the same consensus nucleotide, where consensus is defined as the most common nucleotide in a position, or ‘N’ in the case of a tie. Slight variations to these thresholds do not affect our findings. To exclude TFs which cannot be compared across species, we discarded binding motifs corresponding to TFs with 1-to-1 orthologs in fewer than two non-reference species. This filtering process resulted in 64 binding specificity motifs for 52 genes. These motifs were properly formatted for use by fimo with jaspar2meme, available from the MEME suite [68].
The 2000 basepair regions upstream of all genes in D. melanogaster and their alignments to orthologous regions across the other 11 fly species were obtained from the UCSC Genome Browser 15-fly promoter region alignments [49]. For each binding specificity motif, fimo was run on these aligned upstream regions from all 12 fly species to find all predicted TF binding site occurrences.
To obtain a set of high-confidence predicted binding sites in D. melanogaster, we required that each predicted binding site in D melanogaster be found within 25 basepairs in the UCSC genome alignments to binding sites in D. sechellia, D. simulans, D. yakuba, and D. erecta; this allows detection of conserved sites while allowing for slight variations in the genomes and/or slight error in the genome alignment [107, 108]. We note that restricting D. melanogaster binding sites to those found within 15 or 50 basepairs to binding sites in these other four species did not affect results nor significance. Considering alternate definitions of confident binding sites by restricting D. melanogaster binding sites to those found within 25 basepairs in only D. sechellia, only D. sechellia and D. simulans, or only D. sechellia, D. simulans, and D. yakuba also did not affect results nor significance (S6 Fig.).
For each PWM, the set of “bound” promoter regions, or those containing one or more high-confidence binding sites, was obtained in D. melanogaster. For each of these bound promoter regions, the orthologous promoter region in a non-reference species was also considered bound if it contained one or more binding sites within 25 basepairs of a high-confidence D. melanogaster binding site. For each PWM, we were thus able to determine the percent of bound promoter regions in D. melanogaster that were also bound across each other fly species. Similarly, each high-confidence binding site in D. melanogaster was considered conserved in another species if a binding site was found in that species within 25 basepairs of the D. melanogaster binding site. If another binding site was not found in that species within this window, the high-confidence D. melanogaster binding site was considered lost. The proportion of orthologous promoter regions bound and proportion of binding sites conserved were calculated for each binding motif in each species that contained a 1-to-1 ortholog of the corresponding TF (Fig. 6B-C).
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10.1371/journal.pbio.1000170 | Distinct Parietal and Temporal Pathways to the Homologues of Broca's Area in the Monkey | The homologues of the two distinct architectonic areas 44 and 45 that constitute the anterior language zone (Broca's region) in the human ventrolateral frontal lobe were recently established in the macaque monkey. Although we know that the inferior parietal lobule and the lateral temporal cortical region project to the ventrolateral frontal cortex, we do not know which of the several cortical areas found in those regions project to the homologues of Broca's region in the macaque monkey and by means of which white matter pathways. We have used the autoradiographic method, which permits the establishment of the cortical area from which axons originate (i.e., the site of injection), the precise course of the axons in the white matter, and their termination within particular cortical areas, to examine the parietal and temporal connections to area 44 and the two subdivisions of area 45 (i.e., areas 45A and 45B). The results demonstrated a ventral temporo-frontal stream of fibers that originate from various auditory, multisensory, and visual association cortical areas in the intermediate superolateral temporal region. These axons course via the extreme capsule and target most strongly area 45 with a more modest termination in area 44. By contrast, a dorsal stream of axons that originate from various cortical areas in the inferior parietal lobule and the adjacent caudal superior temporal sulcus was found to target both areas 44 and 45. These axons course in the superior longitudinal fasciculus, with some axons originating from the ventral inferior parietal lobule and the adjacent superior temporal sulcus arching and forming a simple arcuate fasciculus. The cortex of the most rostral part of the inferior parietal lobule is preferentially linked with the ventral premotor cortex (ventral area 6) that controls the orofacial musculature. The cortex of the intermediate part of the inferior parietal lobule is linked with both areas 44 and 45. These findings demonstrate the posterior parietal and temporal connections of the ventrolateral frontal areas, which, in the left hemisphere of the human brain, were adapted for various aspects of language production. These precursor circuits that are found in the nonlinguistic, nonhuman, primate brain also exist in the human brain. The possible reasons why these areas were adapted for language use in the human brain are discussed. The results throw new light on the prelinguistic precursor circuitry of Broca's region and help understand functional interactions between Broca's ventrolateral frontal region and posterior parietal and temporal association areas.
| Two distinct cortical areas in the frontal lobe of the human brain, known as Broca's region, are involved with language production. This region has also been shown to exist in nonhuman primates. In this study, we explored the precise neural connectivity of Broca's region in macaque monkeys using the autoradiographic method to achieve a level of detail impossible in the human brain. We identified two major streams of connections feeding into Broca's area: a ventral stream from the temporal region, which includes areas processing auditory, multisensory, and visual information and a dorsal stream originating from the inferior parietal lobule and the adjacent superior temporal sulcus. Our detailed connectivity analysis illuminates the pathways via which posterior cortical areas can interact functionally with Broca's region, in addition to contributing to an understanding of the evolution of language. We suggest that a fundamental function of Broca's region is to retrieve information in a controlled strategic way from posterior cortical regions and to translate this information into action. This fundamental function was adapted during evolution of the left hemisphere of the human brain to serve language.
| In the ventrolateral frontal lobe of the left hemisphere of the human brain, two distinct architectonic areas, areas 44 and 45, are involved with various aspects of language production and are considered to constitute the anterior language zone, which is also known as Broca's region (Figure 1A) [1]. Electrical stimulation of Broca's region during brain surgery leads to interference with speech production (e.g., [2]–[4]). Broca's region lies immediately anterior to the ventral part of the precentral gyrus, which, in both the human and nonhuman primate brains, is involved with the motor control of the orofacial musculature [2],[5],[6]. In the human brain, architectonic area 44 lies immediately anterior to the ventral precentral gyrus (i.e., in front of premotor area 6) and occupies the pars opercularis of the inferior frontal gyrus. It is succeeded, rostrally, by area 45 on the pars triangularis of the inferior frontal gyrus (Figure 1A) [1],[7]–[9]. While the primary motor cortex (area 4) and the ventral premotor cortex (area 6) have been consistently identified in the ventral part of the precentral region of the macaque monkey [10]–[12], there has been considerable confusion with the identification of areas 44 and 45. In the classic map of the macaque monkey by Walker [13], which has been adopted with minor modifications by most investigators of the monkey brain, area 44 was not identified and a narrow strip of cortex along the anterior bank of the inferior branch of the arcuate sulcus was labeled as area 45, although Walker repeatedly stated that he could not be certain whether it corresponded to area 45 of the human brain because he had not compared its architecture with that of the human (see [9] for discussion of this issue). Confusion also arose because the dorsal part of Walker's area 45 (i.e., the part immediately ventral to area 8) in the macaque monkey has been considered a visual oculomotor area by some investigators (e.g., [14]) which, clearly, would not be the case for an area that might be the homologue of a language zone.
The above confusing state of affairs led us to reexamine systematically the architecture of the frontal cortex of the macaque monkey in comparison with that of the human brain [8],[9]. This research demonstrated the following facts. First, a dysgranular area lying just anterior to the ventral premotor cortex (area 6) could be identified in the macaque monkey in the depth of the ventral part of the inferior branch of the arcuate sulcus, and this area had the architectonic characteristics of human area 44 (Figures 1B and S1). Furthermore, a combined anatomical–physiological study demonstrated that the neurons in the newly identified area 44 of the macaque monkey were involved with the orofacial musculature [15]. Dysgranular area 44 is succeeded, anteriorly, by area 45, which is a clearly granular cortex (Figure S1) with the architectonic characteristics of area 45 in the human brain: clusters of unusually large neurons in layer III, a well-developed layer IV, and moderate sized neurons in layer V (see [8],[9],[15]). We found that monkey area 45 as defined by criteria comparable to those of the human brain extends anteriorly as far as the infraprincipal dimple (IPD) and that it can be subdivided into a caudal (area 45B) and a rostral (area 45A) part (Figure 1B). Furthermore, monkey area 45 when defined by the criteria of human area 45 is not related to oculomotor function as shown by a combined architectonic–microstimulation study that examined this issue [15]. The part of Walker's area 45 that had previously been linked to oculomotor function does not have the characteristics of human area 45 but rather those of caudal area 8 [15]. Ventral to the newly defined monkey area 45 lies area 12, which is comparable to a part of area 47 of the human brain and we have therefore labeled it as area 47/12 (Figure 1B). Area 47/12 is typical prefrontal cortex, i.e., it has a well developed layer IV, but the clusters of unusually large neurons in layer III that characterize area 45 are not observed in area 47/12 [9].
Several experimental anatomical studies of cortico-cortical connections had previously reported inputs to the ventrolateral frontal convexity of the monkey from the inferior parietal lobule [16]–[20], the superior temporal gyrus, and the cortex within the superior temporal sulcus [21]–[24], as well as from the adjacent dorsal inferotemporal cortex [25],[26]. These earlier studies, however, cannot provide precise information regarding the origin of axons within the many architectonic cortical areas that constitute the large inferior parietal and lateral temporal regions and the pathways by means of which these axons reach the newly identified homologues of Broca's region in the macaque monkey (i.e., areas 44, 45B, and 45A). The reason is that the terminations of axons in earlier studies were described in terms of older architectonic schemes that (a) did not identify area 44 and (b) included the cortex where the two subdivisions of area 45 lie as part of either area 8, or area 12, or area 46. In the few studies in which area 45 was identified, it was defined as a small strip of cortex along the anterior bank of the inferior branch of the arcuate sulcus following the criteria of Walker [13], which are not those used to define the macaque area 45 that is comparable with area 45 of the human brain (see [9],[15]). Thus, the terminations of the parieto-frontal and temporo-frontal fibers within the ventrolateral frontal region of the macaque as described in the older studies cannot be easily related to the homologues of the human Broca's region and, thus, inform our understanding of the connections of the human brain. It should be noted here that, although current diffusion tensor imaging methodology used in the human brain has been useful in providing an overall view of the large pathways that link the inferior parietal lobule and the superior lateral temporal region with the ventrolateral frontal region [27]–[31], it does not have the resolution to demonstrate the exact cortical origin of axons and their precise termination within particular target cortical areas. Thus, apart from stating that the inferior parietal lobule and the superior temporal region connect with the ventrolateral frontal region, such studies in the human brain have provided no information about the precise architectonic areas within the large posterior parietal and temporal cortical regions from which axons originate and the precise ventrolateral frontal architectonic areas within which these axons terminate. This information must be extrapolated from experimental anatomical studies in the macaque monkey in which appropriate anatomical methodology (e.g., autoradiography) can be used to establish such details.
Given the above considerations, the purpose of the present study was to examine, in the macaque monkey, the terminations within ventrolateral frontal areas 44, 45A, and 45B (i.e., the homologues of Broca's region) of axons originating within specific parietal and lateral temporal cortical areas and the pathways utilized to reach Broca's region. We used the autoradiographic method, which involves the injection of radioactively labeled isotopes in a particular cortical area of interest and provides an unambiguous demonstration of the precise course and terminations of axons that originate from the injected region [32]. Such information is critically needed because (a) it cannot be obtained in the human brain and (b) it can be used to guide hypotheses and provide plausible interpretations of connections in the human brain examined with diffusion tensor imaging (e.g., [27]–[31]) or physiological connectivity between cortical areas during baseline activation states (e.g., [33],[34]). Furthermore, such information on the circuitry of areas that, in the left hemisphere of the human brain, were adapted to serve certain aspects of language provides major insights into the prelinguistic antecedents of “language” areas and thus major insights into the evolution of language circuits.
The present study examined the origin, course, and terminations of axonal fiber systems connecting the various architectonic areas of the inferior parietal lobule and the lateral temporal region to the newly defined homologues of Broca's region, i.e., areas 44, 45B, and 45A, in the ventrolateral frontal cortex of the macaque monkey. Our architectonic studies have shown that area 44 lies in the fundus of the inferior branch of the arcuate sulcus, immediately in front of the rostral part of premotor area 6, and is succeeded on the anterior bank of this sulcus by area 45B, which extends onto the lip of the sulcus (Figure 1B). Area 45A extends rostrally as far as the IPD and is succeeded ventrally by area 47/12 [8],[9],[15]. Note that the IPD can be a barely detectable dimple (Figure 2A) or a small vertically oriented sulcus (Figure 2B–2D). Also note that the ventral-most part of the inferior branch of the arcuate sulcus, where the homologues of Broca's region lie, forms a distinct segment that makes a relatively sharp forward turn before continuing ventrally (Figure 2B–2D). It is important to note here that the architectonic areas within which the injections were placed (i.e., origins of the pathways) and the architectonic areas within which terminal label was observed in the ventrolateral frontal cortex was established in each individual case under microscopic examination. The sections shown in Figures 3–13 are drawings of actual histological sections examined and charted under darkfield microscopy to locate the labeled axons in the white matter and the axon terminations in the gray matter. Thus, in each individual case, terminal label was verified to be within the architectonic areas of interest on the basis of the criteria that we had established in earlier studies of the homologues of Broca's region [8],[9],[15] (Figure 1B). The posterior parietal cortical areas were defined according to the criteria of Pandya and Seltzer [35], the superior temporal gyrus according to Pandya and Sanides [36], and the inferotemporal cortex and the cortex of the superior temporal sulcus according to Seltzer and Pandya [37]. The architectonic areas of the parietal and superior temporal region from which axons originate and terminate within the homologues of Broca's region in the macaque monkey brain are also provided in the macaque monkey brain atlas by Paxinos and colleagues [38].
The present study of the parietal and temporal inputs to the ventrolateral frontal convexity of the macaque monkey demonstrated a number of fundamental facts about the connections of areas 44, 45B, and 45A, namely the homologues of Broca's region (Figures 1 and S1). The following facts were established with regard to parietal inputs: (a) Area 44 receives strong input from area PFG of the inferior parietal lobule, which corresponds to the caudal part of the supramarginal gyrus of the human brain. By contrast, the ventral premotor area 6, which controls the orofacial musculature and lies just caudal to area 44, receives most of its input from the most rostral part of the inferior parietal lobule (area PF), which corresponds to the most rostral part of the supramarginal gyrus in the human brain, and only a minor contribution from area PFG (compare Figures 3, 4, and 5). (b) Area 44 also receives input from the caudal half of the inferior parietal lobule, primarily from area PG, which corresponds to the cortex of the angular gyrus of the human brain. (c) Both subdivisions of area 45 (45B and 45A) receive parietal input from areas PFG and area PG. (d) The inputs from the inferior parietal lobule course via the second and third branches of the superior longitudinal fasciculus (SLF II and SLF III). These branches of the superior longitudinal fasciculus were first identified by Petrides and Pandya [16] but, at the time, their terminations in ventrolateral frontal cortex could not be identified as belonging to any of the subdivisions of Broca's region (areas 44, 45B, 45A), because these were only identified much later in the 1990s [8],[9],[15]. A contingent of axons originating from the most ventral part of area PG and the adjacent superior temporal sulcus forms an arch around the posterior end of the lateral fissure (the arcuate fasciculus), and these arching fibers then mingle with those of the superior longitudinal fasciculus as they course towards the ventrolateral frontal lobe. These superior longitudinal/arcuate axons form a dorsal stream of fibers that links the various areas of the inferior parietal lobule and adjacent superior temporal sulcus to the homologues of Broca's region in the frontal lobe (Figure 14).
In contrast to the above dorsal stream of axons that originate from the inferior parietal lobule and the adjacent most caudal parts of the superior temporal sulcus, the intermediate-to-anterior part of the superolateral temporal region sends axons that terminate in the ventrolateral frontal cortex via the extreme capsule (Figure S2B). We had originally demonstrated this ventral temporo-frontal pathway via the extreme capsule in the late 1980s [21], but we could not establish its precise terminations in the homologues of Broca's region because, at the time, these had not yet been recognized. The present study established the following new facts: (a) A very strong contingent of axons that originate in the intermediate and anterior parts of the superolateral temporal region and course in the extreme capsule terminate in area 45 (Figure 14), with a modest contingent of fibers terminating in area 44. (b) These temporo-frontal axons that form the extreme capsule fasciculus originate not only from the auditory superior temporal gyrus (Figures 9–11), but also from the multisensory cortex in the upper bank and depth of the superior temporal sulcus (Figure 12), and from the visual association cortex in the ventral bank of the superior temporal sulcus and the adjacent dorsal inferotemporal region (i.e., areas TEa/m and dorsal part of area TE) (Figure 13). (c) Axons running through the extreme capsule and directed to ventrolateral area 47/12 originate primarily from cortex in the ventral bank of the superior temporal sulcus and the adjacent inferotemporal area TE, while axons running via the uncinate fasciculus terminate in the orbital part of the frontal lobe including the orbital part of area 47/12.
In summary, the present study demonstrated that the long, afferent, monosynaptic, association axons that convey inferior parietal and lateral temporal inputs to the homologues of Broca's region are organized into a dorsal and a ventral stream of fibers (Figure 14). The dorsal stream can be subdivided into a rostral component of fibers via the third branch of the superior longitudinal fasciculus (SLF III) originating from the most rostral inferior parietal lobule and targeting the caudal ventrolateral frontal region (primarily rostral area 6 and area 44) and a caudal component originating from the intermediate-to-caudal inferior parietal lobule via the second branch of the superior longitudinal fasciculus (SLF II) and terminating in mid-ventrolateral areas 45A, 45B, and 44. The more ventral fibers of this system, originating from the parieto-temporal junction, arch around the end of the lateral fissure forming the arcuate fasciculus, which can therefore be thought of as the most ventral contingent of the superior longitudinal fibers. In the macaque monkey brain, the caudal inferior parietal lobule and adjacent caudal superior temporal sulcus exhibit a sharp upward direction (Figure 14). The ventral part of this region of the human brain occupies a much more ventral location (i.e., lies below the level of the end of the lateral fissure) because this parieto-temporal junction region has expanded pushing the lunate sulcus (i.e., the lateral border of the primary visual cortex) caudally as far as the occipital pole. Given the upward direction of the caudal inferior parietal lobule and adjacent caudal superior temporal sulcus in the macaque monkey, a significantly fewer number of fibers need to arch around the end-point of the lateral fissure in the monkey compared with the human brain. Thus, the arcuate fasciculus is not as prominent in the monkey [39] although, as shown here, undoubtedly present and a part of the dorsal stream of axons.
The ventral temporo-frontal stream of fibers originates from the intermediate and anterior parts of the lateral temporal cortex and targets primarily area 45 and to a modest extent area 44. These fibers form the extreme capsule fasciculus (Figures 14 and S2B) [21]. It should be pointed out here that the inferior parietal lobule and adjacent caudal superior temporal sulcus (origins of the dorsal stream) and the intermediate-to-anterior superolateral temporal region (origin of the ventral stream) are known to be massively interconnected via the middle longitudinal fasciculus (Figure 14), which was first established in the macaque monkey by Seltzer and Pandya [37],[40] and more recently demonstrated in the human brain with diffusion tensor imaging [41].
The findings established in the present investigation are of major significance for understanding the precursor neural circuitry in the nonlinguistic nonhuman primate brain, which, in the left dominant hemisphere of the human brain, was adapted to serve language processing. These findings naturally raise the question of the extent to which the precursor neural circuitry is still present in the human brain. The traditional view of human language circuitry has been that the caudal temporal region (Wernicke's region) is connected with the anterior language zone (Broca's region) via the arcuate fasciculus and this view has dominated theoretical attempts to interpret language processing and its disorders (e.g., [42]). The present findings show that, even in the macaque monkey, there is a much richer system of pathways linking posterior parietal and lateral temporal cortex with the ventrolateral frontal region than the traditional view would suggest. As pointed out above, although diffusion tensor imaging in the human brain does not have the resolution to establish the origins and precise terminations of such pathways in the human brain, it has provided evidence that a rich system of comparable pathways also exist in the human brain (e.g., [27]–[31]). For instance, in a recent diffusion tensor imaging study in the human brain [31], we were able to establish a pathway running from the superolateral temporal region via the extreme capsule towards the ventrolateral frontal region and another pathway running from the rostral inferior parietal lobule via the superior longitudinal fasciculus towards this same region, consistent with the present findings in the macaque monkey and also suggestions from earlier diffusion tensor imaging work (e.g., [27]–[30]). On the basis of the present experimental anatomical results in the monkey that provide details of origins and precise terminations of these pathways, we can assume that the target of the extreme capsule fibers from the superolateral temporal cortex and the superior longitudinal fasciculus from the inferior parietal lobule is indeed Broca's region.
Several functional neuroimaging studies that searched for the articulatory/phonological system of the human brain observed coactivation of area 44 (i.e., the articulatory part of Broca's region) with the rostral part of the inferior parietal lobule, namely the supramarginal gyrus [43],[44]. It is therefore of considerable interest that the present experimental anatomical results in the macaque monkey demonstrate that the rostral part of the inferior parietal lobule (areas PF and PFG that correspond to the human supramarginal gyrus) is strongly linked with area 44 and the ventral part of the premotor cortex (area 6), which controls the orofacial musculature, and that these connections are made via a distinct branch of the superior longitudinal fasciculus (SLF III). Electrophysiological studies in the macaque monkey have shown that the rostral part of the inferior parietal cortex is involved with hand and orofacial action control (e.g., [45]–[48]). Thus, the specific part of the inferior parietal lobule that is linked with ventral premotor area 6 and area 44 plays a role in orofacial and hand action control. Furthermore, a number of investigators of human language have recently suggested that there is a dorsal stream involved in the mapping of sound-to-articulation and a ventral stream for mapping sound-to-meaning (e.g., [49],[50]). A study that combined functional magnetic resonance imaging with diffusion tensor imaging has provided evidence that sublexical repetition of speech, which requires sound-to-articulation transformations, involves a dorsal system that includes the superior longitudinal/arcuate fasciculus and the premotor cortical area 6 and area 44 [44]. In other words, there is now evidence that the dorsal stream that we demonstrated here in the macaque monkey and have shown to terminate strongly in premotor area 6 and area 44 has been adapted, in the human brain, for use in sound-to-speech articulation transformations. In addition, in the human brain, the dorsal system with its linkages to posterior temporal cortex is involved in syntactic processing (e.g., [51]). Clearly, these are functional contributions of these systems characteristic of the human brain and have possibly evolved as a result of the considerable expansion of the parieto-temporal region in the human brain, including the associated Broca's region in the ventrolateral frontal cortex. There is also evidence that sound-to-meaning comprehension involves primarily the ventral stream of fibers connecting the intermediate lateral temporal cortex with the ventrolateral region via the extreme capsule [44].
What is the role of Broca's homologues in the macaque monkey? Before addressing this question, we should note that architectonic areas 44 and 45 (Broca's homologues) exist also in the ventrolateral frontal region of the human nondominant right hemisphere, namely the hemisphere that is heavily involved in nonlinguistic spatial and nonspatial processing. We should therefore ask: What might be the general nonlinguistic role of the ventrolateral frontal cortical region in the human nondominant right hemisphere and in the macaque monkey? Petrides [52] has argued that a fundamental contribution of nonlinguistic ventrolateral prefrontal areas 45 and 47/12 is the active controlled retrieval of information from memory, namely controlled retrieval of mnemonic information stored in posterior cortical association areas. In functional magnetic resonance imaging studies, we were indeed able to show that the human ventrolateral prefrontal cortex in the right hemisphere is critically involved in the active controlled retrieval of nonverbal visual information, such as information about abstract designs, faces, and locations [53],[54]. We then used the exact same nonverbal active controlled retrieval paradigm in macaque monkeys and have shown that single neuron activity in areas 45 and 47/12 of the nonhuman primate brain is involved with the active controlled retrieval of visual object and spatial information [55]. Thus, ventrolateral prefrontal cortex in the macaque monkey, as in the right hemisphere of the human brain, plays a major role in the controlled strategic retrieval of nonlinguistic information from memory. We can therefore suggest that this nonlinguistic contribution of prefrontal areas 45 and 47/12 was adapted, in the left hemisphere of the human brain, to serve controlled retrieval of verbal information [56], just as the hippocampal region in the left hemisphere of the human brain has been adapted to serve verbal declarative memory encoding while in nonhuman animals and the human right hemisphere supports nonverbal spatial declarative memory [57],[58]. Indeed, there is now considerable evidence from many laboratories that controlled, strategic, verbal memory retrieval and selection involves left ventrolateral prefrontal cortex, area 45, and the related area 47/12 (e.g., [59]–[62]).
In summary, the evidence reviewed above has suggested that one of the component areas of Broca's region, namely area 45 and the related ventrolateral area 47/12, is involved with the controlled retrieval and selection of information from nonverbal memory in the monkey and in the right nondominant hemisphere of the human brain. How might the other component of Broca's region, namely area 44, be related to function? Area 44 is located between area 45, on the one hand, and ventral premotor cortex (area 6) controlling hand/arm and oral/facial musculature, on the other hand, and is connected with both these adjacent areas. Thus, area 44 is in an ideal position to mediate between strategically retrieved and selected information from posterior temporal and parietal cortex by area 45 and the articulation of such information via hand and orofacial action by the ventral precentral gyrus (i.e., the premotor and motor cortex) (see Petrides [56]). In other words, these results suggest that area 44 might be a ventrolateral frontal control area involved in the highest levels of programming of action that will be instantiated by a series of articulatory acts regulated by the premotor and motor cortex of the precentral gyrus. During the evolution of the human brain, these high-level forms of programming (the basic elements of which are already present in the macaque monkey brain) came to include complex syntactical structure (e.g., hierarchical level of control) that is necessary for language (in the narrow sense), and which has been argued to be a major contribution of Broca's region (e.g., [63],[64]). If we were to extrapolate these arguments on the basis of the present monkey anatomical study, our recording study in the monkey [55], and our functional neuroimaging studies of the human right hemisphere homologue of Broca's region [53],[54], we could say that a common primate circuitry was adapted, during millions of years of evolution, in the human brain for the strategic retrieval and selection of information from verbal memory (including the mental lexicon) in posterior temporo-parietal cortical regions by one component of Broca's region, area 45, and the transformation of this selected conceptual information into gestural/speech acts by the other component of Broca's region, area 44, via its connections with motor structures, such as the premotor cortex, the basal ganglia, and the rostral inferior parietal lobule (see Petrides [56] for this argument). We should emphasize here that, clearly, macaque monkeys do not have complex syntactic processing. Our suggestion here is simply that an area that served higher control of action in the macaque monkey may have been adapted for the control of complex hierarchical sequences of gestural and vocal action with the evolution of communication leading to human speech.
When might this adaptation have begun? There is paleoneurological evidence from fossil endocasts that the asymmetry in Broca's region observed in the modern human brain can be observed in brain endocasts of specimens assigned to Homo erectus/habilis, based on the petalia impressions [65] and the study of the endocast of the Sambungmacan 3 (Sm3) fossil, a Homo erectus calvaria from Indonesia [66]. For instance, Sm3 exhibits left-right cerebral volume asymmetry and marked asymmetry in Broca's cap, i.e., modern human characteristics. Thus, the fossil evidence suggests that this asymmetry is a relatively recent event in the evolution of the human brain. Although the presence of this asymmetry does not necessitate the conclusion that there was modern language lateralization as early as Homo erectus, it can be interpreted as supportive evidence that some precursor of the modern function of Broca's region may have begun evolving in Homo erectus. Although language in the narrow sense is clearly a human characteristic, it is interesting to note the presence of areas 44 and 45 (Broca's region) in the brain of the African great apes [67] and also the presence of various asymmetries in the chimpanzee brain relevant to our understanding of the evolution of language, such as a leftward asymmetry in the planum temporale based on architecture [68] and in the ventrolateral frontal region based on gross morphological features [69]. More recently, behavioral evidence has been presented for left hemisphere dominance in the chimpanzee brain for the control of oro-facial movements associated with learned communicative vocalizations, while emotional stereotyped vocalizations may be controlled by the right hemisphere [70]. In addition, in chimpanzees, handedness for tool use has been linked to asymmetries in the ventrolateral frontal region and the planum temporale [71], suggesting a left hemisphere specialization for the control of complex action using the right hand, paralleling a well-known phenomenon in the human brain [72]. These findings are consistent with suggestions that specialization for the control of action and gesture may have preceded specialization for language (e.g., [72]–[74]). Note also that our close primate relatives, chimpanzees and bonobos, use arm/hand gestures more flexibly in their natural communication across contexts than facial expressions and vocalizations [75]. The above facts suggest that the use of gestures for early forms of communication may have been an adaptation distinguishing the Hominoidea from other primates, and that the use of vocalization in the form of modern speech emerged much later with the evolution of language in the narrow sense, i.e., a uniquely human adaptation. It is interesting in this respect that the supralaryngeal vocal tract of humans differs significantly from those of other primates, making the human vocal apparatus unique in transmitting information at fast rates [76].
In conclusion, the present findings indicate that, even in the nonlinguistic nonhuman primate brain, the precursors of Broca's region have a wide neural circuitry involving specific connections with posterior inferior parietal cortex and adjacent caudal superior temporal sulcus and with the intermediate-to-anterior superolateral temporal cortex. It is likely therefore that these pathways, which are additional to the posterior temporal-to-frontal connection via the arcuate fasciculus (traditionally assumed to be the main language pathway), play a major role in language processing in the human brain. The present anatomical findings indicate a rich dorsal stream of fibers via the superior longitudinal/arcuate fasciculus that can be further subdivided into a rostral and a caudal component and an independent ventral temporo-frontal system via the extreme capsule fasciculus that targets predominantly the prefrontal cortical areas 45 and 47/12. Recent diffusion tensor imaging studies (e.g., [27]–[31]) indicate the existence of similar systems in the human brain. This fact suggests that the circuitry of Broca's homologues demonstrated in the present macaque monkey study, which provides details about cortical origins and terminations of pathways not available in the human brain, is most probably also true (and even further elaborated) in the human brain. These findings indicate rich functional interactions between posterior perisylvian cortical regions and the subdivisions of Broca's region, i.e., areas 44, 45B, and 45A, via pathways other than those made by the traditional arcuate fasciculus and raise hypotheses to be tested in the human brain by combined functional magnetic resonance imaging and diffusion tensor imaging. Furthermore, and equally important, it opens up the possibility of examining the neurophysiological basis of the prelinguistic use of these pathways (e.g., [55]), a use that is still directly relevant to the function of these areas in the nondominant right hemisphere of the human brain (e.g., [53],[54]), and thus provide major insights into neural computations that were adapted to serve language processing with the emerging specialization of the human left hemisphere for language processing. In this manner, the study of the functional contribution of the nonlinguistic macaque monkey circuitry of Broca's homologues can potentially provide important insights into the evolution of language.
Injections of radioactively labeled amino acids were placed in different parts of the posterior parietal cortex and the temporal cortex in 11 rhesus monkeys (Macaca mulatta). The injections were placed in the left hemisphere (cases 1, 3, 4, 5, 6, 8, 9, 11) (Figures 3,5–8,10,11, and 13), except for three cases in which they were placed in the right hemisphere (cases 2, 7, 10) (Figures 4, 9, and 12). In the latter three cases (Figures 4, 9, and 12), the drawings were left/right reversed so that the orientation would be consistent with that in all other figures and help the reader in comparing cases across figures. The care and use of animals were in accordance with the guidelines of the National Institutes of Health and the Canadian Council for Animal Care. The animals were immobilized with ketamine hydrochloride (10 mg/kg) and then deeply anesthetized with sodium pentobarbital (30 mg/kg) (Sigma) administered intravenously. A craniotomy was then performed, under aseptic surgical techniques, over the region of interest. In each case, an attempt was made to place two juxtaposed isotope injections into an architectonic area in the parietal or the temporal cortex. The intracortical injections consisted of radioactively labeled amino acids (3H-leucine and/or proline; volume range, 0.4–1.0 µl; specific activity range, 40–80 µCu, aqueous solution; New England Nuclear Brand Radiochemicals from PerkinElmer). After survival periods ranging from 7 to 10 d, the animals were deeply anesthetized with sodium pentobarbital and perfused transcardially with physiologic saline, followed by a 10% formalin solution.
The brains were divided into two blocks by a coronal cut and photographed from all angles. They were subsequently embedded in paraffin, sectioned at 16-µm thickness, and processed for autoradiography according to the technique described by Cowan et al. [32]. The exposure times varied between 3 and 6 mo. At monthly intervals, trial sections were developed for identification of optimal radiolabeling. The sections were also counterstained with thionine to permit identification of the architectonic areas. A series of coronal sections of the hemisphere were examined microscopically with darkfield illumination (for examples see Figures S2 and S3). The labeled fibers in the white matter and the terminal labeling in the cerebral cortex and subcortical structures were recorded with the aid of an X-Y plotter (Hewlett Packard) that was electronically coupled to the stage of the microscope (Leitz Aristoplan). This information was then used to reconstruct the injection and termination sites, as well as the path of the labeled fibers. The cytoarchitectonic boundaries of the projection areas within the cerebral cortex, as well as the sites of origin of the pathways were established in the experimental material under light field illumination.
The distribution of the terminations of the labeled fibers was transferred onto 2-D reconstructions of the lateral, medial, and ventral surfaces of the examined cerebral hemispheres. These 2-D reconstructions of the hemispheres were made using a precision technical drawing software program (AutoSketch, Release 7, Autodesk, Inc.). In order to minimize the distortion in the normal view of the cerebral hemisphere, which inevitably follows such maps, we unfolded the cortex lying within the major sulci separately. These unfolded sulci are presented next to the lateral views of the hemispheres so that the reader can appreciate the details of the terminations within the sulci. On the coronal sections of the hemisphere that was to be reconstructed, we traced the distance from the midline (i.e., the border of the lateral with the medial surface of the hemisphere) to the first sulcus encountered laterally. We then measured the distance from that sulcus to the next sulcus and so on until the lateral-to-ventral edge of the hemisphere was reached. These measurements were used for the lateral surface reconstruction. For the reconstruction of the medial surface of the frontal lobe, we measured the distance from the dorsal-most part of the midline to the first sulcus encountered ventrally and then to the next sulcus until the ventral-most part of the medial surface was reached. The orbital surface of the frontal lobe was reconstructed by measuring the distance, on each coronal section, from the midline to the first sulcus encountered laterally (i.e., the medial orbital sulcus) and then from there to the lateral orbital sulcus and then to the ventral-to-lateral edge of the hemisphere. The medial/ventral surface of the temporal lobe was included with the reconstruction of the medial surface of the hemisphere and the origin of the measurements was the hippocampal sulcus. The measurements obtained for the lateral, medial, and orbital surfaces, as well as the cortex within the sulci, were thus a series of line segments (the y coordinates) arranged in the anteroposterior direction (x coordinates). In separate spreadsheets, the points (x, y coordinates) were plotted and joined together in order to reconstruct the 2-D flattened outlines of surfaces of the hemisphere and the sulci.
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10.1371/journal.pgen.1005221 | Promotion of Bone Morphogenetic Protein Signaling by Tetraspanins and Glycosphingolipids | Bone morphogenetic proteins (BMPs) belong to the transforming growth factor β (TGFβ) superfamily of secreted molecules. BMPs play essential roles in multiple developmental and homeostatic processes in metazoans. Malfunction of the BMP pathway can cause a variety of diseases in humans, including cancer, skeletal disorders and cardiovascular diseases. Identification of factors that ensure proper spatiotemporal control of BMP signaling is critical for understanding how this pathway is regulated. We have used a unique and sensitive genetic screen to identify the plasma membrane-localized tetraspanin TSP-21 as a key new factor in the C. elegans BMP-like “Sma/Mab” signaling pathway that controls body size and postembryonic M lineage development. We showed that TSP-21 acts in the signal-receiving cells and genetically functions at the ligand-receptor level. We further showed that TSP-21 can associate with itself and with two additional tetraspanins, TSP-12 and TSP-14, which also promote Sma/Mab signaling. TSP-12 and TSP-14 can also associate with SMA-6, the type I receptor of the Sma/Mab pathway. Finally, we found that glycosphingolipids, major components of the tetraspanin-enriched microdomains, are required for Sma/Mab signaling. Our findings suggest that the tetraspanin-enriched membrane microdomains are important for proper BMP signaling. As tetraspanins have emerged as diagnostic and prognostic markers for tumor progression, and TSP-21, TSP-12 and TSP-14 are all conserved in humans, we speculate that abnormal BMP signaling due to altered expression or function of certain tetraspanins may be a contributing factor to cancer development.
| The bone morphogenetic protein (BMP) signaling pathway is required for multiple developmental processes during metazoan development. Various diseases, including cancer, can result from mis-regulation of the BMP pathway. Thus, it is critical to identify factors that ensure proper regulation of BMP signaling. Using the nematode C. elegans, we have devised a highly specific and sensitive genetic screen to identify new modulators in the BMP pathway. Through this screen, we identified three conserved tetraspanin molecules as novel factors that function to promote BMP signaling in a living organism. We further showed that these three tetraspanins likely form a complex and function together with glycosphingolipids to promote BMP signaling. Recent studies have implicated several tetraspanins in cancer initiation, progression and metastasis in mammals. Our findings suggest that the involvement of tetraspanins in cancer may partially be due to their function in modulating the activity of BMP signaling.
| Bone morphogenetic proteins (BMPs) belong to the transforming growth factor β (TGFβ) superfamily of secreted polypeptides that regulate a variety of developmental and homeostatic processes [1, 2]. The TGFβ ligands are synthesized as precursor proteins that can be subsequently processed by proteases [3]. Active TGFβ ligands bind to a heterotetrameric receptor complex composed of type I and type II receptors, leading to the phosphorylation of the type I receptor by the type II receptor. The phosphorylated type I receptor then phosphorylates and activates the receptor-regulated Smads (R-Smads). The activated R-Smads form a complex with common-mediator Smads (Co-Smads) and enter the nucleus to regulate downstream gene expression. Malfunction of the TGFβ pathway can result in numerous somatic and hereditary disorders in humans, including various cancers, bone skeletal disorders, and cardiovascular diseases [4–7]. Multiple levels of regulation ensure proper spatiotemporal activity of TGFβ signaling in the correct cellular context [8–11]. Identifying factors involved in modulating the TGFβ pathway and determining their modes of action in vivo will not only provide valuable insights into our understanding of TGFβ signaling, but may also provide therapeutic targets for the many diseases caused by alterations in TGFβ signaling.
C. elegans, with its wealth of genetic and molecular tools and its well-defined cell lineage, provides an excellent model system to study the functions and modulation of TGFβ signaling during the development of a whole organism at single-cell resolution. There are at least three TGFβ-related pathways in C. elegans: one that controls dauer formation, one that regulates axon guidance and cell migration, and a third BMP-like “Sma/Mab” pathway that regulates body size and male tail formation, among its multiple functions [12]. The Sma/Mab pathway includes a BMP-like molecule DBL-1 [13, 14], the type I receptor SMA-6 [15], the type II receptor DAF-4 [16], the R-Smads SMA-2 and SMA-3, and the Co-Smad SMA-4 [17]. Loss-of-function mutations in any component of this pathway will cause small body size and male tail sensory ray formation defects [12].
We have previously shown that the Sma/Mab pathway also plays a role in patterning the C. elegans postembryonic mesoderm. The hermaphrodite postembryonic mesodermal M lineage arises from a single pluripotent precursor cell, the M mesoblast. During larval development, the M mesoblast divides to produce a dorsal lineage that gives rise to striated bodywall muscles (BWMs) and macrophage-like coelomocytes (CCs), as well as a ventral lineage that produces BWMs and the sex muscle precursor cells, the sex myoblasts (SMs) ([18]; Fig 1A and 1C and 1E). This dorsoventral asymmetry is regulated by the schnurri homolog sma-9 [19]. Mutations in sma-9 lead to a dorsal-to-ventral fate transformation in the M lineage ([20]; Fig 1B and 1D and 1F). We have shown that mutations in the core components of the Sma/Mab pathway (Fig 2A) do not cause any M lineage defect on their own, but they suppress the dorsoventral patterning defects of sma-9 mutants, suggesting that SMA-9 regulates M lineage dorsoventral patterning by antagonizing Sma/Mab signaling [20]. Using this sma-9 M lineage suppression phenotype (Fig 1A and 1C and 1E), we have recently identified two new modulators of the Sma/Mab pathway, the RGM protein DRAG-1 and the DCC/neogenin homolog UNC-40, which directly associate with each other to positively regulate Sma/Mab signaling [21, 22, Fig 2A]. We further showed that their functions in modulating BMP signaling are evolutionarily conserved [22, 23].
In this study, we describe our identification and analysis of additional sma-9 M lineage phenotype suppressors that function in modulating Sma/Mab signaling. One novel modulator is TSP-21, which belongs to a family of transmembrane molecules called tetraspanins [24]. Tetraspanins are a distinct family of integral membrane proteins that have four conserved transmembrane (TM) domains separated by a small extracellular loop (EC1), a small intracellular loop (IL) and a large extracellular loop (EC2). They are known to interact with each other to form homo- and hetero-oligomers, and organize membranes into the so-called tetraspanin-enriched microdomains that are also enriched in cholesterol and glycosphingolipids [24–27]. There are 33 tetraspanins in humans and 21 in the C. elegans genome. The in vivo functions of most of these tetraspanins are not well understood. Here we provide evidence that TSP-21, the C. elegans ortholog of human TSPAN4, TSPAN9 and CD53, is localized to the cell membrane and functions positively to regulate Sma/Mab signaling in the signal-receiving cells at the ligand-receptor level. We further show that two additional tetraspanins that belong to the C8 subfamily of tetraspanins, TSP-12 and TSP-14, also function to promote Sma/Mab signaling. TSP-12 and TSP-14 can physically interact with each other, with TSP-21, and with the type I receptor of the Sma/Mab pathway, SMA-6. In addition, we find that mutants defective in glycosphingolipid biosynthesis exhibit defects in Sma/Mab signaling. Collectively, our results provide in vivo evidence supporting the roles of tetraspanins and glycosphingolipid-enriched membrane microdomains in modulating BMP signaling. Finally, we provide evidence that like TSP-12 and TSP-14, which have been previously shown to function in promoting LIN-12/Notch signaling [28], TSP-21 also appears to function in LIN-12/Notch signaling in a cell type-specific manner.
We have previously shown that mutations in all core components of the Sma/Mab pathway, but not the TGFβ-like dauer pathway, can suppress the M lineage phenotype of sma-9(0) mutants [20]. Here we show that null mutations in unc-129, which encodes a TGFβ-like molecule important for axon guidance [29], or null mutations in genes that regulate body size but do not function in the Sma/Mab pathway, such as the β-spectrin gene sma-1 [30], or the cuticle collagen gene lon-3 [31, 32], do not suppress the M lineage phenotype of sma-9(0) mutants (Table 1). Similarly, mutations in the Sma/Mab pathway do not suppress the M lineage defect of let-381(RNAi), which also leads to a dorsal-to-ventral fate transformation defect in the M lineage by inactivating the FoxF/FoxC transcription factor LET-381 ([33]; Table 1). In contrast, two deletion alleles of sma-10, which encodes a conserved, leucine-rich repeats- and immunoglobulin (Ig)-like domain (LRIG)-containing transmembrane protein that promotes Sma/Mab signaling in regulating body size [34, Fig 2A], do suppress the M lineage phenotype of sma-9(0) mutants (Table 1).
Motivated by the specificity of the BMP-like Sma/Mab pathway mutants in suppressing the sma-9 M lineage defect and our previous success from the sma-9 suppressor screen in the identification of evolutionarily conserved modulators of BMP signaling, such as DRAG-1/RGM and UNC-40/DCC/neogenin [21, 22], we performed a large-scale screen for sma-9 suppressors, named susm (suppressor of sma-9) mutations, with the aim of identifying additional modulators of BMP signaling (see Materials and Methods). Using a combination of linkage analysis, complementation tests and whole genome sequencing (see Materials and Methods), we identified the corresponding genes for 32 susm mutations. As shown in Table 2, our suppressor screen successfully and specifically identified mutations in all core members and known modulators of the Sma/Mab pathway. Intriguingly, we isolated lon-1(jj67) as a sma-9 suppressor and showed that an existing, strong loss-of-function allele, lon-1(e185), also suppresses the sma-9 M lineage phenotype (Tables 1 and 2). lon-1 encodes a member of the cysteine-rich secretory protein (CRISP) family of proteins and is known to function downstream of, and be negatively regulated by, the Sma/Mab pathway [13, 35, Fig 2A]. The suppression of the sma-9 M lineage phenotype by lon-1 mutations and the increased expression of the Sma/Mab responsive reporter RAD-SMAD [21] in lon-1(jj67) mutants (Fig 3B) suggest that LON-1 may exert feedback regulation on Sma/Mab signaling, rather than being strictly regulated by this pathway (Fig 2A). In addition to these factors known to function in Sma/Mab signaling, we also identified a novel factor defined by two non-complementing alleles, jj60 and jj77 (Table 2).
We performed whole genome sequencing (WGS) of the novel complementation group that includes jj60 and jj77 to identify the corresponding gene. Four lines of evidence indicate that the corresponding gene is tsp-21. 1) RNAi of tsp-21 suppressed the sma-9 M lineage phenotype (Table 3). 2) Both jj60 and jj77 contain molecular lesions in tsp-21 (Fig 4A): jj60 contains a G-to-A change in nucleotide 1827, resulting in a Glycine (G) to Glutamic acid (E) change of amino acid 109 (G109E). jj77 contains a T-to-A change in nucleotide 3327, resulting in a Valine (V) to Glutamic acid (E) change of amino acid 236 (V236E). jj77 also carries a 84bp deletion (between nucleotides 3202 and 3287) and a 6bp insertion (ATCTCT), resulting in a 13 amino acid deletion and 2 amino acid insertion between amino acids 209 and 223. 3) A DNA fragment containing the genomic sequence of tsp-21 (5kb upstream, entire coding region including introns, and 1.7kb downstream sequences for C17G1.8 in pJKL1005, Fig 4B) rescued the Susm phenotype of jj77 (Table 3). 4) A deletion allele of tsp-21 that we recently obtained, tm6269, exhibited defects similar to those of jj77 and jj60 animals (Fig 4B and Table 3).
tsp-21 encodes a conserved but previously unstudied 301 amino acid transmembrane protein of the tetraspanin family, TSP-21 (Fig 4C). Based on the number of cysteine (C) residues in the large EC2 loop, tetraspanins can be classified into three groups, C4, C6 and C8 [36, 37]. TSP-21 belongs to the C6a group, with the following configuration of cysteine residues in EC2: CCG——CC——C——C (Fig 4C). The closest vertebrate homologs of TSP-21 are TSPAN4, TSPAN9 and CD53 (Figs 4C and S1). These proteins, except for CD53, share the conserved C6a configuration in the EC2 loop as well as conserved transmembrane (TM) domains (Fig 4C). The G109E mutation in jj60 affects the last residue of TM3 (Fig 4A and 4C), and likely results in the production of a partial loss-of-function TSP-21 protein. The deleted residues of TSP-21 in jj77 mutants include one of the six highly conserved cysteine residues in EC2 (Fig 4C). jj77 also contains a missense mutation in a conserved residue in TM4 (V236E, Fig 4), and is therefore likely a strong loss-of-function, or likely null, allele of tsp-21. We have therefore used jj77 for all our subsequent analyses.
Because our sma-9 suppressor screen is highly specific in identifying components of the BMP-like Sma/Mab pathway, we examined tsp-21(jj77) mutants for any additional Sma/Mab signaling defects, such as body size, male tail patterning and expression of RAD-SMAD, a Sma/Mab signaling reporter that we previously generated [21]. tsp-21(jj77) animals are smaller than wild-type animals (Fig 2B–2E) and exhibited reduced RAD-SMAD reporter expression (Fig 3). Unlike mutants in core members of the Sma/Mab pathway, tsp-21(jj77) mutant males can mate and they do not exhibit any significant male tail patterning defects (based on examining 100 sides of tsp-21(jj77) male tails). These tsp-21 phenotypes are very similar to those exhibited by null mutants in two previously identified Sma/Mab pathway modulators, drag-1 and unc-40 [21, 22]. Furthermore, like mutants in other members of the Sma/Mab pathway [20–22], tsp-21(jj77) mutants exhibited no M lineage defects (Table 3). Finally, tsp-21(jj77) mutants showed no dauer defects, and tsp-21 exhibited no genetic interaction with daf-1 and daf-7, two genes functioning in the TGFβ-like dauer pathway ([12]; S1 Table). Collectively, these phenotypic analyses suggest that TSP-21 positively modulates the BMP-like Sma/Mab pathway, but does not appear to play a role in the TGFβ-like dauer pathway.
The smaller body size of tsp-21(jj77) mutants allowed us to use genetic epistasis to determine where in the Sma/Mab pathway TSP-21 functions. We generated double mutants between tsp-21(jj77) and null mutations in various Sma/Mab pathway components (Fig 2A) and measured their body sizes. As shown in Fig 2E, dbl-1(wk70); tsp-21(jj77) and sma-3(jj3); tsp-21(jj77) double mutants were as small as dbl-1(wk70) and sma-3(jj3) single mutants, respectively. These observations are consistent with TSP-21 functioning in the Sma/Mab pathway in regulating body size. lon-1(jj67); tsp-21(jj77) double mutants were as long as lon-1(jj67) single mutants, while lon-2(e678) tsp-21(jj77) double mutants showed intermediate body size between lon-2(e678) and tsp-21(jj77) single mutants (Fig 2E), suggesting that tsp-21 is likely to function upstream of lon-1, but in parallel to lon-2, in the Sma/Mab pathway. drag-1(jj4); tsp-21(jj77) and unc-40(e1430); tsp-21(jj77) double mutants, or drag-1(jj4) unc-40(e1430); tsp-21(jj77) triple mutants were significantly smaller than each respective single mutant (Fig 2E), which is consistent with tsp-21 functioning in parallel to drag-1 and unc-40. Taken together, these results indicate that TSP-21 functions at the ligand-receptor level to positively modulate Sma/Mab signaling.
To determine how TSP-21 functions in the Sma/Mab pathway, we examined the expression and localization pattern of TSP-21. We first generated integrated transgenic lines carrying a translational TSP-21::GFP fusion (pJKL1004, see Materials and Methods) that contains the entire tsp-21 genomic region including 5kb 5’ sequences and 1.7kb 3’ sequences (Fig 4B). This translational fusion rescued the Susm phenotypes of tsp-21(jj77) mutants (Table 3). Subsequently we generated the same fusion in the endogenous tsp-21 locus via CRISPR-Cas9 mediated homologous recombination (see Materials and Methods). Both reporters showed that TSP-21::GFP is plasma membrane-localized and is expressed in a wide variety of somatic cell types, including the pharynx, intestine and hypodermis starting in embryos after the 100 cell stage (Fig 5A–5C at mid-embryogenesis) and peaking in L1 and L2 larvae (Fig 5D–5L). The TSP-21::GFP signal in these tissues decreases in late larval and adult stage animals. TSP-21::GFP is also present at the surface of M lineage cells from the 1-M stage to the 16-M stage (Fig 5S–5Z). In addition to expression in these tissues, the two TSP-21::GFP lines generated via the CRISPR-Cas9 system also showed GFP expression in the somatic gonad and vulva in L2-L4 larvae and adults (Fig 5M–5O), as well as in the rectal epithelium in L4 larvae (Fig 5P–5R). These observations suggest that the enhancer elements for tsp-21 expression in the somatic gonad, the vulva and the rectal epithelium lie outside of the 10.5kb tsp-21 genomic region included in pJKL1004.
We noticed that TSP-21::GFP is enriched in the basolateral side of intestinal cells while being absent from their apical sides (Fig 5G–5I). A similar localization pattern has been reported for the type I receptor SMA-6 and the type II receptor DAF-4 [38]. In addition, while TSP-21::GFP in the M lineage cells is primarily plasma membrane localized, there is also a significant intracellular distribution of TSP-21::GFP in the M mesoblast (Fig 5S and 5W). At present, the functional significance of either the asymmetric localization of TSP-21::GFP in intestinal cells or its intracellular localization in the M cell is not clear.
The Sma/Mab pathway is known to function in the hypodermal cells to regulate body size and in the M lineage to regulate M lineage development. We next tested whether TSP-21 functions in these cell types to exert its role in Sma/Mab signaling. Using cell-type-specific promoters to drive tsp-21 expression, we found that forced expression of tsp-21 cDNA in hypodermal cells, but not in pharyngeal or intestinal cells, rescued the small body size phenotype of tsp-21(jj77) mutants (Table 4). Similarly, forced expression of tsp-21 cDNA in the M lineage also rescued the Susm phenotype of tsp-21(jj77) mutants (Table 3). Thus TSP-21 functions autonomously in the signal-receiving cells to promote Sma/Mab signaling.
There are 21 tetraspanins in C. elegans. Our finding that TSP-21 functions in Sma/Mab signaling prompted us to ask whether other tetraspanins might also function in the Sma/Mab pathway. We therefore screened through the remaining 20 tetraspanin tsp genes by RNAi injection, testing whether any of them are involved in Sma/Mab signaling using the sma-9 suppression assay. Only tsp-12(RNAi) resulted in a low penetrance (9.4%, n = 767) Susm phenotype (Table 5). We then tested a deletion allele of tsp-12, ok239, and found that it also exhibited the Susm phenotype (Table 5), suggesting that the tetraspanin TSP-12 also plays a role in modulating Sma/Mab signaling. Dunn and colleagues [28] have previously reported that TSP-12 and TSP-14 function redundantly to promote Notch signaling. We asked whether tsp-14 and tsp-12 might also share a redundant role in the Sma/Mab pathway, and found that tsp-14(RNAi) enhanced the penetrance of the Susm phenotype of the tsp-12(ok239) mutation (Table 5). This effect appears to be specific since tsp-10(RNAi) failed to enhance the penetrance of the Susm phenotype of the tsp-12(ok239) mutation (Table 5). Thus, TSP-12 and TSP-14 also function redundantly to promote Sma/Mab signaling, in addition to their role in Notch signaling.
The dual functions of TSP-12 and TSP-14 in both the Notch and the Sma/Mab signaling pathways prompted us to examine whether TSP-21 also functions in the Notch signaling pathway. The LIN-12/Notch signaling pathway is known to function in the M lineage to promote the ventral fate: loss of LIN-12/Notch function results in a ventral-to-dorsal fate transformation in the M lineage, namely the loss of M-derived SMs and the gain of M-derived CCs ([39, 40]; S2 Fig). tsp-21(jj77) single mutants exhibit no M lineage defects. We therefore examined whether tsp-21(jj77) could enhance the M lineage defect of the lin-12 temperature sensitive, partial loss-of-function allele lin-12(n676n930ts) by scoring the number of M-derived CCs. As shown in Table 6, tsp-21(jj77) significantly enhanced the M lineage defect of lin-12(n676n930ts) at both 20°C and 22°C, suggesting that TSP-21 functions to promote LIN-12/Notch signaling in the M lineage. However, tsp-21(jj77) failed to enhance the sterility and embryonic lethality of bn18ts, a mutation in the second Notch receptor gene in C. elegans, glp-1 [41]. The lack of genetic interaction between tsp-21(jj77) and glp-1(bn18) is consistent with the absence of TSP-21::GFP expression in the germline and early embryo, as described above.
Tetraspanins often associate with each other and with other membrane or membrane-associated proteins to organize membranes into tetraspanin-enriched microdomains [24–26]. Our finding that in addition to TSP-21, TSP-12 and TSP-14 also function in promoting Sma/Mab signaling suggested that these tetraspanins might interact with each other. We tested this hypothesis by using the mating-based split-ubiquitin system (mbSUS, [42]) in budding yeast. The mbSUS is based on the observation that a full-length ubiquitin can be reconstituted when the N-terminal ubiquitin domain (Nub) and the C-terminal ubiquitin domain (Cub) are brought into close proximity [43, 44]. This system can be used to identify potential interactions between full-length membrane proteins or between a membrane protein and a soluble protein: a mutant form of Nub, NubG, that has reduced affinity for Cub, can only reconstitute with Cub via two interacting proteins. The reconstituted ubiquitin will direct ubiquitin-specific proteases to liberate PLV (protein A, LexA and VP16) from Cub, which then enters the nucleus and activates transcription of reporter genes. We generated TSP-Cub fusions and Nub-TSP or TSP-Nub fusions (see Materials and Methods, and S2 Table for a list of the plasmids generated), and tested pairwise interactions among the three tetraspanins, as well as interactions between these tetraspanins and the type I and type II receptors SMA-6 and DAF-4, respectively. Results from these experiments are summarized in Fig 6. TSP-12-Cub appeared to auto-activate reporter expression, while the TSP-14-Cub was not detectable on western blots (see Materials and Methods). For the remaining three Cub fusions (TSP-21, SMA-6 and DAF-4), we found that TSP-21 can associate with itself, as well as with TSP-12 and TSP-14 (Fig 6). In addition, SMA-6 can associate with both TSP-12 and TSP-14, but not TSP-21. We also detected a very weak interaction between DAF-4 and TSP-14 (Fig 6). The use of multiple positive and negative controls in these experiments (see Materials and Methods, and Fig 6) indicated that the observed interactions are highly specific. For example, TSP-21 did not show any interaction with the C. elegans LKB homolog PAR-4 ([45]; Fig 6), or the plant potassium channel KAT1 ([42]; Fig 6), or with the C. elegans ABC transporter HMT-1 [46]. Except for the weak DAF-4-TSP-14 interaction, the other observed interactions all appeared to be particularly strong, as yeast growth on SC-Trp,-Leu,-Ade,-His,-Ura,-Met plates supplemented with 0.3mM of methionine was detectable only 2 days after streaking the mated yeast. Thus, TSP-21 can form both homo-oligomers and heteromeric complexes with TSP-12 and TSP-14. These findings are consistent with our genetic evidence that all three tetraspanins function to promote Sma/Mab signaling. The strong interactions between SMA-6 and TSP-12 and TSP-14 suggest that these tetraspanins might function by directly recruiting the receptor molecules to specific membrane microdomains.
Tetraspanin-enriched microdomains are also enriched in cholesterol and glycosphingolipids [24–26]. We therefore tested whether cholesterol and/or glycosphingolipids are required for Sma/Mab signal transduction. Our results suggest that Sma/Mab activity is influenced by glycosphingolipids but not cholesterol.
C. elegans worm survival requires exogenous cholesterol [47, 48]. In the lab, worms are normally fed with E. coli bacteria on agar plates supplemented with 5μg/mL cholesterol [49]. Using a method that can lead to nearly complete cholesterol depletion ([50, 51] and see Materials and Methods), we grew L1 or L4 worms on cholesterol-depleted plates and scored their phenotypes or their progeny’s phenotype, respectively, at the adult stage. We found no suppression of the M lineage phenotype when sma-9(cc604) worms were grown on cholesterol-depleted media, even though the worms were sterile, a known phenotype resulting from cholesterol depletion [47, 48]. Thus cholesterol does not seem to be essential for Sma/Mab signaling.
To determine the requirement of glycosphingolipids in Sma/Mab signaling, we generated double mutants between sma-9(cc604) and mutations that reduce or eliminate the activity of enzymes involved in glycosphingolipid biosynthesis [52], S3 Fig), and examined their Susm phenotype. As shown in Table 7, mutations in cgt-3 and bre-5 partially suppressed the sma-9 M lineage phenotype. cgt-3 encodes the ceramide glucosyltransferase that converts ceramide to glucosylceramide, a precursor of complex glycosphingolipids [53, 54]. Previous work has shown that CGT-3 is the major enzyme among the three worm CGT proteins [54]. We found that a deletion allele of cgt-3, ok2877, which deletes most of the coding exons of cgt-3, resulted in a late L1 or early L2 larval arrest and a partial suppression of the sma-9(cc604) M lineage defects (Table 7). cgt-3(ok2877) mutants exhibited additional defects in Sma/Mab signaling: the relative fluorescence intensity of the RAD-SMAD reporter in cgt-3(ok2877) mutants is only 58% of that in stage-matched wild-type animals (see Materials and Methods) and cgt-3(ok2877) mutants exhibited a smaller body size compared to stage-matched wild-type control animals (72% of wild-type body length, n = 23). We also observed a low penetrance of the Susm phenotype in ye17, an allele of bre-5 that encodes a β-1,3-galactosyltransferease involved in glycosphingolipid biosynthesis (Table 7, [55, 56]). Taken together, our data suggest that glycosphingolipids are required for Sma/Mab signaling. The lack of a Susm phenotype for the other mutations affecting glycosphingolipid biosynthesis (Table 7) may be because many of them are partial loss-of-function alleles, since null mutations in many of these genes result in lethality [52]. Alternatively, proper Sma/Mab signaling may require specific type(s) of glycosphingolipids.
cgt-3 is widely expressed in multiple cell types in C. elegans [53, 54]. We tested whether cgt-3, and therefore glycosphingolipids, are required in the signal-receiving cells for proper Sma/Mab signaling. Expression of cgt-3 in the M lineage using the hlh-8 promoter partially, but significantly, rescued the Susm phenotype of cgt-3(ok2877) mutants (Table 7), suggesting that proper Sma/Mab signaling requires glycosphingolipids in the signal-receiving cells. The lack of complete rescue suggests that glycosphingolipids are also required outside of the signal-receiving cells to promote Sma/Mab signaling.
During the course of our study, we observed that both cgt-3(ok2877) and bre-5(ye17) single mutants exhibited a low penetrance M lineage phenotype like that of a lin-12(lf) mutant: extra M-derived CCs due to the fate transformation of M-derived SMs to CCs ([39, 40]; Table 8 and S2 Fig). We further found that cgt-3(ok2877) enhanced the penetrance of the M lineage defects of a hypomorphic lin-12 temperature sensitive allele, n676n930, at a semi-permissive temperature (Table 8). These observations are consistent with previous findings by Katic and colleagues [57] showing that enzymes required for glycosphingolipid biosynthesis, such as BRE-5, are required for promoting LIN-12/Notch signaling. The requirement of glycosphingolipids in LIN-12/Notch signaling appears to be distinct from their requirement in Sma/Mab signaling. Mutations in the Sma/Mab pathway fully restore the sma-9(0) M lineage phenotype back to that of wild-type animals ([20–22, 40]; S2 Fig). However, lin-12(0); sma-9(0) double mutants exhibit a reversal of the M lineage dorsoventral polarity, so that the double mutants have 2 SMs born on the dorsal side and 2 M-derived CCs located on the ventral side ([39, 40]; S2 Fig). Careful examination of the position of the M-derived CCs in cgt-3(ok2877);sma-9(cc604) and bre-5(ye17);sma-9(cc604) mutants showed that a majority of the double mutant animals have their M-derived CCs located on the dorsal side (S3 Table), indicating a suppression rather than a reversal of polarity. Taken together, our results support the notion that glycosphingolipids are required for both LIN-12/Notch and Sma/Mab signaling.
In this study, we identified TSP-21, a C6a class tetraspanin, as a key factor promoting the BMP-like Sma/Mab signaling in C. elegans. tsp-21 mutants exhibit small body size and Susm phenotypes similar to that shown by mutants in core Sma/Mab pathway components. The TSP-21 protein is localized to the plasma membrane, and tsp-21 is expressed and functions in the signal-receiving cells at the ligand-receptor level to promote Sma/Mab signaling. We found that among the remaining 20 C. elegans tetraspanins, TSP-12 and TSP-14 function redundantly to also promote Sma/Mab signaling. How do these three tetraspanins function to promote Sma/Mab signaling? We envision two possible, non-mutually exclusive, scenarios.
In the first scenario, the three tetraspanins might promote clustering of the receptor complexes or the ligand-receptor complexes to modulate Sma/Mab signaling. Tetraspanins are known to homo- and hetero-oligomerize to organize membranes into tetraspanin-enriched microdomains, which are also enriched in tetraspanin-associated proteins [24–26]. Previous work has shown that in mouse, TSPAN12 promotes Norrin/β-catenin signaling by enhancing clustering of the Norrin receptor FZD4 [58, 59]. In particular, TSPAN12 and Norrin can each enhance FZD4 clustering but work together cooperatively to further increase the clustering of the ligand-receptor complex to promote Norrin/β-catenin signaling [58]. We have shown that C. elegans TSP-12, -14 and -21 can interact with each other in yeast and that both TSP-12 and TSP-14 can interact with the type I receptor SMA-6. In addition, we found that glycosphingolipids, which are enriched in tetraspanin-enriched microdomains, are also required for proper Sma/Mab signaling. These findings suggest that TSP-21, TSP-12 and TSP-14 may function by recruiting the receptor complex, or the ligand-receptor complex, to glycosphingolipid-enriched membrane microdomains containing TSP-21-TSP-12-TSP-14, thereby increasing the local concentration of the receptors, or the ligand-receptor complexes, to promote Sma/Mab signaling (Fig 7). Supporting this model, SMA-6, DAF-4 and TSP-21 are all localized to the basolateral membranes of the polarized intestinal cells ([38]; this work). We envision that several previously identified positive modulators of the Sma/Mab pathway, including DRAG-1/RGM, UNC-40/neogenin, and SMA-10/LRIG, might be localized in these microdomains as well, as all three proteins are plasma membrane-localized, are expressed and function in the signal-receiving cells, and interact with the ligand and the receptors (for DRAG-1), or the receptors (for SMA-10), or with each other (for DRAG-1 and UNC-40) [21, 22, 34]. Further biochemical and cell biological experiments are needed to determine the presence and subcellular localization of TSP-21-TSP-12-TSP-14-containing membrane microdomains, whether the Sma/Mab pathway receptors and modulators are indeed localized to these microdomains, and what other factors are also present there.
Alternatively, but not mutually exclusively, the three tetraspanins might be involved in the trafficking of essential Sma/Mab pathway components. Tetraspanins have been found to be present in the plasma membrane or various types of intracellular membranous organelles, and multiple tetraspanins are known to regulate the processing and trafficking of associated proteins [60]. In C. elegans, TSP-12 and TSP-14 have previously been shown to function redundantly in promoting Notch signaling [28]. Their Drosophila and mammalian homologs, the TspanC8 tetraspanins, interact with the ADAM (a disintegrin and metalloprotease) protease ADAM10 to promote its maturation and trafficking to the cell surface, which in turn promotes Notch signaling [61–63]. TSP-12 and TSP-14 may function in a similar manner in promoting Sma/Mab signaling. Since both TSP-12 and TSP-14 can bind to the type I receptor SMA-6 in yeast, they may promote Sma/Mab signaling by regulating the trafficking of SMA-6 (Fig 7), and/or other players in the Sma/Mab pathway. Further work is needed to test this hypothesis. Since the role of TspanC8 tetraspanins in promoting Notch signaling is evolutionarily conserved [61–63], it will be interesting to determine whether the role of TspanC8 tetraspanins in modulating BMP signaling is also evolutionarily conserved, and whether these tetraspanins function in a similar manner in promoting both BMP and Notch signaling.
Using C. elegans as a model, Gleason and colleagues recently showed that the type I receptor SMA-6 and the type II receptor DAF-4 utilize distinct mechanisms for their intracellular recycling, providing physiological evidence supporting the roles of endocytosis and intracellular trafficking in regulating BMP signaling [38]. In light of the roles of multiple tetraspanins in regulating the processing and trafficking of associated proteins [60], our findings, together with that of Gleason and colleagues [38], highlight the usefulness of C. elegans as a model system in identifying cell biological mechanisms that regulate BMP signaling.
The family of tetraspanin proteins is large: there are 21 tetraspanins in C. elegans and 33 tetraspanins in humans. Recent studies have implicated tetraspanins in multiple diseases and physiological processes in humans [60]. In particular, several tetraspanins, such as CD151 [64], TSPAN12 [65], and TSPAN8 [66], among others, have been implicated in cancer initiation, progression and metastasis in mammals. These and other tetraspanins have emerged as diagnostic and prognostic markers, and possible therapeutic targets, for tumor progression (for reviews, see [27, 67]). However, the mechanism by which the mis-regulation of these tetraspanins contributes to cancer is not fully understood [27, 67]. It is well known that mis-regulation of TGFβ signaling contributes to cancer initiation and progression [6, 68]. CD151 is the only tetraspanin whose role in cancer has been directly linked to altered TGFβ signaling [69]. Sadej and colleagues showed that CD151 is required for TGFβ1-induced proliferation and scattering of breast cancer cell line MDA-MB-231 through regulating TGFβ-induced p38 phosphorylation, rather than canonical TGFβ-induced Smad phosphorylation. Furthermore, this function of CD151 in TGFβ signaling requires its interaction with the integrins [69]. How CD151-integrin interaction regulates TGFβ-induced p38 phosphorylation is not clear. Recently a study on the tetraspanin-interacting protein EWI-2 indirectly implicates two other tetraspanins, CD9 and CD81, in regulating TGFβ signaling in melanoma growth and metastasis [70]. But the detailed mechanism on how these two tetraspanins regulate TGFβ signaling is not known.
We have provided a direct in vivo link between BMP signaling and three tetraspanins, TSP-21, TSP-12 and TSP-14, in living animals using C. elegans as a model. Our genetic epistasis results showed that TSP-21 acts through SMA-3, one of the R-Smads in the canonical BMP-like Sma/Mab signaling pathway (Fig 2E). Due to the embryonic arrest of null mutants in the C. elegans integrin genes, we could not determine whether the function of TSP-21 in Sma/Mab signaling is dependent on integrins. We have found that strong-loss-of function mutations in one of the two C. elegans genes encoding the α subunit of integrin, ina-1(gm39) and ina-1(gm144) [71], did not exhibit any Susm phenotype (n = 53 for gm39, and n = 109 for gm144). But we cannot rule out the possibility that in these mutants residual ina-1 function or function of pat-2, another gene encoding the α subunit of integrin [72] is sufficient to mediate Sma/Mab signaling.
TSP-21 is orthologous to human TSPAN4, TSPAN9 and CD53, but is much more distantly related to CD151 (whose C. elegans ortholog is TSP-17; S1 Fig). It is therefore possible that the differences between CD151 and TSP-21 in regulating TGFβ signaling are due to intrinsic biochemical differences between the two types of proteins. Alternatively, since TSP-21 regulates a BMP-like Sma/Mab signaling pathway, it is likely that tetraspanins can regulate both TGFβ signaling and BMP signaling, but via distinct downstream effectors. Interestingly, each of the three human orthologs of TSP-21 (TSPAN4, TSPAN9 and CD53), as well as two out of the six human orthologs of TSP-12 and TSP-14 (TSPAN10 and TSPAN33), are expressed at elevated levels in certain cancer cell lines or tumors [73–75] In addition, one human ortholog of TSP-12 and TSP-14 (TSPAN14) is genetically altered in non-small-cell lung cancer [76]. However, the functional significance of their overexpression or mutation in human cancers is not fully understood. We propose that the involvement of these tetraspanins in cancer may be partially due to their role in modulating the activity of TGFβ and/or BMP signaling.
Previous genetic studies in C. elegans have led to the identification of key players in BMP signaling (for example, [16, 17]). A screen based on the body size phenotype has also been fruitful in identifying factors involved in modulating Sma/Mab signaling, such as SMA-10/LRIG [32] and LON-2/glypican [34, 77]. Potential modulators of the Sma/Mab pathway may also exist among a collection of mutants with a small body size phenotype [78]. However, it may be difficult to identify the genes for which mutations produce only a subtle effect on body size, such as tsp-21(jj77). Furthermore, since genes not functioning in the Sma/Mab pathway also regulate body size (for example, [30–32, 79]), not all mutations affecting body size will identify factors specifically functioning in the Sma/Mab pathway. The sma-9 suppressor screen appears to be a highly specific and sensitive means to identify new components of the Sma/Mab pathway: (1) Mutations in all (except for crm-1, see Table 1) previously identified Sma/Mab pathway members suppress the sma-9 M lineage phenotype (Table 1 and Table 2). In general, partial loss-of-function alleles for a given gene exhibited lower penetrance of the Susm phenotype compared to putative null alleles (Table 2), demonstrating that the suppression of the sma-9 M lineage phenotype is highly sensitive to altered levels of Sma/Mab signaling. (2) Mutations in other signaling pathways, such as the dauer pathway or the Wnt pathway, or mutations that exclusively affect body size without affecting Sma/Mab signaling, do not suppress the M lineage phenotype of the sma-9 mutant ([20]; Table 1). (3) Using this screen, we have identified three evolutionarily conserved modulators of the Sma/Mab pathway, DRAG-1/RGM [21], UNC-40/neogenin/DCC [22], and TSP-21/TSPAN4,9 (this study). Additional modulators of this pathway probably exist, as our screen has only recovered single alleles of several genes known to function in Sma/Mab signaling and is, therefore, unlikely to be genetically saturated (Table 2).
In summary, we have developed a highly specific and sensitive way to identify new modulators of the BMP pathway in C. elegans. This genetic approach has confirmed known regulators and identified novel players. Because of the high degree of conservation of the BMP pathway, the factors that we identify in our screen and the mode of their action that we decipher in C. elegans will be broadly relevant in understanding modulation of BMP signaling in other metazoans, including humans.
Strains were grown using standard culture conditions, as described by Brenner [49]. Analyses were performed at 20°C, unless otherwise noted. Cholesterol depletion conditions were following those described in Merris et al. [80] by replacing agar with agarose, and by growing bacteria OP50 and C. elegans worms on defined media, which contains 3.5mM Tris.HCl, 2mM Tris, 34mM NaCl, and 3.1g/L of ether-extracted peptone. Eggs or L4 hermaphrodite animals were placed on cholesterol-depleted plates and the resulting adult animals were scored for M lineage phenotypes.
The following mutations and integrated transgenes were used: Linkage group I (LG I): drag-1(jj4), arIs37(secreted CC::gfp), bre-4(ok3167), bre-5(ye17); LG II: sma-6(e1482), pod-2(ye60), cgt-3(ok2877)/mIn1[mIs14 dpy-10(e128)], jjIs2437[CXTim50.19[pCXT51(5*RLR::deleted pes-10p::gfp) + LiuFD61(mec-7p::rfp)], sptl-1(ok1693); LG III: daf-4(m63), daf-7(m62), sma-2(e502), sma-3(e491), sma-4(e729), lon-1(e185), cup-5(ar465), ina-1(gm144), ina-1(gm39), bre-2(ye31), bre-3(ye26), bre-3(ye28), lin-12(n676n930ts), hT2[qIs48], ccIs4438[intrinsic CC::gfp]; LG IV: daf-1(m40), daf-1(m213), fat-2(wa17), fat-3(wa22), fat-6(tm331), tsp-12(ok239), nT1[qIs51]; LG V: dbl-1(wk70), fat-7(wa36), sma-10(wk89), sma-10(ok2224), sma-1(ru18), lon-3(ct417), crm-1(tm2218), him-5(e1467), bre-1(ye4), bre-5(ye17), cgt-1(ok1045), acs-1(gk3066)V/nT1[qIs51]IV;V; LG X: lon-2(e678), tsp-21(tm6269), sma-9(cc604), jjIs2433[RAD-SMAD: CXTim50.1[pCXT51(5*RLR::deleted pes-10p::gfp) + LiuFD61(mec-7p::rfp)]].
tsp-21 and sma-9 are located 0.79 map unit apart from each other on the X chromosome. We therefore separated the tsp-21(jj77) mutation from sma-9(cc604) via recombination. Specifically, progeny from tsp-21(jj77) sma-9(cc604)/+ + heterozygous parents were scored for the number of CCs. Animals with 6 CCs (jj77 cc604/+ + or + +/+ + or jj77 cc604/jj77 +) were genotyped for jj77 homozygosity by PCR. jj77 cc604/jj77 + animals were selected and their progeny were further genotyped by sequencing the sma-9 gene in order to obtain jj77 +/jj77 + animals. Four independent recombinants were obtained, #570, #778, #898 and #954. Each recombinant was then outcrossed with N2 three more times before further phenotypic analysis. All four recombinants behaved similarly regarding body size, RAD-SMAD and male tail patterning phenotypes.
The lon-2(e678) tsp-21(jj77) double mutant was generated from a lon-2(e678) egl-15(n484)/tsp-21(jj77) heterozygous worms by identifying Lon-non-Egl recombinants, and scoring for the presence of the tsp-21(jj77) and the lon-2(e678) mutations by PCR genotyping.
let-381(RNAi) was performed via feeding following the protocol described in [33]. Other RNAi experiments were performed by injection. In general, gene specific fragments were amplified using RNAi clones from the Ahringer library [81] or the Vidal library [82], or using N2 genomic DNA as template. dsRNAs were generated using the T7 Ribomax RNA Production System (Promega) and injected into gravid adult hermaphrodite animals of specific genotypes carrying CC::gfp. The resulting progeny were scored at the adult stage for the number of CCs.
arIs37(secreted CC::gfp) I; cup-5(ar465) III; sma-9(cc604) X animals lacking M-derived coelomocytes (having a total of 4 CCs) were treated with 50 mM ethyl methanesulfonate (EMS). Individual F1 animals were picked to 3F1s per plate and their combined F2 progeny were screened for the restoration of M lineage-derived coelomocytes (having a total of 5–6 CCs) by direct visual examination using a fluorescence stereomicroscope. Plates that segregated 5–25% of animals with 6 CCs were kept for further analysis, including determining whether the mutations bred true, the degree of suppression for each suppressor mutation when homozygous and whether the mutations are dominant or recessive.
By screening through 5,300 haploid genomes using the above method, we isolated 37 true-breeding sma-9 suppressors, named susm (suppressor of sma-9) mutations (jj49-jj85, Table 2). Four of these, jj68, jj80, jj81 and jj84 showed a relatively low degree of suppression (near 30%, Table 2), and were not further characterized in this work. jj58 might be a dominant mutation and was not further analyzed. All of the remaining susm alleles appear to be recessive, single locus mutations, although some suppressors exhibited partial dominance in their Susm phenotype (Tables 1 and 2). The suppressor mutations were then mapped to chromosome X or chromosome III based on their linkage to sma-9(cc604) X or to cup-5(ar465) III. Further complementation tests were carried out between each suppressor mutation and mutations in each known members of the Sma/Mab pathway, and between different suppressor mutations that did not affect known genes in the Sma/Mab pathway.
LW0214, which has arIs37(secreted CC::gfp) I and sma-9(cc604) X introgressed into the CB4856 Hawaiian strain by 6x backcrossing, was used for mapping the sma-9 suppressors via snip-SNP mapping [83] and whole genome sequencing (WGS) [84]. LW0214 was tested using a panel of SNP markers and subsequently by WGS, and found to contain CB4856 SNPs for all six chromosomes except for the following regions that still contain N2 SNP markers: chromosome I—from the left end to -12 and from +24 to the right end; chromosome II—from the left end to -18; and chromosome X—between +1.73 and +11. Snip-SNP markers used were described in Wicks et al. [83] and Davis et al. [85].
For the sma-9 suppressor mutations that appeared to affect known genes in the Sma/Mab pathway, either by complementation tests, or by whole genome sequencing (WGS, see below), their molecular lesions were identified by sequencing PCR products spanning the entire genomic regions of the corresponding genes, which include dbl-1, daf-4, sma-6, sma-2, sma-3, sma-4, lon-1, sma-10 and unc-40. For jj69 that contains a single base pair change in the upstream regulatory region of sma-6, a plasmid pJKL1060, which contains 3kb of upstream sequences, the genomic coding region and 2kb of downstream sequences of sma-6, was used to rescue the Susm phenotype of jj69.
Direct WGS of the homozygous suppressor mutant DNA was performed for some sma-9 suppressors. For others, the suppressors were simultaneously mapped and identified using the SNP-WGS method of Doitsidou et al. [84]. For the SNP-WGS method, each sma-9; suppressor mutant was crossed with LW0214, which has sma-9 introgressed into the polymorphic Hawaiian strain CB4856 (described above). Between 36 and 59 F2 progeny that were homozygous for both sma-9 and the suppressor mutation were collected. F3 generation worms from these F2 progeny were pooled for DNA extraction and library construction. Worm genomic DNA was prepared using the Qiagen Gentra Puregene Kit. 5μg of genomic DNA was used to prepare the sequencing library using the NEBNext DNA Sample Prep Master Mix Set 1. Single-end 50bp short-read (51 cycle) sequencing was performed on the HiSeq 2000 instrument (Illumina), yielding 38 ~ 78 million reads (20 ~ 41 fold coverage) per sample.
For direct WGS (jj58, jj60, jj61, jj71, jj77), data analysis was done using the MAQGene platform [86, 87] with the default setting. SNP variants on the X chromosome compared to the reference C. elegans genome ce6 W221 were analyzed. Genes with missense SNP variants in jj60 and jj77, but not in jj58 and jj71, were among the candidate genes that were targeted by RNAi for their ability to suppress the sma-9(cc604) M lineage defects by injection. These included C41A3.1, K09C4.8 and C17G1.8. Further PCR and sequencing confirmed the jj60 and jj77 mutations in C17G1.8 (tsp-21).
For mapping additional suppressors using either direct WGS (for jj2, jj5, jj7, jj50, jj52 and jj70) or SNP-WGS (for jj49, jj57, jj62, jj69, jj71, jj73, jj78 and jj83), sequence data were aligned to C. elegans reference genome version WS220 using BFAST [88] with default parameters. SNP calling was performed by SAMTOOLS [89]. A valid SNP call required a minimum read depth of three. ANNOVAR [90] was used for annotation of SNP coding potential. For SNP-WGS, Hawaiian SNPs were annotated with a custom Perl script. Scatter plots of heterozygous (0.2–0.7 fraction of total reads) Hawaiian SNPs were generated as chromosome position vs. fractional total graphs. Mapping intervals were defined by visual inspection for gaps (i.e., Hawaiian SNP fraction <0.2). Candidate suppressor genes were identified as homozygous (fraction >0.8), non-Hawaiian, nonsynonymous SNPs in the mapped interval. The SNPs in the identified suppressor genes were verified by PCR and sequencing.
jj61 was mapped via SNP-WGS to the region on the X chromosome where lon-2 is located. Direct inspection of the sequence reads around the lon-2 region showed that jj61 contains a large deletion (11.8kb) spanning the lon-2 region, which was subsequently verified by PCR and sequencing.
sma-6 reporter and rescuing constructs
pJKL840: sma-6p::nls::rfp::lacZ::unc-54 3’UTR
pJKL1048: sma-6(jj69)p::nls::rfp::lacZ::unc-54 3’UTR
pJKL1060: sma-6p::sma-6 rescuing construct
tsp-21 reporter constructs
pJKL1005: 5kb tsp-21p::tsp-21 genomic ORF::1.7kb tsp-21 3’UTR
pJKL1004: 5kb tsp-21p::tsp-21 genomic ORF::gfp::1.7kb tsp-21 3’UTR
pJKL998: 5kb tsp-21p::nls::gfp::lacZ::unc-54 3’UTR
pZL11: 5kb tsp-21p::tsp-21 genomic ORF (sgRNA target site modified)::gfp::1.7kb tsp-21 3’UTR
Constructs for tissue-specific expression of tsp-21
pJKL1015: tsp-21p::tsp-21 cDNA::tsp-21 3’UTR
pJKL1017: rol-6p::tsp-21 cDNA::tsp-21 3’UTR
pJKL1018: elt-3p::tsp-21 cDNA::tsp-21 3’UTR
pJKL1019: elt-2p::tsp-21 cDNA::tsp-21 3’UTR
pJKL1020: hlh-8p::tsp-21 cDNA::tsp-21 3’UTR
pJKL1021: myo-2p::tsp-21 cDNA::tsp-21 3’UTR
The full-length tsp-21 cDNA clone yk1449c02, which contains a SL1 trans-splice leader sequence, and full length 5’ and 3’ UTRs, was kindly provided by Dr. Yuji Kohara (National Institute of Genetics, Japan). A point mutation in the coding region of tsp-21 in yk1449c02 was corrected by site-directed mutagenesis to generate pJKL994. Transgenic animals were generated using the plasmid pRF4 or pJKL449 (myo-2p::gfp::unc-54 3’UTR) as markers. Integrated transgenic lines carrying pJKL1004[TSP-21::GFP] (jjIs3113 and jjIs3114) were generated using gamma-irradiation. pJKL840[sma-6p::nls::rfp::lacZ::unc-54 3’UTR] was used for co-localization of TSP-21::GFP and sma-6p::nls::rfp. pTAA1[hlh-8p::cgt-3.1a ORF::unc-54 3’UTR] was used to test for function of cgt-3 in the M lineage.
The following mix of plasmid DNAs was injected into the N2 gravid adults: (1) a Cas9 expression plasmid pDD162 [91], (2) a tsp-21-specific sgRNA plasmid pZL10, which has GAAACTGACACGGTAGAAGATGG replacing the unc-119 sgRNA in plasmid 46169 [92], (3) the homologous repair template pZL11: 5kb tsp-21p::tsp-21 genomic ORF (sgRNA target site modified)::gfp::1.7kb tsp-21 3’UTR, (4) a co-injection marker pCFJ90[myo-2p::mCherry] [93]. GFP knock-in events were screened via PCR using a primer in GFP (ZL21: CGCATATCTTGGACGCCTAATTTG) and a primer in the tsp-21 3’ region outside of the sequences included in pZL11 (ZL22: TCCACACAATCTGCCCTTTCG). Single worm PCR of 250 F1s failed to detect any germline integration event. However, we checked the F2 generation for high transmission efficiency lines (myo-2::mCherry positive) and screened via PCR 5–10 F3 progeny from each of the three high transmission efficiency lines (>50%). One of the three transgenic lines gave us two homozygous GFP knock-in strains: LW3670: jj93(tsp-21::gfp) and LW3671: jj94(tsp-21::gfp).
Total RNA was isolated from mixed-stage N2 or sma-6(jj69) worms using TRIzol Reagent (Invitrogen). Reverse transcription was performed with SuperScript III First-Strand Synthesis System (Invitrogen) following the manufacturer’s instructions. The primers used to detect the cDNAs of sma-6 and act-1 are: sma-6, MLF-34 and MLF-44; act-1, NMA-163 and NMA-164.
Body size measurement and RAD-SMAD reporter assay were carried out as described in Tian et al. [22]. Dauer formation assay was carried out as described in Tian et al. [21]. Statistical analyses were performed using Microsoft Excel and GraphPad Prism (http://www.graphpad.com/scientific-software/prism/).
GFP and RFP epifluorescence in transgenic animals was visualized either on a Leica DMRA2 compound microscope, where the images were captured by a Hamamatsu Orca-ER camera using the OPENLAB software, or on a Zeiss LSM 710 confocal microscope. Subsequent image analysis was performed using ImageJ and Photoshop CC.
We identified tetraspanin homologs by running hmmsearch from HMMER 3.1b1 [94] with the hidden Markov model (HMM) profile for tetraspanins (PF00335.15) from PFAM 27.0 [95] against the reference proteome set of the Quest for Orthologs consortium ([96]; source URL, ftp:/ftp.ebi.ac.uk/pub/databases/reference_proteomes/QfO/QfO_release_2014_04.tar.gz). hmmsearch was run with the arguments '-E 1e-06—domE 1e-06—incE 1e-06—incdomE 1e-06-A [alignment]', which generated aligned regions of similarity to these core domains. Since the regions of homology were extracted from full-length proteins with an HMM, the specific residues extracted were generally a subset of the full protein; moreover, it was possible for two or more such regions to be independently extracted from a single protein chain, although this proved rare for tetraspanins.
These regions were then realigned with MAFFT v7.158b [97] in L-INS-i, its slowest and most reliable mode, using the arguments '—localpair—maxiterate 1000'. The resulting alignments were purged of poorly aligned members by first running trimal v1.4.rev15 [98] using the argument '-gt 0.5', and then running BMGE 1.1 [99] using the arguments '-t AA-h 1-g 0.5:1'. This purged the alignments of any columns in which over 50% of the columns' positions consisted of gaps rather than amino acid residues, and then any sequences in which over 50% of the residues were gapped, yielding global alignments that lacked excessive loops and gaps. From the filtered alignments, we computed protein maximum-likelihood phylogenies, with a WAG model of amino acid evolution [100] and with pseudocounts for gaps, via FastTree 2.1.7 [101], using the arguments '-pseudo-wag'. Confidence values for the branches of trees (ranging from 0.00 to 1.00) were automatically computed by FastTree with 1,000 internal replicates. We visualized the resulting trees with FigTree 1.4.2 (http://tree.bio.ed.ac.uk/software/figtree). Branch lengths were measured in average substitutions (when comparing full sequences or their profiles) among non-gap positions in the aligned sequences, with distances derived from the BLOSUM45 matrix, a correction for multiple substitutions, and an allowed maximum of 3.0 substitutions per individual site [102].
The split-ubiquitin yeast two-hybrid experiments were carried out following the detailed method described in Grefen et al. [103]. The bait CubPLV and prey NubG constructs were generated via PCR and recombinational in vivo cloning in yeast [103]. The resulting fusion constructs were recovered from yeast and transformed into E. coli and confirmed by sequencing. The primers, cDNA templates, and the names of the resulting bait and prey constructs are summarized in S2 Table. The bait and prey constructs were transformed into the haploid yeast strains THY.AP4 (MATa) and THY.AP5 (MATα), respectively, and the resulting yeast strains were mated to generate diploid yeast cells carrying specific combinations of bait and prey constructs [103]. Interactions among each pair of bait and prey constructs were visualized by streaking diploid cells on SC-Trp,-Leu,-Ade,-His,-Ura,-Met plates that were supplemented with four different concentrations of methionine: 0mM, 0.075mM, 0.150mM and 0.300mM, respectively. Methionine can repress the expression of the CubPLV fusion, which is under the control of the Met-repressible MET25 promoter [42]. Growth was monitored for 2–9 days at 30°C.
The plasmids KAT-1-Cub-PLV, NubG-KAT-1 (in pNX33 vector) and KAT-1-NubG (in pXN21 vector) [42], HMT-1-Cub-PLV and HMT-1-NubG (in pXN21 vector) [46] were kindly provided by Sungjin Kim (Cornell University) and used as specificity controls. NubG fusions for PAR-4, a protein unexpected to interact with any of the proteins tested, was included as another control for specificity of the interactions. The empty NubG vector was used as a control to determine if any Cub-PLV fusions can auto-activate the reporters. The vector expressing soluble wild-type Nub (NubWT) was used as a control to indicate expression of the Cub-PLV fusion. Additional confirmation of expression of each fusion protein came from western blot analysis using rabbit polyclonal anti-VP16 antibodies (ab4808, Abcam, for CubPLV fusions) and monoclonal anti-HA antibodies (Clone 12CA5, Sigma, for NubG fusions).
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10.1371/journal.pgen.1001038 | A Genome-Wide Analysis Reveals No Nuclear Dobzhansky-Muller Pairs of Determinants of Speciation between S. cerevisiae and S. paradoxus, but Suggests More Complex Incompatibilities | The Dobzhansky-Muller (D-M) model of speciation by genic incompatibility is widely accepted as the primary cause of interspecific postzygotic isolation. Since the introduction of this model, there have been theoretical and experimental data supporting the existence of such incompatibilities. However, speciation genes have been largely elusive, with only a handful of candidate genes identified in a few organisms. The Saccharomyces sensu stricto yeasts, which have small genomes and can mate interspecifically to produce sterile hybrids, are thus an ideal model for studying postzygotic isolation. Among them, only a single D-M pair, comprising a mitochondrially targeted product of a nuclear gene and a mitochondrially encoded locus, has been found. Thus far, no D-M pair of nuclear genes has been identified between any sensu stricto yeasts. We report here the first detailed genome-wide analysis of rare meiotic products from an otherwise sterile hybrid and show that no classic D-M pairs of speciation genes exist between the nuclear genomes of the closely related yeasts S. cerevisiae and S. paradoxus. Instead, our analyses suggest that more complex interactions, likely involving multiple loci having weak effects, may be responsible for their post-zygotic separation. The lack of a nuclear encoded classic D-M pair between these two yeasts, yet the existence of multiple loci that may each exert a small effect through complex interactions suggests that initial speciation events might not always be mediated by D-M pairs. An alternative explanation may be that the accumulation of polymorphisms leads to gamete inviability due to the activities of anti-recombination mechanisms and/or incompatibilities between the species' transcriptional and metabolic networks, with no single pair at least initially being responsible for the incompatibility. After such a speciation event, it is possible that one or more D-M pairs might subsequently arise following isolation.
| Species are defined such that organisms of the same species can produce fertile offspring, whereas organisms of different species are either unable to mate, or when they do, they produce inviable or sterile progeny. A well-known pair of species that can mate yet produce sterile offspring is the horse and donkey, which produce an infertile hybrid, the mule. A long-standing idea for the species barrier is that when certain pairs of genes from the two different species are combined, the genes can no longer function properly, thus causing death or sterility. Identification of these incompatible genes may allow us to determine how organisms form distinct species, and understand the process of speciation itself. We used two closely related yeasts to look for these incompatible genes by isolating rare viable hybrid offspring, and looking for excluded gene combinations. We did not find any pairs of incompatible genes, but instead found that there appear to be more than two genes involved in such incompatibilities. We speculate that the accumulation of large numbers of sequence differences in their DNA may cause defects in how genes are controlled in hybrids, causing these two yeasts to be independent species.
| Dobzhansky and Muller independently proposed the genic incompatibility model as the genetic basis for the barrier to gene flow in postzygotic speciation [1], [2], whereby epistatic interactions at two or more loci between two species can cause sterility or inviability in a hybrid organism. Their model became known as the Dobzhansky-Muller (D-M) model of speciation, with the simplest form of the model involving interaction of a pair of genes, referred to as a D-M pair. This genic incompatibility model of postzygotic speciation has been widely accepted and has been supported both theoretically and experimentally by a large body of literature [3], [4], [5], [6], [7], [8], [9]. However, the identities of these speciation genes have largely remained elusive. Only a few genes involved in reproductive isolation have been identified, mostly in Drosophila [5], [6], [7], [10], [11], [12]. Most speciation genes identified have been either located on the X chromosome or are incompatible with loci on the X chromosome, consistent with Haldane's rule [13]. For example, the Odysseus gene (OdsH), on the X chromosome in Drosophila, causes hybrid male sterility between D. simulans and D. mauritiana [5]. The D. mauritiana OdsH protein was recently shown to localize to and interact with the Y chromosome of D. simulans, possibly causing decondensation of the heterochromatin, resulting in hybrid sterility [14]. The D. simulans nucleoporin-96, NUP96, is incompatible with an unknown allele on the X chromosome of D. melanogaster [7]. The identity of the first pair of interacting D-M genes were recently reported in Drosophila, where the Lethal hybrid rescue (LHR) gene in D. simulans is incompatible with the Hybrid male rescue (HMR) gene from D. melanogaster [10]. Recently, a speciation gene in mice was identified to be Prdm9, which encodes a meiotic histone H3 lysine 4 trimethyltransferase [15].
The members of the Saccharomyces sensu stricto group of yeasts provide an ideal model system for investigating the molecular mechanisms of speciation. There are currently six known members of the Saccharomyces sensu stricto group, with S. paradoxus being the closest relative of S. cerevisiae, with an overall DNA sequence identity of approximately 85%, and S. bayanus being the farthest relative with an overall sequence identity of approximately 62%. The members of the Saccharomyces sensu stricto group of yeast can mate readily, where two haploid strains from different species and of the opposite mating type can form a viable heterozygous hybrid diploid (F1 hybrid). Such F1 hybrids can undergo meiosis (sporulation) to produce spores (haploid gametes), but the spore viability is less than 1% [16], [17]. Thus, the Saccharomyces sensu stricto yeasts are considered to be postzygotically isolated. In addition to postzygotic isolation, studies have shown potential mating preferences between S. cerevisiae and S. paradoxus [18], suggesting that prezygotic isolation also plays a role in the reproductive isolation of these yeasts. It has been shown that hybrids made between members of the Saccharomyces sensu stricto group can produce rare viable progeny, and that these progeny themselves are postzygotically separated from one another and their parents [19], and thus, by the classic definition, are distinct species. Studies of the Saccharomyces sensu stricto yeasts have shown that genome rearrangements [20], [21] and the mismatch repair system [17], [22] contribute to the mechanisms of postzygotic isolation between different species in this group. However, prior work has suggested that dominant genic incompatibilities do not exist between S. cerevisiae and S. paradoxus [16], and a recent effort to identify recessive genic incompatibilities between these two species, by replacing individual chromosomes from S. cerevisiae with the S. paradoxus versions, was unable to identify any such incompatibilities [23]. However, in that study only 9 of the possible 16 chromosomal replacements could be made and the resulting strains did not undergo meiosis and germination, thus not making it possible to conclusively determine whether any recessive genic incompatibilities exist as a reproductive barrier in hybrids between the two species. Most recently, a D-M pair of interacting genes was identified in the Saccharomyces sensu stricto yeasts. Hybrids were generated by replacing chromosomes in S. cerevisiae with the corresponding ones from S. bayanus, and the homozygous diploid hybrid was created via self-fertilization, which was then tested for sterility [12]. The identified incompatibility involved a nuclear encoded gene from S. bayanus, AEP2, whose product is mitochondrially targeted, and a mitochondrial gene encoding an ATP synthase subunit in S. cerevisiae, OLI1 [12], whose 5′ UTR is bound and regulated by Aep2. Cells containing the incompatible pair are unable to respire and thus unable to sporulate. Unlike most of the other speciation genes identified so far, the AEP2 gene does not appear to be under positive selection, suggesting that positive selection may not be a criterion for genic incompatibility. However, due to several reciprocal translocations between S. cerevisiae and S. bayanus, Lee et al [12] were not able to examine the effects of all the individual chromosomes, since these translocation-containing chromosomes needed to be replaced together, which was technically infeasible. In addition, the presence of any genic incompatibilities that involve multiple loci (residing on different chromosomes) would likely not have been detected via the replacement of individual chromosomes.
Thus far, no comprehensive, genome-wide effort has been made to determine whether D-M genic incompatibilities (at least between nuclear genomes) play a role in the postzygotic isolation between members of the Saccharomyces sensu stricto group. We have exploited the complete genome sequences of S. cerevisiae and S. paradoxus [24], [25] to take a novel approach to identifying such loci. We have used these genome sequences to design dual species microarrays for Comparative Genome Hybridization (array-CGH) to assay the genomes of rare viable F1 spores at high resolution to locate potential speciation loci. Our hypothesis is that there exist genetic determinants in S. cerevisiae and S. paradoxus that are incompatible, resulting in failure of spores to germinate or form colonies. This study differs from the study by Lee et al, which in essence looked at the fertility of the F2 gametes. To determine whether D-M genic incompatibilities exist in their nuclear genomes, we assayed the genome content of more than one hundred rare viable spores produced from F1 hybrids between S. paradoxus and S. cerevisiae. If Dobzhansky-Muller type genic incompatibilities exist between these species, we would expect to see patterns in the genome contents of the viable F1 spores, where combinations of incompatible loci will be excluded or at least underrepresented in the viable F1 spores. Our results show that there are no simple classic D-M pairs of interacting genes between the nuclear genomes of the two species. However, we do identify some underrepresented combinations of loci, and these combinations typically involve more than two loci, suggesting more complex D-M interactions. We also find chromosome 4 to be preferentially inherited from S. cerevisiae, indicative of the presence of a potential incompatible locus on this chromosome. Our results suggest that genic incompatibilities within the nuclear genomes between members of the Saccharomyces sensu stricto yeasts involve multiple incompatible loci, with weak individual effects.
To identify candidate speciation genes in the Saccharomyces sensu stricto yeast, we used S. cerevisiae and S. paradoxus as the parental species, since their genomes are essentially collinear with no gross chromosomal rearrangements between them [24], eliminating chromosomal rearrangements as a major contributor of postzygotic isolation in our study. The mismatch repair system has been shown to play a role in the reproductive isolation between these two species [22], [26]; however, it is not the sole contributor to hybrid sterility in these organisms, as the viability of hybrid spores in the mismatch repair deficient strains is still only 10% [17] (it is not clear how much of the remaining sterility may be explained by mismatch repair independent anti-recombination mechanisms). We thus derived rare F1 spores from both mismatch repair proficient and deficient (due to MSH2 deletion) F1 hybrids of S. cerevisiae and S. paradoxus, and their genome contents were then determined using array CGH.
Dual species DNA microarrays were designed for S. cerevisiae and S. paradoxus for the determination of the genomic contents of the viable F1 spores. The microarray contains 7,134 S. cerevisiae probes and 7,047 S. paradoxus probes, at a resolution of approximately 2 kb across both genomes. The array also contains probes that were designed based on the sequence of the S. cerevisiae mitochondrion. The 60-mer oligonucleotide probes were chosen such that they were best able to distinguish between the two parental genomes (see Materials and Methods for details of probe design and microarray validation). Using these arrays, we interrogated the genomes of 58 spores derived from two independent mismatch repair proficient F1 hybrids, and 48 spores derived from two independent mismatch repair deficient F1 hybrids. Using the software Caryoscope [27], we visualized which portions of the genome were inherited from which parent (either S. cerevisiae or S. paradoxus) (see Figure 1A and B for examples).
The F1 spores derived from mismatch proficient parents showed extensive aneuploidy (defined here as the presence of a particular chromosome from both parental species), with the majority of the genomes assayed containing at least one, and up to five, aneuploid chromosomes. In addition, the rate of recombination was also reduced in these F1 spores, with an average of only 2.7 crossovers observed per viable spore, confirming previous reports that chromosome nondisjunction, due to the mismatch repair system preventing homeologous recombination, may be involved in F1 sterility in the Saccharomyces yeasts [17], [26]. Mismatch repair mutants have been shown to increase recombination between homeologous chromosomes [17], [22]. The spores derived from mismatch repair deficient F1s (generated by deleting MSH2) showed a dramatic decrease, of approximately 10 fold compared to the wild-type, in the number of aneuploid chromosomes per strain. The number of recombinations also increased by more than 6 fold, to an average of 17.8 recombinations, per strain, which is approximately one per chromosome (see Table 1), though lower than would normally be seen in a non-hybrid strain (17.8 recombination events compared to 39 per spore in intraspecific meiotic products, as reported in Mancera et al [28]). The assayed F1 spores between S. cerevisiae and S. paradoxus showed no overall bias in inheritance of the genome from one parent over the other.
From the 58 F1 spores derived from the wild-type F1 hybrid, we observed no crossovers in chromosome 4 in 39 wild-type spores, 30 of which had inherited the S. cerevisiae chromosome 4, while only 9 inherited the S. paradoxus chromosome 4. Using a binomial distribution, the S. paradoxus chromosome 4 appears to be underrepresented in the spores from the wild-type F1 hybrid, with a p-value of 0.0004. This was confirmed by array-CGH of pooled genomic DNA from roughly 1000 viable spores from a mismatch proficient F1 hybrid (Figure 2A), and has been replicated from independent F1 hybrids. Since the mismatch repair deficient hybrids have increased meiotic recombination, we performed a similar pooling experiment with the spores from the mismatch repair deficient F1 hybrids, but were unable to identify any specific region on chromosome 4 to be biased in its inheritance from one species (Figure 2B).
For each pair of homeologous chromosomes, we compared the sequence similarities, GC contents, frequencies of observed recombination in the viable F1 spores, and frequency of meiotic recombination in S. cerevisiae [28] (Figure S1). It has been observed that the recombination frequency in yeast tends to be higher in regions with higher GC content [29]. We calculated the GC content across each S. paradoxus and S. cerevisiae chromosome to see if there were dramatic differences between the local GC content between the two species, but found them to be highly similar (see Figure S1). Comparisons between regions with high local GC content and the local sequence similarity across each chromosome between the two species revealed no bias in low sequence similarity and high GC content (See Table S1). Lower sequence similarities between two homeologous chromosomes may result in lower frequency of recombination. However, as shown in Table S1, we found no correlations between overall sequence similarities and frequency of observed recombination in the viable F1 spores.
The simplest form of the D-M model involves only two interacting loci; thus, to determine whether simple D-M pairs exist between S. cerevisiae and S. paradoxus, we performed pair-wise linkage analysis separately for each genome to determine if any two loci derived from one of the parent's genomes showed a dependency. Such a dependence would likely manifest as an altered pattern of segregation of the two loci from the same genome with respect to one another compared to what would be expected by chance, as determined using a Chi-square test. Our reasoning is that if there is a D-M pair, we are likely to observe these two loci being co-inherited from the same parental genome in rare viable F1 spores. Because we observed reduced meiotic recombination in the viable F1 spores, we expected that chromosomal segments on smaller chromosomes would be co-inherited anyway, while the segments within larger chromosomes would segregate randomly, depending on how far apart they were. By looking at the relationship between the distance between intrachromosomal segments and the Chi-square statistic between them, we found this to be mostly true (See Figure S2). For all 16 chromosomes, we found an inverse relationship between the distance between segments, and the Chi-square statistics between the pairs of segments within each chromosome. The minimum distance between loci within the same chromosome for which there is no significant linkage (significance as determined by an arbitrarily chosen FDR of 0.01) was approximately 180 kb. Thus, if there are potential dependencies between linked loci within approximately 180 kb of the same chromosome, we will not be able to identify them using our analysis. For the remainder of the analysis, we only performed linkage analysis between segments from different chromosomes. To perform this analysis, we first identified all the locations on each chromosome for both parental genomes where a meiotic recombination event had occurred in the production of any of the viable F1 spores assayed. We then segmented each chromosome in both genomes (S. cerevisiae and S. paradoxus) for each of the 106 F1 spores, at the observed recombination locations (irrespective of whether a given F1 spore had a recombination event at that location). For example, there were 19 observed recombination locations on the S. cerevisiae chromosome 1 across all F1 spores, resulting in 20 segments for the S. cerevisiae chromosome 1. After segmentation, each segment for each F1 spore was scored for its presence or absence in that strain for a particular parental species (e.g. S. cerevisiae), where the segment was given a score of 1 if present from S. cerevisiae, and 0 if present from S. paradoxus. If a segment is aneuploid (having inherited both S. cerevisiae and S. paradoxus sequences), then it was given a score of 2. The data for the chromosomes of each parental species were analyzed separately. An example is illustrated in Figure 3A. There were a total of 834 and 830 segments for S. cerevisiae and S. paradoxus genomes, respectively.
We analyzed these data, with each segment scored for its presence, absence, or aneuploidy for a particular parental species (as defined above), to determine the pattern of segregation for all pairwise combinations of segments within a parental genome (excluding segments on the same chromosome). There are a total of nine possible categories for each pair of segments. We initially excluded the categories that involved aneuploid segments, and thus each pair of segments from a given F1 spore was classified into one of four categories, with respect to the segments having been inherited from one or both of the parents (as shown in Figure 3B), for example: i) both segments present from S. cerevisiae, ii) one present from S. cerevisiae and one present from S. paradoxus, iii) vice versa, and iv) both from S. paradoxus. The total numbers of interchromosomal pair-wise comparisons were 322,258 and 318,545 for S. cerevisiae and S. paradoxus genomes, respectively. If there was no dependence between the loci in such a pair, random segregation of two loci would predict that the values for each of these categories would not be significantly different from the expected values. However, if the two loci contain genes that participate in a D-M pair between the two species, we would expect the distribution in the four categories to be skewed. For example, if there exist two loci, A and B in the S. cerevisiae genome, with alleles a and b from S. paradoxus, we would expect that in the case of a two-way dependency, only the parental genotypes, AB and ab, would be viable, with Ab and aB being inviable. In the case of a one-way dependency, we might observe that A is compatible with both B and b, but a is only compatible with b, where the only incompatible genotype is aB. In the two-way dependency case, where the two loci always need to be from the same species, the pairwise analysis for these two loci should reveal no entries in both categories ii and iii, since the presence of one locus from a given parent requires the presence of the other derived from the same parent to function. The one-way dependency scenario is what was found in most prior studies on reproductive isolation in Drosophila [30], [31] and yeast [12]. In the one-way dependency scenario, for the two loci, either category ii or iii would contain no entries. To determine significance, the chi-square test was used for the linkage analysis. To remove the bias in the frequency of inheritance of each segment, we normalized the expectation for the chi-square test. An expectation was calculated based on the number of F1 spores having inherited each segment. For example, if segment A and segment B were inherited from S. cerevisiae in 20 out of 106 and 50 out of 106 total F1 spores, respectively, then the probability of an F1 spore having inherited both A and B from S. cerevisiae would be (20/106)*(50/106) and the expectation for the number of F1 spores having inherited both segments would be (20/106)*(50/106)*106. For statistical significance, a false discovery rate (FDR) for each chi-square statistic was determined by permutation, randomizing each segment between the 106 strains and calculating the pair-wise chi-square statistics for each pair of randomized segments. The false discovery rate was then estimated by dividing the average number of pairs with a chi-square statistic greater than x in the randomized samples over 1000 iterations by the numbers observed in the data set (Tables S2 and S3 contain all the data).
From this analysis, we found no pair of segments that have either a one-way or a two-way simple D-M dependency. That is, there were no pairs of segments from different chromosomes for which any of the patterns of inheritance were excluded, clearly demonstrating the absence of any simple D-M pairs of incompatible genes on different chromosomes within the nuclear genomes between S. cerevisiae and S. paradoxus. However, our linkage analysis revealed several pairs of segments that may be involved in more complex D-M genic incompatibilities involving more than two loci, as these segments show distributions that are statistically significantly different than what would be expected by chance, using an FDR of 0.01. Statistically significant pairs of regions of the S. cerevisiae and S. paradoxus genomes are shown in Tables S4 and S5. In addition, we also analyzed the entire dataset (all nine categories, including the aneuploid segments). However, due to the large number of categories, we did not have sufficient power to identify any significant pairs of segments using an FDR of 0.01.
While our data revealed no simple D-M pairs of interacting genes, the data do suggest more complex genic incompatibilities, involving more than 2 loci. The results of S. cerevisae and S. paradoxus genomes were reciprocal to one another, as expected. If these incompatibilities include two-way dependencies, then we would expect categories ii and iii to have similar behaviors (similar deviations from the expected). Several pairs of chromosomes show potential two-way dependency. Chromosome pairs 1-10, 2-14, 3-13, 4-8, and 7-16 are more likely to be co-inherited from the same parent (FDR <0.01), suggesting potential dependencies involving loci residing on these chromosomes. Interestingly, chromosomes 2 and 9, chromosomes 4 and 13, and chromosomes 11 and 14 are less likely to be both inherited from the same parent (FDR <0.01). Approximately 12% of the nuclear genome is involved in potential interactions.
To estimate whether there exist any 3 interacting loci that may be involved in F1 hybrid spore inviability, we conducted linkage analysis for all possible combinations of 3 loci (A, B, and C). There were a total of 8 categories: 1) all 3 loci present from S. cerevisiae, 2) A, B from S. cerevisiae, but C from S. paradoxus, 3) A and C from S. cerevisiae, but B from S. paradoxus, 4) A from S. cerevisiae, B and C from S. paradoxus, 5) B from S. cerevisiae, A and C from S. paradoxus, 6) A from S. paradoxus, B and C from S. cerevisiae, 7) C from S. cerevisiae, A and B from S. paradoxus, and 8) all 3 loci from S. paradoxus. Linkage analysis between all possible combinations of 3 loci identified 138,322 groups with a zero entry in any one of the categories (not taking into account aneuploidies) (compared to an average of 28,081 from permutations analysis; data not shown), indicative of potential dependencies between the loci. However, due to an insufficient sample size, we cannot confidently determine whether these categories are truly excluded from the viable F1 spores, or whether what we have observed has simply arisen by chance.
We tested the F1 spores for the ability to grow on glycerol as their sole carbon source. Approximately 85% of the F1 spores were able to grow on the non-fermentable carbon source. Thus, at least 15% of the spores will form sterile F2 zygotes. This suggests the presence of incompatibilities between the nuclear genome and the mitochondrial DNA between the two species (or the absence of mitochondrion), as these particular combinations of the S. cerevisiae and S. paradoxus genomes did not allow the resulting F1 spore to grow on non-fermentable carbon source. However, the small number of spores with this phenotype precluded the identification of any loci that might be responsible for this incompatibility.
Our data represent the first comprehensive genome-wide effort to determine genic incompatibility, which is responsible for failure of F1 spores to germinate and form colonies, between members of the Saccharomyces sensu stricto yeasts. We found no simple Dobzhansky-Muller pair of speciation genes within the nuclear genomes of S. cerevisiae and S. paradoxus. Prior reports have suggested that neither dominant nor recessive genic incompatibilities exist between members of the Saccharomyces sensu stricto group of yeasts [16], [23], and our data further confirm this. A recent survey of sequence variation in subpopulations of S. paradoxus and their gamete viabilities in crosses between different isolates revealed a direct correlation between sequence divergence and spore viabilities [32], further supporting the notion that sequence divergence plays a major role in the reproductive isolation between the Saccharomyces sensu stricto yeasts. Thus, the current predominant theory regarding postzygotic speciation in this group of yeasts is the failure of proper segregation due to the mismatch repair system [17], [23]. However, even in mismatch repair deficient S. cerevisiae and S. paradoxus F1 hybrid, the spore viability was still approximately 10% [17], leaving a large percentage of inviability unexplained by mismatch repair system alone. Reduced frequency of recombination caused by mismatch repair system independent anti-recombination mechanisms [33] may also contribute to the reduced spore viability of F1 hybrids.
The first interacting pair of speciation genes was recently identified between the mitochondrion of S. cerevisiae and a nuclear gene in S. bayanus [12]; incompatibilities between the nuclear genome and the mitochondria between other members of the Saccharomyces sensu stricto group were apparently also observed, but not detailed. Our data support the presence of speciation genes involving the nuclear genomes of S. cerevisiae and S. paradoxus, but these are complex interactions involving multiple loci. While it is possible that the presence of speciation genes are masked by compensatory mutations in the viable spores, the mutation rate of approximately 45×10−8 [34] suggests that it would be unlikely in our study. If no complex genic incompatibilities (or if the effects of the incompatibilities were insignificant) exist between these two species, then we would have expected no pairs of statistically significant loci from our linkage analysis of the 106 viable F1 spores. Instead, we identified several loci having segregation distributions that significantly differ from expectation, indicative of more complex interactions likely involving multiple loci. Assuming that these complex interactions involve groups of 3 speciation genes, then we would expect there to exist 7–8 groups of 3 interacting loci for a reduction of 88%–100% in hybrid spore viability. Among the 106 F1 hybrid spores analyzed, we identified 138,322 groups of 3 loci that showed potential dependencies (compared to an average of 28,081 from permutation analysis). However, due to insufficient sample size, we cannot confidently conclude that these dependencies exist. It is however clear that the presence of multiple potential interacting pairs of loci identified in the viable spores of F1 hybrids is indicative of the involvement of multiple loci with weak effects, rather than the involvement of few loci with strong effects, contributing to genic incompatibilities between these two species. Similar “multilocus weak allele interactions” were also observed in studies of reproductive isolation in Drosophila [35].
Interestingly, we found the S. cerevisiae copy of chromosome 4 to be preferentially inherited by the viable F1 spores, based on both statistical analysis of the viable spores derived from mismatch repair proficient F1 hybrids and verification by pooling approximately 1000 viable spores of F1 wild-type hybrids in two independent crosses (Figure 2A). Unfortunately, attempts to narrow down the region on chromosome 4 that is preferentially inherited from S. cerevisiae by pooling spores from the mismatch repair deficient F1 parent failed to reveal the identity of the significant locus on this chromosome (Figure 2B). This discrepancy between the mismatch repair proficient and deficient F1 hybrid spores may a result of the significantly increased rate of recombination (approximately 6 fold) in the mismatch repair deficient F1 hybrids. For example, if such an incompatibility involves chromosome 4 and two loci, A and B, on another chromosome (we would not be able to detect these due to the tight physical linkage of intrachromosomal segments as described in the Results section), A and B would typically be co-inherited in the mismatch repair proficient F1, due to a lack of recombination. However, the increased recombination in the mismatch repair deficient F1 hybrid will dramatically decrease the probability of both A and B being inherited from S. cerevisiae, and result in the lack of preferential inheritance of chromosome 4 from S. cerevisiae in the pooled mismatch repair deficient F1 spores. In addition, even though extensive aneuploidy was observed in the spores from wild-type F1 hybrids, with most chromosomes showing multiple aneuploidy events, the number of aneuploidies observed for chromosome 4 was significantly lower than would be expected by chance (Bonferroni corrected p-value of 0.02 using a binomial distribution), with only a single observed event (See Table 1). It is the only chromosome to exhibit a statistically significantly lower rate of aneuploidy (using a corrected p-value cut-off of 0.05). Difficulties in isolating S. cerevisiae strains aneuploid for certain chromosomes (most notably in chromosomes 4 and 6) have been observed previously [36], [37], [38]. Lethality due to an extra copy of chromosome 6 has been partly attributed to imbalance in the copy number of the beta-tubulin gene, TUB2, which resides on chromosome 6, to that of the alpha-tubulin genes, TUB1 and TUB3, which reside on chromosome 13 [36], [39]. Among the 58 F1 spores generated from mismatch repair proficient hybrids, we only observed 2 aneuploidies in chromosome 6; however, this was not statistically significant after Bonferroni correction. Aneuploidy in chromosome 4 has been shown to cause longer delay in entry into cell cycle [37] and has been attributed to the extra burden of protein synthesis due to an extra copy of the largest chromosome. Thus, it is possible that hybrids containing extra copies of chromosome 4 were selected against due to their slower growth rates.
The lack of any simple pair-wise genic incompatibilities between the nuclear genomes, and the identification of multiple significant pairs of regions may be indicative that postzygotic isolation is due to the combined effects of multiple interactions, each with small effects. Examining known interactions between genes within the significant pairs of loci, we found several pairs of segments containing multiple pairs of genes with known interactions (see Table S6). Thus, it is possible that the sum of all incompatible pairs (no matter how small the effect) inherited by the F1 spores plays a bigger role in the reproductive isolation between these two species than simple D-M genic incompatibilities. However, it is noteworthy that a recent study on incipient speciation in Neurospora generated from divergently evolved populations identified a two-loci asymmetric interaction that resulted in a large decrease in meiotic efficiency [40].
Gene expression regulation has been implicated as a mechanism for reproductive isolation in Drosophila interspecific hybrids [41], [42], [43]. Our recent work demonstrated that even a single nucleotide change in the yeast genome can result in large changes in the global transcriptional profiles [44]. With the genome sequences between S. cerevisiae and S. paradoxus being diverged by approximately 15% in the intergenic regions and approximately 10% in the coding regions, it is likely that there has been significant rewiring in the transcriptional regulatory network, including both cis and trans regulatory changes, that may contribute significantly to reproductive isolation in the Saccharomyces sensu stricto yeasts. Indeed, recent work has demonstrated that in F1 hybrids between S. paradoxus and S. cerevisiae that there are significant cis and trans regulatory differences [45]. Thus, the potential interacting loci identified from our analyses may not necessarily be involved in functional or physical interactions, but may be involved in the proper timing and regulation of gene expression. Even though we were not able to identify the exact genes involved, due to the loci containing multiple genes, these significant pairs of loci identified in our studies will be helpful in narrowing down potential candidates with additional research.
Recent work showed that when clones derived from haploid populations of S. cerevisiae that have evolved for 500 generations in either high saline or low glucose conditions were crossed, the resulting diploid had a reduced sporulation efficiency [46]. This inferred “incipient speciation” was not seen when crossing clones independently evolved under the same conditions. Our earlier work has shown that adaptive clones derived from haploid S. cerevisiae evolved under glucose-limited conditions for approximately 450 generations have only a handful (certainly less than 10) of mutations ([44] and G. Sherlock and D. Kvitek, unpublished results). It is likely that comparable small numbers of mutations exist in the clones derived by Dettman et al [46] in their laboratory evolved populations; thus, the reduced meiotic efficiencies observed in their work are unlikely to have arisen due to classic D-M interacting proteins, but may be due to a few mutations causing large and incompatible changes in the transcriptional networks. Therefore, in addition to anti-recombination mechanisms, the 15% sequence divergence between S. cerevisiae and S. paradoxus likely results in two possible mechanisms of incompatibilities: 1) combinations of multiple potential genic incompatibilities with small effects (as no simple D-M pair was identified from our analysis) and 2) transcriptional regulatory network effects due to misregulation in the level and timing of expressions of genes in hybrid F1 spores, whose network will contain a mix of parts from both parents. It is unclear whether observed classic D-M pairs are frequently the cause of speciation, or whether they arise after the fact, due to these other factors that successively reduce hybrid fertility.
The yeast strains used are listed in Table 2. All S. cerevisiae strains are derivatives of S288c. All S. paradoxus strains used are derivatives of the sequenced type strain CBS432 (NRRL Y-17217).
To generate the msh2 mutants, the 5′ and 3′ regions of the msh2 genes in S. cerevisiae and S. paradoxus were amplified by PCR. These PCR products were fused to KanMX6 from pFA6-KanMX6 [47] via crossover PCR. Diploid heterozygous mutants in msh2 were generated by transforming the resulting fragments for S. cerevisiae and S. paradoxus into the diploid S. cerevisiae strain GSY145×GSY896 or the diploid S. paradoxus strain GSY82 (See Table 2 for genotype), respectively, via a lithium acetate method [48] and plated on YPD plates containing 200 µg/ml G418. Two independent successful transformants (msh2::KAN/MSH2) were chosen for each species and verified via colony PCR using the species-specific verification primers listed in Table 3. These chosen transformants were sporulated in sporulation medium (1% potassium acetate and 0.02% raffinose) for 3 days and resulting spore products were screened for G418 resistance. Since the S. paradoxus diploid strain GSY82 is homozygous wild-type for the HO gene (HO/HO), the resulting spore products will be diploids, due to mating type switching. The S. cerevisiae diploid strain used is homozygous mutant for the HO gene (ho/ho), and thus the resulting spore products are haploids.
S. cerevisiae and S. paradoxus strains were mated to generate either mismatch repair proficient or mismatch repair deficient F1 zygotes by mixing the specified strains listed in Table 2 on YPD plates for 2–3 hours.
To generate the mismatch proficient F1 hybrid strain, zygotes were isolated using a micromanipulator (Carl Zeiss MicroImaging, Inc., Thornwood, NY), and selecting for prototrophs. The F1 hybrids were confirmed by PCR for the presence of both parental genomic DNA sequences on 4 chromosomes (chromosomes 6, 7, 9, and 12).
To generate mismatch repair deficient F1 hybrids an msh2::KAN and leu2Δ S. cerevisiae spore and a diploid msh2::KAN/msh2::KAN and ura3-1/ura3-1 S. paradoxus were used. The S. paradoxus msh2 mutant was sporulated for 3 days before mass mating by mixing with the S. cerevisiae msh2 mutant on YNB plates with no supplementation. Surviving prototrophic colonies were confirmed to be F1 hybrids by checking for the presence of both parental chromosomes at two loci (chr6 and chr7); two independent F1 hybrids were kept for further use.
F1 spores were generated by sporulating a diploid F1 hybrid in sporulation medium for 3 days with aeration. Random spore analysis [49] was performed to isolate potential viable spores of the F1 hybrids. Due to possible F1 diploid contamination in the random spore analysis, every colony was assayed for the presence of S. cerevisiae or S. paradoxus chromosomes (2 loci each on chromosomes 6, 7, 9, and 12 for a total of 8 loci). If a clone contained both parental copies of all 4 chromosomes, then it was assumed to be a surviving F1 diploid (rather than being a strain that is aneuploid for all 4 chromosomes), and was not used for further analysis.
The contig sequences of S. paradoxus and the genomic sequences of S. cerevisiae were downloaded from the Saccharomyces Genome Database [50]. Each contig and chromosome was divided into 2 kb segments. ArrayOligoSelector [51] was used to find 60 mer probe sequences for each of the 2 kb segments from each organism, using the combined sequences of S. paradoxus and S. cerevisiae genomes as a mask, to eliminate cross hybridization potential, either within or between species. The parameters used were: 38% GC, 60 mers, up to 3 oligonucleotides per segment. The oligo sequences produced by ArrayOligoSelector were blasted against the mask file using blastn with e-score cutoff of 1×10−10. The oligonucleotides having more than one match were eliminated. For each segment that had more than one oligonucleotide, the oligonucleotide with the lowest Gibb's free energy of binding was chosen, unless the GC content was outside of the 30–50% range, then the oligonucleotide with the more optimal GC content was chosen. The oligonucleotides that had more than one hit as determined by the ArrayOligoSelector program were also eliminated. The gap distance between adjacent probes was minimized by re-running ArrayOligoSelector on the largest gap regions to find additional oligonucleotides. In addition, we designed oligonucleotide probes for control sequences used by van de Peppel et al [52] using this same approach.
Genomic DNA was isolated and purified using the YeaStar yeast genomic DNA kit (Zymo Research, Orange, CA) and then quantified using the Qubit fluorometer (Invitrogen, Carlsbad, CA). The genomic DNA was fragmented with HaeIII (New England Biolabs, Ipswitch, MA) at 37°C for 1 hour, and the products were purified using Microcon-30 columns (Millipore, Billerica, MA). For all arrays, a mixture of equal molar amounts of S. cerevisiae and S. paradoxus genomic DNA was used as reference. The fragmented genomic DNA of an F1 spore and the reference genomic DNA mix were differentially labeled using the Ulysis labeling kit with Alexa fluors 546 and 647 (Invitrogen, Carlsbad, CA) following manufacturer's instructions and hybridized to the custom dual-species Agilent arrays (Agilent Technologies, Santa Clara, CA). The arrays were washed and scanned following manufacturer's instructions.
For the pooling experiments, after sporulation and germination, roughly 1000 viable F1 spores were picked with sterile toothpicks and combined for genomic DNA extraction and subsequent array CGH analysis. Independent sporulations and pooling experiments were performed for both mismatch repair proficient and deficient F1 hybrids.
The software Feature Extraction v 9.1.5 (Agilent Technology, Santa Clara, CA) was used to extract and normalize the microarray data using a LOWESS based normalization. The normalized arrayCGH results are presented as log base 10 ratios of hybrid genomic DNA over the reference. The results are visualized using the software Caryoscope [27]. S. paradoxus contigs were mapped to the S. cerevisiae genome by blasting the contigs against the S. cerevisiae chromosomes; the contig order was then used to create an input file for Caryoscope wherein the chromosomes were collinear between the species.
All data have been deposited in the GEO database with accession number GSE19683.
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10.1371/journal.pntd.0005383 | The One Health approach to identify knowledge, attitudes and practices that affect community involvement in the control of Rift Valley fever outbreaks | Rift Valley fever (RVF) is a viral mosquito-borne disease with the potential for global expansion, causes hemorrhagic fever, and has a high case fatality rate in young animals and in humans.
Using a cross-sectional community-based study design, we investigated the knowledge, attitudes and practices of people living in small village in Sudan with respect to RVF outbreaks. A special One Health questionnaire was developed to compile data from 235 heads of household concerning their knowledge, attitudes, and practices with regard to controlling RVF. Although the 2007 RVF outbreak in Sudan had negatively affected the participants’ food availability and livestock income, the participants did not fully understand how to identify RVF symptoms and risk factors for both humans and livestock. For example, the participants mistakenly believed that avoiding livestock that had suffered spontaneous abortions was the least important risk factor for RVF. Although the majority noticed an increase in mosquito population during the 2007 RVF outbreak, few used impregnated bed nets as preventive measures. The community was reluctant to notify the authorities about RVF suspicion in livestock, a sentinel for human RVF infection. Almost all the respondents stressed that they would not receive any compensation for their dead livestock if they notified the authorities. In addition, the participants believed that controlling RVF outbreaks was mainly the responsibility of human health authorities rather than veterinary authorities. The majority of the participants were aware that RVF could spread from one region to another within the country. Participants received most their information about RVF from social networks and the mass media, rather than the health system or veterinarians. Because the perceived role of the community in controlling RVF was fragmented, the probability of RVF spread increased.
| Rift Valley fever (RVF), is a neglected, emerging, mosquito-borne disease that has caused outbreaks in Africa and the Arabian Peninsula. RVF outbreaks have a severe negative impact on livestock, human health and economy, placing further demands on communities already experiencing high levels of poverty. We believe there is an immediate need to develop new approaches that will tackle the ongoing spread of RVF. One such approach would prioritize outbreak prevention by involving local communities in the surveillance of emerging zoonotic diseases, empowering local communities as agents of change rather than relegating them as passive victims. RVF is a disease with global implication, but it starts at a local level. Therefore, to control zoonotic disease such as RVF, it is important to understand the local communities’ knowledge, attitudes and practices related to RVF. Using a bottom-up perspective, we investigated the factors that keep the local community from participating in the control of RVF outbreaks at the interface between humans, animals, and the environment. By devising acceptable and cost-effective interventions, we believe local communities can be encouraged to be the first line of defense against RVF outbreaks. Furthermore, policies aimed at curtailing RVF outbreaks would benefit from involvement of the local communities.
| Global outbreak of zoonotic infections, not only affect human and animal health but also affect food security, and socio-economic stability. To control such outbreaks require local as well regional cooperation. Most zoonotic outbreaks begin and occur in settings where resources are poor and where the outbreak severely affect the local community [1, 2]. These outbreaks spread both within and outside the country of origin, often with devastating consequences [3]. Zoonotic infections originate and spread at the interface between humans, animals, and their environments, making them candidates for the One Health approach to disease control [4–8]. Although international organizations, government authorities, and academic institutions, believe the One Health concept should be a part of a local community’s response to zoonotic infection, the One Health concept is rarely implemented at the community level. It is undeniable that community involvement is crucial in reducing the risk of zoonotic diseases at the interface between animal-human and their ecosystem.
Rift Valley fever (RVF) is a mosquito-borne viral disease affecting both humans and animals. It can be transmitted by mosquito bites or by direct contact with infected animals, their fluids, or products derived from them [9]. The disease in humans varies from a mild influenza-like illness to more severe forms such as hemorrhagic fever, renal failure, encephalitis, retinitis and miscarriage [9, 10]. Because there is no approved human vaccine or treatment available for RVF, RVF poses a major threat to public health [9]. The infection causes so called “abortion storm” in livestock and deadly epidemics in young animals, with severe consequences for local and national economies [11]. RVF is present in Africa, and as of 2000, it has spread to the Arabian Peninsula [12, 13]. Environmental changes, international travel, trade, and the spread of RVF virus (RVFV) and vectors outside of Africa highlights the potential for its global spread [14–17].
Sudan is an agricultural country with diverse ecology and has the second largest livestock population in Africa. Most of the Sudanese population depends on livestock for food and income. Our previous studies of the RVF 2007 outbreak in Sudan found a gap in knowledge regarding the role of the local community [2, 18]. In this study, we used a bottom-up approach to evaluate the knowledge, attitudes and practices that affect how the local agro-pastoralist community in Sudan confronts an RVF outbreak.
In accordance with the guidelines of strengthening and reporting of observational studies in epidemiology (STROBE), a cross-sectional community-based study was conducted in March 2013 at the Mabroka Sagadi village in the Managil locality, Gezira State, Sudan (Fig 1).
The Managil locality is close to irrigation canals. The local people are mainly farmers and shepherds and the locality is home to about 2 million livestock (mainly cattle, sheep and goats). The annual rainfall in the region varies between 100 and 350 mm; however, in 2007 the rain level reached up to 400 mm [19]. The rainy season is from mid-June to September. Managil is located in the Savannah zone and the temperature ranges from 25°C to 46°C.
The study area included all the states affected by the 2007 RVF outbreak (Fig 1). Most of the reported human cases (n = 402) [20] were found in Gezira, so this state was selected. In Gezira, the Managil locality reported the highest number of cases and the village of Mabroka Sagadi had nine recorded human RVF cases during the 2007 outbreak. At the time of the study, there were 5,830 inhabitants in Mabroka Sagadi village. Of the 641 households in the village, 240 households were randomly selected, with a response rate of 98% (n = 235). The household head was defined as the person who in charge of the household and any dependents. We interviewed the household heads and all of them were men or women at least 15 years of age.
To develop the One Health questionnaire to collect data about RVF at the human-animal-environment interfaces, we intensively searched the literature for information that would help us develop questions regarding the disease and we searched for the best possible answers to these questions supported by the medical literature. This research resulted in the One Health questionnaire that was designed to compile relevant data on RVF in humans, animals, and the environment. Originally written in English, the questionnaire was eventually translated into Arabic (S1 Table). The questions were open ended and the participants were allowed to provide more than one answer. The participants’ answers were compared to the answers listed in the questionnaire but these answers were hidden from the participants to avoid leading questions. If the participant’s answer was not in the listed answers, it was added to the category named “other”.
A two-day training workshop was held for data collectors (public health officers acquainted with the study area) to discuss the objectives of the study, the contents of the questionnaire, and the methods of the study.
A pilot study was conducted in the village of Algila (Fig 1). Algila had similar socio-cultural and ecological characteristics to those of the final study area, and it was also affected by the 2007 RVF outbreak. The findings were analyzed and used to update the questionnaire for the full-scale study.
A pre-study field visit to the study area was conducted to build trust, explain the study objectives, mobilize the community leaders, and ask the community to be involved in all parts of the study. This led to a sense of ownership and empowerment. Thus, successful face-to- face interviews with the heads of households at their home could take place in a friendly environment.
The thematic areas covered by the One Health questionnaire were socio-demographic considerations, knowledge of RVF in animals and humans, attitudes and practices regarding RVF, and environmental aspects of RVF (S1 Table).
The data were coded, entered into Microsoft Access, and checked for data entry errors by re- entering 10% of the data from the questionnaires. The data were exported and analyzed using STATA version 12 (Stata Corp LP. College Station, TX, USA).
Ethical clearance was granted from the Ministry of Health, Gezira State, Sudan. All participants were informed about the objectives of the study and about the confidentiality of the information and results. The participants signed an informed consent document for participation and they were free to leave the study at any time.
The study included slightly more women (53%) than men (47%), and almost 67% had a low level of education (less than higher secondary school level) with no significant gender difference. Just over half (55%) were above 35 years of age, the vast majority (87%) of the heads of households were married, and their household had at least six members (Table 1). Most women were housewives, while men were mostly occupied with farming (Table 1).
Most of the households bred animals (72%) (Table 1). Cattle were most common, followed by goats and sheep. Animals were kept at home (44%), near the home (22%), or far away from home (6%). Around 25% of the households had members who worked as temporary herdsmen. Most of the households (71%) used their own livestock products as the main source of food, and just under half (43%) sold livestock as a source of income. The 2007 RVF outbreak negatively affected many households (46%), including disrupting the availability of food and livestock trade for about 33% of the households.
The awareness about RVF in general was high: 80% of the heads of households heard about the disease. About 9% of the heads of households had seen people who had contracted RVF during the 2007 outbreak. More than half of those interviewed stated that RVF is a zoonosis that affects both animals and humans, and that RVF had been a serious health problem in the area during the 2007 outbreak. The most common sources of information in the community about an RVF outbreak was their social networks of relatives and friends (54%) and mass media (23%); a less common source of information was the health system (6%) or others such as veterinarians (5%).
According to the respondents, there was higher livestock mortality in the area during the 2007 RVF outbreak than the year before (2006) and the year after (2008) the outbreak. During the 2007 RVF outbreak, 34% of the heads of households had experienced death of livestock; in 2006 and 2008 only 17% had experienced death of livestock. This difference, however, could not be confirmed from official reports.
Although the community experienced higher livestock mortality, only some mentioned known symptoms in livestock that died, such as nasal and ocular discharge and hemorrhagic diarrhea (Table 2). Regarding sick livestock, known RVF symptoms were not mentioned by the majority: only 20% stated hemorrhaging and less than 9% mentioned fever and refusal to eat (Table 2). Likewise, RVF symptoms in humans were not well known to the majority of the respondents: 25% stated that fever and hemorrhagic symptoms were most common.
Most of the respondents did not know how livestock become infected with RVF. For human infection, (21%) stated that humans are infected through uncooked meat while 13% suggested direct contact with livestock. Only a few respondents suggested other mode of transmission such as raw milk (9%), and mosquito bites (6%). The most common answer on how humans should avoid RVF, was to avoid uncooked meat and handling of sick livestock. Avoiding livestock that had suffered miscarriage was the least important according to the results of the survey.
Although few thought that RVF is a contagious disease, more than half of the respondents expected to get RVF when it was present in their area―either due to animal-to-human contact or human-to-human contact (Table 3). One-quarter said that they would avoid contact with neighbors who they suspected of having RVF. About half of the participants (45%) were in favor of patient isolation as a preventive measure during outbreaks and 12% stated that they would avoid contact with RVF patients. Mosquito nets on beds were used by 60% of the respondents, but less than half of these (40%) were impregnated. The majority (60%) had noticed an increase in mosquito population during 2007. To prevent RVF in livestock, the respondents suggested isolation of sick livestock (21%) and vaccination (14%). In addition, the majority of respondents (73%) strongly recommended quarantine of RVF-infected livestock.
The survey revealed that two-thirds of the participants had a positive attitude about medical treatment against RVF in humans. However, only one-third thought that medical treatment could be used for livestock. They also indicated that veterinarians should diagnose RVF in livestock and health workers should diagnose RVF in human. Around 75% of the heads of households knew where to seek medical treatment, either in the public or the private health sector.
If an outbreak occurs, about 40% said they should notify the veterinary authorities about the death of livestock. Almost all the respondents (99%) stressed that they would not get any compensation for their dead livestock if they notified the authorities. When asked if any of their own livestock had had RVF during the 2007 outbreak, 13% said yes. These suspected cases were not confirmed by the authorities. When we asked which disciplines need to work together in order to control RVF, only eight participants (3%) stated that veterinary and health authorities should work together. The majority believed that human health authorities (50%) rather than veterinary authorities (15%) should work to control RVF. With regard to the community’s role in confronting RVF, the respondents suggested that the community should improve its hygienic measures (22%), its health education (18%) and its vector control (8%).
The majority of the study participants (70%) were aware that RVF could spread from one region to another within the country. In addition, 66% of the participants revealed that they were aware of the livestock trade ban associated with RVF outbreak inside and outside the country.
We used the 2007 RVF outbreak in Sudan as a case study to investigate, the knowledge, attitudes and practices, from the One Health perspective that affect the involvement of the local community in disrupting an emerging zoonotic infection. Most of the measures aimed to control RVF were formulated by authorities (i.e., health and veterinary) to be implemented by the local communities (a top-down). For RVF, it seems as these actions are not enough as disease emergence continues. We believed that insights from the local community (a bottom-up approach) about RVF prevention and control could help stop the spread of RVF outbreaks. This bottom-up approach could be a tool to better combat the transmission and the spread of RVF in the regions where the outbreaks are initiated, and could enhance a top down approach. To identify the important factors we used the unique One Health approach to gather information about livestock, people, and the environment. We also investigated whether the community considered the integration of the One Health approach of health, veterinarians, and environment authorities as the best strategy to confront RVF and how they could contribute to RVF control.
Although the community studied had experienced a large outbreak, few had proper knowledge of RVF regarding cause, mode of transmission, symptoms, prevention, and control. This lack of knowledge increases the chances of RVF spreading to neighboring areas and would prevent the community from confronting the disease. The knowledge of risk factors for RVF was insufficient, because the community only practiced some of the measures known to prevent RVF [21] (e.g., eating cooked meat and staying away from sick livestock) while exposing themselves to other risks (e.g., exposure to mosquitoes and handling miscarriage livestock). As in most rural areas, these communities in rural Sudan depend on their livestock as a source of food and income, and often breed and raise their livestock inside or near their homes [22]. The majority did not know that handling livestock that had miscarried was a serious risk factor for RVF [23]. This lack of knowledge could pose a significant risk, especially for rural women, as they tend to take care of livestock at home.
Although the community lives near irrigation canals, which serve as a breeding site for mosquito vectors, the majority ignored mosquitoes as a source of infection. The lack of knowledge of risk factors for transmission could explain the high number of cases reported in the 2007 RVF outbreak in Sudan [18]. Like other mosquito-borne diseases, RVF is associated with heavy rains [23], so the authorities could update communities about rain forecasts. This knowledge would encourage a participatory role of local communities in integrated vector control management similar to the malaria control programs that have been established in most countries where malaria is endemic. The infrastructure of such programs such as established vector control management and the use of impregnated mosquitoes bed nets could also help control other types of mosquito-borne viruses in endemic regions[24, 25].
Therefore, human behavior contributes to the disease emergence. To control the spread of RVF, it is essential to understanding how local communities interact with livestock and the environment. We expect that social scientists, who are well equipped to deal with human behavior changes, will be able to find acceptable ways for rural communities to practice better animal husbandry [26].
For control of RVF, we found the main focus in the community was human health and access to regional hospitals, particularly in the rainy season when the roads are difficult to navigate. Notably, the animal dimension to confronting a zoonosis such as RVF was not well understood. To better implement the One Health approach, authorities could work together with communities to prevent and control emerging zoonotic diseases. This is particularly important because the veterinary services might not be able to cover a vast country like Sudan, where the veterinarians are based in the capital of each locality. The veterinarians visit remote villages for vaccination or investigation of suspected cases of abnormal livestock diseases and visits during rainy season are very difficult due to flooded and otherwise impassable roads. In such a context, voluntary animal health workers from the local communities could be trained to identify livestock diseases, including RVF. This work would take place in collaboration with veterinarians, who have an increasingly important role in global health [27]. Similarly, human voluntary health workers could be trained to identify human diseases. These volunteers could be selected in cooperation with community leaders, which would ensure successful collaboration and communication between health care providers and the community. The sustainability of such a system would depend on a rapid response and support from the authorities when needed by the local communities. These suggestions are in keeping with a participatory approach that regards farmers as being effective partners to curb zoonoses [28, 29].
The rural household economy is affected by RVF outbreaks, regarding both food security and disrupted incomes. There were two opposing interests. The community was only interested in interventions to curb the disease that would not result in the culling of livestock without compensation. The absence of a compensation system weakened the motivation to report early cases in livestock to veterinary authority, a requirement if RVF is to be halted before infecting humans. This lack of compensation could be a possible explanation for the delay in reports of RVF in livestock. If RVF had been identified in livestock early, then livestock as well as human RVF outbreaks in Sudan in 2007 and in Kenya in 1997‒1998 might have been prevented [18, 30]. To support the devastated rural economy due to RVF outbreaks [3], a new policy of compensation for culled or dead livestock must be developed. The respondents stressed the importance of safe vaccination at the right time to prevent their livestock from contracting RVF and preventing the spread of RVF to humans, [31]. Normally, RVF vaccination of livestock is not free in Sudan, an expenditure that might impede locals from regularly vaccinating their livestock, bearing in mind that a new episode of RVF might take some years to re-emerge. Therefore, subsidizing vaccinations for emerging zoonotic diseases might encourage farmers to regularly vaccinate their livestock. In general, RVF livestock vaccines are either inactivated or live attenuated [32, 33]. However, the inactivated vaccine needs multiple doses to booster immunity, making it more expensive and more difficult to distribute. Because the vaccine requires multiple shots, establishing immunity requires time and this vulnerability decreases the vaccine’s usefulness during outbreaks. The live attenuated vaccine is administered as a single dose, but it has shown some teratogenic effects that can lead to abortion among inoculated pregnant animals[34, 35]. However, safe vaccine remains the effective way to protect animals and humans [33, 36].
The respondents were aware of the possibility of RVF spreading inside the country, especially through the free mobility livestock grazing system. They also knew about the economic consequences of a ban on livestock trade after an RVF outbreak. This awareness is important to consider when early warning systems are developed to avoid bias in disease surveillance. The community’s main sources of information on RVF were social network and mass media such as radio, not veterinarians or health workers, who were mainly involved in case notification rather than increasing public awareness [37]. The strong dependence on social networks, rather than on medical and veterinary professionals, could increase the risk of misconceptions if the wrong information is spread. Thus, the World Health Organization recommends that during zoonotic outbreaks interdisciplinary teams of health providers, veterinarians and environmentalists, provide main communication with the public [38]. These teams can communicate through social networks and mass media such as radio, which is one of the most common sources of information in remote areas of many countries. This local involvement will empower the community, allowing them to contribute to notification and control of the outbreaks, and lead them to play proactive roles. This cooperation could strengthen the national surveillance system, which depends mostly on passive notification, a system that might overlook health related events in remote areas. Empowering livestock owners is an opportunity to strengthen the surveillance system for zoonoses, including RVF [39].
Although our study was conducted in 2013 in an area that was affected during the 2007 RVF outbreak in Sudan, up-to-date questions about RVF were also asked at the time of the study. For the questions related to the 2007 RVF outbreak, we considered recall bias. Since the 2007 RVF outbreak, no other recorded hemorrhagic fever outbreaks had occurred in this area according to the participants, so the participants would not have mixed the information about RVF with other similar diseases. In addition, the 2007 outbreak was severe, affecting humans, livestock and the economy in a unique way, which made it easier to remember, decreasing the possibility of recall bias.
In general, the results of this survey are generalizable for the agro-pastoralist regions of Sudan due to the similarity of the context as well as for other countries that experience endemic RVF with similar knowledge, attitudes and practices.
This study addressed the challenges and opportunities of including local communities in controlling RVF outbreaks at the interface between humans, animals, and their environment. The suddenness of the outbreaks, the lack of treatment, the lack of vaccines, and the complex transmission cycle of RVFV highlights the need to increase community participation in disrupting RVF outbreaks. Crucial challenges include improving the knowledge and correcting misconceptions about RVF that result in risky behaviors. However, by empowering rural communities through education and motivating them to recognize cases early, the authorities could be notified and could act accordingly to support the local community. The willingness of the community to participate in curbing RVF outbreaks is an opportunity that can be effectively managed in a bottom-up approach: the more we know about a community’s knowledge, attitudes and practices related to the emergence of RVF, the better we will be embowering local communities with the best information and strategies to prevent the spread of RVF. That is, this bottom-up approach may result in mutually acceptable and cost-effective interventions that can be used to disrupt transmission of RVF in affected communities.
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10.1371/journal.pntd.0003425 | Population Structure of the Chagas Disease Vector Triatoma infestans in an Urban Environment | Chagas disease is a vector-borne disease endemic in Latin America. Triatoma infestans, a common vector of this disease, has recently expanded its range into rapidly developing cities of Latin America. We aim to identify the environmental features that affect the colonization and dispersal of T. infestans in an urban environment. We amplified 13 commonly used microsatellites from 180 T. infestans samples collected from a sampled transect in the city of Arequipa, Peru, in 2007 and 2011. We assessed the clustering of subpopulations and the effect of distance, sampling year, and city block location on genetic distance among pairs of insects. Despite evidence of genetic similarity, the majority of city blocks are characterized by one dominant insect genotype, suggesting the existence of barriers to dispersal. Our analyses show that streets represent an important barrier to the colonization and dispersion of T. infestans in Arequipa. The genetic data describe a T. infestans infestation history characterized by persistent local dispersal and occasional long-distance migration events that partially parallels the history of urban development.
| The colonization and dispersal of disease vectors in new and expanding urban areas pose important health risks. The population and demographic dynamics of these vectors are often unclear, and their temporal and spatial associations with urbanization are unknown. Here, we use molecular markers to describe the genetic structure of populations of T. infestans, an important vector of the etiologic agent of Chagas disease, in an expanding urban environment. Samples were obtained along a transect in Arequipa, Peru, that includes old, well-developed communities and new communities characterized by rudimentarily constructed houses. We assessed the clustering of subpopulations and the effect of distance, sampling year, and city block on genetic distance among pairs of insects. Our analyses show that streets represent an important barrier to the colonization and dispersion of T. infestans in Arequipa. The genetic data describe a T. infestans infestation history characterized by persistent local dispersal and occasional long-distance migration events that partially parallels the history of urban development.
| Chagas disease is a vector-borne disease endemic in Latin America that poses important public health risks [1]. Triatoma infestans, a true bug that commonly harbors the etiologic agent of Chagas disease, Trypanosoma cruzi, has historically occurred throughout southern South America [2,3]. Its range has expanded from sparsely populated rural areas into densely populated urban areas, and it often lives within the walls of rudimentarily built houses [4–6]. Rapid urbanization results in proximity between humans and pest species, including rodents and insects, many of which carry pathogens that are transmittable to humans. The rate at which these pest species disperse through human communities is closely associated with the disease risk of the human population [7] and with economic costs [8].
Urban landscapes are a patchwork of habitats composed of a heterogeneous mosaic of city blocks separated by inhospitable streets [9]. Features of urban landscapes provide unique challenges and opportunities for a species to colonize and proliferate. Identifying features that promote or hinder colonization and migration in cities enables a mechanistic understanding of the distribution and abundance of organisms in urban environments [10,11]. Furthermore, the recognition of these environmental barriers is important for designing effective control measures. Studies assessing the impact of anthropogenic landscape alterations on species still living in the remnants of their unaltered habitat are common. In contrast, few studies have examined how species colonize and disperse throughout the urban landscape itself. This bias is surprising given the proximity between humans and these pest species and the economic, ecological, social, and public health implications of infestations [8]. Here we use molecular methods to elucidate the environmental features of a growing urban landscape that affect the colonization and dispersal of T. infestans along a west-to-east transect within the city of Arequipa, Peru.
The study transect is located in the Mariano Melgar district of Arequipa, Peru (Fig. 1). The transect follows a gradient from old, well-developed communities in the west near the city center to new communities in the east. The new communities are characterized by recent and rudimentarily constructed homes that extend to the current eastern border of the urbanized area. A lack of mortar between the blocks of volcanic stone and bricks that make up the walls of these rudimentarily built homes creates an ideal habitat for T. infestans [4].
The T. infestans samples used in this study were collected in 2008 and 2011 from homes in the transect area; Tetramethrin (Sapolio, Mata Moscas) was sprayed into the cracks in stone walls or other small structures that provide suitable T. infestans habitats to flush out the insects. More than 3,000 individuals were collected, placed into individual tubes, and stored at -20°C. From this total, 180 were chosen for genetic analyses (90 from each sampled year). To choose specific samples for analysis, 30 houses were randomly chosen from each third of the transect (equally divided along the length of the transect) and one fifth instar or adult individual was randomly selected from each chosen home.
Genomic DNA was extracted from two legs of each sample following the insect tissue protocol provided with the Qiagen DNEasy Blood and Tissue kit (Valencia, CA, USA). The 13 microsatellites used in this study are well characterized, commonly used for both macrogeographical and microgeographical studies [e.g., 6,12–14], and have been found to be in linkage equilibrium [15–17]. They were amplified using a fluorescent-tagged forward primer (ABI dyes: 6-FAM, PET, VIC, or NED) using standard protocols. Complete sequences, cycling conditions and source literature are described in S1 Table. Fragment sizing was completed at the DNA Sequencing Center (Applied Biosystems 3100 Capillary Sequencer and GeneMApper) of the University of Pennsylvania. Electropherograms were visualized in PeakScanner (ABI) to confirm the automated allele sizing. Ambiguous peaks were re-amplified and re-genotyped until clear allele sizes were obtained. Alleles were binned according to the established repeat size with TANDEM [18]. Negative controls were run with all PCR reactions to prevent cross-contamination and included in fragment sizing analyses.
Potential differences between collection years were assessed with the exact test of sample differentiation in Arlequin version 3.5.1.2 [19] using 100,000 Markov steps. Population genetic structure was assessed using the Bayesian clustering algorithm implemented by STRUCTURE version 2.3 [20–22]. We ran five independent iterations of the analysis for each number of genetic clusters (K, ranging from 2 to 12) assuming correlated allele frequencies, admixture, and no location data as a prior, with 500,000 Markov Chain Monte Carlo (MCMC) iterations and a 20% burn-in. The output of the STRUCTURE analyses was extracted in STRUCTURE HARVESTER [23] and the optimal alignment of the five iterations was determined using CLUMPP [24]. The ΔK method implemented by Structure Harvester was used to determine the optimal number of clusters [25]. The cluster assignment of each individual was plotted in geographic space in ArcMap 10 [26] as a pie chart. The individual alleles were plotted in a similar manner to verify the detected trends.
We used a permutation-based linear regression to quantify the effect of Euclidean distance, sampling year, and city block (same or different city block coded as 1 and 0, respectively) on the genetic distance between pairs of insects using the Brat-Curtis dissimilarity index [27]. Null distributions of the effect of each variable on genetic distance were constructed by randomly permuting genotypes among individuals collected in the same year, and the effect size of each variable on the genetic distance between pairs of individuals was calculated as the regression coefficient in a linear regression [28]. The regression coefficients calculated from the unpermuted (observed) data were compared to the null distribution of coefficients calculated from 10,000 permutations to derive a p-value for each parameter estimate. Violin plots were used to visualize the effect of environmental factors on genetic distance using R [27]. Additionally, a restricted data set consisting of matching equal-distance pairs of samples (with 1 m precision) located either in the same city block or in different blocks was analyzed in JMP version 10 [29]. This analysis allows a direct comparison of pairs found at the same spatial scale to determine the effect of streets on the fine-scale structure of the insect population.
Point estimates of population genetic diversity across the transect were calculated using sGD [28]. The analyses calculate the observed heterozygosity, the inbreeding coefficient (FIS), and the allelic richness around each individual sample in the defined radius considering all samples within that radius. Data points with fewer than 10 individuals within the radius were excluded from the analyses. The optimal distance was defined as the distance at which the number of valid samples levels off. Radii of 100, 125, 150, 175, 200, 225, and 250 m were considered. The indexes obtained with the selected radius were plotted using ArcMap 10. To assess the presence of trends across the transect, the calculated indexes (observed heterozygosity, inbreeding coefficient (FIS), and allelic richness) were regressed against the horizontal position in the transect using JMP 10.
Microsatellite data generated in this study have been deposited to DRYAD and are available from the Dryad Digital Repository: http://doi.org/10.5061/dryad.5tt50.
Using the exact test of sample differentiation, we found no difference (p > 0.05) in genotype frequencies between the samples collected in 2008 and those collected in 2011. The genetic analyses showed that T. infestans populations are very finely spatially structured within the sampled transect. The majority of city blocks can be characterized by a single, dominant subpopulation as defined by the STRUCTURE algorithm, which incorporates information from all analyzed loci (Fig. 2). Samples belonging to the same block are assigned to the same subpopulation more frequently than expected by chance (56% observed vs. 27% expected; χ2 = 84.96, p < 0.001). The rapid changes in subpopulations from one city block to the next and the patchy distribution of these subpopulations are easily visualized by mapping the STRUCTURE subpopulation probability of each sample onto the study area. These patterns and the dominant subpopulation on each city block were consistent in both of the sampling periods (Fig. 2A, B). The optimal number of subpopulations is assumed to be four (S1 Fig.) by the ∆K method [25]; however.
The fine-scale spatial patterns are also visible for each of the 13 analyzed loci (S2 Fig.). In nearly all cases, a single allele from each locus dominates a city block; however, there are also intermediate zones where two alleles occupy the same block, resulting in heterozygous individuals (S2 Fig.). Furthermore, each allele is found throughout the transect, reflecting a similar pattern that was previously found with the STRUCTURE algorithm wherein each identified subpopulation was also found throughout the transect (Fig. 2).
The location of samples within or between city blocks has an important effect on the genetic dissimilarity among samples, which is visually evident in the violin plots (Fig. 3). The genetic dissimilarity between pairs of samples located on different blocks is substantially greater than the dissimilarity between pairs of samples located on the same block (Table 1). This effect remains statistically significant (p > 0.001) after controlling for the Euclidean distance between pairs of samples (S3 Fig.). The location of the samples (either within or between a city block) and the Euclidean distance among samples were statistically significant explanatory variables of the genetic dissimilarity among sampled insects (Table 1). In the reduced dataset that includes equally distanced pairs of individuals in the same or different blocks, no correlation was found between Euclidean distance and genetic distances (S4 Fig.; p > 0.05). A significant but very low correlation (S4 Fig.; R2 = 0.01, p < 0.001) was found in the complete dataset.
The optimal radius distance for the neighborhood genetic diversity analyses was 225 m (S5 Fig.). The ages of the communities are related to the fine-scale genetic structure of the vector population, and statistical trends were detected from the west (older communities) to the east (newer communities) of the transect. The older parts of the transect had higher observed heterozygosity (Fig. 4A, 5A) and lower inbreeding coefficients (FIS; Fig. 4B, 5B). Allelic richness did not vary across the transect, (Fig. 4C, 5C), which is consistent with the observation that most common alleles (S2 Fig.) and STRUCTURE subpopulations (Fig. 2) can be found across the complete extension of the transect. The complete set of calculated indexes is presented in S2 Table. The patterns in local genetic diversity indexes are similar in the two collection years (Fig. 4).
The expansion of cities alters the environment and creates new habitats for urban species. In an expanding city, the genetic signature of an invading population is expected to be closely related to the history of urban development. However, urban environments contain many migratory barriers that may interfere with such expansion [30]. The data and analyses presented here confirm that city streets act as barriers to the dispersion of T. infestans and, consequently, that particular genotypes tend to be specific to a given city block.
The population genetic data from the study transect, which spans a gradient of urban development from established communities to recently-inhabited areas, describe a demographic history of T. infestans characterized by constant local dispersal and occasional long-distance migration events. Local dispersal events mainly involve households on the same city block, resulting in city blocks colonized by closely-related individuals (Fig. 3). Ambulatory movement, which occurs at all stages of the T. infestans life-cycle, is relatively rapid, as shown by sentinel habitat studies performed in this same system [31]. However, our data suggest that dispersal to neighboring houses across a city street is relatively rare. Multivariate regression models controlling for all potential confounding variables indicate that city streets remain strong environmental barriers to gene flow for T. infestans (Table 1, Figs. 3, S3, S4).
In several instances, closely related individuals can inhabit distant areas of the transect despite being absent from intervening blocks (Figs. 2, S2), suggesting occasional long distance migration events. These long distance migration events are relatively rare, as evidenced by the fact that most blocks are colonized by one single group of closely-related insects. Long distance dispersal may occur due to the natural flight capabilities of T. infestans or via human-mediated dispersal. Only T. infestans adults are capable of flight. In general, adults initiate flight only in response to severely limited resources [32], at which time they can cover distances of up to two kilometers [15,32,33], readily traversing several city blocks. Human-mediated dispersal can also promote dispersal events across several city blocks [34]. We cannot distinguish between aerial and human-mediated long distance dispersal in our transect, as both processes can result in the observed patterns.
Previous reports using presence-absence data have suggested that streets can act as barriers to dispersal for several insect species [35–38] including T. infestans [9]. However, these conclusions are potentially confounded by other factors such as clustering of environmental factors on either side of the putative barrier. Because of the danger posed by T. infestans, no mark-recapture studies that could elucidate their movement through the populated environment have been conducted. Our population genetics analysis allowed us to isolate the effects of streets on the dispersal of T. infestans from potential confounding explanations and thereby better describe the migratory and colonization processes of these insects in urban environments.
The genetic signatures typically associated with the recent introduction of a population, including limited heterozygosity and high inbreeding coefficients, were mostly associated with the newest section of the transect (Fig. 4, 5). These results support the temporal association between the time of first occurrence of T. infestans and the age of the communities in the transect, which was first described by Levy et al. (2014). These results confirm that T. infestans has been present for much longer in the older parts of the transect than in the newer parts. However, the analysis of the allelic richness adds a layer of complexity, as high levels of allelic richness are scattered throughout the transect (i.e., in both old and new communities). This mixed pattern can be explained by the connectivity of the transect to surrounding areas. These sections of great allelic richness could represent the location of the initial colonization or the location of subsequent contact with other T. infestans invasions.
The transition from new, recently urbanized communities to older, better established communities coincides with physical changes in housing structures; more specifically, land tenure tends to result in the acquisition of fully mortared walls and more domestic animals. Further changes can occur with increased capital, including the presence of fewer food animals and more companion animals [30]. These changes in host populations may have indirect effects on the population dynamics of T. infestans; however, these effects are very difficult to rigorously quantify. The genetic signatures detected in this study confirm that the demographic dynamics of T. infestans infestations are significantly affected by urbanization.
Our conclusions may guide the research and development of strategies to control the emergence and re-emergence of vector populations in urban environments. Isolated instances of T. infestans infestation or reinfestation are typically controlled by the application of insecticide around infested households in a ring-like fashion. Our results suggest that the initial application of insecticide should be focused on the city block where the infestation was first detected. Moreover, even though streets represent barriers for these insects, our results and those of previously published works [9] indicate that they are not impervious barriers and may be breached over time. Most importantly, our results call into question whether a purely spatial strategy of vector control such as the ring insecticide treatment can realistically lead to vector elimination, as this strategy can be overcome by repeated long-distance dispersal events. A better understanding of the social and migratory interactions of residents of infested houses may improve the long-term prospects of eliminating the vector from urban environments [11,15,30,34,39–42].
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10.1371/journal.pntd.0004617 | Identification of Novel Chemical Scaffolds Inhibiting Trypanothione Synthetase from Pathogenic Trypanosomatids | The search for novel chemical entities targeting essential and parasite-specific pathways is considered a priority for neglected diseases such as trypanosomiasis and leishmaniasis. The thiol-dependent redox metabolism of trypanosomatids relies on bis-glutathionylspermidine [trypanothione, T(SH)2], a low molecular mass cosubstrate absent in the host. In pathogenic trypanosomatids, a single enzyme, trypanothione synthetase (TryS), catalyzes trypanothione biosynthesis, which is indispensable for parasite survival. Thus, TryS qualifies as an attractive drug target candidate.
A library composed of 144 compounds from 7 different families and several singletons was screened against TryS from three major pathogen species (Trypanosoma brucei, Trypanosoma cruzi and Leishmania infantum). The screening conditions were adjusted to the TryS´ kinetic parameters and intracellular concentration of substrates corresponding to each trypanosomatid species, and/or to avoid assay interference. The screening assay yielded suitable Z’ and signal to noise values (≥0.85 and ~3.5, respectively), and high intra-assay reproducibility. Several novel chemical scaffolds were identified as low μM and selective tri-tryp TryS inhibitors. Compounds displaying multi-TryS inhibition (N,N'-bis(3,4-substituted-benzyl) diamine derivatives) and an N5-substituted paullone (MOL2008) halted the proliferation of infective Trypanosoma brucei (EC50 in the nM range) and Leishmania infantum promastigotes (EC50 = 12 μM), respectively. A bis-benzyl diamine derivative and MOL2008 depleted intracellular trypanothione in treated parasites, which confirmed the on-target activity of these compounds.
Novel molecular scaffolds with on-target mode of action were identified as hit candidates for TryS inhibition. Due to the remarkable species-specificity exhibited by tri-tryp TryS towards the compounds, future optimization and screening campaigns should aim at designing and detecting, respectively, more potent and broad-range TryS inhibitors.
| Parasites from the genus Trypanosoma and Leishmania are etiologic agents for a group of neglected diseases with high morbidity and mortality rates in the developing world. Inasmuch as vaccine development is hampered by the successful mechanisms employed by the pathogens to evade the host immune response, chemotherapy remains as a safe option to fight these diseases. However, new drugs with better pharmacological performance (i.e. safety, efficacy and ease of administration) than those in current use are urgently needed. The thiol-redox metabolism of trypanosomatids offers an excellent opportunity for the development of more selective and efficacious medicines because it depends on a molecule, trypanothione (a bis-glutathionyl derivative of spermidine), unique and indispensable to the pathogens. Here we report the identification of novel inhibitors of trypanothione synthetase from three major trypanosomatid species of medical and veterinary relevance. Although highly conserved in sequence, trypanothione synthetases display significant species-specifity towards compounds, pointing to structural differences as determinants of ligand selectivity. Most of the active compounds presented two-digit μM inhibitory activity and serve as primary scaffolds to develop more potent inhibitors. Among them, N,N'-bis(benzyl)-substituted diamine and paullone derivatives are interesting candidates because of their potent and/or selective anti-trypanosomal and anti-trypanothione synthetase activity.
| Protozoan parasites from the genus Trypanosoma and Leishmania are responsible for diseases affecting humans and their livestock. The zoonotic character of these diseases, which involve different insect species as vectors and wild animals as reservoirs, hamper the implementation of successful control strategies [1]. Immuno-prophylaxis is not yet available and for some species, such as T. brucei spp. and T. cruzi, appears unfeasible due to complex immune-evasion mechanisms [2, 3]. So far, and probably for several decades ahead, chemotherapy remains as the sole choice of treatment. Only a handful of drugs are available to fight Chagas’s disease (T. cruzi), sleeping sickness (T. brucei gambiense and T. b. rhodesiense) and the different forms of leishmaniasis (Leishmania spp.). Unfortunately, they suffer from several drawbacks encompassing low efficacy, resistance and route of administration [4–7]. Moreover, several of these drugs (e.g. nifurtimox, benznidazole and melarsoprol) present a non-specific mode of action that accounts for their high toxicity [8, 9]. Thus, the discovery of new chemical entities targeting specific and indispensable components of parasite metabolism is a priority for trypanosomiasis and leishmaniasis.
The thiol-dependent redox metabolism is one of the unique metabolic features that distinguish trypanosomatids from humans and offer reliable molecular targets for selective drug development [10]. An example of genetic hallmarks of trypanosomatids is the lack of genes coding for glutathione reductase and thioredoxin reductase [11–13], which fuel the major redox systems of most living organisms (i.e. the glutathione/glutaredoxin system and the thioredoxin system) with reducing equivalents. Instead, trypanosomatids rely on the low molecular mass thiol N1, N8-bis(glutathionyl)spermidine [trypanothione, T(SH)2] and the flavoenzyme trypanothione reductase for sustaining the intracellular redox homeostasis (Fig 1). T(SH)2 delivers reducing potential to different redoxin proteins, which, by acting on different targets, control vital functions (for a review see [10]; Fig 1). Proposed functions of T(SH)2 further comprise neutralization of xeno- and endobiotics (e.g. methylglyoxal, iron and nitric oxide), coordination of iron-sulfur complexes and reduction of ascorbate [14, 15].
The biosynthesis of T(SH)2 is achieved in two consecutive steps each involving the ligation of a glutathione (GSH) molecule by its glycine carboxyl group to the free N1 and N8 amine groups of spermidine (SP). Both reactions are catalysed by the C-terminal ligase domain of trypanothione synthetase (TryS; EC 6.3.1.9) at the expense of ATP (Fig 1). Some trypanosomatids species harbour (L. infantum, L. donovani, L. mexicana and T. cruzi) or express (Crithidia fasciculata) a gene coding for glutathionylspermidine synthetase (GspS; EC 6.3.1.8), which synthesizes the reaction intermediate N8 mono-glutathionylspermidine.
The importance of TryS activity for parasite viability has been demonstrated in vitro and in vivo for T. b. brucei [16–19] and L. infantum [20] by means of genetic and pharmacological approaches. In addition, TryS presents several advantages as a drug target candidate: (i) it is encoded by a single copy gene [11–13], (ii) the structure of TryS from L. major has been elucidated [21], (iii) TryS has been shown to provide metabolic control to the trypanothione pathway in T. cruzi [22], and (iv) kinetic information is available for several TryS [18, 22–27].
At an early state of knowledge, the rational inhibitor design was undertaken using GspS of C. fasciculata (CfGspS) and Escherichia coli (EcGspS) as test enzymes and compounds isosteric with GSH or related transition state analogues as chemical scaffolds [28–34]. Preliminary studies with GSH analogues identified the γ-glutamyl moiety as critical for molecular recognition [28]. Further work revealed that addition of acidic groups to the L-γ-Glu-L-Leu dipeptide resulted in CfGspS inhibitors of reasonable potency such as the phosphonic (Ki ~ 60 μM) [29], the boronic (Ki ~ 81 μM and Ki* ~ 18 μM) [34] or the diaminopropionic acid derivative (Ki ~ 7.2 μM and Ki* ~ 21 μM) [35].
Transition state mimics, previously identified as potent inhibitors of EcGspS [30–32], proved to be equally active against recombinant CfGspS. Notably, a Gsp-phosphinate derivative was capable to inhibit recombinant TryS from L. major, T. cruzi and T. b. brucei albeit with apparent Ki values 16–40-fold higher than that obtained for CfGspS (Ki of 18.6 nM) [36]. Unfortunately, these compounds displayed null biological activity against pathogenic trypanosomatids at 100 μM. Nevertheless, this phosphinate remained the only compound able to target TryS from three different pathogenic trypanosomatids.
The anti-proliferative activity of GSH derivates (N,S-blocked GSH diesters, S-2,4-dinitrophenyl-GSH) [37–39] and GSH-related phosphinopeptides [40] ranked from 35 to 0.2 μM in different trypanomatids. The poor biological activity exhibited by these inhibitors has been ascribed to their peptidic nature susceptible to hydrolysis by esterases and amidases [37].
Based on the ATP-dependency of TryS, a compound library of protein kinase inhibitors was screened against recombinant CfTryS. This led to the identification of N5-acetamide paullones (benzo[2,3]azepino[4,5-b]indol-6-ones) as potent inhibitors of CfTryS [10, 41]. Recently, we reported the finding of a related 10-trifluormethylated acetamide derivative of this paullone as nM inhibitor of LiTryS (IC50 350 nM) [20]. Information on the activity of these paullones against TryS and parasites from other pathogenic species is lacking.
More recently, a HTS campaign against TbTryS with a library of ~ 62000 compounds identified several hits that upon optimization yielded leaders with inhibitory activity against TbTryS in the nM range (IC50 values of 45, 95 and 140 nM for DDU86439, DDD85811 and DDD86243, respectively) and EC50 values towards infective T. b. brucei that ranked from 5 to 10 μM. The compounds allowed the chemical validation of TbTryS as drug target [17, 18].
We here describe the setting-up of a screening technique to detect TryS inhibitors and report the identification and chemical validation of inhibitors from TryS from three major trypanosomatid species (Trypanosoma brucei brucei: T. b. brucei, Trypanosoma cruzi: T. cruzi and Leishmania infantum: L. infantum).
Unless otherwise stated all reagents were of analytical grade and purchased from Sigma-Aldrich, J.T. Baker, Carlo Erba Reagents SA, Gibco, Invitrogen, Life Technologies, Enzo Life Sciencies, Roche.
TryS from different trypanosomatids was produced in recombinant form with an N-terminal His-tag. The constructs pET-15b TbTryS [27], pRSET-B TcTryS and pET-28c(+)LiTryS [20] were kind gifts of Alan Fairlamb (Dundee University, Dundee, Scotland), Sergio Guerrero (Universidad Nacional del Litoral, Santa Fe, Argentina) and Helena Castro (Institute for Molecular and Cell Biology, Porto, Portugal), respectively. They were used to express TryS of T. b. brucei 427 (MITat1.4, GenBank accession protein id CAC87573.1), T. cruzi strain Tulahuen 0 (GenBank accession protein id AAO00722.1) and L. infantum JPCM5 (GenBank accession protein id CAM69145.1). E. coli strain BL21 (DE3) or Tuner (DE3) (Novagen) served as expression host. For a detailed description of the expression and purification protocols see S1 Text.
Protein concentration was determined using the Bicinconinic Acid assay with bovine serum albumin as standard. The protocols described above yielded 4–8 mg of recombinant TryS per liter of culture medium with ≥ 95% purity and homogeneous specific activity.
The kinetic characterization of His-tagged TryS was performed using the LDH/PK assay which couples ATP regeneration to NADH oxidation. The end-point assay based on detection of inorganic phosphate (Pi) by the BIOMOL GREEN reagent was used to estimate the apparent Ki for ADP. All reactions were performed at room temperature (RT, 20–25°C) and a detailed description of both assays is provided in S1 Text. The apparent kinetic parameters (KM and Vmax) were calculated by fitting plots of initial velocity (v) vs. substrate concentracion ([S]), determined at saturating concentration of co-substrates, to the Michaelis Menten equation assisted by the software OriginPro 8. For GSH, the KM and Ki values were determined using the following equation v = Vmax / (1 + KM / [GSH] + [GSH] / Ki), which considers the nonproductive binding of GSH to the substituted enzyme [42]. The apparent Ki for ADP was estimated from linear fitting of the plots [E]/v vs. [ADP] at different concentrations of ATP, where [E] is enzyme concentration.
The compound library involves 144 chemical entities that are clustered by chemical scaffold as follow: (A) 6-arylpyrido[2,3-d]pyrimidine-2,7-diamine derivatives (APPDA; S1 Table) developed as ATP-competitive inhibitors of bacterial D-Alanine:D-Alanine ligase [43] and biotin carboxylase [44]; (B) 1-(benzo[d]thiazol-2-yl)-4-benzoyl-3-hydroxy-5-phenyl-1H-pyrrol-2(5H)-one derivatives (BBHPP; S2 Table); (C) N,N'-bis(3,4-substituted-benzyl) diamine derivatives (BDA; S3 Table) that display potent anti-malarial or -trypanosomal/leishmanial activity [45, 46] and are simplified derivatives of compounds interfering with the parasite’s polyamine metabolism [45, 47–50]; (D) benzofuroxan (BZ; S4 Table) [51–55]; (E) 4,5-dihydroazepino[4,5-b]indol-2(1H,3H,6H)-one derivatives (AI; S5 Table), some of which with reported anti-TryS or -T. b. brucei activity [56]; (F) 1H-purine-2,6(3H,7H)-dione (PD), including the 3-butyl-7-methyl-8-((3-(trifluoromethyl) phenylthio)methyl)3,4,5,7-tetrahydro-1H-2,6-dione) (kindly provided by Dr. Luise Krauth-Siegel, Heidelberg University, Germany; S6 Table) that was reported as inhibitor of the T(SH)2-dependent oxidoreductase tryparedoxin [57]; (G) 2-aminooxazole-5-carboxamide derivatives (AOCA; S7 Table); (H) and several singletons: N,N-dibenzyl-1-ethyl-3-methyl-1H-pyrazole-5-carboxamide; 2-amino-N,N-dibenzyl-4-methyl thiazole-5-carboxamide; prochlorperazine; tert-butyl 8-aminooctylcarbamate; tert-butyl 12-aminododecylcarbamate; N-(8-aminooctyl)acetamide.
AAO00722.1; CAC87573.1; CAM69145.1
The kinetic studies of TryS employed in this work were performed with three major aims, first to test the quality of the recombinant enzymes and establish optimal assay conditions, second, to disclose kinetic data previously not addressed for TryS from T. cruzi strain Tulahuen 0 and L. infantum (strain JPCM5) and a reaction product (ADP), and third, to compare the kinetic behavior of different TryS and its implication for enzyme regulation and inhibition. The kinetic data available for TryS from earlier [18; 22–27] and the present work are presented in Table 1 and S1 Fig.
LiTryS presented apparent KM values of 166 ± 62 μM for GSH, 1335 ± 167 μM for SP and 42 ± 10 μM for ATP, and a apparent Ki for GSH of 680 ± 160 μM, all of which are in the same order of magnitude as those reported for non-tagged TryS from the related species L. major [26].
The apparent kinetic parameters of TcTryS strain Tulahuen 0 (KM GSH = 123 ± 23 μM, KM SP = 685 ± 105 μM, KM ATP = 41 ± 6 μM and Ki GSH = 1600 ± 230 μM) are similar to those published for TcTryS strain Silvio X10 clone 7 [25] and strain Ninoa [22], except that inhibition by GSH was not observed for TryS from the last strain [22].
The apparent KM values for substrates and Ki for GSH obtained for His-tagged TbTryS (KM GSH = 135 ± 43 μM, KM SP = 238 ± 51 μM, KM ATP = 18 ± 6 μM and Ki GSH = 242 ± 102 μM) were almost 3- to 7-fold higher than those reported for the untagged version of this protein (KM GSH = 23.8–56.2 μM, KM SP = 37.8–92 μM, KM ATP = 6.6–8.6 μM and Ki GSH = 36.5–143 μM) by different laboratories [24, 27], which may be ascribed to different assay conditions (summarized in S8 Table) as highlighted in previous publications [23, 24]. Nonetheless, all values determined here for His-tagged tritryp TryS differed in less than one order of magnitude from those reported for tag-free versions from identical or homologue proteins, hence the recombinant form of the enzymes were rated as suitable for the screening assay.
Inhibition by ADP has been reported to occur for CfGspS with a Ki of 80 μM [64] but information is lacking for related enzymes from pathogenic trypanosomatids. As shown here, ADP competed for the ATP-binding site of TbTryS, LiTryS and TcTryS (S1 Fig) with apparent Ki values of 40 ± 5 μM, 90 ± 8 μM and 60 ± 6 μM, respectively (Table 1). It is worth to note that these Ki values represent overall estimates of product inhibititon because the assay conditions used do not allow distinguishing a preferential inhibition of the first or second biosynthetic step catalyzed by TryS.
Comparison of the kinetic parameters for TryS from three major trypanosomatid species obtained under similar experimental conditions (this study), shows that the enzyme from African trypanosomes presents KM values for ATP and SP that are remarkable lower (2.3‒5.6-fold) than those for TryS from L. infantum and T. cruzi (Table 1). A similar conclusion can be drawn from studies carried out for related TryS in other laboratories [18, 22, 24–27]. At variance with Leishmania spp. and T. cruzi, T. brucei spp. is an extracellular pathogen that fully relies on de novo synthesis of polyamines and glycolysis to fulfil its metabolic and energetic needs [58, 65]. Thus, TryS from African trypanosomes presents kinetic parameters that guarantee the production of the indispensable metabolite trypanothione [16, 17] under conditions of restricted supply of ATP and SP that the parasite may face during its complex life cycle (e.g. differentiation, which entails drastic reprogramming of energetic metabolism, and different nutrient availability in vector and host). All three tritryp TryS displayed similar KM values for GSH, which contrast with earlier studies reporting a higher affinity of T. b. brucei TryS for this substrate [18, 22, 24, 27]. As previously reported [18, 23–27], tritryp TryS were susceptible to substrate inhibition by GSH, although the inhibitory efficiency varied between species as follows: T. b. brucei (apparent Ki/KM GSH = 1.8) > L. infantum (apparent Ki/KM GSH = 4.1) > T. cruzi (apparent Ki/KM GSH = 13.0). This suggests that the T. b. brucei enzyme is particularly sensititive to inhibition by the substrate GSH. However, the physiological role of this inhibition mechanism, if any, remains questionable because substrate accumulation will further enhance TryS inhibition and GSH cannot surrogate T(SH)2 functions in T. brucei [16]. For the leishmanial and T. cruzi TryS, substrate inhibition will become relevant only at high concentrations of GSH (e.g. > 1.5 mM), which, except for a few examples of L. donovani parasites grown to mid-log phase (e.g. 2.27 mM GSH for axenic amastigotes from the strain BOB and 1.68 mM for promastigotes from the strain LV9) [14], appears to be a non-physiological condition. Indeed, L. infantum parasites harbouring a single trys allele and with a TryS content about 50% lower than that of wildtype cells show no phenotype in vitro and in infected animals [20].
The Ki ADP / KM ATP ratios for tri-tryp TryS range from1.5 to 2.2-fold, suggesting that ADP may be a physiological modulator of TryS activity. However, taking into account that the intracellular concentration of ATP for trypanosomatids (e.g. 2–4 mM and 0.58 mM for infective T. b. brucei [65, 66] and T. cruzi [67], respectively, and 0.87 mM for L. donovani promastigotes [68]) is > 14-fold in excess with respect to the respective KM values for tritryp TryS (e.g. 18–60 μM; Table 1) and that the ATP/ADP ratio reported or estimated by us for trypanosomatids is between 3 to 10 [65, 66, 69–71], such regulatory role of ADP in T(SH)2 biosynthesis may be questioned. A recent kinetic analysis of TbTryS assisted by computational modeling of the enzymatic mechanism [24] highlighted that the activated enzyme is particularly sensitive to inhibition by GSH and T(SH)2. Because the experimental setup employed in this study did not consider inhibition by ADP and excluded ADP as an integral component of the enzyme activated complex, the real contribution of this product to TryS inhibition remains to be addressed.
The biological activity of the most active inhibitors of TbTryS (i.e. IC50 ≤ 30 μM; MOL2008, ZEA10, EAP1-47, EAP1-63, APC1-89, APC1-99, APC1-101, APC1-111) and LiTryS (i.e. IC50 < 1 μM; MOL2008 and FS-554) was evaluated against the infective form of T. b. brucei or promastigotes of L. infantum and murine macrophages. Except for the paullone MOL2008 (EC50 4.3 ± 0.7 μM), all other TbTryS inhibitors presented anti-T. b. brucei activity in the nM range. The most active being the BDA APC1-99 (EC50 15 ± 1 nM), APC1-111 (EC50 40 ± 1 nM), APC1-89 (EC50 61 ± 1 nM) and EAP1-63 (EC50 90 ± 7 nM), followed by ZEA10, EAP1-47 and APC1-101 with EC50 between 200–280 nM (Table 2). In comparison to paullone FS-554 (EC50 112.3 ± 1.1 μM) [20], MOL2008 displayed a 10-fold higher anti-leishmanial activity (EC50 12.6 ± 1.6 μM).
The selectivity of the anti-parasitic effect was assessed using murine macrophages. Most compounds, except for MOL2008, FS-554 and ZEA 10, presented a selectivity index (SI) ≥ 10 with EAP1-63 (SI = 124) and APC1-89 (SI = 164) having the highest selectivity towards bloodstream T. b. brucei (Table 2).
In order to get an insight into the on-target activity of promising compounds, the intracellular thiol content of WT parasites exposed for 24 h to EAP1-47 and MOL2008 was determined. For bloodstream T. b. brucei treated with 50 nM EAP1-47, T(SH)2 content decreases by 28% while GSH level increases by 39% with respect to untreated cells, which overall resembles the metabolite changes observed for parasites with 48 h RNAi-downregulated expression of TryS (S11 Table and [16]). A compound acting on T(SH)2 metabolism is expected to display an increased cytotoxicity against parasites depleted in TryS. The cytotoxic effect of 100 nM EAP1-47 towards trypanosomes with a TryS content that is 1/3 of that corresponding to non-induced RNAi or wild-type cells was slightly increased (1.3-fold; Fig 5). For comparison, nifurtimox, a drug inducing thiol depletion [73], added at 5 μM (EC50 determined for WT cells) displayed a 1.8-fold increased potency towards TryS-depleted parasites. Strikingly, identical assays performed with the chemically-related and trypanosome-selective compounds EAP1-63 and APC1-99 did not show differences in the potency of these compounds towards WT or TryS-depleted parasites. Taking together, these results shows that EAP1-47 is interfering with T(SH)2 biosynthesis, although the almost two order of magnitude difference between TbTryS inhibition and EC50 indicates that the compound has also other molecular targets in vivo. In contrast, the lack of enhanced cytotoxicity displayed by EAP1-63 and APC1-99 towards the TryS-RNAi induced cell line, is an strong indication that their potent trypanocidal activity is unrelated to interference with T(SH)2 metabolism.
On the other hand, L. infantum promastigotes in the log growth phase exposed to MOL2008 at its EC50 (12 μM) for 24 h presented a marked decrease in the intracellular pool of thiols. From two independent experiments, T(SH)2 and GSH content decreases ≥ 90% and > 30%, respectively, upon MOL2008 treatment (S11 Table). Interestingly and at variance with the metabolic changes observed for the genetic (RNAi) or chemical (EAP1-47) silencing of TryS in T. b. brucei, inhibition of TryS by MOL2008 did not lead to accumulation of the substrate GSH in L. infantum. Further experiments are required to establish whether the reduction on GSH level is caused by paullone-mediated inhibition of any of the two ATP-dependent enzymes in charge of glutathione synthesis or is a consequence of a species-specific regulatory mechanism triggered by T(SH)2 depletion.
The screening of a compound library consisting of 7 major chemical scaffolds and several singletons against TryS from three major pathogenic species of trypanosomatids at near physiological concentration of substrates led to the identification of 15 inhibitor molecules with μM (APPDA, BDA, BZ, AI) and sub-μM (AI) potency and a remarkable species-specificity for the molecular target. Despite the high sequence identity between tri-tryp TryS (e.g. the amino acid sequence identity is 71.6% for TbTryS/TcTryS, 64.7% for TbTryS/LiTryS and 63.4% for TcTryS/LiTryS) and the almost strict conservation of residues involved in substrate binding [20, 21], only 4 compounds were able to target TryS from different species, with only one of them (a BDA) inhibiting all three TryS with moderate potency (50% at 30 μM). This behavior highlights the existence of structural differences between tritryp TryS that determine their specificity for ligands. In support of this observation, single aminoacid substitutions in the homologue enzyme from C. fasciculata were previously reported to produce drastic changes in enzyme activity [23, 36] and our comparative analysis of the kinetic parameters of tritryp TryS obtained under similar assay conditions reveals considerable differences between species.
Early investigations have shown that substitutions at different positions of the paullone scaffold yield nM inhibitors of TryS from C. fasciculata or L. infantum (N5-substituted AI) [10, 20] and potent anti-T. b. brucei agents with moderate inhibition towards TbTryS (11-substituted 4-azapaullone) [56]. By extending the analysis of AI activity towards tritryp TryS, we have confirmed that the inclusion of a N-[2-(methylamino)ethyl] acetamide in position N5 confers SP-competitive inhibition of LiTryS activity, since compounds lacking this substitution were almost inactive. From the tritryp TryS evaluated here, LiTryS presented the higher KM for SP, which indicates the enzyme binds less efficiently this substrate probably due to an unfavorable conformation of the polyamine binding site. However, the shape adopted by the SP-binding site of LiTryS appears suitable for accommodating the N5-substituent present in MOL2008 and FS554. In support of this hypothesis, the trypanosomal TryS displayed a 2- to 6-fold lower KM values for SP and a marked refractoriness to inhibition by N5-substituted paullones (> 200-fold higher IC50). The opposite behavior was observed for prochlorperazine, a SP-competitive inhibitor of TbTryS [18]. In this case, the degree of enzyme inhibition was inversely proportional to the SP KM value for each TryS, with TbTryS being the most sensitive to inactivation followed by TcTryS, whereas LiTryS was refractory to inactivation (300 μM prochlorperazine) even at a sub-KM concentration of SP (240 μM vs. KM of 1335 μM). Altogether, these data strongly point to the existence of remarkable differences in the polyamine binding site of tritryp TryS that should be carefully considered for the design of new AI with multi-species activity. In this respect, our study also demonstrated that in spite of the potent anti-T. b. brucei activity [56], AI substituted with α,β-unsaturated carbonyl chains in position 9 or 11 should be disregarded as ligands for future optimization because of their null to marginal anti-TryS activity.
Both N5-substituted paullones identified as potent LiTryS inhibitors displayed an IC50 slope (Hill coefficient) below the unity (~0.35), which suggests binding of the inhibitor to non-equivalent binding pockets or to partitioning of the compounds into an inactive, less potent or inaccessible form at higher concentrations [74]. Since both paullones displayed good solubility in the concentration range tested (~1 nM-50 μM) and produced 100% LiTryS inhibition, we ruled out insolubility or aggregation phenomena as responsible of this behavior. On the other hand, the dose-response plots do not show the presence of a second inflection point that would be indicative of a second binding pocket for paullones. TryS are monomeric but mechanistically complex (trisubstrate) enzymes [23, 24] whose substrate binding sites display large conformational changes during catalysis [21, 75, 76], hence, it is possible that the low Hill slopes reflect an equilibrium between two or more forms of the enzyme that interact differentially with the paullones. Further mechanistic studies are needed to shed light on this issue.
From this screening, MOL2008 was the most potent inhibitor identified for LiTryS that, in addition, shows potential for further optimization of its anti-trypanosomal TryS activity (IC50 ~30 μM). The related paullone FS554 was chemically validated in a previous study using transgenic cell lines of L. infantum [20]. Here we show that MOL2008 is almost one order of magnitude more potent towards L. infantum promastigotes than FS554 and, more importantly, that it targets in vivo trypanothione biosynthesis. The future design of MOL2008 analogues should also aim at improving its biological properties, which according to our data are far from optimal (EC50 in the μM range and SI ≤ 2) and suggest off-target effects in host cells.
Precedent studies demonstrated that disustituted polyamines are potent antiproliferative agents that interfere with the polyamine metabolism of Plasmodium and African trypanosomes [47–50]. On the basis that the substituted diamine moiety may eventually be recognized as ligand by TryS, a collection of simplified derivatives was screened for their anti-synthetase activity. Two BDA (EAP1-47 and APC1-111) displayed inhibitory activity, albeit moderate (IC50 ~ 30 μM), against multiple TryS. SAR analysis revealed the need to fulfill a specific steric demand on the enzyme interacting pocket. A rational optimization of these compounds seems difficult, since the screening performed here included a wide diversity of derivatives (mono- and di-substituted, with bulky groups or halogen atoms, and with linker of different length) that yielded only weak TryS inhibitors. Nevertheless, as in a fragment-based approach, certain moieties of the most active BDA can be selected as substituents of novel or known TryS inhibitors.
With few exceptions, most APC derivatives tested here were previously shown to display low to sub-μM potency towards infective T. b. brucei [46]. Our study confirmed and disclosed the potent (EC50 15–280 nM) and selective (SI = 10–164) anti-parasitic activity of APC analogues and of two halogenated BDA from the EAP series, respectively, all of which exherted a moderate inhibition of TbTryS (IC50 ~ 30 μM). Interestingly, EAP1-47 induced changes in the intracellular pool of low molecular thiols that overall resembled those triggered by RNAi-mediated silencing of TryS [16] and displayed a moderate increase in its cytotoxicity towards TryS-defficient cells that was not paralleled by the more potent and selective molecules APC1-99 and EAP1-63. Nonetheless, as pointed out before, the higher in vivo (anti-trypanosomal activity) vs. in vitro (TbTryS inhibition) activity of EAP1-47 suggests that this compound is targeting other essential molecular target(s) in addition to TryS.
The positive loop of substrate inhibition of TryS by GSH has a direct impact on the design of inhibitors. On the one hand, it suggests that groups competing with GSH can be excluded from the ligand scaffold to avoid unnecessary substrate competition and to reduce the molecular mass of the inhibitor. On the other hand, it shows that full inhibition of TryS is not required because the phenomenon of substrate inhibition will amplify in vivo the inactivation of the enzyme, which represents a pharmacological advantage of this molecular target.
The four APPDA identified as moderate inhibitors of, preferentially, the T. cruzi TryS, are structuraly related to compounds originally designed as inhibitors of bacterial D-Ala:D-Ala ligase. Interestingly, these derivatives presented a 10-fold lower IC50 towards the trypanosomal enzyme than the analogue compounds against the corresponding bacterial target (best IC50 = 260 μM) [43]. The APPDA may serve as primary scaffolds to develope analogues with improved anti-TryS activity and, consequently, superior selectivity index. TcTryS, and to minor extent LiTryS, was target of inhibition by a BZ substituted with an imidazolone. A BZ with an additional imidazolone group retained activity against TcTryS but enhanced ATP hydrolysis by LiTryS and TbTryS, indicating that these ligands present a molecular pattern recognized by the active site of the enzymes that should be further investigated.
Our work also disclosed several compounds enhancing ATP consumption by trypanosomal TryS. Adding value to the existence of distinctive structural features between tritryp TryS, TbTryS was targeted specifically by large heterocyclic compounds from the BBHPP whereas TcTryS was the most promiscuous enzyme, being activated by a PD, an APPDA and the BZ mentioned above. A single compound, a bis-imidazolone BZ, increased LiTryS activity. According to our results, the activation of ATPase activity in trypanosomal TryS by these compounds takes place only when both co-subtrates, namely GSH and SP, are present in the reaction. More detailed studies are required to establish whether such allosteric activation of TryS is paralleled by an increased production of T(SH)2. Compounds promoting the non-productive consumption of ATP are attractive candidates for biological evaluation, because the metabolic outcome of their action should be similar to that of true enzyme inhibitors.
In summary, this study: (i) led to the identification and on-target validation of a potent inhibitor of leishmanial TryS and of chemical scaffolds that can be further developed into inhibitors of wide spectrum against TryS from different trypanosomatid species, (ii) highlights the existence of remarkable kinetic and structural differences between tritryp TryS that prompt to obtain 3D structures of TryS from different trypanosomatids in order to guide a structure-based rationale design of multi-TryS inhibitors; iii) demonstrates the relevance of running a HTS under near physiological concentration of subtrates to minimize the possibility to detect weak competitive inhibitors with low potential to act in vivo
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10.1371/journal.pcbi.1005567 | Landscape and variation of novel retroduplications in 26 human populations | Retroduplications come from reverse transcription of mRNAs and their insertion back into the genome. Here, we performed comprehensive discovery and analysis of retroduplications in a large cohort of 2,535 individuals from 26 human populations, as part of 1000 Genomes Phase 3. We developed an integrated approach to discover novel retroduplications combining high-coverage exome and low-coverage whole-genome sequencing data, utilizing information from both exon-exon junctions and discordant paired-end reads. We found 503 parent genes having novel retroduplications absent from the reference genome. Based solely on retroduplication variation, we built phylogenetic trees of human populations; these represent superpopulation structure well and indicate that variable retroduplications are effective population markers. We further identified 43 retroduplication parent genes differentiating superpopulations. This group contains several interesting insertion events, including a SLMO2 retroduplication and insertion into CAV3, which has a potential disease association. We also found retroduplications to be associated with a variety of genomic features: (1) Insertion sites were correlated with regular nucleosome positioning. (2) They, predictably, tend to avoid conserved functional regions, such as exons, but, somewhat surprisingly, also avoid introns. (3) Retroduplications tend to be co-inserted with young L1 elements, indicating recent retrotranspositional activity, and (4) they have a weak tendency to originate from highly expressed parent genes. Our investigation provides insight into the functional impact and association with genomic elements of retroduplications. We anticipate our approach and analytical methodology to have application in a more clinical context, where exome sequencing data is abundant and the discovery of retroduplications can potentially improve the accuracy of SNP calling.
| We developed an approach and performed comprehensive discovery of retroduplications from 26 human populations, utilizing whole-exome and whole-genome sequencing data. Our high-resolution landscape of retroduplications reveals that variable retroduplications are effective markers of human populations and can track population divergence. We observed that novel retroduplications come from genes with relatively high expression level and co-inserted L1 elements belong to young L1 families, indicating recent retroduplication activity in human migration contributing to genetic diversity. We have also detected several interesting intragenic insertion events, including SLMO2 retroduplication and insertion into CAV3, which worth further investigation for disease predisposition.
| Retrotransposons are class I transposable elements. In retrotransposition events, they are first transcribed into RNA and then reverse transcribed back into DNA, which are eventually inserted into a new position in the genome. It has been found that L1 retrotransponsons, the only autonomous mobile elements in human genome, also occasionally pick up cellular mRNAs as templates for reverse transcription and insertion [1–3]. Although RNA-mediated retroduplication is less common and widespread than DNA-mediated duplication [4], recent studies have revealed extensive retroduplication polymorphism in human genomes [5–7].
Retroduplication of genes contribute to new gene generation and genome evolution [4,8,9]. While most of the retroduplications suffer from lack of promoters, 5’ truncation, mutations, inactive local chromatin environment and other unfavorable factors that hinder the expression of functional protein products, they do exhibit functional impact at times. In some cases, cellular environment change, such as cancer initiation, can “activate” retroduplications, and both transcription and translation evidence of such cases have been observed [10–12]. In other cases, transcription products play a role in the expression regulation of their parent genes [13,14]. Two known transcriptional level regulatory mechanisms are RNA interference [15–17], and transcription products serving as competitive miRNA binding targets [18,19]. Sometimes retroduplications can have high impact on genomic functions when inserting into functional regions. Studies have confirmed cases in which germline intragenic retroduplications result in liver cancer susceptibility [20] and primary immunodeficiency [21]. Besides germline events, a number of studies have reported massive somatic retroduplication events and their critical roles in tumor development [20,22–25] and neuron development [26,27].
Retroduplications carry several distinctive features: exon-exon junctions, genome locations distant to parent genes, poly-A tails, and L1 transposition markers such as target-site duplications (TSDs) and human L1 endonuclease preferential cleavage sites. In this study, we developed an integrative approach to exploit these features for novel variable retroduplication identification, and successfully applied it to 2,535 individuals from 26 populations sequenced by the 1000 Genomes Project Phase 3 [28–30]. Our study adds an additional category of genetic variation to the released Phase 3 categories [29,30]. We further performed extensive population genetic analysis, association analysis, event mechanism inference, and functional analysis of retroduplications. Our study is indicative of human migration and evolution history, and provides valuable insight into retroduplications' functional impact and their association with genomic elements.
First, we performed retroduplication discovery for each individual, using the exon-exon junction strategy on high-coverage whole-exome sequencing (WES) data (see Supplementary Methods, and Fig 1). We controlled the false discovery rate (FDR) using decoy exon junction libraries. As a result, we have called a total of 15,642 retroduplications from 2,533 individuals (with two outlier samples excluded) for 503 unique parent genes (Figs A, B in S1 Text; Table A in S1 Text; S2 Text). On average, an individual has 6 novel retroduplications identified based on exon-exon junctions. Next, we identified retroduplication insertion sites for 152 of the parent genes based on discordant paired-end reads, using shallow-sequenced whole-genome sequencing (WGS) data pooled by population (Fig 1; S3 Text). Multiple genomic features are exploited in this pipeline, in order to achieve high sensitivity in calling, while conservatively controlling the false discovery rate. The retroduplications identified in our study adds an additional category of genetic variation to the released Phase 3 categories [29,30].
Compared to previous studies of human germline retroduplications, which relied on about 1,000 shallow-sequenced individuals [5–7] from the 1000 Genomes Project Phase 1 [31], the population set and sequencing coverage in Phase 3 has scaled up the data about 10-fold combined (Fig C in S1 Text). Besides the retroduplication calls shared among callsets, there are also large number of calls unique to our callset, which is likely due to newly enrolled populations in Phase 3 data, and the higher sensitivity of our methods. We resolved 152/503 (30.2%) insertion sites for our predicted retroduplications, a percentage higher than previous studies [5,7]. Functional enrichment analysis for the 503 unique parent genes shows the most enriched functions are related to ribosome/structural molecule activity, intracellular organelle lumen/nucleoplasm, and protein complex assembly. This observation is in accordance with previous study [5], indicating retrotransposition is coupled with cell division.
We have identified novel retroduplications, which are insertions relative to the reference genome. There are also retroduplications that are deletions relative to the reference genome (i.e. absent in the individuals but present in the reference genome). These events can be detected by overlapping known processed pseudogenes in the reference genome with 1000 Genomes Phase 3 deletion set. We carried out this in the supplement, finding 50 such deletion events (S4 Text). This type of events is far less common than retroduplication insertions, thus we suggest focusing on retroduplication insertions in the study.
The high-resolution landscape of germline retroduplication polymorphism presented by our callset gives us the power to perform extensive analyses of retroduplication variation. Among all 503 parent genes with novel retroduplications, 361 (71.8%) are exclusively identified in a single population, while only 29 (5.8%) are commonly identified in more than 10 populations (Fig B in S1 Text). Retroduplications are larger events than SNPs. It is known that individual structural variations are more likely to lead to phenotypic differences than individual SNPs [32]; thus, retroduplications might be more influential and population-specific than SNPs. From all identified parent genes, we found 43 that can differentiate superpopulations (with significantly large fixation index FST, adjusted empirical p-value < 0.001; see Table B in S1 Text).
We hypothesize that many of the exclusive retroduplications emerged after population divergence. The frequency spectrum of retroduplication parent genes (Fig 2A and 2B; Fig E in S1 Text) implicates population relationships. We further constructed phylogenetic trees of human populations based on novel retroduplication variations (Fig 2C), from which we observed expected and confident cluster cohesion of superpopulations measured by approximately unbiased bootstrap probability (AU) [33,34] (African AU = 99%, East Asian AU = 81%, European AU = 96%, and South Asian AU = 78%). The phylogenetic trees can confidently represent the superpopulation structure and also show mixed populations (Ad Mixed American) mingling with other superpopulations. These observed population relationships are consistent with human migration history. We also compared our retroduplication set with the SNP set generated by the 1000 Genomes Project Phase 3 [29], and found that there are proportionally more novel retroduplications (78.9%) than SNPs (68.7%) private to a superpopulation (Table C in S1 Text). All the above suggests the effectiveness of retroduplications as population markers, as well as validates our approach to retroduplication discovery.
For each population enrolled in the Geuvadis RNA-sequencing project (i.e. CEU, FIN, GBR, TSI, and YRI) [35], we tested whether having novel retroduplication(s) is associated with the parent gene’s expression level. We did not observe any significant association from this analysis (S6 Text), i.e. no retroduplication event was identified as an eQTL. However, while comparing expression level of retroduplication parent genes to all genes, we see a weak but ubiquitous and statistically significant trend that novel retroduplications came from highly expressed genes (p-value < 1.4 × 10−5 for each population, calculated from omnibus tests, see S7 Text). It is consistent with our expectation that the more mRNAs a gene produces, the higher probability that it will be converted into complementary DNA and inserted back into the genome.
To investigate local genomic features around insertion sites which might explain localization preference and imply retroduplication mechanism [36], we tested the association of genomic features with insertion sites. Inheritable retroduplication events occurred in germline so we focused on gametes, especially sperm. The germline mutation rate in male is higher than that in female, maybe due to the greater number and continuous nature of cell divisions in sperm formation [37–40]. We found that retroduplication insertion sites are enriched within hypomethylated regions in sperm (2.0-fold, empirical p-value < 0.0012). It is likely that retroduplication events exhibit certain preference in insertion sites associated with open chromatin. Furthermore, we characterized nucleosome positioning [41,42] around insertion sites. Overall, insertion sites show high regularity of nucleosome location (empirical p-value from permutation test 2×10−4) (Fig 3A). High nucleosome regularity often indicates the presence of chromatin remodeling and DNA binding proteins [43], which creates favorable loosely packed microenviroment for insertion.
Insertion points can be supported by discordant reads from both sides or just one side. We hypothesized that the insertion points with support only from a single side are the insertions with L1 element co-insertion. This is because that L1 involved in retroduplication is sometimes co-duplicated and co-inserted next to the retroduplicated segment. This type of co-insertion event can be detected by looking at the insertion sites that only have discordant read support on one side. In these cases, we found co-inserted L1 tend to belong to young L1 subfamilies, represented by L1HS (4.7-fold, p-value < 0.001) and L1PA (1.9-fold, p-value < 0.001) (Fig 3B). Contrastingly, for insertion sites without evidence for co-insertion (i.e. insertion sites that are supported by both sides) we did not observe such young L1 preference (p-value > 0.05). There is no fundamental preference for retroduplicated DNA segments to insert into other retroelements such as L1 elements. Enrichment of young and active L1 subfamilies involving in speculated L1 transductions suggests these novel retroduplication variants happened very recently.
In order to investigate the functional impact of retroduplication insertions on genomic functions, we tested the significance of overlap between retroduplication insertion sites and genomic elements compared to random genomic background (Fig 3C). As expected, ultraconserved regions are significantly depleted (p-value < 0.001). This observation is consistent with our knowledge that in general population, variable retroduplications should not interrupt with evolutionary or functionally constrained regions. Besides, we observed that intron regions are also depleted (p-value < 0.01), which might be due to negative selection that maintains conserved alternative splicing by avoiding interruption from insertion into introns. In addition, we observed segmental duplication (SD) regions to be enriched. Kim et al. [44] also found an association between SDs and processed pseudogenes, and observed significant amount of SDs flanked by matching pseudogenes. This is consistent with our observation. One reason of this association might be that the repeats generated by retroduplications are associated with non-allelic homologous recombination (NAHR) which contributes to SD formation [45–47]. It is known that NAHR is associated with open chromatin [36] and we also observed that retroduplication insertion has preference on open chromatin, which indicates open chromatin might play a role in the co-localization tendency of SDs and processed pseudogenes.
Among the 43 parent genes that differentiate superpopulations (see the top 43 genes in Table B in S1 Text), we have detected several potentially impactful intragenic insertion events. For example, we observed that SLMO2 (slowmo homolog 2, ENSG00000101166) is retroduplicated and inserts into the last intron of CAV3 (caveolin 3, ENSG00000182533). SLMO2 retroduplication insertion sweeps through all seven African populations almost exclusively. Based on exon-exon junction evidence, we found 30 cases in African populations and only one case in MXL (Ad Mixed American; S5 Text). CAV3 variants are strongly associated with cardiac dysrhythmia, such as long QT syndrome [48] and sudden infant death syndrome [49]. Epidemiological studies have shown that African descendant is a risk factor for prolongation of QT interval [50] and sudden infant death syndrome [51]. Such insertion events might warrant further investigation for susceptibility of diseases. We have identified a total of 12 intragenic insertion events could be related to diseases, and report the full list and affected populations (see Table D in S1 Text).
A final point about retroduplications: they could have an eroding effect on the correctness of SNP genotyping in parent genes or create a false image of mosaicism. We showed in a simple model that if a retroduplication carries an alternative allele, the SNP genotyping quality deteriorates significantly inside the parent gene (Fig F in S1 Text). We found that as the sequencing depth increases, SNP calling performance deteriorates, regardless of genotypes.
In summary, we developed an integrative approach for variable retroduplication discovery and successfully applied it to whole-exome and whole-genome sequencing data of 2,535 individuals from 26 populations. We have shown the power of leveraging high-coverage whole-exome sequencing data in retroduplication identification. Furthermore, we performed comprehensive analysis of our large retroduplication dataset, which reveals variational landscape of novel retroduplications, and shed a light on population differentiation, and functional impact of retroduplications on the genome.
Whole-exome sequencing and whole-genome sequencing data of 2,535 individuals from 26 populations were generated by the 1000 Genomes Project Phase 3 (whole-genome sequencing with mean depth 7.4x and read length of 100bp; targeted exome sequencing with mean depth 65.7x and read length of 76bp) [28–30]. Population description can be found at http://www.1000genomes.org/category/frequently-asked-questions/population. Protein-coding gene expression data (Peer-factor normalized RPKM) is obtained from the Geuvadis RNA-sequencing project [35], which generated RNA sequencing data from lymphoblastoid cell lines of 462 individuals from 5 populations (CEU, FIN, GBR, TSI and YRI) enrolled in the 1000 Genomes Project. We use human reference genome build 37 [52] and GENCODE v19 human genome annotation [53] in the study.
The calling pipeline is developed and customized for generating retroduplication calls from high-coverage exome sequencing data. A simplified flowchart of the current pipeline is shown in Fig 1. We also provide the code for download (http://retrodup.gersteinlab.org).
We check population differentiation due to retroduplication polymorphism, based on retroduplication frequencies in different superpopulations. Herein we pool the 26 populations into 5 superpopulations (African, Ad Mixed American, East Asian, European, and South Asian) as defined by the 1000 Genomes Project. For each given retroduplication parent gene, we calculate the population differentiation measure equivalent to the fixation index [58]. We define the test statistic
FST=p(1−p)−∑i=15cipi(1−pi)p(1−p),
in which i = 1,…, 5 corresponds to the ith superpopulation, p is the retroduplication frequency of a given parent gene in the total population, pi is the retroduplication frequency of the same parent gene in the ith superpopulation, and ci is the relative population size of the ith superpopulation. ci is calculated as the number of individuals in the ith superpopulation divided by the number of individuals in the total population. The larger the FST, the more different the retroduplication frequencies among superpopulations. One-tailed empirical p-value is calculated comparing the observed FST versus the null distribution of FST. The null distribution is calculated from 1,000 fake population sets generated by shuffling individual labels, while maintaining the size unchanged for each population. By the significance of FST, i.e. the p-value adjusted by Benjamini-Hochberg procedure [59], we can detect the retroduplications that can differentiate populations.
We utilize our retroduplication callset and the Geuvadis gene expression data (Peer-factor normalized RPKM) [35] to analyze the association between retroduplication occurrence and gene expression. Matching data of the individuals enrolled in both the 1000 Genomes Project and the Geuvadis project are used. The association tests are performed for each population, respectively, in order to rule out the confounding by population stratification.
To test the association between sperm methylation patterns and retroduplication insertion sites, we intersect out insertion sites with hypomethylated regions in sperm [61]. L1 annotation (RepeatMask), ENCODE HESC DNase I hypersensitive data and genomic GC contents are downloaded from the USCS Genome Browser [62]. Well-positioned nucleosome data is obtained from a recent study on multiple individuals [63].
We randomly shuffle the locations of insertion sites for 10,000 times on the same chromosome, excluding the gap regions, to obtain an empirical distribution of the null hypothesis. For fold changes, we use the mean of this distribution as the best estimate of the expected value. Calculation of p-value is empirical in order to be conservative. We use Bonferroni correction in case of multiple hypothesis testing. Unless specified otherwise, we only report corrected p-value. In order to avoid any effect of the difference of location precision across different insertion sites, we enlarge the insertion site region to 500 bp while keeping the middle point of insertions unchanged. We also exclude insertion points on alternative locus in the genome.
For aggregation plot on well-positioned nucleosome and GC content, we use 200 bp bins to calculate the base overlap, and the final plot is further window-smoothed with window size of 10. Normalization is performed by taking the mean value of the first and last 20 bins as background. We use the GC content from UCSC browser track, which is binned in 5 bp.
We test the significance of overlap between retroduplication insertion sites and genomic elements, including gene, CDS, exon, UTR, intron, pseudogene and lincRNA annotated in GENCODE v19, and ultraconserved regions (evolutionary constraint regions across species), ultrasensitive non-coding regions (regions particularly sensitive to disruptive mutations) and TF (transcription factor) peak regions obtained from ENCODE RNA-seq data [10] and literature [30,64–67]. The overlap between a genomic element type and the insertion sites is measured by the partial overlap statistic, which is the count of genomic elements that have at least 1 bp overlap with the detected insertion sites.
We randomly shuffle the locations of insertion sites for 1,000 times on the same chromosome, excluding the Hg19 gap regions, to obtain an empirical distribution of the null hypothesis. In the permutation tests, the null distribution of the overlap measures is calculated from true genomic elements intersecting randomly shuffled insertion locations. The enrichment of overlap is represented by log2 fold change of the observed overlap statistic versus the mean of its null distribution. Empirical p-value is calculated.
In order to avoid any effect from different location precisions, we enlarge the insertion intervals uniformly to 1,000 bp, while keeping the middle point of insertions. We only use insertion sites on chromosomes (i.e. exclude alternative locus) in the analysis.
We use DAVID [68] to annotate functional terms for retroduplication parent genes, and survey functional term enrichment.
We generate a list of genes where the novel retroduplications insert into. We then search these genes in the DISEASES database [69] to find disease-gene associations reported in literature.
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10.1371/journal.pgen.0040028 | Genome-Wide Association Identifies a Common Variant in the Reelin Gene That Increases the Risk of Schizophrenia Only in Women | Sex differences in schizophrenia are well known, but their genetic basis has not been identified. We performed a genome-wide association scan for schizophrenia in an Ashkenazi Jewish population using DNA pooling. We found a female-specific association with rs7341475, a SNP in the fourth intron of the reelin (RELN) gene (p = 2.9 × 10−5 in women), with a significant gene-sex effect (p = 1.8 × 10−4). We studied rs7341475 in four additional populations, totaling 2,274 cases and 4,401 controls. A significant effect was observed only in women, replicating the initial result (p = 2.1 × 10−3 in women; p = 4.2 × 10−3 for gene-sex interaction). Based on all populations the estimated relative risk of women carrying the common genotype is 1.58 (p = 8.8 × 10−7; p = 1.6 × 10−5 for gene-sex interaction). The female-specific association between RELN and schizophrenia is one of the few examples of a replicated sex-specific genetic association in any disease.
| Schizophrenia is a complex mental disease, which includes symptoms of delusions, hallucinations, disorganized speech, aberrant behavior, lack of emotional expression, diminished motivation, and social withdrawal. The cause of schizophrenia is unknown, but there is extensive evidence that genetics play a significant role in its aetiology. We studied the genetic basis of schizophrenia by analyzing around 500,000 genetic variants distributed across the whole human genome in DNA from schizophrenic patients and controls. We analyzed separately the DNA from men and women, and identified a genetic variant that increases the risk of developing schizophrenia in women only. The genetic variant is estimated to increase the risk of schizophrenia for women carrying the risk variant by 1.4-fold. The genetic variant is in a gene called reelin, which is known to play a part in brain development. However, it is still unclear how this genetic variant predisposes to schizophrenia nor why it is specific to women only.
| Schizophrenia (181500) is a common psychiatric disorder of unknown aetiology. Individual twin studies and meta-analyses of twin studies [1] estimate that the heritability of schizophrenia is approximately 80%. Analysis of family, adoption and twin data indicate that inheritance acts in a complex fashion, in combination with the environment, to mediate the risk of developing the illness [2]. However, despite the relatively large heritability of schizophrenia, efforts to identify the molecular risk factors have so far yielded equivocal results (reviewed in [3]).
Sex differences in the risk of a disorder can provide clues about its pathogenesis. For schizophrenia, the age of onset, premorbid functioning, symptomatic characteristics, and course of illness differ significantly between men and women [4]. Two systematic reviews have demonstrated a sex difference in the risk of developing schizophrenia [5,6]; both studies report that the male to female risk ratio is 1.4. Sex-specific associations with schizophrenia have previously been reported for a number of loci [7–11], but the robustness of these claims is open to doubt; results have yet to be corroborated [8–10] or replication has not been found with the same single nucleotide polymorphism (SNP) in the same direction in the same sex (e.g. [7,11]). This difficulty has afflicted attempts to establish sex-specific association in other diseases. An empirical assessment of 432 published sex differences in genetic association studies for different conditions found a single valid interaction that was consistently replicated in at least two other studies [12].
In the present study, we carried out a genome-wide association study using DNA pools of cases and controls constructed separately for men and women to allow the identification of sex-specific effects. Several studies have shown that DNA pooling detects the most promising loci with considerable savings in time and costs [13–18]. We previously scanned a three Mb region spanning the 22q11 microdeletion for association with schizophrenia using DNA pools. Our previous study [7,19] showed that the pools are representative of the allele frequencies in the sample and are adequate for detection of even modest association signals (odds ratios of about 1.3–1.5).
Four separate pools of DNA were constructed as follows, 419 male cases, 241 female cases, 1,807 male controls and 964 female controls. Increasing the number of controls for a given number of cases significantly increases the power of the experiment. For example, our experiment with 660 cases and 2,771 controls is equivalent in power to about 1,100 cases and 1,100 controls [20]. Since estimating allele frequencies in pooled DNA samples introduces measurement errors, each of the four pools was independently analyzed in ten replicates with Affymetrix 500K SNP arrays. The results from the ten replicates were used to rank the SNPs (the 1,000 top rank-ordered SNPs are listed in Table S1).
Our previously reported findings with several SNPs around both the COMT (116790) and DGCR2 (600594) genes in Chromosome 22 acted as a positive control for the performance of the current study. The association of multiple SNPs in the vicinity of both genes was clearly identified using our method (Figure 1). The best SNPs were selected for individual genotyping using an integrative approach taking into account their statistical significance, ranking and potential biological relevance (see Methods). As a result, a total of 194 SNPs were selected for individual genotyping.
The 194 selected SNPs were individually genotyped. At this stage, we used a sample of 759 controls and 745 patients from the Ashkenazi Jewish population. We genotyped a subset of the control sample used in the pools and enlarged the sample of cases (compared to the sample used in the pools), in order to differentiate in a cost-effective way between SNPs with real differences in allele frequencies between cases and controls and SNPs showing spurious differences due to technical anomalies in the DNA pooling procedure. Fifty-two SNPs out of 167 SNPs that passed our quality control (including a manual examination of genotype clusters and test of Hardy Weinberg equilibrium) showed p-values below 0.05 at any of the tests (male, female and combined) and nine SNPs had p-values below 0.005 (specific results for all SNPs are presented in Table S2).
The lowest p-value was found for SNP rs7341475 (G→A), for women only. SNP rs7341475 is located in Chromosome 7 (bp position = 103192051, NCBI Build 36), in the fourth intron of the reelin gene (RELN (600514)). This particular SNP was prioritized for individual genotyping because it resides within a gene previously studied for association with schizophrenia, in addition to having a high rank in the pools result (top 99.98% in the female pools). The common genotype of this SNP (GG) has a higher frequency in women with schizophrenia (75.5%) relative to female controls (59.3%) with an odds ratio (ORGG) of 2.1 (pgenotype = 9.8 × 10−5), and a significant gene-sex interaction (pinteraction = 5.3 × 10−3). In men, however, no effect was observed (ORGG = 1.1, pgenotype = 0.47, GG frequency = 60.6%). Since the AA genotype is relatively rare, the genotype distribution in cases and controls was analyzed with the GA and AA genotypes grouped together. Similarly, the G allele was significantly overrepresented in women with schizophrenia (frequency = 86.6%) relative to female controls (frequency = 76.2%). The odds ratio for the G allele was ORG = 2.0 with a corresponding allelic statistical significance of pallele = 1.9 × 10−5. Again in men no effect was observed (ORG = 1.1, pallele = 0.38). The most significant result was obtained when the allele frequencies in female cases were compared to a combined sample of male and female control individuals (pallele = 4.8 × 10−7). While a p < 5 × 10−7 cannot on its own be considered statistically significant under a stringent Bonferroni correction, it is suggestive evidence for true association under some assumptions [21], and therefore was further studied. We expanded the size of the Ashkenazi Jewish sample by increasing the number of controls (656 female and 1,988 males). We found increased evidence for the genotype association (pgenotype = 2.92 × 10−5; ORGG = 2.0; Table 1) and the gene-sex interaction (pinteraction = 1.8 × 10−4). The association for males remained non-significant (pgenotype = 0.62).
We considered whether the result could be due to population stratification effect. We categorized parents of the Ashkenazi Jews individuals by their country of origin and compared the allele frequencies of rs7341475. We examined subjects whose parents were from the same country (77% of all individuals) and focused on those countries with more than 100 individuals (80% of individuals): Argentina, Germany, Russia, Poland, Ukraine, and USA (Figure S1). There was no significant difference in allele frequency between Ashkenazi Jewish individuals originated from different areas of the world (p = 0.9405).
The association results and the linkage disequilibrium (LD) structure of RELN are presented in Figure 2. Based on HapMap LD data (CEU), there are no other SNPs in high LD (maximum r2 = 0.4) with rs7341475 on the 500K Affymetrix array. One other SNP (rs2106173) in the RELN gene was also tested by individual genotyping but no statistically significant difference was found. The association signals for other SNPs in RELN (based on DNA pools) were moderate (maximum rank: for men = 1,062, for women = 1,969; Figure 2). We studied the patterns of LD in RELN in the Ashkenazi Jewish population using the genotypes of 129 SNPs (distributed between 102783336 and 103533335, NCBI Build 36) in 197 unrelated control individuals (obtained in the course of another study [22]). We found that the block structure in our sample from the Ashkenazi Jewish population is very similar to that of HapMap CEU. High correlations (r2 > 0.8) are observed between rs7341475 and other SNPs distributed up to 39.8 kb upstream to rs7341475. The SNPs that are highly correlated with rs7341475 (rs10435342, rs6951931, rs17290575, rs6954479 and rs39327) are all located in the third or fourth intron of the gene. There is a substantial correlation (r2 > 0.5) between rs7341475 and other SNPs in a 77.4 kb interval (103188857–103266305). There are no SNPs in neighboring genes in high correlation with rs7341475.
To confirm the female-specific association between rs7341475 and schizophrenia, we tested rs7341475 in four other sample sets, three of European ancestry (UK, Ireland and USA) and one Chinese. In this replication study we tested a total of 2,274 cases (768 women and 1,506 men) and 4,401 controls (2,194 women and 2,207 men). We separated all samples by sex and tested the association for male and female subjects. Based on the association in the Ashkenazi Jewish population, in the replication samples we tested the prediction that the frequency of the GG genotype is increased in women with schizophrenia. The female-specific association of rs7341475 with schizophrenia was replicated in the UK case-control group with an effect in the same direction (ORGG = 1.85; pgenotype = 1.8 × 10−3), and a significant sex effect (pinteraction = 3.2 × 10−3). All other populations showed an effect in the same direction, although individually the effects were not significant (Figure 3; Table 1). Combining all replication samples yielded a genotype ORGG of 1.41 (95% CI = 1.13–1.76) with a corresponding pgenotype of 2.1 × 10−3 for women and ORGG of 0.97 (95% CI = 0.83–1.15, pgenotype = 0.76) for men. The odds ratio for women in the combined replication set was significantly higher than men (pinteraction = 4.2 × 10−3). The association in the combined replication samples remained significant (pgenotype = 0.045) even after excluding the UK sample, which shows that the result of the meta-analysis is not attributable to a single highly significant sample.
The female specific ORGG, from all samples, including the initial Ashkenazi Jewish sample is 1.58 (1.31–1.89) with a corresponding pgenotype of 8.8 × 10−7, pinteraction of 1.6 × 10−5, and a female-population attributable risk of 50%. Note that the estimated risk effect including the Ashkenazi Jewish sample might be slightly inflated, because rs7341475 was selected as one of the best performing SNPs from a large number of SNPs examined. Thus an unbiased estimate for the risk of this SNP, should be based on the replication samples (ORGG = 1.41, or 40% for the female attributable risk), even though there was no statistical evidence of heterogeneity of the odds ratios across the studies (p = 0.20). The frequency of the GG genotype, however, varies between populations (Table 1) – highest in the Chinese sample (frequency = 82.8%) and lowest in the Ashkenazi Jewish population (frequency = 61.6%).
We have carried out a genome-wide association analysis of schizophrenia, using pooled DNA. We identified one SNP in the fourth intron of RELN that confers a sex-specific risk of schizophrenia. Although the significance of the association between rs7341475 and schizophrenia in the Ashkenazi Jewish sample would not withstand a Bonferroni correction for multiple testing, we were able to replicate the sex-specific association in other samples from different populations that were tested for this specific SNP. The same allele and genotype are overrepresented in women, but not men, with schizophrenia in three different populations, Ashkenazi Jews, Europeans and Chinese (although the overrepresentation is not independently statistically significant in all populations tested). This observation, together with the fact that the association in the combined replication samples is significant and robust (even to the removal of the sample showing the most significant association) increases our confidence that we have found a genuine association.
The association observed in this study is unlikely to be the result of population stratification. The samples from the UK showing robust replication of the initial association were individually genotyped for SNPs across the whole genome. As such, this sample was rigorously evaluated for possible population stratification [21]. We also found no evidence for stratification in the Ashkenazi sample, indicating that the increase in allele frequency observed in women with schizophrenia cannot be caused by population structure in the Ashkenazi Jewish sample.
Genetic association studies have so far failed to report any consistent association between Reelin gene polymorphisms and schizophrenia [23–27]. However the gene has not so far been systematically screened. According to HapMap data, 183 tag SNPs would be needed to capture common variation at the gene (with r2 > 0.8, MAF > 5%), while the SNPs on the Affymetrix 500K arrays capture 60% of SNPs. Most studies report data on a SNP in the promoter region, or on a CGG repeat polymorphism in the 5′-untranslated region; none have tested rs7341475. The two previously reported polymorphisms are located in LD blocks that do not contain rs7341475.
Our finding is important on two counts: first, it supports the hypothesis of a neurodevelopmental origin for schizophrenia, assuming that the genetic association reflects variation in the function of the RELN gene. Reelin, the protein product of RELN, is a serine protease [28] that acts via a number of receptor-mediated pathways on neurons [29]. It plays a key part in corticogenesis, as demonstrated by the cytoarchitectural abnormalities of the null mutant reeler (rl −/−) mouse [30]. RELN mutations in humans are associated with an autosomal recessive form of lissencephaly [31], a mental retardation syndrome that does not include psychosis.
Second, while a sex difference in the risk for schizophrenia has been found, its molecular basis has so far been unclear. Here we establish a replicated sex-specific association. Intriguingly, there is prior evidence for sexual dimorphism at the reelin locus. Sex effect have been noted in one study, reporting that RELN expression was higher in women compared to men (in layer I neurons) and a reduction in RELN expression observed only in men with schizophrenia (in the superficial interstitial white matter neurons). However, as the authors acknowledge, these have not been noted in other brain regions [32]. In mice, loss of Purkinje cell was observed in male mice with only one functional reelin gene (rl +/−) but not in reelin-deficient female mice [33].
Finally we note that sex hormones are likely to mediate changes in RELN expression: for example, administration of testosterone decreases reelin expression in the brains of male European starlings [34]. Our result of a female-specific association of RELN with schizophrenia may suggest a possible pathway where sex hormones modulate gene expression, which by altering cortical structure, increases susceptibility to psychosis.
Samples from individuals diagnosed with schizophrenia were collected from hospitalized patients from seven medical centers in Israel (described in [7]). Schizophrenia was diagnosed by Diagnostic and Statistical Manual criteria (DSM-IV, American Psychiatric Association). Ashkenazi controls were collected from volunteers in blood banks. All four grandparents of each subject were of Ashkenazi Jewish origin, and all subjects (or the subject's legal representative) signed an informed-consent form.
Chinese Han schizophrenia patients, their families and control subjects were recruited from Sichuan Province, SW China and consisted of 415 unrelated patients (222 males and 193 females) and 458 normal Han Chinese controls (229 males and 229 females). All patients were interviewed by an experienced psychiatrist using the SCID, and a diagnosis of schizophrenia was made according to DSM-III-R or DSM-IV criteria. Information was also collected from examination of medical records and all other available sources. Genomic DNA was extracted from peripheral blood according to standard phenol-chloroform methods. This study was approved by the South London and Maudsley Trust ethical committee and informed consent was obtained from all patients and control individuals.
The Irish Case – Control Study of Schizophrenia (ICCSS) samples were collected in Northern Ireland and the Republic of Ireland. Individual genotypes were obtained for 980 affected cases (669 males and 311 females) and 582 controls (337 males, 245 females). The affected subjects were selected from in-patient and outpatient psychiatric facilities in the Republic of Ireland and Northern Ireland. Subjects were eligible for inclusion if they had a diagnosis of schizophrenia or poor-outcome schizoaffective disorder by DSM-III-R criteria, which were confirmed by a blind expert diagnostic review. Excluding the relatively small number of schizoaffective female patients (n = 36) did not significantly alter the female-specific association result. Controls, selected from several sources, including blood donation centers, were included if they denied a lifetime history of schizophrenia. Both cases and controls were included only if they reported all four grandparents as being born in Ireland or the United Kingdom.
The UK sample consisted of unrelated subjects with schizophrenia (320 males and 155 females). All were white and born in the British Isles. All patients had a consensus diagnosis of schizophrenia according to DSMIV criteria made by two independent raters following a semi-structured interview by trained psychiatrists or psychologists using the Present State Examination or the Schedules for Clinical Assessment in Neuropsychiatry (SCAN) interview and review of case records. Cases were screened to exclude substance-induced psychotic disorder or psychosis due to a general medical condition. The mean age at first psychiatric contact was 23.6 (SD 7.7) years and the mean at ascertainment was 41.7 (SD 14.6) years. Multicentre and Local Research Ethics Committee approval were obtained, and all subjects gave written informed consent to participate. The control sample used by the Wellcome Trust Case Control Consortium (WTCCC) is described in detail elsewhere [21]. Briefly, controls (n = 2938) came from two sources, the 1958 British Birth Cohort (58C) and UK blood donors. At a genome wide level, the two groups do not significantly differ with respect to allele frequencies, justifying their use as a single control group. Individuals included in the study were living within England, Scotland and Wales. Individuals (n = 26) with non-Caucasian ancestry as determined by multidimensional scaling (MDS) were previously removed by the WTCCC from the sample. Controls were not screened for psychosis, but given the expected modest frequency in an unscreened sample, this has little effect on power. For the genome-wide association study from which these data were taken (manuscript submitted), λ was estimated at 1.06 genome-wide, which is at the lower end observed for other phenotypes when compared with the same controls. The call rate at this test locus for subjects in the genome-wide association study was >99.5% in each of cases and controls.
The USA sample consisted of unrelated subjects with schizophrenia (295 males and 105 females) and unrelated controls (202 males and 232 females) selected from a family-based sample that was ascertained as part of the Clinical Brain Disorders Branch/National Institute of Mental Health Sibling Study. All subjects were diagnosed using the Structured Clinical Interview (SCID). Probands met DSM-IV criteria for broad schizophrenia diagnosis. Control individuals were ascertained from the National Institutes of Health Normal Volunteer Office and were screened by SCID diagnosis for psychiatric disorders and excluded also if they had a first degree relative with a schizophrenia spectrum diagnosis. All participants gave informed consent and self-identified as Caucasian of European ancestry. All subjects also underwent extensive physical and laboratory screening to rule out complicating medical conditions and substance abuse.
The DNA pools were constructed using DNA samples from 660 schizophrenic patients (419 males and 241 females) and 2,771 controls (1,807 males and 964 females). Pools were created by using equal aliquots of each sample, as described in ref. [7,19]. The pools were constructed using DNA samples from both sexes separately. Each pool (n = 4) was allelotyped ten times with the 500K Affymetrix arrays sets as previously described [18].
We selected 194 unique SNPs using three different ranking methods (Figure S2), in addition to prioritizing SNPs in genes previously studied for association with schizophrenia (schizophrenia candidate genes). We used the SchizophreniaGene database [35] to identify genes that were previously tested for association with schizophrenia (515 genes were listed at the time of access [11/05/07]). SNPs with minor allele frequencies below 5%, according to HapMap (CEU) were excluded from further analysis due to the limited power to detect a significant association and the decrease in the accuracy of DNA pooling with rare variants.
Eighty SNPs were selected by the following criteria (some SNPs were selected by more than one method). We calculated for each SNP a modified chi-squared statistic, which includes the error variance introduced by the DNA pooling procedure [18,19] . SNPs were ranked based on logarithm (base 10) of the p-value (logP). Sixty first-ranked SNPs (30 from each array type, Nsp & Sty) were selected for individual genotyping. Additional twenty SNPs with logP > 3 were selected because they reside in candidate genes.
Fifty-five SNPs were selected by the following criteria. We used the GenePool software with silhouette statistic and Manhattan distance as the distance measure [36]. The SNPs were ranked based on the silhouette statistic separately for males and females patients, ranging from 1 for the highest silhouette score to 510,552 for the lowest score. We generated a combined rank for all samples based on the average rank in males and females weighted for the different size of samples. The first ten ranked SNPs with a minor allele frequency above 5% (based on HapMap, CEU) were selected for individual genotyping. This was done for each of three sets: male, female and combined. Twenty-five additional SNPs in candidate genes for schizophrenia were also selected for individual genotyping if they were also ranked in the first 1,000 SNPs in any of the sets.
Eighty-two SNPs were selected by the following criteria: first, we calculated the mean rank score in a sliding-window of nine consecutively neighboring SNPs. For each SNP we assigned a rank based on the mean score in the window showing the largest score out of the different possible windows. The sliding window method was used to identify regions where neighboring SNPs consistently show differences in allele frequency between cases and controls. This method minimizes spurious differences arising from technical anomalies in the analysis of pooled DNA [37], but could preferentially select SNPs in large blocks of LD. The analysis was carried out separately for male, females and the combined sets. SNPs in the first 1,000 ranked windows were sorted based on the SNP's individual rank score. The first 60 SNPs for the combined sample and the first ten for males and females samples were selected for individual genotyping. Two additional SNPs in candidate genes for schizophrenia that were also ranked in the first 1,000 SNPs in the combined analysis were also selected for individual genotyping.
DNA samples from the Ashkenazi Jewish and Chinese populations were genotyped using the Sequenom iPLEX system. LD in the Ashkenazi Jewish control sample was assessed using genotypes obtained from an Illumina HumanHap300 BeadChip in the course of another study [22]. Samples from the Irish Case – Control Study of Schizophrenia and from the US were genotyped using a Taqman 5′-exonuclease allelic discrimination assay. Quality checks for the Sequenom system included concordance rate (>98%) for genotypes of 83 DNA samples from HapMap cell lines, call rate (>95%), Hardy-Weinberg equilibrium in control (p > 0.001), manual checks of genotypes clusters and concordance rate (>98%) for re-genotyping. Genotypes for the UK sample were taken from a genome-wide association study performed in concert with the WTCCC study of common diseases [21] based upon the GeneChip 500K Mapping Array Set. Samples were genotyped at the Affymetrix service laboratory in San Francisco (USA) using the same pipeline as the WTCCC disease and controls samples.
All data analysis was performed using the R language and environment for statistical computing (http://www.r-project.org/). Single-SNP analysis for the individual genotyping data was carried out using a χ2 test on allele and genotype counts. For the replication samples a one-tailed test was employed, since we tested a specific hypothesis of an increase in GG frequency in female patients and not other possible directions of association. We combined the rare homozygote genotype with the heterozygotes for the genotype association analysis. Hardy-Weinberg was assessed using the χ2 statistic with one degree of freedom. To test for a gene by sex interaction, a z-score was calculated using a ratio between the difference in the natural logarithm of the odds ratio between males and females and the square root of the variance of the difference. We used the Mantel-Haenszel method to combine the data of different populations with a fixed effect model and Cochran's Q statistic to test for heterogeneity as implemented in the R package ‘meta' (version 0.5). LD was calculated using the Haploview software package. Population attributable risk of rs7341475 was calculated for women as (K − 1)/K, where K = ∑fi × gi; fi is the frequency of the i genotype, and gi is the estimated genotype relative risk of the i genotype assuming multiplicative model.
The Online Mendelian Inheritance in Man (OMIM) (http://www.ncbi.nlm.nih.gov/Omim/) accession numbers for disease and genes mentioned in the paper are Schizophrenia (181500), RELN (600514), COMT (116790), and DGCR2 (600594). |
10.1371/journal.pgen.1006590 | Re-wiring of energy metabolism promotes viability during hyperreplication stress in E. coli | Chromosome replication in Escherichia coli is initiated by DnaA. DnaA binds ATP which is essential for formation of a DnaA-oriC nucleoprotein complex that promotes strand opening, helicase loading and replisome assembly. Following initiation, DnaAATP is converted to DnaAADP primarily by the Regulatory Inactivation of DnaA process (RIDA). In RIDA deficient cells, DnaAATP accumulates leading to uncontrolled initiation of replication and cell death by accumulation of DNA strand breaks. Mutations that suppress RIDA deficiency either dampen overinitiation or permit growth despite overinitiation. We characterize mutations of the last group that have in common that distinct metabolic routes are rewired resulting in the redirection of electron flow towards the cytochrome bd-1. We propose a model where cytochrome bd-1 lowers the formation of reactive oxygen species and hence oxidative damage to the DNA in general. This increases the processivity of replication forks generated by overinitiation to a level that sustains viability.
| In most bacteria chromosome replication is initiated by the DnaA protein. In Escherichia coli, DnaA binds ATP and ADP with similar affinity but only the ATP bound form is active. An increased level of DnaAATP causes overinitiation and cell death by accumulation of DNA strand breaks. These strand breaks often result from forks encountering gapped DNA formed during repair of oxidative damage. We provide evidence that cell death in overinitiating cells can be prevented by rewiring the metabolism to favor the micro-aerobic respiratory chain with the cytochrome bd-1 as terminal oxidase. Cytochrome bd-1 is found in aerobic as well as anaerobic bacteria. Its role is to reduce O2 in micro-aerobic conditions and work as an electron sink to prevent the formation of reactive oxygen species. Our results suggest that bacteria can cope with replication stress by increasing respiration through cytochrome bd-1 to reduce the formation of reactive oxygen species, and hence oxidative damage to a level that does not interfere with replication fork progression.
| Initiation of chromosome replication from the unique replication origin of E. coli oriC, is tightly controlled and happens once and only once per cell cycle [1, 2]. Chromosome replication is initiated by the DnaA initiator protein. DnaA is an AAA+ ATPase that exists in an ATP bound form and an ADP bound form [3]. DnaA associated with either ATP or ADP binds a set of strong recognition sites in oriC throughout the cell cycle [4] to form the origin recognition complex (ORC;[5]). Upon initiation the DnaA protein associated with ATP forms the orisome by binding to numerous additional sites in oriC. This displaces Fis (Factor for Inversion Stimulation), a protein that binds oriC for most of the cell cycle. With Fis gone, the IHF (Integration Host Factor) protein can bind oriC, which ultimately leads to duplex opening [6, 7], helicase loading and assembly of two replisomes [8].
The level of DnaAATP fluctuates during the cell cycle and is high at the time of initiation [9]. Following initiation, DnaAATP is converted to DnaAADP by the RIDA (Regulatory Inactivation of DnaA) and DDAH (datA-dependent DnaAATP hydrolysis) processes. During RIDA, the Hda protein complexed with the DNA loaded β-clamp stimulates the intrinsic ATPase activity of DnaA thereby converting DnaAATP to the non-active DnaAADP [10, 11]. DDAH is less efficient and takes place at the datA locus where a complex of datA and IHF promotes DnaAATP hydrolysis [12]. If extra initiation events are triggered by loss of RIDA or by conditional mutations in DnaA [13], DNA strand breaks progressively accumulate, eventually resulting in cell death. It was shown that the lethal accumulation of strand breaks in such cells resulted from replication forks encountering DNA damage repair intermediates, particularly resulting from oxidative damage to the DNA during normal aerobic growth. Therefore, growth could be restored in the absence of oxygen or by removing the predominant glycosylase of oxidized bases [14].
During aerobic growth, a proton gradient is generated by a respiratory chain made of the type I dehydrogenases containing iron-sulfur proteins and the cytochrome bo that is efficient and has low affinity for oxygen (Fig 1)[15]. It is controversial how Reactive Oxygen Species (ROS) are produced in E. coli [16]. Respiration per se is not generating ROS [17, 18]. In contrast, respiration is thought to limit ROS formation by pulling away electrons from potential ROS-sources [19]. For example, mutants lacking NAD dehydrogenases I and II or cytochrome oxidases bo and bd-1 produces more H2O2. The main cellular sources of ROS are thought to be free iron, flavins and iron sulfur cluster proteins with the dehydratase enzymes of the TCA cycle as the main culprits [20].
During micro-aerobic growth, another set of proteins which are less efficient in generating a proton gradient dominates the respiratory chain. These consist of a copper containing dehydrogenase (NDHII) and the cytochrome bd-I terminal oxidase that has a high affinity for oxygen (Fig 1). This micro-aerobic chain is also predominant when iron is scarce or during oxidative stress [21] (for review [15, 22]). During these stress conditions the cytochrome bd-I is thought to act as an electron sink to reduce the ROS level [19].
We previously identified two mutations in the iscU and fre genes (iscUC63F and freΔ68) that suppress RIDA deficiency [23]. These genes encode an Iron-Sulphur cluster scaffold protein and Flavin reductase, respectively. Here, we provide evidence that the mechanism of suppression is not linked to DnaA or replication initiation activity. Global transcription analysis of iscUC63F and freΔ68 cells showed that genes encoding enzymes of the TCA cycle were down regulated in both mutants while respiration was altered to favor the use of the micro-aerobic respiratory chain. Therefore, these two mutants may tolerate overinitiation in a manner similar to cells growing in the absence of oxygen. For the freΔ68 mutant, we show that the ArcA regulon plays a crucial role for suppression in part by upregulating cyd transcription to overproduce cytochrome bd-1 [24, 25].
Hda mutant cells accumulate strand breaks under aerobic conditions resulting in progressive growth inhibition, and loss of colony forming ability, unless a suppressor mutation is acquired [23, 26, 27]. The nature of several suppressor mutations was previously identified [23]. One suppressor is a missense mutation in iscU resulting in cysteine being replaced with phenylalanine at position 63 of the scaffold protein for assembly of iron sulfur clusters, IscU (IscUC63F). Iron sulfur clusters are used in a variety of cellular activities such as respiration, amino acid synthesis and DNA repair. A second suppressor is a 380 bp deletion between two imperfect repeats starting at position 497bp after the start codon of the fre gene and ending in the intergenic fre-fadA region. This results in a premature stop codon and loss of the 68 C-terminal amino acids of the flavin reductase (FreΔ68). The Flavin reductase catalyzes the reduction of free flavins by NAD(P)H. It is thought that Fre accounts for more than 80 percent of the free Flavin reduction in E. coli [28, 29] and may serve as a general cytosolic source of electrons [30].
Cells carrying iscUC63F and freΔ68 mutations grew somewhat slower than the wild-type (Fig 2A). Analysis of chromosome replication by flow cytometry revealed that both mutants initiated in synchrony with a small reduction in numbers of origins per cell corresponding to the slower growth rate but the origin concentration remained unchanged (Fig 2A). Loss of RIDA activity by deletion of hda (referred to as loss of RIDA throughout this work), resulted in initiation asynchrony, increased the average number of origins per cell from around 4 to 7.8 and 8.9 for iscUC63F and freΔ68, respectively, and increased the origin concentration (Fig 2A). Note that these are minimum estimates for numbers of origins per cell as runout was too poor to allow for an exact enumeration. This shows that the iscUC63F and freΔ68 mutations restore growth of Hda deficient cells despite of continued overinitiation.
In order to determine whether the iscUC63F and freΔ68 mutations resulted in change or loss of function of their respective proteins we proceeded to delete iscU and fre from otherwise wild-type cells. Complete loss of Flavin reductase resulted in a cell cycle profile similar to that observed for the freΔ68 mutant cells suggesting that the FreΔ68 protein is not functional (Fig 2A). Loss of iscU on the other hand resulted in cells that grew slower than iscUC63F cells and that had a reduced number of cellular origins (Fig 2A). Loss of iscU restored growth of Hda deficient cells while these continued to overinitiate in an asynchronous manner (Fig 2A). The relative increase in origin concentration caused by the deletion of hda in ΔiscU relative to iscUC63F may be a simple consequence of a better runout in ΔiscU cells. This suggests that ΔiscU is a better suppressor than iscUC63F but altogether the data indicate that the IscUC63F protein is partly functional which is in agreement with an earlier report showing that an IscUC63A mutant protein is able to form and transfer [2Fe-2S] clusters with reduced activity compared to the wild-type enzyme [31]. The ori/ter ratio of iscUC63F and freΔ68 also increased from a level of 1.8 similar to wild-type cells, to about 3 and 3.8 upon loss of Hda (Fig 2B). Taken together, the origin concentration and ori/ter ratios indicates that replication fork processivity was uncompromised in Hda deficient cells also carrying iscUC63F and freΔ68 mutations, despite of overinitiation. A Pulse Field Gel Electrophoresis analysis confirmed this, as the levels of fragmented chromosomes in hda iscUC63F and hda freΔ68 was greatly reduced compared to Hda deficient cells (S1 Fig).
We previously suggested that formation of lethal double strand breaks in an hda mutant is intimately linked to the number of replicative forks moving on the chromosome [14]. The number of ongoing replication forks in a cell can be reduced simply by reducing growth rate. We therefore streaked anaerobically generated hda cells on minimal medium plates supplemented with glycerol (S2A Fig) and with glucose and casamino acids (S2B Fig) and incubated aerobically at 37°C. Hda cells failed to form colonies on minimal plates with glucose and casamino acids, but formed colonies as well as wild-type cells on minimal medium plates supplemented with glycerol. To ensure that the cells had not accumulated suppressor mutations, glycerol grown hda cells were restreaked on LB agar in aerobic condition (S2C Fig). These cells failed to form colonies, implying that the glycerol selected hda clones had not acquired suppressor mutations. The growth rate of hda cells in minimal medium supplemented with glycerol was similar to that of the wild-type but initiation of replication was asynchronous and origin concentration was increased (S2D Fig). Hda cells were then shifted from growth in glycerol to glucose and casamino acids (S2E Fig) or LB medium (S2F Fig) for three mass doubling time. As expected, this resulted in an increased origin concentration (relative origin per mass equal to 1.7 or 1.8) reminiscent of the shift observed between anaerobic to aerobic conditions [14]. Overall, these observations indicate that hda is dispensable in slow growing cells, consistent with what has been observed for other overinitiation mutants [32].
Cells carrying iscUC63F and freΔ68 mutations had a reduced growth rate in minimal medium supplemented with glucose and casamino acids relative to wild-type cells (46 and 38 versus 33 minutes, respectively; Fig 2A) and contained fewer origins and hence fewer ongoing replication forks per cell. A possibility was therefore that the ability of these mutants to suppress Hda deficiency resulted from slow growth. The iscUC63F and freΔ68 mutations were originally isolated as hda suppressors from cultures grown in LB [23, 26]. In this medium, the mutants grew with doubling times of 30 and 21 minutes, respectively, and contained a high number of origins and ongoing replication forks per cell, yet loss of Hda was tolerated (S3 Fig). A reduced growth rate is therefore not the main mechanism of suppression in these cells.
Since overexpression of Ribonucleotide Reductase Ia (RNRIa) encoded by nrdAB genes or RNRIb encoded by nrdEF genes were previously shown to suppress the loss of hda [27, 33], we tested whether an increase in amount or activity of these enzymes is present in iscUC63F and freΔ68 cells. To assess the Ribonucleotide Reductase activity we made use of the RNR inhibitor hydroxyurea (HU). The sensitivities to hydroxyurea of iscUC63F and freΔ68 mutant cells were similar to wild-type in our assay (Fig 2C) although a fre mutant has been previously shown to have a 10 to 50% growth reduction in the presence of 10 to 40 mM HU. In the absence of Hda, both the iscUC63F and freΔ68 mutants became hypersensitive to HU, indicating that the RNR activity is indeed critical for survival and that the dNTP pool is limiting in such cells.
To gain insight into the mechanism of hda suppression by the iscUC63F and freΔ68 mutations we performed a global transcription analysis of cells grown in minimal medium supplemented with glucose and casamino acids using microarrays. The two mutants had both changes in gene expression that were specific to each mutant and changes that were in common. Genes that were specifically upregulated in the iscUC63F mutant included those encoded by the iscRSUA hsbB and sufABCDSE operons (S1 Table) which is in agreement with a poorly functioning IscUC63F protein [34–36]. Note that we also observe an increased expression of nrdHEIF (see discussion). In the freΔ68 strain, specifically the cyd operon encoding cytochrome bd-1 was upregulated and the majority of dehydrogenases were downregulated (S2 Table). Overproduction of Fre has little consequences on the transcription profile (S2 Table). Similarities between iscUC63F, freΔ68 and Δfre cells included expression of genes whose products are involved in cellular respiration (Fig 3A, S1 and S2 Tables). The sdh operon encoding succinate dehydrogenase (succinate-coenzyme Q reductase; SDH), the suc operon encoding 2-oxoglutarate dehydrogenase (OGDHC), the nuo operon encoding NADH dehydrogenase I (NDH-I)and the fdo operon encoding formate dehydrogenase (FDH O), most of which contain [Fe-S] clusters, are all part of the respiratory chain and are down-regulated. In addition the cyo genes encoding cytochrome o oxidase (Cyt bo) (S2 Table), are downregulated in freΔ68 and fre deleted cells (Fig 3A, S1 and S2 Tables). On the other hand ndh encoding the type II NADH dehydrogenase (NDH-II), which is a metalo enzyme dependent on copper, is overexpressed in iscUC63F, freΔ68 and Δfre cells. This is in accordance with a report showing that in iscU mutants, NDH-I activity is reduced to background level while NDH-II activity is increased [37]. Finally, the cydAB genes encoding cytochrome bd-1 (Cyt bdI) (S2 Table) and to some extent the genes encoding appBC cytochrome bd-2 (Cyt bdII) are overproduced in freΔ68 and fre deleted cells. The microarray results are coming from single experiments, therefore, the expression patterns of cydA, sdhD and cyoA genes were confirmed by RT-qPCR (S3 Table).
Overall these expression data points towards IscUC63F and freΔ68 cells being shifted from a normal aerobic respiration pattern towards what would be characteristic of cells growing under micro-aerobic or iron limited conditions (Fig 1).
We proceeded to determine whether IscUC63F and freΔ68 cells had their metabolism shifted from the normal aerobic respiration towards the less efficient Cytochrome bd-1 dependent respiratory pathway or even fermentation, despite of growing in aerobic conditions. In order to obtain precise measurements of metabolites, cells were grown in minimal medium supplemented with 0.04% glucose. Surprisingly, we found that the ATP/ADP ratio was increased by 50–60% in both IscUC63F and freΔ68 cells (Table 1). This rules out that these mutations suppress overinitiation by lowering the ATP/ADP ratio which in turn could lower the DnaAATP/DnaAADP ratio, as both nucleotides bind DnaA with similar affinities [3]. The NADH/NAD+ ratio was similar in wild-type and IscUC63F cells but was increased by more than two-fold in freΔ68 cells. The production of acetate per mole of glucose was also increased by 100% and 40% in freΔ68 and IscUC63F cells, respectively (Table 1; Fig 3B). Formate, ethanol, succinate, citrate and lactate were not produced in any of the strains. The IscUC63F and freΔ68 strains also reached lower optical density when glucose consumption ended, i.e. had a lower yield of biomass per mole of glucose (Fig 3C).
These results agree with the microarray data (Fig 3A) and show that the metabolism of iscUC63F and freΔ68 cells is partly re-routed toward acetate production as would be expected when the TCA cycle is downregulated. The increase in acetate production in freΔ68 cells is consistent with a high NADH/NAD+ ratio [38, 39], which in turn agrees with the suggestion that flavin reductase accounts for a significant part of the NADH cellular oxidation [28]. The high ATP/ADP ratio in iscUC63F and freΔ68 suggests that anabolic reactions are reduced in the strains. This increased ratio could also decrease the flux in the TCA cycle by allosteric inhibition [40]. Our microarray analysis indicates that respiration in iscUC63F and freΔ68 cells could be shifted towards the less efficient Cytochrome bd-1, therefore it is expected that ATP production through oxidative phosphorylation is reduced in these strains. The oxygen consumption of iscUC63F and freΔ68 cells was decreased somewhat as expected (Table 1).
The transcription profiles of freΔ68 and Δfre cells are by and large consistent with the activation of the ArcA regulon [25, 41]. The ArcBA (anoxic redox control) two-component system, senses the redox state of the cell and reprograms the metabolism to increase the availability of NAD+ required for glycolysis. ArcA activity is coupled to the NADH/NAD+ balance and conditions that artificially increase the ratio NADH/NAD+ have been shown to activate the ArcA regulon [38].
When oxygen becomes limiting, ArcA represses genes involved in the generation of NADH, including those specifying enzymes of the TCA cycle, while genes encoding enzymes involved in fermentative regeneration of NADH into NAD+ are activated [25].
We cloned arcA under control of the IPTG regulated promoter pA1/O4/O3 promoter [42] in the R1 based plasmid pNDM220 [43] and proceeded to delete hda in the presence of IPTG. Restreaking of the resultant colonies indicated that colony formation depended on IPTG (Fig 4A) and suggests that ArcA is necessary for freΔ68 dependent suppression of Hda deficiency. Flow cytometry analysis revealed that initiation of replication in independent clones of hda cells overexpressing arcA was more frequent and asynchronous than in wild-type cells (Fig 4B). Similar to what was observed for freΔ68Δhda cells (Fig 2), the ori/ter ratio was somewhat increased (Fig 4C). When ArcA was depleted by shifting the cells to a medium lacking IPTG for six hours, the number of origins per cell increased dramatically as did the ori/ter ratio (Fig 4B and 4C). The relative origin concentration was also increased. This indicates that cells were not able to complete chromosome replication, probably due to DNA damage associated arrest or collapse of replication forks, while initiation of replication remained unperturbed [14].
Altogether these results indicate that the freΔ68 mutation results in a high NADH/NAD+ ratio which activates the ArcA regulon to shift cells from the normal aerobic respiration towards the less efficient Cytochrome bd-1 dependent respiratory pathway, despite of growing in aerobic conditions. In the absence of ArcA overproduction, initiation of replication continued but cells were not able to complete chromosome replication.
We proceeded to mimic the metabolism of fermenting cells while growing aerobically by deleting the atpA or atpB genes encoding the F1F0 ATP synthase. In such cells the respiration rate increases but is uncoupled, i.e. does not result in ATP production end energy is solely produced by fermentation. Consequently the cellular ATP/ADP ratio is reduced to about one third of the wild-type level [44]. In ATPase deficient cells, cytochrome bo remains unchanged, cytochrome bd-1 and NDHII are overproduced while the TCA cycle is repressed leading to a redirection of the glycolytic flux towards acetate production [45]. The composition of the respiratory chain used by ATPase deficient cells thus resembles the one used under micro-aerobic conditions and the one used by freΔ68 cells (Fig 1), which is thought to favor NAD+ regenerations [45]. We tested whether cells lacking ATPase could tolerate the loss of Hda similar to freΔ68 cells. Hda was deleted in ΔatpA or ΔatpB cells under anaerobic conditions. Restreaking in the presence of oxygen demonstrated that Hda is indeed dispensable for growth of ATPase deficient cells (Fig 5A). Cells deleted for atpA or atpB had doubling times and cell cycle parameters similar to wild-type cells (Fig 5B). A further loss of hda in the atpA and atpB mutant cells resulted in an elevated number of chromosomes per cell, an increased origin concentration and an increased ori/ter ratio (Fig 5C) relative to wild-type. Note that although increased, the ori/ter ratio in hda atpA and hda atpB is still lower than that of hda cells shifted from anaerobic to aerobic growth for 4 hours (Fig 5C), indicating that replication fork progression is affected in hda atpA and hda atpB but to a tolerable level. Again this demonstrates that the cellular ATP/ADP ratio has little influence on replication initiation, and that loss of ATPase function permits cells to survive despite of overinitiation similar to what we observed for iscUC63F and freΔ68 cells.
The cytochrome bd-1 complex is upregulated in fre (S2 and S3 Tables) and ATPase mutants [45] and may provide protection against ROS because of its high affinity to O2 and potential peroxidase activity [21, 46–48].
We deleted the cydB gene, essential for the cytochrome bd-1 activity, in wild-type, freΔ68 and freΔ68 hda, mutant at 32°C under anaerobic conditions. The low temperature was chosen as cydB mutants grow poorly at 37°C [49]. Colonies were restreaked anaerobically and aerobically and revealed that loss of cydB in the freΔ68 hda mutant led to severe growth inhibition under aerobic conditions (Fig 6A). Therefore cytochrome bd-1 is instrumental in the freΔ68 mechanism of hda suppression.
We also deleted cydB in atpA hda and atpB hda mutants and similar to freΔ68 hda, the cells were unable to grow aerobically showing that cytochrome bd-1 is also essential for hda suppression through loss of ATPase activity (S4 Fig). Although not overproduced in iscU mutants the cytochrome bd-1 was also found essential when hda iscUC63F cells were grown aerobically, indicating its function is critical despite the presence of the cytochrome bo (S4 Fig).
We proceeded to overproduce cytochrome bd-1 (pNDM-Cyd) and found that hda could be deleted in such cells without loss of ability to form colonies (Fig 6B) although the small colony size suggested that pNDM-Cyd was a relatively poor hda suppressor. In agreement with this, independent clones of hda cells overexpressing cytochrome bd-1 grow with a relative high cellular origin concentration but were all similar when analyzed by flow cytometry (Fig 6C). When depleted for cytochrome bd-1, the origin concentration increased overtime although this was difficult to assess due to incomplete runout after rifampicin and cephalexin treatment. The ori/ter ratio was increased from about 10 to about 20 following 16 hours of cytochrome bd-1 depletion (Fig 6D).
In E. coli, a series of dedicated enzymes detoxify endogenously generated reactive oxygen species. Among the different systems that sense ROS, the OxyR system that perceives and reacts to the threat of hydrogen peroxide accumulation is the best understood. To preserve the cellular homeostasis in case of an H2O2 assault, OxyR activates the transcription of genes involved in protective or detoxifying processes [50]. Although OxyR activation is mostly considered a sensor of extracellular elevated H2O2 levels [50], down regulation of the OxyR regulon in iscUC63F, freΔ68 cells could imply that these cells generate less H2O2 endogenously. The microarray data did however not reveal down regulation of the OxyR regulon in iscUC63F, freΔ68 cells (S5 Fig). As microarray data may not reveal minor differences in gene expression we decided determine expression of katG, encoding a catalase strongly induced by OxyR, by using a katG::lacZ transcriptional fusion carried on the chromosome [51]. We did not observe major changes in katG expression in iscUC63F and freΔ68 (S6 Fig) which could indicate that the cytoplasmic level of H2O2 is not reduced in the mutants. We did not find the Sox regulon is affected in iscUC63F and freΔ68 cells either (S5 Fig; S1 Appendix). Although this may indicate that superoxide levels are not changed in the mutants, the SoxS/R system is now believed to react to redox cycling molecules rather than superoxide directly [52].
We also assessed the impact of superoxide dismutase sodA and sodB mutations on survival of overinitiating cells. Deletion of sodA had little effect on the growth of wild-type, hda freΔ68 cells while deletion of sodB appeared deleterious (S7 Fig). This implies that superoxide accumulation is toxic in overinitiating cells but tolerated in wild-type cells. SodA and SodB differ especially by the nature of the metal cofactor Mn and Fe respectively [53]. MnSodA is involved in oxidative stress response, while FeSodB normally provides the generic scavenging activity. The sensitivity of hda freΔ68 to the loss of sodB only may reflect the fact that sodA is repressed by ArcA directly at the transcriptional level and potentially also indirectly at the translational level through the small RNA FnrS (not present in our microarray) [54]. Altogether, this indicates that sodB hda freΔ68 may be low in cytoplasmic superoxide dismutase activity, a stressing situation already for wild-type cells.
In E. coli excessive initiations from oriC result in progressive growth inhibition due to the accumulation of DNA strand breaks [13, 14]. The isolation and characterization of second site suppressor mutations that overcome this inviability has revealed that they fall into two categories. The first category includes mutations that reduce oriC activity and are found in oriC itself [55], in dnaA or in genes that affect DnaA function or activity [23, 27, 33, 56–59]. The second category of suppressor mutations have in common that they do not reduce initiation frequency but allow cells to survive in spite of overinitiation. These mutations facilitate replication fork progression along the chromosome either by increasing the size of the dNTP pool (overexpression of Ribonucleotide Reductase; [27, 33]), by altering the DNA topology (ex: mutation in hns; [60]), or by limiting the repair of DNA damages (i.e. mutM;[14]). Here we characterize three hda suppressor mutations in iscU, fre and atpAB that seemingly promote viability by limiting oxidative damage to DNA.
The iscUC63F, freΔ68 and atpAB mutants all have in common that the TCA cycle is downregulated and all, or parts of the micro-aerobic respiratory chain is upregulated (Fig 7). While the micro-aerobic respiratory chain is relatively inefficient in generating a proton gradient (Fig 7), the ATPase mutants cannot even utilize the proton gradient for ATP production and one might therefore suspect that the ATP/ADP ratio is lowered in all of these cell types. Because DnaA has the same affinity for ATP and ADP [3] a lowered ATP/ADP ratio would result in generation of less DnaAATP upon rejuvenation or de novo synthesis, which could contribute to a lowered overall DnaAATP/DnaAADP ratio. In turn, this could explain the ability of the iscUC63F mutation to suppress RIDA deficiency by lowering initiations from oriC. However, two lines of evidence argue against this. First, we found that initiation of replication was not significantly affected in any of the mutants with respect to origin concentration and initiation synchrony. Second, the ATP/ADP ratio is actually increased in iscUC63F and freΔ68, and only reduced in atpAB mutants [44].This argues that the changes in ATP/ADP ratio observed here has little influence on initiation frequency. This also implies that either the ATP/ADP ratio must be changed more dramatically to affect initiation of replication or that other mechanisms dedicated to maintain the balance DnaAATP/DnaAADP such as RIDA, DDAH, and DARS mediated rejuvenation counteract any gross variations in the cellular ATP/ADP ratio to maintain an initiation frequency that is tightly coupled to cell mass increase.
It should also be noted that the energy charge of E. coli is normally relatively invariable [61–63] at different growth rates and whether cells are grown aerobically or anaerobically. This is thought to be controlled by changes in the glycolysis flux in response to the demands in ATP [44]. In wild-type cells, the cellular energy charge is only affected during adaptation to environmental changes or stress [62, 63].
Overexpression of the Ribonucleotide Reductase is known to suppress the loss of hda [27, 33]. In iscUC63F cells we observed that transcription of nrdHEIF genes encoding RNR1b was increased about two-fold (S1 Table). RNRIa and RNRIb differ mostly in the dinuclear metal cluster required for their activity: a 2Fe-tyrosyl radical (RNRIa) or a 2Mn-tyrosyl radical (RNRIb). However, the activation of nrdHEIF transcription is assumed to palliate a deficiency of the RNRIa only during iron limitation or oxidative stress [64, 65] and requires a concomitant overexpression of the manganese transporter MntH [64] which is not upregulated in the iscUC63F mutant (S1 Table). The RNRIa activity is likely to be reduced in iscUC63F mutant cells because RNR1a function is normally helped by the [Fe-S] cluster protein YfaE. The net result of the iscUC63F mutation is therefore unlikely to be cells with an increased dNTP pool. This is corroborated by a similar sensitivity of IscUC63F cells to the RNR inhibitor hydroxyurea as wild-type cells. Importantly, Hda deficient cells suppressed by overproduction of RNRIa or RNRIb no longer overinitiate replication, i.e. the origin concentration is similar to wild-type. RNR overproduction therefore reduces oriC activity in hda cells [27]. This is quite different from the continued overinitiation of hda iscUC63F and hda freΔ68 cells, which suggest that suppression is not mediated through an increased dNTP level in these mutants. The efficient HU-mediated killing of Hda deficient iscUC63F cells also shows that RNR activity is critical for survival of these cells and therefore that the dNTP pool is still limiting. The situation is similar in the freΔ68 mutant. Fre is a well-known in vitro activator of RNRIa [66]. The freΔ68 mutant sensitivity to HU was similar to wild-type in our assay but hda freΔ68 cells were effectively killed by HU treatment. This implies that in absence of Hda, the dNTP pool is limiting in iscUC63F and freΔ68, although we cannot exclude that the dNTP pool is larger in comparison to what would be found in Fre+ hda and IscU+ hda cells had they been viable.
The enzymes of the TCA cycle are downregulated in iscUC63F, freΔ68 and atpAB mutants (Fig 7) resulting in reduced ROS production arising from the dehydratases [16]. ROS production is expected to be further reduced in iscUC63F cells as this mutant is partly defective in synthesis of the [Fe-S] clusters required for function of many of the dehydratases (Fig 7). ROS production is also likely to be further reduced in the freΔ68 mutant because the generation of FADH2 is affected in the flavin reductase mutant [28]. FADH2 reduces Fe+++ to Fe++ which participates in the Fenton reaction to generate ROS [17] (Fig 7).
The aerobic respiratory chain is also altered in the mutants (Fig 7). For the iscUC63F mutant, this is due to the lack of Fe-S cluster required for several key enzymes in the pathway along with a transcriptional reprograming towards expression of enzymes devoid of Fe-S (S1 Table). For freΔ68 and atpAB mutants, an increased NADH/NAD ratio triggers a transcriptional repression of major aerobic chain components while the micro-aerobic chain is induced, notably by overproduction of cytochrome bd-1 (S2 Table; [38]), an enzyme known to scavenge or even process ROS species (Fig 7) [48, 67–69]. Cytochrome bd-1 was, although not upregulated, also necessary for suppression of overinitiation in IscUC63F cells, indicating that cytochrome bd-1 plays an essential role for growth even when the aerobic terminal oxidase cytochrome bo is also expressed. The importance of cytochrome bd-1 was demonstrated by showing that overproduction of cytochrome bd-1 directly or indirectly through ArcA overproduction could suppress RIDA deficiency and by showing that cytochrome bd-1 was absolutely required for freΔ68 mediated suppression of RIDA deficiency. There are in principle two known ways that cytochrome bd-1 can act as ROS scavenger. First, through its peroxidase activity, where the cellular localization of cytochrome bd-1 is found would suggest that it sanitizes the periplasm or exogenously created H2O2. Second, and probably most relevant in this context, by oxidizing quinone’s, that are also a source of ROS [70], cytochrome bd-1 could maintain a flux of electrons, thereby preventing flavoproteins (and quinones) to retain electrons and adventitiously passed them on to O2. Because ArcA overproduction did not significantly reduce the origin concentration of wild-type cells it is unlikely that ArcA binding to oriC [71] contribute to its ability to suppress overinitiation.
We previously proposed that aerobic inviability of Hda deficient cells results from an increased number of replication forks of which some may encounter an intermediary in the repair of primarily 8-oxodG to create double stranded breaks [14]. As 8-oxodG arises by oxidation of guanine residues in the DNA, primarily by hydroxyl radicals [72], growth can be restored in anaerobic conditions or by removal the GO-repair system, that is responsible for repairing 8-oxodG lesions.
During aerobic growth hydrogen peroxide (or superoxide) is generated as a consequence of flavin auto oxidation in dehydratases such as aconitase and fumarase of the TCA cycle, a process that could be reduced in freΔ68 and iscUC63F cells, due to down regulation of the flavoproteins in freΔ68 cells (S2 Table) and or inactivation of these enzymes due to the absence of iron sulfur clusters in iscUC63F cells. As ROS levels are notoriously difficult to determine directly [16], we turned to the OxyR regulon that is induced by H2O2. We did not observe major changes in expression of the OxyR regulated katG gene in iscUC63F and freΔ68 cells (S6 Fig) which could indicate that the cytoplasmic level of H202 is not reduced in the mutants. However, the observation that katG expression is not reduced during anaerobic growth either [73], suggest that katG is not a good reporter for H2O2 levels close to or especially below wild-type level. Our observation that katG expression was reduced when ArcA was overproduced could be independent of H2O2 and result in repression of rpoS which in turn resulted in production of less KatG [74]
Although we have not been able to measure a reduction in the cellular ROS level, we favor a model in which iscUC63F, freΔ68 and atpAB mutants sustain growth of overinitiating cells by reducing ROS and hence oxidative damage to the DNA. This is supported by the observation that deletion of sodB which presumably result in accumulation of superoxide, is deleterious for overinitiating cells, but tolerated by wild-type cells. Overall we suggest that iscUC63F, freΔ68 and atpAB mutations are just a few of many putative RIDA suppressors that affect the cellular redox balance and ultimately chromosome stability. We do however recognize that this is a model only, and that iscUC63F, freΔ68 and atpAB mutations have pleiotropic effects on cellular physiology and that mechanisms not involving ROS reduction may at least in part be involved.
RIDA deficient cells carrying the iscUC63F, freΔ68 and atpAB suppressor mutations grow relatively poorly compared to other suppressors that downregulate initiation of replication (i.e. affecting DnaA, SeqA; [23]). This most likely reflects the partial suppression of DNA damages, but a too high DNA concentration could per se be a challenge for the cells in relation to processes such as supercoiling, repair and segregation. This may explain the slow growth of this type of suppressors relative to those that affect the initiation of replication. This also stresses the fact that E. coli has evolved a multitude of mechanisms that ensure that the origin concentration remains relatively invariant regardless of the growth conditions [2].
Cells were grown in Luria–Bertani (LB) medium or AB minimal medium [75] supplemented with 0.2% or 0.04% glucose, 0.5% casamino acids and 10 μg/ml thiamine. LB with 0.2% glucose medium was used for anaerobic growth. Unless specified, all cells were cultured at 37°C. When necessary, antibiotic selection was maintained at the following concentrations: kanamycin 50 μg/ml; chloramphenicol, 20 μg/ml; ampicillin, 150 μg/ml. Cell growth was monitored by measuring optical density at 450 nm for AB minimal medium. Anaerobic growth was performed in an anaerobic jar using anaerobic atmosphere generation bags (BD).
All strains used are derivatives of MG1655 (F-λ-rph-1) [76]. The hda deletion described previously [26] was moved by P1 mediated generalized transduction [77].
Mutations from the KEIO collection [78] were moved by P1 transduction into MG1655 using lysates of: JW4364 (ΔarcA), JW0723-2 (ΔcydB), JW3712-1 (ΔatpA), JW3716-1 (ΔatpB), JW2513 (ΔiscU) JW3879 (ΔsodA), JW1648 (ΔsodB).
The katG::lacZ gene fusion from strain AL441 [51] was moved into MG1655, iscUC63F and freΔ68 by P1 transduction.
The cyd operon and arcA gene were amplified from strain MG1655 using primers pairs 5’-CTCTAGATTAAGGAGGCCATATGTTAGATATAGTCGAACT-3’/ 5’-AGAGAATTCTGATTTAAAAGAA-3’ and 5’-CTCTAGATTAAGGAGGCCATATGCAGACCCCGCACATTCT-3’/ 5’-CGAATTCTTAATCTTCCAGATCACCGC-3’ respectively. The PCR products were digested with XbaI / EcoRI and cloned into plasmid pFH2102 digested with the same enzymes resulting in plasmids pFH2102CYDOP and pFH2102ARCA. The cyd operon and the arcA gene were then amplified from pFH2102CYDOP and pFH2102ARCA using a primer annealing upstream of the Shine Dalgarno GTTGACTTGTGAGCGGATAA and 5’-AGAGAATTCTGATTTAAAAGAA-3’ (for cyd) or 5’-CGAATTCTTAATCTTCCAGATCACCGC-3’ (for arcA). The PCR products were digested with XhoI/EcoRI and cloned into plasmid pNDM220 digested with the same enzymes, resulting in plasmids pNDM-cyd and pNDM-arcA
Strains were grown exponentially in ABTG supplemented with 0.5% casamino acids. At an optical density OD450 = 0.3, 35 ml of culture was transferred to a cold tube containing 5 ml frozen water and centrifugated at 4°C at 10000g for 5min. Pellets were resuspended in 0.5ml ice cold TE-Buffer, transferred to an Eppendorph tube containing 250μl lysis buffer (2% SDS, 16mM EDTA and 200mM NaCl) and 750μl Phenol, whirly mixed and placed at 65°C for 10min with whirly mixing every 3–4 min. Following phenol extraction, each sample was treated with Dnase I for one hour at 4°C and an RNA clean up was made using the RNeasy Mini Spin Column kit from QIAGEN.
cDNA was synthesized using the Revert Aid H Minus first strand synthesis Kit from Thermo Scientific. The cDNA was fragmented with DNAse I in One-Phor-All buffer (Amersham Biosciences) and end-labeled with Biotin using the Enzo BioArray Terminal Labeling Kit. The fluorescent labeled cDNA hybrizized to GeneChip® E. coli Genome 2.0 Array was scanned as described in the Affymetrix UserGuide (www.affymetrix.com) and analyzed using GeneChip Analysis Suite software. Following Robust Multi-array Average (RMA) normalization, a cut off for low expression was applied and genes whose expression varied in the wt strains excluded from the analysis. Raw RMA normalization data can be found in S1 Appendix.
Was done by quantitative PCR was performed as previously described [14] with modifications. One milliliter of exponentially growing cells (OD450 ~ 0.2) is harvested and put on ice, centrifuged 5 minute 8000 g, the supernatant discarded and cells resuspended in 100 μl of cold 10 mM Tris pH7.5 and fixed by adding 1 ml of 77% ethanol. The samples were stored at 4°C. For qPCR analysis, 100 μl of ethanol fixed cells were centrifuged 7 minutes at 17000 g, the supernatant discarded and the samples were centrifuged for another 30 seconds at 17000 g and the remaining ethanol removed. The cell pellet was resuspended in 1ml cold water and 2 μl of the cell suspension was used as template for qPCR analysis. The Quantitative-PCR was performed using Takara SYBR Premix Ex Taq II (RR820A) in a BioRAD CFX96. All ori/ter ratios were normalized to the ori/ter ratio of MG1655 treated with rifampicin for 2h. The origin and terminus were quantified using primers 5′-TTCGATCACCCCTGCGTACA-3′ and 5′-CGCAACAGCATGGCGATAAC-3′ for the origin and 5′-TTGAGCTGCGCCTCATCAAG-3′ and 5′-TCAACGTGCGAGCGATGAAT-3′ for terminus as previously reported [26].
Flow cytometry was performed as described previously [79] using an Apogee A10 Bryte instrument. For each sample, 30 000 to 200 000 cells were analyzed. Numbers of origins per cell and relative cell mass were determined as described previously [79].
Intracellular ATP and ADP were extracted as previously described [80]. ATP/ADP ratios were measured using a luciferin-luciferase ATP kit (Microbial ATP Kit HS, BioThema AB, Sweden). Intracellular NADH and NAD+ was extracted as previously described [81]. The NADH/NAD+ ratio was quantified by a luciferase assay provided by the kit NAD+/NADH-Glo Assay (Promega). The luminescence from each assay was measured using the Infinite® M1000 PRO microplate reader (TECAN) with the 96-well microplates from Greiner Bio-one (Cat. No. 655901).
High-performance liquid chromatography (HPLC) was used to measure the concentration of glucose, acetate, formate, ethanol, succinate, citrate and lactate as previously described [81]
Quantification of oxygen consumption was made in a Bioreactor (Sartorius Biostat Q, 500 mL working volume) equipped with an O2 electrode (Mettler Toledo, Switzerland).
Was done as described previously [14]
RT qPCR was performed on phenol extracted total RNA using QuantiNova SYBR Green RT-PCR Kit using supplier recommendations and specific primers for cydA (5’-TGCGGCCTGTATACCCTGTTCC-3’ and 5’-CGTGCCGGCTGAGTAGTCGTG-3’), cyoA (5’-CCGCTGGCACACGACGAGA-3’ and 5’-AAGCGATTTCATTCACGGTAGCA-3’) and sdhD(5’-GATCGGTTTCTTCGCCTCTG-3’ and 5’-CGGTCAACACCTGCCACAT-3’) [82] and normalized to rpoA (5’-TTGATATCGAGCAAGTGAGTTCG-3’ and 5’- GCATCGATGAGAGCAGAATACG-3’) [27].
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10.1371/journal.ppat.1004527 | Intraspecies Competition for Niches in the Distal Gut Dictate Transmission during Persistent Salmonella Infection | In order to be transmitted, a pathogen must first successfully colonize and multiply within a host. Ecological principles can be applied to study host-pathogen interactions to predict transmission dynamics. Little is known about the population biology of Salmonella during persistent infection. To define Salmonella enterica serovar Typhimurium population structure in this context, 129SvJ mice were oral gavaged with a mixture of eight wild-type isogenic tagged Salmonella (WITS) strains. Distinct subpopulations arose within intestinal and systemic tissues after 35 days, and clonal expansion of the cecal and colonic subpopulation was responsible for increases in Salmonella fecal shedding. A co-infection system utilizing differentially marked isogenic strains was developed in which each mouse received one strain orally and the other systemically by intraperitoneal (IP) injection. Co-infections demonstrated that the intestinal subpopulation exerted intraspecies priority effects by excluding systemic S. Typhimurium from colonizing an extracellular niche within the cecum and colon. Importantly, the systemic strain was excluded from these distal gut sites and was not transmitted to naïve hosts. In addition, S. Typhimurium required hydrogenase, an enzyme that mediates acquisition of hydrogen from the gut microbiota, during the first week of infection to exert priority effects in the gut. Thus, early inhibitory priority effects are facilitated by the acquisition of nutrients, which allow S. Typhimurium to successfully compete for a nutritional niche in the distal gut. We also show that intraspecies colonization resistance is maintained by Salmonella Pathogenicity Islands SPI1 and SPI2 during persistent distal gut infection. Thus, important virulence effectors not only modulate interactions with host cells, but are crucial for Salmonella colonization of an extracellular intestinal niche and thereby also shape intraspecies dynamics. We conclude that priority effects and intraspecies competition for colonization niches in the distal gut control Salmonella population assembly and transmission.
| Salmonella enterica serovars infect various mammalian hosts, causing disease ranging from self-limiting diarrhea to persistent systemic infections such as typhoid fever. Here we investigated the impact of an established intestinal S. Typhimurium population on fecal shedding in the presence of another challenging strain. This scenario arises during host-to-host transmission, as well as during chronic host-adapted infections when systemic Salmonella reseed the intestinal tract to be transmitted in feces. In a mouse model of persistent Salmonella infection, we found that distinct subpopulations formed in intestinal and systemic tissues. Expansion of the intestinal subpopulation was responsible for increases in fecal shedding, rather than increased secretion of systemic Salmonella. Furthermore, the Salmonella that initially colonized the gut excluded challengers from the cecum, colon, and feces. A challenging systemic strain could only be shed upon ablation of the established intestinal strain. This intraspecies colonization resistance requires Salmonella hydrogenase-mediated invasion of the distal gut and is maintained by the virulence effectors SPI1 and SPI2. We describe novel observations indicating that Salmonella virulence effectors that have been shown to subvert the host immune response and microbiota, also play a role in intraspecies competition for colonization of transmission niches.
| The Salmonella enterica serovars are important pathogens that cause disease ranging from a self-limiting gastroenteritis to persistent systemic infections. The human-adapted Salmonella enterica Typhi and Paratyphi serovars are the causative agents of typhoid fever, and penetrate the intestinal epithelium to disseminate to systemic tissues [1]. Approximately 1–6% of infected patients become chronic carriers and serve as the reservoir of disease, remaining asymptomatic while excreting Salmonella in their stool [1], [2]. S. Typhimurium causes a typhoid-like disease in mice, but also infects a wide-range of mammalian hosts, including livestock [3], [4]. S. Typhimurium is a major cause of foodborne diarrheal disease in humans, but can also cause invasive non-typhoidal Salmonella (NTS) disease in immunocompromised individuals [5], [6]. NTS can persist in the gastrointestinal tract and be excreted in feces in certain patients [7], with elevated levels of NTS fecal shedding associated with antibiotic therapy [8]. Surprisingly little is known about Salmonella fecal shedding dynamics, particularly during persistent infection. However, this aspect of the Salmonella life cycle is fundamentally important for understanding transmission to new hosts.
Transmission of this enteric pathogen occurs via the fecal-oral route. During invasive disease with host-adapted serovars, Salmonella invade the Peyer's patches (PP) in the small intestine and breach the epithelium. Trafficking through the blood and lymphatics results in systemic dissemination of the pathogen to the mesenteric lymph nodes (mLN), bone marrow, spleen, liver, and gallbladder [9]. It is thought that systemic Salmonella in gallbladder bile secretions reseed the small intestine to be transmitted in feces [1], [10]. However, the fate of the initial invading Salmonella in the intestine and whether they contribute to fecal shedding has not been determined. A deeper understanding of the within-host population biology of Salmonella infections is crucial for determining treatment strategies and preventing spread. The mammalian host can be viewed as an ecosystem, with different tissues functioning as interconnected habitats. In this landscape, pathogens develop into population structures based on processes of dispersal, diversification, environmental selection, and coevolution within the host [11]. During host-to-host spread, each individual acts as an independent ecosystem, and a pathogen must adapt to a new environment in order to be successfully transmitted. Principles in ecology can thus be applied to explain and predict the resulting infection dynamics [11], [12].
Since host-adapted Salmonella serovars first enter the gastrointestinal tract before spreading to systemic tissues, we hypothesized that distinct groups of communities would assemble within these two host compartments. In population ecology, this is referred to as a subpopulation, or a local group of individuals that interact within a certain habitat [13]–[17]. A metapopulation then consists of a collection of subpopulations with various interactions and rates of dispersal between their habitats. Indeed, studies utilizing tagged isogenic strains have revealed formation of metapopulations in other systemic infections. Due to differing replication rates and dispersal routes within host tissues, independent pathogen subpopulations form during Listeria monocytogenes, Yersinia pseudotuberculosis, and uropathogenic Escherichia coli infections [18]–[21], although the impact of these subpopulations on transmission is unknown.
Wild-type isogenic tagged Salmonella (WITS) strains have been developed to resolve the early kinetics of acute infection in the susceptible C57BL/6 mouse background. In the streptomycin-treated diarrhea model, WITS were applied to generate a mathematical model describing replication and immune clearance of Salmonella in the cecal lymph node 24 hours post-infection [22]. Analysis of an intravenous model of infection revealed that concomitant death and rapid bacterial replication resulted in the formation of independent WITS subpopulations in the liver and spleen, although hematogenous mixing led to the homogenization of these systemic communities after 48 hours [23]. A study of early dissemination determined that founder bacteria initiated infection independently in Peyer's patches and systemic compartments 4 days post-infection [24]. However, the WITS technique has not been utilized to dissect the spatiotemporal population dynamics during chronic infections. It is not known whether different subpopulations of Salmonella form during persistent infection, or how they contribute to the pool of Salmonella that is ultimately shed in the feces. Furthermore, it is important to determine whether Salmonella that are carried long-term in systemic tissues and/or in the gallbladder contribute to fecal shedding in the presence of a previously established intestinal subpopulation. The effect of an established intestinal subpopulation on subsequent super-infections is also unclear. However, this scenario could arise in endemic regions and outbreaks, and therefore has implications on human disease and livestock husbandry. It is also unclear whether humans can be co-infected with multiple Salmonella strains due to difficulties in obtaining consistent patient samples, but this scenario could arise in endemic regions and outbreaks.
Studies in ecology have determined that immigration order dictates community structure through a priority effect, in which early colonization affords one member an advantage over future colonizers [25]–[27]. These competitive interactions are often mediated by resource availability [26]–[28]. Darwin's naturalization hypothesis posits that challenging species are more successful in habitats in which their close relatives are absent [29], as the more closely related they are, the more strongly they will compete for the same resources. Following this logic, we hypothesized that different subpopulations of Salmonella will compete for colonization of niches important for fecal shedding.
In this study, we employed tagged isogenic S. Typhimurium strains in a mouse model of persistent systemic infection. We show that a Salmonella metapopulation structure forms during persistent infection, with distinct subpopulations in intestinal and systemic tissues. We further found that established subpopulations of intestinal Salmonella colonize crucial extracellular niches in the cecum and colon that are required for fecal shedding. Systemic Salmonella from the gallbladder, as well as challenging strains from other infected donor mice, are excluded from the distal gut niche in a novel observation of intraspecies colonization resistance by an enteropathogen. Salmonella hydrogenase, an enzyme that mediates acquisition of microbiota-derived hydrogen [30], is required to exert priority effects in this crucial transmission niche. In addition, we demonstrate that maintenance of this intraspecies colonization resistance is dependent on the Salmonella pathogenicity islands SPI-1 and SPI-2 during persistent infection.
To define the Salmonella population structure that arises during chronic infection, we employed a previously established tagged strain approach using a mixture of barcoded, phenotypically equivalent S. Typhimurium strains [23]. These Salmonella wild-type isogenic tagged strains (WITS) each carry a unique 40 base pair tag in between the malX and malY pseudogenes, are equally fit, and have been applied to studies of acute infection [23]. Utilizing these previously published sequence tags, we constructed 8 WITS strains in the S. Typhimurium SL1344 background (W1–W8; Table S1) and confirmed each strain to be equally fit when grown in broth culture (Figure S1A). 129X1/SvJ mice, which possess a wild-type Nramp1 allele and can be persistently colonized with S. Typhimurium [31]–[33], were orally inoculated with 108 colony forming units (CFU) of an equal mixture of strains W1–W8 (Figure S1B–C). Total WITS CFU were enumerated by plating (Figure S1D) and qPCR was performed to determine the WITS abundances in systemic (spleen, liver, gallbladder) and intestinal (PP, small intestine, cecum, colon, feces) sites after 35 days of infection. Individual mice had WITS profiles that were distinct from other animals, with certain WITS comprising the majority of Salmonella found within infected tissues that varied on a mouse-by-mouse basis (Figure 1A). However, combined analysis of all infected mice revealed that all 8 WITS strains were represented in every tissue compartment (Figure 1A) and there was no statistically significant difference between the relative abundances of the WITS strains in each of the tissues, indicating all 8 WITS are equally represented in vivo (Table S2, one-way ANOVA and Kruskal-Wallis tests). A control experiment in which 4 of the 8 WITS were underrepresented in the inoculum resulted in their subsequent underrepresentation within infected tissues (Figure S2), indicating that these 4 WITS did not have any fitness advantage during infection.
The WITS compositions in systemic and intestinal tissues were compared in order to determine whether Salmonella subpopulations arose after 35 days of persistent infection, a time after which the bacteria have breached the intestinal epithelium and have spread systemically to the liver and spleen. The strain composition within individual mice varied depending on the site of infection (Figure 1A). In order to quantify potential differences in WITS abundances, we utilized a Bray-Curtis dissimilarity statistic, which has been commonly used in community abundance analyses in ecology and studies of the microbiota [34]–[36]. This calculation was applied to our model to obtain population-level distance values of WITS compositions in different sites. Bray-Curtis values were calculated between the WITS relative abundances of two tissues (see Materials and Methods), in which a score of 0 indicates an identical WITS profile in both organs and a score of 1 indicates completely dissimilar populations. A dissimilarity matrix was calculated for all tissue comparisons (Table 1). The subpopulation in the liver closely matched that of the spleen with a low mean dissimilarity score of 0.248 (Figure 1B, Table 1), which is consistent with these environments being highly connected by migration pathways through the bloodstream and/or lymphatics.
In addition to colonizing systemic sites, Salmonella persisted within intestinal tissues for 35 days. However, in contrast to the spleen and liver, which contained an average of 3–4 WITS, the intestinal tissues were colonized by 1–2 strains (Figure 1A). This suggested that while there was some bottlenecking in dissemination to systemic sites, stronger selection pressures likely existed within intestinal tissues. Further analysis of the WITS profiles indicated that the strain compositions in proximal gut tissues (PP and small intestine) were dissimilar from those present in distal gut tissues (cecum and colon, Figure 1A), with dissimilarity scores of 0.416–0.575 (Figure 1B, Table 1, Figure S3A). In contrast, the WITS composition in the cecum and colon were very similar with a score of 0.101 (Figure 1B, Table 1), which was significantly lower than the dissimilarity scores observed in the proximal gut (Figure S3A). Together, these data suggest that during persistent infection, different subpopulations of Salmonella form between proximal and distal gut tissues.
It is thought that Salmonella in the liver and gallbladder reseed the intestinal tract via bile, followed by subsequent shedding in the feces. If the bile ducts provided highly connected migration pathways between these sites, the WITS profiles should be similar between systemic and intestinal tissues. Although not all of the mice were colonized by Salmonella in the gallbladder (Figure 1A), the WITS profiles in the gallbladder were most similar to the compositions of the spleen and liver from these mice (Table 1, Figure S3B). In contrast, the WITS compositions in the gallbladder were very different from the composition within the intestinal tissues (Figure 1B, Table 1, Figure S3B). In addition, the WITS compositions in the distal gut were distinct from those in the systemic tissues with high dissimilarity scores >0.816 (Figure 1B, Table 1). Collectively, analysis of the WITS compositions in various compartments within each infected mouse demonstrate that spatially delimited Salmonella subpopulations form during persistent infection, with systemic organs containing populations that are distinct from those in intestinal tissues.
Since host-to-host transmission requires high levels of Salmonella shed in the feces [4], [33], we wished to elucidate the kinetics and population dynamics of Salmonella shedding. Fecal samples were collected at various time points throughout the 35-day infection period (Figure 2A). An average of 6–7 WITS were present in feces after one day of infection, indicating some initial bottlenecking effects in the oral infection route may have occurred (Figure 2A–B). However, even greater dynamic changes in WITS compositions were observed at early time points in infection, with different strains shed at 7 and 14 days post-infection compared to day 1 (Figure 2A). Importantly, there was a dramatic decrease in the number of strains detected in the feces to an average of 1–2 WITS, which did not change during the 35-day infection (Figure 2A–B). Importantly, the sharp decrease in the number of strains shed in the feces on day 7 correlated with an increase in total fecal Salmonella CFU (Figure 2B), suggesting that clonal expansion of dominant WITS strains was responsible for increased fecal shedding.
To ascertain the tissue compartment that served as the source of clonal Salmonella expansion, we compared the WITS relative abundance profiles of the feces to both systemic and intestinal tissues to identify similarities. Although Salmonella initially invade the PP, the WITS compositions in the PP compared to the feces were significantly different at 35 days post-infection (Figures 2C, Table 1). In addition, the compositions of the Salmonella populations within systemic sites compared to the population composition in the feces were even more dissimilar (Table 1). This further corroborated our earlier finding that distinct Salmonella subpopulations arose between systemic and intestinal compartments. Furthermore, we did not observe an increase in the number of WITS strains present during increased fecal shedding (Figure 2B), which would be expected to occur if increased reseeding of systemic Salmonella was the source. Instead, these analyses revealed that the WITS profiles in both the cecum and colon very closely matched the composition of Salmonella shed in the feces (Figure 2B; Table 1). Importantly, the dissimilarity values between the distal gut sites and the feces were significantly lower than that of any other tissue compartment analyzed (Figure 2C, Table 1, Figure S3). Taken together, our results indicate that a clonal expansion of cecal and colonic Salmonella is responsible for the increases in fecal shedding.
The results of our WITS experiment demonstrated that distinct subpopulations formed in systemic and intestinal tissues by 35 days post-infection (Figure 1). However, even though high Bray-Curtis scores were computed between systemic and distal gut tissues, values were <1 indicating there were small percentages of shared WITS in these sites. One limitation of our mixed inoculum approach was that we could not discern the directionality of dissemination. For example, it could not be determined whether WITS present in the distal gut were part of the initial population or if they arrived secondarily by seeding the intestinal tract from systemic sites. In order to determine the relative contribution of systemic and intestinal strains to fecal shedding, it required a strategy to mark Salmonella in these different sites within the host.
To address this, we developed a co-infection model that employed isogenic marked strains rapidly identifiable by differential plating on antibiotics. We used the parental streptomycin-resistant SL1344 strain that has a missense mutation in hisG, which is not required for virulence, and an isogenic SL1344-kanR strain containing a kanamycin resistance cassette inserted at this site (hisG::aphT). These strains are equally fit in single and in mixed infections in mice inoculated by oral or IP routes [33]. In our co-infections, each mouse received 108 of one strain by oral inoculation and 103 of the isogenic strain by intraperitoneal (IP) injection. The IP route bypasses the gastrointestinal tract, such that Salmonella colonize systemic tissues first [32]. To confirm that successful reseeding occurs in our model, Salmonella shedding and tissue burdens were compared in control mice that received single IP infections or those that received single oral infections. Systemic IP-delivered Salmonella reseeded the small intestine, where they reached the same range of fecal shedding levels by 14 days post-infection as mice infected orally (Figure S4). However, the oral inoculation route resulted in >1,000-fold more Salmonella fecal CFUs 1 day post-infection compared to the IP route, and reached peak fecal shedding levels more rapidly (Figure S4A). Thus, in the co-infection model, the oral strain establishes an infection in the gut before the systemic strain reaches the intestine, allowing us to test the strength of priority effects in Salmonella population assembly.
Mice injected IP with a single Salmonella strain shed this strain in the feces as soon as 1 day post-infection (Figure S4A). This was in contrast to what occurred in mice that had been co-infected orally with an isogenic WT strain (Figure 3). The systemic strain was detected in the feces of only 5 of the 54 mice throughout the 30 days of infection (Figure 3A–C). Importantly, shedding of the systemic strain only occurred on a single day and did not persist. Since mice shed variable levels of Salmonella [33], [37], we wondered whether this would influence the ability of the IP strain to be shed. Surprisingly, the oral strain was exclusively shed in the feces of low (<104 CFU/gram), moderate (<108 CFU/gram), and super (≥108 CFU/gram) shedder mice (Figure 3A–C). In addition, when the reciprocal combination of strains (oral: SL1344-kanR, IP: SL1344) was used the same result was obtained throughout 60 days of infection (Figure 3, Figure S5A). Taken together, these results indicate that the established intestinal strain prevents colonization of the cecum and colon by Salmonella disseminating from systemic tissues.
We next wondered what the composition of the Salmonella strains were within systemic tissues of mice that had been co-infected for 30 days. In contrast to the cecum and colon, the IP and oral strains were both present within systemic tissues after 30 days of co-infection. The spleen and liver were comprised of similar abundances of both strains (Figure 4), indicating that intestinal Salmonella effectively disseminated to systemic sites. Although the orally inoculated strain was present in the gallbladder, the IP strain comprised >80.44% of the total Salmonella population in this organ (Figure 4). In addition, the IP strain was present as a minority of the population present in the PP (19%), small intestine (30%), and mLN (38%) (Figure 4). The IP strain was not detected in the cecum and colon in 25 out of 28 mice, and comprised <8% in the remaining animals (Figure 4). Strikingly, the oral strain remained dominant in the cecum and feces during the 60-day infection (Figure S5). Thus, our results from the co-infection model and the WITS analyses suggest that Salmonella that are established in the cecum and colon prevent systemic subpopulations from colonizing important niches that are required for fecal shedding.
One possible explanation for the dominance of the oral strain in the distal gut and feces could be that there is insufficient reseeding of systemic Salmonella into the gastrointestinal tract. To test this possibility, we utilized an established gallstone model of infection, in which S. Typhimurium biofilm formation on gallstones increased reseeding and subsequent fecal shedding by 1,000-fold [38]. We fed mice a lithogenic diet for 10 weeks to induce gallstone formation that resulted in 1–9 stones/mouse as confirmed by ultrasound imaging (Figure S6). In contrast, mice on a standard diet never developed gallstones (Figure S6). As previously demonstrated, mice with gallstones that were infected with 103 S. Typhimurium by IP injection shed >1,000-fold higher levels of bacteria 7 days post-infection compared to control mice (Figure S7).
To determine whether increased levels of S. Typhimurium in the gallbladder would allow systemic bacteria to colonize the cecum and/or colon, mice with diet-induced gallstones were co-infected orally with SL1344 and IP with SL1344-kanR. By 14 days post-infection, mice with gallstones had a mean Salmonella gallbladder burden >10,000-fold higher than mice without gallstones (Figure 5A). This represented an increase in systemic Salmonella, as the gallbladders were exclusively colonized by the IP strain (Figure 5B). In addition, mice with diet-induced gallstones had significantly higher levels of S. Typhimurium in the small intestine, indicating that increased numbers of systemic bacteria had reseeded this site (Figure 5B). Despite this drastic increase in the levels of systemic Salmonella reseeding the intestine, the established intestinal strain remained dominant in the cecum, colon, and feces (Figure 5C). Taken together, our data suggest that in the presence of an established Salmonella strain, systemic Salmonella are excluded from colonizing crucial transmission niches in the distal gut.
Although the presence of gallstones increased the numbers of S. Typhimurium in the gallbladder to 103–107 CFU/organ as well as subsequent reseeding of the small intestine, it is possible that these levels were insufficient to compete with the established intestinal subpopulations (Tables S3, S4). Indeed, we have measured the levels of Salmonella in gastrointestinal sites and found that there is a range of 101–108 total CFU (Table S3). To address this issue, we performed sequential infections in which resident intestinal Salmonella were challenged with a high oral dose of a second strain. First, mice were inoculated with 103 SL1344 by IP injection to establish a systemic infection. This initial Salmonella strain was detected in the feces after 5–7 days and was persistently shed for 35 days (Figure 6A). These mice were then super-infected with 108 SL1344-kanR orally. Although, the orally inoculated strain was detected in the feces 1 day post-infection (dpi), it was not detected in the feces for the remaining 7 days post-oral inoculation (35–42 dpi, Figure 6A). The challenging oral strain was not detected in any systemic or intestinal tissues by 7 days post-challenge (42 dpi, Figure S8A). This demonstrates that super-infecting strains are excluded from colonizing the intestine in the presence of a resident, persistent intestinal Salmonella infection, regardless of the route of inoculation. Collectively, our results suggest that there is intraspecies competition for a transmission niche in the distal gut.
Based on our evidence of intraspecies competition for a distal gut niche, we proposed that the dominance of established Salmonella in the cecum and colon is attributed to priority effects that govern distal gut colonization and subsequent fecal shedding. To test this notion, we performed sequential S. Typhimurium infections to evaluate the duration and strength of these competitive interactions. Mice were infected with 108 SL1344 orally, and fecal shedding of Salmonella was monitored. All of the mice continued to shed Salmonella over the 102 days of infection (Figure 6B). After 102 days, the mice were inoculated orally with 108 CFU of a second competing strain, SL1344-kanR. The competing strain was detected in the feces during the first 3 days post-infection (Figure 6B). However, by 14 days post-challenge (116 dpi), 28 of the 45 mice were no longer shedding the competing Salmonella strain (Figure 6B). Finally, by 35 days post-challenge (137 dpi), the competing strain was not detected in the feces (Figure 6B), intestinal compartments, or systemic tissues of co-infected mice (Figure S8B). The reciprocal strain combinations were also tested: mice were first infected with 108 SL1344-kanR orally for 60 days before subsequent challenge with 108 SL1344, in which the competing strain was cleared from the feces by 20 days post-challenge (Figure S9A). Thus, this colonization resistance against the same Salmonella species was maintained during the chronic stages of infection.
We next sought to determine whether the levels of the initial oral strain (SL1344) in the colon and in the feces would influence the clearance kinetics of the second competing oral strain (SL1344-kanR). One day after the second oral inoculation, the percentage of the competing strain varied depending on the level of shedding of the resident strain. For example, in mice that were shedding >108 CFU/g feces (super shedder mice), the competing SL1344-kanR strain comprised 4.88% of the total population on the first day post-secondary inoculation (Figure S9B). In contrast, for low and moderate shedder mice, the competing strain comprised 42.02% and 35.58% of the total population, respectively (Figure S9B). These differences remained significant 5 days after infection with the second competing SL1344-kanR strain. However, by days 10 and 14, the second SL1344-kanR strain was no longer detected in the feces of any of the mice (Figure S9B). These results indicate that more robust and rapid priority effects are exhibited in mice that are colonized with higher colonic Salmonella loads.
Finally, to determine whether we would see the same intraspecies priority effects in the distal gut during host-to-host transmission, we utilized our previously established model of transmission from an infected, super shedder mouse to uninfected mice in the same cage [33]. In this experiment, the donor mouse was orally infected with SL1344-kanR and was shedding >108 CFU/g at 14 days post-inoculation (Figure 6C). As a positive control for host-to-host transmission, the donor mouse was co-housed with uninfected mice. Similar to our previous results, naïve mice began shedding SL1344-kanR within 24 hours and continued to shed even after the donor was removed (Figure S10A). In contrast, recipient mice that had been infected for 14 days with SL1344 required 10 days of cohousing before low levels of the donor strain (<0.02% of all Salmonella) were detected in the feces (Figure 6C). In addition, shedding of the donor strain in the previously infected recipient mice was transient, and was not detected in the feces or tissues 10 days post-cohousing (Figure 6C, Figure S8C). Similar results were obtained when a SL1344 super shedder was cohoused with SL1344-kanR infected mice. Cohousing for 12 days was required before the donor strain could be detected in the feces of recipient SL1344-kanR mice (Figure S10B). The super shedder donor was left in the cage for an additional 6 days before removal, but consistent with previous findings, the donor strain was not detected in the feces of recipient mice by day 23 post-cohousing (Figure S10B). Together, these experiments show that priority effects determine Salmonella population assembly in intestinal transmission niches, where established subpopulations exert colonization resistance against incoming challengers.
Since the established subpopulation of Salmonella in the cecum and colon exerts colonization resistance, we proposed that their removal would allow challengers to occupy vital transmission niches. To test this idea, mice co-infected with 108 SL1344 orally and 103 SL1344-kanR IP for 7 days were then treated with a single dose of kanamycin. Kanamycin is not absorbed systemically and thus was used to ablate the extracellular, kanamycin-sensitive bacteria in the gastrointestinal tract. Within 24 hours of antibiotic administration, fecal shedding of the established intestinal SL1344 decreased by ∼5 logs (Figure 7A, left). Concomitant with the decrease in the established strain, over 107 CFU of the systemic SL1344-kanR strain was shed per gram of feces (Figure 7A, left). By 4 days post-antibiotic treatment, the systemic strain was exclusively shed in the feces (Figure 7A, left) and was transmitted to naïve recipients (Figure 7A, right). Thus, priority effects arose during the first 7 days of infection, which coincided with the clonal expansion in the distal gut and feces observed in the WITS studies (Figure 2).
Based on these findings, we hypothesized that Salmonella strains were competing for limited nutrient or spatial resources within the cecum and colon, which inhibited the ability of systemic strains to colonize the distal gut. We tested this notion by gavaging co-infected mice (SL1344 oral, SL1344-kanR IP) with 5 mg streptomycin in order to disrupt the microbiota and make more of these resources available [37], [39]–[41]. Both Salmonella strains are streptomycin-resistant, and previous studies have shown that streptomycin treatment of infected mice increases Salmonella fecal shedding to super shedder levels [33], [37]. We observed that all streptomycin-treated mice became super shedders, yet the increase in fecal Salmonella CFU reflected expansion of the oral SL1344 strain (Figure S11). This indicated that disrupting the microbiota with streptomycin treatment was insufficient to permit shedding of the systemic strain, as the newly available resources were likely immediately utilized by established intestinal Salmonella. Furthermore, since kanamycin does not enter mammalian cells, these results collectively indicate that established intestinal Salmonella occupy an extracellular transmission niche in the distal gut and exclude the bacteria that are reseeding the intestine from systemic sites.
Our data show that intraspecies priority effects govern Salmonella population assembly in the distal gut. Since our previous results demonstrated that clonal expansion and priority effects in the cecum and colon could occur by 7 days post-infection (Figure 2, 7A), we hypothesized that nutrient acquisition was very important during this stage of colonization. Indeed, ecological theory has implicated competition for nutrients as an important determinant in priority effects and community structure [28].
S. Typhimurium hydrogenase (hyb) is a key mediator of cecal ecosystem invasion and is required to consume a microbiota-derived metabolite [30]. In the un-inflamed gut of conventional mice with complex microbiota, hydrogenase enzymes facilitate consumption of hydrogen (H2) intermediates in a SPI1- and SPI2-independent manner [30]. Similarly, we show here that Hyb is important for gut colonization and fecal shedding in 129SvJ mice with an intact conventional microbiota (Figure S12). To test the role of Hyb in intraspecies priority effects, co-infections were performed in which all mice were injected IP with 103 wild-type (WT) SL1344 bacteria and one group of mice was co-inoculated orally with 108 ΔhybΔSPI1ΔSPI2 isogenic mutant S. Typhimurium while control mice were co-inoculated orally with 108 WT SL1344-kanR. The hyb mutation was constructed in a ΔSPI1ΔSPI2 background to assess the need for hydrogenase in the context of a non-inflamed gut. The relative levels of each strain in the feces were monitored over 15 days of co-infection (Figure 7B–C). Importantly, the Salmonella shed in the feces at 4 and 7 days contained systemic WT bacteria and by 15 days post-infection were entirely comprised of the systemic WT strain in mice that received ΔhybΔSPI1ΔSPI2 orally (Figure 7B). Total levels of fecal Salmonella were significantly lower in the ΔhybΔSPI1ΔSPI2 co-infection group compared to controls (Figure S13A), which corresponds to the decreased fecal shedding of ΔhybΔSPI1ΔSPI2 mutants during single oral infections (Figure S12B). The strain compositions in the feces of these mice throughout infection indicated that the increase in total fecal CFU on day 15 reflected rapid reseeding and shedding of the systemic WT strain concomitant with declining levels of the oral ΔhybΔSPI1ΔSPI2 strain (Figure 7C). Taken together, we have demonstrated that the hydrogenase mutant was unable to effectively invade the cecal and colonic niche (Figure 7D), thereby nullifying any priority effects and allowing systemic Salmonella to colonize the distal gut with subsequent transmission in feces (Figure 7C–D).
To determine whether intraspecies colonization resistance was still dependent on the maintenance of the extracellular intestinal niche during persistent infection, co-infected mice (oral: SL1344, IP: SL1344-kanR) were treated with a single dose of kanamycin 42 days post-infection. Within 24 hours of antibiotic administration, fecal shedding of the established intestinal SL1344 was decreased by ∼6 logs concomitant with a ∼5 log increase in the systemic SL1344-kanR strain (Figure 8A, left). The systemic strain was exclusively shed in the feces 2 days post-antibiotic treatment (Figure 8A, left), and comprised the entire Salmonella population in the cecum and colon after 7 days (Figure 8A, right). These data thus indicate that the extracellular niche in the cecum and colon is required to maintain intraspecies colonization resistance during persistent infection, which actively inhibits successful fecal shedding of systemic Salmonella.
To gain more insight into how S. Typhimurium competitively excludes incoming challengers from colonizing the distal gut niche, we tested the potential role of the key virulence factors Salmonella Pathogenicity Islands SPI1 and SPI2, which encode type III secretion systems that deliver effector proteins required for persistence in host tissues [32], [42]–[44] and fecal transmission [33]. Co-infections were performed in which mice simultaneously received 103 WT SL1344 by IP and 108 isogenic ΔSPI1ΔSPI2 mutant bacteria orally. In the ΔSPI1ΔSPI2 co-infected mice, the IP-injected WT bacteria were not present in significant numbers at day 7 (Figure 8B). However, by day 25, 21.43% of all fecal Salmonella were WT bacteria, and by day 70, 98.86% were WT S. Typhimurium (Figure 8B). In addition, the total fecal Salmonella CFU in the control (SL1344 oral, SL1344-kanR IP) and the ΔSPI1ΔSPI2 co-infected mice were similar (Figure S13B), which is consistent with our result that the systemic WT strain reseeded and replicated within the intestinal tract once the ΔSPI1ΔSPI2 mutant was cleared (Figure 8C). Indeed, examination of strain abundances in intestinal tissues after 70 days of co-infection confirmed that the systemic IP strain had predominantly colonized the mLN, small intestine, cecum, and colon while the initial ΔSPI1ΔSPI2 mutant was cleared from these sites (Figure 8D). These studies demonstrate that SPI1 and SPI2 are required for the established intestinal Salmonella population to maintain active colonization resistance against systemic reseeding bacteria.
Microbial fecal shedding by chronically infected hosts is the major source of new infection and disease for many enteropathogenic microbes. However, very little is known about the dynamics of Salmonella subpopulations within mammalian hosts and what their relative contributions are to host-to-host transmission. Community assembly theory provides a framework for understanding infection processes, and in this study, we defined the S. Typhimurium metapopulation structure that arose during persistent infection. We then applied ecological principles that govern community assembly to determine the contribution of different Salmonella subpopulations to fecal shedding.
Our tagged strain approach revealed that distinct S. Typhimurium subpopulations arose within different host tissues, resulting in a metapopulation structure with variable migration between sites. After 35 days of infection, the WITS compositions between the liver and spleen closely matched each other, suggesting that robust migration pathways in the blood and lymphatics exist between these tissues. Previous studies of acute infection in susceptible C57BL/6 mice determined that hematogenous spread 48 hours post-infection resulted in S. Typhimurium mixing between the spleen and liver [23]. Expanding our WITS analyses to include a more comprehensive set of infected tissues, we determined that Salmonella in systemic sites were distinct from subpopulations in the intestinal tract. Interestingly, we found that the WITS profiles in the PP and small intestine were also dissimilar from those in the cecum and colon. This likely represents stochastic invasion of the PP by a subset of individual WITS strains, while different subsets initiate separate infection foci in other tissues. Indeed, a recent study of early infection dynamics determined that PP invasion fueled spread to the mLN while an independent pool of bacteria initiated splenic and hepatic infection [24]. Our work suggests that these initial colonization dynamics shape the metapopulation structure that arises and is maintained throughout persistent infection. Quantifying the differences in systemic, proximal and distal gut sites with Bray-Curtis dissimilarity scores, we were able to gain new insights into the importance of the distal gut as a transmission niche.
Surprisingly, we have shown that systemic Salmonella can only colonize the distal gut upon clearance of the established intestinal subpopulation with an oral kanamycin treatment. In contrast, treatment with streptomycin, to which the SL1344 strain is resistant, was insufficient to permit shedding of the systemic strain. This suggests that disrupting microbiota-mediated colonization resistance does not create new niches for systemic bacteria to colonize. Previous studies found that administration of ciprofloxacin killed extracellular Salmonella and permitted tolerant bacteria within dendritic cells of the cecal lymph node to colonize the cecum [45], [46]. Although this fluoroquinolone treatment also ablated systemic Salmonella [45], these studies all highlight the intensely competitive dynamics between Salmonella within the distal gut. Competition for gut colonization was also reported with E. coli K12 strains in germ-free mice, although differences in colonization ability was due to varying fitness costs of antibiotic resistances [47]. Our study with isogenic strains support the idea that intraspecies competition for nutrients excludes systemic bacteria from colonizing the distal gut, in which established Salmonella has saturated a required niche. Intraspecies priority effects have recently been described for commensal species of Bacteroides [48] and E. coli [49], but our findings with an enteropathogen that causes persistent systemic infection is novel. There may also be evidence of these competitive interactions during Yersinia enterocolitica microcolony formation within intestinal tissues, in which previously infected PP were less likely to be super-infected [21]. However, it remains unknown whether colonization can proceed if established Yersinia are eliminated. It is possible that this may be unique to Salmonella rather than a broad enteropathogen phenomenon, as this colonization resistance was not seen between isogenic Campylobacter jejuni strains in a transmission study involving chickens [50].
Host-adapted Salmonella serovars infect the gastrointestinal tract before disseminating to systemic sites such as the gallbladder, which has been classically thought to be the source of Salmonella transmitted in feces [51]. However, the contribution of systemic reseeding in the presence of an established Salmonella intestinal tract infection had never been investigated. We show that an established intestinal strain persisted in the cecum and colon, even when gallstone formation increased gallbladder levels of S. Typhimurium >10,000-fold. It is interesting to speculate that intraspecies colonization resistance may occur in other hosts that are persistently infected by Salmonella. For example, humans can carry S. Typhi for long periods of time possibly in the gallbladder [52]. Although gallbladder removal sometimes cures patients, over 20% of carriers continued to shed S. Typhi and S. Paratyphi in their stool [53], [54], which indicates an alternative persistent reservoir. While circulating S. Typhi in Kathmandu are resistant to nalidixic acid and several fluoroquinolones, patient gallbladder isolates are more sensitive to nalidixic acid, gatifloxacin, and ofloxacin, indicative of a limited role in typhoid transmission [55]. The relative contributions of systemic versus intestinal populations of S. Typhi to transmission are not known. Perhaps the presence of fecal “showers” of S. Typhi [4] are due to reseeding bacteria from systemic sites that gain access to spatial and nutritional resources in the gut. In our co-infection model, the oral strain comprised 88.83% of fecal Salmonella after 60 days, which was lower than the 97.96% observed after 30 days (p = 0.06, unpaired Mann-Whitney). Though this was not a significant difference, it is possible that the intestinal strain may lose its dominance at even later time points, at which point systemic Salmonella may reseed from mesenteric lymph node macrophages [31], [56], [57] and/or the gallbladder [1], [2], [10]. Intraspecies Salmonella colonization resistance could be shaping typhoid epidemiology in endemic regions, but future work is required to determine whether this occurs in other Salmonella serovars besides Typhimurium.
We have found that the clonal expansion of the intestinal subpopulation is responsible for increases in S. Typhimurium fecal shedding. The mechanisms by which this subpopulation expands and establishes intraspecies colonization resistance are likely multifactorial. S. Typhimurium fimbriae and adhesins are important for attachment to intestinal tissues [58]–[60] and may play a role in this intraspecies dynamic. Host immune responses contribute to Salmonella clearance [61]–[63], and could also be involved in influencing intraspecies colonization resistance. However, intraspecies colonization resistance was observed at 14 days post-infection and lasted over 102 days in the context of co-infections, cohousing experiments, and sequential infections. This suggests that neither the innate nor the adaptive immune responses alone could be responsible for the exclusion of systemic reseeding Salmonella.
Microbial communities undergo local diversification in different habitats within the host [12], [64]–[68], and we considered the possibility that genetic mutations could be responsible for Salmonella expansion and intraspecies colonization resistance. Previous studies with marked isogenic strains determined that spontaneous mutations alone do not shape S. Typhimurium colonization dynamics or fecal transmission during persistent infection in 129Sv mice. The dominance of a re-isolated strain was lost upon subsequent infection or passage in broth, and exhibited the same infectious dose (ID50) as a culture-grown strain [24], [33]. A study of systemic S. Typhimurium infection revealed that enhanced growth of bacteria were not due to the selection of mutants, but rather were transient phenotypic changes dependent on gene regulation [69]. Systemic Salmonella did not accumulate attenuating mutations during our experiments. This subpopulation adapted to the intestinal environment following ablation of the resident strain, and replicated to supershedder levels with rapid transmission to naïve mice. Salmonella transcriptional responses likely play an important role in expansion in the distal gut, and insight into these changes will elucidate other mechanisms by which priority effects are exerted.
Our studies with a hydrogenase mutant revealed that Salmonella competition for a microbiota-derived nutrient is one mechanism by which a challenging systemic strain is excluded from the distal gut transmission niche. According to the monopolization hypothesis, rapid population growth upon colonization of a new habitat results in the effective monopolization of resources, resulting in a strong inhibitory priority effect [70]. Since Salmonella are mainly localized in extracellular regions of the distal gut [33], it is tempting to speculate that other Salmonella factors required for nutrient acquisition play a role in intraspecies colonization resistance. The importance of nutrient acquisition in establishing priority effects could be applied to the development of novel therapies, in which targeting key metabolic pathways could potentially prevent pathogen colonization and transmission.
We have found that SPI1 and SPI2 contribute to intraspecies colonization resistance up to 70 days post-infection. Importantly, co-infected mice that received 108 ΔSPI1ΔSPI2 orally shed significant levels of WT systemic Salmonella beginning 25 days post-infection, with no significant changes in the total fecal shedding of Salmonella. This suggests that as soon as nutrient and/or spatial resources are made available by the clearance of the initial ΔSPI1ΔSPI2 mutant, WT Salmonella spread from systemic tissues and rapidly expand within the intestinal tract. The T3SS encoded by these Salmonella pathogenicity islands deliver over thirty effectors with diverse functions [42]–[44], [71]. These effectors could act on Salmonella directly, or create an environment that kills strains reseeding from systemic tissues. These mechanisms could involve Salmonella-induced inflammation and modulation of the host immune response [72]. Inflammation also disrupts the host microbiota and allows the pathogen to metabolize newly available nutrients 1 [73]–[76]. Future work will seek to determine which of these are involved in establishing priority effects and exerting intraspecies colonization resistance.
Priority effects have long been known to shape community assembly in a variety of ecological systems, ranging from bacteria to larger eukaryotic organisms [27], [64]–[66], but this is the first time the phenomenon has been described for pathogen subpopulations during persistent infection within a host. In this landscape, the order in which S. Typhimurium arrive to the intestinal ecosystem dictates which bacteria are subsequently shed in the feces. The results presented herein demonstrate that colonization of distal gut tissues is a bottleneck for successful transmission, which subpopulations of Salmonella compete for. These studies may inform disease processes in host-adapted Salmonella serovars that cause invasive disease, yet are still transmitted fecal-orally. S. Typhimurium is a generalist pathogen that also infects livestock and humans, and thus our work has direct implications on public health [3], [4]. Our findings also highlight the potential for the application of ecological principles to epidemiology in order to predict dominant circulating strains during outbreaks. This work also sheds light on potential mechanisms that influence human-to-human transmission of non-typhoidal diarrheal infections, which can also be invasive in certain patients [5], [6]. A better understanding of these mechanisms might reveal novel therapeutic approaches, or even preventive measures in thwarting disease spread.
Experiments involving animals were performed in accordance with NIH guidelines, the Animal Welfare Act, and US federal law. All animal experiments were approved by the Stanford University Administrative Panel on Laboratory Animal Care (APLAC) and overseen by the Institutional Animal Care and Use Committee (IACUC) under Protocol ID 12826. Animals were housed in a centralized research animal facility certified by the Association of Assessment and Accreditation of Laboratory Animal Care (AAALAC) International.
129X1/SvJ and 129S1/SvImJ mice were obtained from Jackson Laboratories (Bar Harbor, ME). Male and female mice (5–7 weeks old) were housed under specific pathogen-free conditions in filter-top cages that were changed weekly by veterinary personnel. Sterile water and food were provided ad libitum. Mice were given 1 week to acclimate to the Stanford Research Animal Facility prior to experimentation.
The S. Typhimurium strains used in this study were derived from the streptomycin-resistant parental strain SL1344 [77]. A missense mutation (hisG46) in SL1344 results in histidine auxotrophy [78]. The isogenic SL1344-kanR strain was created by replacing the hisG coding sequence with that of a kanamycin-resistance casette (hisG::aphT) using the methods of Datsenko and Wanner [33], [79]. Genetic manipulations were originally made in the S. Typhimurium LT2 background before being transferred to SL1344 by P22 transduction. This methodology was also used to construct wild-type isogenic tagged Salmonella (WITS strains: W1–W8), in which a unique 40-bp signature tag and the kanamycin-resistance cassette were inserted between the malX and malY pseudogenes. Grant et. al. previously established this approach and published the unique 40-bg sequence tags of 8 WITS strains [23], which were employed in this study (Table S1). Growth curves of W1–W8 in LB broth cultures were performed by optical density readings and plating for colony forming units (CFU) per milliliter (Figure S1A). ΔSPI1ΔSPI2 (orgA::tet, ssaV::kan) was generated previously for use in other studies [80]. The Δhyb (hypOhybABC::cm) deletion was constructed as described by Maier et. al., with P22 phage transduction to insert the deleted genomic region into the ΔSPI-1ΔSPI-2 strain ([30], Table S1). All constructs were verified by PCR.
All S. Typhimurium strains were grown at 37°C with aeration in Luria-Bertani (LB) medium containing the appropriate antibiotics: streptomycin (200 µg/ml), kanamycin (40 µg/ml), tetracycline (15 µg/ml) and chloramphenicol (8 µg/ml). For mouse inoculation, an overnight culture of bacteria was spun down and washed with phosphate-buffered saline (PBS) before resuspension to obtain the desired concentration.
Food was removed 16 hours prior to all mouse infections. In WITS experiments, mice were inoculated with an equal mixture of strains W1–W8 via oral gavage of 108 CFU in 100 µl PBS. For intraperitoneal (IP) infections, mice were injected with 103 CFU in 100 µl PBS as previously described [32]. In the co-infection model, mice drank an oral dose of 108 SL1344 in 20 µl PBS, then received an IP injection of 103 SL1344-kanR immediately afterwards. Co-infection experiments were repeated using the reciprocal combination of strains, SL1344-kanR (oral) and SL1344 (IP), which had no effect on the trends observed.
Individual mice were identified by distinct tail markings and tracked throughout the duration of infection. Between 2–3 fresh fecal pellets were collected directly into eppendorf tubes and weighed at the indicated time points. Pellets were resuspended in 500 µl PBS and CFU/gram feces were determined by plating serial dilutions on LB agar plates with the appropriate antibiotics. Low (<104 CFU/gram), moderate (<108 CFU/gram), and super shedder (≥108 CFU/gram) mice were identified based on previously established criteria [33], [37].
Following collection of fresh fecal pellets, animals were sacrificed at the specified time points. Blood was collected by cardiac puncture and animals were euthanized by cervical dislocation. Sterile dissection tools were used to isolate individual organs, which were weighed prior to homogenization. The entire gastrointestinal tract was removed, and the small intestine was immediately separated from the distal gut and transferred to a new sterile petri dish. Visible PP (3–6/mouse) were isolated from the small intestine using sterile fine-tip straight tweezers and scalpels. PP, mLN, spleens, livers, and gall bladders were collected in 1 ml PBS. The small intestine, cecum, and colon were collected in 3 ml PBS. Homogenates were then serially diluted and plated onto LB agar containing the appropriate antibiotics to enumerate CFU/gram tissue. For co-infections with SL1344 and SL1344-kanR, several dilutions were plated to ensure adequate colonies (>100 CFU per sample) for subsequent patch plating to determine strain abundance.
For WITS experiments, 300 µl of tissue homogenate was inoculated into LB broth containing streptomycin (200 µg/ml) and kanamycin (40 µg/ml) as a recovery method to enrich for low abundance strains. An UltroSpec 2100pro spectrophotometer (Amersham Biosciences, Piscataway, NJ) was used to obtain optical density readings of the resulting bacterial cultures. Genomic DNA (gDNA) was extracted from 2×109 S. Typhimurium from each sample in duplicate using a DNeasy blood and tissue kit (Qiagen, 69506) as per the manufacturer's protocol for Gram-negative bacteria.
All qPCRs were performed on an Applied Biosystems 7300 real-time PCR system. A 25 µl reaction contained 12.5 µl of FastStart SYBR Green Master Mix with Rox (Roche, 04913914001), 8 µl DNase/RNase-free water, 0.75 µl of forward and reverse (10 µM) primers (Table S1), and 3 µl of gDNA (1–10 ng). Standard curves were generated using gDNA from each W1–W8 strain. Reaction conditions were 50°C for 2 min; 95°C for 10 min; 40 cycles of 95°C for 15 s and 60°C for 1 min; followed by a dissociation stage of 95°C for 15 s, 60°C for 1 min, 95°C for 15 s, and 60°C for 15 s.
To determine presence of a WITS strain, the qPCR value had to be above a minimum threshold value. This measure of primer specificity was determined by a negative control matrix, in which a specific primer pair was tested on ∼11.25 ng of non-template gDNA from each of the other 7 WITS strains. To test primer sensitivity, detection limits were determined by test plates containing known CFU of each strain. Briefly, colonies were washed off the plates with PBS and gDNA was extracted from plates with varying abundances of WITS (i.e. 1 CFU Strain A with 103–105 CFU Strain B). qPCR was performed and revealed a detection limit of 1 CFU/strain amidst over 4800 CFU from non-target strains. To verify that our method of broth recovery and qPCR analyses accurately rendered WITS abundances, we compared relative abundances of an equal mixture of culture-grown W1–W8 as determined by our qPCR strategy versus plating CFU of individual dilutions of each strain (Fig. S1B).
Plating onto selective LB agar containing streptomycin (20 µg/ml) and kanamycin (40 µg/ml) was used to determine strain abundances in co-infections, sequential challenges, and transmission experiments. In addition to patch plating a minimum of 100 CFU per sample, undiluted samples were plated on selective plates to increase detection limits. For super shedder mice, this permitted detection of a strain comprising just 0.00000001% of the total S. Typhimurium population.
The strain relative abundances were determined for each tissue in all of the 19 mice infected with the 108 equal mixture of 8 WITS. The relative abundances of each WITS strain were analyzed by one-way ANOVA (parametric) and Kruskal-Wallis (non-parametric) tests in Prism statistical software. These analyses were performed for each tissue collected from infected mice. Non-significant P values indicated that a particular WITS was not under or over represented in any tissue type (Table S2). To further verify that certain WITS strains were not preferentially selected for, a control experiment was performed in which mice were orally infected with an inoculum comprised of a skewed WITS mixture (Figure S2A). Underrepresented strains: W2, W3, W5, W6 (4.17%–7.32% of inoculum), overrepresented strains: W1, W4, W7, W8 (17.39%–20.94% of inoculum). Relative abundances of WITS in infected tissues were determined by qPCR after 35 days of infection. For each of the 8 WITS, defined bins were constructed for a range of strain relative abundances, with which the observed frequencies were used to generate histograms (Figure S2B).
Bray-Curtis dissimilarity scores were computed to quantitatively compare Salmonella population compositions in different sites. The relative abundance (y) of each WITS (n) was compared between two tissue sites i and j. The Bray-Curtis dissimilarity (dBCD) was calculated by:A value of 0 indicates an identical WITS composition between two sites, while a value of 1 signifies that two samples are completely dissimilar without any overlap in WITS representation.
The lithogenic diet established by Crawford et. al. ([38]) was modified in our experiments to include the normal rodent diet (Harlan, Teklad 2018) supplied in the Stanford Research Animal Facility. Mice were fed normal base chow supplemented with 1% cholesterol and 0.5% cholic acid (Harlan, Teklad custom research diet) for 10 weeks to induce cholesterol gallstone formation. Mice on control and lithogenic diets were anesthetized with isoflurane and shaved in the abdominal area for ultrasound imaging. A Vevo 2100 system (VisualSonics) was used to confirm gallstone formation. Mice were given 1 week to recover prior to infection with S. Typhimurium.
For sequential infections in which the IP strain served as the initial strain, mice were first injected with 103 SL1344 and the infection was allowed to establish for 35 days. Following that time period, mice were challenged with an oral dose of 108 SL1344-kanR. In experiments with sequential oral infections, mice first received 108 SL1344 orally by drinking. A persistent infection was allowed to establish for 102 days before oral challenge with 108 SL1344-kanR. This sequential oral infection was performed with the reciprocal order of strains, in which SL1344-kanR was given as the initial strain and SL1344 given as the challenge strain.
Mice were infected orally with either 108 SL1344 or SL1344-kanR and fecal shedding of Salmonella was monitored over 14 days prior to the start of the experiment. A SL1344-kanR super shedder donor was then cohoused with mice previously infected with SL1344, in addition to a naïve uninfected mouse as a control. Cohousing was continued for 10 days before the super shedder donor was removed. The reciprocal cohousing experiments were performed in which a SL1344 super shedder donor was cohoused with mice previously infected with SL1344-kanR.
The aminoglycoside was administered orogastrically in a single dose of 20 mg (Sigma Aldrich, K4000) dissolved in 200 µl of water. Mice were transferred to new cages with autoclaved bedding, chow (Harlan, Teklad 2018S), and water at the time of administration.
Prism (GraphPad) was used to create all figures and perform all statistical analyses. Intergroup comparisons of Bray-Curtis dissimilarity values (e.g. spleen-cecum versus colon-cecum) were analyzed by paired t-tests. Comparisons of oral and IP strain abundances within the same group of mice were evaluated with Wilcoxon matched-pairs signed rank tests. Differences in CFUs and strain composition between groups were examined by unpaired nonparametric Mann-Whitney tests. Significance was defined by p≤0.05.
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10.1371/journal.pgen.1007310 | Effector gene birth in plant parasitic nematodes: Neofunctionalization of a housekeeping glutathione synthetase gene | Plant pathogens and parasites are a major threat to global food security. Plant parasitism has arisen four times independently within the phylum Nematoda, resulting in at least one parasite of every major food crop in the world. Some species within the most economically important order (Tylenchida) secrete proteins termed effectors into their host during infection to re-programme host development and immunity. The precise detail of how nematodes evolve new effectors is not clear. Here we reconstruct the evolutionary history of a novel effector gene family. We show that during the evolution of plant parasitism in the Tylenchida, the housekeeping glutathione synthetase (GS) gene was extensively replicated. New GS paralogues acquired multiple dorsal gland promoter elements, altered spatial expression to the secretory dorsal gland, altered temporal expression to primarily parasitic stages, and gained a signal peptide for secretion. The gene products are delivered into the host plant cell during infection, giving rise to “GS-like effectors”. Remarkably, by solving the structure of GS-like effectors we show that during this process they have also diversified in biochemical activity, and likely represent the founding members of a novel class of GS-like enzyme. Our results demonstrate the re-purposing of an endogenous housekeeping gene to form a family of effectors with modified functions. We anticipate that our discovery will be a blueprint to understand the evolution of other plant-parasitic nematode effectors, and the foundation to uncover a novel enzymatic function.
| Plants and their pathogens/parasites are locked in an evolutionary arms race, with considerable attention directed towards the specific functions of the parasites’ “weapons”: the effectors. While we are beginning to understand these functions, we have very little understanding of how plant parasitic nematodes have bolstered their effector repertoire. Here we provide an example of how plant parasites of global economic importance have populated their effector repertoire by the unprecedented duplication and subsequent re-deployment of the endogenous housekeeping gene, glutathione synthetase. We hypothesise that many aspects of the “weaponization” programme detailed here will be common to the genesis of other plant-parasitic nematode effectors. Given that parasitic nematodes deploy a battery of effectors, many arising from the adaptation of either endogenous genes or loci acquired by horizontal gene transfer, this paradigm will have substantial impact on the effort to understand and ultimately undermine devastating USDA and EPPO quarantine organisms.
| The ability of nematodes to exploit living plants as a food resource has arisen independently in four of the twelve major lineages of the phylum Nematoda [1]. As a result, plant-parasitic nematodes occupy a diverse range of niches and climates, and infect a wide range of host species globally. Clade 12 of the phylum encompasses representatives of all major modes of parasitism; migratory ectoparasites, migratory endoparasites, and the most economically important and highly specialized obligate biotrophs—the sedentary endoparasites [2]. The latter induce the re-differentiation of root cells to form a unique nutrient-rich feeding site which is maintained for several weeks in a prolonged biotrophic interaction. For the cyst and reniform nematodes this takes the form of a large syncytium that arises through local cell wall dissolution and fusion of neighbouring protoplasts. Nematodes deploy hundreds of effector proteins to induce profound molecular and physiological changes associated with feeding site induction and maintenance. The majority of all described effectors are secreted from three pharyngeal gland cells (one dorsal and two subventral) through a hollow, protrusible needle-like stylet, into the plant.
While the basis for the evolution of nematode parasitism is largely unresolved and widely debated [3, 4], it is likely that a series of evolutionary transitions gave rise to the biologically complex sedentary plant endoparasites [5, 6]. Surprisingly little is known about the genetic changes that occurred with these transitions. In general, parasites lose functions as they further rely on their host. For the sedentary endoparasitic cyst nematodes, this is evidenced by a reduction in genes involved in detoxification of xenobiotic compounds, and the absence of whole classes of antibacterial and antifungal genes [7]. However, concurrent with this process, cyst nematodes have evolved a large repertoire of effectors that facilitate their remarkable abilities to suppress plant immunity and induce plant cells to re-differentiate into a novel tissue. The evolution of sedentary endoparasitism must therefore be additionally characterized by acquisition of novel functions.
Both acquisition of new genes by horizontal transfer [8] and selective expansion of particular gene families [9] are associated with the evolution of a parasitic lifestyle. For example, a large expansion of the astacin protease and CAP gene families coincided with the emergence of parasitism in the animal parasitic Strongyloides nematodes [10] whilst gene duplication is proposed to have been an important driver of parasitism in the Orobanchaceae plant parasites [11]. Examples of such expansions are also present in the genome of the potato cyst nematode Globodera pallida. The most unusual expansion comprises more than 50 predicted genes with similarity to glutathione synthetase enzymes [12]: almost all eukaryotes possess only a single glutathione synthetase gene.
Glutathione, the tri-peptide γ-L-glutamyl-cysteinyl-glycine, is the main low molecular weight thiol antioxidant in both plant and animal cells, often present at millimolar concentrations in vivo [13]. It is synthesised in a two-step, ATP-dependent, process: glutamate-cysteine ligase (GCL) catalyses the formation of γ-glutamylcysteine (γ-EC) from glutamate and cysteine, followed by the addition of glycine by glutathione synthetase (GS) to form glutathione. Glutathione has a fundamental, multifunctional role in modulating the redox status of cells, protecting them against oxidants and electrophiles, and detoxifying xenobiotics [14]. In both plants and animals, glutathione has a role in cellular defence responses against abiotic and biotic stress [15, 16]. Glutathione has been particularly implicated in plant responses to pathogens. Its abundance decreases during compatible interactions but it accumulates in response to avirulent pathogens [17] and can induce the expression of plant defence genes [18, 19]. Glutathione is also involved in biotic stress signalling [20], in particular NPR1-dependent/independent salicylic acid (SA)-mediated pathways [21, 22] and plants with reduced glutathione levels are generally more susceptible to pathogens and herbivorous insects [23, 24].
Given the diverse functions of glutathione it is clear that an expanded family of glutathione synthetases could be involved in numerous aspects of the plant-nematode interaction. Here we demonstrate that the expansion is not restricted to G. pallida and multiple subsequent expansions of GS genes have co-occurred with the evolution of complexity in plant-nematode interactions. The most recent expansion has been coupled with a diversification of structure and biochemical function that has given rise to enzymes that are introduced into the host cell, and likely possess novel substrate specificities.
We analysed the genomes and/or transcriptomes of eleven plant-parasitic and non-plant-parasitic nematode species (Caenorhabditis elegans, Bursaphelenchus xylophilus, Longidorus elongatus, Pratylenchus penetrans, Meloidogyne incognita, Nacobbus aberrans, Rotylenchulus reniformis, Globodera rostochiensis, G. pallida, Heterodera schachtii and H. avenae) to explore the expansion of genes encoding glutathione synthetase-like domains (Pfams PF03917 and PF03199 –Fig 1). Although almost all plant and animal genomes encode only a single housekeeping glutathione synthetase gene (GS), some plant-parasitic nematode genomes encode an unprecedented number (up to ~70 in R. reniformis). A Bayesian phylogeny inferred from an alignment of all 180 GS-like loci from these eleven species reveals two major expansion events, resulting in three Clades (Fig 1; complete GS-like gene sequences are available under Dryad accession doi:10.5061/dryad.7vd0160). Clade 1 contains one sequence from each nematode in the phylogeny (with the exception of M. incognita that, due to its polyploid genome, contributes two genes to the Clade [25]). It includes the only sequence from the free living nematode C. elegans, and the relative topology of the sequences in Clade 1 is similar to the most recent single and multi-gene phylogenies of plant parasitic nematodes for those species included [1, 26]. Taken together, this suggests that Clade 1 contains the housekeeping progenitor GS of each species.
The first expansion of GS-like genes (Clade 2) was present in the last common ancestor of present day migratory and sedentary plant endoparasites belonging to the order Tylenchida. Species vary in the number of Clade 2 genes from two (P. penetrans and M. incognita) to 12 (R. reniformis), with a mean, mode, and median of ~5. Clade 2 does not include sequences from the free living nematode C. elegans, the migratory ectoparasite L. elongatus or the non-Tylenchid migratory endoparasite B. xylophilus. We note that many Clade 2 GS share a short and somewhat variable C-terminal extension of the approximate consensus sequence P[A|S][A|S][E|Q][F|L], which has no known function (S1 Fig). This peptide is absent in all other clades and is not recognised as a canonical signal by TargetP.
A second larger expansion of GS-like genes (Clade 3) was present in the last common ancestor of the syncytia-forming cyst and reniform nematodes, which both lie on one side of a major bifurcation in the evolution of plant-sedentary endoparasitism (Fig 1). On average, sequences in Clade 3 share 36% protein identity with one another, and are much more diverse than those of Clades 1 and 2 (51% and 52% identity respectively) despite encompassing a narrower range of species. The fact that 68% of G. pallida and G. rostochiensis Clade 3 GS-like genes (26/38) are more similar to a Clade 3 GS from another cyst nematode in the phylogeny than they are to another Clade 3 GS from their own species, suggests that the majority of the diversity in cyst nematode Clade 3 GS was probably present in their last common ancestor. The R. reniformis, H. avenae, and H. schachtii GS-like gene complements were largely or entirely assembled from de novo transcriptome data rather than genomic data, precluding similar conclusions. Nevertheless we note that 8/14 H. avenae Clade 3 GS are more similar to a Clade 3 GS from other cyst nematodes than they are to other Clade 3 GS from their own species. In contrast all R. reniformis Clade 3 GS are contained within two sub-clades, and are more similar to other R. reniformis Clade 3 GS than Clade 3 GS from the cyst nematodes.
Given that species which have GS-like genes from the first expansion (Clade 2) have retained the progenitor housekeeping gene (Clade 1), and those that have GS-like genes from the second expansion (Clade 3) have retained both Clade 1 and 2, this suggests that the role of each expansion does not supersede its predecessor(s). They likely represent gain of novel function during the evolution of endoparasitism.
Each expansion of GS-like genes has a distinctive temporal expression pattern compared to its predecessor (Fig 1A). Clade 2 GS-like genes exhibit up-regulation predominantly in pre/non-parasitic stages, while Clade 3 GS-like genes are highly up-regulated, and often specifically expressed, during the plant-parasitic stages of endoparasitism. Given that the average within-species identity of G. pallida GS-like coding sequences is 49%, we can be confident in the distinct expression patterns of each Clade by RNAseq mapping. The disparity in differential temporal expression of GS-like genes further supports their functional diversification at different points of the life cycle. More detailed transcriptional analysis of all G. pallida GS-like genes throughout the lifecycle demonstrates that those of Clade 3 are highly up-regulated upon early interaction with the host plant and this expression is generally maintained throughout the parasitic stage (7, 14, 21, 28, and 35 days post infection; S2 Fig). This is strongly indicative that the most recently evolved Clade 3 GS-like paralogues are involved in prolonged sedentary endoparasitism.
Analysing the promoters of G. pallida and G. rostochiensis GS-like genes, we discovered that specifically those in Clade 3 harbour multiple copies of the DOG box, a promoter motif associated with dorsal gland cell expression in Globodera spp. [27, 28]. Globodera Clade 1, 2 and 3 GS-like gene promoters contain on average 0.5, 0.2, and 1.31 DOG boxes respectively (S1 Table). While there is no correlation between the number of DOG box motifs per promoter and temporal expression of the corresponding gene (R2 = 0.03 and [28]), Clade 3 GS-like genes with >1 DOG box in the first 1000 bp upstream of the coding start site are approximately twice as highly up-regulated during parasitism as those in Clade 3 without DOG boxes (24-fold (n = 16), 13-fold (n = 6)).
The presence of the DOG box in the promoter of previously uncharacterised genes is used to predict dorsal gland cell-expressed effectors in cyst nematodes [27]. Consistent with DOG box enrichment, all GS-like genes from Clade 3 analysed by in situ hybridisation were highly and specifically expressed in the secretory dorsal gland cells in a range of parasitic stage nematodes (Fig 2A and S3 Fig), while Clade 1 and Clade 2 GS-like genes were expressed in both female and male nematodes, with often preferential expression in the intestine (Fig 2A and S3 Fig. Gland cell expression holds true for both major groups of nematodes that encode Clade 3 GS, the cyst and the reniform nematodes (Fig 2A). The gland cells are the major effector-producing tissue, and secreted proteins expressed in the dorsal gland cell are delivered into the host plant [29, 30]. Strikingly, almost all Clade 3 GS-like genes, but none of those from Clades 1 and 2, encode a canonical N-terminal secretion signal (Fig 1A, black bars). These 102 putatively secreted Clade 3 GS-like proteins are the only known GS from the plant or animal kingdoms that possess such a signal. While we cannot rule out the possibility that Clade 3 GS proteins that do not encode a signal peptide are in fact secreted by non-canonical pathways, as presumed for some nematode effectors (eg. [20, 31]), we restrict our analysis in Clade 3 to signal peptide-encoding GS-like genes. Taken together, a clear distinction can be made between Clade 3 GS-like genes and those of Clades 1 and 2 based on their promoters, their spatial expression patterns, the presence of a signal peptide, and the likelihood of the sequence being upregulated in parasitic stages.
Tissue-specific expression of a putatively secreted protein in secretory dorsal gland cells is a strong indicator of a nematode effector that is delivered in planta. To confirm this, an affinity-purified polyclonal antibody was raised against recombinant Gpa-GSS17 and shown to be specific for this protein (Fig 2C). Using this antibody we are able to detect the abundant presence of Gpa-GSS17 in the large dorsal gland cell of parasitic stage G. pallida nematodes, the cytoplasmic gland extension, and the ampulla at the base of the stylet where secretions accumulate prior to their release (Fig 2B). The same native protein was delivered into the host across the plasma membrane, and is localized within the syncytial feeding site induced by the nematode in potato roots (Fig 2B). No similar fluorescence was observed with the FITC-labelled 2o antibody control. Hereafter, Clade 3 genes are thus referred to as GS-like effector genes. While it is highly unusual for a 100 kilodalton pathogen effector to be translocated to the host cell cytoplasm (GS-like effectors are obligate homo-dimers of 50 kDa per subunit, S4 Fig), plant parasitic nematodes clearly have the ability to construct organelle-sized structures inside the host cell at the plasma membrane where it meets the stylet orifice (reviewed in [32]). The demonstrations that a native GS-like effector protein is secreted from the nematode, delivered into the host, translocated across the host plasma membrane, and localised within the host cell during parasitism are strong evidence of involvement in parasitism.
Taken together, this illustrates how these plant-parasitic nematodes have exploited multiple gene gain events to deploy a novel effector family during parasitism. Interestingly, this programme of effector evolution is potentially more broadly applicable to other well-documented gene gain events in plant-parasitic nematodes: horizontally transferred genes must acquire a similar set of genetic attributes in order to be deployed as effectors during parasitism [33].
To explore the catalytic capacity of nematode GS-like genes, the G. pallida representative of Clade 1 (Gpa-gss1), one representative from Clade 2 (Gpa-gss5) and a number from Clade 3 (Gpa-gss12, Gpa-gss17, Gpa-gss22, Gpa-gss24 and Gpa-gss30) were heterologously expressed in, and their proteins purified from, bacteria (Fig 3A). For comparison, the only GS gene in the corresponding plant host of G. pallida (Solanum tuberosum, St-gss1), was similarly expressed and its product purified. All GS-like proteins were purified as obligate homo-dimeric pairs (S4 Fig). Purified GS were tested for canonical glutathione (Glu-Cys-Gly) synthetic capacity using a spectrophotometric assay where phosphate release from ATP is used as a molar equivalent proxy for glutathione synthesis from the substrates γ-glutamyl-cysteine (γ-EC) and glycine. Using this approach, the maximum rate of the host GS (St-GSS1) phosphate release was 7532 (±1358) umol/mg/min (S2 Table), consistent with a previous report for Arabidopsis GS of ~7500 umol/mg/min [34], demonstrating the suitability of the assay.
Remarkably, each stage of the evolutionary process that gave rise to GS-like effectors has witnessed at least a 10-fold reduction in apparent glutathione synthetic rate, such that Clade 3 GS-like proteins have not retained canonical enzyme activity (Fig 3 and S2 Table). We argue this loss of canonical activity is probably associated with a gain of non-canonical activity. We have analysed 180 GS-like protein sequences, each approximately 500 amino acids in length, from 11 species across the phylum. Within the context of an average sequence identity of only ~34%, there are just 5 residues that are absolutely conserved in all 180 sequences: three of these are in the ATP binding pocket, and one of these is required for catalysis. Perfect conservation of the catalytic residue in itself implies individual catalytic functionality. Given that GS effectors have no appreciable rate of ATP turnover when supplied with canonical substrates, yet all display perfect conservation at the catalytic arginine, we infer that GS-like effectors possess a distinct catalytic activity, which may involve alternative substrate(s).
GS-like enzymes that vary in substrate usage have been described in plants, yet in all cases this variation is restricted to the terminal amino acid: the same γ-EC backbone is universally used as a scaffold [35, 36]. For example, the homo-glutathione synthetase (hGS) of Glycine max (soya bean) catalyses the addition of β-alanine to γ-EC giving rise to homoglutathione (hGSH, γ-glu-cys-β-ala). The ability of purified GS-like proteins to incorporate a range of natural and non-natural terminal amino acids onto the γ-EC backbone was tested. St-GSS1, Clade 1 Gpa-GSS1, and Clade 2 Gpa-GSS5 all exhibited a strong preference for glycine, and a high affinity for γ-EC (S5 Fig). In contrast, none of the Clade 3 GS-like effectors accepted any of the tested terminal amino acid substrates in combination with the γ-EC backbone. One possible explanation for this is that Clade 3 GS-like effectors represent a novel diversification at the site of the di-peptide acceptor.
To create a structural basis for the exploration of novel substrate specificities, we initially solved the first crystal structure of a canonical plant GS (St-GSS1, S. tuberosum, host of G. pallida) in complex with ADP and the canonical di-peptide substrate γ-EC (2.5 Å, PDB 5OES). The crystal structures of two non-canonical parasite GS-like effectors were subsequently solved: Gpa-GSS30 in its apoform (2.6 Å, PDB 5OET) and Gpa-GSS22 in both its apoform (2.2 Å, PDB 5OEV) and ADP-bound closed conformation (2.6 Å, PDB 5OEU, S3 Table). Comparison between Gpa-GSS22-open and–closed reveals a functioning ATP grasp fold (Fig 4).
Residues in the ATP binding pocket of St-GSS1 are highly conserved in sequence and position in the Gpa-GSS22 structure (13/15 residues, S4 Table), and similarly conserved in sequence across G. pallida Clade 3 (structure guided alignment, ~12/15, n = 24, S4 Table). In contrast, there is considerably more variation in the di-peptide binding pocket of G. pallida GS-like effectors, yet, this variation is not evenly distributed around the pocket. The position of γ-EC in St-GSS1 is coordinated at both the glutamate and the cysteine residue. Two of the three residues that coordinate the cysteine interact with the C-alpha backbone. Corresponding residues in all G. pallida GS-like effectors are not conserved in sequence but are preferentially small and uncharged (Fig 3B inset), while the third residue, arginine, is 100% conserved (Fig 3B and S4 Table): consistent with permitting interactions with a cysteine residue. However, the glutamate of the di-peptide is exclusively coordinated by interactions with charged side chains of residues in the binding pocket (R, E, 2xQ, N, and Y). All six of these positions are substituted in Gpa-GSS22 and Gpa-GSS30, thus suggesting that the lack of canonical activity is because γ-EC is not accepted despite the potential for cysteine to be accommodated (Fig 3C and S4 Table). Variability in the glutamate portion of the di-peptide binding pocket is ubiquitous in G. pallida Clade 3 GS: of the 24 GS-like effectors there are 21 different amino acid compositions in these 6 positions, not one of which is canonical (S4 Table). Such diversity is highly unusual among Eukaryotes: a canonical arrangement has been conserved in GS enzymes for ~1 billion years of evolution in three kingdoms of life (Fig 5, Plantae (St-GSS1-closed, PDB 5OES), Fungi (Saccharomyces cerevisiae, PDB 1M0T), and Animalia (Homo sapiens GSS1, PDB 2HGS)).
In summary, conserved amino acids are only absent from one half of the acceptor di-peptide binding pocket. The space that would accommodate the cysteine thiol—the “active residue” of glutathione—is well conserved in sequence and structure, and is non-variable in all GS-like effectors tested. Coupled with the high degree of conservation in the ATP-binding pocket, the functioning ATP-grasp fold, and the perfect conservation of a catalytic residue, these data support the hypothesis that the loss of canonical glutathione synthetic activity is associated with a gain of non-canonical activity. The reaction product is probably a thiol-containing compound: ultimately implicating novel thiol biology in plant-nematode parasitism.
To determine the extent of thiol involvement in syncytia induced by cyst nematodes, we initially employed a qualitative analysis to specifically stain and visualize free thiols in plant tissue during infection. We used the Arabidopsis-Heterodera schachtii pathosystem because the thin and transparent host roots are amenable to such studies, whereas those of other syncytial-forming nematodes (e.g. potato-G. pallida) are not. The H. schachtii transcriptome encodes a number of putatively secreted Clade 3 GS-like effectors (Fig 1). Using ThiolTracker Violet we discovered that thiols are abundant in, and largely localized to, the cytoplasm of syncytia induced by H. schachtii, throughout infection (Fig 6A). Following this support, short sections of infected potato root harbouring syncytia of G. pallida at 21 days post infection were collected, separated from their corresponding nematode, and both samples retained for analysis. Several hundred infection sites and nematodes were collected in this manner and pooled. Control uninfected adjacent root tissue was collected from the same plants. Low molecular weight (LMW) thiols present in the three samples were extracted, derivatized with mono-bromobimane and analysed by Hydrophilic Interaction Liquid Chromatography (HILIC, Fig 6C). We note that any increase in syncytial thiol abundance is not explained by an increase in glutathione, but a series of other LMW thiols (Fig 6C). Surprisingly, analysis of the area under each curve allowed us to roughly estimate that glutathione accounts for only approximately 2.5% of LMW thiols detected using this HPLC protocol in potato control roots, and 2.7% in syncytial segments. Furthermore, some of the novel LMW thiols are not present in uninfected potato root tissue, not present in nematodes, but only present in syncytia (Fig 6D). Although the detected novel thiols were recalcitrant to analysis by mass spectrometry (e.g. Fig 6D), their lower retention by HILIC allows us to infer they are more hydrophobic than glutathione.
Our demonstration of GS-like effectors and abundant thiols in syncytia, coupled with the importance of rbohD-dependent ROS production in syncytial development [37], point towards fine-tuned redox homeostasis during parasitism. We cannot target GS-like effectors in the nematode to disrupt redox homeostasis in planta. The lack of a transformation system precludes the generation of gene knockouts whilst the likely functional redundancy of GS-like effectors and their low sequence similarity would require the combined use of one RNAi construct per effector (n≈20) in order to achieve host-induced gene silencing. We therefore exploited the availability of Arabidopsis plants mutant in the first step of glutathione synthesis (GSH-1 (pad2-1)) and thus compromised in the endogenous glutathione portion of the syncytial thiol pool by approximately 50–80% [38].
Both cyst nematode and syncytial development is significantly retarded at 10–12 days post infection in pad2-1 compared to wild type (WT) plants (Student’s T-test p ≤ 0.001, n = 147 and 82 respectively, Fig 7A, 7B and 7D). Specifically, syncytia and nematodes supported by pad2-1 plants are both on average ~50% the size of WT. However, despite that feeding site size and nematode size significantly co-vary in pad2-1 and WT (Pearson’s correlation, p ≤ 0.05 and 0.001, n = 49 and 66 respectively), the correlation is weak: most of the variation in nematode size (83–89%) is not explained by syncytium size (Fig 7C). This suggests that the lack of plant glutathione synthesis is associated with at least two linked but largely unrelated processes during infection which individually contribute to nematode size, and feeding site size. Furthermore, in pad2-1 Arabidopsis, localised necrosis could often be seen surrounding syncytia, and aborted syncytia were common (Fig 7D and S6 Fig). Given that it is technically intractable to measure aborted syncytia and those obscured by localised necrosis, the effect may be even greater than we report.
In a parallel approach, we reduced glutathione synthetic capacity to approximately 16% of wild-type levels by the exogenous application of 1 mM L-buthionine-sulfoximine (BSO). BSO is an irreversible chemical inhibitor of the first step in the glutathione synthesis pathway [38]. At this dramatically reduced level, infective nematodes fail to maintain a compatible interaction. Under these conditions nematodes are able to penetrate root tissue, migrate to the vascular cylinder and initiate the formation of a syncytium. However, unlike the interaction with pad2-1, syncytia induced in plants treated with BSO are always aborted (Fig 7E). Necrosis occurs in the area of the developing syncytium, as evidenced by propidium iodide staining, and often spreads non-distal to the site of infection (Fig 7F). This phenotype is similar to, but more frequent and severe than, the necrosis seen in pad2-1 plants. Necrotic patches on both pad2-1 plants and BSO treated wild-type plants were associated only with nematode infection sites, while uninfected root was comparable to that of untreated wild-type plants, albeit with reduced proliferation (S7 Fig). We cannot rule out a direct effect of BSO on the nematodes during infection, however, infective stage nematodes incubated for 48 hours on water agar plates containing 1 mM BSO were largely unaffected in mortality or motility (Mann-Whitney U Test, p = 0.408; n = 20. Fig 7G and 7H).
Here we report a paradigm of effector gene birth for a plant pathogen of global economic importance. Cyst nematodes have exploited a series of gene gain events to redeploy glutathione synthetase-like enzymes during parasitism, within the syncytial feeding cell formed in the host root. We predict that the attributes acquired by GS-like paralogs that allow them to be deployed as effectors (e.g. DOG box promoter motif, change in spatial expression, change in temporal expression, gain of signal peptide) constitute a programme of effector evolution common to the genesis of other plant-parasitic nematode effectors from endogenous loci (e.g. SPRY-SECs [39]). The programme likely also applies to well-documented gene gain events in plant-parasitic nematodes (e.g. effectors derived from horizontal gene transfer events [33]), and perhaps even other pathosystems (e.g. aphids [40]). Studying effector gene birth may therefore contribute towards addressing a priority in the field: characterising effector repertoires of diverse plant-pathogens [41].
The structures of the GS-like effectors led us to employ non-biased approaches to measure and analyse thiol biology during parasitism. Ultimately this resulted in the unexpected discovery of a range of novel thiols associated with the nematode feeding sites in host roots. Whilst cysteine, γ-EC and glutathione are major LMW thiols common to both plants and animals, analysis here of potato, and previously of other species [42], reveals a diverse array of unidentified LMW thiols in plants. Many of the very large number of undescribed compounds (~200) discovered in the Arabidopsis sulfur metabolome [43] could be LMW thiols, representing a pool of potential novel cysteine-containing substrates for the nematode GS-like effectors.
The thiol moiety, a nucleophile occurring predominantly in cysteine residues, is one of the most chemically reactive groups in biological systems and plays a major role in maintenance of cellular redox homeostasis. In most other plant-pathogen interactions described to date, the strong nucleophile glutathione is a negative regulator of pathogenicity [18–22, 24, 44, 45]. For example, the Arabidopsis pad2-1 mutant that has reduced glutathione content is more susceptible to a range of pathogens including Pseudomonas syringae and Phytophthora brassicae [24], whilst increased glutathione enhances plant defence responses [21]. The RipAY effector from the bacterial pathogen Ralstonia solanacearum has recently been shown to specifically target host glutathione in order to promote pathogenicity. It acts as a γ–glutamyl cyclotransferase to deplete intracellular glutathione, further emphasising the important role of this thiol in plant immunity [46–48]. A notable exception is the discovery that homoglutathione deficiency impairs root-knot nematode development in Medicago truncatula [49]. Here we show that Arabidopsis plants deficient in endogenous glutathione synthesis are less susceptible to cyst nematodes. This initially would thus seem unsurprising, however the necrosis and aborted feeding sites that result from depletion of glutathione during cyst nematode parasitism are not apparent for root-knot nematodes [49], suggesting different roles for glutathione in the two interactions. It is also important to note that cyst and root-knot nematodes have independent evolutionary origins of sedentary endo-parasitism, have almost no overlap in effector complement [39], and produce feeding sites that are different in structure and ontogeny [50]. Taken together, we cannot draw clear parallels between these superficially similar discoveries in two dissimilar pathosystems. Nevertheless, we show that plant-derived glutathione is a positive regulator of cyst nematode parasitism.
Interestingly, the expansion of Clade 2 GS enzymes, which preceded that of the GS-like effectors, is common to cyst nematodes, root-knot nematodes, and indeed all endoparasitic nematode species within the Tylenchida (including those that do not establish a feeding site within the host). Clade 2 GS enzymes do not encode a secretion signal and are expressed in the intestine. While the Clade 2 GS enzyme tested clearly has a slower rate of canonical enzyme activity than the Clade 1 GS, it can nevertheless synthesise glutathione: It has a high affinity for the γ-EC dipeptide substrate, and a preference for glycine as the terminal amino acid despite a lack of conservation in the two residues that apparently contribute to this specificity. While we cannot rule out the existence of other substrates, Clade 2 GS-like enzymes have retained canonical activity. Many Clade 2 GS share a short and somewhat variable C-terminal extension that is absent from all other clades and is not recognised as a canonical signal by TargetP. We can assume that the conservation of this C-terminal extension implies the existence of some functional constraints that remain to be elucidated.
In conclusion, we implicate a positive role for novel nucleophiles in parasitism of cyst nematodes. We show three discoveries that are functionally independent but grouped under the banner of redox homeostasis in the plant cell, 1) Nematode-derived GS-like effectors likely accept a thiol substrate, but do not produce glutathione (Figs 3,4 and 5); 2) Syncytia are abundant in novel thiols of unknown origin (Fig 6); and 3) Plant-derived glutathione is a positive regulator of cyst nematode parasitism (Fig 7). In contrast to this, it was shown that rbohD-dependent ROS production is also integral to feeding site development, and is necessary to limit cell death and promote cyst nematode parasitism [37]. Taken together, these data collectively support the hypothesis that nematode-induced syncytia operate within a narrow redox “Goldilocks zone”. The focus of future research will be to determine if any of these discoveries are dependent on one another biochemically, what is the cross talk between the various aspects of redox regulation, and how, together, they contribute to parasitism.
RNAseq reads for Heterodera avenae [51], H. schachtii (doi:10.5061/dryad.7vd0160.) and Rotylenchulus reniformis [52] were trimmed according to previously described methods except that HEADCROP was set to 11, 10 and 12 respectively [26]. Trimmed reads were assembled into de novo transcriptomes using the Trinity pipeline [53] with a minimum Kmer coverage of 2. Proteins were predicted using the Trinity wrapper scripts for transdecoder using the Pfam A and B library. GS genes were predicted from the assemblies generated above, existing transcriptome assemblies for Nacobbus aberrans [26], Longidorus elongatus [54], and Pratylenchus penetrans [55] and existing genome assemblies for Globodera rostochiensis [28], Globodera pallida [12], R. reniformis (doi:10.5061/dryad.7vd0160.), Meloidogyne incognita [25, 56], Bursaphelenchus xylophilus [57], and Caenorhabditis elegans [58] using Pfam (PF03917/PF03199). For R. reniformis, additional GS-like sequences were identified in the genome and transcriptome by sequence similarity searches (BLAST v 2.4.0; [59]) using all G. pallida GS-like proteins as queries. The results of these two identification pipelines were merged and a list of unique R. reniformis GS-like sequences was compiled from both the genome and transcriptome.
Drastically truncated sequences identified following alignment of all encoded proteins (MUSCLE v3.8.3.1; [60]), were removed or, for genomic predictions, manually curated where possible based on transcript coverage and/or homology. Any incomplete genomic predictions that could not be corrected due to missing sequence were also removed from further analysis. For G. pallida, upstream regions (2 kb) of all genomic predictions were analysed for the presence of additional exons that could encode signal peptides. The majority of G. pallida full-length coding regions, including all those where manual curation conflicted with the original gene prediction, were amplified using Phusion polymerase from cDNA prepared from early parasitic stage nematodes. Primers used are detailed in S5 Table. The number of predicted G. pallida GS genes that encoded a signal peptide increased following the manual curation and subsequent cloning. The amino acid sequences of those genes from all species remaining after curation were aligned and refined using MUSCLE v3.8.3.1 [60]. The alignment was trimmed using TrimAL (-gappyout) [61] and subject to model selection (WAG+GAMMA with invariable sites) and Bayesian phylogeny construction (Mr Bayes) with two million five hundred thousand generations, a sample frequency of 10%, and a burn in rate of 30% carried out in TOPALi v2.5 [62]. The phylogeny was out-group routed by the Clade containing the C. elegans and L. elongatus sequences [63] using FigTree v1.4 (http://tree.bio.ed.ac.uk/software/figtree/). Where available (G. rostochiensis, G. pallida, H. avenae, R. reniformis, H. schachtii, and N. aberrans), RNAseq reads were mapped back to either the relevant assembly, or in the case of G. rostochiensis and R. reniformis the manually curated GS-like transcripts, and normalized expression values (TMM) were calculated using the Trinity wrapper scripts for RSEM and EdgeR using default parameters. Expression fold change was calculated by dividing average normalized expression at parasitic life stages (any stages recovered from roots) by that of non-parasitic (eggs, second stage juveniles and, for G. pallida, adult males). Multiple biological replicates were available for G. pallida (two), G. rostochiensis (two), R. reniformis (three) and N. aberrans (three). In depth transcriptional analyses across the life cycle of G. pallida were performed using normalized expression values available [12]. Signal peptides were predicted using SignalP v4.0 [64].
For the cyst nematodes G. pallida and G. rostochiensis, in situ hybridisation was carried out on 3rd (J3) and 4th (J4) stage juveniles and young adult females extracted from roots of potato (Solanum tuberosum) according to previously described methods [28]. For sedentary stage female R. reniformis nematodes extracted from roots of cotton the same methods were followed except that the proteinase K treatment was reduced to 1 hour at room temperature. Single-stranded 100–200 base pair DNA probes corresponding to sequences of interest were prepared as described [24] using the oligonucleotide primers detailed in S5 Table. For each gene of interest, an equivalent sense-strand probe acted as a negative control. More than 100 individual nematodes were examined for each probe and the results presented are representative of the staining patterns observed.
Purified, recombinant Gpa-GSS17 protein was used to raise a polyclonal antibody in rabbit that was affinity-purified against the original antigen by Cambridge Research Biochemicals (Billingham, UK). For detection of the protein in nematodes, mixed parasitic stages of G. pallida were recovered from potato roots, fixed, cut, permeabilized and dehydrated as for in situ hybridisation. Rehydrated nematodes were washed with maleic acid buffer then incubated in the same buffer containing 1% blocking reagent (Roche) for 30 mins at room temperature. Following an overnight incubation at 4°C in blocking buffer containing Gpa-GSS17 antibody at a dilution of 1 in 200, nematodes were washed, reblocked for 30 mins and incubated with FITC-conjugated goat anti-rabbit 2o antibody (Sigma) at a dilution of 1 in 200 for 2h at room temperature. After three washes in maleic acid buffer containing 0.01% Tween-20, nematodes were resuspended in anti-fadent (PBS/glycerol; Citifluor) and visualized using a Leica DMRB microscope with GFP filter set. The experiment was carried out on separate occasions with two batches of fixed nematodes and >100 individual nematodes were observed on each occasion. Images were captured with a QIcam camera (QImaging) and Q-Capture software. The images presented are representative of all those that displayed hybridisation of the antibody. Control nematodes were processed in the same manner with the omission of primary antibody.
For detection of Gpa-GSS17 in syncytia, lengths of potato root 14 days post infection with J2 of G. pallida were fixed in 4% paraformaldehyde in PEM buffer (50 mM PIPES, 10 mM EGTA, 10 mM MgSO4 pH 6.9) for 3 days at 4°C. Samples were dehydrated, resin embedded, sectioned and applied to microscope slides according to Davies et al. [65] Transverse sections through the nematode feeding site were blocked with 5% milk powder in PBS for 3 h, then incubated in primary Gpa-GSS17 antibody at a dilution of 1 in 50 in 0.5% milk powder/PBS overnight at 4°C. After washing in PBS, primary antibody was detected with a FITC-conjugated anti-rabbit secondary antibody at a dilution of 1 in 100. At least 20 sections through each of three separate syncytia were analysed. Control sections were treated identically except for the omission of primary antibody. Plant cell walls were stained by incubation in Calcofluor-White (1 mg/ml) for 5 mins, followed by copious washes with PBS. Antibody localisation was visualized and recorded as described above for nematodes.
All GS-like genes analysed were cloned (without their predicted signal peptide if appropriate (SignalP v4.0)) into the pOPINS3C vector [66] in frame with an N-terminal poly-Histidine tag, a SUMO chaperone to promote protein solubility, and a 3C protease cleavage site (S5 Table). GS-like genes were expressed in, and their encoded proteins purified from, E. coli strain Shuffle to allow disulphide bond formation [67]. Cell cultures were grown in Luria Bertani media at 30°C until an optical density of 0.6–0.8 at A600 was reached. Cell cultures were cooled to 18°C and expression of the GS-like proteins of interest induced with addition of IPTG to a final concentration of 1 mM. Proteins were allowed to express for 14 hours at 18°C and the cells were collected by centrifugation and lysed immediately. Cell pellets were re-suspended in 50 mM Tris-HCl, 500 mM NaCl, 50 mM glycine, 5% (v/v) glycerol and 20 mM imidazole, pH 8.0 with the addition of one EDTA-free protease inhibitor tablet per 50 ml, and lysed by sonication. Cell lysate was clarified by centrifugation and applied to a 5 ml Ni2+-NTA column on an AKTA Xpress. His-tagged proteins were step eluted in resuspension buffer + 500 mM imidazole and injected onto a Superdex 200 26/60 gel filtration column equilibrated to 20 mM HEPES and 0.15 M NaCl, pH 7.5. Fractions containing the protein of interest were pooled, concentrated to ~5 ml, and the His+SUMO tag cleaved by overnight digestion with 3C protease at 4°C at a ratio of 100:1 (protein:protease). Mature GS-like proteins were separated from the His+SUMO tag by passing the solution over a 5 ml Ni2+-NTA column and injecting the flow-through onto a Superdex 200 26/60 gel filtration column. The concentration of each protein was measured by direct detection of the peptide bond (Direct detect), and protein aliquots were stored at -80°C until needed.
All enzyme assays were carried out at 30°C in a typical reaction buffer of 100 mM HEPES (pH 7.5), 20 mM MgCl2, and 5 mM dithiothreitol, with the addition of ATP, γ-EC, and glycine at varying concentrations. The hydrolysis of ATP in the presence of each protein, relative to the control, was used as a molar equivalent proxy for the production of glutathione, and measured by detection of free phosphate using malachite green absorbance at 630 nm. After determining the linear range of the reaction over time, pkat values of each protein in the presence of 1 mM γ-EC, 2.5 mM ATP and 100 mM glycine were measured in triplicate, and compared to a standard curve of free phosphate. To estimate the Michaelis-Menten kinetics of those enzymes with a rate appreciably above their negative control, γ-EC was varied in serial dilution and data analysis carried out in Sigmaplot. Experiments to explore the substrate specificity at the terminal amino acid of all GS-like enzymes were carried out using 2.5 mM ATP and 1 mM γ-EC with the following amino acids at 100 mM: glycine, β-alanine, D-alanine, GABA, AABA, diaminopropionic acid, D-serine).
Purified GS-like proteins were concentrated to between 5 and 10 mg/ml in 20 mM Tris (pH 7,5) and 200 mM NaCl. Sitting drop vapour diffusion crystallization experiments at 20°C were carried out in 96 well format using an OryxNano robot. Gpa-GS22 crystallized readily in a number of conditions in several screens at 5 mg/ml final concentration. Screen JCSG condition D10, was optimized to produce crystals in 0.2 M tri-methylamine N-oxide, 0.1 M Tris pH 9 and 20% w/v PEG 2000 MME. Several crystals were transferred to cryoprotectant (mother liquor with the addition 20% ethylene glycol final concentration) and frozen in a loop in liquid nitrogen. X-ray diffraction data were collected at the Diamond light source beamlines i04-1, processed using the xia2 pipeline [68], and the structure was solved by molecular replacement with 3KAL and named Gpa-GSS22-apo. The submitted structure was obtained through an iterative process of manual building and refinement using COOT [69] and REFMAC5 respectively. Tools of COOT and MOLPROBITY [70] were used for structure validation. Further, Gpa-GSS22, at a final concentration of 2.5 mg/ml, crystallized in the same condition with the addition of ADP (2.5 mM), MgCl2 (5 mM) and glutathione (2.5 mM). Diffraction data (beamline i02), structure solution, refinement, and validation were carried out as for Gpa-GSS22-apo, using the solved structure of Gpa-GSS22-apo in molecular replacement. Protein crystals for Gpa-GSS30 were obtained in JCSG E1 (0.2 M magnesium formate di-hydrate and 20% PEG 3350) and the structure solved/refined/validated as described above, using Gpa-GS22-apo for molecular replacement. Protein crystals for St-GSS1 were obtained by optimisation of JCSG D9 (1.4 M D-malic acid) with the addition of 2.5 mM γ-EC, 2.5 mM ADP, 5 mM MgCl2 and St-GSS1 at 3.75 mg/ml final concentration. The structure was solved/refined/validated as described above, using molecular replacement with the structure of hGS of Glycine max (3KAL).
Intra-cellular free thiols were visualized in syncytia produced by H. schachtii 7, 14 and 21 days post infection by incubating lengths of Arabidopsis root in 5 mM ThiolTracker Violet (Life Technologies) for 2 hours at room temperature. This fluorescent dye reacts with any reduced thiols, including glutathione, in live cells. Samples were rinsed twice in thiol-free PBS (Life Technologies) prior to imaging with a Zeiss LSM700 confocal microscope (excitation at 405 nm and collection from 410–500 nm).
For thiol analysis, roots of potato plants cv Desiree were harvested 28 days after planting tubers into sandy loam soil infested with cysts of G. pallida. Plants were grown at 20°C in a glasshouse with a 16h:8h light:dark cycle. Roots were washed to remove adhering soil particles and maintained in water while infection sites were identified using a stereobinocular microscope. Young adult female nematodes at approximately 21 days post infection were carefully removed intact from the root and collected. The length of root harbouring the syncytium (3–4 mm) was excised and collected separately. Equivalent, uninfected root lengths were harvested from the same root system. A total of ~200 feeding sites/nematodes/control root sections were amassed on each experimental occasion. Tissue in 1.5 ml tubes was flash frozen in liquid N2 and stored at -70°C until use. Samples were thawed in 130 ul of 0.1 M HCL and homogenized with a micro pestle. The cell lysate was clarified by centrifugation twice (10 minutes, 4°C, 20,000 RPM) and 50 μl was removed for derivatisation of thiols by addition of 1.5 μl 1 M DTT, 1.5 μl Mono-bromobimane and 45 μl 1 M CHES pH 9. The reaction was incubated at room temperature for 20 minutes, and stopped by the addition of 50 μl of 50% acetic acid. Samples were analysed using a Shimadzu LC/MS system comprising Nexera UHPLC binary pumps and autosampler, Prominence fluorescence and UV diode array detectors and LCMS-2020 single quadrupole mass spectrometer with ESI/APCI dual ion source. Two microliters of each sample was injected onto a Accucore 150-Amide–HILIC 100mm x 2.1mm column held at 25°C, and low molecular weight thiols were separated by a gradient of 0.1% v/v formic acid (Buffer A) and acetonitrile (Buffer B): a linear gradient from 90–85% B over 6 minutes followed by 85% B for 2 minutes, 60% B for 1.5 minutes, and 90% B for 2.5 minutes. Samples were eluted at 0.4 ml/minute and mono-bromobimane derivatives were detected by fluorescence at excitation/emission 397/480 nm. Mass spectra were collected continuously using a DL temperature of 250°C, a nebulizing gas flow rate of 1.5 L/min, a heat block temperature of 400°C, a drying gas flow rate of 15 L/min and in both ESI and APCI ionisation mode. Mass spectra were collected in scan mode with both +ve (4 kV) and–ve (-3.5 kV) across a range of 100–1200 Da. To determine the elution time of glutathione under these conditions, samples were spiked with 1 μl of 10 mM glutathione standard (Sigma) after clarification of the HCL extract by centrifugation.
Surface sterilised Arabidopsis thaliana (Col-0) or pad2-1 seeds were grown for 15 days on 9 cm vertical plates containing ½ strength Murashige and Skoog medium supplemented with 10 g/l sucrose and 1% Phytagel (½ MS10). Hatched second stage juveniles (J2) of H. schachtii were sterilised [71] and resuspended at a concentration of approximately one nematode/μl. 20 μl of suspension was pipetted onto each of two root points per plant with two plants per plate. Infection points were covered with GF/A paper (Whatman) for two days to facilitate invasion. At 10–12 days post infection, nematode (excluding un-emerged adult males) and syncytium size were estimated from the projected cross-section as viewed under a microscope (Olympus BH2) and measured using Image-Pro Analyser v7 (MediaCybernetics). Lengths of root containing aborted syncytia were incubated in propidium iodide (10 μg/ml) for 5 minutes at room temperature, washed twice in PBS, and imaged with a Zeiss LSM700 (excitation at 488 nm and collection from 590–700 nm). For L-Buthionine-Sulfoximine (BSO) treatment, wild-type seedlings were transferred to ½ MS10 plates containing 1 mM BSO two days prior to infection. BSO toxicity to nematodes over the course of the invasion period was tested separately by incubating J2s on water agar plates with or without 1 mM BSO in the absence of any plants. Nematode mortality was scored by observing each nematode for 5–10 seconds to record movement. Nematode motility was assessed by measuring nematode speed over 2 minutes using the ImageJ (http://imagej.nih.gov/ij/) plugin wrMTrck (http://www.phage.dk/plugins/wrmtrck.html).
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10.1371/journal.ppat.1004253 | Oncogenic Herpesvirus KSHV Hijacks BMP-Smad1-Id Signaling to Promote Tumorigenesis | Kaposi's sarcoma-associated herpesvirus (KSHV) is the etiological agent of Kaposi's sarcoma (KS), a malignancy commonly found in AIDS patients. Whether KS is a true neoplasm or hyperplasia has been a subject of intensive debate until recently when KSHV is unequivocally shown to efficiently infect, immortalize and transform rat primary mesenchymal precursor cells (MM). Moreover, KSHV-transformed MM cells (KMM) efficiently induce tumors with hallmark features of KS when inoculated into nude mice. Here, we showed Smad1 as a novel binding protein of KSHV latency-associated nuclear antigen (LANA). LANA interacted with and sustained BMP-activated p-Smad1 in the nucleus and enhanced its loading on the Id promoters. As a result, Ids were significantly up-regulated in KMM cells and abundantly expressed in human KS lesions. Strikingly, genetic and chemical inhibition of the BMP-Smad1-Id pathway blocked the oncogenic phenotype of KSHV-transformed cells in vitro and in vivo. These findings illustrate a novel mechanism by which a tumor virus hijacks and converts a developmental pathway into an indispensable oncogenic pathway for tumorigenesis. Importantly, our results demonstrate the efficacy of targeting the BMP-Smad1-Id pathway for inhibiting the growth of KSHV-induced tumors, and therefore identify the BMP pathway as a promising therapeutic target for KS.
| Although KSHV exerts multiple mechanisms to promote cell survival by repressing TGF-β signaling, little is known whether KSHV manipulates BMP signaling and contributes to the pathogenesis of KSHV-induced malignancies. In the present study, we have identified Smad1 as a novel binding protein of LANA by tandem affinity purification. We demonstrated that LANA up-regulated Id transcription through BMP-Smad1-Id signaling pathway. Id proteins were significantly up-regulated in KSHV-transformed MM (KMM) cells, and were abundantly expressed in human KS lesions; therefore, they were probably relevant to the development of KS. Importantly, we have shown that Ids are required to maintain the oncogenic phenotype of KMM cells in vitro and in vivo. These findings illustrate a novel mechanism by which a tumor virus hijacks and converts a developmental pathway into an indispensable oncogenic pathway for tumorigenesis. Furthermore, we showed that BMP signaling inhibitors dramatically hampered the tumorigenicity of KMM cells in vitro and in vivo. Our results demonstrate that small inhibitors targeting BMP-Smad1-Id signaling pathway are promising candidates for the treatment of KS.
| Kaposi's sarcoma-associated herpesvirus (KSHV) is the etiological agent of Kaposi's sarcoma (KS), which is the most common malignancy in AIDS patients [1]. The KSHV-infected proliferating spindle cells are the driving force of KS [2]. KSHV mainly displays latency in spindle cells. Viral latent genes were reported to promote cell proliferation and inhibit apoptosis through various mechanisms. In particular, latency-associated nuclear antigen (LANA), a multifunctional major viral latent protein, is responsible for maintaining viral episome, inhibiting viral reactivation, and promoting cell proliferation by targeting p53, pRb and GSK-3β, etc (reviewed in [3], [4]). We have also shown that LANA contributes to cell proliferation by promoting intracellular Notch (ICN) accumulation through inhibition of Sel10-mediated ICN degradation [5], [6].
Due to the lack of in vitro KSHV cellular transformation model and the lack of KS cell lines, the roles of KSHV-deregulated signaling pathways in KSHV-induced cellular transformation remain unclear. The recent development of a robust model of KSHV-induced cellular transformation and tumorigenesis has made this possible [7]. Specifically, KSHV can efficiently infect, immortalize and transform primary rat embryonic metanephric mesenchymal precursor (MM) cells. KSHV-transformed MM cells (KMM) efficiently induce tumors with virological and pathological features of KS. This work has paved a way for studying the intrinsic oncogenic pathways underlying the tumorigenesis driven by KSHV latent genes. Using this system, KSHV-encoded miRNAs and vCylin were recently demonstrated to play critical roles in KSHV-induced cellular transformation and tumorigenesis [8], [9].
Bone morphogenetic proteins (BMPs) belong to the transforming growth factor β (TGF-β) superfamily. BMP signaling pathways play critical roles in diverse developmental phases [10]. In recent years, BMP signaling pathways have increasingly been the focus in cancer research, since these developmental pathways are frequently disrupted in cancer [11]. BMP signaling pathways are involved in both promotion and inhibition of cancer progression depending on the context, which is similar to the TGF-β pathway [12].
Inhibitors of DNA-binding (Id) family are major downstream targets of BMP signaling, and belong to the helix-loop-helix (HLH) family of transcription factors. There are four known members of the Id family in vertebrates (called Id1, Id2, Id3 and Id4) [13]. Id proteins do not possess a basic DNA binding domain and functions as a dominant-negative regulator of basic HLH proteins [14]. Recent evidence has revealed that Id proteins, especially Id1, are able to promote cell proliferation and cell cycle progression. Moreover, up-regulation of Id1 has been found in many types of human cancers and its expression levels are also associated with advanced tumor stage. [15]. Id1 was once reported to be up-regulated in KSHV-infected endothelial cells and in KS tissues [16], however, the mechanism and implication of Id1 up-regulation remains unclear.
In this study, Smad1 was identified as a novel LANA-binding protein. LANA up-regulated Id expression through constitutively sustaining the activation of the BMP-Smad1-Id signaling pathway, and thus contributed to the oncogenicity of KMM cells in vitro and in vivo. These studies have identified a novel viral oncogenic signaling pathway, and our data indicate that small inhibitors targeting BMP-Smad1-Id signaling pathway could be promising candidates for the treatment of KS.
In order to explore the novel function of LANA, we utilized Strep-Flag (SF)-tag based tandem affinity purification (SF-TAP) method to identify novel LANA-binding proteins (Fig. 1A) [17]. Smad1, a critical transducer of BMP signaling [18], was one of the hit proteins co-purified by SF-LANA [19]. We confirmed that LANA physically interacted with Smad1 in 293T cells by reciprocal co-immunoprecipitation (Co-IP) (Fig. 1B, C). We further confirmed their interaction in KSHV-infected cells (Fig. S1). LANA is predominantly located in the nucleus [20], while Smad1 shuttles from cytosol to nucleus in complex with Smad4 resulting in the transcription of BMP target genes following phosphorylation at C terminus S463/465 (SXS motif) by type I BMP receptor [18]. To determine the compartment of LANA-Smad1 interaction, 293T cells were transfected with LANA and Smad1, then treated with BMP2 and harvested for cell fraction. Co-IP assay was performed with cytoplasmic fraction and nuclear fraction respectively. As expected, LANA-Smad1 interaction was only detected in the nuclear but not in cytoplasmic fraction (Fig. 1D). Moreover, Smad1 pulled-down by LANA was recognized by a p-Smad1/5/8 antibody (Fig. 1D). Since LANA did not bind to Smad5 (Fig. S1), these results suggested that LANA interacted with BMP-activated p-Smad1 in the nucleus.
We further mapped out the Smad1-binding domain of LANA. Smad1 could be pulled down by Myc-tagged full length LANA1–1162 and N-terminus LANA1–432, but not by C-terminus LANA762–1162, negative control Intracellular Notch1 (ICN) nor control vector (Fig. S1). Therefore, N-terminus LANA1–432 is responsible for Smad1-binding.
Next, we mapped out the LANA-binding domain of Smad1. Smad1 has highly conserved N- and C-terminal regions known as Mad homology (MH) 1 and MH2 domains, respectively, which are linked by a linker region with a highly variable structure [18]. HA-tagged full length Smad1, Smad1-C (Linker+MH2), Smad1-MH2, but not Smad1-N (MH1+Linker) were pulled down by LANA (Fig. 1E). Therefore, Smad1 MH2 domain is responsible for LANA-binding. To narrow down the LANA-binding domain within Smad1 MH2 domain, we constructed a series of MH2 truncation mutants, termed as MH2-N, MH2-M and MH2-C respectively. Deletion of neither C-terminus of MH2 (MH2-N) nor N-terminus of MH2 (MH2-C) totally abolished its binding to LANA while the center part of MH2 (MH2-M) retained LANA binding activity (Fig. 1F). Therefore MH2-M (Smad1308–407) was critical for LANA-binding.
We then asked whether LANA-Smad1 interaction depended on the phosphorylation of SXS motif of Smad1. The Smad1 mutant with the SXS motif deleted (ΔC3), inactivated (AVA) or constitutively activated (DVD) [21] bound to LANA as efficiently as the wild type Smad1 (Fig. 1G). The differences of the apparent molecular weight between the wild type Smad1 and Smad1 mutants in SDS-PAGE were due to tag sizes. These results indicated that the nuclear location but not the phosphorylation of Smad1 is the restriction factor for the LANA-Smad1 interaction.
BMP signaling regulates fundamental biological processes during embryonic development, postnatal development, as well as tumorigenesis [22]. The Smad1 MH2 domain is responsible for sensing BMP signaling, oligomer formation with other Smads, interaction with various DNA-binding proteins, and transcriptional activation of BMP downstream targets [18]. We wondered whether LANA modulated BMP-Smad1 signaling by regulating the expression and/or function of p-Smad1via interaction with Smad1-MH2 domain. To address this hypothesis, 293T cells were transiently transfected with LANA or a control vector and then treated with BMP2. 293T cells were harvested at different times and subjected to immunoblotting for the levels of p-Smad1 and BMP downstream target Id1. The levels of p-Smad1 activation and Id1 were normalized to their expression levels at 0 hour in two groups, respectively. Activation of p-Smad1 started to decline at 3 hours post BMP2 treatment and reached the basal level at 24 hours in the vector-transfected 293T cell while activation of p-Smad1 did not start to decline until 15 hours and continued to maintain at a relatively higher level at 24 hours in the LANA-transfected 293T cells (Fig. 2A, B). Therefore, BMP-induced p-Smad1 expression was significantly sustained by LANA (Fig. 2A, B). Consistent with these results, the induction of the canonical BMP downstream target Id1 was significantly potentiated in the LANA-transfected cells than the vector-transfected control cells by BMP2 (Fig. 2A, C).
Id1 was once reported to be up-regulated in KSHV-infected endothelial cells and in KS tissues; moreover, expression of LANA and vCyclin seemed to up-regulate Id1 expression in post-transcription level [16]. Since Id1 was well-recognized for its roles in tumorigenesis [13], [23], we sought to determine whether LANA up-regulated Id1 expression through the BMP-Smad1 pathway.
As previously reported, we showed that Id1 was up-regulated in KSHV-infected human primary endothelial cells (Fig. S2). However, LANA but no other KSHV latent genes significantly up-regulated Id1 expression in 293T cells (Fig. S3). Meanwhile, LANA did not obviously alter Id1 protein stability. These results indicated that LANA regulated Id1 expression mainly at transcription level (Fig. S3). Consistent with these results, Id1 transcription was up-regulated more than two fold in LANA-transfected 293T cells (Fig. 3A). Treatment with noggin, which inhibited BMP signaling, abolished LANA induction of Id1 expression (Fig. 3A); while treatment with BMP2 further enhanced LANA induction of Id1 expression (Fig. 3B). We then asked whether LANA was directly involved in Id1 transcription regulation. In a promoter reporter assay, LANA increased the activity of Id1 promoter reporter Id1-985, which contained a Smad1 binding site or BRE (BMP-responding element), but not that of the mutant reporter Id1-956 lacking the BRE [24] (Fig. 3C). Knock-down of Smad1 abolished LANA activation of the Id1-985 promoter reporter (Fig. 3D, Fig. S3). Therefore, LANA up-regulation of Id1 transcription depended on the BMP-Smad1 signaling pathway.
We showed that LANA was directly recruited to the Id1 promoter, together with Smad1 in ChIP-PCR assay (Fig. 3E). Moreover, LANA significantly enhanced the enrichment of Smad1 binding to the Id1 promoter after BMP2 treatment (Fig. 3F). Collectively, these results indicated that LANA promoted Smad1-mediated Id1 transcription activation through sustaining p-Smad1 expression, and probably facilitating and extending the loading of Smad1 on Id1 promoter.
Interestingly, we found that other Id family members, including Id2 and Id3 were also up-regulated in LANA-transfected 293T cells at both mRNA and protein levels (Fig. S4), whereas Id4 was not detected in our system. Furthermore, we showed that Id2 and Id3 were up-regulated in KSHV infected human primary endothelial cells (HUVECs) as Id1 (Fig. S5). Knockdown of Smad1 significantly impaired the expression of Id1, Id2 and Id3 in KSHV infected HUVECs, which suggested that Ids were mainly regulated by BMP-Smad1 pathway in those cells. We also showed that BMP signaling inhibitor Dorsomorphin dramatically repressed Id1, Id2 and Id3 in iSLK.219 cells (Fig. S5). Based on our data, we believed that LANA might generally up-regulated the transcription of Id family members through BMP-Smad1-Id signaling pathway in KSHV infected cells.
Since Ids were important oncogenic proteins, we sought to determine whether Ids were aberrantly expressed in KS tissues. We examined the expression of Id proteins and LANA in 10 cases of classical KS tissues and 5 cases of normal skin tissues by immunohistochemistry. As shown, there were weak to modest staining signals of Id1, Id2 and Id3 only in the basal cells of epidermis and around the hair follicle of dermis in normal skin tissues (Fig. 4). There was no LANA staining in any normal skin tissues (Fig. 4). In sharp contrast to normal skin tissues, there were strong staining signals of Id1, Id2 and Id3 in the spindle cells in the KS lesions. By staining for Ids and LANA in consecutive sections, positive signals of Ids were only observed in the spindle cells in KS lesions, which were also positive for LANA staining. No staining of Ids was observed in the adjacent tissues, which were negative for LANA (Fig. S6). Because of the small sample size, we were not able to perform a valid correlation analysis between Ids expression and the stage of the tumors. Nevertheless, our data suggested that Ids were aberrantly regulated in KS tumors and might be relevant to the development of KS. Because the p-Smad1 antibody was not suitable for immunohistochemical staining, we were not able to examine the expression of p-Smad1 in these KS lesions. Nevertheless, we showed that there was strong staining of Smad1 in the KS lesions but not in adjacent tissues (Fig. S6) indicating that BMP-Smad1-Id signaling might be involved in the aberrant expression of Ids in KS.
KSHV can efficiently infect and transform primary rat embryonic metanephric mesenchymal precursor (MM) cells [7]. KSHV-transformed MM cells (KMM) efficiently induce tumors with virological and pathological features of KS [7]. We asked whether Id family members were up-regulated by LANA in KMM cells. We detected significantly higher levels of Id1∼Id3 (about 3 fold) in KMM cells than in MM cells at both mRNA and protein levels (Fig. 5A, B). Knock-down of LANA dramatically suppressed the expression of Id1, Id2, and Id3 in KMM cells (Fig. 5C). These results indicated that LANA was responsible for the up-regulation of Ids in KMM cells.
Id proteins, especially Id1, are able to promote cell proliferation and cell cycle progression. To determine if LANA deregulation of the BMP-Smad1-Id pathway could contribute to KSHV-mediated tumorigenesis [7], we established KMM-shId1 cell lines with high Id1 knockdown efficiency and determined the effect on cellular transformation (Fig. 6A). Knock-down of Id1dramatically decreased the proliferation of KMM cells (Fig. 6B) and inhibited the formation of foci in culture (Fig. 6C, D), formation of colonies in soft agar (Fig. 6E, F), and induction of tumors in nude mice (Fig. 6G, H, I) In contrast, knockdown of Id1 in MM cells only slightly decreased the proliferation of MM cells (Fig. S7). We also established KMM-shId2 and KMM-shId3 cell lines (Fig. S8). Knockdown of Id2 and Id3 inhibited anchorage-independent growth of KMM cells in soft agar (Fig. S8). Moreover, knockdown of either LANA or Smad1 also severely impaired the anchorage-independent growth of KMM cells (Fig. S9). These results indicated that Ids were required for maintaining the oncogenic phenotype of KMM cells.
We further asked whether Ids were the driving force for KSHV-mediated cellular transformation. We overexpressed Id1 in MM cells, however, no direct cellular transformation was observed as expected (Fig. S10). Nevertheless, ectopic expression of Id1 in KMM cells (Fig. S11A) further accelerated cell proliferation (Fig. S11B), and increased the formation of foci in culture and formation of colonies in soft agar (Fig. S11C, D, E, F).
Collectively, our data provided evidence that LANA increased BMP-Smad1-Id signaling and this pathway was required for KSHV-induced tumorigenesis.
Based on the above findings, we speculated that inhibitors of the BMP pathway might be potential therapeutic agents of KS. Dorsomorphin potently inhibits BMP-mediated Smad1/5/8 phosphorylation [25], while WSS25 disrupts the interaction between BMP and BMP receptor [26], [27]. Indeed, treatment with these two molecules dramatically inhibited BMP2-stimulated p-Smad1 expression (Fig. 7A), and inhibited the anchorage-independent cell growth of KMM cells in soft agar assay (Fig. 7B). Importantly, compared to MM cells, Dorsomorphin showed preferential toxicity to KMM cells (Fig. 7C), indicating Dorsomorphin selectively targeted KSHV-transformed cells. Furthermore, we found that Dorsomorphin dramatically inhibited the expression of Ids (Fig. 7D) while ectopic expression of Id1 significantly rescued Dorsomorphin induced G2/M arrest [28], cellular toxicity in KMM cells (Fig. 7E, F and Fig. S12), and partially rescued Dorsomorphin inhibition of anchorage-independent colony formation (Fig. 7G). These results indicated that Dorsomorphin mainly inhibited the oncogenicity of KMM cells through targeting the BMP-Smad1-Id pathway. To strengthen our conclusion, we showed that overexpression of Id1 significantly rescued Dorsomorphin-induced cellular toxicity in 293T cells in a dose-dependent manner (Fig. S13).
Finally, we determined the efficacy of Dorsomorphin in inhibiting in vivo tumor growth of KMM cells. We subcutaneously injected 1×106 KMM cells into BALB/c nude mice. When tumor volume reached about 50∼100 cm3, the nude mice were randomly divided into 2 two groups. One group was intraperitoneally injected with a single dose of Dorsomorphin at 10 mg/Kg [29] while the other group was injected with vehicle control. Impressively, single treatment with Dorsomorphin was sufficient to significantly inhibit the tumor growth of KMM cells (Fig. 7H). Immunohistochemical staining showed that Dorsomorphin inhibited Id1, Id2, Id3 and Ki67 expression and activated caspase 3 in the tumors (Fig. 7I). Interestingly, LANA remained positive in the Dorsomorphin-treated tumors (Fig. 7I), suggesting that the antitumor activity of Dorsomorphin was not dependent on the inhibition of KSHV infection and replication in KMM cells.
Our results showed that KSHV LANA interacted with BMP-activated p-Smad1 in the nucleus, sustained p-Smad1 expression, and facilitated its loading on the Id promoter leading to aberrant expression of Ids, which were indispensable driving forces for KSHV-induced tumorigenesis. Thus, KSHV hijacks and converts a developmental pathway into an oncogenic pathway, which is essential for KSHV-induced transformation. Furthermore, our results have shown that small inhibitors targeting BMP-Smad1-Id signaling pathway may serve as potential candidates for the treatment of KS (summary in Fig. 8).
Although KSHV exerts multiple mechanisms to promote cell survival by repressing TGF-β signaling [30]–[32], little is known whether KSHV manipulates BMP signaling and contribute to the pathogenesis of KSHV-induced malignancies. Previously, KSHV lytic gene K5 was reported to inhibit BMP signaling by down-regulating BMPR-II through ubiquitination-mediated degradation [33]. However, KSHV is predominantly maintained in the latent state of replication in KS spindle tumor cells and KMM cells, in which K5 is usually not expressed. In this context, we believe that KSHV hijacks the BMP-Smad1-Id pathway to promote tumorigenesis.
We previously reported that Smad1 was not detected in PEL cells [31] and LANA did not interact with Smad5. It is unlikely that LANA is involved in Id regulation in PEL cells. We showed that Id1∼3 were expressed in KSHV-positive BCBL1 and BC3 cells at levels similar to KSHV-negative BJAB cells (Fig. S14). Since BJAB was not an ideal control for BCBL1 and BC3 cells, we further compared the expression of Ids in BJAB and KSHV stably transfected BJAB (KSHV-BJAB) cells. We found that Id2 and Id3, but not Id1 was decreased by about 50% in KSHV-BJAB cells compared to BJAB cells (Fig. S14). Since Id2 and Id3 but not Id1 were reported to be down-regulated in the vFLIP-transfected cells [34], vFLIP might be the main viral gene that regulates the expression of Ids in PEL cells.
Since Id1∼3 are significantly up-regulated in KS lesions compared to adjacent tissue and normal skin and in KMM cells compared to MM cells, up-regulation of Ids by LANA through LANA-Smad1-Id signaling is likely the principal mechanism that KSHV regulates the expression of Ids in KS tumor cells and in KSHV-transformed KMM cells. Interestingly, Id1 and Id3 were induced by EBV latent protein LMP1 [35], [36]. LMP1 inactivates the function of Foxo3a leading to up-regulation of Id1. Id1 increased cell proliferation and conferred resistance to TGFβ-mediated cell cycle arrest in nasopharyngeal epithelial cells [37]. Therefore, Id proteins may serve as conserved targets for oncogenic herpesviruses.
Ids inhibit apoptosis and promote cell proliferation through distinct mechanisms [13]. For example, Id1 had been shown to inhibit E-protein and Ets-protein-mediated activation of the p16/INK4a [38], [39]. Id2 has been found to reverse cellular growth inhibition by the retinoblastoma protein (pRb) through direct interaction with pRb [40]. How individual Ids promote KSHV-mediated oncogenesis remain to be further clarified. Our data showed that LANA up-regulated BMP-Smad1-Id signaling was required but not sufficient for KSHV-induced tumorigenesis. It is likely that additional oncogenic signaling pathways are involved in KSHV-induced cellular transformation and tumorigenesis. Discovery of these additional pathways could help better understanding of how KSHV induces tumorigenesis.
Dorsomorphin is known to potently inhibit the expression of Ids through suppressing BMP-induced Smad1 phosphorylation. Our results showed that Dorsomorphin dramatically inhibited the growth of KMM cells in vitro and tumor growth in vivo. Even though Dorsomorphin might also target other kinases [41], our results showed that Id1 was capable of rescuing Dorsomorphin-induced G2/M arrest and cellular toxicity in KMM cells. Therefore, we have demonstrated that, by targeting the KSHV-deregulated BMP-Smad1-Id pathway, Dorsomorphin inhibits KSHV-induced tumorigenesis. Dorsomorphin might be a promising lead compound for KS therapy.
Currently, it is still unknown how LANA sustains p-Smad1 activation through their interaction. In the basal state, Smad1 constantly shuttles between cytoplasm and nucleus through its N-terminal nuclear localization signal (NLS) motif and C-terminal nuclear export signal (NES) [42]. Upon activation by BMP, the C-terminal of Smad1, which is phosphorylated at SXS motif, undergoes conformation change, and creates an acidic knob to form a trimer with the homologous MH2 domain of another Smad1 molecule and MH2 domain of Smad4 [43]. The ligand-induced phosphorylation promotes the accumulation of the hetero-oligomer in the nucleus by inhibiting the nuclear export and enhancing its import [42]. In the nucleus, Smad1/Smad4 complexes bind to other co-transcription factors and initiate target gene transcription. The signal undergoes rapid termination through dephosphorylation in its C-terminal SXS motif by PPM1A [44] and/or SCP (small C-terminal domain phosphatase) family of nuclear phosphatases [45] or degradation via polyubiquitylation and proteasome-mediated degradation by Smurf1/2 [46] or CHIP [47]. In this study, we have demonstrated the interaction between the N-terminal of LANA and MH2 domain of Smad1. This interaction may have several effects on sustaining the activated Smad1 in the nucleus: 1) it may stabilize the heteromeric complex between the phosphorylated Smad1 and the common mediator Smad4, thus masking the NES motif; 2) it may protect Smad1 from dephosphorylation caused by PPM1A or SCPs; 3) it may disrupt the rapid Smad1 turnover via Ubiquitin-Proteasome Pathway mediated by Smurf1/2 or CHIP. Thus, LANA facilitates the loading of functional p-Smad1 on the Id promoter and ultimately leads to aberrant expression of Ids. However, additional works are required to confirm any of these speculations.
In a summary, our study has revealed that BMP-Smad1-Id signaling pathway is positively regulated by LANA and serves as an intrinsic oncogenic pathway of KSHV-induced tumorigenesis. More importantly, we have shown that the BMP-Smad1-Id pathway is a potential therapeutic target for KS.
The clinical section of the research was reviewed and ethically approved by the Institutional Ethics Committee of the First Teaching Hospital of Xinjiang Medical University (Urumqi, 127 Xinjiang, China; Study protocol # 20082012). Written informed consent was obtained from all participants, and all samples were anonymized. All participants were adults.
The animal experiments were approved by the Institutional Animal Care and Use Committee of the Institut Pasteur of Shanghai, Chinese Academy of Sciences (Animal protocol # A2013010). All animal care and use protocols were performed in accordance with the Regulations for the Administration of Affairs Concerning Experimental Animals approved by the State Council of People's Republic of China.
Rat embryonic metanephric mesenchymal precursor cells (MM cells), KSHV-transformed MM cells (KMM), 293T cells were maintained in DMEM (HyClone) supplemented with 10% fetal bovine serum (HyClone). HUVEC was maintained in EGM (Lonza). KMM/shsmad1, KMM/shId1, KMM/shLANA, and KMM/shControl, KMM/Id1, KMM/Vector, 293T/shSmad1, 293T/shControl, 293T/SF-LANA and 293T/SF-Puro cell lines were established by infection of indicated lentivirus according to the manufacturer's instructions (System Bioscience).
LANA truncation plasmids were previously reported [48]. pCDH-SF-LANA was constructed by sub-cloned full-length LANA into pCDH-SF-EF1-Puro by EcoRI and BamHI sites. Reporter plasmids pGL3-Id1-985 and pGL3-Id1-956 were constructed as previously reported [24]. HA-Smad1and Flag-Smad1, were provided by Dr. Naihe Jing (Shanghai Institutions of Biological Sciences). ShLANA was previously reported [49]. shId1, shId2, shId3 and shSmad1 were constructed in pLKO.1 using the following targeting sequence: Id1 (AAGGTCACATTTCGTGCTTCT); Id2 (CAGCACGTCATCGATTATATC); Id3 (GTGATCTCCAAGGACAAGAGG) ; Smad1(CGGTTGCTTATGAGGAACCAA). Truncated or SXS motif mutated Smad1 plasmids were constructed by cloning the indicated sequence into pCMV-HA vector using the following primers:
Smad1-N (F: 5′-CGCGTCGACAATGAATGTGACAAGTTTATT-3′, R: 5′-CGCCTCGAGTTAAGCAACCGCCTGAACATCTC-3′);
Smad1-C (F: 5′-CGCGTCGACAATGCCTGTACTTCCTCCTGTGCT-3′, R: 5′-CGCCTCGAGTTAAGATACAGATGAAATAGGAT-3′);
Smad1-MH2 (F: 5′-CGCGTCGACAATGTATGAGGAACCAAAACACTG-3′, R: 5′-CGCCTCGAGTTAAGATACAGATGAAATAGGAT-3′);
Smad1-DVD (F: 5′-CGCGTCGACAATGAATGTGACAAGTTTATT-3′, R: 5′-CGCCTCGAGTTAATCTACATCTGAAATAGGATTA-3′);
Smad1-AVA (F: 5′-CGCGTCGACAATGAATGTGACAAGTTTATT-3′, R: 5′-CGCCTCGAGTTAAGCTACAGCTGAAATAGGATT-3′);
Smad1-ΔC3 (F: 5′-CGCGTCGACAATGAATGTGACAAGTTTATT-3′, R: 5′-CGCCTCGAGTTAAGCTACAGCTGAAATAGGATT-3′).
Expression plasmids of truncated Smad1-MH2 were constructed by cloning the indicated sequence into pEGFP vector using the following primers:
MH2-N (F: 5′-cgcAGATCTTATGAGGAACCAAAACACTG-3′, R: 5′-cgcGGATCCTTAATGATGGTAGTTGCAGTTCC-3′)
MH2-M (F: 5′-cgcAGATCTCGTTTCTGCCTTGGGCTGCT-3′, R: 5′-cgcGGATCCTTATGTAAGCTCATAGACTGTCTCA-3′)
MH2-C (F: 5′-cgcAGATCTGGATTTCATCCTACTACTGTTTGC-3′, R: 5′-cgcGGATCCTTAAGATACAGATGAAATAGG-3′)
MH2-F (F: 5′-cgcAGATCTTATGAGGAACCAAAACACTG-3′, R: 5′-cgcGGATCCTTAAGATACAGATGAAATAGG-3′).
The antibodies and reagents were used as follows: anti-LANA (1B5, prepared in our lab), anti-Smad1 (Santa cruz, sc-7965x), anti-pSmad1/5/8 (Cell signaling technology, #9511), anti-Id1 (Santa cruz, sc-488), anti-Id2 (Santa cruz, sc-489), anti-Id3(Santa cruz, sc-490), anti-Ki67 (Novocastra, NCL-Ki67p), anti-cleaved Caspase-3 (Cell signaling technology, #9661). Anti-Flag M2 affinity gel (Sigma, A2220), Strep-Tactin sepharose (IBA, 2-1201-010), desthiobiotin (IBA, 2-1000-001), BMP2 (Sigma, B3555), Cycloheximide (Sigma, C1988), Dorsomorphin (Sigma, P5499) and WSS25 were kindly provided by Dr. Kan Ding from Shanghai Institute of Materia Medica [26].
TAP of SF-LANA was done as previously described [19]. Briefly, 293T-SF-LANA or 293T-SF-Puro cells were harvested and subjected to nuclear extraction as previously described [50]. Dialyzed nuclear extract was loaded into a column of prewashed Strep-Tactin Superflow (0.5 ml bed volume, IBA). The column was washed with 10 bed volume of Buffer W (50 mM Tris pH 7.9, 100 mM KCl, 10% Glycerol, 0.2 mM EDTA, 0.5 mM DTT, 0.1% Triton-X100, 0.2 mM PMSF) and eluted with 3 bed volume of Buffer E (Buffer W containing 2.5 mM D-desthiobiotin). The elute was then subjected to second round of affinity purification by anti-Flag M2 affinity gel for 2 hours at 4°C. The beads were washed with Buffer W for 5 times and eluted with 3×Flag Peptide in Buffer W. The elute was monitored by SDS-PAGE and subjected to mass spectrometry.
Cells were lysed in radio immunoprecipitation assay (RIPA) buffer (50 mM Tris [pH 7.6], 150 mM NaCl, 2 mM EDTA, 1% Nonidet P-40, 0.1 mM PMSF, 1×phosphatase inhibitors [Phospho-Stop, Roche]) for 1 h on ice with brief vortexing every 10 min. The lysate were incubated with antibody or affinity beads as indicated overnight at 4°C. The immunoprecipitations were separated by SDS-PAGE and analyzed by immunoblotting. For cytoplasmic protein and nuclear protein fractionation, cells were harvested and extracted as described [51].
Cells were collected and lysed in Trizol buffer (Life technology), and RNA was isolated according to the manufacturer's instructions. Reverse transcription was performed with a cDNA Reverse Transcription Kit (Toyobo). Real-time RT-PCR was performed with a SYBR green Master Mix kit (Toyobo). Relative mRNA levels were normalized to Actin and calculated by ΔΔCT method. The primers were listed below:
Id1 (F: 5′-CTGCTCTACGACATGAACGG-3′, R: 5′-GAAGGTCCCTGATGTAGTCGAT-3′);
Id2 (F: 5′-GCTATACAACATGAACGACTGCT-3′, R: 5′-AATAGTGGGATGCGAGTCCAG-3′);
Id3 (F: 5′-GAGAGGCACTCAGCTTAGCC-3′, R: 5′-TCCTTTTGTCGTTGGAGATGAC-3′);
Actin (F: 5′-GCACGGCATCGTCACCAACT-3′, R: 5′-CATCTTCTCGCGGTTGGCCT-3′).
Chromatin immunoprecipitation (ChIP) was performed as previously described. Briefly, 5 µg correspondent antibody (anti-HA mAb, anti-Flag-mAb) or control mouse immunoglobulin (IgG) was added into each group of lysate at 4°C overnight. Then 50 µl proteinA/G beads, which had been precleared with binding buffer containing 0.2 mg of salmon sperm DNA per ml for 6 h, were added into each sample at 4°C for 2 h for immunoprecipitation. To extract the DNA fragment, TE buffer with 1% SDS and proteinase K (Beyotime) was added to the washed precipitates. After incubation at 65°C for at least 6 h, the eluted solution was subjected to DNA extraction kit (Bio-Dev). Specific primers used for chromatin immunoprecipitation (ChIP) DNA amplification matched the Id1 promoter region were: Id1-F: 5′-CAGTTTGTCGTCTCCATG-3′; Id1-R: 5′-TCTGTGTCAGCGTCTGAA-3′; GAPDH-F: 5′-TACTAGCGGTTTTACGGGCG-3′; GAPDH-R: 5′-TCGAACAGGAGGAGCAGAGAGCGA-3′.
MTT assay for cell proliferation or toxicity was conducted according to the manufacturer's instructions (Beyotime). For cell proliferation, 1000 cells were seeded per well in 96-well plates as indicated; for toxicity, 4000 cells were seeded per well in 96-well plates with DMEM containing Dorsomorphin of indicated concentrations.
Cell cycle assay was conducted according to manufacturer's instructions (Beyotime). KMM-Vector and KMM-Id1 cells were treated with DMSO or 5 mM Dorsomorphin for 48 hours. Then the cells were harvested and subjected to PI staining and cell cycle analysis by Mod Fit software.
Soft agar assay: Six-well plates were covered with a bottom layer of 1% agar (Invitrogen) in DMEM containing 10% FBS. Then 10000 cells were prepared in DMEM containing 10% FBS and 0.4% agar and seeded onto the solidified bottom layer. After two weeks of cell culture, colonies were photographed by microscopy and stained with 0.005% crystal violet. The number of colonies was analyzed by Quantity One.
Colony formation assay: 1000 cells were prepared in DMEM containing 10% FBS and seeded in six-well plates. After two weeks of cell culture, colonies were photographed by microscopy and stained with 0.005% crystal violet. The number of colonies was analyzed by Quantity One.
The clinical tissue specimens from 10 patients with KS were collected from Xinjiang province, northwestern of China. The clinical section of the research was reviewed and ethically approved by the Institutional Ethics Committee of the First Teaching Hospital of Xinjiang Medical University (Urumqi, 127 Xinjiang, China; Study protocol # 20082012). The expression of LANA, Id1, Id2, Id3, Smad1, Ki67, and activated caspase 3 were analyzed by IHC as described [52].
1×106 KMM-shCtrl cells or KMM-shId1 cells were subcutaneously injected into BALB/c Nude mice. There were 5 mice in each group. The size of tumor was measured every 3 day. Tumor volume was calculated by the formula: (length×width2)/2. Nude mice were sacrificed at the same time when the size of tumors in shCtrl group reaches about 2000 mm3. In another xenograft assay with drug treatment, 1×106 KMM cells were subcutaneously injected into BALB/c Nude mice. When tumor volume reached about 50∼100 cm3, nude mice were divided into 2 two groups randomly. There were 5 mice in each group. One group was intraperitoneal injection with a single dose of Dorsomorphin (10 mg/Kg), the other group was injected with vehicle. Tumor volume was monitored daily and calculated by the formula: (length×width2)/2. These animal experiments were approved by the Institutional Animal Care and Use Committee of the Institut Pasteur of Shanghai, Chinese Academy of Sciences (Animal protocol # A2013010).
Data were analyzed by Student's t test. P<0.05 was considered to be significant (two tailed). Error bars represent standard error of mean (s.e.m.).
Gene IDs:
BMP2: 650
Smad1: 4086
Smad5: 4090
Id1: 3397
Id2: 3398
Id3: 3399
LANA: 4961527
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10.1371/journal.pntd.0002804 | Evaluation of Antiviral Efficacy of Ribavirin, Arbidol, and T-705 (Favipiravir) in a Mouse Model for Crimean-Congo Hemorrhagic Fever | Mice lacking the type I interferon receptor (IFNAR−/− mice) reproduce relevant aspects of Crimean-Congo hemorrhagic fever (CCHF) in humans, including liver damage. We aimed at characterizing the liver pathology in CCHF virus-infected IFNAR−/− mice by immunohistochemistry and employed the model to evaluate the antiviral efficacy of ribavirin, arbidol, and T-705 against CCHF virus.
CCHF virus-infected IFNAR−/− mice died 2–6 days post infection with elevated aminotransferase levels and high virus titers in blood and organs. Main pathological alteration was acute hepatitis with extensive bridging necrosis, reactive hepatocyte proliferation, and mild to moderate inflammatory response with monocyte/macrophage activation. Virus-infected and apoptotic hepatocytes clustered in the necrotic areas. Ribavirin, arbidol, and T-705 suppressed virus replication in vitro by ≥3 log units (IC50 0.6–2.8 µg/ml; IC90 1.2–4.7 µg/ml). Ribavirin [100 mg/(kg×d)] did not increase the survival rate of IFNAR−/− mice, but prolonged the time to death (p<0.001) and reduced the aminotransferase levels and the virus titers. Arbidol [150 mg/(kg×d)] had no efficacy in vivo. Animals treated with T-705 at 1 h [15, 30, and 300 mg/(kg×d)] or up to 2 days [300 mg/(kg×d)] post infection survived, showed no signs of disease, and had no virus in blood and organs. Co-administration of ribavirin and T-705 yielded beneficial rather than adverse effects.
Activated hepatic macrophages and monocyte-derived cells may play a role in the proinflammatory cytokine response in CCHF. Clustering of infected hepatocytes in necrotic areas without marked inflammation suggests viral cytopathic effects. T-705 is highly potent against CCHF virus in vitro and in vivo. Its in vivo efficacy exceeds that of the current standard drug for treatment of CCHF, ribavirin.
| Crimean-Congo hemorrhagic fever (CCHF) is endemic in Africa, Asia, southeast Europe, and the Middle East. The case fatality rate is 30–50%. Studies on pathophysiology and treatment of CCHF have been hampered by the lack of an appropriate animal model. We have employed CCHF virus-infected transgenic mice, which are defective in the innate immune response, as a disease model. These mice die from the infection and show signs of disease similar to those found in humans. First, we studied the liver pathology in the animals, as hepatic necrosis is a prominent feature of human CCHF. Secondly, we used the model to test the efficacy of antiviral drugs that are in clinical use or in an advanced stage of clinical testing. Besides ribavirin, the standard drug for treatment of CCHF, we tested arbidol, a drug in clinical use against respiratory infections, and T-705, a new drug in clinical development for the treatment of influenza virus infection. While ribavirin and arbidol showed some or no beneficial effect, respectively, T-705 was highly efficacious in the animal model. These data hold promise for clinical efficacy of T-705 in human CCHF.
| Crimean-Congo hemorrhagic fever virus (CCHFV) is a negative-strand RNA virus belonging to the genus Nairovirus of the family Bunyaviridae. The virus is endemic in Africa, Asia, southeast Europe, and the Middle East. Hyalomma ticks transmit the virus to humans, wildlife, and livestock. Humans may also be infected by contact with infected livestock. Human-to-human transmission occurs mainly in the hospital setting. In humans, the virus causes a febrile illness that may be associated with hemorrhage, liver necrosis, shock, and multiorgan failure. Further hallmarks of the disease are increased levels of serum aspartate and alanine aminotransferase (AST and ALT, respectively), thrombocytopenia, and disseminated intravascular coagulopathy. The average case fatality rate is 30–50%, but may be higher in nosocomial outbreaks [1]–[5].
The pathophysiology of the disease is poorly understood. Endothelial and liver cell damage, induction of proinflammatory cytokines, and dysregulation of the coagulation cascade are thought to play a role [3]–[8]. Studies on the pathophysiology of Crimean-Congo hemorrhagic fever (CCHF) have been hampered by the lack of an appropriate animal model, as no mammal with fully functional immune system has been described so far — except humans — that develops disease upon infection. The first animal model was neonatal mouse [9]. Recently, two transgenic mouse models for CCHF have been described, first, mice lacking the signal transducer and activator of transcription 1 (STAT1−/− mice) and second, mice lacking the type I (alpha/beta) interferon receptor (IFNAR−/− mice) [10]–[12]. Both knockout mice are defective in the innate immune response, die rapidly from CCHFV infection, and reproduce relevant aspects of human CCHF. Surrogate models for CCHF employ IFNAR−/− mice infected with Dugbe or Hazara virus [13], [14], two CCHFV-related nairoviruses that are not known to cause disease in human. Work with these models can be carried out at biosafety level (BSL)-2, while work with infectious CCHFV requires BSL-4 facilities.
In the present study, we aimed at characterizing the pathological changes in the liver of CCHFV-infected IFNAR−/− mice in more detail. Furthermore, we employed this model to evaluate the antiviral efficacy of ribavirin, arbidol, and T-705 (favipiravir) against CCHFV in vivo. These drugs are either in clinical use or in an advanced stage of clinical testing. Ribavirin inhibits CCHFV replication in cell culture [15] and is administered to CCHF patients, though its clinical benefit is not proven and discussed controversially [16]–[19]. It shows beneficial effects in the neonatal and STAT1−/− mouse models [9], [10]. Ribavirin currently is the only drug available for treatment of CCHF. Arbidol is a broad-spectrum antiviral showing activity against a range of RNA viruses in vitro and in vivo, most notably influenza A virus [20]–[24]. In Russia and China, the drug is in clinical use primarily for prophylaxis and treatment of acute respiratory infections including influenza. Arbidol is assumed to act via hydrophobic interactions with membranes and virus proteins, thus inhibiting viral fusion and entry [25]–[27]. T-705 is a potent inhibitor in vitro and in animal models of influenza virus, phleboviruses, hantaviruses, arenaviruses, alphaviruses, picornaviruses, and norovirus [28]–[35]. Following conversion to T-705-ribofuranosyl-5′-triphosphate, it presumably acts as a nucleotide analog that selectively inhibits the viral RNA-dependent RNA polymerase or causes lethal mutagenesis upon incorporation into the virus RNA [36]–[40]. T-705 (favipiravir) is currently in late stage clinical development for the treatment of influenza virus infection.
This study was carried out in strict accordance with the recommendations of the German Society for Laboratory Animal Science under supervision of a veterinarian. The protocol was approved by the Committee on the Ethics of Animal Experiments of the City of Hamburg (Permit no. 44/11). All efforts were made to minimize the number of animals used for the experiments and suffering of the animals during the experiments. All staff carrying out animal experiments has passed an education and training program according to category B or C of the Federation of European Laboratory Animal Science Associations. The animal experiments in this study are reported according to the ARRIVE guidelines [41]. A total of 162 mice were used for this study and all mice were included in the analysis.
CCHFV strain Afg-09 2990 had been isolated in 2009 in our laboratory from a patient with a fatal course of infection [42] and passaged 2 times before it has been used in this study. The virus stock was grown on Vero E6 cells, quantified by immunofocus assay (see below), and stored at −70°C until use in in vitro and in vivo experiments.
Ribavirin (CAS no. 36791-04-5; PubChem CID 37542) was obtained from MP Biomedicals (order no. 02196066), arbidol hydrochloride (CAS no. 131707-23-8; PubChem CID 131410) from Waterstone Technology, USA (order no. 49823), and T-705 (favipiravir; CAS no. 259793-96-9; PubChem CID 492405) was custom synthesized by BOC Sciences, Creative Dynamics, USA.
The compounds were dissolved in dimethyl sulfoxide (DMSO) at a concentration of about 10 mg/ml and stored at −20°C. Final DMSO concentration in the cell culture supernatant was 0.1%. Vero E6 cells were grown in Dulbecco's Modified Eagle's Medium (DMEM) (PAA Laboratories) supplemented with 5% fetal calf serum (FCS) and streptomycin/penicillin and seeded at a density of 4×104 cells per well of a 24-well plate at 1 day before infection. Cells were inoculated with CCHFV at a multiplicity of infection (MOI) of 0.01 in the BSL-4 laboratory. The inoculum was removed after 1 h and replaced by fresh medium complemented with different concentrations of compound. For arbidol experiments, cells were additionally pretreated with arbidol 18 h before infection. Concentration in cell culture supernatant of infectious virus particles was measured 2–4 days post infection (p.i.) by immunofocus assay. Cell growth and viability under compound treatment was determined by the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl-2H-tetrazoliumbromide (MTT) method as described [43]. A sigmoidal dose–response curve was fitted to the data using Prism GraphPad 6.0 (GraphPad Software). The inhibitory concentrations that reduced the virus titer by 50%, 90%, and 99% (IC50, IC90, and IC99, respectively) and the cytotoxic concentrations that reduced cell growth by 50% and 90% (CC50 and CC90, respectively) were calculated from the sigmoidal functions.
For analysis of combinations of two drugs, an 8×8 concentration matrix was tested. Drugs x and y were tested in the concentrations c = 0; IC90/8; IC90/4; IC90/2; IC90; IC90•2; IC90•4; IC90•8 in all possible combinations (cx,cy). The IC90 values were derived from the prior single-drug experiments. The drug combination data were analyzed using the Bliss independence drug interaction model [44]. This model is defined by the equation Exy = Ex+Ey−(Ex•Ey), where Exy is the additive effect of drugs x and y as predicted by their individual effects Ex and Ey. Ex = (Vobs(0,0)−Vobs(cx,0))/Vobs(0,0) and Ey = (Vobs(0,0)−Vobs(0,cy))/Vobs(0,0), where Vobs(cx,cy) is the observed, i.e. experimentally determined virus titer for (cx,cy). In analogy to the MacSynergy II program [44], [45], which evaluates antivirus data according to the Bliss independence model, a three-dimensional approach was used to identify areas where observed effects are greater (synergy) or less (antagonism) than those predicted by Exy. To this end, the ratio between predicted virus titer Vpred(cx,cy) = Vobs(0,0)•(1−Exy) and observed virus titer Vobs(cx,cy) was calculated for each drug combination (cx,cy). A ratio >1 indicates synergy (i.e. for (cx,cy) the virus titer predicted for additive effect is higher than the experimentally determined virus titer), a ratio <1 indicates antagonism (i.e. for (cx,cy) the virus titer for predicted additive effect is lower than the experimentally determined virus titer).
IFNAR−/− mice (129Sv background) [46] were bred in the Specific Pathogen Free animal facility of the Bernhard-Nocht-Institute. Six to twelve-week-old female animals (weight median 20 g, range 15–24 g) were used for all experiments, except of nine males that were used for determination of the lethal virus dose. A group size of 5 animals was expected to provide sufficiently accurate estimates of survival rate, viremia, and clinical chemistry parameters. It allows to detect an 80% difference in survival rate between control and treatment group with p (alpha) = 0.05 and power (1 – beta) = 0.8. Experimental groups were age-matched. Three to five animals of a group were kept together in a conventional cage without enrichments. They had ad libitum access to food and water. Infection experiments with CCHFV were performed in the animal facility of the BSL-4 laboratory with artificial light/dark cycles.
Three to ten animals per group (depending on whether organ collection was planned) were infected by intraperitoneal (i.p.) injection with 0.3 to 104 focus forming units (FFU) of CCHFV in 100 or 200 µl DMEM containing 2% FCS. The mode of administration was chosen to facilitate comparability with previously described CCHF mouse models [10], [11]. After infection, mice were monitored daily for signs of disease, and body weight and rectal body temperature were measured using thermometer BIO-TK8851 with BIO-BRET-3 rectal probe for mice (Bioseb, France). Animals with severe signs of disease such as seizures, bleeding, abdominal distention, diarrhea, agony, or weight loss of >15% within 2 days were euthanized. Blood samples of 30–80 µl per animal were drawn by tail vein puncture in intervals of 1–4 days over a period of 14 days (≤5 blood drawings in total) for clinical chemistry and viremia measurement. For organ collection, when criteria for euthanasia were fulfilled, and at the end of the experiment, animals were euthanized with an isoflurane overdose followed by cervical dislocation. Organs were collected after death or at day 3 p.i. from 2–3 animals that have been randomly chosen from experimental groups with 7–10 animals, and analyzed for infectious virus titer and histopathological changes. Experiments were not replicated.
Ribavirin was administered once daily by the i.p. route. A stock of 10 mg/ml in 0.9% NaCl was prepared before each application. Animals received a ribavirin dose of 100 mg/(kg×d) (200 µl for a 20-g mouse) or 200 µl of 0.9% NaCl as a placebo. The ribavirin dose that is fatal to 50% of mice (LD50) is 220 mg/(kg×d) [47]. Treatment was commenced 1 h p.i. and continued until death or day 8. Arbidol was administered once daily per os using a stomach probe. Suspensions of 15 or 30 mg/ml in 0.5% methylcellulose was prepared before each application. Animals received an arbidol dose of 75 or 150 mg/(kg×d) (100 µl suspension for a 20-g mouse) or 100 µl of 0.5% methylcellulose as a placebo. Treatment was commenced 1 day before infection and continued until death or day 8. In vivo toxicity of arbidol was evaluated in 26 uninfected animals treated with 0, 25, 75, 150, 300, or 600 mg/(kg×d) for 8 days. No toxic effects were observed in this dose range. T-705 was administered twice daily per os using a stomach probe. Suspensions of 0.75, 1.5, 3, or 30 mg/ml in 0.5% methylcellulose were prepared daily. Animals received a T-705 dose of 7.5, 15, 30, or 300 mg/(kg×d) (100 µl suspension twice daily for a 20-g mouse) or 100 µl of 0.5% methylcellulose twice daily. Treatment was commenced 1 h p.i. or later and continued until death or day 8.
Infectious virus particles in blood and organ samples were determined by immunofocus assay. Organ samples were homogenized in 500 µl DMEM–2% FCS using Lysing Matrix D (MP Biomedicals) in a beat mill. Vero cells in 24-well plates were inoculated with 200 µl of serial 10-fold dilutions of sample. The inoculum was removed after 1 h and replaced by a 1%-methylcellulose–DMEM–6% FCS overlay. After 5 days of incubation, cells were fixed with 4% formaldehyde in phosphate-buffered saline (PBS), washed with water, and permeabilized with 0.5% Triton X-100 in PBS. After washing and blocking with 10% FCS in PBS, infected cell foci were detected with CCHFV nucleoprotein (NP)-specific monoclonal antibody A4 [48]. After washing, cells were incubated with peroxidase-labeled anti-mouse IgG. Foci were visualized with tetramethylbenzidine and counted.
Virus-specific antibodies in blood were detected by immunofluorescence assay (IFA) using cells infected with CCHFV strain Afg-09 2990 as an antigen. Mouse serum was inactivated for 1 h at 60°C and tested at a dilution of 1∶20.
Serum samples were diluted 1∶10 or higher, if required, in 0.9% NaCl and analyzed for AST and ALT activity by using commercially available colorimetric assay kits at 25°C (detection limit for undiluted serum is 2.25 U/l for AST and 2.65 U/l for ALT) (Reflotron, Roche Diagnostics). Parameters were measured for individual animals.
Lung, kidney, heart, spleen, brain, and liver were collected, fixed in 4% formaldehyde in PBS, and embedded in paraffin using a Leica ASP300 S tissue processor and a Leica EG1160 embedding station (Leica). Sections (4 µm) were stained with hematoxylin–eosin (H&E) or processed for immunohistochemistry (IHC). IHC sections were stained using the Ventana BenchMark XT automated staining system (Ventana Medical Systems) and Cell Conditioning solution 1 or 2 (Ventana) for 30–60 min. Sections were incubated with primary antibodies directed against B cell marker B220 (1∶400; eBioscience), apoptosis marker cleaved caspase-3 (1∶100; R&D Systems), T cell marker CD3 (1∶100; Dako), myeloid-lineage cell (e.g. macrophage) marker Iba-1 (1∶2,000; Wako Chemicals), inducible nitric oxide synthase (iNOS) expressed by activated monocyte-derived cells (1∶50; Abcam), and cell proliferation marker Ki67 (1∶250; Abcam) for 1 h. Primary antibodies were detected with anti-mouse IgG, anti-rabbit IgG, or anti-rat IgG Histofine Simple Stain MAX PO immuno-enzyme polymer (Nichirei Biosciences) and stained with 3,3′-Diaminobenzidine (DAB) substrate using the ultraView Universal DAB Detection Kit (Ventana). Cells were counterstained with hematoxylin. IHC with primary antibodies directed against CCHFV NP (monoclonal antibody A4 [48], 1∶500) and neutrophil marker Ly6G (1∶1,000; BD Bioscience) was performed manually. Sections were boiled in citrate buffer (pH 6) for 1 h and incubated with antibody at 4°C overnight. Primary antibodies were detected with anti-mouse IgG Histofine Simple Stain AP or anti-rat IgG Histofine Simple Stain MAX PO immuno-enzyme polymer and stained with Fast Red (Roche) or DAB (Sigma-Aldrich) substrate, respectively. Mayer's hematoxylin solution was used for counterstaining. Sections were coverslipped with Tissue Tek mounting medium (Sakura Finetek).
Statistical analysis was performed with GraphPad 6.0 (GraphPad Software). Unpaired groups were compared with the two-tailed Mann–Whitney U test for continuous parameters and with two-tailed Fisher's exact test for frequencies. Survival curves were compared with the log-rank (Mantel–Cox) test.
Before testing antivirals in the IFNAR−/− mouse model, we aimed at determining the optimal infection dose for CCHFV strain Afg09-2990 and characterizing the disease caused by this particular strain. To this end, IFNAR−/− mice were infected i.p. with 0.3, 1, 3, 10, 100, 1,000, and 10,000 FFU. Animals died from the infection even after inoculation with 0.3 FFU (inoculum, died/infected: 0.3 FFU, 4/5; 1 FFU, 5/5; 3 FFU, 4/5; 10 FFU, 6/8; 100 FFU 13/13; 1,000 FFU, 8/8; 10,000 FFU, 3/3). This indicates that only a few infectious virus particles of CCHFV strain Afg09-2990 are sufficient to initiate a productive infection. A lethal outcome was consistently observed with ≥100 FFU. Therefore, the model was further characterized for the inoculation doses 10, 100, 1,000, and 10,000 FFU (Fig. 1). Animals infected with 100 or 1,000 FFU died between days 3 and 6, while animals infected with 10,000 FFU uniformly died at day 2. Before death, animals lost about 15% of body weight (Fig. 1). At day 2, the mean AST and ALT values were around 300 U/l and 100 U/l, respectively, in animals inoculated with 100–1,000 FFU. Both values were higher in the 10,000 FFU group (AST 1,600 U/l and ALT 500 U/l) (Fig. 1). AST and ALT elevations indicated cell damage, in particular of liver cells. At day 2, virus titer in blood ranged from below detection lime in the 10 FFU group, via 3 log10 FFU/ml in the 100 and 1,000 FFU groups, up to 5 log10 FFU/ml in the 10,000 FFU group (Fig. 1). At day 3, virus was found in all organs analyzed (spleen, kidney, liver, heart, lung, and brain) at titers ranging from 4–7 log10 FFU/g irrespective of the inoculation dose (Fig. 1). The maximum virus concentration was found in liver. As the inoculation with ≤10 FFU was not uniformly lethal and the inoculation with 10,000 FFU leaves only 2 days between infection and lethal outcome for therapeutic intervention, further experiments were conducted with a dose of 100 or 1,000 FFU.
Lung, heart, kidney, brain, liver and spleen of CCHFV-infected IFNAR−/− mice were collected at day 3 and assessed on H&E-stained sections. Virus distribution in all organs and inflammatory response in liver were visualized by IHC. Naïve IFNAR−/− mice served as a control.
The antiviral activity of ribavirin, arbidol hydrochloride, and T-705 against CCHFV strain Afg09-2990 was tested in Vero E6 cells. All three compounds were able to suppress virus replication by 3–4 log10 units at concentrations of ≥10 µg/ml (Fig. 4). IC50 and IC90 values ranged from 0.6–2.8 µg/ml and 1.2–4.7 µg/ml, respectively. IC99 values ranged from 2.0–9.5 µg/ml. Cell toxicity in the test range as measured by MTT test was only evident for arbidol hydrochloride (ribavirin CC50>32 µg/ml; arbidol hydrochloride CC50 8.3 µg/ml, CC90 20 µg/ml; T-705 CC50>15 µg/ml) (Fig. 4). In conclusion, all three compounds showed a potent antiviral effect against CCHFV Afg09-2990 in cell culture. Arbidol displayed toxicity with a therapeutic index of about 10.
Ribavirin was tested in comparison to a placebo group receiving the vehicle (0.9% NaCl solution) (Fig. 5). Both groups of IFNAR−/− mice were infected with 100 FFU CCHFV. Although one animal survived after treatment, ribavirin did not significantly increase the survival rate (p = 0.4). However, the drug prolonged the time to death (median 3 vs. 6 days for placebo vs. ribavirin, p = 0.0007), reduced the levels of AST (p = 0.001) and ALT (p = 0.006) at day 2, reduced the virus titer in blood at day 2 (p = 0.0007), increased the weight at day 2 and 3 (p = 0.03 and p = 0.002, respectively), and reduced the terminal virus concentration in all organs when compared to placebo at day 3 (p<0.001 separately for each organ). Histopathological analysis of organs collected at day 3 from ribavirin-treated mice revealed only small disseminated foci of necrosis; most of the liver parenchyma resembled naïve mice. Markedly reduced hepatocellular necrosis correlated with low numbers of apoptotic hepatocytes (cleaved caspase-3), T-cells (CD3), B-cells (B220), and activated monocyte-derived cells (iNOS). Virus antigen-positive cells (NP) were significantly reduced in liver and spleen compared to untreated or placebo-treated mice (data not shown). However, ribavirin-treated mice that succumbed to infection on days 4–9 showed extensive bridging hepatocellular necrosis at the time of death (Fig. 2). Like in untreated mice, the necrosis was accompanied by presence of numerous Iba-1-positive macrophages (Kupffer cells), showing enlarged cell bodies and focal clustering, and iNOS-expressing activated monocyte-derived cells (Fig. 3). Both alterations are suggestive for strong monocyte/macrophage activation. However, in contrast to untreated mice, virus antigen was hardly detectable in liver tissue of the treated mice (Fig. 2), consistent with the low virus titer in all organs (Fig. 5, bottom). Thus, ribavirin reduces CCHFV load and delays disease progression, but it does not prevent terminal liver necrosis, monocyte/macrophage activation, and lethal outcome in the IFNAR−/− mouse model.
Arbidol hydrochloride [75 and 150 mg/(kg×d)] was tested in comparison to a placebo group receiving the vehicle (0.5% methylcellulose) [Fig. 5 and data not shown for 75 mg/(kg×d)]. Both groups were infected with 1,000 FFU CCHFV. Mice were pretreated one day before inoculation. However, the drug changed neither survival rate and survival time, nor any of the other parameter measured. Even reducing the inoculation dose to 10 FFU had no effect when compared to the historical control group.
T-705 was tested in comparison to a placebo group receiving the vehicle (0.5% methylcellulose) (Fig. 6). All groups were infected with 100 FFU CCHFV. Initially, a high dose of T-705 [300 mg/(kg×d)] was tested. The drug was administered from day 0 to day 8. Placebo-treated animals died between day 3 and 4. At day 3, they showed weight loss of nearly 20%, increase in body temperature up to 40°C, AST values of 1,200–51,000 U/l, and ALT values of 260–6,700 U/l. All animals of the treatment group survived the infection and showed no signs of disease. Virus was detected neither in blood nor in the organs throughout the observation period (Fig. 6 and data not shown). Histopathology and IHC at day 3 revealed largely normal liver tissue with absence of virus antigen and inflammatory cells (Figs. 2 and 3).
To determine the efficacy of the drug at an advanced stage of the infection, time-of-addition experiments were performed (Fig. 6). Treatment with a high dose of the drug was commenced 1 day or 2 days after virus inoculation and continued until day 8. Survival was 100% in both groups and animals showed hardly any signs of disease. Only if treatment started 2 days p.i., minor changes in weight, temperature, and AST were seen at day 3. Virus remained undetectable in blood and organs in both time-of-addition groups throughout the observation period. To provide evidence for infection of the animals in the T-705 treatment groups, the development of CCHFV-specific antibodies was measured 21 days p.i. Only 1/10 (10%) of the animals treated post-exposure, but 10/10 (100%) of the animals treated from day 1 or 2 p.i. developed antibodies, indicating that virus replication under post-exposure treatment with T-705 was even not sufficient to elicit antibodies.
To define the lowest effective dose of T-705, animals received 30, 15, or 7.5 mg/(kg×d) T-705 or 0.5% methylcellulose as a placebo (Fig. 7). Treatment was commenced 1 h p.i. and continued until death or day 8. All animals of the 30 and 15 mg/(kg×d) treatment groups survived and showed hardly any signs of disease. Virus was detected at low level only in blood of one animal of the 15 mg/(kg×d) treatment group at day 11 (Fig. 7). A dose of 7.5 mg/(kg×d) did not prevent a lethal outcome, although it prolonged the time to death (p = 0.0007), and reduced the levels of AST (p = 0.0007) and ALT (p = 0.004) at day 3.Taken together, T-705 is highly efficient against CCHFV in the IFNAR−/− mouse model.
Ribavirin is currently in clinical use for treatment of CCHF [16]–[19]. Therefore, it is important to know if T-705 could be given in combination with ribavirin and how both drugs interact. First, the antiviral activity of 64 combinations of ribavirin and T-705 was determined in cell culture. The 8×8 concentration matrix was designed around the IC90 values of both drugs as determined above. Infectious virus particles were measured 3 days p.i. by immunofocus assay and cell viability was determined by the MTT method. The dose–response surface demonstrates that combinations of ribavirin and T-705 exhibit strong antiviral effects with suppression of virus replication by >5 log units (Fig. 8A). Possible antagonistic or synergistic effects were evaluated using the Bliss independence model in analogy to the algorithms of the MacSynergy II program [44], [45]. This analysis revealed clear synergistic effects when the drugs were combined in concentrations around their IC90. In this area of the matrix, the experimental virus titer was up to 2 log units lower than the titer predicted according to the Bliss independence model for additive effect (Fig. 8B). The MTT test did not reveal drug toxicity over the whole matrix (Fig. 8B).
To test the effects of drug combination in vivo, animals received a T-705 dose of 30 or 7.5 mg/(kg×d) in combination with a ribavirin dose of 100 mg/(kg×d). The 30 mg/(kg×d) T-705 dose, which is protective upon single-drug administration, was chosen to test if addition of ribavirin interferes with T-705 efficacy. To explore if combination of two sub-effective doses may result in an effective treatment, 7.5 mg/(kg×d) T-705 and 100 mg/(kg×d) ribavirin were co-administered. None of the parameters in the 30 mg/(kg×d) T-705 plus 100 mg/(kg×d) ribavirin group (Fig. 9, left) was statistically significantly different from the parameters of the 30 mg/(kg×d) T-705 single-drug group (Fig. 7). On the other hand, the combination of a 7.5 mg/(kg×d) dose of T-705 with a 100 mg/(kg×d) dose of ribavirin (Fig. 9 right) improved the survival rate compared to single-drug treatments (Figs. 5 and 7), although the increase did not reach statistical significance (p = 0.08 for T-705+ribavirin vs. T-705 alone, and p = 0.07 for T-705+ribavirin vs. ribavirin alone; two-tailed Fisher's exact test). In conclusion, T-705 and ribavirin exert synergistic effects according to the Bliss independence model when combined in concentrations around their IC90 in vitro. Co-administration of both drugs in the animal model suggests that a combined treatment yields beneficial rather than adverse effects.
In this study, we have used IFNAR−/− mice as an in vivo model to evaluate the efficacy of antivirals against CCHFV. Main pathological alteration in mice infected with the recently isolated CCHFV strain Afg09-2990 was acute hepatitis with extensive necrosis, reactive proliferation of hepatocytes, mild to moderate inflammatory response, and morphological signs of monocyte/macrophage activation. CCHFV-infected and apoptotic hepatocytes were found in the necrotic areas. Ribavirin, arbidol hydrochloride, and T-705 were active against CCHFV Afg09-2990 in cell culture. However, arbidol hydrochloride was inactive in vivo. Ribavirin was partially active, while T-705 was highly efficient in the mouse model. The latter drug was effective even if the window for therapeutic intervention was less than 2 days.
Three mouse models for CCHF have been described in the past: the neonatal mouse model, STAT1−/− mice, and IFNAR−/− mice [9]–[12]. The latter models take advantage of the defect in the innate immune response, which apparently is essential to protect mice from productive CCHFV infection. In all three models, mice are dying from the infection within a few days. We prefer to work with IFNAR−/− mice, as the genetic defect concerns only the interferon type I signaling, while STAT1 deficiency prevents the upregulation of genes due to a signal by either type I or type II interferons and neonatal mice are immunologically tolerant (neonatal tolerance). IFNAR−/− mice are highly susceptible to CCHFV infection. The inoculum sufficient to initiate a productive infection is very low — 0.3 FFU — which presumably corresponds to just a few infectious virus particles. This is consistent with experiments in AG129 mice lacking interferon type I and type II receptors, in which an inoculum of 0.1 FFU of lymphocytic choriomeningitis virus was sufficient to infect the animals [49].
It has been shown very recently that the IFNAR−/− model mimics hallmarks of human CCHF disease [12]. These findings are extended here by a more detailed immunhistopathological analysis of the liver. Histopathology, virus load measurement, antigen staining in various organs, and the measurement of AST and ALT demonstrate that the liver is the major target organ of CCHFV. Although virus was found in all organs, the titer in liver is the highest and exceeds 7 log10 FFU/g in some experiments. The higher virus titer in liver compared to other organs may explain why the IHC analysis for virus antigen revealed clearly positive cells only in the liver, while the signals in other organs were weak or absent. In exceptional cases, AST and ALT values reached 10,000 U/l and 1,000 U/l, respectively, demonstrating massive liver cell damage. However, while ALT is specific for this organ, AST is also present at high level in the heart, skeletal muscle, kidneys, brain, and red blood cells [50]. Therefore, the high AST/ALT ratio may also indicate extrahepatic cell damage. Overall, the histological and biochemical findings are compatible with the diagnosis of a fulminant liver damage. Our findings are also in agreement with the pathological observations in human CCHF; hepatocellular necrosis with hyperplastic and hypertrophic Kupffer cells and mild or absent inflammatory cell infiltrates is the prominent histopathological finding in humans [3]. Two new aspects, which may provide some clues as to the pathophysiology of CCHF, are noteworthy. First, in the necrotic lesions of the liver, both CCHFV-infected hepatocytes and apoptotic hepatocytes clustered. This may suggest that CCHFV-infected cells undergo apoptosis and necrosis. As the inflammatory response was only mild to moderate, a direct cytopathic effect of the virus on hepatocytes may be involved in the induction of apoptosis and necrosis. Importantly, this hypothesis has been raised in early IHC studies on humans with CCHF as well [3]. Secondly, activated Iba-1-positive macrophages and activated monocyte-derived cells expressing iNOS were found in the liver. These cells may play a crucial role in the strong proinflammatory immune responses following CCHFV infection, as demonstrated by significant increases of serum proinflammatory cytokines and chemoattractant molecules in IFNAR−/− and STAT1−/− mice, as well as in humans [6]–[8], [10], [12].
In this study, we have employed the IFNAR−/− mouse model for testing antivirals against CCHFV in vivo. Ribavirin is the standard treatment in human CCHF — although its clinical efficacy is not proven [16]–[19] — and shows beneficial effects in the neonatal and STAT1−/− mouse models [9], [10]. Therefore, we first evaluated the IFNAR−/− mouse model using this drug. The survival time was prolonged, while the survival rate was not increased, which largely corresponds to the results of the high-dose challenge experiments in STAT1−/− mice [10]. Despite the delay in disease progression and the reduction in virus load in blood and organs as evidenced by virus titration and IHC, ribavirin was not able to prevent the lethal pathophysiological cascade. Importantly, the development of terminal liver necrosis with marked monocyte/macrophage activation in the virtual absence of virus in the organ demonstrates that host pathways, once they are triggered by the virus, mediate pathology and death irrespective of the presence of the trigger.
The second compound tested was arbidol hydrochloride, a broad-spectrum antiviral drug in clinical use against flu [20]–[24]. Arbidol hydrochloride efficiently suppressed CCHFV in cell culture. However, no beneficial effects in the IFNAR−/− mouse model were observed. The drug was administered via the same route but at higher dose than in previous studies that showed beneficial effects against influenza A, coxsackie B, and hantaan virus in mice [20], [22]. The compound had significant toxicity at higher concentrations in cell culture. It is conceivable that its antiviral effect in vitro is at least partially attributable to general cell toxic effects that are not detected in the MTT assay used to assess cell viability. Therefore, it might be that the in vitro data overestimate the true antiviral effect of the drug against CCHFV. In addition, arbidol is extremely hydrophobic, which may reduce its oral bioavailability in mice. It might be worth testing arbidol in pharmaceutical formulations with enhanced solubility in future [51].
An important observation in our study is the strong antiviral effect of T-705 against CCHFV in cell culture and in the IFNAR−/− mouse model. This compound has been shown to be highly active against a range of viruses in vitro and in vivo, including orthomyxoviruses, arenaviruses, and bunyaviruses of the genera hantavirus and phlebovirus [28]–[34]. Therefore, its activity against CCHFV, a bunyavirus of the genus nairovirus, is not unexpected. However, in view of the low or lacking potency of ribavirin and arbidol in vivo — both of which have almost the same IC50 and IC90 values than T-705 — the high in vivo potency of T-705 is surprising. The IC50 for CCHFV is 5–30 times lower than the IC50 values for other bunyaviruses [35], which may indicate that this virus is particularly sensitive to T-705. Even if it was given 2 days before the expected time of death, the animals survived and hardly showed signs of disease. If given immediately post-exposure, the drug suppresses virus replication below the level required to elicit antibodies. We could reduce the dose by a factor of 20 [from 300 to 15 mg/(kg×d)] with the drug still showing post-exposure efficacy.
The mode of action of T-705 against CCHFV is not known. In analogy to other segmented negative strand viruses, T-705-ribofuranosyl-5′-triphosphate may be incorporated into the nascent RNA strand and inhibit further strand extension or induce lethal mutagenesis [36]–[40]. How ribavirin acts against CCHFV is still not known, although the drug is in clinical use since decades. Several mechanisms have been proposed for other viruses: it may be incorporated into the virus RNA causing lethal mutagenesis [52], interfere with capping [53], inhibit the viral RNA polymerase [54], [55] or inhibit the host cell enzyme inosine monophosphate dehydrogenase (IMPDH) resulting in reduced GTP levels [56]–[58]. T-705-ribofuranosyl-5′-monophosphate was 150 times weaker than ribavirin-5′-monophosphate in its IMPDH inhibitory effect, suggesting that IMPDH is not a major target enzyme for T-705 [39], [40]. Given that the mode of action of both drugs is poorly understood, it is difficult to predict how they interact. However, as ribavirin is the standard drug for treatment of CCHF [16]–[19], a ribavirin/T-705 combination treatment would be an obvious option in clinical practice. Our experiments suggest that both drugs do not act in an antagonistic manner in vitro and in vivo. According to the Bliss independence model there is even evidence for synergistic interaction in vitro and the experiments in the animal model point to a beneficial rather than adverse interaction in vivo. In conclusion, our data hold promise for clinical efficacy of T-705 or ribavirin/T-705 combination treatment in human CCHF.
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10.1371/journal.pntd.0001957 | Can Human Movements Explain Heterogeneous Propagation of Dengue Fever in Cambodia? | Determining the factors underlying the long-range spatial spread of infectious diseases is a key issue regarding their control. Dengue is the most important arboviral disease worldwide and a major public health problem in tropical areas. However the determinants shaping its dynamics at a national scale remain poorly understood. Here we describe the spatial-temporal pattern of propagation of annual epidemics in Cambodia and discuss the role that human movements play in the observed pattern.
We used wavelet phase analysis to analyse time-series data of 105,598 hospitalized cases reported between 2002 and 2008 in the 135 (/180) most populous districts in Cambodia. We reveal spatial heterogeneity in the propagation of the annual epidemic. Each year, epidemics are highly synchronous over a large geographic area along the busiest national road of the country whereas travelling waves emanate from a few rural areas and move slowly along the Mekong River at a speed of ∼11 km per week (95% confidence interval 3–18 km per week) towards the capital, Phnom Penh.
We suggest human movements – using roads as a surrogate – play a major role in the spread of dengue fever at a national scale. These findings constitute a new starting point in the understanding of the processes driving dengue spread.
| Dengue fever is a mosquito borne viral infection. It has become a major public health problem during the past decades: only 9 countries were affected in the 1970s; dengue is now endemic in more than 100 countries. In the absence of any vaccine or specific treatment, control of dengue fever is currently limited to vector control measures, which are difficult to implement and hardly sustainable, especially in low income countries. To implement efficient control measures, it is crucial to understand the dynamics of propagation of the disease and the key factors underlying these dynamics. In this study, data from 8-year national surveillance in Cambodia were analysed. Dengue fever follows a recurrent pattern of propagation at the national scale. The annual epidemics originate from a few rural areas identified in this work. This study also suggests additional evidence for the role of human movement in the spatial dynamics of the disease, which should be accounted for in control measures. These results differ from the current knowledge about dengue dynamics and are therefore of interest for future research.
| Cambodia is a low-income tropical country, hyper-endemic for all four serotypes of dengue infection. As such, dengue epidemics occur every year during the rainy season and result in considerable morbidity and economic burden. The basis for these recurrent epidemics is an increased vector activity during the rainy season and complex interactions between hosts and viruses with short lived cross-protective immunity [1]–[3]. In the absence of a vaccine, control is limited to vector control measures. Regarding dengue dynamics, locally, dengue outbreaks are explosive and tend to be focal, perhaps reflecting the limited dispersal of the vector, which visits few houses in a life-time, and have a limited flight range [4], [5]. At an international scale, human movement is known to be a major factor responsible for the virus transportation among big urban centres [6]. At a national scale, little is known about the spatial propagation of the disease. In many endemic countries, urban centres are thought to act as a reservoir of the virus from where it can spread to the rest of the country [6]–[9]. In Thailand [7] and more recently in Southern Vietnam [8], researchers demonstrated the existence of a travelling wave either within the 3-year periodic mode or the annual mode of oscillation. However, the underlying factors responsible for these waves remain unknown [1], [7]–[10]. Hypotheses include immunological host-virus interactions, differences in virus virulence, or heterogeneity of the spatial distribution of the host population [1]. Synchronisation of cases has also been observed in the 3 year periodic band in Thailand [7]. Mechanisms responsible for higher synchronicity could include climatic forcing [8], [10].
The objective of this study was to determine whether there was a spatial-temporal pattern of dengue propagation in Cambodia repeating year after year. The characterisation of such patterns is important to understand the forces driving dengue spatial spread and aid better control and logical allocation of public health resources.
Cambodia is a low-income country located in South-East Asia, divided into 24 provinces and 180 districts covering 181,035 km2. Out of the 13.4 million people in 2008, more than 80% live in rural areas; 1.3 million people (9.9%) live in Phnom Penh, the capital city. Cambodia is lagging behind other countries in South-East Asia in terms of economic or demographic development. For example, unlike Thailand, the demographic transition has not occurred yet. The South-West and the North of the country (see grey districts in Figure 1) are mountainous regions with low population density (mean district density of 18 people per km2). The rest of the country is composed of flat plains with few cities (mainly Phnom Penh, Kampong Cham, Siem Reap and Battambang, see Figure 1) scattered among rural areas. The weather is warm all year long, and the climate is dominated by the annual monsoon cycle, with a dry season (December to April) alternating with a wet season (May to November). Climatic variation from one area to another is limited. Temperature is homogeneous across the country and ranges annually from 21°C to 35°C. As a result, mosquitoes can be active all year long when considering only temperature. Rainfall or water availability are more likely to be the factors limiting the vector's activity.
Cambodian National surveillance recorded 109,332 dengue cases during 2002–2008, a period over which the reporting process was stable. Cases were reported passively from public hospitals and actively from 5 major sentinel hospitals located in the cities of Siem Reap (1 hospital), Kampong Cham (1 hospital) and Phnom Penh (3 hospitals). Cases were clinically diagnosed using the 1997 World Health Organization (WHO) case definitions, allowing clinical and paraclinical (haematocrit and platelets count) distinction between classic dengue fever, dengue haemorrhagic fever and dengue shock syndrome [2]. Of note, the new WHO case definition was only introduced in 2009 [11]. To increase specificity, only severe cases (e.g. dengue haemorrhagic fever) affecting children less than 16 years old were recorded in the database. Assuming that epidemic patterns of dengue would be stochastic in low population density areas, we excluded the 45 (/180) districts with less than 20 people per km2 from the analysis, dismissing 3298 declared cases (3.03% of the total declared cases). Since patients' districts of residence were recorded, we calculated weekly incidence rates for each of the 135 remaining districts. Denominators for incidence rates were interpolated linearly using the 1998 and 2008 national censuses. Based on age distribution similarities between provinces, and on similarities between 1998 and 2008 age structures, we assumed that age structure was homogeneous over the country and have not standardised incidence rates according to age. As national surveillance data were made available to be utilised for such temporal and spatial analyses and have been routinely published nationally and once internationally [2] no specific approval was requested from the Ministry of Health's National Ethics Committee. Moreover, data that were provided by the National Programme were anonymised prior to transfer to the Institut Pasteur du Cambodge. We subsequently randomly assigned new codes to each record and deleted the previous ones to unlink the present dataset to the national database.
Time series of dengue incidence were square-root transformed to stabilise the variance, subsequently centred to zero-mean and normalised to unit variance. We then performed a wavelet transform and used wavelet phase analysis to describe dynamic patterns of dengue. This spectral method is well-suited for the analysis of non-stationary time-series such as epidemic curves. It is particularly useful to filter the data in any given frequency band and extract the phase of any given periodic component [10], [12]–[14] (see Figure 2 for an illustration of the method).
The wavelet transform was done using R software [15] and functions translated from Cazelles' Matlab toolbox. We used a Morlet wavelet as the mother wavelet. All equations used and vocabulary relative to wavelet analysis are detailed in [10], [12]–[14].
The wavelet coefficients corresponding to a period ranging from 0.8 to 1.2 year were used to reconstruct filtered time series corresponding to the annual epidemic in each district. These filtered time series, called “annual component of incidence”, are illustrated in Figure 2B.
The phases of the epidemic have been computed in the periodic band 0.8–1.2 year (see equation (5) in [10]), thus obtaining, for each district, a single time series of the phase of the cyclic annual component of the epidemic (Figure 2C). This phase can be viewed as the timing of the epidemics, and is almost not influenced by the intrinsic value of incidence. For a given week, if the seasonal epidemic occurs at the same time in two districts, the two annual components have the same phase. Another example in Figure 2, district #306 (a rural district located around Kampong Cham) has an advanced phase compared to Phnom Penh: the epidemic in Phnom Penh is lagging behind the one in district #306.
By calculating phase differences, one can then determine in which order districts are affected by the annual epidemic. This ranking allowed us to identify districts hit early by the seasonal epidemic. Time series of the temporal lag between seasonal epidemics at different locations were thus estimated from phase differences, according to equations (6)–(8) in [10]. This temporal lag is proportional to the phase difference.
In some districts identified as early districts, there were not enough cases reported to be confident that there was significant dengue transmission ongoing, given that cases are declared on a clinical basis. Therefore, for each district, we identified epidemic years using the national epidemic threshold [2], [16]. The national weekly threshold [2] for an epidemic was calculated as two standard deviations above a three weeks sliding mean computed over the weekly national incidence rates of the five past available non epidemic years [16] (from 2002 to 2006 in our study). We excluded from the analysis time periods from districts with low incidence, only including epidemic years defined as districts with a weekly incidence rate from January to December above the national threshold for an epidemic during two consecutive weeks. Subsequent analyses were restricted to the years with detected epidemics.
For a given year, to evaluate the speed at which dengue epidemic propagates along a given geographical axis comprised of I districts, we performed a regression analysis: Yi = β0+β1 X1i+εi , with i in [1, I], Yi the annual mean of the temporal lag between district i and a district located along the axis and selected as a time reference to compute temporal lags, and X1i the corresponding distance as the crow flies, in km, between the centres of district i and the reference district. The speed of dengue propagation along the axis was estimated as the inverse of the regression slope β1.
To compare the dynamic pattern along J different axes, we performed, each year, an analysis of covariance [17]: Yij = β0j+β1j X1ij+εij, with i in [1, Ij], j in [1, J], Yij the annual mean of the temporal lag between district i of axis j and a district located along the axis j and chosen as a time reference, X1ij the corresponding distance separating the centres of districts i and the reference district on axis j, and β0j and β1j the intercept and slope of the regression line of axis j. We will call “p-value of interaction” the p-value of the hypothesis that the model slopes β1j are all equal, or in other words, that the speed of propagation is the same along different axes. If the model gave a p-value of interaction below 0.05, the propagation was considered heterogeneous along the different axes, and separate regressions were performed for each axis.
As we wanted to know whether the results were consistent from year to year, we performed this analysis each year.
All analyses and figures were performed using R software [15].
All confidence intervals (C.I.) provided were calculated using classical methods for calculating a confidence interval around a mean, unless stated otherwise.
Figure 3 shows the weekly incidence in the 135 (/180) most populous districts (average district population of 78,000), where 105,598 of the 109,332 reported patients reside. Annual epidemics do not appear synchronous, peaking at different times of the year in different districts (between May and October). Unexpectedly, in Phnom Penh, the capital, where the virus circulates during the dry season [2], the annual epidemic lags behind the one in other districts indicating that Phnom Penh is not the starting point. Inspection of weekly and mean annual incidence maps over the whole period (not shown here, but partly visible in Figure 1, Movie S1, and Figure S1) showed two possible geographic axes seminal in the propagation of dengue: the national road between Kampong Cham and Siem Reap – the busiest one in Cambodia –, and the Mekong River.
To characterise and compare the spatio-temporal pattern of dengue incidence along those two axes and determine the focal starting areas of the annual epidemic, we performed a phase analysis using a wavelet approach (see Methods).
The examination of wavelet power spectra (Figure S2) revealed the annual seasonal component of incidence is the most powerful in 117/135 districts, meaning that in Cambodia, the seasonal cycle of dengue incidence time series has more power than the inter-annual cycle. A significant 2–3 years periodic component was detected in some districts, but our time series were too short in time to study it.
Figure 4 represents, in a time-space domain, the phase of the annual component of incidence filtered in the 0.8–1.2 year periodic band for each district of the two geographic features identified previously (see Movie S2 for maps of these phases in all 135 districts). White parts represent years when no epidemic was detected in the district (see Methods). The phase represents the timing of the epidemic in each district. For a given week, if all districts have the same phase (same colour), the epidemics occur at the same time.
First, the pattern of spatial synchronicity observed is consistent from year to year. To test this result, each year, we ranked the 135 districts according to their phase during the epidemic period (weeks 13 to 39), from the district where the epidemic has the most advanced phase to the one with the highest phase delay. We then performed a Spearman correlation test on these ranks, comparing ranks from year n with ranks of years n+1. Except in 2007, correlation coefficients were all higher than 0.38 and significant (all p-values<0.03), inferring that districts are affected in a similar chronological order year after year. In 2007, the order in which districts were affected by the epidemic was not significantly correlated to the order of the previous year.
This ranking also allowed us, each year, to identify districts where the annual epidemic appears early. We have identified three starting points, all located in rural areas: district #306 and a few rural districts around (district #306 being the most early of them), district #104 and 2 other districts around, and, some years, district #805, located along the Mekong River, South to Phnom Penh, along the Vietnamese border (see Figure 4). These districts are very similar to other rural districts included in the analysis, composed of a flat flood plain, with a mean population density of 155 people per km2. They are all located away from the three urban centres where sentinel hospitals involved in active surveillance are. They are consistently the same, year after year.
Secondly, Figure 4 demonstrates that the propagation of dengue is heterogeneous. Annual epidemics are highly synchronous along the national road linking Kampong Cham to Siem Reap whereas an oriented propagation emanates from a rural area located around Kampong Cham (district #306) and ends in Phnom Penh 11 weeks later (95% C.I. 4–18 weeks). This was obscured when districts were classified independently of the geographic area, taking into account only the distance to Phnom Penh (Figures S3, S4 and S5). Some years, another oriented propagation is seen from a rural area near the Vietnamese border (district #805) towards Phnom Penh (Figure 4B).
To evaluate the speed of propagation along each axis, we calculated the temporal lag of the seasonal pattern in each district relative to the district #306 [10] (see Methods). This district was chosen as a time reference point to calculate time lags because it is located at the intersection of the Mekong River and the national road areas, which was convenient to present results of both geographic areas on the same figure (Figure 5). Moreover, based on the ranking of districts using their phase, district #306 was the one with the most number of districts lagging behind over the study period (132, 134, 131, 118, 124 and 104 districts/134 respectively from 2002 to 2007). The results of the analysis of covariance performed on the mean annual lag to compare the speed of propagation of the epidemic in each geographic axis (Figure 4A) are presented in Table 1. Results show that regardless of the year, the evolution of the temporal lag between epidemics according to the distance differs significantly (α-level of 0.05) from one area to the other. We therefore performed separate regressions for each of the two axes (Figure 5). Propagation is always faster along the national road than along the Mekong River. The speed of propagation of the travelling wave along the Mekong River, evaluated by the inverse of the regression slope, is estimated to be 11 km per week (95% C.I. 3–18 km per week) over the study period. Along the national road, the speed is quasi-instantaneous with respect to our time domain sampling rate.
To test the role Phnom Penh plays in this heterogeneity, we ran the same analysis, but excluding Phnom Penh districts. The propagation remained significantly heterogeneous at an alpha-level of 5%, except in 2002 and 2007, and remained significantly heterogeneous at an alpha-level of 10% in 2002 and 2007 (results not shown).
The removal of districts with low population density has no effect at all on results shown. The exclusion of low incidence years did not modify the two conclusions arrived at in the paper (onset of the seasonal epidemic in highly localised rural areas, and heterogeneous propagation in the country), but removed the potential bias linked with the very low number of cases during the non epidemic years (see Figures S6 and S7 that show the same as Figures 4 and 5 when all years are included).
To sum up, our results show that the seasonal epidemic consistently starts in the same three rural districts in Cambodia. Then the propagation is not homogeneous in the country. In districts located along the busiest road, dengue epidemics appear simultaneously and early (with all districts being hit in less than a month), whereas districts located along the Mekong River get hit by the seasonal epidemic later, with the delay being proportional to the distance to district #306.
In 2007 an exceptional epidemic occurred in Cambodia with DENV-3 as the dominant etiological agent, and a four fold increase in weekly incidence rate compared to the four previous years (Figures 2 and 3). During this epidemic, the wave in the one-year periodic mode travelled from Kampong Cham to Phnom Penh in only 7 weeks, at a speed of 17 km per week, a higher synchronicity reflecting a more rapid propagation during this peculiar event. The sequence in which districts were affected by the seasonal variation was also modified, as shown by non-significant Spearman rank coefficients when correlations were calculated between any year and the year 2007.
Regarding the onset of the national epidemic, there is a tendency for the dengue season to start in few rural districts more often than in any other district moving from rural areas towards urban centres, with, for example, annual epidemics in districts #306 and #104 (Figure 4A) leading epidemics in the rest of the country by 3 weeks on average over the study period (95% C.I. 2–4 weeks). In addition, recent prospective cohort data showed that rural areas were affected by dengue to the same degree as urban areas or, as during the 2007 epidemic, at even higher incidence rates [18]. This finding is not consistent with the common thought that the most populated areas spread the disease [7]–[9] or known mechanisms underlying travelling waves [7], [13]. One plausible explanation for a rural origin of the spread, also supported by a recent study in Vietnam [19], is that more than 80% of rural Cambodians do not have access to public water supply, and store their drinking water in big jars that have been identified as major breeding sites for Aedes mosquitoes in Cambodia [2]. This result (onset in rural areas) has strong implications regarding the control of dengue in low-income countries.
To our knowledge, it is the first time that a recurrent heterogeneous pattern of propagation of dengue is revealed in surveillance data at a national scale. This pattern of propagation could come from actual transmission of dengue viruses between districts, via infected mosquitoes or humans, or correspond merely to spatial differences in the emergence of the epidemic, differences due either to spatial differences in local (within-district) dynamics, or to forcing by extrinsic factors such as climate (Moran effect). Given the limited dispersal range of Aedes spp. vectors and the high synchronicity of the epidemics over a 400 km wide area along the national road, it is unlikely that viral transmission from one place to another via infected mosquitoes can account for the pattern observed.
Climate is quite homogeneous in Cambodia, with a narrow temperature range across the country, and the monsoon striking the country from South to North within a month only, around April-May. Climate, by influencing the vector's life cycle, is clearly driving the dengue seasonality observed in Cambodia. It could easily explain the high synchronicity of epidemics observed along the national road if this synchronisation was observed country-wide. However, two of our observations preclude climatic forcing to be the underlying factor for the spatial-temporal pattern observed. First, climate is more homogeneous in the country compared to the observed spatial-temporal pattern of dengue fever. The existence of a very slow oriented dengue propagation along the Mekong River, going North to South, with the epidemic in some districts lagging more than 3 months behind the one in districts that are only less than 200 kilometres in distance cannot be accounted for by climatic differences between districts. Secondly, the epidemic starts as early as in March in the three districts identified as starting points, whereas the monsoon only begins end of April/beginning of May.
We believe the heterogeneous propagation observed is related to the heterogeneity of traffic on the roads of the country, where traffic allows the movement of human carriers or transported mosquitoes infected with dengue. The national road –the busiest country road connecting the two main economic cities within 4–5 hours – probably explains synchronicity. By contrast, the existence of dirt roads along the entire Mekong River, on which traffic and population movement between smaller villages are slower and more difficult than along the national road supports our hypothesis. Despite its epidemiological relevance, our understanding of the relationship between human movement and pathogen transmission remains limited [20]–[22]. The importance of the relationship between human movement and pathogen transmission in explaining the spatio-temporal dynamics of dengue incidence or other diseases has been increasingly pointed-out during the last decade [5], [8], [9],[23]–[25] and explored mainly through a theoretical modelling approach [22], [26], [27].
The higher synchronisation and higher incidence levels across the country in 2007 argues for an association with higher net reproduction ratios of infection due to lower herd immunity when a new serotype is introduced [25], [28]. This hypothesis is supported by the fact that serotype 3 invaded Cambodia at the end of year 2006, replacing serotype 2, in place since 1999, as the major circulating virus [2]. The fact that heterogeneity of propagation remained despite this serotype change supports the hypothesis that another factor – such as human movement – plays an important role in the dynamics.
Major limitations of our analysis include underreporting inherent to passive surveillance data and potential selection bias leaning towards underreporting from urban centres compared with rural areas. However, underreporting would only affect the amplitude of the epidemics in each district and therefore have little effects on the study of synchronism when using the wavelet phase analysis approach [12]. Secondly, it is unlikely that rural Cambodians were over-represented as most hospitals and those that recruit dengue patients through active surveillance are free of charge and located in urban areas (three cities). One could also assume that collecting data from both an active and a passive surveillance system could affect the timing of detection of the epidemics, with active surveillance sites more likely to reveal a small increase in incidence levels earlier than passive surveillance sites. This would lead to a bias in the analysis. However, after a thorough analysis of the surveillance system (Institut Pasteur Cambodia's not published report), we believe that severe cases are scarcely missed even by the passive surveillance system; we also believe that our results are not affected by this bias, given the fact that districts affected early in the epidemic are located in rural areas, kilometres away from the urban sentinel sites. Another common limitation when analysing surveillance data is the introduction of spatial or temporal biases due to difficulties in standardising surveillance systems in time and space, especially in low income countries. We thus, on purpose, excluded any results or comments that would rely on spatial differences in incidence levels only. We found the wavelet approach very robust to these biases when exploring spatial-temporal patterns: unlike many other temporal methods, the level of incidence in a given district does not influence the calculation of time lags between epidemics.
One could think that not standardising incidence according to age could impair the results obtained. But age standardisation only affects the results by modifying incidence levels, and as our results do not rely on differences in incidence levels, this is not a real concern.
Our findings and speculations require further research for additional evidence. Firstly, reasons as to why the three areas identified as being hit early by the epidemic repeatedly year after year (districts #306, #104 and #805) are unclear. This warrants further filed investigations to identify specific factors that trigger the epidemic in these settings.
Secondly, the data we analysed here can only reveal the spatio-temporal dynamics and help make hypotheses on underlying factors. Given the results of this study, and particularly the heterogeneity of dengue spatial dynamics, surveillance data on the spatial distribution of the serotypes (and genotypes if possible) of the co-circulating dengue viruses would help validate (or not) our hypotheses on dengue dynamics in Cambodia, using independent data.
Lastly, given the potential benefit in term of disease control from demonstrating the efficient role of humans' movement in dengue spatial transmission, one might consider further investigations in this direction, collecting data and using dynamic models for instance.
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10.1371/journal.pgen.0030234 | dAtaxin-2 Mediates Expanded Ataxin-1-Induced Neurodegeneration in a Drosophila Model of SCA1 | Spinocerebellar ataxias (SCAs) are a genetically heterogeneous group of neurodegenerative disorders sharing atrophy of the cerebellum as a common feature. SCA1 and SCA2 are two ataxias caused by expansion of polyglutamine tracts in Ataxin-1 (ATXN1) and Ataxin-2 (ATXN2), respectively, two proteins that are otherwise unrelated. Here, we use a Drosophila model of SCA1 to unveil molecular mechanisms linking Ataxin-1 with Ataxin-2 during SCA1 pathogenesis. We show that wild-type Drosophila Ataxin-2 (dAtx2) is a major genetic modifier of human expanded Ataxin-1 (Ataxin-1[82Q]) toxicity. Increased dAtx2 levels enhance, and more importantly, decreased dAtx2 levels suppress Ataxin-1[82Q]-induced neurodegeneration, thereby ruling out a pathogenic mechanism by depletion of dAtx2. Although Ataxin-2 is normally cytoplasmic and Ataxin-1 nuclear, we show that both dAtx2 and hAtaxin-2 physically interact with Ataxin-1. Furthermore, we show that expanded Ataxin-1 induces intranuclear accumulation of dAtx2/hAtaxin-2 in both Drosophila and SCA1 postmortem neurons. These observations suggest that nuclear accumulation of Ataxin-2 contributes to expanded Ataxin-1-induced toxicity. We tested this hypothesis engineering dAtx2 transgenes with nuclear localization signal (NLS) and nuclear export signal (NES). We find that NLS-dAtx2, but not NES-dAtx2, mimics the neurodegenerative phenotypes caused by Ataxin-1[82Q], including repression of the proneural factor Senseless. Altogether, these findings reveal a previously unknown functional link between neurodegenerative disorders with common clinical features but different etiology.
| The spinocerebellar ataxias (SCAs) are a group of ∼30 neurodegenerative disorders caused by different types of mutations in a variety of unrelated genes. For example, SCA1 and SCA2 are caused by mutations in Ataxin-1 and Ataxin-2, two proteins related in name only. Despite these differences, most SCAs share a number of striking clinical and neuropathological similarities, such as ataxia, tremor, speech difficulties, and atrophy of the cerebellum and brainstem. In addition, many ataxia-causing proteins share interacting protein partners. Together, these observations suggest that many SCAs also share common mechanisms of pathogenesis. Here, we report previously unknown functional interactions between the genes and proteins responsible for SCA1 and SCA2. We find that Ataxin-1 and Ataxin-2 physically interact, and that mutant Ataxin-1 forces Ataxin-2 to accumulate in the nucleus instead of the cytoplasm. Most importantly, using an animal model, we discovered that the Drosophila Ataxin-2 gene is a strong suppressor of Ataxin-1-induced neurotoxicity. Thus, neuronal degeneration may take place through common mechanisms in different SCAs. These findings open the possibility of future common therapies for these neurodegenerative disorders for which there is no effective treatment.
| Inherited ataxias are a genetically heterogeneous group of neurodegenerative diseases characterized by loss of motor coordination and balance. They can be caused by loss-of-function or gain-of-function mechanisms; some ataxias are triggered by missense mutations, while others by triplet repeat expansions, which may occur either in coding or non-coding sequences. Furthermore, the gene products implicated in the different ataxias do not share obvious functional or structural relationships to each other. In spite of this genetic heterogeneity, many ataxias show striking similarities. In particular, it is often difficult to distinguish between Spinocerebellar ataxias (SCAs) based only on clinical and pathological observations, and their differential diagnosis often requires genetic testing. In addition, a common neuropathological feature of SCAs is the atrophy of the cerebellar module (reviewed in [1–3]). These similarities suggest that SCAs, and perhaps other ataxias, may also share common mechanisms of pathogenesis. In support of this hypothesis a recent study reported a network of physical protein-protein interactions among many factors associated with ataxia and Purkinje cell degeneration in humans and mice [4]. However, no specific molecular mechanisms are known that can account for the clinical and neuoropathological similarities among SCAs and other ataxias.
SCA1 is caused by the expansion of a CAG repeat encoding a polyglutamine tract in the protein Ataxin-1 that induces a toxic gain of function [5]. The expanded protein accumulates in neuronal nuclear inclusions (NIs) that also contain transcription factors, chaperones, proteasome subunits, and other components of the protein quality control/degradation machinery like CHIP or Ataxin-3 [6–11]. Abnormally long polyglutamine tracts are the common cause of pathogenesis in at least five other SCAs (SCA2, 3, 6, 7 and 17) and three additional neurodegenerative diseases including Huntington's disease (HD) [1,12]. Protein quality control machinery as well as transcriptional dysregulation are general mechanisms that have been implicated in the pathogenesis of these polyglutamine disorders [13–15].
Although the polyglutamine expansion triggers the toxicity of Ataxin-1, experiments in Drosophila and mouse SCA1 models have shown that protein context plays a key role in expanded Ataxin-1-induced neurodegeneration (reviewed in [15]). The nuclear localization signal[16] and phosphorylation[17] influence the toxicity of expanded Ataxin-1. In addition, certain interacting partners of unexpanded Ataxin-1 are critical to expanded Ataxin-1 toxicity [9,18,19]. In this context, expanded Ataxin-1 was recently found to induce a decrease in the levels of Senseless (Sens) and its murine orthologue growth factor independent 1 (Gfi1) [18]. These are transcription factors that interact with unexpanded Ataxin-1 and are necessary for Purkinje cell survival in mice [18] and for sensory organ development in Drosophila [20]. The importance of the protein framework has also been shown in models of other polyglutamine diseases [15,21].
Genetic screening in Drosophila models of neurodegenerative diseases is a powerful approach to identify modifier genes and pathways implicated in pathogenesis [22–24]. We previously reported an unbiased genetic screen with a Drosophila model of SCA1 [25]. Here we report the identification of the Drosophila homolog of Ataxin-2 (dAtx2) as a major modifier of expanded Ataxin-1-induced toxicity. Ataxin-2 is a widely expressed cytoplasmic protein with no similarity to Ataxin-1 except for the polyglutamine domain. The normal function of Ataxin-2 remains unclear, although it has been implicated in mRNA processing [26–28] and translational regulation in yeast [29,30], C. elegans[31] and Drosophila[32], where it is also required for actin filament formation [33]. However, expansion of its polyglutamine domain leads to SCA2 [34–36]. The functional interactions between Ataxin-1 and Ataxin-2 described here mechanistically tie these two proteins and point to previously unknown pathogenic links between two inherited ataxias.
Expression of Ataxin-1[82Q] in the eye of SCA182Q flies causes external and internal abnormal phenotypes [25]. Externally, the eyes of these animals show severe ommatidial disorganization as well as interommatidial bristle loss when compared with control eyes (Figure 1, compare A and A′ with B and B′). Internally, examination of the retina reveals tissue loss and shortened and curved photoreceptor neurons (Figure 1, compare F with G). In a screen for genetic modifiers of Ataxin-1[82Q]-induced toxicity we recovered EP(3)3145 as an enhancer of the eye phenotype (data not shown). This is an insertion of an EP transposable element [37] in the 5′ end of dAtx2, the Drosophila orthologue of human Ataxin-2. The Drosophila and human proteins share 23% amino acid identity and 36% amino-acid similarity over the entire protein with the most conserved sequences corresponding to the ATX2-N and ATX2-C domains (43% and 62% identity, respectively) [33]. Molecular analysis revealed that the EP element is inserted 3121 bp upstream of the ATG and in the same orientation as the dAtx2 transcription unit (data not shown and [33]). These data suggested that EP(3)3145 over-expresses the dAtx2 transcription unit to enhance the SCA182Q eye phenotype. As described below, this possibility was confirmed using a transgene that over-expresses the dAtx2 cDNA.
Co-expression of a wild-type dAtx2 transgene (dAtx2OE) at low levels enhances the Ataxin-1[82Q]-induced eye phenotype. Externally, the eyes of SCA182Q/dAtx2OE animals show no bristles and increased ommatidial disorganization when compared with the eyes of SCA182Q controls (compare Figure 1D and D′ with B and B′). Internally, photoreceptor cells are considerably shorter (compare Figure 1I with G). Expression of the same low levels of dAtx2 alone in the eye causes relatively mild external disorganization and reduction of the retinal width (Figure 1E, E′ and J). Overexpression of dAtx2 from EP(3)3145 and UAS-dAtx2 also aggravates the phenotypes of other fly models of neurodegenerative diseases besides SCA1 [38,39]. However, since overexpression of dAtx2 causes an eye phenotype by itself (Figure 1E, E′ and J) and it is toxic in many other tissues [33], it is difficult to make strong conclusions about the specificity of these genetic interactions.
To test the specificity of the genetic interaction, we investigated if decreasing the levels of endogenous dAtx2 modifies expanded Ataxin-1-induced toxicity. For this, we used a 1.4 kb deletion in the dAtx2 locus (dAtx2X1) that removes part of the dAtx2 promoter, the ATG codon and extends into the first intron [33]. We find that flies expressing Ataxin-1[82Q] and heterozygous for the dAtx2X1 mutant allele show a strong suppression of the eye phenotype, with much improved arrangement of the ommatidia and bristles compared to eyes from flies expressing Ataxin-1[82Q] with normal dAtx2 levels (compare Figure 1C and C′ with B and B′). This suppression is also evident in the retinas of SCA182Q/dAtx2X1 flies that show elongated photoreceptors and very little tissue loss (compare Figure 1H with G). To further test the specificity of this interaction, and to exclude potential genetic background artefacts, we asked whether adding back dAtx2 to SCA182Q/dAtx2X1 flies eliminates the suppression effect. Figure S1 shows that SCA182Q/dAtx2X1/dAtx2OE flies show an eye phenotype that is very similar to the phenotype of SCA182Q flies. The effects of the dAtx2X1 and dAtx2OE alleles decreasing/increasing dAtx2 levels are demonstrated in Figure S2.
Since dAtx2 is an RNA binding protein, we investigated if the observed suppression of Ataxin-1[82Q] toxicity was the result of dAtx2 affecting the levels of the SCA182Q mRNA transcript or the levels of Ataxin-1 [82Q] protein. As shown in Figure 1K and L neither the levels of SCA182Q mRNA nor the levels of the Ataxin-1[82Q] protein are affected by changing the levels of dAtx2.
To verify that the genetic interaction between Ataxin-1 and dAtx2 is not limited to the eye, we analyzed the effect of altering dAtx2 levels on expanded Ataxin-1-induced neuronal dysfunction.
The motor performance of flies as a function of age can be quantified using a climbing assay [40]. This assay has been used to analyze the effects of toxic proteins on neurons in other Drosophila models of neurodegenerative diseases [41,42]. Control flies show no significant decrease in their motor performance until late in life. Figure 2A shows that ∼74% of control flies still climb after thirty-six days (black triangles). Flies expressing Ataxin-1[82Q] specifically in the nervous system (using nrv2-GAL4) display a progressive impairment of their motor performance (Figure 2A, blue circles). In the context of the Drosophila life span, this is a late onset and progressive phenotype as compared to the performance of control flies in the same period of time. We then analyzed the effect of decreased levels of dAtx2 on the climbing phenotype caused by Ataxin-1[82Q]. As shown in Figure 2A (red squares) in SCA182Q flies also heterozygous for the dAtx2X1 mutation climbing performance is significantly improved compared to flies with normal dAtx2 levels (p<0.0001 for repeated measures anova (rma) between genotypes). Figure 2A show that while all SCA182Q animals fail to climb after 26 days, SCA182Q/ dAtx2X1 flies continue to climb until later in life. Thus the impairments in motor performance caused by neuronal expression of Ataxin-1[82Q] are suppressed by decreased dAtx2 levels.
We also studied the effect of Ataxin-1[82Q] expression in a life span assay. Figure 2B shows that expression of Ataxin-1[82Q] in the nervous system leads to premature death in SCA182Q flies in comparison to GFP controls (Figure 2B, compare blue circles with black triangles). While SCA182Q animals do not survive past 30 days, this early lethality phenotype is suppressed in SCA182Q/ dAtx2X1animals (Figure 2B, red squares).
To investigate whether dAtx-2 also modulates neurodegeneration in other models of polyglutamine disease, we tested the effect of altering the dAtx2 levels in a Drosophila model of Huntington's disease [7,43]. Adult flies expressing an expanded N-terminal fragment of human huntingtin (N-Htt128Q) in the eye show a progressive retinal degeneration which becomes obvious at day 5 after eclosion [7]. N-Htt128Q retinas show disorganized and missing photoreceptors (Figure 2D, compare with control in 2C). N-Htt128Q flies also overexpressing dAtx2 present a more degenerated retina than flies expressing N-Htt128Q with normal levels of dAtx2 (data not shown). However, since overexpression of dAtx2 is sufficient to cause retinal degeneration (Figure 1J), this result is not conclusive by itself. Therefore, we tested the effect of decreasing dAtx2 levels on the N-Htt128Q induced retinal degeneration. As shown in Figure 2D-E, decreasing the levels of dAtx2 (N-Htt128Q/dAtx2X1) does not obviously alter degeneration induced by N-Htt128Q in the Drosophila eye.
We also investigated a possible genetic interaction between dAtx2 and N-Htt128Q in the motor performance assay described above. Like with Ataxin-1[82Q], expression of N-Htt128Q in the nervous system leads to motor performance impairments (Figure 2F) where N-Htt128Q animals stop climbing before day 20. The climbing performance of animals expressing N-Htt128Q with decreased levels of dAtx2 (N-Htt128Q/dAtx2X1) is not significantly different from that of animals expressing N-Htt128Q with normal levels of dAtx2 (Figure 2F). Therefore decreasing the levels of dAtx2 fails to suppress N-Htt128Q induced degeneration both in the retina and in the nervous system.
Senseless (Sens) is a proneural factor that is expressed and required in the sensory organ precursor (SOP) cells of the peripheral nervous system [20]. Expression of high levels of Ataxin-1[82Q] in the thoracic SOPs using scabrous-GAL4 (sca-GAL4) leads to a reduction in Sens protein levels in these cells and loss of large mechanoreceptors (macrochaetae) in the thorax of adult flies [18]. Therefore, scoring adult thoracic macrochaetae in SCA182Q animals provides a quantitative phenotype with a known molecular foundation.
We analyzed the effect of altering Ataxin-2 levels on Ataxin-1[82Q]-induced loss of mechanoreceptors. This was performed by quantifying the number of macrochaetae in the adult thorax of SCA182Q animals with different levels of dAtx2. Using a relatively low expressing Ataxin-1[82Q] line, we defined conditions in which sca-GAL4-mediated expression in the SOP cells causes only 9% of macrochaetae loss (Figure 3A-column-3 and Fig 3C, compare to controls Figure 3A-column-1 and Figure 3B). Expression of wild-type dAtx2 alone (dAtx2OE) leads to no loss of machrochaetae (Figure 3A-column-2 and Figure 3D), but co-expression of dAtx2 and Ataxin-1[82Q] (SCA182Q/dAtx2OE) leads to a severe loss of macrochaetae compared to Ataxin-1[82Q] alone (Figure 3E, compare with Figure 3C). Quantification shows an ∼80% decrease in the number of macrochaetae when both proteins are co-expressed compared to controls (Figure 3A-columns 1–4, p<0.0001, Tukey-Kramer HSD).
In the wing imaginal disc, Sens expression includes the precursors of the large thoracic mechanoreceptor bristles, and the two parallel rows of bristles at each side of the wing margin (Figs. 3F-G). We quantified the amount of fluorescence detected after anti-Sens immunostaining in wing margin cells expressing Ataxin-1[82Q] with normal or increased levels of dAtx2. Expression of either low levels of Ataxin-1[82Q] or dAtx2 with sca-Gal4 (at 25C) produces no detectable decrease in the levels of Sens in the wing margin (Figure 3H and I compare with G and quantification in K). However co-expression of both Ataxin-1[82Q] and dAtx2 in the same conditions induces a strong decrease in the levels of Sens (Figure 3J). Quantification of the Sens signal in SCA182Q/dAtx2OE animals (Figure 3K, fourth column) reveals a decrease of ∼50% in the amount of Sens signal when compared to either wild type, SCA182Q or dAtx2OE controls (p<0.001, Tukey-Kramer HSD).
In addition, we investigated the consequences of decreasing the amount of dAtx2 (using the dAtx2X1 allele) in conditions where Ataxin-1[82Q] reduces Sens levels in the wing margin (i.e. at 27°C and 29°C). However, we did not detect a significant modification (data not shown). Since it is possible that we are not able to detect small changes on Sens levels in wing discs from late third instar larvae, or this changes might happen later in the SOP development, we also investigated the effect of decreasing the amount of Ataxin-2 on Ataxin-1[82Q]-induced mechanoreceptor loss. Expression of Ataxin-1[82Q] at high levels (27°C) in the SOP cells results in the loss of ∼20% of thoracic macrochaetae when compared to controls (Figure 3L compare columns 1 and 2 p<0.0001, Tukey-Kramer HSD; see Figure 3M). Reducing the levels of endogenous dAtx2 with the heterozygous dAtx2X1 mutation partially rescues the Ataxin-1[82Q]-induced bristle phenotype. Loss of macrochaetae in SCA182Q/ dAtx2X1 animals is approximately half of that seen in animals with normal levels Ataxin-2 (Figure 3L, compare columns 2 and 3 p<0.005, Tukey-Kramer HSD, and see Figure 3N).
To further characterize the interactions between Ataxin-1 and Ataxin-2, we investigated possible protein-protein interactions. Lysates from cells expressing Drosophila or human Ataxin-2 and GST-Ataxin-1 [82Q] were subjected to co-affinity purification (co-AP) glutathione-S-transferase (GST) pull-down assays. As shown in Figure 4A (lanes 1 and 2) and Figure 4B (lanes 1 and 4), GST-Ataxin-1[82Q] is able to pull down the Drosophila and human Ataxin-2 proteins, which indicates that both proteins are able to physically interact. We also asked whether this interaction is polyglutamine dependent. As shown in Figure 4B lanes 2–4 we did not detect significant differences in the interactions between GST-Ataxin-1 with 2, 30 or 82 glutamines and human Ataxin-2 in this co-AP assay.
Ataxin-1[82Q] is phosphorylated at Serine residue 776 on the C-terminal portion of the protein, and a (Ser776Ala) mutation inhibits the toxicity of Ataxin-1[82Q] in mice[17]. We investigated the importance of Ser776 for the Ataxin-1[82Q]-dAtx2 interaction. As shown in (Figure 4A lane 3) the interaction of dAtx2 with Ataxin-1[82Q]S776A is weaker than with normal Ataxin-1[82Q], suggesting that this interaction is phosphorylation dependent.
In addition, we investigated if Ataxin-1 can pull down endogenous hATX2. We find that unexpanded Ataxin-1 is able to precipitate endogenous hATX2 from human cells, suggesting that the two proteins may be functional interactors in vivo (Figure 4C).
We also investigated whether the interaction between Ataxin-1 and Ataxin-2 is cytoplasmic or nuclear. We carried out co-AP assays with Ataxin-1 and dAtx2 after nuclear/cytoplasmic fractionation of cultured cells. Figure 4D shows that we were not able to detect differences in protein interactions between these cellular compartments using this assay.
Lastly, we investigated whether specific domains of the hAtaxin-1 protein are responsible for the interaction with hAtaxin-2. Co-AP experiments were carried out with lysates from cells expressing Myc-hAtaxin-2 and one of the following hAtaxin-1 fragments tagged with GST: polyglutamine expanded N-terminal (aa# 1–575, Figure 4E lane-3), C-terminus Ataxin-1 containing the AXH domain (aa# 529–816; Figure 4E lane-4) or the AXH domain alone (aa# 558–700; Figure 4E lane-5). All three fragments pull-down Myc-hAtaxin-2, indicating that each Ataxin-1 fragment can interact independently with hAtaxin-2 (Figure 4E lanes 3–5). However, the interaction of hAtaxin-2 is stronger with the N-terminal Ataxin-1 fragment (Figure 4E, lane-3) as compared to the C-terminal or AXH portions (Figure 4E lanes 4 and 5 respectively), since less N-terminal peptide pulls down more hAtaxin-2.
The co-AP assays with expanded hAtaxin-1 and hAtaxin-2 indicate that the two proteins are able to interact. However, Ataxin-1 normally localizes to the nucleus in Drosophila and many human cell types, while Ataxin-2 is a cytoplasmic protein. To address whether the interaction observed in cultured cells is relevant in vivo, we monitored the localization of Ataxin-2 in Drosophila cells expressing Ataxin-1[82Q]. Since Ataxin-1[82Q]-induced toxicity is suppressed by dAtx2 loss of function in the Drosophila eye; we first analyzed the localization of dAtx2 in retinal cells. dAtx2 is not normally detected in the nuclei of retinal cells from either control eyes or eyes overexpressing dAtx2 (Figure 5A and C respectively). In contrast, we find that endogenous dAtx2 localizes to the nuclei of retinal cells expressing Ataxin-1[82Q]. Furthermore, nuclear dAtx2 signal is detected both diffusely in the nucleoplasm as well as in nuclear inclusions (NIs) (Figure 5B). To confirm this unexpected result, we examined the localization of dAtx2 in other Ataxin-1[82Q]-expressing neurons. Similar to the results obtained in the retina, endogenous dAtx2 normally localizes to the cytoplasm and is not detected in the nuclei of neurons from the ventral nerve cord (Figure 5 E-E′′, ok107-GAL4 pattern shown in D). However, Ataxin-1[82Q]-expressing VNC neurons show nuclear accumulation of dAtx2 (Figure 5 F-F′′). Next we investigated whether dAtx2 and Ataxin-1 colocalize. Co-staining of Ataxin-1 and dAtx2 is not possible since the available antibodies for both proteins were raised in rabbit; however, Ataxin-1 NIs in Drosophila neurons are positive for Ubiquitin [25], so we performed a double staining for dAtx2 and Ubiquitin. Figure 5G-G′′ shows that the dAtx2 NIs present in the neurons of SCA182Q flies are positive for Ubiquitin. These results indicate that expanded Ataxin-1 causes dAtx2 to localize to the nucleus and suggest that both proteins co-aggregate in NIs.
To validate these results and investigate their relevance for SCA1 pathogenesis, we analyzed the localization of hAtaxin-2 in SCA1 neurons from human postmortem brain samples by anti-Ataxin-2 immunohistochemistry. Pontine neurons from control samples consistently show cytoplasmic localization of hAtaxin-2 (Figure 5H). Pontine neurons from SCA1 brain samples display frequent Ataxin-1 NIs that are clearly visible with Hematoxylin staining. We found that approximately twenty percent of these NIs are positive for hAtaxin-2 (Figure 5I); see also[10]. These findings, together with the co-AP data using human Ataxin-1 and Ataxin-2 proteins, suggest that the Ataxin-1-Ataxin-2 interactions observed in Drosophila may be relevant for SCA1 pathology.
SCA1 pathogenesis is triggered by polyglutamine expansion in Ataxin-1 beyond 39–44 residues [5]. To investigate if nuclear accumulation of Ataxin-2 is specific to the pathogenic Ataxin-1 form, we analyzed dAtx2 localization in Drosophila VNC neurons expressing human Ataxin-1 with different polyglutamine lengths: Ataxin-1[2Q], Ataxin-1[30Q] and Ataxin-1[82Q]. The Ataxin-1[2Q] line used has higher levels of protein expression than the Ataxin-1[30Q] and [82Q] lines, both of which have comparable expression levels (Western blot data not shown). Figure 6A-D shows that no nuclear dAtx2 signal is detected in control neurons (Figure 6A), or neurons expressing Ataxin-1[2Q] or [30Q] (Figure 6B and C). In contrast, accumulation of dAtx2 is detected in the nucleus of Ataxin-1[82Q]-expressing neurons (Figure 6D). Although this result does not rule out an interaction between wild-type Ataxin-1 and dAtx2, it indicates that abundant nuclear accumulation of Ataxin-2 is specific to the expanded Ataxin-1 form responsible for SCA1 pathogenesis.
The observations that expanded Ataxin-1 induces nuclear accumulation of Ataxin-2 and that decreasing the levels of endogenous Ataxin-2 suppresses toxicity in SCA182Q flies, suggest that nuclear accumulation of Ataxin-2 may lead to neurotoxicity. To test this hypothesis, we generated dAtx2 transgenic flies with an exogenous nuclear localization signal (NLS) engineered on its C-terminus end (dAtx2NLS) (Figure 7 compare A-A′ with C). Although wild-type Ataxin-2 is only detected in the cytoplasm in Drosophila and human neurons, it is difficult to rule out the possibility that some Ataxin-2 may be present in the nucleus. Therefore, we also generated flies carrying a dAtx2 construct with an exogenous nuclear export signal (NES) (dAtx2NES) to use as an additional control (Figure 7B-B′). Ten transgenic lines were recovered for each dAtx2 construct, with a wide range of expression levels for each transgene. We selected transgenic lines expressing wild-type dAtx2 (dAtx2OE), dAtx2NES or dAtx2NLS at similar levels (Figure 7D compare lanes 2–4), and compared their toxicity in the eye. As shown in Figure 7E-H, dAtx2NLS is more toxic than wild-type dAtx2 or dAtx2NES. While both wild-type dAtx2 and dAtx2NES induce a relatively mild eye phenotype compared to controls (Figure 7 E-G), expression of dAtx2NLS results in strong eye toxicity (Figure 7H). Eyes of dAtx2NLS flies show a severely disorganized ommatidial lattice and a complete absence of interommatidial bristles (Figure 7H). These observations were consistent in several lines for each of the dAtx2 constructs at similar levels of expression (data not shown).
In summary, expression of dAtx2NES and wild-type dAtx2 in the eye cause similar mild phenotypes. This is consistent with dAtx2 being mainly cytoplasmic, and with observations of SCA2 pathogenesis in the cytoplasm[44,45]. Interestingly, increasing the levels of Ataxin-2 in the nucleus is sufficient to cause a much more severe eye phenotype. These observations suggest that the toxicity of expanded Ataxin-1 is mediated in part, by the nuclear accumulation of Ataxin-2.
To further test the hypothesis that nuclear Ataxin-2 contributes to expanded Ataxin-1-induced toxicity, we analyzed the effect of expressing the different dAtx2 transgenes on Sens distribution in the wing margin SOPs. Expression of Ataxin-1[82Q] in the antero-posterior compartment boundary (using dpp-GAL4) induces a cell autonomous decrease of Sens only in the dpp-GAL4 expressing area of the wing margin (Figure 8A, A′ and 8B, B′, arrowhead). This provides a molecular readout of Ataxin-1[82Q]-induced neurotoxicity.
Expression of dAtx2NES from dpp-GAL4 does not reduce the levels of Sens, whose distribution in imaginal wing discs is unchanged (Figure 8C and C′, arrowhead). Next, we tested the effect of dAtx2 with a nuclear localization signal (dAtx2NLS). Like Ataxin-1[82Q], expression of dAtx2NLS induces loss of Sens in the wing margin SOPs (Figure 8D and D′, arrowhead). dAtx2NLS-induced loss of Sens is also observed in other cell types. Salivary gland cells express Sens at high levels [20], which localizes to the nucleus (Figure 8E-H). Salivary gland cells expressing dAtx2NES show no detectable change in Sens levels or distribution (Figure 8I-L). In contrast, expression of dAtx2NLS causes a dramatic decrease in the amount of Sens (Figure 8M-P). The nuclei of dAtx2NLS cells are still present and their morphology is similar to controls, indicating that Sens loss is unlikely a consequence of cell death. Therefore, nuclear dAtx2 mimics Ataxin-1[82Q] in causing loss of Sens protein accumulation.
Expression of Ataxin-1 in the thoracic SOPs (using sca-GAL4) leads to loss of macrochaetae in the adult thorax (Figure 3 and ref.[18]). Therefore, we also investigated the consequences of nuclear or cytoplasmic Ataxin-2 accumulation on the development of adult macrochaetae. As with wild-type dAtx2 (Figure 3), expression of dAtx2NES in the thoracic SOP cells causes no visible change in the number of macrochaetae in the adult thorax (Figure 8Q and Figure 8S column-1). In contrast, expression of dAtx2NLS induces a significant decrease in the number of macrochaetae, with an approximate twenty percent reduction in comparison to control animals (Figure 8R and Figure 8S column-2). These results indicate that increasing the levels of nuclear dAtx2 mimics expanded Ataxin-1 in inducing loss of mechanoreceptors and reducing the levels of Sens protein.
Here we report functional interactions between the proteins causing two distinct Spinocerebellar ataxias. We use a Drosophila model of SCA1 to show that wild-type dAtx2 (the fly homolog of the protein that when expanded causes SCA2) mediates, at least in part, neuronal degeneration caused by expanded Ataxin-1 (the protein triggering SCA1). Ataxin-1[82Q]-induced toxicity is worsened by increasing the levels of dAtx2. More significantly, decreasing the levels of dAtx2 suppresses expanded Ataxin-1-induced neuronal degeneration as shown in several independent assays. The suppression of Ataxin-1[82Q] phenotypes by partial loss of function of dAtx2 argues against a possible mechanism by which sequestration and depletion of Ataxin-2 contributes to expanded Ataxin-1-induced neurodegeneration. This is further supported by lack of cerebellar or other neuronal abnormalities in mice that are deficient for Ataxin-2[46].
We find that the human expanded Ataxin-1 interacts with the dAtx2 and human Ataxin-2 proteins in co-AP assays. Furthermore, overexpressed Ataxin-1 pulls down endogenous hAtaxin-2 in cultured cells. These results suggest that Ataxin-1 and Ataxin-2 may be functional interactors in vivo. Consistent with this, we find that expanded Ataxin-1 induces accumulation of Ataxin-2 in the nucleus, where the two proteins localize in NIs both in Drosophila neurons and SCA1 human brain tissue. These are surprising observations since Ataxin-2 is normally a cytoplasmic protein both in humans and Drosophila. Interestingly, wild-type Ataxin-1 can cause neurotoxicity when overexpressed, although to a much lesser extent than expanded Ataxin-1 [25]. However, nuclear accumulation of dAtx2 is triggered by pathogenic but not wild-type forms of Ataxin-1, at least in detectable amounts. Taken together these data suggested that accumulation of Ataxin-2 in the nucleus contributes to the exacerbated toxicity of expanded Ataxin-1, and is an important mechanism of pathogenesis in SCA1. To investigate this hypothesis we targeted dAtx2 to the nucleus by means of an exogenous NLS signal. We find that dAtx2NLS is sufficient to cause a dramatic increase of its toxicity, when compared to either wild-type dAtx2 or dAtx2 with an exogenous nuclear export signal (dAtx2NES) expressed at similar levels.
To further test the hypothesis that nuclear accumulation of Ataxin-2 contributes to neurodegeneration caused by expanded Ataxin-1 we investigated Sens levels. Sens and its murine orthologue Gfi1 are proneural factors whose levels are decreased in the presence of expanded Ataxin-1[18]; thus providing a molecular readout for the neurotoxicity of Ataxin-1. In Drosophila, reduction of Sens levels leads to the loss of mechanoreceptors [18], so we monitored Sens in the context of flies expressing either dAtx2NLS or dAtx2NES but not carrying the Ataxin-1[82Q] transgene. We find that nuclear targeted, but not cytoplasmic, dAtx2 mimics both the Sens reduction and mechanoreceptor loss phenotypes caused by Ataxin-1[82Q].
Expanded Ataxin-2 accumulates both in the cytoplasm and the nuclei of SCA2 postmortem brains [2,47–49]. In mouse and cell culture models of SCA2, expanded Ataxin-2 accumulates in the cytoplasm and its nuclear accumulation is not necessary to induce toxicity [44,45]. However, nuclear accumulation of expanded Ataxin-2 also occurs in cultured cells [45], and is consistently observed in human SCA2 postmortem brainstem neurons [2,47–49]. These observations suggest that both nuclear and cytoplasmic mechanisms of pathogenesis contribute to neurodegeneration in SCA2, as it is known to occur in other polyglutamine diseases like HD and SCA3 [50–52]. One possibility is that Ataxin-2 shuttles between the nucleus and the cytoplasm although the protein is normally detected only in the cytoplasm. Our data show that accumulation of dAtx2 in the nucleus is more harmful than in the cytoplasm. Thus, neurons with nuclear Ataxin-2 in SCA2 patients may be relatively more compromised than neurons where Ataxin-2 accumulates in the cytoplasm. In agreement with this possibility, expanded Ataxin-2 is found in the nuclei of pontine neurons of SCA2 brains, one of the neuronal groups and brain regions with prominent degeneration in SCA2 [2,47–49].
Reducing Ataxin-2 levels suppresses expanded Ataxin-1 toxicity, strongly arguing against a mechanism of pathogenesis by loss of function of Ataxin-2 in the cytoplasm. Studies of the normal function of Ataxin-2 and its yeast [29,30], C. elegans [31], and Drosophila [32,33] homologs suggest a role in translational regulation. Thus, an attractive possibility is that Ataxin-1 [82Q] requires dAtx2 to impair Sens translation and induce the loss of mechanoreceptors. Consistent with this hypothesis is the finding that partial loss of function of dAtx2 suppresses the loss of mechanoreceptors phenotype caused by expanded Ataxin-1.
The data described here uncover unexpected functional interactions between proteins involved in two different SCAs. Nuclear accumulation of Ataxin-2, normally a cytoplasmic protein, is a common denominator of SCA1 and SCA2, and leads to reduced levels of at least one important proneural factor; i.e. Sens, whose mammalian orthologue Gfi1 is required for Purkinje cell survival [18]. Thus neuronal degeneration may take place through common mechanisms in different ataxias, and one of these mechanisms may involve the abnormal accumulation of Ataxin-2 in neuronal nuclei.
The cDNA GH27029 containing dAtx2 was obtained from the BDGP repository. SV40 nuclear localization signal and PKI nuclear export signal were engineered on the 3′ end of dAtx2 cDNA by PCR. Both constructs were then subcloned first in pGEM®-T (Promega) and then in a previously generated pUAST-flag expression vector[53,54]. The Drosophila transgenic lines UAS-dAtx2NES and UAS-dAtx2NLS were obtained by injecting both constructs following standard procedures. EP(3)3145 was obtained from the Szeged Drosophila Stock Center in Hungary. The wild type UAS-dAtx2 (dAtx2OE) and mutant dAtx2-X1 lines [33] were kindly provided by Dr. Pallanck, L.J.. The UAS-SCA182Q, UAS-SCA130Q and UAS-SCA12Q lines have been previously described [7,25]. N-Htt128Q flies have been previously described [7,43]. All other Drosophila strains were obtained from the Bloomington Drosophila Stock Center at Indiana University.
We used previously published procedures [7,25].
Flies were raised at either 25°C for low Ataxin-1[82Q] expression levels or 27°C for high Ataxin-1[82Q] expression levels. Flies were collected at day 1 and the number of macrochaetae per thorax of same sex flies was counted for 20 animals per genotype. The percentage of lost macrochaetae over a total of 26 was calculated, and the average per genotype was plotted in the chart.
Between 25 and 30 adult females per genotype are collected for periods no longer than 24 hours. Flies are transferred to vials containing new food every day. The assay is carried out in an empty vial. The vial is tapped so all flies fall to the bottom then we score flies that climb past a line 5cm high in 18 seconds, and this procedure is repeated ten times for each day shown in the chart. The average percentage of flies climbing per day is then calculated and plotted in Microsoft Excel. Experiments are always performed at the same time in the day to ensure no circadian rhythm effects. Four replicas (25<n<30) are analyzed per genotype. Motor performance in each replica is measured until all animals failed to reach the 5 cm line.
Gst co-affinity purification assays between GST-ATXN12Q, GST-ATXN130Q, GST-ATXN182Q, GST-ATXN1N-term82Q, GST-ATXN1C-term GST-ATXN1-AXH or GST-ATXN182QS776A, and dAtx2, Flag-dAtx2 or Myc-hAtaxin-2 were carried out as previously published [55]. pEGFP-C1 vector was used for control and constructs were transfected in HEK293T cells. Immunoblots were carried out following standard procedures and stained with mouse anti-Myc (9E10) and rabbit anti-GST antibodies (Sigma).
Tissues were dissected and fixed in 4% formaldehyde. Following standard procedures tissues were incubated with rabbit anti-dAtx2 (1:2000, courtesy of Leo J. Pallanck), Lc2628 anti-nuclear Lamin (1:50, Hybridoma Bank), rabbit anti-flag (1:200, Sigma), 11NQ rabbit anti-Ataxin-1 (1:750), 6C1 mouse anti-Ubiquitin (Sigma), guinea pig anti-Sens (1:1000, Hugo Bellen). Secondary antibodies were obtained from Jackson Labs and Molecular Probes. The fluorescent images were documented in a LSM510 Zeiss confocal microscope.
Sens quantification in the SOP and bristle precursor cells of the wing margins was carried as follows: animals were raised at 25°C. immunofluorescence was done on wing discs of the different genotypes (gp anti-Sens 1:1000, Hugo Bellen). Confocal images were obtained throughout the thickness of the wing margin (20 sections per wing disc with 2 μm interval) and then the signal was stacked and summed. Quantification was carried out by selecting the area covered by each wing margin (dorsal or ventral) separately and calculating fluorescence and area. Data corresponding to 20 wing discs per genotype was analyzed by Tukey-Kramer HSD and plotted in a chart.
Materials were stained as previously described [7].
Eye imaginal discs from ten larvae per genotype were dissected in cold PBS, homogenized in 30 μl of Laemmli buffer (Bio-Rad) using a pellet pestle motor (Kontes) and loaded on a 7.5% Tris-HCl Ready Gel (Bio-Rad). Membranes were stained with primary antibodies rabbit anti-dAtx2 (1:5000, L.J. Pallanck) and mouse anti-tubulin (Hybridoma Bank, 1:1000). Horseradish peroxidase-conjugated anti-mouse or anti-rabbit IgG secondary antibodies (1:5000; Bio-Rad) were used and membranes were developed using ECL Western blot detection kit (Amersham Biosciences).
NCBI protein accession data: NP_002964, NP_732034, NP_650466, NP_732033, NP_524818, NP_002102, NP_000323; OMIM disease reference: #164400, #183090. |
10.1371/journal.pntd.0006360 | Point-of-care tests for syphilis and yaws in a low-income setting – A qualitative study of healthcare worker and patient experiences | The human treponematoses comprise venereal syphilis and the three non-venereal or endemic treponematoses yaws, bejel, and pinta. Serological assays remain the most common diagnostic method for all treponemal infections.
Point-of-care tests (POCTs) for syphilis and yaws allow testing without further development of infrastructure in populations where routine laboratory facilities are not available. Alongside the test’s performance characteristics assessed through diagnostic evaluation, it is important to consider broader issues when rolling out a POCT. Experience with malaria POCT roll-out in sub-Saharan Africa has demonstrated that both healthcare worker and patient beliefs may play a major role in shaping the real-world use of POCTs. We conducted a qualitative study evaluating healthcare worker and patient perceptions of using a syphilis/yaws POCT in clinics in the East Malaita region of Malaita province in the Solomon Islands. Prior to the study serology was only routinely available at the local district hospital.
The POCT was deployed in the outpatient and ante-natal departments of a district hospital and four rural health clinics served by the hospital. Each site was provided with training and an SOP on the performance, interpretation and recording of results. Treatment for those testing positive was provided, in line with Solomon Islands Ministry of Health and Medical Services’ guidelines for syphilis and yaws respectively. Alongside the implementation of the POCT we facilitated semi-structured interviews with both nurses and patients to explore individuals’ experiences and beliefs in relation to use of the POCT.
Four main themes emerged in the interviews: 1) training and ease of performing the test; 2) time taken and ability to fit the test into a clinical workflow; 3) perceived reliability and trustworthiness of the test; and 4) level of the health care system the test was most usefully deployed. Many healthcare workers related their experience with the POCT to their experience using similar tests for malaria. Although the test was considered to take a relatively long time to perform the benefits of improved access to testing were considered positive by most healthcare workers. Qualitative data is needed to help inform better training packages to support the implementation of POCT in low-resource settings.
| Syphilis and yaws are closely related bacterial infections. In many countries where the diseases are found there is limited access to diagnostic testing. Recently a point of care test for both diseases has been developed. In the current study we evaluated the experience of healthcare workers and patients in using the test in the Solomon Islands. Both healthcare workers and patients valued the improved access to testing that provided by the point of care test. Experience of healthcare workers in using similar tests for other diseases, such as malaria, had both positive and negative impacts on their beliefs about the syphilis and yaws test.
| The human treponematoses comprise venereal syphilis and the three non-venereal or endemic treponematoses yaws, bejel, and pinta[1]. Syphilis, caused by Treponema pallidum subsp. pallidum, remains an important cause of both morbidity and mortality worldwide and remains one of the major preventable causes of stillbirth globally [2]. Stillbirths and neonatal death due to mother-to-child transmission of syphilis are almost entirely preventable through appropriate screening and treatment of pregnant women during antenatal care[3,4]; this intervention has been shown to be highly cost-effective [5], but access to syphilis testing is a barrier to implementation in many settings[6,7].
Yaws, caused by T. p. subsp. pertenue is the most common of the endemic treponematoses[1]. Although its aetiological agent is closely related to T. p. subsp. pallidum, yaws is transmitted by non-sexual skin to skin contact affects children living in poor, rural communities in the tropics, where the ambient humidity is high[8]. In 2012, the World Health Organization (WHO) launched a renewed plan to eradicate yaws globally by 2020 using community mass treatment with single dose azithromycin. Currently, when submitting data to WHO, most countries report clinically suspected cases without laboratory confirmation, but clinical diagnosis may be inaccurate, with less than 50% of phenotypically consistent cases confirmed serologically in some studies [9,10]. The development and validation of appropriate point-of-care diagnostics for use in yaws eradication efforts has been highlighted as a research priority[11], and would allow strengthening of national yaws surveillance programmes and accurate reporting of cases.
Serological assays remain the most common diagnostic method for all treponemal infections. Importantly, none of the currently available assays is able to differentiate between infection with syphilis and infection with any of the endemic treponematoses[12]. Standard serological testing consists of both a treponemal specific test, such as the Treponema pallidum particle agglutination assay (TPPA), combined with a non-treponemal test, such as the Rapid Plasma Reagin (RPR) assay. Treponemal tests are highly specific but generally remain positive for life following infection. Non-treponemal tests are less specific but reflect disease activity more accurately, and their titres fall following successful treatment. Testing therefore requires both assays to give an interpretable result.
Whilst traditional serological tests are relatively straightforward to perform, they require a cold chain and electricity, denying access to testing to those living in many remote communities. Point-of-care tests (POCTs) allow testing without further development of infrastructure in populations where routine laboratory facilities are not available. A large number of syphilis POCTs meeting the ASSURED criteria[13] have been developed [6] but the majority of commercially available tests include only a treponemal line. Although these tests allow identification of individuals who have been exposed to treponemal infection, they cannot distinguish between current and previous, successfully treated infection. In the antenatal setting, this results in the unnecessary treatment of women with previously treated syphilis; in yaws eradication programmes it represents a barrier to accurately assessing ongoing transmission following community mass treatment.
A single commercially available test, the Dual Path Platform (DPP-POCT) Syphilis Screen and Confirm test kit (Chembio, Medford, NY, USA) provides both a “treponemal” result (analogous to a TPPA assay) and a “non-treponemal” result (analogous to a qualitative RPR assay)[14], and can therefore distinguish between current and past infection. A number of studies and a meta-analysis have demonstrated that the test has a good sensitivity and specificity for the diagnosis but there have not been any evaluations of implementation of the test in a real world setting[15–19].
Alongside the test’s performance characteristics assessed through diagnostic evaluation, it is important to consider broader issues when rolling out a POCT. Adequate training and quality control (QC) steps must be developed, and both health-care workers and patients may require additional education about the role of POCTs in making a diagnosis. Experience with malaria POCT roll-out in sub-Saharan Africa has demonstrated that both healthcare worker and patient beliefs significantly influence the utilisation and interpretation of POCTs and may play a major role in shaping their use in real-world settings[20,21].
The Solomon Islands reports the third most cases of yaws in the world annually[8], after Ghana and Papua New Guinea. Recent ANC surveys have also show a high prevalence of syphilis[22]. Diagnostic testing in the Solomon Islands is limited to hospital laboratories. As a result testing for syphilis in rural clinics relies on sending tests away to a local hospital, whilst most cases of yaws are never confirmed serologically. These gaps in current diagnostic test provision highlight a potential role for the DPP-POCT in the Solomon Islands to improve access to testing for both yaws and syphilis. We conducted a qualitative study evaluating healthcare worker and patient perceptions of using a syphilis/yaws POCT in clinics in the East Malaita region of Malaita province in the Solomon Islands.
The study was at Atoifi Adventist Hospital (AAH), Uru Harbour, the local referral hospital for the eastern region of Malaita Province. Addition healthcare services are provided in the Province by a number of nurse aid posts and rural health clinics. Serological testing for syphilis and yaws is available in this catchment area but requires venepuncture and delivery of the blood sample to AAH or for the patient to travel to the hospital for venepuncture. In many remote communities this may require travelling for more than eight hours to have blood taken.
DPP-POCT kits were purchased from Chembio for use in the study. The DPP-POCT combines both a treponemal and a non-treponemal line. A finger prick blood sample is collected and placed with buffer into the first well on the POCT. The test is allowed to run for five minutes before additional buffer is added to the second well on the POCT. The test is allowed to run for a further 10–15 minutes before the results are read. Tests were purchased by the London School of Hygiene & Tropical Medicine. Test kits and equipment required to perform the DPP-POCT were provided to all clinics for the duration of the study.
The DPP-POCT was deployed in three settings: the outpatient department of AAH, the antenatal clinic of AAH and four rural health clinics accessible to AAH only by walking or canoe. Clinical care in each of these settings is provided by registered nurses. The rural clinics all provide antenatal care as well as general outpatient care including seeing patients with yaws. Members of the study team visited each location and provided a one-day training package to staff members on the use of the DPP-POCT. Each location was also provided with a standardised protocol for performance, interpretation and recording of results. Briefly this protocol included collection of a finger-prick blood samples followed by performance and interpretation of the assay in line with manufacturer’s instructions. Treatment for those testing positive was provided, in line with Solomon Islands Ministry of Health and Medical Services’ guidelines for syphilis and yaws respectively.
We facilitated semi-structured interviews with both health care workers and patients to explore individuals’ experiences and beliefs in relation to the DPP-POCT. Participants were selected using convenience sampling of health-care workers at each facility who reported having used the DPP-POCT. The interviews covered experience of training, positive and negative experiences of performing the test, perceptions of reliability of the test and what level of the healthcare system that individuals believed the test should be available at. For health care workers interviews were performed by researchers with training and previous experience in qualitative research in the Solomon Islands. We performed interviews at each clinical location one and six months after the rollout of the POCT aiming to interview each healthcare worker in each clinical settings. At six months, we used convenience sampling to additionally interview patients attending clinics about their experience of being tested using the POCT. All interviews were undertaken in either Kwaio (a local language) or Pijin (the lingua-franca of the Solomon Islands). Interviews were digitally recorded and then transcribed for analysis by a member of the study team (TE).
As this was a qualitative study, no formal sample size calculation was undertaken. In each setting purposive sampling of health workers was used to select participants. In each clinical setting only one cadre of staff was available, either nurses in rural clinics and the AAH outpatient department and midwives in the AAH ANC department. We aimed to perform multiple interviews at each clinic and at each time point to ensure a full range of views and experience of the DPP-POCT were obtained until we reached saturation.
Immersion in the data was achieved by verbatim transcribing of the interviews and repeated reading of interview transcripts prior to their analysis. We used thematic analysis to identify common themes or stands in healthcare worker and patient experiences.
The study was approved by the ethics committees of the Atoifi Adventist Hospital, the Solomon Islands Ministry of Health and Medical Services, and the London School of Hygiene & Tropical Medicine. Individuals undergoing testing with the DPP-POCT and individuals participating in interviews provided written informed consent. Where a DPP-POCT was performed on a child we obtained written consent from their parent or guardian and additionally obtained verbal assent from the child.
Over the study period approximately 500 DPP-POCTs were used across the study sites. We interviewed a total of 20 health care workers, 12 at the one month time point post training and a further eight healthcare workers at the six month time point post training. We additionally interviewed four patients who had been tested using the DPP-POCT.
As a result of the thematic analysis we identified four main themes: 1) training and ease of performing the test; 2) time taken and ability to fit the test into a clinical workflow; 3) perceived reliability and trustworthiness of the test; and 4) level of the health care system the test was most usefully deployed.
All but two healthcare workers interviewed (18/20) had received training in the use of the DPP-POCT from members of the study team at the time of the DPP-POCT pilot. All healthcare workers reported that they were already familiar with POCTs for malaria and that this helped them conduct the DPP-POCT;; “bikos hemi similar wetim RDT blo malaria (because the test is similar to the RDT for Malaria)(Healthcare worker, rural health clinic)”. The majority of healthcare workers reported that the test was relatively easy to perform but several noted that it was possible to make mistakes with both the timing and volume of buffer resulting in errors in the test
One healthcare worker explained the withdrawal of the blood for the test was sometimes difficult; “wan nogud samting lo saed lo staff nomoa, lo saet lo withdrawal lo blud. Hem nomoa hem lelbet danger fo saed lo staff” (one negative thing for staff was taking blood. That was a bit of a danger for the staff (Healthcare worker, rural clinic).
Several nurses (4/20) reported that the DPP-POCT took a relatively long time to deliver a result and that this could disrupt workflow in a clinical environment. One health care worker explained,
Despite this delay, healthcare workers at both AAH and the clinics reported that the time taken for the test compared favourably with having to wait a week for a result to come back from the hospital laboratory, or the time it would take patients to reach facilities where standard laboratory tests could be performed. A health care worker from AAH stated that previously,
A rural healthcare worker explained
Most nurses (16/20) reported confidence in the results of the DPP-POCT and that this had been reinforced by concordance between POCT and laboratory tests. Two nurses highlighted the experience of discordant results between laboratory and POCT testing as impacting their confidence in the test. One healthcare worker stated,
Another healthcare worker at a different clinic explained, “wanfala patient na taem mefa checkim lo RDT hem negativ but den mefala cross check lo down hem reactive na blud blo hem so hem na mifala stat kwestion about diss test (One patient was negative on the RDT but reactive on the blood which makes me question the test) (Healthcare worker, rural health clinic)”
Parallels were also drawn between the experience of RDTs for malaria and the DPP-POCT. Two nurses invoked previous experience of discordant results between the POCT used for malaria and microscopic diagnosis of malaria, and reported that this made them worry, by analogy, if the DPP-POCT was accurate.
All health care workers (20/20) reported that providing access to testing at the clinic level was a strength of the POCT. Lack of access to laboratory facilities due to the distance from the hospital was seen as a significant challenge in terms of logistics, time and costs. POCT addressed these challenges by providing access to testing at a lower level of the health care system. One nurse stated “bikos lab hem farawe from mifala. So hem barava best ples na ia. (The lab is far away from us. So here (clinic) is the best place) (Healthcare worker, rural health clinic)”. Another rural health worker explained,
Patients reported that improving access to testing at the clinic level was beneficial due to the delays involved in travelling to hospitals for testing. One patient explained,
The costs of getting to the hospital was also a concern expressed by for health workers.
In this study we provide the first real-world evaluation of the acceptability of the DPP-POCT in routine health care settings, in a country co-endemic for yaws and syphilis. Whilst a number of evaluations have been conducted of the analytic performance of the DPP-POCT[14,18,19], no previous studies have addressed factors affecting its real-world roll-out. Our data highlight the ability of these tests to improve access to diagnostics for patients in remote communities, and the receptiveness to the test of healthcare workers and patients. Our findings also highlight lessons that can be learnt in guiding roll-out of the DPP-POCT to support both yaws and syphilis control programmes.
The majority of nurses reported a high level of trust in the test, and both patients and nurses recognised the benefits of making diagnostic testing available in primary health facilities without recourse to a hospital laboratory. The test was generally reported to be easy to perform based on the training received in the study and despite the relatively long time required to obtain a result, nurses reported the test could be integrated into clinical workflows. Previous studies of the roll-out of rapid syphilis tests in other settings have also demonstrated an overall high level of acceptability and feasibility [23]. In line with our findings, these studies highlighted the potential value of reducing travel time to access tests and the ability to offer same day treatment and testing to patients.
In the current study, many health care workers drew parallels between their experience of the DPP-POCT and their previous experience with malaria RDTs. Whilst familiarity with RDTs was clearly advantageous, it also highlighted how lack of confidence in one test kit could affect the roll-out of a second, unrelated, test kit. Adoption of RDTs for the diagnosis of malaria has not always translated into anticipated reductions in the number of patients treated for malaria in the real world[24]. Studies of RDT roll-out programmes have highlighted the complex interplay between patient and clinician expectations that drives how clinicians interact with, utilise and interpret RDTs[25]. Taking account of these interactions and building more sophisticated training and support packages are important facilitators for successful adoption of RDTs[21]. Our data might inform a refined DPP-POCT training package.
There are a number of limitations to this study. Firstly, we focused on a relatively small number of clinics over a fairly limited timeframe. This reduced our ability to consider the impact of broader health systems factors that may influence the feasibility of rolling out the DPP-POCT. Previous studies on syphilis RDTs have highlighted the importance of supply chain and quality control monitoring systems in maintaining long term effectiveness of RDT scale-up at a national level[23]. These cross-cutting issues would be important to consider in the context of a broader programmatic scale up of the DPP-POCT. Second, we did not consider issues around the cost of introducing the test kit, either from the perspective of eliminating mother-to-child-transmission of syphilis or from the perspective of supporting yaws eradication. Whilst antenatal screening for syphilis is considered a highly cost-effective intervention[5], the optimal strategy depends on the prevalence of syphilis and the cost of the test kits. In many situations, use of a treponemal-only rapid syphilis test is more cost effective, albeit at the cost of over-treatment[5]. Cost-effectiveness studies on the use of RDTs for yaws have advocated that a two stage screening process, an initial treponemal-only test followed by a DPP-POCT if positive, is more cost-effective, given the current cost of the DPP-POCT (Fitzpatrick REF–accepted PLOS NTDs).
This is the first study to assess the acceptability of rolling out the DPP-POCT in a routine health care setting. We highlight the potential added value to both patient and healthcare workers that can be provided by positioning these tests at the primary health care level. Longer term and larger evaluations of DPP-POCT would be valuable to assess the impact and cost-effectiveness of scaling up access to the DPP-POCT on the management of syphilis and yaws.
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10.1371/journal.pntd.0004272 | The Schistosome Esophagus Is a ‘Hotspot’ for Microexon and Lysosomal Hydrolase Gene Expression: Implications for Blood Processing | The schistosome esophagus is divided into anterior and posterior compartments, each surrounded by a dense cluster of gland cell bodies, the source of distinct secretory vesicles discharged into the lumen to initiate the processing of ingested blood. Erythrocytes are lysed in the lumen, leucocytes are tethered and killed and platelets are eliminated. We know little about the proteins secreted from the two glands that mediate these biological processes.
We have used subtractive RNA-Seq to characterise the complement of genes that are differentially expressed in a head preparation, compared to matched tissues from worm tails. The expression site of representative highlighted genes was then validated using whole munt in situ hybridisation (WISH). Mapping of transcript reads to the S. mansoni genome assembly using Cufflinks identified ~90 genes that were differentially expressed >fourfold in the head preparation; ~50 novel transcripts were also identified by de novo assembly using Trinity. The largest subset (27) of secreted proteins was encoded by microexon genes (MEGs), the most intense focus identified to date. Expression of three (MEGs 12, 16, 17) was confirmed in the anterior gland and five (MEGs 8.1, 9, 11, 15 and 22) in the posterior gland. The other major subset comprised nine lysosomal hydrolases (aspartyl proteases, phospholipases and palmitoyl thioesterase), again localised to the glands.
A proportion of the MEG-encoded secretory proteins can be classified by their primary structure. We have suggested testable hypotheses about how they might function, in conjunction with the lysosomal hydrolases, to mediate the biological processes that occur in the esophagus lumen. Antibodies bind to the esophageal secretions in both permissive and self-curing hosts, suggesting that the proteins represent a novel panel of untested vaccine candidates. A second major task is to identify which of them can serve as immune targets.
| Schistosomes feed on blood and we have previously shown that its processing begins in the esophagus, which does not act simply as a conduit. It comprises anterior and posterior compartments, each surrounded by glands that secrete proteins into the lumen. Erythrocytes are ruptured as they pass through the compartments and leucocytes are tethered and killed but blood fails to clot. We wanted to identify the proteins secreted from these glands by sequencing the transcriptomes of head and tail preparations to pinpoint those messenger RNAs predominantly or exclusively present only in the heads. We found approximately 50 such proteins, the largest group of 27 being encoded by microexon genes. A second group comprised hydrolytic enzymes that operate at an acid pH. We showed by hybridisation experiments that expression of these genes is indeed localised to either the anterior or the posterior gland. We have suggested that this complex mixture of secreted proteins act together to perform the biological processes that occur in the lumen or, in the case of O-glycosylated membrane proteins, form a protective lining coat. We now want to discover which of them can serve as immune targets in infected animal hosts.
| Adult schistosome worms reside in the host vascular system actively feeding on blood that contains antibodies, complement factors and effector leucocytes, yet they are apparently unaffected by this ‘toxic’ diet. Indeed, their attested longevity in the hepatic portal system (Schistosoma mansoni and S. japonicum) or the venous plexuses around the bladder (S. haematobium) illustrates the sophisticated yet poorly understood mechanisms they must deploy to evade the host immune response in such a hostile environment [1]. The schistosome alimentary tract comprises an oral sucker around the mouth, a short esophagus and an extended gut caecum that runs to the extreme posterior [2]. The caecum comprises a syncytial gastrodermis that is both secretory and absorptive, and an associated network of muscle fibres responsible for peristalsis. It occupies a larger proportion of body cross section in females (16%) than males (6%) [3], reflecting the disparate balance between nutrient uptake across the body surface and gut in the two sexes [2]. The proteolytic enzymes responsible for breakdown of ingested proteins in the acidic environment of the gut lumen have been well researched [reviewed in 2]. In addition, a proteomic analysis of the vomitus released by worms in short term culture [4] has revealed the presence of other hydrolases, as well as ‘transport’ proteins capable of binding lipids (e.g. saposins) and inorganic ions (ferritin, calumenin). In vitro feeding experiments with labelled dextran have demonstrated the occurrence of endocytosis at the gastrodermal surface [4], while laser capture microdissection has been used to identify genes encoding transporters putatively expressed on the luminal surface of the gastrodermis [5].
In contrast, the role of the esophagus has been under-appreciated and little researched since the first ultrastructural descriptions several decades ago [6, 7]. However, we have recently shown that, instead of being just a conduit, it actually initiates the processing of ingested blood before it reaches the gut lumen [8]. The esophagus is divided into anterior and posterior compartments, each surrounded by an associated mass of cell bodies and lined by a syncytial layer of cytoplasm continuous with the surface tegument. The posterior mass was designated as a gland decades ago and we have recently shown in S. japonicum that the anterior cell mass is also a distinct secretory organ [9]. Both cell masses synthesise proteins for secretion into the lumen. Video recording of feeding [8] and in-vitro experiments with membrane-labelled erythrocytes [4] have revealed their lysis in the lumen; the label transfers primarily to the membranes of the posterior compartment. The two observations explain why intact erythrocytes are seldom seen in the lumen [8]. In contrast host leucocytes accumulate within the posterior lumen as a central plug around which incoming blood flows [8]. Furthermore, these tethered leucocytes are structurally damaged, as are those which reach the gut lumen. Despite intact platelets being observed in the anterior compartment [10], ingested blood does not clot in the lumen, implying the existence of anticoagulant mechanisms. Collectively, these observations confirm the esophagus as a crucial site for interaction of host blood with parasite products. Specific expression of three microexon genes (MEGs [11, 12]), namely MEG-4.1 [13], MEG-4.2 and MEG-14 [8], and one venom-allergen-like (VAL; [14]) gene, VAL-7 [15] was revealed in the posterior esophageal gland of S. mansoni by whole mount in-situ hybridisation (WISH). In addition, seven proteins (six MEGs and VAL-7) have been localised to the posterior esophageal gland of S. japonicum by immunocytochemistry [8, 16]. Furthermore, the demonstration of host IgG binding to the esophageal lumen of both mouse and hamster worms in vivo [8] raised the possibility that esophageal proteins might be targets of the host response. Most recently we have obtained evidence that rhesus macaques self-cure from an established S. japonicum infection by producing antibodies that target esophageal secreted proteins [16]. The functions of the esophagus are disrupted, leading to cessation of feeding, starvation and ultimately death of established worms [16, 17]. Clearly, if we are to understand esophageal function better we need more information about the proteins secreted into the esophagus lumen that interact with incoming blood. We have also suggested such proteins represent an entirely new group of targets that might be exploited for vaccine development, due to their critical role in blood feeding and their accessibility to antibodies [16].
The advent of new and cheaper technologies has made comparative transcriptome analysis by direct sequencing feasible. We have used the massive parallel capacity of ion semiconductor sequencing on an Ion Torrent instrument to investigate differential gene expression in the esophageal region of adult male S. mansoni. Schistosomes possess epithelia (tegument, gastrodermis) and rudimentary organ systems (muscles, nerves and sense organs, alimentary tract, protonephridial system, parenchyma) present throughout the whole body but the solid acoelomate body plan means they are not readily isolated for analysis. However, the cell masses surrounding the anterior and posterior esophageal compartments, plus the paired cerebral ganglia of the nervous system are unique to the esophageal region. We therefore reasoned that a subtractive comparison of the patterns of gene expression in heads and tails would delineate this unique ‘head’ subset. We present here the results of that comparison, which highlighted a group of differentially expressed genes, many encoding secretory proteins, and we have validated the expression of representatives to the cell bodies of the anterior or posterior esophageal glands. These data lay the foundations for a deeper understanding of blood processing in the worm esophagus and provide a panel of proteins that can be screened for immunoreactivity against sera from permissive and self-curing hosts.
The procedures involving animals were carried out in accordance with the Brazilian legislation (11790/2008). The protocol for maintenance of the S. mansoni life cycle was reviewed and approved by the local ethics committee on animal experimentation, Comissão de Ética no Uso de Animais (CEUA), Universidade Federal de Ouro Preto (UFOP), and received the protocol no. 2011/55.
Balb/c strain mice were infected with approximately 200 cercariae and adult worms obtained by portal perfusion of animals at 6–7 weeks later, using RPMI-1640 medium buffered with 10mM HEPES (Sigma-Aldrich, St Louis, MO, USA). After extensive washing in the same medium and removal of tissue debris and any damaged individuals, parasites were fixed instantly by immersion in RNAlater (Invitrogen, Paisley, UK). Approximately 400 male worms were individually viewed at x30 magnification under a dissecting microscope, carefully held with fine watchmakers forceps (Ideal Tek, Chiasso, Switzerland) and the head region detached along the line of the transverse gut using Vannas scissors (John Weiss, Milton Keynes, UK). Two hundred tails, defined as the posterior third of the male body to exclude the testes, were similarly excised in order to obtain the same amount of biological material. Before extraction, the two sample pools were disrupted on ice using a tissue grinder until they appeared completely homogeneous.
Total RNA was extracted using an RNeasy Micro kit (Qiagen, Manchester, UK). Briefly, the homogenized lysate was centrifuged for 3 min at full speed to pellet the debris. The supernatant was transferred to a clean tube and mixed with 1 volume of 70% ethanol. The mixture was then transferred to an RNeasy MinElute spin column and centrifuged for 15s at ≥8000xg. After washing and DNA digestion with DNase I, total RNA was eluted with 10μl RNase free water. Messenger RNA was further purified from total RNA using a Dynabeads mRNA DIRECT kit (Life Technologies, Warrington, UK) according to the manufacturer’s instructions. In short, the Dynabeads Oligo (dT)25 beads were washed in one well of a 96-well plate sitting on a magnetic stand. Total RNA was diluted as required and heated at 70°C for 2 min then mixed with an equal volume of Lysis/Binding Buffer. The denatured RNA mixture was transferred to the well containing the beads and incubated for 5 min to allow mRNA binding. After washing, the mRNA was eluted from beads with pre-warmed (80°C) nuclease-free water. Two rounds of mRNA isolation were performed in the same well in order to achieve a high quality mRNA yield.
All reagents and equipment used was obtained from Life Technologies unless otherwise stated. Libraries were prepared for RNA-sequencing using the Ion Total RNA-Seq Kit v2, and the recommended protocol for whole transcriptome library preparation from <100 ng Poly(A) RNA. In brief, RNA was fragmented using RNaseIII, and Ion Adaptors (Mix v2) ligated to fragmented RNA prior to reverse transcription. cDNA was then purified and amplified using Ion Xpress RNA-seq Barcoded primers, with separate barcodes used for each sample. The yield and size distribution of each amplified cDNA library was assessed using the Agilent High Sensitivity DNA kit with the Agilent 2100 Bioanalyzer. Libraries were then pooled at equimolar concentrations, and diluted to 20 pM in preparation for sequencing. Two independent rounds of Ion Torrent sequencing were performed on pooled libraries to allow comparison of technical replicates, and the data combined for downstream analysis.
In accordance with the recommended protocols provided, sequencing template preparation was performed using the Ion OneTouch system in conjunction with the Ion PGM Template OT2 400 kit, where template positive Ion Sphere Particles (ISPs) were prepared and subsequently enriched. Sequencing was then performed on an Ion Personal Genome Machine System, using an Ion 318 Chip v2 with the Ion PGM Sequencing 400 Kit.
The assumption that all the major organ systems and tissues would be present in roughly equal proportions in both the head and tail samples was tested by compiling lists of signature proteins for comparison. The genes encoding cytosolic proteins were taken from the proteomic analysis of the SWAP fraction of adult worms [23]. The parenchyma was represented by the genes encoding glycogen metabolism proteins, culled from the genome database; together with muscle this tissue is the principal site of such activity [24]. The muscle and cytoskeletal genes were taken from the list of proteins identified in the Tris and UTCS fractions of frozen/thawed adult worms [25]. Tegument and gut-secreted proteins were compiled from the respective proteomics studies [26, 27, 28, 4], supplemented by annexins and tetraspanins annotated in the genome, and saposins from an infection array experiment [29]. The lists of representative glycosyl transferases and nervous system genes were compiled by key-word searching of the data using appropriate terms (glucosyl, galactosyl, fucosyl, xylosyl, mannosyl, transferase and neur, synap, acetylch, dopam, transmit, respectively) followed by manual editing.
Eleven targets were chosen from the subset of genes highly enriched in the head samples for independent validation of the site of expression, using whole mount in-situ hybridization (WISH). These were an Aspartyl Protease, Beta 1,3-galactosyltransferase, a Phospholipase A2 and MEGs 8.1, 9, 11, 12, 15, 16, 17 and 22, with VAL-7 as a positive control [15] and the sense sequence of VAL-7 as the negative control. The method was performed on whole adult male and female worms as described by Dillon et al. [13]. The worms were first fixed in Carnoy’s solution, then in MEMFA (0.1 M MOPS, 2 mM EGTA, 1 mM MgSO4 and 3.7% formaldehyde) before storage in ethanol at -20°C until use. Briefly, for the protocol worms were warmed to room temperature and rehydrated by 2x5 min washes, the first in 75% ethanol/25% phosphate-buffered saline (PBS; pH 7.4) containing 0.1% Tween 20 (PBSAT) and the second in 50% ethanol/PBSAT. They were then transferred to 100% PBSAT for 3x5 min washes. After rehydration, parasites were permeabilized in a 10 μg/ml solution of PCR-grade proteinase K (Roche, Germany) dissolved in PBSAT, and refixed with formaldehyde.
For probe synthesis, sequences of interest (S1 Table) were manufactured by Biomatik (Cambridge, Canada) and cloned into the plasmid pBSK (+). Antisense RNA probes were obtained in vitro incorporating DIG-labelled dUTP (Roche, Germany) with T7 or SP6 RNA polymerase (Promega, USA). The permeabilized worms were then incubated at 60°C for 2h in hybridization buffer (50% formamide, 5 x SSC, 100 μg/ml heparin, 1x Denhardt’s solution, 0.1% Tween 20, 0.1% CHAPS and 5 mM EDTA) with 1 mg/ml total yeast RNA added to block non-specific hybridization. After this step, the solution was replaced with fresh (pre-warmed) total RNA/hybridization buffer containing 1 μg/ml of synthesized DIG-labelled probe and hybridization was performed at 60°C overnight. After several washes, parasites were incubated with alkaline phosphatase-conjugated anti-DIG Fab fragments (Roche) overnight at 4°C. After more washes, parasites incubated with BM-Purple substrate were observed for colour development and photographed using the Microscope Eye-Piece Camera (Dino-Lite, Taiwan).
The full IonTorrent dataset has been deposited on the NCBI SRA site (http://www.ncbi.nlm.nih.gov/sra) under the Study number SRP064960. The S. mansoni heads sample was designated as SRS1120313 and the experiment as SRX1353319. Heads run 1 and heads run 2 have the accession numbers SRR2722034 and SRR 2722095, respectively. The S. mansoni tails sample was designated as SRS1120316 and the experiment as SRX1353321. Tails run 1 and tails run 2 have the accession numbers SRR2722255 and SRR 2722455, respectively. New or improved gene annotations deposited on the EMBL TPA site received accession numbers as follows: MEGs 26–31 & MEGs 10.2, LN898187-LN898193; Aspartyl protease (Smp_018800), LN898196; Phospholipase (Smp_031180) LN898197; Phospholipase (Smp_031190), LN898198; MEG-32.1 (Smp_123100), LN898194; MEG-32.2 (Smp_123200), LN898195.
The Ion Total RNA-Seq Kit v2 requires a minimum of 1ng polyA purified mRNA for library construction. Using the appropriate kits, extraction of the head sample homogenate yielded 190ng of total RNA, from which 1.92 ng mRNA was recovered, adequate for library construction; mRNA recovery from the tails was not a limiting factor. Ion Torrent sequencing of the DNA fragments from the two technical replicates of head and tail mRNA extracts yielded between 0.86 and 1.8 million reads in the four runs (S2 Table). Approximately 65% of these were mapped by the Tophat and Cufflinks programmes to predicted genes in version 5 of the S. mansoni genome, thereby providing identities and Smp gene annotations. Frequency distributions of RPKM values, depicting transcript abundance in heads and tails, were virtually superimposed (S1A Fig). A plot of the RPKM values for the technical replicates of heads (S1B Fig) and tails (S1C Fig) revealed the high degree of reproducibility and linearity (correlation coefficients 0.99 and 0.98 respectively) in the sequencing data, with a dynamic expression range between four and five orders of magnitude. A total of 8856 genes was represented by one or more reads; this reduced to 5010 genes when those with trivial numbers of reads were eliminated (RPKM <16). Of these, 2583 were more highly expressed in the heads and 2427 in the tails. A scatter plot of transcript abundance against the difference in expression between heads or tails (Fig 1) delineated subsets of 97 genes in the head (1.95%) and 80 in the tail (1.6%) that were displaced more than fourfold either side of the equivalence line x = 0. Of these, 23 genes were expressed in the heads only and 10 in the tails only (points lying along the 45° line in Fig 1). The intensity of expression of the head subset was greater (mean RPKM 3963, median 98; S3A Table) than that of the tail subset (mean RPKM 1246, median 84; S3B Table) and there was also a massive bias in differential expression in the head subset. The ratio of mean RPKMs H/T of x166 compared with a value of only x6.2 for the T/H ratio in the tail subset (S3A & S3B Table). A further five genes were excluded from the heads analysis and 12 from the tails because there were five or fewer reads in either replicate, making a total of 92 and 67 genes for detailed analysis of expression, respectively.
The subtractive RNA-Seq approach requires that the major schistosome tissues are equally represented in both head and tail preparations if it is to identify genes uniquely or predominantly expressed in esophageal structures. We tested this proposition by comparing the relative expression of genes encoding signature proteins (S1D Fig; S4 Table). Individual paired RPKM values from the two technical replicates, displayed as a scatter plot, revealed the similarity in gene expression level for heads and tails, with a correlation coefficient of 0.951. The diffuse schistosome nervous system (NS) ramifies through all tissues of the worm body but nerve cells are sparse and the level of expression of NS genes was the lowest for any signature tissue. The mean RPKM score of NS abundance in heads (= 69) thus provides a benchmark for comparison of the levels of transcript abundance of other tissue signatures in the heads. The tegument and gastrodermis are the two principal interfaces with the external environment, where considerable biosynthesis of proteins for export takes place. The mean expression level of genes encoding tegument surface proteins ranged from 7.3 to 10.5 times the NS, apart from those encoding the transporter-linked ATPases at 1.3 times the NS (Fig 2A). The mean relative transcript abundance for proteins secreted by the gastrodermis ranged from 10 to 17 times the NS, indicating a slightly higher level of biosynthetic activity than the tegument cell bodies; genes encoding the extended group of ten saposins, likely involved in lipid binding and transport, were the most active. Muscle and parenchyma are the most abundant tissues in the male schistosome body, with cytoskeletal proteins showing a mean expression level (13.5x NS) similar to the gastrodermis, while the genes encoding cytosolic proteins (e.g. glycolytic enzymes, chaperones) were the most highly expressed of all signature proteins (mean 74x NS). Surprisingly, the genes responsible for glycogen metabolism, indicative of parenchyma, were expressed at only 1.8 times the NS level. The glycosyl transferases, involved in the N or O-linked glycosylation of proteins destined for export, were expressed overall in the same range as the NS genes.
We next examined the bias in gene expression by comparing the ratio of RPKM scores for each signature gene in heads versus tails (Fig 2B). Despite the cerebral nerve ganglia being present in the head preparation, there was only a slight bias (x1.1) towards the heads among the 80 genes scrutinised. The other signature groups showed a bias towards greater expression in the tails with ratios ranging from 0.7 to 0.93, apart from the glycosyl transferases. Expression of these was skewed (x1.36) towards the head preparation and suggestive of specialised glycan production in the region. Finally, scatter plots of the signature groups confirmed that the vast majority of the ~250 signature genes lay within a very narrow range either side of the equivalence line (Fig 3). This allowed us to set a generous margin of fourfold difference either side of the line to define differential expression, and thereby to pinpoint any outliers. On that basis not one of the 80 nervous system genes was differentially expressed (Fig 3A), nor were any muscle and cytoskeleton (Fig 3D) or parenchyma and cytosol genes (Fig 3E). However, four potential tegument genes from the extended family lists of annexins and tetraspanins were heavily skewed in expression (Fig 3B); these have not been identified previously by proteomic analysis of the tegument and one of them (Smp_155580) was particularly abundant in the heads. There was one exception in the bias of the saposin genes towards greater expression in the tails (Fig 3C), with Smp_028840 showing eightfold expression in heads, albeit at low intensity. Among the glycosyl transferases three genes stood out with a much greater bias towards expression in the heads (Fig 3F), Smp_159490, Smp_144260 and Smp_151220 being expressed at 16, 43 and 214 times the level in the tails, respectively.
With the eight exceptions noted above, the discrete group of 92 genes differentially expressed in the heads at more than four times the level in tails did not encode signature proteins. Overall transcript abundance was 57 times that of nervous tissue (median 5x higher) with one fifth of the group exclusively expressed in the heads (S3A Table). The remarkable feature of this list, sorted by abundance (S3C Table), was the identity of the top 20 genes. MEGs accounted for more than half the total, including the three already known to be expressed in the esophageal gland (MEGs 4.1, 4.2 and 14), plus a further eight (MEGs 8.1, 8.2, 9, 11, 12, 15, 16 & 17), whose site of expression was not previously recorded (Fig 4). For this top-20 group, the mean RPKM value for abundance (18638) was 270x the NS level and the expression ratio of heads to tails was x604. Indeed MEG-4.2, the most highly expressed gene in the group, had an RPKM value of 66834, x969 the mean level of signature genes in nervous system tissues that are located adjacent to the gland.
A significant number of reads was not mapped to the genome by Tophat and Cufflinks so we performed a de novo assembly of all reads using Trinity. We examined this de novo assembly for the presence of transposons by BLAST searching with a file of 34 well-characterised elements. A total of 27 was identified, amounting to not less than 0.9% of head and 0.6% of tails reads, counting only the top hit. None showed a marked differential expression between head and tail samples, but the three most highly expressed were Saci-1, -2 and -3, previously described as displaying high transcriptional activity [30]. We then interrogated the Trinity data to identify novel differentially expressed protein coding genes not annotated in the genome. We first confirmed that there was no major discrepancy between the results of Tophat/Cufflinks mapping and Trinity de novo contigs by plotting the abundance and differential expression of 12 known MEGs identified by both methods. The correlation coefficient r for comparisons of their RPKM values was 0.88 and for the ratio of heads/tails was 0.93, confirming a strong positive association between the two methods. The Trinity contigs were annotated by BLAST against v5 of predicted genes in the S. mansoni genome to obtain identities of known genes. The same cut-offs for abundance and difference were then applied as for Tophat/Cufflinks mapping and a total of 51 unannotated contigs was filtered out for manual analysis. In this group, the mean RPKM value for abundance (418; 6x the NS), was much lower than for the 92 differentially expressed genes detected by Cufflinks (= 3963). However, this might be anticipated, given that these unannotated contigs encode potential genes not detected by conventional methods. The mean expression ratio of heads to tails (x62) is still very biased towards the heads and 63% were expressed in the heads alone. Sixteen of the novel genes were identified as encoding unannotated MEGs (Fig 4, S5 Table). These included six previously described (MEGs 8.3, 8.4, 19, 20, 22, 24 [31], and a further eight that were entirely new, namely MEGs 26–31 plus two new members of existing families, MEGs 10.2 and 4.3. Gene annotations for seven of these new MEGs were deposited on EMBL and received accession numbers LN898187-LN898193 (MEG-4.3 was a partial sequence in a genome fragment). Finally hundreds of reads were found in the Trinity assembly for a previously annotated gene, MEG-3.4, which has been removed from the genome database by the curators at GeneDB and was therefore not detected by Tophat/Cufflinks mapping.
We extended the evaluation of MEG expression to the unfiltered Cufflinks and Trinity datasets to locate any MEGs that were not differentially expressed (Fig 4) identifying five (1, 5, 6, 13 and 24) in this way (S3D Table). MEG-6 transcripts were particularly abundant (>3000x the level in the NS) but only 3.7x higher in heads than tails. The other four all had a low or moderate level of expression with MEG-5, previously identified in tegument preparations by proteomics, having an RPKM of 1910 in heads and 832 in tails. One final MEG (MEG-10.1) appears to be an outlier, expressed in tails only and possibly confined to a very scarce tissue.
Analysis of gene structure of the novel MEGs described here (Fig 5) reveals that all of them display the typical characteristics: two long 3' and 5' flanking exons and a central coding portion mostly composed of microexons (<36bp). The portion coding for a signal peptide is mostly or entirely contained in the 5' long exon. Twenty-six of the thirty-three microexons (79%) that encode MEGs 26–32 are symmetrical (i.e. have a length divisible by 3), which indicates an evolutionary pressure to favour alternate splicing without disruption of the open reading frame. As with most MEGs, those enriched in the head preparation tend to encode relatively small proteins (average MW ~10 kDa; median MW ~7kDa). Analysis of all the MEG structures using the IUPred program reveals that 13 out of 27 predicted protein products enriched in the head preparation display more than 40% of their length as intrinsically disordered (Fig 6A; S5B Table). These disordered regions are rich in threonine, serine and proline (TSP) residues (S5C Table). Unsurprisingly, nine of these thirteen proteins (MEGs 4.1, 8.1, 8.2, 14, 15, 19, 20, 29 and 32.1) are predicted to be heavily O-glycosylated, with an average of 16.4 sites, 100% of them being located in the putative disordered regions (Fig 6A). A further five MEGs (8.3, 10.2, 22, 32.1, 32.2) are predicted to be O-glycosylated proteins but without extensive regions of disorder (S5B Table). In the MEG-8 family members there is a hydrophobic C terminus encoded by the long 5’ flanking exon (Fig 6A). Clustal comparisons reveal that it contains conserved protein domains with characteristic signatures for each family member across the three schistosome species for which sequence data is available (Fig 6B). This cross-species conservation reveals that the MEG-8 diversity is ancient, arising before speciation of the Genus Schistosoma occurred. In addition, MEG-15 also displays a relatively hydrophobic C-terminus. Another group of MEGs (9, 12, 26, 27 and 28) preferentially expressed in the head, encode a small peptide that is predicted by Heliquest to contain an amphipathic helix with a hydrophobic interaction face (Fig 6C and 6D). That still leaves approximately one third of the proteins encoded by esophageal MEGs that have no distinguishing features to provide a clue to putative function, other than a signal peptide.
A second prominent group of genes detected by the Tophat/Cufflinks mapping (and confirmed by the Trinity assembly) were nine hydrolases (Fig 7; S3D Table). Homology searching of NCBI nr indicates that these are likely to be of lysosomal origin. The group of six proteases annotated as subfamily A1A unassigned peptidases (A01 family) located on Chromosome 3, were exclusive to the head preparation, and ranged in transcript abundance from 0.4 to 28 times the NS level. BLAST and Clustal searching revealed they encoded closely related aspartyl proteases, referred to as Cathepsin D homologues. Transcripts for two other hydrolases, annotated as Phospholipase A2, were abundant (3.6x and 9x NS, respectively) and almost exclusively expressed in the heads. The final hydrolase (20x NS), palmitoyl protein thioesterase 1 enzyme, removes thioester-linked fatty acyl groups from modified cysteine residues in proteins or peptides. The N terminal sequence of aspartyl protease, Smp_018800, was extended by Clustal mapping (S6A Table) and the updated gene annotation deposited at EMBL under accession number LN898196. The presence of a signal peptide on this and four other group members (Smps 132470, 132480, 136830 and 205390) was confirmed by SignalP. (The sixth protease, Smp_136720, is an incomplete gene model lacking the 5’ end.) The five proteases also contained one to three copies of the consensus N-X-S/T sequence, indicating suitable sites for N-linked glycosylation. Palmitoyl thioesterase possessed a signal peptide plus N glycosylation sites and we were able to improve the gene models for the two Phospholipases using Trinity assemblies (S6B & S6C Table), to reveal the presence of signal peptides and N glycosylation sites in both. These new gene annotations were deposited at EMBL under accession number LN898197 and LN898198. All the evidence indicates that the nine hydrolases are destined for the lysosomal pathway and will have optimal enzymatic activity at an acid pH.
As VAL-7 was already known to be expressed in the esophageal gland, and is a member of a large family of secreted proteins potentially important in modifying host responses, we searched our datasets for other VALs. Only VAL-7 was prominent and provides an internal control for the subtraction approach. The full length CDS for VAL-7 deposited at GenBank is divided without overlap between two genome scaffolds with two Smp designations. Their respective scores for abundance and difference are close on the scatter plot (Fig 7) providing a strong indicator that the subtractive RNASeq method can produce reliable results. Only one further gene for VAL-13, came above the >4-fold difference threshold with a score 2.1x the NS level, compared with a mean of 223x for VAL-7. Five other VALs (16, 8, 12, 11 and 6) were more evenly distributed between heads and tails (5B) and it is notable that three of them (6, 11, 16) belong to Group 2, lacking a signal peptide.
The remaining 37 annotated genes mapped by Tophat/Cufflinks fell into two broad groups, 14 that showed a moderate level of differential expression and abundance, and a more compact group of 23 with a low differential; they were classified by putative function (S3E Table). A cytosolic calmodulin-like calcium binding protein (Smp_096390) was the most abundant transcript. A group potentially most relevant to esophageal secretion, and probably located along the secretory pathway, included genes encoding transmembrane emp24 domain containing protein 7 (Smp_140180) involved in vesicular protein trafficking, transmembrane protein 63A (Smp_143750) inserted in the membrane of lysosomes, GPI ethanolamine phosphate transferase 2 (Smp_155490) involved in GPI-anchor formation and Longevity-assurance gene 1 (LAG 1; Smp_122050) that facilitates transfer of GPI-anchored proteins from the endoplasmic reticulum to the Golgi apparatus. Finally, three putative nervous system transcripts at characteristic low abundance, and not in the list of signature NS genes were of note. Neuropeptide F prepropeptide (Smp_088360), tryptophan hydroxylase (Smp_174920) and Catechol-o-methyltransferase (Smp_198020) could represent markers for the cerebral ganglia, although the most skewed is only 16x the level in tails.
The members of the largest grouping (one third) within the differentially expressed subset were annotated as hypothetical proteins, lacking homology to anything outside the Trematoda. Searching of the longest open reading frame for individual genes, in part hampered by incomplete gene models, yielded few that encoded signal sequences or transmembrane domains, a point dealt with in the Discussion. However, utilising a combination of Trinity assembly data and searching of publicly available EST databases, we were able to extend the models for two genes with abundant and differential expression in the heads, Smp_123100 and Smp_123200, situated on chromosome 6. Moreover, mapping of the exons to the chromosome revealed that both had a central region encoded by microexons; due to their homology we designated them MEG-32.1 and MEG-32.2, respectively (Fig 5). Improved gene annotations for these two MEGs were deposited on EMBL and received accession numbers LN898194 and LN898195. The two proteins are predicted to be membrane-anchored at both N and C termini to form a threonine-rich hairpin loop that is O-glycosylated. This makes a total of 12 previously annotated, 13 novel, and two reassigned MEG genes identified in the head preparation in the present study.
Although not the focus of our study, we also analysed the markedly different set of 72 genes expressed more than fourfold higher in the tails than heads (S3F Table). The largest group of 19 were annotated as encoding hypothetical proteins, primarily with homologs only among other Trematoda. Two of these (Smp_177580, Smp_201270) were the most abundant differential transcripts in the tails (771x and 362x the NS level). The second largest group encoded proteins of the extracellular matrix, and adhesion molecules such as protocadherin involved in cell attachment. A collagen (Smp_135560) and a dynein light chain were both abundant (26x and 19x the NS level) and among the most differentially expressed (8.5x and 52x the level in heads, respectively). Seven genes putatively associated with the gastrodermis, including two cathepsins and a saposin, could indicate some regional specialisation of the gut. The group of genes encoding four female-specific proteins in the posterior half of the male worm seems incongruous since they are involved in egg shell formation and predicted to be expressed in vitelline follicles but such follicles with their associated mRNA have been detected in male worms [13]; the most abundant transcript was present at 14x the NS level. The remaining annotated genes all with low levels of expression, encoded proteins involved in signalling pathways (6), nuclear function (4) and miscellaneous processes (12). Expression of the gene for MEG-10.1 in the tails at 6.3x the NS level was noted above.
Detection of gene expression using WISH was successful for all 12 selected targets in males and eight in females (Fig 8). At low magnification the specificity of target gene expression only in the worm anterior between oral and ventral suckers is confirmed (S2 Fig). At higher magnification, four of the genes, MEGs 12, 16, 17 and Phospholipase A2 were revealed as exclusively expressed in the mass of cells surrounding the anterior esophageal compartment, confirming its status as a distinct gland in S. mansoni. These are the first identified genes expressed in this region. Expression of the remainder, together with the VAL-7 positive control, was confined to the posterior esophageal gland cell bodies. They comprised two hydrolases (aspartyl protease and palmitoyl thioesterase), five MEGs (8.2, 9, 11, 15 and 22) plus a glycosyl transferase (β1,3-galactosyltransferase). Expression of the five MEGs plus aspartyl protease was also detected in the posterior esophageal gland of female worms, whereas only MEG-12 expression was detected in the female anterior esophageal gland. The time for colour development after addition of substrate, and to a lesser extent the intensity of the signal corroborate the estimate of mRNA abundance represented by the RPKM score (S3 Fig). The WISH targets with a high log2 RPKM between 13.3 and 16 all developed within 1–2 hours. The remaining six targets divide into two groups with medium (3–5 hrs) and slow (6–10 hrs) development time. The slow developers have log2 RPKMs between 10.4 and 12.7, the medium developers between 8 and 10. The confounding factor is that the length of probe, containing dig-labelled bases to which the detection antibody attaches, was twice as long in the medium as the slow developers. This illustrates the complexity of the WISH protocol and the difficulties for quantitation.
Our aim in this study was to obtain an insight into those genes expressed in the distinctive tissues of the schistosome esophagus that encode the proteins involved in the initial processing of ingested blood. The difficulties in characterising patterns of gene expression that occur in the discrete organ systems of an acoelomate metazoan with a solid body plan should not be underestimated. Laser capture microdissection [5, 32, 33] has been applied but the amount of tissue obtained and the precision needed to excise the organ of choice without contamination, are major limitations. Moreover, the studies to date have used microarray analysis to detect differences in gene expression between tissues, a technique which has inherent limitations. The fixed design of the array, especially if coverage is partial [32], leaves gaps in the repertoire and furthermore does not permit new genes to be identified. The dynamic range of detection is also limited (typically a maximum of 200-fold), due to high background levels, cross hybridisation and saturation of signals [34].
Rapid advances in technology have quickly led to the adoption of RNA-Seq as the method of choice to characterise transcriptomes from many sources [34]. The existence of a well-annotated gene assembly for S. mansoni [11, 35] is a singular advantage and RNA-Seq can also identify novel coding sequences. The technology has a very low (if any) background, no upper limit for quantification and a dynamic range of 4–5 orders of magnitude [34]. Our RPKM scores of expression ranging from ~2 x 100 to 7.2 x 104 for heads and ~2 x 100 to 5.3 x 104 for tails were of that order. It has been estimated, using stringent criteria, that four million mapped reads of ~35 bases provided 80% coverage of gene expression in yeast [34]. We achieved 1.65 and 1.9 million mapped reads of mean length 117 bases, for heads and tails respectively, which detected 8856 genes or 82% of the predicted total. As we were not seeking an overview of the complete transcriptome, we deliberately excluded from analysis genes with fewer than five detected transcripts in both samples. This still left ~ 5000 genes, representing 42% coverage, to be evaluated. In comparison, the first qualitative, genome-wide analysis of S. mansoni [36] was performed on ~125,000 sequences generated largely from mini-libraries by the ORESTES protocol [37]. Our subtractive RNA-Seq approach would be equally applicable to characterise differential gene expression in other adult worm tissues such as the testis, ovary, uterus and ootype.
The core of our strategy was the isolation by microdissection of the entire esophageal region and matching tails from adult male bodies stabilised with RNALater. We then generated the transcript datasets and used the subtractive approach, based on the dual criteria of abundance and differential expression, to delineate the set of genes exclusively or predominantly expressed in the head preparation. Surprisingly, nervous system genes were not prominent in the heads despite the presence of the cerebral ganglia and we must attribute this to the diffuse nature of the schistosome nervous system throughout the whole body. This left us with the proposition that the ~90 differentially expressed genes mapped by Tophat/Cufflinks, plus the novel genes detected by the Trinity assembly, were expressed in the cell bodies of the two esophageal glands. (Note that there is no protein synthetic machinery in the syncytial lining of the esophagus.) That many of the highlighted gene models are partial and that we found new genes in the most intensively studied schistosome species can be explained by two factors. First, the esophageal glands comprise only a tiny fraction of the worm body so their transcripts will be severely under-represented in the whole worm homogenates used hitherto as a source of mRNA, depriving programmes like Evidence Modeller [38] of the resource they need for gene annotation. The second is that de novo gene finding programmes look for patterns of bases not occurring by chance, thus excluding short runs that comprise the microexons of MEGs.
We compared transcript distribution between heads and tails for signature genes, primarily identified by our previous proteomic studies, to determine whether the major schistosome tissues were equally represented; our data amply confirm this supposition. No outliers were detected in the lists of signature proteins from the cytoskeleton, and cytosol, underlining the ubiquity of muscle and parenchyma in both head and tail preparations. Similarly, no known tegument markers or genes encoding constituent of worm vomitus originating in the gastrodermis were differentially distributed. However, two annexins and two tetraspanins were highly biased. BLAST searching with the most abundant annexin, Smp_155580, indicates that the N-terminus of this protein is missing so no conclusion is possible about whether it is a candidate for release into the esophageal lumen. Similarly, a putative saposin, Smp_028840, was highlighted as possible candidate for esophageal secretion. Unfortunately, evidence for a saposin domain is weak (Prosite & NCBI CDD searches) and the sequence lacks a signal peptide; this gene was also detected as differentially expressed by a laser capture study [33]. The distribution of glycosyl transferase expression was investigated because bioinformatic analysis of MEGs 4.1 and 14 indicated that they were O-glycosylated [8], and the presence of O-glycans in the posterior esophageal gland had been demonstrated by lectin staining [8, 39]. The three differentially expressed transferases, one of them validated by WISH in the posterior gland, raise the possibility that some proteins exported from the esophageal glands are decorated with novel glycan structures not found in other worm tissues, an observation that could have immunological consequences. The importance of the secretory pathway in the esophageal cell masses is also underlined by the enrichment of transcripts from five genes involved in the intracellular vesicle transport pathway.
A major finding of this study was the marked expression of MEGs in the head preparation, both in term of transcript abundance and differential. A total of 27 transcripts from 22 out of 32 MEG families (two-thirds) was detected in the schistosome head region, making it the most intense site for the expression of this enigmatic group of genes so far discovered. Furthermore, combining the results of this and our previous studies [8, 16] using WISH and immunocytochemistry we can now be confident that three MEGs (12, 16 and 17) are expressed in the anterior gland and nine (4.1, 4.2, 8.1, 8.2, 9, 11, 14, 15 and 22) in the posterior gland. The second and complementary observation was the marked differential expression of nine genes encoding lysosomal hydrolases in the head region, with phospholipase A validated by WISH to the anterior gland, and aspartyl protease and palmitoyl thioesterase to the posterior gland. The mean RPKM scores for the four genes we have shown are expressed in the anterior esophagus and the 12 in the posterior esophagus [8, 13, 15, 16] are 6053 and 24767 respectively. The posterior gland is approximately 2.5 times the volume of the anterior [8] suggesting a roughly equal transcriptional activity on a tissue mass basis. However, the RPKM scores are 10 to 20 times the mean values for tegument cell bodies and gastrodermal epithelium (598 and 1192, respectively). We conclude that the glands are indeed a hotspot for gene transcription in the male worm body, potentially of secretory proteins destined for export into the esophagus.
Our recent research has provided ultrastructural evidence for the secretion of vesicle contents from both anterior and posterior glands, into the esophageal lumen [8, 9]. We also noted that the morphology of the ‘light vesicles’ in the anterior gland was akin to that of primary lysosomes, raising the possibility that lysosomal enzymes were secreted into the esophagus lumen [9]. Such lysosomal secretion is a well-established feature of the gastrodermis [4]. Our immunocytochemical observations provide direct evidence for the secretion of five MEG-encoded proteins and VAL-7 into the esophagus lumen [16]. Moreover, we have now identified two MEG proteins (8.2 and 15) and two lysosomal hydrolases (aspartyl protease Smp_136830 & palmitoyl thioesterase) in worm vomitus preparations (WCB & LXN, personal communication). In terms of RPKM score, these identities are #s 2 & 3 on the MEG list and #s1 and 2 on the hydrolase list, illustrating the relative sensitivity of RNA-Seq versus proteomic detection. The above observations make a strong case that the protein products of the differentially expressed microexon and hydrolase genes identified in this study, are secreted into the esophagus lumen to interact with ingested blood. This poses the question as to their role in the esophageal processes that we have delineated [4, 8]. These include erythrocyte lysis, leucocyte tethering and killing, disposal of platelets and prevention of clot formation. It should not be forgotten that there are other unannotated genes among the ~140 differentially expressed transcripts that may have a role in these processes.
Predicting functions and devising assays for proteins with little or no homology to anything outside the Genus Schistosoma is a daunting task. However, we can make some inferences from predicted primary and secondary structures. Several of the MEG-4, -8 and -15 families (eight of the esophageal MEGs) possess a central TSP-rich, intrinsically disordered region predicted to be heavily O-glycosylated. We have already demonstrated that the glycosylation of SmMEG-4.1 causes a gel shift of more than 70 kDa above the MW predicted for the protein alone [8]. We previously showed that the MEG-4 family proteins possessed a conserved C-terminus between schistosome species [8]. We have now shown that the MEG-8 family members all display a hydrophobic region at their C terminus, each again possessing a motif highly conserved between species. In MEG-4 we suggested that this region might target host leucocytes, e.g. by binding via its C terminus to a pan-leucocyte marker such as CD45 [8]. The same is true for the MEG-8 motifs and equally, plasma proteins or leucocyte secretions could be the intended ligands. An alternative possibility is that these O-glycosylated proteins might use the C-terminal motifs to organize themselves in a similar way to secreted mucins. There, unstructured, heavily glycosylated chains (the TSP regions) are connected by the interaction of hydrophobic domains, creating a net that confers to the mucus a gel-like consistency with viscoelastic proprieties [39]. The immuno-localisation of both SjMEG-4.1 [8] and SjMEG-8.2 [16] in a cocoon-like association with tethered leukocytes in the lumen of the S. japonicum esophagus provides visual evidence for a mucus-like complex that traps incoming leukocytes. TEM observations have revealed that the parallel arrays of material (0.08 x 0.1μm) contained in the crystalloid vesicles of the posterior esophageal gland are released intact to the esophageal lumen, [8] and may cluster into larger aggregates, indicating the self-affinity/assembly of some molecular constituents. The largest aggregate measured to date (1.0μm x 0.6 μm) in an electron micrograph represents a ~50-fold accretion. Moreover, the 40% greater repeating unit in the aggregates, compared to the vesicles [8], indicates an expansion of the electron lucent layers after release, consistent with the swelling of O-glycans. It is thus plausible that the parasite adopts a “capture and defuse” strategy in the esophagus lumen whereby leukocytes are quickly immobilised and isolated by a mucus network, so preventing the diffusion of antibodies and defence proteins away from them.
In contrast, those MEGs anchored by a transmembrane helix (e.g. 14, 29, 32.1, 32.2), and also predicted to be O-glycosylated, are similar to the cell-associated mucins in humans in lacking the hydrophobic C-terminal region that might facilitate aggregation [40]. The most likely role for these membrane-anchored MEGs is to provide a protective lining coat of O-glycans for the entire esophagus. This suggestion is corroborated by the detection of a thin layer of neutral muco-substance lining the esophagus of the related blood fluke Schistosomatium douthitti [41]. The third obvious category of esophageal MEGs comprises the small peptides (MEGs 9, 12, 26, 27 and 28) that exhibit amphipathic helicoidal regions, with a hydrophobic face. Such peptides are widespread in animals and have both anti-microbial and haemolytic properties (e.g. [42, 43]). In the context of the schistosome esophagus they are capable of interacting with incoming erythrocytes and leucocytes to destabilize their membranes. Our recent observations on the localisation of S. japonicum MEG-9 confirm its association with the surface of leucocytes in situ in the esophagus lumen [16].
The lysosomal hydrolases we identified all have orthologues in mammals with well characterised properties that provide pointers to their potential function in blood processing. The putative secretion of two phospholipases, at least one of them from the anterior gland, strongly suggests a role in the lysis of erythrocytes as these cells pass through the two compartments. The transfer of the lipophilic dye PKH2 from labelled erythrocytes starts in the anterior compartment and is completed in the posterior [4]. TEM and confocal microscope images of intact erythrocytes in the anterior compartment and only a few ghosts in the posterior [44, 8] are consistent with these observations. The palmitoyl thioesterase may also participate in the process of erythrocyte lysis via its ability to cleave the lipid anchor from proteins on the cytoplasmic face of plasma membranes. One such palmitoylated protein, p55/MPP1 [45], found in the erythrocyte is an important component of the ternary complex that attaches the spectrin-based skeleton to the plasma membrane [46]. Thus we can envisage a cascade where short amphipathic MEG peptides such as MEG-9 or MEG-12 bind to and destabilise the erythrocyte membrane, enhancing the interaction of the two phospholipases with their plasma membrane substrates. Increased permeability then permits the palmitoyl thioesterase to enter and disrupt the cytoskeleton; the erythrocyte loses shape, leaks haemoglobin and is destroyed. Judging from our videos of worm feeding [8] the whole process takes only seconds.
The secretion of six aspartyl proteases, at least one from the posterior compartment, indicates a powerful attack is also made on proteins in the plasma or on the external surface of host blood cells. Note that these enzymes are distinct from the one already described for the worm gut (Smp_013040) [47]. The number of homologs suggests either redundancy of function, potentially as a means of immune evasion, or the existence of subtly different substrate specificities in the target proteins. The most obvious candidates are the components of the clotting cascade since clot formation does not occur in the worm esophagus. However, we have now detected fibrin localised in oval deposits in the anterior esophagus [16], some of it coincident with host antibody, which may contribute to block secretion. A role for the aspartyl proteases in preventing this would require one or more to be synthesised by the anterior gland cells. A second potential function for these proteases could be the destruction of defence proteins released from the leucocytes that are, as revealed by TEM [8], trapped and ultimately destroyed in the posterior lumen. One obvious corollary of the secretion of these lysosomal hydrolases is that they function at an acid pH optimum. The lumen of the schistosome gut has long been known to have a pH of ~4.5 [48] and now it appears that the process of acidification may begin in the esophagus. Nothing is known about the mechanism in schistosomes but in lower animals acidification of both the lysosomal interior and transepithelial compartments is effected by V-ATPases [49]. The transcripts of the genes encoding the complex of ~8 proteins that comprise this pump were all detected in our dataset, but were not differentially expressed—unsurprising given that both gut and esophagus may be acidified by the same process. If acidification of the esophagus lumen is confirmed, it would imply that the co-secreted MEG proteins also operate at an acidic pH.
MEG hotspots described in our previous work were the head gland of the migrating schistosomulum (MEG-3 family) and the subshell envelope of the mature egg (MEG-2 and MEG-3 family) [12, 50]. It has been suggested that the role of these egg- and larval-secreted MEG proteins is to interact with and modify vascular endothelial function [50]. In parenthesis, the only representative of the MEG-2 and 3 families found in the esophageal transcriptome was MEG-3.4, not identified in the egg or larval secretions. It is plausible that the group of five non-differentially expressed MEGs (1, 5, 6, 13, 24) comprise a tegument hotspot since one of their number, MEG-5, was previously detected in tegument fractions by proteomics [12]. The identification of a major hotspot of MEG expression in the worm esophagus, together with the expression of a group of lysosomal hydrolases, confirms the complexity of function that we have previously highlighted [8, 9]. We have also observed binding of host IgG to the esophageal lumen, first in S. mansoni worms from permissive mice and hamsters [8] and more recently in S. japonicum worms from rhesus macaques undergoing self-cure [16]. In this last host the antibodies appear to target structures in both anterior and posterior compartments to alter morphology and disrupt function, potentially causing worm starvation and death [16]. Although we have suggested that the alternative splicing of MEGs generates a heterogeneous mixture of proteins that serve to confuse the immune system [12], it appears that such a ploy can be circumvented by a host like the rhesus macaque. Collectively, the esophageal secretions that we have identified provide a novel and untested panel of vaccine candidates. With many available targets, the task is to discover the worm’s Achilles heel.
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